Eurostat regional yearbook : 2025 edition.
Date: 2025-09-26
Institutions: Eurostat
Subjects: EFTA countries, EU Member State, EU statistics, accession to the European Union, economic and social cohesion, economic statistics, regional statistics

Eurostat regional
yearbook  
2025 edition

F L A G S H I P 
P U B L I C A T I O N S

Eurostat regional
yearbook  
2025 edition

Manuscript completed in July 2025

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Theme: General and regional statistics 
Collection: Flagship publications

Print:  ISBN 978-92-68-29696-7 
ISBN 978-92-68-29695-0 
PDF: 

ISSN 1830-9674 
ISSN 2363-1716 

doi:10.2785/3366990 
doi:10.2785/5478134 

KS-01-25-037-EN-C
KS-01-25-037-EN-N

Foreword

I am delighted to welcome you to the 2025 edition of the Eurostat regional yearbook, a journey through 
the diverse and dynamic regions of Europe. This yearbook provides a rich tapestry of economic, social and 
environmental insights, drawing on data from the 27 EU countries, 9 candidate countries and 4 EFTA countries. 
Through detailed figures, vivid maps and striking infographics, this yearbook brings regional stories to life – 
highlighting both the unique differences among and shared experiences between Europe’s regions.

The 2025 Eurostat regional yearbook transforms statistical data into clear, engaging narratives. By providing 
reliable and accessible information, it empowers decision makers to craft thoughtful strategies that bridge 
regional divides, drive inclusive growth and enhance the quality of life across Europe. The yearbook is also a 
valuable resource for researchers and students in their enquiries and investigations – and generally for all those 
who are curious to learn more about Europe’s regions.

At the heart of Europe’s future lies a bold vision for sustainable prosperity and competitiveness, a priority 
championed by the European Commission.

For the first time, the Eurostat regional yearbook highlights a selection of key indicators that track 
progress for this priority. Look for this special icon accompanying relevant maps, figures and 
infographics to discover how the regions are evolving with regard to sustainable prosperity and 
competitiveness.

Continuing from last year, the yearbook also features several indicators linked to the Sustainable 
Development Goals (SDGs), marked with their own distinctive icon.

For those eager to dive deeper, the yearbook is accessible online through Statistics Explained and in PDF 
format on Eurostat’s website. The most recent and detailed datasets can also be explored via Eurostat’s 
comprehensive database.

I warmly invite you to explore and engage with the diverse regions of Europe, where each data point paints a 
vibrant portrait of the evolving landscape of our continent. 

Mariana Kotzeva

Director-General, Eurostat

3Eurostat regional yearbook 2025Abstract

Abstract

Statistical information is an important tool for understanding and quantifying the impact of political decisions in a specific 
territory or region. The Eurostat regional yearbook 2025 provides a detailed picture relating to a broad range of statistical 
topics across the regions of the EU, EFTA and candidate countries.
Regional data are presented using the classification of territorial units for statistics (NUTS), which was recently updated – 
NUTS 2024. It delineates 92 regions at NUTS level 1, 244 regions at NUTS level 2, and 1 165 regions at NUTS level 3.
Each chapter presents statistical information in the form of maps, figures and infographics, accompanied by a descriptive 
analysis highlighting the main findings. Regional indicators are presented for the following 13 subjects: population, health, 
education, the labour market, living conditions, the digital society, the economy, business, research and development, 
tourism, transport, the environment and agriculture.

Chief editor
Mariana Kotzeva

Editors
Teodóra Brandmüller, Máté Farkas and Réka Fodor  
(Eurostat, Unit E.4., Regional statistics and geographical information)

Map production
Joseph Davies, Julien Gaffuri and Michael Harrop  
(Eurostat, Unit E.4., Regional statistics and geographical information)

Dissemination
Romina Brondino, Catherine Coyette, Andrej Hanzevacki, Nina Jere and Annika Johansson  
(Eurostat, Unit B.4., Dissemination and user support)

Production and layout
Informa s.à r.l. – Giovanni Albertone, Simon Allen and Andrew Redpath – working under contract for Eurostat

Contact details
Eurostat 
Bâtiment Joseph Bech 
5, rue Alphonse Weicker 
2721 Luxembourg 
LUXEMBOURG 
E-mail: estat-user-support@ec.europa.eu

Data extraction
The data presented within this publication were extracted during March to May 2025. The manuscript was completed in 
July 2025.
An online data code available under each map/figure can be used to access directly the most recent data on Eurostat’s 
website.
All statements on policies within this publication are given for information purposes only. They do not constitute an 
official policy position of the European Commission and are not legally binding. To know more about such policies, please 
consult the European Commission’s website.

For more information
Eurostat’s website: http://ec.europa.eu/eurostat/
Statistics Explained: https://ec.europa.eu/eurostat/statistics-explained

Eurostat regional yearbook 20254 
 
Acknowledgements

Acknowledgements

We would like to thank all colleagues involved in the editorial work of each chapter:

• Introduction: Máté Farkas, Jane Schofield and Bruno Supicic (Eurostat, Unit E.4., Regional statistics and geographical 
information); Matej Milanovic and Stefán Logi Sigurþórsson (Eurostat, Unit B.3., Enlargement, neighbourhood and 
development cooperation); Remko Hijman, Friderike Oehler and Åsa Önnerfors  (Eurostat, Unit E.2., Environmental 
statistics and accounts; sustainable development); Alexia Rouby (Directorate-General for Agriculture and Rural 
Development; Unit D.1., Rural areas and networks); Jorge Duran Laguna and Kris Magnus (Directorate-General for 
Regional and Urban Policy, Unit B.1., Policy development and economic analysis); Riccardo Sgaramella (European 
Committee of the Regions, Unit C.I, Coordination, planning, impact – cross-border cooperation, territorial partnerships)
• Population: Encho Bicov, Veronica Corsini, Jose Domingo Martinez Solano, Andreea Beatrice Rusu, Andrea Succi and 
David Thorogood (Eurostat, Unit F.2., Population and migration) Hannes Reuter (Eurostat, Unit E.4., Regional statistics 
and geographical information)

• Health: Ebba Barany, Louise Cliff and Monica Pace (Eurostat, Unit F.5., Education, health and social protection); Anna 

Rybkowska and Teodora Tchipeva (Eurostat, Unit F.4., Income and living conditions; quality of life); Christophe Bredel and 
Julien Gaffuri (Eurostat, Unit E.4., Regional statistics and geographical information)

• Education: Ferenc Galik, Eric Géré and Marco Picciolo (Eurostat, Unit F.5., Education, health and social protection); 

Elodie Cayotte and Sabine Gagel (Eurostat, Unit F.3., Labour market and lifelong learning)

• Labour market: Elodie Cayotte, Sabine Gagel, Riccardo Gatto, Kaja Sõstra, Malgorzata Stadnik and Antti Sykko

(Eurostat, Unit F.3., Labour market and lifelong learning); Daniela Sciranková (Eurostat, Unit E.4., Regional statistics and 
geographical information)

• Living conditions: Alessandro Albano, Emilio Di Meglio, Sigita Geske, Kalvis Okmanis, Merle Paats, Anna Rybkowska 

and Arsela Sturc (Eurostat, Unit F.4., Income and living conditions; quality of life)

• Digital society: Urška Arsenjuk, Andrea Attwenger, Dimo Dimov, Michaela Grell and Mathieu Mballa (Eurostat, Unit 

G.4., Innovation and digitalisation)

• Economy: Panagiota Bisiela, Enrico Infante and Martina Patone (Eurostat, Unit C.1., National accounts methodology; 

standards and indicators)

• Business: Gemma Asero, Saila Kumputie, Gregor Kyi, Pierre Ninane and Sarmite Visocka (Eurostat, Unit G.2., European 

businesses)

• Research and development: Alvaro Díez Soto, Daniela Enache, Chiara Orsini and Stefania Panaitescu (Eurostat, Unit

G.4., Innovation and digitalisation)

• Tourism: Andreia Danila, Christophe Demunter, Renata Lewczuk and Levene Székely (Eurostat, Unit G.2., European 

businesses)

• Transport: Anna Białas-Motyl, Emanuele Dardengo, Evangelia Ford-Alexandraki, Stefanie Hille, Anna Katarzyna Mazurek 

and Joanna Raczkowska (Eurostat, Unit E.3., Transport)

• Environment: Hendrik Doll, Remko Hijman, Christine Mayer, Friderike Oehler and Åsa Önnerfors (Eurostat, Unit E.2., 
Environmental statistics and accounts; sustainable development); Aleksandra Galic (Eurostat, Unit E.4., Regional 
statistics and geographical information); Barbara Bacigalupi (Directorate-General for Environment, ENV.01, Strategy, 
digitalization, better regulation & economic analysis); Lukas Repa (Directorate-General for Environment, ENV.C.1, 
Sustainable freshwater management); Katarzyna Biala, Eva Ivits, Manuel Löhnertz, Mette Lund, Alberto González Ortiz 
and Roberta Pignatelli (European Environment Agency); Monica Crippa and the EDGAR team (Directorate-General Joint 
Research Centre (JRC), Unit C.5., Clean air and climate)

• Agriculture: Klára Anwar, Edward Cook, Santiago Cortes Sutil, Monica Iatan, Nicolas Lampach and Helena Ramos

(Eurostat, Unit E.1., Agriculture and fisheries)

We are also very grateful to:

• Arturo de la Fuente Nuño (Eurostat, (Acting) Director of sectoral and regional statistics) and Márta Nagy-Rothengass

(Eurostat, Unit E.4., Regional statistics and geographical information)

5Eurostat regional yearbook 2025 
 
Contents

Foreword ................................................................................................................................................................................................................................. 3

Abstract .................................................................................................................................................................................................................................... 4

Acknowledgements ................................................................................................................................................................................................. 5

Introduction ....................................................................................................................................................................................................................... 7

Section A: People and society ................................................................................................................................................................  17

1. Population .............................................................................................................................................................................................................18

2. Health .........................................................................................................................................................................................................................32

3. Education ...............................................................................................................................................................................................................48

4. Labour market ..................................................................................................................................................................................................67

5. Living conditions ...........................................................................................................................................................................................85

6. Digital society .................................................................................................................................................................................................106

Section B: Economy and finance ....................................................................................................................................................  123

7. Economy ..............................................................................................................................................................................................................124

8. Business .................................................................................................................................................................................................................140

9. Research and development ..........................................................................................................................................................160

10. Tourism ...............................................................................................................................................................................................................171

Section C: Environment and natural resources ..........................................................................................................  187

11. Transport ...........................................................................................................................................................................................................188

12. Environment.................................................................................................................................................................................................202

13. Agriculture ......................................................................................................................................................................................................213

Eurostat regional yearbook 20256Introduction

Introduction

Eurostat, the statistical office of the European Union 
(EU), collects, compiles and publishes statistics for the 
EU, including statistics for the regions of EU, EFTA and 
candidate countries. 

Today’s world is defined by rapid change and uncertainty. 
Data can play a crucial role in shaping and evaluating 
effective policies and responses to the challenges that 
arise. This edition of the Eurostat regional yearbook 
provides statistical insights into a range of pressing 
challenges at subnational level, from climate change to 
competitiveness. By leveraging accurate and timely data, 
policymakers can make informed decisions that drive 
resilience and sustainable growth. 

This edition of the Eurostat regional yearbook focuses 
on 2 key policy areas.

The European Commission’s new plan for Europe’s 
sustainable prosperity and competitiveness aims 
to strengthen economic growth while ensuring 
environmental and social sustainability. Through balancing 
economic resilience with climate goals, the plan aspires to 
create high-quality jobs, reinforce global competitiveness 
and uphold the EU’s commitment to a fair, green and 
prosperous future for all its citizens. This edition of the 
Eurostat regional yearbook places a logo next to the 
title of relevant figures, maps, infographics and tables, 
identifying regional indicators that can be used to measure 
sustainable prosperity and competitiveness. These 
indicators have been aligned with those that form part of 
The future of European competitiveness, a report produced 
by Mario Draghi for the European Commission. 

The United Nations’ (UN) 2030 Agenda for Sustainable 
Development is a long-term strategy that aims to achieve 
a range of economic, social and environmental goals. 
The UN traces developments by using 17 sustainable 
development goals (SDGs) and 169 targets. Eurostat 
monitors progress towards the SDGs in an EU context. It 
coordinates the review of the EU SDG indicator set and 
publishes annual monitoring reports. This edition of the 
Eurostat regional yearbook places a logo next to the title of 
relevant figures, maps, infographics or tables, identifying 
regional indicators that can be used to measure progress 
towards the SDGs. 

European statistics

SUBNATIONAL STATISTICS 

EU countries are often compared with each other in statistical 
presentations. However, in practice it is sometimes difficult 
to compare smaller and larger countries. For example, Malta 
had 563 000 inhabitants on 1 January 2024 and Luxembourg 
had 672 000 inhabitants, while Germany – the most 
populous EU country – had 83.5 million inhabitants. 

There are considerable differences between and within 
countries as regards their territorial composition. For 
example, Ireland, Finland and Sweden are generally rural 
and sparsely-populated, while Malta and the Benelux 
countries have much higher levels of population density. 

Similarly, there can be considerable diversity within individual 
EU countries: for example, contrast the densely-populated, 
urbanised areas of Nordrhein-Westfalen in the west of 
Germany (which had an average of 532 inhabitants per 
square kilometre in 2023) with the sparsely-populated, largely 
rural, north-eastern region of Mecklenburg-Vorpommern 
(with an average of 70 inhabitants per square kilometre). In a 
similar manner, there are considerable differences between 
regions of France: for example, contrast the bustling pace of 
life in the economic hub and capital region of Paris (which 
had an average of 20 200 inhabitants per square kilometre 
in 2023) with the more sedate pace of rural life in the central/
southern region of Lozère (with an average of 15 inhabitants 
per square kilometre). 

Examining subnational or regional data within EU countries 
often provides clearer insights by highlighting internal 
disparities – such as Italy’s north-south divide or Germany’s 
east-west divide – by revealing significant differences in 
socioeconomic development. For example, Germany and 
Poland have a polycentric economic structure, with several 
relatively large cities spread across their territories. In 
contrast, France and Romania follow a more monocentric 
pattern of development, with economic activity 
concentrated in and around their respective capitals. 

Over the past few years, Eurostat has expanded the range 
of statistics that it provides beyond national and regional 
information to cover other territorial typologies. These 
alternative typologies address the needs of policymakers, 
particularly within the context of cohesion and territorial 
developments, using 2 broad headings: 

•  regional typologies
•  local typologies that are based on data for local 
administrative units (LAUs or municipalities).

With this in mind, Regulation (EU) 2017/2391 of the European 
Parliament and of the Council of 12 December 2017 as regards the 
territorial typologies (Tercet) established a common statistical 
classification of territorial units. This legislative consolidation 
facilitates the collection, compilation and dissemination of 
European statistics at different territorial levels. 

STATISTICS ON REGIONS – THE NUTS 
CLASSIFICATION 

The classification of territorial units for statistics – known 
as NUTS – is at the heart of the EU’s regional statistics. It 
is a classification based on a hierarchy, subdividing each 
EU country into regions, classified from larger to smaller 
regions as NUTS levels 1, 2 and 3. 

Eurostat regional yearbook – 2025 edition8 
Table 1: Number of NUTS 2024 regions and 
statistical regions

EU
Belgium
Bulgaria
Czechia
Denmark
Germany
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
Romania
Slovenia
Slovakia
Finland
Sweden

Iceland
Liechtenstein
Norway
Switzerland
Bosnia and Herzegovina
Montenegro
Moldova
Georgia
North Macedonia
Albania
Serbia
Türkiye
Ukraine

NUTS level 1
92 
3 
2 
1 
1 
16 
1 
1 
4 
7 
14 
1 
5 
1 
1 
1 
1 
3 
1 
4 
3 
7 
3 
4 
1 
1 
2 
3 
Level 1
1 
1 
1 
1 
– 
1 
– 
– 
1 
1 
2 
12 
8 

NUTS level 2
244 
11 
6 
8 
5 
38 
1 
3 
13 
19 
27 
4 
21 
1 
1 
2 
1 
8 
1 
12 
9 
17 
9 
8 
2 
4 
5 
8 
Level 2
1 
1 
7 
7 
– 
1 
– 
– 
1 
3 
4 
26 
27 

NUTS level 3
1 165 
44 
28 
14 
11 
400 
5 
8 
52 
59 
101 
21 
107 
1 
5 
10 
1 
20 
2 
40 
35 
73 
26 
42 
12 
8 
19 
21 
Level 3
2 
1 
17 
26 
– 
1 
– 
– 
8 
12 
25 
81 
138 

Note: as of 01 December 2024.

Source: Eurostat

Introduction

The 2024 edition of the NUTS classification provides the 
basis for classifying regional information in this year’s 
Eurostat regional yearbook. For example, in Germany there 
are 400 NUTS level 3 regions, which make up 38 regions 
at NUTS level 2, or 16 regions at NUTS level 1. Some EU 
countries have a relatively small population and/or area 
and are not therefore subdivided at some (or even all) 
of the different levels of the NUTS classification. Estonia, 
Cyprus, Latvia, Luxembourg and Malta are each composed 
of a single NUTS level 2 region that covers the whole of 
their respective territory, while Cyprus and Luxembourg 
are also each composed of a single NUTS level 3 region. 

For non-EU countries – EFTA and candidate countries – 
the concept of ‘statistical regions’ replaces NUTS. Bilateral 
agreements between the countries concerned and 
Eurostat apply the same principles as those used for the 
NUTS classification. 

Table 1 provides an overview of the number of NUTS 
regions that exist in each of the EU and non-EU countries 
covered in the Eurostat regional yearbook. 

Most of the regional statistics shown in the Eurostat 
regional yearbook are for NUTS level 2 regions. However, 
subject to data availability, some maps and figures are 
shown for either NUTS level 1 regions (more aggregated 
geographical information) or NUTS level 3 regions (the 
most detailed level of regional information). The latter 
are only available for a limited selection of indicators that 
cover topics such as demography, economic accounts, 
business statistics, tourism statistics and environmental 
statistics. No regional data are available for Bosnia and 
Herzegovina, Georgia and the Republic of Moldova; 
national data are included when available. 

The NUTS regulation 

The NUTS classification is defined in Regulation (EC) 
No 1059/2003 of the European Parliament and of the 
Council of 26 May 2003 on the establishment of a common 
classification of territorial units for statistics (NUTS). The 
classification is periodically reviewed by the European 
Commission amending the regulation to reflect 
administrative changes within EU countries. The NUTS 
regulation specifies that there should be a minimum 
period of 3 years stability during which time the 
classification should not be changed. Exceptions are made 
when the accession (or departure) of an EU country occurs. 

9Eurostat regional yearbook – 2025 edition 
Introduction

Since 2003, the NUTS classification has been amended 
several times (see Figure 1), due to: 

•  regular amendments
•  changes in the membership of the EU

Figure 1: History of NUTS

•  changes to the territorial boundaries of existing EU 
countries (for example, the inclusion of data for the 
French region of Mayotte).

Source: Eurostat

The European Commission adopted the 7th amendment 
of the NUTS classification (Commission Delegated Regulation 
(EU) No 2023/674) in December 2022. It applies to any data 
that is transmitted to Eurostat for reference periods starting 
from 1 January 2024. This version of NUTS – commonly 
referred to as NUTS 2024 – is the basis for classifying 
regional statistics in the 2025 edition of the Eurostat 
regional yearbook. NUTS 2024 provides a standardised 
framework for regional statistics in the EU, delineating: 

•  92 regions at NUTS level 1
•  244 regions at NUTS level 2
•  1 165 regions at NUTS level 3.

With this switch to NUTS 2024, Eurostat tried to recode 
historical statistics (data for earlier reference periods) 
to the new classification, although this was not always 
possible. As a result, data are not always available for a 
complete set of regions, especially when simple recoding 
or aggregation was not feasible. The lack of historical data 
under NUTS 2024 makes time-series analyses challenging. 

However, EU countries plan to back-calculate key regional 
statistics in an attempt to improve data availability. 

More about the data: the main principles of the 
NUTS classification 

Principle 1: NUTS favours administrative divisions. If 
available, administrative structures are used for the 
different NUTS levels. In EU countries where there is 
no administrative layer corresponding to a particular 
level of NUTS, so-called non-administrative regions are 
created by aggregating smaller administrative regions. 

Principle 2: the NUTS regulation defines minimum 
and maximum population thresholds for the size of 
NUTS regions (see Table 2) to ensure a basic degree of 
comparability. Different rules apply to administrative 
and non-administrative layers. Deviations from 
these thresholds are only possible when particular 
geographical, socioeconomic, historical, cultural or 
environmental circumstances exist. 

Table 2: Population size constraints for NUTS 2024 regions
(number of inhabitants)

NUTS level 1 regions
NUTS level 2 regions
NUTS level 3 regions

Source: Eurostat

Minimum population

Maximum population

3 000 000 
800 000 
150 000 

7 000 000 
3 000 000 
800 000 

Eurostat regional yearbook – 2025 edition10 
Introduction

OTHER TERRITORIAL TYPOLOGIES 

Grid-based statistics provide an alternative to 
administrative boundaries-based regional statistics. They 
divide territories into uniform grid cells, offering detailed 
spatial data with a high degree of geographical resolution; 
for example, the population and housing census that is 
conducted once a decade provides statistics for a grid 
that is composed of cells measuring 1 square kilometre. 
Eurostat uses grid-based statistics to analyse, among 
other subjects, population, transport, the environment, 
agriculture and accessibility. For this edition of the Eurostat 
regional yearbook, grid-based statistics are included 
for 2 chapters: population and health. 

Previous editions of the Eurostat regional yearbook 
have shown a number of other territorial typologies. 
These allow analyses to be extended to cover topics 
such as cities and commuting zones, or the degree of 
urbanisation. While these statistics remain highly relevant 
for policy debate in the EU, an editorial decision was taken 
when compiling the 2025 edition of the publication to 
concentrate on regional statistics. Readers interested in 
subnational statistics based on other territorial typologies 
can refer to the following publications: 

•  Rural Europe (online publication)
•  Urban Europe (online publication).

A short reading guide

COVERAGE 

Each chapter in the Eurostat regional yearbook presents 
statistical information in the form of maps, figures and 
infographics, accompanied by descriptive analyses 
highlighting the main findings. Information is shown 
for 13 different subjects: population, health, education and 
training, the labour market, living conditions, the digital 
society, the economy, business, research and innovation, 
tourism, transport, the environment and agriculture. 

The Eurostat regional yearbook contains regional statistics 
for the EU countries, alongside data for several non-EU 
countries: EFTA countries (Iceland, Liechtenstein, Norway 
and Switzerland) and candidate countries (Bosnia and 
Herzegovina, Montenegro, Moldova, Georgia, North 
Macedonia, Albania, Serbia, Türkiye and Ukraine). 

The geographical descriptions used within this publication 
to group EU countries – for example, as ‘northern’, ‘eastern’, 
‘southern’ or ‘western’ – are not intended as political 
categorisations. Rather, these references relate to the 
geographical location of 1 or more EU countries, based 
on the geography domain of the European Commission’s 
multilingual thesaurus (Eurovoc). 

The designations employed and the presentation of 
material in maps and figures does not imply the expression 
of any opinion whatsoever on the part of the EU 
concerning the legal status of any country, territory or area 
or of its authorities, or concerning the delimitation of its 
frontiers or boundaries. 

TIMELINESS 

Eurostat compiles the information used in this publication 
from a wide range of surveys and data collection exercises. 
As a result, there may be differences in the latest available 
reference year between chapters and indicators, as each 
aims to show the latest information available. 

In general, 2024 data are available for demography (as 
used in the chapter on population), the labour force survey 
(as used in the chapters on education and training and 
on the labour market), EU statistics on income and living 
conditions (as used in the chapter on living conditions) and 
the information society survey (as used in the chapter on 
the digital society). Otherwise, the most recent reference 
period is generally 2023. 

Eurostat’s website may have fresher data due to the 
continuous nature of data collection and processing 
(resulting in updates and new reference periods being added 
throughout the year). Online data codes are provided below 
each of the maps and figures as part of the source; these 
codes can be used to access the freshest data. 

HOW TO INTERPRET THE MAPS 

A majority of the maps in the Eurostat regional yearbook 
are choropleth maps that use different colour shades to 
highlight regional differences. These maps are generally 
composed of 6 sequential colours, from a light yellow 
(for low values) through to dark blue (for high values). 
The information presented has been normalised. In other 
words, rather than show data for absolute values (which 
could introduce bias linked to the size of each region), 
these maps are generally based on proportions/shares, 
rates or ratios. In some cases, the maps use a larger or 
smaller number of classes or colours, while in others, a 
different colour scheme may apply, such as when specific 
colour conventions are inherent to a particular indicator. 

The class boundaries used in the legend for each map 
are computed exclusively in relation to the distribution 
of regional values across the EU (in other words, 
values for regions outside the EU are not taken into 
account when computing the classes). The boundaries 
for the lower classes are usually based on the 10th 
and the 25th percentiles, the middle boundary on 
the 50th percentile, and the boundaries for the upper 
classes on the 75th and the 90th percentiles. As such, the 

11Eurostat regional yearbook – 2025 edition 
Introduction

lightest shade of yellow and the darkest shade of blue 
portrays those EU regions with approximately the lowest/
highest 10% of values. For a limited number of cases, 
several choropleth maps may be shown together (for 
example, small multiple maps presenting employment 
rates by educational attainment for people with a lower, 
medium or higher level of education). These maps share a 
common classification scheme for their class boundaries to 
assist readers who want to make comparisons across the 
different maps. 

Metadata about individual data cells are usually referred 
to as flags. They provide information about the status of 
the data, for example, detailing whether it is estimated, 
provisional or forecasted. To restrict the amount of 
metadata presented under each map or figure, this 
publication generally excludes information about flags. 
No distinction is made between data values that are 
not available, those of poor quality, or those that are 
confidential; these data points are all simply shown/
labelled as being ‘not available’. 

Some choropleth maps have been produced using a 
diverging colour scheme. These maps usually highlight the 
distribution of regions around a specific EU policy target. 
They usually show shades of teal/turquoise (progressively 
darker for values that are increasingly higher than an EU 
target) and shades of gold (progressively darker for values 
that are increasingly lower than an EU target). For a limited 
number of maps, the order of the colours in diverging 
choropleth maps has been reversed. This has been 
done when there are policy targets that seek to lower 
(rather than increase) the value of a particular indicator; 
for example, the EU seeks to reduce the number of early 
leavers from education and training. Diverging scales 
are also used when trying to highlight the differences 
between regions that have positive and negative values, or 
values that are above/below an index value (for example, 
the EU/national average = 100). 

This publication also contains bivariate choropleth maps. 
They provide information for 2 distinct indicators at the 
same time. An example is a map showing the number of 
hospital beds per 100 000 inhabitants and the number 
of medical doctors per 100 000 inhabitants. These maps 
use 9 different colours/shades organised in a 3*3 grid, 
from a light shade (for those regions that have low values 
for both indicators) to a dark shade (for those regions that 
have high values for both indicators). 

Finally, there are several maps that are based on 
proportional circles. These are used to map regional 
statistics presented as absolute values, for example, 
the number of people employed as human resources 
in science and technology. The size/area of each circle 
represents the underlying number of people employed, 
whereas the colour of each circle may be used to highlight 
complementary information, for example, their share in the 
total labour force. 

METADATA 

Eurostat’s data are consistently published with 
accompanying metadata. These metadata provide 
background information on each of the principal sources, 
as well as more specific information about particular 
indicators and individual data cells. 

Data identified as having a break in series, a differing 
definition, or low reliability are documented in the 
metadata notes provided beneath each figure, map, 
infographic or table. Readers who would like more detailed 
metadata are encouraged to use the online data codes 
that are provided as part of the source. 

MISSING DATA 

Missing data for any region of the EU, EFTA or candidate 
countries are denoted by a dark shade of grey in maps, 
while countries outside the spatial coverage adopted for 
this edition of the Eurostat regional yearbook are denoted 
by a lighter shade of grey. 

In figures, missing data for any of the regions are 
footnoted. Many of the figures presented in the Eurostat 
regional yearbook are based exclusively on data for EU 
regions – for example, charts showing the 10 regions 
in the EU with the highest/lowest rates or shares for a 
particular indicator. 

When processing the data for this edition of the Eurostat 
regional yearbook missing data were identified. An effort 
was made to fill missing cells with data for the previous 
year (at the same NUTS level); if this was not possible use 
was made of more aggregated NUTS levels or national 
data. Any exceptions for different geographical levels or for 
different reference periods are documented in the notes/
footnotes under each figure, map, infographic or table. 

Where few or no regional values exist for a particular 
country, national data have been used to fill the gaps; 
these exceptions are also documented in notes/footnotes. 
Furthermore, the source data (online data codes) are 
adapted so as to reflect any additional national data tables 
that may have been used. 

When statistical information in a particular figure or 
map covers more than 1 territorial level (for example, 
only national data are available for some countries), any 
mention in the accompanying commentaries of the 
number of regions meeting a criterion are based on the 
available data, reflecting the actual NUTS levels or national 
data used. 

Eurostat regional yearbook – 2025 edition12 
Introduction

Policy context

Cohesion policy: how is it delivered? 

European policymaking must navigate a dual challenge: 
setting broad objectives for the EU while addressing the 
specific needs of national and regional territories. The 
territorial dimension of EU policy is increasingly recognised 
as essential for issues such as job creation or the transition 
to a green and digital economy. 

Recent challenges within the EU – such as the global 
financial and economic crisis, security concerns, the 
refugee crisis, the departure of the United Kingdom from 
the EU (Brexit), the COVID-19 crisis, the impact of Russia’s 
war of aggression against Ukraine, or the cost-of-living 
crisis – underscore the complexity of delivering coherent 
policies locally, regionally, nationally and internationally. 

COHESION POLICY 

What is cohesion policy? 

The EU’s cohesion policy is designed to strengthen 
economic, social and territorial cohesion within the EU. It 
aims to promote job creation, business competitiveness, 
economic growth, social inclusion and sustainable 
development, thereby impacting the overall quality of life. 

The bulk of cohesion policy funding is concentrated 
on less developed EU regions with the goal of helping 
them to catch up and to reduce the economic, social and 
territorial disparities that exist across the EU. Although 
cohesion policy covers every region of the EU, most of 
the funds are targeted at regions where gross domestic 
product (GDP) per inhabitant is less than 75% of the EU 
average. 

During the period 2021 to 2027, the framework for regional 
development and cohesion policy focuses on 5 key policy 
objectives: 

•  a more competitive and smarter Europe, promoting 
innovation, digitalisation, economic transformation and 
support to SMEs

•  a greener Europe, transitioning towards a ‘net zero’ 
economy, while implementing the Paris Agreement

•  a more connected Europe, enhancing mobility
•  a more social and inclusive Europe, supporting 
social inclusion, equal access to healthcare, quality 
employment, education and skills

•  a Europe closer to citizens, fostering the sustainable 
and integrated development of locally led strategies.

Cohesion policy is delivered through a number of specific 
funds. They are the main financial instruments used to 
promote economic, social and territorial cohesion across 
the EU. 

The European Regional Development Fund (ERDF) 
aims to strengthen economic, territorial and social 
cohesion in the EU by correcting development imbalances 
between the regions. It focuses on providing funding 
for key policy areas such as innovation and research, the 
digital agenda, support for SMEs and the low-carbon 
economy. The ERDF also supports cross-border and 
transnational cooperation, under the European Territorial 
Cooperation objective (Interreg).

The Cohesion Fund (CF) aims to reduce economic and 
social disparities and to promote sustainable development 
in EU countries with a gross national income per inhabitant 
below 90% of the EU average. It primarily focuses on 
investments in environmental and trans-European 
transport network projects.

The European Social Fund Plus (ESF+) provides support 
for people, with a focus on improving employment and 
education opportunities across the EU, as well as the 
situation of the most vulnerable people (those at risk of 
poverty).

The Just Transition Fund (JTF) aims to support territories 
facing serious socioeconomic challenges arising from the 
transition towards climate neutrality. It address the social, 
employment, economic and environmental impacts of the 
transition and is designed to facilitate the implementation 
of the European Green Deal, with the goal of achieving 
climate-neutrality by 2050.

Cohesion policy: how is the budget decided? 

Political agreement on the legislative package for cohesion 
policy for 2021 to 2027 was adopted in the middle of 2021, 
with the multiannual financial framework including a total 
of €392 billion for regional development and cohesion. 
For more information, see the budget allocations for EU 
cohesion policy 2021 to 2027. 

13Eurostat regional yearbook – 2025 edition 
Introduction

More about the data: the NUTS classification 
– an objective basis for the allocation of 
cohesion policy funding 

Statistics from regional accounts are used in the 
allocation of cohesion policy funds, with the NUTS 
classification providing the basis for regional boundaries 
and geographic eligibility. 

During the period 2021 to 2027, eligibility for cohesion 
policy funds is based on NUTS level 2 regions being 
ranked and split into 3 groups: 

•  less developed regions, where GDP per inhabitant (in 
purchasing power standards (PPS)) was less than 75% 
of the EU average

•  transition regions, where GDP per inhabitant was 75% 

to 100% of the EU average

•  more developed regions, where GDP per inhabitant 

was greater than 100% of the EU average.

Additional criteria, such as youth unemployment, 
education levels, climate change, demographic 
developments and migrant integration, have been 
introduced to address regional challenges better. 

The EU assigns the bulk of the cohesion policy budget 
to regions whose development lags behind the EU 
average, aiming to reduce economic, social and territorial 
disparities. Less developed and transition regions – 
predominantly located in the south or the east of the 
EU, the Baltic countries and several outermost regions – 
benefit from 90% of the ERDF and ESF+ resources. 

OTHER POLICY AREAS THAT IMPACT ON 
SUBNATIONAL AREAS 

Urban development policy in the EU 

The EU’s urban development policy integrates economic, 
social, cultural and environmental aspects through 
coordinated measures such as urban renewal, education 
opportunities, social inclusion, crime prevention and 
environmental protection. Intergovernmental cooperation 
on urban matters began in May 2016 with the Urban 
Agenda for the EU, established by the Pact of Amsterdam. 
It aims to enhance regulation, funding and knowledge 
for cities in Europe through partnerships in 14 priority 
areas including (among others) migration, urban poverty, 
housing and climate adaptation. 

equality, to be followed by water-sensitive cities and 
building decarbonisation. 

The EU has strengthened the urban dimension of cohesion 
policy for 2021 to 2027, requiring that at least 8% of the 
ERDF is allocated to sustainable urban development and 
launching the European Urban Initiative (EUI) to support 
cities in innovation, knowledge-sharing and policy 
development. 

Rural development policy in the EU 

Rural areas are central to the European way of life: they 
are valued for their role in food production, natural 
resource management, landscape preservation and 
tourism. However, globalisation and urbanisation have 
transformed many of the EU’s rural regions, such that 
some are characterised by population decline, fewer job 
opportunities, ageing communities, an erosion of rural 
infrastructure and service provision, and limited transport 
and digital connectivity. 

The European Commission’s Long-term vision for the EU’s 
rural areas aims to create stronger, connected, resilient 
and prosperous rural areas by 2040. It includes a cross-
sectoral rural action plan to address economic, social and 
environmental challenges and a rural pact to enhance 
cooperation between public authorities, private and civil 
society organisations and academia willing to act for 
rural areas and communities. The European Commission’s 
recent Vision for Agriculture and Food commits to update 
the EU rural action plan in 2025, strengthen the rural pact 
and rural proofing. 

The EU’s common agricultural policy (CAP) is composed 
of the European Agricultural Guarantee Fund (EAGF) that 
provides income support and support to agricultural 
markets and the European Agricultural Fund for Rural 
Development (EAFRD) which supports farmers and rural 
sustainability. 

The common agricultural policy for 2023 to 2027 aims to 
secure the future of agriculture and forestry, while making 
the CAP more environmentally friendly by focusing 
on 10 specific objectives that are linked to social, economic 
and environmental sustainability in agriculture and in rural 
areas, for example, fair income for farmers, climate action, 
the preservation of landscapes and biodiversity, and 
support for generational renewal and vibrant rural areas. 

European Committee of the Regions 

In 2021, the agenda underwent renewal, guided by the 
New Leipzig Charter – the transformative power of cities 
for the common good, leading to the Ljubljana agreement. 
This second phase introduced new partnerships on 
greening cities, sustainable tourism, food and cities of 

The European Committee of the Regions (CoR) – which 
is the EU’s assembly for regional and local representatives 
– provides a voice for regions and cities across the EU. 
During the period 2025 to 2030, the CoR has 3 main 
priorities: cohesion, resilience and proximity.

Eurostat regional yearbook – 2025 edition14 
Introduction

Economic, social and territorial cohesion is essential for 
reducing regional disparities and promoting balanced 
development in the EU. The CoR stresses the need for 
stronger partnerships and the involvement of local and 
regional authorities in shaping the post-2027 financial 
framework, ensuring tailored solutions for community-
specific challenges. 

To address these priorities, the CoR advocates for robust 
financial instruments, greater fiscal autonomy for regions, 
and diversified EU revenue sources. This key priority 
includes matters on competitiveness, EU budget, just 
transition and sustainable and affordable housing. 

Resilience involves addressing natural and human-made 
crises, security threats and instability. The CoR emphasises 
empowering local and regional authorities with better 
coordination, funding and tools to manage climate action, 
energy security and water resilience effectively. This key 
priority includes matters on cross-border cooperation, 

climate change, water resilience, clean, secure and 
affordable energy, health, agriculture and security. 

Proximity focuses on connecting the EU to its citizens by 
empowering local and regional governments to address 
community needs. The CoR highlights the role of local 
governance in fostering trust, addressing demographic 
change, enhancing inclusion and promoting democratic 
renewal, youth engagement and gender equality. This key 
priority includes matters on demographic change, local 
democracy, gender equality and youth. 

The European Week of Regions and Cities is an annual 
multi-day event which allows regions and cities to 
showcase their capacity to encourage growth and job 
creation, implement EU cohesion policy and support 
good governance. The 23rd European Week of Regions 
and Cities will be held 13 to 15 October 2025 under the 
headline of ‘Empowering communities’. 

15Eurostat regional yearbook – 2025 edition 
Introduction

Strategic agenda 2024 to 2029 

In June 2024, following the European Parliament 
elections, the European Council adopted a Strategic 
agenda 2024 to 2029. It addresses global instability, 
economic challenges and threats to the rules-based order, 
focusing on 3 main pillars: 

•  a free and democratic Europe – upholding fundamental 

rights and democratic values

•  a strong and secure Europe – enhancing defence and 

security measures

•  a prosperous and competitive Europe – promoting 

economic growth and sustainability.

Sustainable prosperity and competitiveness 

Subsequently, the European Commission re-aligned 
its work programme, detailing 7 key priority areas. This 
publication focuses on 1 of these, namely, A new plan 
for Europe’s sustainable prosperity and competitiveness, 
which is considered central to improving citizens’ 
economic and social well-being, increasing their 
purchasing power, creating good jobs, assuring high-
quality goods and services, and reinforcing Europe’s 
sovereignty in strategic sectors. The plan seeks to bolster 
the EU’s long-term competitiveness by: 

•  simplifying business operations
•  implementing a clean industrial strategy
•  promoting a circular and resilient economy

•  accelerating digital technology adoption, by enhancing 
productivity and positioning the EU as a leader in areas 
such as artificial intelligence, net-zero technologies, or 
semiconductors

•  prioritising research and innovation
•  boosting investment
•  addressing skills and labour shortages.

Sustainable development goals 

The 17 sustainable development goals (SDGs) provide a 
global policy framework until 2030 for stimulating action 
in areas of critical importance related to people, the planet, 
prosperity, peace and partnership. 

On 22 November 2016, the European Commission 
adopted the Communication, Next steps for a sustainable 
European future – European action for sustainability 
(COM(2016) 739 final). It detailed the importance of 
the SDGs, identified EU policies that contribute to the 
implementation of SDGs, and announced plans for regular 
monitoring within an EU context. The EU has made a 
firm commitment towards delivering on the SDGs and 
on the Paris Agreement on climate change. Against this 
background, Eurostat has been called upon to monitor 
regularly the progress towards the SDGs in an EU context. 

Eurostat regional yearbook – 2025 edition16 
APeople and society

1. Population

On 1 January 2024, there were 449.3 million people living 
in the EU. During the course of 2023, the population of 
the EU increased by 1.6 million. The rising number of 
inhabitants resulted exclusively from migratory flows, as 
natural population change was negative, with 1.2 million 
more deaths than births. The EU’s net migration – the 
difference between the number of immigrants and 

the number of emigrants, with statistical adjustments 
– was 2.8 million people in 2023. This figure reflects, 
among other factors, an influx of displaced people linked 
to Russia’s war of aggression against Ukraine, as well as 
migrant arrivals for employment, international protection, 
or family reasons. Population events such as births, deaths 
and migratory flows shape demographic changes over 

(% of total population, by NUTS 3 regions, 1 January 2024)
Note: includes estimates made for the purpose of this publication. The infographic shows the NUTS level 3 region with 
(% of total population, by NUTS 3 regions, 1 January 2024)
the highest young-age dependency ratio, data for the EU average, and the NUTS level 3 region with the highest 
old-age dependency ratio.
Note: includes estimates made for the purpose of this publication. The 
Source: Eurostat (online data code: demo_r_pjangrp3)
infographic shows the NUTS level 3 region with the highest young-age 
dependency ratio, data for the EU average, and the NUTS level 3 region 
with the highest old-age dependency ratio.

Source: Eurostat (online data code: demo_r_pjangrp3)

Eurostat regional yearbook – 2025 edition18A

Population

time to impact the structure of the EU’s population. 
Demographic developments are also impacted by 
irregular shocks, such as the COVID-19 crisis or Russia’s war 
of aggression against Ukraine. The population pyramids 
shown in the infographic above highlight the considerable 
difference in age structures across NUTS level 3 regions. 
On 1 January 2024, the French outermost region of 
Mayotte had the highest young-age dependency ratio in 
the EU, while the Belgian coastal region of Arr. Veurne had 
the highest old-age dependency ratio. 

Statistics based on the population 
grid

More about the data: statistics on the 
population grid 

Unlike the majority of data presented in the Eurostat 
regional yearbook, the information presented in 
the 1st section of this chapter is based on a fixed set of 
uniform, grid cells that measure 1 km². In the remainder 
of this publication, data are presented using the more 
aggregated NUTS 2024 classification. Some of the 
differences observed when viewing regional data 
presented by NUTS may reflect the underlying criteria 
used to determine the administrative boundaries that 
delineate each region, thereby potentially overlooking 
important intra-regional disparities and limiting 
analyses. 

The gridded data presented in this section cover the 
whole of the EU’s territory at a resolution of 1 km². 
Gridded statistics offer greater spatial precision than 
regional data, enabling more detailed, localised 
analyses, while capturing variations within regions 
that might otherwise be averaged out over larger 
administrative units. This is particularly valuable when 
examining high-resolution phenomena, such as 
population density, land use or environmental factors. 

The analyses that follow present gridded statistics 
based on a dataset from the 2021 population and 
housing census. The data references a system of 
uniform, equal-sized grid cells, offering several 
advantages: 

•  analysts can aggregate the data for different areas 

that cross regional and national boundaries
•  the data can be assembled to form areas for 

specific purposes or studies, such as coastal regions, 
mountain regions or water catchment areas

•  the grid system integrates easily with other datasets
•  unlike administrative classifications, grids remain 

stable over time.

There were 4.45 million grid cells of 1 km² used for 
the 2021 population and housing census in the EU, 
with the EU’s population during this census year equal 
to 443.2 million inhabitants/residents. Across the EU, there 
were 1.82 million populated grid cells in the reference 
grid, with an additional 75 673 populated cells in 3 EFTA 
countries (Liechtenstein, Norway and Switzerland). 

Europeans tend to live in quite densely populated cities 
or in towns and suburbs, while the vast majority of 
the territory is more sparsely populated (see Map 1.1). 
Almost 60% of the 1 km² grid cells in the EU and EFTA 
countries – 2.69 million cells – had nobody living in them; 
among EU countries, Spain and Sweden had the largest 
areas of uninhabited land (374 735 and 346 556 cells, 
respectively). 

Data from the 2021 housing and population census 
indicate that 48.9 million people lived in the EU’s capital 
cities; this represented close to 10% of the overall 
population. More than 1 in 5 of these capital city residents 
lived in Paris (France) – the EU’s largest city – where the 
population was roughly twice the size of the next largest 
capital, Madrid (Spain). 

19Eurostat regional yearbook – 2025 editionA

Population

Map 1.1: Population density
(number of inhabitants per 1 km², 2021)

Source: Eurostat (GISCO) census population grid 2021

Looking in detail at individual 1 km² grid cells, some of 
the most densely populated areas of the EU were located 
in and around Barcelona (Spain), with a peak to the 
south-west of the city centre in L’Hospitalet de Llobregat, 
where 1 grid cell had a population of 56 158 inhabitants. 
There were also very high population density figures – 
upwards of 40 000 inhabitants per km² – in some grid cells 
within Paris and in the Spanish cities of Madrid, Valencia/
València and Málaga. 

Demographic developments across the EU are far from 
uniform, with considerable variations both between and 
within individual countries. The EU’s population structure 
is evolving, with a key factor being the increased mobility 
of young people. The EU’s population distribution spans 
various settlement types, each characterised by distinct 
demographic and economic features, including: 

•  major urban areas that tend to attract younger 
populations, with a high share of people living 
alone, relatively high living costs, diverse educational 
opportunities and dynamic labour markets

•  commuter belts/suburban areas, which are often 

populated by families

•  former industrial heartlands, characterised by economic 

decline, lower living costs, relatively high levels of 
unemployment, poverty and social exclusion

•  coastal and countryside locations, some of which are 
viewed as retirement locations for relatively affluent 
pensioners

•  other rural and remote regions which frequently exhibit 
declining population numbers, ageing populations, 
limited job opportunities and lower access to essential 
services.

Eurostat regional yearbook – 2025 edition20A

Population

Population developments can be analysed using data 
from the 2011 and 2021 population and housing censuses. 
According to this source, Germany had the largest 
absolute increase in population, with more than 1.7 million 
additional inhabitants. In relative terms, Malta experienced 
the fastest population growth, up 24.5%. By contrast, 
Romania recorded the largest population decline, losing 
more than a million inhabitants during the period under 
consideration, while Bulgaria recorded the steepest fall in 
relative terms, down 11.5%. 

than 10 000 additional inhabitants per 1 km². They were 
located in: 

•  several different urban areas within Byen København 

(the Danish capital)

•  Malmö, in the southern Swedish region of Skåne län 

(which is at the opposite end of the Öresund Bridge that 
links it to København)

•  Aalborg, in the northern Danish region of Nordjylland
•  3 other capital cities: Miasto Warszawa (Poland), 

Bucureşti/Ilfov (Romania) and Stockholms län (Sweden)

Between 2011 and 2021, several grid cells saw a 
significant increase in their population counts, with more 

•  3 French cities: Boulogne-sur-Mer (Pas-de-Calais), 
Marseille (Bouches-du-Rhône) and Toulon (Var).

Map 1.2: Population change
(overall change in number of inhabitants per 1 km², 2011–21)

Source: Eurostat (GISCO) census population grid 2021

21Eurostat regional yearbook – 2025 editionA

Population

Population structure

Historically, life expectancy at birth in the EU has 
increased at a relatively consistent pace. In 2019, prior 
to the COVID-19 pandemic, life expectancy at birth had 
reached 81.3 years. However, it fell by 0.9 years in 2020 and 
by an additional 0.3 years in 2021. In 2022, life expectancy 
at birth began to rise again, and this upward pattern 
continued in 2023, reaching 81.4 years, which was 0.1 years 
higher than its pre-pandemic level. 

There are a number of drivers that impact inter-regional 
differences in life expectancy, including: 

•  proximity to healthcare services – for example, capital 
regions tend to have a greater number and variety of 
healthcare facilities compared with rural regions

•  the prosperity of a region – for example, life expectancy 
is generally above average in regions characterised by a 
higher standard of living and below average in regions 
characterised by poverty and social deprivation

•  lifestyle and cultural differences – for example, the type 
of work that predominates in a region, the typical diet 
of a region, or the incidence of smoking and alcohol 
consumption

•  climatic conditions – for example, people living in warm 
and relatively dry climates tend to live longer lives than 
those living in regions that experience more extreme 
weather conditions.

Map 1.3 is composed of 2 parts: it presents life expectancy 
at birth for females and for males. Both maps use the same 
class boundaries in their legends to facilitate comparison. 
In 2023, female life expectancy at birth in the EU 
was 84.0 years. This equated to a gap of 5.3 years between 
the sexes, as male life expectancy at birth was 78.7 years. 

Female life expectancy was higher than male life 
expectancy across every region of the EU 

In 2023, all 242 NUTS level 2 regions for which data are 
available had higher female (than male) life expectancy 
at birth. The Baltic countries, along with several regions in 
Poland and Romania, reported some of the largest gender 
gaps. In Latvia, female life expectancy at birth (80.6 years) 
exceeded that for males (70.5 years) by 10.1 years. 

At the other end of the range, all of the regions in the 
Netherlands (subject to data availability) and all but 1 of 
the regions in Sweden – the northernmost region of 
Övre Norrland being the only exception – reported much 
smaller differences between the sexes. Flevoland in the 
Netherlands had the narrowest regional gender gap 
in 2023, with female life expectancy at birth (83.3 years) 

exceeding that for males (80.8 years) by 2.5 years. Several 
regions outside of the Netherlands and Sweden also 
reported gender gaps that were no more than 3.5 years: 

•  Prov. Antwerpen in Belgium
•  Nordjylland in Denmark
•  Eastern and Midland, and Southern in Ireland
•  Ciudad de Melilla in Spain
•  Mayotte in France
•  Luxembourg.

In 2023, the highest levels of female life expectancy at 
birth (as shown by the darkest shade of blue in Map 1.3) 
were located across much of Spain and Italy. There were 
also relatively high values – at least 85.5 years – recorded 
in several regions from Belgium, Greece, France, Austria 
and Portugal, as well as single regions from Slovenia, 
Finland and Sweden. The Finnish archipelago of Åland 
had the highest female life expectancy at birth, peaking 
at 88.5 years. The next highest regional values were 
concentrated in central and northern Spain: Comunidad de 
Madrid (88.3 years), Castilla y León (87.7 years), Comunidad 
Foral de Navarra (87.6 years) and País Vasco (87.4 years). 

Several of the highest levels of male life expectancy at 
birth were concentrated in northern and central regions of 
Italy. Several regions in Belgium, Spain, France and Sweden, 
as well as Åland (Finland), Eastern and Midland (the Irish 
capital region), Luxembourg and Malta also recorded 
relatively high values – at least 81.5 years. Comunidad 
de Madrid had the highest male life expectancy at birth 
(83.4 years in 2023). After the Spanish capital region, 
the next highest values were in Provincia Autonoma 
di Bolzano/Bozen, Provincia Autonoma di Trento (both 
northern Italy) and Stockholm (the Swedish capital region), 
each with male life expectancy at birth of 82.8 years. 

Across the 242 EU regions for which data are available, the 
French outermost region of Mayotte recorded the lowest 
female life expectancy at birth (76.2 years in 2023). There 
were 2 other regions – Severozapaden in north-western 
Bulgaria and Észak-Magyarország in northern Hungary 
– which also recorded female life expectancy at birth 
below 79.0 years, as shown by the lightest 2 shades in 
Map 1.3. 

There were 49 NUTS level 2 regions across the EU where 
male life expectancy at birth was below 76.0 years in 2023 
(as indicated by the lightest shade in Map 1.3). These 
regions were concentrated in eastern EU countries – 
Bulgaria, Czechia, Croatia, Hungary, Poland, Romania and 
Slovakia – but also included all of the regions in the Baltic 
countries, as well as Mayotte. Severozapaden (70.0 years) 
and Latvia (70.5 years) recorded the lowest regional values 
for male life expectancy at birth. 

Eurostat regional yearbook – 2025 edition22A

Population

Map 1.3: Life expectancy at birth
(years, by NUTS 2 regions, 2023)

Note: Türkiye, national data. Liechtenstein: 2022. North Macedonia: 2021. 
Albania: 2020.

Source: Eurostat (online data codes: demo_r_mlifexp and 
demo_mlexpec)

The median age is an indicator that helps summarise 
the pace at which population structures have changed. 
During the last 2 decades, the median age of the EU 
population increased 5.4 years, up from 39.3 years 
on 1 January 2004 to 44.7 years by 1 January 2024. 

The regional distribution of median ages exhibits some 
skewness. On 1 January 2024, 744 NUTS level 3 regions 
recorded a median age that was above the level for the 
EU, 13 had a median age that was the same as the EU 
(44.7 years), while 408 regions had median ages below the 
level for the EU. 

•  At the upper end of the distribution, 178 regions in the 
EU had a median age of at least 50.0 years. Germany 
accounted for 79 regions within this group (nearly half 
of the 178 regions), while there were 40 regions located 
in Italy (almost 1 in 4 of the 178 regions). Most of the 
remaining regions with relatively high median ages were 
located in Bulgaria, Greece, France and Portugal (each 
with 10 regions).

•  At the lower end of the distribution, 50 regions across 
the EU had a median age below 40.0 years. France 
(14 regions) and Germany (11 regions) together 
accounted for half of this group. Many of the regions 
with low median ages were predominantly urban 
regions (including capital regions and regions that 
border/surround their capital).

At the start of 2024, rural regions recorded some of the 
highest median ages … 

Figure 1.1 highlights those regions with the highest 
and lowest median ages, as of 1 January 2024. There 
were 3 regions in the EU that reported a median age 
above 55.0 years: 

•  Evrytania (57.0 years), a mountainous region in central 

Greece

•  Arr. Veurne (56.6 years), a rural/coastal region in western 

Flanders (Belgium)

•  Alto Tâmega e Barroso (56.5 years), a mountainous 

region in northern Portugal.

… while some of the lowest median ages were in and 
around capital cities 

Capital regions often exert a considerable pull on inter-
regional and international migrants, as they tend to 
provide a diverse range of educational and employment 
opportunities. This process can lead to a shift in 
population structures, with younger people accounting for 
a growing share of the population in predominantly urban 
regions; over time, this pattern may self-reinforce, insofar as 
populations with younger age structures are more likely to 
have relatively high birth rates. 

23Eurostat regional yearbook – 2025 editionA

Population

As of 1 January 2024, the outermost regions of Mayotte 
and Guyane (both France) had the lowest median ages in 
the EU (18.1 years and 27.3 years, respectively), reflecting 
high fertility rates and lower life expectancy; this was 
also the case in the autonomous region of Melilla (Spain). 
Beyond these 3 atypical regions, the lowest median ages 
were recorded in: 

•  capital regions, including the Danish capital of Byen 
København (34.1 years), the Belgian capital of Arr. de 
Bruxelles-Capitale/Arr. Brussel-Hoofdstad (36.0 years) and 
the Irish capital of Dublin (37.8 years)

•  regions bordering or surrounding capitals, such as Seine-

Saint-Denis and Val-d’Oise in France

•  other predominantly urban regions that are home to 
renowned academic institutions, diverse populations 
and dynamic labour markets, like Heidelburg and 
Freiburg im Breisgau (both Germany).

Figure 1.1: Median age
(years, by NUTS 3 regions, 1 January 2024)

Note: the figure shows the EU regions with the highest and lowest median 
ages.

Source: Eurostat (online data codes: demo_r_pjanind3 and 
demo_r_pjanind)

As of 1 January 2024, the Belgian coastal region of Arr. 
Veurne had the highest old-age dependency ratio in the EU 

Europe has an ageing population: dependency ratios 
serve as a key measure for analysing the economic and 
social pressures associated with low birth rates, a shrinking 
workforce and a growing number of retirees, driven in part 
by increased longevity. For the purpose of this publication, 
the old-age dependency ratio is defined as the proportion 
of elderly individuals (aged 65 years or over) relative to the 
working-age population (people aged 20 to 64 years); it is 
expressed in percentage terms. 

With a growing share of elderly people in the population, 
the EU faces a number of challenges in relation to pension 
systems, healthcare and labour markets, emphasising 

the need for sustainable policies that support the ageing 
population while at the same time ensuring economic and 
social stability. 

During the past 2 decades, the EU’s old-age dependency 
ratio has increased at a rapid pace. On 1 January 2004, 
it stood at 26.8%, indicating that there were slightly 
fewer than 4 working-age adults for every elderly person 
aged 65 years or over. Fast-forward to 1 January 2024 and 
the ratio had risen to 37.0%, meaning there were fewer 
than 3 working-age adults per elderly person. 

As of 1 January 2024, there were 139 NUTS level 3 regions 
where the old-age dependency ratio was at least 50.0%, 
indicating that there were no more than 2 working-age 
adults for every elderly person. This group of 139 regions, 

Eurostat regional yearbook – 2025 edition24A

Population

shown by the darkest shade of blue in Map 1.4, were mainly 
concentrated in (eastern) Germany and France. In addition, 
Italy, Finland, Portugal, Bulgaria, Greece and Spain also had 
several regions where the old-age dependency ratio was 
at least 50.0%. Most of these regions with high old-age 
dependency ratios were characterised as predominantly 
rural, mountainous and/or relatively remote regions. In other 

words, regions where it is likely that younger people have 
left the region to continue their studies or look for jobs. 

•  Arr. Veurne, a coastal region in the west of Belgium 

had the highest old-age dependency ratio in the EU, 
at 72.8% on 1 January 2024.

•  Alto Tâmega e Barroso in northern Portugal and 

Evrytania in central Greece were the only other regions 
in the EU where this ratio exceeded 70.0%.

Map 1.4: Old-age dependency ratio
(%, by NUTS 3 regions, 1 January 2024)

Note: share of people aged ≥ 65 years relative to people aged 20–64 years. 
Ukraine: national data. North Macedonia and Albania: 2023. Ukraine: 2022.

Source: Eurostat (online data codes: demo_r_pjanind3 and 
demo_pjanind)

Predominantly urban regions generally provide more 
opportunities for higher education and employment 
across a diverse range of occupations. As a result, they 
often attract young people, leading to lower old-
age dependency ratios. Equally, some elderly people 

may choose to leave these urban regions once they 
reach retirement, to avoid some of their perceived 
disadvantages, such as congestion, noise, crime and/or 
higher living costs. 

25Eurostat regional yearbook – 2025 editionA

Population

Leaving aside the atypical outermost regions of Mayotte 
(6.1%) and Guyane (13.8%), on 1 January 2024, the lowest 
old-age dependency ratios were generally recorded in 
some of the EU’s most dynamic and diverse regions, 
including: 

•  capital regions – Byen København in Denmark (17.8%), 
Arr. de Bruxelles-Capitale / Arr. Brussel-Hoofdstad in 
Belgium (20.5%), Dublin in Ireland (22.0%), Luxembourg 
(23.5%) and Groot-Amsterdam in the Netherlands (24.4%)
•  regions bordering or surrounding capital regions – Ilfov 
in Romania (21.0%), Seine-Saint-Denis in France (22.6%), 
Mid-East in Ireland (23.8%)

•  other predominantly urban regions – the German 

regions of Frankfurt am Main (24.3%), its neighbouring 
region of Offenbach am Main (25.0%) and Heidelberg 
(25.0%), as well as Gdański in Poland (24.7%).

Figure 1.2 presents a detailed analysis of population 
structures across NUTS level 3 regions. It highlights 
those regions with the highest and lowest shares of 
their populations across 3 different age groups, as 
of 1 January 2024. 

Young people (less than 20 years) made up 20.0% of the 
EU’s total population. 

•  Notably, there were 2 regions that had shares of young 
people that were more than twice as high as across 
the whole of the EU: the French outermost regions of 
Mayotte (53.8%) and Guyane (40.2%).

•  Several predominantly rural regions in southern EU 
countries recorded particularly low shares of young 
people: the lowest shares were in the north-western 
Spanish region of Zamora (12.7%), the northern 
Portuguese region of Alto Tâmega e Barroso (12.8%) and 
the central Greek region of Evrytania (12.9%).

Working-age people made up 58.4% of the EU’s total 
population. 

•  The highest regional share was recorded in the Spanish 

island region of Eivissa y Formentera, where 68.9% of the 
total population was of working age. Other regions with 
relatively high shares included: 

 ◦ tourism-driven economies – Fuerteventura, Lanzarote 

and Gran Canaria (all in Spain), and Malta

 ◦ urban/metropolitan hubs – Byen København 
(Denmark) and Frankfurt am Main (Germany)
 ◦ university/research centres – Heidelberg and 

Regensburg (both in Germany).

•  There were 4 regions in the EU where less than half of 

the population was of working age: 

 ◦ Mayotte (France) – where a relatively high share of 
the population is composed of young people and 
some individuals seek to migrate for education and/or 
employment opportunities

 ◦ Etelä-Savo (Finland), Arr. Veurne (Belgium) and Lot 

(France) – all of which attract retirees seeking a calmer 
lifestyle centred on their natural surroundings.

Older people accounted for 21.6% of the EU’s total 
population. 

•  There were 7 regions in the EU where at least 1 in 3 of 
the total population was composed of older people, 
several of them facing issues linked to depopulation. The 
highest shares were in: 

 ◦ the mountainous, inland Portuguese regions of Alto 
Tâmega e Barroso (which recorded the highest share, 
at 36.3%), Terras de Trás-os-Montes, Beiras e Serra da 
Estrela and Beira Baixa

 ◦ the mountainous Greek region of Evrytania
 ◦ the coastal Belgian region of Arr. Veurne
 ◦ the lakeland Finnish region of Etelä-Savo.

•  By contrast, older people accounted for less than a 

tenth of the population in the French outermost regions 
of Mayotte (2.7%) and Guyane (7.3%). There were also 
relatively low shares in: 

 ◦ Melilla and Ceuta (both in Spain)
 ◦ urban/metropolitan hubs – Byen København 

(Denmark), Arr. de Bruxelles-Capitale/Arr. Brussel-
Hoofdstad (Belgium), Ilfov (Romania) and Seine-Saint-
Denis (France)

 ◦ tourism-driven economies – Fuerteventura and Eivissa 

y Formentera (both in Spain).

Eurostat regional yearbook – 2025 edition26A

Population

Figure 1.2: Younger, working-age and older people
(% share of total population, by NUTS 3 regions, 1 January 2024)

Note: the figure shows the EU regions with the highest and lowest shares 
of younger people, working-age people and older people. The rankings 
include more than 10 regions if several regions have identical values.

Source: Eurostat (online data codes: demo_r_pjanind3 and 
demo_r_pjangroup)

Fertility and infant mortality

More about the data: measuring fertility 

The total fertility rate is the average (mean) number of 
children who would be born to a woman during her 
lifetime, if she were to spend her childbearing years 
conforming to the age-specific fertility rates of a given 
year. 

The natural replacement rate – the average number of 
live births per woman required to keep the population 
size constant in the absence of migration – is estimated 
to be around 2.10 children per woman for developed 
countries. 

Since 2016, the number of live births across the 
EU declined slowly from 4.4 million in 2016, falling 
over 4 consecutive years. After a modest rebound in 2021, 
the pace of decline accelerated in 2022 and 2023, with 
the number of births falling 5.1% and 5.4%, respectively. 
By 2023, the number of live births stood at 3.7 million; this 
was equivalent to an overall decrease of 16.3% compared 
with 2016. 

In 2023, the EU’s total fertility rate was 1.38 live births per 
woman; this was the lowest rate recorded since the start of 
the time series in 2001. During the last 2 decades, the EU’s 
fertility rate has remained consistently below the natural 
replacement rate, within the range of 1.38 to 1.57 live births 
per woman. 

French outermost regions have some of the highest fertility 
rates in the EU 

Map 1.5 shows the distribution of total fertility rates across 
NUTS level 3 regions. In 2023, the regional distribution 
was somewhat skewed insofar as there were 423 regions 
(38.7% of all regions) where the total fertility rate was 
below the EU level, while there were 671 regions (61.3%) 
where the rate was equal to or above the EU level. 

A more detailed analysis of the latest regional data 
for 2023 reveals that: 

•  some of the highest total fertility rates were recorded 

across Bulgaria and France

•  there were 6 regions across the EU where the total 

fertility rate was higher than the natural replacement 
rate of 2.10 live births per woman (as shown by the 
darkest shade of blue in Map 1.5) 

27Eurostat regional yearbook – 2025 editionA

Population

 ◦ the French outermost regions of Mayotte (4.17 live 

births per woman), Guyane (3.55) and La Réunion (2.31)

 ◦ the Bulgarian regions of Sliven (2.58), Yambol (2.40) 

and Pazardzhik (2.20)

 ◦ leaving aside these Spanish island regions, the next 
lowest rates occurred on the Italian island region of 
Sardegna – in Cagliari and Sud Sardegna – and in the 
central Greek region of Fokida

•  some of the lowest total fertility rates, below 1.00 live 
birth per woman, were concentrated in the southern 
EU countries of Greece, Spain and Italy (as shown by the 
lightest shade of yellow in Map 1.5) 

 ◦ the bottom end of the distribution had 5 island 

regions from Canarias (Spain) – El Hierro recorded the 
lowest rate (at 0.75 live births per woman), while rates 
were only marginally higher in Tenerife, La Gomera, 
Gran Canaria and La Palma

•  it was relatively common for the lowest total fertility rate 
within a country to be recorded in the capital region; 
this was the case for Hlavní město Praha (Czechia), Byen 
København (Denmark), Põhja-Eesti (Estonia), Dublin 
(Ireland), Budapest (Hungary), Malta (Malta) and Wien 
(Austria).

Map 1.5: Total fertility rate
(average number of live births per woman, by NUTS 3 regions, 2023)

Note: the Netherlands, Finland and Norway, NUTS level 2. Portugal: NUTS 
level 1. Latvia: national data. North Macedonia, Albania and Türkiye: 2022.

Source: Eurostat (online data codes: demo_r_find3 and demo_find)

Eurostat regional yearbook – 2025 edition28A

Population

Women in the EU are giving birth later in life 

A growing proportion of women in the EU give birth later 
in life, explaining, at least in part, the relatively low levels of 
fertility. This may be linked, among other factors, to: 

•  higher female participation rates in further education 
and/or more women choosing to establish a career 
before starting a family

•  lower levels of job security (for example, in precarious 

employment)

•  the increasing cost of raising children and of housing.

More about the data: the mean and the 
median age of women at childbirth 

The mean age of women at childbirth is the average 
age of mothers when giving birth; it is calculated by 
summing up all of the mothers’ ages at childbirth and 
dividing by the total number of births. The mean value 
can be skewed by extreme values, such as when a few 
women give birth much earlier or much later in life. 

The median age at childbirth is the middle value when 
all the mothers’ ages at childbirth are arranged in 
order. It usually provides a more reliable measure of the 
‘typical age’ at which mothers give birth, insofar as it is 
less affected by outliers. 

Both measures provide insights into fertility patterns 
across the EU. If the mean age is lower than the median 
age, the distribution is negatively skewed, with some 
younger mothers – teenagers and women in their 
early 20s – pulling the mean down. If the mean age 
is higher than the median age, the distribution is 
positively skewed, with most births concentrated at 
younger or middle reproductive ages, but some older 
mothers raising the mean. 

Across the EU, the median age of women at childbirth rose 
from 30.8 to 31.8 years between 2013 and 2023. During 
this decade, the rise in the median age had a fairly regular 
pattern of development, with modest annual increases 
interspersed with occasional years of stability. 

In 2023, the predominantly urban region of Voreios 
Tomeas Athinon, located to the north of the Greek 
capital, recorded the highest median age for mothers at 
childbirth … 

Figure 1.3 (right-hand side) shows that in 2023, among 
NUTS level 3 regions: 

•  some of the highest median ages of mothers at 

childbirth occurred in and around the Greek capital: 
Voreios Tomeas Athinon (to the north; 35.6 years, the 
highest value across the EU), Notios Tomeas Athinon (to 
the south; 34.7 years) and Kentrikos Tomeas Athinon (the 
capital region; 34.3 years)

•  several regions in the north and north-west of Spain 

had high median ages, including: A Coruña (34.8 years), 
Pontevedra (34.5 years), Bizkaia (34.4 years), Lugo, Ourense, 
Asturias, Araba/Álava and Valladolid (all 34.3 years)

•  3 regions on the Italian island of Sardegna had relatively 

high median ages: Oristano (34.7 years), Cagliari 
(34.4 years) and Sud Sardegna (34.3 years)

•  2 predominantly rural regions in Ireland, West and 

South-West, had relatively high median ages (34.9 years 
and 34.6 years, respectively), as did the capital region of 
Dublin (34.3 years).

At the bottom end of the distribution, the 10 EU regions 
with the lowest median ages of mothers at childbirth 
in 2023 were equally split between Bulgaria and Romania 
(5 regions each). The lowest values occurred in: 

•  the south-eastern Bulgarian regions of Sliven (23.5 years) 
and Yambol (25.1 years), both of which had total fertility 
rates higher than the natural replacement rate

•  the southern Romanian regions of Călăraşi (26.0 years) 

and Ialomiţa (26.2 years).

Between 2001 and 2023, the mean age of 
women at childbirth in the EU steadily increased 
from 29.0 to 31.2 years. Over the last 2 decades, 
this indicator followed a regular upward pattern of 
development, with modest annual increments or 
occasional years of stability. By 2023, the mean age of 
mothers at childbirth stood at 31.2 years. 

Capital regions and predominantly urban regions 
accounted for some of the highest mean ages of mothers 
at childbirth. This may reflect a variety of cultural, 
socioeconomic and personal factors, including: 

•  better access to healthcare in these regions, increasing the 

likelihood of a successful pregnancy for older women
•  more progressive social norms, giving women more 

freedom to decide when they have children.

… while Voreios Tomeas Athinon also had the highest 
mean age for mothers at childbirth 

Figure 1.3 (left-hand side) highlights the NUTS 
level 3 regions with the highest and lowest mean ages of 
mothers at childbirth. In 2023, the Greek region of Voreios 
Tomeas Athinon recorded the highest mean age (it also 
had the highest median age). Several other predominantly 
urban regions in and around the Greek capital appeared 
near the top of the ranking, along with Paris in France 
(34.3 years) and Byen København in Denmark (33.8 years). 

29Eurostat regional yearbook – 2025 editionA

Population

Figure 1.3: Mean and median age of mothers at childbirth
(years, by NUTS 3 regions, 2023)

Note: the figure shows the EU regions with the highest and lowest mean 
and median ages. The rankings include more than 10 regions if several 
regions have identical values. The median age is preferred for a more 
balanced / less skewed representation of fertility trends, while the mean 
age is more sensitive to outliers. Mean age: the Netherlands and Finland, 
NUTS level 2; Portugal, NUTS level 1; Latvia, national data; Utrecht (NL35) 
and Zuid-Holland (NL36), not available.

Source: Eurostat (online data codes: demo_r_find3 and demo_find)

At the opposite end of the distribution, there were 7 EU 
regions where the mean age of women at childbirth 
was below 27.0 years; they were all located in Bulgaria 
(5 regions) or Romania (2 regions). The lowest mean ages 
were recorded in 3 Bulgarian regions – Sliven (25.0 years), 
Yambol (25.8 years) and Pazardzhik (26.4 years) – all of 
which had total fertility rates higher than the natural 
replacement rate. 

Within the EU, a marked reduction in infant mortality rates 
has been a key driver of increased life expectancy. The EU 
has a relatively low infant mortality rate by international 
standards, reflecting well-established healthcare systems, 
access to quality prenatal and neonatal care, and 
comprehensive social support. 

In 1961, the EU recorded an infant mortality rate 
of 38.2 deaths per 1 000 live births. This rate fell almost 
uninterrupted and at a relatively fast pace through to 2000, 
when it stood at 6.0 deaths per 1 000 live births. The 

downward trend continued, albeit at a slower pace, over 
the next 2 decades. In 2023, some 12 280 children across 
the EU died before reaching the age of 1; as such, the 
infant mortality rate was 3.3 deaths per 1 000 live births. 

Capital regions have some of the lowest 
infant mortality rates 

Map 1.6 shows that the distribution of infant mortality 
rates across NUTS level 2 regions in 2023 was somewhat 
skewed. Of the 244 regions for which data are available, 
140 had rates below that of the EU and 96 had rates above; 
the remaining 8 regions had rates identical to that of the 
EU. 

In 2023, 19 regions across the EU had infant mortality rates 
that were below 2.0 deaths per 1 000 live births (as shown 
by the lightest shade of yellow in Map 1.6). These relatively 
low rates were mainly concentrated in Italy (4 regions), 
Finland, Sweden (both 3 regions) and Austria (2 regions). 

Eurostat regional yearbook – 2025 edition30A

Population

A closer look reveals that several of the regions with low 
infant mortality rates were capital regions. Budapest 
(Hungary), Helsinki-Uusimaa (Finland), Stockholm (Sweden), 
Zahodna Slovenija (Slovenia), Praha (Czechia) and Lazio 
(Italy) all recorded infant mortality rates that were i) below 
their respective national averages, and ii) below 2.0 deaths 
per 1 000 live births. These low rates may reflect, among 
other factors: 

•  higher living standards
•  better access to healthcare facilities and/or
•  a concentration of expertise and resources (for example, 
specialised neonatal units for infants requiring advanced 
medical interventions).

At the upper end of the distribution, 35 regions had infant 
mortality rates of at least 4.5 deaths per 1 000 live births 

in 2023 (as shown by the 3 darkest shades of blue in 
Map 1.6). This group included: 

•  all 5 of the French outermost regions – Mayotte 

(10.5 deaths per 1 000 live births), Guadeloupe (9.7), 
Guyane (9.6), Martinique (7.5) and La Réunion (5.6) – with 
Mayotte recording the highest rate in the EU

•  a large number of predominantly rural regions, such as 

Východné Slovensko in Slovakia (9.3 deaths per 1 000 live 
births), Yugoiztochen in Bulgaria (7.8), Nord-Vest in 
Romania (6.9) and Anatoliki Makedonia, Thraki in Greece 
(6.6)

•  some exceptions, as there were a few regions 

characterised by much higher levels of population 
density, including Bremen in Germany, Flevoland in the 
Netherlands, and the capital regions of Wien in Austria 
and Ile-de-France in France.

Map 1.6: Infant mortality rate
(deaths per 1 000 live births, by NUTS 2 regions, 2023)

Note: the infant mortality rate is based on the number of deaths of 
children under 1 year of age. Ukraine: national data. Germany: 2022, except 
Saarland (DEC0) and Leipzig (DED5), 2021. Georgia, Albania, Serbia and 
Türkiye: 2022. Ukraine: 2021.

Source: Eurostat (online data codes: demo_r_minfind and demo_minfind)

31Eurostat regional yearbook – 2025 edition2. Health

Health is an important priority for most Europeans who 
expect to receive efficient healthcare services – for 
example, if they contract a disease or are in an accident 
– alongside timely and reliable public health information. 
The overall health of the European Union’s (EU’s) 
population is also closely linked to that of the environment 
through – among other influences – the quality of the air 
we breathe, the water we drink and the food we eat. 

a Communication on a comprehensive approach to mental 
health (COM(2023) 298 final). This initiative promotes better 
access to mental healthcare, workplace well-being and 
research, aiming to give mental health the same importance 
as physical health. The European Pillar of Social Rights 
further reinforces mental health as a key aspect of well-
being, linking it to employment, education and healthcare 
policies. 

A key principle included within the European Pillar of 
Social Rights is that everyone in the EU should have access 
to affordable, preventive and curative healthcare of good 
quality. As part of its work within this area, the European 
Commission has a range of specific actions, including: 

•  support for the modernisation and digitalisation of 

health systems

•  a pharmaceuticals strategy for Europe
•  Europe’s Beating Cancer Plan
•  a new comprehensive approach to mental health.

This year’s edition of the Eurostat regional yearbook highlights 
mental and behavioural disorders. Initiatives related to these 
conditions are an integral part of the EU’s public health 
policy, which prioritises prevention, treatment and social 
inclusion. In 2023, in response to the lasting impact of the 
COVID-19 pandemic, the European Commission published 

The European Commission addresses alcohol use and 
mental and behavioural disorders through actions under 
the EU4Health programmes. It supports the integration of 
mental health services into primary care and encourages 
early intervention, while collaborating with EU countries 
to reduce alcohol-related harm via taxation, labelling and 
marketing restrictions. 

In 2022, mental and behavioural disorders due to the use 
of alcohol accounted for 0.35% of all deaths within the EU. 
There were 6 NUTS level 2 regions with shares above 1.00% 
(see the infographic below): 

•  the Slovenian regions of Zahodna Slovenija (1.66%) and 

Vzhodna Slovenija (1.51%)
•  Hannover (1.43%) in Germany
•  Pomorskie (1.32%), Mazowiecki regionalny (1.24%) and 

Zachodniopomorskie (1.23%) in Poland.

(% of all deaths, by NUTS 2 regions, 2022)

Source: Eurostat (online data code: hlth_cd_asdr2)

Note: based on standardised death rates per 100 000 inhabitants. Utrecht 
(NL35), Zuid-Holland (NL36), Centro (PT19), Grande Lisboa (PT1A), Península 
de Setúbal (PT1B), Alentejo (PT1C) and Oeste e Vale do Tejo (PT1D): not 
available.

Eurostat regional yearbook – 2025 edition32A

Health

Healthcare resources and 
unmet needs

ACCESSIBILITY OF HOSPITALS 

In 2023, 83.4% of the EU’s population lived 
within 15 minutes driving time of a hospital 

Accessibility broadly refers to how quickly and easily 
people can reach a destination given the available means 
of transport. Policymakers are increasingly prioritising it in 
areas such as land-use, transport and regional planning. 
In rural areas, limited access to key services – such as 
hospitals, pharmacies, schools, banks or supermarkets – 
remains a significant challenge. 

In 2023, 83.4% of the EU’s population lived within 
a 15 minute drive of a hospital (note: EU countries may 
apply different definitions for a ‘hospital’). In 125 NUTS 
level 3 regions, the entire population (100.0%) had this 
level of access, as shown by the darkest shade of blue 
in Map 2.1. Germany accounted for most of this group – 
96 out of the 125 regions – primarily within predominantly 
urban regions. The other 29 regions included: 

•  6 from Belgium
•  6 from the Netherlands (including Groot-Amsterdam)
•  4 from Greece (including Kentrikos Tomeas Athinon 

and 3 others within close proximity)

•  4 from France (including Paris and 3 others within close 

proximity)

•  3 from Poland (including Miasto Warszawa)
•  both from Malta
•  2 from Spain (Ceuta and Melilla)
•  2 from Italy (Milano and its neighbouring region of 

Monza e della Brianza).

In 2023, more than 1 in 4 EU regions (318 out of 
the 1 160 NUTS level 3 regions for which data are 
available) had at least 95.0% of their population living 
within a 15-minute drive of a hospital. These regions were 
typically densely populated and/or predominantly urban. 
Most of the capital regions in the EU had accessibility 
rates of at least 90.0%, although slightly lower values 
were recorded in Estonia, Latvia and Slovakia. By contrast, 
Osrednjeslovenska (the capital region of Slovenia) and 
Stockholms län (the capital region of Sweden) reported 
that less than 80.0% of the population lived within 
a 15-minute drive of a hospital; this was also the case in 
Cyprus. 

In 2023, 91 NUTS level 3 regions (those shown with a light 
yellow shade in Map 2.1) had a minority of their population 
living within a 15-minute drive of a hospital. Most of 
these regions where less than half of the population lived 
within 15 minutes driving time of a hospital were located 
in predominantly rural regions across eastern and southern 
EU countries: 

•  Romania (21 regions) and Greece (15 regions) recorded 

the highest counts

•  followed by Croatia and Spain (both 9 regions), Poland 

(8), Portugal (7) and Slovenia (6).

At the lower end of the distribution, 6 regions in the 
EU had less than 10.0% of their population living within 
a 15-minute drive of a hospital: 

•  Mehedinţi, Covasna and Tulcea in Romania
•  Chalkidiki, Thesprotia and Lefkada in Greece (with 

Lefkada having none of its population living within 
a 15-minute drive of a hospital).

33Eurostat regional yearbook – 2025 editionA

Health

Map 2.1: Accessibility to healthcare services
(% of population living within 15 minutes of a hospital, by NUTS 3 regions, 2023)

Note: 15 minutes driving time.

Source: Eurostat calculations based on TomTom Multinet 2022, Geostat 
population grid 2021, Eurostat-GISCO hospital locations 2023

Eurostat regional yearbook – 2025 edition34A

Health

HOSPITAL BEDS AND MEDICAL DOCTORS 

More about the data: the number of hospital 
beds and medical doctors 

The number of hospital beds and the number of 
medical doctors serve as indicators to measure the 
capacity of healthcare systems. 

•  The number of hospital beds includes beds which 

are regularly maintained and staffed and immediately 
available for the care of patients admitted to hospitals; 
these statistics cover beds in general hospitals and in 
speciality hospitals.

•  The number of medical doctors includes generalists, 

such as general practitioners (GPs), as well as 
medical and surgical specialists. These doctors 
provide services to patients as consumers of health 
care, including: giving advice, conducting medical 
examinations and making diagnoses, applying 
preventive medical methods, prescribing medication 
and treating diagnosed illnesses, giving specialised 
medical or surgical treatment.

Eurostat gives preference to the concept of ‘practising’ 
healthcare staff. The data for Greece, Portugal and 
Finland relate to medical doctors ‘licensed to practice’, 
while the data for Slovakia, North Macedonia and 
Türkiye relate to ‘professionally active’ medical doctors. 

Within this section on healthcare resources: 

•  only national data are available for Germany and the 

Netherlands for hospital beds

•  only national data are available for Germany and 

Ireland for medical doctors.

Zachodniopomorskie (Poland) and Bucureşti-Ilfov 
(Romania) were the only EU regions to report more 
than 1 000 hospital beds per 100 000 inhabitants in 2022 

In 2022, there were 2.31 million hospital beds across the EU. 
This equated to 516 hospital beds per 100 000 inhabitants, 
or – expressed in a different way – there was, on average, 
1 hospital bed for every 194 people. 

Figure 2.1 highlights the NUTS level 2 regions with 
the highest and lowest numbers of hospital beds 
per 100 000 inhabitants in 2022. Zachodniopomorskie 
– a region in north-west Poland that includes the city of 
Szczecin – recorded the highest ratio, with 1 232 beds 
per 100 000 inhabitants. Bucureşti-Ilfov – Romania’s capital 
region – was the only other region in the EU to report 
more than 1 000 hospital beds per 100 000 inhabitants, 
at 1 067 beds. 

Major hospitals, specialised medical centres and teaching 
hospitals are often located in capital regions, potentially 
increasing their hospital bed capacity (although different 

healthcare models, funding and population density can 
also impact the distribution). This pattern of a relatively 
high number of hospital beds in capital regions was 
particularly evident in eastern EU countries: in 2022, 
București-Ilfov (Romania), Budapest (Hungary), Yuzhen 
tsentralen (Bulgaria) and Praha (Czechia) all featured 
among the 10 regions with the highest numbers of 
hospital beds per 100 000 inhabitants. 

At the other end of the distribution, 7 out of 
the 10 regions with the lowest ratios of hospital beds 
per 100 000 inhabitants were located in Sweden, including 
the capital region of Stockholm (203 hospital beds 
per 100 000 inhabitants in 2022). This pattern may reflect, 
at least in part, the Swedish healthcare model that focuses 
on primary and preventative care, outpatient services and 
shorter hospital stays, as well as care at home or in nursing 
facilities. Other regions with very low ratios included: 

•  the autonomous Spanish cities of Ceuta and Melilla
•  the French outermost region of Mayotte, which had 

the lowest ratio across EU regions, at 155 hospital beds 
per 100 000 inhabitants.

Attiki (Greece) had a ratio of medical doctors 
per 100 000 inhabitants that was more than twice as high 
as the EU average in 2022 

In 2022, there were 1.83 million medical doctors 
in the EU, equivalent to a ratio of 408 doctors for 
every 100 000 inhabitants, or 1 doctor for every 245 people. 
This EU average masks significant regional disparities across 
NUTS level 2: Figure 2.1 provides information about those EU 
regions that had the highest and lowest ratios of doctor-to-
population ratios. At the top end of the distribution: 

•  Attiki, the Greek capital region, had the highest ratio, 

at 827 medical doctors per 100 000 inhabitants; this may 
reflect, at least in part 

 ◦ data for Greece being based on licensed professionals, 
rather than practicing doctors; as a result, Greek data 
is likely to be higher (compared with those of other 
EU countries) due to the inclusion of some doctors 
who are engaged in other professional activities, for 
example, teaching, research or consulting

 ◦ the availability of medical education and training that 
encourages both domestic and international doctors 
to settle/remain in the Greek capital

•  Ipeiros, another Greek region, had the 2nd highest ratio, 

with 769 medical doctors per 100 000 inhabitants
•  Bucureşti-Ilfov, the Romanian capital region, had 

the 3rd highest ratio, at 765 per 100 000 inhabitants

•  there were several (other) capital regions with 

high ratios, for example, Praha in Czechia 
(757 per 100 000 inhabitants), Wien in Austria (706), Grad 
Zagreb in Croatia (662) and Bratislavský kraj in Slovakia 
(also 662).

35Eurostat regional yearbook – 2025 editionA

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Low doctor-to-population ratios may result from a variety 
of factors, including the organisation of healthcare 
resources, limited funding and/or a lack of trained staff 
(recruitment issues, excessive workload/burnout). Across 
the EU, it is relatively common to find regions struggling 
with the recruitment and retention of key healthcare 
professionals. 

EU regions with relatively low doctor-to-population ratios 
are predominantly rural and often remote. In 2022: 

•  the French outermost region of Mayotte had the lowest 

ratio among NUTS level 2 regions, with 86 medical 
doctors per 100 000 inhabitants

Figure 2.1: Number of hospital beds and medical doctors
(per 100 000 inhabitants, by NUTS 2 regions, 2022)

•  Åland, an archipelago in Finland, had the 2nd lowest 

ratio, at 106 per 100 000 inhabitants

•  the Dutch regions of Flevoland 

(133 per 100 000 inhabitants) and Zeeland (166) had, 
respectively, the 3rd and 4th lowest ratios

•  several predominantly rural regions located in eastern 

EU countries also recorded relatively low ratios, 
including the southern Romanian regions of Sud-
Muntenia and Sud-Est, the northern Hungarian region 
of Észak-Magyarország, and the central Polish region of 
Mazowiecki regionalny (which surrounds its capital).

Note: the figure shows the EU regions with the highest and lowest rates 
per 100 000 inhabitants. Hospital beds: Germany and the Netherlands, 
national data. Medical doctors: Germany and Ireland, national data. 
Medical doctors: Denmark, Finland and Sweden, 2021. Centro (PT19), 
Grande Lisboa (PT1A), Península de Setúbal (PT1B), Alentejo (PT1C) and 
Oeste e Vale do Tejo (PT1D): not available. Medical doctors: Ciudad de 
Melilla (ES64), Luxembourg (LU00), Utrecht (NL35) and Zuid-Holland 
(NL36), not available.

Source: Eurostat (online data codes: hlth_rs_bdsrg2, hlth_rs_bds1, 
hlth_rs_physreg, hlth_rs_phys and demo_gind)

Eurostat regional yearbook – 2025 edition36A

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UNMET NEEDS FOR MEDICAL EXAMINATION 

Self-reported unmet needs for medical examination refer 
to a person’s own assessment of whether they needed an 
examination or treatment for a specific type of health care, 
but did not have it or did not seek it. In 2024, 3.6% of the 
EU population (aged 16 years or over) that was considered 
having a need for medical care reported being unable to 
receive a medical examination due to financial reasons, 
long waiting lists or distance. 

Figure 2.2 provides information on the NUTS 
level 2 regions with the highest and lowest shares of 
people reporting unmet needs for medical examination. 
Note that the denominator for this indicator concerns only 
those people (aged 16 years or over) who were considered 
having a need for medical care. In 2024, there were 23 out 

of 161 regions for which data are available that recorded 
double-digit shares of people with unmet needs. 

•  The highest shares were reported in Greek regions, with 
peaks in Sterea Elláda (28.9%) and Notio Aigaio (25.5%). 
All 13 Greek regions featured among the 14 regions with 
the highest shares, with Umbria in Italy the only non-
Greek region to record a comparable level.

•  The 9 other regions with double-digit shares comprised: 

 ◦ 5 regions from Finland – Länsi-Suomi, Åland, Helsinki-

Uusimaa, Etelä-Suomi and Pohjois- ja Itä-Suomi

 ◦ Sud-Est (Romania)
 ◦ Estonia
 ◦ Puglia (Italy)
 ◦ Latvia.

Figure 2.2: Self-reported unmet needs for medical examination due to financial reasons, long waiting list or 
distance
(% of people aged ≥ 16 years in need of medical care, by NUTS 2 regions, 2024)

Note: the figure shows the EU regions with the highest and lowest shares. 
The rankings include more than 10 regions if several regions have identical 
values. Länsi-Suomi (FI19) and Åland (FI20) are aggregated (same value 
for both regions). Belgium, Germany, the Netherlands, Austria and Poland: 
national data. Mayotte (FRY5): not available.

Source: Eurostat (online data codes: hlth_silc_08b_r and hlth_silc_08b)

37Eurostat regional yearbook – 2025 editionA

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Mortality and causes of death

A wide range of factors determine regional mortality 
patterns, with causes of death linked, among other issues, 
to age structures, sex distributions, access to and the 
quality of health care, living/working conditions, types 
of occupation, lifestyle choices and the surrounding 
environment. Since a region’s (or a country’s) population 
structure can strongly influence crude death rates, 
statisticians typically compare statistics on the causes of 
death using standardised death rates. 

More about the data: standardised death rates 

This section about causes of death presents information 
that is based on standardised death rates. Statisticians 
compute these rates by taking a weighted average of 
age-specific mortality rates, using the age distribution 
of a standard reference population as the weighting 
factor. 

Since age and sex significantly influence most causes of 
death, standardised death rates can be used to improve 
comparability. By eliminating the impact of different 
age structures between regions – as elderly people are 
more likely to die than younger people – the resulting 
measure allows more accurate comparisons across 
space and/or over time. 

In 2022, there were 5.2 million deaths in the EU 

The COVID-19 crisis led to an increase in the total number 
of deaths across the EU: this figure increased by more 
than 0.5 million in 2020 and by a further 112 000 in 2021 to 
reach a peak of 5.3 million. In 2022, the total number of 
deaths in the EU fell 139 000 to 5.2 million. The downward 
pattern accelerated in 2023, as the number of deaths 
fell below 5.0 million, although it remained above pre-
pandemic levels. 

Diseases of the circulatory system accounted for 
almost 1 in 3 deaths across the EU in 2022 

Statistics on causes of death are based on the 10th revision 
of the International Statistical Classification of Diseases 
and Related Health Problems (ICD-10), with the latest data 
available for 2022. At an aggregated level, based on ICD-
10 chapter headings: 

•  diseases of the circulatory system – including heart 
attacks, heart diseases, hypertensive diseases and 
diseases of pulmonary circulation – accounted 
for 32.4% of all deaths in the EU

•  cancer (malignant neoplasms) was the 2nd most 

common cause of death – accounting for 22.6% of all 
deaths

•  diseases of the respiratory system – including conditions 

such as chronic obstructive pulmonary disease, 
pneumonia or asthma – accounted for 7.0% of all deaths

•  mental and behavioural disorders – which are 

highlighted in this year’s edition of the Eurostat regional 
yearbook (see below) – accounted for 4.0% of all deaths

•  although not covered in this edition, the share 

of deaths attributed to COVID-19 fell from 10.8% 
in 2021 to 6.0% in 2022 reflecting, at least in part, 
widespread vaccination, natural immunity, improved 
treatment and understanding of the disease and less 
overwhelmed health services.

In 2022, diseases of the circulatory system were the 
main cause of death in 78.5% (186 out of 237) of 
NUTS level 2 regions. The 2 darkest shades of blue in 
Map 2.2 highlight the 18 regions across the EU where 
at least half of all deaths were caused by diseases of the 
circulatory system. 

•  The highest regional shares were in Bulgaria and 

Romania, where diseases of the circulatory system 
accounted for more than 50.0% of all deaths in every 
region: 

 ◦ Yuzhen tsentralen (southern Bulgaria) had the 

highest share, at 66.2%, followed by Sud-Vest Oltenia 
(south-western Romania), with 65.1%

 ◦ 4 other regions in Bulgaria reported that more 

than 3 in 5 deaths were accounted for by diseases of 
the circulatory system.

•  The only other regions in the EU where diseases of the 
circulatory system accounted for at least 50.0% of all 
deaths were both regions in Lithuania and Dél-Alföld 
(southern Hungary); this was also the case in Latvia.

By contrast, there were 10 NUTS level 2 regions across the 
EU where diseases of the circulatory system accounted 
for less than 1 in 5 deaths in 2022 (as shown by the light 
yellow shade in Map 2.2). All 10 of these regions were 
located in France, with the lowest shares in the capital 
region of Ile-de-France (18.9%), the southern region of 
Provence-Alpes-Côte d’Azur (18.7%) and the outermost 
region of Martinique (18.3%). 

Eurostat regional yearbook – 2025 edition38Map 2.2: Deaths due to diseases of the circulatory system
(% of all deaths, by NUTS 2 regions, 2022)

A

Health

Note: based on standardised death rates per 100 000 inhabitants.

Source: Eurostat (online data code: hlth_cd_asdr2)

39Eurostat regional yearbook – 2025 editionA

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In 2022, cancer accounted for 22.6% of all deaths in the EU 
and diseases of the respiratory system for 7.0% 

Figure 2.3 presents information for the 3 principal causes of 
death in the EU – diseases of the circulatory system, cancer 
(malignant neoplasms) and diseases of the respiratory 
system – alongside information about deaths from mental 
and behavioural disorders (which are highlighted in 
this year’s edition of the Eurostat regional yearbook – see 
below). 

•  The 1st part of the figure confirms that diseases of the 

circulatory system accounted for more than 50.0% of all 
deaths in Bulgarian and Romanian regions.

•  The Slovenian capital region of Zahodna Slovenija 
recorded the highest share of deaths from cancer 
(malignant neoplasms), at 30.6%. Relatively high shares 
were also reported in several regions of: 

 ◦ Spain, where País Vasco had the highest share, at 27.9%
 ◦ France, where Pays de la Loire had the highest share, 

at 27.7%

 ◦ Ireland, where the capital region of Eastern and 

Midland had the highest share, at 27.2%.

•  Several island regions reported relatively high shares 

of deaths from respiratory diseases. This may be linked, 
among other factors, to long-term exposure to volcanic 
pollutants and ash. Região Autónoma da Madeira 
(Portugal) had the highest share, at 14.5%, followed by: 

 ◦ Malta (12.8%)
 ◦ the eastern Belgian region of Prov. Liège (12.1%), which 
has a history of heavy industry such as coal mining 
and steel production

 ◦ Canarias in Spain (11.8%), where Saharan dust may also 

exacerbate respiratory issues.

•  Mental and behavioural disorders accounted for 4.0% of 
all deaths across the EU. Several Dutch regions recorded 
shares that were more than twice as high as the EU 
average, with Overijssel reporting the highest share 
(9.4%); the northern Italian region of Valle d’Aosta/Vallée 
d’Aoste had a similar share.

The information presented in Figure 2.3 also highlights 
those regions with the lowest shares of deaths from 
specific diseases/disorders in 2022. Low shares may reflect, 
among other factors: a high share of total deaths for 
another particular disease, which overshadows the impact 
of others; under-reporting of certain diseases/disorders, for 
example, due to misdiagnosis or because some diseases 
are not officially recognised on death certificates. 

•  Several regions across France recorded a very low share 
of deaths from diseases of the circulatory system. This 
may reflect, among other factors, healthier lifestyles, diet 
and/or widespread access to preventative screenings/
treatments.

•  The lowest shares of cancer deaths were recorded across 
Bulgaria and Romania, often in regions characterised by 
a high share of deaths from diseases of the circulatory 
system. There were 8 regions in the EU where cancer 
accounted for less than 15.0% of all deaths: all 6 regions 
in Bulgaria, as well as the Romanian regions of Sud-
Muntenia and Sud-Vest Oltenia. The lowest share of 
cancer deaths was recorded in Yugoiztochen in Bulgaria 
(11.9%).

•  Yuzhen tsentralen (Bulgaria) and Sostinės regionas (the 
capital region of Lithuania) had the lowest shares of 
deaths from respiratory diseases, both 2.3%. Several of 
the other regions characterised by low shares of deaths 
from respiratory diseases were Nordic regions, where 
low levels of air pollution may be an explanatory factor.

•  Bulgarian and Romanian regions had the lowest 

shares of deaths attributed to mental and behavioural 
disorders, often with shares close to zero. This was most 
notable in the southern Bulgarian region of Yuzhen 
tsentralen and the Romanian capital region of Bucureşti-
Ilfov (both 0.0%).

Eurostat regional yearbook – 2025 edition40Figure 2.3: Causes of death
(% of all deaths, by NUTS 2 regions, 2022)

A

Health

Note: based on standardised death rates per 100 000 inhabitants. The 
figure shows the EU regions with the highest and lowest shares. Utrecht 
(NL35), Zuid-Holland (NL36), Centro (PT19), Grande Lisboa (PT1A), Península 
de Setúbal (PT1B), Alentejo (PT1C) and Oeste e Vale do Tejo (PT1D): not 
available.

Source: Eurostat (online data code: hlth_cd_asdr2)

41Eurostat regional yearbook – 2025 editionA

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Focus on mental health

The COVID-19 pandemic intensified mental health 
challenges, especially for young people and those 
with pre-existing conditions. Isolation, uncertainty and 
disruptions to education and employment led to higher 
levels of stress, anxiety and depression, with lockdowns 
and restrictions exacerbating feelings of loneliness and 
emotional distress. Beyond individual struggles, the strain 
on mental health placed additional pressures on EU 
healthcare systems, increasing the demand for services, 
therapy and crisis intervention. Governments faced 
rising costs from disability claims and financial support 
for those unable to work, while lower employment rates 
and reduced productivity negatively impacted the EU 
economy. 

Following the COVID-19 pandemic, the EU has significantly 
enhanced its focus on mental health, recognising the 
pandemic’s profound impact on some people’s well-
being. In June 2023, the European Commission introduced 
a comprehensive approach to mental health, committing 
€1.23 billion to support EU countries in prioritising mental 
health alongside physical health. The strategy is anchored 
in 3 guiding principles: 

•  implementing effective measures to prevent mental 

health issues before they arise

•  ensuring high-quality and affordable mental healthcare 

and treatment are accessible to all

•  supporting individuals to re-integrate into society after 

recovery from mental health challenges.

More about the data: mental and behavioural 
disorders 

The statistics presented below on causes of death 
due to mental and behavioural disorders are primarily 
based on information pertaining to Chapter V of 
the 10th revision of the International Statistical 
Classification of Diseases and Related Health Problems 
(ICD-10). This chapter refers to mental and behavioural 
disorders (ICD-10 F00-F99), which include: 

•  organic mental disorders (including dementia in 

Alzheimer disease)

•  mental and behavioural disorders due to substance 

use

•  schizophrenia and delusional disorders
•  mood disorders
•  neurotic and stress-related disorders
•  behavioural syndromes
•  disorders of adult personality
•  mental retardation
•  disorders of psychological development
•  behavioural and emotional disorders with onset 

usually occurring in childhood

•  note these figures exclude intentional self-harm.

Intentional self-harm (ICD-10 X60-X84 and Y87) covers 
a range of purposely self-inflicted activities, including 
(attempted) suicide. The data also include sequelae (or 
‘late effects’) of intentional self-harm, which can result in 
death 1 year or more after the initial incident. Intentional 
self-harm is classified by place of occurrence and/or 
type of activity and includes: 

•  intentional self-poisoning by and exposure to 

 ◦ drugs, medicaments and biological substances
 ◦ alcohol
 ◦ organic solvents, carbon monoxide and other gases
 ◦ pesticides and other chemicals

•  intentional self-harm by hanging, strangulation or 

suffocation

•  intentional self-harm by drowning
•  intentional self-harm by handgun, rifle and other 

firearms discharge

•  intentional self-harm by explosive material, smoke, fire 

or flames

•  intentional self-harm by sharp or by blunt objects
•  intentional self-harm by jumping from a high place, 
jumping or lying before a moving object, crashing a 
motor vehicle.

Regional differences in the level and organisation of care 
for people with mental and behavioural disorders reflect, 
at least in part, variations in healthcare systems, funding, 
cultural attitudes and historical developments. 

•  Northern and western EU countries primarily provide 

mental healthcare in community settings.

•  By contrast, hospital-based models are more common 
across eastern EU countries, where patients are more 
likely to be treated in psychiatric hospitals; stigma 
around mental health issues persists, discouraging some 
individuals from seeking treatment.

•  In southern EU countries there is often limited public 
support for people with mental and behavioural 
disorders, leading to a greater reliance on family 
members to provide informal care.

Mental and behavioural disorders accounted for 4.0% of 
deaths across the EU in 2022 

In 2022, there were 211 000 deaths across the EU 
attributed to mental and behavioural disorders; this was 
equivalent to 4.0% of all deaths. The regional distribution 
was relatively uniform, insofar as 112 out of 237 NUTS 
level 2 regions for which data are available (47.3% of all 
regions) reported mental and behavioural disorders 
accounting for a share of total deaths that was equal to or 
above the EU average. 

Looking in more detail at the top end of the distribution, 
there were 22 regions where mental and behavioural 
disorders accounted for at least 7.0% of all deaths in 2022 
(as shown by the darkest shade of blue in Map 2.3). 

Eurostat regional yearbook – 2025 edition42A

Health

They were mainly concentrated in western EU countries, 
including: 

•  9 out of the 10 Dutch regions for which data are 

mental and behavioural disorders was at least twice as 
high as the EU average; alongside Overijssel and Valle 
d’Aosta/Vallée d’Aoste, the others were: 

available

•  5 regions from Germany
•  the Irish capital region.

Overijssel (eastern Netherlands) and Valle d’Aosta/
Vallée d’Aoste (northern Italy) had the highest shares of 
deaths from mental and behavioural disorders in 2022, 
both 9.4%. An analysis of the underlying death rates 
reveals that Overijssel also had the highest standardised 
death rate for mental and behavioural disorders among 
EU regions, at 95.7 deaths per 100 000 inhabitants. The 
rate in Overijssel was 2.3 times as high as the EU average 
(41.9 deaths per 100 000 inhabitants). There were 7 regions 
across the EU where the standardised death rate for 

•  Groningen, Gelderland and Limburg in the Netherlands
•  Hannover in Germany
•  Övre Norrland in Sweden.

In 2022, there were 22 NUTS level 2 regions where less 
than 1.0% of all deaths were attributed to mental and 
behavioural disorders (as shown by the light yellow shade 
in Map 2.3). Apart from the French outermost region 
of Guyane, this group was concentrated in eastern EU 
countries, including: 

•  every region of Bulgaria (6 regions) and Romania 

(8 regions)

•  4 regions from Poland
•  3 regions from Slovakia.

Map 2.3: Deaths due to mental and behavioural disorders
(% of all deaths, by NUTS 2 regions, 2022)

Note: based on standardised death rates per 100 000 inhabitants. Excludes 
suicides.

Source: Eurostat (online data code: hlth_cd_asdr2)

43Eurostat regional yearbook – 2025 editionA

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Within the EU, women recorded a higher share of deaths 
due to mental and behavioural disorders than men 

In 2022, mental and behavioural disorders claimed 
the lives of 131 000 women across the EU, compared 
with 80 000 men. This higher number reflects, at least 
in part, their greater longevity; on average, women live 
longer than men and are therefore more likely to develop 
a range of mental and behavioural disorders, some of 
which – such as dementia – disproportionately affect 
older adults. Based on standardised death rates, 4.7% of all 
female deaths across the EU in 2022 were due to mental 
and behavioural disorders, compared with 3.3% of all male 
deaths. 

Several NUTS level 2 regions recorded shares of deaths 
from mental and behavioural disorders that were more 
than twice the EU average. 

•  The highest shares among women were observed in 
Overijssel in the Netherlands (11.2%) and Valle d’Aosta/
Vallée d’Aoste in Italy (11.1%). There were 5 other 
regions with double-digit shares: Limburg, Gelderland 
and Noord-Brabant (all in the Netherlands), Mellersta 
Norrland (Sweden) and Malta. In addition, Övre Norrland 
(Sweden), Zeeland (the Netherlands) and País Vasco 
(Spain) recorded shares that were more than twice as 
high as the EU average.

•  The highest shares among men, 7.3%, were recorded in 
Hannover (Germany), Overijssel (the Netherlands) and 
Valle d’Aosta/Vallée d’Aoste (Italy). There were 4 other 
regions in the Netherlands that reported shares that 
were more than twice as high as the EU average: 
Groningen, Gelderland, Limburg and Zeeland, while 
this was also the case in Övre Norrland, Malta and 
Midtjylland (Denmark).

Among older people, there were 194 500 deaths due to 
mental and behavioural disorders across the EU in 2022 

In 2022, there were 194 500 deaths in the EU due to mental 
and behavioural disorders among people aged 65 years 
or over, compared with 16 300 deaths among those aged 
under 65. As with most causes of death, older people 
therefore accounted for the vast majority of deaths from 
mental and behavioural disorders, some 92.2% of all 
such deaths. An analysis by sex reveals a higher share 

among women, with 97.0% of female deaths from mental 
and behavioural disorders occurring among women 
aged 65 years or over, compared with 84.3% for men. 

Based on standardised death rates, mental and behavioural 
disorders accounted for 2.2% of all deaths in 2022 across 
the EU among people who were younger than 65, 
and 4.4% of all deaths among people aged 65 years or 
over. 

For the younger cohort, there were 7 NUTS level 2 regions 
where mental and behavioural disorders accounted for at 
least 5.0% of all deaths: 

•  the German regions of Hannover (7.4%), Saarland (6.6%), 
Bremen (5.9%), Hamburg (5.7%) and Braunschweig (5.6%)

•  both regions from Slovenia – Vzhodna Slovenija (6.5%) 

and Zahodna Slovenija (5.6%).

In 2022, among people aged 65 years or over, the share 
of all deaths due to mental and behavioural disorders 
peaked in the Italian region of Valle d’Aosta/Vallée d’Aoste 
(10.6%) and the Dutch region of Overijssel (10.3%). There 
were 8 other regions across the EU where mental and 
behavioural disorders accounted for more than twice as 
many deaths as the EU average (4.4%): 

•  Malta
•  the Dutch regions of Gelderland, Limburg, 
Noord-Brabant, Zeeland and Groningen
•  the Swedish regions of Övre Norrland and 

Mellersta Norrland.

At the other end of the distribution, many regions in 
eastern EU countries reported very low shares of deaths 
due to mental and behavioural disorders. This may reflect 
lower life expectancy – as fewer people reach the ages 
where these disorders are most common – or different 
disease patterns, such as higher mortality from circulatory 
diseases (which, in turn, reduces the relative share of 
deaths from mental and behavioural causes). However, 
regardless of the region, the shares may reflect under-
diagnosis and/or misreporting – for example, due to 
cultural stigma, reluctance to record mental illness as a 
cause of death, or national practices that prioritise physical 
conditions over mental and behavioural disorders when 
recording the main cause of death. 

Eurostat regional yearbook – 2025 edition44Figure 2.4: Deaths due to mental and behavioural disorders
(% of all deaths, by sex, age and NUTS 2 regions, 2022)

A

Health

Note: based on standardised death rates per 100 000 inhabitants. The 
figure shows the EU regions with the highest shares. Utrecht (NL35), Zuid-
Holland (NL36), Centro (PT19), Grande Lisboa (PT1A), Península de Setúbal 
(PT1B), Alentejo (PT1C) and Oeste e Vale do Tejo (PT1D): not available. 
Ciudad de Melilla (ES64): data for males, not available.

Source: Eurostat (online data code: hlth_cd_asdr2)

Almost 50 000 people across the EU died from intentional 
self-harm in 2022 

Deaths from intentional self-harm represent a significant 
public health issue in the EU. These deaths, often 
reflecting deeper societal and mental health challenges, 
vary considerably by sex. Men are considerably more 
likely to die from intentional self-harm than women, 
partly due to the more fatal methods they typically 
use, whereas a higher proportion of women tend to 
survive attempts to end their own lives. In 2022, the EU 
recorded 37 000 male deaths from intentional self-harm 
compared with 12 000 female deaths; as such, there were 
approximately 3 times as many male (as female) deaths. 

In 2022, the EU’s standardised death rate for intentional 
self-harm was 10.6 deaths per 100 000 inhabitants. The 
male rate (17.2 deaths per 100 000 male inhabitants) 
was 3.6 times as high as the female rate (4.8 deaths 
per 100 000 female inhabitants). Among NUTS 
level 2 regions, the highest standardised death rates: 

•  for both sexes (males and females together) were in the 
Hungarian region of Dél-Alföld, along with the French 
regions of Basse-Normandie and Bretagne

•  for males were in the Hungarian regions of Dél-Alföld 

and Alföld és Észak, and the Lithuanian region of Vidurio 
ir vakarų Lietuvos regionas

•  for females were in the Belgian regions of Prov. 

Luxembourg, Prov. West-Vlaanderen and Prov. Namur.

45Eurostat regional yearbook – 2025 editionA

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In 2022, intentional self-harm accounted for 1.0% of all 
deaths in the EU. The highest regional shares, across NUTS 
level 2 regions, were observed in the French regions of 
Bretagne and Basse-Normandie, where 2.3% of all deaths 
were due to intentional self-harm. There were 5 (other) 
predominantly rural regions in France – Franche-Comté, 
Poitou-Charentes, Pays de la Loire, Auvergne and Limousin 
– where intentional self-harm accounted for at least 2.0% 
of all deaths. 

Map 2.4 presents the regional distribution of deaths from 
intentional self-harm, with the darkest shade of blue 
identifying 32 regions across the EU where intentional self-
harm accounted for at least 1.6% of all deaths in 2022. This 
group included: 

•  15 predominantly rural regions in France (including 

the 7 already mentioned above with the highest shares)

•  7 out of the 11 regions in Belgium
•  4 regions from Austria, 3 regions from Sweden, both 
regions in Slovenia and a single region in Finland.

In 2022, 21 regions across the EU reported that less 
than 0.5% of all deaths were attributed to intentional 
self-harm (as shown by the light yellow shade in Map 2.4). 
Nearly all of these regions were in southern or eastern 
EU countries, with the sole exception being the French 
outermost region of Mayotte. These regions with the 
lowest shares were clustered in Greece (7 regions), Bulgaria 
(4 regions) and Italy (3 regions). Among this group of 21, 
there were several capital regions, including Lazio in 
Italy (0.4%), Attiki in Greece (also 0.4%), Bucureşti-Ilfov in 
Romania (0.3%) and Yugozapaden in Bulgaria (0.1%). 

Map 2.4: Deaths due to intentional self-harm
(% of all deaths, by NUTS 2 regions, 2022)

Note: based on standardised death rates per 100 000 inhabitants.

Source: Eurostat (online data code: hlth_cd_asdr2)

Eurostat regional yearbook – 2025 edition46A

Health

Figure 2.5 highlights those NUTS level 2 regions where 
intentional self-harm accounted for the highest and lowest 
shares of all deaths. In 2022, Bretagne in north-western 
France had the highest share of deaths due to intentional 
self-harm, at 2.3%. 

In 2022, intentional self-harm accounted for 0.6% of all 
female deaths in the EU. However, there were several 
regions in Belgium and France that had considerably 
higher shares. Prov. West-Vlaanderen had the highest 
share (1.7% of all female deaths were attributed to 
intentional self-harm), followed by 3 more Belgian regions: 
Prov. Luxembourg (1.5%), Prov. Namur (1.4%) and the 
capital region of Région de Bruxelles-Capitale / Brussels 
Hoofdstedelijk Gewest (1.3%). The next highest shares 
were in predominantly rural regions of France: Pays de la 
Loire, Bretagne, Basse-Normandie and Auvergne (all 1.3%). 

Figure 2.5: Deaths due to intentional self-harm
(% of all deaths, by sex and NUTS 2 regions, 2022)

Sydsverige in Sweden (1.2%) was the only region from 
outside of Belgium and France to feature among those 
with the highest shares of female deaths attributed to 
intentional self-harm. 

In 2022, intentional self-harm accounted for 1.3% of all 
male deaths in the EU. Much higher shares were recorded 
in several regions across France, with peaks of 2.8% in 
Bretagne, Basse-Normandie and Franche-Comté. They 
were followed by 4 more French regions: Poitou-Charentes 
(2.7%), Auvergne, Pays de la Loire and Limousin (all 2.5%). 
Outside of France, the highest shares of male deaths 
attributed to intentional self-harm were recorded in Tirol 
in Austria (2.5%) and in both Slovenian regions – Vzhodna 
Slovenija and Zahodna Slovenija – each with a share 
of 2.4%. 

Note: based on standardised death rates per 100 000 inhabitants. The 
figure shows the EU regions with the highest and lowest shares. Utrecht 
(NL35), Zuid-Holland (NL36), Centro (PT19), Grande Lisboa (PT1A), Península 
de Setúbal (PT1B), Alentejo (PT1C) and Oeste e Vale do Tejo (PT1D): not 
available. Voreio Aigaio (EL41) and Ciudad de Melilla (ES64): not available 
for females.

Source: Eurostat (online data code: hlth_cd_asdr2)

47Eurostat regional yearbook – 2025 edition3. Education

Alongside the provision of health care, public expenditure 
on education is often considered 1 of the most important 
investments that can be made in people. Education 
has the potential to drive socioeconomic development 
forward: this is particularly the case in a globalised world, 
where a highly skilled workforce can be an advantage in 
terms of productivity, innovation and competitiveness. 

Education and training play a vital role in the economic 
and social strategies of the EU. In February 2021, a Council 
Resolution on a strategic framework for European cooperation 
in education and training towards the European Education 
Area and beyond (2021-2030) (2021/C 66/01) was adopted. It 
sets out several policy targets for the European Education 
Area that are designed to remove barriers to learning and 
improve access to quality education for all. Several of these 
targets are referred to within this chapter. 

EU policymakers place considerable emphasis on skills 
development and lifelong learning as essential drivers of 
competitiveness, innovation and social inclusion. Through 
a broad set of policies, they seek to equip people with 
the skills needed to thrive in a rapidly evolving labour 
market. These efforts focus on increasing participation in 
education and training, addressing skills mismatches and 
promoting equal opportunities. They also support progress 
towards 2 of the EU’s key social targets under the European 
Pillar of Social Rights Action Plan: ensuring that, by 2030, at 

least 60% of adults participate in training every year, and 
that the employment rate rises reaches at least 78%. 

The infographic below presents data for the 10 NUTS 
level 2 regions across the EU with the highest shares of 
people aged 25 to 34 years who have a tertiary level of 
educational attainment. In 2024, several of the highest 
shares were recorded in capital regions. For example, 
71.2% in Sostinės regionas (Lithuania), 68.6% in Warszawski 
stołeczny (Poland) and 68.5% in Ile-de-France (France). 

This chapter presents data following the common 
progression of pupils and students through different levels 
of the education system (according to the International 
standard classification of education (ISCED); see box for 
more details), before looking at educational attainment 
levels, the transition from education into work and 
adult education and training. Administrative data on 
the participation of pupils and students in various levels 
of education generally refer to 2023, while the latest 
information from other datasets (surveys) generally 
concerns 2024. 

In 2023, there were an estimated 94.5 million pupils 
and students enrolled across the EU in all levels of 
education from pre-primary to tertiary (as covered by 
ISCED levels 02 to 8); this estimate is based on the latest 
information available. 

(% of people aged 25–34 years, by NUTS 2 regions, 2024)

Source: Eurostat (online data code: edat_lfse_04) 

Note: Mayotte (FRY5) and Åland (FI20), not available.

Eurostat regional yearbook – 2025 edition48A

Education

More about the data: classifying education 
levels 

Enrolments

PARTICIPATION RATES IN EARLY CHILDHOOD 
EDUCATION 

Research has shown that early experiences of children 
often play a critical role in their long-term development. 
Early childhood education and care programmes, which 
are intentionally designed to support children’s cognitive, 
language, physical and socio-emotional development, are 
considered as educational in the ISCED classification (ISCED 
level 0, early childhood education) (1). 

Early childhood education programmes are typically 
designed to introduce young children to organised 
instruction outside of the family context. Programmes have 
an intentional education component and target children 
below the age of entry into primary education (ISCED 
level 1). Early childhood education programmes constitute 
the 1st level of education and training systems and play 
a key role in redressing ‘unequal’ life chances, tackling 
inequalities by preventing the formation of early skills gaps.

(1)  At this age, learning activities are very different to the traditional 

methods adopted within the context of compulsory schooling, and 
– most of the time – take place alongside / as part of caring activities 
(in other words, supervision, nutrition and health). According to ISCED, 
programmes providing only childcare – without a sufficient set of 
purposeful learning activities – are not classified as early childhood 
education.

As national education systems vary in terms of structure 
and curricular content, statistics on education and 
training are compiled according to the international 
standard classification of education (ISCED). This is the 
reference classification for organising formal education 
programmes and related qualifications by education 
levels and fields into internationally agreed categories. 

The most recent version of the ISCED classification – 
ISCED  2011 – was adopted by the UNESCO General 
Conference in November 2011 and identifies the 
following levels of education: 

•  early childhood education – ISCED level 0 

 ◦ early childhood educational development – ISCED 

level 01

 ◦ pre-primary education – ISCED level 02

•  primary education – ISCED level 1
•  lower secondary education – ISCED level 2
•  upper secondary education – ISCED level 3
•  post-secondary non-tertiary education – ISCED level 4
•  short-cycle tertiary education – ISCED level 5
•  bachelor’s or equivalent level – ISCED level 6
•  master’s or equivalent level – ISCED level 7
•  doctoral (PhD) or equivalent level – ISCED level 8.

School attendance is compulsory – at least for primary 
and lower secondary education – across all of the EU 
countries. 

Young people who have successfully completed lower 
secondary education may enter upper secondary 
education (ISCED level 3), when they generally have 
to choose certain subjects or specialisations to study, 
alongside their future education and/or career paths. 
Upper secondary (or intermediate) education typically 
ends in the EU when students are aged 17 or 18 years. 
These programmes are designed primarily to prepare 
students so that they may continue their studies at a 
tertiary level (general programmes), or to provide them 
with skills and competencies that are relevant for a 
specific occupation or trade (vocational programmes). 

The term ‘tertiary education’ is used to refer to ISCED 
levels 5 to 8. It builds on secondary education, providing 
learning activities at a higher level of complexity. This 
level of higher education – provided by universities 
and other tertiary educational institutions – can play 
an important role in society, fostering innovation, while 
increasing economic development and growth. 

49Eurostat regional yearbook – 2025 editionA

Education

More about the data: statistics on early 
childhood education and care 

Within the strategic framework for European 
cooperation in education and training towards 
the European Education Area and beyond, a key 
policy target concerns the share of young children 
– between 3 years of age and the starting age of 
compulsory primary education – who participate in 
early childhood education and care. Eurostat data on 
early childhood education (ISCED level 0) are used 
to measure progress towards the goal of having at 
least 96% of children in this age group participating in 
early childhood education and care by 2030 (2). 

Within this section, regional statistics presented for 
Germany relate to NUTS level 1 regions, while national 
data are presented for the Netherlands. 

Based on the latest available data, there were an 
estimated 15.7 million children (of any age) enrolled in 
early childhood education across the EU in 2023 (data 
for Belgium, Greece, Malta and Portugal only cover pre-
primary education). Map 3.1 shows a more detailed picture 
for 193 NUTS level 2 regions, it covers pupils between the 
age of 3 and the starting age of compulsory education at 
primary level. While the participation rate in early childhood 
education averaged 94.0% across the whole of the EU, there 
were considerable differences in regional rates. The highest 
rates were generally recorded in the westernmost regions of 
the EU and lower rates across most eastern regions. Capital 
regions had higher than average participation rates in some 
EU countries (for example, Ireland, Hungary and Poland), 
whereas in others they recorded lower than average rates 
(for example, Italy, Romania or Sweden). 

In 2023, there were 19 regions across the EU where 
practically every child (100.0%) between the age of 3 years 
and the age for starting compulsory primary education 
participated in early childhood education 

By 2023, the share of children between the age of 3 years 
and the age for starting compulsory primary education 

participating in early childhood education had already 
reached the EU’s strategic target of 96.0% in almost half 
of all EU regions for which data are available (94 out 
of 193 regions); these regions are shaded using 3 different 
tones of teal in Map 3.1. The 94 regions that had already 
met or surpassed the target were concentrated across 
regions from Belgium, Germany, Spain, France, Italy, 
Hungary, Poland and Sweden. Additionally, 2 regions from 
each of Denmark and Portugal, and single region from 
Ireland, Lithuania and Austria had also reached the policy 
target of 96.0%; this was also the case in Luxembourg. At 
the top end of the distribution, 19 regions in the EU had 
participation rates of almost 100.0% (as shown by the 
darkest shade of teal). More than half of these 19 regions 
were concentrated in France (10 regions), with a 
further 5 regions in neighbouring Belgium. 

In Map 3.1, the regions with participation rates below the 
strategic target of 96.0% are shaded using different golden 
tones. In 2023, the share of young children participating in 
early childhood education was less than 85.0% in 20 out of 
the 193 regions for which data are available. These regions, 
with relatively low participation rates (as shown by the 
darkest shades of gold) were concentrated in eastern EU 
countries, including all 8 regions of Romania, 3 regions in 
Czechia, 2 regions from each of Croatia and Slovakia and a 
single region from Bulgaria. This group also included the 
French island regions of Mayotte and Corse, the German 
region of Bremen and the Austrian region of Steiermark. 

Participation in tertiary education 

The number of people enrolling in tertiary education 
across the EU has risen in recent decades, reflecting several 
factors, such as: 

•  demographic patterns
•  changes in labour force participation (particularly for 

women)

•  increased demand from employers for tertiary education 

qualifications (for jobs that previously required a 
secondary level of education)

•  an increased awareness of the benefits of tertiary 

education

(2) It should be noted that the wording of the EU target is for 

•  access to student finance, scholarships and other 

participation in ‘early childhood education and care’ and not ‘early 
childhood education’. Early childhood education and care refers to 
any regulated arrangement for children from birth to compulsory 
primary school age, regardless of the programme content, whereas 
early childhood education refers specifically to ISCED programmes. 
The former encompasses not only early childhood education but also 
programmes which do not meet the minimum requirements to be 
classified as such (for example, childcare only programmes). Although 
the EU target is for participation in early childhood education and 
care, the Regulation stipulates that Eurostat’s data on participation 
rates in early childhood education will be used to measure progress 
towards this target. This means that, in practice, the EU target for 
participation in early childhood education and care programmes 
concerns only those programmes which meet criteria to be classified 
as early childhood education. For more details on the difference 
between these 2 terms, please consult an article on early childhood 
education statistics.

benefits

•  different patterns of learning mobility (within and from 

outside of the EU)

•  an increased demand for longer tertiary education (such 
as the extension from a bachelor’s degree to master’s or 
doctoral (PhD) studies)

•  an increasing share of adults participating in lifelong 

learning.

Eurostat regional yearbook – 2025 edition50Map 3.1: Participation rates in early childhood education
(%, by NUTS 2 regions, 2023)

A

Education

Note: pupils participating in early childhood education from age 3 years 
to the starting age of compulsory education at primary level, as a share 
of the population of the corresponding age. Germany: NUTS level 1. The 
Netherlands: national data. Liechtenstein and Türkiye: 2022.

There were 18.8 million tertiary education students 
enrolled across the EU in 2023 

There were approximately 18.8 million students enrolled 
in the EU’s tertiary education institutions in 2023. As such, 
tertiary students accounted for almost 1 in 5 (19.6%) of the 
total number of pupils and students enrolled within the 
EU’s education system. A majority of the students enrolled 
in the tertiary education sector were female (54.8% of the 
total). 

In 2023, 11.2 million EU students were enrolled in bachelor’s 
programmes. This figure was approximately twice as high 
as the count of students enrolled in master’s programmes 
(5.5 million). The other 2 types of tertiary programmes 
had fewer students: 1.4 million enrolled in short-cycle 
programmes (either academic or vocational) and 0.7 million 
in doctoral programmes. As noted above, females made 

Source: Eurostat (online data code: educ_uoe_enra22)

up the majority of students enrolled in tertiary education. 
This gender gap was particularly apparent among students 
studying for a master’s degree (where 58.3% were female) 
and somewhat smaller among students studying for a 
bachelor’s degree (where 54.1% were female). By contrast, 
males accounted for a small majority of the students 
pursuing a short-cycle tertiary education qualification 
(50.5%) and a doctoral degree (also 50.5%). 

Unsurprisingly, the highest numbers of tertiary students 
were recorded in urban regions from some of the most 
populous EU countries. The German region of Nordrhein-
Westfalen (805 000) and the French capital region of Ile-
de-France (771 000) were the only regions within the EU 
to record more than 0.5 million tertiary students enrolled 
in 2023. After Nordrhein-Westfalen and Ile-de-France, 

51Eurostat regional yearbook – 2025 editionA

Education

13 regions had between 250 000 and 500 000 tertiary 
students enrolled: 

•  4 regions from Spain – Comunidad de Madrid, Cataluña, 

Andalucía and Comunitat Valenciana

•  3 regions from Germany – Bayern, Baden-Württemberg 

and Hessen (all NUTS level 1)

•  3 regions from Italy – Lombardia, Lazio and Campania
•  the capital regions of Attiki in Greece and Warszawski 

stołeczny in Poland

•  the French region of Rhône-Alpes.

Figure 3.1 shows the proportion of all tertiary students 
enrolled in short-cycle, bachelor’s, master’s or doctoral 
degrees. Each national education system has its own 
specific characteristics, with a particular offer of fields 
or levels of education. This may explain why some 
regions have no students enrolled for short-cycle tertiary 
education qualifications or for a master’s or doctoral (PhD) 
degree, as these educational levels may not be offered. 
In such cases, the shares of students enrolled in other 
types of tertiary education may be higher than elsewhere. 
For example, in the Finnish region of Åland, a bachelor’s 
degree was the only form of tertiary education available 
and therefore accounted for 100.0% of students enrolled in 
tertiary education. 

In 2023, 7.5% of all tertiary students in the EU were enrolled 
to study for a short-cycle tertiary education qualification; 
the share was somewhat higher among male short-cycle 
students (8.3% of all male tertiary students) than it was 
for female short-cycle students (6.7% of all female tertiary 
students). The westernmost Austrian region of Vorarlberg 
was the only NUTS level 2 region to report more than half 
of its tertiary students were enrolled to follow a short-cycle 
tertiary education course, with a 57.6% share. There were 
also relatively high shares of tertiary students following 
short-cycle courses in several (other) regions of Austria and 
in several regions of France and Spain. 

There were 11.2 million students across the EU enrolled 
to study for a bachelor’s education in 2023, a majority of 
them were female – 6.0 million compared with 5.1 million 
male students. Together, these students enrolled to study 
for a bachelor’s education made up 59.4% of all tertiary 
education students, with a higher relative share for male 
students (60.4% of all male students enrolled in tertiary 
education) compared with female students (58.6% of all 
female students enrolled in tertiary education). Among 

NUTS level 2 regions, there were 6 regions where more 
than 9 out of every 10 tertiary students followed a 
bachelor’s education in 2023: the Finnish region of Åland, 
as noted above, the southern Belgian region of Prov. 
Luxembourg and 4 Greek regions, namely, Sterea Elláda, 
Ipeiros, Dytiki Makedonia and Kriti. The highest numbers 
of tertiary students studying for a bachelor’s degree were 
concentrated in Nordrhein-Westfalen (494 000; NUTS 
level 1), followed by the French, Spanish and Greek capital 
regions of Ile-de-France (329 000), Comunidad de Madrid 
(258 000) and Attiki (247 000). 

Of the 5.5 million students enrolled to study for a master’s 
education across the EU in 2023, 3.2 million were female 
and 2.3 million were male. This equated to 29.3% of all 
tertiary students, with a higher share among females 
(31.2% of all females enrolled to study for a tertiary 
education were master’s students) than among males 
(27.0%). The largest numbers of students studying 
for a master’s degree were in Ile-de-France (314 000; 
just 16 000 fewer than were studying for a bachelor’s 
degree in the French capital region) and 2 German regions 
(both NUTS level 1) – Nordrhein-Westfalen (271 000) and 
Bayern (172 000). Burgenland in eastern Austria (58.2%) 
and La Rioja in northern Spain (51.0%) were the only NUTS 
level 2 regions to report a majority of their tertiary students 
were enrolled to study for a master’s education. 

In 2023, the 717 000 doctoral or equivalent students 
enrolled across the EU were distributed relatively evenly 
between the sexes, with 362 000 male and 355 000 female 
students. Doctoral students made up 3.8% of all tertiary 
students in the EU, with a higher share of male tertiary 
education students enrolled to study for a doctoral 
degree (4.3%), while the corresponding share among 
females was 3.4%. Across EU regions, Luxembourg 
(12.7%) and the eastern German region of Sachsen 
(10.7%; NUTS level 1) were the only regions to report that 
more than 1 in 10 tertiary students followed a doctoral 
education. The next highest shares were recorded for the 
Finnish capital region of Helsinki-Uusimaa (8.1%), 4 more 
German regions (NUTS level 1) – Baden-Württemberg 
(8.1%), Saarland (7.7%), Bayern and Berlin (both 7.4%) – 
and the Czech capital region of Praha (also 7.4%). The 
highest numbers of students studying for a doctoral 
degree were recorded in 3 German regions (NUTS level 1): 
Nordrhein-Westfalen (41 000), Bayern (33 000) and Baden-
Württemberg (32 000). 

Eurostat regional yearbook – 2025 edition52Figure 3.1: Students enrolled in tertiary education
(% of female/male tertiary students in each level of tertiary education, by NUTS 2 regions, 2023)

A

Education

Note: the figure shows the 10 EU regions with the highest shares (ranked 
on the average share for both sexes). Germany: NUTS level 1. Subject to 
data availability.

Source: Eurostat (online data code: educ_uoe_enrt06)

53Eurostat regional yearbook – 2025 editionA

Education

Educational attainment

A basic level of education is desirable for all, as it provides 
the opportunity to participate in economic and social life. 
Nevertheless, people with higher levels of educational 
attainment generally tend to experience a wider range 
of job opportunities, higher levels of income and tend to 
be more satisfied with life, while they usually have a lower 
likelihood of being unemployed. 

More about the data: educational attainment 

Educational attainment is measured by looking at 
the highest level of education – based on the ISCED 
classification – that an individual has successfully 
completed. These statistics pertain to the highest level 
of attainment reached prior to the moment of the 
survey interview. Note that some people in the target 
age range might: 

•  still be studying (for a higher level of education)
•  have completed their highest level of education in a 
different region from where they were living at the 
time of the survey.

PEOPLE WITH AT LEAST AN UPPER 
SECONDARY EDUCATION QUALIFICATION 

The strategic framework for European cooperation in 
education and training towards the European Education 
Area and beyond (2021 to 2030) includes a complementary 
indicator that measures progress in relation to educational 
attainment: it is defined as the share of people 
aged 20 to 24 years with at least an intermediate (or upper 
secondary) level of educational attainment. The target is 
that by 2030 at least 90% of young people should meet 
this criterion. 

Almost 1 in 3 regions across the EU have already reached 
the EU’s target of 90.0% 

In 2024, 84.3% of people in the EU aged 20 to 24 years 
had attained at least an intermediate level of education 
(in other words, they had at least an upper secondary 
level of educational attainment as defined by ISCED 
levels 3 to 8). This latest figure marks a steady rise over 
the past 2 decades, up from 76.8% in 2002 when the time 
series began. The upward development was relatively 
consistent, with the only pauses occurring in 2009 (no 
change) and in 2022, when a sharp fall in Germany 
affected the overall EU average. 

Map 3.2 shows information about the share of people 
aged 20 to 24 years with at least an intermediate level 
of education. In 2024, some 31% of EU regions – 75 out 
of 242 NUTS level 2 regions for which data are available 
– had already reached or exceeded the EU’s target 
of 90.0%; they are coloured in the 3 different shades of 
teal. These 75 regions were particularly concentrated 
in (northern) Belgium, Czechia, Ireland, Greece, Croatia, 
Poland and Slovakia, where a large proportion of regions 
reported upwards of 90.0% of people aged 20 to 24 years 
with at least an intermediate level of education. 

At the top end of the distribution, there 
were 25 NUTS level 2 regions where the share of people 
aged 20 to 24 years with at least an intermediate level of 
educational attainment was 94.0% or higher in 2024 (as 
shown by the darkest shade of teal). This group included: 

•  all 3 regions in Ireland
•  all 4 regions of Croatia
•  9 out of 13 regions in Greece, including the capital 

region of Attiki

•  4 out of 17 regions in Poland
•  the capital regions of Yugozapaden (Bulgaria), Praha 

(Czechia), Sostinės regionas (Lithuania) and Bratislavský 
kraj (Slovakia)

•  Alsace in eastern France.

In 2024, the highest proportions of young people 
aged 20 to 24 years who had attained at least an 
intermediate level of education were found in the western 
Greek region of Dytiki Elláda (99.7%) and the Croatian 
capital region of Grad Zagreb (99.2%). 

At the other end of the range, 25 NUTS level 2 regions 
reported that, in 2024, fewer than 71.5% of all young 
people aged 20 to 24 years had attained at least an 
intermediate level of education (as shown by the 
darkest shade of gold in Map 3.2). Germany accounted 
for a majority (17) of these regions, most of which 
were clustered in the northern half of the country. The 
remainder of this group was composed of 4 regions 
from Spain (including the 2 autonomous cities), together 
with single regions from Denmark, France, Portugal and 
Romania. 

In 2024, the northern German region of Lüneburg 
(59.2%) recorded the lowest share of young people 
aged 20 to 24 years who had attained at least an 
intermediate level of educational attainment. It was 
the only region across the EU where this share was 
below 60.0%. The next lowest levels were observed in 
the French outermost region of Guyane (62.3%) and the 
central German region of Gießen (63.3%). 

Eurostat regional yearbook – 2025 edition54Map 3.2: Educational attainment – at least upper secondary
(% of people aged 20–24 years, by NUTS 2 regions, 2024)

A

Education

Note: ISCED levels 3–8. Bosnia and Herzegovina: 2023. Prov. Vlaams-
Brabant (BE24), Trier (DEB2), Ciudad de Ceuta (ES63), Ciudad de Melilla 
(ES64) and Corse (FRM0): low reliability.

Source: Eurostat (online data code: edat_lfse_04)

PEOPLE WITH A TERTIARY LEVEL OF 
EDUCATIONAL ATTAINMENT 

More than 1 in 3 EU regions have already reached the 
policy goal for tertiary educational attainment 

Map 3.3 shows the regional distribution of tertiary 
educational attainment in 2024 (in other words, a higher 
level of educational attainment, as defined by ISCED 
levels 5 to 8). The data refer to people aged 25 to 34 years, 
by which age the vast majority of the population have 
completed their education. The strategic framework for 
European cooperation in education and training towards 
the European Education Area and beyond (2021 to 2030) 
includes an EU-wide target: by 2030, at least 45% of people 
aged 25 to 34 years should have attained a tertiary level of 
education. 

In 2024, 44.2% of the EU population aged 25 to 34 years 
had a tertiary level of educational attainment; some 
people within this age group might still be studying. Of 
the 242 NUTS level 2 regions for which data are available, 
84 had already reached or surpassed the EU’s policy target 
of 45.0% (as shown by 3 shades of teal in Map 3.3). 

At the top end of the distribution, 25 regions reported that 
at least 57.5% of young people aged 25 to 34 years had 
attained a tertiary level of education in 2024. These regions 
attract highly qualified individuals, creating significant ‘pull 
effects’ due to the diverse educational, employment, and 
social opportunities they offer. This group included several 

55Eurostat regional yearbook – 2025 editionA

Education

capital regions and economic hubs, often marked by high-
quality universities and a focus on research and innovation. 
It comprised: 

•  the capital regions of Belgium, Denmark, Ireland, Spain, 
France, Lithuania, Hungary, the Netherlands, Poland, 
Slovakia and Sweden

•  6 additional regions from Spain, clustered in the north 

(with specialisations in, among other activities, advanced 
manufacturing and automotive industries)

Map 3.3: Educational attainment – tertiary
(% of people aged 25–34 years, by NUTS 2 regions, 2024)

•  both remaining regions of Ireland (pharmaceuticals, life 

sciences and electronics)

•  Utrecht and Groningen in the Netherlands (high-tech 

industries and green technologies)

•  Prov. Brabant Wallon in Belgium (knowledge-intensive 

industries)

•  Midi-Pyrénées in France (aerospace)
•  Cyprus and Luxembourg.

Note: ISCED levels 5–8. Bosnia and Herzegovina: 2023. Prov. Vlaams-
Brabant (BE24), Trier (DEB2), Ciudad de Ceuta (ES63), Ciudad de Melilla 
(ES64), Corse (FRM0) and Região Autónoma dos Açores (PT20): low 
reliability.

Source: Eurostat (online data code: edat_lfse_04)

Eurostat regional yearbook – 2025 edition56A

Education

At the bottom end of the distribution, 24 NUTS 
level 2 regions reported that fewer than 26.5% of all 
people aged 25 to 34 years had attained a tertiary level 
of educational attainment in 2024 (as shown by the 
darkest shade of gold in Map 3.3). These regions were 
primarily rural or geographically remote/isolated, often 
characterised by relatively large agricultural sectors and 
limited opportunities for highly skilled employment. In 
several regions, vocational education plays a dominant 
role, with young people entering the labour market via 
apprenticeships or training schemes, rather than through 
a tertiary education qualification. This group of 24 regions 
was mainly concentrated in eastern and southern EU 
countries and included: 

• 7 out of the 8 regions in Romania, the exception being

Bucureşti-Ilfov (the capital region)

• 6 out of the 8 regions in Hungary, the exceptions being

Budapest (the capital region) and the surrounding
region of Pest

• 4 regions in Italy – including the islands of Sicilia and

Sardegna

• 2 regions from Czechia – Severozápad and Střední Čechy
• 2 regions from France – including the island region of

Corse and the outermost region of Guyane

• 3 other regions from across the EU – Sterea Elláda in

Greece, Panonska Hrvatska in Croatia, the island region
of Região Autónoma dos Açores in Portugal.

In 2024, the highest share of people aged 25 to 34 years 
with a tertiary level of educational attainment was 
recorded in the Lithuanian capital region of Sostinės 
regionas, at 71.2% 

Figure 3.2 provides more detail for those EU regions with 
the highest and lowest shares of people having a tertiary 
level of educational attainment. In 2024, there were 5 NUTS 
level 2 regions that reported more than 2 out of 3 people 
aged 25 to 34 years having attained a tertiary level of 
educational attainment. Most of these were capital regions 
– the only exception being the northern Spanish region of
País Vasco (67.8%) – the others were:

• Sostinės regionas in Lithuania (71.2%)
• Warszawski stołeczny in Poland (68.6%)
• Ile-de-France in France (68.5%)
• Eastern and Midland in Ireland (67.7%).

In 2024, Romania accounted for 6 out of the 7 regions 
across the EU with the lowest shares of tertiary educational 
attainment among people aged 25 to 34 years. Sud-
Muntenia had the lowest share (15.3%), followed by Sud-
Est (15.8%) and Sud-Vest Oltenia (17.1%). The only region 
from outside this group was Severozápad in Czechia, 
where 19.1% of people aged 25 to 34 years had attained a 
tertiary level of educational attainment. 

Figure 3.2 extends the analysis of tertiary educational 
attainment by providing information by sex. As was the 
case for the total population, the Lithuanian capital region 
of Sostinės regionas had the highest share of women 
aged 25 to 34 years having attained a tertiary level of 
educational attainment, at 79.0% in 2024. However, the 
pattern for men was different, with the northern Spanish 
region of País Vasco having the highest share, with 66.6% 
of men in this age group having attained a tertiary level of 
education. 

Among the EU regions with the highest shares of people 
aged 25 to 34 years with a tertiary level of educational 
attainment in 2024, 7 regions featured in the top 10 both 
for women and for men. However: 

• Dytiki Makedonia (northern-western Greece), Southern
(Ireland) and Budapest (the capital region of Hungary)
were only present within the top 10 regions for women

• País Vasco (northern Spain), Luxembourg and Noord-

Holland (the capital region of the Netherlands) were only
present within the top 10 regions for men.

Across the EU, approximately half of all women 
aged 25 to 34 years had a tertiary level of educational 
attainment 

In 2024, the EU’s tertiary education gender attainment 
gap for people aged 25 to 34 years was 11.2 percentage 
points. Approximately half (49.9%) of all women in this 
age group had attained a tertiary level of education, 
considerably higher than the corresponding share for 
men (38.7%). The largest gender attainment gaps – with 
higher shares for women – were observed in the Greek 
regions of Dytiki Makedonia (51.2 percentage points) and 
Ipeiros (33.7 points), followed by the central Polish region 
of Świętokrzyskie (30.2 points). At the opposite end of the 
distribution, only 4 regions across the EU had a gender 
attainment gap with a higher share for men. The largest 
of these was in the Greek region of Notio Aigaio, where 
the share for men was 4.4 percentage points higher than 
for women, followed by the German regions of Chemnitz 
(2.7 points), Tübingen (1.3 points) and Bremen (0.6 points). 

57Eurostat regional yearbook – 2025 editionA

Education

Figure 3.2: Educational attainment – tertiary
(% of people aged 25–34 years, by sex and NUTS 2 regions, 2024)

Note: ISCED levels 5–8. The figure shows those EU regions with the highest 
and lowest shares of tertiary educational attainment among people aged 
25–34 years. The rankings include more than 10 regions if several regions 
have identical values. Corse (FRM0): low reliability for total. Guyane (FRY3): 
low reliability for women. Ionia Nisia (EL62): low reliability for men. Mayotte 

(FRY5) and Åland (FI20): not available. Trier (DEB2) and Corse (FRM0): not 
available for women and men. Valle d’Aosta/Vallée d’Aoste (ITC2) and 
Região Autónoma dos Açores (PT20): not available for men.

Source: Eurostat (online data code: edat_lfse_04)

Transition from education to work

When students complete their studies, several barriers 
can restrict their transition into the labour market. These 
include a lack of relevant work experience, insufficient 
skills, limited job opportunities in the region where they 
reside or high unemployment levels during an economic 
downturn. The penultimate section of this chapter 
provides an insight into the situation of young people 
aged 18 to 24 years as they seek to move from education 
into work. 

EARLY LEAVERS FROM EDUCATION AND 
TRAINING 

Within the EU, education policy seeks to ensure that 
everyone (irrespective of age) has the skills, knowledge and 
capabilities to develop their careers. The transition from 
education into work may prove particularly difficult for 
people with low levels of literacy and numeracy, those who 
leave education at an early age, and people coming from 
disadvantaged backgrounds. A particular area of concern is 
the proportion of early leavers from education and training. 
They are defined here as individuals aged 18 to 24 years 
who have at most a lower secondary level of educational 
attainment (ISCED levels 0 to 2) and who were not engaged 
in any further education and training (during the 4 weeks 
preceding the European Union labour force survey (EU-
LFS)). This indicator is 1 of 7 key targets outlined in the 
strategic framework for European cooperation in education 
and training towards the European Education Area and 
beyond (2021 to 2030); the EU has set a goal to reduce the 
proportion of early leavers to less than 9% by 2030. 

Eurostat regional yearbook – 2025 edition58A

Education

Within the EU, the share of young people who were early 
leavers from education and training was 9.3% in 2024 

Over the past 2 decades, the proportion of early leavers 
from education and training aged 18 to 24 years has 
steadily declined across the EU. Starting from 16.9% in 2002 
(the 1st year of the time series), the share of early leavers 
fell each year until 2017, reaching 10.5%. Although the rate 
remained unchanged in 2018, it resumed its downward 
path during the subsequent 6 years. By 2024, 9.3% of 
young people in the EU had at most a lower secondary 
level of educational attainment and were not engaged in 
any further education and training; as such, the latest rate 
was within 0.3 percentage points of the policy target set 
for 2030. 

Within the EU, the number and share of early leavers from 
education and training is influenced by a range of factors, 
including geography and sex. 

Map 3.4: Early leavers from education and training
(% of people aged 18–24 years, by NUTS 2 regions, 2024)

• Spatially, the share of early leavers tends to be higher
in sparsely populated regions and former industrial
heartlands. This may be linked, among other reasons,
to limited educational opportunities and weak local
labour markets, which discourage young people from
staying (longer) in education and training, while acting
as a ‘push factor’ encouraging those with higher levels of
educational attainment to seek opportunities elsewhere.

• Gender differences also play a considerable role, with

young men generally more likely to leave education and
training earlier than young women. This contrasts with
the pattern observed above, whereby young women
were far more likely than young men to have a tertiary
level of educational attainment. In 2024, 10.9% of young
men in the EU were early leavers, 3.2 percentage points
higher than the share for young women, at 7.7%.

Note: Germany, Greece, Austria and Poland, NUTS level 1. Ireland and 
Slovakia: national data. Corse (FRM0), Guadeloupe (FRY1), Martinique (FRY2) 
and Bosnia and Herzegovina: 2023. Molise (ITF2) and Övre Norrland (SE33): 
2022. Includes data with low reliability (too many regions to document).

Source: Eurostat (online data code: edat_lfse_16)

59Eurostat regional yearbook – 2025 editionA

Education

More than half of all regions had attained the EU’s policy 
target for early leavers 

In 2024, more than half (94 out of 180) of the NUTS 
level 2 regions for which data are available had a share 
of early leavers from education and training below 
the 9.0% policy target (as shown by 3 shades of teal in 
Map 3.4). These 94 regions were spread across much of the 
EU. Subject to data availability – with some countries only 
reporting at NUTS level 1 and others with partial coverage 
due to relatively low data reliability – they included: 

•  every region of Greece, Croatia, Poland and Slovenia
•  all but 1 region in Czechia, the Netherlands and Austria
•  as well as Ireland, Latvia, Luxembourg and Slovakia.

At the other end of the range, 18 NUTS level 2 regions had 
a share of early leavers from education and training of at 
least 15.0% in 2024, as shown by the 2 darkest shades of 
gold in Map 3.4. This group included 5 regions from each 
of Spain and Romania and was characterised by sparsely 
populated, island and/or peripheral regions; it is likely that 
a disproportionately high share of students from island 
and/or peripheral regions have to leave home if they wish 
to follow a particular course or programme, leaving behind 
a higher concentration of early leavers. 

In 2024, there were 8 regions across the EU where the share 
of young people who were early leavers from education 
and training was below 3.0% 

Figure 3.3 highlights the NUTS level 2 regions with the 
highest and lowest shares of early leavers from education 
and training in 2024. At the top of the distribution, the 
French outermost region of Guyane reported the highest 
share, with 29.4% of young people aged 18 to 24 years 
classified as early leavers. Other regions with rates 
above 20.0% included: 

•  Sud-Est and Centru (Romania)
•  Ciudad de Melilla and Illes Balears (Spain)
•  Észak-Magyarország (Hungary)
•  Corse (France).

By contrast, at the lower end of the distribution, there 
were 8 NUTS level 2 regions where, in 2024, the share of 
early leavers from education and training was below 3.0%. 
The lowest shares were recorded in: 

•  Praha, the capital region of Czechia (1.3%)
•  Jadranska Hrvatska and Panonska Hrvatska in Croatia 

(1.5% and 2.4%, respectively)

•  Voreia Elláda and Attiki in Greece (2.4% and 2.8%, 

respectively)

•  Jihovýchod in Czechia (2.7%)
•  Ireland (2.8%)
•  Yugozapaden, the capital region of Bulgaria (2.9%).

Figure 3.3 extends the analysis of early leavers from 
education and training, providing information by sex. 
As was the case for young people in general, the French 
outermost region of Guyane had the highest share of 
early leavers among young men aged 18 to 24 years, 
at 35.7% in 2024. However, the pattern for young women 
was different, as the Romanian region of Sud-Est had the 
highest share, at 30.3%. A closer analysis of the data for 
the 10 EU regions with the highest shares of early leavers 
reveals that: 

•  Sud-Muntenia, Sud-Vest Oltenia, Nord-Vest (all Romania), 
Severoiztochen (Bulgaria) and Észak-Alföld (Hungary) 
were only present among the top 10 regions for young 
women

•  Ciudad de Melilla, Illes Balears, La Rioja and Andalucía (all 
Spain) were only present among the top 10 regions for 
young men.

In 2024, the lowest shares of early leavers from education 
and training among young women aged 18 to 24 years 
were recorded in the Greek regions of Voreia Elláda (1.4%) 
and Kentriki Elláda (2.1%), followed by the Czech region of 
Jihovýchod (2.4%). Among young men, the lowest rates 
were recorded in the Croatian region of Jadranska Hrvatska 
(2.2%), Praha in Czechia (2.3%) and Attiki in Greece (2.8%). 

Eurostat regional yearbook – 2025 edition60Figure 3.3: Early leavers from education and training
(% of people aged 18–24 years, by sex and NUTS 2 regions, 2024)

A

Education

Note: the figure shows, subject to availability, those EU regions with the 
highest and lowest shares of early school leavers from education and 
training aged 18–24 years. The rankings include more than 10 regions if 
several regions have identical values. Germany, Greece, Austria and Poland: 
NUTS level 1. Ireland and Slovakia: national data. Corse (FRM0): 2023. 
Includes data with low reliability (too many regions to document).

In more than 4 out of 5 regions across the EU, the share 
of early leavers from education and training was higher 
among young men than young women in 2024 

More than 1 in 10 young men aged 18 to 24 years across 
the EU (10.9% in 2024) were early leavers from education 
and training, compared with 7.7% of young women 
in the same age group; this resulted in a gender gap 
of 3.2 percentage points. A similar pattern was observed 
across most EU regions: among the 115 regions for 
which data are available, 94 reported a higher share of 
early leavers among young men (than young women). 
The largest regional gender gaps for this indicator were 
recorded in Spain: 

•  in Illes Balears, the share of young men who were early 
leavers was 16.0 percentage points higher than for 
young women

•  in La Rioja, the gap was 13.7 points
•  the only other region in the EU with a double-digit 

gender gap was the French outermost region of Guyane, 
where the rate for young men was 11.3 points higher 
than that for young women.

Source: Eurostat (online data code: edat_lfse_16)

Among the 21 EU regions where, in 2024, the share of early 
leavers from education and training was higher for young 
women than for young men, the largest gender gaps were 
recorded in Romania: 

•  in Sud-Muntenia, the rate for young women 

was 9.5 percentage points higher than for young men

•  in Sud-Est, the gender gap was 8.1 points
•  the next largest gap was observed in the Czech region 

of Severozápad, where the rate for young women 
was 6.2 points higher than that for young men.

Map 3.5 presents data for NUTS level 2 regions across 
the EU, highlighting the relationship between the share 
of early leavers from education and training and the 
risk of poverty. By combining these 2 indicators, the 
map affirms that the share of early leavers is a leading 
factor in increasing the risk of poverty (in other words, 
these 2 indicators are positively correlated). Education is 
considered an important determinant of labour market 
outcomes, as people who leave education and training 
early typically lack the skills and qualifications needed for 
stable, well-paid jobs. EU policies and academic research 
have focused on how to reduce the share of early leavers 

61Eurostat regional yearbook – 2025 editionA

Education

as it is considered a root cause of poverty and social 
exclusion. 

In 2024, there were several regions across the EU where at 
least 12.0% of people were early leavers from education 
and training, while at least 20.0% of people were at risk 
of poverty (as shown by the darkest shade in the map). 
Some of the highest shares/rates for both indicators were 
recorded in: 

•  the eastern Bulgarian regions of Severoiztochen and 

Yugoiztochen

•  a number of southern and eastern Spanish mainland 
regions including Castilla-La Mancha, Extremadura, 
Comunitat Valenciana, Andalucía and Región de Murcia, 
as well as the autonomous cities of Ceuta and Melilla, 
and the island region of Canarias

•  the French outermost regions of Guadeloupe, 

Martinique and Guyane

•  the southern Italian region of Campania and the island 

regions of Sicilia and Sardegna

•  the eastern Hungarian regions of Észak-Magyarország 

and Észak-Alföld

•  the Portuguese island region of Região Autónoma dos 

Açores

•  the eastern and southern Romanian regions of Nord-Est, 

Sud-Est and Sud-Vest Oltenia.

By contrast, at the other end of the spectrum, several 
regions highlight the importance that sustained 
investment in education and training may bring, driving 
stronger labour market integration, higher skills levels and 
better social outcomes. In 2024, the following regions 

(shown in the lightest shade) had low proportions of early 
leavers from education and training (below 10.0%) and low 
shares of people at risk of poverty (below 6.5%): 

•  the northern Belgian regions of Prov. Limburg, Prov. 

Oost-Vlaanderen and Prov. Vlaams-Brabant

•  the Czech regions of Praha (the capital), Střední Čechy, 

Severovýchod and Jihovýchod

•  the northern Spanish region of País Vasco
•  the Hungarian capital region of Budapest.

Although there is a positive link between the share of early 
leavers from education and training and the risk of poverty, 
the strength of this relationship varies across EU regions. 
While education may serve as a protective factor against 
the risk of poverty, it does not always guarantee lower 
poverty rates. Likewise, local labour market conditions, the 
quality of jobs and/or social policies may have a stronger 
influence on poverty outcomes than the share of early 
leavers from education and training. 

•  For example, several regions in Greece (NUTS level 1), 

the south of France and Croatia exhibited relatively low 
shares of early leavers in 2024, yet their at-risk-of-poverty 
rates remained above the EU average; a similar pattern 
was observed in Latvia and Luxembourg.

•  By contrast, several regions in the EU, including every 
region of Denmark (except for the capital region of 
Hovedstaden), a majority of the regions in Germany 
(NUTS level 1) and several regions predominantly in 
northern and central Spain, reported higher than 
average shares of early leavers, alongside a lower than 
average risk of poverty.

Eurostat regional yearbook – 2025 edition62Map 3.5: Early leavers and people at risk of poverty
(by NUTS 2 regions, 2024)

A

Education

Note: EU, 9.3% for early leavers from education and training. EU, 16.2% for 
people at risk of poverty. Greece, Austria, Poland and Serbia: NUTS level 1. 
Ireland, Slovakia and Türkiye: national data. Switzerland and Serbia: 2023.

Source: Eurostat (online data codes: edat_lfse_16, ilc_li41 and ilc_li02)

Adult education and training

Lifelong learning plays a crucial role in expanding a 
person’s knowledge, abilities and qualifications, whether 
for personal growth, social engagement, or career 
development. As the demands of the labour market 
evolve, there is a growing need for workers to refine their 
existing skills and acquire new ones – either to adapt 
within their current professions or to explore new career 
paths. Technological progress, particularly in areas like 
artificial intelligence, is expected to render some jobs 
obsolete, making continuous upskilling more essential 
than ever. 

The strategic framework for European cooperation in 
education and training towards the European Education 
Area and beyond (2021 to 2030) sets a specific EU policy 
target in this area. By 2025, the EU aims to have at least 47% 
of people aged 25 to 64 years participating (during the 
previous 12 months) in adult learning. This target was 
revised in June 2021 following its inclusion as 1 of the 
EU 2030 social targets within the European Pillar of Social 
Rights Action Plan. The revised goal is for at least 60% of 
people aged 25 to 64 years to participate in education and 
training every year by 2030. 

63Eurostat regional yearbook – 2025 editionA

Education

More about the data: adult participation in 
education and training 

The EU’s adult participation rate for education and 
training was 28.1% in 2024 

In order to give an appropriate measure of participation 
in adult learning, the reference period for the indicator is 
the last 12 months, in other words, the indicator reflects 
participation in training activities across a whole year. 
Until 2021, the adult education survey (AES) was the 
only source of data for adult participation in learning in 
the last 12 months. However, as the AES only takes place 
every 6 years, variables were introduced into the EU 
labour force survey (EU-LFS) to collect data every 2 years 
(starting in 2022). The aim of this was to enable more 
frequent analysis of initiatives related to employment 
and skills policies, including at the regional level, as the 
AES does not provide a regional breakdown due to 
the relatively low sample size. For more information on 
differences between data available from EU-LFS and 
AES see Participation in education and training during 
the last 12 months – differences between data available 
from two sources. 

This edition of the Eurostat regional yearbook marks 
the 1st time that regional statistics using this longer 
recall period have been published. 

According to the EU-LFS, in 2024, more than 1 in 4 (28.1%) 
adults aged 25 to 64 years across the EU participated in 
education and training during the 12 months prior to 
the survey. This figure was 3.1 percentage points higher 
than in 2022 (the only other reference year for which 
comparable data are available, as the data collection 
exercise is biennial). Among the 234 NUTS level 2 regions 
for which data are available, more than 3 out of 4 reported 
an increase between 2022 and 2024 in their adult 
participation rates for education and training. 

Map 3.6 shows adult participation rates in education 
and training for 2024. These rates were relatively evenly 
distributed across the EU, as just over half of all NUTS 
level 2 regions for which data are available (126 out 
of 243 regions) recorded rates below the EU average 
of 28.1%. The regional distribution was relatively 
homogeneous within individual EU countries, partly 
reflecting national rather than regional education and 
training initiatives. 

•  Every region of Bulgaria, Germany, Greece, Croatia, Italy 
and Romania reported a rate that was below the EU 
average; this was also the case in Latvia.

•  Every region of Denmark, Hungary, the Netherlands, 

Austria, Slovenia, Slovakia, Finland and Sweden reported 
a rate that was above the EU average; this was also the 
case for Estonia, Cyprus, Luxembourg and Malta.

In 2024, Sweden recorded some of the highest adult 
participation rates in education and training, as every 
region reported a rate above 55.0% (as shown by the 
darkest shade of teal in Map 3.6). By contrast, participation 
rates were below 11.0% in every region of Bulgaria (as 
shown by the darkest shade of gold; note the latest 
information for Severen tsentralen refers to 2022). Similarly 
low rates were recorded across Greece and Croatia, where, 
apart from the capital regions of Attiki and Grad Zagreb, all 
other regions had rates below 11.0%. 

Eurostat regional yearbook – 2025 edition64Map 3.6: Participation rate in education and training
(% of people aged 25–64 years, by NUTS 2 regions, 2024)

A

Education

Note: participation during the 12 months prior to the survey. Severen 
tsentralen (BG32), Iceland and Bosnia and Herzegovina: 2022. Includes data 
with low reliability (too many regions to document).

Source: Eurostat (online data code: trng_lfse_22)

Swedish regions have particularly high adult participation 
rates in education and training 

Figure 3.4 provides a more detailed analysis of 
participation rates in education and training in 2024. It 
confirms that some of the highest rates among adults 
aged 25 to 64 years were observed in Sweden: 

and Greece. The lowest rates were recorded in the 
Bulgarian region of Yuzhen tsentralen (2.2%) and the Greek 
region of Sterea Elláda (2.7%). 

A closer inspection of participation rates in education and 
training for men and women aged 25 to 64 years reveals 
that in 2024: 

•  a peak of 64.0% was recorded in the capital region of 

•  the Swedish capital region of Stockholm recorded the 

Stockholm

highest rate among women (69.7%)

•  all 8 regions of Sweden ranked among the 10 EU regions 

•  the Slovak capital region of Bratislavský kraj had the 

with the highest rates

•  the top 10 was completed by 2 other capital regions – 
Bratislavský kraj (Slovakia) and Hovedstaden (Denmark).

In 2024, the 10 regions with the lowest adult participation 
rates in education and training were concentrated in 
south-eastern Europe, with 5 regions from each of Bulgaria 

highest rate among men (65.0%)

•  the 10 highest participation rates for women included 
every region of Sweden, along with the Danish and 
Finnish capital regions of Hovedstaden and Helsinki-
Uusimaa

65Eurostat regional yearbook – 2025 editionA

Education

•  the 10 highest participation rates for men were also 

relatively uniform – leaving aside Bratislavský kraj, they 
were regions exclusively located in either Sweden or 
Hungary

•  the lowest participation rates – for both women and 

men – were recorded in the Bulgarian region of Yuzhen 
tsentralen, 2.2% and 2.3%, respectively.

In 2024, the EU participation rate in education and training 
among women aged 25 to 64 years was 29.0%, which 
was 1.7 percentage points higher than the corresponding 
rate for men (27.3%). This gender gap – with women 
having higher participation rates than men – was apparent 
in approximately 2 out of 3 regions across the EU. Several 
Nordic and Baltic regions had particularly wide gender 

gaps (in favour of women); this pattern was also observed 
in Comunidad Foral de Navarra (Spain). 

•  The largest gender gap, favouring women, occurred 

in the Swedish region of Norra Mellansverige 
(15.7 percentage points difference), followed by Estonia 
(13.7 points) and another Swedish region, Sydsverige 
(13.0%).

•  The largest gender gap favouring men occurred in 

the Hungarian region of Közép-Dunántúl (where the 
participation rate for men was 7.6 points higher than 
that for women), followed by Bratislavský kraj (7.2 points) 
and another Hungarian region, Észak-Magyarország 
(6.0 points).

Figure 3.4: Participation rate in education and training
(% of people aged 25–64 years having participated during the 12 months prior to the survey, by sex and 
NUTS 2 regions, 2024)

Note: the figure shows those EU regions with the highest and lowest 
shares of people aged 25–64 years participating in education and training 
during the 12 months prior to the survey. Severen tsentralen (BG32): 2022. 
Severozapaden (BG31), Severen tsentralen (BG32), Ipeiros (EL54), Ionia Nisia 
(EL62) and Sterea Elláda (EL64): low reliability.

Source: Eurostat (online data code: edat_lfse_22)

Eurostat regional yearbook – 2025 edition664. Labour market

On 4 March 2021, the European Commission set out 
its ambition to build a stronger, more social EU by 
focusing on jobs and skills, paving the way for a fair, 
inclusive and resilient socioeconomic recovery from the 
COVID-19 crisis. The European Pillar of Social Rights Action 

Plan (COM(2021) 102 final) outlines specific actions and 
headline targets to improve employment, skills and social 
protection across the EU. One of the principal targets is to 
have, by 2030, an employment rate of at least 78% among 
people aged 20 to 64 years. 

(people aged 20–64 years, by NUTS 2 regions, 2024)

Source: Eurostat (online data code: lfst_r_lfsd2pwn)

Note: due to rounding, some totals may not correspond with the sum 
of the separate figures. Mayotte (FRY5): not available. Kassel (DE73), Trier 
(DEB2), Lubuskie (PL43) and Opolskie (PL52): unemployment rate, not 
available. Åland (FI20): outside the labour force and unemployment rate, 
not available. Includes data with low reliability (too many regions to 
document).

67Eurostat regional yearbook – 2025 editionA

Labour market

Employment and social policies offer a broad range of 
practical benefits for people living in the EU, including 
employment support, mobility within the labour market, 
skills development, social protection and inclusion, 
as well as range of different rights at work. In 2024, 
there were 260.6 million people aged 20 to 64 years 
in the EU (those considered to be of core working 
age); see the infographic above. Of these, the labour 
force was composed of 197.6 million employed people 
and 11.9 million unemployed people who were actively 
seeking and available for work. There were 51.1 million 
people outside the labour force – in other words, 
economically inactive – for example, students, retired 
people (if under the age of 65), people caring for other 
family members, volunteers and those unable to work due 
to a long-term illness or disability. 

Among NUTS level 2 regions, the Finnish archipelago of 
Åland recorded the highest share of employed people 
within the core working-age population, at 86.4% in 2024. 
By contrast, less than half of this subpopulation was 
employed in the southern Italian regions of Calabria 
(48.5%) and Campania (49.4%). 

This chapter examines employment and unemployment 
patterns across EU regions, offering a detailed analysis of 
regional labour markets, with a focus on qualifications and 
skills. It highlights key indicators such as employment rates, 
unemployment rates and the structure of labour markets, 
offering insights into regional disparities within the EU. 

Employment

More about the data: employment rate targets 
in the European Pillar of Social Rights 

The employment rate is the percentage of employed 
people (of a given age) relative to the total population 
(of the same age). 

The EU prioritises increasing the number and share of 
people in work as 1 of its key policy objectives. This goal 
has been central to EU employment policies since the 
launch of the European employment strategy in 1997; 
it was subsequently integrated into the Lisbon and 
Europe 2020 strategies. 

The employment rate is a key indicator in the social 
scoreboard, which monitors the implementation of the 
European Pillar of Social Rights. By 2030, the EU aims 
to have an employment rate of at least 78% among 
people aged 20 to 64 years. This age range reflects the 
growing share of young people who remain within 
education into their late teens and beyond, potentially 
limiting early labour market participation, while most EU 
residents retire by the age of 65. 

Individual EU countries have different employment 
rate targets. To achieve the overall EU target of a 78% 
employment rate by 2030, EU countries presented 
and agreed national targets in June 2022. These are 
generally higher for countries that already had relatively 
high rates. For example, the target for Hungary has 
been set at 85.0%, for Malta at 84.6% and for Germany 
at 83.0%. 

The employment rate is included in the EU’s sustainable 
development goals (SDGs) indicator set. Goal 8 seeks to 
promote sustained, inclusive and sustainable economic 
growth, through full and productive employment and 
decent work for all. 

In 2024, the EU’s employment rate was 2.2 percentage 
points below its 2030 target 

Prior to the COVID-19 crisis, the EU’s employment rate for 
the core working-age population (20 to 64 years) increased 
steadily for 6 consecutive years, reaching 73.1% by 2019. 
This upward development was interrupted in 2020 when 
the pandemic contributed to a fall of 0.9 percentage 
points. However, the employment rate rebounded 
in 2021, nearly returning to its pre-pandemic levels, and 
continued to rise in subsequent years, with a particularly 
sharp increase in 2022 (up 1.6 points). By 2024, the EU’s 
employment rate had climbed to a historical high of 75.8%, 
which was 2.2 points below the target for 2030, as set by 
the European Pillar of Social Rights Action Plan. 

Map 4.1 shows the employment rate in 2024 for NUTS 
level 2 regions. Almost half (46.5%) of all EU regions – 
113 out of the 243 regions for which data are available 
– had already reached or surpassed the EU’s target 
of 78.0% by 2024 (those regions shown in shades of 
teal). These 113 regions were found in clusters, with 
high concentrations in Czechia (all 8 regions), Denmark 
(all 5 regions), Germany (35 out of 38 regions; the 
exceptions being Bremen, Düsseldorf and Berlin), Ireland 
(all 3 regions), the Netherlands (all 12 regions), Slovakia 
(3 out of 4 regions) and Sweden (all 8 regions); this group 
included Estonia, Cyprus and Malta too. 

At the top end of the distribution, the highest 
employment rates among NUTS level 2 regions in 2024 – 
and the only regions to record rates of more than 85.0% 
– were: 

•  the archipelago of Åland in Finland (86.4%)
•  Warszawski stołeczny, the Polish capital region (86.2%)
•  Bratislavský kraj, the Slovak capital region (85.4%)
•  Budapest, the Hungarian capital region (85.3%)
•  Utrecht in the Netherlands (85.3%)
•  Praha, the Czech capital region (85.1%).

Aside from the 6 regions mentioned above, an 
additional 22 regions recorded employment rates of 

Eurostat regional yearbook – 2025 edition68A

Labour market

at least 83.5% (as shown by the darkest shade of teal in 
Map 4.1). Almost half of them were located in Germany, 
while the others were exclusively from the Netherlands, 
Hungary or Sweden – including the capital regions of 
Noord-Holland (the Netherlands; 84.4%) and Stockholm 
(Sweden; 83.7%). 

In 2024, all multi-regional northern, eastern and southern 
EU countries reported that their capital region had a 
higher employment rate than their national average. For 
example, Romania’s capital region of Bucureşti-Ilfov had 
an employment rate of 81.1%, which was 11.6 percentage 
points above the national average of 69.5%. A similar 
pattern was observed in the western EU countries of 
Ireland, France and the Netherlands. By contrast, the 
pattern was reversed in Belgium, Germany and Austria, 
where capital regions recorded lower employment 
rates than their respective national averages. Région 

Map 4.1: Employment rate
(% of people aged 20–64 years, by NUTS 2 regions, 2024) 

de Bruxelles-Capitale / Brussels Hoofdstedelijk Gewest 
and Wien registered the lowest regional employment 
rates across the whole of Belgium and Austria, at 64.1% 
and 70.6%, respectively. 

In 2024, the southern Italian regions of Calabria, 
Campania and Sicilia recorded the lowest employment 
rates in the EU 

Many of the regions with relatively low employment rates 
were rural, sparsely populated, or regions on the periphery 
of the EU. This pattern was particularly apparent in 
southern regions of Spain and Italy, much of Greece, some 
regions in Romania, and the outermost regions of France. 
These areas typically experienced limited employment 
opportunities, especially for individuals with intermediate 
and high skill levels. 

Note: Prov. Vlaams-Brabant (BE24) and Corse (FRM0), low reliability.

Source: Eurostat (online data code: lfst_r_lfe2emprtn)

69Eurostat regional yearbook – 2025 editionA

Labour market

Another group of regions characterised by relatively 
low employment rates are former industrial heartlands 
that have not adapted economically. Some of these 
have witnessed the negative impact of globalisation on 
traditional sectors of their economies (such as coal mining, 
steel or textiles manufacturing). Examples include a band 
of regions running from north-east France into the Région 
wallonne (Belgium). 

Approximately 1 in 4 (65 out of the 243 regions for which 
data are available) EU regions had an employment rate 
that was below 73.5% in 2024 (as shown by the 2 darkest 
shades of gold in Map 4.1). This group included: 

•  2 regions in southern Italy where less than half of the 

core working-age population was employed – Calabria 
(48.5%) and Campania (49.4%)

•  the island region of Sicilia (also in southern Italy), where 
employment covered just over half of the working-age 
population (50.7%), the 3rd lowest rate in the EU

•  the capital regions of Région de Bruxelles-

Capitale / Brussels Hoofdstedelijk Gewest in Belgium 
(64.1%), Lazio in Italy (69.0%), Wien in Austria (70.6%) and 
Attiki in Greece (71.0%).

In 2024, Italy had the highest regional disparity for 
employment rates 

Several EU countries face considerable labour market 
disparities across their regions, with labour shortages 
in some regions contrasted against persistently high 
unemployment in others. A population-weighted 
coefficient of variation provides a means to analyse these 
inter-regional disparities. Figure 4.1 shows that, in 2024, Italy 
had the highest regional disparities in employment rates, 
with a coefficient of variation of 15.6%. Broadly, there was a 
north-south split: the Alpine region of Provincia Autonoma 
di Bolzano/Bozen recorded the highest employment 
rate (79.9%), while the southern regions of Calabria and 
Campania had the lowest rates (48.5% and 49.4%). 

Figure 4.1: Regional disparities in employment rates
(coefficient of variation in %, people aged 20–64 years, by NUTS 2 regions, 2014 and 2024)

Note: as measured by population-weighted coefficient of variation for 
EU, EFTA and non-EU countries with more than 4 level 2 regions (Estonia, 
Ireland, Croatia, Cyprus, Latvia, Lithuania, Luxembourg, Malta, Slovenia, 
Slovakia, Iceland, Liechtenstein, Bosnia and Herzegovina, Montenegro, 
Moldova, North Macedonia, Georgia and Ukraine: not applicable).

Source: Eurostat (online data code: lfst_r_lmder)

Eurostat regional yearbook – 2025 edition70A

Labour market

Belgium (8.1%), Romania (7.6%) and Spain (6.5%) had 
the next highest coefficients of variation for regional 
employment rates in 2024: 

•  in Belgium, the highest regional employment rates were 
generally recorded in Vlaams Gewest, while lower rates 
were observed in Région wallonne and particularly in 
the capital region

•  in Romania, the highest regional employment rate 

was in the capital region of Bucureşti-Ilfov (81.1%), with 
notably lower rates observed in all other regions, in 
particular, Sud-Est (62.6%) and Sud-Vest Oltenia (63.5%)
•  in Spain, the highest regional employment rates were 
generally recorded in northern and eastern regions, as 
well as the capital city region; lower rates were observed 
in peripheral, southern and western regions.

At the other end of the range, the lowest regional 
disparities in employment rates – with a coefficient of 
variation of 2.0% or less – were recorded in the Nordic EU 
countries and the Netherlands. 

Figure 4.1 shows that regional employment 
rates converged to some degree across the EU 
between 2014 and 2024. The coefficient of variation for 
the EU as a whole fell from 13.0% to 9.5%. In 15 (out of 17) 
EU countries for which data are available, inter-regional 
employment rate disparities narrowed. The largest falls – 
in relative terms – occurred in Finland, the Netherlands, 
Czechia, Portugal and Spain, as regional disparities 
decreased by at least 40%. By contrast, Romania and 
Austria were the only EU countries to report an increase in 
regional disparities, rising 8.6% and 2.0%, respectively. 

The EU’s gender employment gap was 10.0 percentage 
points in 2024 

The gender employment gap is included in the EU’s SDGs 
indicator set. Goal 5 seeks to achieve gender equality by, 
among other actions, ending all forms of discrimination, 
violence, and any harmful practices against women and 
girls, while promoting women’s social and economic 
empowerment. 

Long-standing challenges related to female participation 
in the labour force are reflected in persistent gender gaps 
in employment and pay. These gaps exist for a variety of 
reasons, including: 

•  women often bear a disproportionate share of unpaid 

care and household chores that may limit their 
availability for paid employment

•  gender bias and discrimination when hiring, promoting 

and paying women

•  fewer women in leadership positions to draw attention 
to gender-related policies or to mentor more junior 
female staff

•  a lack of affordable childcare and support for working 

parents

•  disincentives in tax and benefit system that can lead 

to 2nd earners bearing a higher tax burden when they 
choose to participate in the labour market
•  occupational segregation, with women often 

concentrated in specific activities that are characterised 
by lower wages and/or fewer opportunities for career 
development.

In 2024, the EU employment rate for men 
aged 20 to 64 years was 80.8%, while the corresponding 
rate for women of the same age was considerably 
lower, at 70.8%. This resulted in a gender employment 
gap – defined here as the difference between male and 
female employment rates – of 10.0 percentage points. 
As part of its broader target to increase the overall 
employment rate to 78% by 2030, the European Pillar of 
Social Rights Action Plan also includes a subgoal to halve 
the gender employment gap. This implies reducing the 
gap to 5.6 percentage points by 2030 – equivalent to an 
average annual decrease of 0.5 points from 2019 onwards. 

In 2024, there were 4 regions across the EU where 
employment rates for women were higher than those for 
men 

In 2024, 59 out of the 243 NUTS level 2 regions for which 
data are available reported a gender employment gap 
that was 5.6 percentage points or lower – in other words, 
meeting the subgoal for the European Pillar of Social 
Rights Action Plan; they are shown in different shades 
of teal in Map 4.2. This group of 59 regions was mainly 
concentrated in Germany (13 regions), France (10 regions), 
Sweden (7 out of 8 regions), Portugal (6 out of 9 regions) 
and Finland (all 5 regions); it also included the Baltic 
regions and Luxembourg. Those regions with relatively 
small gender employment gaps were often characterised 
by high overall employment rates. 

In 2024, there were only 4 regions within the EU that 
reported a higher employment rate for women (than for 
men): 

•  the Finnish regions of Åland, Etelä-Suomi and Pohjois- ja 

Itä-Suomi

•  the Croatian capital region of Grad Zagreb.

71Eurostat regional yearbook – 2025 editionA

Labour market

Despite some progress made in reducing the gender 
employment gap, female employment rates continue 
to lag behind male rates in the vast majority of EU 
regions. Many of the regions with relatively large 
gender employment gaps were characterised by high 
unemployment rates and a greater share of women 
outside the labour force. In 2024, there were 24 NUTS 
level 2 regions that reported gender employment gaps of 
at least 17.5 percentage points (as shown by the darkest 
shade of gold in Map 4.2). The regions composing this 
group were concentrated in southern and eastern EU 
countries: 

•  11 out of 13 regions in Greece, the exceptions being 

Attiki and Ipeiros

•  8 regions in central/southern Italy, including the capital 

region of Lazio

•  4 regions in Romania
•  Ciudad de Ceuta in Spain.

Sterea Elláda (Greece; 31.2 percentage points in 2024) had 
the largest gender employment gap in the EU, followed 
by the Italian regions of Puglia (29.8 points), Campania 
(29.1 points) and Basilicata (28.1 points). 

Map 4.2: Gender employment gap
(percentage points, people aged 20–64 years, by NUTS 2 regions, 2024)

Note: the gender employment gap is the employment rate of men minus 
the employment rate of women. Prov. Vlaams-Brabant (BE24), Corse 
(FRM0) and Åland (FI20): low reliability.

Source: Eurostat (online data code: lfst_r_lfe2emprtn)

Eurostat regional yearbook – 2025 edition72A

Labour market

Employment – focus on 
qualifications and skills

YOUNG PEOPLE NEITHER IN EMPLOYMENT 
NOR IN EDUCATION AND TRAINING (NEET) 

More about the data: young people neither 
in employment nor in education and training 
(NEET) 

The share of young people (aged 15 to 29 years) who 
are neither in employment nor in education and 
training (NEET) provides a useful measure for studying 
the vulnerability of young people in terms of their 
labour market participation and social exclusion. 

The NEET rate is expressed relative to the total 
population of the same age (15 to 29 years); the 
numerator includes not only young people who are 
unemployed but also young people who are outside 
the labour force for reasons other than education or 
training (for example, because they are caring for family 
members, volunteering or travelling, or unable to work 
for health reasons). 

The NEET rate is included in the EU’s SDG indicator 
set. Goal 8 seeks to promote sustained, inclusive and 
sustainable economic growth, full and productive 
employment and decent work for all. 

Economic crises tend to impact young people 
disproportionately, as they are more likely to work under 
temporary and other atypical contracts that are easier to 
terminate. The NEET rate helps assess the share of young 
people who have not transitioned from education or 
training to employment. It is generally seen as a more 
comprehensive measure than the unemployment rate, as 
it more closely reflects young people’s risk of social and 
labour market exclusion. 

Within the European Pillar of Social Rights Action Plan, the EU 
has set a policy target whereby the NEET rate should decrease 
to less than 9% by 2030. Having peaked at 16.1% in 2013, the 
EU’s NEET rate subsequently fell during 6 consecutive years. 
With the onset of the COVID-19 crisis, it climbed to 13.8% 
in 2020, after which a downward pattern of development 
returned. In 2024, the EU’s NEET rate stood at 11.0%. 

In 2024, the 4 EU regions with the lowest NEET rates were 
all located in the Netherlands 

In 2024, 89 NUTS level 2 regions reported a NEET rate 
of less than 9.0% (the EU’s policy target to be reached 
by 2030); they are shown in teal shades within Map 4.3. This 
group was concentrated in northern Belgium (all 5 regions 
of Vlaams Gewest), Czechia (6 out of 8 regions), Ireland 
(all 3 regions), north-western Hungary (4 regions), the 

Netherlands (all 12 regions), Austria (7 out of 9 regions), 
Slovenia (both regions), Slovakia (3 out of 4 regions) and 
Sweden (all 8 regions); it also included Malta. 

Looking more closely at the data, 26 regions across the 
EU recorded a NEET rate below 6.5% in 2024 (as shown by 
the darkest shade of teal in Map 4.3). This group included 
several capital regions, notably those of Bulgaria, Czechia, 
Denmark, Croatia, Hungary, the Netherlands and Sweden. 
The lowest NEET rates in the EU were observed in the 
Netherlands, with Utrecht recording the lowest share, 
at 4.4%, followed by the capital region of Noord-Holland 
(4.5%), Noord-Brabant and Overijssel (both 4.7%). Praha, 
the Czech capital region, had the lowest rate outside the 
Netherlands, at 4.8% – a share that was also recorded in 
the Dutch region of Zuid-Holland. 

As noted above, some of the lowest NEET rates in the EU 
were recorded in capital regions. More broadly, capital 
regions generally reported lower NEET rates than the national 
average. In 2024, there were only 4 exceptions – among multi-
regional EU countries – where the share of young people 
neither in employment nor in education or training was 
higher in the capital region than the national average: 

•  Wien in Austria (12.7%), where the NEET rate 

was 3.5 percentage points higher than the national 
average (9.2%)

•  Région de Bruxelles-Capitale / Brussels Hoofdstedelijk 

Gewest in Belgium (13.1%), where the rate was 3.2 points 
higher than the national average (9.9%)

•  Berlin in Germany (11.1%), where the rate that 

was 2.6 points higher than the national average (8.5%)
•  Eastern and Midland in Ireland (7.7%), where the rate was 

marginally higher than the national average (7.6%).

By contrast, Bucureşti-Ilfov – the capital region of Romania 
– had a NEET rate in 2024 that was 9.9 percentage points 
lower than the national average. This broadly reflected 
the pattern observed across most eastern EU countries, 
with the capital regions of Bulgaria (6.5 points), Hungary 
(5.4 points), Slovakia (also 5.4 points; 2022 data) and Croatia 
(5.0 points) also recording considerably lower rates than 
their respective national averages. 

In 2024, 24 out of the 240 NUTS level 2 regions for which 
data are available recorded NEET rates of at least 18.0% 
among young people aged 15 to 29 years (these regions 
are shaded in the darkest 2 shades of gold in Map 4.3). 
The highest NEET rates were generally observed in 
predominantly rural regions of southern and eastern EU 
countries, as well as in the French outermost regions. At 
the upper end of the distribution, 5 regions had NEET rates 
of at least 25.0%: 

•  the Italian regions of Sicilia (25.7%) and Calabria (26.2%)
•  the Romanian regions of Sud-Vest Oltenia (25.0%) and 

Sud-Est (29.9%)

•  the French outermost region of Guyane had the highest 

rate, at 34.4%.

73Eurostat regional yearbook – 2025 editionA

Labour market

Map 4.3: Young people neither in employment nor in education and training
(% of people aged 15–29 years, by NUTS 2 regions, 2024)

Note: Burgenland (AT11), Innlandet (NO02) and Bosnia and Herzegovina: 
2023. Oberpfalz (DE23) and Bratislavský kraj (SK01), 2022. Includes data with 
low reliability (too many regions to document).

Source: Eurostat (online data code: edat_lfse_22)

In 2024, the EU’s NEET rate was 2.1 percentage points 
higher among young females than young males 

Across the EU, 12.1% of young females aged 15 to 29 years 
were neither in employment nor in education and training 
in 2024. This figure was 2.1 percentage points higher than 
the corresponding rate for young males (10.0%). Across 
NUTS level 2 regions, nearly 3 out of 4 regions had a higher 
NEET rate among young females. The gender gaps were 
most pronounced in regions located across eastern and 
southern EU countries, where cultural, economic and 
societal factors may hinder some young females’ from 
entering the workforce. 

Figure 4.2 confirms the highest NEET rates among 
young females in 2024 were recorded in the Romanian 
regions of Sud-Est (37.7%), Centru (32.7%) and Sud-Vest 
Oltenia (30.6%), as well as the French outermost region of 
Guyane (33.7%). This may reflect, at least in part, persistent 
structural and societal barriers disproportionately affecting 

young women in accessing education, training and 
employment. For young males, the highest NEET rate, by 
far, was observed in Guyane, at 35.1%; this was followed by 
Severozapaden in Bulgaria (25.7%). 

A closer analysis of the data for regions with the 
highest NEET rates among young females and males 
in 2024 reveals some notable gender differences. For 
instance: 

•  Sud-Muntenia in Romania (28.7%), Észak-Magyarország 
in Hungary (26.7%) and Anatoliki Makedonia, Thraki in 
Greece (24.7%) had some of the highest NEET rates for 
young females, but did not feature among those regions 
with the highest overall NEET rates

•  Notio Aigaio in Greece (22.6%) and Guadeloupe in 

France (21.1%) had some of the highest NEET rates for 
young males, but similarly did not feature among those 
regions with the highest overall NEET rates.

Eurostat regional yearbook – 2025 edition74A

Labour market

In 2024, the lowest overall NEET rates in the EU were 
concentrated in the Netherlands, Germany and Czechia, 
particularly across economically developed and urbanised 
regions that may make labour market integration for 
young people easier. 

•  The lowest female NEET rate was recorded in Grad 
Zagreb (the Croatian capital region; 4.6%), while 
Budapest (the capital region of Hungary; 5.2%) and 
Östra Mellansverige (Sweden; 5.5%) were the only other 
regions from outside of the Netherlands to feature 
among those with the lowest rates.

•  The lowest male NEET rates were recorded in the 

neighbouring Czech regions of Praha (2.4%) and Střední 
Čechy (2.5%). There were 4 other regions from Czechia 
that featured among those with the 12 lowest rates, 
alongside 5 regions from the Netherlands (the lowest 
rate was in Utrecht; 3.6%) and Oberbayern in Germany 
(4.2%).

Figure 4.2: Young people neither in employment nor in education and training
(% of people aged 15–29 years, by sex and NUTS 2 regions, 2024)

Note: the figure shows those EU regions with the highest and lowest 
shares of young people aged 15–29 years who were neither in 
employment nor in education and training (NEET). The rankings include 
more than 10 regions if several regions have identical values. Subject to 
data availability. Includes data with low reliability (too many regions to 
document).

Source: Eurostat (online data code: edat_lfse_22)

75Eurostat regional yearbook – 2025 editionA

Labour market

EMPLOYMENT RATES BY EDUCATIONAL 
ATTAINMENT 

More about the data: employment rates by 
educational attainment 

An individual’s level of educational attainment can play 
a key role when seeking employment. People with a 
tertiary level of educational attainment (as defined by 
the international standard classification of education 
(ISCED 2011 levels 5 to 8)) generally enjoy the most 
success when trying to find work. They also tend to be 
better shielded from the risks of unemployment than 
their peers with lower levels of educational attainment. 

The data presented in this section concern people 
aged 25 to 64 years, as this represents a cohort of 
individuals who have generally completed their 
education or training and are most likely to be actively 
participating in the labour market. As such, it excludes 
younger individuals who may still be studying, as well 
as older individuals who may be in retirement. 

In 2024, 184.7 million people aged 25 to 64 years were 
employed across the EU. The largest share of this 
cohort consisted of people with an upper secondary 
or post-secondary non-tertiary education (ISCED 
levels 3 to 4; 44.4%), followed by those with a tertiary level 
of educational attainment (ISCED levels 5 to 8; 40.6%). 
A relatively small share of the EU workforce had no more 
than a lower secondary education (ISCED levels 0 to 2; 
14.8%). The strong links between educational attainment 
and employment opportunities are reflected in the latest 
employment rates: 

•  59.2% for people with no more than a lower secondary 

education (hereafter referred to as a low level of 
education)

•  78.3% for people with an upper secondary or post-

secondary non-tertiary education (a medium level of 
education)

•  87.8% for people with a tertiary level of education (a high 

level of education).

Map 4.4 presents employment rates in 2024 for people 
aged 25 to 64 years according to these 3 different levels of 
educational attainment. 

•  There were 46 NUTS level 2 regions where fewer 

than half of all people with a low level of education 
were in employment. By contrast, employment rates 
for people with medium or high levels of education 
exceeded 50.0% in every region of the EU.

•  There were 71 NUTS level 2 regions where at least 90.0% 

of all people with a high level of education were in 
employment. By contrast, employment rates for people 
with a low level of education remained consistently 
below 90.0% across every region of the EU, while Åland 
in Finland was the only region to report an employment 
rate of at least 90.0% for people with a medium level of 
education.

In 2024, Východné Slovensko in Slovakia had the lowest 
employment rate among people with a low level of 
education … 

The 1st part of Map 4.4 shows employment rates 
for people aged 25 to 64 years with a low level of 
education. In 2024, there were 46 NUTS level 2 regions 
with employment rates below 50.0% for this cohort. 
These 46 regions were mainly concentrated in eastern EU 
countries (excluding Czechia, Hungary and Slovenia), as 
well as in southern Belgium and southern Italy; among 
others, this group also included the capital regions of 
Région de Bruxelles-Capitale / Brussels Hoofdstedelijk 
Gewest (Belgium), Attiki (Greece) and Wien (Austria). The 
lowest employment rate was recorded in Východné 
Slovensko – the easternmost region of Slovakia – 
where 27.7% of people with a low level of education were 
in employment. There were 4 other regions that recorded 
employment rates below 35.0%: Guyane (France; 31.8%), 
Centru (Romania; 33.4%), Stredné Slovensko (Slovakia; 
34.4%) and Severozapaden (Bulgaria; 34.7%). 

… while 3 regions in southern Italy had the lowest 
employment rates for people with a medium level of 
education 

The 2nd map shows employment rates for people 
aged 25 to 64 years with a medium level of education. 
In 2024, there were 8 NUTS level 2 regions that recorded 
an employment rate of at least 87.0% for this cohort. They 
included 3 regions in Czechia (among them the capital 
region of Praha), Bratislavský kraj (the capital region 
of Slovakia), Malta, Közép-Dunántúl (Hungary), as well 
as the island regions of Região Autónoma dos Açores 
(Portugal) and Åland (Finland). Åland recorded the highest 
employment rate for people with a medium level of 
education, at 90.1%. 

At the lower end of the distribution, there were 3 regions 
within the EU where fewer than 60.0% of people with 
a medium level of education were in employment. 
All 3 were located in southern Italy: Calabria (53.2%), 
Campania (58.2%) and Sicilia (59.8%). The next lowest 
employment rate was recorded in the Belgian capital 
region – Région de Bruxelles-Capitale / Brussels 
Hoofdstedelijk Gewest – at 60.9%. 

Eurostat regional yearbook – 2025 edition76In 2024, Região Autónoma dos Açores in Portugal recorded 
the highest proportion of tertiary-educated people in 
employment 

The 3rd map shows employment rates for people 
aged 25 to 64 years with a high level of education. In 2024, 
there were 71 NUTS level 2 regions across the EU that 
recorded employment rates of at least 90.0% for this 
cohort. These regions were clustered across Bulgaria, 
Lithuania, Hungary, the Netherlands, Poland, Portugal, 
Romania and Slovenia; Malta had a similarly high level 
too. The highest proportion of tertiary-educated people 
in employment was recorded in the Portuguese island 
region of Região Autónoma dos Açores (93.9%), while 
employment rates of at least 93.0% were also observed 
in Småland med öarna (Sweden), Centru (Romania), Pest 
(Hungary) and Bratislavský kraj (Slovakia). 

In 2024, employment rates for people aged 25 to 64 years 
with a tertiary level of education were generally higher in 
capital regions than their respective national averages. This 
may reflect the ability of capital regions to attract highly 
qualified individuals, exerting considerable ‘pull effects’ 
through the diverse educational, employment and social/
lifestyle opportunities that they offer. This pattern was 
particularly evident in Greece, where the employment rate 
for tertiary-educated people in the capital region of Attiki 
was 3.1 percentage points above the national average. A 
similar although less pronounced situation was observed 
in Slovakia, Spain, Czechia, Bulgaria, Croatia and Poland, 
where the gap between these 2 rates exceeded 1.0 points. 
By contrast, in the western EU countries of Belgium, Austria 
and Germany, the national employment rate for this cohort 
was at least 3.2 points higher than that for the capital 
region. This gap was particularly wide in Belgium, as the 
employment rate for tertiary-educated people in Région 
de Bruxelles-Capitale / Brussels Hoofdstedelijk Gewest 
was 6.0 points below the national average. 

There were 10 NUTS level 2 regions where, in 2024, the 
employment rate for people aged 25 to 64 years with a 
high level of education was below 78.5%. These regions 
were exclusively located in the southern EU countries of 
Italy (5 regions), Greece (4 regions) and Spain (1 region, 
Ciudad de Ceuta). Almost all of these regions were 
predominantly rural, characterised by relatively large 
agricultural sectors and limited opportunities for highly 
skilled workers. The southern Italian region of Calabria had 
the lowest rate for this cohort, at 71.1%. 

A

Labour market

Map 4.4: Employment rate by educational attainment 
(% of people aged 25–64 years, by NUTS 2 regions, 2024)

Note: Prov. Vlaams-Brabant (BE24), Corse (FRM0) and Åland (FI20), low 
reliability. Chemnitz (DED4), Grad Zagreb (HR05), Lubuskie (PL43), Opolskie 
(PL52) and Świętokrzyskie (PL72): low reliability for no more than lower 
secondary education.

Source: Eurostat (online data codes: lfst_r_lfe2eedu and lfst_r_lfsd2pop)

77Eurostat regional yearbook – 2025 editionA

Labour market

HIGHLY SKILLED PEOPLE 

Capital regions attract highly qualified talent 

A recent communication from the European Commission, 
Harnessing talent in Europe’s regions (COM(2023) 32 final), 
highlighted the issue of increasing global competition for 
talent, as many developed world economies are expected 
to face shrinking populations in the years ahead. The 
communication identified demographic transformation as 
a cause for concern in several EU regions, where shrinking 
working-age populations and the potential departure of 
young and skilled workforces to other regions/territories 
could result in a ‘talent development trap’. 

More about the data: highly skilled people 

The International Classification of Occupations 
(ISCO) provides statisticians with a framework for 
internationally comparable data about occupations 
(a set of tasks and duties performed, or meant to be 
performed, by 1 person, including for an employer 
or in self-employment). The current version of the 
classification was published in 2008 (its 4th iteration) 
and is known as ISCO-08. It is based on a hierarchical 
classification made-up of 10 major groups, denoted 
by 1-digit codes. 

For the purpose of this publication, highly 
skilled employed people are defined as people 
aged 25 to 64 years in the following occupations: 

•  managers (ISCO 1)
•  professionals (ISCO 2)
•  technicians and associate professionals (ISCO 3).

In 2024, there were 84.2 million highly skilled people 
aged 25 to 64 years employed within the EU; they 
accounted for 45.8% of the total number of employed 
people of the same age. Map 4.5 shows that the 
regional distribution of highly skilled people across 
NUTS level 2 regions was somewhat skewed: 106 out 
of 243 regions for which data are available – or 43.6% of 
all regions – reported a share of highly skilled employed 
people that was above the EU average. 

There were 21 NUTS level 2 regions across the EU 
where, in 2024, at least 60.0% of all employed people 
aged 25 to 64 years were considered highly skilled. Of 
these, 13 were capital regions, which tend to attract 
highly qualified individuals by offering a wide array of job 
prospects in dynamic sectors of the economy, as well as 
diverse cultural and social opportunities. 

•  Stockholm, the Swedish capital region, recorded 

the highest share, with 72.7% of employed people 
considered highly skilled.

•  In the capital regions of Czechia, Poland, Hungary, 
the Netherlands and Denmark, more than 2 out 
of 3 employed people were considered highly skilled; 
the same was true for Luxembourg.

•  The capital regions of France, Croatia, Belgium, Slovakia, 
Finland, Lithuania and Germany also reported shares of 
highly skilled employed people that were above 60.0%.

•  Luxembourg recorded a share of 71.7%.
•  Non-capital regions with shares over 60.0% included: 

 ◦ the Dutch regions of Utrecht (72.0%), Zuid-Holland 

(63.8%) and Groningen (60.6%)

 ◦ the Belgian Prov. Brabant Wallon (68.2%)
 ◦ the Swedish regions of Sydsverige (63.5%) and Östra 

Mellansverige (60.7%)

 ◦ the German region of Hamburg (61.4%).

In 2024, there were 20 NUTS level 2 regions where highly 
skilled employed people accounted for less than 30.0% 
of total employment among those aged 25 to 64 years 
(these regions are denoted by a yellow shade in Map 4.5). 
This group was concentrated in the south-east of the EU, 
comprising 11 regions in Greece and 7 regions in Romania. 
The other 2 regions were both located in Spain: the central 
region of Castilla-La Mancha and the island region of 
Canarias. 

Looking in more detail, the lowest share of highly skilled 
employed people was recorded in the Greek region of 
Sterea Elláda (18.5% in 2024); it was the only region across 
the EU where fewer than 1 in 5 employed people were 
highly skilled. The next lowest shares were observed in 
Romania – Sud-Muntenia (22.5%) and Nord-Est (22.7%); 
these were followed by 3 more Greek regions, namely, 
Anatoliki Makedonia, Thraki (23.3%), Voreio Aigaio (24.4%) 
and Peloponnisos (24.8%). 

Eurostat regional yearbook – 2025 edition78A

Labour market

Map 4.5: Highly skilled employed people
(% of employed people aged 25–64 years, by NUTS 2 regions, 2024)

Note: the indicator covers people employed as managers, professionals, 
and technicians and associate professionals (ISCO 1–3). The indicator 
excludes people who gave no response when asked about their 
occupation. Prov. West-Vlaanderen (BE25), Corse (FRM0) and Åland (FI20): 
low reliability.

PEOPLE FREQUENTLY USING DIGITAL DEVICES 
AT WORK 

Technology has become integral to many job functions, 
from administrative tasks to complex decision-making 
processes. In capital regions, where economies are 
often more service-oriented and knowledge-driven, 
the adoption of digital tools is particularly high. As 
technology advances, digital literacy becomes essential 
for maintaining productivity and competitiveness. In 2022, 
just over 2 out of 5 (40.3%) employed people in the EU 
aged 20 to 64 years reported using digital devices for at 
least half of their time at work. Such devices encompass 
computers, tablets, phablets and smartphones for work-
related tasks; phone calls are excluded. 

Source: Eurostat (labour force survey)

In 2022, approximately 3 out of 4 employed people in 
Stockholm used digital devices for at least half of their 
working time 

Map 4.6 shows the regional distribution of employed 
people aged 20 to 64 years who, in 2022, spent at least 
half of their working time using digital devices. Among 
the 234 NUTS level 2 regions for which data are available, 
127 reported shares above the EU average of 40.3%. Within 
this group, 50 regions reported that at least half of all 
employed people spent at least 50% of their working time 
using digital devices (as shown by the 2 darkest shades of 
blue). 

Looking in more detail, the map highlights the top decile 
of the distribution – regions where at least 54.5% of 
employed people used digital devices for at least half of 
their working time (shown by the darkest shade of blue 

79Eurostat regional yearbook – 2025 editionA

Labour market

in Map 4.6). In 2022, 25 NUTS level 2 regions were in this 
category, almost all of them located in western and Nordic 
EU countries. This group included all 8 Swedish regions 
and 7 out of the 10 Dutch regions for which data are 
available. The remainder comprised: 

•  the capital regions of France, Finland, Austria, Germany, 

Denmark and Ireland

•  2 other predominantly urban German regions – 

Oberbayern and Stuttgart

•  Luxembourg
•  the Polish capital region of Warszawski stołeczny, the 
only region outside western and Nordic EU countries.

The Swedish capital region of Stockholm recorded, by 
far, the highest share of employed people using digital 
devices for at least half of their working time in 2022 
(75.9%). The next highest share was reported in Sydsverige 
(also Sweden; 65.2%), followed by 3 capital regions: Noord-

Holland (the Netherlands; 64.9%), Ile-de-France (France; 
63.5%), and Helsinki-Uusimaa (Finland; 62.0%). 

At the lower end of the distribution, there were 25 NUTS 
level 2 regions where less than 24.0% of employed people 
used digital devices for at least half of their working time 
(as shown by the lightest shade of yellow in Map 4.6). 
These regions were concentrated across southern and 
eastern EU countries: 

•  5 out of 6 regions in Bulgaria, with the exception of the 

capital region, Yugozapaden

•  11 out of 13 regions in Greece, with the exceptions of 

the capital region of Attiki and the island region of Notio 
Aigaio

•  7 out of 8 regions in Romania, with the exception of the 

capital region, Bucureşti-Ilfov

•  the remaining 2 regions in this group were both located 
in Hungary – Nyugat-Dunántúl and Észak-Magyarország.

Map 4.6: Frequent use of digital devices at work
(% of employed people aged 20–64 years, by NUTS 2 regions, 2022)

Note: share of employed people using digital devices at least half of their 
working time. The indicator excludes people who gave no response when 
asked about their use of digital devices. Prov. Vlaams-Brabant (BE24), Corse 
(FRM0) and Martinique (FRY2): low reliability.

Source: Eurostat (labour force survey) and (online data code: 
lfso_22dmsc01)

Eurostat regional yearbook – 2025 edition80A

Labour market

The central Greek mainland region of Sterea Elláda 
recorded the lowest share of employed people using 
digital devices for at least half of their working time, 
at 11.6% in 2022. The next lowest shares were reported in 
Peloponnisos and Dytiki Elláda (also Greece; both 13.0%), 
followed by Severozapaden (Bulgaria; 14.3%) and Ipeiros 
(Greece; 14.8%). 

Unemployment

Unemployment can impact not only the macroeconomic 
performance of a country or a region, for example 
lowering productive capacity, but also the well-being of 
individuals and their families. Rising unemployment results 
in a loss of income for individuals, increased pressure on 
government spending for social benefits and a reduction 
in tax revenues. Additionally, the personal and social costs 
of unemployment are varied and include a higher risk 
of poverty, social exclusion, debt or homelessness, while 
the stigma of being unemployed may have a potentially 
detrimental impact on (mental) health. 

More about the data: the unemployment rate 

Within this section, data are presented for people 
aged 15 to 74 years; this is the standard age range 
employed by Eurostat and the International Labour 
Organization (ILO) for studying unemployment rates 
within the labour force. 

Contrary to what may be thought, the unemployment 
rate is not the direct opposite of the employment 
rate, since the 2 measures do not have the same 
denominator; the unemployment rate uses the labour 
force as a denominator, while the employment rate uses 
the population. 

In 2024, there were 13.1 million unemployed people 
aged 15 to 74 years across the EU. When expressed as 
a share of the labour force (of the same age), the EU’s 
unemployment rate was 5.9%. These figures – the overall 
number of unemployed people and the unemployment 
rate –represent the lowest levels recorded since the start 
of the time series in 2002. 

Looking more closely at recent developments, the 
EU’s unemployment rate fell from a peak of 11.4% 
in 2013, declining over 6 consecutive years to 6.7% 
by 2019. With the onset of the COVID-19 crisis, the rate 
increased 0.4 percentage points in 2020 and remained 
unchanged the following year as the pandemic 
continued to affect much of the EU economy. After a 
marked decrease in unemployment during 2022 – when 
labour shortages became apparent in certain sectors – 
unemployment continued to fall in 2023 and 2024, albeit at 
a relatively modest pace. 

The Czech region of Střední Čechy had the lowest regional 
unemployment rate in 2024 

Map 4.7 presents unemployment rates (for people 
aged 15 to 74 years) across NUTS level 2 regions. In 2024, 
the distribution of regional unemployment rates was 
relatively balanced: of the 204 NUTS level 2 regions for 
which data are available, 100 had rates above the EU 
average of 5.9%, while 102 reported rates below the 
average; Nordjylland (Denmark) and Sostinės regionas (the 
capital region of Lithuania) had the same unemployment 
rate as the EU average. The highest regional 
unemployment rates were generally concentrated in 
southern EU countries and in several of France’s outermost 
regions, while the lowest rates were primarily found across 
eastern EU countries. 

In 2024, there were 21 regions across the EU that 
reported unemployment rates of at least 10.5%, as 
shown by the darkest 2 shades of blue in Map 4.7. The 
Spanish autonomous cities of Ceuta and Melilla were 
the only regions with rates exceeding 20.0%. Leaving 
these 2 regions and the French outermost regions aside, 
the highest rates in the EU were reported by Andalucía 
(16.5%) and Extremadura (15.5%) in south-western Spain, 
Ionia Nisia (16.2%) in western Greece, and Campania 
(15.6%) in southern Italy. 

At the lower end of the distribution, there were 10 regions 
with unemployment rates below 2.5% in 2024; they are 
shown with a yellow shade in Map 4.7. A majority of 
these 10 regions were located in Czechia and Poland, 
while Provincia Autonoma di Bolzano/Bozen (northern 
Italy), Zeeland (the Netherlands), Bucureşti-Ilfov (the 
capital region of Romania) and Bratislavský kraj (the 
capital region of Slovakia) also recorded such low rates. 
The neighbouring Czech regions of Střední Čechy and 
Praha (the former surrounds the latter), recorded the 
lowest unemployment rates in the EU, at 1.3% and 1.8%, 
respectively. 

Unemployment rates in the capital regions of multi-
regional eastern and Baltic EU countries were consistently 
lower than their national averages. In 2024, this pattern 
was particularly notable in Romania and Slovakia, as 
their national unemployment rates were at least twice 
as high as those recorded in the capital regions of 
Bucureşti-Ilfov and Bratislavský kraj. By contrast, the 
capital regions of Belgium, Denmark, Germany, Ireland, 
France, the Netherlands, Austria and Finland reported 
higher unemployment rates than their respective national 
averages. Indeed, in 6 of these 8 countries – with France 
and the Netherlands as exceptions – the capital region 
recorded the highest unemployment rate of any region (in 
Finland, Etelä-Suomi had a rate identical to that recorded 
in Helsinki-Uusimaa). This pattern was especially striking 
in Belgium, as Région de Bruxelles-Capitale / Brussels 
Hoofdstedelijk Gewest had an unemployment rate that 
was more than double the national average. 

81Eurostat regional yearbook – 2025 editionA

Labour market

Unemployment rates vary considerably as a function of 
age. In 2024, EU-wide unemployment rates were highest 
among young people aged 15 to 24 years (14.9%) and 
lowest among people aged 45 to 54 and 55 to 64 years 
(both 4.4%). Subject to data availability, young people 
systematically faced the highest unemployment rates 
in every NUTS level 2 region of the EU. In 6 regions, 
the unemployment rate of young people was at 

least 5 times as high as the regional average (for people 
aged 15 to 74 years): 

•  Corse in France (6.4 times as high)
•  Nord-Vest in Romania (5.7)
•  Praha, the capital region of Czechia (5.4)
•  Prov. Oost-Vlaanderen and Prov. West-Vlaanderen in 

Belgium (5.3 and 5.2, respectively)

•  Molise in Italy (5.0).

Map 4.7: Unemployment rate
(% of labour force among people aged 15–74 years, by NUTS 2 regions, 2024)

Note: Germany, Austria and Poland, NUTS level 1. Norway: national data. 
Includes data with low reliability (too many regions to document).

Source: Eurostat (online data code: lfst_r_lfu3rt)

An analysis by age – covering people aged 15 to 24, 
25 to 34, 35 to 44 and 45 to 54 years – reveals the highest 
regional unemployment rates across all EU regions were 
mainly found in Spanish and French regions (see Map 4.8): 

•  the French island region of Corse and the French 

outermost regions of Guadeloupe and Guyane also 
featured multiple times

•  Ionia Nisia (Greece) had a particularly high rate for 

•  the autonomous Spanish regions of Ciudad de Ceuta 
and Ciudad de Melilla featured across all 4 age groups

people aged 25 to 34 years.

Conversely, the lowest regional unemployment rates 
by age group were primarily recorded in Czechia and 
Hungary: 

Eurostat regional yearbook – 2025 edition82A

Labour market

•  Střední Čechy (Czechia) featured across all 4 age groups
•  Praha, Jihovýchod and Jihozápad (all in Czechia) also 

recorded several particularly low rates

•  Pest and Budapest (both Hungary) had particularly 

low rates for people aged 25 to 34 years and people 
aged 35 to 44 years, respectively

•  Bayern (Germany) and Zeeland (the Netherlands) had 
particularly low rates for people aged 15 to 24 years
•  Prov. Oost-Vlaanderen in Belgium and Gelderland in 
the Netherlands had particularly low rates for people 
aged 45 to 54 years.

Map 4.8: Unemployment rate
(% of labour force among people aged 15–24 years, by NUTS 2 regions, 2024)

Note: Germany, Austria and Poland, NUTS level 1. Norway: national data. 
Includes data with low reliability (too many regions to document).

Source: Eurostat (online data code: lfst_r_lfu3rt)

The EU’s long-term unemployment rate was 1.9% in 2024 

Long-term unemployment is a major challenge in the EU, 
affecting both individual well-being and social cohesion. 
High rates often signal structural issues in the labour 
market, skill mismatches, or economic stagnation, leading 
to income loss, social exclusion and increased pressure 
on public welfare systems and community well-being. 
In March 2024, the European Commission presented an 

action plan to tackle labour and skills shortages driven 
by demographic changes, emerging skill demands and 
poor working conditions. Building on existing initiatives – 
including the European Pillar of Social Rights Action Plan 
– it supports training, mobility and attracting talent – 1 of 
its key measures is the financing of new projects aimed at 
achieving zero long-term unemployment. 

83Eurostat regional yearbook – 2025 editionA

Labour market

In 2024, 4.2 million people across the EU had been 
unemployed for more than a year. The long-term 
unemployment rate – which is defined as the share 
of the labour force (aged 15 to 74 years) that has been 
unemployed for 12 months or more – stood at 1.9%. As 
such, around 1 in 3 unemployed people in the EU had 
been jobless for more than a year. 

Map 4.9 shows long-term unemployment rates (for 
people aged 15 to 74 years) across NUTS level 2 regions. 
Of the 195 regions for which data are available, 
82 recorded rates above the EU average, while 106 had 
rates below, and 7 had the same rate. As with the overall 
unemployment rate, some of the highest long-term 
unemployment rates were observed in southern EU 
countries and several of France’s outermost regions. 

•  The autonomous Spanish regions of Ciudad de Melilla 
(16.3%) and Ciudad de Ceuta (15.8%) had, by far, the 
highest rates.

•  The French outermost region of Guadeloupe (11.4%) was 
the only other region in the EU with a double-digit rate, 
while another French outermost region – La Réunion 
(8.2%) – also had a relatively high rate.

•  There were 3 other regions across the EU that had long-
term unemployment rates of at least 8.0%, each of them 
located in southern Italy – Campania (9.9%), Calabria 
(8.3%) and Sicilia (8.0%).

In 2024, there were 52 regions across the EU where the 
long-term unemployment rate was below 1.0% (as shown 
by the lightest 2 shades in Map 4.9). These regions were 
mainly concentrated in northern Belgium, Czechia, Denmark 
(all 5 regions), north-western Hungary, the Netherlands 
(all 10 regions for which data are available), Austria and 
Poland; Malta also recorded a rate below 1.0%. The lowest 
rate across the EU – 0.4% – was observed in 4 regions: 

•  the neighbouring Czech regions of Praha and Střední Čechy
•  Utrecht and Noord-Brabant in the Netherlands.

Map 4.9: Long-term unemployment rate
(% of labour force among people aged 15–74 years, by NUTS 2 regions, 2024)

Note: Germany, Austria and Poland, NUTS level 1. Norway: national data. 
Several regions only have data for 2023 (too many to document). Includes 
data with low reliability (too many regions to document).

Source: Eurostat (online data code: lfst_r_lfu2ltu)

Eurostat regional yearbook – 2025 edition845. Living conditions

By global standards, most people living in the European 
Union (EU) enjoy relatively high standards of living. This 
reflects the EU’s high income/wealth levels and its network 
of established social protection systems that provide a 
safety net for many less fortunate people. Nevertheless, 
93.3 million people in the EU – or 21.0% of the population 
– were at risk of poverty or social exclusion in 2024. Among 
EU regions (NUTS level 2), the French outermost region of 
Guyane recorded the highest rate, at 59.5%. It was the only 
region in the EU where more than half of the population 
faced the risk of poverty or social exclusion. The next 
highest rate was observed in the southern Italian region 
of Calabria, at 48.8% (see the infographic below for more 
information). 

Sociodemographic characteristics like age, educational 
attainment, sex and country of birth / citizenship can 
influence an individual’s living conditions. Wider societal 
developments, such as the impact of globalisation, 
coupled with unexpected shocks – for example, the 
global financial and economic crisis, the COVID-19 crisis, 
the impact of Russia’s war of aggression against Ukraine 
or the cost-of-living crisis – can also have a considerable 
influence. In some cases, these events can rapidly undo 
more gradual, long-term reductions in inequality. 

Since late 2021, there has been a considerable increase 
in the cost of living in the EU. Some of the most rapid 
increases in prices were experienced for goods like 
energy and food. Price increases for these goods often 
disproportionately impact the poorest individuals in 
society, as they tend to allocate a larger proportion of 
their disposable income to such ‘essential goods’. The 
EU’s annual inflation rate rose from 0.7% in 2020 to 9.2% 
by 2022, before falling back to 6.4% in 2023 and then 2.6% 
in 2024. 

People at risk of poverty or social 
exclusion

Absolute poverty is a lack of basic human needs, for 
example, food, shelter, water, sanitation facilities, health or 
education; in other words, a situation where a household’s 
income is insufficient to afford the basic necessities of life. 
By contrast, relative poverty concerns a situation where a 
household’s income is below a certain percentage of the 
median household income of the country where they live. 

(%, by NUTS 2 regions, 2024)

Source: Eurostat (online data codes: ilc_peps11n and ilc_peps01n)

85Eurostat regional yearbook – 2025 editionA

Living conditions

More about the data: at risk of poverty or 
social exclusion 

The indicator for people at risk of poverty or social 
exclusion is based on measures of relative poverty, 
severe material and social deprivation, and quasi-
joblessness. The number/rate of people at risk of 
poverty or social exclusion combines these criteria 
to cover people who are in at least 1 of the following 
situations: 

•  at risk of poverty – people with an equivalised 

disposable income (after social transfers) below the 
at-risk-of-poverty threshold, which is set at 60% of the 
national median equivalised disposable income

•  facing severe material and social deprivation – people 

unable to afford at least 7 out of 13 deprivation 
items (6 related to the individual and 7 related to the 
household) that are considered desirable, or even 
necessary, to lead an adequate quality of life

•  living in a household with very low work intensity 
– where adults aged 18 to 64 years (excluding 
students aged 18 to 24 years and retired people 
under the age of 65 years) worked for 20% or less of 
their combined potential working time during the 
previous 12 months.

Intersections for the at risk of poverty or social exclusion rate

(million, EU, 2024)

Source: Eurostat (online data code: ilc_pees01n)

For EU statistics on income and living conditions (EU-
SILC): 

•  due to changes in regional boundaries, data are only 
available for reference year 2024 (not any earlier years) 
for Utrecht, Zuid-Holland (both the Netherlands), 
Centro, Grande Lisboa, Península de Setúbal, Alentejo 
and Oeste e Vale do Tejo (all Portugal)

•  there is no information for Mayotte in France
•  an earlier reference year is sometimes used for 

individual regions/countries (see specific footnotes 
under each map/figure for more information).

On 4 March 2021, the European Commission set out its 
ambition for a stronger social EU to focus on education, 
skills and jobs, paving the way for a fair, inclusive and 
resilient socioeconomic recovery from the COVID-19 crisis, 
while fighting discrimination, tackling poverty and 
alleviating the risk of exclusion for vulnerable groups. 

The European Pillar of Social Rights Action Plan outlines a 
set of commitments from policymakers and provides 3 key 
targets for monitoring progress; 1 of which is to reduce, 
between 2019 and 2030, the number of people in the EU 
at risk of poverty or social exclusion by at least 15 million 
people (of which, at least 5 million should be children). 

More than 1 in 5 of the EU’s population was at risk of 
poverty or social exclusion in 2024 

Map 5.1 shows the regional distribution of people at risk 
of poverty or social exclusion across NUTS level 2 regions. 
In 2024, the regional distribution of this indicator was 
somewhat skewed, as less than 40% of all EU regions 
(93 out of the 243 for which data are available) recorded 
at risk of poverty or social exclusion rates that were 
higher than the EU average, 4 regions had the same rate, 
while 146 regions had rates that were lower. 

In 2024, there were 25 NUTS level 2 regions where at 
least 33.0% of the population was at risk of poverty or 
social exclusion (these regions are shown in the darkest 
shade of blue in Map 5.1). The highest risks were principally 
observed in southern, eastern and outermost regions of 
the EU: 

•  the 25 regions were concentrated in Greece (5 regions), 
Bulgaria, Spain, Italy (4 regions each), as well as Romania 
and the outermost regions of France (3 regions each)
•  this group also included 2 predominantly urban regions 
in western EU countries – the capital region of Belgium 
(Région de Bruxelles-Capitale / Brussels Hoofdstedelijk 
Gewest) and Bremen in Germany.

•  the reference period for statistics on income generally 
refers to the calendar year before the year in which 
the survey took place

•  data for the Finnish regions of Länsi-Suomi and Åland 
are aggregated (the same value is shown for both 
regions)

In 2024, there were 5 NUTS level 2 regions across the 
EU that reported at risk of poverty or social exclusion 
rates that was more than double the EU average (21.0%). 
All 5 were located either in the EU’s southern countries 
or its outermost regions, where social and economic 
challenges tend to be more pronounced. 

Eurostat regional yearbook – 2025 edition86A

Living conditions

•  The highest rate was in Guyane (France), where 59.5% of 

•  7 regions from northern and central Italy, including 

the population was at risk.

•  In southern Italy, Calabria had a rate of 48.8%, while the 
neighbouring region of Campania also had a relatively 
high rate, at 43.5%.

Provincia Autonoma di Bolzano/Bozen (6.6%), which had 
the lowest at risk of poverty or social exclusion rate in 
the EU

•  6 out of the 8 regions of Czechia, including Jihozápad 

•  The Spanish autonomous cities of Melilla (44.5%) and 

(8.8%), which had the 3rd lowest rate in the EU

Ceuta (42.2%) completed this group.

At the other end of the distribution, there were 26 NUTS 
level 2 regions where less than 12.5% of the population 
was at risk of poverty or social exclusion in 2024 (these 
regions are shown in a light yellow shade in Map 5.1). This 
group included: 

•  3 regions from Vlaams Gewest in northern Belgium
•  3 regions from Austria
•  2 regions from Poland, including the capital region 

Warszawski stołeczny

•  the capital regions of Croatia, Romania, Slovenia 

and Slovakia; the last of these, Bratislavský kraj, had 
the 2nd lowest rate in the EU (8.6%)

•  the north-western Hungarian region of Közép-Dunántúl.

Map 5.1: People at risk of poverty or social exclusion
(%, by NUTS 2 regions, 2024)

Note: Serbia, NUTS level 1. Türkiye: national data. Länsi-Suomi (FI19) and 
Åland (FI20) are aggregated (same value for both regions). Switzerland and 
Serbia: 2023. Montenegro: 2022.

Source: Eurostat (online data codes: ilc_peps11n and ilc_peps01n)

87Eurostat regional yearbook – 2025 editionA

Living conditions

In 2024, people living in the capital regions of eastern 
EU countries generally faced a lower risk of poverty or 
social exclusion compared with those living elsewhere. 
For example, in Romania, the national at risk of poverty 
or social exclusion rate (27.9%) was 2.3 times as high as 
the rate recorded in the capital region of Bucureşti-Ilfov 
(12.0%). A similar pattern was observed in Slovakia, Croatia, 
Poland, Hungary and Bulgaria, where the national rate was 
at least 50% higher than in the capital region. This pattern 
was also present – although generally less pronounced 
– in several other EU countries: Lithuania, Slovenia, Spain, 
Portugal, Greece, Sweden, Finland, Ireland, Denmark and 
France. 

By contrast, the situation was reversed in several EU 
countries. In Belgium, the rate recorded in the capital 
region was more than double the national average, while 

in Austria it was 1.7 times as high. The capital regions 
of 4 more EU countries – Italy, Czechia, the Netherlands 
and Germany – also recorded somewhat higher risks of 
poverty or social exclusion compared with their respective 
national averages. 

Figure 5.1 highlights the NUTS level 2 regions with the 
highest and lowest at risk of poverty or social exclusion 
rates in 2024. It also presents information on the regions 
that recorded the biggest increases and decreases in 
their rates between 2022 and 2024. Across the EU, the at 
risk of poverty or social exclusion rate fell 0.6 percentage 
points from 21.6% to 21.0%. Looking more closely at 
the 236 regions for which data are available, the at risk 
of poverty or social exclusion rate fell in 134 regions, 
remained unchanged in 2 regions and increased 
in 100 regions. 

Figure 5.1: People at risk of poverty or social exclusion
(by NUTS 2 regions, 2024)

Note: the 1st part of the figure shows the EU regions with the highest 
and lowest rates in 2024, while the 2nd part shows the regions with 
the biggest increases and biggest decreases compared with 2022. The 
rankings include more than 10 regions if several regions have identical 
values. Utrecht (NL35), Zuid-Holland (NL36), Centro (PT19), Grande Lisboa 
(PT1A), Península de Setúbal (PT1B), Alentejo (PT1C) and Oeste e Vale do 
Tejo (PT1D): not available for the comparison with 2022. Mayotte (FRY5): 
not available.

Source: Eurostat (online data codes: iilc_peps11n and ilc_peps01n)

Eurostat regional yearbook – 2025 edition88A

Living conditions

There were 3 NUTS level 2 regions where the risk of 
poverty or social exclusion increased by a considerable 
margin between 2022 and 2024. The French outermost 
region of Guyane recorded the largest increase, 
up 10.3 percentage points (from 49.2% to 59.5%). The 
other 2 regions with notable increases were Trier 
(Germany) and Ionia Nisia (Greece), where rates rose 
by 10.0 and 9.6 points, respectively. There were 6 more 
regions across the EU that reported increases of at 
least 5.0 points: 

•  Rheinhessen-Pfalz in Germany – a neighbouring region 

to Trier (up 6.5 points)

•  Dytiki Makedonia and Voreio Aigaio in Greece 
(up 6.5 points and 5.4 points, respectively)

•  Calabria in Italy (up 6.0 points)
•  Dél-Alföld in Hungary (up 5.3 points)
•  Nordjylland in Denmark (up 5.2 points).

The regions with the largest falls in their at risk of poverty 
or social exclusion rates were all located in eastern or 
southern EU countries. The Italian regions of Liguria, 
Abruzzo and Molise experienced the biggest declines, 
as their rates fell between 2022 and 2024 by 10.5, 
10.2 and 9.7 percentage points, respectively. There 
were 8 more regions across the EU that reported decreases 
of at least 7.0 percentage points: 

•  6 of the 8 Romanian regions – the exceptions being 

Nord-Vest and Nord-Est (where rates also fell, although 
at a more modest pace) – with the largest fall in Sud-
Vest Oltenia (down 9.6 points)

•  the Portuguese regions of Região Autónoma da Madeira 

(down 7.3 points) and Algarve (down 7.0 points).

People at risk of poverty

In 2024, 72.1 million people in the EU were at risk of 
poverty (after social transfers), representing 16.2% of the 
population. Some of the highest regional rates for people 
at risk of poverty were concentrated in southern, eastern 
and Baltic countries. By contrast, some of the lowest rates 
were recorded in a cluster of regions stretching from 
central/northern Italy through Austria and into southern 
Germany, Hungary, Czechia and Poland. Another cluster 
of regions with low rates was found in northern Belgium 
and the Netherlands. Several capital regions also recorded 
relatively low rates. 

More about the data: at-risk-of-poverty rate 

The at-risk-of-poverty rate (after social transfers) 
is 1 of 3 criteria used to identify people who are at risk 
of poverty or social exclusion. It provides information on 
the share of the population with a level of income that 
is below a threshold set relative to the median income. 

The at-risk-of-poverty rate identifies the proportion of 
the population who live in a household with an annual 
equivalised disposable income that is below 60% of the 
national median. While the threshold is the same for 
all EU countries in percentage terms (60%), it varies in 
monetary terms as median incomes differ – sometimes 
considerably – between countries. 

Map 5.2 shows the at-risk-of-poverty rate for NUTS 
level 2 regions. In 2024, the regional distribution of this rate 
was relatively skewed: there were 95 regions that recorded 
a rate equal to or above the EU average of 16.2%, while the 
remaining 148 regions had lower than average rates. 

In 2024, the French outermost region of Guyane reported 
the highest at-risk-of-poverty rate 

The French outermost region of Guyane (53.3%), the 
autonomous Spanish cities of Melilla (41.4%) and Ceuta 
(34.6%), and the southern Italian regions of Calabria (37.2%), 
Campania (35.5%) and Sicilia (35.3%) recorded the highest 
at-risk-of-poverty rates among NUTS level 2 regions. 
These were the only regions across the EU where more 
than 1 in 3 of the population was at risk of poverty 
in 2024. An additional 25 regions had at-risk-of-poverty 
rates covering between a quarter and a third of their 
populations. These 31 regions with the highest at risk of 
poverty rates are shown in the darkest shade of blue in 
Map 5.2. 

… while the lowest rate was recorded in the Romanian 
capital region of Bucureşti-Ilfov 

By contrast, at the other end of the distribution, there 
were 27 NUTS level 2 regions where the at-risk-of-
poverty rate was below 10.0% in 2024 (as shown by the 
lightest shade of yellow). Many of these regions were 
predominantly urban regions (including capitals) or 
regions with lower unemployment rates, higher standards 
of living and/or in countries characterised by strong social 
protection systems. This group of 27 included: 

•  5 regions each from Belgium, Czechia and Italy
•  2 regions each from Hungary, Austria and Poland
•  the capital region from Croatia, Romania, Slovakia and 

Finland

•  a single region from each of Denmark and Spain.

89Eurostat regional yearbook – 2025 editionA

Living conditions

These 27 regions with a relatively low risk of poverty 
in 2024 included 7 capital regions, all but 1 from eastern 
EU countries: Bucureşti-Ilfov in Romania (3.7%), Bratislavský 
kraj in Slovakia (7.0%), Praha in Czechia (7.5%), Warszawski 
stołeczny in Poland (8.0%), Budapest in Hungary (9.5%) 
and Grad Zagreb in Croatia (9.7%); the 7th was Helsinki-
Uusimaa in Finland (9.4%). 

The Romanian capital region of Bucureşti-Ilfov had, by far, 
the lowest at-risk-of-poverty rate (3.7% in 2024), while rates 
below 7.0% were also observed in: 

•  the northern Italian regions of Provincia Autonoma di 
Bolzano/Bozen and Provincia Autonoma di Trento

•  the northern Belgian regions of Prov. Oost-Vlaanderen, 

Prov. Vlaams-Brabant and Prov. West-Vlaanderen

•  Burgenland in eastern Austria.

Across several EU countries – for example, Romania, Italy, 
France and Belgium – there was a considerable degree of 
inter-regional variation for at-risk-of-poverty rates. 

•  In Romania, the at-risk-of-poverty rate in Sud-Vest 

Oltenia was 8.1 times as high as in the capital region of 
Bucureşti-Ilfov.

•  In Italy, the rate in the southern region of Calabria 
was 6.3 times as high as in the northern region of 
Provincia Autonoma di Bolzano/Bozen.

•  In France, the rate in the outermost region of Guyane 

was 5.0 times as high as in the western region of 
Bretagne.

•  In Belgium, the rate in the capital region of Région de 
Bruxelles-Capitale / Brussels Hoofdstedelijk Gewest 
was 4.6 time\s as high as in Prov. Oost-Vlaanderen.

Map 5.2: At-risk-of-poverty rate
(%, by NUTS 2 regions, 2024)

Note: Serbia, NUTS level 1. Türkiye: national data. Länsi-Suomi (FI19) and 
Åland (FI20) are aggregated (same value for both regions). Switzerland and 
Serbia: 2023. Montenegro: 2022.

Source: Eurostat (online data codes: ilc_li41 and ilc_li02)

Eurostat regional yearbook – 2025 edition90A

Living conditions

At the lower end of the distribution, there were 2 NUTS 
level 2 regions where, in 2024, less than 10.0% of the 
population faced the risk of monetary poverty before 
social transfers: 

•  the Romanian capital region of Bucureşti-Ilfov (5.8%)
•  the northern Italian region of Provincia Autonoma di 

Bolzano/Bozen (7.1%).

Relatively low at-risk-of-poverty rates before social transfers 
were also recorded in several other capital regions in 
eastern EU countries: 

•  Bratislavský kraj in Slovakia (10.2%)
•  Praha in Czechia (11.4%)
•  Budapest in Hungary (12.1%)
•  Grad Zagreb in Croatia (12.3%).

The right-hand side of Figure 5.2 shows the regions with 
the highest and lowest at-risk-of-poverty rates after social 
transfers. Taking account of the redistributive impact of 
social transfers, Guyane was the only region in the EU 
where more than half of the population continued to be at 
risk of monetary poverty in 2024, while Ciudad de Melilla 
was the only other region where this risk affected more 
than 40.0% of the population. There were 6 other regions 
where the at-risk-of-poverty rate after social transfers was 
more than twice as high as the EU average, namely: 

•  Calabria, Campania and Sicilia (Italy)
•  Ciudad de Ceuta (Spain)
•  Guadeloupe and La Réunion (France).

Across many regions in western and northern EU countries, 
it was common for social transfers to markedly reduce 
the risk of poverty. In 2024, this risk was reduced by at 
least 8.5 percentage points – the EU average – in: 

•  every region of Belgium, Denmark, Ireland and Finland
•  24 of the 26 regions of France (the exceptions being the 

eastern regions of Franche-Comté and Alsace)

•  6 of the 8 regions of Sweden (the exceptions being the 
capital region of Stockholm and Mellersta Norrland)

•  a similar pattern was also observed in Estonia.

In some cases, the redistributive impact of social transfers 
was considerable. In 2024, the at-risk-of-poverty rate was 
reduced by at least 14.0 percentage points in: 

In 2024, the redistributive impact of social transfers 
reduced the risk of monetary poverty across the EU 
from 24.7% to 16.2% 

More about the data: social transfers 

The at-risk-of-poverty rate before social transfers 
measures a hypothetical situation where social transfers 
are absent; pensions, such as old-age and survivors’ 
(widows’ and widowers’) benefits, are counted as 
income (before social transfers) and not as social 
transfers. 

It is possible to assess the impact and redistributive 
effects of welfare policies by comparing at-risk-of-
poverty rates before and after social transfers. Such 
transfers cover assistance that is given by central, state 
or local institutional units and include, among other 
types of transfers, unemployment benefits, sickness and 
invalidity benefits, housing allowances, social assistance 
and tax rebates. 

Figure 5.2 illustrates how the redistributive impact of social 
transfers reduces the risk of monetary poverty, reflecting, 
among other factors, historical, political, economic and 
cultural influences. In 2024, the EU’s at-risk-of-poverty rate 
before social transfers was 24.7%; this was 8.5 percentage 
points lower, at 16.2%, after social transfers. 

Figure 5.2 is split into 2 parts: the left-hand side presents 
the EU regions with the highest and lowest at-risk-of-
poverty rates before social transfers and the right-hand 
side presents similar information after social transfers. 
In 2024, there were 2 NUTS level 2 regions where more 
than half of the population was at risk of poverty before 
social transfers: 

•  the French outermost region of Guyane (64.3%)
•  the southern Italian region of Campania (52.8%).

The other 9 regions in the EU where, in 2024, more 
than 40.0% of the population was at risk of poverty before 
social transfers were: 

•  2 more outermost regions in France – La Réunion and 

Guadeloupe

•  3 more regions in southern Italy – Calabria, Sicilia and 

Puglia

•  the Spanish regions of Ciudad de Melilla and 

Ciudad de Ceuta

•  the capital region of Région de Bruxelles-

Capitale / Brussels Hoofdstedelijk Gewest (Belgium)

•  Bremen (Germany).

91Eurostat regional yearbook – 2025 editionA

Living conditions

•  several regions of Belgium – Prov. Hainaut (where 

the at-risk-of-poverty rate was 20.5 points lower after 
social transfers, the largest reduction recorded among 
EU regions), Région de Bruxelles-Capitale / Brussels 
Hoofdstedelijk Gewest (18.3 points lower), Prov. 
Luxembourg (14.8 points lower) and Prov. Liège 
(14.0 points lower)

•  the southern Italian regions of Campania (17.3 points 

lower) and Abruzzo (17.0 points lower)

•  the French outermost region of La Réunion (15.9 points 

lower)

•  the German region of Bremen (15.3 points lower)
•  the Irish region of Southern (15.2 points lower)
•  the Danish regions of Nordjylland (14.6 points lower) and 

Midtjylland (14.0 points lower).

In addition to the 11 regions mentioned above, a 
further 104 NUTS level 2 regions across the EU recorded a 
fall of at least an 8.5 percentage point fall in their at-risk-
of-poverty rates due to social transfers – matching or 
exceeding the average reduction observed across the 
whole of the EU. 

At the other end of the scale, there were 31 NUTS 
level 2 regions in the EU where the redistributive impact of 
social transfers reduced the at-risk-of-poverty rate by less 
than 5.0 percentage points in 2024. These regions were 
principally concentrated in Greece, northern and central 
Italy, Portugal, Romania and Spain. In other countries, this 
group included the capital regions of Hungary, Croatia, 
Slovakia, Czechia and Lithuania. 

Figure 5.2: At-risk-of-poverty rate before and after social transfers
(%, by NUTS 2 regions, 2024)

Note: the figure shows the EU regions with the highest and lowest rates 
before and after social transfers. Mayotte (FRY5): not available.

Source: Eurostat (online data codes: ilc_li10_r, ilc_li41, ilc_li10 and ilc_li02)

Eurostat regional yearbook – 2025 edition92A

Living conditions

Severe material and social 
deprivation, and economic strain

The severe material and social deprivation rate measures 
the share of people whose living conditions are severely 
constrained by an enforced lack of items that are necessary 
and desirable to lead an adequate life. It reflects enforced 
inability to afford basic goods, services or social activities 
– such as a meal with protein, adequate heating, or 
spending social time with family and/or friends. 

The EU’s severe material and social deprivation rate rose 
during the cost-of-living crisis, peaking at 6.8% in 2023. A 
year later, it fell 0.4 percentage points, returning to a level 
that was similar to that recorded before the crisis. In 2024, 
there were 27.5 million people across the EU that were 
facing severe material and social deprivation; this was 
equivalent to 6.4% of the total population. 

More about the data: severe material and 
social deprivation 

The severe material and social deprivation rate 
is 1 of 3 criteria used to identify people at risk of 
poverty or social exclusion. It is defined as the share 
of people who are unable to afford at least 7 out 
of 13 items (6 related to the individual and 7 related to 
the household) that are considered desirable – or even 
necessary – to lead an adequate quality of life 

List of items related to the individual 

•  having an internet connection
•  replacing worn-out clothes by some new ones
•  having 2 pairs of properly fitting shoes (including a 

pair of all-weather shoes)

•  spending a small amount of money each week on 

themselves

In 2024, the highest severe material and social deprivation 
rates were recorded in the south-eastern corner of the EU, 
peaking in Ionia Nisia (Greece) and Sud-Est (Romania), 
where more than 1 in 4 of the population faced such 
deprivation 

Map 5.3 shows the regional distribution of severe material 
and social deprivation rates. In 2024, the highest rates 
among NUTS level 2 regions were concentrated across 
Greece, Romania, Bulgaria, the outermost regions of France 
and Hungary. The lowest rates were observed in northern 
and central Italy, Slovenia, Austria, Croatia, the Netherlands, 
Poland and northern Sweden. Of the 243 regions for which 
data are available, 150 had rates below the EU average 
of 6.4%, 3 had the same rate, and 90 recorded higher than 
average rates. 

In 2024, Ionia Nisia in Greece (28.0%) and Sud-Est in 
Romania (26.4%) recorded the highest severe material and 
social deprivation rates. These were the only EU regions 
where more than 1 in 4 of the population was affected 
by the enforced inability to afford basic goods, services 
or social activities. There were 6 other regions in the EU 
where more than 20.0% of the population faced severe 
material and social deprivation: 

•  Calabria in Italy (24.9%)
•  Yugoiztochen (22.7%), Yuzhen tsentralen (22.1%) and 

Severen tsentralen (21.6%) in Bulgaria

•  Anatoliki Makedonia, Thraki in Greece (21.4%)
•  Sud-Muntenia in Romania (20.3%).

At the other end of the distribution, every region of 
Czechia, Denmark, Ireland, Croatia, the Netherlands, 
Poland, Slovenia, Finland and Sweden had a severe 
material and social deprivation rate that was below the EU 
average of 6.4% in 2024; this was also the case in Estonia, 
Cyprus, Latvia, Luxembourg and Malta. 

•  having regular leisure activities
•  getting together with friends/family for a drink/meal 

In 2024, there were 6 regions in the EU with severe material 
and social deprivation rates that were less than 1.0%: 

at least once a month

List of items related to the household 

•  capacity to face unexpected expenses
•  capacity to afford paying for 1-week annual holiday 

away from home

•  capacity to being confronted with payment arrears 
(on mortgage or rental payments, utility bills, hire 
purchase instalments or other loan payments)

•  capacity to afford a meal with meat, chicken, fish (or 

vegetarian equivalent) every 2nd day
•  ability to keep home adequately warm
•  have access to a car/van for personal use
•  replacing worn-out furniture

•  the northern Italian regions of Provincia Autonoma 

di Bolzano/Bozen and Provincia Autonoma di Trento 
recorded the lowest rates in the EU (both 0.1%)

•  the Slovenian capital region of Zahodna Slovenija (0.7%)
•  the Polish region of Mazowiecki regionalny (0.9%)
•  2 more Italian regions – Marche and Valle d’Aosta/Vallée 

d’Aoste (both 0.9%).

93Eurostat regional yearbook – 2025 editionA

Living conditions

Map 5.3: Severe material and social deprivation rate
(%, by NUTS 2 regions, 2024)

Note: Serbia, NUTS level 1. Türkiye: national data. Länsi-Suomi (FI19) and 
Åland (FI20) are aggregated (same value for both regions). Switzerland and 
Serbia: 2023. Montenegro: 2022.

Source: Eurostat (online data codes: ilc_mdsd18 and ilc_mdsd11)

Figure 5.3 highlights the NUTS level 2 regions that 
recorded the biggest increases and decreases in 
their severe material and social deprivation rates 
between 2022 and 2024. Across the EU, this rate fell 
from 6.7% to 6.4% – a decrease of 0.3 percentage points. 
Looking more closely at the 236 regions for which data are 
available, the severe material and social deprivation rate 
fell in 131 regions during the period under consideration, 
remained unchanged in 5 regions and increased 
in 100 regions. 

There were 2 NUTS level 2 regions that recorded 
double-digit increases in their severe material and social 
deprivation rates between 2022 and 2024. The biggest 
increase was in the southern Italian region of Calabria, 
up 13.1 percentage points (from 11.8% to 24.9%). There was 

also a marked increase in Ionia Nisia (Greece), where the 
rate increased 11.0 points (from 17.0% to 28.0%). Anatoliki 
Makedonia, Thraki (also Greece) was the only other region 
in the EU to record an increase of more than 4.5 points, 
with its rate up 8.9 points (from 12.5% to 21.4%). 

There were 10 NUTS level 2 regions that recorded a fall of 
more than 5.0 percentage points for their severe material 
and social deprivation rate between 2022 and 2024. 
This group included 7 out of the 8 regions in Romania 
(the exception being Nord-Vest), 2 regions from Greece 
and a single region from Spain. The Romanian regions 
of Sud-Vest Oltenia and Sud-Muntenia and the Greek 
region of Dytiki Elláda experienced the biggest declines, 
as their rates fell by 10.8, 8.5 and 8.3 percentage points, 
respectively. 

Eurostat regional yearbook – 2025 edition94A

Living conditions

Figure 5.3: Severe material and social deprivation rate
(by NUTS 2 regions, 2024)

Note: the 1st part of the figure shows the EU regions with the highest 
and lowest rates in 2024, while the 2nd part shows the regions with 
the biggest increases and biggest decreases compared with 2022. The 
rankings include more than 10 regions if several regions have identical 
values. Utrecht (NL35), Zuid-Holland (NL36), Centro (PT19), Grande Lisboa 
(PT1A), Península de Setúbal (PT1B), Alentejo (PT1C) and Oeste e Vale do 
Tejo (PT1D): not available for the comparison with 2022. Mayotte (FRY5): 
not available.

Economic strain has gained renewed attention in light of 
the ongoing cost-of-living crisis. Rising inflation, surging 
energy prices and challenges around housing affordability 
have placed considerable pressure on household budgets, 
particularly among vulnerable groups. Indicators of 
economic strain can reveal growing disparities in financial 
resilience, which may be used to understand where 
support is most needed to address economic insecurity. 

Source: Eurostat (online data codes: ilc_mdsd18 and ilc_mdsd11)

The cost-of-living crisis reflects prices increasing at a faster 
pace than wages, such that purchasing power falls. The 
surge in prices of food and energy, as well as other goods 
and services, may be attributed, at least in part, to Russia’s 
military aggression against Ukraine. For example, concerns 
over supply shortages, along with international sanctions 
placed on Russian energy exports, led to an increase in the 
price of energy products, while prices for foodstuffs and 
fertilisers also rose strongly. Another contributing factor to 
rising inflation was a post-pandemic surge in demand. 

95Eurostat regional yearbook – 2025 editionA

Living conditions

More about the data: economic strain 

Economic strain refers to a set of indicators that 
measure self-perceived financial difficulties of 
households. This concept is covered by a group of 
indicators collected as part of the EU-SILC survey, 
designed to capture material hardship and/or financial 
stress. 

While economic strain is not generally used as a 
headline indicator within EU policy development, it 
does play a key role in understanding living conditions 
and poverty (beyond income-based measures). Various 
measures of economic strain are included in composite 
indicators such as the severe material and social 
deprivation rate. Some of the most common measures 
include, whether households: 

•  can keep their home adequately warm
•  can afford a meal with meat, chicken, fish (or 

vegetarian equivalent) every 2nd day
•  can afford unexpected financial expenses
•  have arrears on mortgage/rent, utility bills, hire 
purchase instalments or other loan payments.

The data presented in the 4 maps below relate to the 
share of the population affected by each measure of 
economic strain. The data are shown as indices, relative 
to the EU average (that is set equal to 100.0); higher 
values indicate that people living in a particular region 
have greater difficulty affording the specific goods and/
or services. 

In Kentriki Elláda (Greece) and Sur (Spain), more 
than 1 in 5 people were unable to keep their home 
adequately warm in 2024 

The 1st part of Map 5.4 highlights the share of the 
population living in households that are struggling to 
keep their homes adequately warm. In 2024, this issue 
affected 9.2% of the EU population. Among NUTS 
level 1 regions, the highest incidence was observed in 
Kentriki Elláda (Greece), where 21.2% of people lived in 
households struggling to keep their homes adequately 
warm. This was more than 11 times as high as the 
share recorded in the Polish region of Makroregion 
województwo mazowieckie (1.9%). In comparison with the 
EU average, a notably higher share of people in regions of 
Bulgaria, Greece and Spain lived in households unable to 
keep their homes adequately warm, with regional shares 
sometimes more than twice as high as the EU average. By 
contrast, many regions across the Nordic EU countries and 
Poland, reported significantly lower levels, with shares that 
were often less than half the EU average; this was also the 
case in Estonia, Luxembourg, Austria and Slovenia. 

In 2024, more than 1 in 5 people in the Romanian region 
of Macroregiunea Unu were unable to afford a meal with 
protein (meat, chicken, fish or vegetarian equivalent) 
every 2nd day 

The 2nd part of Map 5.4 depicts the share of people who 
could not afford a meal containing protein every 2nd day; 
this economic strain impacted 8.5% of the EU population 
in 2024. The highest share across NUTS level 1 regions was 
recorded in the Romanian region of Macroregiunea Unu 
(22.9%), which was almost 20 times as high as in Cyprus, 
where the lowest share was recorded (1.2%). The share of 
people who could not afford a meal containing protein 
every 2nd day was more than twice as high as the EU 
average in: 

•  both Bulgarian regions
•  Saarland (Germany)
•  Alföld és Észak (Hungary)
•  Macroregiunea Unu (Romania)
•  Slovakia.

In Bremen (Germany) and Severna i Yugoiztochna Bulgaria 
(Bulgaria), more than half of the population was unable to 
face unexpected financial expenses in 2024 

The 3rd part of Map 5.4 shows the proportion of people 
who were unable to manage unexpected financial costs; 
across the EU, 30.0% of the population was affected by this 
economic strain in 2024. Among NUTS level 1 regions, the 
lowest share was recorded in the northern Belgian region 
of Vlaams Gewest (13.2%). Relatively low shares – less 
than half the EU average – were also observed in 3 out of 
the 4 regions of the Netherlands (with the exception of 
West-Nederland). 

At the other end of the scale, more than half of the 
population was unable to manage unexpected financial 
costs in 2024 in the northern German region of Bremen 
(52.4%) and in Severna i Yugoiztochna Bulgaria (51.5%). 
Relatively high shares – of at least 40.0% – were also 
recorded in Yugozapadna i Yuzhna tsentralna Bulgaria, 
3 out of 4 Greek regions (excluding the capital region of 
Attiki), the Spanish regions of Sur and Canarias, the French 
outermost regions, the Italian regions of Sud and Isole, the 
Baltic countries of Latvia and Lithuania, and the Romanian 
regions of Macroregiunea Doi and Macroregiunea Patru. 

In all 4 Greek regions, more than 40% of the population 
had arrears on mortgage, rent, utility bills or hire purchase 
payments in 2024 

The final part of Map 5.4 focuses on the share of 
individuals falling behind on payments for mortgages, 
rent, utilities or hire purchase agreements. In 2024, these 
issues affected 9.2% of the EU population. The highest 
regional shares (among NUTS level 1 regions) were 

Eurostat regional yearbook – 2025 edition96A

Living conditions

recorded in Greece, with more than 40% of the population 
falling behind on payments in all 4 regions. The highest 
share was recorded in Kentriki Elláda, at 45.8%. The share 
of people who were in arrears on mortgage, rent, utility 
bills or hire purchase payments was more than twice as 
high as the EU average in every region of Greece, the 
Bulgarian region of Severna i Yugoiztochna Bulgaria, the 
French outermost regions and the Romanian region of 
Macroregiunea Doi. 

By contrast, Noord-Nederland (2.1%) had the lowest share 
of people falling behind on payments for mortgages, rent, 
utilities or hire purchase agreements in 2024. There were 
several other regions across the EU where the share of 
people in arrears was less than half the EU average: Vlaams 
Gewest (Belgium), Czechia, Sachsen (Germany), Nord-Est, 
Nord-Ovest and Centro (Italy), 2 more Dutch regions (Oost-
Nederland and Zuid-Nederland), and 5 out of the 7 regions 
in Poland (the exceptions being Makroregion południowy 
and Makroregion południowo-zachodni). 

Map 5.4: Economic strains
(index relative to EU average = 100, by NUTS 1 regions, 2024)

Note: based on the share of the population impacted by each economic 
strain. A meal with protein is a meal with meat, chicken, fish (or vegetarian 
equivalent). Austria, Portugal, Serbia and Türkiye: national data. Switzerland 
and Serbia: 2023. Montenegro: 2022.

Source: Eurostat (online data codes: ilc_mdes01_r, ilc_mdes03_r, 
ilc_mdes04_r, ilc_mdes05_r, ilc_mdes01, ilc_mdes03, ilc_mdes04 and 
ilc_mdes05)

97Eurostat regional yearbook – 2025 editionA

Living conditions

People living in a household with 
very low work intensity

In 2024, there were 26.2 million people (aged 0 to 64 years) 
across the EU living in a household with very low work 
intensity, representing 7.9% of this subpopulation. 
In 2021, the share had been 9.0%. It declined relatively 
sharply in 2022 (by 0.7 percentage points), followed 
by smaller decreases in 2023 (0.3 points) and 2024 
(a further 0.1 points). 

More about the data: very low work intensity 

The share of people living in a household with 
very low work intensity is 1 of the 3 criteria used to 
identify people at risk of poverty or social exclusion. 
Working-age adults with low work intensity are 
defined as people aged 18 to 64 years (excluding 
students aged 18 to 24 years and retired people under 
the age of 65 years) who worked for 20% or less of 
their combined potential working time during the 
previous 12 months. Households composed only of 
children, of students younger than 25 years and/or of 
people aged 65 years or more are excluded from the 
calculation of this indicator. 

Map 5.5: People living in households with very low work intensity
(%, by NUTS 2 regions, 2024)

Note: Serbia, NUTS level 1. Türkiye: national data. Länsi-Suomi (FI19) and 
Åland (FI20) are aggregated (same value for both regions). Switzerland and 
Serbia: 2023. Montenegro: 2022.

Source: Eurostat (online data codes: ilc_lvhl21n and ilc_lvhl11n) 

Eurostat regional yearbook – 2025 edition98A

Living conditions

At the lower end of the distribution, there were 8 NUTS 
level 2 regions where, in 2024, the share of people living 
in a household with very low work intensity was less 
than 2.0%. The Romanian capital region of Bucureşti-Ilfov 
(0.6%) and the Slovak capital region of Bratislavský kraj 
(0.8%) had the lowest shares and were the only regions 
in the EU where fewer than 1.0% of people were living in 
a household with very low work intensity. The 3rd lowest 
share was also recorded in a capital region of an eastern 
EU country, Warszawski stołeczny in Poland (1.1%). The 
other 5 regions with shares below 2.0% included: 

•  Provincia Autonoma di Bolzano/Bozen in Italy (1.3%)
•  the western Austrian regions of Salzburg (1.4%) and Tirol 

(1.6%)

•  Közép-Dunántúl in Hungary (1.7%)
•  Sud-Muntenia in Romania (1.9%).

The right-hand side of Figure 5.4 shows the regions 
that experienced the biggest increases and decreases 
in their share of people living in a household with very 
low work intensity between 2022 and 2024. Across the 
EU, there was a 0.4 percentage points decrease in this 
share. During the same period, the share of people 
living in a household with very low work intensity fell in 
almost 2 out of 3 (150 out of 236) NUTS level 2 regions 
for which data are available, while there was an increase 
in 86 regions. 

The Spanish autonomous city of Melilla recorded the 
largest decrease in its share of people living in a household 
with very low work intensity, down 8.0 percentage points 
between 2022 and 2024. Marked reductions were also 
observed in 3 Italian regions – Calabria (down 7.4 points), 
Abruzzo (down 6.9 points) and Liguria (down 6.7 points) 
– as well as in Spain’s other autonomous city, Ceuta 
(down 6.2 points). No other region in the EU reported a 
decrease that was greater than 4.5 points. 

Among the 86 regions that reported a rising share 
of people living in a household with very low work 
intensity between 2022 and 2024, the highest increases 
were recorded in the western German region of Trier 
(up 8.1 percentage points) and the French outermost 
region of Guyane (up 7.3 points). Relatively large increases 
– ranging from 4.3 to 5.8 points – were also observed in 
Yugoiztochen (Bulgaria), Burgenland (Austria) and Bremen 
(Germany). 

In 2024, low work intensity was concentrated in clusters of 
regions across western EU countries and southern Italy 

Of the 243 NUTS level 2 regions for which data are 
available, there were 104 regions that had shares of 
people living in households with very low work intensity 
that were higher than the EU average (7.9%), while 
there were 139 that had lower than average shares. 
Map 5.5 highlights those regions across the EU with a 
relatively high share of people living in households with 
very low work intensity in 2024 (as shown by the darkest 
shade of blue). There were 26 such regions, each with a 
share of at least 12.5%: 

•  7 (predominantly urban) regions in Germany
•  6 regions in France (4 of which were the outermost 

regions)

•  4 regions in Belgium – the capital Région de Bruxelles-
Capitale / Brussels Hoofdstedelijk Gewest and 3 in 
Région wallonne

•  4 regions in Italy, located either in the south or on islands
•  the 2 autonomous Spanish cities of Melilla and Ceuta
•  Austria’s capital region, Wien
•  Dytiki Makedonia in Greece
•  Severozapaden in Bulgaria.

By contrast, there were 27 regions across the EU where 
fewer than 3.5% of people were living in households with 
very low work intensity in 2024 (as shown by the lightest 
shade of yellow in Map 5.5). These 27 regions were widely 
dispersed across 12 different EU countries, including 
Poland (5 predominantly western regions), Italy (4 northern 
regions), Hungary (3 western regions), Austria (3 western 
regions) and Romania (3 eastern regions). 

Figure 5.4 is split into 2 parts: the left-hand side presents 
information for the NUTS level 2 regions with the highest 
and lowest shares of people living in a household with 
very low work intensity in 2024; the right-hand side 
presents the biggest increases and decreases in these 
shares between 2022 and 2024. In 2024, the French 
outermost region of Guyane (35.4%) had, by far, the 
highest share. The next highest shares – where more 
than 1 in 5, but less than 1 in 4 people were living in a 
household with very low work intensity – were observed 
in: 

•  Campania in Italy (24.4%)
•  the French outermost regions of Guadeloupe (23.5%) 

and La Réunion (21.9%)
•  Bremen in Germany (22.9%)
•  the autonomous Spanish region of Ciudad de Ceuta 

(21.9%)

•  the Belgian regions of Prov. Hainaut (20.5%) and Région 
de Bruxelles-Capitale / Brussels Hoofdstedelijk Gewest 
(20.3%).

99Eurostat regional yearbook – 2025 editionA

Living conditions

Figure 5.4: People living in households with very low work intensity
(by NUTS 2 regions, 2024)

Note: the 1st part of the figure shows the EU regions with the highest 
and lowest shares in 2024, while the 2nd part shows the regions with 
the biggest increases and biggest decreases compared with 2022. The 
rankings include more than 10 regions if several regions have identical 
values. Utrecht (NL35), Zuid-Holland (NL36), Centro (PT19), Grande Lisboa 
(PT1A), Península de Setúbal (PT1B), Alentejo (PT1C) and Oeste e Vale do 
Tejo (PT1D): not available for the comparison with 2022. Mayotte (FRY5): 
not available.

Source: Eurostat (online data codes: ilc_lvhl21n and ilc_lvhl11n)

Eurostat regional yearbook – 2025 edition100A

Living conditions

Income distribution

Gross domestic product (GDP) per inhabitant has 
traditionally been used to assess regional divergence/
convergence in overall living standards. However, this 
commonly used measure fails to account for income 
paid/received across borders. It also fails to capture the 
distribution of income within a population and thereby 
does little to reflect economic inequalities. Alternative/
broader measures can help provide a more comprehensive 
and nuanced understanding of economic and societal 
developments. 

The unequal distribution of income and wealth has gained 
prominence in political and socioeconomic discourse 
since the global financial and economic crisis and, more 
recently, during the cost-of-living-crisis. This inequality 
not only reflects a wider concentration of wealth among 
a small privileged group – raising concerns about social 
cohesion, fairness, and long-term economic stability – but 
also highlights persistent disparities between regions, 
particularly those considered to have been ‘left behind’. 

More about the data: income inequality 

The income quintile share ratio (S80/S20) measures the 
inequality of income distribution. It is calculated as the 
ratio between the share of income received by the 20% 
of the population with the highest income (the top 
quintile) and the share of income received by the 20% 
of the population with the lowest income (the bottom 
quintile). High values for this ratio suggest that there are 
considerable disparities in the distribution of income 
between upper and lower income groups. 

The reference period for income statistics refers to the 
calendar year before the year in which the survey took 
place. Income statistics for Belgium and Serbia refer to 
NUTS level 1 regions, while there are only national data 
available for France and Türkiye. 

In 2024, the EU’s income quintile share ratio was 4.7 – in 
other words, the combined income received by the 20% 
of people with the highest incomes was 4.7 times as high 
as the combined income received by the 20% with the 
lowest incomes. 

Across the EU, the Slovak capital region of Bratislavský kraj 
had the lowest income quintile share ratio, at 2.5 in 2024 

Map 5.6 shows the regional distribution of the income 
quintile share ratio. In 2024, this distribution was skewed: 
148 out of the 210 regions for which data are available had 
ratios below the EU average, 8 regions had the same ratio, 
while 54 regions reported income disparities above the 
EU average. The highest income disparities were recorded 
in the Spanish autonomous cities and parts of Bulgaria 
and Italy, while the most equitable distributions of income 
were observed in parts of Czechia, Slovakia and the 
Netherlands. 

At the top end of the distribution, there were 4 NUTS 
level 2 regions where the income quintile share ratio 
was at least 7.5 in 2024 (as shown by the darkest shade 
of blue in Map 5.6). The Spanish regions of Ciudad de 
Melilla had the highest income quintile share ratio, at 9.7. 
The other 3 regions – the Bulgarian capital region of 
Yugozapaden, the Spanish region of Ciudad de Ceuta and 
the southern Italian region of Calabria – each had a ratio 
of 7.6. 

At the other end of the distribution, the share of income 
held by the highest earning 20% of the population 
in 2024 in Bratislavský kraj – the Slovak capital region 
– was 2.5 times as high as that held by the lowest 
earning 20%. This was the lowest income quintile share 
ratio among NUTS level 2 regions. There were 4 other 
regions across the EU with ratios below 3.0 (as shown by 
the lightest shade of yellow in Map 5.6): 

•  Drenthe and Zeeland in the Netherlands (2.6 and 2.8, 

respectively)

•  Západné Slovensko in Slovakia (2.8)
•  Jihozápad in Czechia (2.9).

In many multi-regional EU countries, income distribution 
in the capital region differed significantly from that in 
the rest of the country. In 2024, capital regions often 
recorded the highest income quintile share ratios, as 
seen in Belgium, Bulgaria, Czechia, Denmark, Ireland, 
Hungary, the Netherlands, Austria, Poland, Finland and 
Sweden. For instance, the ratios in the Belgian and 
Austrian capitals – Région de Bruxelles-Capitale / Brussels 
Hoofdstedelijk Gewest and Wien – were 1.4 times as 
high as their respective national averages. By contrast, 
the pattern was reversed in Germany, Croatia, Lithuania, 
Romania and Slovakia, where national averages exceeded 
the ratios recorded in each capital region. For example, 
income quintile share ratios for Romania and Slovakia 
were 1.3 times as high as those in Bucureşti-Ilfov and 
Bratislavský kraj, respectively. 

101Eurostat regional yearbook – 2025 editionA

Living conditions

Map 5.6: Income quintile share ratio (S80/S20)
(ratio, by NUTS 2 regions, 2024)

Note: Belgium and Serbia, NUTS level 1. France and Türkiye: national data. 
Länsi-Suomi (FI19) and Åland (FI20) are aggregated (same value for both 
regions). Switzerland and Serbia: 2023. Montenegro: 2022.

Source: Eurostat (online data codes: ilc_di11_r and ilc_di11)

Eurostat regional yearbook – 2025 edition102A

Living conditions

Table 1 highlights the region within each country with 
the highest incidence of domestic burglaries, motor 
vehicle thefts and intentional homicides in 2023. 
Note these figures are normalised, using incidence 
rates per 100 000 inhabitants to enable more relevant 
comparisons between regions of different population 
sizes; these rates were subsequently converted to indices 
so that each region may be compared with its national 
average. As such, cross-country comparisons should not 
be made. 

•  In Belgium, Czechia, Lithuania and Slovenia, capital 
regions – Région de Bruxelles-Capitale / Brussels 
Hoofdstedelijk Gewest, Praha, Sostinės regionas and 
Zahodna Slovenija – consistently recorded the highest 
incidence rate for all 3 crimes.

•  Midtjylland (Denmark), Provence-Alpes-Côte d’Azur 

(France), Algarve (Portugal) and Bucureşti-Ilfov (Romania) 
recorded the highest incidence rates for domestic 
burglaries and for motor vehicle thefts.

•  In Spain, Ciudad de Ceuta recorded the highest 
incidence rate for motor vehicle thefts and for 
intentional homicides. A similar situation was observed 
in Italy, as the highest incidence rate for motor vehicle 
thefts was in Campania which also had the joint highest 
rate – with Calabria – for intentional homicides.

•  In Slovakia, Východné Slovensko recorded the highest 

incidence rate for domestic burglaries and for intentional 
homicides.

•  In all of the remaining multi-regional EU countries, 

different regions recorded the highest incidence rates 
for each of the 3 crimes covered in this section.

Capital and coastal regions have some of the highest 
incidence rates for domestic burglaries 

Based on the latest information available, there were an 
estimated 703 500 police-recorded domestic burglaries 
across the EU in 2023 (including 2021 data for Luxembourg 
and excluding Ireland, Cyprus and Hungary). Within several 
EU countries, the capital region – specifically those of 
Belgium, Czechia, Croatia, Lithuania, Austria, Romania and 
Slovenia – recorded the highest incidence of domestic 
burglaries. 

In relative terms (compared with national averages), the 
highest incidence of domestic burglaries was recorded 
in Bremen (Germany), where burglaries were 2.8 times 
as likely as the national average. Relatively high indices 
– more than twice as high as the national average – 
were also observed in Wien (the Austrian capital region; 
2.2 times) and Algarve (southern Portugal; 2.1 times). 

Criminal justice

More about the data: crime and criminal justice 
statistics 

The statistics presented in this section are based on 
official figures for police-recorded offences (criminal 
acts), classified according to the International 
Classification of Crime for Statistical Purposes (ICCS). 

The number of police-recorded crimes varies considerably 
across the EU: this may reflect, among other factors, 
different rates for reporting crimes to the police (especially 
for minor offences), different laws in each EU country and 
different police practices for recording crimes. 

The data shown in this section are based on crime 
rates, the number of police-recorded crimes 
per 100 000 inhabitants for NUTS level 2 regions. These 
rates were subsequently converted into indices to show 
deviations from the national average in each country 
(with the national average = 100). 

Domestic burglary is defined as breaking in and 
stealing, in other words, getting unauthorised access to 
a dwelling for theft or intent of theft (with or without 
forcing locks, doors, windows, and so on). 

Theft is defined as taking property unlawfully – without 
violence, force, threat, coercion or deception – with 
the intent to keep it permanently without consent. 
Theft of motorised land vehicles includes stealing cars, 
motorcycles, buses, coaches, lorries, trucks, bulldozers 
and so on. 

Intentional homicide is defined as killing a human being 
wilfully and illegally; in other words, the intent was to 
cause death or serious injury, but not necessarily that it 
was planned beforehand. This is a wider concept than 
murder, for which also planning and other criteria are 
considered. Intentional homicide statistics: 

•  include murder, deadly assault, assassination, 
terrorism, femicide, infanticide, voluntary 
manslaughter, extrajudicial killings and illegal killing 
by police or military

•  exclude attempted homicide, justifiable self-defence, 

assisted suicide, euthanasia and abortion.

Special care should be taken when analysing the 
statistics presented below, as there may be very low 
counts of intentional homicides in some regions. 
Counts can vary considerably over time, especially in 
jurisdictions with small populations, whereby a small 
increase/decrease in the number of homicides may lead 
to a relatively large change in crime rates. 

You can find more information about how crimes are 
classified across EU regions in the metadata. 

103Eurostat regional yearbook – 2025 editionA

Living conditions

In 2023, there was a relatively high incidence of motor 
vehicle thefts in the capital regions of Romania, Poland 
and Germany 

In 2023, police recorded 477 300 motor vehicle thefts 
across the EU (including 2022 data for Ireland, 2021 data 
for Germany, and excluding Cyprus and Hungary). There 
were 12 multi-regional EU countries where the capital 
region recorded the highest incidence of motor vehicle 
theft, namely, Belgium, Czechia, Germany, Ireland, Greece, 
Lithuania, the Netherlands, Poland, Romania, Slovenia, 
Slovakia and Finland. 

In relative terms, the highest incidence rates of motor 
vehicle theft in 2023 were recorded in: 

•  Romania, where police in the capital region of Bucureşti-
Ilfov were 5.6 times as likely to record an offence as was 
the situation for the national average

•  Poland, where police in the capital region of Warszawski 
stołeczny were 4.3 times as likely to record an offence as 
was the situation for the national average

•  Germany, where police in the capital region of Berlin 

were 4.1 times as likely to record an offence as was the 
situation for the national average.

In 2023, there was a very high incidence of intentional 
homicide in French outermost regions 

In 2023, there were 3 930 police-recorded intentional 
homicides within the EU, marking an increase of 1.5% 
compared with 2022. Most EU countries had a relatively 
narrow range of inter-regional variation for incidence rates 
of intentional homicides. There were 6 multi-regional EU 
countries where the capital region recorded the highest 
incidence of intentional homicide – namely, Belgium, 
Czechia, Denmark, Lithuania, Hungary and Slovenia. By 
contrast, the capital regions of Bulgaria, Spain, France, Italy, 
Slovakia and Finland recorded rates that were below their 
respective national averages. 

However, there were a few outliers with much higher 
incidence rates, including several of the outermost 
regions of France and the autonomous Spanish city of 
Ceuta. In relative terms, the French outermost region of 
Guyane had the highest incidence of intentional homicide 
in 2023, at 16.1 times the French national average. 
The 2nd and 3rd highest rates in France were also recorded 
in outermost regions: Guadeloupe (7.3 times the national 
average) and Martinique (4.7 times). In Spain, the highest 
incidence rate for intentional homicide was recorded in 
Ciudad de Ceuta (8.6 times the national average). 

Eurostat regional yearbook – 2025 edition104A

Living conditions

228.6 

108.3 

133.3 

122.2 

187.5 

– 

133.3 

142.9 

857.1 

Table 5.1: Police-recorded offences
(index relative to national average = 100, by NUTS 2 regions, 2023)

Burglary of private 
residential premises

Theft of a motorised 
land vehicle

Intentional 
homicide

Belgium

Région de Bruxelles-
Capitale / Brussels 
Hoofdstedelijk Gewest (BE10)

166.4 

Région de Bruxelles-
Capitale / Brussels 
Hoofdstedelijk Gewest (BE10)

199.9 

Région de Bruxelles-
Capitale / Brussels 
Hoofdstedelijk Gewest (BE10)

Bulgaria

Severozapaden (BG31)

166.7  Yugoiztochen (BG34)

130.6 

Severoiztochen (BG33) and 
Yugozapaden (BG41)

Czechia

Denmark

Germany

Estonia

Ireland

Praha (CZ01)

149.4  Praha (CZ01)

172.1  Praha (CZ01)

Midtjylland (DK04)

121.2  Midtjylland (DK04)

156.4  Hovedstaden (DK01)

Bremen (DE50)

279.5  Berlin (DE30)

408.7  Hamburg (DE60)

–

–

–  –

–  –

:  Eastern and Midland (IE06)

147.5  Southern (IE05)

Greece

Kentriki Makedonia (EL52)

140.8  Attiki (EL30)

173.9 

Ionia Nisia (EL62) and Sterea 
Elláda (EL64)

Spain

France

Comunitat Valenciana (ES52)

157.7  Ciudad de Ceuta (ES63)

234.3  Ciudad de Ceuta (ES63)

Provence-Alpes-Côte d’Azur 
(FRL0)

138.2 

Provence-Alpes-Côte d’Azur 
(FRL0)

166.7  Guyane (FRY3)

1 608.3 

Croatia

Grad Zagreb (HR05)

155.3 

Jadranska Hrvatska (HR03)

145.9  Sjeverna Hrvatska (HR06)

Italy

Cyprus

Latvia

Emilia-Romagna (ITH5)

143.1  Campania (ITF3)

266.0 

Campania (ITF3) and Calabria 
(ITF6)

–

–

–  –

–  –

–  –

–  –

Lithuania

Sostinės regionas (LT01)

110.2  Sostinės regionas (LT01)

113.1  Sostinės regionas (LT01)

Luxembourg –

Hungary

Malta

–

–

–  –

:  –

–  –

Netherlands

Limburg (NL42)

111.2  Noord-Holland (NL32)

Austria

Wien (AT13)

216.7  Salzburg (AT32)

–  –

:  Budapest (HU11)

–  –

144.1  –

132.5 

Burgenland (AT11) and 
Steiermark (AT22)

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

Dolnośląskie (PL51)

171.5  Warszawski stołeczny (PL91)

425.0  Zachodniopomorskie (PL42)

Algarve (PT15)

205.7  Algarve (PT15)

209.2  Península de Setúbal (PT1B)

Bucureşti-Ilfov (RO32)

149.3  Bucureşti-Ilfov (RO32)

563.8  Nord-Est (RO21)

Zahodna Slovenija (SI04)

117.3  Zahodna Slovenija (SI04)

152.4  Zahodna Slovenija (SI04)

Východné Slovensko (SK04)

126.5  Bratislavský kraj (SK01)

197.8  Východné Slovensko (SK04)

Etelä-Suomi (FI1C)

106.4  Helsinki-Uusimaa (FI1B)

119.8  Pohjois- ja Itä-Suomi (FI1D)

Östra Mellansverige (SE12)

124.7  Norra Mellansverige (SE31)

109.2  –

Norway

–

: 

Agder og Sør-Østlandet 
(NO09)

138.0 

Trøndelag/Trööndelage 
(NO06)

Switzerland

Région lémanique (CH01)

131.4  Région lémanique (CH01)

132.9  Région lémanique (CH01)

Albania

Veri (AL01)

128.6  Veri (AL01)

154.9  Veri (AL01)

Serbia

Türkiye

Autonomous Province of 
Vojvodina (RS12)

132.7  City of Belgrade (RS11)

145.6 

Autonomous Province of 
Vojvodina (RS12)

Hatay, Kahramanmaraş, 
Osmaniye (TR63)

224.1 

Gaziantep, Adıyaman, Kilis 
(TRC1)

253.2  Adana, Mersin (TR62)

185.7 

150.0 

– 

– 

112.5 

– 

128.6 

– 

:

162.5 

162.5 

228.6 

171.4 

116.7 

118.2 

141.7 

:

214.3 

200.0 

150.0 

116.7 

212.0 

Note: based on data per 100 000 inhabitants. The table shows for each 
country and each offence, the region with the highest index. Estonia, 
Cyprus, Latvia, Luxembourg and Malta: single regions at NUTS level 2.

Source: Eurostat (online data codes: crim_gen_reg and crim_off_cat)

105Eurostat regional yearbook – 2025 edition6. Digital society

People use information and communication 
technologies (ICTs) for work, study, news, communication, 
entertainment, online transactions and interacting 
with public services. ICT innovations also create new 
business opportunities. To take full advantage of these 
technologies, fast and reliable internet access is essential. 

To support these goals, the Digital Services Act (Regulation 
(EU) 2022/2065) regulates online platforms to prevent 
illegal activities and misinformation, while protecting 
user rights. Meanwhile, the Artificial Intelligence (AI) 
Act (Regulation (EU) 2024/1689) manages AI risks and 
promotes trust in AI systems. 

Europe’s Digital Decade policy has 12 key targets for 2030, 
among which: 

•  at least 80% of people aged 16 to 74 years should have 

at least basic digital skills

•  at least 75% of EU enterprises should use cloud 

computing, data analytics, or artificial intelligence in their 
business operations.

(%, during the 3 months preceding the survey, people aged 16–74 years, 
by NUTS 2 regions, 2024)

Note: Germany and Greece, NUTS level 1. Croatia: national data. France: 
2023.

Source: Eurostat (online data codes: isoc_r_iuse_i and isoc_ci_ac_i)

Eurostat regional yearbook – 2025 edition106A

Digital society

More about the data: survey on the use of ICT 
in households and by individuals 

Household surveys collect data on ICT usage and are 
usually conducted during the 2nd quarter of each 
year, although the precise date can vary between 
EU countries. In general, the data presented below 
usually refer to the 1st quarter of the reference year; 
they concern activities carried out by people during 
the 3 months prior to the survey. 

Most of the ICT statistics presented below cover people 
aged 16 to 74 years, although the 1st section covers 
household internet access and the final section covers 
workplace use of the internet and enterprise use of 
artificial intelligence. 

Data are generally presented for NUTS level 2 regions. 
Exceptions include Germany, Greece and Türkiye (where 
the data refer to level 1 regions), as well as Croatia, 
Norway and Albania (where national data are usually 
presented). 

When making comparisons over time – Figures 1 to 4 
compare the situation in 2024 with that in 2019; note, 
there are breaks in series for the EU, Germany and 
Ireland in 2021. 

The infographic above shows that in 2024 nearly 2 in 
3 people aged 16 to 74 years in the EU used social 
networks. Among NUTS level 2 regions, all 5 Danish 
regions had participation rates within the range of 88.9% 
to 91.5%, higher than in any other region of the EU. The 
northern Danish region of Nordjylland and the capital 
region of Hovedstaden had the highest rates, both 
exceeding 90.0%. Outside of Denmark, the next highest 
shares were recorded in Cyprus (86.8%), the Finnish capital 
region of Helsinki-Uusimaa (84.1%), the Dutch region of 
Flevoland (83.0%) and the western Hungarian regions of 
Dél-Dunántúl (82.8%) and Nyugat-Dunántúl (82.6%). 

Households with access to the 
internet

Internet access comes in a variety of forms: 

•  broadband access includes cable and fibre-optic 

connections that deliver high-speed internet through 
traditional infrastructure

•  wireless access includes options like Wi-Fi and mobile 
broadband that are generally accessed via routers or 
cellular networks

•  satellite internet reaches remote areas and offers 

connectivity when other technological options are 
limited.

In 2024, every household had access to the internet at 
home in the Dutch regions of Flevoland, Groningen and 
Zeeland 

Within this section, connection to the internet (or ‘access’) 
does not refer to ‘connectability’ (can a connection be 
provided in the household’s street or local area), but rather 
to ‘connectivity’, in other words, whether anyone in the 
household is able to use the internet at home if so desired. 
In 2024, the share of EU households connected to the 
internet at home was 94.2%. 

Map 6.1 shows the regional distribution of household 
access to the internet. These data are crucial for 
understanding the remainder of the information presented 
within this chapter, as restricted internet access reduces 
the share of people who can go online and engage in 
specific internet activities. 

In 2024, there were 19 NUTS level 2 regions across the 
EU where at least 98.0% of households had access to 
the internet at home (as shown by the darkest shade in 
Map 6.1). This group included: 

•  all 12 regions in the Netherlands
•  the capital regions of Spain, Luxembourg and Hungary
•  an additional Spanish region – Ciudad de Ceuta
•  Podkarpackie in Poland
•  Pohjois- ja Itä-Suomi in Finland, and
•  Mellersta Norrland in Sweden.

In 2024, the 7 regions in the EU with the highest 
proportions of households having access to the internet 
at home were all located in the Netherlands: Flevoland, 
Groningen and Zeeland (all 100.0%), Utrecht (99.8%), 
Gelderland (99.2%), Noord-Brabant (99.0%) and the capital 
region of Noord-Holland (98.9%). 

By contrast, there were 23 regions across the EU where 
fewer than 9 out of 10 households had access to the 
internet at home in 2024 (as shown by the lightest shade 
in Map 6.1). These 23 regions were spread across several 
EU countries: 4 from each of (southern) Italy and Portugal, 
3 each in Bulgaria and Germany, 2 each in Greece, France 
and Slovakia, as well as Croatia and single regions from 
Belgium and Lithuania. The central Greek region of Kentriki 
Elláda (80.5%) and the southern Italian region of Calabria 
(84.7%) were the only regions to report less than 85.0% of 
all households having access to the internet at home. 

As well as showing the regions that had the highest and 
lowest shares of households with access to the internet 
in 2024, Figure 6.1 also provides information about the 
regions that experienced the biggest and smallest 
changes compared with 2019. The proportion of EU 
households connected to the internet had been 89.7% 
in 2019. This share rose 4.5 percentage points – during the 

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most recent 5-year period for which data are available – to 
reach 94.2% by 2024. 

Between 2019 and 2024, the Greek region of Nisia Aigaiou, 
Kriti recorded the largest increase in household internet 
access at home, its share rising 23.0 percentage points 
to 91.7%. There were also substantial gains in: 

•  3 Bulgarian regions – Severen tsentralen, Severozapaden 

and Yugozapaden – as their internet access rates 
increased 17.7 to 19.7 points

•  4 additional regions – La Réunion (France), Severozápad 

(Czechia) and 2 more Bulgarian regions, Yuzhen 
tsentralen and Severoiztochen – where the share rose by 
more than 15.0 points.

During the period 2019 and 2024, there were 25 regions 
across the EU that reported a fall in their share of 
households with internet access at home. The biggest 
declines were in 4 German regions – Niedersachsen, 
Thüringen, Sachsen-Anhalt and Bremen – with reductions 
of at least 4.6 percentage points. Bremen, in northern 
Germany, recorded the sharpest decline, as its share of 
households with internet access at home fell 9.2 points. 

Map 6.1: Households with access to the internet at home
(% of all households, by NUTS 2 regions, 2024)

Note: Germany, Greece and Türkiye, NUTS level 1. Croatia, Norway and 
Albania: national data. Switzerland and Albania: 2023. Iceland: 2021. 
Corse (FRM0): low reliability.

Source: Eurostat (online data codes: isoc_r_iacc_h and isoc_ci_in_h)

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Figure 6.1: Households with access to the internet at home
(by NUTS 2 regions, 2024)

Note: the 1st part of the figure shows the EU regions with the highest 
and lowest shares in 2024, while the 2nd part shows the regions with 
the biggest increases and biggest decreases compared with 2019. The 
rankings include more than 10 regions if several regions have identical 
values. Germany and Greece: NUTS level 1. Poland: comparison with 
2019, NUTS level 1. Croatia: national data. Portugal: comparison with 

2019, national data. EU, Germany and Ireland: breaks in series. Ciudad de 
Ceuta (ES63): low reliability. Mayotte (FRY5) and Åland (FI20): not available. 
Utrecht (NL35) and Zuid-Holland (NL36): not available for comparison with 
2019.

Source: Eurostat (online data codes: isoc_r_iacc_h and isoc_ci_in_h)

Daily internet users

Internet users are defined as people aged 16 to 74 years 
using the internet at home, at work, or elsewhere, for 
private or professional purposes, and regardless of the 
device or the type of connection used. 

In 2024, almost 9 out of every 10 people aged 16 to 74 years 
in the EU made use of the internet on a daily basis 

In 2024, 88.3% of people aged 16 to 74 years in 
the EU reported using the internet on a daily basis 
during the 3 months preceding the ICT survey. This 
share was 2.4 percentage points higher than in 2023, 
and 11.4 points higher than in 2019. The last few years 
have been characterised by slower growth in the share 
of people using the internet on a daily basis, as this 
indicator approaches saturation for many groups in society 
(particularly among younger generations). 

In 2024, more than 3 out of 4 people used the internet on 
a daily basis in every region of the EU, except for Yuzhen 
tsentralen (Bulgaria), Podlaskie (Poland) and Kentriki 
Elláda (Greece) 

Map 6.2 shows the share of people making daily use of 
the internet within each NUTS level 2 region. In 2024, 
there were clear disparities along broad geographical 
lines: northern and western regions generally recorded 
higher levels of daily internet use than southern or eastern 
regions. There were 105 regions where the share of 
people using the internet daily was above the EU average 
of 88.3%, while a slightly lower number (102 regions) had 
a share that was below; Extremadura (Spain) had the same 
value as the EU average. 

In 2024, several regions across the Netherlands ranked 
among those with the highest concentrations of daily 
internet users. In fact, the Netherlands accounted for 8 out 
of the 10 EU regions with the highest shares, with Zeeland 
recording a peak of 99.3%. Outside of the Netherlands, 

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2 Irish regions – Eastern and Midland, and Northern and 
Western – were the only other regions in the EU where the 
share of daily internet users exceeded 98.0%. 

There were 17 EU regions where less than 80.0% of 
people used the internet on a daily basis in 2024 (they 
are shown by the lightest shade in Map 6.2). This group 
included 6 regions from Poland, 4 regions from Bulgaria, 
2 (outermost) regions of France, 2 regions from (southern) 
Italy, as well as a single region from each of Germany, 
Greece and Austria. The lowest shares were in: 

•  the southern Bulgarian region of Yuzhen tsentralen 

(73.0%)

•  the north eastern Polish region of Podlaskie (73.6%)
•  the central Greek region of Kentriki Elláda (74.9%).

As well as showing the regions with the highest and 
lowest shares of daily internet users in 2024, Figure 6.2 also 
presents information about the overall change in the share 
of daily internet users between 2019 and 2024. 

•  Daily internet usage in the Nord-Est region 

of Romania increased 33.9 percentage points 
between 2019 and 2024, reaching 83.3%. 
There were 9 other EU regions with increases 
above 25.0 points, including 7 more from Romania, 
as well as La Réunion (France) and Nisia Aigaiou, Kriti 
(Greece).

•  Conversely, there were 7 regions across the 

EU where the share of daily internet users fell 
between 2019 and 2024. They were located exclusively 
in Germany (4 regions) and Finland (3 regions), with the 
Finnish capital region of Helsinki-Uusimaa recording the 
largest fall (down 5.2 points).

Map 6.2: Daily internet users
(% of people aged 16–74 years, by NUTS 2 regions, 2024)

Note: during the 3 months preceding the survey. Germany, Greece 
and Türkiye: NUTS level 1. Croatia, Norway and Albania: national data. 
Switzerland and Albania: 2023. Iceland: 2021. Corse (FRM0): low reliability.

Source: Eurostat (online data codes: isoc_r_iuse_i and isoc_ci_ifp_fu)

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Figure 6.2: Daily internet users
(people aged 16–74 years, by NUTS 2 regions, 2024)

Note: during the 3 months preceding the survey. The 1st part of the figure 
shows the EU regions with the highest and lowest shares in 2024, while 
the 2nd part shows the regions with the highest increases and lowest 
changes (positive and negative) compared with 2019. Germany and 
Greece: NUTS level 1. Poland: comparison with 2019, NUTS level 1. Croatia: 

national data. Portugal: comparison with 2019, national data. EU, Germany 
and Ireland: breaks in series. Mellersta Norrland (SE32): low reliability. 
Mayotte (FRY5) and Åland (FI20): not available. Utrecht (NL35) and Zuid-
Holland (NL36): not available for comparison with 2019.

Source: Eurostat (online data codes: isoc_r_iuse_i and isoc_ci_ifp_fu)

Activities on the internet

The widespread adoption of mobile devices like 
smartphones and tablets has significantly increased 
people’s use of the internet, while the variety of activities 
carried out online has also expanded rapidly. Some of the 
most popular activities on the internet include: 

•  learning and skills development
•  remote work
•  accessing information
•  entertainment
•  staying connected with family and friends
•  engaging with government services
•  online shopping (e-commerce).

PARTICIPATION IN SOCIAL NETWORKS 

Social networks are online platforms that enable 
individuals to connect, interact and share content with 
others. Some of the most popular networks include 
Facebook, Instagram, TikTok, Snapchat or X (formerly 

Twitter). These platforms typically allow users to 
create personal profiles, share photos and videos, and 
communicate with others through messaging, comments 
or other forms of interaction. 

Almost 2 out of 3 people across the EU used social 
networks in 2024 

In 2017, 51.8% of the EU’s population aged 16 to 74 years 
used social networks during the 3 months preceding the 
ICT survey; this was the 1st time that a majority of people 
in the EU used social networks. Despite a small fall in 2021, 
the share of people using social networks generally rose at 
a steady pace across the EU, reaching 64.8% by 2024. 

Social networks are among the most popular activities 
on the internet for young people (defined here as those 
aged 16 to 24 years). In 2024, the share of young people 
using social networks was 2.9 times as high as the 
share recorded for older people (defined here as those 
aged 65 to 74 years). Looking in more detail at recent 
developments, the proportion of young people using social 

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networks across the EU fell from 87.0% in 2018 to a relative 
low of 83.4% by 2023 (note there is a break in series in 2021). 
There was a sharp rebound in 2024, with the share of young 
people participating in social networks reaching a new 
peak of 88.6%. During the same period, the proportion of 
older people using social networks increased every year, up 
from 16.9% in 2018 to 30.3% by 2024. 

In 2024, around 9 out of 10 people across every region of 
Denmark used social networks 

Among NUTS level 2 regions, the proportion of people 
using social networks was evenly distributed around the 
EU average. In 2024, there were 105 regions that had shares 
above the EU average and 102 regions with shares below; 
the Spanish island region of Canarias had a share that was 
identical to the EU average. Contrary to the situation for 
most ICT indicators, there were also relatively high shares 
recorded in several eastern and southern EU countries. 

Map 6.3: Participation in social networks
(% of people aged 16–74 years, by NUTS 2 regions, 2024)

Some of the highest shares of people using social 
networks were concentrated in regions across Denmark, 
Cyprus, the Netherlands and Hungary. 

Map 6.3 shows the regional distribution of people 
aged 16 to 74 years using social networks. In 2024, there 
were 20 NUTS level 2 regions across the EU where at 
least 80.0% of people used social networks (as shown by 
the darkest shade of blue). The 5 regions that compose 
Denmark recorded the highest shares in the EU – within 
the range of 88.9% to 91.5% – with a peak of 91.5% in 
Nordjylland. The rest of this group of 20 consisted of: 

•  Cyprus
•  the Finnish capital region of Helsinki-Uusimaa
•  5 regions from the Netherlands
•  7 out of the 8 regions in Hungary (Dél-Alföld was the 

exception)

•  Nord-Vest in Romania.

Note: during the 3 months preceding the survey. Germany, Greece and 
Türkiye, NUTS level 1. Croatia, Norway and Albania: national data. France, 
Switzerland and Albania: 2023. Iceland: 2021. Corse (FRM0): low reliability.

Source: Eurostat (online data codes: isoc_r_iuse_i and isoc_ci_ac_i)

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At the other end of the distribution, 26 regions across 
the EU reported that less than half of the population 
aged 16 to 74 years used social networks in 2024 (they 
are shown by the 2 lightest shades in Map 6.3). Among 
these 26 regions, 22 were located in France (2023 data). 
The others included Sachsen, Thüringen and Brandenburg 
in eastern Germany, along with Calabria in southern Italy. 
There were 5 French regions recording the lowest shares, all 
below 40.0%: Haute-Normandie (38.5%), Bretagne (38.5%), 
Guadeloupe (30.0%), Martinique (29.2%) and Guyane (22.2%). 

INTERNET BANKING 

In recent years, the EU’s banking sector has seen significant 
growth in online services. Consumers now visit their 
local branch less frequently or not at all, the number 
of branches has contracted, and online transfers and 
e-payments have become the norm. Internet banking 
reduces the demand for local branches and their related 
costs. Furthermore, it may often greater convenience, 
allowing banking transactions and other banking activities 
to be carried out at any time of day and from almost 
anywhere. However, while internet banking has numerous 
benefits, it also presents challenges, such as: 

•  accessibility issues
•  security and privacy concerns
•  technical disruptions to services.

Across the EU, more than 2 out of 3 people made use of 
internet banking in 2024 

In 2024, 67.2% of the EU’s population aged 16 to 74 years 
used the internet for banking during the 3 months 
preceding the ICT survey. Map 6.4 shows the share of 
people using internet banking across NUTS level 2 regions. 
Over 90.0% of people in every region of Denmark, the 
Netherlands and Finland used internet banking. In 
Belgium, Czechia, Ireland, Sweden and the Baltic countries 
a majority of regions also recorded relatively high shares (at 
least 80.0%); this was also the case in Cyprus. 

In 2024, the Dutch region of Utrecht had the highest 
regional share of people using internet banking 

A more detailed investigation of the latest information 
shown in Map 6.4 reveals that there were 3 NUTS 
level 2 regions where the proportion of people using 
internet banking was at least 98.0%, namely: 

•  the central Dutch region of Utrecht (98.3%)
•  the Danish capital region of Hovedstaden (98.2%)
•  the northern Dutch region of Groningen (98.0%).

The share of people making use of internet banking was 
often unusually high in capital regions, compared with their 
neighbouring regions. In 2024, this situation was particularly 
noticeable in Praha (Czechia), Comunidad de Madrid (Spain), 
Budapest (Hungary) and Warszawski stołeczny (Poland). 

In 2024, the lowest shares of people using internet banking 
were recorded in Bulgaria, Romania and southern Italy 

In 2024, several eastern and southern regions of the EU had 
relatively low shares of people using internet banking; this 
was particularly the case in Bulgaria, Romania, and – to a 
somewhat lesser degree – southern Italy. This pattern might 
be attributed to a range of different factors, including: 

•  many of these regions are predominantly rural, 

characterised by low levels of internet connectivity and 
digital literacy

•  many of these regions have relatively elderly population 

structures, with people preferring traditional cash 
transactions and having security concerns about online 
banking.

At the lower end of the distribution, the lowest use 
of internet banking in 2024 was recorded in: Sud-Vest 
Oltenia (17.6%) in Romania, Yuzhen tsentralen (18.4%) and 
Severozapaden (19.3%) in Bulgaria. Less than half of all 
people aged 16 to 74 years made use of internet banking 
in every region of Bulgaria and Romania. This was also the 
case in several other regions across the EU: 

•  the southern Italian regions of Puglia, Molise, Basilicata, 

Sicilia, Campania and Calabria

•  the Polish regions of Małopolskie, Podlaskie, Opolskie, 
Mazowiecki regionalny, Świętokrzyskie, Lubuskie and 
Warmińsko-mazurskie

•  the Greek regions of Kentriki Elláda and Nisia Aigaiou, 

Kriti

•  the French outermost region of Guyane.

There were 10 regions in the EU where the share of people 
using internet banking fell between 2019 and 2024 

The share of people using internet banking rose across 
the EU during a 5-year period from 2019 to 2024, up 
from 54.6% to 67.2%. The proportion of people using 
internet banking increased in the vast majority of EU 
regions (178 out of 188), often a relatively fast pace. 
There were 115 regions where the share rose by at 
least 10.0 percentage points, with 43 of these reporting 
growth of at least 20.0 percentage points. 

In Cyprus, the share of people using internet banking 
doubled, rising from 40.5% in 2019 to 80.5% in 2024. This 
was the largest increase among EU regions (see Figure 6.4). 
Yugozapaden in Bulgaria (up 33.6 percentage points) 
and Northern and Western in Ireland (up 30.0 points) also 
recorded rapid growth. By contrast, at the lower end of the 
distribution, there were 10 EU regions where the share of 
people using online banking fell between 2019 and 2024. 
They were principally located across Germany and 
Sweden, with Ciudad de Ceuta in Spain (-19.2%), Övre 
Norrland in Sweden (-10.7%) and Corse in France (-9.5%) 
experiencing the largest reductions. 

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Map 6.4: Use of internet banking
(% of people aged 16–74 years, by NUTS 2 regions, 2024)

Note: during the 3 months preceding the survey. Germany, Greece 
and Türkiye, NUTS level 1. Croatia, Norway and Albania: national data. 
Switzerland and Albania: 2023. Iceland: 2021. Corse (FRM0): low reliability.

Source: Eurostat (online data codes: isoc_r_iuse_i and isoc_ci_ac_i)

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Figure 6.3: Use of internet banking
(people aged 16–74 years, by NUTS 2 regions, 2024)

Note: during the 3 months preceding the survey. The 1st part of the 
figure shows the EU regions with the highest and lowest shares in 2024, 
while the 2nd part shows the regions with the biggest increases and 
biggest decreases compared with 2019. Germany and Greece: NUTS 
level 1. Poland: comparison with 2019, NUTS level 1. Croatia: national data. 

Portugal: comparison with 2019, national data. EU, Germany and Ireland: 
breaks in series. Ciudad de Ceuta (ES63) and Corse (FRM0): low reliability. 
Mayotte (FRY5) and Åland (FI20): not available. Utrecht (NL35) and Zuid-
Holland (NL36): not available for comparison with 2019.

Source: Eurostat (online data codes: isoc_r_iuse_i and isoc_ci_ac_i)

E-commerce

More about the data: defining e-commerce 

For statistical purposes, e-commerce is defined 
as ‘buying goods or services through electronic 
transactions, including the placing of orders for goods 
or services over the internet; payment and the ultimate 
delivery of the goods or service may be conducted 
either online or offline. Orders via manually typed 
e-mails are excluded’. 

E-commerce has the potential to simplify the process 
of comparing different wholesale or retail offers. It can 
reshape distribution networks and consumption patterns, 
expanding consumer choice and influencing price 
competition, especially in relatively remote regions of the 
EU. Additionally, e-commerce removes the time burden 
of travelling to physical stores and disconnects shopping 
from traditional opening hours. 

In 2024, 60.2% of people aged 16 to 74 years in the EU 
reported that they had ordered goods or services over 
the internet in the 3 months preceding the ICT survey. 
The share of people using the internet to order goods or 
services was higher than the EU average in every region of 
Czechia, Denmark, Ireland, France (except the outermost 
regions), the Netherlands, Slovakia, Finland and Sweden; 
this was also the case in Estonia, Cyprus, Luxembourg 
and Malta. By contrast, the share of the population using 
e-commerce was below the EU average for every region 
of Bulgaria, Italy, Lithuania, Portugal, Romania and Slovenia; 
this was also the case in Croatia (national data) and Latvia. 

In Utrecht, more than 9 out of 10 people ordered goods or 
services over the internet in 2024 

In 2024, there were 23 EU regions where at least 80.0% 
of the population aged 16 to 74 years ordered goods or 
services over the internet in the 3 months preceding the 
ICT survey (as shown by the darkest shade in Map 6.5). 

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•  Approximately half of these 23 regions with a high 

propensity to make use of e-commerce were located in 
the Netherlands (all 12 regions).

•  The remainder were spread across Denmark (4 regions), 
Ireland (all 3 regions), Sweden (3 regions) and the Czech 
capital region of Praha.

At the top end of the distribution, the central Dutch region 
of Utrecht had the highest share of people ordering 
goods or services over the internet (91.5% in 2024). Its 
neighbouring region of Flevoland (89.5%) had the next 
highest share, followed by the Irish region of Northern and 
Western (88.3%). 

In 2024, there were 21 EU regions where less than 40.0% 
of the population ordered goods or services over the 
internet (as shown by the lightest shade in Map 6.5). These 
regions were primarily located in eastern and southern 
regions of the EU, including Romania (6 regions), Bulgaria 
(5 regions) and southern Italy (6 regions). This group also 
included 3 outermost regions of France and 1 autonomous 
region of Portugal, where geographical isolation likely 
impacted the use of e-commerce. 

At the bottom end of the distribution in 2024, the south-
eastern Bulgarian region of Yugoiztochen had the lowest 
share of people ordering goods and services over the 
internet (21.7%). There were 2 other regions in the EU with 
shares below 25.0%: the Caribbean region of Guadeloupe 
(24.2%) and the north-western Bulgarian region of 
Severozapaden (24.9%). 

Map 6.5: Ordering goods or services over the internet
(% of people aged 16–74 years, by NUTS 2 regions, 2024)

Note: during the 3 months preceding the survey. Ordering goods or 
services for private use. Germany, Greece and Türkiye, NUTS level 1. 
Croatia, Norway and Albania: national data. Switzerland and Albania: 2023. 
Iceland: 2021. Corse (FRM0): low reliability.

Source: Eurostat (online data codes: isoc_r_blt12_i and isoc_ec_ib20)

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In the Czech region of Severozápad, the proportion 
of people ordering goods or services over the internet 
increased most between 2019 and 2024 

As well as showing those regions that had the highest and 
lowest shares of people ordering goods and services over 
the internet in 2024, Figure 6.4 also provides information 
about those regions that experienced the biggest and 
smallest changes compared with 2019. Across the EU, 
the proportion of people making use of e-commerce 
increased by 11.2 percentage points, rising from 49.0% 
in 2019 to 60.2% by 2024. 

Between 2019 and 2024, the north-western Czech 
region of Severozápad recorded the largest increase 
in its proportion of people ordering goods or services 
over the internet, with its share rising 40.1 percentage 

points (from 33.8% to 73.9%). Substantial gains were also 
registered in: 

•  the Irish region of Northern and Western, where the use 

of e-commerce increased 38.7 points

•  2 additional regions in Czechia – Moravskoslezsko 
and Střední Čechy – where the use of e-commerce 
increased 37.9 points and 35.5 points, respectively.

During the same period, there were 15 regions across 
the EU where the proportion of people ordering goods 
or services over the internet fell between 2019 and 2024. 
The biggest declines occurred in the German regions 
of Bayern, Sachsen-Anhalt and Bremen; these were 
the only regions in the EU to report decreases of more 
than 10.0 percentage points, with the sharpest decline in 
Bayern, down 12.9 points. 

Figure 6.4: Ordering goods or services over the internet
(people aged 16–74 years, by NUTS 2 regions, 2024)

Note: during the 3 months preceding the survey. Ordering goods or 
services for private use. The 1st part of the figure shows the EU regions 
with the highest and lowest shares in 2024, while the 2nd part shows 
the regions with the biggest increases and biggest decreases compared 
with 2019. Germany and Greece: NUTS level 1. Poland: comparison with 
2019, NUTS level 1. Croatia: national data. Portugal: comparison with 

2019, national data. EU, Germany and Ireland: breaks in series. Ciudad de 
Ceuta (ES63): low reliability. Mayotte (FRY5) and Åland (FI20): not available. 
Utrecht (NL35) and Zuid-Holland (NL36): not available for comparison with 
2019.

Source: Eurostat (online data codes: isoc_r_blt12_i, isoc_ec_ib20 and 
isoc_ec_ibuy)

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ICT usage in enterprises

More about the data: ICT usage in enterprises 

The information presented in this section is based on 
results of an EU survey on ICT usage and e-commerce in 
enterprises. 

The European Commission’s priorities for 2024 to 2029 
focus on boosting productivity through digital 
technologies. This comprises, among others, investing 
in: 

•  digital infrastructures
•  supercomputing, semiconductors, the Internet of 

Things and quantum computing

•  other strategic technologies (such as artificial 
intelligence, space, clean tech and biotech).

Regional ICT statistics are presented for NUTS level 2 
regions. They cover enterprises with 10 or more people 
employed (employees and self-employed people) in 
NACE Sections C to N (excluding K) and Group 95.1, 
in other words, the non-financial business economy. 
Exceptions are Belgium (where the data refer to NUTS 
level 1 regions) and Czechia, Germany, Ireland, Greece, 
France, Italy, the Netherlands, Poland, Portugal, Finland, 
Sweden, Serbia and Türkiye (where national data 
are presented). Data for Romania relate to reference 
year 2023 (instead of 2024). Data for Albania relate to 
reference year 2022 (instead of 2024). 

More than 3 out of 5 people employed in the EU’s non-
financial business economy made workplace use of the 
internet in 2024 

In 2024, some 62.9% of people employed in the EU’s non-
financial business economy used the internet for business 
purposes. Map 6.6 shows that a relatively high proportion 
of the workforce in the Nordic EU countries and the 
Netherlands used the internet for business purposes. By 
contrast, eastern and southern EU regions tended to report 
relatively low shares of their workforces making workplace 
use of the internet. This pattern was repeated both for 
industry/construction and for services. 

The distribution of internet use at work was relatively 
uneven across the EU. A total of 59 regions reported a 
share of their workforce using the internet for business 
purposes that was lower than the EU average in 2024, 
while only 25 regions had above-average shares. However, 
it should be noted that this imbalance is, at least in part, 
the result of only national data being available for some 
EU countries which have many regions: Germany, France 
and the Netherlands all had shares above the EU average 
and are each included as just 1 region in these counts; 
somewhat balancing the situation, the national shares for 
Italy and Poland were below the EU average. At the top 
end of the ranking, Sweden and Finland (both national 
data) were the only regions to report shares of more 
than 85.0%. More broadly, the share of employed people 
using the internet for business purposes was above 75% 
(as shown by the 2 darkest shades of blue in Map 6.6) in: 

•  Sweden
•  Finland
•  the Danish regions of Midtjylland, Sjælland, Nordjylland, 

and Syddanmark

•  the Netherlands (also national data)
•  the Austrian capital region of Wien.

A lack of digital infrastructure and workplace connectivity 
may potentially affect – among other matters – remote 
working opportunities, productivity and competitiveness. 
Across the EU, there were 22 regions that reported 
less than half of their workforce used the internet for 
business purposes in 2024 (as shown by the 2 lightest 
shades in Map 6.6). This group was composed of regions 
from 4 different EU countries, namely: 

•  Romania (7 out of 8 regions, the exception being the 

capital region of Bucureşti-Ilfov; 2023 data)

•  Bulgaria (5 out of 6 regions, the exception being the 

capital region of Yugozapaden)

•  Slovakia (3 out of 4 regions, the exception being the 

capital region of Bratislavský kraj)

•  Spain (7 out of 19 regions that were predominantly rural, 

islands or autonomous cities).

The southerly Romanian regions of Sud-Vest Oltenia and 
Sud-Est recorded the lowest shares of their workforces 
using the internet for business purposes, at 22.5% 
and 23.7%, respectively (2023 data). 

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Map 6.6: Internet use at work
(% of employed people, by NUTS 2 regions, 2024)

Note: covers enterprises with 10 or more people employed. Belgium: NUTS 
level 1. Czechia, Germany, Ireland, Greece, France, Italy, the Netherlands, 
Poland, Portugal, Finland, Sweden, Serbia and Türkiye: national data. 
Niederösterreich (AT12), Kärnten (AT21) and Romania: 2023. Albania: 2022.

Source: Eurostat (online data codes: isoc_r_ci_cm_pn2 and 
isoc_ci_cm_pn2)

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There were only 2 regions across the EU where more 
than 30.0% of enterprises made use of artificial 
intelligence in 2024: Midtjylland in Denmark and 
the Belgian capital region of Région de Bruxelles-
Capitale / Brussels Hoofdstedelijk Gewest 

More about the data: artificial intelligence 

Artificial intelligence (AI) refers to the ability of machines 
or computers to perform tasks that typically require 
human intelligence. The statistics presented below 
concern enterprises making use of at least 1 of the 
following AI technologies: 

•  performing analysis of written language
•  converting spoken language into machine-readable 

format

•  generating written or spoken language
•  identifying objects or persons based on images
•  using machine learning
•  automating different workflows or assisting in 

decision-making

•  enabling physical movement of machines via 

autonomous decisions based on observation of 
surroundings.

As with the previous section on workplace use of 
the internet, the statistics below cover all enterprises 
with 10 or more people employed in NACE Sections C 
to N (excluding K) and Group 95.1, in other words, the 
non-financial business economy. 

In 2024, 13.5% of enterprises with 10 or more people 
employed in the EU’s non-financial business economy 
made use of AI when conducting their business. This 
share was growing at a rapid pace, with the latest annual 
increase equivalent to a gain of 5.5 percentage points (up 
from 8.0% in 2023). 

The Danish region of Midtjylland (35.0%) and the Belgian 
capital region of Région de Bruxelles-Capitale / Brussels 
Hoofdstedelijk Gewest (31.7%) had the highest shares of 
enterprises making use of at least 1 AI technology. Other 
regions with relatively high shares in 2024 included: 

•  another Danish region, Sjælland (28.1%)
•  the Austrian and Slovenian capital regions of Wien 

(26.5%) and Zahodna Slovenija (23.6%)

•  Sweden (25.1%), Finland (24.4%) and the Netherlands 

(23.1%) – all national data

•  Vlaams Gewest in Belgium (24.8%; NUTS level 1)
•  Luxembourg (23.7 %).

The share of enterprises using AI technologies was 
particularly low in many of the same regions that were 
characterised by low levels of workplace use of the 
internet. This suggests that factors – such as the structural 
composition of regional economies or infrastructure 
gaps – may account for some of the differences observed. 
In 2024, the lowest enterprise adoption rates for AI were 
generally recorded in regions located across Bulgaria, 
Spain, Hungary, Romania (2023 data) and Poland. There 
were only 8 regions across the EU that had less than 2.5% 
of all enterprises making use of AI, including: 

•  7 out of the 8 regions in Romania (the exception being 
the capital region of Bucureşti-Ilfov), with the lowest 
shares in Sud-Vest Oltenia (0.2%), Sud-Est (0.4%), Nord-
Vest (0.8%) and Nord-Est (0.9%)

•  the Spanish autonomous region of Ciudad de Melilla 

(1.5%).

There were considerable inter-regional variations in the 
share of enterprises making use of AI. In 2024, the largest 
absolute differences were in: 

•  Denmark, from a high of 35.0% in Midtjylland to a low 
of 18.8% in the neighbouring region of Nordjylland, a 
difference of 16.2 percentage points

•  Spain, from a high of 15.6% in Comunidad de Madrid 
to a low of 1.5% in Ciudad de Melilla, a difference 
of 14.1 percentage points

•  Austria, from a high of 26.5% in the capital region of 
Wien to a low of 12.7% for the easternmost region of 
Burgenland, a difference of 13.8 percentage points.

There were also considerable differences in relative terms, 
for example: 

•  in Romania, the highest share in Bucureşti-Ilfov (3.6%) 
was 18 times as high as the lowest share in Sud-Vest 
Oltenia (0.2%).

Eurostat regional yearbook – 2025 edition120A

Digital society

Map 6.7: Enterprise use of artificial intelligence
(% of enterprises, by NUTS 2 regions, 2024)

Note: covers enterprises with 10 or more people employed. Belgium: NUTS 
level 1. Czechia, Germany, Ireland, Greece, France, Italy, the Netherlands, 
Poland, Portugal, Finland, Sweden, Serbia and Türkiye: national data. 
Romania: 2023.

Source: Eurostat (online data codes: isoc_r_eb_ain2 and isoc_eb_ain2)

121Eurostat regional yearbook – 2025 editionBEconomy and finance

7. Economy

In February 2022, Russia launched a war of aggression 
against Ukraine, prompting ongoing financial, humanitarian 
and military support to Ukraine from the EU. In July 2022, 
the European Central Bank (ECB) raised its key interest rates 
for the 1st time in 11 years, starting a period of successive 
interest rate hikes. By late 2022, euro area inflation was in 
double digits, driven by rising energy costs and supply chain 
disruptions. However, inflation declined significantly during 
the 2nd half of 2023, easing economic pressures. 

Despite the geopolitical situation and several atypical 
economic shocks – such as the global financial and 
economic crisis, the European sovereign debt crisis, Brexit 
and the COVID-19 pandemic – the EU’s economy grew 
overall, in real terms, by 24.3% between 2008 and 2023. 
That said, economic activity increased at a modest pace 
across the EU in 2023. Gross domestic product (GDP) 
grew 0.4% in real terms, which was considerably lower 

than in either of the previous 2 years (following a rebound 
of 6.4% in 2021 and further growth of 3.5% in 2022). In 
the aftermath of the COVID-19 pandemic, the attention 
of policymakers and economists turned to long-term, 
structural challenges, such as population ageing, climate 
change, weak productivity growth, the cost-of-living crisis, 
rising income and wealth inequality, as well as territorial 
disparities within and among EU countries. 

The infographic below shows the EU regions with the 
highest annual growth rates in 2023. The Mediterranean 
island region of Malta saw its economic activity rise 6.7% 
in real terms. There were 4 other regions where GDP 
increased by at least 5.0%: Severen tsentralen in Bulgaria 
(up 5.8%), the Spanish island regions of Illes Balears 
(up 5.7%) and Canarias (up 5.1%), as well as the Danish 
capital region of Hovedstaden (up 5.0%). 

(% annual change in real terms, by NUTS 2 regions, 2023)

Source: Eurostat (online data code: nama_10r_2gvagr)

Regional gross domestic product 
(GDP)

The EU’s GDP at market prices was €17.2 trillion in 2023, 
equivalent to an average of €38 100 per inhabitant. 
Behind these aggregated figures for the whole of the EU, 
the economic performance of individual regions varied 
considerably; these patterns of development are explored 
in more detail below. 

LARGEST REGIONAL ECONOMIES IN THE EU 

In 2023, there were 10 NUTS level 2 regions within the 
EU where GDP was in excess of €250 billion. The capital 
region of France – Ile-de-France – had, by far, the largest 
regional economy (€860 billion of GDP), followed by the 
northern Italian region of Lombardia (€490 billion) and 
the southern German region of Oberbayern (€350 billion). 
The other 7 regions within this group all had levels of GDP 
within the range of €250 billion to €300 billion: Comunidad 
de Madrid (Spain), Eastern and Midland (Ireland), Rhône-

Eurostat regional yearbook – 2025 edition124B

Economy

Alpes (France), Cataluña (Spain), Düsseldorf, Stuttgart 
and Darmstadt (all Germany). These 10 regions with the 
highest levels of GDP collectively accounted for 21.1% of 
the EU’s economic output. By comparison, the 137 regions 
across the EU with the lowest levels of GDP accounted for 
approximately the same proportion of output (21.0%). 

In 2023, the Eastern and Midland region of Ireland had a 
level of GDP per inhabitant that was 2.5 times as high as 
the EU average 

In 2023, the highest levels of GDP per inhabitant in PPS 
were located in: 

More about the data: 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 evaluating overall living standards 
and/or competitiveness, through the derived indicator 
of GDP per inhabitant. 

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

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 data being denominated in an artificial common 
currency unit called a purchasing power standard (PPS). 
In contrast to euro-based (€) series, a series denominated 
in PPS 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. 

Regional economic statistics are usually reported in current 
price (or ‘nominal’) terms; in other words, their current 
value during the particular reference year in question. To 
make comparisons over time, it is usually more revealing to 
use data in constant price (or real) terms, where values are 
adjusted to take account of price changes; in other words, 
they have been deflated. During periods of high inflation 
– such as the recent cost-of-living crisis – a time series 
presented in current price terms will show faster growth 
than a series in constant price terms. For example, imagine 
GDP rose from 1 year to the next from €100.0 billion to 
€110.0 billion, while inflation was 8.0%. In constant price 
terms using the prices of the 1st year, GDP in the 2nd year 
would be €101.85 billion. This results in a growth rate 
of 1.85% in real terms, compared with a 10.0% growth rate 
in nominal (or current price) terms. 

•  isolated regions, including most of the capital regions of 
EU countries and several other regions located in Ireland, 
Spain and France

•  a band of regions running from the Nordic EU countries, 
down through Germany and the Benelux countries into 
Austria and northern Italy.

Map 7.1 is based on regional GDP per inhabitant (adjusted 
for purchasing power and then shown as an index 
based on a percentage of the EU average). The regional 
distribution of GDP per inhabitant was relatively skewed 
insofar as fewer than 40% of NUTS level 2 regions – 
or 92 out of 244 regions for which data are available – 
reported a level of GDP per inhabitant in 2023 that was 
equal to or above the EU average (as shown by the teal 
shades in Map 7.1). 

In 2023, there were 16 NUTS level 2 regions across the EU 
where GDP per inhabitant was at least 50% above the EU 
average – as shown by the darkest shade of teal in Map 7.1. 
Among these relatively ‘wealthy’ regions, Eastern and 
Midland – the Irish capital region – had the highest level of 
regional GDP per inhabitant, with a ratio that was 2.5 times 
as high as the EU average. There were 2 other regions with 
ratios that were more than 2.0 times as high as the EU 
average: Luxembourg (2.4) and the Irish region of Southern 
(2.3). The remainder of this group of 16 regions with 
relatively high levels of GDP per inhabitant included: 

•  9 capital regions, namely those of Czechia, Belgium, 

Romania, the Netherlands, Denmark, Hungary, France, 
Poland and Sweden

•  the German regions of Hamburg and Oberbayern
•  the northern Italian region of Provincia Autonoma di 

Bolzano/Bozen

•  the Dutch region of Utrecht.

In the vast majority of EU countries, the capital region 
had the highest level of GDP per inhabitant in 2023. 
The 3 exceptions were: 

•  Germany, where Hamburg, Oberbayern, Darmstadt, 

Stuttgart, Bremen, Mittelfranken, Karlsruhe and 
Braunschweig recorded higher levels than the capital 
region of Berlin, while Tübingen and Oberpfalz had the 
same level

•  Italy, where Provincia Autonoma di Bolzano/Bozen, 
Lombardia, Valle d’Aosta/Vallée d’Aoste, Provincia 
Autonoma di Trento and Emilia-Romagna recorded 
higher levels than the capital region of Lazio

•  Austria, where Salzburg recorded a higher level than the 

capital region of Wien.

125Eurostat regional yearbook – 2025 editionB

Economy

More about the data: comparing GDP per 
inhabitant across EU regions 

among other possible advantages, housing and other 
costs may be lower. 

Some of the economic differences between regions 
may reflect the (sometimes artificial) administrative 
boundaries that are used to delineate regions. It is often 
the case that part of the income generated in ‘wealthy‘ 
regions may be attributed to labour input from 
commuters who live in surrounding regions where, 

In addition, some regions with high levels of GDP are 
characterised by a strong presence of multinational 
enterprises. This may distort their levels of economic 
activity, especially if assets (for example, technology 
patents) are domiciled in a region. For example, the 
Southern region of Ireland hosts several of the world’s 
top technology and pharmaceutical businesses. 

Map 7.1: GDP per inhabitant
(index relative to EU average = 100, by NUTS 2 regions, 2023)

Note: based on purchasing power standards (PPS). Norway and Albania: 
2021.

Source: Eurostat (online data codes: nama_10r_2gdp and nama_10_pc)

Map 7.1 also highlights that many of the regions with 
relatively low levels of GDP per inhabitant are concentrated 
in the south-east corner of Europe or outermost regions 
of the EU. In 2023, 11 NUTS level 2 regions across the EU 
recorded GDP per inhabitant that was less than 50% of the 
EU average; they are shown by the darkest shade of gold. 
Bulgaria and Greece each had 3 such regions, while France 

and Hungary each had 2, and Romania had a single region. 
The French outermost region of Mayotte reported the 
lowest level of regional GDP per inhabitant, at 28% of the 
EU average. The next lowest ratios were in the Bulgarian 
regions of Yuzhen tsentralen (41%) and Severozapaden 
(42%), another French outermost region, Guyane (42%), 
and the Greek region of Voreio Aigaio (also 42%). 

Eurostat regional yearbook – 2025 edition126B

Economy

Dublin had the highest level of GDP per inhabitant in 2023, 
at 139 500 PPS … 

Map 7.2 provides a similar analysis of GDP per inhabitant 
in PPS, based on more detailed NUTS level 3 regions. 
The increased detail/granularity of the data allows a finer 
analysis of economic disparities, for example, identifying 
how seemingly ‘wealthier’ cities differ from their 
surrounding areas. 

Many of the regions with high levels of GDP per inhabitant 
in 2023 are characterised by advanced economic 
development, serving as important economic hubs, where 
innovation thrives, and wealth is concentrated. 

• The Irish regions of Dublin (139 500 PPS) and South-

West (137 300 PPS) recorded the highest levels of GDP
per inhabitant among NUTS level 3 regions in the EU;
both regions are home to numerous multinational
enterprises in sectors such as information technology,
pharmaceuticals and medical devices.

• The German regions of Wolfsburg, Kreisfreie Stadt

(136 500 PPS) and Ingolstadt, Kreisfreie Stadt
(128 600 PPS) had the next highest levels of GDP per
inhabitant (2022 data); both of these regions are major
hubs for the motor vehicle manufacturing.

• Paris in France (126 900 PPS) and München, Landkreis in
Germany (119 500 PPS; 2022 data) were the only other
NUTS level 3 regions in the EU to report levels of GDP
per inhabitant that were more than 3.0 times as high as
the EU average.

Map 7.2: GDP per inhabitant
(purchasing power standards (PPS), by NUTS 3 regions, 2023)

Note: Iceland, national data. Germany, Greece, Spain, Italy, Austria, Poland 
and Serbia: 2022. Norway and Albania: 2021.

Source: Eurostat (online data code: nama_10r_3gdp)

127Eurostat regional yearbook – 2025 editionB

Economy

… while the French outermost region of Mayotte had the 
lowest level, at 10 500 PPS 

At the opposite end of the spectrum, the NUTS 
level 3 regions with the lowest levels of GDP per inhabitant 
were primarily rural areas, less economically developed 
compared with major urban centres. In 2023, most of the 
regions with the lowest levels of GDP per inhabitant were 
located across Bulgaria, Greece (2022 data) and Romania. 
However, there were also single regions from France, 
Hungary and Poland where economic activity fell below 
the level of 15 000 PPS per inhabitant (as shown by a 
yellow shade in Map 7.2). 

• The French outermost region of Mayotte recorded the

lowest level of GDP per inhabitant, at 10 500 PPS.
• The Bulgarian regions of Sliven, Silistra and Haskovo
– characterised by limited investment, a focus on
traditional agricultural or industrial sectors, and a
peripheral location – reported levels of GDP per
inhabitant ranging from 11 000 PPS to 11 400 PPS.

In 2023, Champagne-Ardenne in France was the most 
specialised region in the EU for agriculture, forestry and 
fisheries 

Agriculture, forestry and fisheries play a crucial role in many 
rural regions of the EU. These regions leverage abundant 
natural resources to shape their local economies, preserve 
cultural traditions, and support both economic and 
environmental sustainability. For example: 

• Toscana and Puglia in Italy are renowned for their olive

oil production

• Normandie and Bretagne in France are known for their

dairy/livestock farming

• several regions across the Baltic and Nordic EU countries

are specialised in forestry

• Galicia and Principado de Asturias in north-western

Spain are among the most important fishing regions in
the EU.

In 2023, agriculture, forestry and fisheries contributed 1.8% 
to the EU’s total gross value added. Figure 7.1 shows 
the relative share of these sectors in the total economic 
activity of NUTS level 2 regions. There were 6 regions 
within the EU where agriculture, forestry and fisheries 
accounted for more than a tenth of total gross value 
added. 

• Champagne-Ardenne in France (12.7%; 2022 data) had
the highest share, followed by the Portuguese region
of Alentejo (12.2%) and the Croatian region of Panonska
Hrvatska (11.4%).

• The Greek regions of Thessalia and Peloponnisos, and
Mazowiecki regionalny In Poland (which surrounds the
Polish capital; 2022 data) were the only other EU regions
where agriculture, forestry and fisheries contributed a
double-digit share to overall economic activity.

Variations in the share of agriculture, forestry and 
fisheries in total gross value added reflect a range of 
factors, including geography, history, culture, economic 
diversification and government policies. Some regions 
are naturally predisposed for agriculture, such as those 
with rolling plains or fertile soils. By contrast, mountainous 
or arid regions often lack the necessary conditions for 
extensive farming, while densely populated regions 
tend to have seen their agricultural land repurposed for 
housing, commercial use or infrastructure. The largest 
inter-regional differences were observed in: 

• France (2022 data), with a 12.6 percentage point gap,

as Champagne-Ardenne (12.7%) had the highest share,
while the capital region of Ile-de-France (0.1%) had the
lowest

• Portugal, with a difference of 12.0 points, with Alentejo
(12.2%) recording the highest share and the capital
region of Grande Lisboa (0.2%) the lowest.

Eurostat regional yearbook – 2025 edition128Figure 7.1: Regional specialisation in agriculture, forestry and fisheries
(% share of total gross value added, by NUTS 2 regions, 2023)

B

Economy

Note: Germany, Ireland, France, the Netherlands, Poland, Liechtenstein 
and Serbia, 2022. Norway, Switzerland and Albania: 2021. Innlandet (NO02), 
Oslo og Viken (NO08) and Agder og Sør-Østlandet (NO09): not available.

Source: Eurostat (online data codes: nama_10r_3gva and nama_10_a10)

129Eurostat regional yearbook – 2025 editionB

Economy

Income

Having analysed GDP from the output side, this section 
focuses on income. Information is presented for primary 
income (from paid work and self-employment, as well 
as from interest, dividends and rents) and for disposable 
income. 

HOUSEHOLD PRIMARY INCOME 
RELATIVE TO GDP 

More about the data: analysing regional 
statistics for net primary income 

Within regional accounts, GDP is recorded where it is 
generated (a person’s place of work), whereas income 
is recorded at their place of residence (where people 
live). As a result, the ratio of net primary income to GDP 
is generally lower in regions that concentrate economic 
activity, such as capital regions and other major urban/
metropolitan centres, and higher in regions where 
inter-regional commuters – people who work and live 
in 2 different regions – live. 

Furthermore, the kind of economic activity that takes 
place in a region affects the relationship between net 
primary income and GDP. For regions specialised in 
capital (tangible or intangible) intensive activities, the 
remuneration of workers will generally constitute a 
lower share of the value added than in regions that are 
specialised in more labour intensive activities. 

Regional statistics on primary income are available for 
all the vast majority of NUTS level 2 regions in the EU; 
the only exceptions are Utrecht and Zuid-Holland (both 
in the Netherlands). 

Primary income includes earnings from wages/salaries and 
self-employment, as well as income from investments like 
interest, dividends and rents; it reflects household income 
before taxes and transfers. In 2022, EU households received 
a total of €10.2 trillion of primary income, while the EU’s 
GDP was €16.1 trillion. As such, average household primary 
income was 63.1% of GDP. This ratio was somewhat 
skewed, insofar as 89 out of 244 NUTS level 2 regions for 
which data are available had a ratio that was lower than 
the EU average. 

In capital regions, the impact of commuting often results in 
relatively low ratios of household primary income to GDP 

Across the EU, there were 16 NUTS level 2 regions where 
household primary income accounted for less than 50.0% 
of GDP (as shown by a yellow shade in Map 7.3). 

• A majority of these regions were capital regions,

including those of Belgium, Czechia, Denmark, Ireland,
Croatia, Hungary, Poland, Portugal and Slovakia.
• Other regions in this group included Övre Norrland
(Sweden), Sud-Vest Oltenia (Romania), Hamburg
(Germany), Groningen (the Netherlands), Yugoiztochen
(Bulgaria), Luxembourg and Southern (Ireland).

The lowest ratio of primary income relative to GDP among 
NUTS level 2 regions was recorded in the Southern region 
of Ireland (at 26.6% in 2022). The low value likely reflects 
Southern being the European headquarters for several 
multinational enterprises in sectors such as information 
technology and pharmaceuticals. The low ratios recorded in 
other regions reflect, at least to some degree, a commuting 
impact. 

•  Many people live outside capital regions but work within

them, meaning that GDP is generated in the capital
region, while income is accounted for in neighbouring
regions. For example, the regional economy of the
Czech capital region of Praha benefits from inflows of
commuters from the surrounding region of Střední Čechy.

• In a similar vein, the relatively high level of GDP per
inhabitant in Luxembourg may, at least in part, be
attributed to the impact of cross-border commuters
from the neighbouring regions of Prov. Luxembourg
(Belgium), Trier (Germany) and Lorraine (France).

In 2022, the highest ratios of household primary income 
to GDP were recorded in the Belgian region of Prov. 
Luxembourg and the German region of Trier; a relatively 
high share of their resident populations are employed in 
Luxembourg 

At the other end of the range, there were 16 NUTS 
level 2 regions where the ratio of household primary 
income relative to GDP was equal to or above 80.0% 
in 2022 (as shown by the darkest shade of blue in Map 7.3). 

• The highest ratio was recorded in the Belgian region
of Prov. Luxembourg (at 94.2%), followed closely by
the German region of Trier (94.0%); as noted above,
this reflects many residents working in neighbouring
Luxembourg, while their income is recorded in their
home region.

• There were 3 other regions that had ratios higher

than 90.0%:

◦ the German region of Lüneburg (93.0%), where many

residents worked in Hamburg

◦ the Portuguese region of Península de Setúbal (92.7%),

where many residents worked in Grande Lisboa.

• the Greek region of Ionia Nisia (92.1%), where a relatively
high share of income comes from remittances sent by
emigrants abroad and/or pensions received from other
EU countries.

Eurostat regional yearbook – 2025 edition130Map 7.3: Household primary income relative to GDP
(% of GDP, by NUTS 2 regions, 2022)

B

Economy

Note: Norway, 2020.

Source: Eurostat (online data codes: nama_10r_2hhinc and 
nama_10r_2gdp) 

NET PRIMARY INCOME PER INHABITANT 

As noted above, economic hubs across the EU drive wealth 
creation, with part of their generated output attributed to 
commuters who live in surrounding regions. As a result, 
income per inhabitant in these surrounding regions tends 
to be relatively high when contrasted with their economic 
output (as measured by GDP per inhabitant). 

In 2022, EU primary income per inhabitant 
averaged 22 700 PPS. The use of data in PPS, rather than in 
euro (€), takes account of price level differences between 
countries; the conversion to PPS takes into account the fact 
that household expenditure is predominantly related to 
consumption. 

4 German regions – Oberbayern, Hamburg, Stuttgart 
and Darmstadt – and Utrecht in the Netherlands had the 
highest levels of net primary income per inhabitant in 2022 

There were 26 NUTS level 2 regions with a ratio of net 
primary income per inhabitant of at least 30 000 PPS in 2022. 

• These regions were primarily located in Germany
(15 regions), with some of the highest levels of net
primary income per inhabitant recorded in Oberbayern
(42 100 PPS), Hamburg (35 400 PPS), Stuttgart
(34 600 PPS) and Darmstadt (34 100 PPS).
• The Dutch region of Utrecht (36 900 PPS) and

Luxembourg (33 400 PPS) had the highest levels of net
primary income per inhabitant outside of Germany.
• Apart from Luxembourg, there were 4 capital regions

within this group of 26: Ile-de-France (where net primary
income per inhabitant averaged 32 900 PPS), Noord-
Holland (32 200 PPS), Bucureşti-Ilfov (30 900 PPS) and
Stockholm (30 100 PPS).

131Eurostat regional yearbook – 2025 editionB

Economy

It is worth noting that, in euro terms, Luxembourg had 
the highest level of net primary income per inhabitant 
(€45 700). This figure was slightly above the €45 600 per 
inhabitant recorded for Oberbayern, while the Danish 
capital region of Hovedstaden had the 3rd highest ratio 
(€41 900 per inhabitant). The relatively high cost of living 
in both Luxembourg and Hovedstaden meant that they 
ranked 6th and 41st, respectively, when based on data in 
PPS terms (that compensate for price level differences). 

The results above focus on absolute levels of primary 
income. To have a further insight into income distribution, 
Map 7.4 expresses income levels as indices relative to 
national averages, thereby highlighting regional disparities 
in income distributions. The results for 2022 were skewed 
insofar as across the EU there were: 

• 77 regions that had indices above their national average
• 6 regions that had indices equal to their national average
• 161 regions that had indices below their national

average.

The regions with the highest indices in 2022 and therefore 
a high concentration of net primary income per inhabitant 
were generally capital regions and/or economic hubs. 
This pattern was particularly pronounced in eastern and 
southern EU countries, for example, net primary income 
per inhabitant was: 

• 2.3 times as high as the Romanian average in Bucureşti-

Ilfov

• 1.7 times as high as the Slovakian average in Bratislavský

kraj

• 1.6 times as high as the Hungarian and the Bulgarian

averages in Budapest and Yugozapaden

• 1.5 times as high as the Italian average in Provincia

Autonoma di Bolzano/Bozen.

There were 23 NUTS level 2 regions where the ratio of net 
primary income per inhabitant was at least 20.0% above 
the national average in 2022 (they are shown with a dark 
teal shade in Map 7.4). They included: 

• 14 capital regions, with Ile-de-France (France), Sostinės
regionas (Lithuania), Stockholm (Sweden) and Helsinki-
Uusimaa (Finland) the only capital regions from outside
of eastern or southern EU countries

• 3 regions in northern Italy – Provincia Autonoma di

Bolzano/Bozen, Lombardia and Emilia-Romagna – all
of which recorded higher net primary income per
inhabitant than the capital region of Lazio

• Oberbayern in southern Germany, which had a higher
level of net primary income per inhabitant than the
capital region of Berlin

• Utrecht in the Netherlands, which had a higher level

of net primary income per inhabitant than the capital
region of Noord-Holland

• País Vasco in Spain, which had a relatively high

concentration of net primary income per inhabitant,
though below that recorded in the capital region of
Comunidad de Madrid

• Prov. Vlaams-Brabant in northern Belgium, which had a
higher level of net primary income per inhabitant than
the capital region of Région de Bruxelles-Capitale /
Brussels Hoofdstedelijk Gewest

• the Greek regions of Notio Aigaio and Ionia Nisia, which

had relatively high concentrations of net primary income
per inhabitant, though below that recorded in the
capital region of Attiki.

As outlined above, there were approximately twice as 
many regions (161 out of 244 for which data are available 
across the EU) that recorded net primary income per 
inhabitant below (rather than above) their national average 
in 2022. This may be linked, among others, to factors such 
as geography, demographic patterns, the distribution 
of education and skills, economic diversification/
concentration or regional patterns of innovation and 
investment. 

At the bottom end of the distribution, there were 20 NUTS 
level 2 regions across the EU where net primary income 
per inhabitant in 2022 was less than 75.0% of the national 
average (as shown by the darkest shade of gold in Map 7.4). 
This group of 20 regions was composed of: 

• 5 regions from southern Italy, with the lowest index

recorded in Calabria

• 4 regions from Bulgaria, with the lowest index recorded

in Severozapaden

• 4 outermost regions from France, with the lowest

index – both in France and across the whole of the EU –
recorded in Mayotte

• 3 regions from Romania, with the lowest index recorded

in Nord-Est

• 2 regions from eastern Germany, with the lowest indices

recorded in Chemnitz and Sachsen-Anhalt

• single regions from Spain (Extremadura) and Hungary

(Észak-Magyarország).

Among this group of 20 regions, the highest levels of net 
primary income per inhabitant in 2022 were recorded 
in the 2 German regions of Chemnitz and Sachsen-
Anhalt (both 22 000 PPS). While their income levels were 
slightly below the EU average (22 700 PPS), they were 
nevertheless considerably higher than for any other region 
within this group. The next highest income levels were 
recorded in the French outermost regions of Guadeloupe 
(16 400 PPS) and La Réunion (16 100 PPS). At the lower 
end of the distribution, 8 out of this group of 20 had 
levels of net primary income per inhabitant that were 
below 10 000 PPS. Severoiztochen in Bulgaria was the only 
other region in the EU – outside of this group of 20 – to 
record a level of income below this level. 

Eurostat regional yearbook – 2025 edition132Map 7.4: Net primary income per inhabitant
(index relative to national average = 100.0, by NUTS 2 regions, 2022)

B

Economy

Note: based on purchasing power standards (PPS). Norway: 2020.

Source: Eurostat (online data codes: nama_10r_2hhinc)

Table 7.1 shows the annual change in household 
primary income and the compensation of employees 
for 2022. These rates of change provide an opportunity 
to understand the dynamics of income distribution, the 
health of the labour market and shifts in the sources of 
household income. If the annual change in household 
primary income is rising faster, this suggests that income 
from non-labour sources (for example, investments 

or social transfers) is growing more rapidly than the 
compensation of employees, which may be an indication 
of rising inequalities and/or a shift in how households are 
earning their income. Conversely, if the compensation of 
employees grows at a faster rate than household primary 
income, this suggests that wages are driving income 
growth in households. 

133Eurostat regional yearbook – 2025 editionB

Economy

Table 7.1a: Household primary income
(annual change in %, by NUTS 2 regions, 2022)

Region with highest rate of change

EU / national 
average

Region with lowest rate of change

EU

Yugozapaden (BG41)

Belgium

Bulgaria

Czechia

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Bruxelles-Capitale / Brussels 
Hoofdstedelijk (BE10) 

Yugozapaden (BG41)

Střední Čechy (CZ02)

Hovedstaden (DK01)

Mecklenburg-Vorpommern (DE80)

Eastern and Midland (IE06)

Notio Aigaio (EL42)

Illes Balears (ES53)

Mayotte (FRY5)

Sjeverna Hrvatska (HR06)

Provincia Autonoma di Bolzano/Bozen 
(ITH1)

–

–

Lithuania

Sostinės regionas (LT01)

Luxembourg

–

Hungary

Pest (HU12)

Malta

–

Netherlands

Flevoland (NL23)

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

Salzburg (AT32)

Mazowiecki regionalny (PL92)

Algarve (PT15)

Bucureşti-Ilfov (RO32)

Vzhodna Slovenija (SI03)

Bratislavský kraj (SK01)

Helsinki-Uusimaa (FI1B)

Stockholm (SE11)

21.0 

12.2 

21.0 

20.1 

6.4 

9.9 

12.0 

15.3 

18.6 

8.0 

14.7 

8.3 

–

–

15.7 

–

14.8 

–

9.2 

10.9 

15.9 

17.7 

18.3 

9.2 

11.3 

8.1 

1.9 

8.0 

10.8 

16.2 

16.0 

5.5 

8.1 

13.8 

11.2 

6.9 

8.4 

6.8 

12.9 

6.9 

13.5 

12.0 

15.3 

7.2 

11.0 

7.9 

7.5 

8.9 

11.8 

9.4 

13.6 

9.1 

10.4 

6.0 

1.1 

−4.3 

Åland (FI20) 

9.5 

Prov. Hainaut (BE32) 

5.4 

13.0 

4.3 

7.0 

10.2 

3.4 

4.9 

1.9 

11.1 

Severozapaden (BG31) 

Praha (CZ01) 

Sjælland (DK02) 

Darmstadt (DE71) 

Northern and Western (IE04) 

Anatoliki Makedonia, Thraki (EL51) 

Extremadura (ES43) 

Limousin (FRI2) 

Panonska Hrvatska (HR02) 

4.8 

Friuli-Venezia Giulia (ITH4) 

–

–

–

–

15.1 

Vidurio ir vakarų Lietuvos regionas (LT02) 

–

5.9 

–

6.2 

7.7 

9.8 

6.7 

8.0 

9.0 

9.3 

−4.3 

0.1 

–

Dél-Alföld (HU33) 

–

Groningen (NL11) 

Vorarlberg (AT34) 

Warszawski stołeczny (PL91) 

Região Autónoma dos Açores (PT20) 

Sud-Est (RO22) 

Zahodna Slovenija (SI04) 

Stredné Slovensko (SK03) 

Åland (FI20) 

Småland med öarna (SE21) 

Note: Estonia, Cyprus, Latvia, Luxembourg and Malta do not have any 
regional breakdown at NUTS level 2.

Source: Eurostat (online data code: nama_10r_2hhinc)

In 2022, the highest growth rate for household 
primary income was in the Bulgarian capital region of 
Yugozapaden, up 21.0% ... 

In 2022, household primary income across the EU 
increased 8.0%, while the compensation of employees 
was 6.2% higher; these values are in current price 
terms and therefore do not take into account price 
changes during the period under consideration. Across 
NUTS level 2 regions, the Bulgarian capital region of 
Yugozapaden recorded the highest growth rate for 
household primary income (up 21.0%), followed by the 
Czech region of Střední Čechy (up 20.1%) that encircles the 
capital region of Praha. Household primary income also 
rose at a rapid pace in: 

• several tourism-dependent regions – such as Illes Balears
(Spain) and Algarve (Portugal) – which had experienced
a low level of economic activity during 2021 due to the
COVID-19 crisis

• several capital regions – for example, Bucureşti-Ilfov

(Romania), Sostinės regionas (Lithuania) and Budapest
(Hungary)

• a number of regions that encircle capital regions – for
example, Mazowiecki regionalny (Poland) and Pest
(Hungary)

• the remaining regions of Czechia, as the slowest rate
of change was recorded in the capital region of Praha
(where household primary income nevertheless grew
by 13.0%).

Eurostat regional yearbook – 2025 edition134B

Economy

Table 7.1b: Compensation of employees
(annual change in %, by NUTS 2 regions, 2022)

Region with highest rate of change

EU / national 
average

Region with lowest rate of change

EU

Vidurio ir vakarų Lietuvos regionas (LT02)

17.9 

Belgium

Bulgaria

Czechia

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Bruxelles-Capitale / Brussels 
Hoofdstedelijk (BE10) 

Yugozapaden (BG41)

Střední Čechy (CZ02)

Hovedstaden (DK01)

Trier (DEB2)

–

Eastern and Midland (IE06)

Notio Aigaio (EL42)

Illes Balears (ES53)

Provence-Alpes-Côte d’Azur (FRL0)

Jadranska Hrvatska (HR03)

Valle d’Aosta/Vallée d’Aoste (ITC2)

–

–

Lithuania

Vidurio ir vakarų Lietuvos regionas (LT02)

Luxembourg

–

Hungary

Pest (HU12)

Malta

–

Netherlands

Flevoland (NL23)

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

Tirol (AT33)

Śląskie (PL22)

Algarve (PT15)

Nord-Vest (RO11)

Zahodna Slovenija (SI04)

Bratislavský kraj (SK01)

Helsinki-Uusimaa (FI1B)

Stockholm (SE11)

10.6 

16.0 

13.9 

7.3 

8.8 

–

10.4 

11.3 

15.9 

9.0 

16.5 

8.0 

–

–

17.9 

–

12.4 

–

9.2 

9.8 

11.7 

15.0 

16.8 

8.0 

11.0 

12.4 

2.1 

6.2 

9.4 

14.8 

11.6 

6.7 

6.0 

13.3 

9.6 

4.7 

8.6 

7.3 

14.5 

6.2 

11.4 

12.3 

16.5 

8.4 

9.5 

8.9 

7.2 

7.8 

10.3 

9.8 

14.6 

7.7 

7.7 

6.8 

1.2 

−1.3 

Åland (FI20) 

8.4 

Prov. Luxembourg (BE34) 

12.0 

Severen tsentralen (BG32) 

6.3 

6.0 

5.3 

–

8.4 

2.1 

5.6 

0.8 

12.7 

3.7 

–

–

Praha (CZ01) 

Sjælland (DK02) 

Sachsen-Anhalt (DEE0) 

–

Northern and Western (IE04) 

Anatoliki Makedonia, Thraki (EL51) 

Ciudad de Ceuta (ES63) 

Limousin (FRI2) 

Panonska Hrvatska (HR02) 

Friuli-Venezia Giulia (ITH4) 

–

–

14.3 

Sostinės regionas (LT01) 

–

6.1 

6.0 

6.6 

8.4 

7.3 

11.8 

7.5 

5.7 

−1.3 

−0.1 

–

Dél-Alföld (HU33) 

–

Groningen (NL11) 

Niederösterreich (AT12) 

Warszawski stołeczny (PL91) 

Região Autónoma dos Açores (PT20) 

Sud-Vest Oltenia (RO41) 

Vzhodna Slovenija (SI03) 

Stredné Slovensko (SK03) 

Åland (FI20) 

Mellersta Norrland (SE32) 

Note: Estonia, Cyprus, Latvia, Luxembourg and Malta do not have any 
regional breakdown at NUTS level 2.

Source: Eurostat (online data code: nama_10r_2hhinc)

… while the Lithuanian region of Vidurio ir vakarų Lietuvos 
regionas had the highest growth rate for employee 
compensation, up 17.9% 

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). Most EU regions with 
high annual growth rates for household primary income 
also had relatively high growth rates for the compensation 
of employees. In 2022, there were 8 NUTS level 2 regions in 
the EU where the compensation of employees increased 
by at least 15.0%. They included: 

•  Vidurio ir vakarų Lietuvos regionas in Lithuania, which 

had the highest growth rate (up 17.9%)
•  Nord-Vest and Bucureşti-Ilfov in Romania
•  Yugozapaden and Severozapaden in Bulgaria
•  the tourism-focused regions of Jadranska Hrvatska 

(Croatia), Illes Balears and Algarve.

Among NUTS level 2 regions, the autonomous Finnish 
archipelago of Åland was the only region in the EU to 
experience a decline in household primary income in 2022 
(down 4.3%). Åland also recorded the lowest regional 
rate of change for the compensation of employees 
(down 1.3%). The northern Swedish region of Mellersta 
Norrland was the only other EU region to record a 
negative change, albeit modest, with the compensation of 
employees down 0.1%. 

135Eurostat regional yearbook – 2025 editionB

Economy

Productivity and investment 
indicators

This final section provides further information about 
the compensation of employees (detailing the level of 
remuneration per hour worked), alongside information 
on labour productivity (defined here as gross value 
added per person employed); these indicators may 
be used to analyse patterns/developments of regional 
competitiveness. 

COMPENSATION OF EMPLOYEES 

One of the principal areas of interest/concern for many 
employees is their level of remuneration; this has become 
an even greater preoccupation during the cost-of-living 
crisis. The data presented in Table 7.2 refer to gross (in 
other words, before tax) hourly compensation in euro (€). 

Luxembourg had the highest level of employee 
compensation, €55.0 per hour in 2022 

In 2022, an employee working in the EU received, on 
average, gross compensation of €26.6 for every hour 
that they worked. Luxembourg had the highest level of 
compensation across NUTS level 2 regions in the EU, at 
€55.0 per hour. At the other end of the distribution, the 
Romanian region of Nord-Est had the lowest level of 
compensation (€6.2 per hour). As such, the ratio between 
the highest and lowest levels of employee compensation 
was 8.9 : 1. The distribution of employee compensation 
was symmetrically centred on the EU average, insofar 
as 122 regions had values that were higher than the 
mean, while 121 regions had values that were below; 
there was 1 region that had the same level of employee 
compensation – the Spanish capital region of Comunidad 
de Madrid. 

At the top end of the distribution, there were 26 NUTS 
level 2 regions in the EU where the average level of 
employee compensation was at least €40.0 per hour 
in 2022. These regions were principally concentrated in 
Belgium (8 regions), Germany (also 8 regions), Denmark 
(all 5 regions), the Netherlands (3 regions) and also 
included Luxembourg, as well as the capital region of Ile-
de-France (France). 

There were 41 NUTS level 2 regions where employee 
compensation was less than €10.0 per hour worked 
in 2022. They were predominantly located across eastern 
EU countries and included: 

• 16 of the 17 regions from Poland, the exception being

the capital region of Warszawski stołeczny

• 7 of the 8 regions from each of Hungary and in Romania,
the exceptions being the capital regions of Budapest
and Bucureşti-Ilfov

• 5 of the 6 regions from Bulgaria, the exception being the

capital region of Yugozapaden

• Panonska Hrvatska and Sjeverna Hrvatska that are both

located in Croatia

• the only exceptions – from outside of eastern EU

countries – were 4 Greek regions, namely, Voreio Aigaio,
Ipeiros, Notio Aigaio and Kriti.

In 2022, the only multi-regional EU countries where 
capital regions did not have the highest level of employee 
compensation were Germany, Greece, Spain and Italy 

Table 7.2 confirms that the highest regional levels of 
employee compensation were usually recorded in capital 
regions. This pattern was repeated in 18 of the 22 multi-
regional EU countries. This is unsurprising given the high 
cost of living in many capitals, while most also play an 
important role as the location for company headquarters, 
financial services and national administrations, which 
tend to offer above average levels of compensation. The 
only exceptions, where the highest level of employee 
compensation was not recorded in the capital region, were: 

• Oberbayern in Germany (€46.2 per hour)
• Dytiki Makedonia in Greece (€13.0 per hour)
• País Vasco in Spain (€27.0 per hour)
• Provincia Autonoma di Bolzano/Bozen in Italy (€30.4 per

hour).

Several EU countries had regional distributions of 
employee compensation that were heavily skewed. This 
was generally because the capital region had a much 
higher level of compensation than other regions of the 
same country. For example: 

• someone working in the Romanian capital region of
Bucureşti-Ilfov (€13.6 per hour) could expect to earn
more than twice as much as an someone working
in Nord-Est (€6.2 per hour), where the lowest level of
compensation in Romania was recorded

• someone working in the Polish capital region of

Warszawski stołeczny (€14.2 per hour) could expect to
earn more than twice as much as an someone working
in Warmińsko-mazurskie (€6.6 per hour), where the
lowest level of compensation in Poland was recorded.

Eurostat regional yearbook – 2025 edition136B

Economy

Table 7.2: Compensation of employees
(€ per hour worked, by NUTS 2 regions, 2022)

Region with highest value

EU / national 
average

Region with lowest value

EU

Luxembourg (LU00) 

Belgium

Bulgaria

Czechia

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Bruxelles-Capitale / Brussels 
Hoofdstedelijk (BE10) 

Yugozapaden (BG41) 

Praha (CZ01) 

Hovedstaden (DK01) 

Oberbayern (DE21) 

–

Eastern and Midland (IE06) 

Dytiki Makedonia (EL53) 

País Vasco (ES21) 

Ile-de-France (FR10) 

Grad Zagreb (HR05) 

Provincia Autonoma di Bolzano/Bozen 
(ITH1) 

–

–

Lithuania

Sostinės regionas (LT01) 

Luxembourg

–

Hungary

Budapest (HU11) 

Malta

–

Netherlands

Noord-Holland (NL32) 

Austria

Poland

Wien (AT13) 

Warszawski stołeczny (PL91) 

Portugal

Grande Lisboa (PT1A) 

Romania

Slovenia

Slovakia

Finland

Sweden

Iceland

Norway

Switzerland

Bucureşti-Ilfov (RO32) 

Zahodna Slovenija (SI04) 

Bratislavský kraj (SK01) 

Helsinki-Uusimaa (FI1B) 

Stockholm (SE11) 

–

–

–

Serbia

City of Belgrade (RS11) 

55.0 

52.9 

10.6 

23.7 

49.3 

46.2 

–

37.5 

13.0 

27.0 

47.0 

12.6 

30.4 

–

–

16.9 

–

10.0 

–

45.2 

39.5 

14.2 

16.6 

13.6 

22.8 

19.7 

37.8 

37.7 

–

–

–

7.7 

26.6 

44.6 

8.4 

15.6 

45.0 

38.4 

17.0 

35.5 

10.7 

23.2 

37.0 

10.6 

27.4 

16.7 

12.2 

13.4 

55.0 

9.5 

17.2 

40.0 

36.1 

8.9 

13.8 

8.5 

21.6 

14.6 

34.0 

31.9 

48.9 

50.7 

62.9 

7.3 

6.6 

Yuzhen tsentralen (BG42) 

38.6 

Prov. Luxembourg (BE34)

6.6 

12.0 

41.2 

31.3 

–

31.5 

9.4 

19.6 

31.8 

8.4 

Yuzhen tsentralen (BG42) 

Severozápad (CZ04) 

Sjælland (DK02) 

Chemnitz (DED4); Sachsen-Anhalt (DEE0)

–

Northern and Western (IE04) 

Notio Aigaio (EL42) and Kriti (EL43)

Región de Murcia (ES62) 

Poitou-Charentes (FRI3); Guyane (FRY3)

Panonska Hrvatska (HR02) 

20.8 

Puglia (ITF4) 

–

–

–

–

11.5 

Vidurio ir vakarų Lietuvos regionas (LT02) 

–

–

8.9 

Pest (HU12); Dél-Dunántúl (HU23); Észak-
Alföld (HU32)

–

–

35.9 

30.6 

6.6 

12.3 

6.2 

20.1 

13.0 

31.0 

28.3 

–

–

–

6.5 

Flevoland (NL23) 

Burgenland (AT11) 

Warmińsko-mazurskie (PL62) 

Algarve (PT15); Oeste e Vale do Tejo 
(PT1D)

Nord-Est (RO21) 

Vzhodna Slovenija (SI03) 

Východné Slovensko (SK04) 

Pohjois- ja Itä-Suomi (FI1D) 

Småland med öarna (SE21) 

–

–

–

Region Šumadije i Zapadne Srbije 
(RS21) 

Note: Estonia, Cyprus, Latvia, Luxembourg and Malta do not have any 
regional breakdown at NUTS level 2. Norway and Switzerland: national data.

Source: Eurostat (online data codes: nama_10r_2lp10 and 
nama_10_lp_ulc)

137Eurostat regional yearbook – 2025 editionB

Economy

REAL LABOUR PRODUCTIVITY 

More about the data: analysing regional 
statistics for labour productivity 

Labour productivity can be defined as GDP (or gross 
value added) divided by a measure of labour input, 
typically the number of people employed or the total 
number of hours worked. The information presented 
in Map 7.5 is based on labour productivity per hour 
worked, which should not be influenced by changes in 
the structure of the employment market. For instance, 
the ratio is not impacted if there is a shift from full-time 
to part-time work, or if working hours are curtailed 
(for example, due to labour market restrictions such as 
those imposed during the COVID-19 crisis). 

High 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 levels of employee 
compensation). 

In 2022, people working in the EU generated an average 
of €46.7 in value for each hour worked. This figure, when 
based on GDP per hour worked in 2015 prices, can be 
used to create indices which measure real productivity 
improvements adjusted for inflation; these indices are 
presented relative to the EU average, set at 100.0. 

Map 7.5 shows the development of labour productivity 
across NUTS level 2 regions compared with the EU 
average from 2015 to 2022. If the value for a region 
exceeds 100.0 then that region’s productivity growth 
outpaced the EU average during the period under 
consideration; if a region’s value is below 100.0, then 

that region’s productivity growth lagged behind the 
EU average. As such, the information shown highlights 
changes in labour productivity over time (relative to the 
EU average); the map does not compare absolute levels 
of labour productivity. Regions with an above average 
rate of change either closed the productivity gap with 
the EU average or widened their advantage, while regions 
with a lower than average rate of change either fell 
further behind the EU average or saw their productivity 
advantage narrow. 

Real changes in labour productivity were evenly distributed, 
insofar as 112 (out of 225) NUTS level 2 regions had indices 
that were above the EU average in 2022, the same number 
of regions that had indices that were below the EU average; 
in the Italian region of Provincia Autonoma di Bolzano/
Bozen, labour productivity grew at the same pace as the 
EU average. There were 22 NUTS level 2 regions where real 
labour productivity grew at least 15% faster than the EU 
average between 2015 and 2022 (as shown by the darkest 
shade of teal in Map 7.5). They were predominantly located 
in eastern EU countries with the highest counts in Poland 
(6 regions), Bulgaria (4 regions) and Romania (3 regions), 
while there were also single regions from each of Croatia 
and Hungary. The remainder of this group was composed of 
all 3 regions from Ireland, 2 regions from France, the capital 
region of Lithuania, as well as Estonia. 

Between 2015 and 2022, 14 NUTS level 2 regions across 
the EU experienced a decline in real labour productivity 
or recorded growth that was at least 10% slower than the 
EU average (as indicated by the darkest shade of gold in 
Map 7.5). This group of 14 regions included: 

• 6 regions from Greece
• 6 regions from France, 4 of which were outermost
regions, together with Auvergne and Languedoc-
Roussillon

• 2 regions from Romania, Sud-Muntenia and Sud-Vest

Oltenia.

Eurostat regional yearbook – 2025 edition138Map 7.5: Development of real labour productivity
(index relative to EU average = 100.0, by NUTS 2 regions, 2015–22)

B

Economy

Note: based on € per hour worked in 2015 prices. The map highlights 
changes in labour productivity over time (relative to the EU average), it 
doesn’t compare absolute levels of labour productivity. The Netherlands, 
Portugal, Norway and Switzerland: national data.

Source: Eurostat (online data codes: nama_10r_2rlp, nama_10_gdp and 
nama_10_a10_e)

139Eurostat regional yearbook – 2025 edition8. Business

Presented according to the activity classification NACE, the 
information presented in this chapter is split into 3 parts. 

• The 1st is based on a selection of regional business
demography statistics – indicators from this dataset
include enterprise birth and death rates, as well as the
share of high-growth enterprises.

• The 2nd presents structural business statistics for the

business economy, manufacturing and market services;
these statistics provide valuable information about
regional patterns of specialisation and concentration.
• The 3rd is based on business innovation statistics which

provide information on the introduction of new or
significantly improved goods, services and/or business
processes.

As part of the European Commission’s priorities 
for 2024 to 2029, A new plan for Europe’s sustainable 
prosperity and competitiveness sets out a strategic vision 
for strengthening the EU’s economic resilience, while 
advancing environmental sustainability and social well-
being. This includes: 

• making business easier and faster
• building a clean industrial deal
• working to make business activities more circular and

sustainable

• making the economy more resilient
• increasing productivity with digital technological

diffusion

• putting research and innovation at the heart of the EU

economy

• turbo charging investment
• tackling skills and labour gaps.

In line with these priorities, this year’s edition of the 
Eurostat regional yearbook focuses on knowledge-intensive 
market services, knowledge-intensive high-technology 
services and innovation-active enterprises. 

The Clean Industrial Deal: A joint roadmap for competitiveness 
and decarbonisation (COM(2025) 85 final) will mobilise over 
€100 billion to support clean manufacturing. The European 
Commission will adopt a new framework to accelerate 
the rollout of renewable energy, decarbonise industry 

(% share of regional business economy employment, by NUTS 2 regions, 
2022)

Note: manufacturing activities are covered by NACE Section C. Voreio Aigaio 
(EL41) and Ionia Nisia (EL62): not available. The infographic is based on non-
confidential data (some NACE activities are not available which may impact 
the total calculated for the overall size of the business economy).

Source: Eurostat (online data codes: sbs_r_nuts2021 and sbs_sc_ovw)

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Business

and ensure sufficient manufacturing capacity for clean 
technologies. The aim is to boost demand for EU-made 
clean products by introducing sustainability, resilience 
and ‘made in Europe’ criteria into both public and private 
procurement. 

In 2022, manufacturing activities accounted for 18.7% 
of total employment within the EU’s business economy. 
In the Czech regions of Severovýchod and Střední 
Morava, more than 2 in 5 people were employed in the 
manufacturing sector (see the infographic above). 

Business demography

More about the data: business demography 
statistics 

Business demography statistics are presented for the 
business economy – defined here as NACE Sections 
B to N and P to R and Divisions 95 and 96 – covering 
industry, construction, distributive trades and other 
market services. 

Business demography indicators are presented for 2022; 
the latest information for enterprise death rates is 
provisional. 

High-growth enterprises are of particular interest 
to policymakers insofar as they can improve the 
economic performance of a region, create employment 
opportunities and, if sustained, change its economic 
structure. A high-growth enterprise is an enterprise that 
grows over a specified period of time at a rapid pace 
when measured in terms of its number of employees. In 
this publication, the time period from 2019 to 2022 is used. 
As such, high-growth enterprises are specifically defined 
as those which meet – simultaneously – 2 conditions: 

•  at least 10 employees in 2019
•  average employee growth of more than 10% per year 

between 2019 and 2022.

High-growth enterprises might create new markets 
or compete in existing markets with domestic or 
foreign companies. Therefore, their growth does 
not translate one-to-one into growth of the national 
economy. However, their capacity to create or conquer 
markets correlates positively with the overall economic 
performance of a country. 

For the enterprise birth rate, data are not available for 
NUTS level 2 regions in Belgium, Serbia and Türkiye; 
national data are shown instead. For the enterprise 
death rate, data for Voreio Aigaio and Notio Aigaio in 
Greece refer to 2021 (not 2022). 

Business demography statistics provide information about 
the events in the life cycle of an enterprise. They cover, 
among other stages: 

•  the birth of new enterprises
•  the growth and survival of existing enterprises (with 

particular interest centred on their employment impact)

•  enterprise deaths.

These indicators provide an important insight into business 
dynamics, as new enterprises / fast-growing enterprises 
tend to be very agile and may improve the overall level of 
efficiency and productivity within an economy. 

ENTERPRISE BIRTHS AND DEATHS 

In 2022, the EU’s enterprise birth rate was 10.5% 

The enterprise birth rate measures the number of new 
enterprises born during a year in relation to the total 
population of active enterprises in the same year. In 2022, 
there were 3.4 million enterprise births within the EU’s 
business economy. When expressed relative to the total 
number of enterprises (32.6 million), this equated to an 
enterprise birth rate of 10.5%. 

In 2022, the regional distribution of enterprise birth rates 
for the business economy was relatively skewed insofar 
as 37.2% of NUTS level 2 regions (87 out of 234) reported a 
rate that was equal to or above the EU average, while the 
remaining 62.8% of regions had rates below the average. 
This indicates a tendency for entrepreneurial activity to be 
geographically concentrated in the EU. 

Map 8.1 shows that enterprise birth rates were relatively 
homogeneous in some EU countries, highlighting that the 
underlying national business environment, administrative, 
macro- and socioeconomic conditions likely have an 
important impact. For example, every NUTS level 2 region 
of France (except Limousin), Lithuania and Portugal had an 
enterprise birth rate of at least 12.0%; similarly high rates 
were also recorded in Estonia, Latvia and Malta. 

Predominantly urban regions – especially those in and 
around capital cities – often record the highest enterprise 
birth rates. In 2022, the capital regions of Czechia, Denmark, 
Germany, Ireland, Greece, Italy, Lithuania, Austria, Poland, 
Romania, Slovenia and Finland posted the highest rates 
of enterprise creation in their respective countries. Several 
factors may drive these high rates of business formation, 
including the presence of an innovative environment, 
research clusters, technology hubs, easier market access, 
modern infrastructure, or a concentration of skilled labour. 

In 2022, 23 NUTS level 2 regions across the EU recorded an 
enterprise birth rate of at least 14.0% within their business 
economies (as indicated by the darkest shade of blue in 
Map 8.1). 

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Business

•  The highest rates were observed in Portugal 

(all 9 regions) and France (8 regions). 

 ◦ A peak of 20.5% was recorded in the southern 

Portuguese region of Península de Setúbal; Grande 
Lisboa, Algarve and Região Autónoma da Madeira also 
featured among the 10 EU regions with the highest 
enterprise birth rates.

 ◦ In France, the outermost regions posted the highest 
rates of enterprise creation: Guyane, Martinique and 
Mayotte recorded rates of 19.4%, 19.1% and 18.8%, 
respectively – the 2nd, 3rd and 4th highest among 
EU regions. Outside of its outermost regions, the 
capital region of Ile-de-France had the highest rate of 
business formation in France, at 16.2%.

•  This group of 23 regions with enterprise birth rates of 
at least 14.0% also included all 4 regions in the Baltic 
countries, along with Bucureşti-Ilfov (the capital region 
of Romania) and Malta.

Rural and less economically developed regions often have 
lower enterprise birth rates. This may reflect economies 
which are stable or where existing enterprises successfully 
adapt to change; alternatively, it may reflect barriers to 
entrepreneurial activity, for example, cultural attitudes 
towards risk-taking and entrepreneurship or limited access 
to capital or skilled labour. In 2022, 18 NUTS level 2 regions 
had enterprise birth rates for the business economy 
below 6.5% (they are shown with a yellow shade in Map 8.1). 
These regions were primarily located in Austria (7 regions) 
and Germany (6 regions), with the remainder of this group 
consisting of 3 regions from Italy and 2 from Spain.

Map 8.1: Enterprise birth rate
(% of enterprise births among active enterprises, by NUTS 2 regions, 2022)

Note: in the business economy (defined as NACE Sections B to S, excluding 
Section O and Division 94). Belgium, Serbia and Türkiye: national data.

Source: Eurostat (online data codes: bd_hgnace_r and bd_l_form)

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•

In Austria, the lowest enterprise birth rates were
recorded in western regions of Tirol and Salzburg
(both 5.7%), while Vorarlberg, Niederösterreich and
Kärnten also featured among the 10 EU regions with the
lowest rates.

• In Germany, the neighbouring eastern regions of

Thüringen (4.8%) and Chemnitz (5.1%) had the lowest
enterprise birth rates, while Sachsen-Anhalt also featured
among the 10 EU regions with the lowest rates.

• In Spain, the northern region of La Rioja had the lowest
enterprise birth rate, at 5.8% (which was the 5th lowest
rate in the EU).

• In Italy, the northern region of Provincia Autonoma di

Bolzano/Bozen had the lowest rate, at 6.2% (which was
the 10th lowest rate in the EU).

ENTERPRISE DEATH RATE 

In 2022, the EU’s enterprise death rate was 8.7% 

In 2022, 2.8 million enterprises definitively ceased activity 
within the EU’s business economy; expressed relative 
to the total enterprise population, this equated to an 
enterprise death rate of 8.7%. The distribution of enterprise 
death rates was somewhat skewed: almost 2 out of 5 NUTS 
level 2 regions for which data are available (138 out of 234, 
or 59.0%) reported rates below the EU average. 

Enterprise death rates for the business economy tended to 
be within a relatively narrow range across most EU countries. 
There was generally less variation in enterprise death rates 
between different regions of the same country than was the 
case for enterprise birth rates. Nevertheless, it was relatively 
common for regions with high enterprise birth rates to also 
record high enterprise death rates. This is not surprising, as 
dynamic and innovative enterprises entering a market may 
be in a position to drive less productive incumbents out of 
the market (‘creative destruction’). Moreover, a more risk-
prone culture might lead to the creation of new enterprises 
that, however, do not stand the test of time. 

Figure 8.1 highlights the NUTS level 2 regions with the 
highest and lowest enterprise death rates within their 
business economies in 2022. At the top of the distribution: 

• Estonia reported the highest enterprise death rate,

at 25.1%

• the only other regions in the EU with rates above 20.0%
were all located in Bulgaria – Yugozapaden (22.5%),
Severoiztochen (21.6%) and Yugoiztochen (21.1%)

• there were 3 additional Bulgarian regions that recorded
enterprise death rates in the range of 15.0% to 20.0%
– Yuzhen tsentralen, Severozapaden and Severen
tsentralen – which was also the case for the outermost
region of Mayotte (France) and the capital region of
Eastern and Midland (Ireland).

At the other end of the spectrum, the lowest enterprise 
death rates were consistently observed in Greece. 
All 13 Greek regions occupied the bottom ranks of the 
distribution in 2022, with rates consistently below 5.0%. 
The island regions of Notio Aigaio (1.6%; 2021 data), 
Voreio Aigaio (1.9%; 2021 data) and Kriti (2.0%) had the 
lowest death rates. Outside of Greece, the Austrian 
regions of Salzburg (5.1%) and Tirol (5.2%) had the lowest 
enterprise death rates; Belgium (5.2%; national data) had a 
comparable rate. 

HIGH-GROWTH ENTERPRISES 

In 2022, 9.2% of all enterprises in the EU were 
high-growth enterprises 

There were 155 600 high-growth enterprises across the EU 
in 2022 (see the shaded box above for a definition). High-
growth enterprises accounted for 9.2% of the total number 
of active enterprises with at least 10 employees in the EU’s 
business economy. There was a relatively even distribution 
of high-growth enterprises among NUTS level 2 regions: 
110 regions out of the 234 for which data are available 
(or 47.0%) recorded shares that were equal to or above the 
EU average. 

The presence of high-growth enterprises reflects, at least 
in part, the underlying structure of each region’s business 
economy. Several factors tend to support rapid enterprise 
growth: 

• relatively small enterprises often grow faster than larger

ones – a pattern often referred to as the ‘catch-up’
process

• enterprises located in regions specialised in dynamic
sectors of the economy such as information and
communication technologies, life sciences or
knowledge-intensive services typically grow more
rapidly

• a favourable business environment – such as a well-

developed innovation ecosystem, easy access to capital,
and a skilled workforce – also helps enterprises expand.

The final part of Figure 8.1 highlights the NUTS 
level 2 regions with the highest and lowest shares of 
high-growth enterprises. In 2022, at least 1 in 5 enterprises 
in 4 Swedish regions – Övre Norrland, Stockholm, Norra 
Mellansverige and Västsverige – were classified as 
high-growth enterprises. The northernmost region of 
Övre Norrland recorded the highest share, at 21.0%. In 
fact, the 8 Swedish regions had the 8 highest shares of 
high-growth enterprises. The next highest shares were 
observed in the Lithuanian capital region of Sostinės 
regionas (17.0%) and in the central Greek region of Sterea 
Elláda (14.6%). 

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In 2022, most EU countries reported that their capital 
region had the highest share of high-growth enterprises 
on their national territory. This pattern likely reflects factors 
such as a favourable business climate (as mentioned 
above) alongside a critical mass of potential business and/
or household clients. Exceptions included Greece, Spain, 
France, Italy, the Netherlands, Romania, Slovenia and 
Sweden, although: 

•  Comunidad de Madrid had the 2nd highest share in 
Spain, behind the autonomous Ciudad de Melilla
•  Ile-de-France had the 4th highest share in France, 

behind the outermost regions of Mayotte, Guyane and 
La Réunion

•  Stockholm had the 2nd highest share in Sweden, behind 

Övre Norrland.

Figure 8.1 also highlights the 10 NUTS level 2 regions 
with the lowest shares of high-growth enterprises. They 
consisted of 9 regions from France and Cyprus; the latter 
had the lowest share of high-growth enterprises in the 
EU, at 1.8%. Looking more closely and leaving Cyprus 
aside, the next 15 lowest shares were all located in 
France, including 5 predominantly rural regions – Haute-
Normandie, Franche-Comté, Champagne-Ardenne, 
Bourgogne and Corse – where the share of high-growth 
enterprises was below 5.0%. 

Figure 8.1: Business demography
(by NUTS 2 regions, 2022)

Note: high-growth enterprises are defined as those with employment 
growth of more than 10%; rates of change are calculated as average 
annualised rates over a 3-year period for the number of (paid) employees 
in the business economy; to be classified as high growth, an enterprise 
must have had at least 10 employees at the beginning of the period. 
Enterprise death rate: Voreio Aigaio (EL41) and Notio Aigaio (EL42), 2021.

Source: Eurostat (online data codes: bd_hgnace_r, bd_l_form and 
bd_hg)

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Regional patterns of employment 
specialisation and concentration

MANUFACTURING 

Lombardia had the largest regional workforce for its 
manufacturing sector in 2022, with 906 500 people 

In 2022, the EU’s business economy – comprising NACE 
Sections B to N and P to R, as well as Divisions 95 and 96 
– employed 160.4 million people. Manufacturing (NACE 
Section C) – which involves the production of goods 
and the provision of industrial services for domestic use 
(investment, further processing or consumption) and/or 
for export – accounted for 18.7% of the business economy 
workforce, approximately 30.0 million people. For context, 
employment in the manufacturing sector was similar to 
that for distributive trades, which had an 18.6% share of the 
business economy workforce. 

More about the data: structural business 
statistics 

Structural business statistics (SBS) provide detailed 
data on economic activities across hundreds of 
sectors, broken down by enterprise size (micro, small, 
medium-sized and large enterprises) and by region. 
They provide data covering issues such as labour input, 
value added, productivity, profitability and investment. 
This information can be used to explore (among other 
issues) structural shifts in an economy, national or 
regional specialisations, and sectoral patterns. 

The legal basis for European business statistics changed 
with the adoption of Regulation (EU) 2019/2152 
on European business statistics in November 2019. 
It created a cross-cutting legal framework for the 
collection, compilation, transmission and dissemination 
of statistics, covering the economic activities of 
market producers within the business economy 
(as defined by NACE Rev. 2 Sections B to N, P to R and 
Divisions 95 and 96). 

Map 8.2 shows the relative importance of the 
manufacturing sector in terms of its employment share 
within business economies. In 2022, 119 out of 242 NUTS 
level 2 regions for which data are available – equivalent 

to 49.2% of all regions – reported a higher share of 
manufacturing employment than the EU average of 18.7%. 

The northern Italian region of Lombardia had the largest 
manufacturing workforce in the EU, with 906 500 people 
employed in 2022; this region is specialised in mechanical 
engineering, metal working, textiles and clothing. 
Only 2 other regions across the EU employed more 
than 0.5 million people within manufacturing: 

•  Stuttgart (south-west Germany), where 614 200 people 

worked in the manufacturing sector, with a specialisation 
in the manufacture of motor vehicles

•  Veneto (north-east Italy), where 548 700 people worked 

in the manufacturing sector, with specialisations in 
textiles and clothing, woodworking and furniture.

Across the EU, 24 NUTS level 2 regions had at least 32.0% 
of their business economy workforce employed within 
manufacturing in 2022 (as shown by the darkest shade 
of blue in Map 8.2); all but 1 of these 24 regions were 
located in eastern EU countries. The manufacturing sector 
provided work to at least 32.0% of the business economy 
workforce in: 

•  7 out of the 8 regions in Czechia (the capital region 
of Praha being the only exception) – the highest 
employment shares across the EU were reported in 
Severovýchod (41.8%) and Střední Morava (40.3%)
•  3 regions from each of Bulgaria, Hungary, Poland and 
Romania, with the highest shares in each country 
reported in Severen tsentralen (38.4%), Közép-Dunántúl 
(36.6%), Podkarpackie (34.8%) and Vest (35.7%)

•  2 Croatian regions, with a peak of 37.7% in Sjeverna 

Hrvatska

•  the regions of Západné Slovensko in Slovakia (36.3%), 
Vzhodna Slovenija in Slovenia (35.9%) and Comunidad 
Foral de Navarra in Spain (32.0%).

At the other end of the scale, there were 21 NUTS 
level 2 regions where manufacturing activities accounted 
for less than 8.0% of the business economy workforce 
in 2022. This group primarily included capital regions and/
or regions with well-known tourist destinations, both of 
which are typically dominated by service activities. Leaving 
aside the atypical autonomous Spanish cities of Ceuta and 
Melilla, the lowest shares were recorded in the: 

•  southern Portuguese region of Algarve (3.3%)
•  Greek island region of Nisia Aigaio (3.4%).

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Map 8.2: Employment in manufacturing
(% of business economy employment, by NUTS 2 regions, 2022)

Note: Switzerland, Albania and Serbia, national data.

Source: Eurostat (online data codes: sbs_r_nuts2021, sbs_sc_ovw and 
sbs_ovw_act)

The EU’s manufacturing base has migrated eastwards 

The manufacturing sector has long been regarded as a 
cornerstone of economic prosperity in the EU. However, 
over several decades it has experienced wide-ranging 
transformations, through outsourcing, globalisation, 
changes to business paradigms (such as just-in-time 
manufacturing), the growing importance of digital 
technologies, or concerns linked to sustainable production 
and the environment. 

There has generally been an eastward shift in the EU’s 
manufacturing base during the last 2 to 3 decades, 
reflecting, among other factors, differences in: 

• labour costs
• taxes and subsidies
• flows of foreign direct investment (FDI) and the presence

of multinational enterprises

• natural resource endowments
• environmental standards.

Eastern EU countries have been increasingly used as 
manufacturing bases by enterprises from other EU 
countries (in particular neighbouring countries such as 
Germany) and by enterprises from non-EU countries 
wanting a manufacturing base within the EU’s single 
market. These enterprises often form an integral part of 
international supply chains, with relatively highly skilled 
workforces. 

In 2022, the 3 largest manufacturing subsectors in the EU 
– in employment terms and as defined by NACE divisions
– were the:

• manufacture of food products (NACE Division 10)

with 4.3 million people or 2.7% of the business economy
workforce

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Business

•  manufacture of fabricated metal products, except 
machinery and equipment (NACE Division 25), 
with 3.7 million people, or 2.3%

•  manufacture of machinery and equipment not 

elsewhere classified (NACE Division 28), with 3.1 million 
people, or 1.9%.

There were only 3 other manufacturing subsectors which 
accounted for at least 1.0% of the EU’s business economy 
workforce: the manufacture of motor vehicles, trailers and 
semi-trailers (NACE Division 29; 1.5%), the manufacture 
of rubber and plastic products (NACE Division 22; 1.0%) 
and the manufacture of electrical equipment (NACE 
Division 27; 1.0%). 

Figure 8.2 presents data for 24 different manufacturing 
subsectors. Each bar represents employment in a specific 
manufacturing subsector as a share of the business 
economy workforce. The left- and right-hand ends of each 
bar show the regions with the lowest and highest shares, 
while the point where the blue and green segments of 
each bar meet indicates the EU average. For example, in 
the manufacture of food products, the Greek region of 
Ipeiros recorded the highest regional share, with 8.7% of 
its business economy workforce employed in this activity 
in 2022 – 3.3 times as high as the EU average of 2.7%. 

Primary processing activities are often located close to the 
source of raw materials 

Figure 8.2 also highlights how employment in various 
manufacturing subsectors was often highly skewed. 
In some activities, a few regions recorded very high 
employment shares, while most other regions had 
relatively low shares. In statistical terms, this kind of 
distribution can be referred to as being ‘positively skewed’ 
and typically results in a long right-hand tail. In Figure 8.2, 
this can be seen for those manufacturing activities that 
have long green bars (representing the upper end of the 
distribution) and relatively short blue bars (the lower end). 
For example, the 5 EU regions with the largest workforces 
in the manufacture of leather and related products (NACE 
Division 15) – Toscana, Veneto, Marche (all in Italy), Norte 
(Portugal) and Comunitat Valenciana (Spain) – together 
accounted for 40% of the EU’s total employment in this 
subsector. 

Manufacturing subsectors that involve primary processing 
were often located close to the source of their raw 
materials. This was the case for the manufacture of food 
products, as there were 4 regions where this subsector 
accounted for at least 7.5% of employment within the 
business economy in 2022: Ipeiros and Thessalia (both 
in Greece), Bretagne (France) and Mazowiecki regionalny 
(which surrounds the capital region of Poland). La Rioja 
in Spain (2.9%), Champagne-Ardenne in France (2.1%), 

Alentejo in Portugal (1.6%) and Trier in Germany (also 1.6%) 
had the highest employment shares for the manufacturing 
of beverages (NACE Division 11). Regions specialised in 
the manufacture of textiles (NACE Division 13) were often 
located close to an abundant supply of water; Norte 
in Portugal (2.6%) had the highest regional share. The 
Croatian region of Panonska Hrvatska had the highest 
employment share (4.5%) for the manufacture of wood 
and wood products, except furniture (NACE Division 16), 
while Mellersta Norrland in Sweden had the highest share 
(3.1%) for the manufacture of paper and paper products 
(NACE Division 17). 

German regions often specialise in export-orientated 
subsectors 

Germany exports a relatively high proportion of its 
manufacturing output; this is particularly the case for 
its motor vehicles, machinery, electrical and chemical 
subsectors. In 2022 and among NUTS level 2 regions of the 
EU: 

•  Braunschweig in northern Germany had the highest 

employment share (12.0%) for the manufacture of motor 
vehicles, trailers and semi-trailers

•  Tübingen in south-west Germany had the highest 
employment share (7.8%) for the manufacture of 
machinery and equipment not elsewhere classified

•  Rheinhessen-Pfalz in western Germany had the highest 

employment share (5.6%) for the manufacture of 
chemicals and chemical products (NACE Division 20)
•  Oberpfalz in south-eastern Germany had the highest 
employment share (4.8%) for the manufacture of 
electrical equipment (NACE Division 27).

The manufacture of transport equipment is characterised 
by clusters of economic activity 

In the EU, clusters of economic activity and highly 
integrated production chains characterise the manufacture 
of transport equipment. Over time, some production has 
moved to exploit efficiency gains in global value chains. 
For example, this process has seen a redeployment of 
production towards several eastern EU countries. 

In 2022, aside from Braunschweig in Germany (mentioned 
above), the highest levels of employment specialisation 
for the manufacture of motor vehicles, trailers and semi-
trailers were observed in: 

•  the Romanian region of Vest (with an 11.0% share of 

business economy employment)

•  Střední Čechy and Severovýchod in Czechia (10.3% 

and 7.7%, respectively)

•  the westernmost Hungarian region of Nyugat-Dunántúl 

(9.9%).

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The French region of Midi-Pyrénées was the most 
specialised region in the EU for the manufacture of other 
transport equipment (NACE Division 30), as these activities 

provided employment to 3.5% of its business economy 
workforce, with a significant presence in the aerospace 
subsector. 

Figure 8.2: Regional specialisation among manufacturing activities
(% of business economy employment, by NUTS 2 regions, 2022)

Note: includes estimates made for the purpose of this publication. The 
EU average is shown by the point where the green and blue parts of 
each bar meet; the range of regional values across NUTS level 2 regions 
is shown by the bar (above/below the EU average in green/blue); the 
name of the region with the highest value is also shown. NACE codes are 

given in brackets after each of the activity labels. The figure is based on 
non-confidential data (some activities are not available for a small number 
of regions).

Source: Eurostat (online data codes: sbs_r_nuts2021, sbs_sc_ovw and 
sbs_ovw_act)

MARKET SERVICES 

Some market service activities are widely spread across the 
EU territory, whereas others are concentrated within close 
proximity to a mass of potential clients 

In 2022, market services – comprising NACE Sections 
G to N, P to R, as well as Divisions 95 and 96 – 
employed 112.7 million people, accounting for 70.3% of 
total employment across the EU’s business economy. 

There were 15 NUTS level 2 regions across the EU where 
market services provided more than 85.0% of the business 
economy workforce in 2022: 

•  the capital regions of Berlin (Germany), Attiki (Greece), 
Comunidad de Madrid (Spain), Noord-Holland (the 
Netherlands), Wien (Austria) and Grande Lisboa (Portugal)

•  the popular holiday destinations of Ionia Nisia, Notio 

Aigaio, Kriti (all in Greece), Canarias (Spain) and Algarve 
(Portugal)

•  3 additional regions from the Netherlands – Flevoland, 
Utrecht and Zuid-Holland – as well as the northern 
German region of Hamburg.

Figure 8.3 presents information for 43 different market 
service activities, highlighting the NUTS level 2 regions 
with the highest degrees of employment specialisation. 
Variations in employment specialisation may reflect, 
among other issues, access to skilled labour, the adequate 
provision of infrastructure, climatic and geographic 
conditions, proximity to a critical mass of customers, 
market access or legislative constraints. 

Retail trade (NACE Division 47) accounted for 10.3% of the 
EU’s business economy workforce in 2022. Wholesale trade 
(NACE Division 46) had the next highest share, at 6.0%, 

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Business

followed by food and beverage service activities (NACE 
Division 56; 5.2%) and human health activities (NACE 
Division 86; 5.1%). These are all examples of service activities 
that are found in every NUTS level 2 region of the EU. 

Capital regions were among some of the most specialised 
regions for a range of service activities that rely on the 
close proximity of a large number of potential clients (be 
these other businesses, households or governments). For 
example, in 2022: 

•  the Área Metropolitana de Lisboa (Portugal) had the 
highest employment share for office administrative/
support and other business support activities, at 9.1%

•  Yugozapaden (Bulgaria) for computer programming, 

consultancy and related activities (8.6%)

•  Comunidad de Madrid for activities auxiliary to financial 

services and insurance activities (4.5%)

•  Budapest (Hungary) for legal and accounting activities 

(4.4%)

•  Bruxelles-Capitale/Brussels Hoofdstedelijk (Belgium) for 
insurance and pension funding, except social security 
(2.9%)

•  Praha (Czechia) for other professional, scientific and 

technical activities (2.4%)

•  Bratislavský kraj (Slovakia) for advertising and market 
research (2.3%) and for telecommunications (2.2%).

Figure 8.3: Regional specialisation among market service activities
(% of business economy employment, by NUTS 2 regions, 2022)

Note: includes estimates made for the purpose of this publication. The 
EU average is shown by the point where the green and blue parts of 
each bar meet; the range of regional values across NUTS level 2 regions 
is shown by the bar (above/below the EU average in green/blue); the 
name of the region with the highest value is also shown. NACE codes are 

given in brackets after each of the activity labels. The figure is based on 
non-confidential data (some activities are not available for a small number 
of regions).

Source: Eurostat (online data codes: sbs_r_nuts2021, sbs_sc_ovw and 
sbs_ovw_act)

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KNOWLEDGE-INTENSIVE SERVICES 

More about the data: knowledge-intensive 
services 

Knowledge-intensive services are defined as services 
that rely heavily on knowledge, skills and innovation 
to provide value. These services are typically found in 
subsectors such as information technology, financial 
services, business consulting, legal services and 
other specialised professional services. They require a 
highly-educated workforce and are often considered 
as essential drivers of economic growth for advanced 
economies. 

Within this section a distinction is made 
between 2 different aggregates: 

•  knowledge-intensive market services (excluding 
financial intermediation and high-tech services), 
composed of the following activities: water 
transport (NACE Division 50); air transport (NACE 
Division 51); legal and accounting activities (NACE 
Division 69); activities of head offices, management 
consultancy activities (NACE Division 70); architectural 
and engineering activities, technical testing and 
analysis (NACE Division 71); advertising and market 
research (NACE Division 73); other professional, 
scientific and technical activities (NACE Division 74); 
employment activities (NACE Division 78); security 
and investigation activities (NACE Division 80)

•  knowledge-intensive high-technology services, 

composed of the following activities: motion picture, 
video and television programme production, sound 
recording and music publishing activities (NACE 
Division 59); programming and broadcasting activities 
(NACE Division 60); telecommunications (NACE 
Division 61); computer programming, consultancy 
and related activities (NACE Division 62); information 
service activities (NACE Division 63); scientific research 
and development (NACE Division 72).

In Helsinki-Uusimaa – the capital region of Finland – 
knowledge-intensive market services accounted for 20.0% 
of the business economy workforce in 2022 

The information presented in Map 8.3 highlights regional 
patterns of employment specialisation and concentration 
for knowledge-intensive market services (excluding 
financial intermediation and high-tech services). In 2022, 
these services employed 17.5 million people across the 
EU, accounting for just over a tenth (10.9%) of the business 
economy workforce. 

Among NUTS level 2 regions, the highest numbers of 
people employed in knowledge-intensive market services 
in 2022 were found in: 

•  the French capital region of Ile-de-France 

(961 700 people employed)

•  the northern Italian region of Lombardia (510 000)
•  the Spanish capital region of Comunidad de Madrid 

(439 000).

In 2022, knowledge-intensive market services accounted 
for at least 16.0% of the business economy workforce 
in 27 regions across the EU (as shown by the darkest shade 
of blue in Map 8.3). This group consisted mainly of capital 
regions, but also included several densely-populated 
regions in Belgium and the Netherlands. 

The highest regional share was recorded in Helsinki-
Uusimaa – the capital region of Finland – where, in 2022, 
20.0% of the business economy workforce was employed 
in knowledge-intensive market services (almost double 
the EU average). The next highest regional shares were 
recorded in: 

•  the Czech capital region of Praha (19.3%)
•  the French capital region of Ile-de-France, as well as the 
Belgian region of Prov. Brabant wallon and the Dutch 
region of Zuid-Holland (all 19.2%)

•  the Dutch capital region of Noord-Holland (18.9%)
•  the Hungarian and Slovakian capital regions of Budapest 

and Bratislavský kraj (both 18.0%).

Within some EU countries there were considerable inter-
regional contrasts, suggesting an urban-rural divide in the 
development of knowledge-intensive market services. This 
was particularly apparent in several eastern EU countries, 
where rural regions face structural challenges related to 
infrastructure, outward migration and low investment 
levels. For example, the share of knowledge-intensive 
market services in the business economy workforce of: 

•  the Slovakian capital region of Bratislavský kraj (18.0%) 

was 3.6 times as high as in the eastern region of 
Východné Slovensko (5.0%)

•  the Bulgarian capital region of Yugozapaden (12.7%) 

was 3.2 times as high as in the north-western region of 
Severozapaden (4.0%)

•  the Romanian capital region of Bucureşti-Ilfov (16.3%) 
was 3.0 times as high as in the north-eastern region of 
Nord-Est (5.4%).

Eurostat regional yearbook – 2025 edition150Map 8.3: Employment in knowledge-intensive market services
(% of business economy employment, by NUTS 2 regions, 2022)

B

Business

Note: Norway, Switzerland, Albania and Serbia, national data.

Source: Eurostat (online data codes: sbs_r_nuts2021, sbs_sc_ovw and 
sbs_ovw_act)

KNOWLEDGE-INTENSIVE HIGH-TECHNOLOGY 
SERVICES 

In 2022, knowledge-intensive high-technology 
services employed 6.9 million people across the 
EU. There were 6 NUTS level 2 regions where more 
than 150 000 people were employed in these services – all 
of them either capital regions or economically dominant 
hubs for high-tech employment: 

Knowledge-intensive high-technology services accounted 
for 4.3% of the EU’s business economy workforce in 2022. 
The regional distribution of this workforce was heavily 
skewed: only 57 out of 244 NUTS level 2 regions for which 
data are available reported an employment share equal to 
or greater than the EU average – equivalent to 23.4% of all 
regions. Notably, the 14 regions with the highest shares of 
employment in these services were all capital regions, with 
double-digit shares observed in: 

•  the French capital region of Ile-de-France (447 100)
•  the Spanish capital region of Comunidad de Madrid 

(256 600)

•  the German regions of Oberbayern (194 700) and the 

capital Berlin (172 500)

•  the northern Italian region of Lombardia (180 900)
•  the Polish capital region of Warszawski stołeczny 

(177 200).

•  the Bulgarian capital region of Yugozapaden (12.7%)
•  the Hungarian capital region of Budapest (11.0%)
•  the Romanian capital region of Bucureşti-Ilfov (10.5%)
•  the Croatian capital region of Grad Zagreb (10.3%)
•  the Slovakian capital region of Bratislavský kraj (10.1%)
•  the German capital region of Berlin (10.0%).

151Eurostat regional yearbook – 2025 editionB

Business

The high concentration of knowledge-intensive high-
technology services employment in capital regions may 
reflect a number of different factors, including: 

•  the presence of universities and research institutes, 

supplying skilled labour

•  higher levels of digital skills among urban populations
•  capital regions acting as magnets for internal migration, 
attracting talent from smaller cities and rural regions
•  relatively high levels of foreign direct investment and 

offshoring (when global enterprises establish operations 
in capital cities).

At the lower end of the distribution, there 
were 21 predominantly rural NUTS level 2 regions – often 
in southern EU countries – where knowledge-intensive 
high-technology services accounted for less than 1.5% 
of the business economy workforce in 2022. Specifically, 
this group included 6 regions from Greece, 5 regions from 
France, 3 regions from both Spain and Portugal, as well as 
individual regions from Belgium, Bulgaria, Germany and 
the Netherlands. The lowest shares – below 1.0% – were 
observed in the French outermost region of Mayotte, the 
autonomous Spanish cities and some of the Greek islands 
(Ionia Nisia and Notio Aigaio). 

Map 8.4: Employment in knowledge-intensive high-technology services
(% of business economy employment, by NUTS 2 regions, 2022)

Note: Norway, Switzerland, Albania and Serbia, national data.

Source: Eurostat (online data codes: sbs_r_nuts2021, sbs_sc_ovw and 
sbs_ovw_act)

Eurostat regional yearbook – 2025 edition152B

Business

Innovative enterprises

INNOVATION-ACTIVE ENTERPRISES 

Regional analyses of innovation statistics across the EU 
are essential for understanding how innovative capacity 
and performance vary across different territories. This may 
help identify strengths, disparities and emerging trends, 
enabling policymakers to design targeted strategies that 
promote innovation as a key driver of economic growth, 
resilience and sustainability. 

More about the data: innovation 

The EU’s Community Innovation Survey (CIS) is the 
EU’s primary source of data on innovation activity 
within businesses. Conducted biennially across the 
EU, EFTA and candidate countries, the CIS collects 
harmonised statistics on various types of innovation 
– ‘product innovation’, ‘business process innovation’ 
– for enterprises of different sizes, across a wide range 
of economic sectors. It offers a detailed insight into 
the drivers, barriers and outcomes of innovation. The 
survey supports comparative analysis across countries 
and regions, tracking changes over time and informing 
initiatives to strengthen Europe’s innovation capacity 
and competitiveness in a rapidly changing global 
economy. 

The CIS was first launched in the 1990s and became a 
regular biennial data collection in 2004. The information 
presented below relate to the CIS 2022 exercise, 
which is based on international standards for 
conceptualising and collecting data on innovation, 
as laid out in the Oslo Manual (4th edition). The data 
for CIS 2022 were collected on the basis of the new 
European business statistics framework, as defined in 
Regulation (EU) 2022/1092. The target population of 
the CIS 2022 concerns enterprises within NACE Sections 
B to E, H, J and K, as well as Divisions 46, 71, 72 and 73, 
hereafter referred to as the ‘business economy’. For 
more information, refer to the European business 
statistics methodological manual for statistics on 
business innovation – 2024 edition. 

The statistics presented below are based on a 3-year 
reference period from 2020 to 2022 and cover: 

•  NUTS level 2 data for 21 EU countries
•  NUTS level 1 data for Belgium and Austria
•  national data for Denmark, Germany, France and 

the Netherlands; this is also the case for Norway and 
Türkiye.

Approximately 50% of all SMEs in the EU were considered 
as innovation-active during the period 2020 to 2022 

An innovation-active enterprise is 1 which has engaged 
in any form of innovation activity during a given period, 
irrespective of whether it resulted in a successful 
innovation, a new product sold on the market or a new 
process brought to use, during the last 3 years. As such, it 
may refer to an enterprise that: 

•  introduces a product or business process innovation
•  has on-going innovation activities
•  has abandoned an innovation project (in other words, 

started but did not complete an innovation)

•  or exhibits some or all of the above at the same time.

Based on the latest information from CIS 2022, there 
were 350 000 small and medium-sized enterprises (SMEs) 
with 10 to 249 employees that were innovation-active 
during the period 2020 to 2022; they represented 50.3% 
of all SMEs across the EU. Map 8.5 shows the regional 
distribution within the EU, with the highest shares of 
innovation-active SMEs concentrated in Belgium, Greece, 
Italy, Slovenia, Finland and Sweden; more than 50% of all 
SMEs were innovation-active in Denmark, Germany, France 
and the Netherlands too. 

During the period 2020 to 2022, the highest regional 
counts of innovation-active enterprises were in: 

•  the northern Italian regions of Lombardia (17 800), 

Veneto (8 800), Emilia-Romagna (6 600) and Piemonte 
(5 300)

•  the Belgian region of Vlaams Gewest (6 900)
•  the Spanish region of Cataluña (5 100).

Over the same period, only 5 regions across the EU 
reported more than 70.0% of SMEs being innovation-
active: 

•  the Greek regions of Kriti (77.8%), Voreio Aigaio (75.8%) 

and Ionia Nisia (72.9%)

•  the Belgian region of Vlaams Gewest (74.7%)
•  the southern Italian region of Calabria (70.3%).

Note that several of these regions had very low overall 
counts of innovation-active SMEs within the survey’s 
activity coverage. For example, there were fewer 
than 100 such enterprises in the Greek regions of Voreio 
Aigaio and Ionia Nisia. 

153Eurostat regional yearbook – 2025 editionB

Business

Map 8.5: Innovation-active SMEs
(business economy, by NUTS 2 regions, 2020–22)

Note: enterprises with 10–249 employees. Belgium and Austria: NUTS 
level 1. Denmark, Germany, France, the Netherlands, Norway and Türkiye: 
national data.

Source: Eurostat (online data codes: inn_cis13_bas_r and inn_cis13_bas)

Map 8.6 and Map 8.7 provide more detailed information 
about the innovative activities across the EU, distinguishing 
between SMEs introducing product innovations and SMEs 
introducing business process innovations. The former 
include innovations that introduce a good or service 
that is new or significantly improved with respect to its 
characteristics or intended uses (in other words, significant 
improvements in technical specifications, components 
and materials, incorporated software, user friendliness or 
other functional characteristics). 

During the period 2020 to 2022, there were 184 500 SMEs 
across the EU’s business economy that introduced 
product innovations; together, they represented more 
than 1 in 4 (26.5%) SMEs in the EU. There were 6 NUTS 
level 2 regions that reported more than 3 000 SMEs 
introducing product innovations: 

•  the Italian regions of Lombardia (10 700), Veneto (4 700), 

Emilia-Romagna (4 100) and Piemonte (3 200)

•  the Belgian region of Vlaams Gewest (3 500)
•  the Greek capital region of Attiki (3 200).

Eurostat regional yearbook – 2025 edition154B

Business

In relative terms, there were 3 regions across the EU where 
more than 50% of all SMEs introduced product innovations 
during the period 2020 to 2022, all in Greece: Voreio Aigaio 
(67.4%), Ionia Nisia (65.4%) and Kriti (61.2%). In addition to 
these 3 Greek regions, there were 11 other regions (as 
well as Denmark; national data) across the EU where more 

than 40.0% of SMEs introduced product innovations (as 
shown by the darkest shade of blue in Map 8.6): 

•  7 other Greek regions (the exceptions – with shares 
below 40.0% – were Notio Aigaio, Ipeiros and Dytiki 
Makedonia)

•  3 Italian regions – Calabria, Lombardia and Piemonte
•  the Swedish capital region of Stockholm.

Map 8.6: SMEs introducing product innovations
(% of all SMEs in the business economy, by NUTS 2 regions, 2020–22)

Note: enterprises with 10–249 employees. Belgium and Austria: NUTS 
level 1. Denmark, Germany, France, the Netherlands, Norway and Türkiye: 
national data.

Source: Eurostat (online data codes: inn_cis13_bas_r and inn_cis13_bas)

155Eurostat regional yearbook – 2025 editionB

Business

Map 8.7 presents information on the share of SMEs 
introducing business process innovations. A process 
innovation involves the implementation of a new or 
significantly improved production or delivery method 
(including substantial changes in techniques, equipment 
and/or software). These innovations also cover new 
or significantly improved techniques, equipment and 
software in ancillary support activities, such as purchasing, 
accounting, computing and maintenance. Process 
innovations can reduce the unit production or delivery 
costs, improve quality, or enable the provision of new or 
significantly improved products. Examples include the 
introduction of GPS tracking devices in transport services, 
the implementation of a new reservation system in a travel 
agency, the development of new project management 
techniques in a consultancy firm, or the adoption of new 
methods for pre-producing parts and assembling them 
into finished products in manufacturing. 

During the period 2020 to 2022, there were 290 400 SMEs 
across the EU’s business economy that introduced 
business process innovations. Together, they represented 
more than 2 out of 5 (41.7%) SMEs in the EU. 

The highest regional count of SMEs introducing business 
process innovations was recorded in the northern Italian 
region of Lombardia (15 900) – the same region that 
had the highest count of SMEs introducing product 
innovations. There were 12 other regions across the EU 
that had more than 3 000 SMEs introducing business 
process innovations: 

•  6 of them were located in Italy – Veneto, Emilia-

Romagna, Toscana, Piemonte, Campania and Lazio

•  the Belgian region of Vlaams Gewest
•  the Greek capital region of Attiki
•  the Spanish region of Cataluña
•  the Portuguese region of Norte
•  the Austrian regions of Westösterreich and Ostösterreich 

(NUTS level 1).

In relative terms, there were 16 NUTS level 2 regions where 
at least 57.0% of all SMEs introduced business process 
innovations (as shown by the darkest shade of blue in 
Map 8.7). These 16 regions were concentrated in Greece 
(8 regions) and Italy (7 regions), with the Belgian region 
of Vlaams Gewest (NUTS level 1) being the only other 
region recording a relatively high share of business process 
innovators. 

More than 3 in 4 SMEs in the Greek island regions of Kriti 
(75.9%) and Voreio Aigaio (75.8%) introduced business 
process innovations during the period 2020 to 2022. The 
next highest share was also recorded in a Greek island 
region – Ionia Nisia (72.0%) – followed by Vlaams Gewest 
in Belgium (66.6%) and Calabria in southern Italy (65.2%). 
As noted above, several of the Greek regions with relatively 
high shares had quite low overall numbers of SMEs (within 
the survey’s activity coverage) introducing business 
process innovations; for example, Voreio Aigaio and Ionia 
Nisia had fewer than 100 each. 

Eurostat regional yearbook – 2025 edition156Map 8.7: SMEs introducing business process innovations
(% of all SMEs in the business economy, by NUTS 2 regions, 2020–22)

B

Business

Note: enterprises with 10–249 employees. Belgium and Austria: NUTS 
level 1. Denmark, Germany, France, the Netherlands, Norway and Türkiye: 
national data.

Source: Eurostat (online data codes: inn_cis13_bas_r and inn_cis13_bas)

Map 8.5 indicated that just over half (50.3%) of all SMEs in 
the EU’s business economy were innovation-active during 
the period 2020 to 2022. A higher share of SME employees 
– 58.0% – worked in innovation-active enterprises, 
suggesting that, on average, these SMEs were larger than 
their non-innovating counterparts. 

Map 8.8 shows a skewed distribution for the share of 
employees in innovation-active enterprises: 36 NUTS 
level 2 regions had shares of employees working in 
innovation-active SMEs that were above the EU average, 
while 112 regions had below-average shares. It should be 
noted that Denmark, Germany, France and the Netherlands 
are all counted as single regions within the group 
of 36 with above-average shares, whereas in practice they 

would represent far larger numbers of regions (if NUTS 
level 2 data were available). In particular, the German 
average (71.3%) pulls the EU average up substantially. 

The Italian region of Lombardia had the highest regional 
count of employees working in innovation-active 
SMEs, at 432 000. This was considerably larger than 
in any other region. Only 3 other regions had more 
than 200 000 employees working in innovation-active 
SMEs: 

•  the Belgian region of Vlaams Gewest (284 000; NUTS 

level 1)

•  the Spanish region of Cataluña (232 000)
•  the Italian region of Veneto (219 000).

157Eurostat regional yearbook – 2025 editionB

Business

By contrast, there were fewer than 5 000 employees 
working in innovation-active SMEs in 14 regions, most of 
which were located in southern EU countries: 

• 5 regions in Greece (3 of which were island regions)
• 3 regions in each of Italy and Romania
• 2 island regions in Portugal
• the island region of Åland in Finland.

Map 8.8: Employees in innovation-active SMEs
(% of all employees in SMEs in the business economy, by NUTS 2 regions, 2022)

Note: enterprises with 10–249 employees. Belgium and Austria: NUTS 
level 1. Denmark, Germany, France, the Netherlands, Norway and Türkiye: 
national data.

Source: Eurostat (online data codes: inn_cis13_bas_r and inn_cis13_bas)

In the Belgian region of Vlaams Gewest, more 
than 4 out of 5 SME employees (80.2%) were working for an 
innovation-active enterprise during the period 2020 to 2022. 
There were 13 other regions across the EU that recorded 
shares of at least 65.5% (as shown by the darkest shade of 
blue in Map 8.8), including the capital regions of Greece, 
Finland, Slovenia, Belgium, Portugal and Sweden. There was 
also a relatively high share of SME employees working for 
innovation-active enterprises in Germany (71.3%). 

the market and/or new to the firm. In relative terms, the 
share of total turnover among SMEs that was derived from 
innovative products was 8.8%. There was a somewhat 
skewed distribution: 48 NUTS level 2 regions had shares 
that were above the EU average; the Austrian region of 
Südösterreich had the same share as the EU; 71 regions had 
below-average shares. In particular, the German and French 
averages (5.3% and 4.4%, respectively) pulled down the EU 
average. 

In 2022, EU SMEs generated €725.9 billion of turnover from 
innovative products; this figure includes turnover from 
new or significantly improved products that were new to 

In absolute terms, the Spanish capital region of Comunidad 
de Madrid recorded the highest level of turnover derived 
from innovative products (€23.2 billion in 2022). The next 

Eurostat regional yearbook – 2025 edition158B

Business

highest value was recorded in another Spanish region, 
namely, Cataluña (€18.7 billion), followed by the Greek, 
Portuguese and Irish capital city regions. It should however 
be noted that there are no regional data available for 
Denmark, Germany, France, Italy and the Netherlands. 

Map 8.9 shows there were 13 regions across the EU 
where the share of total turnover among SMEs derived 
from innovative products was at least 15.0% in 2022 (as 
shown by the darkest shade of blue). These regions were 
predominantly located in southern EU countries, with a 
cluster of 5 regions in Greece. Within this group of 13, there 
were 4 regions that recorded shares of more than 20.0%: 

• Panonska Hrvatska in Croatia (33.9%)
• Northern and Western in Ireland (25.4%)

• Denmark (national data only; 24.2%)
• Grande Lisboa in Portugal (22.4%).

By contrast, there were 15 NUTS level 2 regions across the 
EU that reported a share of total turnover among SMEs 
derived from innovative products that was below 2.5% (as 
shown by the lightest shade in Map 8.9). The regions in this 
group were predominantly located in eastern EU countries, 
with: 

• 6 regions in Poland
• 5 regions in Romania
• 2 regions in Bulgaria
• 1 region in Hungary
• a single southern region, Malta.

Map 8.9: Turnover from innovative products in SMEs
(% of all turnover in SMEs in the business economy, by NUTS 2 regions, 2022)

Note: enterprises with 10–249 employees. Belgium and Austria: NUTS 
level 1. Denmark, Germany, France, Italy, the Netherlands, Norway and 
Türkiye: national data.

Source: Eurostat (online data codes: inn_cis13_prodt_r, inn_cis13_prodt, 
inn_cis13_bas_r and inn_cis13_bas)

159Eurostat regional yearbook – 2025 edition9. Research and development

Research and development (R&D) is a driver of economic 
growth, productivity and long-term competitiveness, 
both within the European Union (EU) and beyond. It has 
the potential to help solve some of the world’s largest 
economic, societal and ecological challenges; in doing so, 
it can improve the daily lives of millions of people. 

Education, training and lifelong learning play a vital role in 
developing a region’s capacity to innovate. To build and 
expand its knowledge-based economy, the EU requires a 
consistent supply of highly skilled and qualified individuals. 
This chapter presents key statistics on human resources 
in science and technology (HRST) and employment in 

high-technology sectors, with a special focus on the role 
of women in research and development, where EU-wide 
initiatives aim to increase female participation in science 
and technology through mentorship, education and 
targeted support. 

The infographic below shows that the Polish capital region 
of Warszawski stołeczny had the highest share of HRST 
across NUTS level 2 regions, with nearly 3 out of 4 of its 
labour force classified as such (74.3%) in 2024. Prov. Brabant 
wallon (Belgium), along with 2 other capital regions – 
Budapest (Hungary) and Praha (Czechia) – were the only 
other regions to report shares above 70.0%. 

(% of the labour force, by NUTS 2 regions, 2024)

Source: Eurostat (online data code: hrst_st_rcat)

Note: Mayotte (FRY5), not available.

Core human resources in science 
and technology

In 2024, there were 126.2 million people aged 15 to 74 years 
in the EU classified as HRST, representing 49.2% of the 
EU’s labour force. Among them, 101.1 million people 
met the HRST educational criterion, 80.5 million met the 
occupational criterion and 55.4 million met both the 
educational and occupational criteria. This latter group – 
those who are both qualified in science and technology 
and employed in related occupations – constitutes what is 

often referred to as ‘core HRST’. These people are the main 
focus of the information presented in this section. 

Map 9.1 shows the distribution of core HRST across NUTS 
level 2 regions. Regions with high shares of core HRST 
in their labour force are likely to experience a number 
of benefits, such as higher productivity, higher wage 
levels, and clusters of research and technology activity. 
Factors such as these, in turn, are likely to reinforce their 
attractiveness to graduates and to (new) businesses, 
thereby generating spillover effects. 

Eurostat regional yearbook – 2025 edition160Research and development

B

More about the data: statistics on core human 
resources in science and technology 

The criteria used to define human resources in science 
and technology statistics (HRST) are generally broader 
than those used to delineate R&D personnel and 
researchers; the latter refer specifically to occupations. 
R&D personnel include all people directly engaged 
in R&D as well as people providing direct services for 
R&D activities (such as R&D managers, administrators, 
technicians and clerical staff). Researchers are 
professionals engaged in the conception or creation 
of new knowledge, products, processes, methods and 
systems, as well as in the management of the projects 
concerned. 

HRST are defined as people who meet at least 1 of the 
following criteria. 

•  HRST by education – have successfully completed 
a tertiary education, as defined by the international 
standard classification of education (ISCED) 
levels 5 to 8.

•  HRST by occupation – are employed in a science 
and technology occupation, as defined by the 
international standard classification of occupations 
(ISCO) major groups 2 and 3.

Core HRST are defined as people who meet both of 
the above criteria. These individuals are particularly 
relevant as they combine formal qualifications with 
practical engagement in the field. Common roles taken 
by people classified as core HRST include scientists and 
engineers, teaching professionals, health professionals, 
business and administration professionals, information 
and communications technology (ICT) professionals, lab 
technicians, medical and pharmaceutical technicians, 
business and administration associate professionals, and 
ICT technicians. 

In 2024, some of the EU’s most populous NUTS 
level 2 regions were also home to large numbers of 
core HRST (as denoted by the size of the circles in 
Map 9.1). The French regions of Ile-de-France and 
Rhône-Alpes (2.4 million and 1.1 million, respectively), 
the Spanish regions of Comunidad de Madrid, Cataluña 
and Andalucía (1.2 million, 1.1 million and 0.9 million, 
respectively), the Italian region of Lombardia (0.9 million), 
the German region of Oberbayern (0.8 million) 
and the Polish region of Warszawski stołeczny 
(also 0.8 million) were the only EU regions to report more 
than 0.75 million core HRST. An additional 15 regions each 
had between 0.50 and 0.75 million core HRST. This group 
included 5 regions from Germany, 2 regions from each of 
the Netherlands and Poland, and a single region from each 
of Ireland, Greece, Spain, France, Italy and Sweden. 

Core HRST accounted for approximately 1 in 4 (25.2%) of 
the EU’s labour force in 2024. The regional distribution 
was uneven: around 1 out of 3 NUTS level 2 regions 
(88 out of 243 regions for which data are available) 
reported a share of core HRST above the EU average, 
while 154 regions had below-average shares and a single 
region had a share that matched the EU average. 

In 2024, there were 60 NUTS level 2 regions where the 
share of core HRST in the labour force was at least 27.5% 
(as shown by the darkest shade of green in Map 9.1). This 
group included: 

•  all 3 regions in Ireland
•  7 out of 8 regions in Sweden
•  9 out of 11 regions in Belgium
•  the capital region of every multi-regional EU country, 

except Lazio (Italy)

•  at least 1 other region in Denmark, Germany, Spain, 

France, the Netherlands, Poland and Finland

•  Cyprus and Luxembourg.

Looking in more detail, Luxembourg (48.4%) and the 
Hungarian capital region of Budapest (47.3%) recorded 
the highest shares of core HRST in their labour forces 
in 2024. There were 5 other regions that had shares of at 
least 40.0%: 

•  3 capital regions – Warszawski stołeczny in Poland 

(44.8%), Stockholm in Sweden (41.4%) and Grad Zagreb 
in Croatia (41.0%)

•  Prov. Brabant wallon in Belgium (40.7%)
•  Utrecht in the Netherlands (40.0%).

At the lower end of the distribution, 62 NUTS 
level 2 regions had a share of core HRST in the labour force 
below 18.5% in 2024 (as shown by the lightest shade of 
green in Map 9.1). These regions were mostly rural and/or 
peripheral and were predominantly located in eastern and 
southern EU countries. Among others, they included: 

•  18 out of 21 regions in Italy
•  10 out of 13 regions in Greece
•  6 out of 8 regions in Hungary
•  6 out of 8 regions in Romania
•  5 out of 8 regions in Czechia.

Looking more closely, the Greek region of Sterea Elláda 
recorded the lowest share of core HRST in its labour force 
in 2024, at 10.9%. There were 4 other regions that recorded 
shares below 12.5%: 

•  2 more regions from Greece – Ionia Nisia (11.1%) and 

Notio Aigaio (12.2%)

•  Sud-Muntenia in Romania (11.7%)
•  Severozápad in Czechia (12.4%).

161Eurostat regional yearbook – 2025 editionB

Research and development

Map 9.1: Core human resources in science and technology
(by NUTS 2 regions, 2024)

Note: EU = 55.4 million people employed as core human resources in 
science and technology, equivalent to 25.2% of the labour force. Prov. 
Vlaams-Brabant (BE24), Corse (FRM0) and Åland (FI20): low reliability.

Source: Eurostat (online data code: hrst_st_rcat)

Eurostat regional yearbook – 2025 edition162Research and development

B

Figure 9.1 provides a summary of the NUTS level 2 regions 
with the highest numbers of core HRST, as well as those 
with the highest shares of core HRST in the labour force. 
In 2024, the capital regions of France, Poland, Germany, the 
Netherlands and Sweden were the only ones to appear in 
both rankings: 

•  Ile-de-France had 2.4 million core HRST, equivalent 

to 38.8% of its labour force

•  Warszawski stołeczny had 0.8 million core HRST, 

equivalent to 44.8% of its labour force

•  Berlin had 0.7 million core HRST, equivalent to 34.2% of 

its labour force

•  Noord-Holland had 0.6 million core HRST, equivalent 

to 34.9% of its labour force

•  Stockholm had 0.6 million core HRST, equivalent to 41.4% 

of its labour force.

The data shown in Figure 9.1 highlight 2 main patterns: 
1st, the absolute number of core HRST is generally 
higher in the EU’s most populous metropolitan regions; 
2nd, capital regions often register the highest proportions 
of core HRST relative to their labour force. Among 
the 20 NUTS level 2 regions with the highest shares of core 
HRST, only a few were non-capital regions, namely: 

•  Prov. Brabant wallon, Prov. Vlaams-Brabant and Prov. 

Oost-Vlaanderen in Belgium

•  Utrecht in the Netherlands
•  Sydsverige in Sweden.

Figure 9.1: Core human resources in science and technology
(by NUTS 2 regions, 2024)

Note: the figure shows the 20 EU regions with the highest number of core 
human resources in science and technology and the highest share of core 
human resources in science and technology in the labour force. Mayotte 
(FRY5): not available.

Source: Eurostat (online data code: hrst_st_rcat)

163Eurostat regional yearbook – 2025 editionIn 2024, 22 NUTS level 2 regions employed at 
least 100 000 people in high-technology sectors (as shown 
by the largest circles in Map 9.2). In line with most science 
and technology indicators, these regions were principally 
located in some of the EU’s largest metropolitan regions, 
with half of this group (11 out of the 22) being capital 
regions. 

•  Almost 0.5 million people (498 000) were employed in 
high-technology sectors in the French capital region of 
Ile-de-France.

•  There were 2 other regions in the EU where more 

than 250 000 people were employed in high-technology 
sectors: the Spanish capital region of Comunidad de 
Madrid and Lombardia in northern Italy.

To illustrate the skewed nature of the distribution, 
the 22 regions with at least 100 000 people employed 
in their high-technology sectors together accounted 
for 4.0 million people in 2024, equivalent to 37.7% of 
the EU total. This was similar to the cumulative share of 
the 175 regions where fewer than 60 000 people were 
employed in these sectors, which together accounted 
for 4.2 million people (39.1%). 

At the lower end of the distribution, the lowest counts 
across the EU of people employed in high-technology 
sectors in 2024 – all around 2 000 people – were observed 
in the Greek regions of Dytiki Makedonia, Ipeiros and 
Thessalia, as well as in the southern Italian region of Molise. 

B

Research and development

Employment in high-technology 
sectors

In 2024, there were 10.7 million people employed in high-
technology sectors across the EU 

High-technology sectors drive economic growth and 
productivity, and often provide well-paid employment 
opportunities. In 2024, there were 10.7 million people 
employed in high-technology sectors across the EU, which 
represented 5.2% of total employment. 

More about the data: employment in high-
technology sectors 

High-technology sectors comprise high-technology 
manufacturing sectors and knowledge-intensive high-
technology services. The statistics presented for these 
sectors cover all people (including support staff) who 
work for enterprises in these sectors, not the number of 
highly qualified workers. 

High-technology sectors are identified in terms of a 
sectoral approach. Based on division level data: 

•  high-technology manufacturing covers NACE 
Divisions 21 and 26 (manufacture of basic 
pharmaceutical products and pharmaceutical 
preparations; manufacture of computer, electronic 
and optical products)

•  high-tech knowledge-intensive services cover 

NACE Divisions 59 to 63 and 72 (motion picture, 
video and television programme production, sound 
recording and music publish activities; programming 
and broadcasting activities; telecommunications; 
computer programming, consultancy and related 
activities; information service activities; scientific 
research and development).

A distinction is made between high-technology 
manufacturing sectors and knowledge-intensive high-
technology services due to the existence of 2 different 
methodologies: 

•  R&D intensities are used to distinguish between high, 
medium-high, medium-low and low-technology 
manufacturing industries

•  the proportion of the workforce that has completed 
a tertiary education is used to distinguish between 
knowledge-intensive services and other services.

Eurostat regional yearbook – 2025 edition164Research and development

B

Map 9.2: Employment in high-technology sectors
(by NUTS 2 regions, 2024)

Note: EU = 10.7 million people employed in high-technology sectors, 
equivalent to 5.2% of total employment. High-technology sectors: high-
technology manufacturing and knowledge-intensive high-technology 
services. Ipeiros (EL54) and Guadeloupe (FRY1): 2023. Includes data with 
low reliability (too many regions to document).

In 2024, more than 100 000 females were employed in 
high-technology sectors in the French and Spanish capital 
regions 

In addition to identifying the regions with the highest 
numbers of people employed in high-technology sectors 
for both sexes, Figure 9.2 also highlights the importance of 
these sectors in providing female and male employment. 

•  In absolute terms, the highest counts of females working 
in high-technology sectors in 2024 were recorded in 
the French and Spanish capital regions of Ile-de-France 
(197 000) and Comunidad de Madrid (114 000).

Source: Eurostat (online data code: htec_emp_reg2)

•  8 other EU regions had more than 50 000 females 
employed in high-technology sectors in 2024: the 
capital regions of Berlin (Germany), Eastern and Midland 
(Ireland), Lazio (Italy) and Warszawski stołeczny (Poland), 
as well as Oberbayern and Köln (Germany), Cataluña 
(Spain) and Lombardia (Italy).

•  For men, Ile-de-France (302 000), Lombardia (194 000) 
and Comunidad de Madrid (190 000) recorded the 
highest counts of male employment in high-technology 
sectors in 2024.

•  10 other EU regions reported more than 100 000 males 
employed in high-technology sectors in 2024. This 
group included all of the regions mentioned above 
with the highest counts of female employment, as well 
as Darmstadt (Germany), Rhône-Alpes (France) and the 
Swedish capital region of Stockholm.

165Eurostat regional yearbook – 2025 editionB

Research and development

Across the EU, high-technology sectors accounted for 5.2% 
of total employment in 2024. This share peaked, among 
NUTS level 2 regions, in the Slovak capital region of 
Bratislavský kraj (14.1%). 

There were 10 other regions – all capital regions – with 
at least 1 in 10 people from their respective workforces 
employed in high-technology sectors: those of Hungary, 
Czechia, Croatia, Ireland, Sweden, Denmark, Poland, 
Lithuania, Finland and Bulgaria. 5 more capital regions 
ranked among the top 20 regions with the highest shares 
– those of Germany, Portugal, Romania, Spain and France. 

There was a considerable gender gap in the share of 
employment within high-technology sectors in 2024. 
While 3.7% of the EU’s female workforce was employed in 
these sectors, the corresponding share among males was 
notably higher, at 6.4%. 

•  The Hungarian capital region of Budapest recorded the 

highest share of female employment in high-technology 
sectors, at 9.6%. It was followed by 2 other capital 
regions – Grad Zagreb in Croatia (9.3%) and Eastern 
and Midland in Ireland (8.9%) – which were the only 
other regions in the EU with shares exceeding 8.0% for 
females.

•  For men, the Slovak capital region of Bratislavský kraj had 
the highest share, at 20.3%. There were 3 other regions 
that recorded shares above 15.0% for males: Budapest 
(16.5%), the Czech capital region of Praha (15.7%) and 
Stockholm (15.1%).

•  Among the 20 regions with the highest shares of female 
employment in high-technology sectors, 17 were capital 
regions. The only exceptions were Karlsruhe (Germany), 
Southern (Ireland) and Pest (Hungary).

•  A similar pattern was observed for men, with 16 of 
the 20 regions with the highest male employment 
shares being capital regions. The exceptions included 
Karlsruhe and Southern (which also appeared in the 
female rankings), as well as Oberbayern (Germany) and 
Prov. Brabant wallon (Belgium).

Figure 9.2: Employment in high-technology sectors
(% of total employment, by sex and NUTS 2 regions, 2024)

Note: the figure shows the EU regions with the highest share of 
employment in high-technology sectors. High-technology sectors are 
defined as high-technology manufacturing and knowledge-intensive 
high-technology services. Includes data with low reliability (too many 
regions to document).

Source: Eurostat (online data code: htec_emp_reg2)

Eurostat regional yearbook – 2025 edition166Research and development

B

Women in research and innovation

than 200 000 females were employed as scientists and 
engineers: 

In 2024, the EU had 19.6 million people employed as 
scientists and engineers, this equated to 8.9% of the total 
labour force. 

More about the data: scientists and engineers 

Scientists and engineers refer to people who, working 
in those capacities, use or create scientific knowledge 
and engineering and technological principles, in other 
words: 

•  people with scientific or technological training who 
are engaged in professional work on science and 
technology activities

•  high-level administrators and personnel who direct 
the execution of science and technology activities.

Within the context of statistics on human resources in 
science and technology (HRST), scientists and engineers 
are people who conduct research, improve or develop 
concepts, theories and operational methods and/or 
apply scientific knowledge relating to fields which are 
covered by the following occupations – as defined in 
the International standard classification of occupations 
(ISCO-08): 

•  science and engineering professionals (ISCO 2-digit 

code 21)

•  health professionals (ISCO 2-digit code 22)
•  information and communications technology 

professionals (ISCO 2-digit code 25).

HRST indicators on scientists and engineers are 
compiled as head counts; these data may be used 
to derive the share of scientists and engineers in the 
total population or their share of the labour force. 
Statistics are collected for NUTS level 1 and NUTS level 2 
regions and cover people aged 15 to 74 years. The data 
presented here – analysed by sex – are only available at 
the more aggregated level of detail. 

Monitoring female participation in the fields of science 
and technology provides insight into gender gaps and 
supports efforts to promote equality and make full use 
of the potential talent available within the labour force. 
In 2024, of the 19.6 million people employed across the 
EU as scientists and engineers, 7.9 million were female – 
equivalent to 40.4% of the total. 

Map 9.3 presents information on the number and 
share of female scientists and engineers across NUTS 
level 1 regions. In 2024, the highest counts of female 
scientists and engineers were recorded in the French 
capital region of Ile-de-France (327 000) and the western 
German region of Nordrhein-Westfalen (286 000). 
There were 5 other regions across the EU where more 

•  Este in Spain
•  West-Nederland in the Netherlands
•  Bayern and Baden-Württemberg in Germany
•  Continente in Portugal.

Female scientists and engineers are generally 
underrepresented across the EU. Out of 91 NUTS 
level 1 regions for which data are available, 79 had a higher 
number of male than female scientists and engineers. 
The 12 regions where females made up a majority of the 
scientists and engineers in employment (as shown by the 
darkest shade of green in Map 9.3) included: 

•  4 of the 7 regions in Spain – Canarias, Centro, Noroeste 

and Sur

•  Makroregion centralny and Makroregion wschodni in 

Poland

•  Regiões Autónomas dos Açores e da Madeira in Portugal
•  Severna i Yugoiztochna Bulgaria in Bulgaria
•  Corse in France (2023 data)
•  Norra Sverige in Sweden
•  Latvia.

As mentioned above, females accounted for 40.4% of 
all scientists and engineers in the EU. The 3 regions with 
the highest shares of female scientists and engineers 
in 2024 were all island regions: Canarias (58.8%), Região 
Autónoma dos Açores (57.3%) and Região Autónoma 
da Madeira (56.4%). Several factors may, at least in part, 
explain this pattern in island regions: 

•  public sector institutions – such as universities and 

research institutes – often dominate the local labour 
market, focusing on fields like environmental science, 
marine biology or sustainable tourism, which tend to 
attract more women

•  public employers may be more likely to adopt gender 
equality measures in recruitment and employment
•  higher rates of male outward migration may leave a 

greater share of women in the local workforce

•  as some of these island regions are among the least 
populated regions in the EU, the relatively small size 
of their labour force may influence the data; relatively 
small populations may result in greater year-on-year 
fluctuations and make proportional measures – such 
as the share of female scientists and engineers – more 
sensitive to local labour market dynamics.

Indeed, despite having high proportions of female 
scientists and engineers, many island regions have 
relatively low overall employment in these professions. 
For example, the lowest numbers of female scientists and 
engineers among NUTS level 1 regions were recorded in 
Região Autónoma da Madeira (4 000), Região Autónoma 
dos Açores (5 000) and Corse (6 000; 2023 data). 

167Eurostat regional yearbook – 2025 editionB

Research and development

In 2024, females made up fewer than 37.0% of all scientists 
and engineers in 23 NUTS level 1 regions across the EU 
(as shown by the lightest shade of green in Map 9.3). This 
group included: 

•  10 regions in Germany, including the capital region of 

Berlin

•  4 regions in France
•  3 regions in Italy
•  all 3 regions of Hungary
•  Manner-Suomi in Finland
•  Luxembourg
•  Slovakia.

Looking in more detail, there were 9 NUTS level 1 regions 
where women accounted for fewer than 1 in 3 scientists 
and engineers in 2024. The lowest shares were recorded 
in the Hungarian region of Közép-Magyarország (30.0%), 
Manner-Suomi in Finland (30.7%) and Sud in Italy (31.1%). 
This group of 9 regions also included 4 German regions – 
Baden-Württemberg, Saarland, Hessen and Bremen – as 
well as Luxembourg and Bretagne (France). 

High-technology sectors include high-technology 
manufacturing and knowledge-intensive high-technology 
services. These activities employed 3.6 million females 
in 2024, equivalent to just over 1 in 3 (33.4%) people 
employed in high-technology manufacturing and 
knowledge-intensive high-technology services. 

Map 9.3: Female scientists and engineers
(by NUTS 1 regions, 2024)

Note: EU = 7.9 million female scientists and engineers, equivalent to 
40.4% of all scientists and engineers. Corse (FRM): 2023. Bremen (DE5), 
Mecklenburg-Vorpommern (DE8), Saarland (DEC), Corse (FRM), Região 
Autónoma dos Açores (PT2) and Região Autónoma da Madeira (PT3): low 
reliability.

Source: Eurostat (online data code: hrst_st_rsex)

Eurostat regional yearbook – 2025 edition168In 2024, the highest numbers of females employed in 
high-technology sectors were, unsurprisingly, found 
in some of the EU’s most populous and research-
intensive regions. There were 10 NUTS level 2 regions 
where more than 50 000 females worked in high-
technology sectors. Together, these regions accounted 
for almost 900 000 females in employment – around 25% 
of the EU’s total female workforce in high-technology 
sectors. This was roughly equal to the cumulative number 
of females employed in high-technology sectors across 
the 138 regions with the lowest counts. The 10 regions 
with more than 50 000 females employed in high-
technology sectors included 6 capital regions. 

•  The highest employment counts were recorded in the 
French and Spanish capital regions of Ile-de-France 
(197 000 females employed in high-technology sectors) 
and Comunidad de Madrid (114 000).

•  Other capital regions with more than 50 000 females 
employed in high-technology sectors included Lazio 
(Italy), Berlin (Germany), Warszawski stołeczny (Poland) 
and Eastern and Midland (Ireland).

•  The 4 non-capital regions with a high count of females 
employed in high-technology sectors were all densely 
populated, metropolitan areas from some of the largest 
EU countries: Lombardia (northern Italy), Cataluña 
(eastern Spain), Oberbayern and Köln (both Germany).

Although some progress has been made in terms of 
female participation, men continued to dominate the 
workforce. Among the 218 NUTS level 2 regions for which 
data are available in 2024, men had a majority of the 
high-technology workforce in every region (see Map 9.4), 
underlining the gender employment gap in these sectors.

Research and development

B

In 2024, almost 50.0% of Haute-Normandie’s high-
technology workforce were women 

In 2024, there were 55 NUTS level 2 regions where females 
accounted for more than 36.5% of all employment in 
high-technology sectors (as shown by the darkest shade 
of green in Map 9.4). In several eastern EU countries, a 
large proportion of their regions had relatively high female 
shares, including: 

•  all 8 regions of Hungary
•  both regions in Slovenia
•  3 out of 4 regions in Croatia
•  4 out of 6 regions in Bulgaria.

In addition, there were several regions with relatively high 
female shares in some of the larger western and southern 
EU countries, including 8 regions in France, 6 regions in 
Germany and 4 regions in each of Spain and Italy. 

At the top end of the distribution, there were a few 
cases where female participation in high-technology 
sectors approached parity with men. The highest 
share was recorded in the northern French region 
of Haute-Normandie, where females accounted for 
almost 1 in 2 (49.5%) people employed across high-
technology sectors in 2024. The next highest shares were 
observed in the Hungarian regions of Észak-Magyarország 
and Észak-Alföld (both 48.3%), followed by the French 
region of Languedoc-Roussillon (48.0%). 

Female representation was considerably lower across 
a broad range of regions. In 2024, there were 54 NUTS 
level 2 regions where females made up less than 29.0% of 
the workforce in high-technology sectors (as shown by the 
lightest shade of green in Map 9.4). The lowest shares were 
concentrated in: 

•  9 out of 12 regions in the Netherlands
•  7 regions in Germany
•  7 regions in Italy
•  4 out of 8 regions in Sweden
•  4 out of the 9 Greek regions for which data are available
•  Luxembourg
•  Malta.

169Eurostat regional yearbook – 2025 editionB

Research and development

Map 9.4: Female employment in high-technology sectors
(by NUTS 2 regions, 2024)

Note: EU = 3.6 million females employed in high-technology sectors, 
equivalent to 33.4% of all people employed in high-technology sectors.  
Severozapaden (BG31) and Poitou-Charentes (FRI3): 2023. Includes data 
with low reliability (too many regions to document).

Source: Eurostat (online data code: htec_emp_reg2)

Eurostat regional yearbook – 2025 edition17010. Tourism

Tourism plays a key role in the European Union (EU) 
economy, contributing significantly to economic 
growth, employment and regional development. With 
diverse landscapes, rich cultural heritage and world-
class attractions, the EU is a leading tourist destination. 
Tourism has the potential to support regional economies, 
particularly in coastal, mountain or outermost regions, 
while also fostering cross-border connections; jobs created 
or maintained by tourism can help counteract industrial or 
rural decline. 

Tourism can also have negative consequences or 
externalities. It presents economic challenges, such as 
seasonal employment fluctuations or excessive demand 
during peak seasons, placing a strain on local infrastructure 
and/or acting as a nuisance for local communities. 
Tourism also has an environmental impact: locally 
through noise, pollution, waste generation, wastewater 
discharge and habitat loss, and globally through transport 
emissions. Understanding regional tourism patterns 
helps policymakers develop strategies for sustainable 
growth, balancing economic benefits with social and 
environmental impacts. 

In 2019, before the COVID-19 crisis, the number of nights 
spent in EU tourist accommodation stood at 2.88 billion 
nights spent. Tourism demand plummeted in 2020, 
as the number of nights spent by domestic tourists 
fell 33.4%, while international tourism was hit even harder, 
down 69.7%. The gradual easing of travel restrictions, 
renewed consumer confidence and the enduring appeal 
of EU destinations drove a steady recovery, initially led 
by domestic tourists. A marked increase in the number 
of international tourists followed in 2022, as the number 
of nights spent accelerated. In 2023, the total number of 
nights spent in EU tourist accommodation surpassed the 
pre-pandemic total, reaching 2.94 billion. 

The infographic below provides information on the EU 
regions with the highest numbers of nights spent in tourist 
accommodation during 2023. The list includes popular 
destinations for: 

•  beach holidays, such as Mallorca, Tenerife, Alicante/

Alacant or Málaga in Spain

•  city destinations, such as Barcelona and Venezia, as well 
as the French, Italian and Spanish capital regions Paris, 
Roma and Madrid

•  mountain destinations, such as Bolzano-Bozen in 

northern Italy.

(million nights, by NUTS 3 regions, 2023)

Source: Eurostat (online data code: tour_occ_nin3)

Note: there were 2.94 billion nights spent in tourist accommodation across 
the EU.

171Eurostat regional yearbook – 2025 editionB

Tourism

Number of nights spent in tourist 
accommodation

•  the coastal region of Venezia (38.2 million) and the 

mountainous region of Bolzano-Bozen (36.1 million), 
both in Italy.

More about the data: tourism statistics 

Tourism, in a statistical context, refers to the activity of 
visitors taking a trip to a destination outside their usual 
environment, for less than a year. This definition is wider 
than the common everyday definition, insofar as it 
encompasses not only private leisure trips but also visits 
to family and friends, as well as business trips. 

Tourism statistics are traditionally collected from 
suppliers of tourism services through surveys of tourist 
accommodation establishments or from administrative 
data. These establishments include all types of 
accommodation which provide, as a paid service, 
accommodation for tourists. They are defined according 
to the activity classification NACE and include: 

•  hotels and similar establishments (NACE Group 55.1)
•  holiday and other short-stay accommodation (NACE 

Group 55.2)

•  camping grounds, recreational vehicle parks and 

trailer parks (NACE Group 55.3).

The annual statistics presented in this chapter generally 
refer to 2023. They are typically presented for NUTS 
level 3 regions, although a more detailed breakdown 
between domestic and international tourists is only 
available at NUTS level 2 for Belgium. For Albania (2023) 
and for Türkiye (2022), only national data are available 
for the latest year. 

In 2023, there were 2.94 billion nights spent in tourist 
accommodation across the EU, 53.2% of which were 
accounted for by domestic tourists and 46.8% by 
international tourists. The number of nights spent in tourist 
accommodation reflects both the length of stay and the 
number of tourists and is considered a key indicator for 
examining tourism developments, even if it does not cover 
stays at non-rented accommodation, nor same-day visits 
(without an overnight stay). 

Map 10.1 presents regional information on the total 
number of nights spent in tourist accommodation 
during 2023 (as denoted by the size of each circle). There 
were 9 NUTS level 3 regions where the number of nights 
spent was above 30.0 million, including: 

•  the Spanish island regions of Mallorca (51.2 million nights 

spent) and Tenerife (34.3 million)

•  the capital regions of Paris in France (43.9 million), Roma 
in Italy (41.1 million) and Madrid in Spain (30.2 million)

•  the coastal regions of Barcelona (39.8 million) and 

Alicante/Alacant (32.0 million), both in Spain

Domestic tourists accounted for a majority (53.2%) of 
the total nights spent in EU tourist accommodation 
in 2023. They visited a wide range of destinations, as they 
accounted for a majority of the nights spent in 943 out 
of 1 107 NUTS level 3 regions. By contrast, international 
tourists were often concentrated in a small number of 
regions that rank among the most frequented tourist 
destinations in the EU; this may create tourism pressures 
and have an impact on sustainable development. 

Map 10.1 shows information on the origin of EU tourists, 
identifying 3 specific groups, namely those regions 
characterised by: 

•  a relatively high proportion of nights spent by domestic 
tourists – gold-coloured circles indicate the 827 regions 
where domestic tourists accounted for at least 65.0% of 
the total nights spent

•  a relatively equal share of nights spent between 

domestic and international tourists – green circles 
indicate the 190 regions where the share of domestic/
international tourists was greater than 35.0%, but less 
than 65.0%

•  a relatively high proportion of nights spent 

by international tourists – blue circles indicate 
the 90 regions where international tourists accounted for 
at least 65.0% of the total nights spent.

Looking in more detail, the regions most favoured 
by international tourists were primarily island, coastal, 
alpine and capital regions, where tourism generally 
plays an important role in the local economy. Most 
of these 90 regions experience considerable tourism 
pressures, with pronounced peaks in demand, typically 
during the summer and/or winter seasons. Such 
pressures need to be balanced with the needs of local 
communities, with sustainable development contributing 
to the preservation and protection of natural and urban 
environments. In addition to 15 capital regions – those of 
Belgium, Bulgaria, Czechia, Estonia, Ireland, Greece, Croatia, 
Italy, Latvia, Hungary, Malta, the Netherlands, Austria, 
Portugal and Slovenia – this group was composed of: 

•  20 predominantly island regions in Greece
•  11 predominantly mountainous regions in Austria
•  10 predominantly island and/or coastal regions in Spain
•  10 predominantly urban regions in Italy
•  9 predominantly island and/or coastal regions in Croatia
•  4 Slovenian, 3 Bulgarian, 3 Portuguese, 2 Irish 

and 1 Dutch region

•  Cyprus and Luxembourg.

Eurostat regional yearbook – 2025 edition172Map 10.1: Nights spent in tourist accommodation
(by NUTS 3 regions, 2023)

B

Tourism

Note: the origin of tourists is defined as a share of total nights spent 
by mainly domestic (≥ 65.0%), mixed (> 35.0% and < 65.0%), mainly 
international (≥ 65.0%) tourists. Belgium: NUTS level 2. Several regions in 
Germany (too many to document): 2022.

Berlin had the highest count of nights spent by domestic 
tourists in 2023 … 

Figure 10.1 shows the most frequented tourist destinations 
in the EU, based on NUTS level 3 regions. It provides 
information for those regions with the highest numbers of 
nights spent in tourist accommodation, detailing the top 
regions for all tourists, domestic tourists and international 
tourists. 

Capital regions serve as economic, political and financial 
hubs, attracting professionals for meetings, conferences and 

Source: Eurostat (online data code: tour_occ_nin3)

other corporate activities. Furthermore, they are also cultural 
destinations, with historical sites, museums, concert venues, 
theatres and sporting events. As such, capital regions serve 
as both business and cultural centres, often with demand 
from domestic travellers for both professional and leisure 
purposes. In 2023, the EU regions with the highest number 
of nights spent by domestic tourists included: 

•  Berlin in Germany (17.5 million)
•  Paris in France (15.5 million)
•  Madrid in Spain (14.2 million)
•  Roma in Italy (12.9 million).

173Eurostat regional yearbook – 2025 editionB

Tourism

After the 4 capital regions of the EU’s largest economies, 
the regions with the highest number of nights spent by 
domestic tourists were primarily coastal areas. In 2023, 
domestic tourists spent at least 12.0 million nights 
in 4 other regions: 

•  Hérault (12.7 million) and Var (12.0 million) along France’s 
southern coast, which include popular destinations such 
as Montpellier, Sète, Saint-Tropez and Fréjus

•  Alicante/Alacant (12.5 million) on the south-eastern coast 
of Spain, which includes popular destinations such as 
Benidorm, Altea or Calpe

•  Hamburg (12.4 million) in northern Germany, a bustling 

port city known for its maritime heritage, cultural 
attractions and vibrant neighbourhoods.

… while Mallorca had the highest number of nights spent 
by international tourists 

In 2023, the Spanish island region of Mallorca had, 
by far, the highest count of nights spent in tourist 
accommodation by inte\rnational tourists, at 47.2 million. 
There were 6 regions within the EU where international 
tourists spent between 26.9 million and 29.8 million nights: 

•  2 more Spanish regions – Tenerife (29.8 million) and 

Barcelona (29.7 million)

•  2 Italian regions – Venezia (29.1 million) and Roma 

(28.2 million)

•  the French capital region of Paris (28.4 million)
•  the Croatian coastal region of Istarska županija 

(26.9 million).

Figure 10.1: Top tourist regions in the EU
(million nights spent in tourist accommodation, by NUTS 3 regions, 2023)

Note: the scales on the numeric axes are different.

Source: Eurostat (online data code: tour_occ_nin3)

(1) Includes also Karpathos – Iroiki Nisos Kasos, Kos, Rodos.
(2) Splitsko-dalmati\nska županija.

Figure 10.2 presents, for each EU country, the most 
frequented region by domestic and by international 
tourists (based on the share of the total number of nights 
spent in tourist accommodation in each country). These 
relative shares reflect, to some degree, the number of 

regions in each EU country; Cyprus and Luxembourg are 
single regions at NUTS level 3 (and hence are not shown). 

There were 11 EU countries (out of 25 for which data 
are presented) where the same region was the most 
frequented by both domestic and international tourists: 

Eurostat regional yearbook – 2025 edition174B

Tourism

•  in Denmark, Germany, Estonia, France, Latvia, Malta, 

•  Miasto Kraków in Poland (with its historic old town and 

Finland and Sweden this was the capital region – Byen 
København, Berlin, Põhja-Eesti, Paris, Rīga, the island of 
Malta, Helsinki-Uusimaa and Stockholms län

•  in Bulgaria, Austria and Portugal this was a region other than 
the capital – Burgas (a coastal region on the Black Sea coast), 
Pinzgau-Pongau (an Alpine region south of Salzburg) and 
Algarve (a coastal region on the Atlantic coast).

In half of the remaining EU countries (7 out of 14), 
the capital region was the most frequented among 
international tourists; this was the case in Belgium, Czechia, 
Lithuania, Hungary, the Netherlands, Romania and Slovakia. 
In the other 7 countries, the most frequented regions for 
international tourists included: 

•  the coastal regions of Kalymnos, Karpathos, Kasos, Kos, 
Rodos in Greece, Mallorca in Spain, Istarska županija in 
Croatia and Venezia in Italy

•  the mountainous region of Gorenjska in Slovenia (which 

includes the popular tourist attraction of Lake Bled)

•  the Border region of Ireland (which includes the rugged 

landscapes and coastline of Donegal)

rich cultural heritage).

Among the same 14 EU countries (where a different region 
was most frequented by domestic and by international 
tourists), the most frequented regions by domestic tourists 
were often less well-known internationally. Leaving aside 
the 3 landlocked countries, all but 2 of the most frequented 
regions for domestic tourists were coastal: Prov. West-
Vlaanderen in Belgium, South-West in Ireland, Andros, 
Thira, Kea, Milos, Mykonos, Naxos, Paros, Syros, Tinos in 
Greece, Primorsko-goranska županija in Croatia, Roma in 
Italy, Klaipėdos apskritis in Lithuania, Koszaliński in Poland, 
Constanţa in Romania and Obalno-kraška in Slovenia. 
The only exceptions were the Spanish capital region of 
Madrid and the forested region of Veluwe in the centre 
of the Netherlands. In the landlocked countries, the most 
frequented regions were Královéhradecký kraj (including 
the Krkonoše and Orlické mountain ranges) in Czechia, 
Veszprém (a region including many castles, hills and 
vineyards near Lake Balaton) in Hungary and Prešovský kraj 
(including parts of the Tatra mountain range) in Slovakia. 

Figure 10.2: Regional concentration of nights spent in tourist accommodation in each EU country
(% share of nights spent, by NUTS 3 regions, 2023)

Note: the figure shows the regions which recorded the highest share of 
nights spent by tourists (separately for domestic and international tourists) 
in each EU country. Belgium: NUTS level 2. Cyprus and Luxembourg: single 

regions at NUTS level 3. Several regions in Germany are not available (too 
many to document).

Source: Eurostat (online data code: tour_occ_nin3)

175Eurostat regional yearbook – 2025 editionB

Tourism

The number of nights spent in EU tourist accommodation 
increased 6.8% in 2023 

Having recovered strongly from the impact of the 
COVID-19 pandemic during 2022 (up 50.2%), the number 
of nights spent in tourist accommodation across the EU 
continued to grow in 2023, albeit at a considerably slower 
pace, with annual growth of 6.8%. 

A majority of NUTS level 3 regions saw the number of 
nights spent in their tourist accommodation increase 
between 2022 and 2023. This was the case for 907 out of 
the 1 117 regions for which data are available (as shown 
by the teal shades in Map 10.2). At the top end of the 
distribution, there were 49 regions where the number of 
nights spent rose by at least 25.0%; they were dispersed 

Map 10.2: Nights spent in tourist accommodation
(% annual change, by NUTS 3 regions, 2023)

across most EU countries. Focusing on those regions 
that already had at least 1.0 million nights spent in tourist 
accommodation during 2022, the highest annual rates of 
change for 2023 were observed in: 

•  the Border region of Ireland, where the number of 

nights spent more than doubled, rising from 4.0 million 
to 8.1 million (up 101.8%)

•  Peiraias, Nisoi, a region in close proximity to the Greek 

capital (up 39.4%)

•  the Austrian capital region of Wien (up 30.5%)
•  the predominantly urban region of Dortmund in 

Germany (up 30.0%)

•  Blagoevgrad in south-western Bulgaria (up 29.7%).

Source: Eurostat (online data codes: tour_occ_nin3 and tour_occ_ninat)

Eurostat regional yearbook – 2025 edition176B

Tourism

In 204 NUTS level 3 regions, the number of nights spent 
in tourist accommodation fell between 2022 and 2023 (as 
shown by the golden shades in Map 10.2). At the lower 
end of the distribution, 23 regions experienced a fall of 
more than 10.0%. Poland (7 regions), Belgium, Hungary, the 
Netherlands (each 3 regions) and Ireland (2 regions) were 
the only EU countries to report more than a single region 
with a double-digit decline. Focusing on those regions 
that already had at least 1.0 million nights spent in tourist 
accommodation during 2022, the lowest annual rates of 
change for 2023 were observed in: 

•  Mid-East Ireland, which includes, among other towns, 

Kildare and Wicklow (down 23.2%)

•  Monza e della Brianza, located to the north of Milano in 

Italy (down 19.4%)

•  South-East Ireland, which includes, among other 

cities and towns, Waterford, Kilkenny and Wexford 
(down 14.1%)

•  Arta, Preveza in north-western Greece (down 11.7%).

Seasonality

Seasonality has a considerable impact on tourism. It is 
linked to various environmental factors, such as climate or 
geographical location, as well as socioeconomic factors 
like public and school holidays or factories closing for 
annual leave. Tourist arrivals are generally more evenly 
spread than the number of nights spent across the 
calendar year, due to a higher concentration of longer 
stays in the summer months. 

In the 3rd quarter of 2023, tourists spent 1.2 billion nights 
in EU accommodation. As such, the months of July, 
August and September together accounted for 41.9% of 
the total nights spent during the year. The 2nd quarter 
of 2023 accounted for more than 1 in 4 (26.5%) nights 
spent during 2023, with lower shares for the 4th quarter 
(17.0%) and the 1st quarter (14.6%). 

Map 10.3 shows the total number of nights spent in tourist 
accommodation during each quarter of 2023 for NUTS 
level 1 regions. 

•  In the Austrian region of Westösterreich, more than 1 in 3 

(34.7%) of all nights spent in tourist accommodation 
were during the 1st quarter of the year; the highest 
regional share in the EU.

•  Nearly 1 in 3 (32.5%) of all nights spent in the Finnish 

archipelago of Åland occurred during the 2nd quarter of 
the year; the highest regional share in the EU.

•  More than 2 out of 3 of all nights spent in Severna 
i Yugoiztochna Bulgaria (69.3%) and Croatia (68.8%) 
occurred during the 3rd quarter of the year; the highest 
regional shares in the EU.

•  The Spanish island region of Canarias had the highest 

share of nights spent during the 4th quarter of the year, 
at 25.9%, followed closely by the Spanish capital region 
(25.8%). The distribution of nights spent in Canarias was 
particularly notable, as it remained evenly balanced 
throughout the year: the lowest share was 22.6% in 
the 2nd quarter, while the highest share was 26.5% in 
the 3rd quarter.

Warm temperatures and relatively high sunshine hours 
during the winter months may, at least in part, explain 
the year-round demand for tourist accommodation in 
Canarias. A similar pattern was also observed in the French 
outermost regions (Régions Ultrapériphériques Françaises). 
Many of the other EU regions with relatively stable tourist 
demand throughout 2023 were predominantly urban 
regions, where professional travel and weekend visitors 
help balance summer tourism. This was the case, for 
example, in the capital regions of Région de Bruxelles-
Capitale / Brussels Hoofdstedelijk Gewest (Belgium) and 
Comunidad de Madrid (Spain). 

At the other end of the spectrum, tourist accommodation 
in Croatia, Severna i Yugoiztochna Bulgaria (on the 
Black Sea coast) and the French island region of Corse 
experienced high levels of seasonal tourism demand. 
In these 3 coastal destinations, at least 65.0% of the 
total number of nights spent in tourist accommodation 
occurred during the 3rd quarter of 2023. This pattern 
reflects the peak in demand for beach tourism and a 
substantial influx of visitors for relatively long stays from 
July to September. 

177Eurostat regional yearbook – 2025 editionB

Tourism

Map 10.3: Nights spent in tourist accommodation
(by NUTS 1 regions, 2023)

Note: the shares of all nights spent in EU tourist accommodation were 
14.6% during the 1st quarter, 26.5% during the 2nd quarter, 41.9% during 
the 3rd quarter and 17.0% during the 4th quarter.

Source: Eurostat (online data code: tour_occ_nin2m)

Eurostat regional yearbook – 2025 edition178B

Tourism

•  Fuerteventura and Lanzarote, which are both island 
regions in Canarias (Spain), had the next highest 
ratios, at 118 700 nights spent per 1 000 inhabitants 
and 117 800 nights spent per 1 000 inhabitants, 
respectively

•  3 more Greek island regions also featured in this group: 

 ◦ Kalymnos, Karpathos – Iroiki Nisos Kasos, Kos and 

Rodos in the Aegean Sea

 ◦ Andros, Thira, Kea, Milos, Mykonos, Naxos, Paros, Syros 

and Tinos (also in the Aegean Sea)

 ◦ Kerkyra in the Ionian Sea

•  while 2 mountainous regions in western Austria also 

recorded very high ratios of tourism intensity – Tiroler 
Oberland and Pinzgau-Pongau.

By contrast, there were 128 NUTS level 3 regions across 
the EU with fewer than 1 000 tourist nights spent 
per 1 000 inhabitants in 2023 (as shown by the lightest 
shade of yellow in Map 10.4). Most of these regions were 
clustered in 5 EU countries: 

•  Poland (29 regions) – with the southern region of 

Rybnicki recording its lowest ratio, at 351 nights spent in 
tourist accommodation per 1 000 inhabitants
•  Romania (24 regions), with the southern region of 

Teleorman having the lowest ratio, at 22 nights spent in 
tourist accommodation per 1 000 inhabitants

•  Germany (20 regions), with the western city of Herne 

in North Rhine-Westphalia recording its lowest 
ratio, at 403 nights spent in tourist accommodation 
per 1 000 inhabitants

•  Bulgaria (14 regions), with the north-eastern region of 

Targovishte having its lowest ratio, at 332 nights spent in 
tourist accommodation per 1 000 inhabitants
•  Belgium (11 regions), with Arr. Soignies, located 
to the north-east of Mons in the Hainaut region, 
recording its lowest ratio, at 284 nights spent in tourist 
accommodation per 1 000 inhabitants.

Alongside Teleorman in Romania, the Greek region of 
Dytikos Tomeas Athinon – a densely populated region 
to the west of the capital, largely composed of industrial 
and/or residential neighbourhoods – was the only other 
region in the EU with fewer than 100 tourist nights 
per 1 000 inhabitants in 2023. 

Tourism pressures

For every 1 000 inhabitants in the EU, an average 
of 6 574 nights were spent in tourist accommodation 
during 2023 

Since the rise of mass tourism in the 1950s and 1960s, 
EU regions have been affected by tourism in different 
ways. Some regions continue to attract very few visitors, 
while others have seen a significant increases in tourism. 
This reflects – at least in part – developments such as 
the emergence of package holidays, low-cost airlines, 
online booking platforms and alternative forms of 
accommodation. Additionally, an increasing share of the 
EU’s workforce is choosing to work as digital nomads. 

Sustainable tourism aims to preserve and enhance cultural 
and natural heritage, including the arts, gastronomy 
or biodiversity. The success of tourism depends on its 
sustainability, with the quality of destinations often 
shaped by their natural and cultural environments 
and their integration into the local community. For the 
purpose of this publication, tourism intensity is defined 
as the number of nights spent in tourist accommodation 
per 1 000 inhabitants. 

In 2023, there were 6 574 nights spent in EU tourist 
accommodation per 1 000 inhabitants. The regional 
distribution of tourism intensity was heavily skewed, 
highlighting that mass tourism tends to be concentrated 
in relatively few regions. The ratio of tourist nights spent 
per 1 000 inhabitants was higher than the EU average 
in fewer than 3 out of 10 NUTS level 3 regions (320 out 
of 1 117 for which data are available). The statistics 
presented are likely to underestimate the true extent of 
tourism pressures, given the numerator for the ratio does 
not include same-day visitors, some people staying in 
short-stay accommodation offered via online platforms, 
or people staying in non-rented accommodation (such 
as 2nd homes, or staying with friends/relatives). 

Map 10.4 shows those EU regions that experienced the 
highest tourism pressures in 2023. A total of 40 NUTS 
level 3 regions had at least 40 000 nights spent in tourist 
accommodation per 1 000 inhabitants (as indicated by the 
darkest shade of blue). At the upper end of the distribution, 
there were 9 regions with more than 100 000 tourist nights 
spent per 1 000 inhabitants: 

•  Zakynthos, a Greek island region in the Ionian Sea, had 
the highest ratio of tourism intensity, at 149 900 nights 
spent per 1 000 inhabitants

•  Istarska županija on the Adriatic coast in Croatia, 

had the 2nd highest ratio, at 133 400 nights spent 
per 1 000 inhabitants

179Eurostat regional yearbook – 2025 editionB

Tourism

Map 10.4: Nights spent in tourist accommodation
(per 1 000 inhabitants, relative to resident population, by NUTS 3 regions, 2023)

Note: Albania and Türkiye, national data. Türkiye: 2022.

Source: Eurostat (online data codes: tour_occ_nin3 and tour_occ_ninat)

For each square kilometre of the EU, an average 
of 697 nights were spent in tourist accommodation 
during 2023 

An alternative indicator for analysing tourism pressures is 
tourism density, defined here as the total number of nights 
spent in tourist accommodation per square kilometre 
(km²) of area. In 2023, the EU average was 697 nights spent 
per km². 

Map 10.5 shows that this measure of tourism pressure 
was concentrated in predominantly urban regions, which 
generally have much smaller areas. In 2023, 51 NUTS 
level 3 regions had a ratio of tourism density of at 
least 10 000 nights spent per km² (as shown by the darkest 
shade of blue). 

The French capital region of Paris was a clear outlier, 
recording by far the highest concentration of tourists 

in 2023, with 418 300 nights spent per km². This 
was 4.7 times as high as in any other region of the EU: the 
Greek capital region – Kentrikos Tomeas Athinon – had 
the 2nd highest ratio, with 88 500 nights spent per km². 

There were 15 NUTS level 3 regions in the EU where the 
ratio of tourism density was higher than 20 000 nights 
spent per km² in 2023. Just over half of this group was 
composed of capital regions: those of France and Greece 
(already mentioned above), together with the capital 
regions of Denmark, Belgium, Austria, Malta, Czechia and 
Germany. There were also very high ratios recorded in: 

•  4 predominantly urban regions of Germany – München, 

Frankfurt am Main, Düsseldorf and Hamburg

•  2 suburban regions located around the French capital – 

Hauts-de-Seine and Seine-Saint-Denis

•  the popular island region of Lanzarote that forms part of 

Canarias in Spain.

Eurostat regional yearbook – 2025 edition180Map 10.5: Nights spent in tourist accommodation
(per km2, relative to area, by NUTS 3 regions, 2023)

B

Tourism

Note: Albania and Türkiye, national data. Türkiye: 2022.

Source: Eurostat (online data codes: tour_occ_nin3 and tour_occ_ninat)

At the other end of the distribution, 113 regions had 
tourism density ratios below 75 nights per km² in 2023 
(as shown by the lightest shade of yellow in Map 10.5). 
These regions were generally concentrated in rural areas 
of northern, eastern and southern EU countries. The only 
exceptions were Elbe-Elster in eastern Germany and the 
French regions of Haute-Saône, Meuse (both in eastern 
France), Creuse (central France) and Guyane (an outermost 
region). 

At the very bottom of the distribution, 3 regions recorded 
particularly low tourism density ratios: Călăraşi and 
Teleorman in southern Romania, as well as Guyane 
(an outermost region of France), each reporting fewer 
than 10 tourist nights per km². 

181Eurostat regional yearbook – 2025 editionB

Tourism

Guest nights spent at short-stay 
accommodation offered via online 
collaborative economy platforms

In 2023, Málaga in southern Spain was the most 
frequented region for nights spent in short-stay 
accommodation offered via selected online booking 
platforms 

Developments in information and communication 
technologies have had a major impact on the tourist 
accommodation market. The emergence of online 
platforms has made it easier for small-scale service 
providers to advertise/offer their rooms, apartments and 
holiday homes to potential guests, with a rapid expansion 
of this market. 

The rapid expansion of guest nights spent in short-term 
accommodation offered via the selected online booking 
platforms has brought significant benefits to some regions 
in the EU. Popular destinations have seen increased visitor 
numbers, driving economic activity, while investment 
in short-term accommodation can help to revitalise 
underused or vacant properties. 

More about the data: experimental statistics 
on short-stay accommodation offered via 
online platforms 

The information presented in this chapter has, up to 
this point, been based on official tourism statistics, 
compiled according to Regulation (EU) No 692/2011. 
These statistics provide limited coverage of holiday and 
short-stay accommodation, as data for holiday homes, 
apartments and rooms in otherwise private buildings 
are often outside the scope of tourism registers and 
surveys. 

Official statistics on holiday and short-stay 
accommodation are generally underestimated, given 
that several EU countries limit the scope of observations 
to establishments with, for example, at least 10 bed 
places. In recent years, this coverage issue has been 
further compounded by the emergence of online 
platforms that provide relatively simple methods for 
private individuals and small enterprises to offer short-
stay accommodation; this has led to a surge in the 
provision of this type of accommodation. 

For this reason, Eurostat embarked on an experimental 
data collection exercise aimed at improving the 
completeness/coverage of tourism statistics. It is based 
on a previously unexplored channel, namely data 
on listings/bookings obtained directly from 4 major 
online platforms (Airbnb, Booking.com, Tripadvisor 
and Expedia Group). The exercise is currently restricted 
to the collection of information on holiday and short-
stay accommodation (NACE Group 55.2), reflecting the 
principal type of accommodation for service providers 
within the collaborative economy. 

On the other hand, housing affordability has become a 
pressing issue in some regions experiencing a surge in the 
supply of short-stay accommodation, as long-term rental 
availability decreases and property prices rise. Additionally, 
an influx of tourists can lead to overcrowding, straining 
infrastructure and public services, while communities may 
be displaced as the character of neighbourhoods changes 
and local culture and identity are eroded. 

Figure 10.3 shows the NUTS level 3 regions that recorded 
the highest number of guest nights spent at short-stay 
accommodation offered via online booking platforms 
in 2023. The southern Spanish coastal region of Málaga 
recorded the highest count, at 17.9 million guest nights, 
while 7 more regions reported at least 10.0 million guest 
nights: 

•  the capital regions of Roma in Italy (13.5 million), Paris in 
France (12.2 million) and Madrid in Spain (10.1 million)

•  2 more Mediterranean coastal regions in Spain – 

Barcelona (12.7 million) and Alicante/Alacant (12.0 million)

•  2 coastal regions in other EU countries – Algarve in 

Portugal (10.4 million) and Splitsko-dalmatinska županija 
in Croatia (10.0 million).

The information presented in Figure 10.3 on the 
overall number of guest nights spent at short-stay 
accommodation offered via online booking platforms can 
be analysed according to the origin of guests. For example, 
international guests accounted for approximately 3 out 
of 4 guest nights spent in Málaga during 2023. 

In 2023, the 20 most frequented NUTS level 3 regions for 
short-stay accommodation booked by domestic guests 
via the selected online booking platforms were almost 
exclusively located in Spain and France, with 2 regions 
from Poland the only exceptions. 

•  In Spain, the most frequented regions for domestic 

guests were generally developed, coastal regions on 
the southern and eastern coastlines – Málaga, Alicante/
Alacant, Cádiz and Valencia/València – the only 
exceptions being the capital region of Madrid and the 
north-western Atlantic coastal region of Asturias.

Eurostat regional yearbook – 2025 edition182B

Tourism

•  In France, several of the regions most frequented 
by domestic tourists were relatively rural areas, 
characterised by their coastline and/or mountains, 
including: 

 ◦ Haute-Savoie and Savoie in the Alps
 ◦ Bouches-du-Rhône, Hérault, Var and Alpes-Maritime 

on the Mediterranean coast

 ◦ Gironde, Charente-Maritime, Morbihan, Vendée, 
Calvados and Finistère on the Atlantic coast.

•  In Poland, the 2 most frequented regions included: 

 ◦ the predominantly urban region of Trójmiejski on the 
northern coastline that includes Gdańsk, Gdynia and 
Sopot

 ◦ the predominantly rural, southern region of 

Nowotarski, which acts as a gateway to the Tatra 
mountains.

In the list of the 20 most frequented regions by domestic 
guests, it was generally the case that domestic visitors 
accounted for a higher share (than international visitors) 
of the overall number of guest nights in 2023. This 
pattern was observed in 15 out of the 20 regions, with the 
exceptions being Málaga, Alicante/Alacant, Madrid, Alpes-
Maritimes and Valencia/València. 

Figure 10.3: Top tourist regions in the EU for short-stay accommodation offered 
via selected online booking platforms
(million guest nights, by NUTS 3 regions, 2023)

Note: the scales on the numeric axes are different. The figure shows the 
regions with the highest number of guest nights spent by all tourists, 
domestic guests and international guests. Several regions in Germany, 
Latvia, the Netherlands, Portugal and Finland (too many to document): not 
available.

(1) Splitsko-dalmatinska županija.
(2) Área Metropolitana do Porto.
(3) Primorsko-goranska županija.

Source: Eurostat (online data code: tour_ce_oan3)

In 2023, 8 different countries featured in the ranking of 
the 20 most frequented NUTS level 3 regions for short-
stay accommodation booked by international guests via 
the selected online booking platforms. The vast majority 
of these 20 regions were among Europe’s most popular 
destinations: 

•  Spain had the highest number of regions, with 6: 

Málaga, Barcelona, Alicante/Alacant, Tenerife, Madrid and 
Mallorca

•  Italy followed with 4 predominantly urban regions: 

Roma, Milano, Firenze and Venezia

•  there were 3 Croatian regions along the Adriatic 

coastline: Splitsko-dalmatinska županija (with Split as its 
capital), Istarska županija (with Pula as its capital) and 
Primorsko-goranska županija (with Rijeka as its capital)

183Eurostat regional yearbook – 2025 editionB

Tourism

•  the list also included 2 French regions: Paris and Alpes-

Maritimes

•  2 Portuguese regions: Algarve and Área Metropolitana 

do Porto

•  the capital regions of Budapest (Hungary) and Wien 

•  the Spanish region of Este – which includes, among 
other cities, Barcelona, Alicante/Alacant and Valencia/
València – had the highest numbers of guest nights 
during the 2nd and the 4th quarters, with 13.7 million 
and 9.1 million, respectively.

(Austria), as well as

•  Cyprus.

In the list of the 20 most frequented regions by international 
visitors for 2023, the number of guest nights spent by 
international visitors was consistently higher than the 
number for domestic visitors. In absolute terms, the largest 
differences were recorded in those regions with the highest 
overall numbers of guest nights: for example, international 
visitors in Roma spent 10.1 million more guest nights in 
short-stay accommodation offered via the selected online 
booking platforms than domestic visitors. The next largest 
differences between guest nights spent by international and 
domestic visitors were recorded in Barcelona (9.9 million), 
Splitsko-dalmatinska županija (9.4 million), Málaga 
(9.0 million) and Paris (8.6 million). In relative terms, Croatian 
regions had the biggest disparity, as the number of guest 
nights spent by international visitors outnumbered that 
for domestic visitors by a factor of 30 : 1 in both Splitsko-
dalmatinska županija and Istarska županija. 

Online booking platforms have made it easier for 
travellers to book accommodation year-round. Although 
these platforms provide the opportunity for a more 
even distribution of tourist flows, some destinations 
continue to experience considerable peaks in demand. 
Map 10.6 illustrates the seasonality of guest nights spent 
at short-stay accommodation offered via the selected 
online booking platforms in each quarter of 2023. Across 
the EU, there were 719 million guest nights spent at such 
accommodation in 2023. Of this total, 13.4% were in 
the 1st quarter of the year, 25.0% in the 2nd quarter, 43.2% 
in the 3rd quarter and 18.4% in the final quarter. 

During the 3rd quarter of 2023, there were 24.6 million 
guest nights spent in Croatia in short-stay accommodation 
offered via selected online booking platforms 

Map 10.6 shows the number of guest nights spent at 
short-stay accommodation offered via the selected online 
booking platforms during each quarter of 2023; the figures 
are reflected in the size of the circles for each region. Across 
NUTS level 1 regions, the highest count of guest nights 
spent was recorded in Croatia, with 24.6 million nights 
in the 3rd quarter of 2023. A similar analysis for the other 
quarters reveals that: 

•  Auvergne-Rhône-Alpes in France had the highest 

number of guest nights spent during the 1st quarter 
of 2023, at 6.3 million, reflecting its position as a leading 
destination for winter sports

In all but 4 of the NUTS level 1 regions for which data are 
available, the 3rd quarter had the highest share of guest 
nights spent during 2023, coinciding with the traditional 
summer peak season. This was particularly evident in 
Croatia, where 71.2% of all guest nights were spent during 
this period. Additionally, 16 other regions across the EU 
reported that the 3rd quarter accounted for more than 
half of all guest nights spent in 2023. However, 4 regions 
exhibited a different seasonal pattern: 

•  in Westösterreich (Austria) and Régions 

Ultrapériphériques Françaises (France), the 1st quarter 
of 2023 had the highest share, at 36.3% and 32.1%, 
respectively

•  in the Spanish regions of Canarias and Comunidad 

de Madrid, the final quarter of 2023 accounted for the 
highest share, at 30.0% and 28.6%, respectively.

Looking in more detail at seasonal variations, the share of 
all guest nights spent during: 

•  the 1st quarter of 2023 ranged from a low of 1.8% in 

Nisia Aigaiou, Kriti (Greece), Corse (France) and Croatia to 
a high of 36.3% in Westösterreich

•  the 2nd quarter of 2023 ranged from a low of 15.9% in 
Westösterreich to a high of 31.6% in West-Nederland
•  the 3rd quarter of 2023 ranged from a low of 20.9% in 

Régions Ultrapériphériques Françaises to a high of 71.2% 
in Croatia

•  the 4th quarter of 2023 ranged from a low of 4.6% in 

Croatia to a high of 30.0% in Canarias.

As was the case for tourist accommodation (see above), 
predominantly urban regions tended to experience 
relatively stable demand for short-stay accommodation 
offered via the selected online booking platforms 
throughout the year. This was particularly evident 
in 2023 for the metropolitan region of Nordrhein-Westfalen 
(Germany), as well as in the capital regions of Comunidad 
de Madrid (Spain), Région de Bruxelles-Capitale / Brussels 
Hoofdstedelijk Gewest (Belgium) and Yugozapadna i Yuzhna 
tsentralna Bulgaria (Bulgaria). 

Many regions across the EU exhibited seasonal patterns 
of tourism demand, particularly during the summer 
months. This was particularly evident in Croatia, Severna 
i Yugoiztochna Bulgaria (on the Black Sea coast), Kentriki 
Elláda and Nisia Aigaiou, Kriti (both Greece), as well as in 
the French island region of Corse. In each of these coastal 
regions, the 3rd quarter of 2023 accounted for at least 63.8% 
of all guest nights spent in short-stay accommodation 
offered via the selected online booking platforms. 

Eurostat regional yearbook – 2025 edition184Map 10.6: Guest nights spent at short-stay accommodation
(by NUTS 1 regions, 2023)

B

Tourism

Note: guest nights spent at short-stay accommodation offered via 
selected booking platforms. EU shares were 13.4% during the 1st quarter, 
25.0% during the 2nd quarter, 43.2% during the 3rd quarter and 18.4% 
during the 4th quarter.

Source: Eurostat (online data code: tour_ce_omn12)

185Eurostat regional yearbook – 2025 editionCEnvironment and 

natural resources

11. Transport

Transport is vital to the EU economy: it supports global 
supply chains and daily life, enabling the free movement of 
people and goods within the EU’s single market. However, 
transport also imposes considerable negative externalities 
in the form of societal, environmental and material costs, 
such as greenhouse gas emissions, air pollution, noise, 
congestion and injuries/fatalities from accidents. With the 
transport sector accounting for around 25% of the EU’s 
greenhouse gas emissions, EU transport policy seeks to 
promote environmentally friendly, safe and efficient travel 
through integrated networks. 

The EU’s Sustainable and Smart Mobility Strategy 
(COM(2020) 789 final) part of The European Green Deal sets 
out policy initiatives to achieve a greener and more resilient 
transport system that remains accessible, affordable and 
inclusive, while promoting high levels of safety and security 
across all modes of transport. Within the strategy, there are a 
number of key milestones, including: 

• 30 million zero-emission cars in use by 2030
• a doubling of high-speed rail traffic by 2030
• nearly all new cars, vans, buses and heavy-duty vehicles

to be zero-emission by 2050

• a fully operational, multimodal Trans-European Transport
Network (TEN-T) connecting Europe sustainably by 2050

• zero-emission large aircraft ready for market by 2050.

The passenger car transformed personal mobility, granting 
freedom and flexibility while playing a crucial role in 

economic development. It also carries cultural significance 
– symbolising status and progress for some, while others
choose to forego the use of a car. As the EU transitions
towards greener technologies, the passenger car remains
central to sustainability and innovation debates.

In 2023, the EU had 256 million registered passenger cars, 
of which electric vehicles made up 1.7%. On average, there 
were 9.9 electric cars per 1 000 inhabitants across the EU 
(see the infographic below). The highest motorisation 
rate across EU regions was in the Flevoland (central 
Netherlands), with 141.7 electric cars per 1 000 inhabitants 
– this high value may reflect, at least in part, the presence
of vehicle leasing companies headquartered in the
region. Prov. Vlaams-Brabant in Belgium (54.5 electric
cars per 1 000 inhabitants) and Provincia Autonoma di
Trento in Italy (49.5 per 1 000 inhabitants) had the 2nd
and 3rd highest rates. These figures may reflect specific
regional characteristics, such as favourable tax schemes,
subsides for new vehicle registrations and local policies
supporting the expansion of charging infrastructure, all of
which make electric vehicle registration/ownership more
attractive in these regions.

The selection of information presented for regional 
transport statistics within the Eurostat regional yearbook 
changes on an annual basis (covering different modes 
of transport and focusing on different passenger/freight 
indicators). Previous editions of the publication can be 
found at the bottom of this webpage. 

(number of electric passenger cars per 1 000 inhabitants, by NUTS 2 
regions, 2023)

Source: Eurostat (online data codes: tran_r_elvehst, road_eqs_carpda, 
demo_r_d2jan and demo_pjan) 

Note: France, NUTS level 1. Bulgaria and Portugal: national data. Corse 
(FRM), Utrecht (NL35) and Zuid-Holland (NL36): not available.

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Road transport and accidents

Roads are by far the most common transport mode in the 
EU for passenger and inland freight transport. The EU’s 
Sustainable and Smart Mobility Strategy – putting European 
transport on track for the future (COM(2020) 789 final) 
underlines that, although road mobility provides a wide 
range of benefits to people living in the EU, these are not 
without costs (for society at large). These externalities 
include, among other issues, greenhouse gas emissions, 
pollution, accidents and congestion. The Sustainable and 
Smart Mobility Strategy identifies 10 flagship areas and a 
range of intermediate targets for 2030 and 2040 in order 
to help achieve the EU’s ambitious goal of becoming the 
‘first climate-neutral continent by 2050’. As part of this 
work, the EU has set itself a target, namely, to ensure that 
at least 30 million zero-emission vehicles are operating on 
its roads by 2030. 

ELECTRIC PASSENGER CARS 

Within the EU, the stock of electric passenger cars 
reached 4.4 million in 2023; accounting for 1.73% of 
all passenger cars. The relative importance of electric 
passenger cars was highest in the Nordic countries, 
Benelux countries, Austria and Germany. By contrast, 
electric passenger cars made up a relatively small share of 
the total stock in most southern and eastern regions of the 
EU. 

The uptake of electric passenger cars reflects, among 
other factors, income levels, price differentials between 
electric and other vehicles, subsidies and incentives, 
infrastructure investment, battery technology, fuel prices, 
urban policies, the availability and cost of public transport, 
and environmental consciousness. 

During the most recent decade for which data are 
available, the number of electric cars in the EU has shown 
remarkable growth: 

• their total number passed the threshold of 1.0 million
vehicles by 2020 and more than 3.0 million by 2022

• the highest annual growth rates occurred

in 2020 and 2021, when the number of electric
passenger cars increased 85.3% and 77.5%, respectively

• in absolute terms, the largest annual increase was

recorded in 2023, as the number of electric passenger
cars rose by 1.4 million.

In 2023, the regional distribution of the share of electric 
cars among all passenger cars was slightly skewed: 
in 121 out of the 215 NUTS regions with available data, in 
other words, 56.3% of the regions, the share of electric 
passenger cars was below the EU average. Map 11.1 shows 
that this share was relatively homogeneous within most 
EU countries, suggesting that factors such as national 
subsidies and incentives or other national factors likely 
play an important role in determining the uptake of these 
vehicles. For example, every region in Vlaams Gewest 
(northern Belgium), Denmark, the Netherlands, Austria 
and Sweden recorded a share above the EU average, while 
Budapest – the capital region of Hungary – was the only 
region across all of the eastern EU countries to do so. 

A total of 17 regions reported that electric cars made up at 
least 4.00% of all passenger cars (as shown by the darkest 
shade of blue in Map 11.1): 

• the largest number were located in Denmark

(all 5 regions) and Sweden (5 out of 8 regions), including
the capital regions of Hovedstaden and Stockholm
• there were 3 regions in the Netherlands, among them

the capital region of Noord-Holland

• there were 2 regions in Belgium, including the capital

region of Région de Bruxelles-Capitale / Brussels
Hoofdstedelijk Gewest

• this group also included the Finnish capital region of

Helsinki-Uusimaa
• and Luxembourg.

At the top end of the distribution, the central Dutch 
region of Flevoland recorded by far the highest share of 
electric cars in 2023, at 17.07%. This unusually high figure 
may reflect the presence of vehicle leasing companies 
based in the region, which register large fleets of electric 
vehicles and thereby inflate the number of registrations 
relative to the size of the regional population. The Swedish 
capital region of Stockholm (10.74%) was the only other 
region in the EU to record a double-digit share, followed 
by Hovedstaden in Denmark (8.64%) and Prov. Vlaams-
Brabant in Belgium (7.60%). 

At the lower end of the distribution, 46 regions reported 
that electric cars accounted for fewer than 0.25% of all 
passenger cars in 2023; these are shown by the lightest 
shade of yellow in Map 11.1. This group was largely 
concentrated within Czechia (5 out of 8 regions), Greece 
(11 of 13), Poland (14 of 17) and Slovakia (3 of 4). There 
was also a cluster in southern Italy (6 regions), while the 
remainder of the group consisted of 4 regions from Spain, 
2 regions from Romania, as well as a single region from 
Croatia. 

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Map 11.1: Electric passenger cars
(% of all passenger cars, by NUTS 2 regions, 2023)

Note: France, NUTS level 1. Bulgaria, Portugal, Albania and Serbia: national 
data. North Macedonia and Albania: 2022.

Source: Eurostat (online data codes: tran_r_elvehst and tran_r_vehst)

ELECTRIC RECHARGING POINTS 

Regulation (EU) 2023/1804 on the deployment of alternative 
fuels infrastructure aims to support the EU’s climate goals 
by ensuring the coordinated roll-out of alternative fuels 
infrastructure and a smooth transition to renewable (‘zero-
carbon’) fuels. The regulation emphasises the need to 
increase the capacity of public recharging points for electric 
cars across the EU, alongside stimulating the deployment 
of fast charging stations and dedicated infrastructure for 
heavy-duty vehicles. It sets binding targets for EU countries, 
including minimum fast-charging densities on some of the 
EU’s principal routes by 2025 and a total of 3.5 million semi-
public or public charging units by 2030. 

The majority of recharging points in the EU are 
concentrated in predominantly urban regions, leaving rural 
areas and inter-city routes less well-served. Cross-border 

travel by electric car may be hindered by fragmented 
payment systems and varying standards, although 
ongoing efforts aim to improve the interoperability of the 
EU’s charging network. 

In 2023, there were 145 600 semi-public and public electric 
recharging points across the EU, with a combined capacity 
of 5.4 million kilowatts (kW). Relative to the fleet of electric 
vehicles, each charging point provided an average 
capacity of 1.1 kW per vehicle. It is important to note that 
across the EU the predominant modes of recharging are 
private charging at home, followed by workplace charging, 
while semi-public and public recharging points are used 
less frequently. When people recharge their electric 
vehicles at home or work, the charging outlets typically 
have lower power output, resulting in longer charging 
durations but lower costs compared with faster, more 
expensive public charging points. 

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Transport

Semi-public and public electric charging infrastructure is 
unevenly distributed across the EU. In urban parts of some 
western EU countries – such as across Germany, France, the 
Netherlands and Austria – the network of charging points 
is relatively dense, reflecting the large stock of electric 
vehicles in relatively small areas; this was also the case in 
several regions across northern Belgium and northern 
Italy. By contrast, eastern EU countries generally have fewer 
charging points, although these often provide higher 
recharging capacity. 

In 2023, there were 42 700 semi-public or public 
recharging points in the Netherlands, equivalent to 29.3% 
of the EU total. More than 1 in 5 points were located in 
Germany (21.8%), while France (12.2%) and Italy (10.0%) 
were the only other countries with double-digit shares. 
In terms of capacity, Germany’s semi-public or public 
recharging infrastructure delivered 1.6 million kW of power 
to their network (equivalent to 30.3% of the EU total); 
France (15.5%) and the Netherlands (13.0%) were the only 
other countries with double-digit shares. 

Map 11.2: Electric recharging points
(kW capacity per electric vehicle, by NUTS 2 regions, 2023)

Note: semi-public and publicly available recharging points. France: NUTS 
level 1. Bulgaria, Denmark, Greece and Portugal: national data.

Source: Eco-Movement, Eurostat (GISCO) and Eurostat (online data 
code: tran_r_elvehst, road_eqs_carpda, road_eqs_busmot and 
road_eqs_lormot)

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Map 11.2 shows the average charging capacity per electric 
vehicle (denoted in kW). In 2023, approximately 2 out 
of 3 EU regions for which data are available – 133 out 
of 198 (67.2%) – recorded a rate above the EU average 
of 1.1 kW per electric vehicle. This group included: 

• every region in Czechia, Slovenia and Slovakia
• all but 2 regions in Italy (with Provincia Autonoma

di Trento and Valle d’Aosta/Vallée d’Aoste being the
exceptions)

• all but 1 region in Austria (with the capital region of

Wien being the exception)

• Bulgaria (only national data available), Estonia, Cyprus

and Latvia.

Note that some regions with high rates of charging 
capacity per electric vehicle may have relatively few 
electric vehicles, with charging points installed primarily 
to ensure minimum coverage across large, sparsely 
populated areas. 

By contrast, in 2023, the average recharging capacity per 
electric vehicle was below the EU average in every region 
of Ireland, as well as in Denmark, Greece, Portugal (for all of 
which only national data are available), Luxembourg and 
Malta. 

The Slovak regions of Stredné Slovensko (6.4 kW per 
electric vehicle), Západné Slovensko (6.2 kW) and 
Východné Slovensko (5.3 kW) recorded the highest 
average recharging capacities per electric vehicle across 
the EU in 2023. The next highest value was observed 
in the western Polish region of Lubuskie (5.1 kW). There 
were 12 other EU regions where the average capacity 
was at least 4.0 kW per electric vehicle: half of these were 
in Italy, while the remainder included 3 Spanish regions, 
2 regions in Czechia and an additional Polish region. 

At the other end of the range, 3 EU regions recorded an 
average capacity of less than 0.05 kW per electric vehicle. 
These included 2 southern French regions – Auvergne-
Rhône-Alpes and Provence-Alpes-Côte d’Azur – as well as 
the Finnish archipelago of Åland. 

ROAD FREIGHT TRANSPORT 

The road freight transport sector is a key part of modern 
economies, offering vital services that link producers, 
traders and consumers. It supports supply chains, enables 
regional trade and adapts to evolving demands such as 
digital logistics, green mobility and just-in-time delivery 
systems. 

More about the data: road freight statistics 

Road freight transport statistics relate to transport 
by heavy goods vehicles registered in any of the 
EU countries. Transport by light goods vehicles is 
excluded. The threshold for inclusion as a heavy goods 
vehicle may be based on the load capacity (maximum 
permissible weight of goods) or the legally permissible 
maximum weight (the vehicle, the load, the driver 
and other persons carried). Some countries have a 
somewhat broader coverage as they apply lower 
inclusion thresholds. 

A tonne-kilometre is a unit of measure of freight 
transport which represents the transport of 1 tonne 
of goods (including packaging and tare weights of 
intermodal transport units) by a given transport mode 
(road, rail, air, sea, inland waterways, pipeline and so on) 
over a distance of 1 kilometre. 

Regional statistics for road freight transport should 
be interpreted with care as the data presented may 
reflect, to some extent, the size of each region, as those 
regions characterised by a large area normally transport 
more freight. In a similar vein, those regions that are 
characterised by transporting bulk products that tend 
to weigh a lot (such as raw materials) are also likely to 
report higher values. The information presented in 
this section refers to those regions where goods were 
unloaded. 

In 2023, the total weight of goods transported by heavy 
goods vehicles registered in the EU was 13.1 billion tonnes. 
When taking account of the distance travelled for each 
transport operation, the transport performance of the 
EU’s road freight transport sector was 1 857 billion tonne-
kilometres (tkm). 

In 2023, some of the most populous regions of the EU 
had the highest volumes of road freight unloaded by 
EU-registered vehicles. A peak was recorded in the eastern 
Spanish region of Cataluña (42.8 billion tkm of road freight 
unloaded). Lombardia in northern Italy and Andalucía in 
southern Spain also had high levels, at 39.7 billion tkm 
and 34.1 billion tkm, respectively. There were 8 regions 
with road freight performance within the range 
of 22.1 billion tkm to 30.0 billion tkm: 

• the Spanish regions of Comunitat Valenciana and

Comunidad de Madrid

• the Italian regions of Veneto and Emilia-Romagna
• the Polish regions of Wielkopolskie and Śląskie
• the French regions of Rhône-Alpes and Ile-de-France.

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Transport

In 2023, the north-eastern Spanish region of Aragón had 
the highest level of road freight transport per inhabitant 

When expressed in relation to the number of inhabitants 
in each region, Aragón in north-east Spain recorded the 
highest level of road freight unloaded per inhabitant 
in 2023, at 10 300 tkm. Despite its relatively small 
population, Aragón serves as a logistics hub for freight 
traffic along key east–west and north–south corridors that 
link, among other places, Spain’s 2 largest cities, as well as 
northern Spain with France and the rest of Europe. 

The next highest levels of road freight transport – ranging 
from 8 000 tkm to 8 500 tkm per inhabitant in 2023 – were 
observed in 2 more Spanish regions, Castilla-La Mancha 
and Comunidad Foral de Navarra, the German regions 
of Kassel and Bremen, and the Swedish region of Övre 
Norrland

An additional 16 regions across the EU recorded an 
average of at least 6 500 tkm of road freight unloaded 
per inhabitant (as shown by the darkest shade of blue in 
Map 11.3): 

• the Belgian region of Prov. West-Vlaanderen
• the Czech region of Střední Čechy
• the German regions of Sachsen-Anhalt, Brandenburg,

Oberpfalz and Weser-Ems

• the Spanish regions of Castilla y León and La Rioja
• the Polish regions of Wielkopolskie, Lubuskie, Łódzkie,

Podlaskie and Mazowiecki regionalny
• the Finnish region of Pohjois- ja Itä-Suomi
• the Swedish regions of Småland med öarna and

Mellersta Norrland.

Map 11.3: Road freight transport
(1 000 tonne-kilometres per inhabitant, by NUTS 2 region of unloading, 2023)

Note: EU estimate made for the purpose of this publication (based on 
available data, excluding Malta).

Source: Eurostat (Transport statistics) and Eurostat (online data code: 
demo_r_d2jan)

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ROAD FATALITIES 

As noted above, while transport mobility brings many 
benefits, it also comes with environmental and societal 
costs – including greenhouse gas emissions, pollution, 
congestion and accidents – all of which impact our health 
and well-being. 

In a statistical context, the number of road fatalities refers 
to people who died immediately in a road traffic accident 
or within 30 days as a result of injuries sustained in a road 
traffic accident. In 2023, there were 20 617 road fatalities 
across the EU’s territory, equivalent to 46 deaths per million 
inhabitants. 

The EU’s roads are among the safest in the world. In 
October 2021, the European Parliament adopted a 
resolution on the EU Road Safety Policy Framework 2021–
2030 – Recommendations on next steps towards ‘Vision 
Zero’ (2021/2014), reaffirming the EU’s commitment 
to reduce the number of road deaths to near zero 
by 2050. The strategy set an intermediate target of 
halving the number of road fatalities and serious injuries 
between 2019 and 2030. 

Lombardia (northern Italy) had the highest number of road 
fatalities – 377 deaths in 2023 … 

In 2023, the northern Italian region of Lombardia recorded 
the highest number of road fatalities among NUTS 
level 2 regions, with 377 deaths – 25 fewer than in 2022. 
The only other regions in the EU where the number of 
road fatalities reached at least 300 were: 

• Lazio, the Italian capital region (346 deaths)
• Andalucía in southern Spain (310)
• Veneto in northern Italy (309)
• Rhône-Alpes in southern France (308).

At the other end of the range, 6 NUTS level 2 regions 
recorded fewer than 10 road fatalities in 2023. The sparsely 
populated Åland archipelago in Finland and the small 
Spanish autonomous region of Ciudad de Melilla were 
the only regions in the EU to report no road fatalities – 
the 2nd consecutive year without any such fatalities for 
Åland. The other regions with fewer than 10 deaths were: 

• Ciudad de Ceuta in Spain (1 death)
• Valle d’Aosta/Vallée d’Aoste in northern Italy (5)
• the Belgian capital region of Région de Bruxelles-

Capitale / Brussels Hoofdstedelijk Gewest (6)
• the French outermost region of Mayotte (7).

… while Severozapaden (Bulgaria) had the highest 
incidence rate – 166 road fatalities per million inhabitants 

In 2023, the EU recorded 46 road fatalities per million 
inhabitants. These fatalities were relatively evenly 
distributed: 123 out of 234 NUTS level 2 regions (or 52.6% 
of all regions) reported a rate above the EU average, 
104 had a value that was below, while there were 7 regions 
that had the same number of road fatalities per million 
inhabitants as the EU average. 

Map 11.4 highlights that several rural/peripheral regions 
of the EU reported some of the highest incidence rates 
for road fatalities. In 2023, 7 NUTS level 2 regions recorded 
at least 100 fatalities per million inhabitants (as shown by 
the darkest shade of blue in the map). These regions were 
mostly clustered in the south-east corner of the EU. The 
highest rates were observed in: 

• Bulgaria – Severozapaden recorded the

highest rate in the EU, with 166 road fatalities
per 100 000 inhabitants; the neighbouring region of
Severen tsentralen also reported a high rate (107 fatalities
per 100 000 inhabitants)

• Greece – the island regions of Ionia Nisia (120 fatalities
per 100 000 inhabitants) and Notio Aigaio (119 fatalities
per 100 000 inhabitants)

• France – the outermost region of Guyane (117 fatalities

per 100 000 inhabitants)

• Romania – Sud-Vest Oltenia (107 fatalities

per 100 000 inhabitants) and Sud-Est (102 fatalities
per 100 000 inhabitants).

By contrast, urban and capital regions tended to report 
much lower incidences of road fatalities. This may reflect 
factors such as more extensive public transport networks, 
lower motorisation rates and lower average speeds – for 
example, due to stricter speed limits in built-up areas or 
frequent congestion on urban motorway networks. 

In 2023, 26 NUTS level 2 regions recorded fewer 
than 25 deaths per million inhabitants (they are presented 
in the lightest yellow shade in Map 11.4). As noted above, 
2 regions reported no road fatalities at all: Åland (Finland) 
and Ciudad de Melilla (Spain). Leaving these atypical 
cases aside, most of these 26 regions were urban areas, 
with 11 of them being capital regions. Among those with 
the lowest incidence rates were: 

• Région de Bruxelles-Capitale / Brussels Hoofdstedelijk

Gewest in Belgium (5 road fatalities per million
inhabitants)

• Wien in Austria (6 fatalities per 100 000 inhabitants)
• Berlin in Germany (NUTS level 1; 9 fatalities

per 100 000 inhabitants).

Eurostat regional yearbook – 2025 edition194Map 11.4: Road fatalities
(per million inhabitants, by NUTS 2 regions, 2023)

C

Transport

Note: Portugal and Serbia, national data.

Source: Eurostat (online data codes: tran_r_acci and tran_sf_roadus)

ROAD ACCIDENTS 

In 2023, there were 868 800 accidents on the EU’s roads 
that resulted in a death or injury; this total excludes 
information for Ireland, Greece, Cyprus, Latvia, Malta and 
Sweden. Some of the highest numbers of road accidents 
were, unsurprisingly, recorded in relatively large and 
densely populated regions: 

• Lombardia recorded the most accidents in Italy

(29 200 accidents)

• Cataluña had the highest number in Spain (23 600)
• Ile-de-France recorded the most accidents in France

(15 900); note that for France, data are only available for
NUTS level 1 regions

• Nordrhein-Westfalen had the highest count in Germany
(63 300); note that for Germany, data are only available
for NUTS level 1 regions.

195Eurostat regional yearbook – 2025 editionC

Transport

More about the data: road accident statistics 

The information presented in this section on transport 
accidents comes from the Community database 
on road accidents (CARE), which is managed by the 
European Commission’s Directorate-General for 
Mobility and Transport. 

CARE contains information on road crashes that lead to 
death or injury; as such, it does not contain information 
on damage-only crashes, where there are no fatalities 
or injuries. Its main purpose is to provide evidence to 
identify and quantify road safety problems throughout 
the EU, evaluate the efficiency of road safety measures, 
determine the relevance of EU actions and facilitate the 
exchange of experiences in this field. 

Map 11.5: Road accidents
(per 1 000 motor vehicles, by NUTS 2 regions, 2023)

Road accident statistics also include fatalities and 
injuries in vehicles which are in transit through a region 
as well as fatalities and injuries of non-residents staying 
in a region on holiday, for business or another reason. 
As such, and other things being equal, regions that have 
transit corridors or regions with high numbers of visitors 
may well experience a higher incidence of fatalities and 
injuries. 

The regional statistics presented in this section for 
Germany and France concern NUTS level 1 regions, 
while the latest information for Portugal concerns 
national data. 

Note: Germany and France, NUTS level 1. Portugal: national data. EU: 
estimate based on available data.

Source: Eurostat (online data codes: tran_sf_roadnu and tran_r_vehst)

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Map 11.5 shows the incidence of road accidents 
in 2023. When expressed in relation to the number of 
motor vehicles, there were, on average, 2.8 accidents 
per 1 000 motor vehicles across the EU. The regional 
distribution of road accidents was asymmetrical, with 
approximately 40% of regions (68 out of the 171 for which 
data are available) recording an accident rate above the 
EU average, while 98 regions had a lower-than-average 
incidence; 5 regions recorded a rate equal to the EU 
average. 

In 2023, the highest road accident rates – at 
least 5.0 accidents per 1 000 motor vehicles (as shown 
by the darkest shade of blue in Map 11.5) – were mainly 
concentrated in western EU countries, with relatively high 
values in several regions across Belgium, Germany (NUTS 
level 1 regions) and Austria. Rates above this threshold 
were also recorded in 2 Croatian regions, 2 Spanish regions 
and 1 Italian region; this was also the case for Portugal 
(national data). 

The German capital region of Berlin (NUTS level 1) had 
the highest accident rate among EU regions in 2023, with 
an average of 9.2 accidents per 1 000 motor vehicles; 
this was 3.3 times as high as the EU average. Berlin was 
followed by the 2 other German city states (also NUTS 
level 1): Bremen (8.4) and Hamburg (8.2). The next highest 
rates were observed in 2 of Austria’s Alpine regions, 
namely, Vorarlberg (7.3) and Tirol (7.1). 

Across all EU regions, the number of road accidents 
per 1 000 motor vehicles ranged from a high 
of 9.2 in Berlin, down to lows of 0.4 in the Polish regions of 
Podlaskie and Warszawski stołeczny (the capital region). 
As such, relative to the number of motor vehicles, the 
likelihood of having a road crash that resulted in a fatality 
or injury was 23 times as high in Berlin as it was in either of 
these 2 Polish regions. 

Air traffic

In recent decades, liberalisation measures have 
contributed to the (rapid) growth of low-cost airlines and 
the expansion of smaller regional airports; the latter are 
typically less congested and charge lower landing fees 
than major international hubs. 

The regional statistics presented in this section generally 
refer to airports handling more than 150 000 passenger 
units per year. It is also worth noting that some EU 
regions – for example, the capital region of France – host 
multiple airports within their boundaries, while others 
have none. Based on this specific subset of airports, 
there were 1.4 billion air passengers carried in the EU 
during 2023. 

The French capital region of Ile-de-France – home 
to Paris-Charles de Gaulle and Paris-Orly airports – 
had 99.7 million air passengers in 2023 

In 2023, 37 NUTS level 2 regions (out of 168 for which data 
are available) recorded at least 10.0 million air passengers 
carried. These regions were often located in capital regions 
and other major economic centres across some of the EU’s 
largest countries, as well as in regions with popular holiday 
destinations. 

The left-hand side of Figure 11.1 highlights the 20 regions 
in the EU that had the highest counts of air passengers 
in 2023. The French capital region of Ile-de-France had 
the largest number, at 99.7 million air passengers carried. 
The Dutch capital region of Noord-Holland – home 
to Amsterdam Schiphol airport – had the 2nd highest 
number (61.9 million), followed by the Spanish capital 
region of Comunidad de Madrid, where Adolfo Suárez 
Madrid–Barajas Airport is located (60.1 million). 

This group of 20 regions included: 

• 9 capital regions, namely, those of France, the

Netherlands, Spain, Italy, Portugal, Ireland, Greece,
Denmark and Sweden

• 5 more regions located in Spain – Cataluña (which has

a high number of tourists, but is also an economic hub),
as well as the major tourist destinations of Canarias, Illes
Balears, Andalucía and Comunitat Valenciana

• 3 regions in Germany – Darmstadt (home to Frankfurt

Main airport), Oberbayern (home to Munich’s Franz Josef
Strauss International airport) and Brandenburg (home to
Berlin Brandenburg Airport Willy Brandt)

• 1 more region in Italy – Lombardia (that includes the city

of Milano)

• 1 region in Austria – Niederösterreich (home to Wien–

Schwechat airport)

• 1 more region in France – Provence-Alpes-Côte d’Azur

(with many tourist destinations).

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Transport

Figure 11.1 also provides information on the 20 NUTS 
level 2 regions in the EU with the highest increases 
in passenger numbers between 2019 and 2023 – in 
other words, comparing the situation before the 
COVID-19 pandemic with the latest available data. The 
number of air passengers carried increased by 11.7 million 
during this period in the eastern German region of 
Brandeburg. This rapid increase largely reflects the 
consolidation of air traffic at Berlin Brandenburg airport 
after 2020, following the closure of Berlin-Tegel airport in 
November 2020. 

Air passenger numbers also increased at a relatively fast 
pace between 2019 and 2023 in several popular tourist 
destinations, with gains of between 2.0 and 3.3 million 
passengers in: 

• the Spanish regions of Canarias, Illes Balears, Andalucía

and Comunitat Valenciana

• the Italian regions of Sicilia and Lombardia
• the Greek regions of Attiki and Notio Aigaio
• the Portuguese regions of Grande Lisboa and Norte (that

includes the city of Porto).

Figure 11.1: Air passengers carried
(million passengers, by NUTS 2 regions, 2023)

Note: the 1st part of the figure shows the EU regions with the highest 
number of air passengers carried, while the 2nd part shows the regions 
with the biggest increases compared with 2019.

Source: Eurostat (online data code: tran_r_avpa_nm)

There were 4 regions across the EU that handled more than 
a million tonnes of air freight and mail in 2023 – Ile-de-
France (France), Darmstadt, Leipzig (both Germany) and 
Noord-Holland (the Netherlands) 

The regional statistics presented in this section generally 
refer to airports handling more than 100 tonnes of freight 
and mail per year. Based on this subset of airports, the 
quantity of air freight and mail loaded and unloaded in 
the EU was 14.7 million tonnes during 2023. This marked 
a decrease of 3.1% compared with the pre-pandemic 
situation in 2019. 

The information presented in Figure 11.2 shows data for 
the 20 NUTS level 2 regions in the EU with the highest 
quantities of freight and mail in 2023. The vast majority of 
air cargo handled in EU airports is extra-EU international 
traffic to/from non-EU countries, where speed of delivery 
provides a competitive advantage. While some airports 
that handle freight also serve as major hubs for passenger 
traffic, others have developed a more specialised focus 
on cargo operations. This often reflects factors such 
as a strategic location near large population centres 
or key transport corridors, combined with operational 
advantages like the absence of night-time curfews. 

Eurostat regional yearbook – 2025 edition198C

Transport

In 2023, the French capital region of Ile-de-France recorded 
the highest quantity of air freight and mail, at 2.0 million 
tonnes. There were 3 other regions in the EU that reported 
at least 1.0 million tonnes: 

• Darmstadt in Germany (home to Frankfurt am Main

airport) – 1.9 million tonnes

• Leipzig in Germany (home to Leipzig/Halle airport, a

major express courier hub) – 1.4 million tonnes

• Noord-Holland in the Netherlands (home to

Amsterdam/Schiphol airport) – also 1.4 million tonnes.

Figure 11.2 also shows the main changes – 
between 2019 and 2023 – in the quantity of air freight and 
mail loaded and unloaded. The quantity of goods carried 
increased most rapidly in Leipzig (up 164 000 tonnes) and 

in the Spanish capital region of Comunidad de Madrid 
(up 143 000 tonnes). 

In 4 other regions across the EU, there were relatively 
large gains in the quantity of goods carried, ranging 
from 47 000 to 92 000 tonnes: 

• Prov. Liège (Liège airport serves as an express logistics

hub)

• Köln (Cologne Bonn airport is a hub for express parcel

operations)

• Podkarpackie (Rzeszów–Jasionka airport, in south-east
Poland, has become a key node for cargo operations
since the onset of Russian military aggression against
Ukraine, acting as a staging post for humanitarian aid
and military supplies)

• Budapest (the Hungarian capital region).

Figure 11.2: Air freight and mail
(1 000 tonnes, by NUTS 2 regions, 2023)

Note: the 1st part of the figure shows the EU regions with the highest 
volumes of air freight and mail, while the 2nd part shows the regions with 
the biggest increases compared with 2019. Freight loaded and unloaded. 

Source: Eurostat (online data code: tran_r_avgo_nm)

199Eurostat regional yearbook – 2025 editionC

Transport

Inland waterways

In 2023, 917.8 million tonnes of freight were handled 
by inland waterways across the EU (goods loaded and 
unloaded). Figure 11.3 presents the volume of freight 
handled across NUTS level 2 regions; note that many 
regions in the EU do not have any inland waterways that 
are used for transporting goods. 

In 2023, Zuid-Holland (the Netherlands) accounted for 
more than 15% of the EU’s freight handled (in other words, 
loaded and unloaded) by inland waterways

Inland waterway freight transport in the EU is highly 
concentrated in a relatively small number of regions – 
primarily in the Netherlands, Germany and Belgium – that 
are linked to major river systems. Zuid-Holland in the 
Netherlands recorded the highest freight volume handled 
in 2023, with 159.8 million tonnes of goods loaded or 
unloaded in this region. Its principal city, Rotterdam, plays 
a central role as Europe’s largest seaport and a gateway 
to the Rhine-Meuse-Scheldt river system. It facilitates 
efficient cargo transfer between ocean shipping and 
inland waterways through its extensive port and canal 
infrastructure. 

There were several other regions from the Netherlands 
that also featured prominently at the top of the list of 
the EU’s principal regions for handling inland waterways 
freight transport in 2023: Noord-Holland (67.9 million 
tonnes), Zeeland (44.3 million tonnes), Noord-Brabant 
(31.0 million tonnes), Limburg (23.7 million tonnes) 
and Gelderland (20.8 million tonnes) all reported more 
than 20 million tonnes of freight loaded and unloaded, 
reflecting the Netherlands dense and extensive inland 
waterway network. Some 65.0% of all inland waterway 
freight handled in the EU in 2023 was concentrated 
in just 11 NUTS level 2 regions, each handling more 
than 20 million tonnes. Outside of the Netherlands (see 
above), there were 5 other regions that surpassed this 
threshold: 

• the Belgian region of Prov. Antwerpen (99.5 million
tonnes), with the port of Antwerp lying on the tidal
section of the Scheldt river

• Düsseldorf (65.0 million tonnes), situated on the Rhine,

is a key hub for inland waterway freight transport within
Germany and further afield

• Prov. Oost-Vlaanderen (33.9 million tonnes) in Belgium,

which contains the port of Ghent

• the Sud-Est region of Romania (30.6 million tonnes),
crossed by the Danube – Europe’s 2nd-longest river
• Ile-de-France (20.2 million tonnes), primarily served by
the river Seine, a major inland waterway that facilitates
freight transport to and from the French capital region.

Figure 11.3 also presents data on the top EU regions where 
freight containers were loaded or unloaded on inland 
waterways, presenting the volume of goods handled in 
different EU regions. These containers – which measure at 
least 20 feet in length – are specially designed to facilitate 
the carriage of goods across 1 or more transport modes 
without intermediate reloading. 

In 2023, Zuid-Holland (the Netherlands) 
recorded 3.3 million twenty-foot equivalent units (TEUs) 
of inland waterway container freight handled, followed by 
Prov. Antwerpen (Belgium) with 1.9 million TEUs. Together, 
these 2 regions accounted for nearly half (48.4%) of the 
EU’s inland waterway container freight handling. Other 
regions with relatively high volumes included Noord-
Brabant (the Netherlands) and Düsseldorf (Germany), both 
handling 0.7 million TEUs, followed by Noord-Holland 
(0.4 million TEUs), Rheinhessen-Pfalz (Germany), Limburg 
(the Netherlands) and Ile-de-France (France) – each 
with 0.3 million TEUs. 

Container transport on inland waterways is therefore 
heavily concentrated in a limited number of EU regions, 
primarily in the Netherlands, Belgium and Germany. 
These countries benefit from extensive inland waterway 
networks that are integral to their transport systems, 
linking major ports such as Rotterdam, Antwerp-Bruges, 
Tilburg, Düsseldorf, Amsterdam, Ludwigshafen and Venlo – 
all vital hubs for container shipping. 

Eurostat regional yearbook – 2025 edition200Figure 11.3: Inland waterway freight transport
(by NUTS 2 regions, 2023)

C

Transport

Note: the 1st part of the figure shows the EU regions with the highest 
volumes of freight handled (loaded and unloaded) on inland waterways, 
while the 2nd part shows the regions with the highest volumes of 
containers handled (loaded and unloaded) on inland waterways. The 
scales on the numeric axes are different.

Source: Eurostat (online data codes: iww_go_atygofl and 
iww_go_actygofl)

201Eurostat regional yearbook – 2025 edition12. Environment

Climate change and environmental degradation are 2 of 
the most serious threats with far-reaching consequences for 
ecosystems, human health and the global economy. These 
threats are interrelated: climate change affects biodiversity, 
while healthy ecosystems provide services that are critical 
for climate change mitigation (carbon sinks and stocks) and 
adaptation (water retention, protection against floods and 
desertification, urban heat reduction, protection against air 
pollution, and so on). 

The United Nations (UN’s) 2030 Agenda for Sustainable 
Development is a long-term strategy that aims to achieve a 
range of social, economic, environmental and institutional 
targets. The targets are divided into a set of 17 Sustainable 
Development Goals (SDGs). For the EU, specific indicators 
are in place that are used to measure progress towards 
achieving the SDGs in a national and European context. 
For example, SDG 15 ‘life on land’ aims to protect, restore 
and promote the sustainable use of terrestrial ecosystems. 
The infographic below highlights that, in 2022, terrestrial 
protected areas covered almost 75% of the total land area 
of Arnsberg, a western German region characterised by its 
forests, hills and reservoirs. 

(% of land area, by NUTS 2 regions, 2022)

Note: comprises nationally designated protected areas and Natura 
2000 sites (protected areas designated under the EU Habitats and Birds 
Directives with the goal to maintain or restore a favourable conservation 
status for habitat types and species of EU interest).

Source: European Environment Agency (EEA) and Eurostat (online data 
code: sdg_15_20) 

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Environment

Climate change mitigation

SDG 13 ‘take urgent action to combat climate change 
and its impacts’ is designed to strengthen resilience 
and adaptive capacity to climate-related hazards and 
natural disasters, integrating climate change measures 
into national policies, while improving education and 
raising awareness and institutional capacity on climate 
change mitigation, adaptation, impact reduction and early 
warning. 

EU countries are legally committed to fighting climate 
change by shifting to a climate-neutral economy with 
net-zero greenhouse gas emissions by 2050. Regulation 
(EU) 2021/1119 establishing the framework for achieving 
climate neutrality – hereafter referred to as the European 
Climate Law – seeks to create a cleaner and healthier EU 
by transforming the way enterprises and people behave, 
both in terms of production and consumption patterns. 
This legal act provides the EU’s contribution to the Paris 
Agreement, which set an ambitious global goal ‘to limit 
the temperature increase to 1.5°C above pre-industrial 
levels’. The European Climate Law, together with a package 
of policy initiatives, enshrines the European Green Deal’s 
goal of climate neutrality by 2050. It sets an interim 
target of reducing EU greenhouse gas emissions by at 
least 55% by 2030 (compared with 1990 levels) and has 
been reinforced by a proposal to add a 2040 target to cut 
emissions by 90%. Such reductions will require profound 
and transformative changes, for example, to energy and 
transport systems, industrial processes and agriculture, 
as well as increased carbon removal by ecosystems. 
Map 12.1 shows the movement made towards these 
targets, with greenhouse gas emissions across the EU 
falling 33.9% between 1990 and 2023. Note: data exclude 
international shipping and aviation – although both form 
part of the overall targets – as these items are excluded 
from the regional datasets. 

Greenhouse gas emissions fell in 83.6% of EU regions 
between 1990 and 2023 

There were 204 NUTS level 2 regions across the EU 
that recorded a fall in greenhouse gas emissions 
between 1990 and 2023, while 40 regions experienced 
an increase. Every region of Belgium, Bulgaria, Czechia, 
Denmark, Germany, Lithuania, Hungary, Romania, 
Slovenia, Slovakia, Finland and Sweden recorded a fall 
in greenhouse gas emissions during the period under 
consideration; this was the case in Estonia, Latvia, 
Luxembourg and Malta too. 

• Emissions generally declined most strongly in eastern

and Baltic EU countries. By contrast, within these
countries the Croatian capital region and 3 Polish regions
stood out for recording marked increases in emissions.

• Most regions in western and Nordic EU countries

experienced modest reductions in greenhouse gas
emissions between 1990 and 2023. Nevertheless, some
regions in Ireland, the Netherlands and Austria saw
emissions rise, as was the case for the French outermost
regions.

• Developments in southern EU countries were more

varied, with several regions recording sharp increases,
while others registered sizeable reductions in emissions;
this mixed pattern was observed in all of the southern
EU countries with more than 1 region.

Between 1990 and 2023, 25 out of 244 regions for which 
data are available reported their greenhouse gas emissions 
decreasing by more than 55.0% – the target set for the EU 
by 2030. These 25 regions were geographically diverse – as 
shown by the darkest shade of teal in Map 12.1 – including: 

• 5 predominantly urban regions in Germany
• 4 regions in Romania (including the capital region)
• 3 regions in Italy
• 2 regions in Denmark (including the capital region)
• 2 regions in Hungary
• the capital regions of Bulgaria and Lithuania
• single regions in Czechia, Greece, Spain, Poland and

Portugal

• the neighbouring Baltic countries of Estonia and Latvia.

Greenhouse gas emissions declined in most EU 
regions between 1990 and 2023. While the average 
reduction across the EU was 33.9%, some regions 
recorded considerably larger decreases. Among NUTS 
level 2 regions, the largest decline in emissions was in 
Dytiki Makedonia, Greece (down 76.8%), where lignite-
based electricity generation was phased out. Sostinės 
regionas in Lithuania (down 74.6%) and Região Autónoma 
da Madeira in Portugal (down 73.7%) also registered 
significant reductions in their emissions. 

Between 1990 and 2023, greenhouse gas emission 
increased in a majority of the NUTS level 2 regions in 
Ireland and Spain, as well as in Cyprus. At the upper end 
of the distribution, the largest increases were recorded in 
Ciudad de Ceuta (Spain), where emissions rose by 305.0%, 
and in Mayotte (France), with an increase of 238.9%. 
Excluding the autonomous Spanish cities and the French 
outermost regions: 

• the most substantial increases occurred in Región de

Murcia (southern Spain) and in Cyprus, where emissions
almost doubled over the period under review (up 91.8%
and 90.4%, respectively)

• the next largest increase was observed in La Rioja

(Spain), where emissions rose 73.2%

• 2 other regions saw their emissions grow by more

than 50.0% – Opolskie in Poland (up 53.4%) and the
Croatian capital region of Grad Zagreb (up 52.0%).

203Eurostat regional yearbook – 2025 editionC

Environment

Map 12.1: Change in greenhouse gas emissions
(index relative to 1990 = 100.0, by NUTS 2 regions, 2023)

Note: index calculated as change in tonnes of CO2-equivalents based on 
global warming potential values from the IPCC AR5. International shipping 
and aviation are excluded from the calculation. Ukraine: national data.

Source: EDGAR_GHG_NUTS2_v3.0. GHG emissions at 
subnational level, European Commission (JRC), see 
https://edgar.jrc.ec.europa.eu/dataset_ghg2024_nuts2

Climate change impacts

DROUGHT IMPACT 

SDG 15 ‘life on land’ aims to protect, restore and promote 
sustainable use of terrestrial ecosystems, sustainably 
manage forests, combat desertification, halt and reverse 
land degradation, and halt biodiversity loss. 

Most regions in the EU have sufficient water resources: 
however, water scarcity and drought are becoming 
increasingly frequent and widespread phenomena. Severe 
and frequent droughts may, among other impacts, lead to 
a reduction in water resources, reduce agricultural output, 
accelerate the process of soil erosion and release carbon 

stored in soils. Droughts can also impact biodiversity, the 
restoration of nature through habitat loss, the migration 
of species and the spread of invasive alien species, as 
identified in the EU’s Nature Restoration Regulation (EU) 
2022/869 – a key element of the EU’s biodiversity strategy 
for 2030. 

Information on the severity of drought conditions – which 
arise when soil moisture availability to plants drops to 
such a level that it adversely affects crop yield (and hence, 
agricultural production) – can be used as a measure for 
drought impacts. Map 12.2 shows the average share of 
land area exposed to a severe soil moisture deficit during 
the period between 2013 and 2023; note, a lengthier time 
series is available on the EEA website covering the period 
from 2000 onwards. 

Eurostat regional yearbook – 2025 edition204C

Environment

Between 2013 and 2023, the average annual area affected 
by drought in terms of reduced vegetation productivity in 
areas with severe soil moisture deficit during the growing 
season was approximately 186 000 km². Above average 
areas of land were impacted in 4 of the last 6 years, 
with 2021 and 2023 being the exceptions. 

In 2022, the EU experienced its hottest summer to date 
and its 2nd warmest year on record; nearly 650 000 km² 
of land were affected by drought. By contrast, 
pressure on European ecosystems eased in 2023, with 
approximately 144 000 km² affected – less than 25% of 
the area impacted a year before. In 2023, the largest share 
of land impacted by drought was cropland (46.3% of 
the total), followed by forest and woodland (36.1%) and 
grassland (7.8%). While detailed information on drought 
impact is not yet available for 2024, the fact that it was 
Europe’s warmest year on record suggests continued 
pressure on ecosystems. 

The impact of drought was particularly prevalent across 
German and Belgian regions in 2023 

In 2023, drought impacts were relatively evenly distributed 
across regions: 50.1% of all EU regions recorded a drought-
affected area above the EU average of 4.6%. At the 
top end of the distribution, 225 NUTS level 3 regions 
were impacted by drought. On average, at least 8.4% 
of these region’s land area was affected during the 
period 2013 to 2023 (corresponding to the darkest shade 
of brown in Map 12.2). They were primarily concentrated 
in a band of regions running from central France, into 
the Benelux countries, Germany, Poland and the Baltic 
countries. Germany (115 regions), Belgium (29 regions), 
France (19 regions) and Poland (16 regions) together 
accounted for approximately 80% of all regions with a 
relatively high share of land impacted by drought. 

Map 12.2: Average area with reduced vegetation productivity during the growing season 
(% of land area, by NUTS 3 regions, 2013–23)

Source: European Environment Agency (EEA), Copernicus Land 
Monitoring Service, Copernicus Emergency Management Service and 
Eurostat (online data code: sdg_15_42)

205Eurostat regional yearbook – 2025 editionC

Environment

Between 2013 and 2023, the highest average area 
impacted by drought was recorded in the southern 
Belgian region of Arr. Namur, where 22.3% of the land 
was affected. There were 2 German regions that also had 
shares that exceeded 20.0%: Dessau-Roßlau, Kreisfreie 
Stadt in the east (20.6%) and Borken in the west (20.5%). 
In addition, 11 other regions reported their average area 
impacted by drought within the range of 15.0–18.0%: 

• the Dutch regions of Twente and Achterhoek
• the German regions of Fulda, Wesel and Coburg,

Kreisfreie Stadt

• the Belgian regions of Arr. Nivelles, Arr. Bastogne, Arr.

Huy, Arr. Dinant and Arr. Soignies

• Luxembourg.

By contrast, there were 258 NUTS level 3 regions 
where the average area impacted by drought 
between 2013 and 2023 was less than 2.3% of the total 
land area (they are shown with the 2 lightest shades of 
brown in Map 12.2). They were widely distributed across 
the EU, with the exception of northern areas. Notably, 
there were 2 main clusters: 1 group concentrated in 
southern Germany and eastern Austria, and the other 
concentrated in south-eastern parts of the EU. The highest 
counts of regions with relatively low shares of their area 
impacted by drought were found in: 

• Germany (68 regions)
• Italy (24 regions)
• Greece and Spain (each 23 regions)
• Romania (21 regions)
• Bulgaria (18 regions)
• Austria (14 regions)
• Poland (12 regions)
• France (11 regions).

During the period 2013 to 2023 there were 24 regions 
which reported no drought impact. This group included 
several outermost regions in the Atlantic, comprising 
the 7 regions that form Canarias (Spain) and the Regiões 
Autónomas dos Açores e da Madeira (Portugal). In 
addition, 5 regions in western Bulgaria also recorded no 
drought-impact. 

Among the 1 112 NUTS level 3 regions for which data are 
available, 408 experienced some impact from drought 
during 2023, while 704 recorded no drought impact. 
Figure 12.1 highlights the regions with the largest areas 
of land affected by drought in 2023. The information 
is presented in absolute figures (in km²) and therefore 
reflects – at least to some degree – the overall size of 
each region, alongside the meteorological conditions 
experienced throughout the year. The central Swedish 
region of Jämtlands län had the largest drought-impacted 
area, at 4 100 km². The next highest value was in the 

south-western Spanish region of Badajoz, with 4 000 km² 
impacted. 

All 20 EU regions with the largest drought-impacted 
areas in 2023 – each with at least 1 990 km² affected – 
were concentrated either in the north-eastern parts of 
the EU (the Baltic and Nordic EU countries or Poland) or 
in Spain. As noted above, these figures reflect absolute 
areas impacted, meaning that larger regions experiencing 
moderate drought may still rank relatively high in terms of 
their total affected area. 

Although north-eastern parts of the EU typically experience 
a temperate climate with relatively high rainfall, 2023 was 
marked by extended periods of low precipitation and 
above-average temperatures. By contrast, much of Spain has 
a semi-arid climate with hot summers, making its regions 
particularly vulnerable to drought on a regular basis. 

Alongside the 2 regions with the largest areas of 
drought-impacted land – Jämtlands län and Badajoz – 
the remaining 18 EU regions with at least 1 990 km² of 
drought-affected land were located in: 

• Spain (6 additional regions)
• Lithuania, Poland (4 regions each)
• Estonia (2 regions)
• Lativa and Finland (1 region each).

The 2nd part of Figure 12.1 shows the NUTS level 3 regions 
with the biggest changes in the area of land affected by 
drought, comparing 2023 – the latest year for which data 
are available – with the average for 2013 to 2022. Across 
the EU, an average of 190 000 km² of land was affected 
by drought annually between 2013 and 2022, which 
was 46 500 km² more than the area impacted in 2023. 

Among the 1 112 regions for which data are available, 
973 recorded a smaller area of land impacted by drought 
in 2023 compared with the average for 2013 to 2022. 
In 15 regions, the affected area was unchanged, 
while 124 regions recorded a larger area impacted. 

In 2023, the most pronounced increase in drought-
impacted area – compared with the average for 
the period 2013 to 2022 – was recorded in the 
north-eastern Polish region of Olsztyński, where an 
additional 3 200 km² of land was impacted. The next 
largest increases were observed in Badajoz in Spain (an 
additional 3.0 thousand km²) and Kauno apskritis in 
Lithuania (up 2 900 km²). The next largest developments, 
with increases of 2 600 km² to 2 800 km², were reported in 
Šiaulių apskritis (Lithuania), Lõuna-Eesti (Estonia), as well as 
Córdoba and Barcelona (both Spain). 

Eurostat regional yearbook – 2025 edition206C

Environment

The largest reduction in drought-impacted areas 
in 2023 – compared with the average area affected 
during the period 2013 to 2022 – was recorded in the 
northern Swedish region of Västerbottens län, where 
the impacted area was 1 500 km² smaller than the 
previous decade’s average. The 2nd-largest decrease also 

occurred in Sweden, as Västra Götalands län recorded 
a fall of 1 100 km². There were notable decreases – 
between 800 km² and 1 100 km² – in Cyprus, several 
French regions (Saône-et-Loire, Aisne, Yonne, Allier and 
Côte-d’Or) and 2 Spanish regions (Murcia and Salamanca). 

Figure 12.1: Average area with reduced vegetation productivity during the growing season
(1 000 km² of land area, by NUTS 3 regions, 2023)

Note: the 1st part of the figure shows the EU regions with the largest 
areas impacted by drought, while the 2nd part shows those regions with 
the biggest increases and biggest decreases compared with the average 
impact for 2013–22. Germany, France, Latvia, the Netherlands, Portugal 
and Finland: several regions for which data are not available (too many to 
document). The scales on the numeric axes are different.

Source: European Environment Agency (EEA), Copernicus Land 
Monitoring Service, Copernicus Emergency Management Service and 
Eurostat (online data code: sdg_15_42)

Protected land

SDG 15 ‘life on land’ seeks to conserve and sustainably use 
terrestrial ecosystems. The EU biodiversity strategy for 2030 – 
Bringing nature back into our lives (COM(2020) 380 final) sets 
targets to protect a minimum of 30% of the EU’s land and 
sea area, with specific commitments to protect nature and 
reverse the degradation of ecosystems. 

The information presented in this section comprises 
Natura 2000 sites and other nationally designated 
protected areas. Natura 2000 – established in 1992 – is 
the world’s largest network of protected areas that are 
designated under the EU Habitats and Birds Directives with 

the goal to maintain or restore a favourable conservation 
status for habitat types and species of EU interest. In 2022, 
the Natura 2000 network extended over 18.6% of the 
EU’s land area and 9.0% of its marine territory. Alongside 
Natura 2000 sites, many EU regions have other nationally 
designated protected areas. 

Map 12.3 shows the proportion of terrestrial protected areas 
relative to the total land area in each NUTS level 3 region. 
In 2022, the regional distribution of this indicator was 
relatively balanced around the EU average of 26.1%. Out 
of 1 160 regions for which data are available, 586 reported 
an above-average share, a single region had the same share, 
while 573 regions had lower-than-average shares. 

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Environment

Many of the regions with the highest shares of protected 
areas were broadly spread across Germany, while there 
were also several regions in Poland (particularly along its 
southern border). Other notable clusters were located in 
southern France, southern Bulgaria and northern Greece, 
as well as several coastal regions of Croatia, Alpine regions 
of Austria and the island regions of Corse (France) and 
Região Autónoma da Madeira (Portugal). 

There were 20 additional regions across the EU where, 
in 2022, protected areas covered more than 75.0% of the 
land area: 

• the majority of these – 15 regions – were located in
Germany, most of them in North Rhine-Westphalia

• the remaining 5 included 3 regions in Poland and single

regions from each of Croatia and Austria.

There were 116 regions across the EU which had at 
least 55.0% of their total area designated as protected 
in 2022 (as shown by the darkest shade of blue in Map 12.3). 
In 3 regions, protected areas covered almost the entire 
territory: 

• Berchtesgadener Land in south-eastern Germany (99.9%)
• Lungau in central Austria (99.8%)
• Ahrweiler in western Germany (94.8%).

At the lower end of the distribution, 118 regions had less 
than 8.0% of their total area designated as protected 
in 2022 (they are shown in a light yellow shade in 
Map 12.3). Many of these relatively low shares were in 
Finland, Sweden, eastern Ireland, north-western Spain, 
central Portugal, northern France extending into Belgium 
and the Netherlands, northern Italy and a band of regions 
stretching from Bavaria (Germany) into northern Austria. 
A few countries had just 1 region with a relatively low 
share of their total area designated as protected; in Czechia 
and Hungary, this was the capital region. 

Map 12.3: Terrestrial protected areas
(% of land area, by NUTS 3 regions, 2022)

Note: terrestrial protected areas comprise nationally designated protected 
areas and Natura 2000 sites.

Source: European Environment Agency (EEA) and Eurostat (online data 
code: sdg_15_20)

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Environment

The data presented in Figure 12.2 reflects, to some degree, 
the size of various administrative units; some sparsely 
populated regions are particularly large when measured 
in terms of their area. In 2022, the 2 NUTS level 3 regions 
with, by far, the largest terrestrial protected areas – 
Natura 2000 sites and nationally designated areas – were: 

•  the Swedish region of Norrbottens län (31 700 km²)
•  the Finnish region of Lappi (31 300 km²).

The protected areas in both of these Nordic regions were 
mainly forest and semi natural areas, but they also had 
quite large wetlands and water bodies. The next largest 
protected areas – with between 5 100 km² and 12 300 km² 
– were recorded in: 

•  Västerbottens län and Jämtlands län (Sweden)
•  Cáceres, Badajoz and Jaén (Spain)
•  Vidzeme (Latvia)
•  Tulcea (Romania)
•  Olsztyński and Szczecinecko-pyrzycki (Poland).

Figure 12.2: Terrestrial protected areas
(km², by NUTS 3 regions, 2022)

Note: the figure shows the 20 largest areas across NUTS level 3 regions. 
Comprises nationally designated protected areas and Natura 2000 sites.

Source: European Environment Agency (EEA) and Eurostat (online data 
code: sdg_15_20)

Air quality

SDG 11 ‘sustainable cities and communities’ aims to make 
cities and human settlements inclusive, safe, resilient 
and sustainable. A key aspect of this goal is to reduce 
the environmental impact of urban areas, particularly by 
improving air quality and enhancing the management of 
municipal and other forms of waste. 

Air quality can deteriorate considerably due to human 
activities such as industrial processes, the burning of solid 
fuels, housing, transport, agriculture and the generation 
or treatment of waste. In addition, natural sources of air 
pollution include volcanic eruptions, desert dust and forest 
fires. 

Air pollution remains a major cause of premature death 
and disease across the EU, with fine particulate matter 
(PM2.5) identified as having the most serious human 
health impacts. Some of the most common causes of both 
illness and premature death attributed to air pollution 
include heart disease, stroke, lung disease, lung cancer 
and asthma; these illnesses also have an associated 
economic cost through lost working days and healthcare 
expenditure. 

209Eurostat regional yearbook – 2025 editionC

Environment

To address this, the EU’s zero pollution action plan has 
set a target to reduce premature deaths caused by 
fine particulate matter by at least 55% by 2030 (relative 
to 2005 levels). More broadly, the plan aims to bring 
air, water and soil pollution down to levels no longer 
considered harmful to human health and natural 
ecosystems by 2050. The final section in this chapter 
provides statistics on the magnitude of the health impacts 
of air pollution resulting from exposure to fine particulate 
matter. 

More about the data: air quality guidelines, 
commitments and targets 

Fine particulate matter refers to particles with a 
diameter of 2.5 micrometres or less (otherwise referred 
to as PM2.5). In September 2021, the World Health 
Organization (WHO) issued new global air quality 
guidelines, emphasising the need to safeguard public 
health: the long-term guideline level was set as an 
annual average of 5 µg/m³ for PM2.5. These guidelines 
reflect growing scientific evidence that even low 
concentrations of air pollution may pose significant risks 
to human health. 

As part of its goal to eliminate pollution by 2050, the 
European Commission has outlined key targets and 
initiatives. Among these, an intermediate goal to 
reduce premature deaths resulting from exposure to air 
pollution by at least 55% between 2005 and 2030. 

In December 2024, a revised Directive on ambient 
air quality and cleaner air for Europe (Directive (EU) 
2024/2881) came into force. Under this directive, the 
European Commission will review the latest scientific 
evidence on air pollution and its health impacts by 
the end of 2030 and every 5 years thereafter – or 
more often if new findings warrant it – to ensure the 
standards continue to be appropriate. The directive 
sets a new annual limit value for the EU of 10 µg/m³ for 
PM2.5, to be met by 1 January 2030. 

The revised directive also aims to strengthen air quality 
plans and roadmaps for territorial units where pollution 
exceeds EU standards. It updates air quality limits 
for 12 pollutants, including fine particulate matter. In 
addition, it also provides an opportunity for individuals 
to seek compensation if their health is harmed due to 
violations of EU air quality rules. 

In 2022, there were 4 486 premature deaths attributed to 
fine particulate matter in the northern Italian region of 
Milano 

The European Environment Agency (EEA) estimates that 
fine particulate matter resulted in 239 100 premature 
deaths across the EU in 2022, which equated 

to 78 premature deaths per 100 000 inhabitants 
aged 30 years or more. Note all of the information 
in this section on premature deaths attributable to 
fine particulate matter concerns a cohort of people 
aged 30 years or more – sometimes referred to as the 
‘affected population’ – since this is the population age 
range generally considered within epidemiological studies. 

The regional distribution of premature deaths attributed 
to fine particulate matter was skewed: in 724 out 
of 1 061 NUTS level 3 regions for which data are available 
in 2022 – or approximately 2 out of every 3 regions 
in the EU (68.2%) – the number of premature deaths 
per 100 000 inhabitants was below the EU average. 

Unsurprisingly, the highest absolute numbers of premature 
deaths associated with fine particulate matter occurred 
in some of the most populous and polluted NUTS 
level 3 regions, primarily in southern and eastern EU 
countries, particularly in predominantly urban regions. 
The northern Italian region of Milano (4 486) recorded the 
highest number of premature deaths attributed to fine 
particulate matter in 2022. High counts were also observed 
in Barcelona in Spain (3 357) and the Italian capital region 
of Roma (2 862). 

At the other end of the distribution, there were 10 NUTS 
level 3 regions in the EU with no premature deaths 
associated with fine particulate matter in 2022. All of 
them were located in Sweden, the majority being in 
the northern part of the country. Note that, as only 
partial data are currently available for Finland based on 
the NUTS 2024 classification, national data have been 
published for this country. Information from an earlier 
version of the NUTS classification suggests that several 
regions – particularly in northern Finland – also recorded 
no premature deaths associated with fine particulate 
matter. 

In 2022, the Bulgarian capital region of Sofia (stolitsa) had 
the highest number of premature deaths attributed to fine 
particulate matter per 100 000 inhabitants 

While the absolute number of premature deaths 
attributed to exposure to fine particulate matter was 
generally high in some of the most populous NUTS 
level 3 regions of the EU, the most significant impacts 
of air pollution when normalised by population (among 
those people aged 30 years or more) were often observed 
in industrialised and urbanised regions across eastern EU 
countries. These regions are typically characterised by high 
concentrations of industrial activity in traditional sectors 
such as coal mining, steel plants, cement manufacture and 
chemical industries. In addition, some of them also have a 
relatively high share of households burning coal or wood 
for heating, and/or suffer from traffic congestion – other 
factors which may exacerbate air pollution. 

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Environment

In 2022, there were 253 NUTS level 3 regions in the EU 
where the number of premature deaths attributable to 
air pollution was at least 100 per 100 000 inhabitants 
(these regions are shown in the 2 darkest shades of blue in 
Map 12.4). Broadly speaking, the highest rates of premature 
deaths attributable to air pollution were observed across 
eastern EU countries, Greece and northern Italy. The largest 
clusters were in Bulgaria and Hungary, where all regions 
recorded rates above 100 per 100 000 inhabitants. 

Sofia (stolitsa), Vidin and Plovdiv (all Bulgaria), Miasto 
Kraków and Katowicki (both Poland) and Brodsko-posavska 
županija (Croatia) were the only regions in the EU to record 
more than 200 premature deaths per 100 000 inhabitants 
attributable to air pollution in 2022. There were 73 other 
regions with a rate of at least 150 premature deaths 
per 100 000 inhabitants. This group included 17 regions in 
Italy, 14 (additional) regions in each of Bulgaria and Poland, 

13 regions in Greece, 10 regions in Romania, 4 (additional) 
regions in Croatia, as well as a single region in Hungary. 

In Italy, the highest rates were concentrated in the densely 
populated plains of the Po valley, where geographic 
conditions hinder air circulation and trap pollutants from 
agriculture, industry and transport. Cremona, Milano 
and Monza e della Brianza recorded the highest rates of 
premature deaths per 100 000 inhabitants in 2022. 

At the other end of the range, there were 33 NUTS 
level 3 regions where the number of premature deaths 
attributed to exposure to fine particulate matter was 
less than 15 per 100 000 inhabitants in 2022. This group 
– where the rate of fatalities linked to this type of air
pollution was relatively low – included every region of
Estonia, all but 2 regions from Sweden, as well as 3 regions
from the Belgian Ardennes, 2 regions from each of Ireland
and France, and a single region from Austria; Finland
(national data only) had similarly low rates too.

Map 12.4: Premature deaths – exposure to fine particulate matter
(per 100 000 inhabitants aged 30 years or more, by NUTS 3 regions, 2022)

Note: fine particulate matter is airborne particles with a diameter of 
2.5 micrometres or smaller. Latvia, the Netherlands, Portugal, Finland and 
Norway: national data.

Source: European Environment Agency (EEA)

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Environment

The left-hand side of Figure 12.3 highlights the NUTS 
level 3 regions in the EU with the highest and lowest rates of 
premature deaths resulting from exposure to fine particulate 
matter in 2022. The right-hand side shows the regions with 
the highest and lowest indices relative to 2005 (2005 = 100); 
in other words, it shows the change between 2005 and 2022. 
Only 3 regions across the EU had indices above 100, 
indicating an increase in their rate of premature deaths 
per 100 000 inhabitants between 2005 and 2022. 
The highest increase was recorded in the Spanish island 
region of Menorca (up 4.4% between 2005 and 2022), while 
the other 2 regions were both located in Poland – Bydgosko-
toruński (up 3.8%) and Miasto Poznań (up 1.7%).

Note that these indices only indicate the change in the rate 
of premature deaths attributed to fine particulate matter 
between 2005 and 2022; they do not reveal whether 
the initial or final rates were comparatively high or low, 
but rather show the direction and magnitude of change 
from the initial starting point. For example, the number 
of premature deaths due to exposure to fine particulate 
matter in Menorca was lower than the EU average in 2022, 
despite the 4.4% increase between 2005 and 2022. 

Figure 12.3: Premature deaths – exposure to fine particulate matter
(by NUTS 3 regions, 2022)

Note: fine particulate matter is airborne particles with a diameter of 
2.5 micrometres or smaller. The 1st part of the figure shows the EU regions 
with the highest and lowest rates in 2022, while the 2nd part shows the 
regions with the highest and lowest indices based on data per 100 000 
inhabitants aged 30 years or more with a base year of 2005 = 100. The 
rankings include more than 10 regions if several regions have identical 
values. The scales on the numeric axes are different.

Source: European Environment Agency (EEA)

Eurostat regional yearbook – 2025 edition21213. Agriculture

Farms are crucial to providing safe and affordable food 
across the European Union (EU). Furthermore, farmers 
are increasingly encouraged to balance food production 
with managing the countryside for the public good, 
contributing to rural landscapes, biodiversity and 
environmental sustainability. 

Agricultural products, food and culinary traditions 
play a significant role in shaping regional and cultural 
identity, with climate, landscape and farming techniques 
contributing to the broad variety of different agricultural 
goods and services across the EU. In 2020, there 
were 9.1 million farm holdings in the EU, collectively 
managing 1.55 million km² of land – equivalent to 37.8% of 
the overall land area. 

This final chapter of the Eurostat regional yearbook presents 
agricultural statistics, focusing on 3 principal datasets. 

• Agricultural production statistics detailing the

number of head of bovine animals and pigs for NUTS
level 2 regions.

• Agricultural census data, providing regional information
on the relative importance of young farm managers.
• Economic accounts for agriculture providing regional

analyses of crop output, animal output, and the output
of agricultural services and secondary activities.

In 2023, crop output accounted for about half (50.9%) 
of the total value of the EU’s agricultural industry. Based 
on the latest regional information available across NUTS 
level 2 regions with at least €100 million of crop output, 
Wien (Austria) – that is specialised in viticulture and 
horticulture – had the highest relative share (95.1% in 2023; 
see the infographic below). The Austrian capital region was 
followed by Provence-Alpes-Côte d’Azur in France (89.7% 
in 2023) and Rheinhessen-Pfalz in Germany (89.5% in 2022). 

Source: Eurostat (online data codes: agr_r_accts and aact_eaa01)

(% of the output value of the agricultural industry, by NUTS 2 regions, 
2023)

Note: EU crop output accounted for a 50.9% share of the output value 
of the agricultural industry. Ranking based on regions where the value 
of crop output was at least €100 million. Poland and Slovenia: national 
data. Rheinhessen-Pfalz (DEB3), Canarias (ES70), Severozapaden (BG31) 
and Peloponnisos (EL65): 2022. Région de Bruxelles-Capitale / Brussels 
Hoofdstedelijk Gewest (BE10), Praha (CZ01), Ciudad de Ceuta (ES63) and 
Ciudad de Melilla (ES64): not available.

213Eurostat regional yearbook – 2025 editionC

Agriculture

Animal production

Animal production plays a crucial role in the EU’s 
agricultural sector. The livestock population at any given 
moment describes the production system through the 
stock of animals being farmed; the data presented in this 
section refer to the situation as of December 2024. 

The duration of a production cycle indicates the time 
required for animals to reach their slaughter weight. 
However, other animals may be kept for milk or to act as 
breeding stock. The typical lifespan of an animal varies: 
for example, veal calves will typically be slaughtered 
within 8 months, beef cattle within the first 2.5 years and 
dairy cows within 5 years. 

More about the data: agricultural production 

Livestock statistics provide essential insights into the 
populations of different farm animal species, detailing 
the number of head at various stages of rearing. These 
figures typically distinguish between animals raised for: 

•  fattening and slaughter, contributing to meat 

production

•  herd renewal, which includes breeding and milking 

livestock to sustain future production cycles.

The EU initially aimed to ensure a stable and sufficient 
food supply within the EU through the common 
agricultural policy (CAP). Over time, this focus evolved to 
emphasise market transparency, helping policymakers 
and stakeholders monitor supply and demand 
fluctuations. Reliable livestock statistics play a key role 
in this process by providing feedback on production 
trends, price signals and sustainability. 

Regional livestock data are generally shown for 
NUTS level 2 regions. However, data for Germany are 
presented at NUTS level 1, while only national data are 
available for Albania. 

Cattle farming is primarily concentrated in western EU 
countries that are characterised by a temperate climate 
and relatively high levels of rainfall. These conditions 
encourage pasture-based farming, specialising in both 
dairy and beef production. 

Across EU regions, Southern in Ireland had the largest 
bovine population: 3.4 million head as of December 2024 

As of December 2024, the EU’s bovine herd 
totalled 71.9 million head. Map 13.1 shows the share of 
each NUTS level 2 region in the EU’s overall population of 
bovine animals. There were 15 regions that accounted for 
at least 1.5% of the total (as shown by the darkest shade 
of green). Together this group of 15 regions accounted for 
more than 1 in 3 (35.6%) of the EU’s bovine population. The 
highest concentrations of bovine animals were in Ireland 
and the north-west of France, while high shares were also 
recorded in a band of regions running from north-west 
Spain, through central France and into the Alpine regions 
of southern Germany and northern Italy. There were also 
clusters of intensive cattle farming in northern Germany 
and selected regions of Poland. 

Map 13.1 also presents regional information on the total 
number of bovine animals (as denoted by the size of each 
circle). 

•  The Southern region of Ireland had the highest bovine 

population among the EU’s NUTS level 2 regions, 
at 3.4 million head. This region is particularly specialised 
in dairy production, reflecting its abundant pastures. The 
Northern and Western region of Ireland also featured 
among the 7 EU regions that had at least 1.5 million 
bovine animals.

•  In Germany, Bayern (located in the south-east) 

had the 2nd highest bovine population in the EU, 
with 2.7 million head. Niedersachsen (located in north-
west Germany) had 2.2 million head. Note the data for 
Germany are reported at NUTS level 1, which can result 
in absolute values being misleading when compared 
with NUTS level 2 data for other countries.

•  In France, the leading regions for cattle farming were 

in the north-west of the country, where the temperate 
climate and prevailing weather patterns shaped by the 
Atlantic give rise to specialisation within the dairy sector: 
Pays de la Loire had 2.1 million head of bovine animals, 
while Bretagne had 1.7 million head.

•  Outside of these 3 countries, Lombardia in northern 
Italy was the only other region in the EU to have at 
least 1.5 million bovine animals.

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Agriculture

Map 13.1: Bovine animals
(by NUTS 2 regions, as of December 2024)

Note: EU = 71.9 million head of bovine animals. Germany: NUTS level 
1. Albania: national data. Spain, Iceland, Bosnia and Herzegovina, 
Montenegro, North Macedonia, Albania and Türkiye: as of December 2023.

Source: Eurostat (online data codes: agr_r_animal and apro_mt_lscatl)

Within the EU, pig farming primarily occurs in regions 
characterised by intensive livestock production, often 
tied to large-scale commercial farming operations. 
As of December 2024, the EU’s pig population stood 
at 132.1 million head. 

Across EU regions, Aragón in Spain had the largest pig 
population: 9.9 million head as of December 2024 

Map 13.2 illustrates the share of each NUTS level 2 region 
in the EU’s overall pig population. In December 2024, 
10 regions accounted for at least 2.5% of all pigs in the 
EU (as shown by the darkest shade of green in the map). 
Together, this group of 10 regions accounted for almost 
half (43.9%) of all pigs in the EU. The highest numbers 
and shares were found in the northern half of Spain and 
in a band of regions stretching from Denmark through 
northern Germany and into the Netherlands. There were 
also regional pockets with high numbers of pigs in north-
west France and northern Italy. 

215Eurostat regional yearbook – 2025 editionC

Agriculture

Map 13.2 also shows the total number of pigs found in 
each region as of December 2024 (as indicated by the size 
of each circle). 

•  The north-eastern Spanish region of Aragón had the 
largest pig population among NUTS level 2 regions, 
at 9.9 million head (or 7.5% of the EU total). The 
neighbouring region of Cataluña had the 2nd highest 
count, with 8.0 million head (or 6.1% of the EU total), 
while Castilla y León also ranked among the 10 EU 
regions with more than 2.5 million head of pigs.

•  In Germany, Niedersachsen had the 3rd largest pig 

population in the EU, with 7.0 million head (5.3% of the 
EU total). North Rhine-Westphalia also ranked among 
the principal pig-farming regions, with 5.8 million head. 
As mentioned above, please note the data for Germany 
refer to NUTS level 1 regions.

•  In Denmark, 2 regions from Jutland – Midtjylland 

(4.1 million head) and Syddanmark (3.4 million head) – 
are characterised by high-numbers of pigs.

•  Outside of Spain, Germany and Denmark, there were 
also relatively large pig-farming activities in Bretagne 
(north-west France), Noord-Brabant (the Netherlands) 
and Lombardia (northern Italy).

Map 13.2: Pigs
(by NUTS 2 regions, as of December 2024)

Note: EU = 132.0 million head of pigs. Germany: NUTS level 1. Albania: 
national data. Estonia, Iceland, Bosnia and Herzegovina, Montenegro, 
North Macedonia, Albania and Türkiye: as of December 2023.

Source: Eurostat (online data codes: agr_r_animal and apro_mt_lspig)

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Agriculture

Young farm managers

Across the EU, agricultural holdings vary considerably, from 
small, semi-subsistence farms to large, intensive operations. 
These structural differences are often reflected in the way 
that farms are owned and managed: smaller farms tend 
to be family-run, while larger farms are typically overseen 
by professional managers employed by agricultural 
enterprises. 

Farm managers are the people responsible for the normal 
daily financial and production routines of running a farm, 
such as what and how much to plant or rear and what 
labour, materials and equipment to employ. Often the 
farm manager is also the owner (otherwise referred to as 
the ‘holder’) of the farm but this need not be the case, 
especially when the farm has a separate legal identity. 

Many farm managers in the EU work well beyond what 
would be a typical retirement age in other sectors of the 
economy. This slow pace of generational renewal may 
pose risks for the long-term sustainability of EU agriculture, 
potentially limiting innovation and productivity due to the 
lack of a more dynamic and diverse farm workforce. 

In 2020, slightly more than 1 in 10 farm managers in the EU 
were under the age of 40 

In 2020, there were 1.1 million farm managers in the 
EU that were younger than 40. By contrast, there 
were 3.0 million older farmers aged 65 or over. Younger 
farmers are more likely to manage medium-sized or large 
farms, which may reflect their higher levels of agricultural 
education and training, including exposure to modern 
and innovative practices. Conversely, older farm managers 
predominantly work on small or subsistence farms, which 
often yield low agricultural income. 

The structural differences in agricultural holdings across 
EU regions are reflected in the distribution of young 
farm managers. The highest regional count was in 
the Romanian region of Nord-Est (64 000 young farm 
managers) with 3 other Romanian regions – Sud-Muntenia, 
Nord-Vest and Sud-Vest Oltenia – each reporting 
between 40 000 and 50 000 young farm managers. 
Outside of Romania, the highest numbers of young farm 
managers were found in the Polish regions of Mazowiecki 
regionalny (39 100) and Lubelskie (35 300). 

In 2020, young farm managers accounted for 11.9% of all 
farm managers across the EU. Their share was noticeably 
higher in a broad area extending from France, through 
the Alps into Czechia, Slovakia and Poland. This pattern 
may reflect targeted policy support aimed at encouraging 
more young people into agriculture, including initiatives 
such as start-up grants, low interest loans, reduced land 
prices, support with land access, income support, training 

programmes, and opportunities to foster innovation and 
diversification. Notably, young farm managers were over-
represented in organic farming: they accounted for 20.7% 
of organic farm managers across the EU (compared 
with 11.9% of all farm managers).

By contrast, the lowest shares of young farm managers 
were concentrated in southern EU countries. This may 
be linked to challenges in generational transition, such 
as financial barriers, limited access to land, capital and 
knowledge, declining rural fertility rates and the appeal 
of alternative career prospects. The share of young farm 
managers was also relatively low in several regions of 
Denmark and the Netherlands, where consolidation of 
farming may have created significant barriers to young 
people wishing to enter the industry. 

Map 13.3 highlights the regional distribution of young 
farm managers in 2020. There were 31 NUTS level 2 regions 
where the share of young farm managers was at 
least 20.0% (as shown by the darkest shade of blue in 
Map 13.3). This group included: 

•  8 out of 9 regions in Austria, with the capital region of 

Wien being the only exception

•  11 out of 17 regions in Poland
•  7 regions in France
•  3 regions in Czechia
•  2 regions in Slovakia.

In 2020, Oberösterreich in northern Austria (25.6%) and 
Franche-Comté in eastern France (25.5%) were the only 
regions in the EU where more than 1 in 4 farm managers 
were aged under 40. The next highest shares were close to 
this level and were recorded in the central Polish regions 
of Kujawsko-pomorskie (24.9%) and Wielkopolskie (24.5%), 
followed by the Austrian regions of Salzburg (24.3%) and 
Kärnten (23.8%). 

Young farm managers were particularly scarce in southern 
EU countries. There were 29 regions across the EU where 
young farm managers accounted for fewer than 7.5% 
of all farm managers (as shown by the lightest shade 
of yellow in Map 13.3). This group was concentrated 
in Greece (9 regions) and Spain (6 regions), and also 
included 3 regions in Portugal and 2 in Italy; Cyprus 
likewise had a very low share of young farmers. Outside 
the southern EU regions, relatively low shares were also 
recorded in the capital regions of Denmark, Germany, 
Romania and Sweden, as well as in Sjælland (Denmark), 
Bremen (Germany), and the French outermost regions of 
Martinique and Mayotte. 

By contrast, in the Portuguese regions of Algarve (60.8%) 
and Norte (50.4%), as well as the Spanish region of 
Comunitat Valenciana (50.1%), a majority of farm managers 
were aged 65 years or over. This top-heavy age structure 
underlines the policy relevance of farm succession. 

217Eurostat regional yearbook – 2025 editionC

Agriculture

Map 13.3: Young farm managers
(% of all farm managers, by NUTS 2 regions, 2020)

Note: young farm managers are defined as those younger than 40 years.

Source: Eurostat (online data code: ef_m_farmang)

Economic situation of farms

Farming serves as the cornerstone of many rural 
economies, supporting both ‘upstream’ sectors (such as 
animal healthcare providers and wholesalers of agricultural 
inputs) and ‘downstream’ sectors (such as food processing, 
packaging and transport businesses). The economic 
accounts for agriculture (EAA) provide an overall picture of 
the performance of the EU’s agricultural industry. 

In 2023, the EU’s agricultural industry generated output 
that was valued at €537.0 billion: 

•  more than half of this total (50.9%) came from crop 
output (€273.6 billion), with cereals, vegetables and 
horticultural plants making the largest contributions
•  some 39.9% of the total came from animals and animal 
products (€214.2 billion), with milk and pigs providing 
the largest shares

•  the remaining 9.2% of output in the agricultural industry 
was relatively evenly split between agricultural services 
(€25.3 billion) and non-agricultural secondary activities 
(€23.9 billion).

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Agriculture

More about the data: economic accounts for 
agriculture (EAA) 

EAA provide detailed information about the agricultural 
industry, focusing on production processes and the 
income generated from them. As a satellite account to 
the European System of Accounts (ESA), the EAA adapts 
the general accounting concepts of the ESA to the 
specific characteristics of agriculture. 

Primary production and secondary activities that 
contribute to a farm’s economic performance are 
classified as: 

•  crop output – the value of sales from crop products, 
changes in stock levels and crop products used as 
animal feed, for processing or for the farm’s own final 
use (for example, seed, storage)

•  animal output – the value of sales from animals and 
animal products, changes in stock levels and animal 
products used for processing or for the farm’s own 
consumption (for example, animal feed, breeding 
stock)

•  agricultural services output – the value of services 

directly linked to a farm’s operations, such as renting 
machinery or providing custom services (for example, 
harvesting or irrigation)

•  non-agricultural secondary activities – the value of 

non-separable economic activities related to the farm 
but not strictly agricultural (for example, cheese-
making, agritourism or renewable energy production 
from farm waste).

The EAA are compiled for NUTS level 2 regions, based 
on values at current prices. In this section, no regional 
statistics are available for Poland or Slovenia (they plan 
to provide this data towards the end of 2025); national 
data are presented instead. In Belgium, Bulgaria, 
Czechia, Denmark, Germany, Greece, Spain, Croatia, Italy, 
Lithuania, Hungary, the Netherlands, Romania, Finland 
and Norway, the latest data refer to 2022 (instead 
of 2023). 

The southern Spanish region of Andalucía had the highest 
value of crop output, at €13.1 billion in 2022 

Map 13.4 illustrates both the overall level of crop output 
(depicted by the size of each circle) and the share of 
crop output within the agricultural industry (indicated 
by the colour of each circle) for NUTS level 2 regions. 
The patterns depicted in the map are shaped by a 
broad range of factors, including soil conditions, terrain, 
local economic structures, historical farming practices, 

climatic and environmental conditions. Regions with 
fertile soils, expansive plains and temperate climates tend 
to focus more on crop production. By contrast, regions 
characterised by less favourable growing conditions, 
poorer soil quality and/or hilly and mountainous terrain 
tend to focus more on livestock farming. 

In 2023, the highest levels of crop output occurred in: 

•  Andalucía in southern Spain (€13.1 billion; 2022 data)
•  Zuid-Holland in the west of the Netherlands (€5.8 billion; 

2022 data)

•  Champagne-Ardenne in north-east France (€5.3 billion)
•  Veneto in north-east Italy (€5.2 billion; 2022 data).

Crop output accounted for more than half (50.9%) of the 
total output of the EU’s agricultural industry in 2023. The 
regional distribution of crop output was relatively even: 
in 126 out of 223 NUTS level 2 regions for which data are 
available (56.5% of all regions), the share of crop output 
exceeded the EU average. 

Several eastern and Baltic EU countries reported relatively 
high shares of crop output within their respective 
agricultural industries. In 2022, every region in Bulgaria, 
Czechia and Lithuania, along with all but 1 region in 
Greece, Croatia, Romania and Slovakia (2023 data), 
recorded a crop output share above the EU average; this 
was also the case for Latvia and Slovenia (both national 
data; 2023 data). 

Looking more closely at the relative importance of crop 
output, 6 regions reported that crops accounted for more 
than 85.0% of their agricultural industry output in 2023: 

•  the Austrian capital region of Wien (95.1%); specialised 

in the production of grapes, fruit, vegetables, herbs and 
flowers

•  the southern French region of Provence-Alpes-Côte 

d’Azur (89.7%); specialised in the production of olives, 
grapes, fruit, vegetables and herbs

•  the south-western German region of Rheinhessen-Pfalz 

(89.5%; 2022 data); specialised in the production of 
cereals, grapes, fruit and vegetables

•  the southern Portuguese region of Algarve (88.8%); 
specialised in the production of citrus fruits, olives, 
grapes and vegetables

•  the French outermost region of Guyane (86.7%); which 
had a relatively small agricultural industry, with output 
valued at €165 million

•  the French capital region of Ile-de-France (85.9%); 

specialised in the production of cereals and vegetables/
market gardening.

219Eurostat regional yearbook – 2025 editionC

Agriculture

Map 13.4: Crop output
(in value terms, by NUTS 2 regions, 2023)

Note: Poland and Slovenia, national data. Includes data for 2022 (too many 
regions to document).

Source: Eurostat (online data codes: agr_r_accts and aact_eaa01)

The north-western French region of Bretagne had the 
highest value of animal output, at €7.7 billion in 2023 

Map 13.5 shows the overall level of animal output 
(represented by the size of each circle) and the share of 
animal output within the agricultural industry (indicated 
by the colour of each circle) for NUTS level 2 regions. The 
patterns shown in Map 13.5 tend to mirror those presented 
in Map 13.4; in other words, those regions that are 
specialised in either livestock farming or in crop farming 
tend to have a low degree of specialisation in the other 
type of farming. 

In 2023, the highest levels of regional animal output 
occurred in: 

•  Bretagne (€7.7 billion) and Pays de la Loire (€5.1 billion) in 

north-west France

•  Weser-Ems in north-west Germany (€6.8 billion; 

2022 data)

•  Lombardia in northern Italy (€5.5 billion; 2022 data).

Animal output accounted for 39.9% of the total output 
of the EU’s agricultural industry in 2023. The regional 
distribution of animal output was relatively even: in 130 out 
of 223 NUTS level 2 regions for which data are available 
(58.3% of all regions), the share of animal output stood 
below the EU average. 

Several temperate regions – particularly those bordering 
the Atlantic coast, which experience higher levels of rainfall 
– reported a high degree of specialisation in livestock 
farming; this pattern was also apparent in several northern 
and several hilly/mountainous regions. For example, 
animal output accounted for a high share of output in the 
agricultural industries of every region in Ireland, as well as 

Eurostat regional yearbook – 2025 edition220C

Agriculture

in clusters of regions across north-west France, northern 
Spain (2022 data) and the Alps. 

•  the German region of Weser-Ems (74.0%; 2022 data), 

specialised in pig farming

•  the Spanish regions of Cantabria and Principado de 

Asturias (72.6% and 70.6%, respectively; 2022 data), both 
specialised in cattle farming

•  the Hungarian capital region of Budapest (72.5%; 

2022 data), which had a relatively small agricultural 
industry, with output valued at €19.3 million

•  the French region of Bretagne (70.1%), specialised in pig, 

poultry and dairy farming.

Looking more closely at the relative importance of animal 
output, 8 regions within the EU reported that animals 
accounted for more than 70.0% of the total output in their 
agricultural industries: 

•  the Portuguese island region of Região Autónoma dos 
Açores (76.4%), specialised in the production of dairy 
products

•  the Irish regions of Northern and Western (76.3%) and 

Southern (75.2%), the former is specialised in cattle and 
sheep farming, while the latter is specialised in dairy 
farming

Map 13.5: Animal output
(in value terms, by NUTS 2 regions, 2023)

Note: Poland and Slovenia, national data. Includes data for 2022 (too many 
regions to document).

Source: Eurostat (online data codes: agr_r_accts and aact_eaa01)

221Eurostat regional yearbook – 2025 editionC

Agriculture

The western French region of Poitou-Charentes had the 
highest value of output for agricultural services and non-
agricultural secondary activities, at €2.3 billion in 2023 

Map 13.6 shows the overall level of output for agricultural 
services and non-agricultural secondary activities 
(represented by the size of each circle) and their share in 
the agricultural industry (indicated by the colour of each 
circle) for NUTS level 2 regions. 

In 2023, the highest levels of regional output for 
agricultural services and non-agricultural secondary 
activities occurred across: 

•  France, with the uppermost values in Poitou-Charentes 
(€2.3 billion), Champagne-Ardenne (€1.7 billion) and 
Bretagne (€1.0 billion)

•  Italy (2022 data), with peaks recorded in Emilia-Romagna 

(€1.4 billion), Lombardia (€1.4 billion) and Veneto 
(€1.0 billion).

In 2023, agricultural services and non-agricultural 
secondary activities contributed 9.2% of the EU’s output 
in the agricultural industry. The regional distribution was 
somewhat skewed: in 135 out of 223 NUTS level 2 regions 
for which data are available (60.5% of all regions), this share 
was below the EU average. 

Several regions reported a relatively high contribution 
from agricultural services and non-agricultural secondary 
activities to the output of their agricultural industries. 
This pattern was most evident in Italy, Lithuania, the 
Netherlands and Finland (all 2022 data), where every 
region recorded shares above the EU average, while the 
same was true for all but 1 region in Romania (2022 data) 
and Slovakia. 

In 2023, 5 regions within the EU reported agricultural 
services and non-agricultural secondary activities 
accounting for more than 33.3% of all output in their 
agricultural industries: 

•  Bucureşti-Ilfov, the Romanian capital region (56.4%; 

2022 data)

•  Poitou-Charentes in France (40.2%)
•  Stockholm, the Swedish capital region (39.7%)
•  the Italian regions of Provincia Autonoma di Bolzano/

Bozen and Valle d’Aosta/Vallée d’Aoste (39.7% and 39.1%, 
respectively; both 2022 data).

In these 5 regions, non-agricultural secondary activities 
generally accounted for a higher level of output than 
agricultural services. For example: 

•  in Stockholm, the output of non-agricultural secondary 
activities was 18 times as high as that for agricultural 
services, while in Provincia Autonoma di Bolzano/
Bozen, output of non-agricultural secondary activities 
was 7 times as high

•  the only exception was Bucureşti-Ilfov, where the value 

of output from agricultural services was 45 times as high 
as that of non-agricultural secondary activities.

Eurostat regional yearbook – 2025 edition222C

Agriculture

Map 13.6: Agricultural services output and secondary activities
(in value terms, by NUTS 2 regions, 2023)

Note: Poland and Slovenia, national data. Includes data for 2022 (too many 
regions to document).

Source: Eurostat (online data codes: agr_r_accts and aact_eaa01)

223Eurostat regional yearbook – 2025 editionGETTING IN TOUCH WITH THE EU

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Eurostat regional
yearbook  
2025 edition

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Statistical information is an important tool for understanding and 
quantifying the impact of political decisions in a specific territory or 
region. The Eurostat regional yearbook 2025 provides a detailed picture 
relating to a broad range of statistical topics across the regions of the 
EU, EFTA and candidate countries.

For more information
https://ec.europa.eu/eurostat/

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