European Cluster Panorama 2016

Abstract

The European Cluster Panorama 2016, launched at the European Cluster Conference in Brussels on 1st December 2016, provides an updated perspective on clusters and emerging industries across Europe. It documents the role and benefits of strong clusters, and shows how emerging industries continue to outperform the rest of the economy. An entirely new data set adds insights on skills, entrepreneurship, and the evolution of clusters over time.
The Panorama is a flagship publication of the European Cluster Observatory, a single access point for statistical information, analysis and mapping of clusters and cluster policy in Europe. The Observatory is an initiative run by the 'Clusters, Social Economy and Entrepreneurship' unit of the European Commission's Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs.

Full text

Internal Market,
Industry,
Entrepreneurship
and SMEs
European Cluster Panorama 2016
European Cluster Observatory
REPORT
Prepared by:
Christian Ketels and Sergiy Protsiv
Center for Strategy and Competitiveness
Stockholm School of Economics
November 2016

European Cluster Panorama 2016
This work has been carried out under a service contract for the European Commission
’
s Directorate-General for Internal
Market, Industry, Entrepreneurship and SMEs. It is financed under the Competitiveness and Innovation Framework pro-
gramme (CIP) which aims to encourage the competitiveness of European enterprises. The views expressed in this docu-
ment and the information included in it do not necessarily reflect the opinion or position of the European Commission.
Internal Market,
Industry, Entre-
preneurship and
European Cluster Observatory in Brief
The European Cluster Observatory is a single access point for statistical information, analysis and mapping
of clusters and cluster policy in Europe. It is primarily aimed at European, national, regional and local policy-
makers and cluster managers and representatives of SME intermediaries. It is an initiative run by the ‘Clus-
ters, Social Economy and Entrepreneurship’ unit of the European Commission’s Directorate-General for
Internal Market, Industry, Entrepreneurship and SMEs and aims to promote the development of more world-
class clusters in Europe, notably with a view to promoting competitiveness and entrepreneurship in emerg-
ing industries and facilitating SMEs’ access to clusters and internationalisation activities through clusters.
The ultimate objective is to help Member States and regions to design smart specialisation and cluster
strategies that will help companies to develop new, globally competitive advantages in emerging industries
through clusters, and in this way to strengthen the role of cluster policies in boosting Europe’s industry as
part of the Europe 2020 Strategy.
In order to support evidence-based policy-making and partnering, the European Cluster Observatory pro-
vides an EU-wide comparative cluster mapping with sectoral and cross-sectoral statistical analysis of the
geographical concentration of economic activities and performance. The European Cluster Observa-
tory provides the following services:
■ a biannual ‘European Cluster Panorama’ (cluster mapping) providing an update of and exten-
sion to the statistical mapping of clusters in Europe, including for ten related sectors (i.e. cross-
sectoral) and a correlation analysis with key competitiveness indicators;
■ a ‘European Cluster Trends’ report analysing cross-sectoral clustering trends, cluster
internationalisation and global mega trends in industrial transformation; identifying common inter-
action spaces; and providing a forecast for industrial and cluster opportunities;
■ a ‘Regional Ecosystem Scoreboard’ setting out strengths and weaknesses of regional and na-
tional ecosystems for clusters, and identifying cluster-specific framework conditions for three cross-
sectoral collaboration areas;
■ a ‘European Stress Test for Cluster Policy’, including a self-assessment tool accompanied by
policy guidance for developing cluster policies in support of emerging industries;
■ a showcase of modern cluster policy practice, provided in the form of advisory support
services to six selected model demonstrator regions. The services offered include expert anal-
ysis, regional survey and benchmarking reports, peer review meetings and policy briefings in sup-
port of emerging industries. The policy advice also builds on the policy lessons from related initia-
tives in the area of emerging industries;
■ the European Cluster Conferences 2014 and 2016, which bring together Europe’s cluster pol-
icy-makers and stakeholders for a high-level cluster policy dialogue and policy learning, and fa-
cilitate exchange of information through, e.g. webpages, newsletters and videos.
More information about the European Cluster Observatory is available at the EU cluster portal at:
http://ec.europa.eu/growth/smes/cluster/observatory/.

European Cluster Panorama 2016
Table of Contents
Key Facts at a Glance ........................................................................................................................... i
1.Introduction ........................................................................................................................... 1
2.Concepts and data sources explained ................................................................................ 3
3.The Role of Clusters in Europe ........................................................................................... 9
3.1Measuring clusters in the European economy ................................................................... 9
3.2Strong clusters and their performance ............................................................................. 12
3.3Regional cluster hotspots ................................................................................................ 16
3.4Entrepreneurship ........................................................................... .................................. 18
3.5Reindustrialisation ........................................................................................................... 19
4.Emerging Industries in Europe .......................................................................................... 21
4.1Overall Observations ....................................................................................................... 21
4.1.1Profiling the Performance of Emerging Industries ................................................................................... 21
4.1.2Hotspots of Emerging Industries .............................................................................................................. 24
4.2Profiling the Ten Emerging Industries .............................................................................. 27
4.2.1Advanced Packaging ............................................................................................................................... 27
4.2.2Biopharmaceuticals .................................................................................................................................. 31
4.2.3Blue Growth Industries ............................................................................................................................. 35
4.2.4Creative Industries ................................................................................................................................... 39
4.2.5Digital Industries ....................................................................................................................................... 44
4.2.6Environmental Industries .......................................................................................................................... 48
4.2.7Experience Industries ............................................................................................................................... 52
4.2.8Logistical Services .................................................................................................................................... 56
4.2.9Medical Devices ....................................................................................................................................... 60
4.2.10Mobility Technologies ........................................................................................................................ 64
Methodological Appendix ................................................................................................................. 68

European Cluster Panorama 2016
i
Key Facts at a Glance
The Role of Clusters in the European Economy
…3 000 strong clusters across Europe account for more than 54 million jobs and 45% of all traded indus-
tries’ wages (23% of the overall economy)
…wages in strong clusters are close to 3% higher than in industries not located in such regional hotspots,
and the wage gap towards both other traded industries and the overall economy is growing
…103 leading clusters are in the top 20% of European peers across all four performance dimensions meas-
ured (size, specialisation, productivity, and dynamism)
…all parts of Europe have clusters; 55% of all European regions have between 30% and 60% of traded
industries employment in strong clusters
…strong clusters have shown resilience through the crisis; their share in total traded industry employment
and wages has from 2008 to 2014 increased slightly to 45% (jobs) and 51% (wages)
…the industrial cluster landscape is constantly evolving as a result of changes in market conditions, tech-
nologies, and competition; about one fifth (20%) of all clusters significantly changed in their market position
(strong, medium, weak) between 2008 and 2014
Emerging industries: Overall trends and hotspots
…account together for about 46% of all traded industry employment
…continue to outperform the rest of the traded economy with average wages about 9% higher than in all
traded industries
…have about 40% of Europe economic activity in cross-sectoral, emerging industries concentrated in 20%
of all European locations
…tend to be strongest in regions that have a strong portfolio of traditional sectoral clusters and above
average economic performance with average value added per employee 37% above the EU average
…have each their own geographical footprint, indicating location-specific opportunities for specialisation
and diversification
…the list of European hotspots of cross-sectoral, emerging industries differs significantly from the list of
hotspots of traditional clusters

European Cluster Panorama 2016
1
1. Introduction
The European economy has in the recent past made progress in putting one of the deepest economic
crisis in its existence behind it. Exports are up, growth and investment have improved, and labour mar-
kets are slowly moving towards their pre-crisis state. Still, the economic challenges that EU member
countries are facing remain formidable. The heterogeneity across Europe is high, and especially South-
ern European countries continue to struggle with high unemployment, particularly among the young.
The macroeconomic climate still hinges on the support given by historically unprecedented monetary
policies. And it is not only these current economic issues that are a concern: The result of the UK refer-
endum on leaving the EU has been a shock, and has raised broader economic and political questions.
Successful upgrading of European competitiveness is critical for the EU to meet these challenges, and
create new dynamism in the European economy. The European Commission has in the context of Com-
mission President Juncker’s Agenda for Jobs, Growth, Fairness and Democratic Change1 launched a
wide range of initiatives with this goal in mind. Focusing on microeconomic dimensions of this overall
agenda key recent efforts include the Single Market Strategy, the Digital Single Market, the Digitisation
of Industry strategy, the Energy Union, the Circular Economy Package, the Start-up and Scale-up Initi-
ative and the New Skills Agenda with the Investment Plan for Europe and set of up of Thematic Platforms
to facilitate the implementation of smart specialisation strategies that guide innovation-related invest-
ments under the European Structural and Investments Funds being other important contributors.
Clusters are a key dimension of this policy agenda. They have powerful roles in diagnostics, design,
and delivery of effective policies in order to contribute to the number one objective of achieving more
jobs, growth and investments. Clusters offer a fertile ground for fostering industry transformation and
the development of emerging industries. Accordingly, the European Commission has had a long stand-
ing focus on clusters, providing data, policy tools, and support for cross-European linkages among clus-
ter organisations.
The European Cluster Panorama, provided by the European Cluster Observatory, focuses on providing
policy makers and business leaders across the EU with fresh insights into trends of cross-industry link-
ages and the regional footprint of these groups of related activities. The Panorama applies evidence-
based categories for understanding the likely directions of industrial change and industry emergence,
mapping the performance and economic geography of ten specific emerging industries across Europe.
Its first version, the European Cluster Panorama 2014, documented the strong economic performance
of these broader cross-sectoral, emerging industries in terms of productivity and dynamism, outperform-
ing both the average of existing clusters and the broader EU economy. The economic geography of
emerging industries shows opportunities across Europe. But it also revealed a significant role of legacy
effects and underlying competitiveness driving future opportunities: many of the European regions reg-
istering the strongest position in these ten emerging industries are also in the leading group in terms of
current cluster portfolio strengths and prosperity.
1 Jean-Claude Juncker, A New Start for Europe: My Agenda for Jobs, Growth, Fairness and Democratic Change,
Strasbourg, 15 July 2014. https://ec.europa.eu/priorities/sites/beta-political/files/juncker-political-guidelines-
speech_en_0.pdf.

European Cluster Panorama 2016
2
This European Cluster Panorama 2016 provides an updated perspective on clusters across Europe,
focusing again in more detail on the evolution of the ten emerging industries identified in 2014. The
analysis is based not only on two additional years of data, but can draw on a significantly enhanced and
broadened data set (see the methodological appendix for a more detailed discussion of how the data
set was constructed):
■ One key novelty is the introduction of firm-level data to supplement the statistical data from
national and EU statistical offices. This firm-based data significantly increases the robustness
of the data, especially in countries like Germany that collect regional data through samples
rather than reporting by all firms. It also enables performance of individual firms to be tracked
over time, gaining more granular insights into patterns of entrepreneurship.
■ Another key novelty is the inclusion of new indicators, in particular data on skills. Skills are a
critical dimension of the quality of the business environment, including for its ability to adapt to
industrial transformation processes. It can thus sharpen our understanding of how clusters in-
teract with other factors to influence economic performance. Skills are also a signal for the type
of activities that a cluster within a specific category is engaged in.
Based on this enhanced data set, a deeper analysis is presented compared to the last Panorama in
2014. Updated information is provided on the ten emerging industries, tracking their further evolution in
the post-crisis recovery with data now available up to 2014. The information on the role of existing clus-
ters in Europe is also updated, looking at their overall size, dynamics, and patterns of regional distribu-
tion. The combination of these two views provides comprehensive insights in the current status and
future opportunities inherent in the economic structure of European regions.
Moreover, the enhanced data set also enable a number of new analyses that provide a more granular
perspective on clusters, emerging industries, and their dynamics. A focus is placed on two particular
aspects: the heterogeneity of cluster profiles, and the dynamics of cluster evolution:
■ The profile of individual clusters is measured within a given cluster category or emerging
industry through differences in the mix of occupations most prevalent in different locations. How
clusters with different profiles perform, and what type of regions they are located in can provide
important additional insights for policy.
■ The dynamics of cluster evolution are measured through a closer look at firm entry and exit
data as well as through exploiting the longer time-series of data. This helps to identify where
entrepreneurship in terms of new business formation is most prevalent, both in terms of the
regions and the specific clusters and emerging industries. It also allows to track the dynamics
of cluster evolution, i.e. how much change is observed over time in the economic geography of
clusters and emerging industries and the economic composition of regions.
All underlying data used in this report are available at the cluster mapping tool of the European Cluster
Observatory under the web pages of the EU Cluster Portal set up by the European Commission’s Di-
rectorate General for Internal Market, Industry, Entrepreneurship and SMEs.2 The definitions of specific
analytical categories, in particular the definitions of all clusters and emerging industries, as well as the
analytical approaches used to develop them, are also all available on the EU Cluster Portal.
2 https://ec.europa.eu/growth/smes/cluster/observatory/cluster-mapping-services/mapping-tool_en.

European Cluster Panorama 2016
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2. Concepts and data sources explained
What are clusters?
Clusters are regional concentrations of activities in groups of related industries. Clusters emerge natu-
rally in market processes, because local spill-overs among such activities enhance performance at the
firm and regional level. Examples of such positive effects – that usually grow with the critical mass in a
given location – are a labour market with specialised skills, local supplier networks with specialised
capabilities, and a local knowledge pool driven by the research and innovation activities of local firms
and knowledge institutions.
The evolution of clusters is driven by the benefits of agglomeration. Some of these are the automatic
result of market forces, such as firms growing faster or choosing to locate in clusters, while others de-
pend on purposeful action, e.g. collaboration among firms that enhances spill-overs or government ac-
tion that improves the cluster-specific business environment. But the evolution of clusters is also affected
by economic forces encouraging dispersion: as clusters become larger, there are increasing ‘congestion
costs’ that emerge as disadvantages of the agglomeration effect. For instance, firms bidding up prices
for scarce inputs in clusters, such as wages for specialised workers. There is also the potential risk of a
‘lock-in’ effect in the face of technological change, i.e. all firms in a cluster opting for one technology that
might get disrupted by innovation in other locations. The interplay between these forces of agglomera-
tion and dispersion shapes the evolution of clusters over time,
Clusters differ from cluster organisations, which are the organisations that manage the networks of firms
and other entities within a given cluster. Cluster organisations can help firms to better engage with other
local actors within their cluster and to organise collective action to strengthen the local context. And they
can reduce the transaction costs for firms, especially SMEs, in building linkages to firms and collabora-
tion partners in other locations. The stronger the local cluster, the higher the potential for building suc-
cessful international linkages.
Clusters are also different from both narrow specialisation in individual industries and broad agglomer-
ation of economic activity in cities: clusters reflect the positive spill-overs among a set of related indus-
tries, neither driven only by economies of scale in one industry nor by the economy-wide benefits of
economic density across all industries.
Clusters have a distinct geographic dimension, reflecting the dynamics of local spill-overs. They are also
deeply embedded in a broader geographic context: they serve markets elsewhere and are connected
to other clusters with complementary strengths in regional, interregional or global value chains. This
mirrors the role of location for firms: while local conditions provide the unique context for building distinct
capabilities and strategic positions, national and international linkages are critical to access other mar-
kets, suppliers, and collaboration partner.
More explanations can be found in the Smart Guide to Cluster Policy3 that was published in June 2016
to give guidance on how to make better use of clusters for promoting regional industrial modernisation,
supporting the growth of SMEs and encouraging smart specialisation.
3 The Smart Guide explains what cluster and cluster policies are and what not, what makes them successful and
why they matter. It presents eight Do's and Don'ts and many cluster programme examples and practical instru-
ments. See http://ec.europa.eu/DocsRoom/documents/16903/attachments/1/translations/en/renditions/native.

European Cluster Panorama 2016
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What are emerging industries?
Emerging industries have been defined as “the establishment of an entirely new industrial value chain,
or the radical reconfiguration of an existing one, driven by a disruptive idea (or convergence of ideas),
leading to turning these ideas/opportunities into new products/services with higher added value”.4 They
are in many cases new combinations of existing industrial sectors that are changing in response to new
technologies, new business models and market demands.
The 2014 Cluster Panorama developed a method to empirically identify broad sets of ‘emerging indus-
tries’ in which such new combinations of related activities were seen as likely to emerge.5 Data on weak
linkages across industries were used as indicators of the potential for stronger future linkages to emerge.
Emerging industries based on these weak linkages can be contrasted with the clusters based on existing
strong linkages visibly today, often reflecting the cumulative effect of past connections. The figure below
indicates how the ten emerging industries identified relate to the traditional cluster categories.
Figure 1: Emerging Industries and Clusters
4 This definition developed by Heffernan & Phaal (2009) was used in the policy roadmap of the European Forum
for Clusters in Emerging Industries that is available at http://www.emergingindustries.eu/Up-
load/CMS/Docs/Policy_roadmap.pdf.
5 The detailed process and reasons for choosing such an approach are explained in the “Methodology and
Findings Report for a Cluster Mapping of Related Sectors”, available at http://ec.europa.eu/growth/smes/clus-
ter/observatory/cluster-mapping-services/cluster-panorama_en.
Adv.
Logistics
Creative
Economy
Experience
Economy
Biopharma
Adv.
Packaging
Medical
Devices Digital
Environ.
Services
Mobility
Plastics
Upstr.
Metal
Light.
Appl IT
Med
Dev
Downst.
metal
Tran sp .
Autom
Aerosp.
Comm
Bioph.
Downs.
Chem
Tou ri s m Perf
Arts
Marke‐
tng
Music
Bus
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P&P
Env
Serv
Video
Oil
&
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Prod.
Tec h
Wood
Prod
Metalw.
Vulc.
Mat.
Agricult.
Blue
Growth
Elect.
Power
Fishing
Upstr.
Chem

European Cluster Panorama 2016
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What regional level is used?
Regions are the geographical areas in which the local spill-overs that drive cluster evolution have a
meaningful influence on economic performance. Depending on the specific sector, activity, and exter-
nality the scope of the appropriate area varies: It is, for example, the area in which it makes sense to
speak about an integrated labour market where people can find jobs without moving. It also refers to the
area in which there is a significant likelihood for knowledge to be spread through unplanned meetings
or chance observations of what others do.
This notion of regions is applied in the Panorama by using data for specific administrative regions, gen-
erally at the so-called NUTS 2 level. The European economy is made up of 327 such regions,6 each
with its own profile and economic performance. These regions are used as a pragmatic choice because
they are likely to encompass the ‘economically relevant’ regions, there is data available, and in most
cases there is some level of government that can take action for this specific region.
Why are regions important?
The European economy is ultimately a combination of linked regional economies. While macroeconomic
conditions are well tracked at the level of nations, microeconomic circumstances differ significantly
across regions, even when they are part of the same national economy. An effective strategy to under-
stand and strengthen European competitiveness, especially its microeconomic dimensions, has to
acknowledge this heterogeneity across regions.
One of the key dimensions in which regions differ is their specialisation pattern. Previous studies have
shown that the strength of a region’s cluster portfolio is related to the overall level of prosperity that the
region is able to support7. The European Cluster Panorama adds an additional step and explores the
presence of emerging industries, build up from core clusters and further related industries, across Eu-
rope. It ultimately wants to enable European regions to pursue more evidence-based strategies as they
explore their unique opportunities in emerging industries, a key ambition of the entrepreneurial discover
process part of smart specialisation strategies.8
What data are used?
The 2016 Panorama is based on a new dataset that is compiled specifically for analysing detailed pat-
terns of cluster evolution. The core of the dataset is the firm- and plant-level data sources from the Orbis
Historical dataset supplied by Bureau van Dijk (June 2016 release). This dataset provides detailed data
on the economic performance of firms. It allows to use data of firms’ turnover, wage bill, capital, materials
and employment9 totalling more than 1 billion data points. The coverage is very good in most countries
in Europe, and especially for larger limited liability companies, however significant gaps were still pre-
sent.
6 The analysis covers all EU-28 countries (comprising 276 NUTS-2 regions) as well as Albania, Bosnia and Herze-
govina, Iceland, Israel, FYROM, Kosovo (regarding the political status of which no claims are implied), Montene-
gro, Norway, Serbia, Switzerland, and Turkey by applying the NUTS (Nomenclature of Territorial Units for Statis-
tics) standard for the subdivisions of countries for statistical purposes.
7 For example in Ketels, C. & S. Protsiv (2013). Clusters and the New Growth Path for Europe. WWWforEurope
working paper series, issue 14.
8 See for more detail the material on the European Commission’s Smart Specialisation Platform: http://s3plat-
form.jrc.ec.europa.eu.
9 Employment is usually the only variable available on plant level, the rest are for the firm as a whole.

European Cluster Panorama 2016
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Therefore, three Eurostat datasets were used for calibration: Business demography to obtain counts of
businesses in missing legal forms,10 Structural Business Statistics to provide aggregate values to match
to, and Regional Economic Accounts to calibrate the final numbers to be comparable across countries.11
Using this firm-level data allows to get a more consistent picture across European businesses and com-
puting entrepreneurship indicators that would otherwise be unavailable.
While the dataset is completely new, it resembles the one used in the 2014 Panorama and most of the
values remained fairly stable in the countries where data quality was good in both 2014 and 2016. The
main difference is that the employment indicator is now using a uniform definition across countries,
defining a full-time employee as equivalent to 1 800 hours worked per year. This has the advantage that
it makes the labour input more comparable across countries and does not depend on local legislation
specifying a standard work week (although of course the measurement of working hours is imprecise).
The main outcome of this is that the countries where the average work time is significantly lower than 1
800 hours (e.g. Germany and the Netherlands) see their employment numbers lowered while their
productivity is increased, while the opposite occurs in the Eastern countries with longer working hours.
Another major addition to the dataset are the skills indicators obtained from Labour Force Surveys (LFS),
which were used to obtain the relative sophistication of workers in each cluster according to the following
four skill levels12 using broad sections of the ISCO 08 occupational classification:
■ Officials, Managers, Professionals, Technicians (“Managers”): jobs usually employing abstract
cognitive tasks (ISCO sections 0, 1, 2, 3)
■ Clerical Support Workers (“Clerks”): jobs using routine cognitive tasks, usually in offices (ISCO
group 4)
■ Craft, Trade, Operators, Assemblers (“Crafts”): routine manual jobs, usually on the factory floor
(ISCO sections 6, 7, 8)
■ Service, Sales, Elementary (“Services”): basic non-routine manual jobs like sales or cleaning
(ISCO sections 5 and 9).
Finally, the dataset is complemented with new data on fast-growing new firms (so-called “gazelles”) that
was derived using the same data source. Data on company births, their ownership structure (to remove
subsidiaries of the existing firms), as well as the financial indicators computed in the previous step were
used to select the firms that are less than 5 years old and grew by at least 10% a year over 3 years.13
Other differences between the 2014 and 2016 editions of the Panorama include the per-industry dis-
counting of monetary indicators over time (i.e. the inflation adjustment for output is based on the prices
for this industry’s output within a country, and similarly for the inputs a company uses based on input-
output tables). This, together with purchasing parity adjustments, makes the monetary values across
regions more comparable and in general increases the relative stance of poorer regions since they also
generally have lower prices.
10 Usually sole traders, but in some countries also partnerships. More details on this procedure can be found in
methodological appendix.
11 This calibration was necessary due to different definitions of employment and wages used in different countries
and to ensure that the dataset is consistent with regional and national accounts.
12 Acemoglu, D., D. Autor (2011) Skills, Tasks and Technologies: Implications for Employment and Earnings. Hand-
book of Labor Economics, Volume 4b.
13 More details follow in the section dedicated to entrepreneurship.

European Cluster Panorama 2016
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All data (other than skills) is now from a uniform source: company accounts (as opposed to, for example,
employee surveys), which further enhances the comparability across regions and industries, though
some inconsistencies remain.
How is cluster performance measured?
The strength of a cluster is a complex multi-faceted concept, capturing aspects of overall size, speciali-
sation, productivity, and dynamism (see figure 2).
Figure 2: Measuring cluster performance
In the analysis of the European Cluster Panorama, a two-stage approach is used. In the first stage,
‘strong clusters’ are identified, i.e. situations in which a region is specialised in a set of related indus-
tries relative to peers. This notion is operationalised by identifying the top 20% of European locations by
location quotient, subject to a cut-off of at least 500 employees.
14
■ Specialisation, measured by the relative size of regional employment in a given (sectoral or
cross-sectoral) cluster category reflected in its location quotient (LQ). This relative measure
indicates how much stronger a region is in a cluster category than would be expected given its
overall size, compared to the average employment size in the specific cluster category across
all regions
In the second stage, up to three additional ‘performance stars’ are awarded to capture how well a
location is leveraging the presence of a cluster. These further stars are awarded if a location falls into
the top 20% of European regions in any of the following three dimensions:
14
The Location Quotient is a measure of a region’s specialisation in an industry and is computed as the ratio of this
industry’s shares of a) this region’s employment and b) of the whole European employment across all regions.
Thus, the values above one imply high regional specialisation, with LQ of 2 corresponding to twice as many
employees in an industry than expected if all employment was distributed evenly.

European Cluster Panorama 2016
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■ Absolute size, measured by the number of employees and establishments. This measure is
based on the observation that the number of linkages within a cluster is growing exponentially
with the number of participants. Only when economic activity in a given cluster category moves
beyond a threshold of critical mass do cluster effects become significant.
■ Productivity, measured by the wages paid in a regional cluster (adjusted for local cost levels).
This measure reflects not only what is being done in a region, but how well it is being done,
influenced by the strength of cluster effects. Wages are also influenced by the structure of labour
markets and other factors but are strongly correlated with productivity.
■ Dynamism, measured by a simple average of measures on employment growth and the pres-
ence of fast-growing new firms (gazelles). This measure aims to capture whether a cluster con-
tinues to benefit from strong cluster effects in its development, or not. The cluster may be hin-
dered in its growth because it has already reached a level where costs are greater than the
benefits or other factors such as industry-specific growth trends.
The two first employment-based indicators formed the basis of the initial ‘three-star’ methodology used
by the European Cluster Observatory of the first European cluster mapping in 2007.15 Our current star-
rating is comparable to the one used in the 2014 European Cluster Panorama. The one conceptual
change is the inclusion of data on gazelles, i.e. consistently fast growing new companies, to measure
dynamism. Regions that grow through creating new firms rather than through employment growth in
established firms score higher compared to the method used in 2014. The overall effect of this change
on the report rankings is, however, relatively minor.
The strength of a region’s cluster portfolio is measured by summing up the performance across its
individual clusters. For the analysis of overall regional performance, the total number of stars across all
sectoral clusters or cross-sectoral, emerging industries is used as the core measure. There are other
measures that are reported in some tables, in particular the share of employment in strong clusters for
regions. This measure essentially weights clusters by their employment size, which provides another
useful perspective on the strength of the cluster portfolio. Because it is also more affected by whether
specific large cluster categories are strong, in particular business services, the star methodology is used
as the main indicator of regional cluster or emerging industry strength.
While these indicators enhance the understanding of economic geography across Europe, some cave-
ats should be kept in mind: First, some indicators, particularly the new ones, are measured with error
and depend on changes in how industries are captured in the different statistical systems16. Second, all
indicators have some biases: Absolute employment size can be a sign of low productivity. Large regions
benefit in the size measure, but are less likely to have high location quotient. More established clusters
tend to have higher wages, while they generally grow slower due to their already large size. High wages
measure not only superior productivity but are also reflective of the general cost and wage levels in a
region.17 With these different possible ‘biases’ often working in different directions, the four-star clusters
really stand out with strong performance across all dimensions.
15 See “The concept of clusters and cluster policies and their role for competitiveness and innovation”, Communi-
cation from the European Commission of 17 October 2008 available at http://bookshop.europa.eu/is-
bin/INTERSHOP.enfinity/WFS/EU-Bookshop-Site/en_GB/-/EUR/ViewPublication-
Start?PublicationKey=NBNA23591.
16 For example, while the attempt is to only use the data supplied using NACE 2.0 industry codes, sometimes the
older NACE 1.1 classification needs to be used complicating the growth computations. In other cases, the detailed
regional wage data was missing and had to be imputed using a combination of the detailed national data and less
detailed regional data.
17 This is to some degree controlled for using purchasing power adjusted data.

European Cluster Panorama 2016
9
3. The Role of Clusters in Europe
3.1 Measuring clusters in the European economy
Industries that show significant levels of geographic concentration account for 47% of all European em-
ployment covered in our data. While the data is not fully comparable given differences in sectoral cov-
erage, this ratio is significantly higher than in the United States where traded industries account for 36%
of employment.
18
Firms operating in these industries serve markets beyond their home location, com-
pete with rivals from other locations, and have a choice where to locate. Because of these characteris-
tics, similar to firms engaged in international trade, these industries are often called “traded”.
19
Figure 3: Employment and wage dynamics in Europe in the post-crisis period
The recent job dynamics, in Europe as well as in the US and other countries for which comparable data
is available, has seen employment in traded industries to be decreasing as a share of total employment:
Figure 3 above illustrates this by local employment displaying higher growth than traded employment.
In Europe there has been a net increase of 522 000 traded industry jobs since 2008 (most of it in non-
EU countries, Turkey in particular, covered in our data; employment in traded industries has remained
stable within the EU), compared to the net gain of more than 11 million in the rest of the economy.
Productivity and wage dynamics, however, have pointed in the opposite direction. In terms of wages
paid traded industries generate a stable 51% of the European total. Lower relative employment numbers
have been compensated by higher relative wages. Industries that cluster register an average annual
wage of 34 800 Euro in Purchasing Power Parity (PPP; a measure that accounts for differences in local
price levels) per full time employee.
20
This is 17% higher than the wage in other industries, called local
or non-traded.
18
For data on the US see the US Cluster Portal at www.clustermapping.us.
19
Note, however, that trade across national borders is not the defining aspect, even though by definition all exporting
firms are part of traded industries.
20
The monetary unit used throughout the report is 2010 PPP (i.e. all monetary values are deflated both to account
for inflation and relative prices across regions).

European Cluster Panorama 2016
10
These higher wages in traded industries are likely to be driven by higher levels of productivity, based on
higher capital or skill intensity as well as potentially the benefits of clusters. The wage gap between
traded and local industries has been slowly rising over time.
Traded industries can be further organised in 51 cluster categories, i.e. groups of industries that exhibit
strong linkages in terms of co-location, skill use, and so-called input-out relationships.21 These cluster
categories differ significantly (see Table 1), such as in the absolute number of jobs they represent: The
three largest cluster categories Distribution and Electronic Commerce, Business Services, and Hospi-
tality and Tourism, account, with between 15.8 million and 11.2 million employees each, i.e. for about
one third of all traded employment Europe-wide and are present essentially everywhere. The smallest,
Tobacco, registers only about 38 000 employees, with only 60 regions across Europe accounting for at
least 100 employees each.
Table 1: Profile of 51 Traded Cluster categories
Cluster Plants Employment
(1000) Avg Wage
(PPP) Manager
share Clerk
share Craft
share Service
share
AerospaceVehiclesand
Defence 926 297 38809 50% 9% 31% 11%
AgriculturalInputsand
Services 5479 463 20270 29% 5% 46% 19%
Apparel 10302 1343 12721 24% 7% 57% 12%
Appliances 1839 235 26522 36% 9% 41% 13%
Automotive 19162 2528 35778 33% 7% 51% 9%
Biopharmaceuticals 2350 518 51706 58% 9% 21% 12%
BusinessServices 70239 11990 45574 74% 10% 8% 8%
CoalMining 955 189 22975 36% 6% 47% 11%
Communications
EquipmentandServices 7242 828 45220 56% 12% 19% 13%
ConstructionProducts
andServices 44053 3411 28279 33% 8% 44% 15%
Distributionand
ElectronicCommerce 167110 15855 34046 42% 13% 22% 22%
DownstreamChemical
Products 22432 2572 29258 39% 9% 33% 19%
DownstreamMetal
Products 10500 1012 32846 30% 9% 50% 12%
EducationandKnowledge
Creation 24299 4866 35121 69% 9% 7% 15%
ElectricPowerGenera‐
tionandTransmission 10864 1078 39223 44% 11% 36% 10%
EnvironmentalServices 18252 1373 27152 34% 8% 20% 38%
FinancialServices 45084 7861 40982 38% 21% 24% 17%
FishingandFishing
Products 2698 457 18068 28% 7% 46% 18%
FoodProcessingand
Manufacturing 37836 2370 30252 31% 9% 42% 18%
21 Delgado, Porter, Stern (2016), Defining Clusters of Related Industries, Journal of Economic Geography, Vol. 16,
No. 1. Input-Output relationships measure to what degree the products and services generated by one industry
enter into the production processes of another industry.

European Cluster Panorama 2016
11
Cluster Plants Employment
(1000)
A
vg Wage
(PPP) Manager
share Clerk
share Craft
share Service
share
Footwear 3062 472 17681 21% 7% 62% 11%
Forestry 5052 500 17630 31% 4% 49% 16%
Furniture 35338 3688 28037 26% 6% 60% 8%
HospitalityandTourism 79029 11286 33574 55% 15% 7% 23%
InformationTechnology
andAnalyticalInstruments 21663 1666 45583 48% 9% 35% 8%
InsuranceServices 16684 2841 39527 39% 22% 23% 16%
JewelleryandPrecious
Metals 4286 175 23758 25% 7% 54% 14%
LeatherandRelated
Products 2078 118 22507 24% 7% 58% 11%
LightingandElectrical
Equipment 23259 2081 35340 35% 8% 51% 7%
LivestockProcessing 7679 894 24317 23% 7% 50% 20%
Marketing,Design,and
Publishing 31558 2055 37546 70% 12% 8% 9%
MedicalDevices 12361 726 39293 49% 7% 35% 8%
MetalMining 534 47 34352 36% 8% 40% 16%
Metalworking
Technology 22625 2987 33458 27% 6% 60% 7%
MusicandSound
Recording 2835 127 38165 53% 12% 21% 14%
NonmetalMining 5669 303 28378 26% 7% 52% 15%
OilandGasProduction
andTransportation 5140 451 63698 54% 7% 33% 6%
PaperandPackaging 16184 1059 32638 29% 9% 48% 15%
PerformingArts 21206 3354 43032 83% 5% 7% 5%
Plastics 20435 1921 31612 30% 9% 48% 14%
PrintingServices 19809 1305 31745 32% 8% 48% 12%
ProductionTechnology
andHeavyMachinery 36917 3330 40642 38% 9% 46% 7%
RecreationalandSmall
ElectricGoods 11912 793 27266 36% 9% 38% 17%
TextileManufacturing 17256 1185 21673 28% 8% 50% 13%
Tobacco 496 38 43391 41% 9% 35% 14%
Transportationand
Logistics 23933 7112 26819 26% 12% 47% 14%
UpstreamChemical
Products 6738 418 39334 38% 8% 41% 13%
UpstreamMetal
Manufacturing 27125 1639 37797 30% 7% 54% 9%
VideoProductionand
Distribution 6992 377 46375 72% 7% 9% 12%
VulcanisedandFired
Materials 34177 2100 27792 28% 8% 49% 15%
WaterTransportation 11711 893 42380 38% 12% 37% 13%
WoodProducts 26355 2506 23667 27% 7% 50% 16%
TRADED 1061720 117693 34782 44% 11% 30% 15%
TOTAL 1735261 251676 32103 40% 9% 23% 28%

European Cluster Panorama 2016
12
Table 1 shows that cluster categories differ significantly in average wage levels – likely reflecting the
differences in capital and skill intensity. Across Europe, Oil and Gas is with more than 63 000 Euro in
PPP the highest wage cluster category. Its wages are about five times higher than the lowest wages
cluster category, which is Apparel.
Table 1 also shows the differences in skill s, by showing the allocation of skills for the four job categories
of managers/professionals, clerks, crafts/operators, and service workers. Management and craft tend to
be the largest groups but still differ widely in terms of their relative importance for specific cluster cate-
gories: cluster categories comprising creative industries (Design, Music, Performing Arts, Video) have
around 70% of all jobs among management/professionals, while the footwear cluster category has only
21%. Footwear also has the highest crafts share at 60% of all jobs, while several of the knowledge-
intensive services have less than 10%. Environmental services had the highest service share at 38% of
their jobs, insurance and financial services the highest clerk share at more than 20%.
3.2 Strong clusters and their performance
In total, 3043 strong regional clusters have been identified in Europe; they are defined to capture the
leading 20% of regions by specialisation (i.e. location quotient) in each of the 51 cluster categories.
Strong clusters account for 46% of all traded industries employment. Strong clusters have thus on av-
erage about twice as much presence of economic activity in a specific cluster category than the Euro-
pean average. Strong clusters register an average wage of 36 100 Euro (PPP), which is about 3% more
than all traded industries.22
About 2 550 of these strong clusters register at least 500 employees and a location quotient (i.e. number
of employees relative to what would be expected on average given the size of the region) larger than
1.5. Most of the strong clusters have two stars (the one for specialisation by the definition of “strong”,
and another one), while there are 103 four-star clusters.
Table 2: Number of regions by total stars in strong regional clusters
1 Star 2 Stars 3 Stars 4 Stars
618 1 536 786 103
Cluster categories differ in the dispersion of activity across locations, measured by the degree that
strong clusters (i.e. the top 20% specialised clusters) dominate overall activity in the cluster category
across Europe. For the majority of the sectoral categories strong clusters account for between 40% and
60% of economic activity. But in some larger categories the share is lower, for example about one third
for Distribution and Electronic Commerce. Conversely, in categories related to natural resources (coal
and metal mining) but also in aerospace vehicles and defence, the share is instead above 80%.
22 While this benefit of being in a strong cluster might seem relatively small, it is obscured by the impact of cross-
cluster locational effects on wages. If clusters in a specific cluster-category, for example food processing, are
predominantly concentrated in less advanced regions, the average in strong clusters in this cluster category will
be lower than in generally higher wage regions with less food processing activity.

European Cluster Panorama 2016
13
Looking more narrowly at the ten leading clusters by category, one can find that they tend to account
for about 25% of all economic activity, again with significant variation. On average, the next 50 locations
then account for about the same amount of activity as the leading ten.
Table 3 shows that the profile and performance of strong clusters differs across cluster categories. The
size of the average strong cluster in Business Services (91 700 employees) is much larger than those
in smaller categories like Jewellery. On the other hand, the wages in Oil are among the highest of all
cluster categories at close to 70 000, more than five times those in Apparel (even as all the wage num-
bers are corrected for price differences across regions).
Table 3: Performance of Strong Clusters in 51 Traded Cluster categories
ClusterStrong Otherlocations
Average
Employ‐
ment
Average
Wages
Average
Annual
Empl.
Growth
2008‐14
Average
Employ‐
ment
Average
Wages
Average
Annual
Empl
Growth
2008‐14
AerospaceVehiclesandDefence512835951N/A2322652749N/A
AgriculturalInputsandServices4013169235.17%807244325.51%
Apparel1499481672.14%135425470‐0.59%
Appliances320222864N/A19440366N/A
Automotive20811326407.03%4521393891.02%
Biopharmaceuticals5761522595.23%790507490.33%
BusinessServices91655503473.23%22762407132.08%
CoalMining59231922015.26%78607781.72%
CommunicationsEquipmentand
Services8744456875.55%997441943.32%
ConstructionProductsandServices2419723265‐0.01%6951326940.45%
DistributionandElectronic
Commerce77406266885.73%41035376220.51%
DownstreamChemicalProducts22508253284.09%4164346300.64%
DownstreamMetalProducts6422331461.72%2255326290.42%
EducationandKnowledgeCreation28583346713.67%11460354074.58%
ElectricPowerGenerationand
Transmission8853358247.26%1899432454.62%
EnvironmentalServices10194208673.51%2900326751.02%
FinancialServices62477422171.27%14320396211.95%
FishingandFishingProducts8550164711.27%294254872.05%
FoodProcessingand
Manufacturing10836248141.99%6357325521.11%
Footwear6848167292.56%28523306‐1.02%
Forestry552315208N/A75721956N/A
Furniture24739204350.13%7810342421.46%
HospitalityandTourism61096369013.77%27793317243.73%
23 N/A = data not available.

European Cluster Panorama 2016
14
ClusterStrong Otherlocations
Average
Employ‐
ment
Average
Wages
Average
Annual
Empl.
Growth
2008‐14
Average
Employ‐
ment
Average
Wages
Average
Annual
Empl
Growth
2008‐14
InformationTechnologyand
AnalyticalInstruments13419441933.05%3053471100.97%
InsuranceServices25051406692.57%4627379932.42%
JewelleryandPreciousMetals195121503‐1.66%26427184‐0.02%
LeatherandRelatedProducts2154210864.90%131263282.50%
LightingandElectricalEquipment1281030203‐1.88%4751388550.63%
LivestockProcessing7053203940.38%166428443‐0.01%
Marketing,Design,andPublishing17980390170.91%3325355351.16%
MedicalDevices5592394672.16%1460391290.82%
MetalMining161232163N/A5444822N/A
MetalworkingTechnology17553337620.77%7004332650.42%
MusicandSoundRecording273540521‐3.92%137324443.79%
NonmetalMining2700209251.31%649341600.42%
OilandGasProductionandTrans‐
portation5040685215.24%543533590.98%
PaperandPackaging715430450‐0.08%2268343510.02%
PerformingArts24654558173.23%6640309811.51%
Plastics12106274863.20%4317345490.13%
PrintingServices942631747‐2.27%266931743‐1.55%
ProductionTechnologyandHeavy
Machinery25179429782.91%6390383150.18%
RecreationalandSmallElectric
Goods7036254502.40%1263298330.87%
TextileManufacturing11808175193.53%160029306‐0.39%
Tobacco120039211‐0.26%8849028N/A
TransportationandLogistics4701521431‐0.31%15361309900.81%
UpstreamChemicalProducts354236627‐2.37%74242439‐0.10%
UpstreamMetalManufacturing11981363612.97%3310390960.87%
VideoProductionandDistribution4554504090.90%453374912.13%
VulcanisedandFiredMaterials1512421513‐0.55%4221334810.10%
WaterTransportation9155416642.16%1171437470.60%
WoodProducts1811417788‐0.37%5040290310.84%
Wages in a specific regional cluster are driven by cluster effects and by location effects. The stronger
the cluster and the better the location-specific business environment, the higher the cluster’s productivity
and wages. In some cluster categories, strong clusters tend to be in locations with weak business envi-
ronments; the negative location effect then results in a relatively low wage in strong clusters. Accounting

European Cluster Panorama 2016
15
for this effect, however, wages in strong clusters are higher.24 When it comes to growth, strong clusters
outperform weaker ones in about half the industries. In some clusters (e.g. Automotive) the difference
in growth is substantial. Here, the cluster effects are strong enough to compensate for the usual con-
vergence, i.e. the normally faster growth in locations with lower current levels of economic activity,
across locations that economic theory predicts.25
Clusters are constantly evolving: Market conditions and demand are changing, technologies and busi-
ness models are changing, and the local cluster and its business environment is changing too. At the
same time, there are forces that will drive path-dependency, which means that clusters with strong per-
formance yesterday have capabilities that make it more likely that they will also be strong today. The
analysis of the new dataset, which includes comparable time-series data over the 2008-2014 period,
allows to track these two forces. To do so, all regions have been categorised into three different groups
depending on whether they display weak, medium, and strong cluster strength overall. The share of
regions was then calculated that either stayed within the same cluster strength category or changed its
position into a different strength category.
Table 4: Regional overall cluster strength development (2008 to 2014)
ClusterstrengthWeak2014Medium2014Strong2014
Weak200892%8%1%
Medium200813%78%9%
Strong20082%14%85%
Strong: following the definition of strong clusters used above, i.e. top-20% specialised (i.e. location quotient, LQ in
short); Medium: capturing clusters above the median LQ, but not in top 20%; Weak: the clusters below the median
LQ.
The data in table 4 reveals that there is indeed significant evidence of both churn and path dependency.
Between 78% (medium) and 92% (weak) of all clusters by category remain in the same group over this
six-year period, which covers the crisis and its aftermath. About 20% of all clusters did change the group
they were in. Among strong clusters there was more stability: 89% of the clusters strong in 2014 were
already strong in 2008. This data is consistent with a view that locations with little existing assets find
developing cluster strength very hard, while among those that have some assets and those that already
have strong clusters there is significant mobility in terms of changing market success.
24 A more appropriate comparison is thus to look at the wage in strong clusters taking regional and sectoral effect
into account. Controlling for these effects yields the effect of strong cluster equal to approximately a 3% increase
in average wage. However, this coefficient is driven down by non-EU countries (where our data is also weaker)
and the effect of a strong cluster within EU is close to 8%.
25 Delgado, Porter, Stern (2014), Clusters, Convergence, and Economic Performance, Research Policy, Vol. 43,
Issue 10, pp. 1785 – 1799.

European Cluster Panorama 2016
16
3.3 Regional cluster hotspots
Most regions have between 5 and 15 strong clusters according to the definition applied by the Pano-
rama. 75% of all regions fall into this bracket. Regions then tend to achieve 10 to 30 stars in their strong
clusters; 66% of all regions fall into this bracket.
There are roughly 50 regions with fewer than 10 stars, and 50 regions with more than 30 stars. Among
the top five regions in terms of total number of stars in strong clusters there are three Polish regions
(Slaskie, Wielkopolskie, and Dolnoslaskie), one German region (Mittelfranken), and one from the UK
(Western Wales). The roughly 20 regions with five or less stars in strong clusters are predominantly
from Southern Europe, especially Greece and Turkey.
Consistent with the analysis conducted in the 2014, figure 4 and table 5 apply a broader measure of
regional cluster portfolio strength, counting all stars achieved in individual cluster categories, not just in
strong clusters. Locations like the Istanbul region but also Ile de France and Lombardia with a larger
overall size, generally display higher growth, or higher overall wage levels. A look at the strong clusters
in these regional hotspots (one other measure) reveals that there are many different specialisation pro-
files that are consistent with an overall strong cluster portfolio. Each region has its own distinct profile of
activities supporting value creation and prosperity.
Figure 4: European regional hotspots for sectoral clusters by number of stars

European Cluster Panorama 2016
17
Table 5: Leading Regions by Cluster Stars
RegionRegion
Name
Largest
City
Total
Stars
1‐star
clus‐
ters
2‐star
clus‐
ters
3‐star
clus‐
ters
4‐star
clus‐
ters
Empl.
Shareof
Strong
Clusters
Top3ClustersbyLQ
TR10IstanbulIstanbul10133112058.14%Appliances
TextileManufacturing
Biopharmaceuticals
DE21Ober‐
bayern
Munich9292311148.34%AerospaceVehiclesand
Defense
Biopharmaceuticals
VideoProductionand
Distribution
FR10Îlede
France
Paris9210326063.75%PerformingArts
VideoProductionand
Distribution
Marketing,Design,and
Publishing
DE11StuttgartStuttgart8310207356.29%ProductionTechnology
andHeavyMachinery
Automotive
MetalworkingTechnology
ITC4Lombar‐
dia
Milan8017197163.65%TextileManufacturing
InsuranceServices
FinancialServices
DEA2KölnKöln807267044.26%VideoProductionand
Distribution
MetalworkingTechnology
InsuranceServices
DE71Darm‐
stadt
Frankfurt
amMain
7613217053.28%Biopharmaceuticals
FinancialServices
InsuranceServices
DE60HamburgHamburg7313148267.49%WaterTransportation
MetalMining
MedicalDevices
DEA1Düssel‐
dorf
Düssel‐
dorf
7316159032.04%ProductionTechnology
andHeavyMachinery
CommunicationsEquip‐
mentandServices
UpstreamChemical
Products
PL22SlaskieKatowice7215251161.61%CoalMining
LightingandElectrical
Equipment
Furniture
PL41Wielko‐
polskie
Poznan7213253056.89%Appliances
Furniture
LivestockProcessing

European Cluster Panorama 2016
3.4 Entrepreneurship
For the 2016 Cluster Panorama firm-level data was used to identify so-called ‘gazelles’, i.e. firms that
have grown quickly over an extended period of time.26 Gazelles are defined here as companies less
than 5 years old that have grown their employment at least 10% annually over a period of three years.
This definition is more inclusive than the common 20% growth requirement used for gazelles, enabling
us to capture a larger share of the dynamics of new business formation. At the same time, the focus is
placed on traded industries and thus those parts of the economy where companies are not constrained
in their growth potential by the size of their local market.27
While about half of all gazelles are in local industries, by their nature this activity is more likely to reflect
churn and the displacement of less productive existing firms than net addition of economic activity.
Due to the novelty of this data source and differences in coverage across countries, the results have to
be interpreted with caution. In particular, it seems likely that country-specific rules and regulations, for
example on taxation, have an important influence on the presence of new business formation that is not
directly linked to the overall dynamism of the economy.
Table 6: Top regions by presence of Gazelles
RegionRegionNameLargestCity Gazelles GazelleEmployeesShareofregion's
employment,%
FR10ÎledeFranceParis1998579081.8%
ES51CataluñaBarcelona1455337942.2%
ES30MadridMadrid1358376762.7%
FR71Rhône‐AlpesLyon1292222182.2%
HU10Kozep‐MagyarorszagBudapest1145285612.6%
ES61AndalucíaSevilla1090252732.4%
ITC4LombardiaMilan1008633732.6%
LT00LietuvaVilnius979279273.9%
SE11StockholmStockholm977138862.3%
ES52ValenciaValencia930220512.3%
There are more than 67 700 gazelles in traded industries in Europe employing 1.9 million workers or
1.6% of all employees. Of these, 25 000 or 38% of gazelles are located in strong clusters. These new
firms are also substantially larger in strong clusters: their share of overall gazelle employment is 46%
and they employ 35 employees on average compared to 24 outside of strong clusters.
Many of the new enterprises are located in Southern and Eastern European regions, particularly in
countries like Spain and Italy that have historically a very high share of small, family-owned enterprises.
26 Similar analysis has recently been presented for regions and broad sectors; see http://ec.europa.eu/euro-
stat/documents/2995521/7706167/4-26102016-AP-EN.pdf. The Cluster Panorama puts this data into the con-
text of regional clusters, and focuses more specifically on fast growing firms that have been newly established.
27 Guzman/Stern (2015), Nowcasting and Placecasting Entrepreneurial Quality and Performance, NBER Working
Paper No. 20954, MBER: Cambridge, MA, find being part of a traded industry to be a core driver of future growth
opportunities for new businesses.

European Cluster Panorama 2016
19
In some of these regions, the gazelles constitute more than 3% of the overall employment. Since the
thresholds are relatively low, it is not clear whether many of these new businesses have ambitions to
grow beyond a few employees.
3.5 Reindustrialisation
Manufacturing has in the post-crisis period attracted renewed interest from policy makers, both in Eu-
rope and North America. Manufacturing is seen as important for building capabilities over time, helping
regions to create more competitive and resilient economies. Cases of ‘reshoring’ of industrial activity
from Asia offered hope that manufacturing could be a driver of growth in the post-crisis period. The
European Commission’s 2014 Communication 'For a European Industrial Renaissance’ stresses in this
context the need for Europe to focus on the post-crisis modernisation of its economic structure, espe-
cially in industry.28
This message was strengthened with the 2016 Communication on “Digitising European Industry”.29 The
data set compiled for the Panorama tracks the dynamics of economic activity in manufacturing between
2008 and 2014 by following the traditional narrowly defined sectors for manufacturing activity.30 The vast
majority of manufacturing is included in traded industries, and thus captured in our cluster analysis. Most
clusters are fully manufacturing or services driven. This is partly the result of a lack of granularity in the
available data. There are a few, however, that mix both types of industries: Communications Equip. &
Services, Electric Power Gen. & Transmission, as well as most natural resource driven ones (Mining,
Forestry).
Overall, manufacturing accounted for 37.4% of traded industries employment in 2014, down from 39.9%
in 2008 (see figure 5). In absolute terms manufacturing employment has decreased from 46.7 million to
44 million in the same period. Despite decreasing employment shares, the share of gross value added
(GVA) in manufacturing has remained stable at about 33%, and the share of total wages declined from
35% to 34%. There are strong indications of rising productivity in the sector: wages grew 4% from 33
600 to 35 000, and value added per employee grew from 63 400 to 67 900 (a 7% increase).
These trends have been very similar when looking only at strong clusters in manufacturing and in the
economy overall. Manufacturing activity tends to be somewhat more concentrated in strong clusters
than other traded industries but experienced a similar decrease from 42.6% to 40.5% as a share of all
strong cluster employment.
This data is consistent with the view that (advanced) manufacturing is achieving a rate of productivity
growth that is outpacing demand growth for manufacturing goods. Production requires a decreasing
number of employees that are more and more productive, and thus able to secure a growing wage
premium versus the average worker. Clusters in manufacturing are affected by these broader trends but
not differently from manufacturing activities in other locations. The changes in productivity seem to be
fairly broad-based across all locations.
28 European Commission (2014), For a European Industrial Renaissance, COM (2014) 14 final.
29 Digitising European Industry - Reaping the full benefits of a Digital Single Market (COM(2016) 180 final), available
at https://ec.europa.eu/digital-single-market/en/digitising-european-industry.
30 Traded industries part of section C in the NACE 2.0 classification of economic activities are used as the definition
of manufacturing.

European Cluster Panorama 2016
20
Figure 5: Dynamics of manufacturing in Europe
One much discussed aspect is the distribution of manufacturing activity within Europe. With the acces-
sion of Eastern European countries in 2004 there was a significant relocation of labour-intensive manu-
facturing activity to the East; this was a core driver of their robust catch-up. Our data indicates that this
process of west-east relocation of manufacturing activity has largely been completed. While the overall
level of manufacturing employment remains larger in Eastern Europe,
31
much in line with their factor
endowments, the decrease in the share of manufacturing in traded industry employment has been sim-
ilar to the EU overall, dropping from 51.6% to 48.3% between 2008 and 2014.
A look at individual regions within EU confirms these broader trends as the level of cross-region variation
is relatively modest. When regions are ranked by their manufacturing employment share in 2008, the
top 59 regions have all seen this share decreased. Only 9 regions have increased their manufacturing
share by more than one percentage point, while many regions have lost more than 10 percentage points.
Very few regions have grown their manufacturing employment share.
31
Defined as the EU members that joined the EU in 2004 and after, excluding Cyprus and Malta.

European Cluster Panorama 2016
21
4. Emerging Industries in Europe
4.1 Overall Observations
4.1.1 Profiling the Performance of Emerging Industries
The ten emerging industries (see them listed in table 7) identified in the 2014 edition of the European
Cluster Panorama continue to play an important role in European economic development. The 492 000
companies comprising the sector employ 54 million employees, or 46% of the overall traded industry
employment.32 The average wage at 37 900 Euro (PPP) is 9% larger than traded industries overall and
all but two emerging industries have a higher average wage than traded industries overall.
While the cross-sectoral, emerging industries categories are more similar in size and performance than
the sectoral cluster categories due to being broader and partially overlapping, they do exhibit substantial
heterogeneity in skill compositions. Creative and Digital industries rely on managerial and professional
talent nearly twice as much as traded industries in general. At the same time, Logistical Services and
Mobility Technologies employ much more manual craft labour, while Experience Industries focus on
service workers.
Table 7: Profile of Emerging Industries
EmergingIndustryPlants Employment
(1000)
Average
Wage
(PPP)
Manager
share
Clerk
share
Craft
share
Service
share
AdvancedPackaging3891348540233583929%8%53%10%
Biopharmaceuticals2096123151574753354%11%21%14%
BlueGrowthIndustries75235129532823232039%12%38%12%
CreativeIndustries107276141668794476574%10%8%7%
DigitalIndustries8053599947674494962%9%21%8%
EnvironmentalIndustries7188287257093846646%11%32%11%
ExperienceIndustries113445152489613532356%14%10%20%
LogisticalServices2831676148982639026%12%49%14%
MedicalDevices4386348261574352749%10%32%9%
MobilityTechnologies78787108185473825136%9%45%9%
Compared to 2014, figure 6 shows relative wage levels remaining similar while there have unsurprisingly
been more significant changes in terms of employment growth.33 Employment dynamics have improved
in traded sectors as the European economy is emerging from the crisis, and the same is true for most
emerging industries. Overall the ten emerging industries continue to outperform the average of all traded
industries on at least wage level or employment growth. But over the last two years one of them, Logis-
tical Services, has dropped below this benchmark. While the industries captured in this category had
32 Note that since the emerging industry definitions are overlapping, the overall employment across all emerging
industries is lower than the simple sum of employment in each industry separately.
33 Comparisons over time are made within the new data set. The figures cannot be directly compared to the data
reported in the 2014 Cluster Panorama, even though the patterns are generally similar.

European Cluster Panorama 2016
22
registered below average traded industry wages before, they also had less dynamic employment growth
over the last two years.
Experience industries, a category dominated by tourism including also some business services, per-
forming arts, and other industries, is now the emerging industry with the highest employment growth.
Medical Devices, a category that includes around a small medical device core large segments of infor-
mation technology, production technology and other industries, saw employment growth drop to the
lowest level among all emerging industries, falling further behind also the traded industries average.
Figure 6: Performance of Emerging Industries
Taking the same strength measure as for cluster categories in the previous section, table 8 profiles the
performance in strong emerging industry clusters.
34
Strong emerging industry clusters account for about
51% of employment and 53% of wages paid across all emerging industries. In all categories apart from
Blue Growth and Logistical services strong clusters report faster growth than weaker ones, highlighting
the possible presence of positive feedback loops. In 8 out of 10 emerging industries the average strong
cluster has positive growth, compared to 6 out of 10 among weaker locations. Wages tend to be higher
in locations in which the respective emerging industry is strong, but again in some cases these locations
are predominantly in less-advanced lower wage regions, dragging down the average; this is just like for
the traditional clusters as discussed earlier in this report.
34
Emerging industries are broader conceptual categories than the cluster categories defined in the previous section,
and there is no evidence that cluster dynamics extend to the full breadth of cross-sectoral industries they com-
prise. The term cluster is here used to characterise the presence of critical mass in the set of more weakly related
industries captured by emerging industries.

European Cluster Panorama 2016
23
Table 8: Performance of strong locations in Emerging Industries
EmergingIndustryStrong OtherLocations
AverageEm‐
ployment
Average
Wages
AvgEmpl
Growth
2008‐14
AverageEm‐
ployment
Average
Wages
Avg Empl
Growth
2008‐14
AdvancedPackaging34 012 36 393 1.28% 11 09337679‐0.17%
Biopharmaceuticals15 400 58 486 3.80% 489648409‐0.18%
BlueGrowthIndustries53 214 35 314 1.34% 34 605361790.69%
CreativeIndustries116 924 50 199 3.35% 30 217395341.81%
DigitalIndustries71 102 51 790 0.75% 22 041408360.88%
EnvironmentalIndustries44 917 45 305 1.17% 23 271383720.47%
ExperienceIndustries97 663 39 283 3.84% 37 625338522.80%
LogisticalServices37 870 26 332 ‐0.30% 16 078322050.62%
MedicalDevices35 043 48 050 0.19% 10 10842829‐1.22%
MobilityTechnologies79 420 39 784 0.51% 23 74039764‐0.31%
When it comes to entrepreneurship, emerging industries register 15 100 gazelles in strong clusters (48%
of all gazelles in emerging industries) with employment of 415 000 (51%). This corresponds roughly to
the geographic footprint of the existing activities in these industries. Among the emerging industries
there is a clear pattern that service-dominated sectors, like Creative Industries and Logistical Services,
have relatively more high-growth companies and they constitute a larger share of employment. This is
driven partially by the recent shift towards services, but also by the generally lower capital requirements
and other barriers to entry.
Table 9: Gazelles in Emerging Industries
EmergingIndustryGazelles GazelleEm‐
ployees
Shareof em‐
ploymentinga‐
zelles
Shareofga‐
zellesin
strong
locations
Shareofgazelle
employeesin
stronglocations
AdvancedPackaging1567623021.3%36.6%47.9%
Biopharmaceuticals783210230.9%29.9%33.4%
BlueGrowthIndustries81292765682.1%21.6%25.4%
CreativeIndustries145392898102.0%53.2%49.9%
DigitalIndustries59401488341.5%35.6%42.6%
EnvironmentalIndustries51721873052.1%23.8%41.6%
ExperienceIndustries90272322541.5%34.7%45.8%
LogisticalServices39751214551.6%28.9%32.5%
MedicalDevices1518509221.1%35.9%41.8%
MobilityTechnologies33001261571.2%35.5%44.8%
The geographic distribution of gazelles thus follows roughly the same patterns as the geographic foot-
print of existing activity in the respective emerging industry: it is the same 20% of clusters that account
for twice as much current economic activity and entrepreneurship as the average of all locations.
The share of gazelles in strong clusters varies from nearly one half in Logistics and Creative Industries
to one quarter in Environmental Services. These variations likely reflect industry-specific differences in

European Cluster Panorama 2016
24
barriers to entry but potentially also location-specific conditions relevant for entrepreneurship in the type
of locations an emerging industry is predominantly found.
4.1.2 Hotspots of Emerging Industries
While the present analysis is generally focused on individual emerging industries, it also identified re-
gions with cluster strengths across all emerging industries. Similar to the hotspots analysis of regional
sectoral cluster portfolios, the total number of stars registered across the four cluster performance di-
mensions for the ten cross-sectoral, emerging industries was also identified for each region.
Table 10 shows strong differences in size and economic performance across groups of regions by their
overall number of stars for emerging industries. Regions with more stars are not only significantly larger
(this gives them a better chance to capture stars for absolute size) but register also much higher wages,
value added, and patenting intensity.
Table 10: Regional competitiveness outcomes and emerging industry cluster portfolio strength
Clusterstarratingrange
Average
0‐45‐910‐1415‐1920+
GDP,million€26342 37673 68883 111967 18189641815
GDPpercapita,PPP23450 25300 30400 37200 4455027200
Employment4533155352657307309897252144157570227
AverageWage,PPP/Employee309302912636251421914164132949
GrossValueAdded,PPP2124927214455207772518287931386
PatentsperMillionPeople90661070514402191184123411405
Numberofregions100 111 77 29 10
As in the 2014 edition of the Panorama, most of the top regions come from Southern Germany with
Stuttgart as the leader in 2016 compared to Darmstadt two years ago. Due to the substantial changes
in the underlying datasets it is hard to compare the relative performance of the regions directly, but the
stability of the top-10 is a sign of the robustness of the main results.
Many of these hotspots are concentrated in large urban areas and traditional manufacturing regions.
This is due to the nature of emerging industries that combine strong service-oriented industries like
Creative and ICT and the industries that build upon the accumulated manufacturing knowledge. The
former are much more likely to be strong in urban centres, and in fact most of the capitals and large
cities in Europe score high on the number of stars. While the latter prosper in historically strong techno-
logical areas stretching from Cologne to Milan, as well as some Eastern European regions.

European Cluster Panorama 2016
25
Figure 7: European regional hotspots of cross-sectoral, emerging industry clusters
Table 11 profiles the European regions with 15 or more stars for emerging industries. These 39 regions
have more than 51% of traded industry employment in emerging industries, compared to 43% in re-
maining 288 regions. The corresponding shares for wages paid are 57% and 45% respectively. These
regions are overall considerably richer and more productive having 37% larger average value added
per employee suggesting the possible impact of favourable business environment.
Table 11: Europe’s emerging industries hotspots (15 stars or more)
RankRegionRegionNameLargestCity Size
stars
Spec.
stars
Productivity
stars
Dynamism
Stars
Total
stars
1NO01OsloogAkershus Oslo 5 5 10 525
2DE11StuttgartStuttgart 10 6 7 124
3DE71DarmstadtFrankfurtam
Main
10 6 7 023
4DE21OberbayernMunich 10 5 7 022
4DEA1DüsseldorfDüsseldorf 10 5 4 322
4DEA2KölnKöln 10 3 9 022
7DE12KarlsruheKarlsruhe 10 7 3 121
7DE14TübingenTübingen 5 5 8 321

European Cluster Panorama 2016
26
RankRegionRegionNameLargestCity Size
stars
Spec.
stars
Productivity
stars
Dynamism
Stars
Total
stars
9FR10
Î
ledeFranceParis 1037020
9TR10IstanbulIstanbul 10001020
11DE13FreiburgFreiburg 774119
11DE25MittelfrankenNürnberg 667019
11DEA5ArnsbergDortmund 955019
11IE02Southernand
Eastern
Dublin 754319
15CH06ZentralschweizLuzern 0410418
15DE60HamburgHamburg 864018
15DEB3Rheinhessen‐Pfalz Mainz 656118
15DK01HovedstadenCopenhagen 548118
15ITC4LombardiaMilan 1080018
15SE11StockholmStockholm 645318
21BE21AntwerpenAntwerpen 2410117
21BG41YugozapadenSofia 710917
21DE23OberpfalzRegensburg 355417
24AT13WienWien 443516
24DE27SchwabenAugsburg 565016
24NO04AgderogRo‐
galand
Kristiansand 239216
24TR33ManisaManisa 360716
24TR42Kocaeliİzmit 170816
24UKI7OuterLondon‐
WestandNorth
West
London
438116
30CH04ZürichZürich 3210015
30FR71Rhône‐AlpesLyon 1023015
30ITC1PiemonteTurin 1040115
30ITH3VenetoVenice 950115
30ITH5Emilia‐RomagnaBologna 960015
30NL32Noord‐HollandAmsterdam 726015
30NL33Zuid‐HollandRotterdam 725115
30NO05VestlandetBergen 149115
30UKI6OuterLondon‐
South
London 128415
30UKJ1Berks,Bucksand
Oxon
Oxford 537015
Comparing the regional hotspots for the ten cross-sectoral, emerging industries with those for the 51
sectoral cluster categories, it is evident that while all the emerging industry hotspots are also sectoral
cluster hotspots, the reverse is not true. In general, regions in the Eastern and Southern Europe, partic-
ularly in Poland, Baltic States and Spain, are much stronger in sector-based cluster measures then they
are in emerging industry cluster based ones. This could reflect a different industrial composition of these
regions, but also substantial difference in the overall business environment.
There are seven regions (five in Germany) that are in top-10 by cluster stars according to both the
emerging industry definition and the sector-based one: Istanbul, Stuttgart, Paris, Düsseldorf, München,
Köln, and Frankfurt am Main. Some regions have stronger emerging industry cluster portfolios (Oslo,
Dublin, Karlsruhe, Nürnberg, and Dortmund), while others are much stronger in sectoral cluster portfo-
lios (Milan, Hamburg, Lithuania, Poznan, Katowice).

European Cluster Panorama 2016
27
The strongest regions exhibit very different patterns of strength. Some, like Oslo, Antwerpen, and Swiss
regions, have high wages in every emerging industry and score high on productivity. Regions in Eastern
Europe, particularly Bulgaria, Hungary and Romania, score high on dynamism due to high entrepre-
neurship indicators. Many regions have reached large sizes in all of the sectors, but it is by definition
hard to specialise in many areas. This makes Lombardia’s eight emerging industries with high speciali-
sation a particularly strong achievement. Top regions usually combine two or three strong dimensions,
but are weaker in the remaining ones. The most balanced regions among the top 25 are Stockholm and
Dublin which score at least three stars along each dimension.
4.2 Profiling the Ten Emerging Industries
The following section reports key new data on each of the ten emerging industries that were defined in
the 2014 European Cluster Panorama. It also reproduces a basic profile of the activities included in each
emerging industry, including a graphic representation of industry composition. For a more qualitative
discussion of the emerging industries and key trends that they are exposed to please see the 2014
document.
4.2.1 Advanced Packaging
Basic Facts Level in 2014 Change since
2012 Share of traded
clusters Share of
overall economy
Employment 4 854 023 2.01% 4.12% 1.93%
Establishments 38 913 -1.93% 3.67% 2.24%
Average Wage 35 839 2.38% 103.04% 111.64%
Gazelle Employment 62 302 N/A 3.26% 1.65%
Figure 8: Employment over time, 2008 - 2014

European Cluster Panorama 2016
28
The core of the Advanced Packaging industry is the Paper and Packaging cluster, complemented with
packaging-related industries from Plastics, Automotive, Metalworking and other clusters that are often
significantly larger in overall employment. Packaging of goods occurs throughout industry value chains,
from early steps in manufacturing, any distribution actions (transport packaging) until the end product
having arrived at the final user (the consumer package).
Figure 9: Advanced Packaging Industry composition
35
35
The size of the different boxes is proportional to industry employment

European Cluster Panorama 2016
29
Table 12: Occupational profile of employment in Advanced Packaging
OccupationEmploymentEmploymentshare
Craft,Trade,Operators,Assemblers 2161 00053.7%
Metal,MachineryandRelatedTradesWorkers 769 10019.1%
StationaryPlantandMachineOperators 663 90016.5%
Assemblers214 8005.3%
Other513 20012.8%
Officials,Managers,Professionals,Technicians 1157 60028.8%
ScienceandEngineeringAssociateProfessionals 353 8008.8%
Other803 80020.0%
Service,Sales,Elementary397 2009.9%
LabourersinMining,Construction,ManufacturingandTransport 270 2006.7%
Other127 0003.2%
Clerks355 3008.8%
NumericalandMaterialRecordingClerks 224 8005.6%
Other130 5003.2%
Figure 10: Leading regions in Advanced Packaging

European Cluster Panorama 2016
30
Table 13: Europe’s top locations36 in Advanced Packaging
#RegionRegionNameLargestCityEmploymentLQAvg.Wage,
PPP
Annual
Growth
Gazelle
Empl.
Share
Stars
1HU22Nyugat‐DunantulGyör453694.692213419.6%3.3%3
2TR42Kocaeliİzmit260883.523491640.1%1.4%3
3DE14TübingenTübingen395132.4353297‐0.3%0.4%3
4DE11StuttgartStuttgart1021502.3862906‐0.1%0.3%3
5DE23OberpfalzRegensburg245022.27589340.8%2.5%3
6DE27SchwabenAugsburg344862.1553179‐11.6%1.2%3
7RO42VestTimisoara362562.11118888.5%3.2%3
8DE26UnterfrankenWürzburg244902.0758313‐1.8%1.1%3
9DEA5ArnsbergDortmund590201.95528472.3%2.1%3
10DE25MittelfrankenNürnberg335301.93751027.8%0.8%3
11DE12KarlsruheKarlsruhe532261.93814534.7%1.7%3
12DE13FreiburgFreiburg378301.85540331.6%0.3%3
Table 14: Strategic profiles of top locations in Advanced Packaging
RegionRegionNameLargestCity Top3Occupations
HU22Nyugat‐Du‐
nantul
Györ82Assemblers
72Metal,MachineryandRelatedTradesWorkers
81StationaryPlantandMachineOperators
TR42Kocaeliİzmit75FoodProcessing,Woodworking,GarmentandOtherCraftandRe‐
latedTradesWorkers
DE14Tübingen Tübingen 72Metal,MachineryandRelatedTradesWorkers
31ScienceandEngineeringAssociateProfessionals
81StationaryPlantandMachineOperators
DE11StuttgartStuttgart72Metal,MachineryandRelatedTradesWorkers
31ScienceandEngineeringAssociateProfessionals
81StationaryPlantandMachineOperators
DE23OberpfalzRegensburg 72Metal,MachineryandRelatedTradesWorkers
31ScienceandEngineeringAssociateProfessionals
81StationaryPlantandMachineOperators
DE27SchwabenAugsburg 72Metal,MachineryandRelatedTradesWorkers
81StationaryPlantandMachineOperators
31ScienceandEngineeringAssociateProfessionals
RO42VestTimisoara 82Assemblers
93LabourersinMining,Construction,ManufacturingandTransport
72Metal,MachineryandRelatedTradesWorkers
DE26UnterfrankenWürzburg 72Metal,MachineryandRelatedTradesWorkers
31ScienceandEngineeringAssociateProfessionals
81StationaryPlantandMachineOperators
DEA5ArnsbergDortmund 72Metal,MachineryandRelatedTradesWorkers
81StationaryPlantandMachineOperators
31ScienceandEngineeringAssociateProfessionals
DE25MittelfrankenNürnberg 72Metal,MachineryandRelatedTradesWorkers
81StationaryPlantandMachineOperators
93LabourersinMining,Construction,ManufacturingandTransport
36 We sort locations here and in all following sections by the number of stars, followed by LQ.

European Cluster Panorama 2016
31
4.2.2 Biopharmaceuticals
Basic Facts Level in 2014 Change since 2012 Share of traded
clusters Share of overall
economy
Employment 2 315 157 0.79% 1.97% 0.92%
Establishments 20 961 -1.96% 1.97% 1.21%
Average Wage 47 533 1.13% 136.66% 148.06%
Gazelle Employment 21 023 N/A 1.10% 0.56%
Figure 11: Employment over time, 2008 - 2014
The Biopharmaceutical emerging industry is an expansion of the cluster category with the same name
with industries added from upstream (chemical), downstream (wholesale and packaging), as well as the
core activities (research and development). The Biopharmaceuticals industry is producing medical drugs
by biotechnology methods (involving live organisms or bioprocessing). A basic distinction is made be-
tween biopharmaceuticals, manufactured by biotechnology methods and involving complex biological
molecules, and drugs, manufactured by chemical (non-biological) means and involving small molecules
and other chemical substances.
37
The two largest parts of the Biopharmaceuticals category are re-
search and development and manufacture of pharmaceuticals, which together constitute about one half
of the overall wages paid in the industry. This reflects on the strong scientific basis of the sector. The
other half consists of roughly equally upstream activities, such as chemical inputs needed for the man-
ufacturing of pharmaceuticals, and downstream activities like packaging and wholesale.
37
There is no consensus on the use of biopharmaceutical or related terms in the scientific community. Those con-
cerned with biopharmaceuticals are divided among a large number of scientific and industrial disciplines and
professional associations. None have taken a visible position concerning terminology.

European Cluster Panorama 2016
32
Figure 12: Biopharmaceuticals Industry composition
Table 15: Occupational profile of employment in Biopharmaceuticals
OccupationEmploymentEmployment
share
Officials,Managers,Professionals,Technicians 96930060.0%
ScienceandEngineeringProfessionals 20410012.6%
BusinessandAdministrationAssociateProfessionals 16570010.3%
ScienceandEngineeringAssociateProfessionals 16070010.0%
BusinessandAdministrationProfessionals 1034006.4%
Other33540020.8%
Craft,Trade,Operators,Assemblers 29150018.1%
StationaryPlantandMachineOperators 1582009.8%
Other1333008.3%
Service,Sales,Elementary20710012.8%
LabourersinMining,Construction,ManufacturingandTransport 890005.5%
Other1181007.3%
Clerks18900011.7%
NumericalandMaterialRecordingClerks 1045006.5%
Other845005.2%

European Cluster Panorama 2016
33
Figure 13: Leading regions in Biopharmaceuticals
Table 16: Europe’s top locations in Biopharmaceuticals
#RegionRegionNameLargestCityEmployment
LQ
Avg.
Wage,
PPP
Annual
Growth
Gazelle
Empl.
ShareStars
1DEB3Rheinhessen‐PfalzMainz290463.3878772‐1.5%0.0%3
2BE24Vlaams‐BrabantLeuven 9771 2.84 97 897 ‐0.8%0.7% 3
3DK01HovedstadenCopenhagen 23 113 2.79 67 934 1.7%0.3% 3
4BE10BrusselsBrussels150622.63868901.7%0.1%3
5DE71DarmstadtFrankfurtam
Main
434492.5179307‐0.4%0.3%3
6BE21AntwerpenAntwerpen 15 368 2.36 98 449 ‐1.3%0.0% 3
7UKI6OuterLondon‐
South
London 13 250 2.04 54 251 ‐5.3%0.0% 3
8IE02Southernand
Eastern
Dublin353001.995693118.4%0.4%3
9UKI7OuterLondon‐
WestandNorth
West
London156381.6693380‐21.4%1.0%3

European Cluster Panorama 2016
34
#RegionRegionNameLargestCity Employment
LQ
Avg.
Wage,
PPP
Annual
Growth
Gazelle
Empl.
ShareStars
10DE13FreiburgFreiburg162021.66815450.4%1.3%3
11UKJ1Berks,Bucksand
Oxon
Oxford222991.6669881‐0.2%0.0%3
12SE22SydsverigeMalmö 10 590 1.58 137 469‐7.3%0.2% 3
13SE11StockholmStockholm 19 230 1.55 68 171 0.6%0.4% 3
14NO01OsloogAkershusOslo102231.52795794.0%0.0%3
15DED2DresdenDresden100071.482793526.6%0.9%3
16CH06ZentralschweizLuzern 6891 1.39 81 115 ‐16.4%0.1% 3
17DEA2KölnKöln 23 003 1.33 74 636 ‐5.0%2.0% 3
18DEA3MünsterMünster128571.29677791.2%0.3%3
19DE73KasselKassel65921.246443711.7%4.3%3
20SE12ÖstraMellans‐
verige
Uppsala94041.24636465.3%0.4%3
Table 17: Strategic profiles of top locations in Biopharmaceuticals
RegionRegionNameLargestCity Top3Occupations
DEB3Rheinhessen‐PfalzMainz81StationaryPlantandMachineOperators
21ScienceandEngineeringProfessionals
31ScienceandEngineeringAssociateProfessionals
DE71DarmstadtFrankfurt
amMain
31ScienceandEngineeringAssociateProfessionals
43NumericalandMaterialRecordingClerks
21ScienceandEngineeringProfessionals
UKI6OuterLondon‐SouthLondon33BusinessandAdministrationAssociateProfessionals
12AdministrativeandCommercialManagers
26Legal,SocialandCulturalProfessionals
IE02SouthernandEasternDublin24BusinessandAdministrationProfessionals
33BusinessandAdministrationAssociateProfessionals
75FoodProcessing,Woodworking,GarmentandOther
CraftandRelatedTradesWorkers
UKI7OuterLondon‐West
andNorthWest
London33BusinessandAdministrationAssociateProfessionals
12AdministrativeandCommercialManagers
13ProductionandSpecialisedServicesManagers
DE13FreiburgFreiburg21ScienceandEngineeringProfessionals
81StationaryPlantandMachineOperators
31ScienceandEngineeringAssociateProfessionals

European Cluster Panorama 2016
35
4.2.3 Blue Growth Industries
Basic Facts Level in
2014 Change
since 2012 Share of
traded
clusters
Share of overall
economy
Employment 12 953 282 2.03% 11.01% 5.15%
Establishments 75 235 -1.45% 7.09% 4.34%
Average Wage 32 320 1.63% 92.92% 100.68%
Gazelle Employment 276 568 N/A 14.45% 7.34%
Figure 14: Employment over time, 2008 - 2014
“Blue Growth” is here defined as the development and use of the potential of oceans, seas, and related
infrastructures as well as of any inland fresh-water sources and their exploitation. The “Blue Growth
Industries” therefore include all sectors and industries related to a maritime environment as well as
sectors producing, making use of, and treating fresh-water sources.

European Cluster Panorama 2016
36
Figure 15: Blue Growth Industries Industry composition
Table 18: Occupational profile of employment in Blue Growth Industries
OccupationEmploymentEmployment
share
Officials,Managers,Professionals,Technicians 391510040.9%
ScienceandEngineeringProfessionals 101160010.6%
ScienceandEngineeringAssociateProfessionals 8220008.6%
BusinessandAdministrationAssociateProfessionals 6184006.5%
Other146300015.3%
Craft,Trade,Operators,Assemblers 367360038.3%
DriversandMobilePlantOperators 228440023.8%
Metal,MachineryandRelatedTradesWorkers 5827006.1%
Other8064008.4%
Clerks120930012.6%
NumericalandMaterialRecordingClerks 6396006.7%
Other5697005.9%
Service,Sales,Elementary8932009.3%

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37
Figure 16: Leading regions in Blue Growth Industries
Table 19: Europe’s top locations in Blue Growth Industries
#Re‐
gion
RegionNameLargestCityEmploy‐
ment
LQAvg.
Wage,
PPP
Annual
Growth
Gazelle
Empl.
Share
Stars
1NO05VestlandetBergen1128014.41594708.5%5.2%4
2UKM5NEScotlandAberdeen456463.576022020.0%0.6%3
3NO04AgderogRogalandKristiansand662482.9151170‐8.4%3.3%3
4NO01OsloogAkershusOslo649461.7375048‐0.1%3.7%3
5NL33Zuid‐HollandRotterdam956171.3449930‐5.3%0.4%3
6LV00LatvijaRiga772491.28163686.6%4.5%3
7LT00LietuvaVilnius999181.24223781.5%8.9%3

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Table 20: Strategic profiles of top locations in Blue Growth Industries
Re‐
gion
RegionNameLargestCity Top3Occupations
NO05VestlandetBergen 31ScienceandEngineeringAssociateProfessionals
81StationaryPlantandMachineOperators
72Metal,MachineryandRelatedTradesWorkers
UKM5NEScotlandAberdeen 21ScienceandEngineeringProfessionals
81StationaryPlantandMachineOperators
72Metal,MachineryandRelatedTradesWorkers
NO04AgderogRogalandKristiansand 31ScienceandEngineeringAssociateProfessionals
72Metal,MachineryandRelatedTradesWorkers
81StationaryPlantandMachineOperators
NO01OsloogAkershusOslo 21ScienceandEngineeringProfessionals
33BusinessandAdministrationAssociateProfessionals
31ScienceandEngineeringAssociateProfessionals
NL33Zuid‐HollandRotterdam 21ScienceandEngineeringProfessionals
83DriversandMobilePlantOperators
31ScienceandEngineeringAssociateProfessionals
LV00LatvijaRiga 75FoodProcessing,Woodworking,GarmentandOther
CraftandRelatedTradesWorkers
31ScienceandEngineeringAssociateProfessionals
42CustomerServicesClerks
LT00LietuvaVilnius 83DriversandMobilePlantOperators
33BusinessandAdministrationAssociateProfessionals
72Metal,MachineryandRelatedTradesWorkers

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4.2.4 Creative Industries
Basic Facts Level in
2014 Change
since 2012 Share of
traded
clusters
Share of
overall
economy
Employment 14 166 879 4.82% 12.04% 5.63%
Establishments 107 276 -0.45% 10.10% 6.18%
Average Wage 44 765 1.56% 128.70% 139.44%
Gazelle Employment 289 810 N/A 15.15% 7.70%
Figure 17: Employment over time, 2008 - 2014
The European Commission’s 2010 Green Paper defines creative industries as “industries which use
culture as an input and have a cultural dimension, although their outputs are mainly functional. They
include architecture and design, which integrate creative elements into wider processes, as well as sub-
sectors such as graphic design, fashion design or advertising.”
38
For this report any further activities
driven by intellectual inputs and which are delivering intellectual outputs only (not being complemented
with delivery of any hardware or product), are also be considered as part of this industry. Such activities
include market research, opinion polling, translation, business and management consulting.
38
European Commission (2010) Green paper – Unlocking the potential of cultural and creative industries, Commu-
nication COM (2010) 183.

European Cluster Panorama 2016
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Figure 18: Creative Industries Industry composition
Table 21: Occupational profile of employment in Creative Industries
OccupationEmploymentEmploymentshare
Service,Sales,Elementary893 2009.3%
Officials,Managers,Professionals,Technicians 8988 30077.3%
InformationandCommunicationsTechnologyProfessionals 1591 00013.7%
ScienceandEngineeringProfessionals 1424 10012.3%
BusinessandAdministrationProfessionals 1361 60011.7%
BusinessandAdministrationAssociateProfessionals 978 6008.4%
AdministrativeandCommercialManagers 685 8005.9%
Legal,SocialandCulturalProfessionals 649 5005.6%
Other2297 70019.8%
Clerks1255 00010.8%
Craft,Trade,Operators,Assemblers 885 4007.6%
Service,Sales,Elementary771 2006.6%

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Figure 19: Leading regions in Creative Industries
Table 22: Europe’s top locations in Creative Industries
#RegionRegionNameLargestCityEmploymentLQAvg.
Wage,
PPP
Annual
Growth
Gazelle
Empl.
Share
Stars
1NO01OsloogAkershusOslo870232.126944114.7%0.5%4
2DEC0SaarlandSaarbrücken 113 459 3.43 53 484