Sectoral analysis and assessment of geographical concentration of EU industries.
Date: 2020-01-21
Institutions: Joint Research Centre
Subjects: economic activity, economic geography, industrial concentration, regional development, specialisation of trade

Sectoral Analysis and Assessment of 
Geographical Concentration of EU 
Industries  

Study carried out for the 
European Commission, DG 
JRC, under contract 
JRC/SVQ/2017/B.5/0034/NC 

Written by Ben Gardiner and An Vu (Cambridge 
Econometrics) and Ron Martin (Cambridge 
University) 

2020 

 
 
 
 
 
 
 
 
 
  
 
 
This publication is a report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to 
provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy 
position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is 
responsible for the use that might be made of this publication. For information on the methodology and quality underlying the data used 
in this publication for which the source is neither Eurostat nor other Commission services, users should contact the referenced source. The 
designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part 
of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation 
of its frontiers or boundaries. 

Contact information  
Name: Robert Marschinski 
Address: Joint Research Centre, c/ Inca Garcilaso 3, E-41092 Seville/Spain 
Email: robert.marschinski@ec.europa.eu 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC119363 

PDF 

ISBN 978-92-76-15039-8 

doi:10.2760/575675 

Luxembourg: Publications Office of the European Union, 2020  

© European Union, 2020  

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How to cite this report: Gardiner, B., Vu, A., and Martin, R., Sectoral Analysis and Assessment of Geographical Concentration of EU 
Industries, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-15039-8, doi:10.2760/575675, JRC119363. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Main Insights ............................................................................................................................................................................................................................................................... 4 

1 

Introduction ......................................................................................................................................................................................................................................................... 9 

1.1  Context and purpose of the report ......................................................................................................................................................................................... 9 

1.2  Structure of the report ................................................................................................................................................................................................................... 9 

2  Methodological Approach ....................................................................................................................................................................................................................... 11 

2.1 

Introduction ....................................................................................................................................................................................................................................... 11 

2.2  EU Integration and the Single Market .............................................................................................................................................................................. 11 

2.3  Specialisation, Concentration, and Clustering.............................................................................................................................................................. 24 

2.4  Summary ............................................................................................................................................................................................................................................. 38 

3  Database .......................................................................................................................................................................................................................................................... 43 

3.1 

Introduction ....................................................................................................................................................................................................................................... 43 

3.2  Data sources and availability ................................................................................................................................................................................................ 44 

3.3  Database construction method ............................................................................................................................................................................................ 61 

3.4  Findings and conclusions .......................................................................................................................................................................................................... 70 

4  Sector Specialisation and Geographical Concentration ...................................................................................................................................................... 80 

4.1 

Introduction ....................................................................................................................................................................................................................................... 80 

4.2 

Implications from Theory and Findings of Previous Studies ............................................................................................................................. 80 

4.3  Results from measures of sector specialisation ....................................................................................................................................................... 83 

4.4  Results from Measures of Geographical Concentration .................................................................................................................................... 101 

5  Clustering and Agglomeration ......................................................................................................................................................................................................... 110 

5.1 

Introduction .................................................................................................................................................................................................................................... 110 

5.2 

Implications from Theory and Findings of Previous Studies .......................................................................................................................... 110 

5.3  Review of existing literature ................................................................................................................................................................................................ 112 

5.4  Results from Measures of Clustering and Agglomeration ............................................................................................................................... 115 

6  References .................................................................................................................................................................................................................................................... 153 

Appendices ............................................................................................................................................................................................................................................................. 158 

Appendix A 

Balassa Index Heat Maps ........................................................................................................................................................................................ 159 

Appendix B 

Manufacturing Sector Coverage for Theil Indices .................................................................................................................................... 164 

Appendix C 

Detailed EG Statistics ................................................................................................................................................................................................. 166 

3 

 
 
 
 
 
Main Insights 

Introduction to the study 

The aim of this JRC-Seville commissioned study is to gain a deeper understanding of how 
the process of EU integration has affected patterns of industrial specialisation, geographic 
location and clustering across Europe and its regions. To carry out this study required a 
theoretical definition and assessment of the key terms (specialisation, concentration, and 
clustering); data collection (across time periods, Member States, regions and sectors); 
formulation and construction of relevant indicators; and assessment of their evolution and 
the role played by the Single Market. 

The general prediction from theory is that increasing economic integration across the EU, as 
expressed by the four freedoms (of movement of people, capital, goods and services) and 
underpinned by harmonisation of regulation, mutual recognition of qualifications and 
standards, the introduction of a common currency, and similar such measures, should lead 
to both Member States and their regions becoming more economically specialised, whether 
because of inherent and inherited comparative advantages, or because of increasing returns 
associated with scale of production effects. New Economic Geography goes further and 
argues that such increasing returns effects both lead to and are enhanced by the 
geographical concentration and agglomeration of economic activity and localised industrial 
interdependencies. More recent theoretical developments, however, contend not only that 
that technological changes have permitted such interdependencies – such as supply chains 
and intra-industry trade – to become geographically dispersed (delocalised), but also, as a 
result, for countries and regions to become specialised in functions and tasks rather than in 
particular industries.  

The period of study, the past two decades, required an assessment of the extent to which 
EU integration had progressed over these years, because evidence of the effects of 
integration (on specialisation, concentration, and agglomeration) would most likely be 
observable in areas (Member States, sectors, etc) where integration had strengthened the 
most. This is particularly the case when other developments, such as the increase in 
globalisation of supply chains, and the financial crisis and great recession which followed, 
are coincidental to this period and created their own impacts and dynamics which could 
obscure any integration-driven effects. Particular (integration-related) events occurring 
during this period include the accession of many eastern European countries, the launch of 
the euro, and several initiatives (such as the Services Directive and the Single Market Act I 
and II) intended to deepen integration into areas of the economy less affected by earlier 
directives (which tended to focus more on tradeable goods). 

Main findings 

The results present little evidence to suggest that, as EU integration has progressed over the 
past two decades or so, Member States have become more export specialised. Rather, 
overall, the Member States have become more similar in terms of their degree of 
specialisation, as measured by their average sector export structures over this period. There 
have only been a few exceptions to this trend, most notably the UK’s increase in 
specialisation in financial services. 

At the same time, the geographical specialisation of sectors, when export performance is 
averaged across Member States, has also tended to become more similar. Also, when 
analysed in groups according to their accession date, Member States that increase in 
specialisation (Malta and the UK) are the exception to the rule. In other words, it would 

4 

 
 
appear that there is little support at the Member State level for the hypothesis that integration 
encourages nations to increase their specialisation in those sectors in which they have a 
comparative or competitive advantage. As the results are averages, it is feasible that these 
mask changes in specialisation within particular sectors, although the general agreement 
with findings from the analysis of export trends would tend to support the ‘becoming 
generally more similar in sectoral structure’ argument. 

As in the case of specialisation, there is little evidence to suggest that, as EU integration has 
progressed over the past two decades or so, economic activities – measured by employment 
in individual sectors – have become more geographically concentrated, either between or 
regionally-within Member states. While the limitations of data have to be borne in mind, it 
does not appear to be the case that agglomeration forces have been sufficient to produce 
any significant increase in the geographical concentration of employment within industries, at 
least at the spatial scale for which analysis has been possible. 

Finally, our analyses find some evidence of pairwise co-location or geographical 
agglomeration of certain activities.  The findings are strongest for pairs of industries for 
which there are clear supply-chain, input-output linkages, such as textiles and apparel; 
electrical equipment and computer, electronic and optical products; chemicals and chemical 
products; rubber and plastic products and other non-metallic mineral products; and basic 
metals with fabricated metal products, except machinery and equipment; and manufacturing 
of wood and wood products, with that of pulp, paper and paper products. There is also 
limited evidence that some of these co-location concentrations have increased over the 
period of the study. Overall, however, the findings do not suggest that there has been any 
substantial increase in geographical agglomeration and co-location across sectors since the 
early-2000s. 

Comparison with previous research 

Several empirical papers have assessed whether specialisation and concentration of 
economic activity have increased across the EU over recent decades, though much of this 
work refers to the 1980s and 1990s and is also at national level.  

  Sapir (1996), using the Herfindahl index to measure country specialisation across 100 
manufacturing industries, found that specialisation remained constant over the 1977-
1992 period in Germany, Italy and the UK, but increased in France after the mid-1980s.  

  Amiti (1999) using data for manufacturing industries and the Gini coefficient, found that 

specialisation had increased for most of the countries in her samples.   

 

 

In his study of 32 manufacturing industries across 13 Member States over the 1972-1996 
period, and using a variety of measures, Brülhart (2001) found that national employment 
specialisation had generally increased, not only in traditional resource and labour-
intensive sectors, but also in technology-intensive industries. However, he also found 
that specialisation in exports, though higher than in employment, fell over his study 
period. In other words, while countries appeared to have become more specialised in 
terms of the sectorial distribution of their manufacturing employment, at the same time 
they had become more diversified in terms of their manufacturing exports.  

In her study of EU regions, Cutrini (2010) employed a new version of the Thiel Index to 
examine trends in both specialisation and concentration of manufacturing, covering 12 
sectors, across some 145 NUTS2 regions for 10 European countries, at three points in 
time, 1985,1993 and 2001. Overall, she found a decline in the localisation (spatial 
concentration) of almost all of the 12 manufacturing activities over these years, and a fall 

5 

 
 
in regional specialisation in almost all countries. In short, her analyses suggest a process 
of regional de-agglomeration and de-specialisation of manufacturing activity, and thus 
run somewhat counter to the cross-national findings identified by the above previous 
studies. 

 

In their study for the European Commission, Middlefart-Knarvik et al (2000) looked at the 
period from the 1970s through to the mid-1990s and found, using detailed industrial data 
at Member State level, divergence in industrial structure (from an EU benchmark) from 
the 1980s onwards. They also identified certain (often low-skill) sectors that were 
concentrating activities, while the mid to high-end technology sectors were showing 
signs of dispersion. 

Empirical findings have thus varied, according to the period studied, whether the analysis is 
for countries or regions, and the data sets used. A key problem affecting all such studies is 
that of data availability, both on a sectoral basis and in terms of geographical (especially 
regional) coverage. The additional challenge is that of imputing the trends identified with the 
evolution (both integration, or deepening, and expansion or widening) of the European 
Union, when so many other processes and developments have been underway over recent 
decades (not least technological change and accelerating globalisation). Constructing a 
counterfactual of what would have happened to regional specialisation across the EU had 
integration not taken place is a major challenge. 

The findings from this study are closest to those of Cutrini (2010), whose work was also 
replicated and extended with a reduced sample of sectors and regions. The analysis would 
seem to confirm the trends that Cutrini’s earlier studies established, that if anything both 
economic specialisation and spatial concentration have continued to decline, on average. 
The findings also lend support to Krugman’s (2008) general argument that perhaps within 
advanced economies the era of regional specialisation, whether due to comparative 
advantage or the increasing returns effects of industrial localisation, has passed. This is in 
line with growing evidence that production processes are fragmenting across borders with 
countries trading tasks and functions rather than products. 

Lessons learned 

A major lesson learned during the empirical analysis has been the limitations of regional-
sector data quality and quantify in Europe. A large proportion of the available resources were 
spent on developing the most comprehensive and consistent regional-sector database which 
could be seen as representative of the EU in terms of Member State coverage. Despite 
these efforts, the best that could be achieved was four time periods (2003, 2007, 2001 and 
2015) covering around 200 NUTS2 regions and some 20 sectors. This is an order of 
magnitude below the sector-regional coverage being used in similar studies based on US 
metro level data. 

At the same time, assessing the precise contribution of increasing integration on the 
economic geography of the EU is complicated by the fact that other transformative 
processes and events have also been at work over the period of the study, including 
globalisation (and competition from emerging economies, especially China), technological 
developments, the global financial crisis and the national and EU policy responses to that 
crisis (including fiscal consolidation). All of the latter can be expected to have impacted 
differentially across the Member States and regions of the EU, thereby interacting with, 
possibly intensifying or possibly countering, the effect of increasing EU integration. The 
implications of this is that a longer, consistent time series would be needed to disentangle 
the multitude of effects, as well as having a meaningful measure of EU integration itself. 

6 

 
 
Another way forward would be to focus only on one sector in more detail, in the form of a 
case study, which would allow more effort to be made on understanding the supply-chain 
structures through qualitative techniques such as surveys and stakeholder workshops.  

In addition, in today’s turbulent times, policies are needed that assist regions – especially 
those that been left behind over recent decades – to develop the adaptive resilience to 
navigate future economic, technical and social challenges and opportunities. Policies that 
promote regional economic diversification may be more consistent with that aim than those 
aimed at increasing the specialisation and agglomeration of activities across the EU. 

Suggestions for future research 

A key lesson from this study is that much better data are needed in order to allow more 
incisive tests of the impact of EU integration - and especially the impact of specific individual 
integration measures and policies – on the geographical distribution of economic activity 
across Member States and their regions. More particularly, data need to be at a much more 
detailed level of sectoral disaggregation, since the data that are available, and which were 
used in this study, may well mask important aspects of regional specialisation, co-location, 
and concentration to be found for certain activities. Further, and relatedly, there is a need for 
more spatially disaggregated data, since, again, the geographical units used in this study are 
arguably too coarse to reveal localised clusters of industrial specialisation and co-location. 
Perhaps, most crucially, what will be needed for future research are firm or establishment 
level data. These would allow a much more detailed and precise investigation of 
geographical patterns and shifts in the spatial distribution of specialised and related 
activities, ideally with information on the sizes of firms, their ownership (domestic versus 
foreign), and so on. Allied to this, and now regarded as of key importance, data on supply 
chains and inter-and intra-firm trade are possibly more relevant than data on sectors and 
should be the focus of studies aimed at assessing the impact of economic integration (and 
disintegration) processes. A previous EU research initiative, MICRO-DYN1, attempted to 
achieve such an objective by establishing a pan-European firm-level database from Member 
State sources. It is the view of this study’s authors that such an initiative should be revisited 
and given similar priority and status to sectoral databases such as EU-KLEMS and the 
macroeconomic AMECO maintained by DG EcFin. 

This latter point has implications for how the impact of economic integration is, or should be, 
theorised. Most the economic-geographical theory used to predict how increasing economic 
integration (such as removal of trade barriers, and improving the geographical mobility of 
labour, etc), impacts on the spatial distribution of economic activity assumes, explicitly or 
implicitly, that sectors are the key economic entities that will be affected.  This was the 
theoretical framework used in this study. However, more recent theoretical developments 
have moved away from this straightforward sectoral focus to consider the process of 
regional and local economic development in somewhat different terms. For example, one 
theme has been based on the idea of ‘related variety’. This is the notion that what drives 
regional development is the presence of, and changes in, local sets of complimentary 
(‘related’) activities, technologies and skills. ‘Related variety’ is argued to be conducive to 
regional growth, resilience and adaptability. The question then follows as to how far and in 
what ways related variety influences how regions respond to measures to promote economic 
integration, of the sort adopted by the EU. Another theoretical theme argues that what 
matters for regional development is the role of regions in, and their connection to, supply 
chains, and whether such supply chains are localised or geographically dispersed. How 

1 https://www.case-research.eu/en/microdyn-competitiveness-in-the-knowledge-based-economy  

7 

 
 
                                           
different regions are linked into different supply chains will have implications for how those 
regions are impacted by EU integration policies. However, exploring these two aspects of 
regional development in the context of further integration across the EU would require 
relevant data sets, of the sort mentioned in the previous paragraph, that are not at present 
collected or available. 

8 

 
 
1 

Introduction 

1.1  Context and purpose of the report 

The JRC-Seville is interested in gaining a deeper understanding of how the 
process of EU integration, through the development of the Single Market, has 
affected patterns of industrial specialisation and geographic location and 
clustering across Europe and its regions. To do this, it commissioned a study 
to develop indicators for each of these areas and to calculate their evolution 
over recent history, so that judgement can be made as to the role that market 
integration has had on their patterns of development. 

The work involves a mix of: 

 

theoretical definition and assessment of the key terms (specialisation, 
concentration, and clustering); 

  data collection (across time periods, Member States, regions and sectors); 

 

formulation and construction of relevant indicators; 

  assessment of their evolution and the role played by the Single Market. 

This consolidated report brings together all the separate reports in once 
publication, with each chapter reformulated from these studies. focuses on the 
theory, measurement, and empirical findings concerning sector specialisation 
across Europe and its links to economic integration and the Single Market. 

1.2  Structure of the report 

The remaining parts of this report are as follows. 

Main insights 

Preceding this introduction is a stand-alone ‘main insights section’. This… 

Methodological 
considerations 

Database 

Specialisation 
and 
concentration 

Chapter 2 revisits the implications from economic theory of how economic 
integration could affect sector specialisation, while also summarising the 
findings from the literature which have already looked at this issue (particularly 
across Europe). It also reports on the development of the Single Market, what 
measures of EU integration are available, and what they say about how far 
and how fast this has happened across Member States and the sectors within 
them. 

Chapter 3 describes the data collected for empirical analysis. Two datasets 
were compiled. Firstly, a national-sector export-share database for 
constructing Balassa indices of revealed comparative advantage (and which 
feed into the analysis of specialisation). Secondly, a national-regional-sector 
database to be used for constructing measures of specialisation, 
concentration and clustering as outlined in the methodological approach, and 
which feed into subsequent analytical work. 

Chapter 4 focuses on the theory, measurement, and empirical findings 
concerning sector specialisation and geographical concentration across 
Europe and its links to economic integration and the Single Market. The 
empirics include the Balassa indices of revealed comparative advantage and 
the Theil indices. Findings are presented and discussed in the context of the 
theory and previous literature. 

9 

 
 
 
Clustering and 
agglomeration 

Chapter 5 revisits the implications from economic theory of how economic 
integration could affect clustering and agglomeration, while also summarising 
the findings from the literature which have already looked at this issue 
(particularly across Europe). It then reports on the measurement of clustering 
and agglomeration, through the use of Location Quotient correlations and the 
Elisson-Glaeser index of co-agglomeration. Findings are presented and 
discussed in the context of the theory and previous literature. 

References and 
appendices 

This is followed by references, and any further technical detail is provided in 
Appendices. 

10 

 
 
 
2  Methodological Approach 

2.1  Introduction 

This chapter focusses on the Single Market has developed over time and how 
this is reflected in measures of EU integration. This is important because the 
premise for the study, prominent in some seminal New Economic Geography 
works such as Krugman and Venables (1990), is that increased EU integration 
(through the harmonisation of rules and regulations and general removal of 
trade costs) has provided an impetus to trends in specialisation, concentration 
and clustering of activities across Europe and its regions. But the process is 
neither smooth or regular in terms of pace. Different regulations come into 
operation at different periods and take time to have effect. Also, it is possible 
that the pace of harmonisation differs across different Member States and 
different sectors and so this also needs to be taken into account.  

The chapter goes on to provide details on the measures of regional industrial 
specialisation, regional concentration, and clustering which will be developed 
and applied in later chapters. It outlines the theoretical definition of these 
concepts and highlights any ongoing academic debate as to the formulation of 
indices to capture them. Finally, a proposition is made for each theme of 
analysis as to which is the most appropriate indicator to use, and what the 
implications are in terms of data collection. 

2.2  EU Integration and the Single Market 

The European Union evolved gradually from the aftermath of World War II, 
initially as an aid to peacekeeping in the region with six members2, to become 
the world’s largest economy with an integrated network of 28 Member States. 
During this time a number of treaties have been signed, institutions and 
organisations established, and policies formed, all with the aim of increasing 
the degree of political and economic integration among the EU’s members. 

For the purposes of the current study, we are more interested in recent 
initiatives and developments (i.e. over the past 15-20 years). These are the 
ones that are most likely to have deepened the level of integration among the 
Member States which we will be observing over the period of analysis, and 
which (a priori) we expect to be reflected in the measures of regional-industry 
specialisation, concentration and clustering to be calculated in subsequent 
studies. Developments may fall into two broad categories – there may be 
background effects which facilitate general integration or focus on a Member 
State (i.e. when it joined the EU), or there may be industry-specific initiatives 
which have been designed to focus on particular sectors. 

Development of the 
EU Single Market 

Background 

Specific focus for 
current study 

Timeline of 
developments 

This sub-section reports on recent EU developments and initiatives. The 
purpose is mainly to identify things which are relevant to the study and point to 
how they might have an impact, but not to discuss in any great depth. 

  EU Accession 

2 Belgium, France, (West) Germany, Italy, Luxembourg, the Netherlands formed the European 
Steel and Coal Community in 1951, and subsequently signed the Treaty of Rome in 1957. 
11 

 
 
 
 
                                           
Since 2000, the following countries have acceded to the EU: 

-  Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, 

Poland, Slovakia, Slovenia (2004) 

-  Bulgaria, Romania (2007) 
-  Croatia (2013) 

Although the process leading up to accession involves a gradual adoption of 
EU laws, regulations and conditions, it can be expected that integration within 
the EU system will continue to take place for some years after joining. 
Therefore, for the more recently-joined members, one might expect to observe 
an increasing level of integration through the study period which may have an 
effect on measures such as specialisation, concentration, and clustering. 

 

Introduction of the euro currency 

Although some years in the preparation3, the euro was officially launched at 
the start of 1999. Prior to this, however, some countries were part of the 
Exchange Rate Mechanism (ERM) where participating currencies floated 
within fixed margins. Other countries opted to have their currencies pegged to 
the Deutschmark. The euro can be argued to be a part of the European 
integration process, as the removal of currency fluctuation between members 
removes a friction to trade and should thus lead to previously unprofitable 
transactions being made viable. In addition, membership of the euro was 
based on achieving convergence criteria (covering budget deficit and debt 
ratios, inflation, interest rates, and exchange rate stability) which themselves, 
in theory, should mean that integration and euro area membership reinforce 
each other. 

In terms of grouping countries over the period of the analysis: 

-  a core group that were members throughout the period or which have 

maintained a fixed exchange rate to the euro (Austria, Belgium, 
Denmark, Finland, France, Germany, Greece, Ireland, Italy, 
Luxembourg, the Netherlands, Portugal, Spain); 

-  more recently acceded Member States which adopted the euro on 

membership of the EU (Bulgaria, Cyprus, Estonia, Latvia, Lithuania, 
Malta, Slovakia, Slovenia); 

-  countries which have mostly maintained a floating exchange rate 

(Croatia, the Czech Republic, Hungary, Poland, Romania, Sweden, the 
UK). 

  Other initiatives 

3 The principles of the currency were laid out in the Maastricht Treaty of 1992. 

12 

 
 
                                           
A recent report for the American Chamber of Commerce to the EU (2017) 
provided a summary timeline of events which can be used by our study to 
point towards where integration is still occurring in particular areas of the 
economy. 

Figure 2.1 Timeline of EU Initiatives 

Integration and 
the Geography 
of Industry: 
Propositions 
from Economic 
Theory 

Traditional trade 
theory 

Source:  AmChamEU (2017, p9) 

While tariff barriers have long-since been eliminated, there remain frictions 
(i.e. non-tariff barriers) which continue to impede the process of integration4 in 
some areas of the economy, particularly services but also in energy. It is 
therefore likely that any sector-based measures of integration will show less 
integration in these areas than in manufacturing, but also that integration is 
continuing to increase. 

A central tenet of traditional (‘old’) trade theory is that (free) trade between 
nations encourages countries to specialise in those sectors and industries in 
which they have some comparative advantage. This might be particular 
natural endowments (resources), cheap abundant labour, superior technology, 
certain expertise, or other factor-specific attributes that confer a competitive 
advantage in particular industries in international markets. In such 
circumstances, under free trade, country A could become specialised in, say, 
industry i, while country B would become specialised in industry j.  

Extended to 
geographical 
location 

Trade theory has undergone several developments over the past four decades 
or so. One such innovation is the recognition that it is not necessarily 
particular factor endowments as such that confer a comparative advantage, 
but that the geographical localisation of industries within nations may itself be 

4  The  Single  Market  is  ultimately  based  on  four  freedoms  of  movement  (goods,  people, 
services and capital), but not all of these have proceeded at the same pace as the EU has 
developed. 

13 

 
 
 
 
 
 
 
                                           
New Economic 
Geography 

The link with 
integration 

Industrial 
specialisation 
and geographic 
concentration 

a source of competitive advantage. Such geographical localisation can 
generate various external economies which give rise to increasing returns 
effects to the firms in the industry in question. Thus it was observed that, in 
many instances, the sectors in which a country enjoys a trade advantage are 
often geographically localised (or ‘clustered’) within that country. This idea 
underpins the so-called New Trade Theory, where trade is driven not by 
comparative advantage based relative cost (price) differences, but by 
increasing returns effects associated with economies of scale, monopolistic 
competition, and geographical agglomeration externalities. 

This revised view of trade has become a key component of the New Economic 
Geography (NEG) models of the spatial distribution of economic activity (for 
example, Krugman, 1991; Fujita, Krugman and Venables, 1999; Fujita and 
Thisse, 2002; Baldwin et al, 2003; Brakman, Garretsen and Marrewijk, 2009). 
The models combine the (increasing returns based) ‘New Trade Theory’, 
urban Marshallian economics, and the ‘New Urban Economics' to explain how 
firms and households locate across geographic space, and how those 
distributions can change in response to changes in transport costs, wage 
costs, labour mobility and the like. One issue such models have been used to 
explore is what happens to the geographical distribution of economic activity 
as the level and nature of economic integration increases.  

Economic integration between countries, such as that has occurred between 
the Member States of the EU, can be viewed as lowering the transactions 
costs between the countries and the regions within them. Transaction costs 
can take the form of transportation costs, tariffs, differences in regulatory 
arrangements, restrictions on the movement of labour and capital, or 
exchange rate controls, or other such barriers and frictions. According to NEG 
theory, a reduction in such transaction costs will make it more likely that any 
given degree of external economies will be sufficient to lead to the 
geographical concentration of an industry. This runs something as follows. 
Before integration assume that all regions of the set of countries under 
consideration operate a particular industry, so that the industry is essentially 
geographically dispersed. What happens to the spatial distribution of the 
industry if there is a move towards economic integration of the countries in 
question depends on three key issues: the extent and pattern of cost 
differences (and hence advantages) in the industry between regions across 
the countries, the pre-existing pattern of regional specialisation in the industry, 
and the extent to which the industry in question is subject to location-specific 
external economies. A regional cost advantage may be the result of either a 
larger local market in the region concerned, or because of some other local 
factor cost advantage.  

NEG models typically assume a prior geographical distribution to be an 
equilibrium one. Now assume that the process of economic integration lowers 
the transaction costs between countries and hence between regions across 
those countries. If there were no cost differences amongst regions for the 
industry in question then a geographically dispersed pattern of the industry will 
be a stable (assumed to be ‘equilibrium’) state. If some regions enjoy cost 
advantages, then the reduction of transaction costs, such as a removal of 
tariffs, will encourage a shift in the geography of the industry towards those 
regions with a pre-existing cost advantage and away from those with a cost 
disadvantage. So, according to this theory, increasing economic integration 
should ordinarily lead to a divergence in economic structure (ie increased 

14 

 
 
Krugman’s EU 
reference 

Additional forces 
of technology 
and globalisation 
supply chains 

Measuring EU 
Integration 

specialisation) between regions in terms of their industrial structure, and to an 
increasing geographical concentration of the industry in particular regions, 
which then becomes a new stable (‘equilibrium’) pattern.  

Krugman (1993) used this line of argument to suggest that as the countries 
belonging to the European Union become progressively integrated 
economically, so – other things being equal – they should become 
increasingly specialised in particular industries, with those industries 
concentrated in those regions in which the external economies benefits of 
localisation were greatest. His argument was based not only theoretical 
reasoning, but also on appeal to the United States, an internally integrated 
economy with a common currency – the sort of economic space to which 
Europe was moving.  The fact that the United States had a higher degree of 
internal spatial economic differentiation and regional specialisation than the 
European Union at that time indicated, in his view, what to expect in the 
European Union as it moved closer towards the sort of unified integrated 
economy exemplified by the United States, and especially as the EU moved 
towards monetary union. He also argued that regions across the EU became 
increasingly specialised, so too, like their US counterparts, they might be 
expected to become increasingly prone to idiosyncratic demand shocks and 
cyclical fluctuations. In summary, Krugman argued that increasing integration 
across the European Union would result in a European economy that was 
characterised by (i) greater regional specialisation, (ii) increased region-
specific shocks, and (iii) possible greater disparity in regional growth rates, 
because with increased factor mobility (aided by the integration process), both 
labour and capital are likely to move to and concentrate in those regions in 
which economic activity is already concentrated, agglomeration economies 
are greater and productivity is higher (see Bertola, 1993).   

As more recent work suggests, these predictions need to take into account 
another trend in the spatial organisation of economic activity that is both an 
outcome and a source of increasing integration, namely the rise of complex, 
geographically dispersed production networks and supply chains (see 
Baldwin, 2016). In combination with technological advances, the removal of 
tariff barriers, the harmonisation of regulatory arrangements, and the like, 
permits regional specialisation by function as well as or instead of by sector.  
Instead of vertically integrated industries in particular locations, It is now 
possible, and common, for an industry to be horizontally integrated across 
geographic space, with different stages of production or different functions 
located in different regions.  Such spatial webs or networks of supply and 
functional specialisation are now an important feature of the European 
economy. The car industry is a typical example, with most European 
producers having supply chains that span several regions and indeed 
countries. 

EU Single Market Integration can be measured in various ways but is possibly 
best captured by indicators which follow the four fundamental freedoms of 
movement which underpin it: goods, people, services and capital. All elements 
are examined for evidence of integration patterns, although it is the freedom of 
goods, services and capital that can be mostly tied to particular sectoral 
activity. 

15 

 
 
Movement of 
goods and 
services 

The flow of goods and services can be measured by trade within EU Member 
States (intra) and between EU Member States and non-member countries 
(extra). Intra and extra EU imports and exports are available from E3ME (a 
global-sectoral model maintained by Cambridge Econometrics5) from 1970 to 
2015 for 69 industry sectors6 . Specific indicators such as the share of total 
intra-EU trade in total trade, total or intra-trade as a percentage of GDP, can 
then be calculated.  

  Total trade 

A priori, one would expect trade between EU members (intra-EU) to increase 
as the integration brought about by the Single Market process deepens 
further. The graph below shows the percentage of intra-EU trade to GDP (left-
hand-axis) between 1992-2015, which (apart from the disruption caused by 
the great recession) follows the expected pattern. Alongside is shown the 
equivalent pattern of extra-EU trade (right-hand axis). 

Figure 2.2 EU Trade as a % of GDP 

Note:   The term EU refers to the number of Member States included in the EU-wide aggregate 

during the period for which the label is shown in the figure (respectively, EU-15 (1992-
2003), EU-25 (2004-06), EU-27 (2007-12) and EU-28 (2013-)). Intra-EU trade is equal to 
the average of intra-EU exports and imports expressed as a percentage of the total GDP. 
This includes trade in goods and services. 

Source: Cambridge Econometrics 

Interestingly, when compared against extra-EU trade it appears that the 
increase in intra-EU trade shares is no greater than that of extra-EU trade, and 
in more recent years has been on a slower trend. At least part of the increase 

5 See https://www.camecon.com/how/e3me-model/ for more information. 
6  Aggregate  trade  in  services  is  derived  from  the  bilateral  trade  in  services  data  from  the 
OECD  EBOPS  (Latest  series:https://stats.oecd.org/Index.aspx?DataSetCode=TISP_EBOPS2010)  to 
derive  the  sectoral  detail.  The  bilateral  flows  are  then  aggregated  across  trade  partners 
into within the EU and external to the EU. The sectoral estimates are then scaled to the 
Eurostat  data  on  total  trade  in  services  within  the  EU  and  external  to  the  EU  for  each 
member state from the series (nama_10_exi). 

16 

 
 
                                           
in the intra-EU measure must simply be following patterns of globalisation and 
increased general openness to trade. 

The overall picture (for intra-EU trade) presented by Figure 2.2 can be split 
across Member States and sectors to provide a more detailed view of 
integration. The findings are summarised below. 

  Trade by Member State 

In this section, we measure the individual Member States’ integration into the 
Single Market for each sector by using the total of a country’s intra-EU exports 
and imports. To compare this indicator across all MS, we are using the share 
of total trade to the country’s GDP, i.e. the same measure as in Figure 2.2, but 
at Member State level. It would be expected that when a country has acceded 
to the EU its share of intra-EU trade will increase as it integrates within the 
trading system, while those countries which have been members of the EU for 
longer periods of time would show less increase in integration, having already 
achieved this in earlier periods. Figure 2.3 also separates out Member States 
between those established members of the European Union and those which 
joined from 2004 onward.  

17 

 
 
Clearly, and as exemplified by Member States such as Luxembourg and 
Belgium, the ratio of trade to GDP is as much a measure of size and 
openness of an economy as it is of the degree of EU integration. Also, 
Figure 2.3 Intra-EU Trade by Member State 

Note:  

Intra-EU trade is equal to the average of intra-EU exports and imports expressed as a 
percentage of the total GDP. This includes trade in goods and services. 

Source: Cambridge Econometrics 

historical linkages (e.g. UK with its Commonwealth trade relations, and 
Finland with Russia and other international partners) also complicate the 
picture. Among the more recently acceded Member States, most are among 
those that have increased their trade intensity with the EU quite rapidly while a 
few (Romania, Croatia, Cyprus, Malta, Slovenia and Poland) do not look much 
different from the more established Member States7. 

  Trade by sector 

The same analysis can also be undertaken by averaging intra-EU trade as a 
% of GVA across Member States for each sector. Figure 2.4 provides a 
summary of the findings, with the 69 sectors grouped into broad 
categorisations in order to visualise patterns more easily. 

Overall, the ratio of intra-EU trade to GDP in most of manufacturing sectors is 
much higher than for services sectors. It is also the case that those 

7 Further investigation of whether the more recently acceded Member States had experienced 
a  shift  in  their  trading  patterns  (towards  the  EU)  revealed  mixed  findings.  For  Bulgaria 
and  Romania  their  accession  coincided  with  the  onset  of  the  Great  Recession,  and  so 
nothing  can  be  concluded,  while  for  Croatia  accession  is  too  recent.  Among  the  other 
Member States, the Czech Republic, Hungary, Malta, Slovakia and Slovenia display slight 
upward trends post-accession, although this assessment is from visually assessing trends 
and is not a statistical conclusion. 

18 

 
 
                                           
 
manufacturing sectors with the highest ratios in 2000 (this includes Electrical 
equipment, Motor vehicles, Textiles, Basic metals, Chemicals, and Transport 
equipment) are also among those with the largest increase over the following 
period.   

Those service sectors of note include two which have increased their ratios by 
around 25pp over the period (R&D, and Other professional services) and two 
others which have higher than average ratios of intra-EU trade, but which 
have not shown any sizeable change over the period (Security and 
investigation, and Rental & leasing). 

Figure 2.4 Intra-EU Trade by Sector 

Note:  

Intra-EU trade is equal to the average of intra-EU exports and imports expressed as a 
percentage of the total GDP. This includes trade in goods and services. 

Source: Cambridge Econometrics 

Movement of 
capital  

The movement of capital between countries can be captured by Foreign Direct 
Investment (FDI), and in particular the extent of EU integration can be 
expressed as the ratio of intra-EU FDI (inward plus outward stock) to total FDI.  

The OECD FDI statistics8 contain sufficient information to undertake the 
analysis: 

  65 industries according to according to the industry of the direct 

investment enterprise; 

8 Benchmark Definition 3rd Edition (BMD3). 

19 

 
 
 
 
                                           
 
 

time coverage can be as far back as 1995 for some countries and 
some sectors up to 2013, but the more certain period is 1998-2013; 

  countries available: 21 out of EU28 plus Norway (countries not 

covered: Bulgaria, Malta, Cyprus, Latvia, Lithuania, Romania and 
Croatia); 

  FDI flow as well as stock9. 

As FDI flows are not a direct component of GDP, and the sector 
disaggregation from the OECD database are different from those presented in 
the trade section, the average ratios of intra-EU FDI calculated for Member 
States and sectors are made relative to total FDI. 

  FDI by Member State 

Due to the quality of the annual FDI data series, Figure 2.5 presents the 
average ratio for Member States over the 2008-16 period. There is some 
difference between pre- and post-2004 accessions Member States, with the 
latter cohort mostly grouped at the high FDI-share end of the chart. This could 
reflect the attractive (lower-cost) location for European FDI of the newly-
acceded Member States. 

Figure 2.5 Intra-EU FDI by Member State 

Note:  

Intra-EU FDI is equal to the average of intra-EU inward and outward FDI assets expressed 
as a percentage of the total FDI assets. 

Source: OECD 

  FDI by sector 

9 When looking at capital movement, this analysis uses FDI positions, also known as stock of 
FDI, rather than FDI flows, because FDI stock is more stable as it measures the total of 
FDI at a given point in time, rather than the flow which measures the change in level of 
FDI in a given period. 

20 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
Figure 2.6 shows the same ratio calculation by sector, over 2008-15 (again 
period averages only due to data limitations). 

The service sectors dominate the higher ratios, while the manufacturing 
sectors are at the lower end. This is most likely a reflection of the importance 
of physical trade as a means of exchange and competition for manufacturing 
sectors, while services rely more on direct foreign investment due both to the 
nature of their activities being less amenable to physical trade, and also the 
continued lack of harmonisation that persists across Europe. 

Figure 2.6 Intra-EU FDI by Sector 

Note:  

Intra-EU FDI is equal to the average of intra-EU inward and outward FDI assets expressed 
as a percentage of the total FDI assets. 

Source: OECD 

  FDI Regulatory Restrictiveness Index 

The OECD’s FDI Regulatory Restrictiveness Index provides a measure of 
friction to FDI in the form of ‘all discriminatory measures affecting foreign 
investors, including market access restrictions and departures from national 
treatment’10. The dimensions of this database are as follows: 

time frame: 1997, 2003, 2006, 2010-2016 

- 
-  sectoral coverage: 22 economic sectors 
-  Member States: 24 countries out of EU28 (countries not covered are 

Bulgaria, Malta, Cyprus and Croatia) 

It should be noted that the index does not focus on restrictions to EU28 FDI in 
particular, but is a general measure of restrictiveness for any FDI regardless of 

10 See https://www.oecd.org/daf/inv/investment-policy/FDIRRIndexPPT.pdf for more information. 

21 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
 
origin. It does, however, still provide another way of assessing openness to 
international movement and ownership of capital.  

Figure 2.7 shows how FDI restrictiveness has changed between 2003 and 
2016 across available Member States11. 

Clearly, the majority of Member States were already relatively open towards 
FDI by 2003, and so there has been little change over time since then. Austria 
and Finland stand out as being more restrictive (though still quite low on the 
overall index), while Austria remains so at the end of the period, showing 
much less reduction than Finland (which by 2016 is no different than the 
majority of other countries). 

Figure 2.7 FDI Restrictiveness by Member State 

Note:   A value of 1 implies complete closure to FDI, a value of zero complete openness. 
Source: OECD 

Data by sector are less available and so are not reported in detail. What data 
do exist do not show much variation over time (mirroring the aggregate 
country results). For Austria, the most closed of Member States covered by 
the dataset, the Electricity sector is completely closed to FDI, while the 
Transport sector has opened up dramatically in both Austria and Finland in the 
late-2000s, as have other service sectors in Finland (which mostly explains it 
shift in Figure 2.7). 

  Movement of people  

The movement of people can be captured by migration statistics which 
monitor gross intra-EU migration flows (in + out) as a proportion of total 

11 For Romania the start date is 2010 as earlier data were not available. 

22 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
 
population, and also employment data which capture the citizenship of 
employees in each country. In addition, there exist data on the recognition and 
movement of professional activities around the EU12 which can be used as a 
proxy for regulation and acceptance of human capital movement. Both 
aspects of labour mobility are considered below. 

  EU employment patterns 

The Eurostat LFS data allows the identification of employment by citizenship 
for each Member State. Figure 2.8 shows non-native (EU-28) employment as 
a proportion of total employment (obtained by adding up across all Member 
States). 

Figure 2.8 Share of EU workers from outside host country employed in host country (EU 
average) 

Note:   EU28 average (EU27 average pre-2011) 
Source: Eurostat 

In 2006 most of the restrictions on movement of labour from the newly-
acceded Member States expired, leading to a continually rising trend (seen 
above) from what had previously been a fairly stable indicator. 

For a Member State perspective, Figure 2.9 provides the level (2006) and 
change (2006-16) of the share of non-host country workers in total host 
country employment. Luxembourg immediately stands out as a haven for 
cross-border workers, with both the highest share and highest increase in 
share of non-native workers. The vast majority of Member States remain low 
on both level and change, with only a handful having shown much increase in 
the last 10 years and most of these being among the more prosperous 
Member States where (presumably) most jobs have been created to attract 
workers from other countries. 

  Movement of professional activities 

12 See http://ec.europa.eu/growth/tools-databases/regprof/.  

23 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
Various Directives (in particular 2005/36/EC and, more recently, 2013/55/EC) 
allow for an EU-wide system of recognition of professional experience and 
qualifications. These initiatives have allowed for free movement for 
professionals such as doctors, nurses, and architects within the EU. Indeed, 
examination of the database for free movement of professionals reveals that 
the three most mobile professions across the EU are nurses, secondary 
school teachers, and doctors of medicine. 

The process remains far from complete, however, and in 2016 the EC created 
the European Professional Card (EPC)13 to aid the recognition process further 
– the system is currently available for general care nurses, physiotherapists, 
pharmacists, real estate agents and mountain guides. Such developments will 
aid the process of integration in the services sector, where the majority of 
professional qualifications reside (and differ across Member States). 

Figure 2.9 Share of EU workers from outside host country employed in host country 

Note:   EU28 average (EU27 average pre-2011) 
Source: Eurostat 

2.3  Specialisation, Concentration, and Clustering 

Different 
Dimensions of 
the Geographical 
Distribution of 
Economic 
Activity 

Four major dimensions, or aspects, of the distribution of economic activity 
across geographic space can be identified from the vast literature that now 
exits of the subject: regional specialisation, regional concentration, regional 
agglomeration and local clustering. Concentration and agglomeration are both 
concerned with the question of whether a particular part of economic activity – 
a particular industry - can be found at a few locations, be these certain cities, 
regions or countries. Although both concentration and agglomeration deal with 

13  See  https://ec.europa.eu/growth/single-market/services/free-movement-professionals/european-professional-

card_en  

24 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
 
location of economic activity, their focus is somewhat different. According to 
Brülhart (1998), typically concentration analyses the location across space of 
an individual sector of activity, whereas agglomeration analyses the location 
across space of several sectors, for example several different manufacturing 
industries, or several different types of services. In the view of Brakman, et al 
(2009), studies of agglomeration analyse how aggregate economic activity, 
say the broad category of manufacturing, is distributed across geographic 
space; whereas, the empirical analysis of concentration does the same only 
for a particular type of economic activity, say the production of motor vehicles, 
and then tries to show at this lower level of aggregation how the production of 
motor vehicles is distributed geographically. 

Concentration and agglomeration can be distinguished from specialisation 
(see Hallet, 2000; Gorter, 2002, Brakman et al, 2005). Specialisation deals 
with the question of whether or not a location’s (nation, region, city, etc.) 
economic structure (in terms of shares of employment or output) is dominated 
by one or just a few related sectors, for example, motor vehicles or textiles. It 
is usually measured by comparing the location’s economic structure against a 
relevant benchmark, for example in the case of say a member state of the 
European Union with the aggregate EU’s economic structure, or in the case of 
a region or city, with the economic structure of the nation of which the region 
or city is a part. The issue of specialisation has long been a key notion in trade 
theory, where it is argued that nations, cities and regions will tend to 
specialisation in those activities in which they have a comparative advantage. 
Thus, the degree of economic specialisation in a city or region is typically 
taken to be an indication of the extent of that city’s or region’s ‘revealed’ 
comparative advantage in the sector(s) concerned. In certain circumstances, 
however, specialisation and concentration can be the two sides of the same 
coin, since a very high level of concentration of an industry in a particular 
region may well mean that the region is in fact specialised in that sector, in as 
much that it dominates the region’s employment structure.  

Thus, as Brakman, Garretsen and Marrewijk (2009) point out, concentration, 
specialisation and agglomeration may or may not coincide, depending on 
contingent circumstances. Further, when considering the regional effects of 
the economic integration within the European Union, there are (at least) two 
geographical levels involved: the country level, and the regional.  Thus, it is 
possible to have specialisation between nations (that make up the EU), but not 
necessarily between regions within those nations. For another, it is possible to 
have concentration of an industry between nations, but no regional 
specialisation within nations.  Some examples are illustrated stylistically in 
Figure 2.10, in which there are two industries and two countries each with four 
regions. For simplicity, assume a pre-integration pattern in which both 
industries are equally distributed across the four regions in each country 
(Figure 1a). Following integration of the two countries, and assuming this 
reduces transport costs, removes barriers to trade and factor movements, and 
so on, then a variety of outcomes are possible, depending on the specifics of 
the industries, regions and countries.  

In one case (Figure 1b), integration leads to a shift in both industries such that 
industry 1 becomes concentrated in country A, which is thus specialised in 
that industry, while industry 2 shifts to Country B, which thus becomes 
specialised in that industry. But note there need be no regional concentration 
or specialisation in either country. Other possible outcomes are illustrated in 

25 

 
 
Figure 1c, Figure 1d and Figure 1e. In Figure 1c, country A becomes 
specialised in industry 1, which is also concentrated in one of its regions 
(which thereby specialises in that industry), while country B becomes 
specialised in industry 2, which is also concentrated in one of its regions. In 
Figure 1d there is a concentration of both industry 1 and 2 in country 1, and an 
agglomeration of the two industries in one particular region of that country. In 
Figure 1e, both industry 1 and industry 2 are concentrated in country A, there 
is regional specialisation (of both industries in country A, and of industry 1 in 
country B), and regional agglomeration in country A. These examples merely 
illustrate the complex range of outcomes that are possible and which might 
follow integration, and the different geographical levels at which such 
outcomes can be analysed. They also suggest that decomposability would be 
a desirable feature of any measure used to assess the geographical 
distribution of economic activity in an integrated multi-country, multi-region 
system such as the European Union. 

The notions of specialisation and concentration is closely related to that of 
clustering. The spatial concentration of an industry in a particular location (city 
or region) is often described as being geographically clustered. Such 
clustering may form one of the specialisations of the region in question.  
Indeed, the presence of clusters is often associated with specialisation.  
However, the concept of a cluster is usually taken to have a rather more 
specific meaning, based on the extensive work of Michael Porter (see for 
example, Porter, 1990), who defines a cluster as a “geographically proximate 
group of interconnected companies and associated institutions in a particular 
field, linked by commonalities and complementarities” (1990, p. 199).  

Thus, while a cluster implies local specialisation, it goes beyond the mere 
localisation of an industry to highlight the interdependencies, both upstream 
and downstream, of that industry with related activities, which typically will 
include “end-product or service companies, suppliers of specialised inputs, 
components, machinery and services, financial institutions, and firms in 
related industries”, as well as “government and other institutions providing 
specialised training, education information, research and technical support”.  
Although both specialisation and concentration are implied in this definition of 
a cluster and can be used to identity the likely presence of a cluster, much 
else is required for a specialisation or concentration to function as a Porter-
type cluster. 

To summarise our definitions: 

 

 

regional specialisation is the degree to which the (proportional) economic 
sectoral composition of a region differs from the (proportional) economic 
sectoral composition of all other regions taken together; 

regional concentration (of a given sector of economic activity) is the 
degree to which that sector is localised, i.e. where it is based more in just a 
few, rather than in all, of the regions; and finally, 

  agglomeration is a situation where more than one sector is co-localised 

with other sectors in certain regions. 

26 

 
 
Figure 2.20 Some Stylised Geographical Outcomes of Economic Integration 

27 

(cid:9) 
 
Measuring the 
Geographical 
Distribution of 
Economic 
Activity 

The literature on devising measures of the geographical distribution of 
economic activity, on regional industrial concentration, specialisation, and 
agglomeration, is vast, and stretches several decades. One of the earliest 
discussions is that by the founding father of regional science, Walter Isard 
(1960), in which various measures are compared and their inter-relatedness 
highlighted. Another early comparison of alternative measures is that by 
Thirlwall and Harris (1967).  Since then, numerous additional measures have 
been added, although many are in fact versions of one another: in this sense, 
one may talk of a ‘family’ of measures and indices. Most seek to measure the 
degree to which industry is localised across geographic space, and, more 
especially, whether particular industries are localised in particular regions. And 
most involve comparing the actual pattern of industry across regions against 
some ‘reference’ pattern. This might be a hypothetical distribution (such as 
one of equal shares of an activity between regions), or the national industrial 
structure (which is of course itself a weighted average of regional structures).  

One thing all measures have in common is that they are ‘cause-free’, in that 
they imply no particular regional development theory or process is necessarily 
at work, although in many cases inferences are made from conceptual 
arguments about processes (such as those of Marshallian localisation 
economies, or external economies claimed to derive from the spatial 
agglomeration of activity) to specific indices and measures; or conversely, 
particular measures are intended (or assumed) to reveal the presence of such 
processes.   

The literature is simply too extensive to refer to all of the many relevant papers 
individually. However, there are certain studies that have a particular 
relevance to this project, in that they explicitly develop and utilise specific 
indices and measures to examine recent trends in the geographical 
distribution of economic activity across the European Union (among the more 
important papers may be listed Amiti, 1999; Hallet, 2000; Midelfart-Knarvik et 
al., 2000; Ciccone, 2002; Brülhart 2001a, 2001b; Bülhart and Traeger, 2005; 
Brülhart and Mathys, 2008; Cutrini, 2010). In his study, Hallet (2000) uses a 
number of indices to measure changes in regional specialisation (the Krugman 
Specialisation Index) and regional concentration of industry (a variety of 
indices) across the EU over the period 1980-1995 (his study is for 119 regions 
and 17 sectors of economic activity). He finds a general trend of declining 
regional specialisation over this period, and that the degree of concentration 
for most sectors has remained more less unchanged. 

Brülhart and co-authors (see references above) use a variety of measures to 
investigate the (changing) geographical concentration and specialisation of 
economic activity across the EU, including location quotients, Gini coefficients, 
Balassa indices, and entropy measures (the latter affording a decomposition 
into between-country and within-country effects). Among their findings they 
suggest that industrial specialisation of EU members states increased over the 
period covered by the 1970s, 1980s and 1990s, that this process appeared to 
have been boosted by the Single Market project, but that there was neither 
increased geographic concentration in core EU countries nor movement 
towards peripheral EU countries. It should be noted, however, that Brülhart 
and his co-authors often use the terms specialisation, concentration and 
clustering more or less interchangeably, whereas in our we argued they 
should be kept distinct. 

28 

 
 
Industrial 
Specialisation  

Definition and 
measurement 

In her interesting work, Cutrini (2006, 2010) also derives Theil indices for both 
specialisation and concentration that again test for both within-country and 
between-country effects across the EU. She also relates these measures to 
the Balassa index.  Her analysis is for some 145 regions and 12 
manufacturing industries, over the period 1985-2001. The findings are 
particularly significant since they suggest that, as far as manufacturing is 
concerned, and for the period covered, Krugman’s (1993) prediction, that as 
integration in the EU deepens regional specialisation and the spatial 
agglomeration of economic activity should both increase, has not been borne 
out. 

The debate over whether regional economic (industrial) specialisation is 
advantageous or disadvantageous is long-standing (for a recent discussion, 
see Kemeny and Storper, 2012). Interregional differences in both the level and 
pattern of industrial structure are of interest for two main reasons. First, 
because many studies have argued that differences in industrial structure are 
an important source of interregional differences in economic performance. 
Second, because of a possible relationship between regional specialisation 
and comparative (or absolute) advantage, for example with respect to trade.  

Due to the increasing interest in the possible effects of economic integration 
on economic specialisation, the measurement of interregional and indeed 
inter-country industrial composition has attracted considerable attention, as 
mentioned above. Empirical analyses of international and interregional 
specialisation use a wide range of statistical tools. Yet there is no general 
agreement on which measure or index best captures the level of 
specialisation. 

  Absolute and relative specialisation 

One of the most useful surveys is that by Palan (2010), who compares the 
strengths and weaknesses of a number of such indices, both in terms of their 
statistical properties and their empirical application to industrial specialisation 
among European countries.  Palan distinguishes between those indices that 
measure absolute specialisation and those that focus on relative 
specialisation.   

A region (or country) would be considered to be characterised by absolute 
specialisation if a small number of industries account for high shares of that 
region’s (or country’s) total employment (Palan, 2010, p2).  Indices of relative 
specialisation are concerned with measuring the deviation of a region’s (or 
country’s) industrial structure from that of another economy. The difference 
between the two types of index is in the type of benchmark they use. The 
benchmark used for absolute measures is typically that of a hypothetical 
statistical distribution, most typically a uniform (equal) distribution of 
employment across industries. Thus in the case of N industries this would be 
where each industry has an employment of 1/N (the lower limit of such an 
index).   Increasing departures from an equi-share distribution would indicate 
rising degrees of absolute specialisation, with complete specialisation being a 
situation with all employment in just one industry, (equivalent to a maximum 
value of 1.0). With measures of relative specialisation, the benchmark is the 

29 

 
 
 
 
distribution of industry shares in some other actually existing economy. Where 
regional specialisation is the focus of attention, this would typically be the 
relevant national economy being studied. In the case of a countries, the 
reference might be the trading bloc to which the country belongs.  It is clearly 
also possible to compare regions across a group of countries using the 
industrial distribution of the group of countries as a whole as the benchmark.  

  Decomposability 

This latter case raises the issue of decomposability.  A decomposable 
specialisation index is one which permits specialisation to be split into a 
weighted average of the specialisation existing within and between groups. In 
the present context, this would be where it is possible to decompose a 
country’s specialisation into comparative advantages inherent in a given 
country (member of the European Union) in relation to other countries in the 
group (the European Union as a whole) – the ‘between country’ component - 
and regional comparative advantage within the given country, that is 
comparative advantages of some regions compared to the national level – the 
‘within-country’ component.  

Palan’s very useful discussion covers five absolute specialisation measures 
and four relative specialisation indices (Table 1). Of the absolute measures 
both the Hirschman-Herfindahl index and the Shanon Entropy index possess 
this decomposability feature. Of the relative indices, only the Theil index 
satisfies the decomposability criterion. When applied to the analysis of 
specialisation among European countries (but not regions), the Palan analysis 
is quite revealing, leading to a number of key conclusions.  First that there is 
little correlation between absolute and relative measures of specialisation: the 
two types of index measure different aspects of specialisation. Second, among 
the absolute measures, the Hirschman-Herfindahl index is the most closely 
correlated with the other absolute measures. Third among the relative 
measures, the Krugman Index is the most closely correlated with the other 
relative measures. To these conclusions should be added two further salient 
points. As is well known, all measures of specialisation are sensitive to the 
level of sectoral and geographical disaggregation used.  Although there is no 
generally agreed preferred levels of sectoral and spatial disaggregation, it may 
be possible to undertake some ‘robustness’ tests by carrying out the analyses 
for different sectoral levels and different scales of geographical unit. And while 
all give some indication of the degree of specialisation, none of itself tells us 
which industries a region (or country) is specialised. Finally, none of the 
indices tells us about the underlying processes driving specialisation (or 
diversification). 

Table 2.1 Regional Specialisation Measures (based on Palan, 2010) 

Type of 
Measure 

Absolute 

Index 

Decomposable?  Bounded? 

Hirschman-Herfindahl 

Yes 

Shannon Entropy Index  Yes 

Ogive 

Diversification Index 

Absolute Gini Index 

No 

No 

No 

Yes 

Yes 

Yes 

No 

Yes 

30 

 
 
 
 
 
 
Type of 
Measure 

Relative  

Index 

Decomposable?  Bounded? 

Krugman Index 

Index of Inequality in 
Productive Structure 

Relative Gini Index 

Theil Index 

No 

No 

No 

Yes 

Yes 

Yes 

Yes 

No 

Note: While having bounds might be a desirable feature of a measure, in that it 
provides a direct way of comparing different instances or cases, it is not a necessary 
feature. Arguably what is more important in the case of analysing specialisation (or 
concentration) of economic activity across the EU is whether a measure or index is 
decomposable, into between and within member country effects. 

Probably the most frequently used measure of regional relative specialisation 
is the Krugman Specialisation Index (KSI), also sometimes known as the 
Coefficient of Regional Specialisation (CRS).  This is calculated as sum of the 
absolute differences between each industry’s share of regional employment 
and that industry’s share of national employment. In effect, it is an index of 
regional structural dissimilarity (in relation to the national industrial structure), 
that is 

where   
𝐸𝑖𝑟 is employment in industry i in region r 
𝐸𝑟 is total employment in region r 
𝐸𝑖𝑁 is national employment in industry i, and  
𝐸𝑁 is total national employment  
Krugman has used this index to compare the degree of regional specialisation 
in Europe with that in the United States (eg. Krugman, 1993).  

It is usually stated that the index has a range of between 0 and 1. If a region 
has exactly the same industrial structure as the nation of which it is a part, 
(that is the shares of employment are the same for all industries) then the 
index takes the value 0. Whether the maximum value of 2 is possible depends 
on the reference benchmark. If the benchmark were, say, another region, then 
the maximum of 2 is possible since the two regions could have completely 
non-overlapping industry structures, so that each industry share would be 
counted twice. But if the benchmark is the national economy, which is usually 
the case, then the maximum must be less than 2 since by definition if the 
region in question has a certain industry i, then so must the nation of which 
that region is a part.  In this case, the maximum is given as [2(N-1)]/N.  It 
should also be pointed out that even if a region has an index of 0, meaning it 
has the same industrial structure as the national economy, it does not 
necessarily mean that the region is not specialised, only that it has the same 
structure as the national economy, which itself may be specialised. 

31 

 
 
 
 
 
 
Preferred 
indicator 

However, and importantly, the Krugman Index is not decomposable. Our 
preferred measure of regional specialisation is the version of the Theil index 
developed by Cutrini (2006, 2008, 2009, 2010), as this does permit a 
decomposition of specialisation into a ‘within-country’ component and a 
‘between-country’ component. The added advantage of using her index is that 
it allows a comparison with the trends she identified across the EU up to 2001, 
while at the same time we are able to extend her analysis by applying the 
technique to a greater degree of sectoral disaggregation over a more recent 
time period.  

Let E denote employment, and subscripts c, r and k denote country, region 
and industry respectively. Then, 

𝐸𝑖𝑗𝑘  = employment in industry k, in region j belonging to country i 

𝐸𝑖𝑗  = Total employment in region j of country i 

𝐸𝑖𝑘  = total employment in industry k in country i 

𝐸𝑖  = Total employment in country i 

𝐸𝑘 = Total employment in industry k in the European Union 

𝐸= Total employment in the European Union  

N = number of industries 

R = number of regions in the European Union 

Then, following Cutrini (2010), concerning specialisation, it is possible to 
evaluate the dissimilarity between the economic structure of a given region 
and that of a supra-national economy, here the EU, into three ‘raw’ indices:   

First, the specialisation of region j in country i relative to the European Union, 
given by 

𝑁
𝑇𝑖𝑗 =   ∑
𝑘=1

𝐸𝑖𝑗𝑘
𝐸𝑖𝑗

𝑙𝑛 (

𝐸𝑖𝑗𝑘 𝐸𝑖𝑗⁄
𝐸𝑘 𝐸⁄

) 

Second, the specialisation of region j in country i relative to that country - the 
‘within-country’ component, given by 
𝑁
𝑊𝑖𝑡ℎ𝑖𝑛 =   ∑
𝑘=1

𝐸𝑖𝑗𝑘 𝐸𝑖𝑗⁄
𝐸𝑖𝑘 𝐸𝑖⁄

𝐸𝑖𝑗𝑘
𝐸𝑖𝑗

𝑙𝑛 (

𝑇𝑖𝑗

) 

And third, the national specialisation of country i relative to the European 
Union – the ‘between-country’ component - given by  

𝑁
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 =   ∑
𝑘=1

𝑇𝑖

𝐸𝑖𝑘
𝐸𝑖

𝑙𝑛 (

𝐸𝑖𝑘 𝐸𝑖⁄
𝐸𝑘 𝐸⁄

) 

These indices can be calculated for different years, and mapped accordingly 

As Cutrini (2010) points out, the national specialisation relative to Europe, that 
𝐵𝑒𝑡𝑤𝑒𝑒𝑛,  can be envisaged as a residual of the average regional 
is 𝑇𝑖
specialisation in country i relative to Europe once differences in regional 

32 

 
 
 
 
 
 
industrial structures within that country have been accounted for.  Thus, if we 
define  

𝑁

𝑎𝑅𝑆𝑖 =   ∑ 𝑇𝑖𝑗

𝑗=1

𝐸𝑖𝑗
𝐸𝑖

as the average regional specialisation of all the regions of country i relative to 
the European industrial structure, and  

𝑎𝑅𝑆𝑖

𝑊𝑖𝑡ℎ𝑖𝑛 =  ∑ 𝑇𝑖𝑗

𝑁

𝑊𝑖𝑡ℎ𝑖𝑛 𝐸𝑖𝑗
𝐸𝑖

𝑗=1
as the average regional specialisation of all the regions within country i relative 
to that country’s industrial structure, then the average regional specialisation 
of a country relative to Europe, 𝑎𝑅𝑆𝑖, is composed of two elements: the within 
country component and the country bias (Cutrini, 2010). Both of these average 
indices can be graphed to indicate differences between countries in trends in 
average regional specialisation over time.  Finally, it follows that 

𝑎𝑅𝑆𝑖 =   𝑎𝑅𝑆𝑖

𝑊𝑖𝑡ℎ𝑖𝑛 + 𝑇𝑖

𝐵𝑒𝑡𝑤𝑒𝑒𝑛 

National 
specialisation 
and revealed 
comparative 
advantage 

In studies of national specialisation, the Balassa index (BAL) is commonly 
applied to sectoral export data to provide an indicator of ‘revealed comparative 
advantage’ (RCA), first introduced in Balassa (1965). This is usually defined 
as  

𝐵𝐴𝐿𝐴𝑆𝑆𝐴𝑘𝑗 = 𝑅𝐶𝐴𝑘𝑗 = (

𝑋𝑘𝑗
∑ 𝑋𝑗 𝑘𝑗

⁄

∑ 𝑋𝑘𝑗
𝑘
∑ ∑ 𝑋𝑘𝑗
𝑗

𝑘

)  

Geographic 
concentration  

Definition and 
measurement 

where 𝑋𝑘𝑗 is the value of exports by sector k from region j. Although this index 
could be calculated for EU member states, using national export statistics, the 
requisite data on regional exports are not available for most EU countries. In 
this case, the index can be calculated using regional employment (or regional 
output), though it should be recognised that employment or output need not 
necessarily equate directly to export activity, so that in this instance the 
assumption cannot be made that the index measures ‘revealed comparative 
advantage’ as such. 

Whereas the measurement of regional specialisation is viewed from the 
perspective of the region – the extent to which region’s employment or output 
is accounted for by one or at most just a few industries -  the idea of 
concentration is essentially an industry focused notion: the extent to which a 
given industry is based more in certain regions than in others. The idea of 
concentration is often described in terms of the degree of localisation of an 
industry, as outlined by the work of Brülhart and co-authors discussed in  

33 

 
 
 
 
 
 
 
 
 
Section 3.3. Indeed, one simple way of measuring the degree of concentration 
of any given industry across geographic space is the Coefficient of 
Localisation (CL), given as 

where the individual employment terms are as defined above.  This gives a 
geographical concentration (or localisation) index for each industry, but it does 
not reveal in which regions the industry in question is concentrated.  

To overcome this limitation, the Location Quotient (LQ) is a commonly used 
index, which can be computed for each industry and each region. In its 
simplest form this is the ratio of a region’s share of a given national industry to 
its share of total national activity, that is:  

If LQ is greater than unity then the industry in question is generally considered 
to be more localised in region r than in other regions: the higher the value of 
the index the greater the degree of regional concentration. Thus, mapping the 
LQs for a given industry i can reveal where and to what extent that industry is 
concentrated geographically. In fact, many authors use location quotients as a 
measure of regional specialization in a given industry, and it has also been 
used to identify local clusters. There is a relationship between the location 
quotient, in terms of ratios, to the components (expressed in terms of 
differences) of the measure of the level of regional specialisation (the KSI or 
CRS) and industrial localisation (CL) above.  

As with all measures of the geographical distribution of economic activity, the 
spatial scale of the areal units of observation can influence the findings. This is 
particularly the case with concentration (and agglomeration), where spatial 
dependence may be important. For this reason, some authors have argued 
that the calculation of location quotients should be modified so as to take the 
location quotients of neighbouring areas into account. To do this Cromley and 
Hanink (2012) define what they call a focal locational quotient (FLQ) (see also 
Liu, 2014), which takes the form 

34 

𝐶𝐿𝑖=∑ 𝐸𝑖𝑟𝐸𝑖𝑁−𝐸𝑟𝐸𝑁 𝑟 𝐶𝐿𝑖=∑ 𝐸𝑖𝑟𝐸𝑖𝑁−𝐸𝑟𝐸𝑁 𝑟  
 
 
 
 
 
 
 
 
 
 
Where 𝑤𝑖𝑘  is the weight linking region r to region k determined from the form 
of spatial dependence specified. There are several possible spatial weight that 
could be specified, although the simplest is first-order neighbouring regions. 
The region’s own spatial weight, 𝑤𝑖𝑟  could be set to unity, or to 1/s, where s 
the total number of contiguous regions for region r, including that region itself. 
In effect this version of the location quotient can be seen as the ratio of 
relative specialization of the industry of interest at the regional level, 
geographically larger than the individual spatial units because of the use of 
spatial weights, to its relative specialization at the national level.   

Other approaches to measuring the geographical concentration of economic 
activity include the Hirschman-Herfindahl index, which in this case compares 
the distribution of an industry across regions compared to a uniform 
distribution in which employment (or output) is equally spread across all 
regions (when the index has the value 1/n where n is the total number of 
regions). The value of the index increases with the degree of concentration 
reaching its upper limit of unity when the industry in question is concentrated 
in just region. As an absolute measure, the Hirschman-Herfindahl measure of 
geographical concentration displays a bias towards large regions, since the 
latter are likely, other things being equal, to have larger shares of employment 
in any given industry. 

Another index used by economic geographers is the Locational Gini 
coefficient. This is a relative measure of geographical concentration of a 
specific industry in comparison to a reference distribution rather than a uniform 
one. The coefficient takes the value zero if the industry’s employment (or 
output) is distributed across regions in the same proportion as the distribution 
across those regions of total employment. The coefficient takes values greater 
than zero the more the distribution of the industry’s employment is skewed 
compared to that of total employment. If total employment is not uniformly 
distributed, then an industry which is uniformly spread across all regions will 
appear to be concentrated in areas with little other employment. In contrast 
with the Hirschman-Herfindahl Index which gives greater weight to large 
regions, the Locational Gini, as a relative measure, gives greater weight to 
small areas.  

In her study of the distribution of economic activity across the regions of the 
European Union, Cutrini (2010) uses another version of the Theil index she 
developed to measure regional specialisation (referred to above) to compute a 
corresponding measure of concentration that distinguishes between the 
within- country relative concentration of an industry and the between-country 
relative concentration of that industry. This is our preferred measure of 
regional industrial concentration. The relevant indices are defined as: 

Total relative concentration of industry k 

35 

Preferred 
indicator 

𝑇𝑘= ∑∑𝐸𝑖𝑗𝑘𝐸𝑘𝑅𝑖𝑟=1𝑀𝑚=1𝑙𝑛(𝐸𝑖𝑗𝑘𝐸𝑘⁄𝐸𝑖𝑗𝐸⁄)  
 
 
 
 Within-country relative concentration of industry k 

𝑀

𝑅𝑖
𝑊𝑖𝑡ℎ𝑖𝑛 =   ∑ ∑
𝑟=1

𝑚=1

𝑇𝑘

𝐸𝑖𝑗𝑘
𝐸𝑘

𝑙𝑛 (

𝐸𝑖𝑗𝑘 𝐸𝑖𝑘⁄
𝐸𝑖𝑗 𝐸𝑖⁄

) 

Clustering and 
agglomeration  

Definition and 
measurement 

Between-country relative concentration of industry k 

𝑚
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 =   ∑
𝑖=1

𝑇𝑘

𝐸𝑖𝑗𝑘
𝐸𝑘

𝑙𝑛 (

𝐸𝑖𝑘 𝐸𝑘⁄
𝐸𝑖 𝐸⁄

) 

where the definition of the terms is that used above for the Theil specialisation 
indices above. 

As mentioned above, many studies have used the Location Quotient (LQ), or 
some variant thereof, to identify industrial ‘clusters’, in the sense that such an 
approach  reveals  those  local  areas  in  which  a  given  industry  has  a  high 
degree  of  localisation,  in  terms  of  its  share  of  local  employment  (or  output). 
Given that most analyses of this sort utilise sectoral employment (or out) data, 
much obviously depends on the level of sectoral disaggregation used, as well 
as the geographical  size of the  areal  units for  which  such  data  are  available.  
A  more  accurate version  of this  approach would  use  micro-level  data sets  of 
individual  firms,  to  examine  the  co-location  of  firms  in  given  sectors.  When 
micro-data of this sort are available, with precise (geocoded) information as to 
the  actual  addresses  of  individual  firms,  various  statistical  proximity  (eg 
distance) procedures are available which can be used to identify clusters that 
are  not  confined  or  conditioned  by  the  limitations  of  pre-given  geographical 
administrative units (such as NUTS areas).  

But, as also mentioned above, the spatial and industrial identification of proper 
functioning  clusters,  as  defined  by  Porter  (op  cit),  requires  more than  finding 
particular localisations of a given industry or even numerous co-located firms 
in  that  industry.  It  also  requires  empirically  demonstrating  that  the  firms  in 
question  have  interactions  and  interdependencies  both  with  one  another  and 
with firms  and  activities  in  related  and  associated  industries,  as  well  as  local 
institutions  of  various  kinds.  The  notion  of  a  functioning  cluster  is  more  than 
just a set of co-located firms  

in 

the  discussion  of  Figure  2.10,  the 

As  suggested  above, 
idea  of 
agglomeration  is  best  defined  in  terms  of  the  co-location  of  groups  of 
industries  (or  firms  in  different  industries).  Thus,  while  a  cluster  (especially  a 
large  one)  might  be  said  to  involve  a  degree  of  agglomeration,  an 
agglomeration does not necessarily constitute a cluster in the Porterian sense.  

In the urban and regional economics literature, the identification of industrial 
agglomeration is most usually done using the approach devised by Ellison and 
Glaeser (1997).  Interestingly in their work there is something of a confusion, 

36 

 
 
 
 
 
 
 
or at the very least a blurring, between the notions of clustering and 
agglomeration. In Ellison, Glaeser and Kerr (2010), for example, the authors 
state “Why do firms cluster near one another? We test Marshall’s theories of 
industrial agglomeration by examining which industries locate near one 
another, or co-agglomerate”.  This statement conflates the idea of ‘cluster’ 
which seems to be equated with Marshall’s work on ‘industrial districts’ (not 
industrial agglomerations; indeed, Alfred Marshall’s interest was in the 
economies of localised industrial specialisation), with the notion of 
agglomeration. 

Preferred 
indicator 

In  their  original  paper,  Ellison  and  Glaeser (1997)  treat  agglomeration  as  the 
combined effect of natural advantage and industry spillovers. In this model, Q 
firms  sequentially  choose  amongst  R  locations  (regions).  An  individual  firm 
must  choose  whether  to  follow  a  prior  firm’s  location  decision  or  choose  a 
location randomly, ‘by throwing a dart’ at a map. From this model, Ellison and 
Glaeser  drive  their  index  of  agglomeration  for  a  given  industry  k  across 
regions as  

𝐴𝑔𝑔𝑙𝑜𝑚 =

𝐸𝐺𝑘

𝑅
𝑟=1

∑ (𝐸𝑟𝑘 − 𝐸𝑟̂)2 − (1 − ∑
𝑅
𝑟=1

(1 − ∑

𝐸𝑟̂2

)(1 − ∑

𝑄
𝑝=1

2
𝑧𝑝

)

𝑅
𝑟=1

𝐸𝑟̂2

) ∑

𝑄
𝑝=1

2
𝑧𝑝

where  𝐸𝑟̂ measures the aggregate size of region  r, typically modelled by its 
share  of  total  (say,  EU-wide)  employment,  the  sums  are  over  all  regions  or 
areas  (say,  in  the  EU),  and  the {𝑧𝑝} are  the  sizes  of  the  firms  (or  plants)  in 
industry  k.  The  last  term  in  both  the  numerator  and  denominator  of  the  EG 
measure  is  the  Hirschman-Herfindahl  index,  hence,  the  agglomeration 
measure is often written as  

𝐴𝑔𝑔𝑙𝑜𝑚 =

𝐸𝐺𝑘

∑ (𝐸𝑟𝑘 − 𝐸𝑟̂)2 − (1 − ∑

𝑅
𝑟=1

𝑅
𝑟=1

𝐸𝑟̂2

) 𝐻𝑘

(1 − ∑

𝑅
𝑟=1

𝐸𝑟̂2

)𝐻𝑘

where  𝐻𝑘 is the Hirschman-Herfindahl index for industry k. 

Clearly,  this  measure  of  industry  agglomeration  requires  micro-level  (that  is 
firm-level) data. Ellison, Glaeser and Kerr (2010) use a simpler version of the 
measure  to  study  the  ‘co-agglomeration’  of  pairs  of  industries,  namely,  for 
industries k and 𝑙,  

𝐶𝑜−𝑎𝑔𝑔𝑙𝑜𝑚 =

𝐸𝐺𝑘𝑙

𝑅
𝑟=1

∑ (𝐸𝑘𝑟 − 𝐸𝑟̂)(𝐸𝑙𝑟 − 𝐸𝑟̂)
𝑅
𝑟=1

1 − ∑

𝐸𝑟̂2

37 

 
 
 
 
 
 
 
 
 
 
 
Purpose (and 
relevance) of the 
study 

This measure thus requires industry (sectoral) level data only. Other (more 
sophisticated) versions of the Ellison-Glaeser indices can be found in the 
literature. 

2.4  Summary 

There have been numerous investigations of the (changing) patterns of 
economic activity across the European Union, with special reference to 
whether and in what ways specialisation, concentration and agglomeration 
differ across member states and across the regions within them. Many of 
these studies have used the economic geography of the European Union to 
test the empirical validity of the various theories or explanations of these 
phenomena.  Some studies have sought explicitly to determine whether and in 
what ways patters of regional specialisation, concentration and agglomeration 
have changed in response to the process of increasing integration 
(deepening) of the EU. Again, there are some theoretical arguments as to 
what should be expected as a result of this process (see below). 

Most studies deal with the period from around the beginning of the 1970s to 
around 2000.  Much has happened to the economic context and conditions of 
the European Union since then, including another phase of enlargement of the 
membership of the Union, an acceleration in the process of globalisation, and 
the disruption caused by the global financial crisis of 2008-9 and the Great 
Recession this triggered.  There is, therefore, in the light of these and other 
events, a strong case for revisiting the topic of the changing geographies of 
economic activity across the Union. There have also been some advances in 
the techniques and indices used to measure specialisation, concentration and 
agglomeration, as well the availability of improved and contemporary data. In 
addition, in recent years there has been something of a reappraisal of the 
importance of specialisation, concentration and agglomeration as empirical 
trends in the global economy. For example, a decade ago Paul Krugman 
(2008)  voiced some doubt as to whether increasing return to regional 
specialisation, concentration and agglomeration are now as important as they 
once were, that 

there’s  good  reason  to  believe  that  the  world  economy  has,  over  time, 
actually  become  less  characterised  by  the  kinds  of  increasing  returns 
effects emphasized by new trade theory and new geography… In the case 
of geography, in fact, the peak impact of increasing returns occurred long 
before the theorists arrived on the scene. (p. 161) 

And some recent empirical studies find that agglomeration may not be the 
most important factor driving the economic growth of regions and cities (see 
for example, Martin, Gardiner and Tyler, 2011). For these reasons also, a new 
appraisal of the geographical pattern of economic activity across the EU is 
warranted. 

Possible 
geographical 
outcomes of 
increasing 
integration 

The precise impact of increasing economic integration (and enlargement) 
within the European Union on the geographical distribution of economic 
activity across its member states and their regions is not possible to predict a 
priori with any certainty. There are several potential outcomes, and these in 
fact may change over time, for example as the technologies of production 
evolve, extra-EU conditions change (for example the rise of major overseas 
competitors, such as China), and shifts in policies and regulatory 

38 

 
 
 
arrangements occur. At least four alternative scenarios can be distilled from 
the literature: 

(i) 

(ii) 

(iii) 

(iv) 

That increasing economic integration will encourage regional 
specialisation and concentration of activity, as different areas are 
able to exploit comparative advantages and increasing returns to 
scale in particular sectors due to the improvements in trade and in 
flows of capital and labour. This is what might be called the 
‘traditional’ view, as found for example in standard trade theory and 
in Krugman-style ‘new economic geography’.  

That with improvements in transport and advances in technology 
that allow the ‘de-verticalisation’ of production, increasing 
integration could encourage the spatial dispersal and regional de-
concentration of economic activity and the emergence of a more 
geographically even distribution of production, including the 
‘delocalisation’ of supply chains and networks. If there are also 
limits to the returns from ever more geographical concentration of 
activity, this too could reinforce this tendency. Interestingly, this 
was what Krugman has more recently suggested is happening in 
the United States, where, for example the car industry, which was 
previously concentrated in the industrial mid-west regions, 
especially around Detroit, has become more geographically 
dispersed across the country.  

In the case of the EU, a third possibility is for a geographically-
differentiated combination of these first two possibilities. 
Progressive integration of the EU has gone hand in hand with 
increasing enlargement, specifically with the addition of new 
member states in Eastern Europe. Typically, these countries have 
lower wages and costs than the original ‘core’ member states. They 
thus have a competitive advantage in this regard and have 
attracted substantial growth-enhancing FDI as a result (see, for 
example, Sapienza, 2009; Popescu, 2014). There has also been a 
shift of manufacturing operations in certain sectors from the core 
member states to these new members. Further, as the new 
member countries have become exposed to a larger market, this 
may have allowed them (in certain sectors) to benefit from 
increasing returns to scale from trade with the rest of the EU.  In 
short, while economic activity may be dispersing within the core 
members, and regional specialisation falling there, the converse 
could well characterise the new member states, where both 
regional concentration and specialisation might be expected to 
have increased.  

A yet further possibility follows the line of argument advanced by 
Baldwin, referred to above, namely a decline of regional sectoral 
specialisation across the EU, but a shift to functional specialisation, 
as different stages and functions of an activity (such as motor 
vehicle production, or finance) are carried out in different regions. 
As discussed above, a lack of suitable data (principally on 
occupations and skills) makes it difficult to explore this possibility 
for the EU. 

39 

 
 
The extent to which any of these four scenarios can be identified as playing 
out across the countries, regions and sectors of Europe depends mainly on 
two things: 

(i)  The available data - how detailed regionally and sectorally they can be 

obtained, as well as temporally (how long is the period of data 
obtainable). 

(ii)  Other forces and factors acting on the economy. The process of 
integration and enlargement in Europe has not taken place in a 
vacuum. There have been other events occurring, the most important 
of which is the financial crisis and subsequent recession which spread 
across most of Europe from 2008 onwards, and which is likely to have 
disrupted patterns of underlying industrial behaviour. 

A summary of findings on European integration is presented below. The 
general conclusion would be, however, that the longer the time period (i.e. 
outside of the effects of the financial crisis) that is available to the study, the 
easier it will be to identify patterns of development related to integration. In 
addition, a sector-by-sector approach to expected findings is also likely to be 
necessary given the different forces of specialisation, concentration and 
agglomeration which might be at play, and which may work differently 
depending on the type of activity being analysed. 

Data and indicators on different measures of integration which follow the four 
freedoms of the EU (goods, services, capital, and people) have been 
investigated to see how these measures have evolved over the study period 
and across Member States and sectors. In particular: 

-  EU trade (goods and services) as a % of GDP 
- 
-  Non-native (but within EU) employees as % of Member State 

Intra-EU FDI as a % of total FDI 

workforce 

-  Amount of regulatory restrictions by Member State 

The recent financial crisis seems to have disrupted the EU integration process 
through lowering the proportion of intra-EU trade as a % of GDP (see Figure 
2.2). However, this downturn is not evident when looking at the share of EU 
workers from outside the host country (see Figure 2.8), probably because the 
financial crisis led to an exodus of workers looking for work in those Member 
States with relatively strong jobs markets. 

In addition, when comparing the evolution of intra- and extra-EU trade shares 
there is no apparent difference in trend between the two for the majority of the 
period, and even a slight slowing of intra-EU trade relative to extra-EU trade in 
the last few years. This suggests that much of the increasing trade shares 
could simply be related to globalisation forces rather than anything to do with 
EU integration, and in addition that EU integration has struggled to get back 
on track following the financial crisis. Balanced against this is the argument 
that more countries are becoming open to trade around the world and it would 
be difficult to expect EU trade shares to keep pace with the rest of the world 
as more opportunities have opened up. 

Empirical 
findings  

Measuring 
integration 

Period of study 

Member States 

40 

 
 
 
 
Using indicators to proxy integration is a difficult process, as any single 
indicator can reflect a variety of forces. For example, with EU trade 
proportions, it is typically the case that small countries, and those which act as 
trade hubs, will be more open to trade and will thus have higher shares of 
intra-EU trade. This largely explains the situation for Luxembourg, the 
Netherlands, Ireland, and Belgium in Figure 2.3.  

Among the more recently-acceded Member States it is possible to detect 
increased levels of integration. Looking at the change in intra-EU trade shares, 
Slovakia, Hungary, Lithuania, Czech Republic, and Bulgaria have all 
increased fairly rapidly (as shown in Figure 2.3). Many of the newer Member 
States are also at the top end of the scale when looking at intra-EU FDI 
proportions, with the flow of capital (from western / more established Member 
States to east) having been both a symptom and factor in the integration 
process. 

Finland and Austria stand out from most other Member States on measures of 
FDI restriction (see Figure 2.7). This is due largely to utility and service sector 
limitations, many of which have decreased over the period of analysis 
meaning that by the end of the study period there is little difference between 
them. 

Sectors 

As would be expected, manufacturing dominates the EU trade-GDP shares, 
both in level and change (see Figure 2.4). There are, however, some service 
sector activities (R&D, and Other professional services) which have seen 
reasonable increases over the study period. 

Integration across service sectors, and the related recognition of professional 
qualifications remains a work in progress. Recent Directives and initiatives 
(such as the Digital Single Market Strategy and the European Professional 
Card) are moving the process along, and so these are the areas most likely to 
be showing signs of change over the study period. 

Data 
implications 

Use of 
employment as 
the benchmark 
indicator 

From reviewing the literature on specialisation, concentration and 
agglomeration, it is clear that the majority of studies and indicators use 
employment as their standard variable of choice - this is due to several main 
advantages: 

-  employment generally has more complete coverage than other 

measures of activity such as GVA; 

-  employment is a less controversial indicator than GVA, particularly for 
the services sector where measurement of output (and by corollary 
productivity) is more difficult to measure; 

-  employment does usually not require additional manipulations (to do 
with currency or inflation adjustments) to ensure comparability across 
countries. 

Because of this employment is seen as a more robust indicator, and due to its 
wide use in the literature it makes sense to continue this in the current study 
so that results can be compared directly without having to take account of the 
implications of different variables being used. 

The only exception to this will be for the national Balassa Index calculations, 
where export shares from the COMEXT database will be calculated. 

41 

 
 
 
 
Sectoral vs firm-
level data 

The calculation of most indices can be undertaken on sectoral data, whether 
these are at national or regional level. The exception to this is the calculation 
of the Ellison-Glaeser index for measuring agglomeration, which can be 
calculated at both sector and firm level.  

42 

 
 
 
 
Aims of the 
database 

National-sector 
export share 
database 

National-
regional-sector 
database 

3 

Database 

3.1  Introduction 

This chapter is concerned with the collection and construction of a national-
regional-sector database suitable for the construction of indicators to be used 
to calculate measures of specialisation, concentration and clustering. The 
database deliverable can be split into two components: 

A database suitable for applied statistical analysis, containing sufficient 

(i) 
country coverage, regional-sectoral detail, and time periods (of annual data). 

An accompanying user guide which describes the process of data 

(ii) 
sourcing, collection and construction. 

The database has two components. 

As no detailed data on regional-sector export shares exist for Europe, these 
data are confined to national analysis. The OECD’s STAN database and the 
export data from WIOD (World Input-Output Database) were identified as the 
most promising sources for detailed sector export shares over a sufficient 
period of time and sectoral detail. 

At the outset it was not considered feasible to include all Member States in the 
analysis, as both data quality and resource issues would prevent such a task. 
Instead, the aim was to construct a database of sufficient Member State 
coverage that the findings would be representative for the EU as a whole. At 
the outset, Eurostat’s SBS database was considered to be the main source of 
data, to be supplemented by data from National Statistical Offices as 
necessary. On further investigation it was decided to place additional focus on 
the Bureau van Dijk (BvD) firm-level database as a supplementary source of 
information. 

  Countries and regions (NUTS2) 

A total of 16 Member States were proposed for inclusion in the regional-sector 
analysis – a stipulation of the work was that these should include the largest 
five Member States (France, Germany, Italy, Spain, and the UK). The 
remaining Member States considered to have sufficiently good quality regional 
data for inclusion were: Belgium, Bulgaria, Czech Republic, Denmark, Estonia, 
Greece, Hungary, The Netherlands, Slovakia, Slovenia and Sweden. Taken 
together, this selection accounted for 88% of EU GDP in 2015 and 81% of 
population. 

  Sectors 

For most of the countries (and their regions) mentioned above, it was 
considered that a full coverage of the market-based sectors was desirable. 
This includes Manufacturing (all sectors) and services sectors including: 
Wholesale and retail trade; repair of motor vehicles and motorcycles, 
Transportation and storage, Accommodation and food service activities, 
Information and communication, Real estate activities, Professional, scientific 
and technical activities, Administrative and support service activities.  

  Time period 

43 

 
 
 
Export data by 
country and 
sector 

From prior analysis it was considered that the most robust data could be found 
over the period 2005-2015. However, given the occurrence of the global 
financial crisis near to the start of this period, it was considered desirable to 
push the data back further (to 1995) if possible. 

3.2  Data sources and availability 

In considering the sources for the export database, the three main contenders 
were the OECD’s STAN database, Eurostat’s COMEXT database, and the 
WIOD export data. The criteria on which the databases were assessed were 
that they should provide full coverage of EU Member States, detailed sectoral 
detail (sufficient to calculate the Balassa index of “revealed comparative 
advantage” – following the work by Brülhart (2001a)), and span a long period 
of time (preferably back to 1995). 

  OECD STAN 

The OECD’s STAN (STructural ANalysis) database14 is a comprehensive 
source of industry covering a range of indicators, including exports. Within 
this, the Bilateral Trade Database by Industry and End-use category (BTDIxE) 
provides sufficient coverage to be of interest for the project. 

  Eurostat COMEXT 

COMEXT15 is a Eurostat database dealing with international trade (exports 
and imports) in goods which dates back to the early 1990s and provides one 
of the most detailed sources of European trade data available.  

  WIOD exports 

WIOD is an internationally recognised source of input-output data for countries 
across the world, which has been in operation for many years and is part-
funded by the European Commission. There are two releases of data 
available on the WIOD website16.  

Coverage 

The table below provides a comparison between the three options. 

Table 3.1: Export database comparison 

STAN 

1995-2016 

COMEXT 

1995-2016 

Time 
Period 

WIOD 

1995-201117 (2013 
release) and 2000-14 
(2016 release) 

Countries  All Member States 

All Member States 

EU27 (no data for HR) 

Sectors 

Once aggregations 
have been 
removed and 
compatibility with 

The COMEXT 
database does 
not allocate 
exports by sector, 

For the 2013 release 59 
sectors (NACE Rev1), 
while for the 2016 
release, 64 sectors 

14 See http://www.oecd.org/sti/ind/stanstructuralanalysisdatabase.htm.  
15 See http://ec.europa.eu/eurostat/web/international-trade-in-goods/data/focus-on-comext.  
16 See http://www.wiod.org.  
17  1995-2009  for  BG,  CY,  EE,  EL,  LV,  LT,  LU,  MT,  PT,  RO,  SK,  SI,  1995-2011  for  all  other 

countries. 

44 

 
 
 
 
 
                                           
STAN 

COMEXT 

WIOD 

but only 
distinguishes data 
by products 
(about 4000). 

(NACE Rev2). Both 
releases include services, 
i.e. complete coverage of 
the economy. 

NACE codes 
established, the 
remaining 
coverage is 31 
sectors. The export 
data deal mostly 
with trade in goods, 
with a limited 
coverage of 
services and 
utilities18. 

Coverage  Data coverage of the 

is very good with 
only a few missing 
data observations,  
Some countries also 
start a bit later19. 

Not investigated as 
it was considered 
too time-consuming 
to pursue the 
COMEXT option. 

Other 
notes 

As with STAN, data 
coverage is very good 
although possible missing 
observations have been 
noted across a range of 
sectors and countries20. 

WIOD data for services 
exports are based on 
estimated data21. 
However, they are based 
on reliable sources and 
are considered the best 
option available in lieu of 
no directly observed data. 

The disadvantage of the STAN database is its inability to cover service 
sectors, which are an important part of the structure of many EU Member 
States and can also provide strong export earnings (e.g. Luxembourg, UK). In 
this context, the WIOD database was explored as either a substitute for, or 
complement to, the STAN data. 

Comparison between the databases has revealed some differences in levels 
of exports, while the trends seem broadly similar. Given that the use of these 
data is for relative (Balassa index) calculations, we consider that they are 
mostly complements (as the STAN provides additional manufacturing 
disaggregation) and so the proposal is to use them alongside one another in 
the analysis of revealed comparative advantage.  

The Structural Business Statistics database contains detailed enterprise data 
for a wide range of activities across the European Union (including services). 
Most importantly for the project, it also has a regional dimension and it is this 
part of the SBS database that is to be used as the backgrounds structure for 
the project data. 

18  Taken  together,  the  last  three  sectors  in  account  for  under  1.5%  of  total  exports  of  EU 

goods and services in 2016. 

19 Luxembourg starts in 1999, Slovakia in 1997 and Bulgaria in 1996. 
20  In  the  raw  WIOD  data  there  are  several  instances  of  zero  entries,  which  seem  strange  as 

other periods have non-zero entries, and thus could be missing. 

21 See http://www.wiod.org/publications/source_docs/WIOD_sources.pdf (p11) for details. 

45 

Eurostat SBS 
database 

 
 
 
 
 
 
                                           
Coverage 

The SBS regional data are split into two sub-datasets which are identified by 
the time period covered. 

  Database 1: 2008 – 2015 (sbs_r_nuts06_r2) 

These data are a mix of different spatial definitions (NUTS 2013 and NUTS 
2010) – this mix varies by Member State. On a sectoral basis, activities are 
according to NACE Rev.2 definitions22, and range from Section B (Mining and 
Quarrying) to N (Administrative and support service activities) at two-digit 
level. The table below summarises any issues of completeness and 
consistency with Database 1, focussing on employment data for the 16 
Member States which are part of the national-regional-sector analysis, and 
providing detail on whether the main issues are to do with time period 
coverage, regional mix, or available sectoral detail (or some combination 
thereof). 

Table 3.2: SBS Database issues (2008 – 2015) 

Member 
State 

Belgium 

Issues with Employment Data  

Periods: No major problems. 

Regions: No major problems. 

Sectors: Missing data for Section B sub-sectors.  

Bulgaria 

Periods: No major problems. 

Regions: No major problems. 

Sectors: Missing data for Section B sub-sectors and some sub-sectors 
within Section E (Water supply; sewerage, waste management and 
remediation activities). 

Czech 
Republic 

Periods: No major problems. 

Regions: No major problems. 

Sectors: Missing data for Section B sub-sectors. 

Denmark 

Periods: No major problems. 

Regions: No major problems. 

Sectors: Missing data for Section B and some Section C sub-sectors. 
Missing data for sub-sectors within Section H for some regions.  

Estonia 

Periods: No major problems. 

Regions: No disaggregation - the country is a NUTS2 region. 

Sectors: Missing data for Sections B, E and H sub-sectors. 

France 

Periods: Large number of missing data in 2008 and 2009.  

Regions: Mixed between two NUTS classification (2010 and 2013) - data 
available in NUTS2010 covers period 2008-2012 and NUTS2013 covers 
period 2013-2015. 

Sectors: Missing data for Section B and Section H and Section J sub-
sectors 

22 See Appendix A for a list of NACE Rev.1 and 2.2 definitions and how they match together. 

46 

 
 
                                           
Member 
State 

Greece 

Issues with Employment Data  

Periods: No major problems. 

Regions: Mixed between two NUTS classification (2010 and 2013), data 
available in NUTS2010 covers period 2008-2012 and NUTS2013 covers 
period 2013-2015 

Sectors: Missing data for Section B sub-sectors and some Section E sub-
sectors.  

Germany 

Periods: No major problems. 

Regions: Mixed between 2 NUTS classification including NUTS2013 and 
NUTS2006.  

Sectors: Missing data for Section D and Section E. Missing data for 
Section B sub-sectors in some regions.  

Hungary 

Periods: No major problems. 

Regions: No major problems. 

Sectors: Missing data for Section B sub-sectors.  

Italy 

Periods: No major problems. 

Regions: Mixed between 2 NUTS classification including NUTS2013 and 
NUTS2006. 

Sectors: Missing data for Section F in some regions.  

Netherlands  Periods: No major problems. 

Regions: No major problems. 

Sectors: Missing data for Section B sub-sectors. Missing data for some 
Section C and Section E sub-sectors in some regions.  

Slovakia 

Periods: No major problems. 

Regions: No major problems. 

Sectors: Missing data for Section B sub-sectors and some Section C, 
Section E and Section H sub-sectors.  

Slovenia 

Periods: No major problems. 

Regions: Mixed between 2 NUTS classification including NUTS2013 and 
NUTS2010. 

Sectors: Missing data for Section B sub-sectors.  

Spain 

Periods: No major problems. 

Regions: No major problems. 

Sectors: Missing data for Section B sub-sectors. Missing data for Section 
H in some regions.  

Sweden 

Periods: No major problems. 

Regions: No major problems. 

Sectors: Missing data in Section B sub-sectors and some Section C sub-
sectors.  

47 

 
 
Member 
State 

UK 

Issues with Employment Data  

Periods: 2011-15 missing for some regions (UKD2, UKD5) and for 2008-
10 (UKD6, UKD7) and for 2013-15 (UKI1, UKI2) and for 2008-12 (UKI3, 
UKI4, UKI5, UKI6, UKI7) most likely due to changes in NUTS 
classification mentioned below. 

Regions: Mixed between 2 NUTS classification including NUTS2013 and 
NUTS2006.  

Sectors: Missing data in Section B sub-sectors. Missing data in Section E 
sub-sectors in some regions.  

  Dataset 2: 1995 – 2007 (sbs_r_nuts03) 

For the earlier time period dataset, the regional disaggregation is a mix 
between NUTS 2006 and NUTS 2010 at NUTS2 level. For sectoral split, the 
coverage at NACE Rev.1.1 is from Section C (Mining and Quarrying) to K 
(Real estate, renting and business activities). 

Table 3.3: SBS Database issues (1995 – 2007) 

Member 
State 

Belgium 

Issues with Employment Data 

Periods: Missing data for 1995 and 2002 in all levels of geography. Large 
number of missing data in 1996 to 1998.  

Regions: No major problems. 

Sectors: Missing data in Section J and sub-sectors in Section C and 
Section D.   

Bulgaria 

Periods: Missing data for 1995 in all levels of geography. Data is available 
only at 2007 for regional level.  

Regions: Disaggregated into only 2 regions, with only 2007 data available.  

Sectors: Missing data for Section J.  

Czech 
Republic 

Periods: Missing data from 1995 to 2001 and 2003 in all levels of 
geography.   

Regions: No major problems 

Sectors: Missing data for sub-sectors within Section C, Section D and 
Section I. Missing data for Section J.  

Denmark 

Periods: Large number of missing data from 1995 to 1998. Data is 
available only at 2007 for regional level.  

Regions: Missing data for regional level.  

Sectors: Missing data for sub-sectors within Section C and Section D. 
Missing data for Section J.  

Estonia 

Periods: Missing data from 1995 to 1999 and 2003.  

Regions: No regional disaggregation.  

Sectors: Missing data in Section J and sub-sectors within Section C and 

48 

 
 
 
Member 
State 

Issues with Employment Data 

Section D.  

France 

Periods: Large number of missing data in 1995.  

Regions: Missing data from 1995-2000 in FR10.  

Sectors: Missing data in Section J and sub-sectors within Section C and 
D.  

Greece 

Periods: Large number of data missing in 1995-1999.  

Regions: No major problems. 

Sectors: Missing data in Section J and sub-sectors within Section D.  

Germany 

Periods: Large number of data missing in 1995-1998.  

Regions: Large number of data missing for each region, especially from 
1995-1998.  

Sectors: Missing data in Section J and sub-sectors within Section C, 
Section D and Section E.  

Hungary 

Periods: Missing data from 1995 to 2000.  

Regions: No major problems. 

Sectors: Missing data in Section C sub-sectors and in Section J.  

Italy 

Periods: Large number of missing data in 1995 and 1998.  

Regions: No major problems. 

Sectors: Missing data in Section C and D sub-sectors.  

Netherlands  Periods: Data missing in 1998. 

Regions: No major problems. 

Sectors: Missing data in Section J and sub-sectors within Section C and 
Section D. 

Slovakia 

Periods: No major problems. 

Regions: No major problems. 

Sectors: Missing data in Section J and sub-sectors within Section C, 
Section D and Section I.  

Slovenia 

Periods: Data missing in 1995 and 1996. Large number of data missing in 
2006.  

Regions: Data only available at 2007 in regions.  

Sectors: Missing data in Section I and Section J and sub-sectors within 
Section C and Section D.  

Spain 

Periods: Large number of data missing from 1995 to 1998.  

Regions: No major problems. 

Sectors: Missing data in Section J and sub-sectors within Section C and 
Section D.  

Sweden 

Periods: Large number of data missing between 1995-2006.  

49 

 
 
Member 
State 

Issues with Employment Data 

Regions: Data is available only at 2007 in some regions and some 
missing data from 1995 to 1997.  

Sectors: Missing data in Section J and sub-sectors within Section C and 
Section D.  

UK 

Periods: Missing data from 1995-1997. Large number of missing data in 
2001.  

Regions: No major problems. 

Sectors: Missing sub-sectors within Section C and Section D. Missing 
data in Section J. 

The tables above provide an indication of the challenges of obtaining 
consistent data across time, space and activity. As a way of summarising 
these findings and allowing some degree of comparability, Figures 3.1 and 3.2 
look at the proportion of available data, firstly across countries and secondly 
across time. A value of 0% on the y-axis would imply no available data at all, 
while 100% would be a full dataset (across regions and sectors). 

The charts clearly show several things: 

  There is a real mix in the quantity of data covered across Member 

States. Typically, the longer established Member States are stronger 
within the earlier database, while for the 2008-15 period the coverage 
is more even. 

  France, Greece and (marginally) Italy are the only Member States for 

which coverage decreases in the more recent database. 

  The quantity of SBS data has gradually improved over time, levelling 

off at around 85% coverage across the 16 Member States in the more 
recent database, while continually improving in the previous (1995-
2007) database. 

  A dip in coverage during the first couple of years of the new database 

being established. 

  A continuous time series approach to the analysis might thus be 

problematic, while a snapshot of several years (eg 2003, 2007, 2011, 
2015) could be a better option. 

50 

 
 
 
Figure 3.1: SBS data coverage by country 

Note:   The calculation is approximate as it includes national and sector aggregates 

as part of the coverage. This explains why Denmark is non-zero for 1995-
2007. 

Source: Eurostat SBS database, CE calculations 

Figure 3.2: SBS data coverage over time 

Note:   The calculation is approximate as it includes national and sector 

aggregates as part of the coverage. 

Source: Eurostat SBS database, CE calculations 

51 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Additional data 
sources 

NSO 
investigations 

The various temporal, spatial and sectoral issues identified within the SBS 
database led to investigations at country (NSO) level to see whether there 
were any additional data available which might not have been released to 
Eurostat. These data could then be used to supplement the SBS data, 
potentially helping to bridge or fill gaps, aid consistency checking, and so on. 

In the period leading up to submission of this report, numerous 
communications were sent or received during the process of discovering what 
additional data might be available. The table below provides a summary of the 
findings from this exercise. 

Table 3.4: NSO data investigations 

Member 
State 

Belgium 

Bulgaria 

Czech 
Republic 

Denmark 

Estonia 

France 

Greece 

Germany 

Data findings 

Employment data (employees and self-employment) have been provided 
according to Nace Rev. 1 (2-digit level) and NUTS 2013 for the period 
1995-2009 at NUTS2 level. 

To date, no communication has been received back from the Bulgarian 
NSO. 

The Czech statistical office has provided detailed sectoral data for 2003. 
No data are available before that date. 

Due to the 2007 Danish Municipal Reform NUTS2 data are only available 
from 2008 onwards. However, Danmark Statistics have said they are able 
to construct equivalent NUTS2 data going backwards over 1995-2006 at 
NACE Rev 2 classification. Unfortunately, these data are not free, and 
would cost DKK 9000 to obtain. 

NA – data are complete from SBS database. 

A weblink (https://www.insee.fr/fr/information/1302154) was provided 
whereby you can consult older publications of INSEE. However, the 
website also notes that “because of the moving of INSEE's offices, the 
INSEE library will be closed to the public from March 12th, 2018 until the 
end of 2018.” 

Further communications with INSEE suggested that, partly due to the 
ongoing move of their library, there would be no supplementary data 
available at this time. 

Weblinks were provided for Greek regional data for 2008-15 for Sections 
H-S. For Section G the data go from 2000-15, although there are breaks in 
the data. There are no sub-divisions, and nothing for manufacturing, 
mining or other missing sectors. Pre-2008 data are the on NACE Rev 1.1. 
definition. 

Communications received indicate that the Destatis did not compile any 
statistics in the service sectors (they only started to compile them as they 
became mandatory due to EU legislation). In addition, there are no 
archives or non-public sources available. A weblink to data compiled by 

52 

 
 
 
 
 
Member 
State 

Data findings 

Hungary 

Italy 

Destatis for a variety of activities was provided, but this did not yield 
anything useful.  

To date, no communication has been received back from the Hungarian 
NSO. 

Although communication channels were established, data are sufficiently 
complete from the SBS not to require supplementary sources. 

Netherlands  Communications are ongoing regarding the availability of supplementary 

data. 

Slovakia 

Slovenia 

Spain 

Sweden 

UK 

Communications are ongoing regarding the availability of supplementary 
data. 

Communications revealed that no supplementary data were available from 
the NSO. 

NUTS2 2-digit employment data were provided from the Spanish regional 
accounts for 1995-2002. 

Communications are ongoing regarding the availability of supplementary 
data. 

N/A – Cambridge Econometrics maintain an ONS-compatible UK local 
authority level database which can be used to construct the necessary 
spatial-sectoral-temporal configuration. 

Cambridge 
Econometrics 
European 
regional 
database 

The Cambridge Econometrics (CE) database23 is a NUTS3 level dataset which 
covers the period 1980-2015 for 6 aggregate sectors. It has been maintained 
and updated by CE over many years and is designed to be consistent with 
regional accounts from Eurostat. While the sector aggregation is insufficient 
for the analysis required of specialisation, concentration and clustering, it 
could potentially be used to provide a background structure from which more 
detailed sector proportions can be calculated using firm-level databases listed 
below.  

An additional advantage is that the database is now available free of charge 
through the JRC data platform24, with future updates being provided annually 
by the European Commission (JRC-ISPRA). 

Firm-level 
databases 

The proposed combination of the SBS and NSO databases still leaves large 
gaps in time and sector coverage, as well as a problem of conversion between 
NACE Rev versions to resolve. For this reason, the possibility of using firm-
level databases (from which regional-sector aggregates could be constructed, 
bottom-up) has also been explored. 

LISA database 
(NL) 

LISA (Landelijk Informatiessysteem van Arbeidsplaatsen en vestigingen25) is 
an establishment-based employment dataset which covers the entire 
population of firms in the Netherlands (where paid work takes place), and thus 
provides the most comprehensive picture of micro employment data available. 

23 See https://www.camecon.com/european-regional-data/ for more information. 
24  See  http://urban.jrc.ec.europa.eu/t-pedia.  Search  for  "Cambridge"  or  "Econometrics:  and  you  find 

the list of the currently available indicators (metadata and downloadable zip file). 

25 See https://www.lisa.nl/home.  

53 

 
 
 
                                           
Bureau van Dijk 
database 

The data for this work have kindly been processed by Prof Frank van Ort of 
the Utrecht University, who has made use of these data in the past for his own 
analyses (see, for example, van Ort et al (2010)). Prof van Ort provided a 3-
digit database (NACE Rev2) at NUTS2 level for the period 1996-2015.  

The Bureau van Dijk (BvD) Orbis database26 is operated by Moody’s Analytics 
is a private company dataset covering approximately 300 million companies 
worldwide. The companies are geo-coded and the associated industry codes 
can be aggregated into NACE classifications. The JRC-Seville has access to 
the Orbis database and has worked to produce region-sector databases for 
the Member States being analysed.  

  Sectors 

Unlike LISA, the BvD database still represents a sample of firms (with a bias 
against smaller firms who will not be registered). In addition, as it includes only 
private sector companies, it does not cover the public and own-service NACE 
sections (O-T) very well. This, however, is not considered a major 
disadvantage as it is mainly the market-oriented sectors which are of interest 
for the study (and their relation to EU integration). 

Finally, there is also a potential issue of over-representation of some sectors 
in the database, by which is mean that the proportions of sectors for any given 
year will not match those of the population due to some sectors being more 
likely to be reporting in the BvD database than others. 

To test this final potential property of the BvD data, sector shares for the BvD 
were aggregated across regions to calculate national equivalents, within three 
of the broad aggregate sectors which the CE database uses: 

(i) 

Industry – not including construction (Sections B-E); 

(ii)  Wholesale and retail trade, Transportation and storage, 

Accommodation and food service activities, and Information and 
communications services (Sections G-J); and  

(iii) 

Financial and Business Services (Sections K-N) 

These sector shares were compared with equivalent values for the LISA 
database (for NL), the CE detailed sector data (for UK), National Accounts 
data from Eurostat. The following figures (3.3 – 3.6) visualise the results. 

The findings can be summarised as follows: 

For the Netherlands: 

-  For LISA and the National Accounts (Figure 3.3), the sector 

proportions within Industry (with the exception of the repair and 
installation of machinery and equipment) and Services (G-J) 
correspond extremely well with high correlations.  

-  The situation is not so good with Financial Business Services (FBS), 
however, particularly with some sectors (‘Employment agencies’ and 
‘Security and investigation, service and landscape, office administrative 
and support activities’ both have a much higher proportion in the 
National Accounts, whereas ‘Legal and accounting activities; activities 

26 See https://www.bvdinfo.com/en-gb.  

54 

 
 
                                           
of head offices; management consultancy activities’ is consistently 
higher in the LISA data).  

-  Between the BvD and National Accounts (Figure 3.4) the degree of 

correspondence is not so good, even for industries, with some sectors 
(Manufacture of food, beverages and tobacco products, and 
furniture/other manufacturing) showing deviations. 

-  Moving to the service sectors, Wholesale and retail trade show some 

opposing (over and under representation) proportions in 1996, but this 
seems to settle down in later periods. 

-  Probably the largest concern is with the FBS sector, and in particular 
Financial service activities (consistently over-represented in the BvD) 
and Employment agencies and Security, landscaping and 
admin/business support services, which show continued under-
representation. 

For the UK: 

-  The comparison between CE data and the National Accounts (Figure 

3.5) reveals no major deviations across any sub-sectors. 

-  For the National Accounts vs BvD (Figure 3.6) there are some 

deviations for industry sub-sectors (notably food, drink and tobacco 
and clothing and leather) but these seem to improve over time. 

-  BvD service sub-sector proportions seem reasonable, but for the FBS 

sector there are some persistent deviations, notably Legal & 
accounting activities, and Security and investigation services, while 
Financial services is also under-represented in the earlier years. 

55 

 
 
Figure 3.3: LISA vs Eurostat National Accounts (NL) sector proportions 

1996 

2005 

2015 

Note:  
   Shares are calculated as proportions of the aggregate sector. 
Source(s): Eurostat National Accounts database, LISA database, CE calculations. 

56 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.4: BvD (NL) vs Eurostat National Accounts (NL) sector proportions 

1996 

2005 

2015 

   Shares are calculated as proportions of the aggregate sector. 
Note:  
Source(s): Eurostat National Accounts database, BvD database, CE calculations. 

57 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.5: Cambridge Econometrics UK data vs Eurostat National Accounts (UK) sector proportions 

1996 

2005 

2015 

Note:   Shares are calculated as proportions of the aggregate sector. 
Source:  Eurostat National Accounts database, CE database, CE calculations. 

58 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.6: BvD (UK) vs Eurostat National Accounts (UK) sector proportions 

1996 

2005 

2015 

Note:   Shares are calculated as proportions of the aggregate sector. 
Source:  Eurostat National Accounts database, BvD database, CE calculations. 

59 

 
 
 
 
 
  Time periods 

Across the sample of Member States being analysed, there is a range of 
starting years for the data. The established Member States do go back to 
1995, but typically the more recently-acceded Member States do not. For 
example, Hungary and Poland start in 1997, Slovakia in 2004 and Slovenia in 
2007. 

  Sample robustness 

There are also some concerns over the sample coverage going back through 
time, meaning that even if the data are reported as going back to 1995 the 
number of companies being surveyed may be relatively small (to the entire 
population of the Member State) which would make the data less robust than it 
otherwise appeared. 

To investigate this possibility, the charts below (split between the more 
established Member States and those acceding from the 2000s onwards) 
reports on the number of companies being surveyed going back through time 
across the Member States. 

- 

Figure 3.7: BvD sample sizes (relative to 2015) across selected Member States 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

Source: Eurostat SBS database, CE calculations 

- 

Perhaps surprisingly, 2015 is not the best year in terms of sample size, across 
all countries – for NL (2011), FR (2007), and ES (2009) one must look at 
earlier years – but it is generally the case among the established Member 
States. There are similar patterns with those more recently acceded, although 
for SK (2014) and HU (2012) it also holds true. 

There is a general rule, however, that the more recent years have a larger 
sample size, and this cannot simply be due to more companies being in 
existence (i.e. a larger population size). This throws open the question at what 
point in time the sample of companies being surveyed in the BvD no longer 
becomes representative. In the case of Italy, for example, there seems to be a 

60 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
SBS database 

Changing 
classifications 

rapid decline in sample after only a few years, while for most others the 
decline is more gradual. 

3.3  Database construction method 

Having reviewed the availability of data from official sources, this section looks 
at how best to blend them together to form a consistent database. The focus 
here is entirely on the national-regional-sector database as the export 
databases from STAN and WIOD are sufficiently complete to not require 
additional processing. 

Two distinct options are presented: 

1.  The SBS database, supplemented by national level (NSO) data; 

2.  The BvD database, used to create sector-region proportions which 
could then be applied to more aggregated national/regional data. 

As mentioned in the previous chapter, to provide a consistent database for the 
study period, the two SBS databases need to be blended together. This 
section describes what needs to be done for this to happen. 

  Regional (NUTS) classifications 

Changes to regional classifications are the first issue to deal with, as when 
calculating the indicators for a particular sector it is important to make sure 
that they are identified for the same spatial area, as otherwise this will lead to 
inconsistencies in measurement. Not all Member States have been affected 
by changes to NUTS classifications during the study period, however, and so 
only those for which this is a relevant issue are included in the table below.  

Table 3.5: Regional classification issues and proposed solutions 

Member 
State 

NUTS 2006 / 
2010* 

NUTS 2013 

Comments and Proposed Solution (in 
italics) 

Denmark  DK 

DK01 – 
DK05 

France 

FR9  

FRA 

FR91–94 

FRA1-A5 

For Denmark the change in NUTS 
classification is very important due to the 
municipal reform which, in 2007, created 
five NUTS2 regions where previously 
there had been only one (the whole 
country). This means that pre-2008 
there are no NUTS2 data available for 
Denmark. 

It would seem the main solution is to 
treat the country as a single region for 
the entire period or to simply exclude it 
from the analysis. 

An additional region (FRA5) has been 
added implying some boundary shifts 
(from FRA1) 

This shift concerns the overseas regions 
of France (Départements d'Outre-Mer). 
Because these are peripheral and small 

61 

 
 
 
Member 
State 

NUTS 2006 / 
2010* 

NUTS 2013 

Comments and Proposed Solution (in 
italics) 

regions the solution proposed is to only 
focus on mainland France and thus 
ignore these changes. 

Regions have merged. Solution is to 
only use DE40 for whole period. 

The changes are superficial (codes and 
names but not boundary changes) and 
so there are no real inconsistencies to 
deal with. 

The changes are superficial (codes and 
names but not boundary changes) and 
so there are no real inconsistencies to 
deal with. 

Germany  DE41, DE42 

DE40 

DED1, DED3 

DED4, DED5 

Greece 

EL1*, 
EL11*-13*, 
EL14*,  
EL2*, 
EL21*, 
EL22* - EL25* 

EL5,  
EL51-53, 
EL61, 
EL6, 
EL54 
EL62 - EL65 

Italy 

ITD,  
ITD1-D5,  
ITE,  
ITE1-E3 

ITH,  
ITH1-H5 
ITI, 
ITI1-I4 

The changes are superficial (codes and 
names but not boundary changes) and 
so there are no real inconsistencies to 
deal with. 

Slovenia 

SI01*, SI02* 

SI03, SI04 

UK 

UKI1*,  
UKI2* 
UKD2 
UKD5 

UKI3-I4, 
UKI5-I7 
UKD6 
UKD7 

The changes are superficial (codes and 
names but not boundary changes) and 
so there are no real inconsistencies to 
deal with. 

Cambridge Econometrics maintain an 
ONS-compatible UK local authority level 
database which can be used to 
construct the necessary spatial-sectoral-
temporal configuration. However, for the 
purposes of this study it was decided to 
use the NUTS 2006/2010 for UKI and 
not add the additional split for the 
London NUTS2 regions. 

Sectoral (NACE) 
classifications 

The changes in sector classification concern the shift from NACE Rev 1.1 in 
the 1995-2007 database to NACE Rev 2 for the 2008-15 data. The shift in 
definitions is well known and correspondence tables exist27 along with some 
papers discussing the issues of matching data (e.g. Perani and Cirillo, 2015). 

The main question here is whether a correspondence process is possible, 
either going forward (i.e. convert NACE Rev 1.1 to NACE Rev 2), or 
backwards and produce a database with NACE Rev 1.1 definitions. Although 
Perani and Cirillo (op cit, Figure 1) already have produced a conversion 
matrix, this is based on Italian employment data which cannot very easily be 
tested28 for validity against other countries. 

27 See http://ec.europa.eu/eurostat/web/nace-rev2/correspondence_tables  
28 Detailed micro data for all Member States in the sample would be required. 

62 

 
 
 
 
 
                                           
For this reason, an alternative process was chosen based on matching 
sectors which have the closest degree of correspondence between the NACE 
versions29. A degree of correspondence of 80% or above was considered 
sufficient enough for this purpose. 

Table 3.6 shows the two-way degree of correspondence across the sectors 
that could potentially be covered using the SBS data. Those sectors shaded 
red are the ones with two-way correspondence of 80% or above. Two further 
(transport-related) sectors30 are shaded orange as, even though they do not 
meet the correspondence threshold, they are considered interesting from an 
EU-integration perspective to warrant taking forward. It should be noted, 
however, that a high degree of correspondence is no guarantee of sufficient 
data being available to mean they will be included in the subsequent empirical 
analysis.  

Based on the coverage of the SBS databases, it will not be sufficient to simply 
put them alongside one another, post-region/sector classification sorting, and 
to then undertake the analysis. Further filling is required to make the database 
more complete across time, space and sector. 

Table 3.4 provided a summary of communications with, and data received 
from, the various NSOs for the sample of Member States being covered in the 
analysis. Following the joining of the SBS databases, supplementary data that 
have been obtained from the NSOs (i.e. for Belgium, Greece, Spain and the 
UK31) have been assessed against this to see if it can provide a useful role in 
bridging gaps or pushing data forward and back, all the while seeking to 
maintain consistency with the original data. 

Interpolation involves filling in gaps between known values whereas 
extrapolation is pushing the data forwards or back time. Both can help to 
improve coverage of the database, but there are costs to using ‘estimated’ 
data because (a) there is certain to be some degree of error involved, which 
ideally we would like to be random, and (b) there is a risk that the method of 
filling data may affect the quality (and therefore usefulness) of the resulting 
database for subsequently calculating indicators of specialisation, 
concentration and agglomeration. An example would be if, to extrapolate a set 
of industries within a region going forward, fixed shares from the last year of 
actual data were imposed – such a technique would clearly mean that the 
method of filling would influence the conclusions drawn from the indicators of 
specialisation, etc. 

Further filling-in 
mechanisms 

NSO data 

Interpolation and 
extrapolation 

29 Using this product conversion matrix this way means assuming that all products have equal 
economic  weight,  which  is  naturally  an  approximation  but  all  that  can  be  done  in  the 
circumstances. 

30 For Manufacture of motor vehicles, trailers and semi-trailers, and for Manufacture of other 
transport  equipment,  it  was  also  felt  that  the  product  %  rule  gave  undue  weight  to 
smaller sectors, although this could not be tested due to lack of data. Given the slightly 
lower correspondence however, the findings from the empirical analysis will be weighted 
accordingly due to the higher degree of uncertainty over comparison across periods. 
31  As  previously  mentioned,  local  area  sector  data  for  the  UK  come  from  Cambridge 

Econometrics, although they are based on original ONS sources. 

63 

 
 
                                           
Table 3.6: NACE Revision Sectoral Correspondence  

Rev 1.1 

Sector Name 

Rev 2 

Sector Name 

DA 

Manufacture of food products, beverages 
and tobacco 

C10, C11 

Manufacture of food products; Manufacture of 
beverages32 

0.96 

Share of 
Rev 1.1 in 
Rev 2 sector 

Share of 
Rev 2 in 
Rev 1.1 
sector 

0.9933 

DD 

Manufacture of wood and wood products 

C16 

Manufacture of wood and of products of wood and 
cork, except furniture; manufacture of articles of straw 
and plaiting materials 

0.89 

0.94 

DE21 

DE22 

DF 

Manufacture of pulp, paper and paper 
products 

C17 

Manufacture of paper and paper products 

Publishing, printing and reproduction of 
recorded media 

C18, J58 

Printing and reproduction of recorded media; 
Publishing activities 

0.90 

0.95 

0.86 

0.69 

Manufacture  of 
coke, 
products and nuclear fuel 

refined  petroleum 

C19 

Manufacture of coke and refined petroleum products 

0.61 

0.81 

DG 

Manufacture of chemicals, chemical 
products and man-made fibres 

C20, C21 

Manufacture of chemicals and chemical products; 
Manufacture of basic pharmaceutical products and 
pharmaceutical preparations 

0.92 

0.96  

32  The  quality  of  data  within  the  NACE  Rev  2  sector  C12  (tobacco  products)  was  considered  too  poor  to  include  in  the  calculations,  while  the  aggregate  sector  (food,  beverages  and 
tobacco) also did not exist at all. So although there is not complete correspondence between the Rev 1.1 and Rev 2 classifications,  it was decided (because tobacco is a relatively 
small sector) that this would not greatly affect subsequent calculations. 

33 In the correspondence table the Nace Rev 2 relates to food, beverages and tobacco, and not just food and beverages. 

64 

 
 
                                           
Rev 1.1 

Sector Name 

Rev 2 

Sector Name 

DH 

Manufacture of rubber and plastic products 

C22 

Manufacture of rubber and plastic products 

DI 

Manufacture of other non-metallic mineral 
products 

DJ27 

Manufacture of basic metals 

Manufacture of fabricated metal products, 
except machinery and equipment 

C23 

C24 

C25 

Manufacture of other non-metallic mineral products 

Manufacture of basic metals 

Manufacture of fabricated metal products, except 
machinery and equipment 

Manufacture of machinery and equipment n.e.c. 

C28 

Manufacture of machinery and equipment n.e.c. 

Manufacture of electrical and optical equipment 

C26, C27 

Manufacture  of  computer,  electronic  and  optical  products; 
Manufacture of electrical equipment 

DJ28 

DK 

DL 

Share of 
Rev 1.1 in 
Rev 2 sector 

Share of 
Rev 2 in 
Rev 1.1 
sector 

0.83 

0.91 

0.88 

0.58 

0.71 

0.64 

0.84 

0.97 

0.97 

0.84 

0.88 

0.86 

DM34 

Manufacture of motor vehicles, trailers and 
semi-trailers 

C29 

Manufacture of motor vehicles, trailers and semi-
trailers 

0.70 

0.72  

DM35 

Manufacture of other transport equipment 

C30 

Manufacture of other transport equipment 

DN 

Manufacturing n.e.c. 

C31, C32, C33 

Manufacture  of  furniture;  Other  manufacturing;  Repair  and 
installation of machinery and equipment 

Sale, maintenance and repair of motor 
vehicles and motorcycles; retail sale of 
automotive fuel 

G50 

G45 

Wholesale and retail trade and repair of motor vehicles 
and motorcycles 

0.74 

0.71 

0.96 

0.86 

0.32 

1.00 

65 

 
 
Rev 1.1 

Sector Name 

Rev 2 

Sector Name 

G51 

G52 

H 

I60 

I61 

I62 

I63 

I64 

K70 

K73 

Wholesale trade and commission trade, 
except of motor vehicles and motorcycles 

G46 

Wholesale trade, except of motor vehicles and 
motorcycles 

Retail trade, except of motor vehicles and 
motorcycles; repair of personal and 
household goods 

Hotels and restaurants 

Land transport; transport via pipelines 

Water transport 

Air transport 

Supporting and auxiliary transport activities; 
activities of travel agencies 

G47 

I 

H49 

H50 

H51 

H52 

Retail trade, except of motor vehicles and motorcycles 

Accommodation and food service activities 

Land transport and transport via pipelines 

Water transport 

Air transport 

Warehousing and support activities for transportation 

Post and telecommunications 

H53, J61 

Postal and courier activities; Telecommunications 

Real estate activities 

Research and development 

L68 

M72 

Real estate activities 

Scientific research and development 

Share of 
Rev 1.1 in 
Rev 2 sector 

Share of 
Rev 2 in 
Rev 1.1 
sector 

0.97 

1.00 

0.91 

0.99 

1.00 

0.98 

0.93 

0.93 

0.62  

0.99 

0.48  

1.00 

1.00 

1.00 

1.00 

1.00 

0.81 

1.00 

1.00 

1.00 

66 

 
 
 
BvD combined 
with aggregate 
national-regional 
databases 

Accessing firm-
level databases  

Interpolation is therefore less ‘risky’ than extrapolation, because there are 
known/actual datapoints with which to anchor the data. This can take place 
using adding constraints across space (total NUTS2 should sum to NUTS1) or 
activity (sub-sectors must add to a Section total). 

With extrapolation, higher-level data can also be used, for example if NUTS2 
sector data are missing but NUTS1 or Member State level detail are available 
they can be used as adding-up constraints or guides for how development is 
occurring. As mentioned, however, given the purpose for which the data are to 
be used, care needs to be taken in how these methods are applied and 
sensitivity testing undertaken to see how the techniques might affect end 
results. 

A final point worth noting is that some of the filling techniques were required 
also at the reclassification stage. The reason for this is that the move from 
NACE Rev 2 to NACE Rev 1.1 only works if the sectors are complete, 
otherwise incomplete proportions will be reallocated and the resulting series 
will not match one another. 

The other option to consider is the use of firm-level databases, which can be 
aggregated to appropriate NUTS and NACE classifications. To avoid 
disclosure issues, proportional share can be calculated and applied to more 
aggregated / robust datasets and thus create employment levels.  

  For each Member State 

These exist for most Member States as business register databases which 
record information on firms liable for value-added taxation (and thus they 
exclude small enterprises but do cover the vast majority of the population of 
firms). Examples of these databases are the LISA database (the Netherlands), 
and for Germany through the Forschungsdatenzentrum (FDZ). However, the 
lengthy application process and general difficult in gaining access and 
required learning and processing time meant that this was not a viable option 
for the current project, although there have been attempts in the past to do this 
which could be explored as part of further research34. 

As mentioned previously however, the project team were able to assemble 
two complete Member States databases. Firstly, for the Netherlands, where 
an aggregated version of the LISA database has been provided by Prof Frank 
van Ort. Secondly for the UK, where Cambridge Econometrics have 
aggregated their detailed sector-local area database to NUTS2 classifications. 

  Pan-European 

The BvD database presents another possibility for remaining Member States. 
Some of the drawbacks (sample size, sector proportions) of the BvD database 
were examined earlier in the chapter. The remaining part of this section 
examines more closely the degree of correspondence of sector-region 

34  See, 

for  example,  the  MICRODYN  FP7  project:  http://www.case-research.eu/en/microdyn-
competitiveness-in-the-knowledge-based-economy.  The  project’s  own  website  (www.micro-dyn.eu)  is 
no longer operational. 

67 

 
 
 
 
 
                                           
Creating and 
testing sector 
proportions 

proportions, particularly for NL and UK where a reliable and complete data 
comparison exists (in the form of the LISA and CE local area database, 
respectively). 

In respect of calculating and applying sector-region proportions, there are two 
options: 

(i) 

(ii) 

Calculating sub-sector shares which can be applied the CE 
European Regional database, which provides a long time series of 
6-sector regional (NUTS2) data for all Member States. This means 
that for each of the 6 CE sectors (for each NUTS2 region), the sub-
sector proportions within this can be calculated and applied to 
create employment levels. 

As an alternative, for any detailed sub-sector at national level, the 
regional proportions can be calculated from the BvD data and 
applied to the national accounts data, again creating the necessary 
employment levels. 

Both techniques are valid but rely on different distributional properties of the 
BvD data, both across space, activity, and time – properties which can only be 
tested empirically. 

The data extracted from the BvD has been re-proportioned so that they 
represent, for each available year, the region-sector employment shares within 
each of the three aggregate sectors from the CE regional data. These are then 
the proportions which can be applied to create levels of sector-region 
employment over time. However, before this occurs, a further round of 
robustness testing has been undertaken to compare the sector-region shares 
with those for the Netherlands and the UK (using the LISA and CE local area 
data). 

  Robustness testing 

The following sub-section reports on robustness testing of the BvD database 
to check how employment proportions and trends seem when compared to the 
LISA and SBS databases. This has already been done previously at national 
level. Here, the comparison is at regional-sector level to see whether the same 
issues tend to occur. This is only possible for NL and UK, however, as these 
are the countries for which consistent and detailed region-sector data exist35. 

For the purposes of assessing the robustness of the BvD database, three 
years (199636, 2005, and 2015) were chosen to assess the degree and quality 
of coverage. In addition, subsectors within manufacturing (Section C), and 
services - Wholesale and Retail (Section G), Transport and Storage (Section 
H), Food and Accommodation (Section I) and Information and Communication 
(Section J) - are compared for similarity of employment shares, as it is this 
(rather than levels of employment per se) that are important for assessing 
robustness of the data source. Missing values (within the BvD, of which there 
are quite a few in the earlier periods) were not included in the calculations. 

35  The  SBS  was  considered  as  a  comparison  for  other  Member  States,  but  the  different 
sampling,  regional  and  sector  definitions  described  previously  would  make  this  a  highly 
uncertain exercise. 

36 Although the BvD extraction is from 1995, the LISA database begins in 1996. 

68 

 
 
                                           
Figures 3.8 and 3.9 summarise the position for the Netherlands and UK 
respectively, within individual subsectors categorized (but not named as there 
are too many in most circumstances). 

  NL comparison 

-  Within the manufacturing section, there is some evidence of grouping 

(whereby some BvD subsectors are consistently over or under-
represented compared to the LISA proportions). Often these are small 
sectors (such as Manufacturing of other products nec) but the largest 
differences are for the manufacture of Coke and petroleum products 
(which are likely to be located for particular reasons). The situation 
improves somewhat as the time period becomes more recent, with the 
2015 snapshot more centred around the 45-degree line than in earlier 
years, but the clustering largely remains. 

-  For services, the situation seems much improved. With fewer sub-
sectors the groupings are more evident but the clustering is more 
consistent and centred around the 45 degree line. There is clear 
improvement between the 1995 and 2005 period. 

-  For FBS, there is clear over-representation of Financial services 

(except insurance and pension funding) which was already apparent 
from the national accounts comparisons previously analysed. Over-
representation in one sector will naturally affect the proportions of other 
sub-sectors, although employment agencies seem to also be 
increasingly over-represented as time moves on (again, as highlighted 
in the previous national accounts charts). 

  UK comparison 

-  For manufacturing there are many missing values in the 1995 period, 
which results in a rather extreme picture in Figure 3.9 where the main 
sector with values is Pharmaceuticals. Rather worryingly, this situation 
does not seem to improve when moving to more recent time periods. 

-  A similar position is noted with the services sub-sectors, although there 
are fewer missing values. What seems to be happening is a broadly 
consistent proportion across the UK NUTS2 regions within the BvD 
data, which is then spread out when compared to the CE local data. 
This position improves a little over time, but the lines of subsectors are 
still very much evident. 

-  Within the FBS sector, more sub-sector lines are evident as with the 
services sector. This seems to be symptomatic of consistent shares, 
especially for Business Support Services which has the highest 
proportion within the BvD data, followed by the Finance & Insurance 
subsector. This situation does not seem to improve much over time, 
but rather become more entrenched. 

Creating and 
testing regional 
proportions 

In a process analogous to the sector proportion analysis, the data extracted 
from the BvD was re-proportioned for three sample years (1996, 2005, 2015) 
to create region shares of the most important (i.e. well-represented in 
sampling terms) sub-sectors for each of the three aggregate sectors from the 
CE regional data. These proportions are then compared again the equivalent 
calculations from the Netherlands and the UK (using the LISA and CE local 
area data, respectively). Figure 3.10 visualises the findings.  

69 

 
 
  NL comparison 

The results perform very well across all the sub-sectors (Manufacture of 
fabricated metal products, except machinery and equipment; Wholesale trade 
(no motor vehicles and motorcycles); and Financial institutions, except 
insurance and pension funding). Region proportions are close to the 45-
degree line and show little sign of change across the three sample years. 

3.4  Findings and conclusions 

Data quality and 
robustness 

National-sector 
export data 

The quality and robustness of the export data are considered very good and fit 
for purpose. 

National-sector- 
region 
employment data 

It is evident from the previous analysis that the combination of SBS databases 
and NSO supplementary information is insufficient to create a consistent 
database across all time period. There are too many missing data in the early 
years to undertake extrapolation without risking the quality of the database. 

In addition, the BvD data alone are not of sufficient robustness to deal with all 
Member States/regions/sectors/time periods due to sample sizes and 
coverage. 

An alternative strategy was therefore required. 

Moving forward 

Selected periods 
rather than a 
time series 

The combination of SBS-ONS data cover more Member States than the BVD 
data over a longer time period but do have issues on time coverage in early 
periods, as well as limitations on sector consistency and completeness. So 
although the SBS data cannot be used for all time periods, a selection of years 
and sectors could be made which are sufficient to undertake empirical 
analysis. It has been proposed that these years should be 2003, 2007, 2011, 
and 2015. 

Matching sectors 
across NACE 
revisions 

The process of matching sectors was discussed and, in particular, Table 3.7 
showed that 16 sectors met the correspondence matching criteria, with a 
further two transport-related sectors given further consideration for their 
importance and links to the EU integration process. 

Data completion 
method 

To fill-in any remaining gaps, the following ‘algorithm’ has been adopted: 

1.  For any given year (ie 2003, 2007, 2011 or 2015), Identify which 

sectors/regions the data need to be completed for. 

2.  Do the data exist through NSO sources? If so complete with this. 

3. 

4. 

If not, do the SBS data exist for adjacent years (eg 2002 or 2004 for 
the 2003 period)? 

If yes, test these regional proportions against equivalent BvD data. If 
proportions match well enough then use BvD proportions for year in 
question. If proportions don’t match then just use adjacent year shares 
from SBS. 

5. 

If data do not exist for adjacent years find closest year and repeat 3-5, 
except use a linear trend to calculate the appropriate SBS share. 

70 

 
 
 
 
 
Further round of 
sector trimming 

Final sample of 
Member States 

6. 

If there are no SBS data at the NUTS2 level, consider the option of 
moving up to NUTS1 level if these data exist, as a way of maintaining 
the sample of Member States as consistent as possible. 

7.  Always ensure that regional totals scale to the national. 

During this process of filling out, it became evident that additional sectors 
would need to be removed as there was either not sufficient data from the 
SBS and/or insufficient data from the BvD to fill in any remaining gaps. This 
was unfortunate, but it was felt that maintaining data quality was of paramount 
importance and otherwise there was a risk that the filling process might unduly 
influence the findings from subsequent sectoral and spatial analysis. The 
sectors in Table 3.7 are those that remain in the process37. 

Matching the definitions thus allows a sub-set of sectors to be analysed which 
mostly crossover between the two datasets. There is, however, one other 
complicating factor which is the coverage across Member States. From 
preceding analysis we know that Denmark does not have regional data 
publicly available before 2008, while for Slovenia no data exist before 2007. In 
addition, Estonia is only a single region.  

Therefore, if the SBS sample is to be restricted to sectors which match across 
the NACE revisions and which cover countries which exist for all four periods 
and have regional data, the sample of Member States covered would be 
narrowed to the final set of 13 countries: Austria, Belgium, France, Germany, 
Greece, Hungary, Italy, the Netherlands, Poland, Slovakia, Spain, Sweden, 
and the UK. Table 3.8 provides a % coverage of EU employment across each 
final selected sector. 

Exclusion or 
replacement of 
some smaller 
regions 

Some small regions were removed from the country totals as data were 
missing and it was not possible to fill them in from other sources. This was 
considered a better option than removing the entire country, particularly as this 
issue occurs mostly within Spain and France which are large Member States 
that are important for the analysis. Specifically: 

-  For all sectors, all FRA NUTS 2 regions (Départements d'Outre-Mer), 

and ES63 (Ceuta) and ES64 (Melilla) for Spain. 

Further, for two sectors, the NUTS2 data were replaced by NUTS1 aggregates 
to avoid their removal from the analysis (given the size/importance of the 
Member States concerned). 

-  For the Manufacture of basic metals, French regions (FR81, FR82, 

FR83: Languedoc-Roussillon, Provence-Alpes-Côte d'Azur, Corsica) 
were replaced by the FR8 aggregate. 

- 

For Manufacture of paper and paper products, AT11, AT12, AT13 (Burgenland, 
Niederösterreich, and Wien) were replaced by the AT1 aggregate, as were the FR81-
83 NUTS2 regions for France. 

37  In  particular,  it  should  be  noted  that  the  two  transport-related  sectors  highlighted 
previously in Table 3.2 did not make it through this data filling process. For both of these 
sectors,  missing  data  at  NUTS2  level  could  not  be  substituted  at  NUTS1  level  due  to 
further missing data for regions in France, Germany, and Greece.  

71 

 
 
                                           
Table 3.7: Final Sector Selection (sufficient NACE correspondence and with good country coverage) 

Rev 1.1 

Sector Name 

Rev 2 

Sector Name 

DA 

Manufacture of food products, beverages 
and tobacco 

C10, C11 

Manufacture of food products; Manufacture of 
beverages 

DD 

Manufacture of wood and wood products 

C16 

Manufacture of wood and of products of wood and 
cork, except furniture; manufacture of articles of 
straw and plaiting materials 

DE21 

DH 

DI 

Manufacture of pulp, paper and paper 
products 

C17 

Manufacture of paper and paper products 

Manufacture of rubber and plastic products 

C22 

Manufacture of rubber and plastic products 

Manufacture of other non-metallic mineral 
products 

DJ27 

Manufacture of basic metals 

Sale, maintenance and repair of motor 
vehicles and motorcycles; retail sale of 
automotive fuel 

Wholesale trade and commission trade, 
except of motor vehicles and motorcycles 

G50 

G51 

C23 

C24 

G45 

G46 

Manufacture of other non-metallic mineral products 

Manufacture of basic metals 

Wholesale and retail trade and repair of motor 
vehicles and motorcycles 

Wholesale trade, except of motor vehicles and 
motorcycles 

Share of Rev 1.1 
in Rev 2 sector 

Share of 
Rev 2 in 
Rev 1.1 
sector 

0.96 

0.89 

0.90 

0.83 

0.91 

0.88 

0.96 

0.99 

0.94 

0.95 

0.84 

0.97 

0.97 

1.00 

0.97 

1.00 

72 

 
 
Rev 1.1 

Sector Name 

Rev 2 

Sector Name 

Retail trade, except of motor vehicles and 
motorcycles; repair of personal and 
household goods 

Hotels and restaurants 

Land transport; transport via pipelines 

Research and development 

G47 

I 

H49 

M72 

G52 

H 

I60 

K73 

Retail trade, except of motor vehicles and motorcycles 

Accommodation and food service activities 

Land transport and transport via pipelines 

Scientific research and development 

0.91 

1.00 

0.98 

1.00 

Share of Rev 1.1 
in Rev 2 sector 

Share of 
Rev 2 in 
Rev 1.1 
sector 

0.99 

1.00 

1.00 

1.00 

73 

 
 
 
Table 3.8: EU Employment Coverage by Selected Sector 

NACE Rev 2 Sector 

Member 
Sample 
Share 
(%) 

State 
EU 
in  2015 

C10, C11 - Manufacture of food products; Manufacture of beverages 

C16 - Manufacture of wood and of products of wood and cork, except 
furniture; manufacture of articles of straw and plaiting materials 

C17 - Manufacture of paper and paper products 

C22 - Manufacture of rubber and plastic products 

C23 - Manufacture of other non-metallic mineral products 

C24 - Manufacture of basic metals 

G45  -  Wholesale  and  retail  trade  and  repair  of  motor  vehicles  and 
motorcycles 

G46 - Wholesale trade, except of motor vehicles and motorcycles 

G47 - Retail trade, except of motor vehicles and motorcycles 

I - Accommodation and food service activities 

H49 - Land transport and transport via pipelines 

M72 - Scientific research and development 

82 

71 

84 

83 

80 

8538 

85 

84 

86 

88 

81 

89 

Databases for 
assessment 

The result of the work described above is a dataset contained in an Excel 
database accompanying this report. The data are as follows: 

National-sector 
exports 

This excel files are largely a re-configuration of information extracted from the 
STAN and WIOD databases, with some limited treatment of missing data. 
Following an introductory page, the data are sorted by country tabs, with each 
country having sector x time information. Along with export values (in $), 
national export shares are also included to allow for consistency checking. 

Some minimal filling in of data has been necessary, and two flags have been 
put alongside the data to identify this: 

  a: no data available; 

  b: missing data have been replaced with zeros, as in the vast majority of 

cases this is most likely the case. 

National-region-
sector 

employment   

For these data, recorded as both levels and national shares, a more elaborate 
flag system has been devised to reflect the fact that the original SBS data 
were not as good in terms of coverage and alternative sources and techniques 
were required to arrive at a complete set of data. 

  a: LISA (NL) or CE (UK) data shares used; 

38 There was no EU total for this sector in 2015, so the proportion for 2011 is shown instead. 

74 

 
 
 
                                           
  b: filled with SBS share interpolated from adjacent periods; 

  c: filled with BvD share; 

  d: filled by subtracting the total of all other regions from the national total; 

  e: no NUTS 2 data available, substitute with NUTS 1 data. 

In both the export and employment data, no flag implies original data.K 

75 

 
 
 
76 

 1996 2005 2015  
 
 
77 

 1996 2005 2015  
 
 
78 

Manufacturing sub-sector Services sub-sector 1996 2005 2015 FBS sub-sector NL UK  
 
 
 
  UK comparison 

Here the results are not so promising. For all three sub-sectors 
(Pharmaceuticals; Land transport; and Business support services) the 
proportions do not seem comparable and this casts serious doubt on the 
usefulness of the regional proportions application method. 

Written by Cambridge Econometrics 

79 

      
 
 
 
 
 
4 

Sector Specialisation and Geographical 
Concentration 

4.1  Introduction 

This chapter revisits the implications from economic theory of how economic 
integration could affect sector specialisation and geographical concentration, 
while also summarising the findings from the literature which have already 
looked at this issue (particularly across Europe). It reports on the 
measurement of sector specialisation, namely the Balassa indices of revealed 
comparative advantage and the Theil indices. Findings are presented and 
discussed in the context of the theory and previous literature. Finally, it reports 
on the measurement of geographical concentration, namely through the Theil 
indices discussed in the theoretical report which are the counterpart of the 
sector specialisation measures. Findings are presented and discussed in the 
context of the theory and previous literature. 

4.2  Implications from Theory and Findings of Previous 

Studies 

According to traditional trade theory, nations – and regions – will tend to 
specialise in those industries and sectors in which they have a comparative 
advantage. The latter may be because of certain raw materials and natural 
resources, because of cheaper labour, or some technical advantage. Trade 
arises because of the different comparative advantages of nations and 
regions, and in turn tends to reinforce the patterns of export specialisation 
among nations and regions according to those comparative advantages. In 
new trade theory, it is argued that a key source of a nation’s comparative 
advantage in any particular sector is the increasing returns (external 
economies) that accrue when that sector is spatially agglomerated: or put 
another way, when that sector is geographically concentrated in one or just a 
few locations within the country concerned.  As we showed in the 
methodological section (Chapter 2), different combinations of spatial 
concentration, agglomeration and speciation are possible among a group of 
multi-region nations. 

The key point is that the assumption is that, other things being equal, the 
removal or lessening of barriers to trade among countries should promote 
greater specialisation among those countries and among their regions. This 
was the argument advanced in relation to the European Union by Paul 
Krugman in his fusion of new trade theory and the New Economic Geography. 
According to the New Economic Geography, the spatial distribution of 
economic activity is the outcome of a tension between spatial agglomeration 
forces on the one hand, and spatial dispersion forces on the other, with both 
dependent on the level of trade costs. In most NEG models the dispersion 
force is assumed to be stronger than agglomeration forces when trade is 
closed: as trade openness increases, the dispersion force is weakened 
relative to the agglomeration force.  Under this theory, therefore, removal of 

Written by Cambridge Econometrics 

80 

Re-cap on theory 
and implications  

Traditional trade 
theory 

New Economic 
Geography 

      
 
 
 
 
trade barriers and the lowering of trade costs should lead both to the 
geographical concentration of economic activity between regions, and as part 
of this process to increased regional specialisation, reflecting the inherent (and 
initial) comparative advantage of different regions in different industries. 
Following this line of reasoning, Krugman’s hypothesis was that as the EU 
became increasingly integrated, so it would be characterised by greater 
regional specialisation. However, at the same time, increased regional 
specialisation across the EU would expose regions to increased region-
specific (idiosyncratic) shocks. Further, given increased specialisation and 
region-specific, increased factor mobility - also aided by the integration 
process - both labour and capital are likely to move to and concentrate in 
those regions in which economic activity is already concentrated, 
agglomeration economies are greater, and productivity is higher. As a result, 
integration could possibly lead to greater disparity in regional growth rates 
across the EU.  

From a policy perspective, therefore, regional specialisation is something of a 
double-edged sword. On the one hand, increased specialisation can promote 
faster growth because of the economies of localisation and therefore might be 
focus for policy intervention. But on the other hand, following Krugman’s 
argument, increased regional specialisation could ultimately widen regional 
growth disparities.  Indeed, in the vast literature in economic geography and 
regional development studies there has long been a debate over the relative 
merits and demerits of regional economic specialisation versus regional 
diversification. The evidence on the matter is best described as equivocal.  In 
terms of the EU case, a basic hypothesis from both traditional trade theory 
and the New Economic Geography variants is that as integration of the EU 
has progressed, so we should expect to find that both the member states and 
the regions within them have become more sectorally specialised. We might 
also hypothesise that this process is likely to have been faster among the 
more recent Eastern European member states that have joined the EU in 
recent years, since the core member states have been integrated into a single 
market for some time. 

The above line of reasoning is based on a sectoral view of the economy and 
of trade. But, as we also discussed in the methodology report, over the past 
few decades, the creation and expansion of global production networks and 
supply chains has recast the nature of trade, away from sectors to functions or 
stages in the production process. Advances in technology have driven a mode 
of production in which the different stages that make up a product, such as a 
motor vehicle, can be functionally separated and potentially carried out at 
different locations. Thus, the issue of functional specialisation and its role in 
trade has attracted attention. In this perspective, countries and regions will 
tend to specialise in that function, or those functions, in which they have a 
comparative advantage. Using a broad fourfold division of functions into R&D, 
management, marketing and fabrication, and using the Balassa export 
specialisation index, Timmer et al (2018) find some evidence of functional 
specialisation across countries. For example, high income countries (such as 
Germany, France, UK) have higher specialisation in R&D, while lower income 
countries (such as China, Brazil, Mexico, Poland) tend to specialise more in 
fabrication.  This obviously has different policy implications than those founded 
on specialisation by sector.  Timmer et al also find, for the case of China, 

Written by Cambridge Econometrics 

81 

Functional 
versus sectoral 
views on trade 

      
 
 
Review of 
empirical studies 

Country-level 
studies 

evidence that the provinces there also tend to specialise by function.   An 
interesting question, then, is if increasing EU integration leads to greater 
regional functional specialisation. To answer this question, however, would 
require detailed data on the occupational composition of export activities, 
which at present are not available for the European regions. But this approach 
to trade and regional specialisation is one that would certainly reward future 
research. 

As summarised in Chapter 2, both the theory and the empirical evidence on 
whether and to what extent increasing economic integration across the EU 
increases the degree of sectoral specialisation among the Member States and 
their constituent regions, is not unequivocal.  According to traditional trade 
theories, national and regional specialisation will reflect differences in 
comparative advantage, whereas new trade theories suggest that national and 
regional specialisation will be driven by scale economies. In an early paper 
and using a combination of new trade theory and ‘new economic geography’ 
theory, Krugman (1993) suggested that, as the EU became more integrated 
over time, it would become more akin to the United States, i.e. a more 
integrated economy where, he argued, there is a higher degree of regional 
specialisation. In a later commentary however (Krugman, 2006), he took a 
different view, arguing that in the advanced economies, the era of increasing 
regional specialisation and geographical concentration of industry had passed, 
in part because the pull of agglomeration and other such external economies 
and increasing returns effects had become less pronounced, at least for 
manufacturing activity.  

Several empirical papers have assessed whether specialisation has increased 
across the EU over recent decades, though much of this work refers to the 
1980s and 1990s and is also at national level.  Sapir (1996), for example, uses 
the Herfindahl index to measure country specialisation across 100 
manufacturing industries and concluded that specialisation remained constant 
over the 1977-1992 period in Germany, Italy and the UK, but increased in 
France after the mid-1980s. Amiti (1999) used both EUROSTAT data (for 65 
manufacturing industries) for Belgium, France, Germany Italy and the UK, and 
UNIDO data (for 27 manufacturing industries) for the 10 European countries of 
Belgium, Denmark, France, Germany, Greece, Italy, the Netherlands, 
Portugal, Spain and the UK over 1968-1990. For both analyses, and using the 
Gini coefficient as her measure of specialisation, she found that specialisation 
had increased for most of the countries in her samples.  In his study of 32 
manufacturing industries across 13 Member States over the 1972-1996 
period, and using a variety of measures, Brülhart (2001) found that national 
employment specialisation had generally increased, not only in traditional 
resource and labour intensive sectors, but also in technology-intensive 
industries. However, he also found that specialisation in exports, though 
higher than in employment, fell over his study period. In other words, while 
countries appeared to have become more specialised in terms of the sectorial 
distribution of their manufacturing employment, at the same time they had 
become more diversified in terms of their manufacturing exports. 

Sub-national 
studies 

Unlike the above studies, all of which (mainly for data reasons) were confined 
to analyses of national level specialisation, Cutrini (2010) used a new version 
of the Theil Index and Eurostat’s Region-SBS (Structural Business Statistics) 

Written by Cambridge Econometrics 

82 

      
 
 
Balassa Index of 
‘Revealed 
Comparative 
Advantage’ 

data to examine trends in both specialisation and concentration of 
manufacturing, covering 12 sectors, across some 145 NUTS2 regions for 10 
European countries, at three points in time, 1,85,1993 and 2001. Overall, she 
found a decline in the spatial concentration (geographical localisation) of 
almost all of the 12 manufacturing activities over these years, and, at the 
same time, a fall in regional specialisation in almost all countries. In short, her 
analyses suggest a process of regional de-agglomeration and regional de-
specialisation of manufacturing activity, and thus run somewhat counter to the 
cross-national findings identified by the above previous studies.   

4.3  Results from measures of sector specialisation 

It will be recalled from Chapter 2 that a standard way of measuring the 
comparative advantage of a country, in terms of its success in international 
markets, is by assuming this is ‘revealed’ by the country’s relative 
specialisation in particular exports. This assumption follows Ricardo’s ‘law’ or 
principle of comparative advantage which holds that under conditions of free 
trade an economic agent (such as a nation or a region) will produce more of a 
good (or service) for which it has an advantage relative to other nations 
(regions) arising from particular factor endowments and/or technical knowhow 
that enable the nation (region) to produce that good (or service) more 
efficiently (at a lower price) or more effectively (at a higher quality).  Thus, 
assuming the law or principle holds, and that international trade is indeed free, 
then nations (and indeed regions) will tend to specialise in those sectors in 
which they have a trading advantage compared to other nations (regions). 
Hence, actual observed patterns of national (and regional) sectoral 
specialisation are assumed to reflect or ‘reveal’ (ex post) each nation’s (and 
region’s) comparative advantage. 

Measuring national (or regional) revealed comparative advantage is usually 
done using the index developed by Balassa (1965), defined as: 

𝐵𝐴𝐿𝐴𝑆𝑆𝐴𝑘𝑗 = 𝑅𝐶𝐴𝑘𝑗 = (

𝑋𝑘𝑗
∑ 𝑋𝑗 𝑘𝑗

⁄

∑ 𝑋𝑘𝑗
𝑘
∑ ∑ 𝑋𝑘𝑗
𝑗

𝑘

)  

where 𝑋𝑘𝑗 is the value of exports by sector k from country (or region) j.  To the 
extent that the European Union has become increasingly integrated over 
recent decades, and internally has increasingly moved towards a free trade 
area, then, other things being equal, member states and their regions might be 
expected to have become increasingly specialised in those sectors of 
economic activity in which they have a comparative advantage. As discussed, 
if export data were available for EU regions, the index could be used to 
measure each region’s revealed comparative advantage, i.e. its specialisation 
in particular export activities, and how this has changed over time. 

The Balassa index has the main advantage that it is relatively straightforward 
to calculate (simply requiring export data) and it is also a relative index which 
is consistent with the concept of comparative advantage. Certain drawbacks 

Written by Cambridge Econometrics 

83 

      
 
 
 
 
Recap on 
available data 

Empirical 
findings 

Findings across 
countries 

should be borne in mind when interpreting results, however. For example, the 
index cannot account for import trade flows, particularly in cases of re-exports 
where a country both exports and imports large quantities of a good, perhaps 
by being integrated in an international value chain (and so the actual value 
added may be low). A related distortion is that the effects of vertical 
specialisation (where different parts of the value chain are separated and 
located in different countries) cannot be captured. Recent work such as 
Gnidchenko and Salnikov (2015) explore these drawbacks in more detail and 
suggest alternative indices. 

Since export data are not available across EU regions in anything like a 
comprehensive or consistent way, our analysis here is for the EU member 
states only. Two data sets are used, the World Input Output Database (WIOD) 
covering the period 2000-1439, and the Structural Analysis Database (STAN) 
compiled by the OECD, which covers the period 1995-2016. The WIOD 
database covers 64 sectors, although for the analysis this was reduced to 
5540, and the STAN database, which excludes marketable services, covers 33 
sectors. Thus, while the WIOD data have a better sectoral coverage, the 
STAN data provide an analysis over a longer time period.  For this reason, 
both data sets have been used. 

The detailed matrix of Balassa indices for each sector by each Member State 
using these two data sets are shown as ‘heat maps’ in Appendix A. To assist 
in the detection of trends over time in revealed comparative advantage 
(specialisation), Figures 4.1-4.2 report the average indices for countries, and 
Figures 4.3-4.4 report the change in the spread (standard deviation) around 
the average Figures 4.3 and 4.4 look at the same changes across sectors for 
the start and end years stated above and Figures 4.5 and 4.6 provide the 
equivalent standard deviation changes. 

Some key features emerge from Figures 4.1 and 4.2.  

Firstly, export specialisation declined in the majority of EU Member States in 
both dataset periods, with the decline being especially evident in many of the 
more-recently acceded Member States (in particular Estonia, Croatia, Latvia, 
Bulgaria and Cyprus in the STAN data and Cyprus, Poland, Latvia, and the 
Czech Republic in the more narrowly-defined WIOD data).  

By comparing the heat maps across the beginning and end periods in 
Appendix A, the larger declines in the WIOD data are caused by reductions in 
the relative export shares in forestry and fishing products, and also in the 
related sector of wood and wood products (Estonia and Latvia) but also in 
some public services (particularly public administration and defence, and 
education). 

39  Another  WIOD  database  also  exists  for  1995-2011,  but  this  is  on  a  different  sectoral 

classification.  

40 Certain  WIOD  sectors  were excluded  from  the  subsequent  analysis  as  their Balassa  values 
were considered too erratic/unstable, which is often the case with very low export value 
sectors.  A  specific  rule  was  adopted  whereby  sectors  with  an  average  share  in  total  EU 
exports  over  the  2000-14  period  below  0.1%  were  removed.  This  led  to  the  following 
sectors being taken out: CPA_E36 - Natural water; water treatment and supply services; 
CPA_Q86  -  Human  health  services;  CPA_Q87_Q88  -  Social  work  services;  CPA_R93  - 
Sporting services and amusement and recreation services; CPA_S94 - Services furnished 
by membership organisations; CPA_S95 - Repair services of computers and personal and 
household goods; CPA_S96 - Other personal services; CPA_T - Services of households as 
employers; undifferentiated goods and services produced by households for own use; and 
CPA_U - Services provided by extraterritorial organisations and bodies. 

Written by Cambridge Econometrics 

84 

      
 
 
                                           
Figure 4.1: Balassa Index Average across EU Member States based on WIOD data 

Note:   Balassa Index is calculated as a simple arithmetic average of country-sector export 

shares relative to EU27 total exports of goods and services.  

Source:  WIOD export data, CE calculations 

Although there are a few Member States where the Balassa averages 
increase, by far the most notable case is that of Malta, due (for the WIOD 
data) to the increase in the sector ‘Creative, arts and entertainment services; 
library, archive, museum and other cultural services; gambling and betting 
services’, which covers Malta’s rapidly expanding on-line gaming sector, and 
also in Printing and recording services.  

In the STAN data, the increase is mostly driven by increased revealed 
comparative advantage in Fishing and aquaculture, Printing and reproduction 
of recorded media, and Coke and refined petroleum products.  

The spread of Balassa indices across the broader set of WIOD sectors, as 
shown in Figure 4.3, also shows some notable declines41 among the more 
recently-acceded Member States, with some exceptions (Malta), while Ireland 
and Slovakia also show large increases. For the tradeable goods sectors in 
the STAN dataset, the decreases are more uniform although Malta and 
Belgium stand out as having increased over the 1995-2016 period. 

41 Croatia is not shown as it’s Balassa standard deviation decreases from 62.4 to 6.9 

Written by Cambridge Econometrics 

85 

      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
Figure 4.2: Balassa Index Average across EU Member States based on STAN data 

Note:   All sectors included, Balassa Index is calculated as a simple arithmetic average. 
Source:  STAN export data, CE calculations 

Figure 4.3: Balassa Index Standard Deviation across EU Sectors based on WIOD data 

Note:   EU27 Member States included in averages (Croatia is excluded) 
Source:  WIOD export data, CE calculations 

Written by Cambridge Econometrics 

86 

      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The changing pattern across Member States, rather than sectors, is shown in 
Figures 4.5 and 4.6, as represented by the standard deviation (the average of 
the Balassa has no meaning when averaged across Member States). On 
these terms, sectoral specialisations in raw materials and goods across the 
EU, traditional sectors such as forestry, wood products, and apparel have all 
become less geographically concentrated between Member States, as have 
many public services such as health and education. 

A few sectors show a marked rise in geographical concentration, including the 
creative, arts and education (due mostly to the rise in Malta, as mentioned 
previously) but also printing and recording, and real estate services and 
energy supply. 

Figure 4.4: Balassa Index Standard Deviation across EU Sectors based on STAN data 

Note:   EU27 Member States included in averages (Croatia is excluded) 
Source:  STAN export data, CE calculations 

From the tradeable goods focus of the STAN data, extractive sectors such as 
most of the mining and quarrying sub-sectors have declined along with more 
traditional manufacturing sectors such as tobacco products, wood products 
and apparel. Among those showing an increase in concentration are fishing 
and aquaculture, other mining and quarrying, and coke and petroleum 
products. 

Written by Cambridge Econometrics 

87 

      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 4.5: Balassa Index Standard Deviation across EU Sectors based on WIOD data 

Note:   All EU28 Member States included in averages 
Source:  WIOD export data, CE calculations 

 Figure 4.6: Balassa Index Standard Deviation across EU Sectors based on STAN data 

Note:   All EU28 Member States included in averages 
Source:  STAN export data, CE calculations 

Written by Cambridge Econometrics 

88 

      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Analysis over 
time (by EU 
accession) 

A final way of looking at the Balassa indices is their evolution over time. 
Although a comparison has already been done for the beginning and end 
periods of the data, looking in more detail at the time series is interesting from 
the perspective of when different Member States acceded to the European 
Union, and whether any shift in specialisation patterns can be observed / 
linked to this. Broadly speaking, three cohorts can be identified among 
Member States: 

(i) 

Those which are members for the whole time period: Austria, 
Belgium, Denmark, Finland, France, Germany, Greece, Ireland, 
Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and 
the UK. 

(ii) 

Those joining in 2004: the Czech Republic, Cyprus, Estonia, 
Hungary, Latvia, Lithuania, Malta, Poland, Slovenia, and Slovakia. 

(iii) 

Those joining in 2007: Bulgaria and Romania42. 

Figure 4.7 (a-c) shows the movement of the Balassa Index over time for these 
three cohorts, focussing on the STAN (tradeable goods) database, while 
Figure 4.8 (a-c) uses the WIOD data (all sectors) 

Figure 4.7a: Balassa Index across EU Member States – Cohort 1 (STAN) 

Source:  STAN export data, CE calculations 

42 Croatia, although not joining until 2013, is also included in this group. 

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Figure 4.7b: Balassa Index across EU Member States – Cohort 2 (STAN) 

Source:  STAN export data, CE calculations 

Figure 4.7c: Balassa Index across EU Member States – Cohort 3 (STAN) 

Source:  STAN export data, CE calculations 

Among the core (EU15) group of Member States, there is a general stability 
with the exception of Greece and Portugal, for whom the index is quite high 
and volatile in comparison, while Belgium and Sweden show a marked 
increase (from 2008 and 2013 respectively).  

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Figure 4.8a: Balassa Index across EU Member States – Cohort 1 (WIOD) 

Source:  WIOD export data, CE calculations 

Figure 4.8b: Balassa Index across EU Member States – Cohort 2 (WIOD) 

Source:  WIOD export data, CE calculations 

The 2004 cohort mostly show a decline over time, which is particularly rapid in 
Cyprus and Poland. Malta is the exception, but its rising trend started before 

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accession (around 2000). In the final cohort both Bulgaria and Romania show 
some decrease in the run-up to accession but, with the former reversing this 
pattern slightly in the following years. 

Figure 4.8c: Balassa Index across EU Member States – Cohort 3 (WIOD) 

Theil Indices 

Source:  STAN export data, CE calculations 

For the WIOD data, among the first cohort of EU15 Member States, again 
there is mostly stability with the exception of the UK which is rising throughout 
the period, Greece which is mostly on a downward trend, and Belgium which 
shows a shift up from 2007 onwards. In the 2004 accession cohort there is 
more movement, with two groups identified – a lower value Balassa group of 
Hungary, the Czech Republic, Slovakia43, Slovenia, Poland and Lithuania; and 
a higher value group comprising Malta, Estonia, Latvia, and Cyprus which 
(with the exception of Malta which shows increasing specialisation) all tend to 
decline over time. Among the final cohort there is a decline in Bulgaria up to 
2007 and then little change thereafter, while Romania is relatively stable 
throughout. 

As discussed in Chapter 2, although several (interrelated) measures of 
regional sectoral specialisation have been used in the academic literature, one 
of the most common is the entropy-based Theil Index. As Cutrini (2006, 2009, 
2010) has shown, this particular index has the advantage that it is 
decomposable, and has been the preferred method used in her analyses of 
regional specialisation across the European Union (as discussed above).   

Here we follow Cutrini (2010) and decompose the sectoral (industrial) 
specialisation of any specific region relative to the  European Union as a 
whole into two parts: the specialisation of that region in the sector concerned 
relative to the country in which the region is located – the ‘within-country’ 
component; and the specialisation of that country relative to the European 
Union as a whole – the ‘between-country’ component. Using the notation set 
out in Chapter 2, we define three ‘raw’ indices:   

First, the specialisation of region j in country i relative to European Union, 
given by 

43  Slovakia  shows  a  marked  increase  in  2012  due  to  a  large  value  in  Real  Estate  Services, 

which prior to this had been zero throughout. So this may be a spurious result. 

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𝑁
𝑇𝑖𝑗 =   ∑
𝑘=1

𝐸𝑖𝑗𝑘
𝐸𝑖𝑗

𝑙𝑛 (

𝐸𝑖𝑗𝑘 𝐸𝑖𝑗⁄
𝐸𝑘 𝐸⁄

) 

Second, the specialisation of region j in country i relative to that country - the 
‘within-country’ component, given by 
𝑁
𝑊𝑖𝑡ℎ𝑖𝑛 =   ∑
𝑘=1

𝐸𝑖𝑗𝑘 𝐸𝑖𝑗⁄
𝐸𝑖𝑘 𝐸𝑖⁄

𝐸𝑖𝑗𝑘
𝐸𝑖𝑗

𝑙𝑛 (

𝑇𝑖𝑗

) 

And third, the national specialisation of country i relative to the European 
Union – the ‘between-country’ component - given by  

𝑁
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 =   ∑
𝑘=1

𝑇𝑖

𝐸𝑖𝑘
𝐸𝑖

𝑙𝑛 (

𝐸𝑖𝑘 𝐸𝑖⁄
𝐸𝑘 𝐸⁄

) 

Each of these can be mapped, for different points in time, although particular 
interest focuses on the first index. 

𝐵𝑒𝑡𝑤𝑒𝑒𝑛, can be envisaged as a residual of the average regional specialisation 

As Cutrini (2010) points out, the national specialisation relative to Europe, 
𝑇𝑖
in country i relative to Europe once the divergence of regional industrial 
structures within that country have been accounted for.  Thus, if we define  

𝑁

𝑎𝑅𝑆𝑖 =   ∑ 𝑇𝑖𝑗

𝑗=1

𝐸𝑖𝑗
𝐸𝑖

as the average regional specialisation of all the regions of country i relative to 
the European industrial structure, and  

𝑎𝑅𝑆𝑖

𝑊𝑖𝑡ℎ𝑖𝑛 =  ∑ 𝑇𝑖𝑗

𝑁

𝑊𝑖𝑡ℎ𝑖𝑛 𝐸𝑖𝑗
𝐸𝑖

𝑗=1
as the average regional specialisation of all the regions within country i relative 
to that country’s industrial structure, then the average regional specialisation 
of a country relative to Europe, 𝑎𝑅𝑆𝑖, is composed of two elements: the within 
country component and the country bias. Both of these average indices can 
be graphed to indicate trends over time.  Finally, it follows that 

𝑎𝑅𝑆𝑖 =   𝑎𝑅𝑆𝑖

𝑊𝑖𝑡ℎ𝑖𝑛 + 𝑇𝑖

𝐵𝑒𝑡𝑤𝑒𝑒𝑛 

Re-cap on 
available data 

The database described Chapter 3 was modified in the light of the 
specialisation analysis. The reason is because the Theil index is a relative 
measure, and so any findings from the analysis must be made relative to a 
meaningful benchmark (the denominator). The theoretical construct of the 
Theil index described in the equations above has the all-sector EU total as the 
benchmark, but when analysing across sectors and regions the data simply 
are not available for a full picture. Furthermore, the collection of sectors 
gathered together in the initial database comprised a mix of manufacturing 

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and services which, added together, do not add up to anything meaningful to 
compare against. 

To find a way forward, attention has focussed on the manufacturing sector as 
the most likely benchmark for which employment data would be available 
across sectors and regions. This links closely to the work of Cutrini (2010), 
which also focussed on the manufacturing sector for the very same reason. 
Using the filling-out methods described in Chapter 3, the following dataset was 
constructed for the empirical analysis: 

  Time periods: 2003, 2007, 2011, 2015 

  Sectors: 2-digit disaggregation is available. For 2003 and 2007, the NACE 
1.1 disaggregation was used44, while for 2011 and 2015 NACE 2 codes 
were used45. Appendix A contains a full list of sectors. 

  Member States: NUTS2 level46 for AT, BE, DE, EL, ES, FR, HU, IT, NL, 

PL, PT, SE, SK, and UK. 

In terms of coverage, i.e. the proportion of total EU manufacturing 
employment, the available data accounts for 78% in 2003/07, and 64% in 
2011/15. The lower proportion for the NACE Rev2 dataset occurs due to the 
additional sectors that were omitted for this dataset. 

Empirical 
findings  

This section presents the results of the new Theil statistics and puts them in 
the context of those produced by Cutrini (2010). 

 

Initial findings and comparability of results 

Figure 4.9 shows the initial findings for the overall Theil statistic across all the 
Member States. Clearly there is step change between the two sets of periods 
that needs to be resolved because the results are likely to be sensitive to the 
sample being used, i.e. both the sectoral disaggregation and number of 
Member States covered by the dataset. This means there are some issues to 
contend with in how much it is possible to compare the three different sets of 
results: 

(i) 

(ii) 

Cutrini’s (2010) results (for 1985, 1993 and 2001) which are based 
on NACE1 sectors and a limited number of Member States (BE/LX, 
DE, EL, ES, FI, FR, IT, NL, and the UK); 

The new set of NACE1 results (for 2004 and 2007) which are 
based on the same sectors as Cutrini (2010) but which have an 
expanded set of Member States (see above); 

(iii) 

The new set of NACE2 results (for 2011 and 2015) which have a 
different sectoral disaggregation but the same countries as in (ii). 

44 For NACE Rev 1.1, DC - Manufacture of leather and leather products and DF - Manufacture 
of  coke,  refined  petroleum  products  and  nuclear  fuel  were  excluded  from  the  database 
because there were too many missing observations that could not be reliably filled. 

45  Similarly,  the  following  sectors  were  excluded  from  NACE  Rev  2  due  to  lack  of  data 
availability:  C12  -  Manufacture  of  tobacco  products;  C15  -  Manufacture  of  leather  and 
related  products;  C19  -  Manufacture  of  coke  and  refined  petroleum  products;  C21  - 
Manufacture of basic pharmaceutical products and pharmaceutical preparations, and C30 
- Manufacture of other transport equipment. 

46  NUTS2  regions  were  used  wherever  possible.  However,  data  limitations  led  to  some 
restrictions.  The  following  NUTS  1  are  used  instead  of  corresponding  NUTS  2  regions: 
UKM  (for  2003  only),  ES6  (all  years),  FR8  (all  years),  DE7,  DE9,  DEA,  DEB,  DED  (all 
years),  AT1  (all years). In  addition,  some  regions were  excluded from  the  database  due 
to lack of data, and that they are also non-mainland regions: FR9, PT2 and PT3. 

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To make sense of the results, a bridging exercise was undertaken to establish 
a link between (i) and (ii), and (ii) and (iii) – otherwise it would not be possible 
to know whether changes between these results were being driven by the 
sample make-up or the actual process of specialisation. 

Figure 4.9: Overall Theil Statistic Results – combined NACE1 and NACE2 samples 

Source:  CE calculations 

The bridge between (i) and (ii) was established by reducing the sample of 
Member States in the new set of results to match Cutrini (2010) as closely as 
possible. The main difference was that Cutrini’s results included Luxembourg 
and Finland, which are not covered in the update, but as they are relatively 
small Member States it is hoped that this will not unduly influence the 
comparability. 

The bridge between (ii) and (iii) was established by using detailed results for 
NL from the LISA database, which allows the NACE2 results to be pushed 
back across all four periods, and thus any shift in the Theil results for NL 
(between (ii) and (iii)) should thus be mostly attributable to the change in 
sectoral disaggregation. If reliable, this shift factor could then be applied to the 
other Member States to enable a comparison. 

  Cutrini (2010) compared with new data with restricted Member State 

sample 

Figures 4.10a-c show the original Cutrini results alongside the two extra years 
of results added on using the restricted sample. 

In general, the bridging approach seems to be successful. The additional 
periods also seem to confirm Cutrini’s finding that manufacturing specialisation 
(i.e. degree of dis-similarity relative to the EU benchmark) has been on a 
decreasing trend, or at best mostly stable, in all the Member States in the 
sample. Given that all the Member States in the sample are relatively well-
established EU members, for which integration within the manufacturing 

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sector is fairly well established with little or no regulatory changes occurring, 
this is perhaps not surprising. 

Figure 4.10a: Overall Theil Index – restricted (Cutrini, 2010) sample 

Source:  CE calculations 

Figure 4.10b: Between Country Theil Index – restricted (Cutrini, 2010) sample 

Source:  CE calculations 

Another way of interpreting the findings in Figures 4.10a-c is that 
manufacturing structures across Member States have converged, that is 
become more similar (though Greece is still more specialised than other 
states) (Figure 4.7b). This is not overly surprising, perhaps, given that 
manufacturing employment has been steadily declining across Europe since 
the early-1980s. That decline has lessened in recent years, and has probably 
now more or less stabilised. Hence further major reductions in the 
specialisation of member states is probably not likely. 

A somewhat similar story is evident with respect to the degree of 
specialisation across regions within member states (Figure 4.10c). In almost 
all member states, regions have become less specialised (more similar) in 
terms of their manufacturing structures (Figure 4.10c).  

Figure 4.10c: Within Country Theil Index – restricted (Cutrini, 2010) sample 

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  NL NACE2 results for 2003 and 2007 compared to NACE1 results over full 

Member State sample 

The NL results for NACE2 for all four periods, compared with those using the 
SBS data (NACE1 and NACE2) are shown in Figures 4.11a-c. 

Figure 4.11a: Overall Theil Index – LISA versus SBS data 

Source:  CE calculations 

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Figure 4.11b: Between Country Theil Index – LISA versus SBS data 

Source:  CE calculations 

Figure 4.11c: Within Country Theil Index – LISA versus SBS data 

Source:  CE calculations 

Even with this approach there are differences for the 2011 and 2015 periods, 
and these are likely to be due to two factors. Firstly, the EU benchmark, 
created by SBS totals in the original series and EU national accounts for the 
LISA version (this was required because the SBS totals did not extend back to 
all periods). Secondly, there was a difference with the sector disaggregation 
as the EU national accounts data provide aggregations in some cases (e.g. 
food, beverages and tobacco are grouped as one activity). These two 
differences are likely to cause a shift for the latter two periods, but beyond this 
the additional shift for the 2004 and 2007 periods could be caused by the shift 
to the NACE1 disaggregation. 

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Figure 4.12a: ‘Adjusted’ Overall Theil Statistic Results 

Source:  CE calculations 

Figure 4.12b: ‘Adjusted’ Between Country Theil Statistic Results 

Source:  CE calculations 

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The findings for NL on the LISA data show, as with the extended Cutrini 
results, a pattern of broad stability / gradual decline across all the Theil 
components. By adjusting the other Member States by the ‘shift factor’ 
between the two lines in Figures 4.11a-c, the resulting patterns are shown in 
Figures 4.12a-c. These findings should obviously be interpreted with a degree 
of uncertainty / caution attached to them, as they rely on an adjustment factor 
derived from the NL data and there is no way of knowing how far this 
adjustment holds across other Member States. Furthermore, the application of 
the multiplicative adjustment factor means that the Theil statistics no longer 
add up.  

However, with these caveats in mind, the findings do, again, generally indicate 
a pattern of declining / stable specialisation patterns, with the exception of the 
between-country Theil statistic for Greece and Portugal which rises strongly 
between 2011-2015. 

Figure 4.12c: ‘Adjusted’ Within Country Theil Statistic Results 

Source:  CE calculations 

Summary 

There is little evidence to suggest that, as EU integration has progressed over 
the past two decades or so, Member States have become more export 
specialised. Rather, overall, the Member States have become more similar in 
terms of their degree of specialisation, as measured by their export structures 
over this period. There have only been a few exceptions to this trend, most 
notably the UK’s increase in specialisation in financial services (as seen by 
comparing Tables A1 and A2).  

At the same time, the geographical specialisation of sectors across Member 
States has tended to become more similar. In other words, it would appear 
that there is little support at the Member State level for the hypothesis that 

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integration encourages nations to increase their specialisation in those sectors 
in which they have a comparative advantage. The Theil statistics used to draw 
these conclusions are national averages (across regions and sectors), the 
same as those reported in Cutrini (2010). As they are averages, it is feasible 
that the average masks changes in specialisation within particular sectors, 
although the general agreement with findings from the Balassa analysis would 
tend to support the ‘becoming generally more similar in sectoral structure’ 
argument. 

In this respect, the findings lend support to Krugman’s (2008) general 
argument that perhaps within advanced economies the era of regional 
specialisation, whether due to comparative advantage or the increasing 
returns effects of industrial localisation, has passed.  Indeed, there is growing 
evidence that production processes are fragmenting across borders with 
countries trading tasks and functions rather than products.  That is, regional 
sectoral (‘horizontal’) specialisation may be giving way to regional functional 
(‘vertical’) specialisation. (Timmer, et al, 2019; see also, for example, Yi, 2003; 
French, 2017). With cross-border production sharing, countries and regions 
can specialise in particular stages of the production process – such as R&D, 
fabrication, marketing, management. Data classified by sector will not 
necessarily reveal this type of specialisation: different types of data are 
required (for example, Timmer, 2018, uses occupational labour incomes). 
Such forces are also linked to the globalisation of production processes, which 
has been progressing at a rapid rate during the period under analysis, and has 
encouraged geographically dispersed production networks. It is quite possible, 
therefore, that the economic forces caused by EU integration are relatively 
smaller compared to the forces caused by globalization, and thus what is 
observed reflects more on the latter than the former. It is possible that some 
degree of regional specialisation of tasks or functions is replacing regional 
sectoral specialisation, but the detailed data required to investigate this are not 
available on a consistent basis.  

These discussions and arguments are brought together more consistently 
within the next stage of analysis, which investigates changes in patterns of 
geographical concentration. 

4.4  Results from Measures of Geographical 

Concentration 

Whereas the Theil index of specialization used above provides a measure of 
the degree to which each EU region is economically specialized relative to the 
EU as a whole, the corresponding Theil index of industrial concentration 
calculates the degree to which any given economic sector is geographically 
concentrated across the EU. Again, following Cutrini (2010) it is possible to 
decompose the total relative geographical concentration of any given industry 
into two sub-components: the within- country relative concentration of an 
industry, and the between-country relative concentration of that industry. The 
relevant indices are defined as: 

Total relative concentration of industry k 

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Theil Indices of 
Geographical 
Concentration 

𝑇𝑘= ∑∑𝐸𝑖𝑗𝑘𝐸𝑘𝑟𝑖𝑗=1𝑚𝑖=1𝑙𝑛(𝐸𝑖𝑗𝑘𝐸𝑘⁄𝐸𝑖𝑗𝐸⁄)       
 
 
 Within-country relative concentration of industry k 

𝑚

𝑟𝑖
𝑊𝑖𝑡ℎ𝑖𝑛 =   ∑ ∑
𝑗=1

𝑖=1

𝑇𝑘

𝐸𝑖𝑗𝑘
𝐸𝑘

𝑙𝑛 (

𝐸𝑖𝑗𝑘 𝐸𝑖𝑘⁄
𝐸𝑖𝑗 𝐸𝑖⁄

) 

Between-country relative concentration of industry k 

𝑇𝑘

𝑚
𝐵𝑒𝑡𝑤𝑒𝑒𝑛 =   ∑
𝑖=1

𝐸𝑖𝑗𝑘
𝐸𝑘

𝑙𝑛 (

𝐸𝑖𝑘 𝐸𝑘⁄
𝐸𝑖 𝐸⁄

) 

where the notation and definitions of the terms are those used for the Theil 
specialisation indices above in section 4.3 (k refers to industry, j to region and 
i to member country). 

As the measure of concentration is sector-specific, the services data created 
in the original database can also be used alongside the additional 
manufacturing data from the subsequent data work. What matters most is the 
consistency of the regional / country coverage to ensure that the indices can 
be compared over time. Additionally, a reduced regional sample can be used 
to replicate the results of Cutrini (2010). 

This means that the following set of calculations can be performed: 

(i) 

(ii) 

(iii) 

Using the Cutrini sub-sample (of Sectors and Member States), re-
produce the numbers for Figs 1 (EU-wide manufacturing), 2 (high 
vs low tech) and also Fig 3 (EU-wide, particular sectors). This will 
allow us to add two more observations (2003, 2007) to see whether 
trends have continued. 

Extend the sample to include our full set of Member States and 
regions but stick with manufacturing to see how Fig 1 changes. We 
know that the sectors are different between the Rev 1 and Rev 2 
datasets, but there may be an average level of comparability – we’ll 
only know when we do this. 

Again, use the whole sample and the sectors from the originally 
constructed database from Chapter 3 (where we know there is 
reasonable two-way consistency in definition) to look how they 
have changed over all four periods.  This will include some service 
sectors. 

Figure 4.13 presents a combination of three sets of results. 

(i) 

Firstly, it re-creates the Cutriini (2010) results for overall 
manufacturing. 

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Recap on 
available data 

Empirical 
findings 

Replication and 
comparison of 
Cutrini (2010) 
results 

      
 
 
 
 
 
 
 
(ii) 

(iii) 

Then, it extends this for 2003 and 2007 with the new NACE Rev1 
dataset, using the same sectors and Member States as in the 
original study. 

Finally, the full sample of 13 Member States collected through this 
project is used to create a revised set of manufacturing results. 
However, because the 2011 and 2015 data are based on NACE 
Rev2 sectors it is to be expected that some discontinuity would be 
observed.  

The findings are that, broadly speaking, the manufacturing sector in Europe 
has continued to decline in its degree of concentration – more or less in line 
with Cutrini’s previous finding. Expanding the coverage of Member States 
alters the levels of the Theil statistics for 2003 and 2007, but the same rate of 
decline is observed between the two years. As mentioned above, given the 
differences in Rev1 and Rev2 sectors it does not make sense to see the 
increase between 2007 and 2011 as anything meaningful. The movement 
between 2011 and 2015 can be analysed, however, and shows a slight 
increase in concentration, although nothing that would reverse the declines 
seen in previous years. At best it can probably be seen as a stabilisation of 
previous trend decline. 

Figure 4.13: Extension of Cutrini (2010) results on geographic concentration - manufacturing 

Source:  CE calculations 

Moving on to look at the individual manufacturing sectors, Figures 4.14a – c 
replicate Figure 3 from Cutrini (2010) and then extend by two periods (2003 
and 2007). 

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Figure 4.14a: Extension of Cutrini (2010) results on geographic concentration – manufacturing sectors 

Source:  CE calculations 

Figure 4.14b: Extension of Cutrini (2010) results on geographic concentration – manufacturing sectors 

Source:  CE calculations 

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Figure 4.14c: Extension of Cutrini (2010) results on geographic concentration – manufacturing sectors 

Source:  CE calculations 

Using the 
database from 
Chapter 3 with 
comparable 
sectors 

The work in the database chapter (Chapter 3) established a set of 12 sectors 
which had sufficient two-way compatibility between them to enable 
comparison across these classifications, and thus across all four periods. 
These sectors are outlined in Table 4.1 

Table 4.1: Selected sectors from original database 

NACE Rev 2 Sector 

Share  of  Rev 
1.1  in  Rev  2 
sector 

Share  of  Rev 
2  in  Rev  1.1 
sector 

C10,  C11  -  Manufacture  of  food  products; 
Manufacture of beverages 

0.96 

0.99 

C16  -  Manufacture  of  wood  and  of  products  of 
wood  and  cork,  except  furniture;  manufacture 
of articles of straw and plaiting materials 

C17 - Manufacture of paper and paper products 

C22  -  Manufacture  of  rubber  and  plastic 
products 

0.89 

0.94 

0.90 

0.83 

0.95 

0.84 

C23 - Manufacture of other non-metallic mineral 
products 

0.91 

0.97 

C24 - Manufacture of basic metals 

G45  -  Wholesale  and  retail  trade  and  repair  of 
motor vehicles and motorcycles 

0.88 

0.96 

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0.97 

1.00 

105 

      
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Summary 

G46 - Wholesale trade, except of motor vehicles 
and motorcycles 

0.97 

1.00 

G47 - Retail trade, except of motor vehicles and 
motorcycles 

0.91 

0.99 

I - Accommodation and food service activities 

H49 - Land transport and transport via pipelines 

M72 - Scientific research and development 

1.00 

0.98 

1.00 

1.00 

1.00 

1.00 

Figures 4.15a and 4.15b report the Theil Concentration Indices (Total, 
Between and Within) for these sectors. The grouping of the sectors is simply 
by the scale of their statistics, and not by any economic typology, for ease of 
viewing their variation over time. Although some care and attention has gone 
into selecting these sectors, the degree of correspondence as reported in 
Table 4.1 shows that some caution should still be used in judging the shift 
between the pre-2007 (Rev 1.1) and post-2011 (Rev 2) periods for certain 
activities. 

As in the case of specialisation, there is little evidence to suggest that, as EU 
integration has progressed over the past two decades or so, economic 
activities – here measured by employment in individual sectors – have 
become more geographically concentrated, either between or regionally-within 
Member states. While the limitations of data, discussed here and in the other 
reports, have to be borne in mind, it does not appear to be the case that 
agglomeration forces have been sufficient to produce any significant increase 
in the geographical concentration of employment within industries, at least at 
the spatial scale for which analysis has been possible.  Indeed, our analysis 
would seem to confirm the trends that Cutrini’s earlier studies established, that 
if anything both economic specialisation and spatial concentration have 
continued to decline. Of course, a finer spatial lens might well reveal different 
degrees and trends in local specialisation and spatial concentration. But for 
the geographical scales studied here, it would be difficult to argue that 
increasing integration has led to increasing specialisation and agglomeration. 
It might also be argued that in any case, the process of EU integration itself 
has slowed or reached limits for some sectors.  

At the same time, as noted above, the theoretical argument that increasing 
economic integration should lead to both increased geographical 
concentration (agglomeration) and increased regional specialisation, may no 
longer be an appropriate view of how the economies of Europe – or indeed in 
other advanced parts of the global economy – now function. In the case of the 
European Union, a declining proportion of trade is in fact intra-Union, between 
Member States, compared to two decades or so ago.  The historical shock of 
the global financial crisis in 2008, and the deep recession this caused, will also 
have had an impact. Further, dramatic changes in the global economy, in 
global trade patterns, in technology and in production systems, possibly all 
render the relationship between national and regional ‘openness’ and 
connectivity, on the one hand, and the locational dynamics of economic 
activity, on the other, much more complex and diffuse than in the past. Finally, 

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future developments – such as the gathering pace of artificial intelligence and 
its impact on employment and production, the mounting pressure for shifting to 
a low-carbon or carbon-neutral economy, and the uncertainty of trade 
arrangements, for example, between Europe and the United States and China 
– will all leave some imprint on the economic geography of the European 
Union.  

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Figure 4.15a: Full sample results for Rev1 – Rev2 consistent sectors 

Source:  CE calculations 

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Figure 4.15b: Full sample results for Rev1 – Rev2 consistent sectors 

Source:  CE calculations 

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5 

Clustering and Agglomeration 

5.1  Introduction 

This chapter revisits some of the theories relating to clustering and 
agglomeration, and considers how this inter-relates with the process of 
economic integration. It also looks back over relevant literature which has 
studied agglomeration and clustering, and summarises their findings. 

5.2  Implications from Theory and Findings of Previous 

Studies 

Re-cap on theory 
and implications for 
clustering and 
agglomeration 

Clusters as 
specialised 
geographical 
concentrations 

Over the past three decades or so, there has been a veritable flood of 
academic literature in urban economics, the New Economic Geography, 
business studies and economic geography, on the topics of clustering and 
agglomeration.  The notion of clustering was strongly promoted by the 
business economist Michael Porter in the 1980s and 1990s. Porter defines 
business clusters as 

Geographic  concentrations  of  interconnected  companies, 
specialised  suppliers,  service  providers,  firms  in  related 
industries,  and  associated 
(for  example, 
universities,  standards  agencies,  and  trade  associations)  in 
particular  fields  that  compete  but  also  co-operate  (Porter, 
1998, p. 197)  

institutions 

Clusters, it is claimed, raise the innovation and productivity of their constituent 
firms, thereby giving them a competitive advantage in external markets. 
Indeed, it is frequently argued that a country’s most export-competitive sectors 
of activity often tend to be geographically clustered. 

According to Porter, then, key to the existence of a cluster is the spatial co-
location - or geographical concentration - of firms in the same and closely 
related industries. Clusters thus imply a degree of local industrial 
specialisation. In fact, all of the examples of clusters in the academic literature 
refer to local concentrations of specialised industry.  In many respects, the 
cluster concept is a modern re-incarnation or reworking of Alfred Marshall’s 
(1890) notion of ‘industrial district’, and Porter’s own ‘cluster theory’ draws 
heavily on Marshall’s ‘triad’ of localisation economies (a pool of specialised 
labour, local supporting and ancillary activities, and specialist knowledge 
accumulation and spillovers among the local firms).   These external 
economies associated with localised specialisation are deemed to raise the 
innovation and productivity of the firms concerned. It should be noted that 
Italian economists have also devoted considerable effort in resurrecting 

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Marshall’s notion of ‘industrial district’, embellishing it in the process, and have 
used it to describe and analyse the myriad specialised local industrial districts 
of the so-called ‘Third Italy’ (for example, see, Gilbert, 2016). The Italian 
literature rarely uses the term cluster to refer to these modern-day industrial 
districts, which are also given an explicit social-institutional-cultural dimension 
(see Sforzi, 2015).  

In the Anglophone business economics and economic geography literature, 
however, it is the cluster notion that is the preferred concept. This is not to 
say, however, that the cluster concept has become accepted without some 
reservations, criticisms and caveats (see for example, Martin and Sunley, 
2003; Duranton, 2011).  For one thing, the geographical scale over which a 
cluster should be defined, and the mechanisms that underpin its functioning, 
are often left vague. Just how localised does a cluster have to be to be 
deemed such? In practice, defining the geographical boundaries of clusters 
may be highly dependent on the sort of data available, and the areal units on 
which such data are based.   For another, the simple co-location of similar 
firms, even in close spatial proximity, does not necessarily mean that the 
group of firms constitute a functioning cluster (as a dense network of interfirm 
interactions, interdependencies and exchanges). Yet further, the evidence that 
clustering raises firm productivity, and thence competitive advantage, is far 
from unequivocal.  Nevertheless, the creation and promotion of clusters have 
become widely accepted policy goals in many European states. 

Whilst the modern concept of the cluster has been developed mainly in 
business economics, the concept of agglomeration derives mainly from the 
urban economics literature. Although it too is intended to capture the spatial 
concentration of economic activity, as discussed in Chapter 2 strictly speaking 
agglomeration refers to the co-location or geographical concentration of a 
variety of different economic activities together, rather than of firms in the 
same or interrelated industries.  The term agglomeration should really be 
reserved for geographical concentrations of diverse activities, and is most 
often used in discussions of externalities of the so-called Jacobsian type 
(named after the urbanist Jane Jacobs, 1969).  These Jacobsian urban or 
agglomeration externalities include firms’ access to a large labour pool, a large 
‘home’ market giving opportunities for new firm entry, access to diverse 
ancillary activities and services, and various forward and backward linkages 
between local firms. Typically, such agglomeration economies are discussed 
in relation to cities, not clusters. Again, the presence of these positive 
externalities is argued to raise the productivity of local firms. 

However, in various writings the terms cluster and agglomeration have come 
to be used interchangeably, making for some confusion. Whilst spatial co-
location of firms is a basic requirement for both concepts, in the case of 
clusters the co-locating firms are in the same or closely related sectors, 
whereas in agglomerations the co-locating firms are in diverse activities, which 
may not be directly or closely related. 

Agglomerations 
as diversified 
concentrations 

The implications 
of economic 
integration 

To the extent that increasing economic integration across geographic space 
lowers transport costs and removes the barriers to trade (and frees up the 
ability of labour to move geographically), then it should encourage the 
concentration of firms in (specialised) clusters and major agglomerations 
(large cities and urban regions). And such spatial concentration in turn, 

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Clusters can rise 
and fall 

according to assumption, should increase the productivity and 
competitiveness of firms in such clusters and agglomerations, thereby 
attracting both more firms and workers. According to theory, then, the overall 
implication of increasing economic integration - such as across the European 
Union – should be the increasing concentration (co-location) of firms, both in 
specialised clusters and in major urban centres and urban regions.  

It should be noted, however, that economic integration – in the form of 
reductions in barriers to trade – can also expose existing clusters and urban-
regional agglomerations to increased competition from other (including 
foreign) competition. The economic landscapes across the major advanced 
economies are littered with the vestiges of once highly-competitive and 
successful clusters which have since undergone decline in the face of cheaper 
or more innovative similar clusters elsewhere.  

Porter for example, chose the Portuguese footwear clusters as one of his key 
examples. Yet those clusters have faced intense overseas competition from 
cheaper production in the Far East, Russia and South America. Clusters (like 
other forms of sectorally-specialised geographical concentrations, including 
whole cities), can undergo ‘life cycles’, rising to prominence but then in some 
subsequent historical phase slipping into relative or even absolute decline, 
through a combination of foreign competition and a lack of technological or 
product upgrading. In fact clusters can experience quite complex evolutionary 
patterns (see for example, Martin and Sunley, 2003). Little is known about 
how such evolutionary development paths relate to changes and shifts in trade 
and economic integration. 

5.3  Review of existing literature 

European 
context 

Although there has been considerable discussion of cluster policy in the 
European context, much of it within or associated with the European 
Commission, there has been surprisingly little detailed empirical analysis that 
attempts to map actual clusters across the EU. See, for example: 

  European Secretariat for Cluster Analysis (ESCA): https://www.cluster-

analysis.org;  

  European Cluster Policy Forum: Improving Linkages and Synergies in 
Cluster Policy (https://ec.europa.eu/growth/content/third-european-cluster-
policy-forum-improving-linkages-and-synergies-cluster-policy_en;  

  Ketels, (2004): The ‘mapping tool’ that appears on the European Cluster 

observatory is actually non-functional (European Commission, 
(https://ec.europa.eu/growth/smes/cluster/observatory/cluster-mapping-
services/mapping-tool_en). 

One of the few detailed studies is that by Domenech et al (2011), which uses 
the Amadeus micro database to map clusters of creative industries across 
Europe. Other studies are usually country specific, for example for the UK 
(Duranton and Overman, 2005) for Italy and Spain (Lazzeretti et al, 2008), or 
Germany (Brachert et al, 2011). The resultant findings vary according to the 
data used and the geographical scales adopted. Not all of the ‘clusters’ so 
identified are necessarily functioning clusters. Ideally, mapping clusters 
requires firm-level data. In the absence of such data, studies often simply 
revert to the use of location quotients on an industry by industry basis.  This 

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index was discussed in Chapter 2. It does have the virtue of assigning a value 
of relative local concentration in an industry for each geographic area making 
up a national economy (or group of such economies, as in the European 
Union).  

Measuring 
agglomeration 

There are different statistical approaches to measuring agglomeration, and 
those of which are most applicable to the current situation are described 
below. 

Location 
Quotient (LQ) 

As described in Chapter 2, the location quotient for an industry i in region r is 
the ratio of the region’s share of that industry’s national employment (subscript 
N) to that regions share of total national employment, that is: 

In the present case, if the reference economy (N) is that of the European 
Union as a whole, then EU-wide figures would be used instead of the national 
figures in the above formula.   

If the LQ for industry i in region r is greater than unity then the industry in 
question is generally considered to be more ‘localised’ or ‘clustered’ in region r 
than in other regions: the higher the value of the index the greater the degree 
of regional localisation or ‘clustering’. Thus, mapping the LQs for a given 
industry i can reveal where and to what extent that industry is geographically 
‘clustered’.  

In addition, since clusters typically involve two or more related or 
complementary sectors, a further step in the use of location quotients is to 
examine the correlations between pairs of industries that is: 
Corr (𝐿𝑄𝑖𝑟, 𝐿𝑄𝑗𝑟) 
across regions. Since these are conventional correlations they can be tested 
for statistical significance. 

An alternative and arguably more sophisticated approach to identifying co-
agglomeration and clustering of industries (and firms) is provided by Ellison 
and Glaeser (1997, 1999, 2010), who have devised a family of indices of 
‘geographic concentration’ and co-agglomeration. These indices are derived 
from an underlying theory of firm location under conditions of increasing 
returns and spillover effects. As such, in all but the simplest co-agglomeration 
index, their measures require employment data not only for industries but also 
on the sizes of individual firms in those industries; that is, they require micro-
data.  Ellison and Glaeser’s early work has since attracted a significant body 
of literature, on refining and extending their original indices, on applying the 
indices to different empirical contexts, and on identifying the driving forces 
underpinning local industrial concentration agglomeration (for example, 
Duranton et al, 2005; Felkner et al, 2011; Howard et al 2015). 

In the absence of firm-level data, Ellison-Glaeser (EG) proposed a simple 
index of industry co-agglomeration requiring only employment data at an 
industry by industry level, namely: 

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The Ellison-
Glaeser (EG) 
statistic 

𝐿𝑄𝑖𝑟=(𝐸𝑖𝑟𝐸𝑖𝑁/𝐸𝑟𝐸𝑁 )=(𝐸𝑖𝑟𝐸𝑟/𝐸𝑖𝑁𝐸𝑁)        
 
 
 
 
 
𝐸𝐺𝑖,𝑗 =

𝑅
𝑟=1

∑ (𝑠𝑟𝑖 − 𝑥𝑟)(𝑠𝑟𝑗 − 𝑥𝑟)
𝑅
𝑟=1

1 − ∑

2
𝑥𝑟

Summary 

where r indexes the regions covered in the analysis, 𝑠𝑟𝑖 is the share of industry 
i’s employment in region r, 𝑠𝑟𝑗 is the share of industry j’s in region r, and 𝑥𝑟 is 
the mean employment share in region r across all industries. As mentioned 
above, various elaborations of this index have been advanced in the literature, 
but all require firm-level data. 

One issue with the EG index is that it yields a measure that is in effect the 
average co-agglomeration of two industries, i and j, across all regions in the 
larger geographical area being studied. It does not provide an index of co-
agglomeration on a region by region basis.  Another issue is that there are no 
standard statistical tests of the significance of the index. Cassey and Smith 
(2014) have proposed a simulation method for estimating the significance of 
the EF index of concentration or agglomeration of a single industry (although 
this again requires plant level data), but no such corresponding test appears to 
exist for the co-agglomeration shown above. 

The notion and potential benefit of developing clusters remains strong in the 
agenda of policy makers at all levels of government. The strength of this 
concept resonates mostly from the seminal work undertaken in the 1980s and 
1990s by Michael Porter, and more latterly adopted within the field of new 
economic geography. While the cluster concept originates in Porter’s work on 
business economics, agglomeration (the benefits that accrue from 
densification of activity) largely stems from the urban economics literature. 
Agglomeration benefits are generally divided into two types, urbanisation (the 
benefits from locating in more densely-populated areas) and localisation (the 
benefits from locating near firms from similar or related sectors). It is mostly 
the latter (localisation) agglomeration benefits that are associated with 
clusters. From the perspective of the study and the interest of how increasing 
integration across Europe might affecting clustering and agglomeration, the 
theory would suggest that increasing concentration (co-location) of activity 
should result. 

Within Europe, the European Cluster Observatory represents the main pan-
EU focus for analysis. However, exploration of this site has revealed little in 
the way of detailed regional empirical work, possibly due to the difficulty of 
obtaining reliable data, an issue which the current study has tried to address. 
What work does exist is often firm-based, using for example the Amadeus 
database, or country-specific where access to detailed datasets is more 
readily available. 

For the current study, in terms of measuring agglomeration, lack of firm level 
data restrict the analysis somewhat, but it is still possible to use location 
quotient cross-correlations and the simplified version of the Ellison-Glaeser 
statistic with the regional-sector data to hand. The results of this analysis are 
presented in the next section. 

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Re-cap on 
available data 

5.4  Results from Measures of Clustering and 

Agglomeration 

As noted previously, the majority of cluster analysis relies on access to 
detailed firm-level data, or at least sources that allow for a high detail of 
sectoral disaggregation. Unfortunately this is not the case with the database 
that has been established for this study, and this therefore limits the extent to 
which clusters can be analysed (and identified). This sub-section reviews the 
available data and analyses which sectors will be included in the cluster 
analysis. 

Chapter 3 and 4 
databases 

Two sets of sector databases have been developed with different 
sector/geographical coverage. 

  Chapter 3 sectors 

For the database constructed in Chapter 3, sectors were chosen which 
allowed sufficient correspondence between the NACE versions covering the 
years 2003 / 2007 (Rev 1.1) and 2011 / 2015 (Rev 2). Table 5.1 provides a 
recap. 

Table 5.1: Chapter 3 sector coverage 

NACE Rev1.1 

Code  Sector Name 

NACE Rev2 

Code  Sector Name 

Manufacture of food products, 
beverages and tobacco 

DA 

C10, 
C11 

Manufacture of food products; 
Manufacture of beverages 

Manufacture of wood and wood 
products 

DD 

C16 

Manufacture of wood and of products of 
wood and cork, except furniture; 
manufacture of articles of straw and 
plaiting materials 

Manufacture of pulp, paper and 
paper products 

DE21 

C17  Manufacture of paper and paper products 

Manufacture of rubber and plastic 
products 

Manufacture of other non-metallic 
mineral products 

C22 

C23 

DH 

DI 

Manufacture of rubber and plastic 
products 

Manufacture of other non-metallic mineral 
products 

DJ27  Manufacture of basic metals 

C24  Manufacture of basic metals 

Sale, maintenance and repair of 
motor vehicles and motorcycles; 
retail sale of automotive fuel 

Wholesale trade and commission 
trade, except of motor vehicles and 
motorcycles 

Retail trade, except of motor 
vehicles and motorcycles; repair of 
personal and household goods 

G50 

G51 

G52 

Wholesale and retail trade and repair of 
motor vehicles and motorcycles 

G45 

Wholesale trade, except of motor vehicles 
and motorcycles 

G46 

Retail trade, except of motor vehicles and 
motorcycles 

G47 

H 

Hotels and restaurants 

I 

Accommodation and food service activities 

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NACE Rev1.1 

Code  Sector Name 

NACE Rev2 

Code  Sector Name 

Land transport; transport via 
pipelines 

I60 

H49 

Land transport and transport via pipelines 

K73 

Research and development 

M72 

Scientific research and development 

  Chapter 4 manufacturing sectors 

For the analysis of specialisation and geographical concentration, attention 
also focussed on the manufacturing sector as the work attempted to replicate 
and extend the analysis of Cutrini (2010). Table 5.2 shows the sector 
coverage available. 

Table 5.2: Chapter 4 manufacturing sector coverage 

NACE Rev1.1 

NACE Rev2 

Code  Sector Name 

Code  Sector Name 

Manufacture of food products, 
beverages and tobacco 

C10 

Manufacture of food products 

C11 

Manufacture of beverages 

Manufacture of textiles and textile 
products 

C13 

Manufacture of textiles 

C14 

Manufacture of wearing apparel 

Manufacture of wood and wood 
products 

Manufacture of pulp, paper and 
paper products; publishing and 
printing 

Manufacture of chemicals, 
chemical products and man-made 
fibres 

Manufacture of rubber and plastic 
products 

Manufacture of other non-metallic 
mineral products 

Manufacture of basic metals and 
fabricated metal products 

C16  Manufacture of wood and of products of 
wood and cork, except furniture; 
manufacture of articles of straw and plaiting 
materials 

C17 

Manufacture of paper and paper products 

C18 

Printing and reproduction of recorded 
media 

C20  Manufacture of chemicals and chemical 

products 

C22  Manufacture of rubber and plastic products 

C23  Manufacture of other non-metallic mineral 

products 

C24 

Manufacture of basic metals 

C25 

Manufacture of fabricated metal products, 
except machinery and equipment 

C28  Manufacture of machinery and equipment 

n.e.c. 

DK 

Manufacture of machinery and 
equipment n.e.c. 

DL 

Manufacture of electrical and 

C26  Manufacture of computer, electronic and 

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DA 

DB 

DD 

DE 

DG 

DH 

DI 

DJ 

      
 
 
 
 
NACE Rev1.1 

NACE Rev2 

Code  Sector Name 

Code  Sector Name 

optical equipment 

optical products 

C27 

Manufacture of electrical equipment 

DM 

Manufacture of transport 
equipment 

C29  Manufacture of motor vehicles, trailers and 

semi-trailers 

DN 

Manufacturing n.e.c. 

C31 

Manufacture of furniture 

C32 

Other manufacturing 

C33 

Repair and installation of machinery and 
equipment 

  Geographical coverage 

The additional manufacturing detail provided for the Chapter 4 analysis came 
at the cost of reducing the regional coverage of the data. Table 5.3 provides 
the detail. 

Table 5.3: Geographical coverage of the datasets from Chapter 3 and 
Chapter 4 

Member State 

Number of NUTS regions 

Original database in 
Chapter 3 

Manufacturing only 
database in Chapter 4 

Austria 

Belgium 

France 

Germany 

Greece 

Hungary 

Italy 

Netherlands 

Poland 

Slovakia 

Spain 

Sweden 

United Kingdom 

Portugal 

Total 

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7 

11 

22 

38 

13 

7 

21 

12 

16 

4 

17 

8 

37 

N/A 

213 

7 

11 

20 

19 

13 

7 

21 

12 

16 

4 

16 

8 

34 

5 

193 

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Choice of 
sectors and 
geography for 
cluster analysis 

Both the LQ and EG co-agglomeration statistics are largely atheoretical, in 
that they will generate a set of average pairwise correlations for all sectors in 
the analysis regardless of whether they are likely to be directly connected. 
However, a priori reasoning can be used to judge which sector combinations 
are more likely to provide promising results, which can also help to narrow 
down the analysis to those considered more meaningful. 

Use of input-
output linkages 

In particular, input-output (IO) tables can be used to establish the strongest 
sector supply linkages for those in the sample, and therefore provide a 
justification for co-location. Using IO tables from Eurostat, Table 5.4 lists the 
strongest47 supply-chain (intermediate demand) relationships for each of the 
sectors in Tables 5.2 and 5.3, highlighting any that are available in the current 
sample. 

Table 5.4: Sector supply-chain linkages 

Sector 

Supply-Chain Sectors 

Food products, beverages and tobacco products 

Textiles, wearing apparel and leather products 

Chemicals and chemical products 

Wholesale trade services, except of motor vehicles 
and motorcycles 

Wood and of products of wood and cork, except 
furniture; articles of straw and plaiting materials 

Wholesale trade services, except of motor vehicles 
and motorcycles 

Paper and paper products 

Wholesale trade services, except of motor vehicles 
and motorcycles 

Printing and recording services 

Paper and paper products 

Wholesale trade services, except of motor vehicles 
and motorcycles 

Chemicals and chemical products 

Rubber and plastics products 

Chemicals and chemical products  

Other non-metallic mineral products 

Chemicals and chemical products 

Basic metals 

Fabricated metal products, except machinery and 
equipment 

Basic metals 

Wholesale trade services, except of motor vehicles 
and motorcycles 

Computer, electronic and optical products 

Electrical equipment 

Wholesale trade services, except of motor vehicles 
and motorcycles 

Electrical equipment 

Basic metals 

Fabricated metal products, except machinery and 

47  To  define  what  we  mean  by  strong  supply-chain  linkages,  the  combined  use  matrix  was 
analysed for a selection of the larger Member States (DE, ES, FR, PL, and the UK) and the 
proportions of intermediate demand that each sector supplies to the other was analysed. 
Proportions of 10% and above are listed in Table 3.4, aside from the own-sector demand 
which is typically the dominant proportion. 

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Sector 

Supply-Chain Sectors 

equipment 

Computer, electronic and optical products 

Machinery and equipment n.e.c. 

Basic metals 

Fabricated metal products, except machinery and 
equipment 

Wholesale trade services, except of motor vehicles 
and motorcycles 

Motor vehicles, trailers and semi-trailers 

Basic metals 

Furniture; other manufactured goods 

Fabricated metal products, except machinery and 
equipment 

Wood and of products of wood and cork, except 
furniture; articles of straw and plaiting materials 

Wholesale trade services, except of motor vehicles 
and motorcycles 

Repair and installation services of machinery and 
equipment 

Fabricated metal products, except machinery and 
equipment 

Machinery and equipment n.e.c. 

Wholesale trade services, except of motor vehicles 
and motorcycles  

Motor vehicles, trailers and semi-trailers 

Motor vehicles, trailers and semi-trailers 

Wholesale and retail trade and repair services of 
motor vehicles and motorcycles 

Wholesale trade services, except of motor 
vehicles and motorcycles 

Land transport services and transport services via 
pipelines 

Retail trade services, except of motor vehicles 
and motorcycles 

Land transport services and transport services via 
pipelines 

Wholesale trade services, except of motor vehicles 
and motorcycles 

Accommodation and food services 

Food products, beverages and tobacco products 

Wholesale trade services, except of motor vehicles 
and motorcycles 

Scientific research and development services 

Chemicals and chemical products 

A few points emerge from Table 5.4: 

  Clearly the wholesale trade services sector is linked with many of the 

sectors, but this is unlikely to mean that the activities would be located 
near to one another. Indeed, the main link in the other direction is with land 
transport services, where co-location could possibly occur due to the 

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presence of strong infrastructure linkages or a central location (for 
distributional purposes). 

  Similarly, accommodation and food services is linked with food, beverages 

and tobacco most likely due to a third factor, namely urbanisation or 
tourism locations. 

  Some traditional manufacturing linkages emerge (e.g. rubber and plastics 
with chemicals, motor vehicles with basic metals) and it is possibly here 
that co-location is more likely. 

Geography of 
analysis 

The EG statistic provides an average (across the geography being analysed) 
of location correlations. In a similar way, the LQ statistic (which is by industry 
and region) can be correlated across regions for pairwise industry 
combinations. 

Location 
quotient analysis 

For this reason, outside of an overall EU average, only countries with a large 
enough number of regions can be analysed, otherwise the power of the 
statistics calculated will not be robust. Based on Table 5.3, the following 
Member States could possibly be analysed individually, alongside a pan-
European calculation: Germany, France, Spain, Italy, Poland, and the UK. 

The industry location quotients have been calculated for each of the region-
industry combinations in the sample, using the formula described in the 
previous chapter, with the correlation coefficients (and associated 
significance) calculated from this, i.e. for each industry, the correlation takes 
places across the regions in the sample. The location quotients are calculated 
over each of the four years of data. 

Pan-EU results 

  Nace Rev1 sectors 

Tables 5.5a-d show the cross-correlation coefficients for all the sectors in the 
sample, with coefficients coloured red if they are significant at 95% confidence 
limits (i.e. p-value <= 0.05), with the top-10% (highest) of correlations also 
shaded red. The tables are presented as triangular matrices48, because the 
results are symmetric, and the two-letter abbreviations are the industry codes 
as described in Tables 5.1 and 5.2. 

The combinations that stand out as being particularly strong (across both 
years) and which are justified by potential supply-chain relationships, are as 
follows: 

-  DK (Manufacture of basic metals and fabricated metal products) and 

DJ (Manufacture of machinery and equipment n.e.c.). 

-  DN (Manufacturing n.e.c.) with DD (Manufacture of wood and wood 

product), particularly as DN contains furniture manufacturing; 

-  Retail service sectors (G50, G52) and H (Hotels and restaurants) 
which have more to do with demand-side linkages related to co-
incidental factors such as urbanisation. 

Generally speaking, there is little evidence, at EU level, that the LQ values 
have increased over the two periods. Table 5.6a shows the difference, with 

48 The leading diagonal has been removed as all these correlations are equal to 1. 

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red numbers highlighting changes from significant correlations (though not 
necessarily significant changes) while the yellow highlights signify those 
sectors for which supply-chain linkages might be expected. The average 
change across the significant correlations is -0.007. 

  Nace Rev2 sectors 

For the periods 2011 and 2015 which use Nace Rev2 sector definitions, there 
is more variation of significance. Tables 5.5c and 5.5d show the results, with 
the significant (positive) values highlighted in red, while the top 10% (highest 
correlations) are shaded in red, as with the Rev1 tables. 

Rather than go through all the top combinations (some of which might be 
spurious and not worth reporting) the combinations which are justified by the 
supply-chain linkages mentioned in Table 5.4 are as follows: 

-  Manufacture of furniture (C31), and Wood and of products of wood 

(C16); 

-  Computer, electronic and optical products (C26) and Electrical 

equipment (C27); 

-  Machinery and equipment n.e.c. (C28) and Fabricated metal products, 

except machinery and equipment (C25); 

-  Chemicals and chemical products (C20), Rubber and plastic products 

(C22), and Other non-metallic mineral products (C23); 

-  Basic metals (C24) with Fabricated metal products, except machinery 

and equipment (C25); 

-  Manufacture of machinery and equipment n.e.c. (C28) and 

Manufacture of motor vehicles, trailers and semi-trailers (C29). 

Unlike the Rev1 differences, the change between 2015 and 2011 for those 
sector combinations of interest is generally positive, implying increasing co-
location activity. The changes are quite small, however, and are likely a 
reflection of both the short time period between the observations and also the 
generally limited progress of integration across the whole of the EU sample. 
Table 5.6b shows the period change, using the same colour coding as 
described for Table 3.6a above. The average change for significant cross-
sector correlations is 0.009. 

  Changes across sectors in all four periods 

There are a few sectors where the definitions transcend the change from Rev1 
to Rev2, and even less where supply-chain linkages can be used to judge 
whether a meaningful concentration measure has strengthened over the 
whole period. Possibly the only combination that meets these criteria is 
between the manufacturing of wood and wood products (DD/C16), with that of 
pulp, paper and paper products (DE/C17). Here there is evidence of 
increasing concentration, with the LQ correlation increasing from 0.103, to 
0.159, 0.429 and 0.468 in the four periods. 

Written by Cambridge Econometrics 

121 

      
 
 
Table 5.5a: Location Quotient Correlation Coefficients (EU Sample, 2003) 

Written by Cambridge Econometrics 

122 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA0.3200.319-0.084-0.0670.3500.2600.1620.0300.058-0.0170.299-0.035-0.068-0.100-0.2360.233-0.350DB0.233-0.081-0.0930.1630.3430.2300.1870.209-0.0700.372-0.1120.041-0.118-0.1880.062-0.295DD0.103-0.2360.1870.3190.3130.2850.092-0.0500.614-0.034-0.107-0.187-0.0650.121-0.351DE0.2530.166-0.2090.1230.2960.2580.1340.1660.1320.2820.072-0.0790.1730.334DG0.229-0.0120.1110.1340.2010.090-0.0490.0240.190-0.061-0.1680.0460.196DH0.1590.5180.4190.3940.2680.385-0.023-0.117-0.128-0.2260.030-0.097DI0.3490.2110.030-0.0300.309-0.025-0.059-0.161-0.1070.141-0.363DJ0.6020.3570.2640.390-0.064-0.120-0.308-0.2160.076-0.244DK0.6000.3850.440-0.164-0.024-0.325-0.271-0.058-0.019DL0.3210.203-0.210-0.129-0.259-0.3050.0430.076DM0.134-0.117-0.035-0.150-0.208-0.0390.038DN0.0260.091-0.052-0.1660.036-0.207G50 0.1540.5480.3930.0040.088G51 0.1900.0650.2670.287G52 0.575-0.0350.183H -0.0830.044I60 -0.119K73       
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.5b: Location Quotient Correlation Coefficients (EU Sample, 2007) 

Written by Cambridge Econometrics 

123 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA0.2930.301-0.137-0.0940.4160.2830.1640.0090.0350.0340.3010.001-0.024-0.218-0.2220.173-0.359DB0.208-0.098-0.1050.2240.2960.2060.1700.194-0.0070.431-0.0130.072-0.156-0.1570.040-0.305DD0.159-0.2060.2660.3490.3820.2970.1470.0610.6390.058-0.095-0.204-0.0680.201-0.365DE0.2130.138-0.2070.1660.2920.1850.0650.1530.1480.2040.007-0.1090.1860.261DG0.179-0.0070.0630.1290.1350.046-0.0870.1030.148-0.080-0.1840.0280.139DH0.2300.5060.4480.4870.3670.451-0.025-0.149-0.242-0.3500.194-0.149DI0.3500.1840.0770.0720.348-0.005-0.039-0.168-0.1230.144-0.379DJ0.6000.4150.3080.413-0.067-0.165-0.336-0.2610.149-0.290DK0.6110.4350.421-0.126-0.060-0.383-0.2940.061-0.015DL0.4170.260-0.174-0.132-0.293-0.3220.1380.046DM0.165-0.107-0.118-0.204-0.2690.044-0.040DN0.0120.083-0.128-0.2030.148-0.282G50 0.1140.4710.3120.0250.100G51 0.2420.0420.1850.186G52 0.512-0.1300.146H -0.1390.004I60 -0.128K73       
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.5c: Location Quotient Correlation Coefficients (EU Sample, 2011) 

Written by Cambridge Econometrics 

124 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.2620.2090.2350.2090.046-0.0630.0280.3290.244-0.0410.178-0.0650.040-0.0290.0430.266-0.0280.1020.019-0.099-0.068-0.2540.147-0.327C110.0750.0690.109-0.022-0.068-0.0070.1980.202-0.0010.1820.0360.0700.0010.0770.084-0.060-0.081-0.0540.047-0.1100.0420.079-0.157C130.3570.1240.1670.2410.0660.2730.2610.0310.4010.1060.2180.2250.0820.2830.2250.010-0.0520.082-0.083-0.1000.009-0.209C140.2520.041-0.054-0.1220.1650.320-0.0260.2660.0660.1930.1060.0790.3310.1980.103-0.1870.038-0.065-0.1570.095-0.245C160.429-0.097-0.1420.2510.3650.2600.4360.0340.2200.2320.0930.5510.1250.192-0.069-0.127-0.237-0.1190.334-0.360C170.1320.2220.2320.1020.4800.3770.1430.3210.3540.1640.1810.1440.1490.168-0.077-0.207-0.2360.265-0.096C180.1340.060-0.123-0.1110.0630.1170.1280.150-0.0440.0760.290-0.1070.2750.2620.0920.0130.0600.194C200.2010.1050.0950.1780.1440.1530.1880.147-0.0290.0680.1130.2160.143-0.086-0.1850.0660.111C220.2340.1240.5540.3180.4570.3670.3360.3790.3640.2160.106-0.127-0.287-0.3530.092-0.194C230.1380.3730.0990.2200.1910.2380.3850.0650.091-0.023-0.057-0.097-0.1070.205-0.367C240.3720.0770.2220.2740.200-0.0090.0110.1460.049-0.104-0.083-0.0690.152-0.125C250.3290.6150.6280.3610.4150.4270.2720.052-0.085-0.285-0.2820.153-0.248C260.5210.4380.4490.0650.4640.2270.069-0.046-0.089-0.1370.1540.142C270.6770.4420.2780.4970.2430.0540.004-0.228-0.2130.084-0.019C280.5040.2300.5350.1740.1190.068-0.219-0.166-0.0340.010C290.1250.1720.2050.0070.032-0.197-0.2570.194-0.012C310.2370.163-0.0570.025-0.130-0.2410.145-0.256C320.2230.2740.085-0.045-0.054-0.0060.001C33-0.0280.062-0.230-0.3420.300-0.124G45 0.0980.2830.053-0.0310.128G46 0.1560.0080.2190.196G47 0.478-0.0390.183I -0.0930.104H49 -0.160M72       
 
 
 
 
 
 
Table 5.5d: Location Quotient Correlation Coefficients (EU Sample, 2015) 

Written by Cambridge Econometrics 

125 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.2180.2720.2090.2020.0720.0320.0000.2420.251-0.0510.145-0.0970.022-0.015-0.0010.2580.0070.047-0.048-0.024-0.023-0.0980.110-0.282C110.0870.0590.0710.014-0.042-0.0050.1070.1090.0050.124-0.0270.029-0.0120.0420.031-0.015-0.098-0.097-0.013-0.1370.0440.053-0.052C130.4110.1630.2000.2900.0780.3360.2890.0160.4250.1650.2780.2510.1260.3300.2680.057-0.0360.104-0.046-0.1230.062-0.211C140.2070.0750.048-0.1040.1790.334-0.0030.2530.0470.1990.1280.0840.3080.2690.117-0.1880.041-0.153-0.1510.119-0.224C160.4680.030-0.0850.3040.3730.2470.4710.0830.2850.2280.1270.6210.1310.1930.086-0.090-0.238-0.1780.335-0.374C170.1150.1970.2510.1630.3770.3990.1550.3690.3230.1190.2230.1730.1220.214-0.067-0.122-0.2380.210-0.174C180.1170.152-0.001-0.1020.1740.0750.1870.139-0.0200.1880.244-0.0150.3070.2870.179-0.1300.0170.023C200.2050.2010.0910.1890.1620.1550.2060.1220.0100.1000.2220.1720.118-0.020-0.2140.071-0.001C220.3220.1950.5740.3450.4810.3890.3560.4230.4000.2000.176-0.059-0.177-0.3190.124-0.225C230.1730.3880.1730.3040.2660.2610.3880.1430.2050.037-0.035-0.165-0.1590.234-0.328C240.4030.1000.2730.2950.2220.0260.0790.2170.087-0.126-0.052-0.0810.110-0.162C250.4210.6370.6330.3650.4180.4900.2710.1140.004-0.178-0.3020.152-0.275C260.5160.5170.4100.0830.5530.1570.169-0.035-0.046-0.1680.1190.058C270.6690.4530.3270.5210.1950.100-0.016-0.170-0.2540.161-0.081C280.4920.2270.5240.1520.1630.111-0.120-0.1820.014-0.069C290.1240.2380.2030.0660.067-0.120-0.2220.292-0.086C310.2410.1790.0360.084-0.077-0.1970.215-0.267C320.1700.1420.064-0.036-0.1570.040-0.076C330.0570.144-0.108-0.2900.290-0.193G45 0.0780.360-0.095-0.0770.110G46 0.249-0.0410.2340.117G47 0.374-0.1190.172I -0.0880.148H49 -0.168M72       
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.6a: EU Location Quotient Correlation Coefficients Changes (2003-2007) 

Written by Cambridge Econometrics 

126 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA-0.027-0.017-0.053-0.0270.0660.0240.002-0.021-0.0230.0510.0020.0360.045-0.1180.014-0.060-0.009DB-0.025-0.017-0.0120.061-0.047-0.024-0.016-0.0140.0640.0590.0990.031-0.0380.030-0.022-0.010DD0.0560.0310.0790.0300.0690.0130.0550.1110.0250.0920.012-0.017-0.0030.080-0.015DE-0.040-0.0270.0010.043-0.005-0.072-0.069-0.0120.016-0.079-0.066-0.0300.013-0.073DG-0.0500.005-0.048-0.006-0.065-0.043-0.0380.080-0.041-0.020-0.016-0.018-0.057DH0.072-0.0120.0290.0940.0990.066-0.002-0.033-0.115-0.1230.163-0.051DI0.002-0.0270.0470.1020.0390.0200.020-0.008-0.0170.003-0.016DJ-0.0020.0580.0440.023-0.003-0.044-0.028-0.0450.073-0.045DK0.0110.050-0.0190.038-0.037-0.057-0.0230.1190.004DL0.0960.0570.036-0.003-0.034-0.0170.095-0.030DM0.0310.009-0.083-0.054-0.0610.084-0.078DN-0.014-0.008-0.076-0.0370.112-0.075G50 -0.039-0.077-0.0800.0210.012G51 0.051-0.023-0.083-0.101G52 -0.063-0.096-0.037H -0.056-0.040I60 -0.009K73       
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.6b: EU Location Quotient Correlation Coefficients Changes (2011-2015) 

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127 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C10-0.0440.063-0.027-0.0080.0260.095-0.028-0.0880.007-0.010-0.033-0.032-0.0180.014-0.044-0.0080.035-0.055-0.0670.0750.0450.156-0.0380.045C110.012-0.010-0.0370.0360.0260.001-0.091-0.0930.006-0.059-0.062-0.042-0.013-0.035-0.0530.045-0.016-0.042-0.059-0.0270.002-0.0260.104C130.0540.0380.0330.0490.0130.0630.028-0.0150.0240.0590.0590.0250.0440.0460.0430.0480.0160.0220.037-0.0230.053-0.002C14-0.0450.0330.1020.0180.0140.0140.023-0.013-0.0180.0060.0220.005-0.0240.0720.015-0.0010.002-0.0880.0060.0240.021C160.0390.1270.0560.0530.008-0.0130.0350.0490.065-0.0040.0340.0700.0070.0010.1550.037-0.001-0.0590.001-0.014C17-0.018-0.0250.0180.062-0.1030.0220.0120.048-0.031-0.0450.0430.030-0.0280.0460.0100.085-0.002-0.055-0.077C18-0.0170.0910.1220.0090.111-0.0420.059-0.0110.0240.111-0.0450.0910.0310.0250.087-0.143-0.043-0.171C200.0040.096-0.0040.0110.0180.0020.018-0.0250.0400.0310.110-0.044-0.0250.066-0.0300.005-0.113C220.0880.0710.0210.0270.0240.0220.0200.0440.036-0.0150.0700.0680.1100.0340.032-0.031C230.0350.0150.0740.0830.0750.0230.0020.0780.1140.0600.022-0.068-0.0520.0290.039C240.0310.0230.0500.0210.0220.0350.0680.0710.038-0.0230.031-0.012-0.042-0.037C250.0920.0220.0050.0040.0020.0630.0000.0610.0880.106-0.020-0.002-0.027C26-0.0050.078-0.0390.0180.089-0.0690.1000.0110.043-0.031-0.035-0.084C27-0.0080.0110.0490.024-0.0490.046-0.0200.058-0.0420.077-0.062C28-0.012-0.003-0.011-0.0220.0440.0430.100-0.0170.048-0.079C29-0.0010.066-0.0020.0590.0350.0770.0350.098-0.074C310.0040.0170.0920.0590.0530.0430.070-0.011C32-0.053-0.131-0.0200.009-0.1040.046-0.078C330.0850.0810.1220.052-0.010-0.069G45 -0.0200.077-0.148-0.045-0.018G46 0.093-0.0480.015-0.080G47 -0.104-0.080-0.011I 0.0050.044H49 -0.009M72       
 
 
 
 
 
 
 
 
Individual 
Member States 

As noted previously, part of the reason behind limited change in the LQ 
correlations could be because they are calculated across an average of all EU 
regions in the sample, where a range of EU Member States at different stages 
of integration are present, thus potentially obscuring results. By analysing 
individual Member States the degree of heterogeneity would be reduced. With 
this in mind, the LQ correlation coefficients were reproduced for some of the 
larger Member States in the sample (Poland and the UK).  

Poland 

Of the larger countries, all except Poland are well-established (and therefore 
well-integrated) EU members and this would point to using Poland as an 
example of where change over time might be most easily observed. The 
disadvantage of using Poland is that it only has 16 regions over which to 
average the LQ coefficient correlations. 

The results of the analysis (see Tables 5.7a-d) do not generally yield much 
evidence either way. There is no more evidence of increases in the LQ 
correlations for Poland than there is for the EU sample data. Most of the 
strong correlations occur among the service sectors, which could be related to 
the strong urbanisation forces and development of the capital city region over 
this time. It could also be that the loss of observations from moving to Member 
State level offsets the greater focus obtained by looking at individual countries. 
Tables 5.8a-b show the changes in the correlation coefficients, using colours 
in the same way as for the EU equivalents in Tables 5.6a and b. The average 
changes of significant coefficients over the two periods are -0.040 and -0.061. 

UK 

For reasons stated above, while Poland is an interesting example from an 
integration perspective, it is less promising in terms of the number of regions. 
For this reason, the UK was also selected as it has the largest number of 
NUTS2 regions of any Member State in the sample (see Table 5.3). Tables 
5.9a-d thus show the equivalent LQ correlations for the UK over the four 
available periods from the SBS database, while Tables 3.10a and b show the 
coefficient changes across the two sets of periods. 

Compared to Poland, the significant correlations in the UK are located mostly 
amongst the manufacturing sectors and less in services, but on the whole 
there are few combinations where a significant correlation also coincides with 
a strong supply-chain linkage which is also positive. In fact, the only 
combination for the 2003-07 change which meets these criteria is DI 
(Manufacture of other non-metallic mineral products) and DH (Manufacture of 
rubber and plastic products), while for the 2011-15 period change no such 
coincidence occurs. Overall, the average changes of significant coefficients 
over the two periods are -0.036 and -0.084.  

Written by Cambridge Econometrics 

128 

 
 
 
 
 
Table 5.7a: Location Quotient Correlation Coefficients (Poland, 2003) 

Written by Cambridge Econometrics 

129 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA-0.0270.2140.138-0.1490.116-0.626-0.316-0.344-0.128-0.1350.389-0.052-0.092-0.238-0.152-0.100-0.172DB-0.191-0.0430.0090.0350.136-0.132-0.0560.142-0.1940.0690.0620.010-0.210-0.150-0.195-0.123DD-0.320-0.4360.225-0.184-0.286-0.415-0.0530.1540.638-0.195-0.393-0.1770.220-0.368-0.481DE0.6930.099-0.2910.073-0.2570.4980.022-0.1070.5500.7700.5990.1750.7590.704DG-0.033-0.1250.1590.0030.4780.031-0.2310.3770.4980.3810.1690.4700.444DH-0.2120.0960.0840.2150.4300.4480.2740.3260.0410.130-0.0220.021DI0.3830.633-0.0400.030-0.173-0.129-0.197-0.066-0.226-0.165-0.195DJ0.6770.5130.248-0.0570.4830.3320.3610.282-0.0660.136DK0.2400.302-0.0870.2580.1080.022-0.049-0.162-0.018DL0.4150.2910.8150.7090.6100.5570.3300.515DM0.0940.3210.3610.2170.571-0.0570.115DN0.124-0.137-0.0990.138-0.281-0.289G50 0.8400.7470.5820.4390.557G51 0.8010.4680.7110.846G52 0.6940.6720.756H 0.1160.276I60 0.890K73  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.7b: Location Quotient Correlation Coefficients (Poland, 2007) 

Written by Cambridge Econometrics 

130 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA0.0630.4160.3340.0400.351-0.625-0.191-0.261-0.129-0.1940.4350.2890.2000.1600.069-0.097-0.158DB-0.2230.047-0.0030.1510.079-0.118-0.0810.155-0.1130.061-0.0270.062-0.133-0.168-0.212-0.129DD-0.155-0.3050.313-0.174-0.114-0.2710.0170.2180.6580.000-0.265-0.1260.360-0.291-0.414DE0.6400.171-0.3610.164-0.2270.420-0.0150.0980.7030.8380.7920.2530.6830.541DG0.125-0.1970.2430.1240.4120.079-0.0220.5030.5910.5580.2650.4390.401DH-0.1410.2100.3250.2130.4070.6090.3120.3250.1060.134-0.147-0.104DI0.3870.5200.0490.214-0.067-0.216-0.324-0.289-0.264-0.258-0.255DJ0.6150.5660.4720.1300.5730.3340.2460.355-0.0940.136DK0.3330.4920.1390.2480.107-0.036-0.110-0.2410.025DL0.5650.3050.7030.5940.4270.4690.2750.459DM0.2300.4400.3340.2060.483-0.0460.139DN0.303-0.0010.0010.051-0.250-0.247G50 0.8230.7830.5570.4010.456G51 0.8560.4390.6700.734G52 0.5720.6550.729H 0.1750.346I60 0.712K73  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.7c: Location Quotient Correlation Coefficients (Poland, 2011) 

Written by Cambridge Econometrics 

131 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.3350.0020.0070.4080.3190.3010.1930.212-0.466-0.4230.080-0.292-0.0570.126-0.3280.4790.1870.0900.2450.1770.1730.0330.061-0.257C11-0.130-0.059-0.185-0.1990.6740.205-0.150-0.4750.041-0.284-0.257-0.027-0.109-0.120-0.1380.2680.0110.4750.5580.6530.2030.4580.505C130.753-0.1840.3100.005-0.1060.3720.213-0.0450.0640.2870.6460.1790.2890.1930.475-0.0720.0760.1680.058-0.137-0.054-0.129C14-0.2540.0670.033-0.1670.1620.069-0.225-0.2210.0590.190-0.347-0.207-0.0420.138-0.153-0.2490.025-0.106-0.187-0.139-0.095C160.059-0.339-0.1400.193-0.026-0.3150.178-0.054-0.2340.1630.0260.693-0.0090.1120.123-0.291-0.0640.314-0.076-0.447C170.3110.4640.324-0.137-0.2230.4520.3860.1740.1990.0570.2960.3830.1420.2750.3520.228-0.0210.244-0.100C180.3060.046-0.413-0.095-0.0900.0020.018-0.221-0.293-0.0660.243-0.0040.4800.8010.7850.2080.6120.632C200.125-0.2430.1090.4240.1790.243-0.1020.098-0.1430.2050.3700.4660.4230.3940.3500.4730.224C22-0.074-0.0040.2760.2860.4500.3900.1360.5180.4610.1400.2270.2780.1500.145-0.035-0.088C230.3620.3840.0140.3740.3470.5090.021-0.158-0.110-0.177-0.388-0.256-0.253-0.322-0.258C240.483-0.1130.4880.2780.690-0.3060.0210.2210.1550.018-0.0360.066-0.0560.129C250.3010.5130.4350.5640.1220.3810.6480.4840.1700.1150.3260.151-0.096C260.3100.1520.146-0.0350.6460.5600.4000.3670.2560.4350.3720.390C270.5270.6620.1520.5230.2670.4290.2720.2290.1190.0620.075C280.6140.4170.4280.0790.227-0.051-0.056-0.254-0.228-0.263C290.1040.3210.2320.3750.0390.0300.0410.088-0.036C310.112-0.1400.149-0.1010.087-0.010-0.123-0.364C320.5940.7200.6430.5240.3950.4170.293C330.6630.4310.2680.6950.4960.361G45 0.7920.8200.6930.7900.548G46 0.8370.4740.8410.754G47 0.5860.7450.645I 0.5980.450H49 0.793M72  
 
 
 
 
 
Table 5.7d: Location Quotient Correlation Coefficients (Poland, 2015) 

Written by Cambridge Econometrics 

132 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.5640.1520.0900.4720.2800.516-0.0420.184-0.365-0.3330.075-0.3830.0120.247-0.3320.5020.346-0.1280.3680.3190.4600.1310.278-0.177C110.0010.023-0.054-0.1530.5430.231-0.013-0.2730.131-0.120-0.3480.1520.2830.0400.0250.2810.0740.3990.4610.5320.0620.3570.186C130.727-0.0690.3690.087-0.0570.4330.168-0.0450.0960.2340.6740.1790.3040.3380.669-0.0450.2190.2400.297-0.0690.306-0.075C14-0.2310.0140.089-0.1590.0450.145-0.151-0.236-0.0190.237-0.227-0.141-0.0330.257-0.280-0.1930.0290.026-0.186-0.019-0.081C160.126-0.170-0.0420.236-0.051-0.2790.230-0.009-0.0490.238-0.0050.6290.010-0.0140.274-0.2300.0430.3730.095-0.380C170.2370.2800.241-0.067-0.1620.5110.2450.2610.2360.1060.3450.369-0.0260.2830.2280.242-0.0850.447-0.157C180.1260.059-0.3320.0880.006-0.0750.057-0.083-0.1640.0980.363-0.0780.5530.8170.8190.2560.5920.601C200.228-0.0980.1760.4240.1090.2870.0000.233-0.1760.1800.4260.3360.2390.2170.3810.3790.169C220.1020.2580.4790.1840.4670.4950.3940.5100.6480.1220.3750.2640.2740.1350.139-0.103C230.4250.349-0.0500.4020.3190.5320.025-0.157-0.167-0.099-0.292-0.158-0.285-0.222-0.346C240.475-0.0200.4410.3640.693-0.2550.1370.3010.3370.1790.1390.1480.0820.184C250.2360.4680.4930.5530.1720.3940.5180.5380.1920.1890.2810.327-0.119C260.1390.0160.287-0.0250.4460.4750.2550.2160.1470.3140.3680.432C270.3420.6740.2740.5280.3260.4680.2490.3350.1720.3780.021C280.6100.2550.4570.0870.3460.1000.153-0.1820.144-0.389C290.0850.3630.3580.5150.1830.2210.0950.3720.026C310.262-0.2360.3070.0410.330-0.0340.160-0.278C320.4090.6350.6220.6200.2970.5410.233C330.4930.2840.1410.6160.3650.381G45 0.7560.8250.6280.8510.461G46 0.8240.3480.8120.725G47 0.4210.7380.551I 0.4630.508H49 0.562M72  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.8a: Poland Location Quotient Correlation Coefficients Changes (2003-2007) 

Written by Cambridge Econometrics 

133 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA0.0900.2030.1960.1890.2350.0010.1250.083-0.001-0.0590.0460.3410.2930.3980.2200.0040.014DB-0.0330.090-0.0130.116-0.0570.014-0.0250.0130.081-0.008-0.0890.0520.076-0.017-0.018-0.006DD0.1650.1300.0880.0100.1710.1440.0700.0640.0200.1950.1280.0520.1400.0770.066DE-0.0530.071-0.0700.0910.030-0.078-0.0370.2050.1530.0680.1920.078-0.076-0.162DG0.158-0.0720.0840.121-0.0660.0480.2090.1250.0930.1770.096-0.031-0.042DH0.0720.1140.241-0.003-0.0230.1610.0370.0000.0660.004-0.125-0.125DI-0.1140.0890.1840.106-0.086-0.127-0.223-0.038-0.093-0.059DJ-0.0620.0530.2240.1870.0900.002-0.1160.073-0.0280.000DK0.0930.1900.227-0.009-0.001-0.058-0.061-0.0800.043DL0.1500.014-0.111-0.115-0.183-0.089-0.055-0.057DM0.1350.119-0.027-0.010-0.0870.0110.024DN0.1780.1360.100-0.0870.0310.042G50 -0.0170.036-0.025-0.038-0.101G51 0.055-0.029-0.041-0.112G52 -0.122-0.016-0.026H 0.0580.070I60 -0.178K73  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.8b: Poland Location Quotient Correlation Coefficients Changes (2011-2015) 

Written by Cambridge Econometrics 

134 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.2300.1500.0830.064-0.0380.215-0.235-0.0280.1010.090-0.004-0.0910.0690.121-0.0040.0240.159-0.2190.1230.1410.2870.0980.2170.079C110.1310.0820.1310.046-0.1310.0260.1370.2020.0900.164-0.0910.1790.3920.1590.1630.0130.063-0.077-0.097-0.121-0.141-0.100-0.319C13-0.0270.1150.0590.0810.0480.061-0.0450.0000.032-0.0530.0280.0000.0150.1450.1940.0270.1440.0710.2400.0680.3600.054C140.023-0.0520.0560.009-0.1170.0760.074-0.015-0.0780.0470.1200.0660.0090.119-0.1270.0570.0040.1320.0010.1200.014C160.0670.1690.0980.043-0.0250.0360.0520.0450.1850.076-0.031-0.0640.019-0.1250.1510.0600.1070.0590.1710.068C17-0.074-0.184-0.0830.0710.0610.059-0.1410.0870.0360.0490.049-0.014-0.1680.008-0.1250.013-0.0640.203-0.057C18-0.1800.0120.0810.1820.096-0.0770.0390.1380.1290.1650.119-0.0740.0730.0160.0330.048-0.020-0.031C200.1030.1450.0670.000-0.0700.0450.1020.136-0.033-0.0240.056-0.129-0.185-0.1760.031-0.095-0.055C220.1760.2620.203-0.1020.0170.1050.259-0.0080.187-0.0170.147-0.0140.124-0.0100.174-0.015C230.063-0.036-0.0640.028-0.0290.0230.0040.001-0.0570.0790.0970.098-0.0320.100-0.088C24-0.0080.093-0.0470.0850.0030.0510.1160.0800.1820.1610.1740.0820.1380.054C25-0.065-0.0440.057-0.0110.0500.013-0.1300.0540.0220.074-0.0450.176-0.024C26-0.171-0.1370.1410.010-0.199-0.085-0.144-0.151-0.109-0.121-0.0040.042C27-0.1850.0120.1220.0050.0590.039-0.0230.1060.0530.316-0.054C28-0.003-0.1620.0300.0080.1190.1510.2090.0720.372-0.126C29-0.0190.0420.1260.1390.1450.1910.0540.2840.061C310.150-0.0960.1570.1410.243-0.0240.2830.086C32-0.185-0.086-0.0210.096-0.0980.124-0.060C33-0.171-0.147-0.127-0.080-0.1310.020G45 -0.0360.005-0.0660.061-0.087G46 -0.013-0.127-0.029-0.029G47 -0.165-0.008-0.094I -0.1360.058H49 -0.231M72  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.9a: Location Quotient Correlation Coefficients (UK, 2003) 

Written by Cambridge Econometrics 

135 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA0.2290.814-0.0380.0360.4360.2120.1360.179-0.301-0.0400.3610.347-0.0420.1180.1020.623-0.286DB0.2020.2520.1620.4340.2020.1000.196-0.0840.2600.437-0.0390.227-0.119-0.1840.217-0.380DD-0.0760.0850.6130.2760.2760.341-0.1560.1270.4710.441-0.0260.3250.1020.442-0.233DE0.042-0.198-0.269-0.399-0.174-0.285-0.2990.042-0.1520.364-0.220-0.164-0.0100.202DG0.2020.0090.033-0.051-0.1640.2040.2700.1480.0760.233-0.1370.287-0.023DH0.4730.6530.6480.0930.4510.7230.2870.0930.116-0.1510.460-0.229DI0.4130.5200.0790.1310.2010.2390.0890.051-0.0960.266-0.309DJ0.5890.1010.5040.5620.1250.050-0.078-0.2170.436-0.274DK0.4000.2780.4310.2870.285-0.088-0.4260.086-0.021DL0.178-0.0400.1990.0970.090-0.357-0.3820.178DM0.4720.202-0.0130.074-0.1120.038-0.309DN0.2070.0580.120-0.1920.312-0.277G50 0.2320.5760.1050.1170.030G51 -0.112-0.4460.1480.398G52 0.372-0.029-0.032H -0.0270.037I60 -0.145K73  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.9b: Location Quotient Correlation Coefficients (UK, 2007) 

Written by Cambridge Econometrics 

136 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA0.1930.616-0.0210.0380.4220.3050.1390.229-0.2790.0650.1670.354-0.0510.1670.2480.443-0.334DB0.0380.2470.1360.4950.2380.1160.250-0.1450.2510.6340.0220.247-0.094-0.1980.156-0.381DD-0.3100.0720.5490.4730.4180.259-0.0780.2730.2300.512-0.2470.3970.1720.320-0.416DE-0.042-0.281-0.253-0.390-0.255-0.158-0.331-0.067-0.3150.192-0.267-0.0440.1320.258DG0.2490.0670.1200.058-0.1530.2330.0990.2900.0920.321-0.1290.472-0.015DH0.5440.6070.5610.0020.5230.6840.5380.0290.254-0.0200.375-0.386DI0.5120.581-0.1070.2050.3390.3150.0430.059-0.2040.368-0.389DJ0.4870.0090.4930.5900.275-0.0340.024-0.2080.260-0.392DK0.3170.3060.4240.4640.334-0.039-0.2970.129-0.094DL0.0870.0460.4580.3490.229-0.279-0.3510.473DM0.5740.360-0.0510.1200.0430.021-0.318DN0.2740.0660.110-0.2080.028-0.462G50 0.3460.6930.0550.3190.069G51 0.006-0.4280.1370.311G52 0.2170.257-0.140H -0.008-0.103I60 -0.114K73  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.9c: Location Quotient Correlation Coefficients (UK, 2011) 

Written by Cambridge Econometrics 

137 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.2410.2100.2360.6090.5800.0520.2610.5100.3190.2390.125-0.3030.1680.104-0.0990.189-0.1620.1100.3080.0930.2470.0910.521-0.329C110.1590.1050.2450.109-0.109-0.1490.0310.139-0.1010.1020.0950.1300.266-0.067-0.029-0.1300.1080.0970.130-0.029-0.0450.098-0.013C130.5810.1390.3720.4810.2410.3120.2150.0030.150-0.2340.2150.201-0.0760.668-0.012-0.184-0.0430.163-0.167-0.265-0.015-0.369C140.0960.2880.3940.0820.2670.153-0.0640.204-0.219-0.0510.234-0.0300.3710.026-0.1490.0250.317-0.210-0.194-0.079-0.329C160.6490.0270.1610.6630.3700.6420.320-0.0940.4150.2280.0160.1270.0160.0050.280-0.1700.3710.2150.330-0.436C170.3610.4060.5650.1860.4490.064-0.0920.1920.039-0.0340.276-0.100-0.0780.1770.0980.126-0.0010.307-0.254C180.2420.0490.043-0.272-0.0870.1560.1650.2580.0580.4120.041-0.0380.1100.2920.045-0.263-0.031-0.002C200.2780.3160.1790.260-0.131-0.0520.1220.3940.298-0.2590.0050.0800.1280.033-0.2470.2510.226C220.5190.6160.569-0.1170.3860.4160.2780.4050.254-0.0470.4830.1540.174-0.0770.218-0.405C230.3000.385-0.2230.2170.3800.2260.403-0.043-0.1770.2770.0830.220-0.3070.259-0.256C240.368-0.2620.083-0.0830.0990.1250.062-0.0090.108-0.1840.1310.2660.399-0.300C25-0.1840.3470.5870.6380.3730.2580.0710.3890.079-0.080-0.3440.121-0.293C260.3780.158-0.142-0.1900.3950.3600.3030.2950.150-0.020-0.4520.417C270.5260.1440.1930.3180.0960.381-0.1550.147-0.053-0.002-0.289C280.5300.3280.214-0.1080.5150.180-0.113-0.268-0.086-0.108C290.1380.052-0.0270.2390.035-0.126-0.3110.0540.025C310.224-0.0980.2620.3260.014-0.4710.128-0.295C320.4720.5750.2530.123-0.016-0.360-0.170C330.2920.0290.2970.1930.060-0.028G45 0.4060.408-0.0170.125-0.046G46 -0.088-0.479-0.2990.221G47 0.2840.369-0.136I 0.224-0.001H49 -0.158M72  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.9d: Location Quotient Correlation Coefficients (UK, 2015) 

Written by Cambridge Econometrics 

138 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.3390.2230.0030.6570.5130.2020.1200.5800.2100.1850.1420.0730.1600.326-0.0640.392-0.2820.4050.1830.1240.3830.0990.419-0.344C110.081-0.0480.1520.037-0.089-0.154-0.0100.036-0.113-0.0470.1670.0110.189-0.045-0.113-0.2190.2040.0990.097-0.0730.2640.0550.150C130.1730.0190.4550.3450.2850.3930.138-0.0020.125-0.0670.1850.301-0.0880.507-0.131-0.073-0.1770.1890.004-0.194-0.067-0.305C14-0.0700.1150.246-0.1470.1160.095-0.1160.003-0.025-0.2340.086-0.0870.205-0.253-0.162-0.3220.034-0.285-0.271-0.041-0.139C160.2370.2780.3750.6510.4860.3090.429-0.0410.1680.3750.1490.595-0.1040.5520.3260.0980.122-0.1180.535-0.295C170.2520.2450.5760.0540.5120.1550.1070.3230.244-0.0410.361-0.0540.0360.012-0.0100.3120.0940.282-0.270C180.3150.2880.194-0.2090.204-0.0660.2550.4440.1770.5380.2320.2920.0170.079-0.135-0.3940.059-0.101C200.3110.2470.0380.277-0.2350.2720.2040.2120.5400.0370.265-0.058-0.0690.047-0.2600.217-0.062C220.4960.4280.6360.0340.3320.4480.2560.673-0.0650.3090.3310.2000.291-0.1740.464-0.338C230.1350.4980.0260.1620.3990.2400.332-0.0520.2430.1480.2600.069-0.2920.403-0.279C240.278-0.1190.253-0.192-0.0180.0850.1080.0740.027-0.2500.1470.3660.464-0.271C250.0000.4130.5010.6160.4530.2870.4050.3160.259-0.090-0.3810.322-0.230C260.3160.359-0.184-0.1490.2170.1670.5050.2480.197-0.182-0.2720.290C270.3980.2350.1960.3980.3150.278-0.2210.1720.0770.077-0.244C280.3360.392-0.0780.2980.4430.360-0.015-0.3860.109-0.042C290.0410.0990.0390.1070.183-0.198-0.2460.222-0.149C31-0.0110.2870.1730.0920.019-0.4090.258-0.236C320.3840.199-0.2230.004-0.113-0.1790.085C330.389-0.0020.096-0.0240.141-0.196G45 0.2980.373-0.0880.0650.102G46 -0.169-0.483-0.0570.139G47 0.2110.298-0.252I -0.133-0.108H49 -0.384M72  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.10a: UK Location Quotient Correlation Coefficients Changes (2003-2007) 

Written by Cambridge Econometrics 

139 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA-0.036-0.1980.0170.002-0.0140.0930.0030.0510.0230.106-0.1940.007-0.0090.0490.146-0.179-0.048DB-0.164-0.004-0.0270.0610.0360.0160.054-0.061-0.0090.1970.0610.0200.025-0.014-0.061-0.001DD-0.233-0.013-0.0650.1970.141-0.0820.0780.145-0.2410.071-0.2220.0720.070-0.121-0.183DE-0.084-0.0830.0160.009-0.0810.127-0.032-0.108-0.164-0.172-0.0480.1200.1420.056DG0.0470.0570.0870.1090.0110.029-0.1710.1420.0160.0880.0080.1850.008DH0.071-0.047-0.087-0.0920.073-0.0380.250-0.0640.1390.131-0.085-0.158DI0.0990.060-0.1860.0740.1380.076-0.0460.008-0.1080.102-0.080DJ-0.102-0.092-0.0120.0280.151-0.0840.1020.009-0.176-0.119DK-0.0820.029-0.0070.1770.0490.0500.1290.043-0.073DL-0.0910.0860.2590.2520.1400.0770.0320.295DM0.1020.158-0.0380.0450.155-0.017-0.009DN0.0670.009-0.010-0.015-0.284-0.185G50 0.1140.118-0.0500.2030.038G51 0.1190.018-0.010-0.087G52 -0.1550.286-0.108H 0.019-0.139I60 0.031K73  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.10b: UK Location Quotient Correlation Coefficients Changes (2011-2015) 

Written by Cambridge Econometrics 

140 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.0980.013-0.2330.047-0.0670.149-0.1420.070-0.109-0.0550.0170.376-0.0080.2220.0350.203-0.1200.296-0.1250.0310.1360.009-0.103-0.015C11-0.078-0.152-0.093-0.0710.020-0.005-0.041-0.103-0.012-0.1490.071-0.118-0.0770.022-0.083-0.0900.0960.001-0.033-0.0440.309-0.0430.163C13-0.408-0.1200.083-0.1360.0440.081-0.078-0.005-0.0250.166-0.0310.099-0.011-0.161-0.1190.112-0.1340.0260.1710.071-0.0520.063C14-0.166-0.173-0.148-0.230-0.151-0.058-0.052-0.2010.194-0.183-0.148-0.057-0.166-0.279-0.013-0.347-0.283-0.075-0.0780.0380.190C16-0.4120.2510.214-0.0120.116-0.3330.1090.053-0.2470.1470.1340.468-0.1200.5460.0460.268-0.249-0.3330.2050.141C17-0.109-0.1600.011-0.1320.0630.0910.1990.1320.205-0.0070.0850.0450.114-0.165-0.1080.1860.096-0.025-0.016C180.0740.2390.1500.0630.291-0.2220.0900.1860.1190.1260.1920.330-0.094-0.213-0.180-0.1310.090-0.099C200.032-0.069-0.1400.018-0.1040.3240.083-0.1820.2420.2960.260-0.138-0.1960.015-0.013-0.034-0.288C22-0.023-0.1880.0670.151-0.0540.032-0.0210.268-0.3190.356-0.1530.0460.117-0.0970.2460.067C23-0.1660.1120.249-0.0560.0190.014-0.070-0.0100.420-0.1280.176-0.1510.0150.145-0.023C24-0.0900.1430.170-0.108-0.117-0.0410.0470.083-0.082-0.0670.0150.1010.0650.029C250.1850.066-0.086-0.0220.0800.0300.334-0.0730.180-0.010-0.0370.2010.063C26-0.0620.200-0.0420.040-0.178-0.1930.203-0.0470.047-0.1620.179-0.126C27-0.1290.0920.0040.0800.220-0.103-0.0660.0250.1300.0790.044C28-0.1940.064-0.2910.406-0.0710.1800.098-0.1180.1950.066C29-0.0970.0470.066-0.1310.148-0.0720.0650.168-0.174C31-0.2350.385-0.090-0.2340.0050.0620.1310.059C32-0.087-0.376-0.476-0.119-0.0980.1810.255C330.097-0.031-0.201-0.2170.081-0.169G45 -0.108-0.035-0.071-0.0600.148G46 -0.081-0.0050.242-0.081G47 -0.073-0.071-0.116I -0.357-0.107H49 -0.226M72  
 
 
 
 
 
 
 
 
EG co-
agglomeration 
statistic 

EU-level results 

Table 5.6a-d provides a list of the highest (top-10) pairwise co-agglomerations 
according to the EG statistic calculated for all European regions in the sample, 
over each of the four years for which data are available (2003, 2007, 2011, 
and 2015). The first two years use NACE 1.1 sector classifications, while the 
final two years are defined on NACE 2 classifications. 

These top-10 lists do not include those pairwise relationships identified in the 
previous section which are likely to occur for reasons other than supply-chain 
reasons, e.g. most wholesale trade services linkages. Full details of the tables, 
including those statistics omitted from the top-10 list, are provided in Appendix 
C. 

Table 5.6a: Highest EU-wide Pairwise Co-agglomerations (2003) 

Rank 

Industry 1 

Industry 2 

EG Stat 

0.0032 

0.0030 

0.0030 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of electrical and optical 
equipment 

Manufacture of textiles and textile 
products 

Manufacturing n.e.c. 

Manufacture of basic metals and 
fabricated metal products 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of electrical and optical 
equipment 

Manufacture of textiles and textile 
products 

Manufacture of transport equipment 

0.0029 

Manufacture of wood and wood products 

0.0029 

Manufacture of textiles and textile 
products 

Manufacture of basic metals and fabricated 
metal products 

Manufacture of rubber and plastic 
products 

Manufacture of machinery and equipment 
n.e.c. 

0.0026 

0.0025 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of transport equipment 

0.0025 

1 

2 

3 

4 

5 

6 

7 

8 

9 

Manufacture of wood and wood products  Manufacturing n.e.c. 

10 

Manufacture of chemicals, chemical 
products and man-made fibres 

Manufacture of machinery and equipment 
n.e.c. 

Table 5.6b: Highest EU-wide Pairwise Co-agglomerations (2007) 

Rank 

Industry 1 

Industry 2 

1 

2 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of electrical and optical 
equipment 

Manufacture of textiles and textile 
products 

Manufacturing n.e.c. 

Written by Cambridge Econometrics 

0.0024 

0.0021 

EG Stat 

0.0034 

0.0034 

141 

 
 
 
 
 
 
Rank 

Industry 1 

Industry 2 

EG Stat 

3 

4 

5 

6 

7 

8 

9 

Manufacture of textiles and textile 
products 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of electrical and optical 
equipment 

Manufacture of wood and wood products 

0.0030 

Manufacture of transport equipment 

0.0030 

Manufacture of transport equipment 

0.0029 

Manufacture of textiles and textile 
products 

Manufacture of basic metals and fabricated 
metal products 

Manufacture of wood and wood products  Manufacturing n.e.c. 

Manufacture of basic metals and 
fabricated metal products 

Manufacture of machinery and equipment 
n.e.c. 

0.0027 

0.0026 

0.0023 

Manufacture of rubber and plastic 
products 

Manufacture of transport equipment 

0.0021 

10 

Manufacture of electrical and optical 
equipment 

Research and development 

0.0020 

Table 5.6c: Highest EU-wide Pairwise Co-agglomerations (2011) 

Rank 

Industry 1 

Industry 2 

1 

2 

3 

4 

5 

6 

7 

8 

9 

Manufacture of textiles 

Manufacture of wearing apparel 

Manufacture of electrical equipment 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of motor vehicles, trailers and 
semi-trailers 

Manufacture of computer, electronic and 
optical products 

Manufacture of motor vehicles, trailers and 
semi-trailers 

Manufacture of electrical equipment 

Manufacture of motor vehicles, trailers and 
semi-trailers 

Manufacture of chemicals and chemical 
products 

Manufacture of basic metals 

Manufacture of wearing apparel 

Manufacture of wood and of products of 
wood and cork, except furniture; manufacture 
of articles of straw and plaiting materials 

Manufacture of wearing apparel 

Manufacture of furniture 

Manufacture of basic metals 

Manufacture of machinery and equipment 
n.e.c. 

EG Stat 

0.0097 

0.0072 

0.0059 

0.0054 

0.0053 

0.0051 

0.0050 

0.0047 

0.0046 

10 

Manufacture of basic metals 

Manufacture of electrical equipment 

0.0039 

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142 

 
 
 
 
 
 
Table 5.6d: Highest EU-wide Pairwise Co-agglomerations (2015) 

Rank 

Industry 1 

Industry 2 

Manufacture of textiles 

Manufacture of wearing apparel 

Manufacture of electrical equipment 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of motor vehicles, trailers and 
semi-trailers 

Manufacture of computer, electronic and 
optical products 

Manufacture of motor vehicles, trailers and 
semi-trailers 

Manufacture of chemicals and chemical 
products 

Manufacture of electrical equipment 

Manufacture of basic metals 

Manufacture of basic metals 

Manufacture of motor vehicles, trailers and 
semi-trailers 

Manufacture of machinery and equipment 
n.e.c. 

EG Stat 

0.0093 

0.0079 

0.0065 

0.0062 

0.0060 

0.0058 

0.0050 

Manufacture of computer, electronic and 
optical products 

Manufacture of electrical equipment 

0.0047 

Manufacture of wearing apparel 

Manufacture of furniture 

Manufacture of wood and of products of 
wood and cork, except furniture; 
manufacture of articles of straw and 
plaiting materials 

Manufacture of furniture 

0.0047 

0.0044 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

Tests of 
significance 

The calculation and ranking of the EG statistics does not tell us very much 
unless we can establish the results are significant or not.  

Figure 5.11: Histogram from combined 2003/2007 EG statistics 

Written by Cambridge Econometrics 

143 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figures 5.11 and 5.12 show the histograms of the combined 2003/2007 and 
2011/2015 EG statistics, alongside distributional characteristics and a Jarque-
Bera test for normality. Both distributions are shown to be non-normal which 
means we cannot use +/- 2 standard deviations as a method for determining 
significance. 

Figure 5.12: Histogram from combined 2011/2015 EG statistics 

Instead we can attempt bootstrapping to work out the statistical properties of a 
distribution, whereby we undertake repeated random sampling to allow for a 
more rigorous estimation of variance and, ultimately, significance. 

Bootstrapping 
results 

We undertook repeated (1000) random sampling of the regional-sector 
employment and reproduced the equivalent histograms for the ones shown in 
Figures 5.11 and 5.12. These are shown in Figures 5.13 and 5.14 
respectively. 

Figure 5.13: Bootstrapped EG statistic histogram for 2003/2007  

Written by Cambridge Econometrics 

144 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.14: Bootstrapped EG statistic histogram for 2011/2015  

Comparing LQ 
and EG results 

Country-specific 
analysis 

The histograms generated by the bootstrapping method show that the top-10 
EG statistic values reported in Tables 5.6a-d are all significant49 and can then 
be compared with the significant LQ correlations, although there is little 
indication that the extent of co-agglomeration has increased over the periods. 

As with the LQ statistic, the sector combination that is most comparable 
across the NACE revisions (the manufacturing of wood and wood products, 
with that of pulp, paper and paper products) does not appear in any of the top-
10 lists in Tables 5.6a-d, but examination of the EG statistic for this does 
reveal again a gradual increase over the whole period50. 

The degree of correspondence between the LQ correlations and the EG 
statistics was assessed by undertaking a correlation for each year. The results 
showed little change across the two sets of period, with a correlation of around 
0.7 for the Rev1 periods (2003 and 2007) and 0.6 for the Rev2 period (2011 
and 2015). The lower correlation in the Rev2 periods can most likely be 
explained by the greater sectoral disaggregation. 

To round off the analysis, an individual Member State was selected to see 
how a single country differed from the averaged EU results. This time the 
focus was only on the UK, as the LQ analysis of Poland was limited by the 
number of regions available. The UK results are at two levels, firstly at NUTS2 
using the SBS data as previously, and secondly using CE’s local area 
database which extends the number of areas to around 400. The point of 
doing this is firstly to see what difference adding an order of magnitude to the 
spatial disaggregation has on the EG statistic results, and also because the 
local area data are available for a much longer and consistent time series 

49  The  thresholds  for  significance  at  the  95%  level  of  confidence  are  0.000099  for  the  Rev1 

periods and 0.00031 for the Rev2 periods. 

50 The values for this combination are -0.001, -0.0008, 0.0006, and 0.001 for the 2003, 2007, 

2011 and 2015 periods, respectively. 

Written by Cambridge Econometrics 

145 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
NUTS2 level 
analysis 

(1981-2017) and so the evolution of the EG statistics over the period when the 
UK was integrating as part of the EU. 

As noted in Table 5.3, among the other Member States in the sample, the UK 
contains a high number of NUTS2 regions, and so in theory should provide a 
more robust set of results than most other countries. Tables 5.7a-d report the 
top-10 combinations51, replicating the EU results from Tables 5.6a-d. The 
degree of correspondence (correlation) between the Rev1 periods is much 
higher 0.97) compared to the later Rev2 periods (0.69), possibly due to the 
smaller number of sectors in the Rev1 classification. Both are strongly positive 
and suggestive of persistence in co-location patterns, however. 

Table 5.7a: Highest UK Pairwise Co-agglomerations (2003) 

Rank 

Industry 1 

Industry 2 

Manufacture of transport equipment 

EG Stat 

0.0150 

Manufacture of basic metals and 
fabricated metal products 

Manufacture of rubber and plastic 
products 

Manufacture of transport equipment 

0.0075 

Manufacture of rubber and plastic 
products 

Manufacture of basic metals and fabricated 
metal products 

Manufacture of other non-metallic mineral 
products 

Manufacture of basic metals and fabricated 
metal products 

Manufacture of basic metals and 
fabricated metal products 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of rubber and plastic 
products 

Manufacture of other non-metallic mineral 
products 

Manufacture of other non-metallic mineral 
products 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of transport equipment 

Manufacturing n.e.c. 

Manufacture of textiles and textile 
products 

Manufacture of other non-metallic mineral 
products 

0.0072 

0.0069 

0.0067 

0.0066 

0.0066 

0.0061 

0.0060 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of transport equipment 

0.0060 

Table 5.7b: Highest UK Pairwise Co-agglomerations (2007) 

Rank 

Industry 1 

Industry 2 

1 

2 

3 

Manufacture of basic metals and 
fabricated metal products 

Manufacture of transport equipment 

Manufacture of other non-metallic mineral 
products 

Manufacture of basic metals and fabricated 
metal products 

Manufacture of rubber and plastic 
products 

Manufacture of other non-metallic mineral 
products 

EG Stat 

0.0102 

0.0081 

0.0078 

51  The  95%  confidence  limit  for  the  Rev1  periods  is  calculated  to  be  0.00072  while  for  the 

Rev2 periods it is 0.00172. 

Written by Cambridge Econometrics 

146 

 
 
 
                                           
4 

5 

6 

7 

8 

9 

1 

2 

3 

4 

5 

6 

7 

8 

9 

Rank 

Industry 1 

Industry 2 

Manufacture of textiles and textile 
products 

Manufacture of other non-metallic mineral 
products 

Manufacture of other non-metallic mineral 
products 

Manufacture of machinery and equipment 
n.e.c. 

0.0066 

Manufacture of rubber and plastic 
products 

Manufacture of transport equipment 

0.0066 

Manufacture of rubber and plastic 
products 

Manufacture of basic metals and fabricated 
metal products 

0.0065 

Manufacture of transport equipment 

0.0065 

Manufacture of other non-metallic mineral 
products 

Manufacture of textiles and textile 
products 

10 

Manufacture of basic metals and 
fabricated metal products 

Manufacturing n.e.c. 

Manufacturing n.e.c. 

Table 5.7c: Highest UK Pairwise Co-agglomerations (2011) 

Rank 

Industry 1 

Industry 2 

Manufacture of textiles 

Manufacture of wearing apparel 

Manufacture of basic metals 

Manufacture of motor vehicles, trailers and 
semi-trailers 

Manufacture of textiles 

Manufacture of furniture 

Manufacture of beverages 

Manufacture of wood and of products of 
wood and cork, except furniture; manufacture 
of articles of straw and plaiting materials 

Manufacture of textiles 

Manufacture of paper and paper products 

0.0138 

Manufacture of fabricated metal products, 
except machinery and equipment 

Manufacture of motor vehicles, trailers and 
semi-trailers 

Manufacture of basic metals 

Manufacture of fabricated metal products, 
except machinery and equipment 

Manufacture of beverages 

Manufacture of textiles 

Manufacture of machinery and equipment 
n.e.c. 

Manufacture of motor vehicles, trailers and 
semi-trailers 

10 

Manufacture of textiles 

Manufacture of chemicals and chemical 
products 

EG Stat 

0.0066 

0.0064 

0.0061 

EG Stat 

0.0213 

0.0207 

0.0169 

0.0139 

0.0138 

0.0132 

0.0114 

0.0113 

0.0110 

Written by Cambridge Econometrics 

147 

 
 
 
 
 
 
 
 
1 

2 

3 

4 

5 

6 

7 

8 

9 

Table 5.7d: Highest UK Pairwise Co-agglomerations (2015) 

Rank 

Industry 1 

Industry 2 

Manufacture of basic metals 

Manufacture of motor vehicles, trailers and 
semi-trailers 

Manufacture of fabricated metal products, 
except machinery and equipment 

Manufacture of motor vehicles, trailers and 
semi-trailers 

Manufacture of basic metals 

Manufacture of fabricated metal products, 
except machinery and equipment 

EG Stat 

0.0231 

0.0167 

0.0149 

Manufacture of textiles 

Manufacture of paper and paper products 

0.0144 

Manufacture of beverages 

Repair and installation of machinery and 
equipment 

Manufacture of textiles 

Manufacture of furniture 

Manufacture of textiles 

Manufacture of rubber and plastic 
products 

Manufacture of wood and of products of 
wood and cork, except furniture; 
manufacture of articles of straw and 
plaiting materials 

Manufacture of chemicals and chemical 
products 

Manufacture of basic metals 

Manufacture of basic metals 

0.0133 

0.0129 

0.0126 

0.0113 

0.0107 

10 

Manufacture of other non-metallic mineral 
products 

Manufacture of basic metals 

0.0106 

Local area 
analysis 

The sector classification for the CE database follows the Rev2 system, and as 
many of the corresponding sectors52 that are covered there are included in the 
co-location analysis. Tables 5.8a-d show the equivalent results using local 
areas instead of NUTS2 regions53. These generally tend to show co-location is 
strongest among the manufacturing sectors (where IO linkages might be 
expected to be strongest), and is highly persistent with correlations of 0.97, 
0.96, and 0.94 between the years of between the EG results for 2003-07, 
2007-11, and 2011-15, respectively. 

Table 5.8a: Highest UK Local Area Pairwise Co-agglomerations (2003) 

Rank 

Industry 1 

Industry 2 

1 

2 

3 

4 

Accommodation 

Metals & metal prods. 

Machinery, etc 

Other professional services54 

Motor vehicles, etc 

Motor vehicles, etc 

Electrical equipment 

Machinery, etc 

EG Stat 

0.0018 

0.0017 

0.0013 

0.0012 

52 The correspondence is not exact, as the sector disaggregation in CE’s local area database is 

a mix of two-digit activities, and aggregates thereof. 

53  The  relevant  95%  confidence  limits  are  0.00008,  0.00012,  0.00018,  and  0.00019  for  the 

respective years. 

54 This sector is included because it contains R&D services, as part of a wider aggregation of 

activities. 

Written by Cambridge Econometrics 

148 

 
 
 
                                           
Rank 

Industry 1 

Industry 2 

5 

6 

7 

8 

9 

Electrical equipment 

Motor vehicles, etc 

Wood & paper 

Chemicals, etc 

Non-metallic min. prods. 

Metals & metal prods. 

Non-metallic min. prods. 

Machinery, etc 

Non-metallic min. prods. 

Motor vehicles, etc 

10 

Metals & metal prods. 

Machinery, etc 

EG Stat 

0.0012 

0.0011 

0.0011 

0.0011 

0.0011 

0.0011 

Table 5.8b: Highest UK Local Area Pairwise Co-agglomerations (2007) 

Rank 

Industry 1 

Industry 2 

EG Stat 

1 

2 

3 

4 

5 

6 

7 

8 

9 

Accommodation 

Metals & metal prods. 

Electrical equipment 

Wood & paper 

Wood & paper 

Chemicals, etc 

Metals & metal prods. 

Electrical equipment 

Textiles, etc 

10 

Non-metallic min. prods. 

Other professional services 

Motor vehicles, etc 

Motor vehicles, etc 

Chemicals, etc 

Non-metallic min. prods. 

Non-metallic min. prods. 

Machinery, etc 

Machinery, etc 

Chemicals, etc 

Machinery, etc 

0.0014 

0.0014 

0.0013 

0.0011 

0.0011 

0.0011 

0.0011 

0.0011 

0.0010 

0.0010 

Table 5.8c: Highest UK Local Area Pairwise Co-agglomerations (2011) 

Rank 

Industry 1 

Machinery, etc 

Textiles, etc 

Industry 2 

Motor vehicles, etc 

Chemicals, etc 

Metals & metal prods. 

Motor vehicles, etc 

Wood & paper 

Metals & metal prods. 

Electrical equipment 

Food, drink & tobacco 

Chemicals, etc 

Accommodation 

Chemicals, etc 

Machinery, etc 

Machinery, etc 

Wood & paper 

Non-metallic min. prods. 

Other professional services 

10 

Wood & paper 

Non-metallic min. prods. 

1 

2 

3 

4 

5 

6 

7 

8 

9 

EG Stat 

0.0017 

0.0014 

0.0014 

0.0013 

0.0013 

0.0013 

0.0012 

0.0012 

0.0012 

0.0011 

Table 5.8d: Highest UK Local Area Pairwise Co-agglomerations (2015) 

Written by Cambridge Econometrics 

149 

 
 
 
 
 
Rank 

Industry 1 

Industry 2 

EG Stat 

1 

2 

3 

4 

5 

6 

7 

8 

9 

Wood & paper 

Non-metallic min. prods. 

Metals & metal prods. 

Motor vehicles, etc 

Wood & paper 

Chemicals, etc 

Electrical equipment 

Motor vehicles, etc 

Food, drink & tobacco 

Wood & paper 

Chemicals, etc 

Non-metallic min. prods. 

Non-metallic min. prods. 

Metals & metal prods. 

Wood & paper 

Electrical equipment 

Electrical equipment 

Machinery, etc 

10 

Wood & paper 

Metals & metal prods. 

0.0015 

0.0014 

0.0013 

0.0013 

0.0012 

0.0012 

0.0012 

0.0012 

0.0012 

0.0011 

To review all the EG statistics over time would be a task well beyond the remit 
of this study, and so a single co-location (Metals & metal products – Motor 
vehicles, etc) has been chosen as one which is consistently strong in the 
tables, as well as having a prior expectation from the IO analysis. Figure 5.15 
charts the evolution of this co-location statistic from 1981-2017. The 
downward trend is suggestive of a gradual weakening of ties between the 
location outcomes of the sector, possibly caused by the increasing importance 
of global supply chain linkages. 

Figure 5.15: Metals & Metal Prods – Motor Vehicles EG statistic 1981-
2017  

0.0025

c
i
t
s
i
t
a
t
S
G
E

0.0020

0.0015

0.0010

0.0005

0.0000

1981

1986

1991

1996

2001

2006

2011

2016

General input-
output analysis 

As a final exploration of the link between the co-location statistics and input-
output linkages, correlation analysis was attempted between the EG results 

Written by Cambridge Econometrics 

150 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Summary 

and the IO tables, firstly to see if such correlations recorded positive results 
and secondly whether there was evidence of changing strength in the 
correlation over time. This line of research broadly follows the analysis 
undertaken by Diodato et al (2018)55, whereby the strength of supply-chain 
linkages as a driver of co-location was assessed relative to other factors such 
as access to skilled labour. 

Unfortunately, the correspondence between the input-output table sectors 
(which are only available at Nace Rev2 classification, covering two of the 
years 2011 and 2015) and those from the SBS is not complete56 and so only a 
partial analysis could be undertaken. Another limitation of this analysis is that 
the EG statistic matrix is symmetric, i.e. the co-location takes no account of 
the expected direction of the supply-side linkage, which is relevant for the 
input-output table. 

With this in mind, the limitations of the data are too great to prove anything 
substantial when compared to the data available for the Diodato study, and so 
this part of the analysis was not undertaken. 

Testing for the degree of geographical co-location, agglomeration and 
clustering of economic activities across the EU, and how far and in what ways 
such spatial phenomena have varied over time is far from straightforward. 
Previous studies using data from the 1980s and 1990s have yielded mixed 
results, some finding evidence of increasing geographical concentration and 
agglomeration, others finding little or no evidence of such trends. 

Our analyses, focussing on the period since 2003, was constrained by data 
issues that have imposed limitations on all aspects of the project, as 
presented in previous reports and chapters. The lack of detailed consistent 
firm-level data severely constrains any attempt to identify clusters, which 
functionally consist of numerous co-located firms in related sectors, linked in 
complex supply chains, input-output structures, knowledge exchange 
relationships, innovation networks and shared labour pools. Using sectoral 
data, as was the case here, input-output tables can be used to suggest 
possible supply-chain linkages that can generate expected co-location 
patterns of industries, but such patterns may not necessarily be functioning 
clusters in the strict Porterian sense.   

Even with our sectoral data there are other limitations, including the break in 
series as between NACE Rev 1.1 and NACE Rev 2, gaps in regional 
coverage, the restricted information on services, and the short overall time 
period for which analysis was conducted (2003-2015).  

Notwithstanding these issues, and using location quotients and the basic 
Ellison-Glaeser statistic, our analyses find some evidence of pairwise co-
location or geographical concentration of certain activities.  The findings are 
strongest for pairs of industries for which there are clear supply-chain, input-
output linkages, such as textiles and apparel; electrical equipment and 
computer, electronic and optical products; chemicals and chemical products; 
rubber and plastic products and other non-metallic mineral products; and basic 

55 It should  be  noted  that  the Diodato  (2018)  study  was based  on  US  and  Mexico  data, with 
access to over 1000 metropolitan areas (the preferred geographical unit of analysis) and 
120  (traded)  industries.  Such  level  of  detail  is  important  when  contrasted  against  the 
limitations of the available EU data. 

56 In particular, in the IO tables food, beverages and tobacco are combined, as are textiles and 

wearing apparel. Finally, furniture is combined as part of other manufacturing. 

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151 

 
 
                                           
metals with fabricated metal products, except machinery and equipment; and 
manufacturing of wood and wood products, with that of pulp, paper and paper 
products. There is also limited evidence that some of these co-location 
concentrations have increased over the period of the study. Overall, however, 
the findings do not suggest that there has been any substantial increase in 
geographical agglomeration and co-location across sectors since the early-
2000s. 

How then do our findings relate to what theory would lead us to expect? The 
dominant view (for example in the New Economic Geography) is that 
increasing economic integration promotes trade, which in turn promotes 
increasing returns from greater regional specialisation and geographical 
concentration of activities.  Thus, increasing economic integration in the EU, to 
the extent that it leads to increased intra-EU trade, and assuming other things 
being equal, should tend to promote greater geographical specialisation, 
agglomeration and clustering of industries, as both member states and their 
regions are able to take advantage of the increasing returns effects associated 
with scale of production among localised interrelated and linked  firms and 
industries. In a classic paper, Krugman (1993) used the case of the United 
States to demonstrate this argument, and moreover to argue that as the EU 
became more integrated (especially in terms of monetary union), so economic 
activity there would become more geographical agglomerated and specialised.  
However, interestingly, by the time of his  Nobel Prize lecture in 2008, 
Krugman had changed his mind, and argued that the process of regional 
specialisation and agglomeration in the advanced economies of the world had 
actually probably peaked in the 1930s, long before the New Economic Theory 
of agglomeration was developed (in large part by Krugman himself in the 
1990s) - and long before the EU was formed.  In Krugman’s view, the 
phenomenon of increasing spatial agglomeration of industry is now largely 
confined to younger and emerging economies (such as China). 

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152 

 
 
 
 
 
6 

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Appendices 

The contents of the appendices is as follows: 

  Appendix A: Balassa Index Heat Maps 

  Appendix B: Manufacturing Sector Coverage for the Theil Indices 

  Appendix C: Detailed Ellison-Glaeser Statistics 

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Appendix A  Balassa Index Heat Maps 

This Appendix contains sector-based heat maps across each Member State 
for  the  periods  2000  and  2014  for  the  WIOD  database,  and  for  1995  and 
2016 for the STAN database. 

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Figure A1: Balassa Index heat map across Member State Sectors based on WIOD database, 2000 

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160 

CPAProductATBEDEDKELESFIFRIEITLUNLPTSEUKCYCZEEHULTLVMTPLSISKBGROHRAverageCPA_A01Products of agriculture, hunting and related services0.311.010.451.183.632.770.331.380.710.840.142.590.340.160.330.850.690.981.121.280.190.120.850.240.493.211.590.851.02CPA_A02Products of forestry, logging and related services1.231.050.940.780.350.761.530.990.000.500.880.872.881.030.160.024.4165.892.704.3854.403.551.482.085.651.4511.415.896.33CPA_A03Fish and other fishing products; aquaculture products; support services to fishing0.020.310.415.471.442.470.041.130.000.600.031.141.112.411.241.950.6910.770.150.051.791.680.340.010.050.110.082.581.36CPA_BMining and quarrying0.070.830.171.410.500.120.090.050.020.060.015.530.330.202.060.060.340.250.140.160.250.390.980.010.061.030.110.060.55CPA_C10-C12Food products, beverages and tobacco products0.721.550.632.500.011.150.291.212.410.840.081.570.690.300.750.520.490.830.821.220.690.271.140.370.310.650.250.740.82CPA_C13-C15Textiles, wearing apparel and leather products0.890.910.720.791.101.040.240.860.192.860.220.494.090.290.770.080.962.141.383.941.701.362.521.541.301.732.691.071.35CPA_C16Wood and of products of wood and cork, except furniture; articles of straw and plaiting materials3.441.150.651.040.120.675.470.530.320.590.430.335.593.280.160.032.0314.371.045.3721.320.014.414.461.801.464.233.483.13CPA_C17Paper and paper products1.950.780.950.430.200.698.570.770.140.680.200.612.593.590.400.050.790.910.440.200.320.031.112.361.970.290.380.611.14CPA_C18Printing and recording services8.970.361.750.090.050.030.740.076.380.331.280.410.130.040.110.000.8214.000.130.040.034.410.060.100.050.030.000.061.44CPA_C19Coke and refined petroleum products 0.262.010.470.783.491.561.430.770.010.800.351.920.531.270.9714.250.500.170.495.830.240.020.750.121.853.303.052.471.77CPA_C20Chemicals and chemical products0.451.801.210.420.240.800.451.161.630.750.131.070.400.480.960.020.630.580.490.200.170.160.700.820.770.920.670.730.67CPA_C21Basic pharmaceutical products and pharmaceutical preparations0.660.500.802.650.220.560.201.013.160.980.021.220.181.441.160.200.280.200.440.290.660.510.162.160.170.380.091.470.78CPA_C22Rubber and plastics products1.081.411.240.790.351.040.690.980.301.351.070.651.240.780.740.061.470.630.820.290.241.211.231.741.290.230.270.470.85CPA_C23Other non-metallic mineral products1.271.420.880.750.641.750.580.810.302.240.880.402.230.490.560.232.831.280.810.120.440.211.431.881.771.162.051.581.11CPA_C24Basic metals1.581.781.110.281.380.831.471.050.220.871.560.620.721.270.690.091.070.410.590.131.760.081.661.924.045.553.170.351.30CPA_C25Fabricated metal products, except machinery and equipment1.540.901.270.720.160.930.880.810.201.611.020.631.421.080.660.062.421.910.850.380.470.531.642.031.200.670.400.640.97CPA_C26Computer, electronic and optical products0.510.530.900.530.000.352.210.902.660.420.261.750.441.421.330.020.380.471.880.340.131.780.320.280.380.070.470.120.74CPA_C27Electrical equipment1.300.661.310.640.260.781.161.000.851.270.220.520.991.060.790.022.030.822.100.350.191.221.223.221.450.251.600.651.00CPA_C28Machinery and equipment n.e.c.1.210.651.630.920.060.531.100.810.231.810.220.340.321.180.820.050.990.350.510.210.220.240.560.970.810.390.560.080.63CPA_C29Motor vehicles, trailers and semi-trailers1.091.321.660.140.001.760.291.160.090.650.070.311.231.040.650.031.410.291.480.360.060.031.161.021.940.020.110.090.70CPA_C30Other transport equipment1.880.191.180.310.530.560.702.170.070.910.050.280.240.581.370.410.280.490.130.600.190.011.000.330.420.060.681.070.60CPA_C31_C32Furniture; other manufactured goods1.051.850.841.380.110.690.340.631.292.430.060.500.310.820.700.130.891.820.561.181.161.852.182.640.510.131.510.931.02CPA_C33Repair and installation services of machinery and equipment4.450.381.212.420.000.004.751.400.000.090.081.454.010.940.000.660.002.580.720.001.4013.901.310.920.001.020.366.421.80CPA_D35Electricity, gas, steam and air-conditioning1.182.802.170.750.140.650.960.740.010.270.150.540.090.650.050.002.661.862.270.190.790.010.541.410.113.150.320.570.89CPA_E37-E39Sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management services 0.860.020.800.400.670.790.321.570.001.411.281.260.830.461.240.243.421.930.322.444.300.161.480.991.821.890.482.281.20CPA_FConstructions and construction works3.231.510.244.749.150.020.360.000.000.480.712.502.120.000.490.542.753.323.482.001.530.437.611.311.164.960.735.932.19CPA_G45Wholesale and retail trade and repair services of motor vehicles and motorcycles1.570.010.281.450.000.120.844.220.000.050.760.000.005.840.120.000.061.593.750.000.000.060.000.000.000.400.020.000.76CPA_G46Wholesale trade services, except of motor vehicles and motorcycles1.690.481.632.512.020.260.501.930.860.490.780.383.751.030.060.690.191.301.376.041.040.460.000.921.582.121.430.001.27CPA_G47Retail trade services, except of motor vehicles and motorcycles0.000.000.020.0013.150.000.465.480.000.003.780.000.003.260.005.810.003.300.000.000.000.000.000.000.007.196.223.011.85CPA_H49Land transport services and transport services via pipelines2.050.890.161.380.213.030.701.460.100.901.781.010.930.210.290.245.041.773.463.9412.610.023.712.974.253.035.126.012.40CPA_H50Water transport services0.110.500.947.7520.430.370.560.760.060.780.010.700.151.190.832.770.051.970.030.222.070.350.170.150.000.100.173.951.68CPA_H51Air transport services0.480.280.491.580.612.720.851.280.900.702.291.643.260.450.892.841.780.721.490.431.313.610.490.460.032.910.612.291.33CPA_H52Warehousing and support services for transportation0.692.470.330.960.842.220.300.580.171.850.541.350.702.300.393.750.627.031.772.015.704.390.940.540.380.730.704.271.73CPA_H53Postal and courier services2.391.880.481.880.310.430.980.583.260.240.812.680.340.000.790.620.500.260.350.820.910.042.020.020.691.160.240.540.90CPA_IAccommodation and food services3.041.091.240.140.000.000.580.003.470.000.020.001.880.002.920.001.430.120.861.870.000.080.000.000.000.174.510.000.84CPA_J58Publishing services1.001.061.020.450.130.810.270.306.140.770.400.910.150.710.970.601.940.200.620.360.620.192.060.580.700.290.012.010.90CPA_J59_J60Motion picture, video and television programme production services, sound recording and music publishing; programming and broadcasting services0.530.280.850.102.240.160.040.780.001.354.240.820.860.822.150.011.450.184.230.430.190.071.240.080.110.490.280.430.87CPA_J61Telecommunications services1.281.670.370.961.091.730.100.590.942.353.900.691.191.000.900.501.280.890.751.861.720.460.990.320.211.272.963.761.28CPA_J62_J63Computer programming, consultancy and related services; information services0.660.220.771.450.332.181.930.204.990.630.670.910.241.941.051.821.570.580.930.310.850.731.020.240.230.460.301.091.01CPA_K64Financial services, except insurance and pension funding1.360.390.540.210.520.900.320.311.670.1721.170.740.260.582.420.720.130.060.260.071.236.850.180.040.260.120.370.211.50CPA_K65Insurance, reinsurance and pension funding services, except compulsory social security1.100.270.460.560.730.410.230.224.220.966.170.230.100.343.130.290.390.070.060.120.540.520.390.090.070.072.700.420.89CPA_K66Services auxiliary to financial services and insurance services0.090.930.000.400.000.970.060.000.620.771.020.070.490.105.321.572.200.130.030.000.000.990.030.000.020.000.000.560.58CPA_L68Real estate services0.790.142.250.030.000.222.660.000.002.331.750.000.070.000.821.140.200.750.002.680.000.454.470.000.000.040.000.000.74CPA_M69_M70Legal and accounting services; services of head offices; management consulting services1.262.260.870.250.271.360.910.640.180.360.411.760.391.271.070.152.460.580.700.150.465.990.830.190.350.290.200.430.93CPA_M71Architectural and engineering services; technical testing and analysis services0.870.820.861.340.132.530.611.370.510.920.140.690.561.121.060.060.670.280.280.080.280.120.590.160.170.820.561.360.68CPA_M72Scientific research and development services1.060.950.970.400.230.833.150.950.211.361.010.610.071.201.480.000.310.080.480.110.260.020.440.090.240.630.290.390.64CPA_M73Advertising and market research services0.980.550.670.580.703.490.260.410.141.560.280.570.900.651.460.472.861.431.061.184.250.412.391.300.310.921.021.111.14CPA_M74_M75Other professional, scientific and technical services; veterinary services0.450.110.140.191.370.010.220.020.851.810.310.361.940.604.201.240.320.371.361.150.350.032.501.392.060.000.001.000.87CPA_N77Rental and leasing services0.540.150.140.130.100.730.691.951.871.310.172.770.191.780.2434.860.340.680.930.220.659.400.080.130.130.170.010.002.16CPA_N78Employment services0.630.181.190.360.000.001.470.000.002.140.560.220.000.003.250.080.041.380.000.003.240.090.000.000.000.000.000.080.53CPA_N79Travel agency, tour operator and other reservation services and related services0.310.020.000.000.000.000.090.000.003.892.631.170.089.030.3023.520.010.050.000.700.009.681.650.000.002.130.000.001.97CPA_N80-N82Security and investigation services; services to buildings and landscape; office administrative, office support and other business support services0.100.130.091.320.235.560.030.680.000.830.080.130.840.123.170.000.450.220.410.000.380.180.250.000.000.370.000.000.56CPA_O84Public administration and defence services; compulsory social security services0.930.011.071.500.002.791.040.000.120.000.071.050.002.621.4312.404.913.563.539.575.461.500.002.031.507.620.0037.493.65CPA_P85Education services0.180.450.000.181.460.000.670.000.000.300.810.170.062.645.120.001.441.100.330.860.010.451.070.111.7361.840.0545.834.53CPA_R90-R92Creative, arts and entertainment services; library, archive, museum and other cultural services; gambling and betting services0.610.370.410.130.843.890.051.220.250.550.010.470.470.072.550.000.740.110.030.570.096.270.490.020.000.053.461.920.92CPA_TOTALTotal            1.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.00Average1.240.840.801.101.321.111.010.910.961.001.240.941.001.221.232.121.252.911.001.222.521.591.200.850.842.351.242.90 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure A2: Balassa Index heat map across Member State Sectors based on WIOD database, 2014 

Written by Cambridge Econometrics 

161 

CPAProductATBEDEDKELESFIFRIEITLUNLPTSEUKCYCZEEHULTLVMTPLSISKBGROHRAverageCPA_A01Products of agriculture, hunting and related services0.400.760.521.642.332.440.671.481.900.840.161.550.920.290.210.810.800.731.533.362.760.041.260.530.874.222.241.851.32CPA_A02Products of forestry, logging and related services0.700.580.550.440.460.851.550.542.900.330.440.602.690.510.140.184.9214.791.435.2625.210.141.679.094.242.842.249.013.37CPA_A03Fish and other fishing products; aquaculture products; support services to fishing0.020.180.155.143.211.480.350.721.960.410.050.551.0810.421.231.760.421.260.120.251.921.660.090.050.060.220.224.781.42CPA_BMining and quarrying0.170.860.180.640.110.460.160.140.070.070.007.540.200.370.880.070.230.150.140.060.350.060.200.160.231.050.000.100.52CPA_C10-C12Food products, beverages and tobacco products1.091.360.821.880.271.500.421.192.251.200.071.041.060.480.480.210.730.950.971.311.500.241.470.300.191.060.391.300.92CPA_C13-C15Textiles, wearing apparel and leather products0.780.770.831.090.051.590.361.140.033.560.080.502.960.490.370.190.981.070.471.190.890.061.050.700.701.591.541.290.94CPA_C16Wood and of products of wood and cork, except furniture; articles of straw and plaiting materials3.170.760.770.590.150.635.600.440.420.570.240.203.863.080.090.031.6313.990.963.5814.720.012.623.411.081.203.724.802.58CPA_C17Paper and paper products1.850.681.060.430.240.989.570.720.111.080.110.382.513.860.200.001.021.020.640.930.520.031.441.590.790.630.271.071.20CPA_C18Printing and recording services5.711.372.100.130.090.113.810.160.560.180.000.040.540.020.140.000.7215.480.030.070.4139.830.330.130.120.040.000.142.58CPA_C19Coke and refined petroleum products 0.382.440.520.808.492.202.640.670.010.820.231.511.541.610.530.830.380.240.644.890.572.020.701.230.731.660.801.741.46CPA_C20Chemicals and chemical products1.181.931.280.510.231.120.641.191.100.890.071.000.610.660.580.120.730.690.791.000.300.060.730.900.270.540.250.800.72CPA_C21Basic pharmaceutical products and pharmaceutical preparations0.560.981.032.110.370.770.291.054.281.300.010.690.260.900.750.840.500.060.870.470.680.340.342.450.080.690.221.060.86CPA_C22Rubber and plastics products1.080.961.340.530.431.020.980.850.241.350.630.461.760.710.490.102.030.901.561.070.710.361.761.671.591.041.190.930.99CPA_C23Other non-metallic mineral products1.471.011.060.660.851.940.940.710.272.190.250.252.670.500.311.262.001.411.580.911.880.051.631.670.972.030.442.511.19CPA_C24Basic metals1.481.240.930.261.521.231.780.790.101.410.580.400.831.221.540.241.070.440.450.280.920.021.231.591.502.970.810.520.98CPA_C25Fabricated metal products, except machinery and equipment1.830.611.350.790.371.060.930.710.221.870.270.311.851.010.330.192.392.230.920.801.210.101.552.081.670.980.822.001.09CPA_C26Computer, electronic and optical products0.860.361.420.740.000.260.790.900.940.520.071.490.531.240.580.351.762.382.200.561.450.521.240.593.070.470.920.360.95CPA_C27Electrical equipment1.380.401.480.620.250.911.840.800.141.370.050.391.051.000.440.662.041.582.340.860.630.391.452.481.271.182.291.101.09CPA_C28Machinery and equipment n.e.c.1.340.521.621.050.150.561.440.740.132.180.110.500.341.080.510.121.430.730.990.660.360.070.651.040.920.620.790.290.75CPA_C29Motor vehicles, trailers and semi-trailers0.900.861.840.150.001.480.250.790.030.700.060.150.870.830.580.092.440.412.330.400.430.011.061.112.770.240.880.190.78CPA_C30Other transport equipment0.730.200.950.270.690.800.612.810.020.950.040.250.220.162.473.870.430.130.210.160.140.010.550.200.260.230.470.450.65CPA_C31_C32Furniture; other manufactured goods1.181.371.091.040.320.470.620.821.142.200.040.670.670.570.600.271.021.550.732.161.050.751.591.370.790.711.051.320.97CPA_C33Repair and installation services of machinery and equipment3.170.570.743.060.000.730.293.240.000.000.250.944.260.740.150.650.882.121.430.002.183.670.620.870.001.080.5814.611.67CPA_D35Electricity, gas, steam and air-conditioning3.463.340.711.710.270.340.761.050.000.190.300.940.520.980.130.003.174.312.730.651.570.010.755.300.115.510.940.741.45CPA_E37-E39Sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management services 0.761.011.190.410.560.730.621.130.000.400.561.321.110.411.400.551.292.340.611.222.600.170.931.260.842.092.842.131.09CPA_FConstructions and construction works0.752.340.082.762.952.010.020.000.000.550.761.104.030.000.660.911.525.110.841.502.380.377.102.901.071.791.922.241.70CPA_G45Wholesale and retail trade and repair services of motor vehicles and motorcycles0.851.800.910.340.001.430.110.810.060.370.110.390.002.621.660.000.050.290.750.000.000.004.860.000.000.320.000.000.63CPA_G46Wholesale trade services, except of motor vehicles and motorcycles1.091.060.811.430.730.720.221.651.130.321.451.101.980.711.500.280.110.400.761.730.790.170.900.710.760.900.940.000.87CPA_G47Retail trade services, except of motor vehicles and motorcycles0.000.000.000.009.621.460.001.020.601.324.590.000.003.381.073.080.001.850.000.000.000.007.050.000.004.074.645.471.76CPA_H49Land transport services and transport services via pipelines2.340.850.151.070.181.680.741.450.090.540.861.711.800.120.240.711.681.451.785.595.920.022.333.851.192.787.863.781.88CPA_H50Water transport services0.150.730.969.8514.740.250.941.310.100.330.010.750.290.960.812.500.011.040.040.170.480.400.050.210.000.090.592.371.43CPA_H51Air transport services0.570.780.411.671.051.361.711.772.230.391.601.365.160.760.931.560.510.460.950.291.991.400.590.530.072.111.101.271.23CPA_H52Warehousing and support services for transportation1.262.430.481.151.051.000.520.580.001.160.501.001.734.070.518.900.516.681.554.474.613.010.941.030.511.131.963.121.99CPA_H53Postal and courier services1.781.030.660.890.060.150.911.152.610.490.832.230.340.001.801.040.210.670.700.340.900.000.150.150.000.210.150.280.70CPA_IAccommodation and food services2.570.781.040.220.003.080.430.001.440.000.000.002.010.003.380.000.330.170.311.180.000.080.000.000.000.263.590.000.75CPA_J58Publishing services1.000.630.950.530.430.810.060.564.620.433.150.600.251.131.451.881.660.410.360.581.150.120.591.190.750.210.150.690.94CPA_J59_J60Motion picture, video and television programme production services, sound recording and music publishing; programming and broadcasting services0.660.571.060.360.490.530.250.790.370.691.680.211.270.853.530.200.770.730.390.180.220.060.620.240.480.700.130.510.66CPA_J61Telecommunications services0.791.680.350.670.190.830.241.090.661.604.440.491.351.201.870.770.571.160.380.320.860.510.361.832.660.811.381.521.09CPA_J62_J63Computer programming, consultancy and related services; information services0.920.760.680.950.341.252.180.426.350.211.210.870.412.580.803.160.510.910.650.240.840.910.640.240.321.551.910.851.17CPA_K64Financial services, except insurance and pension funding0.440.400.480.160.360.400.310.541.860.1317.420.450.110.342.193.330.070.130.050.031.168.360.090.090.230.210.130.121.41CPA_K65Insurance, reinsurance and pension funding services, except compulsory social security0.740.420.540.141.070.330.350.306.020.493.360.210.110.173.731.110.290.030.030.030.230.580.150.580.070.400.440.170.79CPA_K66Services auxiliary to financial services and insurance services0.101.410.000.180.000.410.030.000.610.422.060.120.250.117.368.230.090.390.010.000.001.280.020.000.020.000.000.420.84CPA_L68Real estate services0.530.040.840.010.000.080.000.000.002.430.230.020.060.000.711.580.141.230.001.230.000.522.500.0036.400.050.000.001.74CPA_M69_M70Legal and accounting services; services of head offices; management consulting services0.872.670.840.380.310.401.631.200.170.230.731.600.531.801.210.210.760.560.770.130.327.010.560.300.430.450.570.800.98CPA_M71Architectural and engineering services; technical testing and analysis services1.380.561.111.820.151.381.031.580.250.760.300.360.811.381.560.060.640.380.460.060.220.340.540.280.401.241.562.520.83CPA_M72Scientific research and development services1.571.331.330.850.300.374.940.940.710.810.430.400.131.921.320.000.200.270.470.060.250.020.360.170.390.970.500.700.78CPA_M73Advertising and market research services1.262.400.590.510.250.990.251.201.500.461.660.211.011.011.740.490.761.170.590.662.880.851.621.420.621.512.032.241.14CPA_M74_M75Other professional, scientific and technical services; veterinary services0.190.130.520.140.380.000.180.013.251.240.900.121.050.494.650.900.080.422.380.530.250.031.311.801.030.000.001.370.83CPA_N77Rental and leasing services0.510.670.170.080.060.062.101.474.100.574.243.100.211.980.1511.250.150.881.420.130.387.850.040.140.090.180.030.001.50CPA_N78Employment services0.500.211.220.410.000.004.070.000.001.980.460.170.000.004.101.410.096.260.000.003.480.150.000.000.240.000.050.100.89CPA_N79Travel agency, tour operator and other reservation services and related services0.340.010.000.170.003.760.000.000.002.761.322.300.058.220.177.460.100.090.000.330.004.260.350.000.443.090.000.021.26CPA_N80-N82Security and investigation services; services to buildings and landscape; office administrative, office support and other business support services0.130.580.261.360.121.510.082.420.000.720.270.140.900.134.350.000.170.230.000.000.300.350.190.000.090.280.000.010.52CPA_O84Public administration and defence services; compulsory social security services0.455.380.601.020.001.040.950.000.240.000.040.760.002.122.2910.000.421.020.751.572.081.370.001.030.510.570.006.741.46CPA_P85Education services0.190.330.000.651.060.420.040.000.000.051.090.100.071.537.950.000.100.280.110.130.000.090.150.050.004.220.008.000.95CPA_R90-R92Creative, arts and entertainment services; library, archive, museum and other cultural services; gambling and betting services0.740.550.410.300.340.530.071.090.700.400.100.180.590.084.141.270.060.550.010.360.1687.170.230.070.000.121.812.723.74CPA_TOTALTotal            1.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.00Average1.101.050.781.031.050.981.130.871.060.881.100.831.121.321.511.560.861.930.790.981.763.231.111.101.341.181.081.87 
 
 
 
 
 
Figure A3: Balassa Index heat map across Member State Sectors based on STAN database, 1995 

Written by Cambridge Econometrics 

162 

Balassa Index for 1995 (relative to EU goods total)Member State (in order of accession)SectorATBEDEDKELESFIFRIEITLUNLPTSEUKCYCZEEHULTLVMTPLSISKBGROHRAverageD01: Crop and animal production, hunting and related service activities0.090.270.100.351.230.700.100.380.120.160.160.670.070.020.101.220.220.110.740.410.080.020.260.080.130.390.370.180.31D02: Forestry and logging0.500.240.230.960.300.300.660.540.100.130.970.140.870.390.060.002.9913.841.766.1434.150.000.350.441.901.090.051.652.53D03: Fishing and aquaculture0.020.330.258.437.841.500.030.962.160.480.101.571.671.191.331.431.071.970.410.490.831.510.760.030.100.370.023.881.45D05: Mining of coal and lignite0.000.410.430.060.040.000.000.190.000.010.000.850.000.000.250.0026.250.690.000.020.190.0049.520.020.000.090.000.002.82D06: Extraction of crude petroleum and natural gas0.010.120.022.451.000.010.000.070.000.040.014.300.000.004.890.000.150.010.000.060.300.000.020.000.050.000.000.660.51D07: Mining of metal ores0.032.430.120.004.211.100.720.213.680.120.060.5915.7012.120.230.000.140.150.090.020.000.001.550.001.2912.971.200.072.10D08: Other mining and quarrying0.918.420.470.492.660.830.590.520.380.420.150.660.630.304.630.991.122.010.181.021.060.181.770.141.311.060.400.601.21D10: Food products0.471.400.613.592.831.180.311.312.910.670.512.310.680.290.640.940.662.522.152.732.170.161.270.460.501.280.461.191.29D11: Beverages0.340.410.240.451.080.800.161.630.880.670.450.611.400.151.002.000.570.461.150.300.680.190.340.290.312.060.220.820.70D12: Tobacco products0.280.740.791.013.710.300.170.260.550.164.554.560.120.080.5984.290.970.230.030.141.851.200.340.330.789.840.031.294.26D13: Textiles1.401.741.080.691.791.040.351.030.412.112.200.722.810.420.752.171.902.330.932.482.590.160.921.350.581.310.751.041.32D14: Wearing apparel0.800.620.521.116.200.430.250.700.422.230.320.576.000.160.643.270.762.902.962.792.063.083.702.921.942.056.355.352.18D15: Leather and related products1.070.290.360.550.591.960.280.630.183.650.080.375.540.120.421.081.340.941.821.151.291.481.191.841.631.863.773.671.40D16: Wood and products of wood and cork, except furniture3.730.890.491.810.560.696.740.630.360.561.520.484.544.430.140.063.108.031.634.6115.510.023.994.072.271.333.214.612.86D17: Paper and paper products2.230.710.840.480.270.768.930.770.150.561.000.812.024.180.540.411.010.390.420.510.360.030.751.741.630.480.350.891.19D18: Printing and reproduction of recorded media0.110.070.280.321.020.090.050.1416.260.180.290.140.030.100.480.050.060.060.110.010.026.930.070.090.210.150.010.020.98D19: Coke and refined petroleum products0.271.810.540.884.391.381.340.640.210.880.033.332.161.311.102.711.333.162.126.660.571.131.570.122.673.955.705.852.06D20: Chemicals and chemical products0.491.791.220.520.520.860.541.481.560.630.551.520.470.351.120.391.060.861.061.270.660.190.810.741.111.931.261.540.95D21: Basic pharmaceutical products and pharmaceutical preparations0.690.780.591.320.220.410.160.721.480.480.040.680.191.000.930.990.310.330.660.590.910.370.301.170.440.700.180.910.63D22: Rubber and plastics products1.711.211.051.050.631.060.541.260.471.143.180.850.560.840.990.351.110.670.920.360.200.890.711.271.410.420.260.580.92D23: Other non-metallic mineral products1.411.040.730.721.881.740.550.910.371.862.110.442.050.450.600.742.931.120.940.800.800.061.351.261.881.271.421.381.17D24: Basic metals1.671.701.080.401.801.181.591.100.110.936.690.830.191.461.000.162.140.461.300.651.030.062.311.343.613.523.240.581.50D25: Fabricated metal products, except machinery and equipment1.560.770.950.990.580.820.700.740.311.251.230.691.060.930.660.331.960.980.890.340.350.271.521.501.120.480.750.600.87D26: Computer, electronic and optical products0.610.520.980.840.140.531.430.942.920.621.201.430.681.341.810.540.230.550.640.540.216.060.230.470.300.100.050.240.93D27: Electrical equipment1.050.481.270.900.590.831.131.000.791.180.720.620.980.930.800.870.850.371.660.610.530.820.791.970.880.470.480.940.88D28: Machinery and equipment n.e.c.1.320.621.501.140.160.561.030.780.361.610.710.570.191.080.970.260.960.590.530.260.560.120.500.630.630.570.480.300.68D29: Motor vehicles, trailers and semi-trailers1.051.351.570.200.092.290.291.080.060.760.280.391.141.160.830.100.790.610.630.360.310.020.471.111.060.110.270.180.66D30: Other transport equipment0.350.160.670.570.410.620.881.540.160.570.120.490.320.691.080.020.360.180.070.400.701.071.230.200.690.480.671.370.57D31T32: Furniture, other manufacturing1.591.610.801.930.280.820.510.760.982.500.240.550.740.920.980.591.301.810.820.721.372.732.142.280.850.412.361.461.22D35: Electricity, gas, steam and air conditioning supply [D]0.930.190.150.350.000.050.032.100.000.000.000.020.000.000.000.000.681.860.002.960.740.000.711.630.050.000.160.050.45D36T99: Other activities1.400.760.961.250.510.980.900.950.780.700.721.110.230.522.430.531.030.260.430.170.180.250.520.641.010.250.040.270.71Note: LX is for 1999, SK for 1997 and BG for 1996Average0.911.090.671.151.530.831.000.841.260.880.971.061.711.191.033.431.911.630.871.282.330.942.640.971.041.641.111.36 
 
 
 
 
 
 
Figure A4: Balassa Index heat map across Member State Sectors based on STAN database, 2016 

Written by Cambridge Econometrics 

163 

Balassa Index for 2016 (relative to EU goods total)Member State (in order of accession)SectorATBEDEDKELESFIFRIEITLUNLPTSEUKCYCZEEHULTLVMTPLSISKBGROHRAverageD01: Crop and animal production, hunting and related service activities0.120.210.090.400.720.630.100.330.050.150.240.690.240.050.080.470.130.310.280.660.770.020.200.200.140.880.680.330.33D02: Forestry and logging0.260.210.212.090.190.280.520.640.100.181.060.360.390.150.060.001.274.520.262.065.950.000.372.970.780.400.103.041.02D03: Fishing and aquaculture0.030.210.144.8411.251.360.310.711.090.320.271.072.1913.011.518.880.370.270.080.281.3514.090.080.030.020.250.034.272.44D05: Mining of coal and lignite0.001.250.240.060.150.460.000.090.260.080.003.630.020.000.410.005.490.000.150.350.090.008.430.060.080.340.021.290.82D06: Extraction of crude petroleum and natural gas0.002.620.872.660.300.270.000.440.010.120.000.040.010.027.670.000.620.060.240.150.040.000.070.672.330.010.040.850.72D07: Mining of metal ores0.001.240.060.010.882.581.790.110.860.110.060.413.417.390.060.000.010.010.050.000.010.010.270.010.1310.380.320.001.08D08: Other mining and quarrying1.0916.370.710.815.471.431.370.820.600.870.351.231.510.591.905.220.583.250.241.627.950.120.461.730.821.540.341.702.17D10: Food products0.861.250.742.562.251.660.341.111.421.001.032.041.210.540.622.070.491.000.861.741.290.731.670.590.411.280.451.491.17D11: Beverages1.130.510.270.630.490.900.202.000.651.080.510.641.210.401.350.940.260.760.220.792.200.130.250.240.160.260.130.720.68D12: Tobacco products0.000.991.070.703.980.280.020.270.090.313.001.323.830.480.227.201.580.100.325.001.021.323.200.010.022.033.841.401.56D13: Textiles1.201.160.871.051.511.270.460.870.132.143.690.833.210.590.740.091.452.200.722.311.460.611.191.160.641.921.861.141.30D14: Wearing apparel0.801.030.632.031.112.120.241.040.142.220.460.882.960.600.950.330.560.900.301.350.860.141.200.440.613.072.332.471.13D15: Leather and related products0.681.290.460.590.491.160.201.440.073.300.250.773.090.270.570.460.580.520.650.400.260.150.670.781.280.801.822.270.90D16: Wood and products of wood and cork, except furniture3.190.640.620.760.230.605.080.510.350.431.660.323.182.770.120.040.998.940.663.7116.680.011.942.430.941.172.975.292.37D17: Paper and paper products1.810.740.920.580.540.889.580.780.090.881.580.752.674.360.430.090.820.990.770.700.660.021.431.840.920.700.340.981.32D18: Printing and reproduction of recorded media0.620.780.480.360.480.070.270.300.360.230.210.440.160.700.690.010.940.910.210.501.1015.010.280.360.120.210.160.390.94D19: Coke and refined petroleum products0.302.100.370.8210.811.332.930.630.180.960.023.512.151.981.018.040.282.360.445.261.179.810.750.900.832.581.222.352.32D20: Chemicals and chemical products0.632.211.040.670.651.050.941.393.580.790.811.510.650.720.940.520.420.580.671.210.510.330.780.820.390.790.330.620.91D21: Basic pharmaceutical products and pharmaceutical preparations0.901.680.872.160.640.610.240.963.820.740.140.580.340.871.222.280.230.090.680.480.603.530.221.270.110.500.191.090.97D22: Rubber and plastics products0.970.671.010.620.610.730.700.800.230.903.100.611.410.610.570.251.190.711.231.010.660.711.421.111.230.861.260.710.92D23: Other non-metallic mineral products1.220.690.840.811.391.750.670.750.271.632.350.472.520.460.502.011.231.131.070.891.770.131.381.130.841.920.432.271.16D24: Basic metals1.771.230.920.382.051.222.560.890.051.374.740.890.751.501.670.490.740.560.370.300.830.111.051.461.262.511.000.541.19D25: Fabricated metal products, except machinery and equipment1.800.521.011.030.530.940.760.640.211.341.050.641.650.860.530.201.781.800.711.151.230.101.491.701.330.640.892.271.03D26: Computer, electronic and optical products1.110.431.120.910.600.320.750.881.660.440.551.850.631.191.031.451.901.941.800.691.451.851.070.502.240.560.720.521.08D27: Electrical equipment1.320.391.211.470.510.871.220.780.390.990.620.601.060.940.660.331.681.542.190.650.571.071.491.921.111.232.091.251.08D28: Machinery and equipment n.e.c.1.370.631.371.180.240.561.340.810.241.841.140.900.411.200.850.231.160.740.870.690.410.270.640.860.840.660.860.740.82D29: Motor vehicles, trailers and semi-trailers0.890.861.490.210.071.570.350.790.020.660.550.330.841.061.010.231.750.491.990.350.420.061.121.332.330.331.610.290.82D30: Other transport equipment0.540.180.900.220.170.570.412.641.030.600.340.460.280.231.890.060.220.160.170.110.170.770.720.170.090.190.520.450.51D31T32: Furniture, other manufacturing1.131.350.891.330.420.550.810.981.661.650.281.201.140.760.970.461.361.470.692.311.061.521.841.210.710.841.271.071.10D35: Electricity, gas, steam and air conditioning supply [D]0.970.130.380.400.580.180.290.740.080.140.000.020.871.020.060.001.122.581.010.500.050.000.432.730.033.270.885.160.84D36T99: Other activities1.120.440.850.630.770.550.361.170.670.530.320.760.200.633.960.281.391.280.280.601.540.391.770.740.430.280.210.710.82Average0.901.420.731.061.620.931.120.850.660.900.980.961.421.481.111.381.051.360.651.221.751.711.221.010.751.370.931.54 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Appendix B  Manufacturing Sector Coverage for Theil Indices 

The table below shows the 2-digit manufacturing sector coverage for the two SBS databases used in the study (NACE Rev1.1 for the 2003 and 2007 
periods, and Nace Rev2 for the 2011 and 2015 periods). 

NACE Rev1.1 

Code  Sector Name 

NACE Rev2 

Code  Sector Name 

DA 

DB 

DD 

DE 

DG 

DH 

DI 

DJ 

DK 

DL 

Manufacture of food products, beverages and tobacco 

C10 

Manufacture of food products 

Manufacture of textiles and textile products 

C11 

Manufacture of beverages 

Manufacture of wood and wood products 

C13 

Manufacture of textiles 

Manufacture of pulp, paper and paper products; publishing and printing 

C14 

Manufacture of wearing apparel 

Manufacture of chemicals, chemical products and man-made fibres 

C16 

Manufacture of wood and of products of wood and cork, except furniture; 
manufacture of articles of straw and plaiting materials 

Manufacture of rubber and plastic products 

Manufacture of other non-metallic mineral products 

Manufacture of basic metals and fabricated metal products 

Manufacture of machinery and equipment n.e.c. 

Manufacture of electrical and optical equipment 

DM 

Manufacture of transport equipment 

Written by Cambridge Econometrics 

C17 

Manufacture of paper and paper products 

C18 

Printing and reproduction of recorded media 

C20 

Manufacture of chemicals and chemical products 

C22 

Manufacture of rubber and plastic products 

C23 

Manufacture of other non-metallic mineral products 

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EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

NACE Rev1.1 

Code  Sector Name 

NACE Rev2 

Code  Sector Name 

DN 

Manufacturing n.e.c. 

C24 

Manufacture of basic metals 

C25 

Manufacture of fabricated metal products, except machinery and equipment 

C26 

Manufacture of computer, electronic and optical products 

C27 

Manufacture of electrical equipment 

C28 

Manufacture of machinery and equipment n.e.c. 

C29 

Manufacture of motor vehicles, trailers and semi-trailers 

C31 

Manufacture of furniture 

C32 

Other manufacturing 

C33 

Repair and installation of machinery and equipment 

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EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Appendix C  Detailed EG Statistics 

Table C1:  Co-agglomeration results across EU regions (2003) 

Source:  CE calculations. 

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166 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA0.00050.0014-0.0007-0.00100.00030.0009-0.0003-0.0008-0.0006-0.00060.00050.0001-0.00030.0001-0.00020.0003-0.0010DB0.0029-0.0006-0.00020.00120.00200.00260.0011-0.0002-0.00310.0030-0.00100.0000-0.0012-0.0010-0.0010-0.0043DD-0.0010-0.00150.00040.00200.0009-0.0002-0.0010-0.00200.0024-0.0001-0.0004-0.0002-0.0004-0.0002-0.0024DE0.0004-0.0004-0.0011-0.0006-0.00030.00010.0003-0.0006-0.00030.00020.00010.00040.00020.0012DG0.0016-0.00100.00190.00210.00140.0001-0.0009-0.0001-0.0001-0.0007-0.0011-0.0004-0.0003DH0.00040.00200.00250.00140.00090.0006-0.0002-0.0009-0.0007-0.0013-0.0012-0.0008DI0.00040.0006-0.0004-0.00070.00150.0000-0.0004-0.0003-0.0004-0.0004-0.0016DJ0.00300.0007-0.00120.0010-0.0003-0.0005-0.0009-0.0014-0.0012-0.0025DK0.00320.00250.0006-0.0004-0.0012-0.0013-0.0019-0.00220.0000DL0.0029-0.0003-0.0003-0.0008-0.0008-0.0010-0.00110.0017DM-0.0013-0.0003-0.0007-0.0006-0.0008-0.00110.0021DN-0.0002-0.0002-0.0004-0.0005-0.0006-0.0019G50 0.00000.00030.00010.00010.0003G51 0.00010.00020.0006-0.0001G52 0.00050.00040.0006H 0.00020.0010I60 -0.0005K73  
               
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C2:  Co-agglomeration results across EU regions (2007) 

Source:  CE calculations. 

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167 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA0.00050.0014-0.0006-0.00090.00040.0009-0.0002-0.0009-0.00050.00020.00070.0001-0.00020.0000-0.00020.0003-0.0011DB0.0030-0.0004-0.00040.00120.00200.00270.0007-0.0001-0.00220.0034-0.00100.0001-0.0012-0.0010-0.0009-0.0044DD-0.0008-0.00170.00060.00190.0009-0.0008-0.0008-0.00060.0026-0.0001-0.0003-0.0003-0.0004-0.0002-0.0026DE0.0004-0.0005-0.0011-0.0006-0.0001-0.0001-0.0006-0.0006-0.00030.00030.00010.00030.00030.0010DG0.0010-0.00120.00100.00180.0013-0.0002-0.0011-0.00010.0001-0.0005-0.00080.00000.0006DH0.00040.00190.00200.00170.00210.0007-0.0001-0.0009-0.0007-0.0013-0.0010-0.0008DI0.0005-0.0001-0.00030.00010.00160.0000-0.0003-0.0003-0.0002-0.0003-0.0020DJ0.00230.0009-0.00020.0011-0.0002-0.0005-0.0008-0.0013-0.0010-0.0023DK0.00340.00300.0001-0.0004-0.0009-0.0012-0.0015-0.00150.0010DL0.0029-0.0002-0.0002-0.0009-0.0008-0.0011-0.00110.0020DM-0.00070.0001-0.0014-0.0005-0.0011-0.00160.0013DN-0.0003-0.0001-0.0004-0.0005-0.0004-0.0022G50 -0.00010.00020.00010.00000.0003G51 0.00020.00020.0006-0.0003G52 0.00040.00030.0004H 0.00010.0009I60 -0.0005K73  
               
 
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C3:  Co-agglomeration results across EU regions (2011) 

Source:  CE calculations. 

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168 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.00070.00010.00120.0015-0.00010.0001-0.00160.00010.0006-0.0018-0.0001-0.0010-0.0015-0.0014-0.00110.00090.00010.00020.0002-0.00020.0003-0.00010.0001-0.0012C11-0.00040.00040.0010-0.0001-0.0001-0.00060.00020.0004-0.0019-0.0002-0.0002-0.0004-0.0007-0.00050.00030.0003-0.00010.0002-0.00030.00010.0002-0.0001-0.0001C130.00970.00340.00130.0015-0.00010.00180.00110.00170.0037-0.00140.00130.0021-0.00370.00320.00240.0001-0.0007-0.0002-0.0009-0.0009-0.0017-0.0039C140.00500.00010.0012-0.00320.00020.0029-0.00220.0024-0.0015-0.0011-0.0006-0.00450.00470.00350.0007-0.00110.0000-0.0007-0.00040.0000-0.0048C160.00060.0004-0.00270.00060.0014-0.00090.0016-0.0018-0.0011-0.0006-0.00300.00380.00140.00030.0000-0.00060.0000-0.0003-0.0005-0.0028C170.00000.00120.00100.00000.00190.0009-0.00010.00130.00190.00120.00080.0002-0.00030.0000-0.0002-0.0004-0.0008-0.0006-0.0010C18-0.0009-0.0004-0.0003-0.00100.0002-0.0006-0.0009-0.0008-0.00200.00040.0003-0.00030.00000.00020.00020.00020.0000-0.0002C200.0014-0.00050.00510.00060.00120.00280.00260.0023-0.0016-0.00120.0002-0.00010.0004-0.0009-0.00110.00030.0003C220.00030.00170.00140.00080.00280.00290.00230.00100.00060.00000.0001-0.0006-0.0006-0.0012-0.0012-0.0007C23-0.00090.00030.00040.00000.00000.00020.00120.00070.0004-0.0002-0.0003-0.0002-0.00030.0001-0.0013C240.0031-0.00130.00390.00460.00020.0002-0.00200.00010.00020.0001-0.0009-0.0019-0.0010-0.0015C25-0.00070.00170.0027-0.00090.00170.00070.0000-0.0001-0.0003-0.0006-0.0009-0.0011-0.0019C260.00340.00250.0054-0.00180.00120.0008-0.0005-0.0004-0.0009-0.00030.00020.0030C270.00720.00530.00050.0008-0.0001-0.0001-0.0007-0.0013-0.0015-0.00230.0014C280.00590.00070.0008-0.00070.0000-0.0009-0.0015-0.0018-0.00350.0004C29-0.0026-0.00040.0000-0.0005-0.0008-0.0013-0.0014-0.00080.0019C310.0014-0.00020.0000-0.0003-0.0002-0.0008-0.0009-0.0025C320.0003-0.0002-0.0004-0.00040.0000-0.0005-0.0004C33-0.00010.0000-0.0002-0.00030.0007-0.0004G45 -0.00010.00020.0000-0.00040.0001G46 0.00000.00000.0006-0.0001G47 0.00040.00010.0002I 0.00030.0010H49 -0.0003M72  
               
 
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C4:  Co-agglomeration results across EU regions (2015) 

Source:  CE calculations. 

Written by Cambridge Econometrics 

169 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.00080.00020.00130.00160.00020.0002-0.00150.00020.0005-0.0018-0.0001-0.0011-0.0014-0.0015-0.00100.00100.00020.00030.0001-0.00010.0002-0.00020.0003-0.0011C11-0.00030.00020.0009-0.0001-0.0001-0.0006-0.00010.0003-0.0023-0.00050.0001-0.0006-0.0011-0.0005-0.00010.00070.00000.0003-0.00030.00010.00030.00020.0006C130.00930.00280.00150.0013-0.00010.00190.00060.00190.0033-0.00160.00070.0017-0.00310.00370.0021-0.0002-0.0006-0.0001-0.0007-0.0008-0.0012-0.0032C140.00390.00050.0013-0.0030-0.00020.0023-0.00220.0020-0.0018-0.0015-0.0009-0.00440.00470.00340.0014-0.00130.0003-0.0007-0.00010.0002-0.0043C160.00100.0005-0.00280.00110.0010-0.00080.0015-0.0020-0.0011-0.0009-0.00260.00440.00120.00010.0002-0.00060.0001-0.0004-0.0001-0.0024C170.00000.00100.00130.00040.00170.0011-0.00010.00120.00160.00090.00130.0004-0.00030.0001-0.0003-0.0003-0.0009-0.0004-0.0011C18-0.0009-0.0003-0.0004-0.00080.0002-0.0010-0.0010-0.0010-0.00210.00070.0003-0.00020.00000.00020.00020.00020.0001-0.0001C200.00130.00030.00600.00080.00180.00290.00290.0019-0.0016-0.00120.0005-0.00010.0004-0.0009-0.00140.00010.0002C220.00050.00250.00180.00050.00300.00330.00210.00170.0006-0.00100.0003-0.0009-0.0005-0.0014-0.0012-0.0008C23-0.00060.00020.00120.00070.00050.00120.00100.00070.0006-0.0002-0.0002-0.0004-0.00060.0001-0.0012C240.0037-0.00100.00440.0050-0.00050.0006-0.0018-0.00010.0002-0.0001-0.0008-0.0022-0.0010-0.0020C25-0.00040.00190.0028-0.00060.00190.0008-0.00030.0000-0.0004-0.0006-0.0011-0.0008-0.0017C260.00470.00390.0062-0.00190.00130.0003-0.0003-0.0006-0.0011-0.0007-0.00040.0027C270.00790.00580.00070.0009-0.00160.0000-0.0012-0.0013-0.0018-0.00200.0013C280.00650.00050.0009-0.00220.0000-0.0014-0.0015-0.0020-0.00280.0001C29-0.0024-0.0001-0.0006-0.0003-0.0010-0.0013-0.0016-0.00110.0012C310.0014-0.0004-0.0001-0.0003-0.0002-0.0011-0.0005-0.0027C320.0002-0.0002-0.0004-0.0004-0.0001-0.0003-0.0002C33-0.00030.0006-0.0001-0.00020.0013-0.0009G45 -0.00020.0002-0.0001-0.00020.0004G46 0.00000.00000.0007-0.0003G47 0.00040.00010.0002I 0.00020.0010H49 -0.0004M72  
               
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C5:  Co-agglomeration results across UK NUTS2 regions (2003) 

Source:  CE calculations. 

Written by Cambridge Econometrics 

170 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA0.00560.0050-0.00510.00140.00330.00500.00100.00220.0010-0.00050.00080.0011-0.0021-0.0003-0.00170.0008-0.0043DB0.00290.00030.00290.00390.00600.00120.0016-0.00200.00080.0043-0.0017-0.0011-0.0012-0.00200.0008-0.0124DD-0.00500.00170.00340.00510.00240.00290.00220.00190.00190.0015-0.0022-0.0002-0.0019-0.0007-0.0033DE-0.0036-0.0062-0.0067-0.0082-0.0054-0.0059-0.0087-0.0026-0.00360.00180.00030.00480.00130.0052DG0.00280.00160.00000.00150.00150.00090.00270.0013-0.00030.0000-0.0024-0.0009-0.0007DH0.00660.00720.00540.00200.00750.00490.0014-0.0011-0.0010-0.0039-0.0007-0.0045DI0.00690.00660.00160.00430.00360.0014-0.0022-0.0011-0.0035-0.0006-0.0093DJ0.00670.00190.01500.00560.0011-0.0014-0.0017-0.0046-0.0003-0.0091DK0.00330.00600.00360.0015-0.0007-0.0009-0.0036-0.0014-0.0009DL0.00270.00040.0021-0.00080.0002-0.0021-0.00190.0022DM0.00610.0017-0.0016-0.0017-0.0048-0.0015-0.0093DN0.0005-0.0006-0.0008-0.0028-0.0010-0.0042G50 -0.00010.0001-0.0016-0.0007-0.0001G51 0.0000-0.00010.00020.0031G52 0.0005-0.00010.0006H 0.00060.0024I60 -0.0011K73  
               
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C6:  Co-agglomeration results across UK NUTS2 regions (2007) 

Source:  CE calculations. 

Written by Cambridge Econometrics 

171 

DADBDDDEDGDHDIDJDKDLDMDNG50 G51 G52 H I60 K73 DA0.00490.0050-0.00510.00170.00390.00480.00200.00240.00060.00160.00110.0014-0.0017-0.0004-0.00180.0003-0.0026DB0.00220.00070.00270.00580.00660.00210.0023-0.00280.00090.0064-0.0010-0.0006-0.0011-0.00180.0001-0.0113DD-0.00620.00180.00310.00550.00360.00310.00280.00270.00100.0019-0.0026-0.0003-0.0016-0.0005-0.0015DE-0.0047-0.0061-0.0066-0.0078-0.0052-0.0057-0.0080-0.0026-0.00450.00090.00040.00460.00130.0041DG0.00370.00250.00140.00260.00230.00270.00200.0022-0.0001-0.0004-0.0027-0.0007-0.0006DH0.00780.00650.00520.00190.00660.00530.0022-0.0009-0.0010-0.0035-0.0011-0.0063DI0.00810.00660.00060.00650.00510.0021-0.0018-0.0012-0.0036-0.0007-0.0098DJ0.00560.00150.01020.00610.0018-0.0012-0.0013-0.0034-0.0013-0.0093DK0.00330.00490.00360.0022-0.0005-0.0011-0.0029-0.0014-0.0015DL0.00240.00050.00280.0000-0.0001-0.0022-0.00160.0045DM0.00590.0023-0.0016-0.0014-0.0033-0.0019-0.0079DN0.0010-0.0003-0.0009-0.0024-0.0014-0.0075G50 0.0001-0.0001-0.0020-0.00070.0004G51 0.0001-0.00050.00000.0011G52 0.00040.00020.0004H 0.00060.0022I60 0.0002K73  
               
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C7:  Co-agglomeration results across UK NUTS2 regions (2011) 

Source:  CE calculations. 

Written by Cambridge Econometrics 

172 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.00750.00860.00490.00680.00750.00040.00440.00470.00560.00400.00260.00020.00280.0037-0.00100.0036-0.00080.00160.0014-0.0006-0.0008-0.00310.0002-0.0064C110.01140.00370.01390.0060-0.0058-0.0041-0.0029-0.0008-0.00780.00070.0012-0.00350.0000-0.0117-0.0072-0.00440.01090.0007-0.00390.0005-0.00040.0022-0.0023C130.02130.00840.01380.00600.01100.00650.00950.00360.0035-0.00400.00450.0059-0.00550.01690.00000.00070.0004-0.0005-0.0014-0.0042-0.0007-0.0167C140.00390.00800.00310.00590.00360.00350.00280.0047-0.0055-0.00040.00410.00050.0075-0.0010-0.0018-0.00110.0006-0.0012-0.0017-0.0005-0.0112C160.0075-0.00060.00360.00450.00550.00610.00460.00220.00400.00590.00110.00280.00110.00450.0020-0.0025-0.0007-0.0028-0.0010-0.0081C170.00330.00730.00590.00590.00610.00260.00140.00410.00470.00030.00680.00060.00170.0018-0.0009-0.0010-0.0037-0.0001-0.0084C180.00320.00140.0021-0.0013-0.00060.00040.00170.0015-0.00020.00440.0004-0.00190.00000.0008-0.0003-0.0011-0.0001-0.0010C200.00450.00550.00570.0030-0.00010.00260.00410.00380.0067-0.0001-0.00050.00060.0003-0.0011-0.0029-0.0012-0.0041C220.00630.00940.00550.00150.00510.00650.00710.00640.00310.00070.00260.0002-0.0012-0.0039-0.0015-0.0083C230.00840.0044-0.00020.00530.00710.00480.00850.0015-0.00120.0022-0.0004-0.0009-0.0042-0.0008-0.0092C240.0132-0.00550.00460.00770.02070.01000.00380.00090.0021-0.0017-0.0018-0.0045-0.0003-0.0168C25-0.00070.00360.00730.01380.00530.00380.00240.0026-0.0002-0.0012-0.0038-0.0013-0.0098C260.00480.0030-0.0016-0.00090.00460.00500.00320.0007-0.0003-0.0017-0.00280.0028C270.00670.00530.00550.00450.00170.0029-0.0005-0.0009-0.0030-0.0020-0.0064C280.01130.00790.00460.00110.0034-0.0001-0.0016-0.0041-0.0025-0.0080C290.00720.0057-0.00020.00380.0004-0.0018-0.0049-0.0018-0.0105C310.0040-0.00210.00230.0014-0.0015-0.0047-0.0017-0.0094C320.00320.00300.0005-0.0008-0.0019-0.0028-0.0031C330.0023-0.0009-0.0001-0.0014-0.0010-0.0030G45 0.0005-0.0005-0.0023-0.0012-0.0027G46 -0.0002-0.0009-0.00060.0015G47 0.00070.00050.0010I 0.00060.0051H49 0.0002M72  
               
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C8:  Co-agglomeration results across UK NUTS2 regions (2015) 

Source:  CE calculations. 

Written by Cambridge Econometrics 

173 

C10C11C13C14C16C17C18C20C22C23C24C25C26C27C28C29C31C32C33G45 G46 G47 I H49 M72 C100.00700.0083-0.00700.00760.0086-0.00010.00450.00670.00630.00630.00320.00260.00360.00480.00080.0056-0.00080.00290.0018-0.0006-0.0008-0.0036-0.0004-0.0061C110.0071-0.00570.01040.0059-0.0091-0.0010-0.00240.0003-0.0099-0.00100.0075-0.00440.0069-0.0104-0.0069-0.00320.01330.0027-0.0034-0.00100.0007-0.00110.0033C13-0.00610.00530.01440.00430.01260.00860.00770.00140.00360.00140.00640.0073-0.00370.0129-0.00110.0022-0.00030.0006-0.0012-0.0038-0.0021-0.0133C14-0.0106-0.01040.0028-0.0146-0.0109-0.0098-0.0152-0.0124-0.0141-0.0168-0.0130-0.0160-0.0055-0.0132-0.0086-0.0116-0.00190.00210.01030.0048-0.0022C160.0074-0.00080.00530.00670.00910.01070.00660.00400.00450.00760.00580.00640.00140.00520.0029-0.0010-0.0015-0.0039-0.0014-0.0049C170.00160.00930.00920.00600.01040.00550.00510.00790.00810.00360.00940.00210.00310.0023-0.0007-0.0015-0.0044-0.0024-0.0060C180.00170.00140.0016-0.00010.0000-0.00150.00190.00030.00040.00480.0011-0.0019-0.00050.0006-0.0001-0.00070.0002-0.0014C200.00630.00580.00680.00480.00250.00730.00530.00480.00690.00290.00230.00150.0002-0.0010-0.0036-0.0020-0.0038C220.00800.01130.00740.00280.00640.00630.00870.00840.00210.00180.00270.0004-0.0012-0.0051-0.0013-0.0062C230.01060.00760.00300.00600.00720.00680.00800.00170.00290.00240.0002-0.0014-0.0048-0.0012-0.0087C240.0149-0.00200.00890.00380.02310.01010.00800.00370.0021-0.0013-0.0025-0.0053-0.0006-0.0173C250.00200.00610.00650.01670.00660.00460.00330.00300.0008-0.0017-0.0048-0.0017-0.0058C260.00570.0056-0.00240.00070.00430.00440.00420.0003-0.0006-0.0026-0.00300.0044C270.00640.00750.00640.00580.00260.00330.0000-0.0012-0.0039-0.0026-0.0045C280.00730.00590.00240.00380.00360.0002-0.0015-0.0042-0.0021-0.0013C290.00590.00480.00030.00310.0016-0.0025-0.0060-0.0007-0.0108C310.00200.00010.00190.0005-0.0013-0.0044-0.0013-0.0064C320.00380.0028-0.0002-0.0006-0.0021-0.00210.0018C330.0025-0.0011-0.0007-0.0016-0.0012-0.0025G45 0.0006-0.0005-0.0028-0.00090.0011G46 -0.0002-0.0011-0.00010.0016G47 0.00070.00050.0002I 0.00060.0022H49 -0.0020M72  
               
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C9:  Co-agglomeration results across UK local areas (2003) 

Source:  CE calculations. 

Written by Cambridge Econometrics 

174 

3456810111213141517212223282924373 Food, drink & tobacco0.00040.00070.00000.00040.00050.0002-0.00010.00030.0002-0.00060.00030.0003-0.0001-0.0001-0.0005-0.00040.0002-0.00114 Textiles etc0.00070.00010.00090.00070.0003-0.00030.00030.0007-0.00020.0004-0.0001-0.0001-0.0002-0.0010-0.0005-0.0001-0.00105 Wood & paper-0.00010.00110.00070.00040.00020.00070.0005-0.00010.00040.0003-0.0002-0.0001-0.0005-0.00050.0000-0.00126 Printing & recording-0.0002-0.0004-0.0006-0.00030.0001-0.0002-0.0008-0.00010.00000.00020.0001-0.0004-0.00010.00010.00028 Chemicals, etc0.00080.00020.00020.00040.0005-0.00040.00050.00020.0000-0.0002-0.0010-0.00040.0000-0.000910 Non-metallic min. prods.0.00110.00020.00080.00110.00110.00070.0003-0.0001-0.0003-0.0013-0.0006-0.0002-0.001511 Metals & metal prods.0.00000.00090.00110.00170.00090.00020.0000-0.0003-0.0016-0.0007-0.0002-0.001612 Electronics0.00030.00050.00070.00030.0003-0.0001-0.0001-0.0003-0.0003-0.0002-0.000713 Electrical equipment0.00120.00120.00080.0001-0.0001-0.0002-0.0011-0.0005-0.0002-0.001314 Machinery, etc0.00130.00070.00030.0000-0.0002-0.0013-0.0006-0.0001-0.001315 Motor vehicles, etc0.00100.0002-0.0001-0.0005-0.0020-0.00090.0000-0.001717 Other manuf. & repair0.00020.0000-0.0001-0.0010-0.0005-0.0001-0.001121 Motor vehicles trade0.00000.0000-0.0006-0.00030.0000-0.000822 Wholesale trade-0.0001-0.0004-0.00010.00000.000123 Retail trade0.00020.00010.00000.000128 Accomodation0.0008-0.00030.001829 Food & beverage services0.00000.000924 Land transport-0.000237 Other professional services 
               
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C10:  Co-agglomeration results across UK local areas (2007) 

Source:  CE calculations. 

Written by Cambridge Econometrics 

175 

3456810111213141517212223282924373 Food, drink & tobacco0.00060.0008-0.00010.00070.00070.00040.00020.00030.0005-0.00020.00030.00030.0000-0.0002-0.0006-0.00050.0002-0.00124 Textiles etc0.00070.00060.00100.00070.0003-0.00010.00010.0007-0.00030.00040.00000.0001-0.0002-0.0009-0.00040.0001-0.00065 Wood & paper-0.00010.00110.00110.00060.00050.00070.00070.00030.00050.00040.0000-0.0001-0.0005-0.00050.0000-0.00126 Printing & recording-0.0002-0.0003-0.0004-0.00010.0000-0.0001-0.0005-0.00010.00000.00020.0000-0.00060.00000.00000.00018 Chemicals, etc0.00110.00050.00020.00050.00080.00010.00050.00040.0001-0.0001-0.0010-0.00060.0001-0.001210 Non-metallic min. prods.0.00100.00030.00080.00100.00080.00070.00040.0000-0.0002-0.0010-0.00060.0000-0.001411 Metals & metal prods.0.00020.00070.00110.00140.00080.00040.0001-0.0003-0.0013-0.00060.0000-0.001612 Electronics0.00060.00070.00050.00060.00040.0001-0.0001-0.0006-0.0004-0.0002-0.000613 Electrical equipment0.00110.00130.00080.00040.0001-0.0001-0.0009-0.0005-0.0001-0.001214 Machinery, etc0.00080.00080.00040.0001-0.0002-0.0012-0.0006-0.0001-0.001415 Motor vehicles, etc0.00060.00030.0000-0.0003-0.0017-0.00060.0000-0.001417 Other manuf. & repair0.00030.0001-0.0001-0.0009-0.0004-0.0001-0.001021 Motor vehicles trade0.00010.0000-0.0007-0.00030.0000-0.000922 Wholesale trade-0.0001-0.0005-0.00020.0000-0.000123 Retail trade0.00020.00010.00000.000028 Accomodation0.0007-0.00020.001429 Food & beverage services0.00000.000724 Land transport-0.000237 Other professional services 
               
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C11:  Co-agglomeration results across UK local areas (2011) 

Source:  CE calculations. 

Written by Cambridge Econometrics 

176 

3456810111213141517212223282924373 Food, drink & tobacco0.00080.00120.00020.00080.00080.00040.00020.00030.0005-0.00030.00030.00040.0001-0.0001-0.0007-0.00050.0002-0.00124 Textiles etc0.00110.00100.00140.00100.0005-0.00030.00030.0010-0.00030.00110.00020.0002-0.0002-0.0012-0.00060.0000-0.00125 Wood & paper0.00040.00130.00110.00080.00050.00080.00080.00060.00060.00050.0000-0.0001-0.0007-0.00060.0000-0.00136 Printing & recording0.00040.0002-0.00010.00020.00040.00030.00000.00020.00020.00010.0000-0.0008-0.00020.0000-0.00048 Chemicals, etc0.00120.00050.00040.00050.00080.00040.00070.00040.0002-0.0002-0.0012-0.00060.0000-0.001110 Non-metallic min. prods.0.00100.00040.00080.00100.00070.00070.00060.0001-0.0002-0.0012-0.00060.0000-0.001311 Metals & metal prods.0.00010.00070.00130.00140.00090.00060.0002-0.0002-0.0015-0.00070.0000-0.001512 Electronics0.00070.0008-0.00030.00040.00040.0001-0.0001-0.0005-0.0003-0.0002-0.000613 Electrical equipment0.00130.00110.00060.00060.0001-0.0001-0.0009-0.0005-0.0001-0.001214 Machinery, etc0.00170.00080.00060.0001-0.0002-0.0014-0.0006-0.0002-0.001415 Motor vehicles, etc0.00030.00070.0000-0.0003-0.0017-0.0007-0.0001-0.001317 Other manuf. & repair0.00050.0002-0.0001-0.0010-0.0004-0.0001-0.001021 Motor vehicles trade0.00020.0000-0.0008-0.00040.0000-0.000922 Wholesale trade-0.0001-0.0006-0.00020.0000-0.000123 Retail trade0.00010.00010.0000-0.000128 Accomodation0.0008-0.00020.001229 Food & beverage services0.00000.000824 Land transport-0.000137 Other professional services 
               
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and Agglomeration 

Table C12:  Co-agglomeration results across UK local areas (2015) 

Source:  CE calculatio

Written by Cambridge Econometrics 

177 

3456810111213141517212223282924373 Food, drink & tobacco0.00060.00120.00020.00070.00100.00040.00030.00050.0006-0.00030.00050.00040.0001-0.0002-0.0005-0.00060.0001-0.00114 Textiles etc0.00060.00060.00040.00060.0001-0.00030.00010.0005-0.00090.0003-0.00030.0001-0.0001-0.0006-0.00020.0000-0.00025 Wood & paper0.00050.00130.00150.00110.00060.00120.00100.00070.00090.00050.0001-0.0002-0.0007-0.0007-0.0001-0.00116 Printing & recording0.00040.00040.00020.00020.00050.0004-0.00020.00040.00020.00010.0000-0.0008-0.00020.0000-0.00028 Chemicals, etc0.00120.00060.00050.00100.0009-0.00010.00090.00040.0002-0.0001-0.0009-0.0006-0.0001-0.000910 Non-metallic min. prods.0.00120.00040.00100.00100.00080.00080.00060.0002-0.0002-0.0010-0.00070.0000-0.001211 Metals & metal prods.0.00040.00090.00110.00140.00090.00050.0003-0.0003-0.0013-0.0007-0.0001-0.001312 Electronics0.00110.0011-0.00060.00050.00050.0001-0.0001-0.0005-0.0004-0.0003-0.000513 Electrical equipment0.00120.00130.00090.00060.0001-0.0001-0.0007-0.0006-0.0002-0.001014 Machinery, etc0.00090.00080.00060.0002-0.0002-0.0010-0.0006-0.0002-0.001015 Motor vehicles, etc0.00020.00060.0002-0.0004-0.0013-0.00070.0000-0.001517 Other manuf. & repair0.00050.0001-0.0001-0.0008-0.00050.0000-0.000821 Motor vehicles trade0.00020.0000-0.0006-0.00040.0000-0.000822 Wholesale trade-0.0001-0.0006-0.00020.0000-0.000223 Retail trade0.00010.00010.0000-0.000128 Accomodation0.0007-0.00040.000829 Food & beverage services-0.00010.000624 Land transport-0.000137 Other professional services 
               
 
 
 
 
 
 
 
 
 
 
 
 
 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and 
Agglomeration 

178 

 
EU Integration and the Impact on Region-Sector Specialisation, Concentration, and 
Agglomeration 

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EU Integration and the Impact on Region-Sector Specialisation, Concentration, and 
Agglomeration 

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doi:10.2760/575675 

ISBN 978-92-76-15039-8