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This article was downloaded by: [Universiti Teknologi Malaysia]On: 04 January 2014, At: 19:39Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Innovation: The European Journal ofSocial Science ResearchPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/ciej20
Is innovation in cities a matter ofknowledge-intensive services? Anempirical investigationRoberta Capello a , Andrea Caragliu a & Camilla Lenzi aa Department of Building, Environment, Science and Technology ,Politecnico di Milano , Piazza Leonardo 32, Milan , 20133 , ItalyPublished online: 19 Apr 2012.
To cite this article: Roberta Capello , Andrea Caragliu & Camilla Lenzi (2012) Is innovation in citiesa matter of knowledge-intensive services? An empirical investigation, Innovation: The EuropeanJournal of Social Science Research, 25:2, 151-174, DOI: 10.1080/13511610.2012.660326
To link to this article: http://dx.doi.org/10.1080/13511610.2012.660326
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Is innovation in cities a matter of knowledge-intensive services?An empirical investigation
Roberta Capello*, Andrea Caragliu and Camilla Lenzi
Department of Building, Environment, Science and Technology, Politecnico di Milano, PiazzaLeonardo 32, 20133 Milan, Italy
(Received 15 March 2011; final version received 10 October 2011)
The presence of large cities in a region represents a potential for regionalinnovation capacity: cities are in fact expected to generate dynamic agglomerationeconomies and knowledge spillovers. The paper adds to previous analyses onthis topic by investigating whether the linkage between the presence of cities inthe region and the innovative performance is mediated by the urban industrialstructure. In fact, a positive correlation is likely to exist between the presenceof large cities in a region and its innovative performance. Such a relationshipcould also depend on the presence of knowledge-intensive service, rather than onadvanced manufacturing activities. In order to verify this statement, we classifyEuropean NUTS2 regions both from an industrial perspective, as well as byspatial typologies. We integrate this classification with a novel data set on regionalinnovation, based on the Community Innovation Survey. On this basis, geo-graphical and descriptive analyses of regional innovation patterns are developedand explained. The descriptive results support our expectations. Regions host-ing large urban areas are the most innovative, and this statement is reinforcedin regions characterized by specialization in knowledge-intensive services. Thesimultaneous presence of advanced manufacturing and knowledge-intensiveservice activities generates synergic effects, fostering innovative performance.
Keywords: innovation; cities; knowledge-intensive services
Introduction
Technologically advanced and science-based sectors represent a major driver of
economic development. New jobs are expected to arise mainly in these new sectors,
while more traditional sectors are subject to restructuring or off-shoring, potentially
causing serious tensions in local labor markets.This stylized fact is certainly not new in the literature and links back to the
traditional and oldest theory explaining innovation through the presence of ‘‘science-
based’’ (Pavitt 1984) or high-technology sectors; regions hosting these sectors were
considered as ‘‘advanced’’ regions, leading the transformation of the economy.
Despite its early birth, this view is still well maintained in scientific discourse, and
it still inspires policy debate, as attested by the inclusion of employment data in high-
tech sectors in score boarding and benchmarking exercises aimed at measuring
innovation performance, also at the regional level (see among the many contributions
*Corresponding author. Email: [email protected]
Innovation � The European Journal of Social Science Research
Vol. 25, No. 2, June 2012, 151�174
ISSN 1351-1610 print/ISSN 1469-8412 online
# 2012 ICCR Foundation
http://dx.doi.org/10.1080/13511610.2012.660326
http://www.tandfonline.com
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the Regional Competitiveness Index, 2010, the Regional and European Innovation
Scoreboard, 2009, and the 5th Cohesion Report).
The focus of studies on innovation has progressively shifted from the sector-
based approach towards what has been called a function-based approach (Camagni
and Capello 2009), which stresses the importance of pervasive and horizontal
functions like R&D and higher education as the main determinants of innovation
capability. ‘‘Scientific’’ regions, hosting large and well-known scientific institutions,
were studied deeply and relationships between these institutions and the industrial
fabric were analyzed, with some disappointment as far as an expected but not often
visible direct linkage was concerned (MacDonald 1987, Massey et al. 1992, Monk
et al. 1988, Storey and Tether 1998). Indicators of R&D inputs (like public and
private research investment and personnel) and increasingly indicators of R&D
output (like patenting activities) were used in order to understand the engagement of
firms and territories on knowledge, intended as a necessary long-term precondition
for continuing innovation (Dasgupta and Stiglitz 1980, Antonelli 1989, Griliches
1990). The most recent step forward made in the literature was including nonmaterial
drivers of innovation as an explanation of innovation capacity, such as in the
learning region theory (Lundvall and Johnson 1994) and the milieu innovateur
approach (Aydalot 1986, Aydalot and Keeble 1988, Camagni 1991).
At the urban level, the same conceptual development in the theoretical iden-
tification of urban innovative determinants has taken place, and for this reason
the industrial structure of urban economies has recently been a relatively neglected
factor of urban innovation. However, the recent idea of ‘‘smart cities’’, generally
referring to the presence of e-economy and e-society, calls once again for a sector-
based approach to explain urban efficiency. An e-society or e-economy, in fact,
implies the presence (and the innovative use) of knowledge-intensive services (KIS)
in cities, generating higher efficiency rates. Addressing the sector based-approach at
the urban scale is also of interest since many of the studies undertaken in this frame
have focused on manufacturing clusters rather than urban agglomerations.
In this paper we claim that an industrial approach is still worth analyzing.
Modern urban economies may benefit from a favorable industrial mix, supporting
innovation; in particular, we expect that metropolitan regions hosting knowledge-
intensive activities will generate higher innovation performance. The capacity to
innovate is expected to be even stronger as metropolitan areas host a combination of
advanced manufacturing and service activities, that synergically foster the emergence
of a creative atmosphere.
Limited evidence is currently available at the European regional and urban scales
on the interplay between high-tech manufacturing, advanced services and innovation
activities. So far, a lack of data on innovation processes and different types of
innovations at the regional scale has unfortunately impaired similar exercises. Also,
this limitation is even more pronounced when the regional settlement structure
has to be introduced as a further dimension of analysis. The present paper moves
in this direction, by providing a systematic empirical analysis at regional level on
31 European countries. Our contribution is therefore mainly on empirical grounds
and based on descriptive evidence drawn from an original dataset covering EU27
plus European Free Trade Association (henceforth, EFTA) countries combining
employment data in high-tech manufacturing and services derived from EUROSTAT
with a novel data set on different types of innovation. This data set has been built by
152 R. Capello et al.
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the authors on the basis of data from the Community Innovation Survey (CIS)
EUROSTAT database.
The paper also describes spatial trends of different types of innovation (e.g.
product vs process innovation) and shows whether the presence of different industrialspecialization in metropolitan regions is related to a particular kind of innovation
activity. We speculate that product and marketing innovation � being dependent
either on a highly qualified workforce or on service sectors � are inclined to be more
intense in metropolitan regions. Finally, the most innovative performance, measured
by the simultaneous presence of firms able to carry out product and process
innovation in a region, is expected to be higher in metropolitan regions.
Research questions and data description
The general aim of the paper is to describe the relationship between the presence of
large cities in regions and the regional innovation performance. In fact, the presence
of large cities in a region represents per se a potential for its innovation capacity,
since cities are expected to generate dynamic agglomeration economies and knowl-
edge spillovers (Anselin et al. 2000, Capello 2001, Frenkel 2001, Simmie 2001).
This paper moves a step beyond previous analyses on this topic by describing
whether this linkage is mediated by the urban industrial structure. In fact, a positivecorrelation between the presence of large cities and the innovative performance in a
region is likely to exist. However, this relationship is expected to depend also on
the presence of knowledge-intensive service more than on advanced manufacturing
activities. This correlation can be even stronger for some specific types of innovation,
like product and marketing innovation, that either require a highly skilled labor
market (the former) or a widespread diffusion of service activities (the latter).
The research questions that are addressed in the empirical analysis are the
following:
(1) Is it true that agglomerated regions, hosting large urban areas, are the most
innovative ones?
(2) Is it true that, among all types of innovation, product and marketing
innovations are the most developed in agglomerated regions?
(3) Is it true that agglomerated regions specialized in knowledge intensive
service are more innovative than those specialized in manufacturing?
In order to answer these questions, this paper relies upon original data being
collected and developed in the frame of an ongoing ESPON (European Spatial
Observation Network) project.1 Data collection is based on the EUROSTAT
NUTS2 classification, and entails three main sources of data: data on the share of
employment in high tech manufacturing and service sectors; indicators on the
settlement structure; and an array of innovation indicators based on national CIS
figures developed at the NUTS2 level. The choice of the administrative areas used in
empirical analyses is a long disputed debate. Our work is based on EUROSTAT’sNUTS2 level for two main reasons. Conceptually, NUTS3 regions are often too
small to encompass functional urban areas; NUTS1 regions, on the contrary, tend to
be too large to contain local effects within their boundaries. In addition, this study is
related to an ongoing project financed by ESPON, whose research is mostly based on
the NUTS2 classification.
Innovation � The European Journal of Social Science Research 153
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The high level of complexity of modern production systems makes defining high-
tech industries an awkward task. Because of the deep technological content of both
manufacturing and service activities, neither should be ex ante excluded from the
definition. The definition of high-tech industries is, however, partly arbitrary;therefore, we decided to choose a broad one, encompassing sectors with medium-
high and high-tech content so as to capture a wide range of sectors characterized
by considerable high-tech creation and deployment. High-tech sectors are iden-
tified according to the OECD definition (Organization for Economic Cooperation
and Development 2005, NACE revision 1.1). Such sectors include manufacturing
of aircraft and spacecraft, pharmaceuticals, office, accounting and computing
machinery, radio, TV and communications equipment, and medical, precision
and optical instruments; in the following, we will refer to these classification as‘‘HT manufacturing’’. As for high-tech services, we considered those defined by the
OECD as ‘‘Knowledge-Intensive Service (henceforth, KIS) Activities’’.2 We classi-
fied regions according to their specialization in high-tech manufacturing and/or
service sectors as compared with the EU average. In detail, specialization is here
captured by the location quotient (LQ) computed with respect to the EU31 average
value on the basis of regional employment data in HT manufacturing and KIS,
available in EUROSTAT. Specialization is computed for two years (2002 and 2007),
in order to detect possible time trends.For the settlement structure, urban areas are defined in this paper as metro-
politan regions. The settlement structure typology here adopted is the one developed
within ESPON projects. Agglomerated regions are defined as those with a city
of more than 300,000 inhabitants and a population density of more than 300
inhabitants per square kilometer, or a population density between 150 and 300
inhabitants per square kilometer. Urban regions are defined as those with a city of
between 150,000 and 300,000 inhabitants and a population density of between 150
and 300 inhabitants per square kilometer (or a smaller population density � 100 and150 inhabitants per square kilometer with a bigger center of more than 300,000).
Rural regions have a population density lower than 100 per square kilometer and a
center of more than 125,000 inhabitants, or a population density lower than 100 per
square kilometer with a center of less than 125,000. Therefore, agglomerated regions
turn out to be those with a high population density and hosting at least one large
city, while urban regions are characterized by the presence of medium-sized cities.
Lastly, innovation indicators are based on national CIS4 wave figures (covering
the 2002�2004 period), redistributed at the NUTS2 level. In particular, we focus onthe following types of innovation activities, captured by different questions of the
CIS: only product innovations, only process innovations, product and process
innovations (both types of innovation simultaneously as well as all the first three
main typologies together), and marketing and/or organizational innovations.
Throughout the paper, maps are drawn according to the natural breaks method
developed by George Jenks. This method is probably the most widely used system
of definition of classes in choropleth maps, in particular because it allows the
minimization of within-classes variance and the maximization of across-classesvariance levels.
For the sake of clarity, however, it is useful to introduce here the following
definitions. In particular, following the CIS, we consider as product innovation new
and significantly improved goods and/or services with respect to their fundamental
characteristics, technical specifications, incorporated software or other immaterial
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components, intended uses, or user friendliness. Process innovation relates instead
to the implementation of new and significantly improved production technologies
or new and significantly improved methods of supplying services and delivering
products. A third category is available, called product and process innovation, whichcaptures the capacity of firms to develop both product and process innovation at
the same time. This category measures the best innovative performers since the
introduction of product and process innovation requires the simultaneous presence
of markedly different innovation skills. Lastly, in the CIS, another type of innovation
is captured, i.e. product and/or process innovation, which represents a general category
entailing either product or process or both of them. Marketing innovation is defined
as the introduction of ‘‘significant changes to the design or packaging of a good or
service’’ or ‘‘new or significantly changed sales or distribution methods, such asinternet sales, franchising, direct sales or distribution licenses’’. Finally, an
organizational innovation is defined as the introduction of either ‘‘new or significantly
improved knowledge management systems to better use or exchange information,
knowledge and skills within your enterprise’’, ‘‘a major change to the organization of
work within your enterprise, such as changes in the management structure or
integrating different departments or activities’’ or ‘‘new or significant changes in
your relations with other firms or public institutions, such as through alliances,
partnerships, outsourcing or sub-contracting’’. The Appendix fully details themethodology followed to obtain innovation figures at NUTS2 level.
Specialization in high-tech sectors in EU metropolitan regions
Location quotients for both HT manufacturing and KIS, on the basis of employment
data, point to a slight decrease in HT manufacturing activities that has taken place
in major industrial countries in the last decade. Regional specialization in the HT
industry markedly declined between 2002 and 2007 in most French, Polish, British,Bulgarian and Greek regions; at the same time, a relative positive shift occurred
in most regions belonging to two belts, one running north�south and the other
stretching west�east on the continent. On average, the location quotient for the HT
industries declined by 0.02 in the EU15 regions and increased by 0.09 in New
Member States (hereafter NMS).
Data also indicate that, although regions characterized by an agglomerated
settlement structure do not display remarkably higher specialization levels in the
HT manufacturing, they do so in terms of KIS. In time, however, a decrease in high-tech manufacturing specialization has taken place in the EU27, with rural regions
showing over the period 2002�2007 an increasing specialization in HT manufactur-
ing, which is mirrored by a simultaneous decrease in more urbanized areas.
The loss of high-tech manufacturing is not necessarily matched by a simultane-
ous process of increasing specialization in advanced services (Figure 1). In fact, on
average EU15 regions show zero variation in the KIS location quotients, whilst
NMS show a slight increase (0.01). Remarkable country effects characterize the
data, with three countries registering significant correlations between the changein HT manufacturing and the change in KIS specializations. In particular, nega-
tive correlation can be found only for Greece, Italy and Sweden, where regions
apparently switched regime, swapping a focus on advanced manufacturing with a
specialization in advanced services. Elsewhere, insignificant relations suggest that
manufacturing jobs flowing to NMS or outside Europe are not necessarily replaced
Innovation � The European Journal of Social Science Research 155
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with similarly advanced functions. Country-wise, however, the negative correlation
between specialization levels in HT manufacturing and KIS industries is remarkable,
although not significant. At the country level, therefore, the shift of modern EU27
economies towards advanced services seems to characterize countries previously
specialized in HT manufacturing.
By examining top 10 performers over the analyzed time span (Table 1), the
picture displays a less dynamic behavior. In fact, from this perspective the situation
seems much more stable, with only one change taking place between 2002 and 2007
for HT manufacturing (Franche-Comte being substituted by Severovychod), while
more changes take place in KIS (five out of 10 regions in the 2007 top 10 table would
not be listed in 2002). The hierarchy of HT manufacturing seems therefore quite
hysteretic, with more change taking place in KIS, where in particular a strong
specialization of capital city-regions seems to take place.
In order to better understand the interplay and the possible synergic effects of
specialization in manufacturing and services, we classified regions according to their
level of specialization in both sectors (in comparison with the EU average value).
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Roma
Riga
Oslo
Bern
Wien
Kyiv
Vaduz
Paris
Praha
Minsk
Tounis
Lisboa
Skopje
Zagreb
Ankara
Madrid
Tirana
Sofiya
London Berlin
Dublin
Athinai
Tallinn
Nicosia
Beograd
Vilnius
Ar Ribat
Kishinev
Sarajevo
Helsinki
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Warszawa
Podgorica
El-Jazair
Ljubljana
Stockholm
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Amsterdam
Bratislava
Luxembourg
Bruxelles/Brussel
Valletta
Acores
Guyane
Madeira
Réunion
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MartiniqueGuadeloupe
0 500250km
© Politecnico di Milano, ESPON KIT Project , 2011
Change in LQ knowledge intensive services 2002-2007NA
-0.15 - -0.12
-0.11 - -0.05
-0.04 - -0.02
-0.01 - 0.01
0.02 - 0.04
0.05 - 0.09
0.10 - 0.16
0.17 - 0.46
Figure 1. Change in the location quotient in knowledge intensive services (2002�2007).
Source: authors’ calculation on EUROSTAT employment data.
156 R. Capello et al.
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We can thus identify four main types of region, as summarized in Figure 2: regions
with specialization simultaneously in HT manufacturing and KIS are labeled as
Technologically Advanced Regions (TARs); regions without any advanced specia-
lization are labeled low-tech regions; while regions with a specialization in either KIS
or HT manufacturing are called, respectively, KIS regions and HT manufacturing
regions.
Figure 3 displays (according to classes of Figure 2) the geography of European
regions according to the proposed classification. Twenty-one regions identified as
TAR are German, 13 British, eight French, five Belgian, four Swiss, three Swedish,
two Finnish and Danish, and one each for Italy, Norway, Slovenia and Slovakia.
The geography of technology in Europe is indeed highly concentrated, although
peripheral regions and regions with capital cities in NMS do play a major role. Over
time (although the time span considered may be too short to draw safe conclusions),
no region acquired or lost the status of TAR.The productive fabric of Europe shows therefore a remarkable concentration
of technology, related either to the advanced manufacturing or services activities.
Table 1. Top 10 regions in terms of location quotients in high-technology (HT) manufactur-
ing and knowledge-intensive services (KIS), 2002�2007.
HT manufacturing Knowledge intensive services
Location
quotient 2002 2007 2002 2007
Region 1 Stuttgart Stuttgart Inner London Inner London
Region 2 Tubingen Braunschweig Stockholm Stockholm
Region 3 Braunschweig Karlsruhe Oslo og Akershus Oslo og Akershus
Region 4 Franche-Comte Tubingen Outer London Hovedstaden
Region 5 Kozep-Dunantul Rheinhessen-Pfalz Brussels Aland
Region 6 Karlsruhe Unterfranken Hovedstaden Zurich
Region 7 Niederbayern Freiburg Ovre Norrland Berlin
Region 8 Unterfranken Severovychod Mellersta Norrland Noord-Holland
Region 9 Rheinhessen-Pfalz Kozep-Dunantul Ile de France Utrecht
Region 10 Freiburg Niederbayern Surrey and Sussex Ovre Norrland
Source: authors’ calculation from EUROSTAT data.
HT manufacturing regions Technologically-Advanced Regions
Low-tech regions
Specialization in KIS
Specialization in high-tech manufacturing
EU average
KIS regions
Figure 2. Sector-based typology of regions.
Innovation � The European Journal of Social Science Research 157
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Moran’s I index, measuring the degree of spatial autocorrelation among regions and
calculated on the basis of a Rook contiguity matrix of second order, is in fact equal
to 0.18, and significant at all conventional levels, for the categorical variable
‘‘Technologically-Advanced Region’’ depicted in Figure 2.
This statement, however, needs qualification. In fact, while specialization in
high-tech manufacturing seems to be much more diffused across the European
space, specialization in KIS displays impressive concentration rates. It is finally
worth stressing that some countries present no specialization type � neither in HT
manufacturing, nor in KIS industries.
Tables 2 and 3 offer a summary of the spatial distribution of differently special-
ized regions by settlement structure. They are based on the same raw numbers
and differ only in the way percentages are calculated: in Table 2, percentages are
calculated by column (i.e. we show how many agglomerated, urban or rural are TAR,
HT manufacturing, KIS, and low-tech, respectively). In Table 3, instead, we show the
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Low tech regions
Advanced manufacturing regions
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Figure 3. Geography of European regions according to the sector-based classification, 2007.
Source: authors’ calculations from EUROSTAT data.
158 R. Capello et al.
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reverse, (i.e. how many TAR, HT manufacturing, KIS and low-tech are respectively
agglomerated, urban or rural).
In Table 2, column one clearly points out that the largest group is composed
of KIS regions, whereas the other three groups are similarly large. Agglomeratedregions are characterized by both advanced manufacturing and service activities and
by KIS specialization. Urban regions are typically advanced manufacturing regions,
while rural regions are characterized, as expected, by low-tech activities. Each
typology of region is significantly different from both the others, and the significance
of such difference is very strong. Two-tailed t-tests (not displayed here) always
rejected the null of equality of the two distributions at the 1% significance level.
Table 3 shows that TARs are mostly characterized by a dense, or hyper-dense,
urban structure (more than 80% of TARs are either agglomerated or urban). Similarconclusions apply to KIS regions, while HT manufacturing regions present mainly
an urban and rural settlement structure. Consequently, almost three-quarters of
low-tech regions are characterized by a rural structure. Once again, each type of
settlement structure characterizes a markedly different distribution of regions, with
the usual two-tailed t-tests (not displayed here) always rejecting the equality null at
the 1% significance level.
An interesting question is whether the innovative performance of agglomerated
regions changes according to their industrial specialization. This is the subject matterof the next section.
Innovation trends in EU metropolitan regions
The fundamental relevance of innovation process in contemporary economies is not
in general matched by quality data. CIS represents one of the best and most updated
Table 3. Advanced sector-based typology of regions by settlement structure.
Typology of regions
All Agglomerated Urban Rural Total
Technologically advanced regions 22% 38.60% 42.11% 19.30% 100%
HT manufacturing regions 21% 15.52% 48.28% 36.21% 100%
KIS regions 33% 36.59% 35.37% 28.05% 100%
Low-tech regions 24% 11.76% 14.71% 73.53% 100%
Source: authors’ calculation from EUROSTAT data.
Table 2. Advanced sector-based typology of regions by settlement structure.
Typology of regions
All Agglomerated Urban Rural
Technologically advanced regions 22% 37.64% 29.97% 12.28%
HT manufacturing regions 21% 15.16% 34.38% 23.05%
KIS regions 33% 35.71% 25.19% 17.86%
Low-tech regions 24% 11.50% 10.46% 46.80%
Total 100% 100% 100% 100%
Source: authors’ calculation from EUROSTAT data.
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attempts to measure innovation activities. As pointed out above, in fact, the CIS
precisely allows the distinction between different types of innovation activities. CIS
data, however, are unequally stratified across space. Since in some EU countries data
are not stratified at NUTS2 level, such spatial detail is not publicly made available.
The authors offer a major improvement in this direction, by providing a robust
methodology to estimate regional CIS data (see the Appendix for a detailed
description of the methodology). This section presents the first descriptive results
drawn from this new database on regional innovation data.
The spatial distribution of innovation activity in European regions does indeed
display consistent autocorrelation, as pointed out above. Table 4 shows the value
of Moran’s I statistic calculated on the basis of a distance weight matrix on the
subsample of 265 NUTS2 regions for which innovation rates are available and
coordinates do not alter significantly the magnitude of calculated statistics.3 All
three variables are characterized by a highly significant pattern of positive spatial
autocorrelation, which implies that regions with high levels of innovation rates tend
to be surrounded by regions with similarly high levels, and vice versa.
Product innovation trends
For product innovation only (depicted in Figure 4), spatial concentration seems to be
prominent. This variable in fact displays considerable concentration in selected
countries,4 the core being in German, Scandinavian, Swiss and British regions, with a
few notable exceptions outside these areas. EU15 regions tend on average to innovate
more, and significantly so, than Eastern ones; the same applies to denser regions,
while rural regions display a relatively lower product innovation rate. In general,
spatial concentration looks pronounced regardless of the country being a strong
or weak product innovator. In particular, the latter is the case for Portugal, where
Lisbon is the only area with some product innovation activity, Spain, with Madrid,
Barcelona and a few Pyrenean regions, Greece, and some NMS. Italy represents
an exception to this pattern, since several regions in the northern and central part
of the country display similar product innovation rates. Overall, spatial patterns
characterize the variable not only across the country, but also within countries;
in fact, capital regions tend to display higher product innovation rates, with some
notable exceptions of regions also registering consistent innovation performance
despite not hosting the capital city (e.g. Rhone-Alps and Toulouse in France).
Spatial concentration can be technically verified by mapping the clusters of high
levels of the variables mapped surrounded by similarly high levels, low�low, high�low and low�high levels as defined with the use of Local Indicators of Spatial
Association (LISA; Anselin 1995). Maps are available in Appendix 2; Table 5 shows
that, among the possible urban settlement structures, agglomerated regions are the
ones that have the highest percentage of firms innovating in product only, belonging
Table 4. Moran’s I statistic for different regional innovation rates.
Type of innovation Moran’s I
Product innovation only 0.17***
Process innovation only 0.21***
Marketing and/or organizational innovation 0.32***
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Zagreb
Ankara
Madrid
Tirana
Sofiya
London Berlin
Dublin
Athinai
Tallinn
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Beograd
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Ar Ribat
Kishinev
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Helsinki
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Share of product innovation onlyNA0 - 3.263.27 - 5.925.93 - 9.129.13 - 12.8012.81 - 17.3017.31 - 23.4323.44 - 33.4533.46 - 44.42
Figure 4. Share of firms developing product innovation only.
Source: authors’ estimations from CIS national EUROSTAT data.
Table 5. Share of firms developing product innovation only by LISA cluster and regional
settlement structure.
LISA cluster Total regions
Agglomerated
regions Urban regions Rural regions
High�high 66 0.47 0.48 0.05
Low�Low 96 0.21 0.20 0.59
Low�high 22 0.18 0.27 0.54
High�low 2 0.50 0.50 0.00
No spatial association 79 0.16 0.42 0.42
Source: authors’ calculations.LISA, Local Indicators of Spatial Association.
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to the cluster displaying positive spatial autocorrelation (high�high). Rural areas, onthe contrary, are characterized by a negative spatial association.
Process innovation trends
Process innovation shows a more dispersed pattern than product innovation
(Figure 5). Countries such as Portugal, Spain, France, Germany and the UK do not
display a remarkable concentration of process innovation within their boundaries.
The variance associated with this variable is much lower than the same measureassociated with product innovation. This finding further strengthens the case for
a more evenly distributed practice. In fact, this is also reflected in the case of
NMS, that are unexpectedly characterized by homogeneous spatial trends. Process
innovation takes place more frequently in densely populated regions and in
metropolitan areas. A relevant dichotomy shows up between western and eastern
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Lisboa
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Zagreb
Ankara
Madrid
Tirana
Sofiya
London Berlin
Dublin
Athinai
Tallinn
Nicosia
Beograd
Vilnius
Ar Ribat
Kishinev
Sarajevo
Helsinki
Budapest
Warszawa
Podgorica
El-Jazair
Ljubljana
Stockholm
Reykjavik
København
Bucuresti
Amsterdam
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Bruxelles/Brussel
Valletta
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Réunion
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Share of process innovation onlyNA0 - 5.405.41 - 8.098.10 - 10.0910.10 - 12.3212.33 - 14.7114.72 - 18.0118.02 - 25.9225.93 - 55.08
Figure 5. Share of firms developing process innovation only.
Source: authors’ estimations from CIS national EUROSTAT data.
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countries, the former averaging process innovation rates higher by about 5% than
regions in NMS. Given the softer nature of process innovation, however, on average
innovation rates are in the case of this variable consistently higher than product
innovation. Overall, process innovation displays an average value, across Europeancountries, higher by just 1 percentage point than product innovation. In particular,
it is worth stressing that process innovation displays on average higher values in
southern European countries than in the rest of Europe, by about 2 percentage
points. However, product and process innovation display remarkable levels of co-
variation. On average, regions displaying large levels of product innovation are also
simultaneously proficient in process innovation. A notable exception is represented
by southern European countries, notably Spain, Greece, Italy and Portugal, where
relatively insufficient performance in terms of product innovation is matched bysuperior performance in process innovation.
Table 6 shows the LISA clusters associated with process innovation and a picture
of spatial association of high values in different spatial typologies, strengthening
the above-mentioned result of a relatively evenly distributed innovation activity.
A remarkable result of this analysis is also the absence of any type of spatial associa-
tion in 65% of all observations analyzed, which further confirms that process
innovation is a more evenly distributed activity than product innovation.
Marketing and/or organizational innovation trends
Differently from product and process innovation, marketing and/or organizational
innovations capture nontechnological elements of innovation progress such as qual-
ity improvements, reductions of environmental damages stemming from firms’ pro-duction, reductions of energy consumption, creation of new markets, reduced labor
costs, reductions of amount of materials required for production, and conformance
to regulations.
Figure 6 highlights a significant concentration of marketing and/or organiza-
tional innovation in regions in the EU15 countries, with particularly high values in
German and Austrian regions. However, the spatial distribution of this soft form of
innovation seems much more even across the European space. The relatively even
distribution is in particular remarkable when observed within countries, witnessing asimilar innovative capability among regions.
This even distribution notwithstanding, spatial patterns characterize marketing
and/or organizational innovation, with a consistently higher tendency to introduce
such improvements in capital regions, and higher innovation rates also as region
Table 6. Share of firms developing process innovation only by LISA cluster and regional
settlement structure.
LISA cluster Total regions
Agglomerated
regions Urban regions Rural regions
High�high 76 0.28 0.32 0.41
Low�Low 0 0.00 0.00 0.00
Low�high 14 0.14 0.21 0.64
High�low 3 0.00 1.00 0.00
No spatial association 172 0.27 0.35 0.38
Source: authors’ calculations.
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Zagreb
Ankara
Madrid
Tirana
Sofiya
London Berlin
Dublin
Athinai
Tallinn
Nicosia
Beograd
Vilnius
Ar Ribat
Kishinev
Sarajevo
Helsinki
Budapest
Warszawa
Podgorica
El-Jazair
Ljubljana
Stockholm
Reykjavik
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Bucuresti
Amsterdam
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Luxembourg
Bruxelles/Brussel
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Share of marketing and organizational innovationNA0 - 9.059.06 - 15.2415.25 - 19.8119.82 - 23.5323.54 - 29.5629.57 - 37.5037.51 - 48.0548.06 - 78.36
Figure 6. Share of firms developing marketing and/or organizational innovation. Source:
authors’ estimations from CIS national EUROSTAT data.
Table 7. Share of firms developing marketing and/or organization innovation by LISA
cluster and regional settlement structure.
LISA cluster Total regions
Agglomerated
regions Urban regions Rural regions
High�high 58 0.28 0.52 0.21
Low�Low 77 0.34 0.26 0.40
Low�high 25 0.12 0.36 0.52
High�low 4 0.50 0.25 0.25
No spatial association 101 0.22 0.31 0.48
Source: authors’ calculations.
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density increases, as well as in regions with large cities, bearing the diversified and
creative environment leading to innovative behavior. NMS innovate in marketing
and/or organization less than EU15 regions, on average by about 9 percentage points.
Similar patterns affect Nordic and Mediterranean countries, regions in the lattersample innovating less in marketing and/or organization by about 5 percentage
points. Marketing and/or organizational innovation, however, is not an activity apart
from product and process innovation. In fact, pure correlation between marketing
and/or organizational innovation, on the one hand, and product and/or process
innovation, on the other, is remarkably high (equal to 0.71 and significant at all
conventional levels).
Table 7 shows mean values for each LISA cluster in agglomerated, urban and
rural regions. Interestingly, 52% of all regions in the high�high cluster are located inurban areas, suggesting the concentration of marketing innovation activities in
medium-size cities. Furthermore, about 40% of the whole sample displays no spatial
association at all.
To reply to the first two research questions in a descriptive way, we present the
innovation rates of agglomerated regions with respect to all other regions by type of
innovation activities. Overall, agglomerated regions show superior innovation rates
to urban and rural regions in all five fields. It seems likely indeed that metropolitan
regions tend to outperform the rest of the EU territory in all respects, the differencebeing strongly statistically significant as shown by the t-tests (Table 8). Moreover,
Table 8 also shows that the difference in innovation performance of regions is
statistically significantly higher in agglomerated for what concerns product innova-
tion and marketing and/or organizational innovation. Our impression that these two
kinds of innovation � being dependent either upon high-level quality workforce or on
service sectors � are those calling for the presence of large urban areas to take place
turns out to be true.
Industry innovation trends in EU metropolitan regions
Since agglomerated regions show greater innovation rates, it is of interest to
understand whether this relates to their industrial specialization. Table 9 gives some
insights in this direction. In particular, Table 9 replies to our third research questionpresented above by showing that KIS regions innovate more than advanced
manufacturing regions, but this is true for product innovation only and for marketing
innovation. Moreover, KIS regions are more inclined to innovate more than other
regions in product rather than in process innovation. This result is rather interest-
Table 8. Innovation rates in agglomerated regions by type of innovation activities.
Typology
Share of
product
innovation
only
Share of
process
innovation
only
Share of both
product and
process
innovation
Share of
product and/
or process
innovation
Share of
marketing and/or
organizational
innovation
Agglomerated 14.88 12.41 15.76 40.94 28.85
Others 8.69 10.57 14.70 33.40 24.83
t-Test 3.65*** 2.97*** 2.03** 3.28*** 16.38***
***, **, * Significant at the 1, 5 and 10% level, respectively.Source: authors’ calculation from CIS data estimations.
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ing, witnessing that innovative activities are related to the production of new and
advanced services rather than to new processes. If this is related to the emergence
of smart cities through e-society, we can claim that the interpretation given by
agglomerated regions of the e-society is the production of new and advanced
services, rather than the mere supply of old services through new ICT networks.
Another interesting result contained in Table 9 is that synergic effects in innovation
performance exist if the region is specialized in advanced manufacturing and service
activities at the same time. Technologically advanced regions, in fact, look more
innovative than the other regions, regardless of the type of innovation being considered
(technological, such as product or process, or nontechnological, such as marketing
and/or organizational) and the settlement typology. These regions systematically
innovate more than the average of the whole sample, but also with respect to the
average innovation rate registered by either high-tech manufacturing or services
regions. This result suggests that the simultaneous presence of advanced manufactur-
ing and KIS may stimulate innovation activities, probably exploiting urbanization (i.e.
diversity) externalities, as suggested in Feldman and Audretsch (1998). In order to
provide support for this statement, however, more in-depth analysis is required.
Conclusions
In this paper we provide empirical evidence on the relationship between specializa-
tion in high-tech sectors and innovation performance at the regional level. We claim
that a sector-based approach is still worth analyzing as it is still shaping both the
scientific discourse as well as inspiring the current policy debate. Looking at urban
innovation process from this standpoint is also of paramount interest, since much of
Table 9. Innovation rates by industrial specialization and types of innovation activities in
agglomerated regions.
Typology
Share of
product
innovation
only
Share of
process
innovation
only
Share of
both product
and process
innovation
Share of
product and/
or process
innovation
Share of
marketing
and/or
organizational
innovation
Technologically advanced regions
Agglomerated 21.05 13.31 19.02 49.48 35.30
Others 13.54 12.34 17.60 42.52 30.87
t-Test 5.48*** 3.88*** 1.91** 4.65*** 2.75***
HT manufacturing regions
Agglomerated 10.46 13.53 16.24 40.24 25.63
Others 9.23 10.37 15.97 35.57 24.98
t-Test 1.78** 1.99** 0.28 0.94 0.04
KIS regions
Agglomerated 14.54 11.80 14.55 38.86 27.06
Others 10.37 10.60 13.29 33.25 23.67
t-Test 2.90** 0.84 0.71 1.70** 1.76**
***, **, * Significant at the 1, 5 and 10% level, respectively.Source: authors’ calculation from EUROSTAT data.
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previous studies undertaken in this frame primarily focused on cluster-like
agglomeration environments rather than urban settlements.
The results of our analysis point to a picture of spatial concentration of both
high-tech manufacturing and services sectors, that have partly increased over time.
Interestingly, most European regions show some degree of specialization in either
services or manufacturing or both (being thus labeled as Technologically Advanced
Regions). The productive fabric of Europe shows therefore a remarkable concentra-
tion in advanced activities, related to either advanced manufacturing or services. This
statement, however, needs qualification. In fact, while specialization in manufac-
turing high-tech seems to be much more diffused across the European space,
specialization in KIS displays impressive concentration rates. Interestingly enough,
this concentration increases within agglomerated regions. Nearly 36% of agglomer-
ated regions are in fact specialized in KIS, while 37% are specialized in both
advanced manufacturing and service activities.
These patterns describe also the regional innovative performance and the type
of innovation activities carried out. Product innovation shows quite a prominent
spatial concentration, especially in central and northern European regions. On the
other hand, the geography of process innovation is less concentrated, and southern
regions seem to perform especially well in this regard. Lastly, marketing and/or
organizational innovation is relatively more evenly distributed across the European
space. Importantly, agglomerated regions are the best performing in all types of
innovation activities, but they look a particularly favorable setting for product and
marketing innovations. Moreover, our descriptive analysis shows that KIS regions
register a higher innovation performance than HT manufacturing regions.
Knowledge-intensive service regions also show their outstanding performance
in product innovation rather than process innovation with respect to other regions.
This is particularly important, since it witnesses that innovation in service is not the
mere supply of old services through new technological means (like e-services), but the
production and creation of new services (e.g. online data base management and data
mining), in which the real efficiency gains lie.
Interpretative analyses of the geographical patterns of innovation will be
developed, where other territorial specificities, playing the role of co-determinants,
shall be taken into account. Our results suggest that innovation in urban areas is still
an interesting research field. In particular, economic modeling of the interaction
between innovation and urban performance is the object of our future research.
Notes
1. The project is called KIT (‘‘Knowledge, Innovation and Territory’’) and is led by thePolitecnico of Milan, and in particular by the authors of this paper. For further details onthe project and the consortium members, see http://www.espon.eu/main/Menu_Projects/Menu_AppliedResearch/kit.html.
2. Medium-high and high-tech manufacturing industries include chemicals (NACE 24),machinery (NACE 29), office equipment (NACE 30), electrical equipment (NACE 31),telecommunications and related equipment (NACE 32), precision instruments (NACE 33),automobiles (NACE 34) and aerospace and other transport (NACE 35); KIS include watertransport (NACE 61), air transport (NACE 62), post and telecommunications (NACE 64),financial intermediation (NACE 65), insurance and pension funding (NACE 66), activitiesauxiliary to financial intermediation (NACE 67), real estate activities (NACE 70), rentingof machinery and equipment (NACE 71), computer and related activities (NACE 72),research and development (NACE 73) and other business activities (NACE 74).
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3. Statistics are calculated with the exclusion of the French Overseas Department, Iceland,Switzerland and Norway.
4. Additional information on spatial patterns in innovation data can be found in Appendix 2.
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Appendix 1. Innovation data: estimation methodology
The CIS is designed to obtain information on innovation activities within enterprises with 10or more employees. National CIS data are available in EUROSTAT. We estimated regionaldata (i.e. NUTS2 level) starting from the national data (i.e. NUTS0 level) in order to ensurecomparability across countries.
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The estimates at NUTS2 level were obtained with a two-step procedure. The first step callsfor the disaggregation of the national data according to specific weights, especially defined foreach category of innovation. The weights aim to capture both a functional as well as anindustrial dimension. The former is captured by looking at the share of professions, the latterby looking at the industrial specialization. In absence of any a priori assumption on differentrelevance of the functional vs the industrial dimension, we attributed equal importance to theselected weights. Table A1 shows the selected weights.
The second step of the estimate requires the robustness of the estimates. To check therobustness of our estimates we implemented a series of benchmark exercises. In detail, weimplemented three types of tests, namely on the equality of means, on the equality of standarddeviation, and of Kolmogorof�Smirnoff, to assess whether our estimates diverge from theoriginal sample distribution.
We performed two sets of comparisons. First, we compared our estimates of the share ofonly product innovators, the share of only process innovators and the share of product andprocess innovators with regional data from National Statistical Offices. These latter wererescaled at the National value available from EUROSTAT, since the national figures availablefrom EUROSTAT and National Statistical Offices may differ according to different strataweighting procedures. The tests could be implemented only on limited set of countries, namelyItaly, Romania and Czech Republic, that publicly release these data on their websites.
Next, to support further our estimates, we made use of data on product and/or processinnovators from Regional Innovation Scoreboard (RIS). In particular, we compared ourestimates of product and/or process innovators, obtained as the sum of the first threecategories of innovators (i.e. only product innovators, only process innovators, product andprocess innovators), with RIS data. The tests could be implemented only on those countrieswhose data are available in the annex to the RIS methodology report.
Still, some problems of comparability remain. For example, the France NUTS0 dataavailable from RIS on the share of product and/or process innovators is different from theFrance NUTS0 data available from EUROSTAT (in particular, the former is smaller than thelatter), which may affect the mean value of our estimates.
Table A2 summarizes the results of these tests. Overall, they indicate that our estimates donot statistically differ in their mean, standard deviation and distribution from the official datareleased either by National Statistical Offices or by RIS. Although for some countries, the testsindicate that either the mean or the standard deviation can be statistically different, the outputof the Kolmogorov�Smirnoff test lends support to our estimates and indicates that thedistribution of the original sample does not statistically differ from that of our estimates.
Appendix 2. Spatial patterns in regional innovation patterns
Figures A1�A3 map LISA clusters for the product innovation only, process innovation only,and marketing and/or organizational innovation rates. Country effects are evident in the firstand the last case. While in terms of product innovation, German and British regions tend to
Table A1. Selected weights.
Type of innovation Weights
Only product Percentage scientists, percentage employment in high-tech
(employment in manufacture of electrical and optical equipment
(DL sector in NACE Rev 1.1. classification))
Only process Percentage employment in manufacturing, percentage technicians,
percentage managers
Both product and process Percentage scientists, percentage employment in high-tech (DL),
Percentage employment in manufacturing, percentage technicians,
percentage managers
Marketing and/or
organizational
Percentage managers, percentage employment in services
Innovation � The European Journal of Social Science Research 169
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Table A2. Robustness checks.
Type of innovation
Sample
Mean
estimates
Mean benchmark
estimates Mean difference
Standard deviation
difference
Kolmogorov�Smirnoff test
(different distribution)
Product only
Italya 4.41 4.53 NS NS Not significant; p-value
equals 0.94.Romaniaa 1.95 1.69 NS �; pB0.05
Process only
Italya 14.27 14.00 NS NS Not significant; p-value
equals 0.95.Romaniaa 4.72 4.82 NS �; pB0.01
Product and process
Czech Republica 14.48 14.38 NS B; pB0.05 Not significant; p-value
equals 0.98.Italya 8.90 9.01 NS NS
Romaniaa 13.87 13.15 NS B; pB0.01
Product and/or process
Austriab 49.03 50.03 NS NS Not significant; p-value
equals 0.98.Belgiumb 42.37 46.61 NS NS
Bulgariab 15.03 15.21 NS NS
Czech Republicb 37.03 36.05 NS NS
Spainb 29.97 29.06 NS �; pB0.01
Finlandb 34.45 34.52 NS NS
Franceb 27.55 24.37 NS �; pB0.01
Greeceb 29.72 39.30 B; pB0.01 NS
Hungaryb 18.09 17.37 NS NS
Italyb 31.77 32.21 NS NS
Polandb 23.07 38.95 NS NS
Portugalb 39.40 38.95 NS NS
Romaniab 20.18 17.74 NS NS
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Table A2 (Continued )
Type of innovation
Sample
Mean
estimates
Mean benchmark
estimates Mean difference
Standard deviation
difference
Kolmogorov�Smirnoff test
(different distribution)
Sloveniab 34.11 23.85 �; pB0.05 NS
Slovakiab 22.43 20.01 NS NS
United Kingdomb 25.80 42.08 NA NA
Italya 31.77 27.59 NS NS
Romaniaa 20.18 20.54 NS NS
Marketing and/or organizational
Austriab 80.52 80.52 NS NS Not significant; p-value
equals 0.51Belgiumb 80.33 70.36 NS NS
Bulgariab 0.76 0.94 NS NS
Czech Republicb 54.83 54.23 NS NS
Spainb 35.72 32.53 �; pB0.05 NS
Finlandb 69.13 72.81 NS NS
Franceb 55.78 56.04 NS �; pB0.05
Italyb 49.12 51.39 NS NS
Polandb 26.88 27.43 NS NS
Portugalb 64.49 67.43 NS NS
Romaniab 33.71 32.10 NS NS
Sloveniab 54.35 54.28 NS NS
Slovakiab 19.65 18.15 NS �; pB0.05
United Kingdomb 42.14 43.44 NS �; pB0.05
aSource of data used as benchmark: National Statistical Offices.bSource of data used as benchmark: Regional Innovation Scoreboard 2009.NS, not significant; NA, not available.
Inn
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represent clusters of high values, in marketing and/or organizational innovation, Germanregions are matched by Finnish ones.
Figure A2 shows how instead process innovation departs significantly from this pattern,by identifying a belt of Southwestern EU countries, whose regions, from Portuguese andSpanish to French, from Italian to German and Austrian ones, tend to identify clusters ofconsistent innovative activity.
In the case of marketing and/or organizational innovation, it is worth stressing how capitalregions (Bucharest, Paris, Prague and Vienna) are characterized by a relationship of negativespatial association with the territory around them. This identifies metropolitan regionssurrounded by regions with relatively low values of marketing and/or organizationalinnovation, suggesting the role of regional attractors of human capital-intensive activitiestypical of this type of innovation.
Figure A1. LISA for the variable ‘‘Share of firms developing product innovation only’’.
Source: authors’ estimations from CIS national EUROSTAT data.
172 R. Capello et al.
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Figure A2. LISA for the variable ‘‘Share of firms developing process innovation only’’.
Source: authors’ estimations from CIS national EUROSTAT data.
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Figure A3. LISA for the variable ‘‘Share of firms developing marketing and/or organiza-
tional innovation’’.
Source: authors’ estimations from CIS national EUROSTAT data.
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