Measuring the expected synergy in Spanish regional and national systems of innovation
Measuring the expected synergy in Spanish regional and national systems of innovation
Loet Leydesdorff 0 1 2
Igone Porto-Gomez 0 1 2
Spain 0 1 2
JEL Classification O 0 1 2
0 Engineering School of Bilbao, University of the Basque Country (EHU-UPV) , Alameda Urquijo s/n, 48013 Bilbao , Spain
1 Amsterdam School of Communication Research (ASCoR), University of Amsterdam , P.O. Box 15793, 1001 NG Amsterdam , The Netherlands
2 & Loet Leydesdorff
This paper examines the effect of synergy at the geographical, technological, and organizational levels on the structure of the innovative system in Spain. Using a unique dataset of more than one million firms in 2010 across geographic regions in Spain, it empirically estimates the synergy within and across regions and sectors. The key findings indicate that Spain's innovation system is largely decentralized into more regionalized systems with the strongest role played by the metropolitan areas. The results have policy implications for Spain as well as other nations and intra-country regions. The paper contributes to the extant literature related to innovation systems in three ways: first, by using a more novel approach adapting the triple helix context; second, by providing empirical evidence on the importance of synergy in influencing the structure of a national innovation system; and third, by providing a case study of Spain.
After a visit to Japan,
, 1988) noted that Japan could be considered as a
national system of innovations (NSI).
further on this concept using case studies.
had argued that interactions
within national contexts might be more effective than across borders. However, one can
ask whether some borders—for example between Scandinavian countries or EU member
states—can still be characterized as national, and one can also question whether regions
within nations may function as systems of innovation
(Braczyk et al. 1998; Cooke, 2002)
Is Italy, for example, a single innovation system, or does Italy as a nation-state house
different innovation models in northern and southern Italy
(e.g., Balconi et al. 2004;
? Have regions with relative autonomy, such as Scotland or Catalonia,
increasingly been able to construct innovation systems in terms of competitive advantages
(Cooke and Leydesdorff 2006)
Some authors have strongly argued for studying regional innovation systems
Braczyk et al. 1998; Cooke 2002; cf. Boschma 2005)
while others have continued to focus
on nations as units of analysis.
discusses also other options such as
sectorial innovation systems. In this study, we do not aim to discuss the advantages or
disadvantages of these perspectives, but consider them as possible definitions and pursue a
comparative analysis for Spain as a specific case study. In our opinion, what counts as an
innovation system should not be determined on the basis of normative definitions, but be
entertained as an empirical question. Is innovativeness indicated at the regional and/or
national level? How can one operationalize and measure innovation-systemness
et al. 2016; Ritala and Almpanopoulou 2017)
In a system, components and elements are concerted and a tendency towards
equilibrium can be expected to prevail. One can test systemness, for example, in terms of the
(e.g., Leydesdorff and Oomes 1999)
. In an innovation system, however,
equilibrium is continuously upset because of the knowledge-based specification of new
(Nelson and Winter 1982; Schumpeter , 1964)
. We argue that systems are
innovative insofar as they generate new options from synergies among geographical,
technological, and organizational factors
(Edquist 1997; Storper 1997; Schwartz 2006)
Synergy favors the entrepreneurial climate for innovation by reducing risks (selection) and
generating options (variation). We propose to measure synergy in terms of redundancy
using an indicator developed in the Triple-Helix context (Leydesdorff 2003). We elaborate
this approach for the case of Spain
(Buesa et al. 2006; Navarro and Gibaja 2012;
ZabalaIturriagagoitia et al. 2007)
Redundancy plus uncertainty (or Shannon-type information) constitutes the maximum
entropy of a system. Consequently, increased redundancy reduces relative uncertainty
(Brooks and Wiley 1986)
. Redundancy can also be considered as options that have not
(yet) been realized, whereas uncertainty provides a measure of the options that have
already been realized. The latter options can be observed historically, whereas the
dynamics of redundancy are evolutionary. However, we are able to specify an expectation.
In practice, the dynamics of information and redundancy can reduce or add to the
uncertainty that prevails; the trade-off can be measured using information theory.
Our measurement instrument—to be elaborated below—was developed for the
measurement of innovation systems in the Triple Helix (TH) context of studying
universityindustry-government relations; but it can also be used outside this context. On the basis of
Nelson et al.’s (2011
) specification of the dynamics of innovation in medicine,
et al. (2016)
, for example, generalized the TH model to ‘‘supply,’’ ‘‘demand,’’ and
‘‘control’’ as three sub-dynamics of innovation. In this study, and following up on a number
of similar studies of nations, we use geographical, organizational, and technological
distributions of firm characteristics and their mutual information. When the resulting indicator
is positive, the historical dynamics prevail and options are exploited. However, when the
resulting indicator is negative, uncertainty is reduced and synergy—operationalized as the
generation of new options—is indicated more than past performance. Options become then
available in the system for exploration
. When feedback and feedforward
loops propel information in clockwise or counter-clockwise cycles with potentially
opposite signs, the loops among three dimensions can also be self-reinforcing or
(Ulanowicz 2009; Ivanova and Leydesdorff 2014)
Spain is an interesting case because after the end of the dictatorship (1975), a new
constitution was drafted in 1978 which gave more autonomy to the regions. Two regions
particularly—Catalonia and the Basque Country—have national aspirations because of
their languages and their in some respects different positions within Spain and the
(Cooke and Morgan 1992; Buesa et al. 2006; Moso and Olazaran 2002;
RibaVilanova and Leydesdorff 2001)
. In which respects (e.g., sectors) can Spain nevertheless
be considered as a national system of innovations, or have regions been able to construct
their own innovation systems; and if so, to what degree?
We analyze the Spanish national and regional innovation systems in terms of the
expected synergies at NUTS2 (19 regions) and NUTS3 (51 provinces) levels. Synergy is
operationalized as generating options for further development among distributions of firm
characteristics (N & 1 M). Regionalization has been an objective in Spain since the
constitution of 1978. Our results indicate that five regions are central: Barcelona in
Catalonia, Madrid and its immediate environment, Andalusia, the Valencian Community, and
the Basque Country. Barcelona and Madrid stand out as metropolitan innovation systems.
The Andalusian innovation system is concentrated in Seville. Synergy generation in the
Valencia region and the Basque Country is lower than in the two metropoles by more than
an order of magnitude. The national level adds marginally to the sum of regional systems
except for the case of high-tech manufacturing. In sum, the national system of innovations
is multi-centered with a focus on cities more than regions.
Data was downloaded from the ORBIS database of Bureau van Dijk on January 18, 2017.
We first contacted Statistics Spain asking for the complete set of firm data—having
received this information for the cases of Norway, Sweden, and Italy—but access was
denied for administrative reasons. Within ORBIS, we used the string ‘‘All active
companies and companies with unknown situation’’ combined (with a Boolean AND) with
‘‘World Region/Country/Region is country: Spain.’’ ORBIS reports that 4,508,010 records
are found as a search result (among 173 M firms worldwide), but the retrieval contains
only 2,520,000 records. These were downloaded in 18 batches of 140,000 records.
Employment information, however, is not available (‘‘n.a.’’) in 1,393,856 of these records;
1,073,452 firms are not assigned to a NACE code; and 55,264 firms are not listed with an
address—that is, either a postcode or a city name. In summary, 1,508,984 (59.9%) of the
retrieved records are not complete in one of the three relevant dimensions, so that
1,011,016 records provide the sample under study.1 In a comparable study about Italy,
1 Within this sample only 2.6% of the records have no turnover information, while this is the case in 74.2%
of the discarded records.
Cucco and Leydesdorff (2013)
retrieved 992,172 firms registered at ORBIS, of which
462,316 contained the full information.2
Spain is organized into 19 regions (‘‘autonomous communities’’) at the NUTS2 level
and 51 provinces at the NUTS3 city level. We used postal codes to organize the data into
these NUTS2 and NUTS3 regions.3 The distribution of the firms over NUTS2 and NUTS3
is provided in the left columns of Tables 3 and 5 (in the Results section), respectively.
Figure 1 shows the administrative organization of the country in NUTS2 and NUTS3
classifications for the orientation of the reader.
Although geographical proximity tends to contribute to better links between the players
located in a given environment
(Knoben and Oerlemans 2006; Carboni 2013)
, the quality
of these ties depends on several other indicators, such as the type of sector (Woerter 2012)
and the size of the firm. Following Storper’s (1997, p. 27) ‘‘Holy Trinity’’ of relations
among geography, technology, and organization—we distinguish these three dimensions
. The classification of firms in terms of the ‘‘Nomenclature ge´ne´rale des
Activite´s e´conomiques dans les Communaute´s Europe´ennes’’ (NACE), Rev. 2 is used for
indicating the (second) technological dimension.4 We disaggregate along this dimension in
term of medium- and high-tech manufacturing, and knowledge-intensive services. Table 1
provides the list of NACE codes associated with these sectors in the economy.
In the third dimension, the number of employees can be used as a proxy for the
organizational classification (Table 2). We could have used yearly turnover which is
available for 1,147,048 of the records—that is, for almost the same subset. However,
turnover rates vary among years more than numbers of employees. The distinction between
small, medium, and large enterprises is standardized (for example, by Eurostat)5 as
• micro enterprises: with fewer than 10 persons employed;
• small enterprises: with 10–49 persons employed;
• medium-sized enterprises: with 50–249 persons employed;
• small and medium sized enterprises (SMEs): with 1–249 persons employed;
• large enterprises: with 250 or more persons employed.
We first experimented with this classification, but then decided to use the finer-grained
classes provided in Table 2 because this scheme produced richer results
(Leydesdorff et al.
2006; Rocha 1999)
. Note that so-called micro-enterprises with fewer than 10 employees
constitute 81.3% of the firms under study.
2 Using the full set of data of Statistics Italy (n = 4,480,473), the results for the indicator were not
significantly different (Spearman’s q = 0.998; p \ .01).
3 NUTS is an abbreviation for ‘‘Nomenclature des Unite´s Territoriales Statistiques’’ (that is, Nomenclature
of Territorial Units for Statistics). The NUTS classification is a hierarchical system for dividing up the
economic territory of the EU.
4 The NACE code can be translated into the International Standard Industrial Classification (ISIC) that is
used, for example, in the USA.
5 This classification is available, for example, at
) information theory, uncertainty in the distribution of a random
variable x can be defined as Hx ¼ P px log2 px. The values of px are the relative
quencies of x: px ¼ fx P f . Using the two-base for the logarithm, uncertainty is expressed
The uncertainty in the case of a system with two variables can analogously be
y pxy log2 pxy
Hx þ Hy
In this case of two variables with interaction, the uncertainty of the system is reduced
because of mutual information Txy as follows:
If the two distributions of x and y are independent, Txy = 0 and Hxy ¼ Hx þ Hy . One
(e.g., McGill 1954; Abramson 1963, pp. 131 ff.)
that in a case of three
dimensions—as in the case we will study below—mutual information corresponds to:
Txyz ¼ Hx þ Hy þ Hz
Hyz þ Hxyz
When the negative terms in Eq. 3 are larger than the positive ones, negative entropy is
argued that this formula is therefore inconsistent with
Shannon’s information theory. The negative entropy is generated by next-order loops in the
Of these sectors, 59 to 63, and 72 are considered high-tech
communication; for example, when meaning is exchanged or different codes of
communication invoked. Each third ‘‘partner’’ in the communication may spuriously feedback or
feedforward on the communication between the other two. In other words, a triangle can be
tumbled to the right or to the left. Uncertainty can be added or reduced when three
dimensions operate by generating mutual information or redundancy, respectively.
Redundancy generation reduces relative uncertainty by providing new options to the
system. For example, meanings can be shared or codes of communication operating as
selection environments can interact. New redundancy adds options to the system that were
hitherto not realized. An innovation system can be prolific in providing new options
because the non-linear dynamics can become self-reinforcing
historical realizations then function as a retention mechanism. Increasing the number of its
options may be more important for the viability of an innovation system than the options
Note that the generation of redundancy indicates an interaction among selection
environments, whereas the generation of uncertainty is a consequence of variation in historical
relations. Our measure, in other words, does not measure action (e.g., academic
entrepreneurship) as input or output, but the investment climate as a structural consequence
of correlations among distributions of relations. However, the distinction between the
structural dynamics and the historical dynamics of relations is analytical. In practice, the
two layers reflect each other in an evolving system. Equation 3 models the trade-off
between variation and selection as positive and negative contributions to the uncertainty
Although this trade-off can also be modeled in terms of the analysis of variance
, the use of information theory has the advantage that all terms are composed from
sigmas and therefore the results are fully decomposable to the micro-level. Thus, the
measurement model is micro-founded. One can examine empirically how much specific
firms, sectors or regions add to the uncertainty or the redundancy. Is emerging systemness
at various levels of aggregation sectorial, regional, or otherwise
, pp. 20f.), furthermore, showed that in the case of groups (or subsamples),
one can decompose the information as follows: H ¼ H0 þ P nNG HG. The right-hand term
PG nNG HG provides the average uncertainty in the groups and H0 the additional
uncertainty in-between groups. Analogously, one can derive
(Leydesdorff and Strand 2013, at p.
In this formula, TG can be considered as a measure of uncertainty at the geographical
scale G; nG is the number of firms at this scale, and N is the total number of firms under
study. One can also decompose across regions or in terms of firm sizes, or in terms of
combinations of dimensions.
T ¼ T0 þ
G N TG
G N TG
However, for comparisons across samples one may have to normalize, for example as
percentages, because the scales are sample-dependent.6 After normalization (Eq. 5), the
geographical contributions of regions or provinces can be compared and aggregated. The
6 The maximum entropy Hmax = log(N).
difference between the sum of the normalized contributions (RGDTGÞ and the next-order
level can be considered as a surplus generated between the groups G.
In this study, we decompose the Spanish innovation system in terms of NUTS2 and
NUTS3 regions and then zoom into the relative weights of knowledge-intensive services
and high- or medium-tech manufacturing, both at the level of the Spanish system and at the
regional levels. In this design, the between-group term T0 provides us with a measure of
what the national system adds in terms of synergy to the sum of the regional systems (given
the sectors under study). The three dimensions are the (g)eographical, (t)echnological, and
(o)rganizational; synergy will be denoted as TGTO. We express synergy in millibits (mbits);
1 bit = 1000 mbits.
3.1 Regions at the NUTS 2 level
Figure 2 provides a map of Spain with the regions (NUTS2) colored according to their
respective contributions to synergy generation in the Spanish innovation system. The total
synergy for Spain is -886 mbits, of which 54.5% is realized in four regions: Catalonia
(-163 mbits or 18.4%), Andalusia (13.8%), the Communidad de Madrid (11.6%), and the
Valencian Community (10.7%). The between-regions synergy at the national level is only
52 mbits or 5.9% of the national synergy (Table 3, column c). This is much less than we
found in previous studies of national systems (except for Hungary)7: Norway (11.7%),
China (18.0%), the Netherlands (27.1%), Sweden (20.4%), and Russia (37.9%). In other
words, the Spanish system does not function as a unified country, but innovation is
regionalized (Fig. 4).
In the case of eastern Hungary,
Lengyel and Leydesdorff (2011)
conjectured that the
relatively high synergy value reflected a previous form of, in this case, state-led
integration. Perhaps, something similar is the case for Andalusia: there is no synergy in the
hightech sector, and the region is also not prominent in medium–high tech. Table 3 shows the
values for (1) all sectors; (2) sectors labeled as high-tech manufacturing in Table 1 above;
(3) medium–high tech; and (4) knowledge-intensive services. The contribution to the
synergy is highest for Catalonia in all the columns. The lead of Catalonia compared to the
Community of Madrid—the official name of this region—is most pronounced in medium–
high tech manufacturing, where Catalonia contributes 32.6% to the national synergy and
Madrid only 11.7%. The national level adds 16.4% between-regional synergy to this. In
high-tech manufacturing, Catalonia (36.2%) and Madrid (30.0%) contribute both, but the
national level prevails in this sector with DT0 = 37.7%.
Figure 3 shows the percentage contributions to synergy generation for the 19 regions
sorted by their contributions to medium–high tech manufacturing. In addition to the
Community of Madrid and Catalonia, the Valencian Community, the Basque Country, and
Andalusia play a role, but to different extents. Andalusia does not play a significant role in
the generation of synergy from high- or medium-tech manufacturing. The Valencian
Community and the Basque Country are as important as Madrid for generating synergy
from medium–high tech industry, but do not contribute to synergy generation in the
hightech sectors. In the knowledge-intensive services, the Madrid region and Catalonia take the
lead, followed by Andalusia (13.6%), the Valencian Community (9.8%), and the Basque
7 In the Hungarian case, there was no surplus synergy at the national level.
Country (5.9%). The between-regional surplus is 6.7% in this case; that is, of the same
order as for ‘‘All sectors.’’
In Table 4, we test whether or to what extent the synergy generation is a function of the
number of firms in a region by providing Pearson correlations among firm numbers and
synergy generation for the four sectorial categories distinguished in Table 3. In the top left
quadrant, one sees that the numbers of firms in all four categories are significantly
correlated. The lowest correlation is for the number of firms in medium-tech manufacturing
versus knowledge-intensive services. While medium-tech manufacturing is more strongly
oriented towards the local economy than high-tech, knowledge-intensive services tend to
be mobile and therefore relatively independent of their geographical location.
Following the first row in Table 4 to the right, we see that the number of firms is not
significantly correlated with the synergy produced in All Sectors or in KIS. (The minus
sign is generated by the negative values of T.) However, the numbers of firms in HT and
MHT are significantly correlated to the generation of synergy. In other words, the presence
of HT firms is associated with synergy (r = .785; p \ .01), etc. In the case of KIS, this
correlation is .073 (n.s.) and for All Sectors it is only .004 (n.s.). In summary, the relation
between synergy production and geographic localization is sectorially specific: while this
relation is significant for HT Manufacturing in the case of Spain, it is virtually absent for
KIS. KIS moves easily between regions and is relatively independent of location
. A knowledge-intensive service can be offered nation-wide.
The bottom right quadrant informs us that the generation of synergy at the level of
the economy (‘‘All Sectors’’) is negatively correlated to the generation of synergy in HT
(r = -.358; n.s.), but it is positively correlated to synergy generation in MHT (r = .389;
n.s.) and KIS (r = .889; p \ .01). Synergy generation in HT and MHT are also correlated
(r = .559; p \ .05). Note that the number of HT firms in the sample is only 2,562 or 2.5%.
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3.2 The NUTS3 level (‘‘Provincias’’)
We repeated the analysis for the 51 provinces of Spain categorized as NUTS3—that is, the
city level. Table 5 shows the results in a format similar to Table 3. Figure 4 provides the
geographical results for each province analogously to Figure 2 at the level of regions. The
T in three dimensions (synergy
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
synergy for Spain is again -886 mbits. The provinces of Barcelona and Madrid realize
12.6% and 11.6%, respectively. Valencia and Alicante, both part of the Valencian
Community, follow with 5.5% and 3.7%, respectively. Seville ranks fifth with 3.0% of the
Twenty-three provinces provide each less than one percent of the synergy. Soria (-1
mbits), Avila (-1 mbits), and Palencia (-1 mbits) located in the province of Castile and
Le o´n, along with Teruel (-2 mbits) form a non-innovative belt around Madrid. The
surplus T0 between provinces is 12.0%, of which 5.9% is realized above the regional level
(between regions and the nation; see Table 3) and, consequently, 6.1% between provinces
and regions. In other words, the regional level adds as much synergy to the sum of the
provinces (6.1%) as the nation does to the sum of the regions (5.9%). In a regionalized
innovation system, however, one would expect more synergy at the regional level between
provinces than between the nation and the regions. Note that the sector breakdowns are
rather similar in Table 5, with no real differences across sectors.
Figure 5 shows the generation of synergy by the provinces in decreasing order and
broken down in terms of sectors. Only the first 12 provinces are shown. At this more finely
grained geographical scale of NUTS3, however, the innovation system is as concentrated
as at the regional level. The synergy generation in Catalonia as a region is realized in
Barcelona to such an extent that, in our opinion, Barcelona can be considered as a
metropolitan innovation system. Note that measured at this finer-grained level, the
between-regional surplus at the national level is considerable in all four categories.
However, there are also considerable differences across sectors. It is much weaker for KIS
than for HT and MHT manufacturing.
The two metropoles (Barcelona and Madrid) function both nationally and regionally as
the generators of opportunities for innovation. Zaragoza and Seville play comparable roles
in their regional environments, but at a much lower level. The Valencian Community—
Valencia and Alicante—and the Basque Country—Alava, Bizkaia, and Gipuzkoa—can be
considered as regional innovation systems that are spread over provinces, but their synergy
levels are much lower than for the two metropoles.
In summary, the Spanish innovation system is regionalized. More than the center in
Madrid, Barcelona and Valencia carry the system along the mediterranean coastline. On
the Atlantic coast, the Basque Country connects to both France and Spain. In the south,
Andalusia has a function in itself, but this innovation system is not high-tech or
knowledge-based and is focused in Seville. The remainder of the country is rather barren in terms
of generating opportunities for innovation. The two metropoles (Barcelona and Madrid) set
the stage. This pattern is reinforced in the case of high-tech or knowledge-intensity.
4 Discussion and limitations
The main constraint of this analysis is obviously the use of ORBIS data. Unfortunately, we
did not have access to the full data at Statistics Spain such as we obtained from the
Scandinavian offices and from Statistics Italy (but not from the Russian Federation or
China). The quality of ORBIS data is beyond our control. Given the statistical character of
the study, however, the results may still be reliable. In a previous study of Italy, we could
use both ORBIS data (N of firms = 462,316) and full data from Statistics Italy (N of
firms = 4,480,473). The results at the NUTS2 level rank-correlated more than 99%
(Spearman’s q = .998; p \ .01; Cucco and Leydesdorff 2013)
. This significant correlation
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inspires some confidence in using ORBIS data for this purpose. However, the numbers are
sometimes small, particularly in disaggregated subsamples such as the number of high-tech
manufacturing firms in NUTS3 regions. A further limitation of ORBIS data is the use of
primary NACE codes, whereas firms may have been attributed more than a single NACE
Using a number of sources, Buesa et al. (2015, pp. 78 ff.) collected similar data about
Spanish firms for an input–output Data Envelopment Analysis. (Unfortunately, the number
of firms in the sample was not specified.) The efficiency of the regional innovation systems
in Spain was analyzed at the NUTS2 level of regions for the same year (2010). On the basis
of an analysis without sectorial differentiation, the best performance was indicated for
Catalonia (100%), the Community of Madrid (100%), and Navarra (100%), followed by
Aragon (99%) and the Valencian Community (95%). When the analysis was repeated for
high-tech manufacturing, the best results were obtained for Aragon (97% efficiency),
followed by La Rioja (87%), Navarra (80%), and the Basque Country (73%). Madrid was
only 70% efficient and Catalonia 60%.
Focusing on high-tech firms,
Zabala-Iturriagagoitia et al. (2007)
report leading roles for
Catalonia and the Community of Madrid, with the Basque Country as a significant third
region. However, Andalusia would be positioned at the bottom, after the Balearic Island,
Extremadura, Castille-La Mancha, and Murcia. From another perspective,
Alberdi Pons et al. (2014
) analyze the types of regional innovation
systems in Spain. According to these authors, the Basque Country, Navarre, Catalonia, and
the Community of Madrid would work as cohesive innovation systems, while the Canary
and Balearic Islands, Andalusia, both Castilles, Asturias, Galicia, Murcia, Extremadura,
and Cantabria are considered as fragmented RIS. Focusing on biotechnology,
Diaz et al.
) points to Catalonia and the Community of Madrid as the metropoles. Andalusia,
Galicia, and the Valencian Community follow with more scattered portfolios.
Obviously, these results are in many respects different from ours. The differences may
be generated both by different sources and by using different methods. We focus on firms
as units of analysis, while the other studies included other knowledge producers such as the
creators of new patents, publications, or subsidies for research projects. Nevertheless, the
message of these authors is the same: Madrid and Barcelona are the innovative
powerhouses of Spain. According to these authors, however, this would be less the case for
hightech manufacturing. Our analysis does not confirm this result. Furthermore, in their results
the regions play a role that is more central than in ours.
Note that our analysis focuses on the possible interactions among the structural
dimensions of innovation systems operating as selection environments. Redundancy
generated at this structural level is traded off against uncertainty generation in historical
relations (Eq. 3 above).
Buesa et al. (2015)
and the other studies focused on the efficiency
in the historical variation and not on the potential of regions and cities to develop new
options for innovation as systems. Thus, the research questions are also very different.
Two metropolitan innovation systems are central: Barcelona and Madrid. On most
indicators of synergy production Barcelona scores above Madrid. The exception is KIS, which
is more synergetic in Madrid than Barcelona. This may be an effect of the state apparatuses
being centralized in Madrid and requiring knowledge-intensive services. Otherwise, our
results do not indicate strong regionalization of the Spanish innovation system. The
relatively pronounced role of Andalusia as a regional innovation system at a level comparable
to the Valencian Community and the Basque Country was not expected.
We conjecture that Andalusia has a pattern of integration comparable to eastern
Hungarian regions that were also successful in maintaining specific characteristics from the
past that are still functional. The Andalusian system is heavily concentrated in Seville as a
semi-metropole. The other two regions—the Valencian Community and the Basque
Country—can be considered as regional innovation systems, but they provide options for
innovation at a much lower rate than Barcelona and Madrid.
Returning to our research questions and methods, we may conclude that the
betweenregional (that is, national) surplus in redundancy is low when compared with other
European nations; but the conclusion is not that the weakness of the national innovation
system has led to strong regional innovation systems. The regionalization is mainly driven
by the two metropoles which are at the center of a metropolitan innovation system.
Andalusia, the Basque Country, and the Valencian Community can perhaps be considered
as regional innovation systems, albeit with far fewer options than the metropolitan systems;
the remainder of the country has hitherto remained peripheral in terms of the development
of a knowledge-based economy. Given the policy objective of regionalization (among
other things) expressed in the new constitution of 1978, these results may appear
At the theoretical level, our contribution is mainly an empirical and methodological one.
We have wished to show that the question of regionalization of national systems of
innovation can be operationalized in terms of synergy production at the various levels.
Furthermore, our argument is that innovation systems should not be inferred from the
behavior of entrepreneurs and enterprises. From a systems perspective, behavior (‘‘action’’)
provides the (potentially stochastic) variation. The system operates deterministically in
terms of selection environments, but the latter change historically and under pressure from
Selection environments are not given, but they can be specified. Two selection
environments operating upon each other may lead to co-evolution and potentially lock-in along
historical trajectories. Three selection environments operating upon one another, however,
may lead to reinforcements and loops among feedback loops. The interactions among the
selection environments can then be expected to generate redundancies which counteract
the variation. This is not a linear process that can be steered by providing the right input,
but a process of self-organization and emergence. The political intervention is then in need
of being reflexively informed about its intended and unintended consequences
(Leydesdorff et al., 2017; Petersen et al. 2016)
. The methodological objective of this study has
been to measure this process without reducing the complexity to indicators of historical
variation (Ashby 1964).
Acknowledgements We are grateful to the anonymous referee for constructive comments.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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