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Since the introduction of the related variety concept in 2007, a number of studies have been undertaken to analyse its effect on economic development. Our review of 21 studies makes clear that most studies find support for the initial hypothesis that related variety supports employment growth, though some studies suggest that the growth effects of related variety may be specific to knowledge-intensive sectors only. From the review, we list a number of further research questions regarding methodology, the role of unrelated variety, different forms of relatedness and the effect of related variety on knowledge production and entrepreneurship.
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European Planning Studies
ISSN: 0965-4313 (Print) 1469-5944 (Online) Journal homepage:
Related variety and economic development: a
literature review
Jeroen Content & Koen Frenken
To cite this article: Jeroen Content & Koen Frenken (2016) Related variety and economic
development: a literature review, European Planning Studies, 24:12, 2097-2112, DOI:
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Related variety and economic development: a literature
Jeroen Content
and Koen Frenken
Utrecht University School of Economics, Utrecht, The Netherlands;
Copernicus Institute of Sustainable
Development, Utrecht University, Utrecht, The Netherlands
Since the introduction of the related variety concept in 2007, a
number of studies have been undertaken to analyse its effect on
economic development. Our review of 21 studies makes clear that
most studies find support for the initial hypothesis that related
variety supports employment growth, though some studies
suggest that the growth effects of related variety may be specific
to knowledge-intensive sectors only. From the review, we list a
number of further research questions regarding methodology, the
role of unrelated variety, different forms of relatedness and the
effect of related variety on knowledge production and
Received 23 June 2016
Accepted 19 September 2016
Related variety; regional
growth; branching;
employment; Jacobs
1. Introduction
In recent research in economic geography, an empirical body of literature has emerged on
the role of related variety in regional development. The concept of related variety was put
forward by Frenken, Van Oort, and Verburg (2007) to further specify the common
hypothesis that regions may benefit from producing a variety of products and services,
as more variety implies more potential for inter-industry knowledge spillovers. Frenken
et al. (2007) emphasized that: one expects knowledge spillovers within the region to
occur primarily among related sectors, and only to a limited extent among unrelated
sectors(p. 688). That is, they hypothesized that inter-industry spillovers occur mainly
between sectors that draw on similar knowledge: knowledge originating from one sector
is most relevant to, and can most effectively be absorbed by, another sector that is
related in the sense that firms draw on similar knowledge (about technology,
markets, etc.).
The concept of related variety was introduced in an attempt to resolve an earlier empiri-
cal question put forward by Glaeser, Kallal, Scheinkman, and Shleifer (1992) whether
regions benefit most from being specialized or being diversified. This controversyis com-
monly referred to as MAR versus Jacobs, referring to the theories of Marshall, Arrow and
Romer suggesting spillovers to take place primarily within a single industry versus the
theory of Jacobs (1969, p. 59), who argued that the greater the sheer numbers and varieties
of divisions of labour already achieved in an economy, the greater the economys inherent
© 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
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CONTACT Koen Frenken
VOL. 24, NO. 12, 20972112
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capacity for adding still more kinds of goods and services. The theories of MAR view
innovation mainly as incremental where firms learn from knowledge and innovation
from same-industry firms (otherwise known as localization economies), while Jacobs
views innovation essentially as a recombinant process that necessarily builds on a pre-
existing variety of knowledge and artefacts that are being combined in new ways,
leading to new products and services, viz. new employment.
As reviewed by De Groot, Poot, and Smit (2016), the many empirical studies on MAR
versus Jacobs, which followed on the seminal study by Glaeser et al. (1992), have provided
very mixed results (Figure 1).
There are almost as many studies that find evidence for the
MAR hypothesis as there are studies that disprove it. And, while a large share of studies
finds evidence confirming Jacobs externalities, still a substantial share of studies finds no
effect of variety on regional growth, or even opposite effects. It also seems evident from the
many studies yielding insignificant results that the theoretical notions of specialization and
variety seem too simplistic to capture the varied effects of an economys composition on its
further development.
Frenken et al. (2007) agreed with Jacobs that innovation is essentially a recombinant
process (what Schumpeter famously called innovative Neue Kombinationen[new com-
binations]), but qualified the notion of recombination arguing that some pieces of knowl-
edge and artefacts are much easier to recombine than other pieces of knowledge and
artefacts. Hence, variety is especially supportive for innovation and regional development
when variety is related, be it in a technological sense or in a market sense. The reasoning
here is similar to that of diversified firms, where it has been argued that firms undergoing
related diversification outperform firms undergoing unrelated diversification, because
only the former profit from economies of scope.
Frenken et al. (2007) specifically hypothesized that related variety would spur employ-
ment growth, as new combinations lead to new products or services and, hereby, to new
jobs. Localization economies stemming from the spatial concentration of firms in the exact
same industry, instead, would enhance process innovation as specialized knowledge is
used to optimize production processes in existing value chains. Such innovations spur
labour productivity, and do not necessarily lead to more jobs. The related-variety thesis
is thus consistent with product lifecycle theory, which poses that young industries with
Figure 1. Overview of outcomes of empirical studies on the effect of MAR (specialization) vs. Jacobs
(diversity) externalities on regional growth. Note that competition is often taken as a third explanatory
variable. Source: De Groot et al. (2016).
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high rates of product innovation create jobs in diverse urban areas, while mature indus-
tries with high rates of process innovation spur productivity in specialized peripheral
areas (Capasso, Cefis, & Frenken, 2016; Duranton & Puga, 2001).
The concept of related variety is also consonant with the concept of product space
introduced by Hidalgo, Klinger, Barabasi, and Hausmann (2007). They argued that
countries develop by diversifying their export portfolio over time. They showed that
countries typically do so by branching out, that is, by entering export products
that are closely related to the products they already export. The reasoning underlying
this phenomenon holds that once a country has developed the capabilities to specialize
in exporting particular products, it can easily diversify in related products that require
very similar capabilities to produce them. By calculating, for each possible new product,
the proximityof related products already present in a countrys export portfolio, the
authors could show that the higher the average proximity of related products vis-à-vis a
new potential product (which they called density), the higher the chance that a
country will diversify into this new product. This idea is in line with related variety,
because the more products a country already exports related to a product that it does
not yet export, the more likely it will start exporting that product in the future. The differ-
ence between the related-variety and the product proximity concepts is that the former is
used to explain aggregate regional or national growth, while the latter is used to explain
diversification events into specific new products or industries at the regional or national
The related-variety hypothesis has motivated a large number of empirical studies on the
effect of related variety in sectoral composition on national and regional economic devel-
opment as indicated by employment, income or productivity, or by diversification
measured as a countrys or regions entry into a new industry. We provide a systematic
review of empirical studies at the regional and national levels in the next section. That
means that we focus on the related-varietyliterature following Frenken et al. (2007), ana-
lysing how related variety affects regional/national growth, as well as the branchinglit-
erature following Hidalgo et al. (2007), analysing how related variety vis-à-vis a specific
industry affects the probability that a region/nation becomes specialized in that specific
We limit our review to papers that have been either published or accepted
for publication in scientific journals.
Hence, we omit current working papers on the topic.
2. Related-variety studies
Below, we review 16 studies we found that analysed the effect of related variety on employ-
ment growth, or another economic performance indicator, at either the national or
regional level. We summarize the set-up and results of each study in Table 1.
The first study to associate variety with regional economic growth is Frenken et al.
(2007), who looked at employment growth in a study on 40 Dutch regions. They
argued that, on the one hand, related variety is expected to increase employment
growth and, on the other hand, unrelated variety is expected to decrease unemployment
growth. Unrelated variety in this respect can be described as a measure of risk-spreading
that cushions the effects of an external demand shock in a certain sector. This is explained
by the fact that a higher degree of unrelated variety in a region will cause that region
overall to be affected just moderately in the case of a sector-specific shock in demand.
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Table 1. Related-variety studies.
Author(s) Unit Coverage Period Data source Main iV(s) Digits dV(s) RV UV
Frenken et al. (2007) NUTS3 Netherlands 19962002 CBS Related variety
Unrelated variety
RV = 5 in each 2
UV = 2
Employment growth + 0
Productivity growth 0
Saviotti and Frenken
National OECD 19642003 OECD trade data Unrelated export variety
Semi-related export
Related export variety
UV = 1
SV = 2 in each 1
RV = 3 in each 2
GDP per cap +
Labour productivity +
Boschma and Iammarino
NUTS3 Italy 19952003 ISTAT Export variety
Related export variety
Unrelated export variety
Import variety
Related trade variety
Variety = 3
RV = 3 in each 2
UV = 1
Employment growth M 0
Value-added growth + +
Bishop and Gripaios (2010) Subnational Great Britain 19952002 NOMIS Related variety
Unrelated variety
RV = 4 in each 2
UV = 2
Employment growth
at two-digit
Quatraro (2010) NUTS2 Italy 19812002 ISTAT
Total variety
Unrelated variety
Related variety
RV = 3 in each 1
UV = 1
TV = 3
Productivity growth M 0
Bosma et al. (2011) NUTS3 Netherlands 19902002 CBS
Chambers of Commerce
Related variety RV = 5 in each 2 Productivity growth M
Falcioglu (2011) NUTS2 Turkey 19802000 Turkish statistical institute Variety
Related variety
Variety = 3
RV = 3 in each 2
Productivity growth +
Boschma et al. (2012) NUTS3 Spain 19952007 INE, Ivie and Agencia
Related variety
Unrelated variety
Porter relatedness
Hidalgo relatedness
RV = 6 in each 2
UV = 1
Value-added growth + 0
Hartog et al. (2012) NUTS4 Finland 19932006 Statistics Finland Related variety
Unrelated variety
Variety = 5
RV = 5 in each 2
UV = 2
Employment growth M 0
Mameli et al. (2012) Local labour
Italy 19912001 ISTAT Variety
Related variety
Unrelated variety
Variety = 3
RV = 3 in each
UV = 1
Employment growth + +
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Cortinovis and Van Oort
NUTS2 Europe 20042012 ORBIS, Bureau van dijk Unrelated variety
Related variety
Technological regime
UV = 1
RV = 4 in each 2
Employment growth M 0
van Oort et al. (2015) NUTS2 Europe 20002010 Amadeus Related variety
Unrelated variety
RV = 4 in each 1
UV = 2
Employment growth + M
Productivity growth 0 0
Caragliu et al. (2016) NUTS2 Europe 19902007 Cambridge Econometrics Related variety
Unrelated variety
RV = 2 in each 1
UV = 1
Employment growth
at the industry level
Notes: iV stands for independent variable; dV stands for dependent variable. The columns RV and UV show the significance of related and unrelated variety on the dependent variables shown in the
column dV(s). + and indicate significant positive or negative effects, respectively, whereas 0 and M indicate no significant and mixed results, respectively.
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However, specialization in one or few sectors will result in the opposite scenario, as the
region is exposed to the probability of a severe slowdown. Empirically, using the Standard
Industrial Classification (SIC) scheme, Frenken et al. (2007) measured related variety as
the average entropy across employment in five-digit industries within each two-digit
class, while unrelated variety is the entropy in employment across 2-digit classes. They
showed that related variety, as hypothesized, enhances employment growth. The results
also confirmed the portfolio effect, as they found that unrelated variety is negatively
related to unemployment growth.
Using OECD export data on a national level, Saviotti and Frenken (2008) later found
related export variety to stimulate GDP growth per capita and labour productivity, while
unrelated export variety only promotes growth with a considerable time lag. They explain
this finding by the type of innovation that benefits from variety. Related variety means that
knowledge is easily recombined in new products, causing direct growth effects. Unrelated
variety is harder to recombine, but if successful, can lead to complete new industries sus-
taining long-term growth. This study, however, did not include control variables and calls
for more refined follow-up studies.
Boschma and Iammarino (2009) used regional trade data of Italy to study the effects of
variety in regional exports and found that variety per se did not explain regional growth.
However, related export variety was found to have a positive and significant association
with regional growth and employment, in contrast to unrelated export variety. The
authors also looked at the similarity between the importing and exporting sectors and
found some evidence that it will support regional employment. This finding, however,
is not robust in the sense that this effect was not found for regional growth in labour pro-
ductivity or value-added growth.
Other studies looked at the effect of related variety on growth indicators other than
employment growth. Boschma, Minondo, and Navarro (2012) showed that Spanish
regions with higher levels of related variety are likely to have higher levels of value-
added growth. They did so using two additional measures of related variety in order to
overcome some limitation of the entropy measure that is based on the SIC, which
defined relatedness ex ante, as Boschma et al. (2012) put it. One of the alternative ex
postmethods they employ is based on Porters(2003) study on clusters, where relatedness
is measured on the basis of the spatial correlation of employment between sectors. The
other measure is based on the proximity index of Hidalgo et al. (2007), based on the
co-occurrence of products in production portfolios. Boschma et al. (2012) found that
related variety is positively related to regional growth using any of the three measures,
and that the effect is stronger for the cluster (Porter) and proximity (Hidalgo) indicators
relative to the entropy (Frenken) measure.
Falcioglu (2011) looked at productivity growth in Turkish regions, and found that
related variety, rather than variety as a whole, of regional economic activity positively
impacts a regions productivity. The author has defined productivity in two ways: as
output divided by labour and value added divided by labour. Instead of looking at the
industrial structure, Quatraro (2010) also analysed regional productivity growth, and
specifically how knowledge affects regional growth in Italy. The results suggest that the
regional knowledge stock affects not only regional productivity growth rates, but also
the composition and the variety of the knowledge stock matter. Related knowledge
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variety seems to positively affect regional productivity, while unrelated knowledge variety
was found to be insignificant.
Yet other studies analysed whether the effect of related variety differs across industries.
Bosma, Stam, and Schutjens (2011) distinguished between total factor productivity growth
in manufacturing and in services for 40 Dutch regions. They found that related variety had
a positive effect on productivity growth in manufacturing, but a slightly negative effect on
productivity growth in services. Mameli, Iammarino, and Boschma (2012) examined the
relationship between related variety and regional employment growth in local labour
systems of Italy. Without making further distinctions, both related and unrelated
variety in general have a positive effect on regional employment growth. Distinguishing
between manufacturing and services, and contrary to Bosma et al. (2011), related
variety positively affects regional employment in services, while unrelated variety posi-
tively affects regional employment growth in manufacturing. Hartog, Boschma, and Sotar-
auta (2012) investigated the impact of related variety in Finland; they did not find evidence
that related variety in itself influences employment growth. Rather when decomposed into
low/medium-tech sectors and high-tech sectors, related variety between high-tech sectors
seems to positively impact regional employment growth. The distinction between sectors
here is based on the R&D intensity and the share of tertiary educated persons employed.
Bishop and Gripaios (2010) looked at the effect of related variety on regional employ-
ment growth per industryin Great Britain. They argue that distinguishing between the
manufacturing and services industry might be an oversimplification as these sectors them-
selves are also heterogeneous, and thus the mechanisms and extent to which spillovers
occur differ between sectors. Motivated by this argument, the authors make use of a dis-
aggregated approach, and look at employment growth in each 2-digit sector as dependent
variables. Their assumed heterogeneity between sectors is reflected in the results, as related
variety has a significant positive impact on employment growth only in 3 out of the 23
sectors (telecom, computing and other business activities), and surprisingly unrelated
variety has a significant positive impact in 8 out of the 23 sectors.
More recently, Cortinovis and Van Oort (2015) conducted their research using a pan-
European data set. Following the original set-up of the study by Frenken et al. (2007), they
hypothesize that related variety is positively related to employment growth due to knowl-
edge spillovers across sectors; unrelated variety is negatively related to unemployment
growth due to portfolio effects associated with a diversified economy and as a result dam-
pened effects of sector-specific shocks. Specialization is positively related to productivity
due to cost-reduction and efficiency gains achieved through localization externalities.
They fail to find evidence supporting these hypotheses. However, when introducing tech-
nological regimes, they found related variety to positively affect employment growth and
productivity in regions characterized by high technology. van Oort, de Geus, and Dogaru
(2015) also looked at the pan-European level and make a distinction between smaller and
larger regionsurban size in order to account for differences in agglomerative forces. They
find that related variety has a positive effect on employment growth, which seems to be
stronger for small and medium urban regions compared to large urban regions. No signifi-
cant effect was found for unrelated variety. In a most recent pan-European study on
employment growth at the sectoral level, Caragliu, de Dominicis, and de Groot (2016)
did not find evidence for the hypothesis that related variety enhanced employment
growth. Instead, they found a positive and significant effect of unrelated variety on
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employment growth. This study is rich in that it looks at 259 NUTS2 regions in the EU
and for an extensive period (19902007). However, given data limitations, the authors
defined unrelated variety as the entropy at the one-digit industry level and related
variety as the weighted sum of the entropy at the two-digit level, within each one-digit
class. Hence, their results are not fully comparable with studies looking at a more fine-
grained industrial level in line with Frenken et al. (2007). Furthermore, their dependent
variable was employment growth within a single sector, as only Bishop and Gripaios
(2010) did before, rather than overall employment growth in a region as most studies
did before.
3. Branching studies
The concept of related variety as introduced by Frenken et al. (2007) associated related
variety in a regional economy with total employment growth of that regional economy.
A complementary perspective is to analyse whether related variety vis-à-vis a specific
industry enhances the growth of that particular industry, because that industry benefits
from spillovers from related industries. This research design was first introduced by
Hidalgo et al. (2007) and later followed by a number of studies at both national and
regional levels. We summarize the set-up and results of each study in Table 2.
Hidalgo et al. (2007) introduced the concept of product space, where each product has a
certain proximity to each other product, indicting its relatedness. They measured related-
ness of products using a proximity indicator based on how often two products co-occur in
countriesexport portfolios. The idea here holds that if many countries have a comparative
advantage both in product A and in product B, apparently A and B are somehow related,
sometimes referred to as revealed relatedness(Neffke & Henning, 2008). Hidalgo et al.
(2007) argue that if a country has a comparative advantage
in producing a certain
product, chances are high it will also obtain a comparative advantage in products that
are related to it in terms of, for instance, what kind of skills, institutions, infrastructure,
physical factors or technology is needed. Their study shows that countries indeed generally
become specialized in new products which are related to products it already is producing.
They also show that some countries are located in the centre of this product space export-
ing products that are related to many other products, while other countries are located
more to the periphery with fewer connections to related products. Being located more
to the periphery thus means having to travela larger distance to the centre, which in
turn might help explain that poorer countries are struggling to develop competitive pro-
ducts and therefore might fail to converge as they are located more to the periphery of the
product space with less connections to related products.
Neffke, Henning, and Boschma (2011) ask the same question as the original study by
Hidalgo et al. (2007), but at the regional level. Indeed, as for countries, regions are most
likely to branch into industries that are technologically related to the preexisting industries
in the region. Using data on products being co-produced at the same plants, they were able
to measure in detail the relatedness structure between products based on co-occurrences.
They then show for 70 Swedish regions during the period 19692002 that industries that
were technologically related to pre-existing industries in a region had a higher probability
to enter the region, as compared to unrelated industries. Furthermore, they show that
unrelated industries had a higher probability to exit the region.
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Table 2. Branching studies.
Author(s) Unit Coverage Period Data source Digits Main iV(s) dV(s)
Hidalgo et al. (2007) National 132 countries 19901995 NBER SITC-4 Density Entry
Neffke et al. (2011) A-region Sweden 19692002 Statistics Sweden SNI69-6 Closeness Membership
Boschma et al. (2013) NUTS3 Spain 19882008 NBER World Trade
Agencia Tributaria
SITC-4 Density at the country level
Density at the province level
Bahar et al. (2014) National World 19622008 World Trade Flows UN &
SITC-4 Density
RCA neighbour
Boschma, Martin, and Minondo (2016) State U.S. 20002012 US Census Bureau
HS-4 Density
RCA neighbour
RCA growth
Boschma and Capone (2015a) National 23 countries 19702010 World Trade Flows and CEPII 6-digits Density
Institution indicator
Boschma and Capone (2015b) National EU27
19952000 BACI 4-digits Density
Import density
Essleztbichler (2015) Metropolitan areas U.S. 19751997 Bureau of Economic Analysis SIC-4 Closeness Membership
Notes: iV stands for independent variable; dV stands for dependent variable. All studies showed a significant effect of density or closeness on the probability of entry into a new product or industry,
or a rise of the RCA.
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Similarly, Boschma, Minondo, and Navarro (2013) analysed the emergence of new
industries in 50 Spanish regions in the period 19882008. A novel element in this study
is the inclusion of a measure indicating how related a local industry is vis-à-vis the national
production profile. In line with Neffke et al. (2011), this study also provides evidence that
regions tend to diversify into new industries that use similar capabilities as existing indus-
tries in these regions. They show that proximity to the regional industrial structure plays a
much larger role in the emergence of new industries in regions than does proximity to the
national industrial structure. This finding suggests that capabilities at the regional level
enable the development of new industries. This result was further confirmed by a more
recent study on 360 U.S. metropolitan areas (Essleztbichler, 2015).
Another question holds whether certain countries or regions are better capable of diver-
sifying into unrelated industries compared to other countries or regions. Boschma and
Capone (2015a) took up this question at the national level, and hypothesized that
certain types of institutions enable unrelated diversification more than other types of insti-
tutions. In particular, following the distinction made by Hall and Soskice (2001), they
found that liberal-market institutions (e.g. the U.S.) are more flexible than coordinated-
market institutions (e.g. Germany) in reallocating labour and capital from one sector to
another unrelated sector. This can be explained by the actors in coordinated-market econ-
omies being primarily oriented towards collaboration and stability. Hence, they will tend
to diversify into related industries as to maximally leverage existing knowledge, insti-
tutional arrangements and collaborative relationships. In liberal-market economies, this
is less so, as firms, suppliers, employees and other stakeholders are relatively more self-
interested and driven by opportunities rather than on preserving existing arrangements
and relationships per se.
A final topic that has been addressed building on the original study by Hidalgo et al.
(2007) is the question of spatial spillovers. If a region or country lacks a certain local capa-
bility rendering it difficult to diversify into related products, it may still be able to do so if it
can leverage the spatial proximity to such capabilities through spillovers. Bahar, Haus-
mann, and Hidalgo (2014) address this question and show that a country is more likely
to start exporting a product when a neighbouring country is already exporting the
product. In addition, they find that having a neighbouring country with a strong compara-
tive advantage in a certain product has a positive predictive power on future growth in the
countrys own comparative advantage of that same product. Their results furthermore
indicated that, regardless of size, income level, cultural and institutional dimensions,
and factor endowments, the variety of products exported by countries is remarkably
similar to that of their neighbours.
Boschma, Heimeriks, and Balland (2014) extended this line of research by analysing the
effect of neighbouring regions and the probability a region develops a new industry for
U.S. states. They show that a region has a higher probability to develop a certain industry
if the neighbouring region is specialized in it. This might be explained by knowledge spil-
lovers that are more easily absorbed at small distances, that is, the strong distance-decay
effect of knowledge spillovers over spatial distance. In addition, they found that neigh-
bouring states show a high similarity in the variety of exported products, suggesting a con-
vergence process. A more recent study by Boschma and Capone (2015b) looked more
specifically at import profiles at the country level. Here, they found that a country
tends to enter into a new product not only when its own product portfolio is close to
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this new product (density), but also when its import portfolio is close to this new product
(import density).
4. Future research
The review of related variety research made clear that although the evidence base is still
rather small with 21 studies most studies find support for the initial hypothesis by
Frenken et al. (2007) that related variety supports some form of regional growth. Those
who looked at inter-industry differences found that the effects of related variety on
growth may be specific to certain industries only, especially manufacturing and knowl-
edge-intensive ones (Bishop & Gripaios, 2010; Bosma et al., 2011; Cortinovis & Van
Oort, 2015; Hartog et al., 2012). Concerning the studies looking on how countries or
regions develop new industries following Hidalgo et al. (2007), it was also found that if
a region or countries already host industries that are related to a specific industry, it is
much more likely to become specialized in that industry.
A number of follow-up research questions come to mind that can be taken up in future
(1) Though evidence is generally in support of the related-variety thesis, the possibility of
publication bias is not inconceivable, given a more general tendency to under-report
negative results, especially in the emerging stage of a new topic area. Future research
would benefit from more standardized research designs as well as more comprehen-
sive reporting of possible model specifications. In particular, various dependent vari-
ables indicating economic development are being used including employment growth,
productivity growth and GDP growth, and sometimes measured in different ways.
Future research could follow the original related-variety theory arguing that related
variety spurs product innovation and, hereby, employment growth. Hence, ideally,
any empirical analysis includes an analysis of the effect of related variety on employ-
ment growth, possibly next to other dependent variables. Regarding the measurement
of related variety with entropy measures or density as the average proximity of pro-
ducts to a new product, authors do use standardized measures. However, the empiri-
cal data on which the measures are applied can be different, for example, different
digit levels or a different population of products. Again, in so far as possible, standard-
ization is needed.
(2) Findings that suggest that related-variety effects on growth are confined to certain
sectors (Bishop & Gripaios, 2010; Cortinovis & Van Oort, 2015; Hartog et al., 2012;
Mameli et al., 2012) deserve further theoretical and empirical elaboration. A
common thread among these studies point to the role of knowledge intensity.
Indeed, one theoretical line of argument may build on the idea that more knowledge
spills over across related industries, when these industries are knowledge-intensive in
the first place.
(3) Methodologically, the key question at present holds: what is the best method and data
source to capture related variety? Frenken et al. (2007) relied entirely on the pre-given
hierarchical classification as provided by the SIC scheme. This has the advantage of
being amenable to entropy decomposition into related and unrelated variety, yet
has the disadvantage that relatedness is defined ex ante from a hierarchical
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classification scheme that was never intended to capture technological relatedness viz.
spillovers. Hidalgo et al. (2007) derive relatedness from the co-occurrences of pro-
ducts in countriesportfolios. This method derives relatedness ex post from data
rather than ex ante from a classification scheme, yet only measures relatedness
indirectly and remains agnostic about the exact source of relatedness causing indus-
tries to co-locate in countries. As an alternative to Frenken et al. and Hidalgo et al., the
work by Neffke and Henning (2013) seems promising. They measure relatedness by
the number of people changing jobs between two industries, thus capturing directly
skill-relatedness. Alternatively you could explore, at least for the industries that
patent large parts of their knowledge base, the relatedness of patents by looking at
patent classes, citations and inventor mobility. The best results are probably obtained
by a smart triangulation of these approaches.
(4) Theoretically, there are many reasons to expect that regions or countries generate
product innovation from related variety (Frenken et al., 2007) and diversify into
related industries (Hidalgo et al., 2007). However, this leaves unexplained why, and
under what conditions, regions/countries with unrelated variety can also yield
product innovation (especially radical ones), and also leaves unexplained why some
regions/countries manage to diversify into unrelated industries. To break with path
dependence and create new growth paths through true new recombinations,
regions will have to rely more on knowledge and resources residing in other
regions. Hence, (policies attracting) multinationals, immigrant entrepreneurs and
mobile scientists may well underlie new path creation. Some evidence on this thesis
is already available, but more research would be needed to come to a more compre-
hensive understanding (Binz, Truffer, & Coenen, 2014; Dawley, 2014; Neffke, Hartog,
Boschma, & Henning, 2014).
(5) Another question concerns the geographical sources of spillovers through related
variety. Rather than solely looking at a regions internal structure, the relatedness
vis-à-vis other regions with which a region intensively interacts may also matter.
That is, most studies did not pay attention to knowledge spillovers originating
from extra-regional activity. These types of spillovers can occur in numerous ways;
for instance, the trading of goods and services, foreign direct investment and global
value chains are relations that may cause otherwise tacit knowledge to spill over
between regions. The extent to which a region can benefit from foreign knowledge
inflows through these types of relationships depends also on the regions own knowl-
edge and know-how, that is, its absorptive capacity. In addition to that, they suggest
that the inflow of knowledge needs to exhibit complementarities to the existing
knowledge. It should be related, however not similar. More research along these
lines would highlight the role of trade, and global value chains in particular, in gen-
erating spillovers between related industries.
(6) A natural extension of the current research both theoretically and empirically is to
look at relatedness in other dimensions than those related to technological knowledge.
For example, Tanner (2014) developed a market relatedness indicator and has showed
how this indicator predicts quite well regionstechnological development in fuel cell
technology. A similar argument can be made regarding institutional relatedness.
Regions are more likely to diversify into industries that are institutionally related to
the industries already present, not only as actors can build on existing institutional
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arrangements and practices, but also as actors are likely to face less resistance moving
into institutionally related industries than into institutionally unrelated industries.
(7) Since most studies focus on the effect of related variety on either employment growth
or the emergence of a new export specialization as dependent variable, the mechanism
howrelated variety leads to growth and export specializations remains rather
implicit. What can be done in future studies is to analyse directly the impact of
related variety on entrepreneurship, knowledge and innovation, which in turn are
expected to lead to employment and exports. Quite some studies already analysed
the effects of related and unrelated variety on patents as the dependent variable
(Castaldi, Frenken, & Los, 2015; Kogler, Rigby, & Tucker, 2013; Rigby, 2015;
Tanner, 2016; Tavassoli & Carbonara, 2014), but fewer of such studies exist
looking at scientific publications (Boschma et al., 2014; Heimeriks & Balland, 2015)
or new firm formation (Colombelli, 2016; Guo, He, & Li, 2016) as dependent
(8) Finally, related-variety studies hitherto focus on how related variety affects economic
development, while research on the geography of knowledge recombination processes
at the micro-level remains rather unconnected to the related-variety literature. A chal-
lenge for future research will be to combine the macro-level work reviewed here with
the emerging micro-level work on related variety, both theoretical (Davids & Frenken,
2015; Strambach & Klement, 2012) and empirical (Aarstad, Kvitastein, & Jakobsen,
2016; Antonietti & Cainelli, 2011), as to come to a better multi-scalar understanding
on how regional conditions and constraints as well as various forms of proximity
affect recombination processes of knowledge among related and unrelated domains.
1. Note that most studies also take into account a competition variable, following Porters
(1990) work on the advantages of competition (in clusters).
2. Analogously, some authors prefer to speak of geographies of scope (Florida, Mellander, & Sto-
larick, 2012) instead of related variety.
3. Given the macro-scope of the review with a focus on regional and national growth, we do not
go into micro-level studies investigating the effect of regional related variety on firm perform-
ance. This is, to a large extent, already covered by a recent review by Frenken, Cefis, and Stam
(2015) on industrial dynamics in clusters. From this review, it became apparent that firms
profit most if co-located with firms in other, but related, industries rather than being co-
located with firms operating in the same industry. In the latter environments, the benefits
from learning from firms in the same industry may well be offset by increased competition
as well as knowledge spillovers to direct competitors, especially for the more advanced firms.
4. We selected papers to review by searching for papers that (i) cited Frenken et al. (2007)in
case of the related variety studies, or (ii) Hidalgo et al. (2007) in case of the branching
studies, or (iii) contained the keyword related variety, or (iv) contained the keywords
revealed comparative advantageand proximity.
5. A country has a comparative advantage in a product if the products share in a country export
portfolio exceeds the products share in total trade worldwide. This is measured by Revealed
Comparative Advantage (RCA).
6. A more extensive study was reported in the working paper Hausmann and Klinger (2007).
7. Hidalgo and Hausmann (2009) later developed a method that captures an economys com-
plexity and show that higher levels of complexity of an economy are associated with higher
levels of income. Their method is based on two dimensions: the first is the ubiquity of the
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products exported (By how many countries is a product exported?) and the second is the
diversification of an economy (How many products does a country export?). They show
there is a negative relationship between these two dimensions, that is, diversified countries
tend to export less ubiquitous products. For further refinements, see Tacchella, Cristelli, Cal-
darelli, Gabrielli, and Pietronero (2012) and Cristelli, Tacchella, and Pietronero (2015).
We thank Johannes Van Biesebroeck, Claire Economidou, Mark Sanders and Erik Stam for their
useful comments. The usual caveat applies.
Disclosure statement
No potential conflict of interest was reported by the authors.
This work has been supported by the Directorate-General for Research and Innovation, the Euro-
pean Commission, under the H2020 FIRES-project (
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... The concept of related variety is meant to capture the notion that related industries are more likely than unrelated industries to benefit from knowledge spillovers (Frenken et al., 2007). For a complete review of the growing literature on related variety and the impacts on regional growth, see Content and Frenken (2016). Finally, there has also been recent analysis working to capture the notion of specialized diversity. ...
... Operationally, resilience has been captured as employment stability, growth stability, and change in unemployment, which growth has been measured as employment growth, income growth, and unemployment. See Beaudry and Schiffauerova (2009) and Content and Frenken (2016) for more an in depth review. ...
... For nearly 400 U.S. Metropolitan Statistical Areas (MSAs) we determine how an MSA's level of economic tightness changed between 2001 and 2018 and how it relates to economic productivity. Tightness is similar to a measure called related variety (Content & Frenken, 2016;Frenken et al., 2007) in that it is an aggregate measure of a region. Given that tightness is an aggregate measure, we compare it to regional productivity and productivity growth as opposed to industry-region employment growth as in earlier studies. ...
Full-text available
The structures of regional economies play a critical role in determining both a region’s productivity and its resilience to shocks. We extend previous work on the regional occupation and skills structure by analyzing the effect of a region’s industry structure. We operationalize the concept of economic structure by constructing a network of interdependent economic components, employing ecological techniques of co-occurrence analysis to infer interactions between industries. For each U.S. metropolitan statistical area, we create an aggregate measure of economic tightness that captures the degree of interconnectedness among a region’s industries. We find that industry tightness, which we find is partly driven by rare industry pairs, is positively correlated with a region’s economic productivity, negatively correlated with a region’s change in productivity following the Great Recession. This study contributes to an understanding of the tradeoff between productivity and resilience, which is intended to help policy makers that face similar real-world tradeoffs.
... Studies on related and unrelated variety for Turkey follow the ex-ante measure for relatedness introduced by Frenken et al. (2007). However, Boschma et al. (2012), Boschma et al. (2013), and Content and Frenken (2016) all stated that Frenken et al.'s (2007) methodology is insufficient in several aspects and argue that Hidalgo et al.'s (2007) proximity approach is a much better measure. This study adopts the ex-post proximity approach developed by Hidalgo et al. (2007) for the related and unrelated variety calculations in Turkey. ...
Bu çalışmanın amacı Türkiye’de 26 Düzey-2 bölgesi bazında ilişkili ve ilişkisiz ihracat çeşitliliği ile gelir dağılımı arasındaki ilişkinin incelenmesidir. Bu amaçla, Türkiye için şimdiye kadar ex-ante ölçütler üzerinden yapılan hesaplamalardan farklı olarak ex-post bir yaklaşım kullanılarak 2014-2020 yılları arasını kapsayan dönem için yıllar itibarıyla ilişkili ve ilişkisiz çeşitlilik değerleri hesaplanmıştır. Analiz bulgularına göre Türkiye'de ilişkili çeşitlilik ve gelir dağılımı arasında anlamlı bir ilişki yokken, Türkiye’nin nispeten gelişmiş batı bölgelerinde ilişkisiz çeşitliliğin artması portföy etkisi üzerinden gelir dağılımını olumlu yönde etkilemektedir.
... The current economic profile of cities is a proxy of deindustrialization where inherited industries did not form the starting point for further structural change through diversification. This finding confirms that the role of relatedness for economic diversification should be considered in conjunction with other major processes, such as deindustrialization, job polarization, urbanization and skill-biased technological change (Content & Frenken, 2016;Holm & Ǿstergaard, 2018). The significance of the transition economies for understanding the related and unrelated economic diversification has been recently acknowledged (Boschma, Coenen, Frenken, & Truffer, 2017) by supporting the need to combine insights from evolutionary thinking and transition studies. ...
... However, it must be emphasized that not only the volume of knowledge produced matters for innovation but also its composition. Several studies based on the Schumpeterian concept of recombinant innovation have indeed investigated the determinants and effects of the intrinsic heterogeneity of regional knowledge bases (Audretsch & Belitski, 2020;Barbosa et al., 2014;Castaldi et al., 2015;Content & Frenken, 2016;Grillitsch et al., 2017;Quatraro, 2010;Schoenmakers & Duysters, 2010). R&D expenditures are comparatively more complex innovative activities than other activities related to the external acquisition of embodied technology, such as the purchase of machinery and software and the acquisition of external knowledge through technological consultancy and personnel training. ...
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Knowledge spillovers have been identified as a factor affecting the unequal distribution of innovation in space. In this paper we aim to understand how territorial factors shape the novelty degree of innovations. Thus, we perform an empirical analysis that relates territorial factors to innovative performance at the firm level. Our results show that local knowledge spillovers from research and development expenditures are positively associated with upper-level innovation, while local knowledge spillovers from total innovation expenditures are not related to the degree of novelty of innovation. Furthermore, the impacts on innovation are also moderated by related and unrelated varieties since firms in regions with higher regional-related variety are less likely to generate upper-level innovation.
... The evolution of specialization or connectedness of urban economies serves as an important indicator of economic resilience of cities (Frenken et al. 2007, Content and Frenken 2016, Farinha et al. 2019. Specialization is complementary to the interdependence ("relatedness" or "connectedness") of labor occupations present in a city's economy, which has been used to assess urban resilience (Shutters et al. 2015). ...
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In this paper we examine the coevolution of individual cities and the city networks to which they belong, during aneconomic shock. We take an individual city and its city network to be the meso and macro levels, respectively, of a social-economicsystem. Focusing on the economic shocks felt by Russian cities in 2014 following the Ukrainian conflict, we demonstrate that the sameshock had different effects at the meso level (a city’s employment structure) and macro level (a city’s interfirm linkages to other cities,both national and international). To explain our findings, we draw on panarchy theory to propose a multilevel perspective of resiliencethrough the coevolution of adaptive cycles at the meso and macro levels of urban economies. To evaluate resilience at each level, wefirst operationalize the panarchy concept of connectedness using a previously developed metric called “tightness,” which quantifies theinterdependencies among economic activities. We next operationalize the panarchy concept of potential by measuring a city’s degreeof economic specialization. At the meso level, we find that larger cities suffered less employment loss than smaller cities during theshock and that by 2019 the structure of the meso level had largely returned to its 2010 structure. On the other hand, at the macro level,we found that the 2019 macro level structure changed considerably from 2010. Thus, we show that the meso level was disturbed butreturned to a previous state (engineering resilience) while the macro level transitioned to a new state (ecological resilience). Resultssuggest that policy makers would benefit from distinguishing between the meso and macro levels, enabling the development of multilevelurban policies to address future shocks.
... It is much easier to draw resources and knowledge from related export products while developing new products, since related products demand similar manufacturing skills and exporting knowledge. On the supply side, related products often contain similar technologies and skills, and thus capabilities a region has manufacturing certain products can be used in the development of some new related products (Content & Frenken, 2016). On the demand side, export markets of related products are likely to have similar features in terms of distribution channels, consumer tastes, and laws and regulations (Roberts & Tybout, 1999;Albornoz et al., 2016). ...
While considerable attention has been paid towards how some long-lasting extra-regional linkages such as foreign direct investments facilitate interregional knowledge transfer, less is known about the role of temporary extra-regional linkages formulated via trade fairs. This paper seeks to fill this gap by focusing on the China Import and Export Fair and examining the relationship between temporary extra-regional linkages forged by trade fairs and regional export dynamics. In doing so, we also speak with the literature on ‘temporary clusters’. Our second contribution is to differentiate export market and product diversification, and to show distinct effects of temporary extra-regional linkages on those two.
... Another form of externalities due to Jacobs (1969) emphasizes the importance of the variety of the industrial structure in an agglomeration and knowledge spillovers across firms in different industries. More recently, Frenken et al. (2007) have introduced the concept of related variety with its explicit focus on inter-industry knowledge spillovers (see Content and Frenken, 2016, for a recent survey). ...
The purpose of this paper is to study how changes in unrelated variety influence individuals’ poverty alleviation. Drawing on the LiTS III database, we employed the Oprobit model to test 5007 individual-level observations from 23 regions in four former Yugoslavian countries. All results imply that the changes in unrelated variety have a U-shaped relationship with individuals’ poverty alleviation. Our findings enrich the unrelated variety research by providing micro-level evidence and offer practical insights for governments, organizations and individuals aiming to reduce poverty.
Previous studies on the impact of firm growth on survival have paid little attention to agglomeration externalities. This study theoretically analyses how different agglomeration externalities affect the relationship between growth and survival. Using data from China’s manufacturing start-ups, we confirm a U-shaped relationship between the growth and survival risks of start-ups. Moreover, we find that different agglomeration externalities have heterogeneous moderating effects on this U-shaped relationship. Specifically, specialization and diversification externalities have significantly negative and positive moderating effects, respectively. The positive moderating effect of diversification externalities comes from the moderating effect of related variety externalities, whereas unrelated variety externalities have no significant moderating effect. The robustness test results support the above conclusions and suggest that the moderating effect of specialization externalities is heterogeneous in terms of firms’ resources.
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This paper aimed at investigating the relationship between the features of local knowledge bases and the creation of innovative start-ups in Italy. The knowledge spillover theory of entrepreneurship has here been combined with the recombinant knowledge hypothesis in order to derive a theoretical framework that could emphasize the heterogeneous nature of knowledge and identify some key dimensions. The empirical analysis has been focused on the patterns of new firm formation in Italian NUTS 3 regions using data on the creation of innovative start-ups that have followed the implementation of a new Italian regulation. The results of the analysis confirm that not only does the size of the knowledge stock play a key role in shaping the creation of innovative start-ups, but also the characteristics of such knowledge, in terms of variety and similarity.
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We conduct multilevel analyses of Norwegian data and find that related industrial variety is a positive regional driver of enterprise innovation. Unrelated variety is a negative regional driver of enterprise productivity. This implies that regions with high levels of related variety and low levels of unrelated variety optimize enterprise performance. We argue that regional specialization is a two-dimensional construct inversely associated with related and unrelated variety. Thus, a specialized region (low in unrelated variety) is in fact a driver of enterprise productivity. In addition, we find that population density is another regional driver of enterprise productivity.
Applying the new economics of organization and relational theories of the firm to the problem of understanding cross‐national variation in the political economy, this volume elaborates a new understanding of the institutional differences that characterize the ‘varieties of capitalism’ found among the developed economies. Building on a distinction between ‘liberal market economies’ and ‘coordinated market economies’, it explores the impact of these variations on economic performance and many spheres of policy‐making, including macroeconomic policy, social policy, vocational training, legal decision‐making, and international economic negotiations. The volume examines the institutional complementarities across spheres of the political economy, including labour markets, markets for corporate finance, the system of skill formation, and inter‐firm collaboration on research and development that reinforce national equilibria and give rise to comparative institutional advantages, notably in the sphere of innovation where LMEs are better placed to sponsor radical innovation and CMEs to sponsor incremental innovation. By linking managerial strategy to national institutions, the volume builds a firm‐centred comparative political economy that can be used to assess the response of firms and governments to the pressures associated with globalization. Its new perspectives on the welfare state emphasize the role of business interests and of economic systems built on general or specific skills in the development of social policy. It explores the relationship between national legal systems, as well as systems of standards setting, and the political economy. The analysis has many implications for economic policy‐making, at national and international levels, in the global age.
Who introduces structural change in regional economies: Entrepreneurs or existing firms? And do local or nonlocal establishment founders create most novelty in a region? We develop a theoretical framework that focuses on the roles different agents play in regional transformation. We then apply this framework, using Swedish matched employer–employee data, to determine how novel the activities of new establishments are to a region. Incumbents mainly reinforce a region’s current specialization: incumbent’s growth, decline, and industry switching further align them with the rest of the local economy. The unrelated diversification required for structural change mostly originates via new establishments, especially via those with nonlocal roots. Interestingly, although entrepreneurs often introduce novel activities to a local economy, when they do so, their ventures have higher failure rates compared to new subsidiaries of existing firms. Consequently, new subsidiaries manage to create longer-lasting change in regions.
To understand how the specialisation patterns of cities differ among scientific fields, we study patterns of knowledge production in Astrophysics, Biotechnology, Nanotechnology and Organic Chemistry in the period 1996–2012. Using keywords from journal publications, we find systematic differences across scientific fields, but remarkable similarities across cities within each field. Biotechnology shows a turbulent pattern with comparative advantages that are short lasting, and with few related topics are available for research locations. Astrophysics—and in later years Nanotechnology—show a pattern of stable rankings, comparative advantages that last longer, and many related topics potentially available for research locations. Organic Chemistry has an intermediate position. Thus, fields of knowledge production have fundamentally different characteristics that require different smart specialisation strategies taking into account the differences in accumulation and relatedness.
The development of new industries demands access to local capabilities. Little attention has yet been paid to the role of spillovers from neighbour regions for industrial diversification, nor has the role of network linkages between neighbour regions been investigated. As the spread of capabilities has a strong geographical bias, we expect regions to develop new industries in which their neighbour regions are specialized. To test this hypothesis, we analyse the development of new industries in US states during the period 2000–2012. We show that a US state has a higher probability of developing a comparative advantage in a new industry if a neighbour state is specialized in that industry. We also show that neighbour US states have more similar export structures. This export similarity seems to be explained by higher social connectivity between neighbour states, as embodied in their bilateral migration patterns.
This article adds to the empirical evidence on the impact of agglomeration externalities on regional growth along three main dimensions. On the basis of data on 259 Europe NUTS2 (Nomenclature of Territorial Units for Statistics) regions and 15 NACE (Nomenclature statistique des Activités économiques dans la Communauté Européenne) 1.1 2-digit industries for the period 1990–2007, we show that agglomeration externalities are stronger in technology-intensive industries, also after controlling for sorting; that specialization externalities are stronger for low density regions, while diversity matters more for denser urban areas; and, finally, that Jacobs externalities comprise a pure diversification effect (related variety) and a portfolio effect (unrelated variety), although evidence of positive effects on regional growth is only found for the latter. An additional contribution of this article is to extend the analysis on the basis of a full geographical coverage of European NUTS2 regions, with the aim to generalize the empirical identification of the impacts of specialization and diversification externalities with respect to the existing literature. Our results are robust to a rich set of consistency checks, including the use of spatial autoregressive models with autoregressive disturbances, used to assess to what extent the effects of agglomeration externalities are localized.
This paper analyzes the process of industrial diversification in the countries that were part of the European Union (EU-27) and those that were the target of the European Neighbourhood Policy (ENP) in the period 1995–2010 by means of world trade data derived from the BACI database (elaborated UN Comtrade data). Our results show that in both the EU-27 and the ENP countries, the evolution of the productive structure—as proxied by the export mix—is strongly path-dependent: countries tend to keep a comparative advantage in products that are strongly related to their current productive structure, and they also diversify in nearby products. However, this effect is much stronger for ENP countries, signalling their lower resources and capabilities to diversify in products that are not very related to their productive structure. We also show that the future export structures of countries are affected by their imports: both the EU-27 and ENP countries keep a comparative advantage in products that are strongly related to their imports, but only EU countries show a strong capability to diversify in new products from related import sectors. Our results also hold when controlling for geographical and institutional proximity.
The varieties of capitalism literature has drawn little attention to industrial renewal and diversification, while the related diversification literature has neglected the institutional dimension of industrial change. Bringing together both literatures, the paper proposes that institutions have an impact on the direction of the diversification process, in particular on whether countries gain a comparative advantage in new sectors that are close or far from what is already part of their existing industrial structure. We investigate the diversification process in 23 developed countries by means of detailed product trade data in the period 1995-2010. Our results show that relatedness is a stronger driver of diversification into new products in coordinated market economies, while liberal market economies show a higher probability to move in more unrelated industries: their overarching institutional framework gives countries more freedom to make a jump in their industrial evolution. In particular, we found that the role of relatedness as driver of diversification into new sectors is stronger in the presence of institutions that focus more on 'non-market' coordination in the domains of labor relations, corporate governance relations, product market relations, and inter-firm relations.