Related variety and economic development: a literature review

Abstract

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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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:
10.1080/09654313.2016.1246517
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Related variety and economic development: a literature
review
Jeroen Content
a
and Koen Frenken
b
a
Utrecht University School of Economics, Utrecht, The Netherlands;
b
Copernicus Institute of Sustainable
Development, Utrecht University, Utrecht, The Netherlands
ABSTRACT
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.
ARTICLE HISTORY
Received 23 June 2016
Accepted 19 September 2016
KEYWORDS
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 ‘controversy’is 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 economy’s inherent
© 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
CONTACT Koen Frenken k.frenken@uu.nl
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VOL. 24, NO. 12, 2097–2112
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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).
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 economy’s 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.
2
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 ‘proximity’of related products already present in a country’s 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
level.
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 country’s or region’s 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-variety’literature following Frenken et al. (2007), ana-
lysing how related variety affects regional/national growth, as well as the ‘branching’lit-
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
industry.
3
We limit our review to papers that have been either published or accepted
for publication in scientific journals.
4
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 1996–2002 CBS Related variety
Unrelated variety
RV = 5 in each 2
UV = 2
Employment growth + 0
Productivity growth −0
Unemployment
growth
0−
Saviotti and Frenken
(2008)
National OECD 1964–2003 OECD trade data Unrelated export variety
Semi-related export
variety
Related export variety
UV = 1
SV = 2 in each 1
RV = 3 in each 2
GDP per cap + −
Labour productivity + −
Boschma and Iammarino
(2009)
NUTS3 Italy 1995–2003 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 + +
Labour-productivity
growth
M0
Bishop and Gripaios (2010) Subnational Great Britain 1995–2002 NOMIS Related variety
Unrelated variety
RV = 4 in each 2
UV = 2
Employment growth
at two-digit
industry
level
MM
Quatraro (2010) NUTS2 Italy 1981–2002 ISTAT
EPO
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 1990–2002 CBS
Chambers of Commerce
Related variety RV = 5 in each 2 Productivity growth M
Falcioglu (2011) NUTS2 Turkey 1980–2000 Turkish statistical institute Variety
Related variety
Variety = 3
RV = 3 in each 2
Productivity growth +
Boschma et al. (2012) NUTS3 Spain 1995–2007 INE, Ivie and Agencia
Tributaria
Related variety
Unrelated variety
Porter relatedness
measure
Hidalgo relatedness
measure
RV = 6 in each 2
UV = 1
Value-added growth + 0
Hartog et al. (2012) NUTS4 Finland 1993–2006 Statistics Finland Related variety
RV-HiTech
RV-LowTech
Unrelated variety
Variety = 5
RV = 5 in each 2
UV = 2
Employment growth M 0
Mameli et al. (2012) Local labour
market
Italy 1991–2001 ISTAT Variety
Related variety
Unrelated variety
Variety = 3
RV = 3 in each
2
UV = 1
Employment growth + +
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Cortinovis and Van Oort
(2015)
NUTS2 Europe 2004–2012 ORBIS, Bureau van dijk Unrelated variety
Related variety
Specialization
Technological regime
UV = 1
RV = 4 in each 2
Employment growth M 0
Unemployment
growth
MM
van Oort et al. (2015) NUTS2 Europe 2000–2010 Amadeus Related variety
Unrelated variety
RV = 4 in each 1
UV = 2
Employment growth + M
Productivity growth 0 0
Unemployment
growth
00
Caragliu et al. (2016) NUTS2 Europe 1990–2007 Cambridge Econometrics Related variety
Unrelated variety
RV = 2 in each 1
UV = 1
Employment growth
at the industry level
0+
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
post’methods they employ is based on Porter’s(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 region’s 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 industry’in 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 regions’urban 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 (1990–2007). 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
countries’export 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
5
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.
6
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 ‘travel’a 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.
7
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 1969–2002 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 1990–1995 NBER SITC-4 Density Entry
Neffke et al. (2011) A-region Sweden 1969–2002 Statistics Sweden SNI69-6 Closeness Membership
Entry
Exit
Boschma et al. (2013) NUTS3 Spain 1988–2008 NBER World Trade
Agencia Tributaria
SITC-4 Density at the country level
Density at the province level
Entry
Bahar et al. (2014) National World 1962–2008 World Trade Flows UN &
COMTRADE & WDI & UNCTAD
SITC-4 Density
RCA neighbour
Entry
Boschma, Martin, and Minondo (2016) State U.S. 2000–2012 US Census Bureau
Comtrade
HS-4 Density
RCA neighbour
RCA growth
Boschma and Capone (2015a) National 23 countries 1970–2010 World Trade Flows and CEPII 6-digits Density
Institution indicator
Entry
Boschma and Capone (2015b) National EU27
ENP16
1995–2000 BACI 4-digits Density
Import density
Entry
Essleztbichler (2015) Metropolitan areas U.S. 1975–1997 Bureau of Economic Analysis SIC-4 Closeness Membership
Entry
Exit
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 1988–2008. 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
country’s 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
research:
(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 countries’portfolios. 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 region’s 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 region’s 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 regions’technological 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
‘how’related 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
variables.
(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.
Notes
1. Note that most studies also take into account a competition variable, following Porter’s
(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 advantage’and ‘proximity’.
5. A country has a comparative advantage in a product if the product’s share in a country export
portfolio exceeds the product’s 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 economy’s 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).
Acknowledgements
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.
Funding
This work has been supported by the Directorate-General for Research and Innovation, the Euro-
pean Commission, under the H2020 FIRES-project (http://www.projectfires.eu/).
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