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Multinational enterprises, industrial relatedness
and employment in European regions
Nicola Cortinovis *
,†
, Riccardo Crescenzi ** and Frank van Oort***
*Department of Human Geography and Spatial Planning, Utrecht University, Princetonlaan 8a, 3584 CB
Utrecht, The Netherlands
**Department of Geography and Environment, London School of Economics, Houghton Street, WC2A 2AE,
UK
***Department of Applied Economics, Erasmus Universiteit Rotterdam, E building, Burgemeester Oudlaan 50,
3062 PA Rotterdam, The Netherlands
†
Correspondence to: email <n.cortinovis@uu.nl>
Abstract
This article investigates the link between multinational enterprises (MNEs) and em-
ployment in their host regions by cross-fertilising the literature on MNE externalities
with the emerging body of research on industrial relatedness. The link between em-
ployment and MNE presence in the same and related industries is tested for
European regions. The results suggest that cross-sectoral MNE spillovers are medi-
ated through industrial relatedness and that they are positively and significantly asso-
ciated with higher employment levels, independently of input–output relations. Our
results indicate that regions characterised by lower factor prices are likely to benefit
the most from the presence of multinationals in terms of employment, but these
benefits are concentrated in high knowledge-intensive sectors, potentially fostering
inequalities within less-developed economies.
Keywords: Employment, foreign direct investment, relatedness, Europe, regions
JEL classifications: O33, F22
Date submitted: 10 May 2019 Editorial decision: 3 April 2020 Date accepted: 8 April 2020
1. Introduction
The capability of firms to control and organise their activities in multiple countries and
the corresponding increase in global investment flows have fostered scholarly and policy
debates on multinational enterprises (MNEs) and their effects on host economies (Narula
and Dunning, 2000;Fu et al., 2011;Javorcik, 2013). These impacts have received signifi-
cant attention in economics, economic geography and international business. Various con-
tributions in these fields have highlighted a number of mechanisms through which MNEs,
especially when pursuing knowledge-intensive and innovative activities in the host econ-
omy (Javorcik et al., 2018), have a beneficial effect on domestic firms in terms of innov-
ation and productivity. Based on this evidence, countries and regions across the globe
have started to actively compete with each other in order to attract foreign investors
(Bitzer et al., 2008;Harding and Javorcik, 2011;Narula and Pineli, 2016). At the same
time, new empirical research has highlighted various potential ambiguities in the link be-
tween MNE presence and local innovation, development and wealth, shedding new light
on the pre-conditions for these positive effects to materialise (Go¨rg and Greenaway, 2004;
Crespo and Fontoura, 2007).
#The Author (2020). Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),which
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Journal of Economic Geography (2020) 0, 1–41 doi: 10.1093/jeg/lbaa010
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Multinationals are often seen as key generators of innovation, accounting for a large
share of global research and development (R&D) spending and possessing superior know-
ledge on the true competitive advantage of their host countries vis-a`-vis international mar-
kets for specific products (Iammarino and McCann, 2013;Crescenzi et al., 2014;Javorcik
et al., 2018). Empirical evidence indeed suggests that multinationals do transfer knowledge
to their foreign affiliates (Arnold and Javorcik 2009;Brambilla, 2009;Guadalupe et al.,
2012). Yet, spillover effects to domestic firms in host economies may still fail to material-
ise or may even be negative. On the one hand, MNEs actively protect their knowledge in
order to minimise knowledge leakages in favour of domestic competitors (Alcacer and
Delgado, 2016). On the other hand, competition from MNEs, both in the product and fac-
tor markets, may lower productivity and innovation efforts in domestic firms (Aitken and
Harrison, 1999). These mechanisms are typically used to explain the limited evidence for
positive horizontal (i.e. intra-industry) spillovers (Javorcik, 2004;Lin and Saggi, 2007;
Havranek and Irsova, 2011;Javorcik et al., 2018). Differently, research has found stronger
support for vertical (i.e. inter-industry following the supply chain) externalities, which are
conceptually justified by the higher incentives for multinationals to provide knowledge
and technological insights to their suppliers (backward spillovers) and their customers (for-
ward spillovers; Lu et al., 2017).
The aim of this article is to add to this debate from a different perspective and explore
the link between MNEs activities and local labour markets by cross-fertilising the MNE
spillover literature with the growing body of research on industrial relatedness. A small
stream of literature has recently emerged on this subject, mainly focussing on the impacts
of industrial or technological relatedness on domestic firm innovation in developing and
transition regions. Lo Turco and Maggioni (2019) show that the relatedness of the produc-
tion portfolio of foreign firms correlates with the diversification into new products by do-
mestic manufacturing firms in Turkish regions. They also observe a higher degree of
complexity for new products, but conditioned upon the presence of relevant absorptive
capacity of domestic firms. The article focuses on the entry of new industries in regions
as dependent variable (following Cortinovis et al., 2017) and argues, in line with Hidalgo
et al. (2007), that developing economies like Turkey are often poorly diversified and their
economy relies on a limited number of traditional products that offer a limited contribution
to long-run economic growth. Following a similar approach, Zhu et al. (2017) look at the
emergence of new sources of competitive advantage in manufacturing firms in Chinese
districts. They show that technological relatedness to the local export mix interacts with
internal and external knowledge sources (including foreign direct investment (FDI)) to es-
tablish new entry of products. Following a similar approach, Elekes et al. (2019) analysed
foreign-owned firms as agents of structural change in Hungarian regions with similar con-
clusions. However, these recent papers do not link their findings to economy-wide out-
comes beyond the firm/industry level. Conversely, the analysis of regional employment
growth takes centre stage in Boschma and Iammarino (2009): using a relatedness frame-
work they find that related regional imports play a particularly important role in Italy
(while correlations with value-added growth and labour productivity are less robust). More
recently, Elekes and Lengyel (2016) analysed regional employment growth contributions
of foreign and domestic firms. In a different conceptual framework, Waldkirch (2009)
looks at the employment impact of FDI across sectors at the country level in Mexico.
The analysis of the local employment consequences of MNE activities in a European-
wide regional perspective is still a largely under-explored area of research, notwithstanding
its importance for public policies. The European Union is heavily relying on the concept
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of relatedness (Thissen et al., 2013;McCann 2015) to underpin its innovation, Smart
Specialization and Cohesion Policy strategies. At the same time, policymakers are coming
to the realisation that these strategies need to embrace the FDI and Global Value Chains
fully, understanding regional development as a (global) connectivity phenomenon
(Crescenzi et al., 2018,2019). Yet, coherent conceptualisations and robust empirical evi-
dence at the regional level on the employment–FDI nexus are still missing. The lack of
consensus in the existent literature on knowledge spillover effects from FDI is magnified
when it comes to employment effects. Local employment effects are the balance between
competition effects and learning effects. Stronger competition might not only push weaker
firms out of the market—with significant employment losses—but might also foster capital
intensity in the most dynamic firms, outweighing direct job creation from new FDI.
Knowledge spillovers might also improve the competitive profile of domestic firms with
positive effect on their expansion and growth (also in terms of employment), but domestic
technological upgrading might also lead to local job losses with the adoption of labour-
saving technologies.
Therefore, the analysis of local employment growth—the direct result of relatedness as
a means of knowledge transmission for MNEs—is a highly needed contribution to the lit-
erature. The European Union (EU)—encompassing a wide range of territorial conditions
from less developed to frontier regions—offers an ideal testing ground for theory-driven
empirical analyses, making it possible to explore the heterogeneity of these links.
In capturing local employment effects, the article furthers the current understanding of
the sectoral nature of MNE effects by adopting a relatedness perspective to capture broad
similarities across industries, which we consider complementary to (vertical) input–output
linkages
1
traditionally explored in the literature. Considering that knowledge-intensive
industries and product relatedness are generally associated with employment opportunities
(Frenken et al., 2007), this article is first looking at sectoral employment in European sub-
national regions in relation to MNE presence both within and across related sectors. The
analysis also explores the heterogeneity of these relations with reference to industrial
knowledge intensity and regional development levels, reflecting the large diversity of ab-
sorptive and labour market conditions in the regions of Europe. The empirical analysis of
related sectors (either in its own right or in addition to input–output relations) as mediators
of MNE employment effects has not been introduced before in an EU context, but may
prove beneficial for understanding spillovers and policy strategies when a convincing rela-
tion is found. The existing literature has focused on individual emerging economies
(Turkey in Lo Turco and Maggioni 2019; Hungary in Elekes and Lengyel , 2016, Elekes
et al., 2019; and China in Zhu et al., 2017). In so doing, it has been able to leverage firm-
and product-level microdata in a single country setting, to distinguish foreign vis-a`-vis do-
mestic firm transmission channels and capability measures and track structural change in a
detailed manner. In this article, the focus is on industry-level employment effects where re-
latedness acts as a mediator (and not as outcome variable as in the existing literature). In
addition, by covering the EU in its entirety (and territorial diversity), this article can cap-
ture a wider heterogeneity of effects. Finally, special attention is given to the identification
of these effects in order to exclude possible endogeneity.
1 As discussed by Hidalgo et al. (2007), relatedness captures different types of linkages and similarities driving the
co-location of firms, in a way that may include but it is not limited to input–output relations. We argue and show
in Table A2 in Appendix A that our relatedness measure encompasses more than input–output linkages.
MNEs, relatedness and employment 3
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The empirical results show that positive and significant cross-sectoral employment effect
from MNE activities materialise among related industries. This provides an initial confirm-
ation to the idea that industrial relatedness—possibly encompassing but not limited to I-O
relations—is an important channel for employment-enhancing effects from MNE activities
in European regions. While the use of relatively aggregated sectors at the EU regional
level does not allow us to capture relatedness at a fine-grained level, the influence of
MNEs on related industries across two-digit NACE sectors, confirms the importance of
looking beyond vertical linkages when exploring the employment consequences of inter-
nationalisation. These results, however, are contingent on the modelling of both regional
and industrial heterogeneity. In relation to regional and industrial heterogeneity, the results
suggest, in line with previous studies (Bitzer et al., 2008;Fu, 2008;Fu et al., 2011), that
inter-industry effects are not negligible and tend to be stronger in relatively less-developed
regions. To address potential sources of bias in our results, we perform various robustness
checks: first, we apply a Bartik-type instrument (Ascani and Gagliardi, 2015;Crescenzi
et al., 2015), in combination with deep lags, to approximate the distribution of MNEs
across regions and sectors, while removing region–industry-specific characteristics; sec-
ondly, we re-estimate our models considering only sectoral employment from domestic
firms (i.e. non-MNEs), as previous research showed that this is an important distinction in
the question where new varieties stem from and spill over into (Zhu et al., 2017;Lo
Turco and Maggioni, 2019). Both robustness checks confirm the validity of our
conclusions.
The article is organised as follows. In Section 2, the relevant literature on MNE exter-
nalities, their preconditions and their intra- and inter-industrial scope is reviewed, in order
to derive four testable hypotheses in Section 3. Empirical strategy and data to test these
hypotheses are presented in Section 4. Results and robustness tests are discussed in
Section 5. The final section acknowledges some key limitations of the article and presents
policy implications and directions for further research.
2. MNE spillover literature
2.1. Ambiguity of MNE effects on domestic firms
MNEs are among the most important actors in the process of knowledge creation and dif-
fusion. Thanks to their technological capabilities and their capacity to control activities in
multiple technological environments, MNEs can leverage their network of subsidiaries and
exploit local knowledge resources in multiple locations (Narula and Dunning, 2000;Ernst
and Kim, 2002;Iammarino and McCann, 2013). On this basis, foreign subsidiaries can
bring about externalities for domestic firms, some of which may lead to higher domestic
productivity and (under certain circumstances) employment growth (Javorcik, 2013;
Crescenzi et al., 2015).
In the last decades, a significant body of research has studied the impact of MNE sub-
sidiaries on their host economy (Burger et al, 2013;Perri and Peruffo, 2016;Karreman
et al, 2017), with multinational companies potentially affecting, either positively or nega-
tively, the host country. Theoretical and empirical contributions have explored the different
channels through which these impacts can unfold. First, local companies can learn and
imitate the technologies and procedures used by MNEs (Ernst and Kim, 2002;Crespo and
Fontoura, 2007). In the same way, foreign MNE networks can also offer new insights
about foreign market opportunities and relational channels, facilitating the
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internationalisation of domestic firms (Go¨rg and Greenaway, 2004). Secondly, domestic
firms can acquire specialised knowledge by hiring workers previously employed by MNEs
(Poole, 2013). Labour mobility, however, can also work in the opposite direction: MNEs
tend to offer higher wages than domestic ones, making them more attractive for the most
talented workers in the local labour market (Javorcik, 2013). Thirdly, the increase in com-
petition due to MNE entry can force domestic companies to become more efficient and
make better use of existing technologies and resources (Jacobs et al., 2014). However,
competitive pressure might also be harmful: more advanced MNEs may push competitors
out of the market or induce local companies to operate on a smaller and less efficient scale
(Fu et al., 2011).
2.2. Inter-industry effects: buyer–supplier linkages and industrial relatedness
In the quest for cross-sectoral MNE spillovers, most of the existing literature has identified
input–output relations as the main channel through which such effects materialise (Lin and
Saggi, 2007;Perri and Peruffo, 2016;Lu et al., 2017). Vertical linkages to MNEs engen-
der productivity-enhancing effects, for instance, through increased demand for local goods
or stronger competition for supplying multinationals (Javorcik, 2004,2013;Alvarez and
Lopez, 2008;Crespo et al., 2009;Javorcik et al., 2018). Besides, to guarantee certain qual-
ity or technical standards, foreign companies have the incentive to share knowledge with
local producers (Ernst and Kim, 2002;Javorcik et al., 2018), through visits and periodic
inspections or training programmes (Fu et al., 2011). Similar dynamics apply to forward
linkages. By sourcing from MNEs, local firms may benefit from goods of higher quality
or technological sophistication, which in turn may streamline their production process, fos-
tering efficiency and productivity (Crespo and Fontoura, 2007;Javorcik, 2004). Specific
knowledge might also be acquired along with the good itself or via after-sale care or sup-
port services.
Whereas studies on within-industry spillovers often give inconclusive results (Fu et al.,
2011), significant evidence exists confirming the relevance of inter-industry effects
(Kugler, 2006;Crespo et al., 2009;Javorcik, 2013;Javorcik et al., 2018). In general, these
analyses suggest that backward linkages positively contribute to the increase in level of
productivity within the local economy (Javorcik, 2004;Lin and Saggi, 2007;Bitzer et al.,
2008;Crespo et al., 2009), with few exceptions (Damijan et al., 2003). Conversely, for-
ward linkages do not have significant effects on local productivity (Crespo and Fontoura,
2007).
Within the debate on inter- and intra-sectoral MNE spillovers, types of linkages other
than input–output relations have received limited attention (some exceptions: Branstetter,
2006;Kugler, 2006). This contrasts with other literatures, which consider a broader set of
dimensions through which industries might be connected. In the economic geography lit-
erature, externalities emerge from the recombination of both proximate (Boschma, 2005;
Frenken et al., 2007) and highly diverse types of knowledge (Jacobs, 1969;Glaeser et al.,
1992). In these respects, the concept of relatedness aims at capturing how local know-
ledge, technologies and assets influence the possibility of knowledge recombination and
diversification of the economy over time (Hidalgo et al., 2007). In other words, the oppor-
tunities to diversify and operate in new (for the region) sectors depend on the industries
already present in the economy: the more two sectors are related, the easier it is for firms
to re-deploy their assets, acquire new capabilities and move from one sector to the other
(Hausmann and Klinger, 2007;Hidalgo et al., 2007;Boschma et al., 2013;Boschma and
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Capone, 2015;Cortinovis et al., 2017). The concept of relatedness thus synthesises the dif-
ferent dimensions in which two sectors can be proximate, be it because of similar technol-
ogies, skills or production processes, because of input–output relations, or because of
similar institutional arrangements (Hidalgo et al., 2007).
Foreign-owned companies, with their ability to gather and use knowledge and technolo-
gies from different locations (Narula and Dunning, 2000;Iammarino and McCann, 2013;
Crescenzi et al., 2015), may bring about significant cross-industrial knowledge flows out-
side of their own supply chains. While more specialised knowledge is more difficult to be
redeployed, this can still happen. For instance, technical expertise may provide valuable
knowledge and insights to successfully operate in similar industries, as in the case of spin-
off dynamics (Boschma and Wenting, 2007;Boschma and Frenken, 2011). On this basis,
confining the impacts of MNEs within the boundaries of backward and forward linkages
might offer at best a partial picture of the cross-sectoral spillovers.
Based on the arguments and evidence outlined above, we propose that knowledge in
one sector can find useful applications also in different but related sectors, influencing
their employment levels. Whereas this idea of industrial relatedness may encompass also
vertical linkages (Hidalgo et al., 2007), it specifically entails the possibility of knowledge
spilling over to proximate sectors outside the supply chain. The channels for knowledge
spillovers already identified in the literature, such as labour mobility, demonstration effects
or other informal linkages (Ernst and Kim, 2002;Perri and Peruffo, 2016), can thus be
expected to work not only within vertical relations, but also by connecting different but
technologically or cognitively similar industries.
If knowledge flows across industries are key to understand the diffusion of MNE effects
into domestic firms, the overall employment implications of these effects have remained
largely unexplored in the existing literature. The ambiguity of the MNE direct local em-
ployment effects discussed in the previous section is mirrored by the ambiguity in the dif-
fusion of local employment effects across sectors. It remains empirically under-explored
how competitive pressures from MNEs coupled by the diffusion of efficiency-enhancing
practices (reducing employment in domestic firms) might be counter-balanced by
employment-enhancing effects associated with higher demand (domestic and through the
opening of new export markets), lower input costs, diversification into related but locally
unexplored markets, and product and value chain upgrading.
2
2.3. Heterogeneity in MNE effects
The existing literature has highlighted how local conditions and MNE characteristics may
affect the ability of domestic firms to benefit (or not) from the presence of foreign compa-
nies (Ernst and Kim, 2002;Perri and Peruffo, 2016). Productivity and knowledge spill-
overs are found to be more marked in economies with higher levels of development
(Crespo and Fontoura, 2007;Meyer and Sinani, 2009), whereas the picture is more mixed
for transition and developing economies (Go¨rg and Greenaway, 2004;Bitzer et al., 2008;
Javorcik, 2013). This relation between local development and MNE spillovers depends,
however, on more fundamental factors, affecting the ability of domestic firms to benefit
from MNE presence (Fu et al., 2011).
2 MNEs may influence industrial employment both directly (e.g. through demand effects, attracting and forming
skilled labour, etc.) and indirectly (e.g. stimulating firm entry, innovation, etc.). Given our empirical approach
and the nature of our data, we cannot disentangle these effects more specifically.
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Among the factors, ranging from institutional and social features (Cipollina et al., 2012;
Karreman et al., 2017) to MNE characteristics (Beugelsdijk et al., 2008;Neto et al.,
2008), which mediate MNE presence and its effects, one of the most relevant is the
technological gap between local firms and multinationals. In these respects, whereas
larger differences in terms of technological endowment between domestic and foreign
firms entail greater room for learning, a larger gap also entails greater investment and risk,
making the assimilation of insights, processes and technologies more difficult (Kokko,
1994;Boschma, 2005;Javorcik et al., 2018). Relatedness in terms of co-occurring sectoral
specialisations, shared labour inputs and skills potentially facilitates the assimilation
process.
A second critical factor mediating externalities from foreign MNEs and domestic per-
formance is ‘absorptive capacity’ (Narula and Dunning, 2000;Blomstro¨m and Kokko,
2003), conceptualised as the stock of prior knowledge (Cohen and Levinthal, 1990). The
fact that firms with stronger absorptive capacity have a greater potential to benefit from
MNE spillovers suggests that certain industries might have a greater potential to benefit
from MNE activities. Given the greater knowledge intensity of advanced industries, both
theory and empirics suggest that MNE have stronger positive impacts in more knowledge-
intensive sectors (Crespo and Fontoura, 2007;Fu et al., 2011). Conversely, in low-
knowledge-intensive sectors competition effects from MNEs might prevail over learning
generating a negative effect on domestic activity and employment.
3. Research setting
The literature on multinational corporations and their effects on the local economy have
witnessed an upsurge in recent years. However, as highlighted in the critical review of the
existing evidence, some significant knowledge gaps still exist.
First, existing research has devoted limited attention to the local domestic employment
consequences of MNE entry. From a conceptual standpoint, MNEs generate employment-
enhancing opportunities for domestic firms but they also increase competitive pressures
(on both the product and the factor markets) and tend to boost capital deepening and prod-
uctivity at the expenses of local jobs. The overall net balance in terms of employment in
domestic firms has remained under-explored.
Secondly, theoretical and empirical research suggests the existence of both intra- and
inter-sectoral spillovers. Existing evidence suggests that the former are weaker given that
MNE actively limits knowledge leakages to potential competitors. The latter type of
effects—referring to spillovers spanning across industrial sectors—are instead associated
with knowledge diffusion along the value and supply chain and have found strong empir-
ical support. However, as the majority of research has concentrated on input–output rela-
tions as channels for spillovers, the broader linkages related to industrial proximity have
been overlooked. Differently from previous contributions, we argue that insights, technolo-
gies and workers from MNE can also flow to industries that are not connected via vertical
linkages but similar in terms of productive processes, skills, competences and knowledge
assets.
Thirdly, because such industrial relatedness co-evolves with sectoral diversity more nat-
urally than with specialisation, beneficial effects are expected for employment (related to
early-stage product innovation) rather than for productivity (due to later-stage process in-
novation; Abernathy and Clark, 1985). In other words, similar to the mechanisms behind
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related variety (Frenken et al., 2007), relatedness-mediated spillovers are likely to mostly
impact the level of employment. Even though this reasoning strongly resonates with the
traditional arguments on agglomeration economies, only a very limited literature (and
none on an EU-wide scale) considered the role of industrial relatedness in MNE spillovers
and its potential employment effects.
Fourthly, the effects of MNEs on domestic firms are mediated and influenced by local
characteristics (Ernst and Kim, 2002;Go¨rg and Greenaway, 2004;Meyer and Sinani,
2009). The characteristics of the local labour markets in which both MNEs and domestic
firms operate—in terms of human capital, knowledge or institutional conditions—shape
the nature and magnitude of the employment effects. Local economic conditions affect
both the creation of new jobs in response to job-enhancing shocks due to MNE entry and
the capability of the local economy to absorb job losses (due to job-adverse effects of in-
coming MNEs). Given the considerable differences in terms of sectoral composition, in-
dustrial sophistication and overall level of development across European regions (Annoni
et al., 2017), our work aims at disentangling the heterogeneity of effects of foreign compa-
nies on the domestic regional economy.
Based on these considerations, we develop four hypotheses on the employment effects
of multinational corporations on industries in European regions. In our baseline models,
we want to study the intra-industry role of MNE presence on local sectoral employment.
Whereas it is difficult to formulate a priori expectations given the ambiguity in previous
contributions, we envisage sectors with higher presence of foreign companies may perform
better due to intra-industries externalities.
Hypothesis 1: The level of employment in a given sector and region is positively related to the
presence of MNEs in the same sector–region.
As argued in the previous sections, the main focus of this article is on industrial related-
ness and its ability to mediate MNE spillovers across sectors, shaping employment levels.
Combining the literature on inter-industry MNE spillovers, diversity externalities (Jacob,
1969; Glaeser et al., 1992;Frenken et al., 2007) and relatedness (Boschma, 2005;Hidalgo
et al., 2007), in Hypothesis 2, we theorise that knowledge spillovers from foreign compa-
nies affect employment in sectors related to that of the MNE.
Hypothesis 2: The level of employment in a given sector and region is positively related to the
presence of MNEs in related industries in the same region.
Our final two hypotheses deal with regional and industrial heterogeneity in our sample.
Knowledge assets and absorptive capacity are necessary for benefitting from foreign com-
panies (Go¨rg and Greenaway, 2004;Crespo and Fontoura, 2007;Fu et al., 2011). Against
this background, we expect that relations to MNEs, both within the same industry and in
related sectors, will have a stronger effect in more knowledge-intensive industries, as they
are better equipped in terms of human capital and R&D resources and therefore in a stron-
ger position to benefit in terms of employment from MNE presence.
Hypothesis 3: The effects of MNE presence on employment, both within-industry and across-
industry, are stronger for knowledge-intensive industries in the target region.
Finally, the effects of MNEs have been shown to depend on the level of development of
the target area, with firms in less-developed regions benefitting more from foreign compa-
nies (Crespo and Fontoura, 2007;Javorcik, 2013). Whereas our sample gathers relatively
developed economies, significant regional differences persist in the EU, with Southern
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(less growing) and Central Eastern European regions (less developed) being on average
less prosperous than Western ones. On these bases, we hypothesise that:
Hypothesis 4: The effects of MNE presence on employment, both within-industry and across-
industry, are heterogenous depending on the level of development of the target region.
4. Models, methods and data
4.1. Modelling framework
This article studies short-term effects of MNE presence, both within the same industries
and in related ones, on employment in the sectors within regions. The empirical investiga-
tion of this relationship poses a number of challenges from an econometric point of view,
both in terms of capturing the effects on related industries, and due to endogeneity and re-
verse causality. In this section, we discuss our modelling choices, providing more details
on endogeneity while discussing the econometric application of this article.
In Model 1, employment in each sector–region is modelled as a function of the number
of MNEs active in the region/sector in the previous year as specified in Equation (1).
yi;r;t¼ai;rþstþdMNEnumi;r;t1þknoMNEi;r;t1þcControli:r;t1þei;r;t;(1)
where yi;r;tstands for the level of employment (in logs) in sector i, in region rat time t,
MNE represents the log count of MNE
3
at time t1, while noMNE is a dummy variable
with value 1 when no foreign company is present in sector i, in region rat time t1.
Our model includes also control variables (Control) as well as sector–region (ai;r) and
yearly (st) fixed effects. Along with sector–region and yearly fixed effects, we thus control
for within-region dependence in the error terms and potential heteroscedasticity by using
robust and regionally clustered errors.
Testing for Hypothesis 2, requires an extension of the baseline model discussed above, so
to include the terms for capturing MNE presence in related industries. In the case of Model
2, the variable MNEnum is interacted with the proximity matrix Wto generate MNEnum rel.
This matrix, as explained in the following sections, captures industrial proximity between
industries based on the co-occurrence of pairwise sectoral specialisation.
yi;r;t¼ai;rþstþdMNEnumi;r;t1þqMNEnum reli;r;t1þknoMNE i;r;t1
þcControli:r;t1þei;r;t;(2)
Finally, we test for Hypotheses 3 and 4 by splitting the sample according to different
types of sectors and regions. In other words, the same models will be estimated separately
for advanced manufacturing industries,
4
knowledge-intensive services and low-knowledge
sectors, as well as for more prosperous EU regions and for less-developed EU regions.
4.2. Methodology
This article aims to test whether MNE regional employment effects are perceived across
industries, based on a measure of pairwise industrial proximity.
3 As discussed more thoroughly in the section on data and in Appendix D, our dataset captures the presence of for-
eign firms, both via M&A and greenfield foreign direct investments.
4 See Appendix B for details on the subdivision of sectors and regions in different categories.
MNEs, relatedness and employment 9
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To do so, we apply the concept of relatedness proposed by Hidalgo et al. (2007), fol-
lowing a method proposed by Van Eck and Waltman (2009) and refined by Steijn (2016).
These methods allow us to create a measure of similarity across industries at the two-digit
of NACE classification. To perform these calculations, we used data on sectoral employ-
ment in 2006 from the Bureau Van Dijk Orbis database (cf. Variables and Data in
Appendix D). Since our analysis will focus on the period 2008–2013, we choose to use
only data from 2006 in order to reduce possible endogeneity.
Following Hidalgo et al. (2007), we start by defining the sectors in which each region
is specialized. We consider region rto be specialised in sector iwhen it’s location quotient
for that sector is larger than 1. In more formal terms:
LQir ¼Eir=Er
Ei=E
;(3)
and
xi;r¼
1;if LQir >1
0;otherwise
8
<
:
(4)
Once the sectoral measure of specialisation is computed, we count how many regions
are jointly specialised in sectors iand j. We then consider iand jrelated if the two indus-
tries tend to systematically co-locate. Our measure of relatedness is calculated as the ratio
between the observed co-occurrences and a random benchmark (Van Eck and Waltman,
2009).
5
Equation 5 represents formally the computation performed:
uij ¼cij
Si
T
Sj
TSi
þSj
T
Si
TSj
T
2
;(5)
where cij is the co-occurrence count of specialisations in sectors iand j,Siand Sjare the
total number of occurrences of iand j, respectively, and Tis the total number of occur-
rences of any sector. In the equation, the nominator is equal to the number of times (i.e.
in how many regions) specialisations in iand joccur together, while the denominator
computes the number of co-occurrences under the assumption of the iand jare
independent.
The result of Equation 5 is a nnWmatrix, with nbeing the number of sectors in our
sample. Each cell in Wcontains the relatedness score between two sectors, with each
value ranging between 0 and infinity and taking value 1 when the expected number of co-
occurrences is the same as expected under the random scenario. In order to capture the
effects of strong relatedness across sectors, we exclude cells in the main diagonal of W
and we set to 0 the cells with relatedness less or equal to 1 (i.e. pairs of industries which
occur less or as frequently as at random). Finally, we rescale the values of the matrix to
make them range between 0 and 1. Simply multiplying the relatedness matrix Wand the
5 Appendix E discusses and motivates in more detail our choice vis-a-vis possible alternative options (e.g. standard
Hidalgo relatedness, Hidalgo relatedness based on bootstrapping procedure). In Appendix E, we also show that
our results are consistent across several specifications of the relatedness matrix.
10 Cortinovis et al.
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sectoral vectors of MNEnum in each region, we generate the variable MNEnum rel, capturing
relatedness-mediated effects of MNE presence. More formally:
MNEnum reli;r;t¼X
j6¼i
ui;jMNEnum j;r;t:(6)
The main intuition behind the construction of this indicator is the same as the one used
in spatial econometrics for computing spatial lag variables (LeSage, 2014), with our
weights capturing proximity in the industrial space rather than geographical distance. In
other words, MNEnum reli;r;tcaptures how exposed sector i, in region rat time tis to
MNEs in related industries. Intuitively, the more exposed the sector is, the larger the chan-
ces of spillover effects from MNEs. While different in the method, this approach is con-
ceptually close to the work of Cicerone et al. (2019 on Italian provinces.
In Figure 1, we give a direct representation of the relatedness measure captured by W.
In Figure 1, each node represents one of the 68 industries we collected data for, and the
position relative to the other nodes is based on the pairwise relatedness scores (Hidalgo
et al., 2007). As shown in the legend, round nodes are low-knowledge industries (LKI),
whereas square nodes represent most advanced sectors. Each of the nodes is coloured
according to the first-digit NACE sector it belongs to. As Figure 1 clearly highlights, sec-
tors are not homogeneously related one to each other. Square nodes have sorted them-
selves in the bottom-left side of the graph, where the network relations appear to be
dense. This indicates that knowledge-intensive industries tend to be more closely related
with each other and less with medium- and lower-knowledge-intensive sectors.
Figure 1 thus gives some preliminary support to the idea that spillover effects may be
stronger within the knowledge-intensive part of the economy (to be tested with Hypothesis
3) compared to spillovers across sectors with various degrees of knowledge intensity. A
mirroring pattern emerges on the top-right part of the graph, where mostly low-
knowledge-intensive manufacturing industries locate. In spite of the fact that lower
knowledge-intensity of these industries may limit MNE externalities, also in this case, the
configuration suggests opportunities for cross-sectoral spillovers.
4.3. Variables and data
In order to construct our dataset, we resort to different data sources, namely Eurostat,
Cambridge Econometrics (CE) and Bureau Van Dijk (BVD). Table 1 reports the sources,
period and descriptive statistics of the variables (pairwise correlation among variables
reported in Table A3 in Appendix B). More details on the sectors and regions included in
this study are in Appendix B, while an overview on the data cleaning process for BVD
data is provided in Appendix D (Kalemli-Ozcan et al., 2015).
As shown in Table 1, we resort to official data for computing our dependent variable,
Empl (ln). The Structural Business Survey (SBS) of Eurostat provides information for 68
two-digit sectors on characteristics, among which the number of employees. Whereas most
of the literature focuses on (total factor) productivity as dependent variable (Javorcik,
2004;Altomonte and Pennings, 2009;Beugelsdijk et al., 2008), we argued that employ-
ment is appropriate for analysing innovative crossover opportunities between sectors in
diversified economies that are prone to spillovers from MNEs in the EU (Frenken et al.,
2007;Content and Frenken, 2016). Besides, the gap in the literature on the relation be-
tween MNEs and the local employment balance, the policy relevance of MNE employ-
ment effects in a context of economic turmoil and a wave of potential relocation of
MNEs, relatedness and employment 11
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international businesses following Brexit (Chen et al., 2017) makes the use of employment
as the dependent variable particularly interesting and relevant.
The main variables of interests in our article are three. As a measure of the presence of
MNE in a given sector, we use a count variable (in logs) for the number of foreign-owned
Figure 1. Network representation of relatedness.
Table 1. Descriptive statistics
Variables Source NMean SD Min Max
Empl (ln) Eurostat 92,309 7.729 1.822 0 13.13
MNE_num (ln) BVD 138,528 1.108 1.377 0 8.214
MNE_num_rel (ln) BVD 138,528 8.116 6.292 0 46.93
MNE_num_bl (ln) BVD 138,528 1.453 1.022 0 5.328
MNE_num_fl (ln) BVD 138,528 1.384 0.961 0 5.431
No_MNE (dummy) BVD 138,528 0.337 0.473 0 1
HK_tert Eurostat 136,960 0.122 0.0449 0.0366 0.328
TotR&D Eurostat 137,520 1.526 1.229 0.0600 11.36
GDP (ln) Eurostat 136,960 3.353 0.984 0.0751 6.242
Firm_num (ln) Eurostat 98,014 5.466 2.003 0 11.81
PhK (ln) CE 136,552 5.273 0.602 0.284 7.029
MNE_num_sp (ln) BVD 138,528 69.34 37.95 0 230.9
iv_b_nor_eu BVD 138,528 12.98 49.33 0 2436
rel_iv_b_nor_eu BVD 138,528 96.37 130.1 0.299 1555
dl_log_f10 BVD 69,264 1.048 1.339 0 8.070
dl_rel_log_f10 BVD 69,264 7.676 6.039 0 43.34
Period 2008–2013
12 Cortinovis et al.
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companies operating (MNE_num (ln) in Table 1).
6
As explained above, MNE_num_rel
reflects the interaction of MNE_num with the relatedness matrix W, and it captures the
effects due to the presence of foreign companies in related industries. Whereas no explicit
hypothesis applies to No_MNE,
7
the coefficient for this dummy variable can be considered
of interest because it captures the average effect of hosting no foreign company.
As mentioned in the presentation of Equations (1) and (2), our models include various
control variables. HK_tert and TotR&D control for the knowledge endowment of each re-
gion (Crespo and Fontoura, 2007;Fu et al., 2011): the former is computed as the share of
employees having obtained tertiary education over the working age population; the latter
is the percentage of total R&D expenditure over regional GDP. Similarly, we included the
level of GDP of the region (GDP (ln)) to control for the economic size of the region.
Whereas these three variables are measured at regional level, PhK is measured for the six
‘macro’ sectors available from CE. Finally, in order to control for local agglomeration
economies and spatial effects, we include two variables. For each two-digit NACE indus-
try, we include the log number of local units (Firm_num (ln)) to capture within-region ag-
glomeration effects. Besides, we capture possible cross-regional effects by including the
total number of MNEs (i.e. all MNEs across all industries) in the neighbouring regions
(MNE_num_sp (ln);Alfaro and Chen, 2014). Specifically, we compute the average of the
total number of MNEs in regions sharing a border with the focal region r(LeSage, 2014).
5. Econometric analysis
The results from our baseline models are reported in Tables 2 and 3. In the tables, the
heading of each column indicates whether the coefficients refer to the economy as a whole
(All), to low- LKI, to high-knowledge industries (HKI)
8
or to knowledge-intensive busi-
ness services (KIBS). KIBS are important knowledge-intensive facilitators of growth
(Jacobs et al., 2014;Content et al., 2019) as well as generators of high-quality high-value-
added jobs, which makes this group of industries interesting to focus on specifically.
The heading also specifies whether the estimates refer to the whole sample, more
advanced regions (with GDP per capita above the EU average in 2010) or less advanced
areas (with GDP per capita below the EU average in 2010). The estimates reported in
Table 2 confirm our Hypotheses 1, 3 and 4. More specifically, a high presence of foreign
companies at time t1 is associated with a high level of employment at time twithin
the same sector. The coefficients for the variable MNE_num are positive and significant
6 We opt for using the log count of MNEs rather than the share for two main reasons. First, using shares may in-
duce a downward bias in our estimates as suggested by Aitken and Harrison (1999) and discussed in Castellani
and Zanfei (2006). To the extent that domestic firms are more susceptible to economic downturns, it would be
likely to induce an increase in the share of MNEs (due to a lower denominator) with lower employment levels.
Secondly, previous contributions (Altomonte and Pennings, 2009) suggest the effect of MNEs is not linear and
that the effect of one additional MNE differs when moving from 0 to 1 MNEs than when moving from 100 to
101 MNEs. Log-transforming the variable helps accounting for such ‘diminishing returns’.
7 The log transformation of our variable of interest would imply that region-sector observations with 0 MNE
would get a missing value. After taking the logs we replace these missing values with 0, and create the No_MNE
dummy to identify ‘true zeros’ (those industry–region observations with 0 MNE) from the cases of region–sec-
tors with only 1 MNE (which become 0 once we log-transform them). We consider this a better approach to the
logþ1 strategy, which effectively creates a bias in the estimations. Our results are nonetheless consistent when
we use either logþ1, share of MNEs over total firms, share of MNE employment over total employment as meas-
ures of exposure to multinationals. For sake of brevity, the results using these alternative approaches are available
on request to the authors.
8 HKI include both more advanced manufacturing and knowledge-intensive business services.
MNEs, relatedness and employment 13
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Table 2. Model 1—intra-industry effects of MNE presence
Whole sample LKI HKI KIBS
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Variables Employment
—All
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
MNE_num (ln) 0.0427*** 0.0266** 0.0149 0.0325* 0.0744*** 0.0446** 0.0935*** 0.0736*** 0.0435* 0.0940***
(0.0113) (0.0113) (0.0134) (0.0170) (0.0164) (0.0199) (0.0234) (0.0177) (0.0232) (0.0253)
No_MNE
(dummy)
0.0127 0.00301 0.0163 0.0174 0.0259* 0.0233 0.0257 0.0162 0.0121 0.0208
(0.0130) (0.0199) (0.0247) (0.0291) (0.0143) (0.0247) (0.0177) (0.0169) (0.0288) (0.0209)
HK_tert 0.952*** 1.063*** 0.866** 1.311** 0.947** 0.861* 0.960 1.096** 1.227** 0.926
(0.340) (0.374) (0.408) (0.608) (0.458) (0.485) (0.760) (0.523) (0.556) (0.912)
TotR&D 0.0107 0.00833 0.0149 0.00159 0.0130 0.0319** 0.0115 0.00511 0.0191* 0.0128
(0.00915) (0.00869) (0.0107) (0.0105) (0.0121) (0.0132) (0.0158) (0.0112) (0.00988) (0.0189)
GDP (ln) 0.393*** 0.424*** 0.485*** 0.392** 0.278 0.461*** 0.212 0.258 0.506** 0.155
(0.137) (0.135) (0.133) (0.166) (0.173) (0.162) (0.218) (0.159) (0.213) (0.204)
PhK (ln) 0.0763*** 0.0484** 0.0768* 0.0345 0.128*** 0.0919** 0.133*** 0.173*** 0.0974** 0.191***
(0.0198) (0.0220) (0.0415) (0.0273) (0.0325) (0.0356) (0.0447) (0.0448) (0.0481) (0.0631)
Firm_num (ln) 0.0940*** 0.0748*** 0.0560*** 0.0964*** 0.138*** 0.0950*** 0.180*** 0.169*** 0.128*** 0.192***
(0.00800) (0.00765) (0.00745) (0.0127) (0.0133) (0.00903) (0.0180) (0.0194) (0.0184) (0.0238)
MNE_num_
sp (ln)
0.00419*** 0.00210*** 0.00368*** 0.00108 0.0114*** 0.0132*** 0.00915*** 0.0106*** 0.0127*** 0.00876***
(0.000742) (0.000782) (0.00106) (0.00109) (0.00207) (0.00335) (0.00262) (0.00221) (0.00295) (0.00310)
Observations 75,547 46,501 18,535 27,966 29,046 12,235 16,811 20,732 8776 11,956
R
2
0.026 0.024 0.021 0.027 0.038 0.042 0.041 0.040 0.048 0.040
Number of id 15,515 9574 3770 5804 5941 2474 3467 4233 1773 2460
Sector_
region FE
Yes Yes Yes Yes Yes Yes Ye s Ye s Ye s Ye s
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Notes: Clustered standard errors in parentheses.
***p<0.01, **p<0.05, *p<0.1.
14 Cortinovis et al.
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Table 3. Model 2—Intra- and inter-industry effects of MNE presence
Whole sample LKI HKI KIBS
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Variables Employment
—All
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
MNE_num (ln) 0.0299*** 0.0193* 0.0187 0.0179 0.0566*** 0.0478** 0.0632*** 0.0577*** 0.0503** 0.0641***
(0.00898) (0.0103) (0.0133) (0.0151) (0.0134) (0.0201) (0.0181) (0.0151) (0.0233) (0.0201)
MNE_num_
rel (ln)
0.0248*** 0.0163** 0.00947 0.0326*** 0.0277** 0.00514 0.0495*** 0.0232** 0.0102 0.0462***
(0.00823) (0.00734) (0.00776) (0.00987) (0.0110) (0.00781) (0.0166) (0.0110) (0.00840) (0.0176)
No_MNE
(dummy)
0.0118 0.00232 0.0160 0.0157 0.0252* 0.0236 0.0259 0.0162 0.0126 0.0225
(0.0130) (0.0199) (0.0246) (0.0291) (0.0143) (0.0246) (0.0176) (0.0169) (0.0288) (0.0208)
HK_tert 0.823** 0.977*** 0.947** 1.250** 0.801* 0.895* 0.825 0.976* 1.297** 0.824
(0.345) (0.370) (0.393) (0.609) (0.464) (0.493) (0.760) (0.517) (0.568) (0.912)
TotR&D 0.0110 0.00861 0.0148 0.000335 0.0132 0.0320** 0.00880 0.00541 0.0194* 0.00939
(0.00906) (0.00869) (0.0107) (0.0106) (0.0119) (0.0133) (0.0158) (0.0108) (0.0101) (0.0188)
GDP (ln) 0.378*** 0.414*** 0.485*** 0.374** 0.264 0.458*** 0.181 0.243 0.498** 0.120
(0.135) (0.134) (0.132) (0.163) (0.170) (0.163) (0.214) (0.158) (0.215) (0.205)
PhK (ln) 0.0753*** 0.0481** 0.0773* 0.0378 0.126*** 0.0913** 0.133*** 0.173*** 0.0940* 0.188***
(0.0196) (0.0222) (0.0416) (0.0280) (0.0314) (0.0355) (0.0424) (0.0438) (0.0476) (0.0600)
Firm_num (ln) 0.0935*** 0.0746*** 0.0555*** 0.0940*** 0.137*** 0.0946*** 0.173*** 0.166*** 0.128*** 0.184***
(0.00760) (0.00760) (0.00756) (0.0127) (0.0123) (0.00926) (0.0149) (0.0179) (0.0184) (0.0202)
MNE_num_
sp (ln)
0.00423*** 0.00215*** 0.00364*** 0.00111 0.0114*** 0.0132*** 0.00919*** 0.0110*** 0.0125*** 0.00960***
(0.000740) (0.000782) (0.00106) (0.00109) (0.00207) (0.00335) (0.00260) (0.00217) (0.00296) (0.00303)
Observations 75,547 46,501 18,535 27,966 29,046 12,235 16,811 20,732 8776 11,956
R
2
0.027 0.024 0.021 0.028 0.040 0.042 0.044 0.041 0.048 0.043
Number of id 15,515 9574 3770 5804 5941 2474 3467 4233 1773 2460
Sector_
region FE
Yes Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s Ye s
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Notes: Clustered standard errors in parentheses.
***p<0.01, **p<0.05, *p<0.1.
MNEs, relatedness and employment 15
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across the different types of sectors. However, the size of the coefficients changes when
the analysis is performed across different groups of industries: the effect of MNEs on local
employment more than doubles when moving from less advanced industries (Tab le 2, column
2) to high-knowledge ones and knowledge-intensive services (Columns 5 and 8). As theorised
in Hypothesis 3, more knowledge-intensive parts of the economy are more strongly influenced
by the presence of foreign companies and are also more prone to expand their employment
levels in response to the new competitive and knowledge landscape. At the same time, sectors
that host no foreign company do not seem to do significantly worse than the others. The coef-
ficients for No_MNE are in fact negative, though only one of them is significantly different
from zero. With respect to Hypothesis 4 and regional heterogeneity, the results of the baseline
model suggest a stronger intra-industry effect of MNE in less advanced regions. Finally,
whereas different control variables did not produce significant coefficients, the levels of invest-
ments (PhK) and of sectoral level agglomerations (Firm_num) are both strongly associated
with higher regional employment rates, as expected.
We investigate the role of industrial relatedness as a mediator for MNE employment
effects in our last two models. Table 3 reports the estimated coefficients for Equation (2).
The estimates reported in the columns of Tab l e 3 highlight heterogeneity in the relation
between the presence of foreign companies and their employment effects on the hosting
economy. Hypothesis 1 finds further support, as MNE_num remains positive and significant
in most of the specifications. The differences in terms of the size of the coefficients between
more and less advanced EU regions and between more and less knowledge-intensive indus-
tries remain unchanged. The coefficients reported in Table 3 relative to the effect of MNE
presence in related industries also provide valuable insights: the number of foreign compa-
nies from related industries appears to significantly impact sectoral employment.
Remarkably, as for the results for MNE_num,alsoMNE_num_rel indicates a stronger effect
of MNE presence in related industries in the case of most knowledge-intensive sectors. In
line with Hypothesis 4, the effect of multinationals in related industries appears to be mostly
driven by less-advanced regions: the coefficients for MNE_num_rel arealwayspositivesig-
nificant except in the case of regions with above-average per capita income.
To summarise, our analysis aimed at studying the employment effects of MNE presence
within and across industries, as well as across different types of sectors and regions. As a
significant innovation compared to previous studies, we use industry pair co-occurrence re-
latedness rather than IO-relations as a framework of capturing spillovers. Our baseline
hypotheses find overall support. Both the intra-industry impact (Hypothesis 1) and inter-
industry effects (Hypothesis 2) of MNE appear to be positive, though with substantial differ-
ences across groups of industries and regions. Domestic firms in knowledge-intensive
industries show the stronger potential to benefit from the presence of MNEs. Stronger posi-
tive employment effects are also concentrated in less-developed regions where the potential
for learning is possibly higher and competition in the product market is lower given that both
domestic firms and MNEs might be serving different (distant) markets. Less sophisticated
local firms might be more oriented to the local markets while MNEs might target more the
export markets benefitting from the price advantage offered by cheap labour locations.
5.1. Instrumental variable estimations
Different methodological issues may be affecting the models and results previously dis-
cussed. A first concern reflects the fact that MNE location choices are endogenous, imply-
ing that the relations found in the previous models may be biased by reverse causality.
16 Cortinovis et al.
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Given the direct relation between MNE location choices and sectoral performance, this
problem is likely to be especially acute in the case of intra-industry effects.
Multinationals, with their location choices, self-select into region–industry pairs and their
choices will be based on considerations pertaining local economic performance and avail-
ability of critical resources, either in the form of infrastructure, human capital or other (in)-
tangible assets (see Crescenzi et al., 2014;Karreman et al., 2017). This implies that the
number of MNEs active in an industry–region might be driven by current (or projected)
performance including employment levels. The direction of the bias induced by this type
of endogeneity is unknown. If MNE location choices are driven by previous region–sector
performance, we would expect an upward bias in our coefficients, leading us to overstate
the effect of MNE spillovers on employment. Conversely, given that MNEs tend to be
more productive and innovative than local firms, they may rely less on local labour as an
input for production. For instance, if MNEs are more prone to automate their production
this would imply that our spillover measure and (unobservable) automation are positively
correlated. As a result, the negative correlation between an omitted variable (automation)
and the dependent variable (employment) would induce a downward bias in our results.
Even without any strong prior on the bias of our baseline results, we address endogeneity
concerns by constructing deep lags and a Bartik-type of instrumental variable (IV) and re-
estimate our models using two-stage panel data techniques.
The IV strategy leverages a shift-share Bartik instrument (Faggio and Overman, 2014;
Crescenzi et al., 2015). The aim of the instrument is to approximate the number of multi-
nationals present in each industry–region group, excluding the effect of characteristics that
may drive the location choices of MNEs. For this purpose, we compute the instrument for
the (log) number of MNEs as specified in Equation (7):
ivbnoreui;r;t¼numfirms2006i;r
Prnumfirms2006i;r
X
r
numMNEi;r;tnumMNEi;r;t
(7)
where irefers to the industry and rto the region. The instrument redistributes the total
number of MNEs (over the entire sample of EU regions) active in sector i(excluding
from the count MNEs in sector iin focal region r) according to the respective share of
firms in sector iin region rin 2006. Specifically, the first term of Equation (7) provides a
weight based on how many firms in industry iare located in region rin 2006. This weight
is interacted with the second term of Equation (7), which captures the time-varying num-
ber of MNEs in industry iacross Europe, excluding those from the focal region r.
Exploiting only the variation over time of the second term of our instrument drastically
reduces the concerns for using the potentially endogenous share of firms by sector in 2006
(the first term in Equation 7). Besides, as our estimates rely on within variation in the sec-
tor–region dimension, the first term of Equation (6) is unlikely to violate the exclusion re-
striction. Similarly, the exclusion of the number of MNEs in the region (the second term
in the second term in Equation 7) helps further addressing the problems with the exclusion
restriction (Faggio and Overman, 2014). To test directly whether our identification strategy
meets the exclusion restriction, we also use deeper lags to instrument for more recent val-
ues of our endogenous variables. Within the limits of our dataset, we maximise the time
gap in our deep lagging strategy using 4-years lagged variables as instruments: for in-
stance, the number of MNEs in 2010 was instrumented using the number of MNEs in
2006. Whereas this leads to a reduction in the number of observations included in our
MNEs, relatedness and employment 17
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models, using data before the 2009 crisis is useful to increase the potential exogeneity of
our instruments.
Estimating IV regressions with more than one endogenous variable is technically chal-
lenging and generally advised against (Angrist and Pischke, 2009). In our case, the num-
ber of potentially endogenous variables, the similarity of the instruments to be used and
the different industrial and regional dimensions cutting across our sample, make the IV es-
timation especially problematic. Considering these challenges, and the fact that reverse-
causality may be a problem especially for intra-industry effects, we focus our robustness
checks on endogeneity of the MNE_num (ln) and MNE_num_rel (ln) variables separately.
9
Tables 4 and 5report the estimates and the statistics referring to IV estimation. The coeffi-
cients for the first-stage regressions are reported in Appendix C.
Overall, the results shown in Table 4 provide more solid confirmation of the tentative
findings presented in the previous part of the analysis. The F-tests reported at the bottom
of Table 4 are mostly above the rule of thumb threshold of 10 (or reasonably close to it),
usually applied in the literature, thus indicating the validity of the chosen instrument.
Besides, the first two columns show that both our instruments are strongly relevant, which
allows us to correctly overidentify the 2SLS regressions: the column marked with ‘(BI)’
refers to the second stage using only the Bartik-type of instrument, while the column
marked with ‘(DL)’ uses the deep lagging approach. From columns 3–12 of Table 4,we
use both instruments and we test whether they meet the exclusion restriction. Throughout
our specifications, the Hansen J-test is consistently insignificant suggesting the validity of
our approach. In terms of the estimated coefficients, the second-stage coefficients are not
found to be significant in the whole sample and in the LKI. However, the effects of MNE
presence on employment in the same industry are positive significant for high-knowledge
sectors and KIBS and the pattern of sectoral heterogeneity in the effects matches the one
in Table 2, with HKI and KIBS presenting bigger and more significant coefficients than
LKI.
10
By comparing the coefficients in the 2SLS with the OLS regressions, the point
estimates are much stronger in our robustness checks, indicating our baseline results were
underestimated.
While trying to instrument for both endogenous variables at the same time strongly
curbs the power of our instruments (see Footnote 4), assuming MNE_num (ln) as exogen-
ous and instrumenting for MNE_num_rel (ln) offers a further confirmation to our results.
Also in the case of Table 5, the F-test for the excluded instruments is above rule of thumb
threshold and the Hansen J-test confirms the validity of our exclusion restriction. As
expected from our baseline results, MNE spillovers mediated via relatedness have a sig-
nificant and positive impact on sectoral employment both in the whole sample (column 3
of Table 5) and in more knowledge-intensive industries (columns 7 and 9 of Table 5).
9 We tried also adopting a similar strategy for instrumenting for the number of MNEs in related industries, by
interacting the instrument iv_b_nor_eu with the previously computed relatedness matrix (Javorcik et al., 2018).
Whereas the IV estimations appear to work solidly for Model 1, which is not the case for Model 2: once both
endogenous variables are included, the instruments do not perform as good.
10 The main difference with the baseline results is the lack of significance in the coefficient concerning LKI indus-
tries. This may suggest some selection issues which were not duly taken into account in our OLS estimates.
Aspects like the introduction of policy interventions for fostering employment (e.g. the European Globalization
Adjustment Fund) or industry- and location-specific MNEs attraction schemes (Crescenzi et al. 2019) are pos-
sible factors confounding OLS estimates, leading to a significant OLS coefficient. Our IVs exclude these factors
(either by taking long lags or by directly excluding region–industry characteristics in the case of the Bartik
instruments), therefore, the 2SLS coefficients for LKI are not as significant as before.
18 Cortinovis et al.
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Table 4. Model 2 (IV)—intra-industry effects of MNE presence
Whole Sample LKI HKI KIBS
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Variables Employment
—All (BI)
Employment
—All (DL)
Employment
—All
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
MNE_num (ln) 0.189 0.229 0.242 0.0770 0.218 0.118 0.949*** 0.751** 1.198*** 0.936*** 0.551 1.201***
(0.131) (0.171) (0.160) (0.161) (0.290) (0.198) (0.279) (0.299) (0.345) (0.294) (0.343) (0.345)
No_MNE (dummy) 0.00380 0.0284* 0.0291* 0.0302 0.0432 0.0313 0.0544** 0.0793** 0.0244 0.0503* 0.0723* 0.0261
(0.0147) (0.0172) (0.0169) (0.0245) (0.0484) (0.0237) (0.0261) (0.0392) (0.0288) (0.0276) (0.0393) (0.0342)
Neigh. MNEs (ln) 0.00307*** 0.00162 0.00157 0.00172 0.00307 0.000364 0.00396 0.00688* 0.000598 0.00435 0.00941** 0.00117
(0.00119) (0.00126) (0.00126) (0.00176) (0.00310) (0.00230) (0.00310) (0.00383) (0.00439) (0.00354) (0.00402) (0.00497)
Observations 75,506 46,071 46,071 28,390 11,173 17,217 17,681 7,364 10,317 12,617 5280 7337
R
2
0.019 0.007 0.009 0.009 0.015 0.010 0.252 0.284 0.291 0.262 0.158 0.303
Number of reg_ind 15,474 15,428 15,428 9511 3747 5764 5917 2465 3452 4217 1765 2452
Sector_region FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Control vars. Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Flogf10 13.21*** 23.44*** 18.54*** 19.47*** 7.391*** 14.23*** 14.22*** 7.620*** 10.21*** 13.68*** 6.342*** 9.557***
Flogf10 p-val 0.000335 2.20e-06 2.97e-08 1.31e-08 0.000989 2.14e-06 1.37e-06 0.000809 6.84e-05 2.24e-06 0.00250 0.000122
Hansen J0 0 0.0248 0.0107 0.453 2.330 0.0822 0.0223 0.103 0.119 0.239 0.303
Hansen p-val 0 0 0.875 0.917 0.501 0.127 0.774 0.881 0.748 0.730 0.625 0.582
Notes: Clustered standard errors in parentheses.
***p<0.01, **p<0.05, *p<0.1.
MNEs, relatedness and employment 19
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Table 5. Model 2 (IV)—inter-industry effects of MNE presence
Whole Sample LKI HKI KIBS
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Variables Employment
—All (BI)
Employment
—All (DL)
Employment
—All
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
Employment Employment
—above
av. Reg.
Employment
—below
av. Reg.
MNE_num (ln) 0.0458*** 0.00394 0.00274 0.00304 0.0223 0.00612 0.0193 0.00780 0.0498** 0.0108 0.0208 0.0412
(0.0156) (0.0163) (0.0161) (0.0225) (0.0165) (0.0372) (0.0183) (0.0276) (0.0232) (0.0222) (0.0360) (0.0270)
MNE_num_rel (ln) 0.00595 0.0315 0.0342* 0.0153 0.0377 0.0126 0.0414* 0.00527 0.0573* 0.0321 0.0199 0.0468
(0.0205) (0.0211) (0.0203) (0.0248) (0.0295) (0.0341) (0.0227) (0.0311) (0.0299) (0.0237) (0.0373) (0.0294)
No_MNE (dummy) 0.0130 0.0186 0.0188 0.0269 0.0294 0.0269 0.00795 0.0234 0.0109 0.0149 0.0445 0.0140
(0.0129) (0.0144) (0.0144) (0.0222) (0.0405) (0.0223) (0.0165) (0.0280) (0.0179) (0.0200) (0.0343) (0.0208)
Neigh. MNEs (ln) 0.00417*** 0.00270** 0.00272** 0.00234* 0.00551*** 0.000294 0.00259 0.00289 0.00129 0.00221 0.00515* 0.00120
(0.000741) (0.00123) (0.00123) (0.00141) (0.00198) (0.00194) (0.00264) (0.00298) (0.00376) (0.00295) (0.00311) (0.00428)
Observations 75,506 46,071 46,071 28,390 11,173 17,217 17,681 7,364 10,317 12,617 5280 7337
R
2
0.026 0.007 0.006 0.011 0.007 0.014 0.002 0.010 0.005 0.001 0.017 0.002
Number of reg_ind 15,474 15,428 15,428 9511 3747 5764 5917 2465 3452 4217 1765 2452
Sector_region FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Control vars. Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Frel 27.61*** 87.73*** 54.01*** 46.94*** 17.30*** 27.60*** 50.44*** 19.21*** 30.22*** 42.90*** 13.46*** 27.58***
Frel p-val 3.08e-07 0 0 0 3.15e-07 5.68e-11 0 7.58e-08 0 0 6.17e-06 5.78e-11
Hansen J0 0 0.276 0.244 0.630 0.0677 0.0206 0.860 0.135 0.00798 1.183 0.326
Hansen p-val 0 0 0.600 0.621 0.428 0.795 0.886 0.354 0.713 0.929 0.277 0.568
Notes: Clustered standard errors in parentheses.
***p<0.01, **p<0.05, *p<0.
20 Cortinovis et al.
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Also in this case, the significance and magnitude of the results are well in line with the
results reported in Table 3. By comparing the results in Tables 4 and 6, we conclude there
is a downward bias in the baseline regressions, though much less sizeable than in the case
of Model 1.
5.2. Robustness check on domestic employment
A second potentially problematic aspect in our estimation relates to the fact that the pres-
ence of multinationals may, by itself, induce a positive effect on employment within the
same sector–region. As multinationals tend to be larger in terms of employment, it cannot
be excluded that their presence may by construction lead to a higher level of sectoral em-
ployment. Therefore, we test our results looking at non-MNE employment in a subsample
of industry–regions.
With respect to the second issue (non-MNE employment), we perform the same analysis
as in Table 3, this time looking only at employment in non-multinational firms. To imple-
ment this robustness check, we use information from Orbis to compute the level of em-
ployment in each industry–region accruing to firms that are not foreign owned. Because
of the low reliability of information for certain countries (Kalemli-Ozcan et al., 2015) and
due to the missing information on firm-level employment, we restrict the sample consid-
ered in our robustness check, selecting only regions in countries for which the minimum
correlation between employment data in Orbis and Eurostat SBS is at least 70%.
11
Having selected only countries with highly reliable data, we compute the (log) number
of employees in domestically owned firms and re-estimate Models 1 and 2 once again.
Both models are also estimated for the HKI, LKI and KIBS industries, whereas we do not
group the regions along the per capita income categories due to the reduced heterogeneity
in the sample for this robustness check.
Tables 6 and 7reproduce the results for the robustness checks on non-MNE employ-
ment. The estimates on the reduced sample highlight positive significant relations between
MNE_num and MNE_num_rel, from the one hand, and non-MNE employment on the
other hand.
All in all, our robustness checks provide a general confirmation of our main findings.
Our IV strategy, based on deep lags and a Bartik-type of instrument, confirms the exist-
ence of positive intra-industry spillovers, as well as their stronger effects in the case of
more knowledge-intensive industries. Whereas we are not able to apply the same IV
method simultaneously including within industry and relatedness mediated spillovers, we
test the validity of our results instrumenting for MNE_num_rel (ln) alone. The results from
this robustness check are in line with those obtained in our baseline regressions, suggest-
ing that relatedness-mediated spillovers positively impact local sectoral employment.
Hypotheses 3 and 4 theorise a stronger effect of MNEs for advanced industries and heter-
ogenous effects across different levels of local development. Hypothesis 3 proves to be ac-
curate. High-knowledge sectors and knowledge-intensive services consistently show higher
and more significant coefficients for MNE presence, both within and across industries.
Results are less clear-cut when investigating regional heterogeneity. From our standard
11 This implies that if even for only one sector in one region, a country has correlation lower than 70%, it will not
be included in the analysis. Finally, region–industries within the following 19 countries are included in the ro-
bustness check: Austria, Belgium, Bulgaria, Czech Republic, Denmark, Finland, France, Hungary, Lithuania,
Latvia, Luxembourg, Norway, Poland, Portugal, Romania, Slovenia, Slovakia, Spain and Swede.
MNEs, relatedness and employment 21
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panel results, less advanced EU regions seem to benefit more than other areas from the
presence of foreign companies (Hypothesis 4).
6. Conclusions
The cross-sectoral effect of MNEs and the existence of preconditions for the local econ-
omy to benefit from foreign companies are nowadays well-established facts. The aim of
this article is to explore the significantly under-researched link between MNEs activities
Table 7. Model 2—intra- and inter-industry effects of MNE presence on non-MNE employment
Whole sample LKI HKI KIBS
Variables Non-MNE
employment—All
Non-MNE
employment
Non-MNE
employment
Non-MNE
employment
MNE_num (ln) 0.0466** 0.0394* 0.0584* 0.0746**
(0.0194) (0.0234) (0.0299) (0.0331)
MNE_num_rel (ln) 0.0544*** 0.0490*** 0.0608*** 0.0499***
(0.0121) (0.0146) (0.0136) (0.0146)
No_MNE (dummy) 0.00440 0.0146 0.0100 0.00555
(0.0250) (0.0311) (0.0423) (0.0500)
Observations 26,980 16,895 10,085 7165
R
2
0.049 0.056 0.044 0.048
Number of id 5426 3403 2023 1438
Sector_region FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
Control variables Yes Yes Yes Yes
Notes: Clustered standard errors in parentheses.
***p<0.01, **p<0.05, *p<0.1.
Table 6. Model 1—intra-industry effects of MNE presence on non-MNE employment
Whole sample LKI HKI KIBS
Variables Non-MNE
employment—All
Non-MNE
employment
Non-MNE
employment
Non-MNE
employment
MNE_num (ln) 0.0863*** 0.0714*** 0.110*** 0.120***
(0.0258) (0.0263) (0.0374) (0.0396)
No_MNE (dummy) 0.00190 0.0125 0.0136 0.0117
(0.0248) (0.0308) (0.0430) (0.0505)
Observations 26,980 16,895 10,085 7165
R
2
0.041 0.049 0.033 0.040
Number of id 5426 3403 2023 1438
Sector_region FE Ye s Yes Yes Yes
Year FE Yes Yes Yes Yes
Control variables Yes Yes Yes Yes
Notes: Clustered standard errors in parentheses.
***p<0.01, **p<0.05, *p<0.1.
22 Cortinovis et al.
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and local employment at the EU-level from the perspective of industrial relatedness.
Therefore, the innovative contribution of the article is 3-fold.
First, the article has unveiled a positive link between MNE presence and local employ-
ment, suggesting that internationalisation through inward FDI is a positive-sum game in
Europe whereby jobs created outnumber jobs losses.
Secondly, the article offers new insights on the cross-sectoral dimension of MNE em-
ployment effects, showing that these are not limited to vertical input–output linkages
(Javorcik, 2004;Alvarez and Lopez, 2008;Liang, 2017;Javorcik et al., 2018). One of the
main contributions of this article is its original use of pairwise industrial relatedness,
through which we capture how knowledge, technologies and assets available locally in a
region influence firms’ opportunities of knowledge and skill recombination. In our frame-
work, the more two sectors are related, the easier it is for firms to re-deploy their assets,
acquire new capabilities, and change, upgrade or expand their portfolio of products and
therefore expand employment. In this sense, we argue that our relatedness measure cap-
tures industrial proximity in a broader sense (Boschma, 2005) than simple input–output
relations. Skill relatedness (Neffke and Henning, 2013) and technological and cognitive
similarities (Cortinovis et al., 2017;Farinha-Fernandes et al., 2019) in products as well as
processes are likely to be the main drivers behind industrial relatedness. The positive and
robust results obtained by our relatedness-based measure provide a confirmation that in-
put–output relations do not function in isolation as channels through which MNEs influ-
ence employment in their host economies.
Thirdly, the article disentangles the heterogeneous employment effects of MNEs by link-
ing them to sectoral and regional differences in Europe (Crescenzi and Iammarino, 2017).
Our results suggest that these sources of heterogeneity have to be adequately taken into ac-
count in order to better grasp the mechanics of MNE employment effects. We show that
within- and cross-sector linkages to foreign company are particularly important for
knowledge-intensive industries and for low-income regions in Europe. Our results indicate
that regions hosting knowledge-intensive industries and characterised by lower factor prices
are likely to benefit the most from the presence of multinationals in terms of employment.
In other words, we confirm the economic geography insights that economies in low-income
European regions have much to gain from MNE’s (Defever, 2006;Crespo and Fontoura,
2007;Javorcik, 2013,Elekes and Legyel, 2016) but we show that this is especially true in
higher knowledge-intensive sectors within these economies. While MNEs can provide
knowledge that can be absorbed by local (knowledge-intensive) industries (Content et al.,
2019) by boosting their job-creation capabilities, building capacity for further absorption is
an important precondition to achieve better overall economic performance and higher em-
ployment levels in the periphery of Europe (Cortinovis and van Oort, 2019).
These results offer a number of key insights to policymakers in Europe and beyond. In
terms of place-based development strategies and policies in European regions, interre-
gional MNE (but also trade and knowledge) network dependencies are an underexplored
yet important aspect (Thissen et al., 2013). Apart from productivity-enhancing effects, the
attraction of MNEs can contribute to regional job creation, especially in a relatedness
framework. Whereas local and industrial conditions appear to be important, place-based
policy tools such as investment promotion agencies (studied by Crescenzi et al., 2019) can
significantly stimulate local employment creation through internationalisation. Importantly,
our contribution also suggests that conditional on the local industrial portfolio, regional
economies can leverage MNE effects in more encompassing ways than suggested by
MNEs, relatedness and employment 23
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previous literature, thus facilitating sectoral revival and employment creation across the in-
dustrial space.
Intra- and inter-industry relatedness (in terms of co-occurring sectoral specialisations,
shared labour inputs, IO relations and skill relatedness) facilitates the assimilation of
MNEs in regional economic development, broadens the scope of regional development
policies and connects our results with an emerging body of research on the regional
impacts of Global Value Chain connectivity (Crescenzi et al., 2018). Building on the no-
tion that innovation and knowledge transfer stem from the recombination and cross-over
capabilities of existing and complementary knowledge and technologies (Frenken et al.,
2007;Enkel et al., 2009), (regional) policy tools targeting (intra- and inter-sectoral) know-
ledge transfer mechanisms have the potential to enhance the MNE-employment nexus.
Policies on labour mobility, education, technology transfer, public–private partnerships, tax
exemptions and intellectual property rights are examples of this (Borras and Edquist,
2013) and they should be carefully assessed on their effectiveness in relation to the em-
ployment generation capacities of MNEs in own and related industries.
Nonetheless, some limitations apply to our study and its results. The lack of more dis-
aggregated data forced us to perform the relatedness analysis based on 68 two-digit
NACE sectors. This implies at least two caveats. First, our intra-regional spillovers are by
definition rather broad, potentially encompassing what other researchers have been able to
capture as cross-sectoral linkages. Although this represents a limitation for this study, it
also highlights the possible drawbacks in the use of input–output relations as channels for
knowledge spillovers. If some effects of relatedness are found across relatively broadly
defined sectors, even stronger results can be expected using relatedness in more disaggre-
gated settings. Secondly, in this article, we account for endogeneity issues as much as pos-
sible by using an IV estimation. Besides, the use of sector–region fixed effects and
different types of control variables further reduces concerns for omitted variable bias.
However further methodological refinements in order to fully capture causal effects de-
serve attention in future work (e.g. following Javorcik et al., 2018).
Funding
The research leading to these results has received funding from the European Research
Council under the European Union Horizon 2020 Programme (H2020/2014-2020) (Grant
Agreement no. 639633-MASSIVE-ERC-2014-STG).
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Appendix
A. Relatedness and I-O matrix
One concern in relation to our definition of relatedness is that this measure would simply cap-
ture buyer–supplier relations. After all, Hidalgo et al. (2007, 482–483) directly refer to ‘the
inputs or outputs involved in a product’s value chain (e.g., cotton, yarn, cloth, and garments)’
as one of the factors potentially driving relatedness. To reduce the concerns of relatedness
being simply capturing input–output linkages, we compare the pairwise relatedness score we
obtained from Equation (5) with each of the national input–output matrices from the World
Input–Output Database for the same year (2006). A graphic representation of the relations
between the two measures of inter-industry linkages is reported in Appendix Figure A1.In
the graphs in Appendix Figure A1, each dot represents one of the 4624 pairs of sectors,
including the diagonal elements which are set to zero in both the relatedness and input–out-
put matrices. As the graphs show, there is no clear association between input–output intensity
and relatedness scores.
This is confirmed also by Table A1: the average correlation score across the different
countries is only 0.06, with the maximum correlation being 0.1. By summing the input-out-
put matrices across European countries, the sectoral pairwise correlation is 0.09. We consider
this evidence reassuring: considering that most part of the variation in relatedness is not
explained by input-output relations, we believe other approach genuinely captures forms of
similarities and drivers of co-location which are other than buyer-supplier linkages.
A.1. Relatedness and/or input–output linkages?
In spite of the relatively low correlation between the relatedness and input–output weights,
the concern that such our relatedness-weighted measure may simply capture buyer–supplier
linkages along with cognitive and technological similarities between industries would re-
main. We explore this potential problem both graphically and using regression analysis.
Appendix Figure A2 shows, by country, the correlation between relatedness-mediated spill-
overs and spillovers through backward and forward linkages. In spite of the relatively low
correlation between relatedness and I-O table, the spillover measures appear to be correlated,
potentially affecting the validity of our study.
To fully address this concern, and as a further identification effort of our approach, we esti-
mate our baseline regression on industrial employment levels including the same measure of
MNE spillovers applied to backward and forward linkages matrices.
12
MNEnumbli;r;t¼X
j6¼i
ni;jMNEnumj;r;t:
Specifically, in Appendix Table A2, we gradually expand our specification by including
one at a time the variables capturing, respectively, spillovers mediated by backward linkages,
12 We compute backward and forward linkages following the standard approach in the literature, in which the in-
tensity of relations across industries is measured as share of total production of sector isupplied to sector j
(backward linkages) and the share of total purchase of sector iprovided by sector k(forward linkages). In the
case of backward linkages, with ni;jbeing the share of output of isupplied to sector j:
28 Cortinovis et al.
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Figure A1. Correlations between relatedness scores and input–output intensity by country.
MNEs, relatedness and employment 29
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forward linkages and relatedness. Interestingly, we see that MNE presence is in general asso-
ciated with higher levels of employment, with backward linkages appearing to dominate over
forward linkages. Whereas obviously these coefficients do not capture causal effects, it is
interesting to notice that our results are in line with those on productivity-enhancing MNE
spillovers (Javorcik, 2004). With respect to relatedness, once we include the measure of
relatedness-mediated spillovers, the coefficient for backward linkages turns insignificant
while the one for relatedness is positive and strongly significant. Whereas this result should
not be interpreted as the primacy of relatedness over buyer–supplier linkages, also in the light
of the high correlation among the three spillover variables (cf. correlations in Appendix
Table A3), it supports the idea that relatedness can capture employment spillovers in a more
encompassing way, thus confirming the potential of our approach. Since our aim is not to
methodologically establish the superiority of relatedness over backward linkages, and consid-
ering the likely overlap between the two spillover measures, the remainder of the analysis
will focus only on relatedness and the heterogenous impacts of MNE externalities across
industries and regions.
Table A1. Correlation between relatedness and national input–output
matrices
Country Correlation
Austria 0.07248859
Belgium 0.08934118
Bulgaria 0.04303621
Cyprus 0.02533528
Czech Republic 0.07352089
Denmark 0.08866313
Estonia 0.06133714
Finland 0.05600721
France 0.07532422
Germany 0.08431157
Greece 0.03813855
Hungary 0.06300398
Ireland 0.09564528
Italy 0.08906841
Latvia 0.03162895
Lithuania 0.05224235
Luxembourg 0.08319002
Malta 0.02225585
Netherlands 0.08941054
Norway 0.07974073
Polonia 0.05801462
Portugal 0.06386989
Romania 0.05993557
Slovakia 0.0556302
Slovenia 0.05888733
Spain 0.03488573
Sweden 0.10063751
United Kingdom 0.0939608
All EU 0.0908432
Average correlation 0.06569685
30 Cortinovis et al.
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Figure A2. Correlation scores by country.
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Table A2. I-O linkages, relatedness and sectoral employment levels
(1) (2) (3) (4) (5) (6)
Variables W WþBL WþFL WþBLþFL WþRL WþBLþFLþRL
MNE_num (ln) 0.0427*** 0.0333*** 0.0356*** 0.0331*** 0.0299*** 0.0300***
(0.0113) (0.00947) (0.00949) (0.00934) (0.00898) (0.00897)
MNE_num_bl (ln) 0.123** 0.110** 0.0120
(0.0485) (0.0485) (0.0555)
MNE_num_fl (ln) 0.0986** 0.0167 0.0255
(0.0457) (0.0434) (0.0419)
MNE_num_rel (ln) 0.0248*** 0.0263***
(0.00823) (0.00996)
No_MNE (dummy) 0.0127 0.0124 0.0123 0.0123 0.0118 0.0118
(0.0130) (0.0130) (0.0130) (0.0130) (0.0130) (0.0130)
HK_tert 0.952*** 0.879** 0.886*** 0.876** 0.823** 0.825**
(0.340) (0.340) (0.339) (0.340) (0.345) (0.345)
TotR&D 0.0107 0.0105 0.0106 0.0105 0.0110 0.0110
(0.00915) (0.00893) (0.00893) (0.00892) (0.00906) (0.00911)
GDP (ln) 0.393*** 0.378*** 0.387*** 0.379*** 0.378*** 0.377***
(0.137) (0.135) (0.136) (0.135) (0.135) (0.135)
PhK (ln) 0.0763*** 0.0756*** 0.0752*** 0.0755*** 0.0753*** 0.0754***
(0.0198) (0.0197) (0.0197) (0.0197) (0.0196) (0.0196)
Firm_num (ln) 0.0940*** 0.0934*** 0.0936*** 0.0934*** 0.0935*** 0.0936***
(0.00800) (0.00765) (0.00770) (0.00764) (0.00760) (0.00760)
MNE_num_sp (ln) 0.00419*** 0.00453*** 0.00434*** 0.00452*** 0.00423*** 0.00423***
(0.000742) (0.000751) (0.000744) (0.000750) (0.000740) (0.000749)
Observations 75,547 75,547 75,547 75,547 75,547 75,547
R
2
0.026 0.027 0.027 0.027 0.027 0.027
Number of reg_ind 15,515 15,515 15,515 15,515 15,515 15,515
Sector_region FE Yes Ye s Ye s Ye s Yes Yes
Year FE Yes Yes Yes Yes Yes Yes
R
2
0.0264 0.0270 0.0268 0.0270 0.0275 0.0275
Notes: Clustered standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.
32 Cortinovis et al.
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B. Further descriptives and data coverage
The table below (A4) provides an overview of the industries included in our sample. The
codes are the two-digit industrial codes of the NACE classification, based on the 2008 revi-
sion. These have been split in low knowledge and high-knowledge sectors based on the clas-
sification provided by Eurostat
13
.
Table A3. Correlation table
Variables 1 2345678910111213141516
Empl (ln) 11
MNE_num (ln) 20.6 1
MNE_num_rel (ln) 30.33 0.56 1
MNE_num_bl (ln) 40.35 0.55 0.89 1
MNE_num_fl (ln) 50.38 0.58 0.89 0.93 1
No_MNE (dummy) 60.5 0.57 0.38 0.39 0.41 1
HK_tert 70.09 0.17 0.29 0.32 0.26 0.13 1
TotR&D 80.12 0.13 0.21 0.24 0.21 0.14 0.46 1
GDP (ln) 90.37 0.31 0.51 0.54 0.52 0.27 0.41 0.44 1
Firm_num (ln) 10 0.79 0.55 0.24 0.19 0.26 0.39 0.02 0.04 0.24 1
PhK (ln) 11 0 0.01 0.01 0.05 0.02 0.05 0.19 0.11 0.17 0.1 1
MNE_num_sp (ln) 12 0.25 0.31 0 0.04 0 0.18 0.01 0.01 0 0.29 0.01 1
iv_b_nor_eu 13 0.31 0.48 0.21 0.19 0.2 0.17 0.09 0.05 0.19 0.35 0.02 0.29 1
rel_iv_b_nor_eu 14 0.29 0.35 0.67 0.54 0.55 0.21 0.28 0.15 0.56 0.29 0.01 0 0.25 1
dl_log_f10 15 0.6 0.98 0.55 0.54 0.57 0.54 0.18 0.13 0.32 0.55 0.03 0.3 0.49 0.35 1
dl_rel_log_f10 16 0.34 0.57 0.99 0.88 0.88 0.37 0.32 0.21 0.53 0.24 0.04 0.01 0.21 0.68 0.56 1
Table A4. List of sectors
High knowledge ¼Advanced manufacturing (in bold)
þKnowledge-intensive services
Low knowledge
20 05 37
21 06 38
26 07 39
27 08 41
28 09 42
29 10 43
30 11 45
50 12 46
51 13 47
58 14 49
59 15 52
60 16 53
61 17 55
62 18 56
63 19 68
69 22 77
70 23 79
71 24 81
(continued)
13 Please find the classification at this link: https://ec.europa.eu/eurostat/statistics-explained/index.php/
Glossary:High-tech_classification_of_manufacturing_industries.
MNEs, relatedness and employment 33
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Table A5. List of regions by income groups
Above EU average in GDP per capita Below EU average in GDP per capita
AT12 DE72 HU10 PT17 AT11 ES11 FR81 ITI2 RO21 UKK3
AT13 DE73 IE02 RO32 BE22 ES12 FR83 ITI3 RO22 UKK4
AT21 DE91 ITC1 SE11 BE32 ES13 GR11 LT00 RO31 UKL1
AT22 DE92 ITC2 SE12 BE33 ES41 GR12 LV00 RO41 UKL2
AT31 DE94 ITC3 SE21 BE34 ES42 GR13 MT00 RO42 UKM2
AT32 DEA1 ITC4 SE22 BE35 ES43 GR14 NL12 SE31 UKM3
AT33 DEA2 ITH1 SE23 BG31 ES52 GR21 NL13 SI01 UKM6
AT34 DEA3 ITH2 SE32 BG32 ES53 GR22 NL23 SI02 UKN0
BE10 DEA4 ITH3 SE33 BG33 ES61 GR23 NL34 SK02
BE21 DEA5 ITH4 SK01 BG34 ES62 GR24 PL11 SK03
BE23 DEB1 ITH5 UKD6 BG41 FI19 GR25 PL21 SK04
BE24 DEB3 ITI1 UKH1 BG42 FI1C GR30 PL22 UKC1
BE25 DEC0 ITI4 UKH2 CY00 FI1D GR41 PL31 UKC2
BE31 DED5 LU00 UKI1 CZ02 FR21 GR42 PL32 UKD1
CZ01 DEF0 NL11 UKI2 CZ03 FR22 GR43 PL33 UKD3
DE11 DK01 NL21 UKJ1 CZ04 FR23 HU21 PL34 UKD4
DE12 DK03 NL22 UKJ2 CZ05 FR24 HU22 PL41 UKD7
DE13 DK04 NL31 UKJ3 CZ06 FR25 HU23 PL42 UKE1
DE14 DK05 NL32 UKK1 CZ07 FR26 HU31 PL43 UKE2
DE21 ES21 NL33 UKM5 CZ08 FR30 HU32 PL51 UKE3
DE22 ES22 NL41 DE40 FR41 HU33 PL52 UKE4
DE23 ES23 NL42 DE80 FR42 IE01 PL61 UKF1
DE24 ES24 NO01 DE93 FR43 ITF1 PL62 UKF2
DE25 ES30 NO02 DEB2 FR51 ITF2 PL63 UKF3
DE26 ES51 NO03 DED2 FR52 ITF3 PT11 UKG1
DE27 FI1B NO04 DED4 FR53 ITF4 PT15 UKG2
DE30 FI20 NO05 DEE0 FR61 ITF5 PT16 UKG3
DE50 FR10 NO06 DEG0 FR62 ITF6 PT18 UKH3
DE60 FR71 NO07 DK02 FR63 ITG1 RO11 UKJ4
DE71 FR82 PL12 EE00 FR72 ITG2 RO12 UKK2
Table A4.. (continued)
High knowledge ¼Advanced manufacturing (in bold)
þKnowledge-intensive services
Low knowledge
72 25 82
73 31 95
74 32
75 33
78 35
80 36
34 Cortinovis et al.
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Maps in Appendix Figure A3 show that, while regions with above-average income tend to
be knowledge-intensive, there are various exceptions to the rule. For instance, the region of
Toulouse (Midi-Pyrenees) stands out in terms of knowledge-intensity while the average in-
come is below the EU average. The difference is even more remarkable for different Central-
Eastern EU regions, such as Western Slovakia, Transdanubia regions in Hungary and Vest re-
gion in Romania.
Figure A3. Maps on regions with above-average income (upper panel) and employment share in
knowledge-intensive activities (lower panel).
MNEs, relatedness and employment 35
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Table A6. First-stage regressions for 2SLS in Table 4 (endogenous variable: MNE_num (ln))
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Variables First
stage—All
First
stage—All
First
stage—All
First
stage—LKI
First stage
—Ab av.
Reg.—LKI
First stage
—be av.
REG.—LKI
First stage
—HKI
First stage
—Ab av.
Reg.—HKI
First stage
—Be av.
Reg.—HKI
First stage
—KIBS
First stage
—Ab av.
Reg.—KIBS
First stage
—Be av.
Reg.—KIBS
iv_b_nor_eu 0.00134*** 0.00128*** 0.000987*** 0.000496** 0.00233*** 0.00364*** 0.00251*** 0.0118*** 0.00343*** 0.00230*** 0.0119***
(0.000369) (0.000385) (0.000331) (0.000206) (0.000863) (0.000738) (0.000652) (0.00286) (0.000742) (0.000663) (0.00310)
dl_log_f10 0.0466*** 0.0445*** 0.0557*** 0.0414** 0.0644*** 0.0185 0.0129 0.0174 0.0327* 0.0236 0.0310
(0.00963) (0.00971) (0.0110) (0.0164) (0.0147) (0.0152) (0.0225) (0.0197) (0.0177) (0.0277) (0.0224)
No_MNE (dummy) 0.0618*** 0.0592*** 0.0594*** 0.0616*** 0.0726*** 0.0504*** 0.0558*** 0.0779*** 0.0331** 0.0439*** 0.0482* 0.0364*
(0.00916) (0.0106) (0.0107) (0.0144) (0.0255) (0.0155) (0.0159) (0.0286) (0.0163) (0.0159) (0.0252) (0.0199)
HK_tert 0.674* 0.724** 0.672* 0.799** 1.417** 0.337 0.389 0.996 0.247 0.539 0.958 0.509
(0.346) (0.364) (0.359) (0.355) (0.617) (0.406) (0.456) (0.773) (0.512) (0.517) (0.917) (0.568)
TotR&D 0.00103 0.00291 0.00328 0.00324 0.0100* 0.00993 0.00409 0.00815 0.000315 0.00328 0.00773 0.00240
(0.00456) (0.00518) (0.00516) (0.00616) (0.00549) (0.00912) (0.00509) (0.00623) (0.00948) (0.00579) (0.00718) (0.0120)
GDP (ln) 0.0328 0.0279 0.0258 0.00692 0.818*** 0.0747 0.0706 1.321*** 0.255** 0.0684 1.430*** 0.213
(0.0814) (0.0952) (0.0940) (0.0909) (0.314) (0.0840) (0.125) (0.358) (0.119) (0.162) (0.496) (0.161)
Firm_num (ln) 0.0209*** 0.0163*** 0.0162*** 0.0157*** 0.0137 0.0183*** 0.0169** 0.0101 0.0225*** 0.0179* 0.00484 0.0312***
(0.00469) (0.00499) (0.00497) (0.00533) (0.0135) (0.00498) (0.00770) (0.0136) (0.00820) (0.00919) (0.0146) (0.00954)
PhK (ln) 0.0129 0.0243 0.0261 0.0451 0.00529 0.0436 0.00279 0.0342 0.0170 0.0207 0.0868 0.0250
(0.0166) (0.0323) (0.0322) (0.0305) (0.0617) (0.0348) (0.0421) (0.0770) (0.0491) (0.0534) (0.106) (0.0651)
Neigh. MNEs (ln) 0.00731*** 0.00325*** 0.00320*** 0.00598*** 0.00719*** 0.00528*** 0.00258 0.00600** 0.000528 0.00356** 0.00694** 0.00149
(0.000681) (0.000974) (0.000975) (0.00126) (0.00194) (0.00165) (0.00160) (0.00268) (0.00194) (0.00176) (0.00307) (0.00208)
Observations 75,506 46,071 46,071 28,390 11,173 17,217 17,681 7364 10,317 12,617 5280 7337
Number of reg_ind 15,474 15,428 15,428 9511 3747 5764 5917 2465 3452 4217 1765 2452
Sector_region FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Notes: Clustered standard errors in parentheses.
***p<0.01, **p<0.05, *p<0.1.
C. First stage of IV regressions
36 Cortinovis et al.
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Table A7. First-stage regressions for 2SLS in Table 5 (endogenous variable: MNE_num_rel (ln))
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Variables First stage
—All
First stage
—All
First stage
—All
First stage
—LKI
First stage
—Ab av.
Reg.—LKI
First stage
—Be av.
Reg.—LKI
First stage
—HKI
First stage
—Ab av.
Reg.—HKI
First stage
—Be av.
Reg.—HKI
First stage
—KIBS
First stage
—Ab av.
Reg.—KIBS
First stage
—Be av.
Reg.—KIBS
rel_iv_b_nor_eu 0.00747*** 0.00525*** 0.00507*** 0.00211 0.00961* 0.00524*** 0.00286** 0.0124** 0.00502*** 0.00256* 0.0123**
(0.00142) (0.00135) (0.00155) (0.00152) (0.00519) (0.00130) (0.00131) (0.00521) (0.00134) (0.00139) (0.00580)
dl_rel_log_f10 0.317*** 0.289*** 0.292*** 0.300*** 0.302*** 0.286*** 0.290*** 0.291*** 0.285*** 0.284*** 0.292***
(0.0338) (0.0357) (0.0372) (0.0620) (0.0466) (0.0371) (0.0713) (0.0435) (0.0390) (0.0775) (0.0457)
MNE_num (ln) 0.468*** 0.371*** 0.363*** 0.291*** 0.384*** 0.185*** 0.471*** 0.617*** 0.286*** 0.559*** 0.737*** 0.333***
(0.0571) (0.0397) (0.0389) (0.0356) (0.0638) (0.0354) (0.0537) (0.0795) (0.0593) (0.0644) (0.0937) (0.0704)
No_MNE (dummy) 0.0577*** 0.0836*** 0.0867*** 0.0781*** 0.0751* 0.0575** 0.0978*** 0.129*** 0.0392 0.116*** 0.166*** 0.0419
(0.0192) (0.0186) (0.0185) (0.0221) (0.0387) (0.0229) (0.0277) (0.0432) (0.0331) (0.0346) (0.0531) (0.0399)
HK_tert 3.103 2.761 1.370 1.534 5.541 0.269 1.172 4.244 0.521 0.916 3.963 0.525
(2.114) (2.084) (2.018) (1.966) (3.492) (2.239) (2.249) (4.065) (2.668) (2.584) (4.775) (3.102)
TotR&D 0.00296 0.0235 0.0339 0.0196 0.0677** 0.0721 0.0572 0.130*** 0.0568 0.0697 0.157*** 0.0604
(0.0291) (0.0401) (0.0392) (0.0360) (0.0310) (0.0490) (0.0456) (0.0442) (0.0575) (0.0521) (0.0517) (0.0661)
GDP (ln) 0.579 0.647 0.661 0.411 6.813*** 0.221 1.128 9.557*** 0.0240 1.143 10.71*** 0.122
(0.512) (0.600) (0.571) (0.509) (1.599) (0.475) (0.743) (1.965) (0.700) (0.899) (2.414) (0.970)
Firm_num (ln) 0.0321 0.0119 0.0128 0.00245 0.00462 0.0168 0.0337 0.0801* 0.0142 0.0391 0.0764 0.0172
(0.0279) (0.0196) (0.0193) (0.0179) (0.0417) (0.0164) (0.0289) (0.0479) (0.0283) (0.0364) (0.0542) (0.0373)
PhK (ln) 0.0213 0.130 0.0755 0.0379 0.167 0.0727 0.123 0.364 0.0306 0.205 0.510 0.00169
(0.118) (0.168) (0.165) (0.160) (0.340) (0.169) (0.194) (0.332) (0.235) (0.259) (0.398) (0.352)
Neigh. MNEs (ln) 0.00393*** 0.0111*** 0.0119*** 0.00853*** 0.0192*** 0.000131 0.0172*** 0.0301*** 0.00593* 0.0182*** 0.0312*** 0.00525
(0.00137) (0.00247) (0.00246) (0.00215) (0.00392) (0.00188) (0.00333) (0.00530) (0.00351) (0.00345) (0.00551) (0.00343)
Observations 75,506 46,071 46,071 28,390 11,173 17,217 17,681 7364 10,317 12,617 5280 7337
Number of reg_ind 15,474 15,428 15,428 9511 3747 5764 5917 2465 3452 4217 1765 2452
Sector_region FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Notes: Clustered standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.
MNEs, relatedness and employment 37
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D. Data development
The main sources used in this article are the Eurostat SBS, Cambridge Econometrics
Regional Database and Bureau Van Dijk Orbis and Zephyr databases. As for the former two
sources, no significant elaboration was performed. The only exception is the nearest neigh-
bour interpolation order to fill gaps in the data, mostly on SBS employment data and
Eurostat R&D and human capital information. Besides, whereas SBS provides data on em-
ployment from 2008, we decided to exclude 2008 as, out of the 15,369 observations for that
year, around 4500 are flagged as potentially problematic.
The treatment of BVD data was instead more complex. Orbis database provides detailed
information at firm level on sector of operation, number of employees, registration date and
last available year. Zephyr instead gathers information on Merger and Acquisition (M&A)
deals, reporting the name and code of the firms involved, the stake of the deal, the date, etc.
Data on 18 million firms in the period 2006–2014 (from Orbis) and on more than
17,000 M&A deals between 1997 and 2014 (from Zephyr) were downloaded, cleaned and
geo-coded
14
(Kalemli-Ozcan et al., 2015). Whereas Orbis provides information on whether a
given firm is foreign owned or not, and with what share of ownership, only the most recent
information is recorded with no historical records about ownership. In other words, past in-
formation on ownership, such as whether a given firm was already foreign-owned and when
it was acquired, is not provided.
In order to overcome this obstacle, information on M&A from Zephyr was used to estab-
lish when a domestic firm acquired or was acquired by a foreign company. After merging
data from Zephyr and Orbis, we proceeded as follows:
1. Firms that are present in both Orbis and Zephyr are considered as MNE from the
year in which the first M&A in which they were involved took place. For instance,
Firm A is acquired by the MNE B in 2008: Firm A becomes MNE from 2008 on.
2. Firms that are recorded as foreign in Orbis but missing in Zephyr are assumed to be
MNE throughout the whole period. In this way, our dataset is also able to capture,
at least to some extent, greenfield investments, previous M&A and deals which
were not reported in Zephyr.
We exclude each firm from the dataset for the years subsequent to the last available year
recorded in Orbis. For example, Firm A, which was acquired by MNE B in 2008, provided
information to its local chamber of commerce only until 2011. As 2011 is its last available
year, Firm A is considered as domestic firm in the years 2006 and 2007 (period preceding its
acquisition), is counted as MNE between 2008 and 2011 (after the first detected M&A), and
excluded from the sample in 2012 and 2013 (since no information is reported after 2011).
Besides, we considered as MNE only those firms which are owned at least at 10% by a for-
eign counterpart.
As the last step, firm-level data from the dataset obtained after these operations have been
collapsed to the second digit of NACE code, in order to be compatible with the data obtained
from Eurostat SBS. As a robustness check, we compute the correlation between the employ-
ment data constructed from Orbis and the one provided by SBS. The overall correlation be-
tween the two datasets is a re-assuring, being above 75%.
14 For our analysis, we included only information on firms with unconsolidated accounts, in order to avoid intro-
ducing some bias (Oberhofer, 2013;Kalemli-Ozcan et al. 2015).
38 Cortinovis et al.
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E. Discussion and robustness of relatedness
While our analysis uses the concept of relatedness introduced by Hidalgo et al. (2007), in our
empirical specification, we use a relatedness measure based on a small sample modification
(Steijn, 2016) of the association measure developed by Van Eck and Waltman (2009).We
compute relatedness using employment data on sectors and regions in 2006. Whereas this
does not allow us to model the possible variation in industrial specialisation over time, we
are convinced it is a solid approach since it reduces the source of variation to changes in the
number of MNEs, rather than combining it with the change in sectoral specialisations.
Besides, considering the relatively short time dimension available in our data, we believe the
distribution of regional specialisations would probably change only to a limited extent. In
what follows we discuss our choices with respect to the methodology applied in our article
and we report a number of robustness checks using different versions of the Hidalgo measure
of relatedness.
Appendix Figures A4 and A5 show the heat maps across different measures of relatedness,
where ‘Relatedness’ refers to the one used in our analysis, ‘Relatedness_HHB’ refers to the
measure introduced by Hidalgo et al. in their 2007 paper, and ‘Relatedness_H90’ and
‘Relatedness_H95’ are the bootstrapped version of Hidalgo relatedness using the 90th and
95th percentile as threshold for defining specialisations (see Cortinovis et al., 2017 for
details). With the only exception of ‘Hidalgo Relatedness’, the matrices appear to capture a
similar structure of relatedness, with more strongly related industries being at the bottom left,
along the diagonal and in the top-right part of the heat maps. The traditional measure of re-
latedness shows considerably less structure, with most industries being strongly related to
most other industries.
However, we have become aware of an issue undetected by previous works using boot-
strapped versions of the Hidalgo relatedness, which is due to the fact that in bootstrapping
procedures only regions above a certain threshold are considered as specialised. As the
threshold is based on a chosen percentile, it implies that for any industry, a relatively constant
number of regions are considered as specialised. This puts a cap on the number of specialised
regions for each industry which, in turn, affects the actual relatedness score, since that is
Figure A4. Comparison between different forms of relatedness.
MNEs, relatedness and employment 39
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Figure A5. Comparison between different forms of relatedness (bootstrapping).
40 Cortinovis et al.
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calculated as the conditional probability of being specialised in any pair of industries. This
leads to the issue that the unique number of scores that the bootstrapped relatedness matrix
takes is a function of the percentile used, and it is remarkably lower than using other related-
ness measures. In our case, this implies that with a 95th percentile threshold we obtain 36
unique values, while with a 90th percentile threshold the unique scores of relatedness are 72.
Using our preferred relatedness matrix, the number of unique values is 1079. In these
respects, using the bootstrapping method has the significant drawback of effectively making
the relatedness scores a categorical indicator (with 36 or 72 levels) rather than a truly con-
tinuous measure. In this sense, the association measure comparing the relatedness score with
a random benchmark may have the indirect advantage of reducing the concern on how spe-
cialisation is defined. While the traditional approach of a score of the location quotient above
1 (LQ >1) has some limitations, the association measure seems to correct, to some extent,
for the case in which many regions may appear specialised. If most of the regions meet the
LQ >1 requirement, it will be implicitly more likely for two industries to appear in the ran-
dom benchmark as well, making the association measure less likely to capture spurious re-
latedness. From our point of view, this is the most likely explanation for the difference in the
structures between the relatedness measure we currently use and the standard relatedness
measure by Hidalgo, which are both based on the LQ >1 definition of specialisation.
Finally, Appendix Table A8 shows that, in spite of the differences in the matrices, the
results we find are very consistent. Regardless of the measure used to capture relatedness, the
different baseline specification shows a positive and significant relation between relatedness-
mediated spillovers and local industrial employment.
Table A8. Robustness check on different relatedness measures
(1) (2) (3) (4)
Variables Employment
—All—Rel
Employment
—All—H90
Employment
—All—H95
Employment
—All—HHB
MNE_num (ln) 0.0299*** 0.0315*** 0.0350*** 0.0311***
(0.00898) (0.00906) (0.00929) (0.00895)
No_MNE (dummy) 0.0118 0.0124 0.0126 0.0127
(0.0130) (0.0130) (0.0130) (0.0130)
MNE_num_rel (ln) 0.0248***
(0.00823)
MNE_num_h90 (ln) 0.0220***
(0.00821)
MNE_num_h95 (ln) 0.0270**
(0.0130)
MNE_num_hhb (ln) 0.00836**
(0.00326)
Observations 75,547 75,547 75,547 75,547
R
2
0.027 0.027 0.027 0.027
Number of reg_ind 15,515 15,515 15,515 15,515
Control variables Ye s Ye s Ye s Yes
Sector_region FE Yes Yes Yes Yes
Year FE Yes Yes Yes Yes
Notes: Clustered standard errors in parentheses.
***p<0.01, **p<0.05, *p<0.1.
MNEs, relatedness and employment 41
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