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Investigating firm heterogeneity in
country-of-origin cluster location
choice decisions
Francisco Puig
Department of Management, University of Valencia, Valencia, Spain
Anoop Madhok
York University, Toronto, Canada and Rey Juan Carlos University, Madrid, Spain, and
Zhi Shen
CIIE, Xian, China
Abstract
Purpose –This paper aims to analyse which firm-level characteristics drive their location decisions when
investing in a foreigncountry. Focusing onorigin clusters, theauthors will study the potential influence of the
home country context and, in particular, the impact of firm-level factors, both investor- and investment-
related, underlying heterogeneity in their location choice decisions.
Design/methodology/approach –The empirical analysis draws on data gathered from mainland
Chinese MNEs that have invested in Germany between 2005 and 2013 (269 firms). The authors chose a single
host (Germany) and a single home (China) country for their representativeness and for methodological
reasons to control for country effects. The authors used a multinomial logit model to assess the effects of the
independent variableson the probability that each of the three location possibilities would be selected.
Findings –The results suggest that investors preferring co-location in origin clusters have distinct
structural and strategic characteristics. From a more structural point of view, Chinese foreign direct
investment (FDI) undertaken by smaller firms and those without prior experience in the EU prefer an area
where there are other Chinese investors. From a more strategic perspective, these FDI flows are more likely to
tap into industry agglomerations when the investors’objective is strategic asset seeking, and they have less
knowledge-intensive investments.
Practical implications –The findings may be of great practical value to practitioners and policymakers.
Knowledge of the advantages and disadvantages of the types of agglomeration networks can help managers
to balance the rewards and risks in their decision-making and to select a suitable development path for their
FDIs. For policymakers, an understanding of the structure and formation of different groups of firms in one
location and the characteristics of investors who may enter the location can help them to improve their
regulatory work and to develop policies to attract investments, thereby enhancing local economic
development and communitystability.
Originality/value –The research shifts the emphasis of the location choice decision beyond just where to
locate toward with whom to collocate. It also contributes to the growing research on emerging market
multinationals by providing further insight into understanding of FDI location behavior by firms from
emerging economies.
Keywords Multinational firms, Location choice, Clusters, Agglomerations, Emerging economies
Paper type Research paper
Introduction
The choice of location within a foreign country is a critical aspect of multinational firms’
outward foreign direct investment (OFDI) strategies, as the decision of where to invest in the
Investigating
firm
heterogeneity
Received 19 July2018
Revised 1 March 2019
3 May 2019
22 June 2019
Accepted 9 July 2019
Multinational Business Review
© Emerald Publishing Limited
1525-383X
DOI 10.1108/MBR-07-2018-0051
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1525-383X.htm
foreign market can directly impact the outcome of the investment as well as direction and
prospects for future growth. A recent line of literature has drawn attention to the
subnational or regional aspects of location (instead of other more established levels of
analysis such as the country or city), emphasizing the economies that arise from the co-
location of firms in specific domains as a result of various externalities and spillovers. The
focus of such research haspredominantly been on clusters and industrial districts, which are
seen to comprise an array of industries related through common technologies, buyers/
suppliers, distribution channels, labor pools, etc. (Porter, 1990;Krugman, 1991).
Although the work on industry clusters (IC) has contributed immensely by explaining
how participation in clusters helps the competitiveness of firms, it by and large neglects
another kind of agglomeration that can be observed in the international business context
where, rather than an industry cluster within a particular host country, a group of foreign
investors originating from the same home country locate in proximity to one other when
investing in a foreign location due to certain underlying commonalities that are embedded in
their home context (Tan and Meyer, 2011;Hernandez, 2014;Stallkamp et al.,2017), thus
forming country-of-origin clusters (COC)[1].
In a recent paper, Beugelsdijk and Mudambi (2013) underlined the potential for advances
to be made by combining international business scholars’knowledge of firm organization to
economic geographers’knowledge of place and space. Nevertheless, a significant limitation
of research emphasizing common parentage at the country level is that it is somewhat
coarse-grained in that it tends to treat all compatriot firms homogenously. In this regard,
researchers (McCann, Arita and Gordon, 2002;Kim and Aguilera, 2016) have called for more
attention toward more fine-grained organization-level and behavioral issues to advance
cluster research further. Along similar lines and in an implicit recognition of this limitation,
Tan and Meyer themselves call for further studies that examine how the co-location choice
may be affected by otherfactors, such as knowledge intensity.
In this paper, we respond to this call and examine firm-level factors that drive
heterogeneity in firms’choices with respect to their location choice decision. Our interest is
in origin clusters, although we also address ICs to draw out the contrast between the two.
Work on origin clusters till date has tended to focus more on investing firms originating
from developed economies. Notably, although FDI from emerging economies has been
increasing rapidly in recent years, there has been relatively little investigation of the country
of origin effects in the case of foreign direct investors from emerging economies. In light of
their increasing visibility, there have been louder calls for more research on emerging
economy firms’outward investment strategies, especially in advanced economy contexts
where these firms face greater disadvantages when investing overseas compared to their
advanced economy counterparts (Peng et al., 2008;Cuervo-Cazurra, 2012;Madhok and
Keyhani, 2012). A particular distinction between the two with respect to the country of
origin effect is that whereas foreign investors from an economically advanced country of
origin benefit from superior attributes such as technology or brands when investing in
emerging economies, this is not the case when firms from an emerging economy invest in an
advanced economy. In the latter case, the emerging economy firm faces a credibility deficit
(Madhok and Keyhani, 2012).
Our empirical context is Chinese firms investing in the European Union (EU). China
stands out as an emerging economy whose firms are rapidly expanding their global
footprint, both in advanced and other economies. Due to initial resistance in the US to
Chinese investment, especially through acquisitions, they increasingly began investing in
Europe and in particular in Germany, which is both Europe’s dominant economy as well as
its locomotive (Clegg and Voss, 2012). Second, unlike some of the other European economies,
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Germany has a number of regions where firms can cluster, e.g. Hamburg, Dusseldorf or
Berlin, rather than having one dominant pole (Hanemann and Huotari, 2015). Third, the
home country institutional context is quite distinct from the host country; this aspect creates
unfamiliarity for the investing firm due to the uncertainty of the host country’s institutional
environment.
Our study builds upon and is yet distinct from some of the extant arguments on COC
agglomerations. For instance, we look at prior compatriot investors rather than immigrant
communities (Hernandez, 2014), even though there might be some overlap between the two.
This also contrasts with Stallkamp et al. (2017), who were more interested in the impact of
initial investment in origin clusters on speed and location choice of subsequent investments
within China. We look into the interorganizational dynamics among firms and the
environment where they operate and the effect of these interactions on firms’behavior
(DiMaggio and Powell, 1983;Scott, 2008). Our paper, closest to Tan and Meyer (2011),
extends and complements their pioneering work by looking at firms located inside and
outside of clusters.
Our paper contributes in a number of ways. By going down to the firm-level, we are able
to better explain the heterogeneity in investing firms’location choices with respect to
agglomeration. It is worth noting in this regard that our treatment is more comprehensive
than extant work in that we also include the isolated (i.e. non-cluster) location option, as
firms can and also choose to locate outside of clusters. Given the existence of the two types
of clusters, origin and industry, the question of what characteristics may drive firms to
prefer which type of cluster also becomes relevant. Collectively, the argument shifts the
emphasis of the location choice decision beyond just where to locate toward with whom to co-
locate.
The rest of the paper is organized as follows. In the next section, we provide an overview
of the difficulties faced by foreign investors, especially from emerging economies, and a
brief review of the agglomeration literature followed by our hypotheses. The subsequent
section discusses the methodology followed by the results. The final section discusses some
of the contributions and implications of the paper. Here, we draw upon an analogy with the
flocking behavior of birds to illuminate our arguments further.
Literature review and hypotheses
Foreign investors confront a number of location-specific disadvantages compared with
national firms when they enter a new foreign market (Hymer, 1960). Broadly speaking, not
knowing the local environment as well as domestic firms, foreign firms are likely unable to
access and absorb the information and resources available in the host country as easily as
the former, who are much more attuned to the local context (Meyer et al., 2011). First and
foremost is the liability of foreignness (Zaheer, 1995), which is rooted in the psychological,
cultural and institutional distance between home and host countries (Zhou and Guillen,
2016), and as a result of which foreign firms face disadvantages stemming from a lack of
knowledge about the political, legislative, economic, market and culture-related
environments of the host country (Anand and Delios, 2002;Meyer et al.,2009). This results
in frictions and increases the cost of doing business broad, in particular in the early stages as
the firm goes about finding its way about in the local market.
Second, foreign investors additionally suffer from a liability of outsidership (Johanson and
Vahlne, 2009). In this argument, instead of an abstraction, markets are considered networks
of relationships in which firms are linked to each other and through which firms learn and
build commitment. It is important to have a relevant network position in the local market
when entering a foreign country and an “insidership”is decisive for the investment to
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succeed. Compared to national firms, foreign entrants are outsiders and usually lack a
developed local business network, which impedes their access to the resources required for
their business development.
A third important barrier to foreign investors, faced specifically by MNEs from emerging
economies investing in advanced economies, is what has been termed the liability of
emergingness (Madhok and Keyhani, 2012). In this argument, the establishment and
maintenance of legitimacy in the host environment is vital for the survival and growth of the
business in the host country (Kostova and Zaheer, 1999). Due to their emerging market
origins as well as limited experience in institutionally more developed as well as distant
countries, MNEs from emerging economies find it difficult to understand and conform to
local norms and practices (Scott, 2008;Asmussen and Goerzen, 2013). This results in a
relative lack of legitimacy and social acceptance from local constituents (Zhou, 2013)–
government, consumers and suppliers –which, in turn, leads to them being discriminated
against, at least relative to other foreign investors from more advanced economies (Madhok
and Keyhani, 2012). In the absence of such legitimacy, foreign investors in such a context
may find it more difficult to develop trust and foster cooperative ties with key local
constituents (Eden and Miller, 2004), which results in more uncertainties for their business
(Zhou, 2013).
Collectively, these three liabilities create considerable uncertainty for foreign firms as
they go about conducting their business abroad. One way to overcome these disadvantages
is the (co)location strategy, whereby firms can gain access to the information or resources
they need by locating close to other related firms, i.e. agglomeration.
Location choice and multi-national enterprises clustering
Ever since Marshall’s(1920)pioneering work examining external economies in industrial
districts in England, there has been strong and sustained interest in what has now come to
be denominated as agglomeration economies (Parr, 2002). Although researchers interested
in the topic come from diverse disciplines, such as geography, economics, sociology and
strategy, all share the common interest of better comprehending the geographical context,
how agglomeration economies come about and the type of advantage they confer upon firms
(Mariotti et al., 2010;Lazzeretti et al.,2014)[2].
The crux of the argument is as follows. Firms operate and evolve in an environment
consisting of other firms and organizations. Accordingly, they learn not only from their own
experience but also by interacting with other organizations in their immediate environment
(Chen and Chen, 1998). This can give rise to clusters of firms who choose particular
locations, giving rise to agglomeration economies (Krugman, 1991), such benefits of
agglomeration being created “through links between independent firms present in
proximity to one another within a defined space”(Kim and Aguilera, 2016,p.146).
Some authors (Boschma, 2005) have criticized economic geographers for
overemphasizing physical proximity in their explanation for agglomeration economies and
have called for more attention to other forms of proximity, such as cognitive, social and
institutional proximity, which also impact resource and knowledge flows across firms. In
contrast to economic geographers’explanations of spatial organization and dynamics,
where physical proximity and economic variables have mainly tended to characterize much
of the work on location choice, the latter argument also addresses more organizational and
institutional considerations (Peng et al.,2008). In the international business context, even
though there is a geographic proximity in the host nation, an equally important underlying
factor is the shared home country context, which has a richer dimension than physical
proximity.
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A number of scholars have devoted substantial energy to understanding the location
aspect of MNEs’international expansion and what underlies their decision to place value-
added activities in particular areas outside the home base (Kang and Jiang, 2012;Kim and
Aguilera, 2016;Mesquita, 2016). Basically, in the international context, firms participate in
and are influenced by two principal environments besides that of the host context. The first
is the industry context in which interactions among firms are driven by business ties,
whereby firms exchange resources and interact with other firms –suppliers, distributors or
even competitors –in similar or related activities but not necessarily from the same country.
The other is the home country context, by which interactions among firms in the host
country are characterized by ethnic, diasporic and other such social ties with other
organizations of similar origin but not necessarily in the same industry. In fact, with the
underlying driver being the shared country of origin, these agglomerations are agnostic to
industry.
In short, the two environments thus provide investors with two potentially different
agglomeration tendencies:
(1) into areas preferred by other investors engaged in the same industry; and
(2) into regions where other investors originating from the same home country have
entered.
Because of the distinctiveness of their constituents, these two environments influence
investors in different ways and provide them with different stimuli and resources.
With the IC argument being quite well-known, we focus our attention more on the origin
cluster. In a nutshell, with respect to the former, firms are attracted toward ICs due to
business-related opportunities to learn as well as to tap into common economies that can
significantly reduce their business development costs (McCann and Folta, 2008;Alcacer and
Chung, 2014), with the downside being that relationships can be more competitive in nature
(Folta et al.,2006). In contrast, COCs are driven by a different set of considerations that are
cemented by common country bonds (Tan and Meyer, 2011).
There are a number of benefits that firms can enjoy by co-locating with their compatriot
firms. First, knowledge does not just flow freely and evenly among firms in physical
proximity within a cluster but is facilitated by social relations (Giuliani, 2007;Jean et al.,
2011). In this regard, it is easier to build trust among compatriots (Chang and Park, 2005;
Stallkamp et al., 2017). Such a high trust relationship can facilitate the communication and
information transfer process among compatriot firms, thus expediting learning about the
host environment and institutions (Liao and Yu, 2012) as well as lowering the cost of
knowledge transfer. Second, new foreign investors can benefit from the legitimacy that their
compatriots have already achieved in that location (Auster and Aldrich, 1984). These
advantages can help foreign investors reduce their liability from being outsiders. In other
words, agglomerations of firms with a common nationality can ease the difficulty of doing
business and can reduce perceived local investment risks (Bangara et al., 2012). Third, due to
their commonalities, it is easier to absorb knowledge from others who have a relatively
similar way of doing things and may have been through similar adaptation processes and
experiences (Tan and Meyer, 2011). Supporting this line of argument, studies (Miller et al.,
2008;Belderbos and Zou, 2009) show that co-locating with ethnically similar firms in a
foreign country significantly increases new entrants’chances of survival.
To sum up, an origin cluster in a sense provides a foreign investor with a friendly ‘start-
up’environment and can reduce its entry costs by helping it learn about the host context as
well as mitigate institutional uncertainty at the country level. Besides, in our particular
context, the different ethnic origin and socio-cultural background, combined with a
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reputation for relative technological backwardness and thus less to contribute, would render
it more difficult for Chinese investors to gain easy access to the benefits of ICs. In other
words, even though the benefits of ICs, especially in advanced economies, may be attractive
for emerging multinationals, they may not be so easily accessible to them. Moreover,
participation in such clusters may be perceived as somewhat riskier since relationships are
more rivalries as a result of similar or overlapping business activities (Iammarino and
McCann, 2006;Wang et al.,2014).
At the same time however, as they are not all influenced equally by the two distinct kinds
of environments, it is unrealistic to expect that all firms from a particular country would act
in a similar manner in their location choices. Put differently, firm-level differences can also
be expected to impact this decision, with both individual investor and investment
characteristics leading to heterogeneity in firms’location choices. This heterogeneous
behavior of investing firms causes the agglomeration tendency to vary across firms (Shaver
and Flyer, 2000).
Hypotheses
Smaller firms sometimes called micro-multinationals (Dimitratos et al., 2003) differ from
larger firms in their independence from as well as interaction with their environment
(Shuman and Seeger, 1986;Brouthers and Nakos, 2004). For one, resource limitations
constrain small firms’ability to build industry networks. As it is, this is usually a costly and
time-consuming process, which is then further exacerbated due to the liabilities of
foreignness, outsidership and emergingness identified earlier. On the other, their small size
limits their credibility further. All this impedes their access to the necessary resources for
their overseas operations, both with respect to business information as well as the necessary
knowledge of the foreign context. Moreover, as the literature has shown, there are costs and
risks associated with working and doing business in clusters due to imitation and
congestion (Krugman, 1991;Iammarino and McCann, 2006). As a result of the above, the net
effect of clustering can be negative.
On the one hand, foreign firms, both large and small, are more likely to decide to co-locate
and interact with other firms in particular areas, forming agglomerations and clusters, to
benefit from information and knowledge spillovers as well as gain from local institutional
linkages. On the other, smaller firms have fewer resources and less confidence than larger
firms (Dimitratos et al.,2003) to withstand any negative impacts from competitive
retaliation by cluster incumbents or from unintended technological spillovers. As a result, in
spite of potential benefits, smaller firms may find participation in ICs particularly
challenging due to resource constraints and weak legitimacy, at the very least in the early
stages till they find their initial footing. As smaller firms usually find it more difficult to
withstand challenges arising from the institutional context (Erramilli and Rao, 1993;
Schwens et al.,2011), this makes them more likely to depend on their home-based resources,
including networks, and be influenced by others with whom they share a common home
context, members of which aremore willing to share with them their information, experience
and legitimacy in the overseas markets. Moreover, the common heritage facilitates
absorption and lowers learning and adaptation costs to local environments (Tan and Meyer,
2011). In addition, if unsure of the appropriate course of action, one way to mitigate such
uncertainty is to imitate referent others, i.e. mimetic isomorphism (Guillen, 2003).
Consequently, SMEs are more likely to locate with compatriot firms, i.e. interact with other
investors from the same home environment:
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H1. Ceteris paribus, in establishing subsidiaries in host countries, smaller (emerging
market) firms are more likely than larger firms to locate (with compatriots) within
an origin cluster.
State-owned enterprises (SOEs) are a significant force in worldwide overseas investments,
especially in the case of emerging economies (Yeung and Liu, 2008;Kolstad and Wiig, 2012).
SOEs usually have both political and economic concerns in their ideology and strategies
(Cuervo-Cazurra et al.,2014) and, accordingly, are frequently required to serve the political
aims and mandates of the state as well as align their interests with their home institutions
while pursuing their business objectives (Scott, 2002;Zhang et al., 2011). Thus, SOEs are
more tied to the home context and their behavior is usually influenced by the considerations
and strategic needs of the home context (Ramasamy et al., 2012). Moreover, the political
affiliation of SOEs makes their interests less likely to be consonant with the expectations of
the external institutions in the local context (Globerman and Shapiro, 2009). Indeed, they are
usually perceived by host country institutions as not simply business entities but also as
political actors (He and Lyles, 2008), which exacerbates the legitimacy problem and acts as
an extra entry barrier, even more so when the investment is from an ideologically or
politically distant foreign country (Cui and Jiang,2009, 2012). The strong ties with the home
context and the greater perceived institutional pressure from the host context makes SOEs
more likely to seek “refuge”among compatriots:
H2. Ceteris paribus, in establishing subsidiaries in host countries, SOEs are more likely
than privately owned enterprises to locate within an origin cluster.
As explained above, the various liabilities confronting foreign investors constitutes a major
obstacle for them. Unfamiliarity with the political, legislative, economic, market and cultural
environments hinders the development of an investor’s business network and the
attainment of legitimacy for business operations in the host context. Knowledge of sensitive
cultural or other institutional aspects of the host environment is not easy to attain or
understand (Tung, 1998;Miller et a., 2008). Although such knowledge required for FDI can
be partly acquired through market transactions, this kind of knowledge exchange lacks the
richness and effectiveness of that based more on primary relationships (Hernandez, 2014).
More direct exposure through prior investment in the host environment facilitates
development of such ties.
Moreover, new foreign entrants may find it difficult to develop trust with local business
partners in a culturally or institutionally distant country (Tsui-Auch and Möllering, 2010).
This raises additional challenges for those lacking prior experience in the host context to
carry out their business operations. Thus, they are more likely to locate next to compatriot
firms to take advantage of the legitimacy that these prior entrants have already generated in
the region. In addition, as mentioned earlier, the interfirm relationships between firms of the
same origin is characterized by a relatively high degree of trust, which can help latecomers
reduce investment uncertainty and facilitate development of their business networks[3].
Even though Tan and Meyer (2011) made a similar argument in the context of
institutionally-deficient environments, the argument above suggests that it is not so much a
question of just the presence of local institutional voids but rather, and more broadly,
institutional differences between home and host country contexts. In this regard, in spite of
country-level variations, EU member countries have a high level of institutional
convergence since they all need to abide by common EU policies on many significant issues,
for which reason they can for all practical considerations be considered the same
institutional region for the purpose of global strategy (Rugman and Verbeke, 2004):
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H3. Ceteris paribus, in establishing subsidiaries in host countries, (emerging market)
firms lacking host environment experience are more likely than those with such
experience to locate within an origin cluster.
Scholars investigating the agglomerating behavior of firms have found that colocation
significantly reduces investors’perception of risks and increases new entrants’chances of
survival (Belderbos and Zou, 2009). Moreover, investors with different aims have quite
different preferences and strategic considerations in FDI (Buckley et al.,2007;Lu et al.,2011).
These motives impact the extent and type of dependence and how they interact with others
in these contexts, which in turn is reflected in their location tendencies (Shaver and Flyer,
2000). Investors engaging in upstream FDI with the objective of seeking specific industry-
related assets such as technologies, brands, and specific management know-how are more
likely to interact with other firms engaged in related activities to acquire or create these
assets. In this context, it is less likely that locating among compatriots would enable them to
attain these objectives, and accordingly makes it less likely that they would choose to locate
among compatriot firms in the host context for this purpose.
On the other hand, investors seeking foreign markets have to compete directly with other
firms with similar activities for distribution channels and markets (Fan et al., 2016). In
contrast to those primarily seeking complementary upstream resources, investors with a
market-seeking aim may perceive more rivalry from the host environment. As it is,
expanding into the markets of developed economies such as the USA and the European
Union (EU), where industries or markets are in a more mature stage of development
characterized by a high degree of competition, is particularly challenging. This is even more
so for emerging market firms, as such firms usually lack significant advantages to compete
in the global market (Luo and Tung, 2007;Deng, 2009). In addition to this, emerging market
firms suffer from a status of outsider and lack of legitimacy resources, which makes it more
difficult for them to build trust and gain acceptance in the market. The lack of legitimacy
and trust poses a daunting obstacle but is an imperative that needs to be overcome. One way
is to learn from and benefit from compatriot resources (Kang and Jiang, 2012). Thus,
emerging market investors seeking foreign markets are more likely to locate among
compatriots to gain legitimacy and develop their business networks.
Although the literature has shown that foreign firms need legitimacy in the host country
to increase their chance of survival and their access to resources, these arguments do not
apply as much to firms seeking strategic assets. The latter are less local market-oriented and
for them the imperative is more frequently to learn from other industry participants for
upgrading and catching up, not just to compete locally but also to become more competitive
at home:
H4. Ceteris paribus, in establishing subsidiaries in host countries, (emerging market)
firms seeking overseas markets are more likely than firms seeking strategic assets
to locate within an origin cluster.
A major advantage of co-locating with other firms is access to knowledge or information
spillover. However, as mentioned, geographic proximity increases competition and rivalry
and raises the appropriation hazard. This competitive relationship reduces the propensity to
interact with others in the same industry, especially for weaker firms, as leakage of specific
knowledge, such as technological and management know-how, to potential competitor firms
with similar activities would erode competitive advantages (Iammarino and McCann, 2006).
Moreover, as Chang and Xu (2008) demonstrate, foreign entrants tend to be crowded out by
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local players from the region due to the competitive aspect. Thus, such firms would be
motivated to avoid such risk by locating among compatriots, where such a threat is weaker
since business activities are more diverse.
However, subsidiaries differ in knowledge intensity of their industries and investments.
Firms in high-knowledge industries require higher investment but demand tends to be more
differentiated, thus they enjoy higher profit margins and are more buffered from
competition relative to less knowledge-intensive firms. In contrast, firms in low-intensity
industries and investment are less able to withstand the effect of competition as they lack
such differentiation. Along similar lines, firms from emerging economies have an even
greater legitimacy deficit in knowledge-intensive sectors because their technology is
considered inferior and not sufficiently up to speed (Madhok and Keyhani, 2012).
Nowadays, investors from emerging economies are increasingly climbing up the
technological ladder and starting to get noticed for some imaginative technology, even
though this may not be leading edge. Thus, firms with higher knowledge-intensity
investments may not suffer a legitimacy deficit to the same extent as their more ‘ordinary’
low knowledge intensity counterparts. In contrast, the latter are less able to endure some of
the negative effects of the lack of legitimacy in the host environment and thus more drawn
toward gaining legitimacy indirectly through compatriots compared to their high
knowledge intensity counterparts:
H5. Ceteris paribus, in establishing subsidiaries in host countries, (emerging market)
firms with lower knowledge intensity are more likely than those with higher
knowledge intensity to locate within an origin cluster.
Methodology
Data collection and sample
Our empirical analysis draws on data gathered from mainland Chinese MNEs that have
invested in Germany between 2005 and 2013. We chose a single host (Germany) and a single
home (China) country for our study for their representativeness and for methodological
reasons to control for country effects (Klossek et al.,2012). Using the AMADEUS database,
considered to be an important secondary research source with a high degree of reliability
(Brouthers and Brouthers, 2003;Siedschlag et al., 2013), we identified firms in Germany that
have owners from China (owned more than 51 per cent of the shares)[4].
Combining the information of AMADEUS with information extracted from firms’
reports[5] and web sites, as well as government publications, we created a database of 269
firms. We then deleted from our sample those firms which did not have complete
information on the focal variables of our analysis. The main sample therefore comprised of
238 firms. These represent the principal Chinese outward FDIs carried out by enterprises in
Germany during the period covered (UNCTAD, 2013) and covers the four types of location
choices: industry clusters, origin clusters, overlapping clusters (i.e. where the two cluster
types overlap) and non-clusters (i.e. locationally isolated investments). Similar information
sources and collection methods have been used in other studies (Dikova and Van
Witteloostuijn, 2007;Siedschlag et al., 2013).
Measurements
The dependent variable in the analysis is the FDI location tendency of Chinese MNEs within
the host country. In our case, this was reflected in three types of agglomeration choices:
industry, origin and isolated (i.e. non-cluster). As highlighted in the footnote below, Tan and
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Meyer (2011) underline the extreme difficulty, both conceptually and empirically, in
separating out the independent effects of industry and origin in situations where there is
cluster overlap. We therefore decided to follow their example and exclude these (40 cases)
from our main sample, reducing the sample to 198 firms[6].
There is no agreed method for identifying and mapping clusters, either in terms of the
measurement or the procedures by which the geographical boundaries of the clusters should
be determined (Martin and Sunley, 2003). To address this aspect we followed the
suggestions of Alcácer and Zhao (2016), who establish a three step process:
the definition of the activity to study –in our case, the sector (industry) and the
phenomenon (Chinese multinationals);
the delineation of the geographic unit of analysis upon which to carry out said
analysis (in our case German regions at a NUT-2 level)[7]; and
the establishment of the number of agglomerated units (firms) to label
agglomeration as a cluster (contingent upon the size of the region).
Consistent with this process and in line with some previous authors (Cader and Leatherman,
2011;Delgado et al., 2014), we used the location quotient (LQ)[8] as the method to identify
agglomerations. As there are no commonly accepted thresholds for defining an
agglomeration (O’Donoghue and Gleave, 2004), and given that the ratio of ratios
representing the location quotient can be interpreted in terms of specialization when greater
than 1, we define and categorize an agglomeration if the LQ >1.0 and 0 otherwise. This is
similar to Wu et al. (2017). It is important to remark that although we could have defined the
boundaries of agglomerations more broadly (NUT-1) or more narrowly (NUT-3) we use the
NUT-2 level, as the selection of the level of analysis has to be suitable to test the theoretical
relationships and data availability. Specifically, for NUT-1 the Germany country is divided
into only seven areas or states, and for NUT-3 in 401 districts, whereas for NUT-2 in 38
regions. Other papers (De Beule et al.,2018) have used a similar criterion:
L:Q:ðorigin agglomerationÞ¼
Number of firms with Chinese shareholderðsÞin a particular region=Number of all firms in the region
Number of all firms with Chinese shareholderðsÞin Germany=Number of all firms in Germany
L:Q:ðindustrial agglomerationÞ¼
Number of firms of a particular sector in a particular region=Number of all firms in the region
Number of all firms of a particularsector in Germany=Number of all firms in Germany
The NACE Rev. 2 (Statistical Classification of Economic Activities in the European
Community) is used for the industry definition. The geographic unit of analysis is the
region at a NUTS-2 level. Our analysis revealed substantial geographical diversity in
our sample: Dusseldorf, Darmstadt, Oberbayern, Koeln, Stuttgart, Unterfranken,
Hamburg and others.
The independent variables in the study were defined in line with the hypotheses, which
focus on five structural and strategic characteristics of the investors when they entered the
host country. The corresponding variables that we created are investor size,investor
ownership,investor exposure,investment motivation and knowledge intensity.
MBR
Investor size was captured by a rating scale using the log of the operating revenue, total
assets, and number of employees of the investors in the year prior to their FDI entry; this
scale has been widely adopted in IB empirical studies (Aw and Lee, 2008;Demirbag et al.,
2008). It is important to highlight that we followed the classification provided by Bureau
Van Dijk (SME, Large, Very large). In other words, we did not use a single criterion for the
measurement to avoid potential bias in analysis resulting from cases where there are large
operational revenues but few total assets or employees, or the reverse.
Investor ownership was measured based on the ownership structure of the investor. In
hypothesizing the effects of the home institutional dependence in the host context, we
argued that the dependence on their home institution context and perceived institutional
pressures from the host environment would be greater for state-owned enterprises.
Following some prior authors such as Duanmu (2012) and Cui and Jiang (2012),we
calculated the total percentage of the equity owned by the Chinese government and its
agencies in the investing firms. We then created a dummy variable, with private investors
being coded “0”and SOEs as “1”.
Investor exposure focuses on investors’exposure to the host context through prior FDI.
This was captured by a dummy variable, where the code “1”was used when the investing
firm had prior FDI experience in the EU and “0”otherwise. Similar measurements related to
MNEs’experience have been employed in research by authors such as Makino et al. (2002).
We had access to information regarding when investors entered a host country in the EU for
the first time. As explained earlier, adherence to the ever-increasing number of common EU-
level policies and regulations has consistently been prompting greater institutional
convergence among EU members over the years, in spite of country-level variations in
political, legal and business systems. This is particularly so in the economic and business
realm, thus justifying their being considered the same institutional region for the purpose of
global strategy (Rugman and Verbeke, 2004). We accordingly considered it justifiable to
operationalize the four countries where the majority of Chinese investments in the EU being
concentrated, i.e. Germany, UK, France and Spain (De Beule et al., 2018), as a common
destination for the first investment in the EU. A similar kind of argument was made by
Cuervo-Cazurra and Genc (2008) in the context of prior investment in countries with
institutional voids serving as a platform for subsequent investments in similar countries in
the region.
Investment motivation measures the Chinese investors’primary purpose for undertaking
their investment. Prior studies have shown that investment motivation has a significant
impact on MNEs’location preference (Chung and Alcácer, 2002;Makino et al.,2002).
Broadly, firms invest internationally to seek access to new markets (market-seeking), to
secure better access to raw materials (resource-seeking), to secure better access to
technologies, brands, distribution channels (asset-seeking and/or to reduce overall
production costs (efficiency-seeking) (Dunning, 1988). Although legitimacy issues are
relevant irrespective of the motive, the type of motive impacts the requisite level of
legitimacy due to the differing dependence on their immediate environment.
Clearly, Germany not being a primary goods producer or a location for low-cost
production, it is especially likely that Chinese direct investments in economically developed
countries as Germany are driven by a motivation to acquire technologies and brands (asset-
seeking) or to expand markets. Such investments potentially augment their competitiveness.
Moreover, a sharper competitive edge through asset-seeking or through more successful
participation in more competitive markets like Germany makes them more competitive back
in the home country (a huge market) as well as elsewhere.
Investigating
firm
heterogeneity
In line with the above authors, and with the information extracted from firms’reports
and websites, as well as government publications, we classified the investment motivations
into two categories. Given the nature of our variables, we coded them as dummies. Strategic-
asset seeking investment (coded “0”) includes activities such as design, research and
development and the acquisition of assets such as technology, patents and some intangible
know-how with the aim of enhancing the home-based capability. Market seeking investment
(coded “1”) includes activities related to overseas market expansion, either by wholesaling or
retailing products or services, and other sales-support activities.
Knowledge intensity measures the intensity of technology or knowledge involved in the
investments of the Chinese MNEs. Due to the limitations of the information that we could
access, we followed the Classification of Manufacturing Based on NACE Rev. 2 and
Classification of Services Based on NACE Rev. 2 provided by Eurostat (2014)[9] as proxies,
which indicates the average level of the technology or knowledge intensity in each sector.
Where MNEs’subsidiaries in the host country operate in higher technology or knowledge
intensity industries, we coded it as “1”and the other cases as “0”.
We also included a set of control variables in our analysis. Investment sector was created
to capture the differences between manufacturing investments and service investments.
Some previous FDI studies have suggested that manufacturing sector investors and service
sector investors perceive environmental uncertainties differently (Brouthers and Brouthers,
2003). We also measured investment size as the log of the size of the subsidiary used by the
foreign investors in the host country. A larger investment is considered to be riskier in FDI
and is likely to receive more institutional pressure from the local institutions (Li and Li,
2010). Finally, establishment mode was created to capture whether the investment was made
through a greenfield operation or an acquisition. This is important because acquisition of an
already existing operation would constrain the location choice of the investing firm.
A summary of the measurement of the independent, dependent, and control variables
and their operated values is given in Table I. The descriptive statistics indicate that of the
total sample size of 198 FDIs 29.3 per cent involved isolated location choices, 33.8 per cent
involved location inICs, and the remaining 36.9 percent involved location in COCs.
Method
We used a multinomial logit model to assess the effects of the independent variables on the
probability that each of the three location possibilities –IC, COC and non-cluster –would be
selected. This is a particularly appropriate and often-used technique when firms are
confronted with a choice among multiple options (Gatignon and Anderson, 1988;Agarwal
and Ramaswami, 1992;Goerzen et al., 2013). In a multinomial logit model, one of the
dependent variable is selected as a case of reference, or base case. In our study, the non-
cluster or isolated option is set as the base case.
Results
Table II presents the means, standard deviations and correlations between variables. The
correlations between the independent and control variables were generally lower than 0.4
except, for understandable reasons, the ones between location choice, investment motivation
and the establishment mode of the subsidiary, i.e. greenfield or acquisition[10]. The VIF
factors (values lower than 2.5) indicate that that there were no serious problems of
multicollinearity (Gujarati, 2003;Hair et al.,2006).
Table III shows the results of the multinomial logistic regression analysis, as estimated
with SPSS-20. To interpret the magnitude of the relationship between an independent
variable and the dependent variable in multinomial regression, the table includes, for each
MBR
independent variable, the estimated coefficient (
b
i), significance (*), standard error (SE). To
display the results, we defined six models. Establishing different models makes it possible
to compare alternative models by isolating changes in model fit and determining the
explanatory power of the variables (Aiken et al.,1991). As mentioned, the isolated location
choice is set as the base case. As such, no empirical statements can be made about which
circumstances favor the base case, as all its coefficients being set to zero, this being the case
from which all deviations (in our case, origin and industrial cluster) are measured, with
(Gatignon and Anderson, 1988).
Table I.
Description of the
variables
Name Description Values (% in sample)
Location mode Agglomeration choice 1 = Isolated (29.3%)
2 = Industry cluster (33.8%)
3 = Origin cluster (36.9%)
Investor size Investor size (group) 1 =SME
a
(13.3%)
2 = Large company (28.1%)
3 = Very large company (58.6%)
Investor ownership Owners of the investor 0 = Private (65.7%)
1 = SOE (34.3%)
Exposure Previous FDIs in the EU 0 = No (68.5%)
1 = Yes (31.5%)
Investment motivation Investment strategic objective in the
host country
1 = Market seeking (48.9%)
2 = Asset seeking (51.1%)
Knowledge intensity Technology and knowledge intensity
of the subsidiary
0 = Low and medium-low (53.9%)
1 = High and medium-high (46.1%)
Investment sector Investment activity in host country 0 =Manufacturing activity (44.0%)
1 = Trading and service activity (56.0%)
Investment size Subsidiary size in host country 1 = SME (66.7%)
2 = Large company (18.7%)
3 = Very large company (14.6%)
Established mode Entry mode 0 = Acquisition (50.5%)
1 = Greenfield (49.5%)
Note:
a
SMEs refer to small- and medium-sized firms
Table II.
Descriptive statistics
and correlation
coefficients
Variables Mean SD 12345678910
1. Isolated 0.28 0.45 –
2. Industry cluster (IC) 0.34 0.47 0.44 –
3. Origin cluster (COC) 0.38 0.49 0.49 0.55 –
4. Investor size 2.45 0.72 0.17 0.15 0.31 –
5. Investor ownership 0.34 0.48 0.07 0.00 0.06 0.33 –
6. Exposure 0.31 0.47 0.04 0.29 0.32 0.31 0.02 –
7. Investment motivation 0.51 0.50 0.05 0.42 0.46 0.35 0.20 0.27 –
8. Knowledge intensity 0.46 0.50 0.11 0.14 0.24 0.27 0.04 0.24 0.48 –
9. Investment sector 0.56 0.50 0.00 0.31 0.30 0.18 0.05 0.21 0.38 0.43 –
10. Investment size 1.48 0.74 0.12 0.20 0.31 0.38 0.23 0.28 0.42 0.25 0.37 –
11. Established mode 0.49 0.50 0.08 0.43 0.49 0.38 0.10 0.40 0.64 0.32 0.40 0.48
Note: Number of observations: 198
Investigating
firm
heterogeneity
COC-Isolated COC-Isolated IC-Isolated IC-Isolated COC-IC COC-IC
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Investor size
SME 1.62** (0.78) 0.21 (1.03) 1.41 (0.94)
Large 0.34 (0.55) 1.01*(0.58) 0.67 (0.60)
Ownership
Private 0.42 (0.50) 0.33 (0.48) 0.75 (0.54)
Exposure
Without experience 1.18* (0.64) 0.89* (0.49) 2.08** (0.65)
Investment motivation
Market seeking 0.34 (0.63) 1.63** (0.61) 1.97**(0.66)
Knowledge intensity
Medium and low Know intensity 0.42 (0.59) 1.19** (0.55) 0.77 (0.61)
Investment sector
Manufacturing 0.27 (0.45) 0.43 (0.59) 0.64 (0.41) 0.88* (0.50) 0.92** (0.43) 0.46 (0.60)
Investment size
SME 0.98 (0.75) 0.27 (0.87) 0.73 (0.52) 0.75 (0.61) 0.24 (0.77) 0.48 (0.89)
Large 1.27 (0.85) 0.67 (0.92) 1.41** (0.59) 1.43** (0.66) 0.13 (0.83) 0.76 (0.91)
Established mode
Acquisition 1.60*** (0.47) 1.42** (0.62) 0.95** (0.46) 0.29 (0.24) 2.56*** (0.49) 1.72**(0.49)
Constant 0.01 (0.79) 1.11 (1.05) 1.56** (0.64) 1.08 (0.86) 1.564* (0.84) 0.031**(1.11)
Observation (N) 198 (125) 198 (125) 198 (121) 198 (121) 198 (133) 198 (133)
Log-likelihood ratio 65.251 196.949 65.251 196.949 65.251 196.949
R
2
(Cox and Snell) 0.315 0.451 0.315 0.451 0.451 0.451
R
2
(Nagelkerke) 0.355 0.509 0.355 0.509 0.509 0.509
Chi square 72.399*** 107.978*** 72.399*** 107.978*** 72.399*** 107.978***
Correctly classified (%) 59.7 83.6 59.7 73.4 59.7 66.1
Notes:
*
p<0.10;
**
p<0.05;
***
p<0.01
Table III.
Multinomial
regression
MBR
Models 1 and 2 estimate the coefficients for the choice of origin clusters vis-à-vis the base
case. Model 1 includes only the control variables, whereas Model 2 tests the complete model
by including all the independent variables. In these models, a positive and significant
coefficient indicates that the corresponding explanatory variable increases the likelihood of
the investor locating in the corresponding type of cluster, in this case COC. Conversely, a
negative and significant coefficient indicates that the corresponding explanatory variable
decreases the likelihood of the same. Models 3 and 4 do likewise for the choice of ICs. We
additionally also examined the choice of COC treating IC as the base case. This is captured
in Models 5 and 6.
The regression results show that all the models have significant explanatory power and
model fit. As one can see, when adding the independent variables, the chi squares and
correctly classified percentages of these models increases significantly. In the case of COCs,
whereas model 1 with only the control variables correctly classified just 59.7 per cent of the
location decisions, the correct classification percentage increased to 83.6 per cent for COCs in
the full model. This is a significant jump in classification accuracy.
In line with our hypotheses, we expected investor size, investor ownership, investor
exposure, investment motivation and knowledge intensity to impact the agglomeration
decision. Analyzing the coefficients of investor size (1.62; p<0.05) and exposure (1.18; p<
0.1) in Model 2, we can affirm that our H1 and H3 are confirmed. That is, smaller size and
lack of experience in the EU increase the likelihood of an investor location in a COC.
Moreover, in the case of IC (Model 4), the coefficients for investor size is 1.01 (p<0.1) and
exposure is 0.89 (p<0.1), the latter suggesting that firms without experience are less
likely to locate in ICs. It is also worth noting that the effect size for these coefficients are
stronger for COCs than ICs. However, only the exposure variable (2.08; p<0.05) in Model 6
(base case IC) is significant. This reinforces the argument that, besides seeking
agglomeration initially, less experienced firms seem to prefer a COC.
With respect to H2, although the coefficient for ownership is negative as expected,
suggesting that private firms are less likely to locate in origin clusters, it is not significant. In
other words, the type of ownership (i.e. state or private) does not have any significant impact
on the location choice decision. It is possible that, as state enterprises increasingly engage
overseas, economic concerns have begun to crowd out political concerns, at least compared
to earlier. In fact, institutional reforms in countries like China may be compelling state-
owned firms to become more competitive and economically-driven, even though the effect
might be greater on non-state-owned firms (Dau, 2012). In such case, and as host countries
become more exposed as well as accustomed to their presence, they become increasingly
accepted and viewed with less suspicion. Both these aspects would mitigate the pressure to
gravitate toward compatriots[11]. Additionally, we did not distinguish between different
types of state-owned firms. In their recent work, Benito et al. (2016) analyzed publicly listed
and non-publicly listed state-owned firms, the former often having some degree of private
ownership, and suggest the arrival of a “modern”state-owned firm more in tune with
competitive considerations.
With respect to the investment motive (H4), the corresponding coefficient for market-
seeking investment in Model 2 was not significant, even though the sign was pointing in the
right direction. However, the coefficient for market seeking (1.63, p<0.05) in Model 4
suggests that firms characterized by market-seeking investments are less likely to locate
within an industry cluster. Moreover, using IC as the base case, the coefficient for market
seeking (1.97, p<0.05) in Model 6 reinforces this argument. Thus, even though H2 is not
directly supported, there is some indirect support for the argument.
Investigating
firm
heterogeneity
Finally, we postulated in H5 that firms with lower knowledge intensity investments are
more likely to locate within a COC. In the case of COCs, however, the corresponding
coefficient in Model 2 was not significant. In contrast, the coefficient for industry cluster
(1.19; p<0.05) in Model 4 suggests that firms with low and medium-intensity investments
prefer ICs. Our argument had more to do with the non-existence of slack in low knowledge-
intensity investments and thus the limited ability to withstand competitive and legitimacy
pressures. However, it is possible that the opportunity to learn and upgrade may offset other
considerations. Also, these firms may be less concerned about competitive spillovers as they
have less to lose.
In conclusion, overall the directionality and significance of the coefficients are broadly
consistent with our hypotheses, with the empirical results supporting many of our
arguments and others seeking mixed support. Besides the direct results, some further
information can be extracted from Table III. For instance, the fact that the corresponding
coefficients for investor size are significant in Models 2 and 4 but not in Model 6 indirectly
appears to suggest that very large firms (58.6 per cent of investors –see Table I) do not
experience the same pressure to agglomerate compared to their other compatriots. Similarly,
the finding in Model 4 that firms with market-seeking motives prefer to avoid ICs (Model 4)
and prefer COCs over ICs (Model 6) indirectly suggests that firms with asset-seeking
motives seem to prefer ICs, the latter point being reinforced by the fact that, as we could
expect, and in line with Zschoche (2016), manufacturing firms prefer ICs relative to those in
services (Model 4, coefficient = 0.88; p<0.1).
Discussion
In this paper, we provide insight into the potential influence of the home country context on
foreign investors’choices with respect to location decisions within a country. Literature on
MNEs’foreign investment strategies have traditionally taken the host country environment
as a whole and paid lesser attention to firms’location patterns at the subnational level. Our
arguments suggest that investors’FDI location choice is not homogeneous within the host
country. By showing how foreign entrants can engage in and benefit from a colocation
strategy in different ways, the argument on location choice goes one step beyond just where
to locate and shifts theemphasis toward with whom to co-locate.
Our results suggest that investors preferring co-location have distinct structural and
strategic characteristics. From a more structural point of view, Chinese FDI undertaken by
smaller firms and those without prior experience in the EU prefer an area where there are
other Chinese investors. From a more strategic perspective, these FDI flows are more likely
to tap into industry agglomerations when the investors’objective is strategic asset seeking
and they have less knowledge-intensive investments.
In sum, the location of one’s own home country firms in a certain area acts centripetally
in drawing membership from similar others, thus resulting in origin clusters. The entry of
these “pioneers”not only provides examples to others with similar backgrounds, namely the
possibility of doing business, but also creates legitimacy for them in the same location. This
is important for the arrival in the same area of other compatriot firms who may lack
knowledge of the local context and suffer from lack of legitimacy. Unlike studies which
focus mainly on factors such as knowledge spillover, specialized labor and input providers
(Marshall, 1920;Porter,1990, 1998), this focus on contextual information and legitimacy
helps explain why investors are encouraged to co-locate with other firms of similar
background.
Our study also contributes to the growing research on emerging market multinationals
by providing further insight into understanding of FDI location behavior by firms from
MBR
emerging economies. In a way, such a location expansion path within the foreign country
would be the locational counterpart to the establishment mode expansion path suggested in
the traditional Uppsala internationalization process model (Johanson and Vahlne,1977,
2009). The Uppsala model emphasizes the gradual and direct accumulation of knowledge
and learning from firms’prior experience. To cope with uncertainty during a firm’s
internationalization process, the model proposes a pattern or order by which firms enter
foreign countries, whereby they tend to commence their foreign operations from more
familiar and culturally similar areas and move gradually to more unfamiliar and dissimilar
countries or regions. The locational perspective complements the model in the
understanding of MNEs’foreign expansion behavior with respect to choice of location
within a country. In this argument, firms’knowledge, as well as legitimacy to carry out
business, can be gained not only from inside through direct experience but also indirectly
from the outside, wherein they can acquire information and resources and capture
opportunities through origin cluster participation.
Moreover, the findings of this study may be of great practical value to practitioners and
policy makers. One important goal of management studies is to improve decision-making. A
knowledge of the advantages and disadvantages of the agglomeration networks can help
managers to balance the rewards and risks in their decision-making and to select a suitable
development path for their FDIs while remaining consistent with their investment
objectives. For policy makers, an understanding of the structure and formation of different
groups of firms in one location and the characteristics of investors who may enter the
location can help them to improve their regulatory work and to develop policies to attract
investments, thereby enhancing local economic development andcommunity stability.
Of course, our investigation is not without its limitations. First, a single home and host
country raises the issue of generalizability and may explain why a number of predicted
relations that were non-significant. In a similar vein, perhaps prior exposure to other EU
countries may only be an imperfect proxy for Germany empirically. Second, with respect to
agglomeration economies, we have examined the independent effects of ICs and COCs. Yet
there is always the possibility that these two coincide in the same territory, i.e. cluster
overlap. Third, though scholars focusing on agglomeration economies usually argue that
new entrants are attracted by the associated benefits, it remains unclear whether the effects
of such agglomeration are actually generated ex post, in other words, whether they are the
outcome of clusters not the cause. Ultimately, of course, the question of whether
agglomeration economies exist before agglomerations form or the reverse is a chicken-and-
egg question that is difficult to tease out empirically.
As a concluding remark, we know that firms do benefit from locating in clusters due to
network externalities, but we know less about the evolution of such clusters. That is,
questions such as how they emerge, how they evolve over time and what role foreign market
entrants play in this process are ripe for further investigation. Researchers have begun to
look into these questions (Crescenzi et al., 2016;Nielsen et al.,2017;Shen and Puig, 2018), but
the focus has been more on industry-based agglomerations. Our arguments potentially
further additional insight into the phenomenon.
Notes
1. We use the term agglomeration or cluster to refer to both kinds.
2. Given their varied backgrounds, researchers use different terms to address what are in essence a
similar or closely related set of concepts (Martin and Sunley, 2003). In line with the IB tradition,
we use the term cluster.
Investigating
firm
heterogeneity
3. In line with Urzelai and Puig (2019), we believe that though both newcomers and firms with prior
experience in the host country would want to benefit from the knowledge and legitimacy, the
newcomers would benefit more.
4. In 73.5% of the cases, the ownership of the subsidiary was completely Chinese (more than 95%
of the shares were owned by a Chinese investor), and in the remaining 26.5%, there was more
than one investor and each had less than 95% of shares (10% 94%, both included). In the latter
case, we selected only those cases in which the Chinese ownership was at least 51%.
5. Mainly their annual and semiannual reports, investment announcements, and other kinds of
internal documents.
6. As Tan and Meyer state: “Conceptually, factors leading to a foreign investor’s choice to co-locate
with compatriots or foreign industry peers are likely to lead investors to co-locate with FDI firms
from the same industry and the same country if such a choice is available. Therefore, it is not
surprising that same country & industry FDI activity is highly correlated with the two variables
of interest in our study –same country FDI activity and same industry FDI activity. We remove it
from the models to untangle individual effects of each type of FDI activity (p. 514) ... As we are
unable to disentangle the factors that lead to either country-of-origin agglomeration or industry
FDI agglomeration, we leave the variable out of the estimation, to avoid multicollinearity”(p.518,
footnote 6).”
7. The NUTS classification (Nomenclature of Territorial Units for Statistics) is a hierarchical
system of the EUROSTAT for dividing up the economic territory of the EU, which has three
layers: NUTS-1 is the major socio-economic regions (states), NUTS 2 are basic regional units
defined for the application of regional policies (within the country) and the NUTS 3 are smaller
regions for specific diagnoses
8. Following Cromley and Hanink (2012), the LQ with reference to Chinese firms at observation
point (or location) i is a ratio of ratios. For example, the ratio for the local unit of observation
(country-of-origin cluster) can be written as e
i
/E
i
, where e
i
is the number of Chinese firms at
region i, and E
i
is the total of firms at region i. The ratio for the aggregate reference can be
written as e/E, where e and E are the total firms in Germany and the total of the overall firms in
the reference economy, respectively. Then: LQi = (e
i
/E
i
)/(e/E). To define a subsidiary as Chinese
at least 51% of the shares were to be held by an investor of Chinese nationality.
9. https://europa.eu/european-union/documents-publications/statistics_en
10. However, as the correlation coefficients between investment motivation and establishment mode
were of some concern, we re-estimated the models without the latter. The results, including the
Chi-squared statistic, not being significantly different, we decided to retain both variables in the
final analysis.
11. As one reviewer noted with regard to that results, “one can argue whether SOEs are welcomed in
COCs or not. Due to their unlimited resources as well as connection to home country government
and their political power, there might be resistance from COCs to SOEs and their potential
influence and domination in the cluster that could have negative impact on the performance of
the firms in COCs”. We acknowledge this comment, and it remains open for future research.
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Further reading
Dunning, J.H. (1998), “Location and the multinational enterprise: a neglected factor?”,Journal of
International Business Studies, Vol. 29 No. 1, pp. 45-66.
Zaheer, S., Lamin, A. and Subramani, M. (2009), “Cluster capabilities or ethnic ties? Location choice by
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Corresponding author
Francisco Puig can be contacted at: francisco.puig@uv.es
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