The Spatial Structure of Foreign Subsidiaries and MNE Expansion Strategy
Guoliang F. Jiang *Dalhousie University
Rowe School of Business6100 University Avenue,
Halifax, Nova Scotia, CanadaB3H 4R2
Tel: (902) 494-8999Fax: (902) 494-1107
Email: [email protected]
Guy L. F. HolburnWestern University
Ivey Business School1255 Western Road,
London, Ontario, CanadaN6G 0N1
Tel: (519) 661-4247Fax: (519) 661-3485
Email: [email protected]
Paul W. BeamishWestern University
Ivey Business School1255 Western Road,
London, Ontario, CanadaN6G 0N1
Tel: (519) 661-3237Fax: (519) 661-3485
Email: [email protected]
* Corresponding author
(Forthcoming at the Journal of World Business)
1
The Spatial Structure of Foreign Subsidiaries and MNE Expansion Strategy
ABSTRACT
Drawing on internalization theory and economic geography research, we examine how the
spatial structure of MNE subsidiaries in supranational regions affects subsidiary location choices.
Our analysis of foreign production investments by Japanese manufacturing firms from 1971-
2006 supports our theoretical predictions: firms were more likely to establish new production
subsidiaries in countries geographically more proximate to existing production subsidiaries, but
not to trading subsidiaries, in the same region. The proximity effect diminished for production
subsidiaries engaged in accessing natural resources or R&D. Performance of production
subsidiaries was also stronger for those closer to other production subsidiaries in the same
region.
Keywords: Spatial transaction costs; Geographic distance; Internalization; Location choice
2
1. Introduction
What geographic factors influence MNE location strategy? Research on country location of
MNE subsidiaries has largely focused on the effect of distance between an MNE’s home country
and a potential host country (Kang & Jiang, 2012; Ragozzino, 2009; Slangen, 2011), and on host
country location-specific attributes such as industry agglomeration (Head, Ries, & Swenson,
1995; Kim, Delios, & Xu, 2010; Wheeler & Mody, 1992), resource access (Kolstad & Wiig,
2012; Schotter & Beamish, 2013), and government policies towards FDI (Mudambi, 1995; Zhou,
Delios, & Yang, 2002). While there is an increasing emphasis on how the regional and global
configuration of MNE subsidiaries affects MNE investment strategy and performance
(Beugelsdijk & Mudambi, 2013; Dunning, 1998), prior studies have not typically accounted for
cross-border spatial linkages between subsidiaries, a defining attribute of MNEs’ international
operations. In this study, we focus on cross-border linkages within the firm and specifically
examine the question: how do spatial relations between subsidiaries influence MNE expansion
strategy?
MNEs are complex geographic networks of activities undertaken at interdependent
subsidiaries, linked by cross-border flows of goods, information, finance and managerial
authority. The cross-border spatial structure of an MNE’s subsidiaries reflects accumulated
country choices for investment locations over time, creating a corporate geography superimposed
on territorial geography. A subsidiary and the country market in which it is located are
simultaneously situated in both territorial geography and corporate geography. Spatial relations
among subsidiaries thus generate a distinct layer of spatial variation that can influence MNEs’
investment strategy. This type of spatial variation differs from that arising from home-host
country distance (Kang & Jiang, 2012; Slangen, 2011) or subnational industry clustering (Head
3
et al., 1995; Kim et al., 2010): the former centers on the firm and its internal relationship whereas
the latter focuses on macro-level country or cluster characteristics that are exogenous to the focal
firm. By examining an understudied yet consequential geographic aspect of the MNE, this study
provides new conceptual and empirical insights to the analysis of MNE expansion strategy.
We augment an internalization theory framework with core concepts from economic
geography research, especially the notion of spatial transaction costs, to explore the relationship
between corporate geography and FDI strategy (McCann, 2008; McCann & Shefer, 2004). The
theoretical premise of our study is that spatial transaction costs between subsidiaries, consisting
of information and transport costs, create barriers to internalization and foreign investment. We
argue that MNEs can reduce cross-border spatial transaction costs by locating new production
subsidiaries closer to existing ones within the same supranational regions, assuming subsidiaries
within the same MNE are willing to cooperate. We argue further that the benefit of proximity
depends on the functional focus (specifically, production and trading) of existing subsidiaries,
and on the strategic mandate of the new subsidiary (such as accessing local natural resources or
conducting R&D).
Our analysis of foreign production investments by Japanese public manufacturing firms
between 1971 and 2006 confirms our central proposition that the proximity of a potential host
country to an MNE’s existing production subsidiaries, but not to trading subsidiaries, in the same
supranational region increases the probability of production entry into that country. However,
this positive influence of geographic proximity diminishes when a new subsidiary is engaged in
accessing local natural resources or conducting R&D. Yet, the proximity to trading subsidiaries
increases the probability of production entries with an R&D mandate. Finally, we find consistent
proximity effects when we examine subsidiary performance, namely that subsidiaries more
4
proximate to other subsidiaries within the same region tend to perform better than more distant
ones.
Overall, this study makes three contributions. First, in response to recent calls for more
research on the organizational spatial dimension of MNE geography (Beugelsdijk, McCann, &
Mudambi, 2010; Beugelsdijk & Mudambi, 2013; Buckley, 2009), our analysis demonstrates that
cross-border spatial relations between subsidiaries constitute an important corporate geographic
characteristic that affects the relative attractiveness of a potential investment location. Second,
we provide a more fine-grained location choice analysis by distinguishing between production
and trading subsidiaries and also between strategic mandates of new entries. Our results reveal
that territorial geography and corporate geography simultaneously influence location choices and
that their relative impact is dependent on a subsidiary’s functional and strategic focuses. Finally,
building on the core concept of spatial transaction costs from the economic geography literature,
this study enriches internalization research by offering new insights into an understudied spatial
determinant of internalization effectiveness – inter-subsidiary proximity. As far as we are aware,
our analysis is among the first to systematically examine how the spatial structure of MNE
subsidiaries affects MNE expansion strategy. A direct practical implication is that intrafirm
spatial proximity at a regional level may offer a competitive advantage when MNE operations
become increasingly interdependent (Ahlstrom, 2015; Iammarino & McCann, 2013).
2. Theoretical Background
Internalization is a central concept in international business that is at the core of theories of
foreign direct investment (Buckley & Casson, 1976; Rugman, 1981). Internalization theory
argues that firms and markets are alternative institutions for organizing interdependent economic
5
activities located in different countries (Hennart, 1982). Conceptualized as a process of making a
market within a firm, internalization is a mechanism for retaining competitive advantages
derived from the firm’s intangible assets and capabilities that cannot be readily transferred across
firm boundaries using market-based mechanisms such as licencing. The MNE thus manages a
complex set of interrelated cross-border activities using a planned system of internal markets
rather than resorting to imperfect or nonexistent external markets.
Despite the broad applicability of internalization theory, the organizational costs of
internalization, especially those associated with MNE spatial structure, are often
underemphasized in previous research, and the benefits and costs of internalization are often
assumed to be invariant to distance (Buckley, 2009, p.234). Although prior research has
combined location factors and internalization in explaining the development of MNEs, it has
primarily focused on cost minimization based on transport cost savings or cost differentials in
inputs, such as labor and natural resources (Iammarino & McCann, 2013; Rugman, 1981). In
particular, Beugelsdijk et al. (2010) argued that researchers should distinguish between two
separate yet related dimensions of MNE geography – ‘place’ and ‘space’. While place is
concerned with location-specific attributes – such as natural resource endowment, institutional
environment, and home-host country distance – and their impact on MNE foreign location
choices, space emphasizes the role of the firm in geographic space based on organizational
connectivity. International business research has largely focused on the ‘place’ aspect of location
strategy (Kang & Jiang, 2012) and the variation in ‘space’ arising from industry clustering (Kim
et al., 2010). The extant literature, however, has developed fewer insights about the geographic
configuration or spatial structure of MNE subsidiaries, overlooking a form of spatial variation
that can impact spatial transaction costs within the MNE.
6
In the economic geography literature, spatial transaction costs are broadly defined as “the
costs associated with engaging in and coordinating activities across space” (McCann, 2008,
p.355). In the context of foreign trade and investment, spatial transaction costs consist of three
distinct types: information transmission costs (hereafter “information costs”), transport costs, and
tariff costs. Unlike the first two, tariff costs are institutional costs that are not geographic in their
construction. Thus, following the tradition in the field, we focus on information costs and
transport costs in this study (McCann & Shefer, 2004). Information costs are associated with
moving knowledge and information across geographic space between transacting parties,
whereas transport costs are associated with moving goods across geographic space. The
definition of these two types of spatial transactions costs are explicitly geographical and the costs
incurred depend on the distance covered (McCann, 2008). The concept of spatial transaction
costs can be readily applied to the analysis of MNE subsidiaries as they are geographically
dispersed units engaged in coordinated activities across space.
The role of information costs is crucial to internalization analysis when examining the
costs of control, monitoring and exploiting proprietary knowledge. Information asymmetry
resulting from geographic distance discourages locating internally coordinated activities in
distant locations (Ragozzino, 2009; Slangen, 2011). Research on equity investment performance
has also established that analysts and investors located proximate to a firm have an information
advantage over those in distant locations, enabling investors to earn superior returns from
geographically proximate investments (Coval & Moskowitz, 1999; Malloy, 2005).
Another aspect of information costs is concerned with knowledge transfer within the MNE.
MNEs synthesize, integrate and disseminate locally originated knowledge, whether it is
developed at the headquarters or subsidiaries, and seek to apply it more broadly across countries
7
(Ahlstrom et al., 2014; Fang et al., 2010; Kogut & Zander, 1993; Qian & Delios, 2008). Prior
studies have shown that spatial proximity between subsidiaries facilitates knowledge transfer
within the firm. Hansen and Lovas (2004) find that large geographic distances between units
reduce the tendency for staff to seek information from other units. Ambos and Ambos (2009)
show that geographic distance limits knowledge transfer between subsidiaries and that the
negative effect of distance is particularly salient when personal coordination mechanisms are
used to facilitate knowledge transfer. These findings imply that geographic proximity between
subsidiaries is advantageous to the firm; however, there is little evidence as to how inter-
subsidiary proximity may influence MNE expansion strategy.
Meanwhile, the MNE can be viewed as a differentiated network within which subsidiaries
possess different types and levels of knowledge (Nohria & Ghoshal, 1994). The development of
a subsidiary’s capabilities can alter its political relations with the headquarters and other
subsidiaries (Mudambi & Navarra, 2004; Mudambi, Pedersen, & Andersson, 2014; Mudambi,
Piscitello, & Rabbiosi, 2014). For instance, knowledge transfer is found to be more effective
between subsidiaries whose main activities are complementary rather than substitutive because a
substitutive relationship may reduce subsidiaries’ willingness to share the knowledge (Andersson
et al., 2015; Gupta & Govindarajan, 2000). In the current study, the initial choice of a new
subsidiary’s location and its activities may be politically influenced by existing subsidiaries;1
however, it is conceivable that market entry decisions largely reflect the strategic direction
designed by the headquarters (Buckley, Devinney, & Louviere, 2007). Our conceptual model
thus assumes that the relationship between the new subsidiary and existing ones is non-
competitive. Although this partial equilibrium approach restricts the conceptual scope of our
1 We thank an anonymous referee for this insight.
8
analysis, the resulting theoretical parsimony enables us to more effectively demonstrate the
effects of inter-subsidiary spatial proximity.
In the case of transport costs, while the transport costs of bulk commodities have fallen in
recent decades, the transportation and logistics components of manufactured goods have
increased (McCann & Shefer, 2004). Geographic distance also has implications for the
opportunity costs associated with delivery time. As the quantity and complexity of market
information increases, production and logistics operations at MNEs have become increasingly
complicated and fragmented as evidenced by the growth of intra-firm trade and reliance on just-
in-time delivery strategies (Schonberger, 1996). As a result, the average lead times of shipments
have fallen over the years whereas shipment frequency has increased. This increased frequency
of transactions, combined with storage, inventory-handling and other forms of logistics costs, has
been found to increase the economic costs of geographic distance (McCann, 1998, 2011). These
findings suggest that the spatial structure of the MNE’s subsidiaries is likely to influence location
choices towards creating the least-cost production system.
Both international business and economic geography research have identified a distinctive
regional configuration to MNEs’ international strategy (Delios & Beamish, 2005; Dicken, 2007;
Rugman & Verbeke, 2004). Supranational regions represent a spatial platform for MNEs to
coordinate the scale and scope of their operations. Although prior research has shown that MNEs
tend to concentrate investment activities, especially production investment, by regional
boundaries (Arregle, Beamish, & Hebert, 2009), few studies have examined the inter-subsidiary
relations within a regional framework. One exception is a recent study that examined how inter-
subsidiary learning improves subsidiary performance (Kim, Lu, & Rhee, 2012). The authors
found that Japanese foreign subsidiaries utilized formal, routinized channels and interpersonal
9
communications to create opportunities for managers and personnel to interact with each other
within the same region. These findings along with other studies on MNE regional configuration
clearly indicate that MNEs’ international strategies substantially coalesce around discernible
geographic regions; therefore, the influence of intra-firm spatiality on location choices of
overseas production is likely to be regionally dependent too.
On the other hand, subsidiaries are established by the parent firm with certain objectives,
which determine the function and strategic mandate of the subsidiary. However, the traditional
internalization framework assumes that foreign subsidiaries equally benefit from market
internalization and few studies have explicitly specified underlying subsidiary mandates, bar a
few exceptions (Goerzen, Asmussen, & Nielsen, 2013; Makino, Lau, & Yeh, 2002). Several
studies have examined different functional activities, such as production, sales and services,
which reflect, though not directly indicate, investors’ objectives (Alcacer, 2006; Castellani,
Jimenez, & Zanfei, 2013; Enright, 2009).
Furthermore, investment in the same functional activity, especially production, can be
motivated by different strategic objectives. Dunning and Lundan (2008) suggest that foreign
production can be classified as natural resource-seeking, market-seeking, efficiency-seeking and
strategic-assets-seeking. Firms exhibit differential responses to the same location attributes, such
as corruption and governance hazards, when they have different mandates (Brouthers, Gao, &
McNicol, 2008; Hakkala, Norback, & Svaleryd, 2008; Slangen & Beugelsdijk, 2010). Though
existing evidence is limited and sometimes inconsistent, it illustrates the importance of
incorporating variation in subsidiary mandates into the analysis of MNE expansion strategy.
Our review suggests that an examination of subsidiary spatial structure in supranational
regions through the lens of spatial transactions costs can yield new explanations for MNE
10
location strategy. Prior studies also indicate that distinguishing subsidiary functions and strategic
mandates can help discern the differential influences of spatial transaction costs. We thus adopt a
theoretical approach that explicitly considers the impact of subsidiary heterogeneity along
functional and strategic dimensions. Our analysis follows the tradition in the internalization and
economic geography literatures of focusing on foreign investment in production subsidiaries
(Buckley & Casson, 1976; McCann, 1998; Schonberger, 1996). However, overseas production
facilities are supported by nonproduction units such as geographically dispersed trading
operations. Production and trading are the most prevalent functions in MNE networks, but they
differ significantly in activities, tangible and intangible assets, and informational needs (Alcacer,
2006; Makino, Beamish, & Zhao, 2004). Therefore, spatial transaction costs associated with
existing production and trading subsidiaries are likely to differ when considering new production
entries, making subsidiary function a potential contingency in the analysis of subsidiary spatial
structure. Similarly, subsidiary strategic mandates also have implications for assessing intrafirm
spatial relations. Spatial transaction costs may become less of a constraint when strategic
mandates, such as accessing local natural resources and R&D, are more directly influenced by
territorial geography than corporate geography. Building on this theoretical approach we develop
a set of hypotheses in the next section that collectively examine the main effect of subsidiary
spatial structure on investment strategy, and its nuanced effects under different subsidiary
functions and mandates.
3. Hypotheses
For several reasons we argue that the proximity of a potential host country to a firm’s
existing production subsidiaries in a region, controlling for home-host country distance,
11
increases the probability of production entry into that country. First, inter-subsidiary proximity
can enhance operational efficiency and flexibility of MNE production systems. One obvious
advantage is that transport costs between subsidiaries, and associated logistics costs, will be
lower when they are located in countries closer to each other. In addition, subsidiaries within
MNE regional networks often rely on timely transmission of goods and information to link
activities along value chains. Since geographic distance between subsidiaries increases
transportation and information transmission costs, more spatially proximate subsidiaries within a
region are better positioned to coordinate operations with each other.
Second, search for investment opportunities is likely to be spatially bounded since greater
distance leads to a higher degree of information asymmetry. Foreign subsidiaries often serve
regional country markets proximate to their locations and rely on regional suppliers from these
countries. Therefore, an MNE is likely to have better knowledge of the institutional and market
conditions of the countries closer to its existing establishments in a region than more distant
ones. This localized knowledge helps lower the liability of regional foreignness and facilitates
information gathering and processing, increasing the probability of identifying investment
opportunities in the proximity of its current operations (Johanson & Vahlne, 2009; Rugman &
Verbeke, 2007).
Third, an MNE’s competitive advantage depends in part on its ability to integrate complex
knowledge, particularly tacit knowledge, residing in geographically dispersed units (Kogut &
Zander, 1993). The transmission of tacit knowledge, which is context-dependent and embodied
in personal interactions, requires shared understanding of knowledge context and mutual trust
that builds on face-to-face contact and informal coordination mechanisms (Ghoshal, Korine, &
Szulanski, 1994). However, greater geographic distance increases communication barriers such
12
as travel time across time zones, discouraging staff in one location from interacting with those in
more distant subsidiaries (Ambos & Ambos, 2009). Moreover, search for knowledge can be
constrained by managers’ cognitive biases related to existing organizational routines (Levinthal,
1997). Managers located in one country are more likely to identify with regional peers in more
proximate markets because they share common beliefs and routines that arise from greater
similarity in business environments (Kim et al., 2012). Familiarity and similarity not only enable
subsidiaries to achieve greater efficiency in knowledge transfer but they may also create bias
against knowledge from distant locations (Hansen & Lovas, 2004).
In summary, geographic proximity between regional production subsidiaries will lower
spatial transaction costs within the firm. In additional to increased efficiency in coordinating and
monitoring cross-border production, lower spatial transaction costs help the firm more
effectively deploy and protect competitive advantages derived from intangible assets and
capabilities. From an internalization perspective, spatial transaction costs can be seen as partly
determining the extent to which markets can be internalized and, in the current context, a firm is
more likely to locate a new production subsidiary in the proximity of existing production
facilities in the same supranational region. Hence, we posit:
Hypothesis 1: The greater the proximity of a country to a firm's existing production
subsidiaries in a region, the greater the probability of the firm locating a new production
subsidiary in that country.
We argue that the effect of proximity to regional subsidiaries is likely to be conditioned by
the functional activities of existing units. Trading subsidiaries create value by linking production
with demand from foreign customers. They can simultaneously engage in a variety of activities,
including market research, intelligence collection, advertising, selecting foreign distributors,
13
after-sale services, logistics, and financing. The main mandate, however, is to stimulate and
generate demand for the MNE’s products and to provide the necessary services to process and
fill customer orders (Balabanis, 2000; Goerzen & Makino, 2007). Since information collection,
due diligence and sales require frequent and close contact with customers, local businesses and
government agencies, physical presence in foreign markets is often critical for trading
subsidiaries. At the same time, the type of services offered by trading subsidiaries and
knowledge created through these activities are closely linked to the local environments they face.
Thus, unlike production subsidiaries which significantly depend on internalized transfer of
proprietary technology, trading operations draw heavily on localized market knowledge.
As is the case between production subsidiaries, geographic distance between trading and
production subsidiaries increases both information and transport costs, making spatial proximity
a favorable condition for future production investment. However, we argue that proximity of
new production facilities to trading operations is not as consequential as that to production
operations. First, the relevance of knowledge originated in the same functional activities is
higher than knowledge created through activities of different functions. Same-function
subsidiaries, thus, are likely to serve as a more important source of knowledge when the firm
invests in a region. Trading units rely on location-specific market knowledge in order to improve
effectiveness in linking supply and demand, whereas production units primarily depend on
proprietary technology and tacit operational knowledge to gain efficiency. Since knowledge
overlap is limited, the quantity and complexity of information exchange between trading and
production subsidiaries will be less significant compared to those between production units.
There is some evidence that regional meetings in MNEs are organized by functional areas to
promote knowledge sharing between subsidiaries (Kim et al., 2012). Thus, we expect that the
14
benefits of spatial proximity attributable to lower information costs will be smaller between
trading and production subsidiaries.
Second, a low level of inter-functional interdependence arises in part due to the differences
between corporate geography and territorial geography. The importance of local physical
presence dictates that the location of trading activities largely conforms to the spatial distribution
of markets (Alcacer, 2006). In contrast, the location of production subsidiaries is to a greater
extent determined by spatial transaction costs among peer production units and the distribution
of factor endowments. Therefore, spatial transaction costs associated with trading units are more
of a function of territorial geography (i.e. the spatial distribution of markets) than of corporate
geography (i.e. the spatial distribution of foreign operations). There are limits to minimizing
spatial transaction costs by locating production in the proximity of trading operations because it
is uneconomical to spread production capacity in the same way that trading operations are
structured. Thus, according to the internalization framework, the impact of spatial proximity
between production and trading subsidiaries on subsequent production investment will be limited
because, in this case, territorial geography is a more influential factor than corporate geography
in determining the extent of market internalization. Thus, we posit:
Hypothesis 2: The effect of a country’s proximity to a firm’s existing production
subsidiaries in a region on subsequent production entry into that country is stronger than that
for proximity to existing trading subsidiaries.
The impact of spatial transaction costs on investment decisions is likely to be contingent on
a subsidiary’s mandate. We argue that location choices of natural resource-seeking subsidiaries
will be less responsive to inter-subsidiary proximity than those of subsidiaries mandated to seek
market, efficiency or strategic assets. Since natural resources are relatively immobile and
15
expensive to transport, the location of a production unit with a natural resource-seeking mandate
is largely determined by the geographic distribution of resource endowments. In other words, the
influence of territorial geography strengthens in relation to corporate geography when seeking
natural resources is part of a production subsidiary’s mandate.
There is also evidence that firms develop location specific capabilities to cope with
business and political risks when seeking natural resources in foreign countries (Cuervo-Cazurra
& Genc, 2008; Holburn & Zelner, 2010; Schotter & Beamish, 2013). This location-specific
knowledge, though valuable, is likely to be less applicable to other units that do not actively seek
local natural sources. In contrast, subsidiaries of market-seeking and efficiency-seeking
mandates occupy the final and intermediate stages within a firm’s international production
network. They rely heavily on the exploitation of common proprietary knowledge and integrated
production processes to enhance their operational efficiency. Similarly, asset-seeking
investments, often through acquisition, are undertaken to strengthen a firm’s ownership-specific
advantage and to complement a firm’s existing foreign operations (Dunning & Lundan, 2008).
Therefore, internal communication and logistic coordination are particularly important for these
investments; consequently, the reduction of spatial transaction costs is likely to be more
beneficial for these production subsidiaries than for those with a natural resource-seeking
mandate. Thus, we posit:
Hypothesis 3: The effect of a country’s proximity to a firm’s existing production
subsidiaries in a region on subsequent production entry into that country will be weaker when
the new subsidiary has a natural resource-seeking mandate.
Analogous to Hypothesis 3, we expect that an R&D mandate will diminish the impact of
inter-subsidiary proximity on the location choice of production subsidiaries. Foreign subsidiaries
16
increasingly assume a significant role not just as a recipient and enabler of knowledge transfer
within the firm but also as a source of innovation (Birkinshaw & Hood, 1998; Feinberg & Gupta,
2004). These subsidiaries tend to engage in exploitative and/or explorative types of R&D. The
former focuses on extending the existing knowledge base through incremental innovation and
product development whereas, in the latter case, subsidiaries are directed toward more basic
research and the development of new technical knowledge (Frost, 2001).
The locations of exploitative R&D activities have been found to be closely related to local
industry strength (Frost, Birkinshaw, & Ensign, 2002). On the other hand, firms engaged in more
explorative research are often drawn to locations with large talent pools and strong knowledge
infrastructure, such as universities and technology centers (Demirbag & Glaister, 2010).
However, regardless of the orientation of R&D activities, external environmental conditions,
especially supply side forces, predominantly determine locational advantages of a potential host
country (Kuemmerle, 1999). The influences of these forces, which reflect territorial geography
rather than corporate geography, are likely to strengthen when R&D is part of a production
subsidiary’s mandate. Therefore, while lower spatial transaction costs resulting from geographic
proximity between production units may remain beneficial, their impact is likely to decline in the
case of an R&D mandate. Thus, we posit:
Hypothesis 4: The effect of a country’s proximity to a firm’s existing production
subsidiaries in a region on subsequent production entry into that country will be weaker when
the new subsidiary has an R&D mandate.
In contrast, while we have argued that proximity to trading subsidiaries is not as influential
for production entries as proximity to other regional production subsidiaries (Hypothesis 2), we
expect its impact to increase when a production subsidiary is mandated to conduct R&D.
17
Overseas subsidiaries create new products or skills in response to knowledge of local conditions,
such as the knowledge about consumer tastes or local regulations. Acquiring and transmitting
knowledge of local markets are considered a main source of information costs for overseas R&D
activities (Buckley & Carter, 2004). A fundamental role of trading subsidiaries is to collect and
disseminate information about local markets through a variety of activities, including market
research, due diligence and after-sale service. Location-specific market knowledge originating in
trading units, though of limited value in improving production efficiency, may prove particularly
useful for R&D activities. Therefore, by lowering spatial transaction costs, proximity to trading
subsidiaries will help address information challenges facing production subsidiaries with an
R&D mandate. Thus, we posit:
Hypothesis 5: The effect of a country’s proximity to a firm’s existing trading subsidiaries
in a region on subsequent production entry into that country will be stronger when the new
subsidiary has an R&D mandate.
4. Method
4.1 Sample
We test our hypotheses using data on the country location choices for foreign production
subsidiaries established by Japanese public manufacturing firms between 1971 and 2006. Japan
has been one of the world’s largest sources of outward FDI for three decades. The manufacturing
sector is particularly active in foreign expansion, and manufacturing firms have entered a wide
range of countries, providing significant variation in location choices during the sample period.
Subsidiary data was compiled from Kaigai Shinshutsu Kigyou Souran (Japanese Overseas
Investment), a directory of operating foreign subsidiaries of Japanese firms. The Japanese
18
Overseas Investment directory provides information on each subsidiary, including its host
country, industry classification and founding year. Data on parent firms was extracted from the
Nikkei Economic Electronic Databank System (NEEDS). NEEDS reports financial information
for firms listed on the Tokyo Stock Exchange and is available from 1971. The study period was
determined mainly by data availability.
Our sample consists of large, diversified companies whose investments span various
industries. The proximity variables and subsidiary counts with regard to production entries are
based on the grouping of subsidiaries sharing the same 2-digit Standard Industrial Classification
(SIC) code (all trading subsidiaries share the same industry code). Production and trading
account for nearly 90% of the subsidiaries reported by sample firms. Other subsidiaries are
engaged in finance, real estate, agriculture, and miscellaneous services, and are excluded from
the operationalization of variables of theoretical interest.
We use the regional classifications of the World Bank and the United Nations to identify
seven regions: Africa, Asia, Europe, Latin America & the Caribbean, Middle East & North
Africa (MENA), North America and Oceania (Arregle et al., 2009) (see Appendix 1). Since our
analysis is concerned with the impact of the spatial structure of existing subsidiaries, we exclude
firms’ first production entries into a region. The resulting pooled dataset for estimating
production entry contains 6,875 entries in 518,342 firm-year-country observations.
4.2 Measures
4.2.1 Dependent Variable
Our unit of analysis is a firm’s entry decision for a potential host country in a given year.
Accordingly, the dependent variable – Entry – is a binary variable. For each firm-year-country
19
observation, “1” indicates that the parent firm established a production subsidiary; “0” indicates
otherwise.
4.2.2 Independent Variables
We construct two proximity variables – Proximity to Regional Production Subsidiaries and
Proximity to Regional Trading Subsidiaries. These are defined as the reciprocal of the average
distance between a potential host country and a firm’s existing subsidiaries of a given function in
a region:
Proximity i=∑
jN j
∑j
DISTANCE ij∗N j
(1)
where ‘DISTANCEij’ is the distance in thousands of kilometers from the largest city (by
population) of the host country i to the largest city of a same-region country j where the firm’s
existing production (or trading) subsidiaries are located, and ‘Nj’ is the number of production (or
trading) subsidiaries in country j. The proximity variables are set to zero when there are no
regional subsidiaries. This measure reflects the theoretical underpinning that spatial transaction
costs directly depend on geographic distance covered (McCann, 2008). It is also consistent with
operationalization approaches adopted in the location choice literature (Chacar & Lieberman,
2003; Chung, 2001; Mitra & Golder, 2002; Nachum & Wymbs, 2005), and is analogous to a
widely used centrality measure in the network literature (Freeman, 1979).
4.2.3 Control Variables
We control for country level factors that existing research finds can influence location
decisions. GDP and GDP Growth Rate, adjusted for purchasing power parity, are proxies for
market size and potential size, two factors that are commonly attractive to investors. GDP
20
Growth Rate is measured as the average annual growth in real GDP in the preceding three-year
period. We include GDP per capita as a measure of a country’s prosperity and consumer
purchasing power. Trade Ratio is the ratio of a country’s total exports and imports to its GDP.
While there is some evidence that FDI complements trade, research on MNE investment
suggests that there may also be a substitution effect between foreign production and export
activities (Blonigen, 2001). We include both existing FDI Stock and FDI as a percentage of
GDP, measured as the annual dollar value of net foreign direct investment in a country
(excluding that from Japan) as a percentage of GDP, to capture the country’s attractiveness to
foreign investors. FDI data was obtained from UNCTAD except for that on Taiwan, which was
obtained from Taiwan’s Ministry of Economic Affairs. Country Resource Exports, measured as a
host country's exports of primary goods (including fuels, minerals, agricultural and food
resources) as a percentage of GDP, is included to account for the country’s natural resource
endowments (Isham et al., 2005). We also include Country Patent Grants, measured as the
logged count of yearly patent grants received by a country, as a proxy for the country’s
innovative capacity (Furman, Porter, & Stern, 2002).
We include three variables to control for a host country’s institutional environment.
Political Stability, constructed using the Henisz’s (2002) POLCONV data, measures constraints
embedded in a country’s political structures, which support credible policy commitments. Rule
of Law is drawn from the Rule of Law index in the Governance Indicators data (Kaufmann,
Kraay, & Mastruzzi, 2008). For missing data years we interpolate using the closest year for
which data is available. We use Hofstede’s (2001) cultural dimensions to construct Cultural
Distance between Japan and a potential host country (Kogut & Singh, 1988), though we exclude
the fifth dimension – long-term orientation – due to the lack of data available for the majority of
21
countries in our sample. We also include Bilateral Geographic Distance between home and host
countries measured as the distance in thousand kilometers between Tokyo and the largest city of
a host country. Finally, to control for the potential influence of spatial relations with non-regional
subsidiaries, we include Proximity to Non-regional Production Subsidiaries using the same
method as described in Equation (1).
We include Country Production Operation and Country Trading Operation measured,
respectively, as the logged count of a firm’s production and trading subsidiaries in a host country
to account for MNEs’ propensity to repeatedly invest in a country and for the differences in the
density of investment activities between firms over time (Guillén, 2003; Kogut & Chang, 1996).
A firm’s foreign investment strategy can also be influenced by the strategic choices of peer firms
(Henisz & Delios, 2001; Jiang, Holburn, & Beamish, 2014). We thus include Industry Country
Production Operation measured as the logged count of production subsidiaries with the same
industry codes established by other Japanese firms in a host country. To distinguish the spatial
element from the firm’s overall propensity to repeatedly invest in a region, we include Region
Production Operation measured as the logged count of production subsidiaries in the same
region excluding the focal host country.
Parent firm size can affect the level of foreign investment so we include Firm Size
measured as the logarithm of parent company sales adjusted by the industry mean. Research has
shown that firms of higher capital intensity tend to expand abroad. Capital Intensity is measured
as the logged ratio of total fixed assets to total sales adjusted by the industry mean (Caves, 2007).
4.3 Estimation Approach and Statistical Interpretation
22
Conditional logit is a common estimation approach in location choice research. Although a
conditional logit model could be applied to our data it has several limitations. First, the
‘independence of irrelevant alternatives’ (IIA) assumption, which means that the probability
ratios do not depend on other alternatives in the choice set, is not satisfied in many empirical
settings (Martin, Swaminathan, & Tihanyi, 2007). Second, the model does not accommodate
chooser-specific attributes that are invariant across choices as independent variables – such as
year of entry and parent firm size.
A logit model is an alternative estimation technique for this study that is not limited by the
constraints of a conditional logit model. However, the small proportion of entry events relative to
non-entry events indicates a rare events data structure. King and Zeng (2001) showed that a
logistic regression can produce biased estimation of event probability when applied to rare
events data, and developed a choice-based sampling procedure to correct for potential estimation
bias. Following recent application of this technique in management research (Cockburn &
MacGarvie, 2011; Folta & O'Brien, 2004; Hallen, 2008; Singh, 2005), we derive a sample
consisting of all entry events and randomly selected non-entry observations. For each entry
event, ten non-entry firm-year-country observations from the same year are included. We then
implement a rare event logit model for the constructed sample. We also include parent firm
industry, year, and region fixed effects using dummy variables to account for unobserved
industry, temporal, and regional heterogeneities.
5. Results
Table 1 presents summary statistics and correlations for the subsample of production
entries constructed using the choice-based sampling procedure. All the individual variables’
23
variance inflation factor values are less than three, indicating that multicollinearity is unlikely to
be a concern. Table 2 reports the rare event logit estimation results: Model 1 is the baseline
specification without proximity variables or interaction terms, which are included in Model 2 and
other alternative specifications. The coefficient estimates are highly consistent between Models 1
and 2, and we use Model 2 as the preferred specification to test Hypotheses 1 and 2.
***************************Insert Tables 1-2 about here***************************
The control variables largely behave as expected. Estimated results for GDP and GDP
Growth Rate indicate that Japanese firms are more likely to enter countries with greater market
size and economic growth rate. A country that has a lower GDP per capita is also found to be
more attractive to MNEs, perhaps to access lower-cost labor. High levels of Country Resource
Exports and Country Patent Grants appear to be attractive locational attributes to Japanese
MNEs. Consistent with expectations, FDI as percentage of GDP is positively related to market
entry. FDI Stock, however, is negatively related to entries; this may reflect the fact that a large
number of production investments occurred in developing countries which tend to have lower
levels of accumulated FDI, especially in the 1980s and 1990s. A higher value of Rule of Law is
positively related to production entries and, as expected, Cultural Distance deters entry.
Bilateral Geographic Distance and Proximity to Non-regional Production Subsidiaries are
negatively related to production entry. This latter result may imply that when a potential host
country is distant from a firm’s subsidiaries in other regions, it is costly to service this market
using those subsidiaries, thereby making direct investment in this country more attractive.
Investment decisions exhibit a strong country-specific focus as indicated by the positive
effects of Country Production Operation and Industry Country Production Operation. Country
24
Trading Operation does not have a significant impact on production entries, implying relative
low interdependence between the two functions as far as country-level aggregation is concerned.
Region Production Operation has a statistically weak yet positive effect on production entry.
Larger Firm Size is negatively associated with foreign investment. One possible explanation for
this result is that dominant firms in the domestic market tend to be less internationalized than
smaller firms (Mascarenhas, 1986). We found no significant influence of Capital Intensity.
Hypothesis 1 predicts that the spatial proximity between a potential host country and a
firm’s existing production subsidiaries in the same region enhances the probability of the firm
locating a production subsidiary in that country. Coefficient estimates in a non-linear model
cannot be directly interpreted as marginal effects as in a linear model (Hoetker, 2007). Hence, in
order to enable accurate interpretation, we adopt a simulation-based procedure to assess the
impact of independent variables on the probability of production entry (King, Tomz, &
Wittenberg, 2000). This procedure uses Monte Carlo simulation to convert the raw output of a
rare event logit model into changes in predicated probabilities. Following common practice, we
simulated the parameters 1,000 times in this study. The simulation results are most clearly
understood through graphic presentation (Zelner, 2009).
Based on the estimation results from Model 2, the upper curve in Figure 1 shows the
simulated effects of Proximity to Regional Production Subsidiaries on the probability of
production entry (measured on the y-axis). Consistent with our prediction, the upward curve
clearly indicates that inter-subsidiary proximity is positively related to production entry. The
simulation enables intuitive assessments of the magnitude of the hypothesized effect: all else
equal, the probability of production entry increases by 30% when Proximity to Regional
Production Subsidiaries increases by one standard deviation from its sample mean.2 The
2 Other variables are set at their sample mean in simulations.
25
simulated confidence interval of this positive change is statistically significant at the 5% level.
These results provide strong support for Hypothesis 1.
***************************Insert Figure 1 about here***************************
Hypothesis 2 proposes that the positive proximity effect derived from the spatial relations
with production subsidiaries is greater than that with trading subsidiaries. The lower curve in
Figure 1 depicts the simulated effects of Proximity to Regional Trading Subsidiaries on the
probability of production entry. The significant difference in the slopes of the upper and lower
curves implies that the magnitude of the impact of Proximity to Regional Production
Subsidiaries is greater than that of Proximity to Regional Trading Subsidiaries. For instance,
entry probability only increases by 1% when Proximity to Regional Trading Subsidiaries
increases by one standard deviation from its sample mean and this increase is not statistically
significant. A Wald test also confirms that the effect of Proximity to Regional Production
Subsidiaries is greater than that of Proximity to Regional Trading Subsidiaries (chi2=62.1(1),
p<0.001). Therefore, Hypothesis 2 is strongly supported.
Hypotheses 3-5 focus on whether the effects of proximities to regional production and
trading subsidiaries differ depending on the strategic mandates of new production subsidiaries.
The Japan Overseas Investment publication provides self-reported investment purposes of
foreign subsidies, including two that are pertinent to our theoretical model – “access natural
resources or materials” and “development of products and planning”. 3 However, only
approximately half of the sampled subsidiaries reported their strategic mandates. Model 3 reports
3 The Japan Overseas Investment classified FDI into fifteen commonly observed purposes. Other investment purposes are “access local market,” “alliance with customers,” “access labor force,” “reverse imports to Japan,” “development of overseas production network,” “preferential treatment of local government” “development of overseas distribution network,” “export to third countries,” “exchange rate risk measures,” “loyalty and information collection,” “expansion into new business,” “enhanced regional headquarters,” and “hedge to trade fiction.”
26
the estimation results when we only include entries whose mandates are reported, and results are
consistent with those from Model 2. We then use a subgroup analysis approach and estimate
entries of different mandates in separate regression models (Hoetker, 2006; Schotter & Beamish,
2013). We divide our sample based on whether “access natural resources or materials” or
“development of products and planning” is listed as an investment mandate.
Hypothesis 3 predicts that the positive effect of proximity to other regional production
subsidiaries on subsequent production entry will diminish when the new subsidiary has a natural
resource-seeking mandate. The results of the subgroup analysis (Models 4 and 5) show a
significant difference in coefficient estimates for Proximity to Regional Production Subsidiaries.
The coefficient is statistically insignificant in Model 4 (natural resource-seeking group) but is
statistically significant (p<0.01) in Model 5 (non-natural resource-seeking group), providing
strong support for Hypothesis 3.
Hypothesis 4 similarly predicts that an R&D mandate will lead to a smaller effect of
proximity to other regional production subsidiaries on subsequent production entry. The
coefficient estimates for Proximity to Regional Production Subsidiaries are both positive in
Models 6 and 7, but only marginally significant (p<0.1) in Model 6 (R&D group), providing
some support for Hypothesis 4. Because we use divided groups, the sample means are different;
therefore, the marginal effect is not directly comparable across the models. Further simulation
shows that the probability of production entry increases by 25% (R&D group) and 29% (non-
R&D group), respectively, when Proximity to Regional Production Subsidiaries increases by one
standard deviation from its sample mean. The lack of stronger support for the hypothesis implies
that tight internal linkages may remain critical even when firms attempt to tap into local
27
intellectual resources (Alcacer & Zhao, 2012), and that the R&D mandate, though important,
may not be the primary strategic objective of production subsidiaries.
Hypothesis 5 further proposes that the effect of proximity to regional trading subsidiaries
on subsequent production entry will be stronger when a new entry has an R&D mandate. The
results of the subgroup analysis (Models 6 and 7) confirm that the coefficient estimates for
Proximity to Regional Trading Subsidiaries are different. The coefficient is statistically
significant (p<0.01) and signed as expected in Model 6 (R&D group), and is statistically
insignificant in Model 7 (non-R&D group). Thus, Hypothesis 5 is strongly supported.
5.1 Subsidiary Performance
An underlying assumption of our conceptual model is that lower spatial transaction costs
facilitate internalization, which in turn improves organizational performance. Thus, to further
analyze the validity of our model, we conduct additional analysis to examine how the spatial
structure of foreign subsidiaries affects their performance.
Due to the absence of detailed financial performance data at the subsidiary level, we utilize
instead a categorical measure provided by the Japanese Overseas Investment data source. Each
year, subsidiary general managers are asked to assess and report their subsidiaries’ performance
in three ascending categories: loss, break-even, and gain. Thus, our dependent variable –
Subsidiary Performance – is an ordinal variable. On average, subsidiaries in our location choice
model reported performance data 5.2 times between 1986 and 2006.4 Prior research has shown
that subjective performance data is highly correlated with objective performance data, alleviating
potential concerns about reporting bias (Isobe, Makino, & Montgomery, 2000).
4 Performance data is only available between 1986 and 2006.
28
We modify other measures in order to properly model subsidiary performance. First, we
extend all time-varying variables, including the main proximity measures, from a subsidiary’s
founding year to all operating years. Second, we include several subsidiary-specific variables.
Subsidiary Size is measured as the logarithm of the number of employees. Subsidiary Age is
measured as the logarithm of the difference between the founding year and the year performance
was reported. Subsidiary Ownership is a categorical variable that classifies a parent firm’s
ownership position in a subsidiary as whole ownership, majority or equal ownership, or minority
ownership. In addition, we include Expatriate Ratio, measured as the ratio of the number of
Japanese Employees to total subsidiary employment, to account for managerial control of the
parent firm at a subsidiary (Peng & Beamish, 2014). Finally, we include Parent Firm
Performance, measured as the preceding three-year average of return on assets to control for the
impact of parent firm performance on subsidiary performance.
Given the ordinal nature of our dependent variable, we employed an ordered logit model to
examine subsidiary performance. Since multiple performance observations from the same
subsidiary are likely to be correlated, we use a clustered variance estimator to control for intra-
group correlation. Our specification also includes region, subsidiary industry, and year fixed
effects. Model 8 in Table 2 presents the estimation results of the ordered logit model. The
coefficient estimate for Proximity to Regional Production Subsidiaries is statistically significant
(p<0.05). When Proximity to Regional Production Subsidiaries increases by one standard
deviation from its sample mean, the simulated probability of a subsidiary reporting a ‘gain’
increases from 0.64 to 0.66 and the simulated probability of reporting ‘loss’ decreases from 0.16
to 0.15. These changes are statistically different from zero at the 5% level. We find no significant
relationship between Proximity to Regional Trading Subsidiaries and subsidiary performance.
29
Overall, the results of our performance analysis are consistent with the central findings from our
location choice analysis. This provides additional support for our overall theoretical prediction
that inter-subsidiary proximity is conducive to foreign production investment.
5.2 Robustness of Findings
We employ alternative estimation approaches to examine the sensitivity of our results to
the choice of specification. Two common methods in prior studies are conditional logit and event
history analysis using discrete time logit (Henisz & Delios, 2001; Kalnins, 2004). Since the
conditional logit model does not accommodate firm-specific variables, Firm Size, Capital
Intensity and year and industry fixed effects are omitted. Due to the longitudinal data structure of
the discrete time logit analysis, measures associated with production subsidiaries are no longer
based on subsidiaries in the same industry. Instead, they are now based on the grouping by the
same parent firm. Industry Country Production Operation is omitted from the model for the
same reason. We also remove all subsidiaries of less than 20 employees to ensure that our
analysis includes subsidiaries with substantial strategic significance (Beamish & Inkpen, 1998).
The results also remain consistent when we include Subsidiary Size in the production entry
models. Furthermore, we substitute the cultural distance measure with the psychic distance data
(covering 25 host countries) provided by Hakanson and Ambos (2010). The coefficient estimates
on terms of theoretical interest in these alternative specifications are consistent with the main
results reported in Model 2, indicating reliability of our findings. Finally, additional analysis
shows that overseas investment rates increased following the signing of the Plaza Accord, but
fell during the economic slump of the 1990s.5
6. Discussion
5 Exhibits are omitted due to space constraints.
30
In this study we argue that country location choices for MNEs are influenced by the cross-
country spatial structure of existing regional subsidiaries. A statistical analysis of foreign
production investments by Japanese manufacturing firms provides substantial empirical support
for our theoretical predictions. We find that the proximity of a host country to a firm’s regional
production subsidiaries, but not to trading subsidiaries, enhances the probability of production
investment in that country. This positive effect, however, diminishes when the new production
subsidiary has a mandate to seek local natural resources or to conduct R&D. In contrast, the
effect of proximity to regional trading subsidiaries strengths when R&D is part of a new
subsidiary’s mandate.
6.1 Contributions
Our study contributes to MNE research in three ways. It first contributes to the growing
literature on the strategic importance of geography by demonstrating intra-firm spatiality as a
distinct source of spatial variation and a significant determinant of MNE foreign investment
decisions. Our findings complement prior location studies that focus on spatial variations
resulting from home-host country distance and subnational industry clustering by showing that
modeling the cross-country spatial linkages between foreign subsidiaries can yield new insights
into MNE location strategies. In addition, this study echoes prior research that shows the cultural
profile of a firm’s existing location portfolio can affect the rate of subsequent internationalization
and corporate performance (Hutzschenreuter & Voll, 2008; Hutzschenreuter, Voll, & Verbeke,
2011).
Second, our analysis advances prior research on the geographic distribution of foreign
investment, which has largely omitted consideration of interdependencies between subsidiary
31
functions and mandates (Alcacer, 2006; Flores & Aguilera, 2007). We explicitly examine
interactions between subsidiary activities and corporate spatial structure embedded in territorial
geography. In particular, the significant differences between the effect of proximity to production
subsidiaries and that to trading subsidiaries imply that operational differences and knowledge
requirements associated with different functions are likely to serve as a boundary condition as to
how much internalization advantage firms can gain through spatial proximity, and how corporate
geography shapes investment strategy. The results also imply more generally that subsidiary
function may be a significant determinant of inter-subsidiary relationships. For instance,
functional differences may directly determine the degree of knowledge complementariness
between a source subsidiary and a receiving subsidiary, which in turn affects knowledge transfer
between them (Andersson et al., 2015).
On the other hand, our unique measures of subsidiary strategic mandate allow us to tease
out how the geographic configuration of MNE subsidiaries and investment motivations interact
to influence location strategies. Results on the differential influences of inter-subsidiary
proximity in the cases of natural resource-seeking and R&D mandates offer novel evidence on
the key role of managerial intention in determining the geographic pattern of foreign expansion
(Hutzschenreuter, Pedersen, & Volberda, 2007). Overall, our findings with respect to subsidiary
level attributes, such as subsidiary function and strategic mandates, provide a more sophisticated
understanding of MNE location strategy.
Finally, our study enriches internalization theory through a focused analysis of inter-
subsidiary spatial relations. Our findings suggest that economizing on spatial transaction costs,
which contributes to overall internalization costs, may enhance the firm’s competitive advantage.
A positive association between a subsidiary’s performance and its geographical proximity to
32
other regional subsidiaries, as shown in our additional analysis, further implies that lower
communication and logistic costs resulting from geographic proximity can improve operational
coordination and the efficiency of the internal market for firm knowledge. Researchers have
repeatedly noted the lack of research on spatial elements in internalization analysis (Buckley,
2009; McCann & Mudambi, 2004). As the MNE’s operation increasingly relies on integrated
subsidiary systems across countries, there is a clear need to extend spatial analysis beyond home-
host country distance and subnational agglomeration to include cross-country connectivity
between subsidiaries. Our analysis provides specifications and insights that can facilitate more
nuanced application of the internalization framework in examining the growth and performance
of the MNE.
Building on the notion of spatial transaction costs, our analysis also establishes a stronger
connection between regional strategy research and internalization theory. We develop new
insights for the literature by showing that concentration and connectivity are distinct aspects of
firms’ regional strategy. Besides the stock of existing regional operations (Arregle et al., 2009;
Rugman & Verbeke, 2004), the spatial relations between the subsidiaries constitute structural
conditions for intermediate levels of international integration, and thus influence subsequent
investment decisions.
6.2 Managerial Implications
The central findings of this study support the notion that both ‘space’ and ‘place’ are
critical dimensions of potential investment locations. While it is imperative to evaluate the
economic and institutional conditions of a location, managers should account for how the spatial
structure and connectivity of a firm’s existing investment portfolio can influence the
33
performance of overseas operations. These two distinct dimensions imply potential trade-offs in
selecting investment locations. Firms are typically attracted to country markets that have
investor-friendly business environments and/or substantial market potential. However, a location
with attractive attributes that is remote from a firm’s existing regional operations can create
significant information and logistic costs. As MNEs increasingly rely on cross-border production
systems and locally-developed expertise to improve operating efficiency, managers are advised
not to base their location decisions solely on environmental factors, such as market size and
national culture. Instead, managers should be aware of the costs of spatial isolation and carefully
weigh the benefits of inter-subsidiary proximity in relation to other environmental factors.
Another key managerial implication is that firms can gain regional competitive advantage
using ‘geographic proximity’ as a tool to reduce cross-border coordination costs in a complex
global MNE system. Intrafirm spatial tightness may prove a structural advantage when MNEs
simultaneously link, reconcile, and integrate an increasing number of issues as their operations
become more diverse and more interdependent. Managers should also recognize that the benefits
of inter-subsidiary proximity vary by function and subsidiary mandate, and thus should monitor
and adjust over time the firm’s geographic configuration to enhance internal communication and
logistic efficiency. Especially, our findings imply that it is necessary to build geographically
disparate innovation processes around multiple trading or sales units that can offer rich
information on local markets. Tight spatial linkages between innovative activities and existing
production and trading operations not only can serve as a mechanism for information sharing,
but also help firms internalize proprietary knowledge and reduce the risk of knowledge outflows
(Alcacer & Zhao, 2012).
34
Although our findings show that geographic distance between subsidiaries can pose
significant costs, there are also ways to alleviate these obstacles, especially information costs.
The more that managers are aware of distance-related costs, the more they will undertake
appropriate measures to address the challenge. Managers are advised to proactively adopt
informal and formal communication mechanisms, particularly face-to-face networking, to
enhance interactions between regional subsidiaries. In this effort, subsidiaries located afar from
others should receive extra resources since they have the most to gain from mitigating the
negative effect of geographic distance.
6.3 Limitations and Future Research
Naturally our results should be interpreted with some caution due to a variety of
limitations, which also suggest directions for future research. First, since the conceptual focus of
our analysis is cross-border spatial linkages within a firm, we did not explore industry clustering
effects at the subnational level. However, we recognize that foreign subsidiaries are often located
in particular agglomerations within a country and are embedded in localized competitive and
institutional settings (Beugelsdijk & Mudambi, 2013). Just as intra-firm spatial relationships
have strategic consequences, connections with local suppliers, competitors, and other external
stakeholders can convey benefits or liabilities. A possible spatial implication is that a strong
agglomeration effect may influence a subsidiary’s dependence on sister subsidiaries in proximate
countries. Future research could extend the scope of our study and explore potential interactions
between local agglomeration, particularly at the state, provincial, or municipal levels, and the
transnational structure of MNE operations.
35
In addition, since granular, street-level location information for many subsidiaries is
incomplete in our data, we chose to use the distances between the largest cities of respective
countries as proxies for geographic distances between subsidiaries. Although this proxy
reasonably approximates the distance between subsidiaries and is commonly used in the
literature (Ambos & Schlegelmilch, 2004; Flores & Aguilera, 2007; Kang & Kim, 2010;
Ramasamy, Yeung, & Laforet, 2012), it nonetheless is suboptimal when a host country has a
large territorial area. Due to the same data constraint, our analysis does not include measures for
distance between subsidiaries in the same country, though Country Production Operation and
Country Trading Operation partially account for firm-specific spatial variation within a country.
To understand better how intra-firm spatial structure affects investment strategy, researchers
could assemble a more complete picture of the physical distribution of foreign operations both
across and within countries for a smaller number of firms.
Second, our conceptual model does not take into account potential competitive
relationships between subsidiaries. However, cooperation and competition can exist
simultaneously between MNE subsidiaries as they vie for internal resources, corporate attention
and political power (Andersson et al., 2015; Bouquet & Birkinshaw, 2008; Luo, 2005; Mudambi,
Pedersen, et al., 2014). Thus, geographic proximity between subsidiaries may enhance
competition at the same time as it improves coordination. Future research may build on our
partial equilibrium analysis to explore whether and how competitive and political dynamics
between subsidiaries moderate the influence of corporate spatial structure. Moreover, subsidiary
mandates may change over time and, as a result, alter the benefits of spatial proximity between
subsidiaries (Birkinshaw & Hood, 1998). An interesting extension of our study would be to
36
investigate how the evolution of subsidiary mandate and inter-subsidiary spatial relations
influence each other.6
Third, the generalizability of our results is also limited by the fact that our sample consists
of firms from Japan only. Japanese MNEs historically have adopted a more centralized approach
to the management of foreign operations than have western firms, and their production networks
tend to be more tightly integrated (Bartlett & Ghoshal, 1998). In addition, while the sample firms
established a large number of plants in North America and Europe during the studied period,
Asia accounted for the largest portion of production investments. Since firms may adjust their
investment strategy depending on where they primarily operate, our results may be partially
driven by the idiosyncrasies of the Asia Pacific region. It will be particularly fruitful to utilize a
sample of MNEs from diverse home countries to provide additional tests of, and to refine, our
theoretical model. Alternatively, researchers may delve deeper into the specific institutional and
cultural characteristics of a home country to examine how attributes such as tolerance of
uncertainty interact with corporate geography in determining firms’ investment decisions.7
7. Conclusion
This paper finds that when an MNE establishes new production facilities it tends to choose
country markets more proximate to its existing regional subsidiaries – and these subsidiaries
subsequently perform better than more distant ones. Spatial proximity to existing production
subsidiaries is less consequential for natural resource-seeking investment; however, proximity to
trading operations is preferable when R&D is part of a new subsidiary’s strategic mandate. In
conclusion, we show that the spatial configuration of existing subsidiaries is a significant
6 We thank an anonymous referee for this insight.7 We thank an anonymous referee for this insight.
37
geographic determinant of MNE expansion strategy, and that location strategies built on
corporate geography can be a source of competitive advantage.
REFERENCES
Ahlstrom, D. (2015). From the editors: Publishing in the Journal of World Business. Journal of World Business, 50, 251-255.
Ahlstrom, D., Levitas, E., Hitt, M., Dacin, M. T., & Zhu, H. (2014). The three faces of China: Strategic alliance partner selection in three ethnic Chinese economies. Journal of World Business, 49, 572-585.
Alcacer, J. (2006). Location choices across the value chain: How activity and capability influence collocation. Management Science, 52, 1457-1471.
Alcacer, J., & Zhao, M. (2012). Local R&D strategies and multilocation firms: The role of internal linkages. Management Science, 58, 734-753.
Ambos, B., & Schlegelmilch, B. (2004). The use of international R&D teams: An empirical investigation of selected contingency factors. Journal of World Business, 39, 37-48.
Ambos, T. C., & Ambos, B. (2009). The impact of distance on knowledge transfer effectiveness in multinational corporations. Journal of International Management, 15, 1-14.
Andersson, U., Gaur, A., Mudambi, R., & Persson, M. (2015). Unpacking interunit knowledge transfer in multinational enterprises. Global Strategy Journal, 5, 241-255.
Arregle, J. L., Beamish, P., & Hebert, L. (2009). The regional dimension of MNEs' foreign subsidiary localization. Journal of International Business Studies, 40, 86-107.
Balabanis, G. (2000). Factors affecting export intermediaries' service offerings: The British example. Journal of International Business Studies, 31, 83-99.
Bartlett, C., & Ghoshal, S. (1998). Managing across borders: The transnational solution (2nd ed.). Boston, MA: Harvard Business School Press.
Beamish, P. W., & Inkpen, A. C. (1998). Japanese firms and the decline of the Japanese expatriate. Journal of World Business, 33, 35-50.
Beugelsdijk, S., McCann, P., & Mudambi, R. (2010). Introduction: Place, space and organization - economic geography and the multinational enterprise. Journal of Economic Geography, 10, 485-493.
Beugelsdijk, S., & Mudambi, R. (2013). MNEs as border-crossing multi-location enterprises: The role of discontinuities in geographic space. Journal of International Business Studies, 44, 413-426.
Birkinshaw, J., & Hood, N. (1998). Multinational subsidiary evolution: Capability and charter change in foreign-owned subsidiary companies. Academy of Management Review, 23, 773-795.
38
Blonigen, B. A. (2001). In search of substitution between foreign production and exports. Journal of International Economics, 53, 81-104.
Bouquet, C., & Birkinshaw, J. (2008). Weight versus voice: How foreign subsidiaries gain attention from corporate headquarters. Academy of Management Journal, 51, 577-601.
Brouthers, L. E., Gao, Y., & McNicol, J. P. (2008). Corruption and market attractiveness influences on different types of FDI. Strategic Management Journal, 29, 673-680.
Buckley, P. (2009). Internalisation thinking: From the multinational enterprise to the global factory. International Business Review, 18, 224-235.
Buckley, P., & Carter, M. (2004). A formal analysis of knowledge combination in multinational enterprises. Journal of International Business Studies, 35, 371-384.
Buckley, P., & Casson, M. (1976). The Future of the Multinational Enterprise. London: Macmillan.
Buckley, P., Devinney, T., & Louviere, J. (2007). Do managers behave the way theory suggests? A choice-theoretic examination of foreign direct investment location decision-making. Journal of International Business Studies, 38, 1069-1094.
Castellani, D., Jimenez, A., & Zanfei, A. (2013). How remote are R&D labs? Distance factors and international innovative activities. Journal of International Business Studies, 44, 649-675.
Caves, R. E. (2007). Multinational Enterprise and Economic Analysis (3rd ed.). Cambridge, U.K.: Cambridge University Press.
Chacar, A. S., & Lieberman, M. B. (2003). Organizing for technological innovation in the U.S. pharmaceutical industry. In J. Baum & O. Sorenson (Eds.), Advances in Strategic Management: Geography and Strategy (Vol. 20, pp. 299-322). Kidlington: JAI.
Chung, W. (2001). Mode, size, and location of foreign direct investments and industry markups. Journal of Economic Behavior and Organization, 45, 185-211.
Cockburn, I. M., & MacGarvie, M. J. (2011). Entry and patenting in the software industry. Management Science, 57, 915-933.
Coval, J. D., & Moskowitz, T. J. (1999). Home bias at home: Local equity preference in domestic portfolio. Journal of Finance, 54, 2045-2073.
Cuervo-Cazurra, A., & Genc, M. (2008). Transforming disadvantages into advantages: Developing-country MNEs in the least developed countries. Journal of International Business Studies, 39, 957-979.
Delios, A., & Beamish, P. W. (2005). Regional and global strategies of Japanese firms. Management International Review, 45, 19-36.
Demirbag, M., & Glaister, K. W. (2010). Factors determining offshore location choice for R&D projects: A comparative study of developed and emerging regions. Journal of Management Studies, 47, 1534-1560.
Dicken, P. (2007). Global Shift: Mapping the Changing Contours of the World Economy (5th ed.). New York: Guilford Press.
39
Dunning, J. H. (1998). Location and the multinational enterprise: A neglected factor? Journal of International Business Studies, 29, 45-66.
Dunning, J. H., & Lundan, S. (2008). Multinational enterprises and the global economy (2nd ed.). Cheltenham, UK: Edward Elgar.
Enright, M. (2009). The location of activities of manufacturing multinationals in the Asia-Pacific. Journal of International Business Studies, 40, 818-839.
Fang, Y., Jiang, G., Makino, S., & Beamish, P. (2010). Multinational firm knowledge, use of expatriates, and foreign subsidiary performance. Journal of Management Studies, 47, 27-54.
Feinberg, S. E., & Gupta, A. K. (2004). Knowledge spillovers and the assignment of R&D responsibilities to foreign subsidiaries. Strategic Management Journal, 25, 823-845.
Flores, R. G., & Aguilera, R. V. (2007). Globalization and location choice: an analysis of US multinational firms in 1980 and 2000. Journal of International Business Studies, 38, 1187-1210.
Folta, T. B., & O'Brien, J. (2004). Entry in the presence of dueling options. Strategic Management Journal, 25, 121-138.
Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215-239.
Frost, T. (2001). The geographic sources of foreign subsidiaries' innovations. Strategic Management Journal, 22, 101-123.
Frost, T., Birkinshaw, J., & Ensign, P. (2002). Centers of excellence in multinational corporations. Strategic Management Journal, 23, 997-1018.
Furman, J., Porter, M., & Stern, S. (2002). The determinants of national innovative capacity. Research Policy, 31, 899-933.
Ghoshal, S., Korine, H., & Szulanski, G. (1994). Interunit communication in multinational corporations. Management Science, 40, 96-110.
Goerzen, A., Asmussen, C., & Nielsen, B. (2013). Global cities and multinational enterprise location strategy. Journal of International Business Studies, 44, 427-450.
Goerzen, A., & Makino, S. (2007). Multinational corporation internationalization in the service sector: A study of Japanese trading companies. Journal of International Business Studies, 38, 1149-1169.
Guillén, M. F. (2003). Experience, imitation, and the sequence of foreign entry: Wholly owned and joint-venture manufacturing by South Korean firms and business groups in China, 1987-1995. Journal of International Business Studies, 34, 185-198.
Gupta, A., & Govindarajan, V. (2000). Knowledge flows within multinational corporations. Strategic Management Journal, 21, 473-496.
Hakanson, L., & Ambos, B. (2010). The antecedents of psychic distance. Journal of International Management, 16, 195-210.
40
Hakkala, K. N., Norback, P., & Svaleryd, H. (2008). Asymmetric effects of corruption on FDI: Evidence from Swedish multinaitonal firms. Review of Economics and Statistics, 90, 627-642.
Hallen, B. L. (2008). The causes and consequences of the initial network positions of new organizations: From whom do entrepreneurs receive investments? Administrative Science Quarterly, 53, 685-718.
Hansen, M. T., & Lovas, B. (2004). How do multinational companies leverage technological competencies? Moving from single to interdependent explanations. Strategic Management Journal, 25, 801-822.
Head, K., Ries, J., & Swenson, D. (1995). Agglomeration benefits and location choice: Evidence from Japanese manufacturing investments in the United States. Journal of International Economics, 38, 223-247.
Henisz, W. J. (2002). The institutional environment for infrastructure investment. Industrial and Corporate Change, 11, 355-389.
Henisz, W. J., & Delios, A. (2001). Uncertainty, imitation, and plant location: Japanese multinational corporations, 1990-1996. Administrative Science Quarterly, 46, 443-475.
Hennart, J. F. (1982). A theory of multinational enterprise. Ann Arbor: University of Michigan Press.
Hoetker, G. (2006). Do modular products lead to modular organizations? Strategic Management Journal, 27, 501-518.
Hoetker, G. (2007). The use of logit and probit models in strategic management research: Critical issues. Strategic Management Journal, 28, 331-343.
Hofstede, G. (2001). Culture's Consequences: Comparing Values, Behaviors, Institutions, and Organizations Across Nations. Thousand Oaks, CA: Sage Publications.
Holburn, G., & Zelner, B. (2010). Political capabilities, policy risk, and international investment strategy: Evidence from the global electric power generation industry. Strategic Management Journal, 31, 1290-1315.
Hutzschenreuter, T., Pedersen, T., & Volberda, H. W. (2007). The role of path dependency and managerial intentionality: A perspective on international business research. Journal of International Business Studies, 38, 1055-1068.
Hutzschenreuter, T., & Voll, J. C. (2008). Performance effects of ‘‘added cultural distance’’ in the path of international expansion: The case of German multinational enterprises. Journal of International Business Studies, 39, 53-70.
Hutzschenreuter, T., Voll, J. C., & Verbeke, A. (2011). The impact of added cultural distance and cultural diversity on international expansion patterns: A Penrosean perspective. Journal of Management Studies, 48, 305-329.
Iammarino, S., & McCann, P. (2013). Multinationals and economic geography. Cheltenham, UK: Edward Elgar.
Isham, J., Woolcock, M., Pritchett, L., & Busby, G. (2005). The varieties of resource experience: Natural resource export structures and the political economy of economic growth. World
41
Bank Economic Review, 19, 141-174.
Isobe, T., Makino, S., & Montgomery, D. (2000). Resource commitment, entry timing, and market performance of foreign direct investments in emerging economies: The case of Japanese international joint ventures in China. Academy of Management Journal, 43, 468-484.
Jiang, G., Holburn, G., & Beamish, P. (2014). The impact of vicarious experience on foreign location strategy. Journal of International Management, 20, 345-358.
Johanson, J., & Vahlne, J. E. (2009). The Uppsala internationalization process model revisited: From liability of foreignness to liability of outsidership. Journal of International Business Studies, 40, 1411–1431.
Kalnins, A. (2004). Divisional multimarket contact within and between multiunit organizations. Academy of Management Journal, 47, 117-128.
Kang, J. K., & Kim, J. M. (2010). Do foreign investors exhibit a corporate governance disadvantage? An information asymmetry perspective. Journal of International Business Studies, 41, 1415-1438.
Kang, Y., & Jiang, F. (2012). China's outward foreign direct investment: Location choice and firm ownership. Journal of World Business, 45, 45-53.
Kaufmann, D., Kraay, A., & Mastruzzi, M. (2008). Governance matters VII: Aggregate and individual governance indicators, 1996-2007. In: World Bank Policy Research Working Paper No. 4654.
Kim, T.-Y., Delios, A., & Xu, D. (2010). Organizational geography, experiential learning and subsidiary exit: Japanese foreign expansions in China, 1979-2001. Journal of Economic Geography, 10, 579-597.
Kim, Y.-C., Lu, J. W., & Rhee, M. (2012). Learning from age difference: Interorganizational learning and survival in Japanese foreign subsidiaries. Journal of International Business Studies, 43, 719-745.
King, G., Tomz, M., & Wittenberg, J. (2000). Making the most of statistical analyses: Improving interpretation and presentation. American Journal of Political Science, 44, 347-361.
King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis, 9, 137-163.
Kogut, B., & Chang, S. J. (1996). Platform investments and volatile exchange rates: Direct investment in the U.S. by Japanese electronic companies. Review of Economics and Statistics, 78, 221-231.
Kogut, B., & Singh, H. (1988). The effect of national culture on the choice of entry mode. Journal of International Business Studies, 19, 411-432.
Kogut, B., & Zander, U. (1993). Knowledge of the firm and the evolutionary theory of the multinational corporation. Journal of International Business Studies, 24, 625-645.
Kolstad, I., & Wiig, A. (2012). What determines Chinese outward FDI? Journal of World Business, 47, 26-34.
42
Kuemmerle, W. (1999). The drivers of foreign direct investment into research and development: An empirical investigation. Journal of International Business Studies, 30, 1-24.
Levinthal, D. A. (1997). Adaptation on rugged landscapes. Management Science, 43, 934-950.
Luo, Y. D. (2005). Toward coopetition within a multinational enterprise: A perspective from foreign subsidiaries. Journal of World Business, 40, 71-90.
Makino, S., Beamish, P., & Zhao, N. (2004). The characteristics and performance of Japanese FDI in less developed and developed countries. Journal of World Business, 39, 377-392.
Makino, S., Lau, C.-M., & Yeh, R.-S. (2002). Asset-exploitation versus asset-seeking: Implications for location choice of foreign direct investment from newly industrialized economies. Journal of International Business Studies, 33, 403-421.
Malloy, C. J. (2005). The geography of equity analysis. Journal of Finance, 60, 719-755.
Martin, X., Swaminathan, A., & Tihanyi, L. (2007). Modeling international expansion. Research Methodology in Strategy and Management, 4, 103-119.
Mascarenhas, B. (1986). International strategies of non-dominant firms. Journal of International Business Studies, 17, 1-25.
McCann, P. (1998). The economics of industrial location: A logistics-costs approach. Berlin and New York: Springer.
McCann, P. (2008). Globalization and economic geography: The world is curved, not flat. Cambridge Journal of Regions, Economy and Society, 1, 351-370.
McCann, P. (2011). International business and economic geography: Knowledge, time and transaction costs. Journal of Economic Geography, 11, 309-317.
McCann, P., & Mudambi, R. (2004). The location behavior of the multinational enterprise: Some analytical issues. Growth and Change, 35, 491-524.
McCann, P., & Shefer, D. (2004). Location, agglomeration and infrastructure. Papers in Regional Science, 83, 177-196.
Mitra, D., & Golder, P. N. (2002). Whose culture matters? Near-market knowledge and its impact on foreign market entry timing. Journal of Marketing Research, 39, 350-365.
Mudambi, R. (1995). The MNE investment location decision: Some empirical evidence. Managerial and Decision Economics, 16, 249-257.
Mudambi, R., & Navarra, P. (2004). Is knowledge power? Knowledge flows, subsidiary power and rent-seeking within MNCs. Journal of International Business Studies, 35, 385-406.
Mudambi, R., Pedersen, T., & Andersson, U. (2014). How subsidiaries gain power in multinational corporations. Journal of World Business, 49, 101-113.
Mudambi, R., Piscitello, L., & Rabbiosi, L. (2014). Reverse knowledge transfer in MNEs: Subsidiary innovativeness and entry modes. Long range planning, 47, 49-63.
Nachum, L., & Wymbs, C. (2005). Product differentiation, external economies and MNE location choices: M&As in global cities. Journal of International Business Studies, 36, 415-434.
43
Nohria, N., & Ghoshal, S. (1994). Differentiated fit and shared values: Alternatives for managing headquarters-subsidiary relations. Strategic Management Journal, 15, 491-502.
Peng, G. Z., & Beamish, P. (2014). MNC subsidiary size and expatriate control: Resource-dependence and learning perspectives. Journal of World Business, 49, 51-62.
Qian, L., & Delios, A. (2008). Internalization and experience: Japanese banks' international expansion, 1980-1998. Journal of International Business Studies, 39, 231-248.
Ragozzino, R. (2009). The effects of geographic distance on the foreign acquisition activity of U.S. firms. Management International Review, 49, 509-535.
Ramasamy, B., Yeung, M., & Laforet, S. (2012). China's outward foreign direct investment: Location choice and firm ownership. Journal of World Business, 47, 17-25.
Rugman, A. M. (1981). Inside the Multinationals: The Economics of Internal Markets. New York: Columbia University Press.
Rugman, A. M., & Verbeke, A. (2004). A perspective on regional and global strategies of multinational enterprises. Journal of International Business Studies, 35, 3-18.
Rugman, A. M., & Verbeke, A. (2007). Liabilities of regional foreignness and the use of firm-level versus country-level data: A response to Dunning et al. Journal of International Business Studies, 38, 200-205.
Schonberger, R. J. (1996). World class manufacturing: The next decade. New York: Free Press.
Schotter, A., & Beamish, P. (2013). The hassle factor: An explanation for managerial location shunning. Journal of International Business Studies, 44, 521-544.
Singh, J. (2005). Collaborative networks as determinants of knowledge diffusion patterns. Management Science, 51, 756-770.
Slangen, A. (2011). A communication-based theory of the choice between greenfield and acquisition entry. Journal of Management Studies, 48, 1699-1726.
Slangen, A., & Beugelsdijk, S. (2010). The impact of institutional hazards on foreign multinational activity: A contingency perspective. Journal of International Business Studies, 41, 980-995.
Wheeler, D., & Mody, A. (1992). International investment location decision: The case of United States firms. Journal of International Economics, 33, 57-76.
Zelner, B. A. (2009). Using simulation to interpret results from Logit, Probit, and other nonlinear models. Strategic Management Journal, 30, 1335-1348.
Zhou, C. H., Delios, A., & Yang, J. (2002). Locational determinants of Japanese foreign direct investment in China. Asia Pacific Journal of Management, 19, 63-86.
44
TABLE 1. Descriptive Statistics and Bivariate Correlations
Variable Mean S. D. Min Max 1 2 3 4 5 6 7 8 9 10 111 Entry 0.09 0.29 0 12 Proximity to Regional Production Subsidiaries 0.33 0.60 0 9.00 0.0763 Proximity to Regional Trading Subsidiaries 0.34 0.67 0 9.00 0.015 0.3704 GDP 0.51 1.27 0.00 10.9 0.444 0.024 0.0315 GDP Growth Rate 0.07 0.05 -0.17 0.44 0.209 -0.004 -0.034 0.0536 GDP per capita 9.39 8.63 0.15 61.9 -0.025 0.305 0.347 0.223 -0.1247 Trade Ratio 0.62 0.50 0.04 4.13 0.032 0.180 0.157 -0.180 0.096 0.2378 FDI as a percentage of GDP 2.22 5.23 -46.1 88.2 0.022 0.107 0.097 -0.009 -0.022 0.277 0.3879 FDI Stock 0.05 0.20 0.00 2.80 0.202 0.059 0.066 0.771 -0.028 0.399 -0.052 0.027
10 Country Resource Exports 41.0 31.3 0 100 -0.146 -0.243 -0.245 -0.231 0.040 -0.400 -0.259 -0.141 -0.16811 Country Patent Grants 4.71 2.90 0.69 11.9 0.190 0.232 0.269 0.506 -0.051 0.547 -0.083 0.042 0.396 -0.45812 Cultural Distance 3.22 1.62 0.72 9.35 0.057 0.061 0.059 -0.037 0.057 0.149 0.252 0.067 0.002 0.015 -0.06713 Political Stability 0.53 0.31 0 0.90 -0.123 0.198 0.242 -0.008 -0.181 0.531 0.176 0.161 0.157 -0.323 0.37814 Rule of Law 0.51 0.99 -1.53 2.36 0.002 0.301 0.348 0.083 0.050 0.752 0.286 0.125 0.231 -0.396 0.48715 Bilateral Geographic Distance 9.63 3.91 1.16 18.6 -0.354 -0.163 -0.121 -0.196 -0.234 -0.084 -0.302 -0.062 -0.022 0.432 -0.25016 Proximity to Non-regional Production Subsidiaries 0.10 0.04 0 0.94 -0.210 0.047 0.091 -0.114 -0.169 0.124 -0.055 0.008 -0.046 -0.056 0.07017 Country Production Operation 0.13 0.36 0 3.37 0.419 0.059 0.060 0.501 0.157 0.060 0.025 0.011 0.311 -0.153 0.24618 Country Trading Operation 0.08 0.27 0 2.8 0.198 0.143 0.186 0.364 0.043 0.227 0.068 0.010 0.305 -0.145 0.27019 Industry Country Production Operation 1.23 1.6 0 6.5 0.519 0.142 0.109 0.599 0.225 0.168 0.136 0.043 0.379 -0.295 0.40320 Region Production Operation 0.67 0.83 0 4.17 0.235 0.375 0.174 0.096 0.128 0.019 0.198 0.042 -0.014 -0.278 0.10721 Firm Size 1.39 1.29 -3.38 5.11 -0.002 0.108 0.148 -0.002 -0.015 0.003 -0.010 -0.020 -0.004 -0.001 0.00322 Capital Intensity 4.78 18.4 -54.0 72.5 -0.001 0.035 0.052 -0.001 -0.022 0.017 0.004 0.004 0.004 -0.013 0.003
Variable 12 13 14 15 16 17 18 19 20 2113 Political Stability 0.08914 Rule of Law 0.297 0.57715 Bilateral Geographic Distance -0.206 0.029 -0.14816 Proximity to Non-regional Production Subsidiaries -0.023 0.107 0.089 0.06017 Country Production Operation 0.043 -0.038 0.058 -0.282 -0.13018 Country Trading Operation 0.035 0.135 0.234 -0.115 -0.041 0.43419 Industry Country Production Operation 0.059 0.032 0.156 -0.518 -0.303 0.574 0.38620 Region Production Operation 0.097 -0.001 0.031 -0.484 -0.097 0.362 0.219 0.39121 Firm Size 0.005 0.006 0.020 0.012 0.081 0.139 0.171 0.001 0.27722 Capital Intensity 0.000 0.014 0.007 0.007 0.049 0.020 -0.002 -0.016 0.044 0.178
N=43,546; Correlation with absolute values greater than 0.008 are significant at the p<0.05
45
TABLE 2. Rare Event Logit Models of Production EntriesVariable
Proximity to Regional Production Subsidiaries 0.44 ** 0.43 ** -0.05 0.44 ** 0.34 † 0.44 ** 0.16 *(0.03) (0.04) (0.31) (0.04) (0.2) (0.04) (0.06)
Proximity to Regional Trading Subsidiaries 0.01 0.07 -0.36 0.07 0.36 * 0.02 -0.01(0.04) (0.06) (0.27) (0.06) (0.17) (0.06) (0.05)
GDP 0.39 ** 0.41 ** 0.51 ** 0.37 † 0.51 ** 0.19 0.51 ** 0.00 †(0.03) (0.03) (0.06) (0.19) (0.06) (0.19) (0.06) (0.00)
GDP Growth Rate 5.62 ** 6.05 ** 6.12 ** 3.15 6.05 ** -3.83 6.22 ** 1.91 *(0.78) (0.81) (0.83) (3.08) (0.87) (5.42) (0.84) (0.94)
GDP per capita -0.04 ** -0.05 ** -0.06 ** -0.06 † -0.06 ** -0.06 † -0.06 ** 0.00 †(0.01) (0.01) (0.01) (0.03) (0.01) (0.04) (0.01) (0.00)
Trade Ratio -0.03 -0.02 -0.15 † -0.16 -0.13 -0.03 -0.14 † 0.06(0.05) (0.06) (0.08) (0.24) (0.09) (0.33) (0.09) (0.09)
FDI as a percentage of GDP 0.04 ** 0.04 ** 0.03 ** 0.02 0.03 ** 0.02 0.03 ** 0.01(0.00) (0.00) (0.01) (0.03) (0.01) (0.03) (0.01) (0.01)
FDI Stock -0.78 ** -0.79 ** -1.36 ** -0.60 -1.27 ** -0.05 -1.4 ** 0.21 *(0.14) (0.15) (0.3) (0.95) (0.31) (0.74) (0.33) (0.09)
Country Resource Exports 0.02 ** 0.01 ** 0.01 ** 0.02 ** 0.01 ** 0.00 0.01 ** 0.00(0.00) (0.00) (0.00) (0.01) (0.00) (0.01) (0.00) (0.00)
Country Patent Grants 0.03 † 0.04 * 0.01 -0.02 0.01 -0.08 0.01 0.04(0.02) (0.02) (0.02) (0.08) (0.02) (0.09) (0.02) (0.02)
Cultural Distance -0.08 ** -0.08 ** 0.03 0.19 * 0.02 -0.29 † 0.04 † -0.02(0.03) (0.03) (0.03) (0.09) (0.03) (0.17) (0.03) (0.05)
Political Stability -0.23 * -0.19 0.17 1.28 * 0.11 -0.14 0.16 -0.30 †(0.12) (0.12) (0.14) (0.55) (0.15) (0.86) (0.15) (0.18)
Rule of Law 0.22 ** 0.16 † 0.32 ** 0.26 0.33 ** 1.08 ** 0.32 ** -0.09(0.08) (0.08) (0.06) (0.22) (0.07) (0.36) (0.06) (0.13)
Bilateral Geographic Distance -0.06 ** -0.05 * -0.18 ** -0.16 * -0.18 ** -0.17 * -0.18 ** 0.00(0.02) (0.02) (0.02) (0.08) (0.02) (0.07) (0.02) (0.00)
Proximity to Non-regional Production Subsidiaries -6.22 ** -9.11 ** -7.64 * -8.53 ** -8.4 * -8.65 ** 2.45 ** (0.84) (1.11) (3.71) (1.18) (4.22) (1.15) (0.69)
Country Production Operation 0.28 ** 0.34 ** 0.22 * 0.68 * 0.19 † 0.91 * 0.19 * 0.10(0.06) (0.06) (0.09) (0.29) (0.1) (0.4) (0.1) (0.07)
Country Trading Operation 0.02 0.00 -0.10 -0.06 -0.09 0.26 -0.11 -0.04(0.07) (0.07) (0.12) (0.43) (0.12) (0.45) (0.12) (0.07)
Industry Country Production Operation 0.63 ** 0.59 ** 0.52 ** 0.35 ** 0.51 ** 0.53 ** 0.51 ** -0.06(0.03) (0.03) (0.03) (0.13) (0.04) (0.17) (0.03) (0.04)
Region Production Operation 0.07 † 0.06 -0.11 * 0.16 -0.12 * -0.65 * -0.08 0.03(0.04) (0.04) (0.05) (0.18) (0.05) (0.3) (0.05) (0.05)
Firm Size -0.06 * -0.03 -0.11 ** -0.2 * -0.11 ** -0.10 -0.10 ** 0.04(0.02) (0.02) (0.03) (0.09) (0.03) (0.16) (0.03) (0.03)
Capital Intensity 0.00 0.00 0.00 0.01 † 0.00 0.01 † 0.00 0.00(0.00) (0.00) (0.00) (0.01) (0.00) (0.01) (0.00) (0.00)
Subsidiary Size 0.10 ** (0.03)
Subsidiary Age 0.84 ** (0.05)
Expatriate Ratio 0.05 (0.28)
Parent Firm Performance 4.10 ** (0.76)
Number of observations† p < .10; * p < .05; ** p < .01. Fixed effects (region, industry, year, and subsidiary ownership) are not reported.a. NRS = Natural Resource-Seeking
20,521 18,726 43,546 43,546 21,438 1,330 20,108 917
Reported Performance
Model 7 Model 8Mandate NRSa Non-NRS R&D Non-R&D Subsidiary
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
46
FIGURE 1: Impact of Proximity to Regional Subsidiaries on Production Entry
11.
52
2.5
Pro
babi
lity
of P
rodu
ctio
n E
ntry
(Nor
mal
ized
to 1
whe
n P
roxi
mity
is e
qual
to z
ero)
0 .5 1 1.5 2Proximity to Regional Subsidiaries
Proximity to Regional Production Subsidiaries
Proximity to Regional Trading Subsidiaries
APPENDIX 1: Regions and Countries
Region CountryAsia Bangladesh, China, Hong Kong, India, Indonesia, South Korea, Malaysia,
Pakistan, Philippines, Singapore, Taiwan, Thailand, Turkey, and Vietnam.Europe Albania, Austria, Belarus, Belgium, Bulgaria, Croatia, Czech, Denmark,
Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Macedonia, Moldova, Netherlands, Norway, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, and the United Kingdom.
North America Canada, Mexico, and the U.S.A.Latin America & the Caribbean
Argentina, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Jamaica, Panama, Peru, Suriname, Trinidad & Tobago, Uruguay, and Venezuela.
Oceania Australia and New ZealandAfrica Ghana, Mali, Nigeria, Rwanda, South Africa, Tanzania, Uganda, Zambia,
and Zimbabwe.Middle East & North Africa
Algeria, Egypt, Iran, Iraq, Israel, Jordan, Morocco, and Saudi Arabia.
47
Top Related