Evaluating the effects of agglomerations on location selection of MNEs based on different equity-based entry modes

Masters Dissertation

Evaluating the effects of agglomerations on

location selection of MNEs based on different

equity-based entry modes

Student: Lars Schumann

Student no: NCL: B8057137 GRO: S3903362

Supervisors: Newcastle University: Prof. Tom McGovern

University of Groningen: Prof. Gary Ge

Programme: NCL: MSc Advanced International Business Management

GRO: MSc International Business & Management

Date of Submission: December 2nd, 2019

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Abstract

This paper identified a gap in existing research as there have been no comparative studies

on the agglomeration levels effect on location decisions for various FDI. For now, there is

research on agglomerations impact on each JV, M&A, and Greenfield location decisions with

smaller firms being more affected than larger ones. However, there is no such research

including all these equity-based FDI methods in order to see which one is affected most. There

is a need for both public sectors, whose policies enable agglomerations, and business

understanding to find out just that: How does agglomeration affect FDI location choice

differently for various equity-based entry modes (Joint ventures, Mergers & Acquisitions,

Greenfields) of MNEs? Initially, the agglomeration levels of each state are being calculated for

both industry and country of origin agglomeration. Thereafter a compelling dataset of FDI from

Japan into the USA was obtained including various independent and control variables –

agglomeration level, knowledge intensity, deal type, subsidiary network and experience of

firms – that might also have an impact on strategic decision-making regarding location choices

of firms. Data is going to be drawn from the Bureau van Dijk resources Zephyr and Orbis. The

findings of the binary regression performed led to conclude that industrial agglomeration has a

bigger effect than country of origin agglomeration and that M&As are most significantly

affected by agglomeration, followed by Greenfields. JVs location choices are not found to be

influenced by industry agglomeration but strongest affected by country of origin agglomeration.

Moreover, this study motivates further researches in the field of agglomerations. For instance,

strategic relocation does not necessarily come with FDI but might as well be carried out within

one country.

Keywords: FDI, Joint Venture, Mergers and Acquisitions, Greenfield, agglomeration, location

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Acknowledgements

Firstly, I would like to express my appreciation for my supervisors Gary Ge and Tom

McGovern. Their invaluable constructive feedback and guidance enabled a challenging but

rewarding experience in writing my thesis. Further, I would like to thank all my friends and

colleagues who I met as part of the AIBM course, both in Newcastle University, and University

of Groningen, for making the last 18 months an amazing experience. Lastly, I must thank my

mum and my girlfriend to offering me support wherever it was needed along my journey during

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Table of Content

2.1 Push and Pull Factors of Agglomerations ... 11

2.2 Industry and Country of Origin Agglomeration ... 14

2.3 Hypotheses ... 17 3 Research Methodology ... 23 3.1 Research Philosophy ... 23 3.2 Data ... 24 3.3 Sample ... 25 3.4 Variables ... 26 3.4.1 Dependent Variable... 26 3.4.2 Independent Variables... 27 3.4.3 Control Variables ... 29 4 Results ... 33 4.1 Preliminary tests ... 33 4.2 Descriptive Statistics ... 34 4.3 Correlations ... 38 4.4 Regression results ... 38 4.5 Robustness checks ... 40 5 Discussion ... 43 6 Conclusion ... 48 6.1 Theoretical implications ... 48 6.2 Practical implications ... 50

6.3 Limitations & Future Research ... 51

References ... 53

Appendices ... 62

Appendix 1: List of states and agglomeration levels ... 62

Appendix 2: Preliminary tests ... 63

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List of Figures

Figure 1 - Conceptual model……….….………17

Figure 2 - Distribution of Greenfield investments in the USA……….……….……….28

Figure 3 - Distribution of M&As across the USA……….……….…28

Figure 4 - Distribution of JVs across the USA………..28

List of Tables Table 1 - Industry and Country of Origin Agglomeration………..……10

Table 2 - Variable description………..………..24

Table 3 - Investments into agglomerations and not into agglomerations………27

Table 4 - Descriptive Statistics and Correlations………...28

Table 5 - Regression - Location choice of subsidiaries……….30

Table 6 - Outlier test………..32

List of Abbreviations

COO Country of Origin Agglomeration

FDI Foreign Direct Investment

IT Internet Technology

JV Joint Venture

KBV Knowledge-based view

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1 Introduction

There is a vast literature trying to link inward foreign direct investment (FDI) to

subsequent agglomeration (Wheeler & Mody, 1992; Head et al., 1995, 1999; He, 2002; Campos

& Kinoshita, 2003; Crozet et al., 2004; Du et al., 2008; Alcacer & Chung, 2014; etc.), however

there is only a little evidence that this creates a sustainable agglomeration as the dependence on

the MNE is too significant. The decision-making power lies solely with the MNE that the

technology transfer between foreign and domestic industries is limited (De Propris, 2001).

However, the attractiveness of agglomerations as an FDI destination is not affected by that lack

of sustainability. Many studies in the past found a strong agglomeration effect for FDI (Wheeler

& Mody, 1992; Head et al., 1995, 1999; He, 2002; Campos & Kinoshita, 2003; Crozet et al.,

2004; Du et al., 2008), however, Guimaraes and Figueiredo’s research (2000) actually found

contrary results and an insignificant negative correlation that indicated a dispersion of FDI. The

paper at hand makes the case that the different results could be up to the entry mode choice that

was looked at during these studies. As there has never been a comparative study amongst

different entry modes and the extends of effects varied between the studies mentioned

previously. Firms entering a new market choose their preferred method of entering based on

the objectives they have. These objectives are different for different entry modes and different

objectives cause different patterns in decision making that can be linked also to location

decisions. While, other predictors such as firm size, experience and knowledge intensity of the

firm have been assessed and controlled for in research before (e.g. Head et al., 1995; Li &

Bathelt, 2014), entry mode was not. Therefore, in a bid to clarify the different extents of the

effects of agglomeration on the locational choice of MNEs this paper addresses the question:

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Relevant entry modes to assess include Joint Ventures (JVs), Mergers and Acquisitions

(M&As), as well as Greenfield investments as all of those, represent equity-based entry modes.

Leading to the entering firm to be exposed to the foreign market directly. JVs and M&As

emerge as possible strategies (Franco et al., 2010) to gain resources, knowledge and customers

amongst other things in the relative environment and then have the potential to use the learnings

elsewhere. The two governance forms mentioned, JVs and M&As, have their distinctions that

cause different effects in knowledge transfer: In the wake of an acquisition one company

purchases majority stake in another company to assume control; a JV is a legally distinct

business that is owned by two or more parent firms (Beamish & Lupton, 2009). While both

forms of investment ensure exposure to the local market, the extent of intertwining between

organisations differ. During a JV both sides own a (often somewhat equal) share of a newly

found enterprise that is there to enhance best practice and technology exchange. However, both

companies have the interest to gain a maximum of new knowledge while not giving away too

much of their expertise to weaken their own position in the competition. Given the differences

outlined before, both forms of FDI have in common that they both are equity-based entry modes

into a foreign market. Further to these investment options, Greenfield investments are gaining

relevance with the softening of regulations amongst more regulated markets including China

(OECD, 2019a). Equity-based FDI creates a knowledge and ownership advantage for firms that

cannot be generated by non-equity based FDI such as licensing which is therefore excluded

from this analysis.

FDI location choices by companies are shaped by two contrary forces: entrenchment

benefits and opportunity costs (Alcacer et al., 2013). Entrenchment benefits occur when a firm

is an early mover into a market. It gets the benefit of local learnings before competitors have

the opportunity to gain those (Alcacer et al., 2013). On the other hand, opportunity costs are a

concept of scarcity of capital: a firm has multiple markets to choose from to enter into but only

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might have to postpone an investment into another. Giving competitors in those geographic

markets valuable lead time to improve their position for competition (Alcacer et al., 2013).

In order to capture entrenchment benefits, often in the form of spillovers, the subsidiaries

proximity to hotspots of innovation and production is necessary. That is where agglomeration

comes into the picture. Agglomeration of industries or companies can be defined as follows:

Agglomerations are “[…] geographic concentrations of industries related by knowledge, skills,

inputs and/or other linkages” (Delgado & Porter, 2015). These agglomerations have different

extent and therefore appeal to different motives behind FDI. Moreover, there are two different

kinds of agglomerations that have to be distinguished for this research. First, industry

agglomerations as they are defined by Delgado & Porter (2015). Second, country of origin

agglomerations suggesting foreign investors often locate near other FDI firms sharing the same

country of origin (Chang & Park, 2005; Chung & Alcacer, 2002;Head & Ries, 1996).

Firms in an agglomeration are characterized by high specialization and complementarity

which creates a dynamic knowledge creation (learning and innovation) and transfer (diffusion

and synergies). Collective learning processes are creating innovation and competitiveness as

well as improving productivity not only for the high-tech sector but also for other industries

(De Propris & Driffield, 2006). For instance, the ceramic tile industrial district in Sassuolo

(Italy) accounts for one-third of the sectors world export (Menghinello, 2003). As the

agglomeration grows, the extent of vertical and horizontal product differentiation increases.

Consequently, the agglomeration becomes a centre of collected competences across various

related industries, and stages of production. These localised centres of knowledge can be very

attractive to outside firms, and thus attractive to inward FDI decision-makers (De Propris &

Driffield, 2006). The extent of agglomerations attractiveness to FDI is measured in multiple

different studies with different focusses around the subject. These studies will be looked at in

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Evidently, agglomerations have effects on companies’ strategic behaviour and decision

making. That includes the location decisions they make when investing abroad. Here the

benefits and risks of agglomerations play a central role in evaluating to what extent the

knowledge and expertise, as well as the efficient infrastructure, outweighs potential

appropriation risks. Multiple literatures all tested agglomeration effects on investment decisions

for M&As (e.g. Basile, 2004; Böckermann & Letho, 2006), JVs (e.g. Tuan & Ng, 2003; Zhao

& Zhu, 1998) and Greenfield investments (e.g. Head et al., 1995; Shaver & Flyer, 2000; Alcacer

& Chung, 2007) showed similar results. That will be assessed in more detail during the literature

review. Agglomeration was significant in attracting firms’ investment to a region with small

companies being relatively more affected by it than larger firms. In contrary Guimares and

Figueiredos (2000) found an insignificant negative correlation that indicated a dispersion of

FDI. However, with all the information provided by these researches, there is, to my best

knowledge, no work that evaluates and compares the strength of the effect for each of different

entry modes which could explain the differing results as well as effect strengths. This is where

the paper at hand is going to contribute to existing research in comparing the attractivity of

agglomerations for various types of FDI. Namely, JV, M&A and Greenfields that have been

used in previous research but were never analysed and compared in one study.

In order to find out how the entry modes can be differently affected in their location

choice, it is important to understand varying objectives behind the choice of entry. JVs are often

driven by the access to resources that otherwise would not be available for the companies

agreeing on cooperation. Namely, intellectual property and exploring new capabilities without

giving away knowledge that was not agreed on with the respective partner (Deloitte, 2016).

Moreover, risk mitigation in terms of a separation between the JV and both partners as well as

regulatory approval, such as FDI restrictions, played a role in the motivations of setting up a JV

(Deloitte, 2016). Many scholars consider that JVs enable easier access to the firms’

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scale, develop new products faster and more cheaply than could be done by either of the

parenting firms acting alone or through acquisition (Beamish & Lupton, 2009; Choi &

Contractor, 2016). Complementary resources and technologies between the two partners play a

major role here as JVs are often contractually limited to a certain time in order to achieve a

certain goal that is in many cases related to technology transfer (Johnson & Turner, 2003). This

indicates a focus on the partners rather than the environment in terms of agglomerations.

However, in setting up a JV the staff is build up from both internal sources of both partners as

well as external skilled personal that helps to achieve said objectives. Therefore, the often

skilled and educated workers within agglomerated areas might be more relevant to gaining

technological advantage – and thereby influencing location choices – than first expected.

In contrary, for M&As the local labour pools are not as relevant. As the company, that is

either acquired, already exists and holds enough labour force as well as distribution channels,

equipment, supplier networks and customer base to operate at near full capacity (Johnson &

Turner, 2003). Therefore, at first, M&As do not require to develop new capacity and are not

dependent on outside benefits like JVs might be. However, given the fact that more companies

than not located in agglomerated areas – agglomeration, in the end, being caused by just that, a

high number of companies in one field – the location choice made by acquiring firms is

predetermined by the location of potential targets for a takeover. Indicating a potential tendency

to co-locate with other companies. While the choice of co-location for JVs might be free and

evaluated after taking cost and benefits into account. The decision of location choice in M&As

has to be made, as one factor of analysis in the target firm. Next to other reasons like strategic

fit, cultural fit, size of investment, economic benefit, consumer base and many others.

Lastly, for Greenfield investments, the location decision is the least restricted for the firm

pursuing FDI. The location choice can be made only based on environmental attributes such as

the extent of competition, supplier networks, availability of skilled labour et cetera. In

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and involvement in a local environment (Johnson & Turner, 2003). In order to adapt to the new

environment spillovers from local knowledge, proximity to suppliers and customers and other

supporting industry is an advantage especially for firms with little to no experience in

international business either in general or in that specific region.

In summary, while the effects of agglomeration on M&As and Greenfield investments

are more explicit, JVs are rather dormant and secondary in strategic decision making. That does

not mean that the effects of agglomeration on location choice as it can be measured is

recognising the difference in drivers for co-location. In this research location decisions of

Japanese firms in cross-border FDI into the United States of America (USA) are being analysed.

The data consists of various investments in the three relevant equity-based entry modes of JVs,

M&As and Greenfield investments and is collected using the databases Orbis (Greenfield

investments) and Zephyr (M&A and JV), both provided by Bureau van Dijk (2019), to create a

compelling dataset of 326 observations between 2010 and 2019. It has been found that M&As,

as well as Greenfield investments locational choice in terms of a state, is related to the industry

agglomeration level of the state. While JVs are not attracted by higher industry agglomeration

of states, country of origin agglomeration proved to be a centripetal force. These results show

that the different entry modes have an influence on the effect strengths of agglomeration on the

location choice. Indicating that discrepancies in prior researches could be down to the different

entry modes assessed. Future research can gain insight for what differences entry modes have

on effect strengths of agglomeration in location choice by building on prior research and the

findings of this paper.

The following section is reviewing relevant literature in order to come up with hypotheses

that will be tested. Subsequently, the methods necessary to test the hypotheses will be

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2 Literature Review

2.1 Push and Pull Factors of Agglomerations

Generally, firms select their branch location according to the expected profits, with

locations that promise high returns clearly in favour (Basile, 2004). Relevant determinants

include transportation cost, local market size, supplier availability amongst incentives like tax

breaks. There are multiple motivations for companies to establish subsidiaries within

agglomerated areas such as agglomerations. Following a cumulative causation approach, it is

suggested that firms tend to locate in areas where other firms are already established (Basile,

2004). Overall, there are two forces that must be considered when analysing the attractiveness

of agglomerations. On the one hand, there are centripetal forces that motivate MNEs to invest

in areas that already have other MNEs. This is down to the fact that foreign firms generate

spillovers in a host country environment that carry potential value for subsequent FDI; namely

specialized supplies and reduced cost of information (Shaver et al., 1997). Moreover, there are

multiple reasons highlighted in various literature: a significant market for local products and

services; reduced costs for transport and the supply chain; opportunities to reach more

customers through the high market concentration; more feasible access to resources,

opportunities for new companies to extend in this environment; increased motivation through

a competitive environment, greater potential cooperation between members of the

agglomeration, high degree of specialization in both services and products; the proximity of

firms of the same or similar industries allow for an exchange of knowledge and ideas through

direct contact and free movement of labour – so called spillovers; the number of competitive

firms in one area will result in a large pool of skilled workers; the direct contact with people in

the same field reduce risks and durations of the innovation process because of information

transfer between partners, companies, clients and research institutions (Malmberg et al., 1996),

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& Swann, 1998), (Sölvell et al., 2003), (Krugman, 1991), (Malmberg & Maskell, 2001), (Boja,

2011). Further to these reasons, it is important to note that firms making their location decision

in a foreign country might be subject to information issues. New investors follow prior

decisions of other companies as an unconventional location could backfire not only financially

but also in a reputational sense (DeCoster & Strange 1993). Essentially leading to a mimicking

of other firms‘ location decisions (Head et al., 1999) which then builds further traction towards

the existing agglomeration.

On the other hand, Fujita et al. (1999) have identified forces that come into play when a

certain level of agglomeration is achieved. Labour cost can be an example of that as it goes up

when the demand for jobs goes up through an influx of incoming investment. This centrifugal

force could be observed in the south-east of China as labour and land prices in Guangdong went

up so dramatically that Foxconn, a supplier for Apple, had to relocate (Pan, 2012).

Further to these centripetal forces, other research indicated further benefits for

subsidiaries and the companies as a whole. Subsidiaries within agglomerations are found to be

more embedded, internationally orientated and autonomous compared to subsidiaries that are

located outside of agglomerations (Birkinshaw & Hood, 2000). Forst (2001) proved that local

context creates an important base of knowledge to draw from for innovative processes of

subsidiaries. While it has been found that subsidiaries in innovative environments are more

likely to generate knowledge benefits over other subsidiaries (Cantwell & Mudambi, 2005). In

this work, subsidiaries are seen to be able to create business advantages, as indicated before,

but also to contribute to the local environment they are established in (Birkinshaw & Sölvell,

2000; Brandstetter, 2006). Considering this, the motivations for firms to invest in

agglomerations should be clear. However, the centrifugal forces emphasized before lead to a

weighing up of the pros and cons of investing in an agglomeration. Companies planning their

FDI strategy are weighting up these centrifugal and centripetal forces to evaluate a location and

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different entry modes, there are usually different objectives. These propositions are based on

the knowledge-based view (KBV) which is an extension of the resource-based view that

highlights knowledge as a strategic resource that can generate increasing returns and does not

depreciate (Curado, 2006). Furthermore, it can be attributed to the FDI that is motivated by

non-marketable asset seeking from firms. The growth of Silicon Valley as a destination of FDI

was driven by firms behaving accordingly to the KBV to give an example (Almeida, 1996).

However, different kinds of agglomerations might also attract different motives for FDI. It is

expected that urban agglomerations or agglomerations around a widely populated area attract

both efficiency as well as market seeking motives in MNEs. Industrial belts like the “Die Region

der Weltmarktführer” (“The region of the world market leaders”) (Zhang & Warken, 2019) in Heilbronn-Franken, Germany, (GGS, 2019) and innovative agglomerations’ appeal to motives

that concern efficiency and knowledge seeking. Especially, innovative agglomerations with

firms of multiple sectors can be interesting for MNEs as many of them are diversifying and

investing in building a business in different sectors such as Google, but also automotive

companies that now own banks, IT platforms, software centres and many more (e.g. Daimler:

Mercedes Benz Cars (incl. Smart), Mercedes Benz Vans, Mercedes Benz Trucks, Mercedes

Benz Financial Services, Mercedes Benz Bank, Car2Go, et cetera). Essentially any MNE that

is active in more than one sector or industry.

Traditionally, the literature expected that MNEs cause agglomerations by investing in a

region and pulling their suppliers and other supporting industry with them which then creates

an industry agglomeration that creates spillovers for the region and other firms (De Propris &

Driffield, 2006). Much like what happened in Alabama as the state prepared everything in order

to achieve an attractive environment for the Mercedes Benz site in Tuscaloosa. However, De

Propris and Driffield (2006) propose that this chain of events is eventually motivated by a

pre-existing agglomeration that, then, in turn, makes the MNE invest and enrich that agglomeration

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local firms and also profit from the knowledge and capabilities held in that environment. Given

that MNEs usually have a vast choice of regions and locations to choose from, considering the

entrenchment benefits and opportunity costs, De Propris and Driffield (2006) point appears to

be valid, as the company attempts to gain the maximum benefit from their location decision.

That benefit can best be realized in an environment that is already existing and proven to be

profitable as an agglomerated area of companies and supporting institutions.

2.2 Industry and Country of Origin Agglomeration

The key term of this research – agglomerations – can be differentiated into two sections.

On the one hand, there is industry agglomeration and on the other hand, there is the country of

origin agglomeration. While earlier literature puts an emphasis on industry agglomeration

(Krugman, 1991; Marshall 1920) only focussing on this one type is not representing the full

picture of the effect that agglomeration can have on the location decisions made by MNEs when

investing in a foreign country. Understanding this shortcoming, more recent works included the

country of origin agglomeration as a factor (Chung & Alcacer, 2002; Tan & Meyer, 2011;

Shaver & Flyer, 2000). Clearly, both kinds of agglomerations have their differences in making

a location interesting for a potential FDI involvement. First, Industry Agglomeration in terms

of co-location with companies in the same industry can help the entrant gain benefits concerned

with the field they are operating in such as industry forecasts, and information on local customer

and supplier behaviours (Mariotti & Piscitello, 1995). This knowledge is commonly of a tacit

nature and therefore requires personal interaction to be exchanged (Polanyi, 1962). The

exchange can be enhanced by geographic proximity as an interaction between individuals is

facilitated. Local participation and networks enable firms to follow new trends and technologies

while also reacting to competitors’ actions (Porter, 1998; Almeida & Kogut, 1997). More than

only providing knowledge on customers and suppliers, also resources such as labour force and

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contrary to the benefits, industry co-location can also be linked to higher cost for the entrant as

he is facing increased competition not only for potentially scare resources but also the

consumers in the market and region. An issue that intensifies with every company that makes

the decision to move in the same area (Folta et al., 2006). Moreover, prior researches such as

Chung and Alcacer (2002) have found that agglomeration might cause knowledge expropriation

by competitors in the same region – causing especially larger and technologically advanced

firms to avoid co-location in a bid to secure their competitive advantage (Shaver & Flyer, 2000).

Second, County of origin agglomeration is related to different benefits than industry

agglomeration that stem from the, often, different fields the companies operate in. Firms that

co-locate in a country of origin agglomeration do this for other motivations than in the case of

an industry agglomeration. For instance, newcomers to a region might find it difficult to trust

local partners due to an increased vulnerability of not understanding the market equally well

(Tsui-Auch & Möllering, 2010). However, when co-locating with firms that have a similar

background these parallels help covering the distance to a foreign environment and increase

trust between FDI compatriots (Table1) (Tan & Meyer, 2011). This comes down to the fact that

individuals often form stronger ties with their same kin (Manev & Stevenson, 2001; Marsden,

1990). Another reason might be a common history of the co-locating firms. Due to common

geographic background firms faced similar challenges in the past such as economic downturns

as well as they could have worked together before. According to Tan & Meyer (2011)

commonalities between expatriates of foreign firms strengthen the bonds between firms as these

individuals socialize with each other in activities like family events. Not only do these ties

provide a potential for exchange of information about the foreign environment but also potential

new business opportunities (Linehan, 2000).

Clearly, the different kinds of agglomerations influence the attractiveness of an

agglomeration according to the entry mode chosen. Greenfield investments of companies with

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market than others. While an M&A takes over local employees that already have knowledge of

local proceedings and consumers so that they can hit the ground running and face competition

immediately – making industry agglomerations more relevant for them. Lastly, a JV faces

similar circumstances as the M&A as it is a collaboration between two or more firms that are

set up only to achieve predetermined objectives rather than setting itself up for a future in the

market. Making the benefits of an industry agglomeration more appealing for this sort of entry.

Table 1: Industry and Country of Origin Agglomeration (adapted from Tan & Meyer, 2011) Industry agglomeration Country of origin agglomeration Inter firm relationships − Often in direct

competition − Contractual-based

cooperation

− Normally not in direct competition

− Benefit from a higher level of trust

Benefits − Access to local, industry-specific knowledge & resources (such as

specialized labour/inputs, partners, customers and infrastructure

− Access to local market knowledge

− Access to local, home-country-specific resources

− Easier to gain legitimacy

Cost − Competition for scarce

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2.3 Hypotheses

The following section is concerned with building up the hypotheses that will subsequently

be tested with quantitative data. As mentioned previously, JVs are often established in order to

gain learnings from national or international partners and the local environment. Therefore, it

can be expected that FDI through JV is positively related to location choice towards

agglomerated areas for several reasons.

First, the benefits of agglomerated areas with many businesses and highly skilled and

experienced personnel attract FDI in the form of JVs. Tuan & Ng (2003) found a positive

significant effect of agglomeration on the location decisions of firms in JVs in China confirming

that claim. Especially smaller firms were found to be more responsive to agglomeration

benefits. Second, agglomerations and effects that are favoured through agglomeration such as

skill intension and market concentration are also considered as positive drivers of location

selection of JVs (Zhao & Zhu, 1998). Those factors, market concentration and skill intension,

are not only addressing knowledge-seeking aspects of FDI but also efficiency and market

seeking motives. A JV is, at times, just used for the gain in knowledge, as presented before.

However, it accounts also as a relatively low-risk option to enter a new market as the financial

exposure of collaboration is always going to be lower than building a company by yourself or

buying into an existing company. In that sense the efficiency bonus of being located in an

agglomerated area can be a further pull in engaging in a JV next to the general intentions with

the partner as distribution channels, suppliers and other supporting companies can be explored

for operations in the future. This again is especially true for smaller companies with less

experience in the international business arena and the target country.

Based on the expectations of JVs being created for knowledge exchange the assumption

of proximity as a catalyst for learning. As well as, the results of prior researches (Tuan & Ng,

2003), that mentioned positive relations of the agglomeration effect on JVs, the following

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H1a: FDI through JV is positively related in its location choice towards agglomerated areas.

More than for JVs the location selection of M&As is predetermined by the selection of

the firm that is taken over. Nevertheless, through the fact that there are more firms in

agglomerated areas than not, the likelihood of one of these to be taken-over is higher. Therefore,

a positive effect of agglomeration on M&A location selection is expected as indicated by Zhang

et al. (2012) from their research into Indian and Chinese M&As in Europe.

Moreover, Brakman et al. (2015) and Guadalupe et al. (2012) found that cross border

M&As in the US tend to concentrate more around agglomerations in comparison to national

M&As. Indicating the effect of liability of foreignness (LOF) that pushes foreign firms to

co-locate with firms from the same country to overcome their disadvantage of higher entry costs.

That potentially puts forward a more significant effect for the country of origin agglomerations.

Nevertheless, also industry agglomerations can assist companies in adjusting to a new

environment by enabling easier connections to those companies around and supporting

networks that already exist thanks to competitors and the target firm in that field and area. Even

if the firm acquires local expertise, it still seeks proximity to other firms in order to optimize

local learning and overcome the LOF as soon as possible. Moreover, efficiency and markets

are also highly relevant here for the investing firm as, for instance, transportation cost can be

lower and lead times quicker if supporting industries such as suppliers are proximate rather than

on the other end of the country. Furthermore, larger agglomerations are found to be more

productive in comparison to smaller ones (Brakman et al., 2015). This also includes the number

of workers is found to be higher in larger agglomerations increasing the potential firm

productivity (Combes et al., 2008) which is key subsequently to an entry into a foreign market.

Especially, considering the disadvantage foreign firms must overcome in comparison to local

competition. The cumulative causation approach, which argues for firms settling near other

firms, (Basile, 2004) implies that there are more potential targets for M&As located within

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are more firms in those areas. Both the need to overcome the LOF and to improve the

embedding of knowledge and transfer learnings from the new environment and to internalize

in the organization as well as the efficiency benefits of larger agglomerations, leading to the

following:

H1b: FDI through M&A is positively related in its location choice towards agglomerated areas.

As for Greenfield investments, a company building a new entity in a foreign country,

there are fewer restrictive forces in terms of their specific location selection. According to Head

et al. (1995), Shaver and Flyer (2000) and Alcacer and Chung (2007; 2014) the exclusion of acquisitions lead to a sample of FDI that is least constrained in the location decisions. Therefore,

the decisions made by the companies that follow this stream of investment make them sorely

on their own strategical interest. Here the centripetal and centrifugal forces may have the

strongest effect as companies might fear aiding competitors when co-locating (Shaver & Flyer,

2000). LOF is yet again a determinant that has to be overcome. To do so co-location into an

agglomerated area is a tool that can be applied by the strategic decision makers as productivity

is increased (Brakman et al., 2015) along with other benefits mentioned before. Moreover, the

same research found that smaller firms have a bigger interest in co-locating and exploiting the

advantages of agglomeration than their larger counterparts (MNEs) (Shaver & Flyer, 2000). As

can be seen from the results of previous research, the type of firm - in terms of its age and

industry - makes a difference in their co-location decisions (Shaver & Flyer, 2000). Based on

what is said previously, a positive effect of agglomerations’ impact on the decision making of

companies that choose Greenfield investments is expected in this research. Taking into consideration varying results and differences related to companies’ size. Leading to the

following hypothesis:

20

In literature, most tested agglomeration effects on investment decisions for M&As

(Brakman et al., 2015; Böckermann & Letho, 2006), JVs (Tuan & Ng, 2003; Zhao & Zhu,

1998) and Greenfield (Head et al., 1995; Shaver & Flyer, 2000, Alcacer & Chung, 2007) showed similar results. Agglomeration was significant in attracting firms’ investment to a

region with small companies being relatively more affected by it than bigger ones. Given the

different levels of freedom in the location selection, different effect strengths – and even

different effects such as the dispersion found by Guimaraes and Figueiredo (2000) – can be

expected when measuring the effects of agglomeration on the location choices. Therefore, this

part is concerned with reviewing the literature on which of the effects is expected to be the

strongest.

Zhang et al. (2012) found that the relation of M&As from India and China towards Europe

being largely attracted towards agglomerated areas such as cities. However, the expected

outcome of Greenfield investments showed a greater effect of agglomerations on the location

choice, as the investment here is not predetermined by the organisation that is being taken over.

However, this expectation is flawed as it rests on a sample that cannot be differentiated by entry

mode and a share of investments that is calculated by an average of overall investments. This

result, therefore, must be viewed critically. Admittedly, Zhang et al. (2012) made a point

insisting that the freer location decisions by Greenfield investments are expected to be more

attracted by agglomeration than M&As. That ignores the fact that there are more companies

existing in agglomerated areas than there are companies in non-agglomerated areas. Leading to

the expectation that mergers are more likely to occur in agglomerated areas than not, based on

the fact that there are more potential targets for the investing firm in addition to the regular

centripetal forces that apply to all entrants. Despite, for M&As local labour is not as relevant as

the company that is acquired already employs enough or near enough employees to operate

near-maximum capacity (Johnson & Turner, 2003). The existing labour force, as well as

21

major role in the investment decision of the acquiring firm and therefore agglomeration has a

stronger effect – especially when focussing on efficiency – here.

In line with what is said before, the lack of local expertise that comes with a Greenfield

investment sees the biggest need for investing nearer the knowledge that is relevant for the

company entering a market. M&As are bound to have a similar requirement of embeddedness

to overcome the LOF, even if the acquiring firm gains local knowledge and distribution

channels through the deal itself end the personnel of the target company. The main difference

here is that the existing companies that are available as potential targets for merging or takeover

happen to be located more frequently than not in agglomerated areas. Following a cumulative

causation approach which implies that companies tend to locate where others are already

established (Basile, 2004) - a principle on that agglomeration theories are built. Based on the

assumption of agglomeration which describes areas with a high propensity of businesses – in

fact, higher propensity than other areas – this means that the majority of companies in a country

is to be located within an agglomerated area. Indicating that agglomerations might have a bigger

impact on the location selection for M&As in comparison to Greenfield investments. Moreover,

M&A investments tend to prefer competitive industrial relations and avoid more rural areas that

are at times connected with higher unemployment and a worse quality of life (Basile, 2004).

Greenfield investments, on the other hand, might, especially for more low-technology

industries, be more attracted by that low employment and potentially cheaper workforce in an

efficiency-seeking setting. Providing the opportunity to enter a market while saving on

production and transportation cost in comparison to the firm’s headquarter.

Considering the lack of information when entering a new market and a new culture

without any local partner, such as in a JV, and without taking over local knowledge, the

proximity to knowledge hubs, skilled labour pools and a functioning distribution network

22

Lastly, a JV between two companies is often formed for the reason of knowledge

exchange between the partners and joining their efforts in one field of interest. Here proximity

to other firms – potentially rivals – appears as more of a centrifugal force than a centripetal one

on many occasions. Therefore, the following hypothesis is formulated:

H2: Agglomeration is expected to have the strongest effect on M&As over Greenfields and the weakest effect on JVs.

The hypotheses that are developed based on these findings are summarised and visually

presented in the following conceptual model (Figure 1).

23

3 Research Methodology

3.1 Research Philosophy

Irrespective of whether a researcher’s epistemological beliefs have been consciously

examined or discussed, these beliefs trickle-down to crucial parts of a paper such as the phrasing

of its research question and the selection of a methodology to study the data according to

Johnson & Duberly (2000). Therefore, there is growing traction in the importance of

management researchers to reflect on their epistemological beliefs and its implications for

research conducted by them.

The research at hand has been conducted in the assumption that there exists a reality that

is independent of the observer or in other terms - positivism (Easterby-Smith, Thorpe, &

Jackson, 2012). The positivist approach allows data to be applied in order to verify or falsify

predetermined hypotheses. To be able to do that data is stated in quantitative form and a large

sample of transactions is collected. As pointed out by Easterby-Smith et al. (2012), large

samples allow the uncovering of patterns and regularity, while it also admitted that it is a matter

of probability that the data collected will provide for an accurate and universal indication of the

underlying situation.

As a central criticism of the positivist approach, Kim & Donaldson (2016) bring forward

the failure to perceive the objective nature of data and methods. Granting that these methods

are in no way perfect in understanding all underlying processes and significance that people

attach to actions, they are useful in understanding a wide range of the reality at hand while being

of relevance to policy considerations (Easterby-Smith et al., 2012). In the paper at hand, a

quantitative approach enables a swift and straightforward analysis of the data. Although the

research has been conducted using a neutral approach, there is some probability that the researcher’s background and beliefs influenced the interpretation of the data to some extent. It

24

interpretations, while it is hoped that insights generated during this project can help managers

and policymakers gain a better understanding of the underlying aspects that motivate strategic

decision making in an FDI location choice context alongside being incorporated into future

research.

3.2 Data

In order to investigate the location choices made by firms’ investments at a regional rather

than a national level, industrial data on various MNEs were analysed. Numerous investments

were gathered using the database ORBIS/ZEPHYR which is maintained by the Bureau van Dijk

(2014). The bureau compiles data from various government and corporate sources and is

updated periodically (Bureau van Dijk, 2014). In order to focus on the highlighted relevant

equity-based investments only investments that resulted in a 10% ownership at least were

selected, as it is suggested by academical sources (OECD, 2000). Leaving the entry modes of

JV, M&A, and Greenfield as the ones to be observed. While previous research left acquisitions

out of the sample as the location choice here is constrained (Head et al., 1995; Shaver & Flyer,

2000; Alcacer & Chung, 2007; Alcacer & Chung, 2014) this paper addresses the research gap

in the analysis just there and explores how these constrained decisions compare to the

unconstrained Greenfield investments. As Greenfield investments are not included in the

Zephyr database, these were identified by Orbis. New subsidiaries opened between 2016 and

2019, with a parent company in Japan, and no takeover was selected and checked on a

transaction to transaction basis to ensure that the transactions at hand are actually Greenfield

transactions or so-called new plants. The number of firms was drawn from the database Orbis

(Bureau Van Dijk, 2019). The data for FDI was thereafter drawn from the sources mentioned

prior in order to test the hypotheses.

To determine the influence of both industry and country of origin agglomeration on the

25 Formula 1:

𝐿𝐺𝑀𝐽 =

𝛽 + 𝑒𝑋𝑖 + 𝑋𝑐𝑜𝑜 1 + 𝛽 + 𝑒𝑋𝑖 + 𝑋𝑐𝑜𝑜

The industry agglomeration in one location is described by 𝑋𝑖 while 𝑋𝑐𝑜𝑜 defines the

number of firms from the investing companies home country in the specific state is modelled.

Both variables will be further broken down and explained in the independent variable part

below.

In an effort to generate a comparable output the regression was carried out three times

producing three different regression tables and descriptive statistics similar to Basile (2002).

Consequently, the results were evaluated in a comparative approach to evaluate differences and

indicate the significance and strength of the effects for the three different entry mode choices (β) – Greenfield investments, M&A, JV.

3.3 Sample

The USA was used as a geographic sample as its availability of every kind of FDI entry

mode and scope of inward investment promises a sufficient sample. Moreover, the 50 states

provide regional boundaries that can be used for agglomeration calculation and agglomeration

definition.

326 Transactions from Japanese companies into the US were looked at in great detail that

allows controlling for the variables presented in the next section. These transactions were made

by 212 different companies, with some companies making more than one investment during

the time. Moreover, the focus on one country for acquiring or entering firms allowed controlling

for factors such as industry agglomeration as well as the country of origin agglomeration and

its different effects on the location choice much like Alcacer & Chung (2014). The sample

consists of a wide variety of firms to ensure diversity. Overall, there were 128 Greenfield

transactions, 29 JV deals and 169 M&A deals of Japanese firms into the US tested in the

26

well as increases in stakes, were eliminated from the sample. After controlling the sample

further, a number of deals were excluded due to limitations in the data available leaving the

finals sample with 111 Greenfield transactions, 29 JV deals and 162 M&A deals. Accumulating

to 302 transactions to be observed in the regression.

The general timeframe of these transactions was from 2015 up until June 2019 as the data

is less constrained by any crisis that would affect investment behaviour such as the financial

crisis in 2008 (Dornean et al., 2012) (extend how the crisis affects FDI). However, in the case

of JVs transactions, the time period had to be extended to 2010 up to June 2019 in order to

come up with a greater sample size as the original timeframe used for the other entry modes

would have only accounted for 15 cases of JVs. Therefore, it was decided to extend the

timeframe in a bid to gain a more insightful sample to test. 2010 was chosen as a starting date

as data availability before that point in time is limited not only on Zephyr but also other

resources are taken into consideration. The necessity to extend the timeframe for this entry

mode is down to the fact that JVs are being used less frequently in the US than in, for instance,

China because of regulatory differences. While this limited use might indicate that JVs are not

relevant to look at, they are still important as it is the most widely used equity FDI choice in

China (Beijing Municipality, 2019), the country with the – by far – second-highest inward FDI

flow (OECD, 2019b). The number of JVs has been increasing, and thus the amount of attention

that this deal type is receiving in literature is also growing (Lane et al., 2001; Yan & Luo, 2016).

3.4 Variables

3.4.1 Dependent Variable

Location choice of FDI (GF/M&A/JV) in terms of whether foreign firms invest in a location that is defined as agglomerated or not was the dependent variable of the analysis. The

27

maximization it was assumed that each investor chooses the state that provides him with the

highest possible profit. The profits mentioned might come from the availability of inputs into

the firms' production including agglomeration effects from proximate businesses in a similar

industry (Head et al., 1995).

To determine whether or not a firm’s subsidiary is located in an agglomerated area,

industrial data was analysed on a geographical level. Similar to, Holmes and Stevens (2004),

Alcacer and Chung (2014) and Li and Bathelt (2017) industry data from ORBIS was analysed

to determine and detect agglomerations in order to then assess if companies invest in the

respectively relevant agglomerated areas. The location choice of FDI was attributed the values

0 and 1. 0 for the states in the lower 80% of the agglomeration while the ten states with the

highest levels of agglomeration – the remaining 20% - were attributed the value of 1. Each state

and its values for industry and country of origin agglomeration can be retraced in Appendix 1.

The calculation can be reviewed in section 3.4.2 as these values serve as the predictors in the

analysis determining the location decision.

3.4.2 Independent Variables

After determining which states can be considered the host to agglomerations or of a

significant agglomeration the decision was regressed against a number of variables. First, the

independent variables will be introduced before control variables are being outlined.

Industry Agglomeration Levels: Formula 2:

𝐴𝑔𝑔𝑙𝑜𝑚𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑖𝑜 𝑝𝑒𝑟 𝑆𝑡𝑎𝑡𝑒 =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑖𝑟𝑚𝑠 𝑝𝑒𝑟 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑝𝑒𝑟 𝑠𝑡𝑎𝑡𝑒 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑥 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑖𝑟𝑚𝑠 𝑝𝑒𝑟 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑥

The given formula, adapted from Du et al. (2008), indicates the level of agglomeration in

each of the states included in the sample. The formula includes the industry level to distinguish

28

Calculating this ratio per state for both high- and low-tech industries provided a percentage of

companies located in each state. This percentage was then regressed against the inward FDI

into the states in order to find out to what extent decisions are driven by these levels of

complementary industries.

Country of Origin Agglomeration Levels (Japanese firms in each US state)

Next to industry level agglomeration, country of origin agglomeration plays a great role

in research as it was tested in various studies (Zhang et al., 2012; Head et al., 1995; Tan &

Meyer, 2011).

Formula 3:

𝐻𝑜𝑚𝑒 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝐴𝑔𝑔𝑙𝑜𝑚𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑖𝑜 𝑝𝑒𝑟 𝑆𝑡𝑎𝑡𝑒

= 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑖𝑟𝑚𝑠 𝑓𝑟𝑜𝑚 ℎ𝑜𝑚𝑒 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑜𝑓 𝑓𝑖𝑟𝑚 𝑝𝑒𝑟 𝑠𝑡𝑎𝑡𝑒 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑥 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑖𝑟𝑚𝑠 𝑝𝑒𝑟 ℎ𝑜𝑚𝑒 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑖𝑛 𝑦𝑒𝑎𝑟 𝑥

Even considering the centripetal forces that come into play once a certain threshold in the

size of the agglomeration is achieved, larger agglomerations are still considered more attractive

to foreign entrants as the increased productivity that is needed to overcome LOF. This research

follows the expectations brought up by Brakman et al. (2015), and De la Roca and Puga (2012)

that bigger agglomerations are a more important factor in the location choice. Indicating a firm

in a larger agglomeration makes for a more attractive target than a firm in a smaller

agglomeration (for that criteria).

States with high levels of agglomeration were defined by the percentage of the firms in

that state. The ten states hosting the highest percentage of firms (top 20%) in terms of industry

agglomeration was labelled a high agglomeration area for this study. Conducting two separate

analysis for each industry and country of origin agglomeration would be out of the scope for

this paper. Moreover, the states with high levels of industry agglomeration and country of origin

29 3.4.3 Control Variables

Experience and capabilities of firms

It can be expected that firms with increased experience are not in the same need of local

knowledge due to their prior knowledge. Therefore, firm age was used as a proxy to represent

these experiences and capabilities as it is proven to be hard to measure (Cohen & Levinthal,

1990; Kogut & Zander, 1992; Phene & Almeida, 2008). It is assumed that older firms have a

more thorough understanding of the industry and their field and therefore strategic decisions

like location selection. Experienced firms can be expected to recognize the value and risks of

agglomerations better.

Related to the argument that is been made by Shaver and Flyer (2000) that bigger firms

are less likely to co-locate than smaller firms it is to be expected that firm experience plays a

major role in the decision making towards agglomerated areas. Firm experience can come from

multiple sources. One of them is the experience of employees being better trained and more

experienced (Stolzenberg 1978; Brown & Medoff 1989; Kalleberg & Van Buren 1996; Troske

1999; Walace & Kay 2009). Le and Kroll (2017) state that the international experience of

executives is related to exposure towards foreign environments. Those create dissonance in

cognitive schemas of people which stimulates learning. In the long run, these learnings affect employees’ adaptability and performance. Obviously, that experience of employees trickles

down to the experience and capabilities of the organisation as a whole and thereby its capacity

to adapt to different environments more easily than firms who do not have these experiences at

their disposal. Applied to the location selection of a company it is expected that local knowledge

can be predicted and adapted to more easily, making the co-location with other companies and

agglomerated areas overall less relevant for the company in question. That is partly because the

company itself already has some international experience and learnings they can build on and,

thereby, adapt the new challenges at hand and also the experiences of employees explained

30 Knowledge intensity

As companies act within different industries (and some across multiple industries) the

sample was sub-grouped into knowledge-intensive ones and the ones who are not. Hatzichronoglou’s (1997) approach of classifying manufacturing industries based on the

technological intensity was applied and extended. Non-knowledge intensive industries such as

mining, agriculture, forestry, retail, logistics, and medium-low and low-technology

manufacturing activities received a value of 0. Knowledge-intensive activities, as defined by

Hatzichronoglou (1997), and finance, motion pictures, engineering, and architectural services

(Li & Bathelt, 2017) were attributed to the value of 1. High technology industries are identified

by the following Standard Industry Classification codes: 28, 35, 36, 37, 38, 42, 48, 41, 44, 45,

737, 966.

Creating a picture of location choice differences between both kinds of firms and

industries. More than only the locational factors of the investment decision, such as the

technological environment, the setup of the firm is also highly relevant. The intensity of

knowledge a firm processes can have a major impact on the decision for whether it is driven by

innovative considerations or solely on a cost and profit basis (Chung & Alcacer, 2002; Li &

Bathelt, 2017). This is evident in high technology industries that create an environment that is

knowledge-intensive (Li & Bathelt, 2017). In contrast, low technology industries, such as

manufacturing and mature industries where low-cost productions play a major role, are driven

by other aspects such as low labour cost. Consequently, and with the argument that innovative

firms prefer new establishments in agglomeration areas, it can be expected that

knowledge-intensive industries are more attracted by agglomerations than their low technology

counterparts. This comes as no surprise as low technology companies are reliant on low labour

cost, which is unlikely to occur in a high competition area such as an agglomeration. On the

other hand, high technology firms and knowledge-intensive firms can tap into that skilled

31 Subsidiary network

Similarly, to the age of an organisation, the number of subsidiaries also affects its

experience in the international context. This is possible in multiple ways. It can be expected

that MNEs with an existing network of subsidies have advantages in accessing knowledge pools

over firms that have only a few sites. However, there can be also costs associated with these

MNEs as the integration of diverse knowledge becomes problematic. Reasons for that can be

broad. First, each organization has hotspots that are more influential in the organisation than

others. Second, managers are cognitively limited which makes the coordination of many

subsidiaries with sometimes conflicting information problematic and more cost-intensive with

growing networks (Simon, 1947; Meyer et al., 2011). Thereby the search for knowledge

becomes increasingly limited (Laursen & Salter, 2006; Leiponen & Helfat, 2010; Love et al.,

2014; Li & Bathelt, 2017) as a negative relation from the size of a subsidiary network towards

location decisions for agglomerations is expected. The number of international subsidiaries can

be seen as another advantage in their experience of investing abroad. Given the experience in

foreign environments and the adaptability and flexibility caused by this experience, the more

international subsidiaries the smaller the need of investing in an agglomerated area to adapt to

the new market. The more internationalized an MNE is the more local and regional knowledge

and a reduced incentive to co-locate with other companies to gain local expertise (for some of

the motives). Table 2 provides an overview of the variables used for this research and where

32 Table 2 Variable description

Variable type Name Data source Measure

Dependent Location choice Orbis/Zephyr 1= into agglomeration

0= away from

agglomeration Independent Industry Agglomeration

within a US state Orbis As of formula 2 Country of Origin Agglomeration within a US state Orbis As of formula 3

Control Experience and capabilities of a firm (represented by firm age)

Orbis/Zephyr 2019- the year of the foundation of the acquirer

Knowledge intensity Orbis/Zephyr According to SIC code in a list separated into high (1) and low tech (0)

Subsidiary network Orbis/Zephyr Number of

33

4 Results

The following section outlines, first, the preliminary tests for the assumptions of binary

regression to ensure the validity of the results obtained. Second, descriptive statistics and

correlations are analysed. Then, third, the results of the main regression analysis are presented.

4.1 Preliminary tests

Normal distribution was first tested for alongside skewness and kurtosis through a

Shapiro-Wilk test across all levels of the independent variable. The results show that skewness

and kurtosis appear to be present in the data and that the data appears not to be normally

distributed. Despite this, normality of the raw data is not an essential requirement for binary

regression, especially for large sample sizes (Lumley, Diehr, Emerson, & Chen, 2002). To

ensure that this skewness and kurtosis does not create non-parametric, non-linear effects the

Pearson and Spearman correlation coefficients were compared. On the whole these delivered

similar results thus this does not seem to disturb the data. When looking at the different variables

these distributions can be explained. For the number of subsidiaries, a majority of companies is

on the smaller end of that scale when, in contrast, there are only a handful of really large

organisations with hundreds or even thousands of subsidies. While other regressions such as

linear regression require normal distribution for reliable results the binary regression used for

the research at hand does not depend on that. Therefore, transformation is not required.

Next multicollinearity was tested by using the variance inflation factor (VIF). To do that

regression for each of the IVs was executed while all remaining IVs stayed in as predictors. The

VIF and tolerance for all but two variables showed an acceptable range of not higher than 5 and

no lower than .2 respectively (Field, 2009). The only two variables for which this was not the

case were low-technology company-related agglomeration and high-tech company-related

agglomeration. In both cases, the VIF excelled the value of 10 while tolerance stayed above .2.

34

related due to the fact that some areas just host more companies than others and thus have a

higher density of corporations. This might stem from a customer base, resources, or skilled

workers to give just some examples amongst those that lead to agglomeration. The decision

was made to eliminate low-tech agglomeration from the model and run the regression tests with

high-technology agglomeration as a lone measure for industry agglomeration.

Next, in order to test for heteroscedasticity, both a Breusch-Pagan & Koenker test was

performed on all regression models used in the main analysis. For all models of the regression,

heteroscedasticity is not present, therefore it is concluded that it was not going to disturb the

results.

Lastly, Autocorrelation between the error terms was tested for using a Durbin-Watson

test to evaluate the independence of the errors. The test for the regression produced a score of

1.933 which are all within the acceptable range of between 1.5 and 2.5 (Field, 2009) and

indicates not correlated residuals. Therefore, it can be concluded that autocorrelation is not

present.

4.2 Descriptive Statistics

For this research 302 investments from Japan into the USA were assessed and analysed.

These transactions are broken down into 111 Greenfield investments, 29 JVs and 162 M&As.

Table 3 shows how the transactions are distributed over the three different entry modes and

whether or not transactions are made into agglomerated areas or not. Moreover, the kind of

agglomeration that is present in the respective area the investment is made into. The ten states

with the highest percentage of agglomeration for each of the cases, industry and country of

origin agglomeration, are used to define the agglomerated areas. In the case of industry

agglomerations those states are: California (12.3% of businesses within the USA), Texas