INTERNATIONAL EXPANSION STRATEGIES

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HOME REGION ORIENTATION

IN INTERNATIONAL EXPANSION STRATEGIES

Elitsa (Ellie) R. BanalievNortheastern University

Charles DhanarajIndiana University



HOME REGION ORIENTATION IN INTERNATIONAL EXPANSION STRATEGIES

Elitsa (Ellie) R. Banalieva

Assistant Professor

Gary Gregg Research FellowInternational Business & Strategy GroupCollege of Business Administration

Northeastern UniversityBoston, MA 02115-5000

Phone: 617-373-4756Fax: 617-373-8628

Email: [email protected]

Charles Dhanaraj*Associate Professor of Management

Schmenner Faculty FellowKelley School of Business

Indiana University801 West Michigan Street

Indianapolis, IN 46202-5151Phone: 317-274-5694

Fax: 317-274-3312Email: [email protected]

Forthcoming 2013 Journal of International Business Studies

*Corresponding author.

We are grateful for the invaluable feedback from the editor, Professor Ulf Andersson, and three anonymous reviewers, which have

sharpened our contribution here. We thank our colleagues who have helped us significantly: Christian Asmussen, Paul Beamish

Allan Bird, Cyril Bouquet, Anthony Goerzen, Shyam Kumar, Harry Lane, Dan Li, Marjorie Lyles, Simon Parker, Ravi RamamurtiSubramanian Rangan, Alan Rugman, K. Sivakumar, Alain Verbeke, and the participants at the research workshops at BEPP

department at Indiana University, Haskayne School of Business at the University of Calgary, IB&S group at NortheasternUniversity, and strategy department at Boston College. The first author acknowledges the support from the Northeastern

University’s Gary Gregg Research Fellowship and the second author acknowledges the support from the Indiana University’s

Schmenner Faculty Fellowship for enabling this research.



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HOME REGION ORIENTATION IN INTERNATIONAL EXPANSION STRATEGIES

ABSTRACT

Despite the emerging consensus that most multinational enterprises (MNEs) are regional,

systematic theory explaining regionalization is conspicuously absent and empirical findings on its

implications for MNE performance remain mixed. Drawing on internalization theory, we suggest that

technological advantage and institutional diversity determine firms’ home region orientation (HRO), and

we posit a simultaneous relationship between HRO and performance. We apply insights from the firm

heterogeneity literature of international trade to explain the influence of technology on HRO. We predict

a negative and non-linear impact of technological advantage on HRO driven by increasing returns logic,

and a negative impact of institutional diversity on HRO driven by search and deliberation costs. We find

empirical support for our model using simultaneous equations methodology on longitudinal data of Triad-

based MNEs. Performance significantly reduces HRO, but HRO does not have a significant effect on

performance.

Keywords: Internalization theory; geographic scope; home region orientation; technological advantage;regionalization debate; institutional diversity; performance; simultaneous equations methodology

Running Head: Home Region Orientation



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“ Internal inducements to growth are not by themselves profitable opportunities

for expansion nor are external inducements by themselves.” Penrose (1995: 87)

INTRODUCTION

How do multinational enterprises (MNEs) determine their geographic scope1? This is a central

concern for international business (IB) research. Yet, ironically the literature has long remained silent

regarding the nuances of location in international expansion2. Simply put, should MNEs focus on markets

close to their home country (i.e., regional), or far from home (i.e., global)? There has been an implicit

assumption that firms expand globally, accelerated by growing technology, transportation, and trade links

across the world (Ghoshal, 1987; Levitt, 1983; Yip, 1992), and popular books have reinforced this notion

(Cairncross, 2001; Friedman, 1999, 2005). Rugman and Verbeke (2004), in their iconoclastic study of the

Fortune Global 500 firms, argued that most firms are not global, but regional; i.e., they limit their

geographic scope to their home region. Ghemawat (2007) independently reported corroborating findings

that the exorbitant cost of operating at a distance (cultural, administrative, geographic, or economic)

between the home and host countries had led to a state of “semi-globalization.” Propelled by this

provocative insight, a number of recent studies explore the nuances of geographic scope and a consensus

is emerging that most MNEs are regional. Yet, systematic theory explaining regionalization is

conspicuously absent and empirical findings on its implications for MNE performance remain mixed.

We focus our research on two related questions. Why do MNEs limit their geographic scope to

their home region? How does this affect MNE performance? Our research interest is driven by the

pressing need to explore the conceptual logic underpinning the limits of firms’ geographic scope. As

Osegowitsch and Sammartino (2007: 46) note, “[t]he IB community needs to urgently confront the

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Despite the wide use of the term “geographic scope,” it has not been well defined in the literature. Geographicscope can refer to the extent, dispersion, and diversity of the foreign markets that a firm expands into. A firm’s

geographic scope is a cumulative effect of its locational choices (Hennart, 2011). Some studies use it synonymously

with degree of internationalization, others use entropy measures to include the dispersion and diversity dimensions

(Goerzen & Beamish, 2003; Hitt, Hoskisson, & Kim, 1997; Lu & Beamish, 2004; Tallman & Li, 1996). Until

Rugman and associates’ work, most of the work had not differentiated between near and far geographic activities, as

the literature’s primary focus had been on domestic vs. foreign markets.

2Dunning (1998) pointed out that location remained a neglected issue in IB research, and a decade later, when

Dunning’s article received the JIBS Decade Award , Cantwell (2009) reminded that the issue still persisted.



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question why, in an age of purported globalization, many of the world’s largest firms appear to have

barely ventured beyond the confines of their home region.” In undertaking such an endeavor, we

recognize the endemic constraints posed by the limited availability of publicly available data since

existing regulations do not mandate disclosure of fine-grained data such as sales across geographic

segments (Herrmann & Thomas, 1996). Even when firms report segment sales, they define geographic

segments in an idiosyncratic manner, making comparison across firms difficult (Rugman & Verbeke,

2007). Our goal in this paper is to advance the field’s understanding of the regionalization phenomenon.

We make the best use of available data by adopting a parsimonious “near/far” bimodal approach to

geographic scope. We recognize that such an approach does not explain everything. However, simplifying

the frame can lead to conceptually interesting and empirically tractable questions.

In developing our conceptual framework, we use the term “home region orientation” (HRO)

(Delios & Beamish, 2005; Rugman & Verbeke, 2008). Broadly, HRO is the propensity of a firm to

expand within the home region as opposed to outside the home region, allowing us to systematically

theorize on the strategic choices faced by the firm and its motivations for these location choices. Over

time, HRO drives the expansion strategies of a firm and, thus, dictates a firm’s geographic scope.

Although the international finance (French & Poterba, 1991; Tesar & Werner, 1998) and international

trade literatures (Hejazi 2005; McCallum, 1995) have used the term “home bias” to describe the

geographic concentration of portfolio and trade activities of investors and countries, respectively, we

refrain from using the term “bias” as it signals an a priori negative connotation of the phenomenon 3.

Technological advantage and institutional environment are the two dominant explanatory

constructs in the IB literature in general, and in the internalization theory in particular (Buckley &

Casson, 1976; Rugman, 1981). In this study, we build on internalization theory to investigate how these

two constructs determine a firm’s geographic scope. We integrate the increasing returns mechanism

central to the technology literature (Arthur, 1989; Ciuriak et al., 2011; Helpman, 2006; Helpman, Melitz

3We are grateful to our anonymous reviewers who gently nudged us to stay neutral and consistent with the

established terminology in the regionalization literature.



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& Yeaple, 2004; Krugman, 1979; Melitz, 2003; Nocke & Yeaple, 2007) with the notion of firm-specific

advantage (FSA) central to the IB literature (Buckley & Casson, 1976; Rugman, 1981) to propose a

negative and non-linear relationship between technological advantage and HRO. We analyze how

institutional diversity within the home region can influence the search and deliberation costs for an MNE

and, thus, affect its HRO (Goerzen & Beamish, 2003; Rangan, 2000). While most of the extant work has

attempted to detect the impact of HRO on firm performance (see Qian et al., 2010 for an overview), we

build a simultaneous equations model focusing on how performance and HRO jointly affect each other.

Our research design uses longitudinal data on 625 Triad-based (USA, Western Europe, and Japan) public

MNEs between 1997 and 2006 and controls for a wide range of alternative explanations.

We contribute to the IB literature in three ways. First, we provide a parsimonious model of how

technology and environment shape firms’ geographic scope. While technological advantage has been

well-recognized as a critical factor determining firms’ overall internationalization and the entry modes

(see Kirca et al., 2011 for an overview), its relevance to HRO is less apparent. In particular, our negative

nonlinear hypothesis of the effect of technological advantage on HRO is novel and builds on the firm

heterogeneity literature (Melitz, 2003; Nocke & Yeaple, 2007). Also, the variance approach that we adopt

in our regional institutional diversity hypothesis sheds new light on why some firms would avoid their

home region despite the proximity. Second, our comprehensive database allows us to test the

generalizability of regionalization patterns that Rugman and Verbeke (2004, 2008) observed using the

Fortune 500 Global data. Our longitudinal data are drawn from a large database of firms from the United

States, Japan, and Western Europe. Finally, our simultaneous equations model provides an insight into the

complex HRO-performance relationship, and can explain the inconsistent empirical findings on the

impact of regionalization on performance.

We first synthesize three debates within the regionalization literature. We then present our

conceptual framework, followed by our research design. Finally, we discuss the implications of our

findings and conclude with a summary of our contributions and ideas for future research.



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GEOGRAPHIC SCOPE IN INTERNATIONAL BUSINESS RESEARCH

Two complementary streams of literature have been invoked in explaining firms’ geographic

scope: the Uppsala internationalization process model (Johanson & Vahlne, 1977) and the Penrosian

capability model (Penrose, 1995). The process model of internationalization posits that MNEs

internationalize incrementally from familiar and proximate to new and distant locations, increasing their

commitment to foreign locations in small steps as they learn about these new markets. This minimizes the

uncertainty inherent in unfamiliar markets and the complexity of engaging with partners embedded in

local networks (Barkema, Bell, & Pennings, 1996; Benito & Gripsrud, 1992; Johanson & Vahlne, 1977;

2009). Hence, MNEs start as home-regionally oriented and gradually adjust their international expansion

toward more distant global locations. The Penrosian perspective (Penrose, 1995) complements this

learning model by emphasizing the growing constraint in managerial resources as MNEs expand

internationally. Managerial attention that can be devoted to complexities of internationalization is scarce

and, accordingly, MNEs are likely to deploy their attention to proximal and familiar opportunities to

minimize the cost of dynamic adjustment, thus favoring a regional strategy (Hutzschenreuter, Voll, &

Verbeke, 2011; Meyer, 2006; Tan & Mahoney, 2005).

Rugman (2000) formalized the regionalization hypothesis in his book, provocatively titled “The

End of Globalization: Why Global Strategy Is a Myth & How to Profit from the Realities of Regional

Markets”. Rugman’s (2000) ingenuity was to present a coruscating insight from simple hand-coded data

on the geographic distribution of sales, overlooked by most scholars (for exceptions, see Hitt et al., 1997).

Researchers in other disciplines have made similar observations. For instance, portfolio research in

finance has observed that U.S. investors tend to hold more than 90% of their equity wealth in U.S. assets

and Japanese investors tend to hold roughly 98% of their assets at home (French & Poterba, 1991; Tesar

& Werner, 1998). The international trade literature has observed that intra-regional trade is substantially

larger than inter-regional trade (Hejazi, 2005; McCallum, 1995). In the strategic management literature,

studies found that diverse industry standards, demand for local differentiation, and the complexity of



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global operations drove businesses to focus regionally (Douglas & Wind, 1987; Morrison, Ricks, & Roth,

1991; Roth & Morrison, 1992). “Regionalism rules” (Ethier, 1998: 1214), but popular media and even

scholarly research have perpetuated the assumption of an integrated global marketplace, leading

Ghemawat (2007: 1) to question, “why—if at all—firms should globalize in a world where distance still

matters.”

A decade of research on regionalization has amassed significant empirical evidence for it, and has

refined the methodology and sharpened the focus of inquiry. Figure 1 synthesizes the evolution of this

research. In essence, this stream posits that there are limits to geographic scope, contrary to the implicit

premise in global strategy research and the ubiquitous “global” claims of CEOs (Bartlett & Ghoshal,

1989; Levitt, 1983; Rugman, 2000). The development in regionalization research is best captured by three

debates that have invigorated the stream for a decade, namely, how to define a “region”, how to measure

regionalization, and how home region orientation matters to firm performance.

***Insert Figure 1 Here***

1. Definition of “Region”: While regionalization has been documented as an important emerging theme

for future IB research (Griffith, Cavusgil, & Xu, 2008), there is little consensus on how to operationalize

a region (see Figure 1). Rugman and associates’ original conceptualization of the Triad followed Ohmae’s

(1985) work. Flores and Aguilera (2007) show, through a longitudinal study, that the growing recent

number of investments outside the core Triad markets demands a more fine-grained regional

specification. Dunning, Fujita, and Yakova (2007), using macro data of foreign direct investment (FDI),

confirm the broad regional patterns, with regions defined by culture clusters. Sensitivity studies (Aguilera,

Flores, & Vaaler, 2007; Vaaler, Aguilera, & Flores, 2007) suggest that differing approaches using

cultural, political, economic, or geographic distances to grouping countries can affect the conclusions

about the regionalization patterns. Arregle, Beamish, and Hebert (2009) defined regions in geographic

terms as “a grouping of countries with physical continuity and proximity,” building on the premise that

“physical immediacy is a precondition for a sense of unity or shared properties” (Aguilera et al., 2007: 8).



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We follow this geographic definition of a region for several reasons. First, geographic proximity is central

to how MNEs organize their international strategy (Buckley & Ghauri, 2004) because it leads to greater

trade and investment linkages (Ghemawat, 2007). Second, the geographic proximity approach is time-

invariant, “which might provide an advantage over other regional schemes” (Aguilera et al., 2007: 9).

Third, while sophisticated regional classifications based on culture or other considerations are valuable,

they are less useful when the focus of the research is on international corporate strategy because they are

“an academic artifact, intellectually appealing but relatively far removed from the practice of international

corporate strategy and geo-political reality” (Rugman & Verbeke, 2007: 203).

2. Measures of Regionalization: In their early studies, Rugman and associates advocated for the use of

the home region sales-divided by-total sales ratio, with the home region sales including the domestic

sales, as a useful measure of firms’ regionalization (Rugman, 2000, 2005; Rugman & Verbeke, 2004).

The authors used thresholds to classify firms into regional, bi-regional, and global (Rugman & Verbeke,

2004). Recent research, however, criticized the thresholds as arbitrary and suggested that the

regionalization hypothesis also needs to be tested longitudinally (Osegowitsch & Sammartino, 2008).

Studies also noted that including domestic sales in measuring regionalization overstates the degree of

regionalization (Delios & Beamish, 2005; Li, 2005). Two alternative measures of regionalization that

have emerged take into account domestic market sales, while also isolating the effect of the international

rest of home region: first, rest of home region sales-to-foreign sales ratio (Banalieva & Eddleston, 2011;

Delios & Beamish, 2005; Li, 2005; Rugman & Verbeke, 2008), and second, rest of home region sales-to-

total sales ratio (Elango, 2004; Rugman & Verbeke, 2008). Asmussen (2009) suggests a measure

normalizing the ratios using GDP data, but the measure has little practical appeal. We use two alternative

measures for HRO4

. The first measure uses the ratio of rest of home region sales-to-foreign sales (Delios

& Beamish, 2005; Rugman & Verbeke, 2008). The second is the ratio of rest of home region sales-to-total

4 If T is the total sales of an MNE, D is the domestic sales, F is the foreign sales, and R is the sales within the home region, and G

is the sales outside the home region, then our measures r 1 and r 2 for regionalization can be represented as: r 1 = (R-D)/F and r 2 =

(R-D)/T – G/T. Note that T = D+F, F=(R-D) + G, and r 1 and r 2 are highly correlated.



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sales minus the ratio of global sales-to-total sales (Asmussen, 2009; Elango, 2004; Rugman & Verbeke,

2008).

3. Regionalization and Performance: Although Rugman (2000, 2005) and Rugman and Verbeke’s

(2004) original work did not assume the implications of regionalization on performance, several follow-

up studies have explored this question, yielding mixed results. While some studies have found a positive

effect of HRO on performance (Qian et al., 2010; Rugman et al., 2007), other studies have found a

negative effect (Delios & Beamish, 2005; Elango, 2004), and more recent studies have advanced

contingency perspectives (Banalieva & Eddleston, 2011; Li, 2005). Delios and Beamish (2005), in an

exploratory study, found that home region-oriented Japanese MNEs were the worst performers in the

sample. Similarly, Elango (2004) theorized a positive HRO-performance relationship but instead found

that a greater HRO reduces performance, although not significantly so, for a set of worldwide MNEs.

These results have raised an interesting but underexplored question: “why is the home-oriented

multinational firm … so prevalent overall when its performance is the lowest in the sample?” (Delios &

Beamish, 2005: 30). None of these studies have explored the simultaneous effect of performance on

HRO. A firm’s strategy and its risk-taking behavior can be constrained by its performance. It is possible

that underperforming firms lack the financial resources to expand beyond the home region so they are

home region oriented. Thus, we explore the simultaneous relationship between HRO and performance to

get a better understanding of the direction of causality between performance and HRO.

THEORY DEVELOPMENT

Internalization theory (Buckley & Casson 1976; Rugman 1981) suggests that market

imperfections—structural or transaction-specific—raise the transaction costs across national borders and

lead to internalization of markets and the creation of MNEs (Hennart, 2007: 428). Internalization

eliminates buyer uncertainty and haggling costs, bypasses government intervention through transfer

pricing, and allows for the use of discriminatory pricing based on market conditions; these benefits can

outweigh the administrative and coordination costs of internalization (Rugman, 1981). This has emerged



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as a general theory to explain MNEs’ foreign expansion (Buckley & Casson, 1976; 2009; Rugman, 1981)

and provides significant insights into firms’ geographic scope. The theory builds on two core constructs:

non-location bound FSAs, which create an edge for the MNE in a foreign country, and the institutional

environment, which determines the costs of exploiting the FSAs (Rugman & Verbeke, 2008). We theorize

a negative, non-linear effect of technological advantage and a negative effect of institutional diversity

within the home region on HRO, and argue for a simultaneous relationship between HRO and

performance. Figure 2 synthesizes our conceptual framework.

*** Insert Figure 2 Here ***

Technological Advantage

Technological advantage refers to the proprietary knowledge developed by an MNE through R&D or

otherwise and embodied in the firm’s processes and products. It is regarded as the most valuable asset

MNEs own (Caves, 1996; Dunning, 1980). Technological advantage is “the most commonly used proxy

variable in the literature to denote the existence of internalization advantage, implying that high degrees

of R&D intensity indicate the presence of intangible assets that lead to competitive advantage in

international markets” (Kirca et al., 2011: 32). It is also a non-location-bound FSA that propagates firms

globally (Anand & Delios, 2002; Meyer, Wright, & Pruthi, 2009; Nocke & Yeaple, 2007; Rugman &

Verbeke, 2008). Rugman and Verbeke (2008: 4060) note that “MNEs can penetrate foreign markets only

if they can build upon non-location bound FSAs, transferable and deployable in a profitable fashion in

host environments.” While the relationship between technological advantage and overall

internationalization is well-established (Kirca et al., 2011), few studies have analyzed how technological

advantage can influence the firms’ distance of international expansion, i.e., HRO (Cerrato, 2009).

Firm-specific technological advantage is a key variable in the “new” new trade literature as well

(Melitz, 2003; Nocke & Yeaple, 2007). In contrast to the earlier new trade literature where Heckscher-

Ohlin models assumed trade gains arose at the country- or sector-level of analysis with countries

operating under constant returns to scale and with the same production technology, the “new” new trade



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theory adopts a firm level of analysis and embeds firm-level heterogeneity within Krugman’s (1980)

model of trade under monopolistic competition and increasing returns (see Ciuriak et al., 2011;

Greenaway & Kneller, 2007 for an overview). Increasing returns, characterized by “the tendency for that

which is ahead to get even farther ahead”5 (Arthur, 1996; Helpman & Krugman, 1985; Krugman, 1980;

Romer, 1986), embodies the notion that as the technological advantage grows, it has an increasingly

larger impact on the competitive advantage of the firm. Krugman (1979) invoked the increasing returns

argument to integrate consumers’ preferences for product diversity and producers’ preferences for

economies of scale, and showed that countries with a larger demand for a product produced a more-than-

proportionate share of that product. By incorporating firm-level heterogeneity along with increasing

returns, the “new” new trade theory aligns well with the empirical reality that only a few firms participate

in foreign markets and those that do export tend to do so by using newer technologies that would allow

them to overcome the large costs of foreign expansion (Ciuriak et al., 2011; Helpman, 2006; Helpman et

al., 2004; Melitz, 2003; Nocke & Yeaple, 2007). While Melitz (2003) and Helpman et al. (2004) treat

firm-level capabilities as a bundle, Nocke and Yeaple (2007) distinguish technological advantage, which

has mobility across geographic borders, from marketing advantage, which lacks such mobility. This

“new” new trade theory has integrated exports and FDI seamlessly, and has invoked technological

advantage as a core determinant of firms’ international activity. This is also confirmed by empirical

studies on firm heterogeneity that have focused on determinants and effects of exporting (e.g., Bernard,

Eaton, Jensen & Kortum, 2003; Bernard & Jensen, 1999; 2004; Bernard, Jensen & Schott, 2006).

Drawing on this research, we argue that technology confers competitive advantage for a firm to

access global markets and overcome the challenges of increasing distance from the home market,

particularly in three areas: diversity of technology standards, demand for differentiation, and global

complexity of management (Douglas & Wind, 1987; Morrison et al., 1991; Roth & Morrison, 1992). We

5Arthur (1996) also worked on increasing returns from a technology competition perspective, focusing on standards

and lock-in, and assuming increasing returns conferred by a combination of the impact of economies of scale,

network externalities and switching costs. Romer (1986: 1002), in his work on long-run growth models, assumed

that knowledge is an input “that has increasing marginal productivity.”



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describe these three mechanisms next, incorporating insights from the firm heterogeneity literature.

First, with increasing technological advantage, the fungibility of the proprietary assets of the firm

across geographic borders and the marginal productivity of technology increase at an increasing rate

(Melitz, 2003; Nocke & Yeaple, 2007). When technology sophistication is low, technological knowledge

tends to be simpler and generic, and firms imitate each other’s technologies or even purchase technologies

from third parties (Hashai & Almor, 2008). This increases the threat of imitability and renders an

insufficient technological advantage for firms to expand beyond their familiar home regions (Douglas &

Wind, 1987). However, as technological sophistication increases, the MNE grows increasingly into a

standard-setter rather than a standard-taker and the degree of adaptation to new and geographically distant

markets decreases at an increasing rate (Bernard & Jensen, 1999; Nocke & Yeaple, 2007). As

technological sophistication grows, it becomes increasingly manageable for the firm to penetrate farther

into more distant global markets and dislodge global rivals there, as the firms lock-in global consumers to

the firms’ own technology standards. For instance, sophisticated technologies can “eventually corner the

market of potential adopters, with the other technologies becoming locked out” (Arthur, 1989: 116).

Second, technology leaders become trail blazers as they set new trends for other firms to follow

(Arthur, 1989, 1996; Nocke & Yeaple, 2007). The proprietary knowledge gives the firm “the resources

and competitiveness to expand in all regions and to benefit from the scale economies of a global plant

configuration” (Belderbos & Sleuwaegen, 2005: 579). When technological advantage is low, the MNEs

are forced to fight against the local competition from both domestic incumbents and foreign MNEs. In

such cases, an MNE’s foreignness is a liability because the MNE with low technological advantage is

unable to match the technology efforts of its rivals and is likely to face eroding global, but accelerating

regional market presence (Autio, Sapienza, & Almeida, 2000). However, rising levels of technological

advantage increasingly enhance the MNEs’ ability to combine their knowledge on a global scale, find

more efficient global distribution channels, and reduce the high costs of new product development (Zahra,

Ireland, & Hitt, 2000). Thus, as technological advantage increases, the MNE becomes less burdened with

cost and instead begins to enjoy a premium pricing advantage at an increasing rate (Helpman et al., 2004;



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Melitz, 2003). Additionally, the capacity to penetrate geographically distant markets increases

disproportionately, with Levitt’s ‘global market’ becoming a closer reality.

Third, increasing technological advantage accelerates the productivity of managerial attention in

international expansion at an increasing rate (Bouquet, Morrison, & Birkinshaw, 2009; Bouquet &

Birkinshaw, 2011). As technological advantage increases, productivity of the firm’s resources also

increases more than proportionately (Griliches, 1986; Hansen & Wernerfelt, 1989; Henderson &

Cockburn, 2000). Penrose (1995) emphasized executive management is a key resource that is necessary,

but often limiting for the growth of a firm. Managerial attention for international expansion is “the time

and effort that headquarter executives invest in activities, communications, and discussions aimed at

improving their understanding of the global marketplace” (Bouquet & Birkinshaw, 2011: 244). It allows

executives to stay abreast of ongoing international expansion opportunities and respond accordingly with

informed strategic actions. As technological advantage increases, organizational costs of coordination

across the markets decrease at an increasing rate and, hence, firms would be able to reach Levitt’s world

of globalized markets rapidly. The increasing ability of the firm to lock in global consumers would also

lower the managerial complexity at an increasing rate. Concurrently, as the capacity of the firm to

penetrate distant markets increases at an increasing rate, a firm’s HRO decreases more than

proportionately. A combination of these increasing returns in technology’s fungibility, market power, and

managerial productivity leads to:

Hypothesis 1: Ceteris paribus, for an internationalizing MNE, as technological advantage

increases, home region orientation decreases at an increasing rate.

Institutional Diversity

MNEs operate in diverse institutional environments (Kostova & Roth, 2002; Rosenzweig &

Singh, 1991). In the IB literature, two approaches, political economy and socio-cultural, have been used

to study institutional environments. The political economy stream emphasizes the risk and complexity of

investing in a country arising from its regulatory policy and uses variables such as political risk, political

hazard, formal (regulatory, legal, administrative, economic, and geographic) institutional distance,



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political predictability, and restrictiveness (Abdi & Aulakh, 2012; Boddewyn, 1988; Campbell, Eden, &

Miller, 2012; Gomes-Casseres, 1990; Henisz, 2000; Salomon & Wu, 2012). The socio-cultural stream

emphasizes the sociological/behavioral similarity (or distance) between informal rules of the home and

host country cultures, often captured with psychic or cultural distance (Abdi & Aulakh, 2012; Campbell

et al., 2012; Hofstede, 1980; Johanson & Vahlne, 1977; Kogut & Singh, 1988). Recent works have

attempted to integrate these approaches using institutional distance (Abdi & Aulakh, 2012; Berry,

Guillen, & Zhou, 2010; Campbell et al., 2012; Kostova & Roth, 2002; Salomon & Wu, 2012; Slangen &

Beugelsdijk, 2010; Xu & Shenkar, 2002). Berry et al. (2010) provide a comprehensive analysis of various

types of institutional distance and their influence on firms’ foreign entry decisions. Broadly, this literature

suggests that MNEs “prefer to locate foreign operations in host countries that are more

‘proximate/similar’ to their home country” (Flores & Aguilera, 2007: 7).

Distance measures are helpful for dyadic (i.e., home-host country) analysis. However, given our

focus is on a regional level of analysis, i.e., HRO, a regional institutional diversity construct becomes

more appropriate (Goerzen & Beamish, 2003) because “[f]irms’ international strategy is set not only on a

country-by-country basis… regional considerations play an important role” as well (Arregle et al., 2009:

104). Institutional diversity at the regional level is the variation in the institutional environments across

the countries within the home region. Unfortunately, “IB research has devoted surprisingly little attention

to comparing the topography of institutional landscapes and understanding their diversity” (Jackson &

Deeg, 2008).

Institutional context is a critical factor in internalization costs as “institutions directly determine

what arrows a firm has in its quiver as it struggles to formulate and implement strategy and to create

competitive advantage” (Ingram & Silverman, 2002: 20). Institutional diversity increases the risk for the

decision making process and raises transaction costs (Kostova & Zaheer, 1999). New contexts demand

higher information processing and coordination costs, and increase the complexity of learning how to

maneuver through these diverse countries (Hitt et al., 1997; Kostova & Zaheer, 1999).



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Furthermore, MNEs, in pursuing new economic opportunities in foreign countries, “engage in a

process of search and deliberation” (Rangan, 2000: 206). Firms incur search costs during the

identification of potential exchange partners and deliberation costs during their assessment of the

capability and reliability of these partners (Rangan, 2000). Incorporating the search and deliberation costs

into the analysis allows us to make an important extension to prior institutional research. Namely, while

spatial proximity can reduce the search costs, the institutional commonality across the partners within the

home region can minimize the deliberation costs. As Rangan (2000: 207) notes, search and deliberation

are “additive to the purchase price.” Internalization theory suggests that firms minimize both.

Institutional diversity is important not only at entry, but all through the life of the MNE. For

instance, governance hazards like expropriation risk of assets at less than full market value, constraints on

the pursuit of business opportunities because of weak enforcement of contracts, liquidity risk caused by

local customers delaying or avoiding payments, etc. can create havoc not only in one particular country

operation but at the regional network level as well (Zhou & Poppo, 2010). As the variance across the

institutional environments within the home region decreases, firms can exploit valuable knowledge

created or learned in one country within the home region to another country within the home region, what

Bartlett and Ghoshal refer to as “worldwide learning,” to create competitive advantage (Chan, Isobe, &

Makino, 2008). While explicit contracts with suppliers, distributors, and partners can work in some

countries within the home region due to their market-based institutions (Zhou & Poppo, 2010), they may

be futile in countries within the home region with less market-based institutional frameworks, which tend

to be characterized by a greater degree of asymmetric information and, hence, risk for a home-regionally

oriented company (Chan et al., 2008). These arguments suggest that spatial proximity is a natural driver

for firms to consider home region markets. However, as regional institutional diversity increases, firms

will find alternative global markets more attractive to avoid the growing regional institutional complexity:

Hypothesis 2: For an internationalizing MNE, the greater the institutional diversity of its home

region, the lower the firm’s home region orientation.



15

The HRO-Performance Relationship

As discussed earlier, we advance a model incorporating a simultaneous relationship between

HRO and performance. We first advance the effect of performance on HRO in hypothesis 3, followed by

the effect of HRO on performance in hypothesis 4.

The impact of performance on HRO. As an MNE’s resources and slack are conditioned by its

performance, its willingness to take risk in new markets will be influenced by firm performance. First,

greater firm profitability suggests that the firm has access to increased wealth or slack resources that can

help the firm grow and allow it to penetrate new, less familiar markets (Fiegenbaum, Shaver, & Yeung,

1997; Nohria & Gulati, 1996; Penrose, 1959). Organizational theorists have suggested that such residual

resources are necessary for flexibility and growth as they provide a cushion to absorb unexpected shocks

and allow firms to take risks in market expansion (Bromiley, 1991; Nohria & Gulati, 1996). Thus,

increased firm profitability provides a buffer to store resources and deploy them in situations of

temporary downturns or difficult competitive circumstances. When a firm performs well, it can afford to

experiment more with new and riskier strategies that can generate greater returns (Tseng et al., 2007).

Global strategy has been associated with higher risk compared to regional strategy (Elango, 2004; Li,

2005). Thus, a better performance could lead to greater global scope and a lower HRO.

Second, a greater availability of wealth obtained from higher performance would also render the

managers of the organization more inclined to take strategic risks in search of new growth opportunities

(Bromiley, 1991). Researchers have long associated higher levels of a firm’s financial wealth with a

greater degree of innovation (Leonard-Barton, 1992). Consistent with the theory of the growth of the firm

(Penrose, 1959), this suggests that managers in well-performing firms are more likely to choose global

markets as regional markets associated with a narrower search that can lead to “increasingly rigid

cognitive maps and highly specialized competencies that may become core rigidities” (Raisch &

Birkinshaw, 2008: 393). Good performance also creates a buffer that allows firms to withstand the

possible dangers from the riskier global segment and to establish a dominant position there in the long

run. This is consistent with the firm heterogeneity literature, which suggests that only the firms with



16

sufficiently high profits will be able to overcome the high sunk costs of exporting and that the foreign

geographic scope increases with the productivity of the firm (Bernard et al. 2006; Greenaway & Kneller

2007; Helpman 2006, Helpman et al., 2004; Melitz 2003). Ciuriak et al. (2011: 5) notes, “high

productivity at the firm level often precedes entry into international markets, suggesting the presence of

significant firm-level sunk costs that raise the productivity threshold that firms must clear to be able to

profitably enter foreign markets.” Thus:

Hypothesis 3: Ceteris paribus, for an internationalizing MNE, the greater its performance, the

lower its home region orientation.

The impact of HRO on performance. As reviewed earlier, empirical results are mixed regarding

the effect of HRO on performance (e.g., Delios & Beamish, 2005; Elango, 2004; Li, 2005; Qian et al.,

2010; Rugman et al., 2007). Internalization theory would cast regionalization as an outcome of the

combination of resource position of the firm and the institutional environment, and does not have specific

predictions on the implications of regionalization for performance. Hennart (2011) convincingly argued

that firms can perform well at different levels of multinationality. While the theoretical argument that

regionalization does not influence performance is convincing, it is an arduous task to theorize such a

claim. Following Hennart (2007; 2011), we set up two competing hypotheses, arguing for positive and

negative effects, and use the empirical data to identify the nature of the relationship.

HRO can improve firm performance because firms attempt to minimize search and deliberation

costs (Rangan, 2000) and maximize the financial gains from economies of regional agglomeration

(Krugman, 1991; Stigler, 1951). Firms that cluster close to one another benefit from the concentration of

their operations as efficiency gains are obtained from co-located suppliers (e.g., shared inputs), consumers

(e.g., larger markets) (Krugman, 1991; Stigler, 1951), and skilled labor-market pooling (Marshall, 1920).

Such co-location allows for easier access to a variety of suppliers, more competitive input prices, and

greater specialization of products (Stigler, 1951). MNEs can benefit from both upstream and downstream

agglomeration, as suppliers are more likely to cluster when MNE operations concentrate in a region.

Additionally, MNEs can benefit from appealing to similar markets within the home region and, hence,



17

enjoy knowledge spillover effects due to frequent interactions of common buyers and suppliers (Alcacer

& Chung, 2007). Adams and Jaffe (1996) found that firms’ plants that are co-located with the firms’

innovation activities are more efficient in their production than their out-of-state plants. This suggests that

geographic distance lessens the benefits from regionalization economies as face-to-face interactions

become more difficult (Rosenthal & Strange, 2003).

HRO can also reduce firm performance because firms can seek to minimize risk (Agmon &

Lessard, 1977; Dhanaraj & Beamish, 2004; Rugman, 1976; 1980) and congestion costs (Almeida &

Kogut, 1997; Pouder & St. John, 1996; Shaver & Flyer, 2000) from diseconomies of regional expansion.

The more MNEs are involved in global markets, the more MNEs are able to share their costs of

production across geographic markets and increase their performance (Capar & Kotabe, 2003).

Globalizing MNEs are also able to reach a larger number of global consumers with their products and,

hence, increase their global market share vis-à-vis rivals. Increasing global diversification is also

associated with increasing risk reduction from international diversification (e.g., Agmon & Lessard, 1977;

Dhanaraj & Beamish, 2004; Rugman, 1976; 1980). Distant global markets are less correlated with one

another than proximate regional markets (Speidell & Sappenfield, 1992), so an MNE pursuing a low

HRO strategy is likely to be less affected by a regional economic crisis as it can readily shift expansion

efforts into another, less affected global region, enhancing its overall profitability. If MNEs confine their

geographic scope to the home region, they miss out on exploring new market opportunities globally,

potentially exhausting the market opportunities within the home region and limiting the firms’ market

share and profitability. Additionally, in a limited geographic space like the home region, congestion costs

in the form of increased competition for valuable labor and capital inputs or increased risk of knowledge

expropriation by geographically proximate rivals are likely to increase and to lead to shortages (Almeida

& Kogut, 1997; Pouder & St. John, 1996; Shaver & Flyer, 2000). Thus:

Hypothesis 4a: Ceteris paribus, for an internationalizing MNE, the greater its home region

orientation, the greater its performance.

Hypothesis 4b: Ceteris paribus, for an internationalizing MNE, the greater its home region

orientation, the lower its performance.



18

RESEARCH DESIGN

Research Context

We tested our conceptual framework using data from the Triad (i.e., the U.S., Western Europe,

and Japan; Ohmae, 1985; Rugman, 2000, 2005; Rugman & Verbeke, 2004). The Triad is an important

geographic space for several key reasons. First, the Triad countries share similar macro-economic

features: e.g., low economic growth, economic and financial infrastructures, government regulations,

relatively homogenous consumer demand, high purchasing power ability of consumers, high urbanization,

etc. (Ohmae, 1985; Rugman, 2000, 2005). Second, “[t]he Triad is home to most innovations in industry,

and includes the three largest markets in the world for most new products” (Rugman & Verbeke, 2004:

4). Third, Rugman (2000) documented that 86% of the Fortune 500 Global MNEs are headquartered in

these core Triad regions. This high Triad concentration is a “useful indicator of the Triad’s enduring

importance” (Rugman, 2005: 59). Thus, by drawing on MNEs from all these markets simultaneously, we

hope to enhance the external validity and comparability of our results.

Data Sources

We used the OSIRIS database to extract the Triad-based firms. OSIRIS is a commercially

available financial database provided by Bureau Van Dijk that includes close to 70,000 companies

(subsidiaries and parent firms) from around the world. The financial data in OSIRIS comes from firms’

annual reports and is provided by World’Vest Base (WVB), Korea Information Service (KIS), Teikoku

Databank, Huaxia International Business Credit Consulting Company, Reuters, and Edgar Online

(OSIRIS Data Guide, 2007). OSIRIS is seen as “one of the most comprehensive databases of listed

companies” (Shao, Kwok, & Guedhami, 2010: 1397) and is being used increasingly for international

research (e.g., Chakrabarti, Singh, & Mahmood, 2007; Chakrabarti, Vidal, & Mitchell, 2011; Rugman,

2007; Rugman, Kudina, & Yip, 2007; Rugman, Oh, & Lim, 2012). However, comprehensive coverage

starts only after 1996, which constrained our observation window. Furthermore, if a company delists, it

stays in the database, but its account is no longer updated annually by the OSIRIS technical staff. None of

the firms in our sample had delisted over the sample period. OSIRIS’ geographic segment data coverage



19

also depends on the way firms report the data in their reports. As mentioned earlier, segment disclosure

requirements are not systematic across countries and limit the data availability by country (Herrmann &

Thomas, 1996). We randomly cross-checked the financial data reported in OSIRIS and in the firms’

annual reports and found both sources are consistent. Adjusting for these data limitations, our final

sample consisted of firms based in 12 Triad countries (i.e., the U.S., Japan, Denmark, Finland, France,

Germany, Ireland, Netherlands, Norway, Sweden, Switzerland, and the U.K.). Over the 1997-2006

period, these countries collectively represented 62.11% in average GDP share as a percentage of the

world’s GDP6, a reasonable coverage.

We used the Bloomberg Terminal, a computer system provided by Bloomberg L.P., that provides

real time and historical financial data of public companies worldwide, to extract the stock market

capitalization for the firms in our sample. The macro-economic data in our analysis came from the World

Bank and the United Nations University—Comparative Regional Integration Studies (UNU-CRIS). The

Regional Institutional Diversity data came from the Business Environment Risk Intelligence (BERI) (e.g.,

Ali, 2003; Chong & Zanforlin, 2000; Knack & Keefer, 1995) and the Fraser Index of Economic Freedom

of the World co-published by the CATO Institute, the Fraser Institute, and more than 70 think-tanks

around the world7

(e.g., DiRienzo et al., 2007; Gwartney et al., 2002; Nachum & Song, 2011).

Appendices A and B describe the two indexes.

We then proceeded by selecting the sample of firms based in the Triad nations. To ensure the

firms were sufficiently independent to determine their own strategy, we excluded firms in which another

entity held more than 25% ownership, as provided by OSIRIS (Bartram et al., 2007; Chen, 2007). We

also excluded firms that are subsidiaries of the sampled firms because their financial statement data are

already accounted for in their parent firms’ consolidated statements. Additionally, to ensure that the firms

were multinational, we focused on firms with at least 10% foreign sales (e.g., Nachum, 2004; Sambharya,

1995). We dropped firms with less than two years of available data due to the panel data structure

6Based on authors’ calculations using World Bank data.

7For more details, please refer to: http://www.freetheworld.com/datasets_efw.html.

https://outlook.neu.edu/owa/redir.aspx?C=0GtSSl8-pE-mQjlvgpHAvQ_pcOAdbs8IMYJ1W7WHts9VS4k8aOIjFKm94UPc3RaRbp6-_RxtqpY.&URL=http%3a%2f%2fwww.freetheworld.com%2fdatasets_efw.html

https://outlook.neu.edu/owa/redir.aspx?C=0GtSSl8-pE-mQjlvgpHAvQ_pcOAdbs8IMYJ1W7WHts9VS4k8aOIjFKm94UPc3RaRbp6-_RxtqpY.&URL=http%3a%2f%2fwww.freetheworld.com%2fdatasets_efw.html



20

requirements of our model. We also excluded the financial firms as they have very different capital

structures, often affected by banking regulations for minimum capital requirements (Rajan & Zingales,

1995). Our approach is consistent with prior international finance research (e.g., Fama & French, 1992;

La Porta et al., 2002; Mehran & Stulz, 2007), which has noted that “financial ratios and valuation metrics

for banks are not directly comparable to financial ratios and valuation metrics for other firms” (Mehran &

Stulz, 2007). IB research has also followed this practice (e.g., Reeb, Kwok, & Baek, 1998). These steps

resulted in 625 MNEs from 1997 to 2006, or 3,061 firm-year observations (33.09% from the U.S.,

28.91% from Western Europe, and 37.99% from Japan). This ten-year period is sufficiently long to

capture the evolutionary nature of internationalization (Lu & Beamish, 2004) and is twice as long as the

average timeframe in prior studies (e.g., Li, 2005; Rugman & Verbeke, 2008).

Measures

Home region orientation. Following prior research, we measured firms’ HRO with the ratio of

regional sales (excluding domestic sales) to foreign sales (e.g., Banalieva & Eddleston, 2011; Delios &

Beamish, 2005; Li, 2005; Rugman & Verbeke, 2008). The higher the ratio, the higher the firms’ HRO.

We also adopted an alternative HRO measure as a robustness check, which we discuss later.

Technological advantage. We captured firms’ technological advantage with the ratio of R&D

expenditures-to-total sales, a widely-used measure of firms’ innovation input (e.g., Anand & Delios,

2002; Kirca et al., 2011; Meyer et al., 2009). We added the square term of R&D expenditures-to-total

sales to test our theoretical arguments that technological advantage decreases HRO at an increasing rate.

We also attempted to measure technological advantage with another time-varying firm-level

measure—firm patents, an output measure of innovation (Hall, Thoma, & Torrisi, 2007)—but we

encountered several data challenges. First, because firms file for patents with patent offices around the

world, ensuring that the patent portfolio for each firm over time is complete becomes a challenging task

(for an overview, see Thoma et al., 2010). Commercial patent data providers typically do not supply

unique firm identifying numbers by which to assign patents to the same focal firm, leading to over- or

under-counting a firm’s patent portfolio if company-patent matching is performed based solely on



21

company names (Thoma et al., 2010). Second, while Thoma et al. (2010) and Hall, Jaffee, and

Trajtenberg (2001) have collected firms’ patent data and provided unique company identifying numbers

to overcome the company name matching problem, their database covers only approximately 58.8% of all

EPO applications granted between 1979 and 2008. We attempted to use the Thoma et al. (2010) and Hall

et al. (2001) patent datasets to match with the firms in our sample. However, this resulted in a sparse

patent portfolio matrix for each focal firm due to the many zeros obtained when firms did not file for

patents, leading to difficulties in interpreting the results. These data-related challenges prevented us from

using patents as an alternative firm-level and time-varying measure of firms’ technological advantage.

Regional institutional diversity. We followed two steps to measure Regional Institutional

Diversity. First, we assigned the countries from the BERI index into “home region” and “global”

segments based on the United Nations (UN) country classifications (Appendix C) following prior

research that also uses these UN country mappings (Arregle et al., 2009; Flores & Aguilera, 2007). Since

the geographic-based approach to country groupings is time-invariant (Aguilera et al., 2007), it allows us

to disentangle the effect of regional institutional diversity from a possible change in region definition over

time. Second, we measured the Regional Institutional Diversity with the coefficient of variation of the

BERI Index across the home region, excluding the focal firm’s home country. The coefficient of variation

is the standard deviation of the distribution divided by its mean. A higher coefficient of variation indicates

greater regional institutional diversity (Pfeffer & Langton, 1993).

Performance. We measured firms’ performance with return on assets (ROA); i.e., earnings

before tax/total assets (e.g., Bashir, 2003; Charumilind, Kali, & Wiwattanakantang, 2006; Manos,

Murinde, & Green, 2007). ROA captures the ability of managers to reap profits from their invested assets.

We also used the ln of Tobin’s Q as an alternative performance measure, which we discuss later.

Control variables. We controlled for a range of additional factors summarized in Table 1.

*** Insert Table 1 Here ***



22

Methodology

The conceptual framework in Figure 2 can be modeled empirically with the following system of

equations for firm i, home region r, identifying and control variable j, and year t:

Eq.1: Performance i,t+1= a0 + a1*HRO i,t+1 +a j*Identifying & Control Variables j,t +e1i,t

Eq.2: HRO i,t+1 = b0 + b1*Performance i,t+1 + b2*Technological Advantage i,t + b3*Technological

Advantage sq.i,t + b4* Regional Institutional Diversity i,r,t +

b5* Identifying & Control Variables j,t + e2i,t

Since Performance and HRO are determined simultaneously, they are correlated with the error

terms e1 and e2, which makes OLS inappropriate (Greene, 2003; Wooldridge, 2009). The proper

estimation methodology is simultaneous equations models, as it explicitly models the simultaneity

between HRO and performance (Greene, 2003). In doing so, the model considers the exogenous variables

to jointly determine each endogenous variable and to construct the set of instruments for the endogenous

variables (Kennedy, 2001; Wooldridge, 2009). Using such simultaneous equations methodology is an

important empirical advancement to the regional/global strategies literature, as prior studies have not

explicitly taken into account this simultaneity (e.g., Delios & Beamish, 2005; Elango, 2004; Li, 2005;

Rugman & Verbeke, 2004). ”[T]he failure to statistically correct for endogeneity can lead not only to

biased coefficient estimates but, more importantly to faulty conclusions about theoretical propositions”

(Hamilton & Nickerson, 2003: 52).

After the Hausman test revealed that fixed effects are better than random, we followed prior

research and took advantage of the panel structure of our data by demeaning the variables with the within

(fixed effects) transformation (e.g., Clougherty, 2006; Wooldridge, 2009). This procedure is identical to

adding dummy variables for each firm in the regression, but demeaning the data instead preserves degrees

of freedom (e.g., Clougherty, 2006; Wooldridge, 2009). Thus, firm heterogeneity that can arise from

time-invariant variables (e.g., industry, country, region, geographic distance, language, colonial ties,

common border, etc.) is accounted for through the firm fixed effects (Wooldridge, 2009). As time-

invariant variables would be perfectly collinear with the fixed effects, their inclusion is not necessary

(Wooldridge, 2009). We lagged the exogenous variables one year with respect to the dependent variables



23

to facilitate the direction of causality (e.g., Elango & Pattnaik, 2007) and standardized the regression

coefficients so they can be readily compared (e.g., Ait-Sahalia & Brandt, 2001).

We next employed two-stage least squares (2SLS) and three-stage least squares (3SLS) to test the

simultaneous equations model. Both the 2SLS and 3SLS estimators use instrumental variables. 2SLS

estimates each equation separately so it keeps possible mis-specification to one equation but it also

ignores the information contained in the correlation between the error terms (Wooldridge, 2009). 3SLS

estimates all equations jointly so it uses the full information in the model but also runs a higher mis-

specification chance (Wooldridge, 2009). However, when the 2SLS and 3SLS models yield similar

results, they increase the robustness of the findings (Kumar, 2009).

In simultaneous equations models, identification of each equation needs to be achieved for proper

estimation by ensuring that the number of exogenous variables excluded from each equation is at least as

great as the number of endogenous variables in the system minus one (a necessary but not sufficient

condition) (Johnston, 1972). Thus, each equation needs to include at least one variable that is not in the

other equation for identification purposes. To identify Eq. 1, we used Leverage (total liabilities-to-total

assets), as higher leverage may impede firm performance as firms borrow more debt that they have to

repay later (Li, 2005). To identify Eq. 2, we used Currency Zone, as firms based in currency zones are

more likely to be regionally oriented; RTA Trade, as firms based in RTAs are more likely to take

advantage of regional integration; and Domestic Market Size, as firms based in larger domestic markets

may be more regionally oriented given their familiarity with greater consumer demand locally. Currency

Zone is equal to 1 for countries with no common currency; i.e., 1997-2006: US, Japan, Denmark,

Norway, Sweden, Switzerland, and the UK; 1997-1998: Germany, Finland, France, Ireland, and

Netherlands; and equal to 2 otherwise. RTA Trade is equal to (HITIi,t - HETIi,t)/(HITIi,t + HETIi,t), where

HITIi,t stands for “homogeneous intra-regional trade intensity” index and HETI stands for “homogeneous

extra-regional trade intensity” index (Iapadre, 2006). Both HETI and HITI are functions of the region’s

share of outsiders’ total trade (Iapadre, 2006; Plummer, Cheong, & Hamanaka, 2010). RTA Trade rises if



24

the intensity of intra-regional trade grows faster than that of extra-regional trade. We also captured

Domestic Market Size with the natural log of GDP (in U.S. dollars).

We checked that the equations are properly identified in three ways. First, we ensured that the

identifying variables for Eq. 2 are correlated with HRO but not with Performance, and that the identifying

variable for Eq. 1 is correlated with Performance but not with HRO. The correlations in Table 2 support

this criterion as Leverage is significantly (p<0.05) correlated only with Performance but not with HRO,

and Currency Zone, RTA Trade, and Domestic Market Size are significantly (p<0.05) correlated only with

HRO but not with Performance. Second, we performed the Sargan test of over-identifying restrictions

(Greene, 2003; Sargan, 1958; Wooldridge, 2009) to confirm that the identifying variables are properly

included in their respective equation and excluded from the other equation. A statistically insignificant p-

value of the Sargan test suggests that the system of equations is properly identified: the Sargan test p-

value was 0.5790, confirming that the identifying variables are indeed exogenous. Third, we performed

the rank condition test (a necessary and sufficient condition for identification) to ensure the model could

be properly estimated (Johnston, 1972). The system of equations passed the rank condition test as well.


RESULTS

The 2SLS and 3SLS regression analyses yielded similar results, so we present the 3SLS findings

throughout our paper as they are more consistent and asymptotically efficient than 2SLS (Kumar, 2009).

The results follow in Table 3. We tested hypotheses 1-3 on column 1 and hypothesis 4 on column 2.


Hypothesis 1 predicted that Technological Advantage will have a negative coefficient for both the

linear and the squared terms. Column 1 shows that, while we find that the signs of Technological

Advantage are as expected, the linear term is not significant. However, the squared term is significant

(p<0.05), and thus providing partial support for hypothesis 1. Hypothesis 2 is fully supported because

column 1 shows that Regional Institutional Diversity had a negative and significant (p<0.001) effect on

firms’ HRO. Hypothesis 3 is also fully supported because column 1 shows that performance significantly



25

(p<0.05) decreased HRO. The competing hypotheses 4a and 4b were not supported because column 2

showed that HRO had no significant effect on firm performance. This finding suggests that the causality

runs from performance to HRO such that when the internal and external inducements to growth are

properly accounted for, they balance out any possible performance gains from a greater HRO preference.

It is possible that prior research finds significant effects of HRO on performance because it has not

controlled for the effects of its antecedents. When the errors are correlated in a simultaneous equations

model, “a statistical relationship between M [HRO in our case] and Y [performance in our case] could be

‘driven’ by an actual relationship between the two variables or by any other factor that affects both M and

Y yet is not explicitly included in the two regression equations” (Shaver, 2005: 337).

The coefficients of the control variables on HRO in column 1 reveal some interesting findings as

well. For instance, larger firm size, larger domestic market size, and faster RTA trade decrease HRO

significantly. Conversely, industry diversification, regional market attractiveness, and currency zone

increase HRO significantly. Similarly, in column 2, a greater industry diversification, regional market

attractiveness, and leverage significantly improved firm performance.

We proceed by graphing the sample HRO vs. the sample Technological Advantage (Figure 3a)

and the average HRO vs. the three categories (low, medium, or high) of Technological Advantage (Figure

3b). For Figure 3b, we split the MNEs by first finding the sample average for Technological Advantage

(0.05), and then finding the average Technological Advantage for the above-sample average group (0.12)

and the average Technological Advantage for the below-sample average group (0.02). Thus, the low-

technology MNEs (1,766 firm-year obs.) had Technological Advantage less than 0.02 and an average

HRO of 0.40. The medium-technology Triad MNEs (976 firm-year obs.) had Technological Advantage

between 0.02 and 0.12 and an average HRO of 0.39. The high-technology Triad MNEs (319 firm-year

obs.) had Technological Advantage above 0.12 and an average HRO of 0.19. These results are consistent

with the previously-documented pyramidal structure of MNEs according to which most firms are regional

and very few expand globally. They are also in line with the firm heterogeneity literature in international

economics, where only a few firms serve foreign markets and most serve their home market (e.g., Bernard



26

et al. 2006; Helpman et al., 2004; Melitz, 2003). Our findings suggest two reasons for this pyramidal

structure. First, the results show that most (89.58%) of the Triad MNEs are low-to-medium technology

firms that lack the necessary technology capability to venture globally, in line with Rugman and Verbeke

(2004, 2008). Second, the results show that the average HRO declines at an increasing rate as

Technological Advantage increases. For example, the average HRO declines by 0.1 point between the low

and medium-technology firms but it declines by a much larger amount—0.20 points—between the

medium and high-technology firms, illustrating the increasing returns mechanism.

*** Insert Figures 3a-3b Here ***

Robustness Tests

We performed additional robustness tests to rule out possible alternative explanations for our

results8. Due to space considerations, we present some of these additional tests in Tables 4a and 4b, with

full results available from the authors upon request.

*** Insert Tables 4a-4b Here ***

First, we retested the models with a market-based performance measure: Tobin’s Q (ln of market

capitalization plus book value of liabilities divided by book value of assets, Cummins, Lewis, & Wei,

2006) and present the results in model 1. Similarly to the model with ROA, we find partial support for

hypothesis 1 and full support for hypothesis 2. We did not find support for hypothesis 3, suggesting that

our argument of performance being indicative of internal perception of slack resources may be more

consistent with accounting-based measures like ROA than with market-based measures like Tobin’s Q

that include investors’ forward expectations about the company. We also did not find support for

hypotheses 4a or 4b, suggesting that HRO does not affect market-based performance significantly.

Second, we replaced the BERI composite measure with each of its three sub-components

(Appendix A) and present the results in models 2-4 in Tables 4a-4b. The results were consistent with our

previous analysis. We next replaced the BERI measure with the alternative Fraser index (DiRienzo et al.,

2007; Gwartney et al., 2002; Nachum & Song, 2011) and report the results in model 5. The results were

8We are grateful for the two anonymous reviewers for suggesting some of these robustness tests.



27

consistent. We next used each of the Fraser sub-components (Appendix B). The results for hypotheses 1

and 3 were consistent. Hypothesis 2 was fully supported only with the Government and Legal Fraser sub-

components. Neither of the competing hypotheses 4a or 4b was supported except for the model with the

Government sub-index, which was the only model where HRO increased performance significantly

(p<0.05). Among other factors, the government sub-component captures marginal tax rates and

government expenditure, which are essential for MNEs and likely drivers for the significant effect.

Third, we tested the models with an alternative HRO measure: rest of home regional sales/total

sales – global sales/total sales adapted from prior research (Asmussen, 2009; Elango, 2004; Rugman &

Verbeke, 2008). This alternative measure is 85% correlated with our main measure of regional

sales/foreign sales. As before, we found partial support for hypothesis 1 and full support for hypothesis 2.

Performance decreased HRO but not significantly so, thus finding no support for hypothesis 3. As before,

we did not find support for either of the competing hypotheses 4a or 4b.

DISCUSSION

Our results present a compelling theoretical explanation for the regionalization phenomenon that

has been gaining a growing scholarly attention. Our empirically supported, theoretical proposition that

non-linear behavior of firms’ technological advantage dictates their geographic scope and their distance in

international expansion opens a major avenue for further research. Specifically, we found that HRO

decreases rapidly as a result of increasing technological advantage, suggesting that MNEs’ geographic

scope increases globally, and more-than-proportionately, as technological advantage increases (i.e., the

increasing returns mechanism). This presents a plausible explanation of Rugman’s (2005) observation of

the pyramidal structure of firms’ geographic scope whereby most firms are regional, some are bi-regional,

and only a few are global. This finding is also consistent with the firm heterogeneity literature in

international economics, where only a few firms serve foreign markets and most serve their domestic

market (e.g., Bernard et al. 2006; Helpman et al., 2004; Melitz, 2003). It also extends Cerrato’s (2009)

finding that innovation in the Italian manufacturing industry proportionately enables a firm to overcome

the liabilities of global foreignness. Our integration of the increasing returns concept with internalization



28

theory fits well with the broad empirical observations within the geographic scope stream in explaining

both the internationalization intensity as well as the nuanced gradation of firms’ locational strategies.

Even though technology has received major attention from IB scholars, very little attention has been

given to the dynamics of technology in determining firm-level phenomena like HRO. Prior studies that

have used the increasing returns notion have tended to focus on industry and country-level analyses. For

instance, Nachum and Zaheer (2005) incorporated the increasing returns notion in their analysis of the

telecommunications industry. Krugman (2010), too, invoked the increasing returns notion and integrated

it with countries’ comparative advantage to explain how focused locations within countries are dictating

countries’ international trade patterns. Little has remained understood as to whether and how increasing

returns can affect firm-level phenomena like geographic scope. Our paper shed new light into this area.

Furthermore, our results on the overwhelming effect of regional institutional diversity present a

window on why firms would go global rather than stay regional, even considering the liabilities of

working across the regions (Rugman & Verbeke, 2008). Internationalization patterns of firms in markets

such as Japan, China, and India are less regional and the institutional diversity may provide an

explanation. Unlike prior research that has analyzed institutional effects in terms of distance, we used a

variance measure on a wide range of institutional components and found consistent and significant

negative effects of regional institutional diversity on firms’ HRO. Unfortunately, our model does not go

far enough to explain the presence of bi-regional firms. Having specific regional boundaries, the Triad in

our model, we were unable to explore the possibility of firms selecting specific countries within the home

region to focus their activities. For example, firms in the U.S. and Canada seem to focus on the NAFTA

region more than outside NAFTA. These are interesting possibilities for future research. Nevertheless, the

fundamental premise we propose here stands: firms seek to minimize their institutional diversity of the

environments that they work in.

We also analyzed the dynamics of HRO evolution over 1997 to 2006. Our t-test with unequal

variance and Welch’s adjustment showed that the average HRO has grown significantly (p<0.05) from

0.325 in 1997 to 0.414 in 2006. We also graphed the longitudinal trends between 1997 and 2006 for each



29

of the Triad branches and present the results in Figure 4. European MNEs were the most regional in the

sample. American firms were the least regional, with an average HRO slightly decreasing over time.

Japanese firms became increasingly regional over the sample period and reached and slightly surpassed

the Western European firms’ HRO in 2006. This evidence of a growing regional trend among Japanese

MNEs is consistent with other findings (Collinson & Rugman, 2008; Delios & Beamish, 2005), perhaps

owing to the growing trade relationship between Japan and China (METI, 2004).

***Insert Figure 4 Here***

Additionally, our post-hoc analysis on the dynamics of HRO over time confirmed the importance

of regional integration. For instance, European MNEs were the most regional while American firms were

the least. The impact of regional trade agreements in diffusing patterns across the region and thus

reducing institutional diversity has been well analyzed in the literature. However, we still find the

persistence of firms’ HRO over time, with our t-test revealing that HRO actually increased significantly

from 1997 to 2006. If we follow the internationalization models derived from the Uppsala school

(Johansson & Vahlne, 1977), we should logically see a diminishing HRO over time. Eventually, as firms

mature, they should increase their geographic scope to global markets. However, it is perplexing to see

the persistence of regionalization, even after multiple decades of firm operations. Such dynamic analysis

poses an interesting question as to what drives the persistence of regional boundaries in international

expansion? Arregle et al. (2009) provide a useful first step in that direction.

The simultaneous effect of performance on HRO depicts a very different picture from prior

studies that have focused exclusively on analyzing the effect of HRO on performance. These prior studies

have treated regionalization as an independent variable, and inferred positive and negative effects on

performance. We extended this prior research by arguing for a simultaneous relationship between HRO

and performance. We showed that while performance drives HRO, HRO does not influence performance

significantly, after accounting for HRO’s antecedents and extensive control variables. We had the

difficult task of proving a null hypothesis, which we overcame by using a set of competing hypotheses. In

line with Hennart (2007, 2011), we found that geographic scope is in itself constrained or driven by other



30

variables and hence, not a strategic option by which performance can be enhanced or diminished. We also

took a very careful approach to properly account for HRO’s antecedents in our model, and perhaps

provide a better prediction of the performance impact than other models, which do not control for them.

Our study has several limitations. We focused our analysis on firms’ HRO in their sales-based

foreign market penetration strategies (e.g., Elango, 2004; Li, 2005; Rugman & Verbeke, 2004, 2008).

However, analyzing firms’ HRO in other types of foreign strategies, such as purchasing or sourcing, or

using subsidiary level data can further enhance the generalizability of our findings. Unfortunately, our

data was limited by the publicly available databases, and thus we do not have specific information on the

internationalization motives of the firm. Our control for domestic market size can mitigate this problem in

some way as firms based in larger domestic markets are more likely to internationalize in search of

cheaper foreign production, while firms based in smaller domestic markets may be more likely to

internationalize in search of new market opportunities (Dunning & Lundan, 2008; Moon, 1994). A major

constraint we had was our technological advantage measure. Even though we used R&D intensity as the

most commonly used measure for technology (Kirca et al., 2011), there is an increasing realization that

we need to expand beyond it to capture a firm’s technological advantage. However, we have not yet, as a

field, generated a comparable-across-different-geographies technology measure as internationally

compatible patent measures are difficult to compile (Thoma et al., 2010). Given the centrality of the

innovation and technology focus, this remains an unresolved issue for cross-country IB research like ours.

CONCLUSION

We used internalization theory to investigate the geographic scope of a firm, paying particular

attention to the decision to concentrate their activities within or outside their home region. We have

developed a theory-driven explanation of geographic scope, empirically validated with Triad-based MNE

data. We showed how technological advantage and regional institutional diversity determine HRO, and

suggested a simultaneous relationship between HRO and performance. Thus, we hope our study serves as

a useful platform to advance future theory development on geographic scope.



31

A decade of research on regionalization with converging empirical data demands that the IB field

take a fresh look at how we construct geographic scope and theorize on its impact on performance. It

demands for a move away from simple notions of domestic vs. international expansion, and take a more

conscious look at the locational aspects. We have attempted to steer the conversation away from different

ontological debates regarding defining regions or measuring regionalization, and towards how firms

expand their geographic scope (Flores & Aguilera, 2007: 16). A longitudinal view of the international

expansion processes, understanding how technology enables penetrating distant markets, and how

institutional diversity enables or diffuses home regional concentration would add useful insights for both

IB theory and practice. Such an approach can integrate other forms of international activity such as

alliances, which can provide a complementary perspective of how firms use diverse entry modes to

exploit their FSAs across diverse geographies. Expanding the regionalization research to frame the

international firms as a geographically distributed network, and to study the dynamic changes in the

network over time, dictated by technology and environment, will also be useful ways to extend future

research. Such network-based approaches can capture the element of randomness or idiosyncrasy, which

seems to be prevalent in many early internationalization decisions. Such studies on the dynamics of

geographic scope will also uncover some of the viscous elements within international strategy that

constrain firms’ internationalization paths, and lead to a persistent focus on the home region. Our model

focusing on technology and environment provides a point of departure for such research.



32

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41

Figure 1 Key Milestones in the Evolution of the Regionalization Perspective

Distance matters Ghemawat (2003)

Regionalization

Perspective (Global 500) Rugman & Verbeke (2004)

Challenging regionalization’smeasure & generalizability

Osegowitsch & Sammartino (2008)

Relevance of the Triad Rugman & Verbeke (2007)

Macro data using culture clusters

Dunning et al. (2007)

Region options & sensitivity

A uilera & Co 2007

2010200920082007200620052004200320012000

Rethinking global strategy

Ghemawat (2007)

Real world managers

think “regional” Morrison et al. (1991)

TransnationalStrategy for a Flat

World

Levitt (1983)

Prahalad & Doz (1987)

Bartlett & Ghoshal (1989)

Porter (1990)

Yip (1992)

Cairncross (1998)

Friedman (1999)

End of globalization Rugman (2000)

TCE theory of regionalization

Rugman & Verbeke (2005)

Regionalization

Japanese dataCollinson & Ru man 2008

Modify regionalization measure

to exclude domestic sales Rugman & Verbeke (2008)

Normalized measure of

regionalization Asmussen (2009)

Regionalization

Italian dataCerrato (2009)

Japanese Data

Longitudinal FDI Arregle et al. (2009):

EMPIRICAL EVIDENCE

DEFINING REGION

REGIONALIZATION MEASURES

2011



42

Figure 2 Conceptual Model and Hypotheses

Performance

Non-Location-Bound

FSA

Technological

Advantage

Institutional

Environment

Regional Institutional

Diversity

H1: (negative

nonlinear)

H4a: (+)

H4b: (-)

H3: (-)

Home Region

Orientation

H2: (-)

Control & Identifying Variables

Location-Bound FSA (Marketing Advantage)

Multinationality

Regional Market Attractiveness

Firm Age

Institutional Distance

Firm Size

Industry Diversification Industry Home Region Orientation

Leverage*

Currency Zone^

Domestic Market Size^

RTA Trade^

* identifying variable in performance eq.

^ identifying variable in home region orientation eq.



43

Figure 3a Sample Home Region Orientation vs. Technological Advantage

Figure 3b Average Home Region Orientation vs. Technological Advantage

NOTE: HRO is rest of home region sales/foreign sales. The “Low”, “Medium,”and “High” categories have 1,766; 976; and 319 observations, respectively.

0

. 2

. 4

. 6

. 8

1

H o m e R e g i o n O r i e n t a t i o n

0 .2 .4 .6 .8Technology Advantage

0.19

0.39

0.40

0.00

0.10

0.20

0.30

0.40

0.50

Low Medium High


A v e r a g e H o m e R e g i o n O r i e n t a t i o




44

Figure 4 Longitudinal Trends in the Home Region Orientation of the Triad

0

0.1

0.2

0.3

0.4

0.5

0.6

1 9 9 7

1 9 9 8

1 9 9 9

2 0 0 0

2 0 0 1

2 0 0 2

2 0 0 3

2 0 0 4

2 0 0 5

2 0 0 6

Year

H o m e R e g i o n O r i e n t a t i o

USA Wes tern Europe Japan



45

Table 1 Control Variables

Measure Operationalization Rationale

Marketing Advantage selling, general, administrative expenses/total sales Control for location-bound FSAs(Anand & Delios, 2002)

Multinationality foreign sales/total sales Control for international expansion(Li, 2005; Rugman & Verbeke, 2008)

Industry

Diversification , is the share of sales from business segment i

Control for industry diversification

(Tallman & Li, 1996)Regional MarketAttractiveness

Rest of home region GDP growth Control for market attractiveness(Goerzen & Beamish, 2003)

Firm Age ln(number of years since incorporation+1) Control for experience effects

Institutional Distance ( Average Global-to-Home Country Distance + Average Rest-to-Home Country

Distance)/2, where:

Average Global-to-Home Country Distance =(Average Global BERI

Score – Home Country BERI Score);

Average Rest-to-Home Country Distance = (Average Rest of Home

Region BERI Score – Home Country BERI Score)

Control for institutional distance

between home country, rest of home

region, and global segment

Firm Size ln total sales (thousands of U.S. dollars) Control for economies of scale

Industry HRO Average HRO for the focal firm’s competitors in each industry (sic2) and year,

excluding the focal firm’s HRO

Control for industry

isomorphism effects

Firm Effects Fixed effects within-transformation of the variables Control for firm fixed effects

(Clougherty, 2006;

Wooldridge, 2009)Year Effects 0-1 dummy variables for each year, 1997 base year Control for business cycle

effects (Li, 2005)

( )∑−

2

1i

si

s



46

Table 2 Descriptive Statistics and Correlations Matrix

Variable Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 ROA 0.05 0.09 1.00

2 Home Region Orientation 0.37 0.31 0.00 1.00

3 Technological Advantage 0.05 0.06 -0.18 -0.23 1.00

4 Marketing Advantage 0.23 0.15 -0.11 -0.26 0.55 1.00

5 Multinationality 0.43 0.24 0.04 -0.06 0.22 0.07 1.00

6 Regional Institutional Diversity 0.21 0.02 -0.14 0.16 -0.15 -0.09 -0.27 1.00

7 Industry Home Region Orientation 0.38 0.12 0.01 0.22 -0.17 -0.13 -0.07 0.14 1.00

8 Institutional Distance -17.26 5.15 0.07 0.01 0.11 0.07 0.40 -0.16 0.04 1.00

9 Firm Age 3.58 1.03 0.06 0.12 -0.28 -0.24 -0.10 0.33 0.15 -0.20 1.00

10 Firm Size 13.28 2.04 0.17 -0.06 -0.17 -0.32 0.08 0.08 0.14 -0.11 0.37 1.00

11 Regional Market Attractiveness 4.43 1.59 0.04 0.21 -0.13 -0.11 -0.15 0.36 0.23 -0.12 0.23 0.03 1.00

12 Industry Diversification 0.40 0.25 -0.03 -0.05 -0.07 -0.09 0.01 -0.04 0.05 -0.05 0.13 0.38 -0.03 1.00

13 Leverage 0.51 0.24 -0.16 0.03 -0.21 -0.27 0.05 0.01 0.04 0.07 0.11 0.31 -0.03 0.20 1.00

14 Domestic Market Size 28.97 1.10 0.01 -0.35 0.07 0.19 -0.43 -0.07 -0.08 -0.36 -0.06 0.03 -0.06 0.12 -0.08 1.00

15 Currency Zone 1.11 0.32 0.00 0.15 0.05 -0.17 0.37 -0.10 0.02 0.29 -0.02 0.13 -0.06 0.05 0.09 -0.45 1.00

16 RTA Trade 0.71 0.21 0.00 -0.05 -0.02 0.04 -0.24 0.24 -0.03 0.35 0.04 0.03 0.06 0.05 0.00 0.29 0.12

NOTE: Bold indicates significance at 5%. N=3,061.



47

Table 3 Three-Stage Least Squares Regression Results

Dependent Variable: HRO ROA

Technological Advantage -0.021 -0.010

(1.001) (0.472)

Technological Advantage sq. -0.007* -0.007*

(2.485) (2.280) Regional Institutional Diversity -0.145*** 0.001

(6.078) (0.028)

Performance -0.240*

(1.999)

Home Region Orientation 0.216

(0.954)

Marketing Advantage -0.021 -0.016

(0.988) (0.774)

Multinationality -0.001 -0.019

(0.061) (0.946)

Firm Size -0.085** -0.161***

(2.845) (6.014)

Firm Age 0.081** 0.016(3.206) (0.628)

Industry Diversification 0.043* 0.046*

(2.082) (2.318)

Regional Market Attractiveness 0.066+ 0.091*

(1.850) (2.447)

Institutional Distance 0.013 0.026

(0.435) (0.911)

Industry HRO 0.023 -0.024

(1.090) (1.140)

Domestic Market Size -0.141***

(3.944)

Currency Zone 0.058**

(2.668)

RTA Trade -0.073**

(2.598)

Leverage 0.170***

(8.036)

Constant 0.212*** 0.244***

(3.696) (4.909)

Chi-sq. 155.68*** 339.86***

NOTE: t-statistics in parentheses. N=2,436.

+p<0.10; *p<0.05; **p<0.01; ***p<0.001.



48

Table 4a Three Stage Least Squares Robustness Tests with Home Region Orientation as the Dependent Variable

Model 1 Model 2 Model 3 Model 4 Model 5

Dependent Variable: HRO HRO HRO HRO HRO

Independent Variables:

Performance -1.019 -0.238* -0.283* -0.235* -0.234+

(1.499) (1.987) (2.297) (1.965) (1.900)

Technological Advantage -0.073 -0.020 -0.019 -0.020 -0.022

(1.577) (0.963) (0.923) (0.982) (1.076)

Technological Advantage sq. -0.011* -0.008* -0.008** -0.007* -0.007*(2.041) (2.540) (2.640) (2.418) (2.500)

Regional Institutional Diversity (BERI) -0.105**

(2.684)

Regional Institutional Diversity (Beri-ORI) -0.108***

(4.836)

Regional Institutional Diversity (Beri-PRI) 0.010

(0.395)

Regional Institutional Diversity (Beri-Rfactor) -0.158***

(6.301)

Regional Institutional Diversity (Fraser) -0.051+

(1.855)NOTE: All control variables were included in models but were omitted here for space consideration. t-statistics in parentheses. Beri-ORI, PRI, and RFactor stand for theOperational, Political, and Remittances sub-components of the BERI index. N=2,436. +p<0.10; *p<0.05; **p<0.01; ***p<0.001.

Table 4b Three Stage Least Squares Robustness Tests with Performance as the Dependent Variable

Model 1 Model 2 Model 3 Model 4 Model 5

Dependent Variable: Tobin's Q ROA ROA ROA ROA

Independent Variable:

Home Region Orientation 0.368 0.233 0.231 0.162 0.221

(1.064) (1.261) (1.233) (0.635) (1.100)NOTE: All control variables were included in models but were omitted here for space consideration. t-statistics in parentheses. N=2,436.

+p<0.10; *p<0.05; **p<0.01; ***p<0.001.



49

APPENDIX A Description of the BERI index

Sub-Index Objective Criteria Continuous Range

Operational Focuses on the operations climate

of doing business in that country.

Captures the degree to which

nationals are given preferential

treatment and the general quality of

the business climate.

Captures policy continuity, attitude: foreign

investors and profits, degree of privatization,

monetary inflation, balance of payments,

bureaucratic delays, economic growth, currency

convertibility, contract enforceability, labor

cost/productivity, professional services and

contractors, communications and transportation,

local management and partners, short-termcredit, long-term loans and venture capital.

70-100 Stable environment typical of an advanced

industrialized economy;

55-69 There are some complications in day-to-

day operations;

40-54 There are major complications in day-to-

day operations;

0-39 Unacceptable business conditions

Political Focuses on socio-political

conditions in a country.

Captures party fractionalization; language and

ethnic fractionalization; coercive measures to

retain power; xenophobic and nationalistic

sentiments; population density and wealth

distribution; organization of forces for a radical

government; dependence on a major hostile

power; negative influence of regional political

forces; societal conflicts; & instability as

perceived by unconstitutional changes and

guerilla wars.

70-100 Stable environment, political changes will

not lead to conditions seriously adverse to business;

55-69 Political changes seriously adverse to

business have occurred in the past, but governments

in power have a low probability of introducing such

changes;

40-54 Political developments seriously adverse to

business exist or could occur;

0-39 Unacceptable political conditions severely

restrict business operations. Loss of assets from

rioting and insurgencies is possible.

Remittances

and

Repatriationof Capital

Focuses on a country's capacity and

willingness for foreign companies to

convert profits and capital in thelocal currency to foreign exchange

and transfer the funds, and have

access to convertible currency to

import components, equipment, and

raw materials.

Includes a legal framework sub-index, foreign

exchange generation sub-index, accumulated

international reserves sub-index, foreign debtassessment sub-index.

0-100 (BERI does not provide specific cut-off

points for this sub-index, but simply indicates that

higher values mean more stable businessenvironment, consistent with the range

interpretations of the other two sub-indexes).

Source: Business Environment Risk Intelligence (BERI) (2008).



50

APPENDIX B Description of the Fraser index

Sub-Index Objective Criteria Continuous Range

Area 1:

Government

Captures size of

government: expenditures;

taxes, and enterprises

General government consumption spending; transfers and subsidies

as % of GDP; government enterprises and investment; top marginal

tax rate.

0-10, with higher values

meaning better

government quality

Area 2:

Legal

Captures legal structure and

security of property rights

Judicial independence; impartial courts; property rights protection;

military interference in rule of law and political process; integrity of

the legal system; legal enforcement of contracts; regulatoryrestrictions on the sale of real property.


meaning better legal

quality

Areas 3 & 4:

Economic

Captures access to sound

money and freedom to trade

internationally

Money growth; inflation; freedom to own foreign bank accounts;

taxes on international trade; trade barriers; size of trade sector; black

market exchange rates; international capital market controls.


meaning better

economic quality

Area 5:

Regulatory

Captures the regulation of

credit, labor, and business

Credit market regulations; labor market regulations; business

regulations.


meaning better

regulatory quality

Source: Gwartney et al. (2002).



APPENDIX C United Nations’ Country Groupings

Asia & Oceania Europe Americas Other

Armenia Albania Argentina* Algeria Australia* Austria* Bahamas Angola Azerbaijan Belgium* Barbados Benin

Bahrain Bosnia & H. Belize Botswana Bangladesh Bulgaria Bolivia Burkina Faso China* Croatia Brazil* Burundi Cyprus Czech Rep.* Canada* Cameroon Fiji Denmark* Chile* Central Afr. Republic

Georgia Estonia Colombia* Chad Hong Kong Finland* Costa Rica Congo, Dem. Republic India* France* Dominican Rep. Congo, Rep. Of Indonesia* Germany* Ecuador* Cote d'Ivoire Iran* Greece* El Salvador Egypt* Israel* Hungary Guatemala Ethiopia Japan* Iceland Guyana Gabon

Jordan Ireland* Haiti Ghana Kazakhstan* Italy* Honduras Guinea-Bissau

Korea, South* Latvia Jamaica Kenya Kuwait Lithuania Mexico* Lesotho Kyrgyzstan Luxembourg Nicaragua Madagascar Malaysia* Macedonia Panama Malawi Mongolia* Malta Paraguay Mali Myanmar Moldova Peru* Mauritania Nepal Netherlands* Trinidad & Tobago Mauritius New Zealand Norway* United States* Morocco* Oman Poland* Uruguay Mozambique Pakistan* Portugal* Venezuela* Namibia Papua New Guinea Romania Niger Philippines* Russia* Nigeria* Singapore* Slovak Rep Rwanda Sri Lanka Slovenia Senegal

Syria Spain* Sierra Leone

Taiwan* Sweden* South Africa* Thailand* Switzerland* Tanzania

Turkey* Ukraine* Togo

UAE United Kingdom* Tunisia Vietnam* Uganda, Zambia, Zimbabwe NOTE: The above countries are covered by the Fraser Index of Economic Freedom. Countries marked with * are

also covered by BERI, which also covers Saudi Arabia. Even though BERI covers only 53 countries compared tothe 139 that Fraser covers, the 53 BERI countries represent the majority of economic activity in the world: e.g., as of

2007, they comprised 95% of the world’s GDP (based on authors’ calculations using World Bank data on GDP).

Documents

INTERNATIONAL EXPANSION STRATEGIES