UNIVERSITEIT VAN AMSTERDAM
Political Risk and Entry Mode Selection
The different impact of governmental, societal
and economic risk on entry mode decisions
UvA
Author Lotte van den Enk – 10255036
Date of submission 29-01-2016
Version Final
Qualification MSc. Business Administration – International Management First supervisor Drs. E. (Erik) Dirksen
Second supervisor Dr I. (Ilir) Haxhi
2 Contents
List of figures ... 4
List of tables ... 4
Introduction ... 5
Chapter 1: Literature review of entry mode selection ... 7
1.1 Defining entry mode... 7
1.2 The resource-based view (RBV) ... 8
1.3 Transaction cost economics (TCE) ... 9
1.4 The institution-based view ... 10
1.5 The eclectic paradigm ... 11
1.6 Conclusion and discussion ... 12
Chapter 2: Literary review of political risk ... 14
2.1 Defining political risk... 14
2.2 Macro and micro political risk ... 16
2.3 Internal and external sources of political risk ... 16
2.3 Societal, governmental and economic political risk... 18
2.4 Conclusion and discussion ... 19
Chapter 3: Theoretical framework ... 21
3.1 Operationalizing government-related political risk... 21
3.2 Operationalizing society-related political risk ... 22
3.3 Operationalizing economy-related political risk ... 23
Chapter 4: Methods ... 25
4.1 Research Design ... 25
4.2 Data collection... 26
4.2.1 Data collection of the dependent variable ... 26
4.2.2 Data collection of the independent variables ... 27
4.2.3 Composition of final dataset ... 28
4.3 Description of the variables... 28
4.3.1 Dependent variable ... 28
4.3.2 Independent variables ... 28
4.3.3 Control Variables ... 30
4.4 Data Analysis ... 32
3
5.1 Descriptive statistics ... 33
5.1.1 Describing the sample ... 33
5.1.2 Principal component analysis ... 35
5.1.3 Multicollinearity statistics ... 37
5.2 Regression analysis ... 38
5.3 Limitations ... 42
Chapter 6: Discussion ... 43
Discussion general expectations... 43
Discussion separate effects of political risk types ... 44
Research recommendations ... 46
Conclusion ... 47
References ... 49
Appendices ... 59
Appendix I: Description of the independent variables ... 59
Appendix II: Host countries ... 61
Appendix III: Residual Statistics ... 62
Appendix IV: Principal components analysis ... 62
Appendix V: Logistic regression Model 1 ... 63
Appendix VI: Logistic regression Model 2 ... 65
4 List of figures
Figure 1: A Framework for Political Risk Evaluation (Haner 1979: 20) ... 17
Figure 2: A General Framework for Political Risk Assessment (Simon 1982: 67) ... 18
Figure 3: Overview conceptualization political risk ... 20
Figure 4: Visual representation of the relationship between political risk types and entry mode selection ... 24
Figure 5: Sample distribution over period 2007-2011 ... 34
List of tables Table 1: Industry classification ... 32
Table 2: Sample distribution of JVs and WOSs among Model 1, 2 and 3 ... 33
Table 3: Sample distribution of JVs and WOSs among secondary and tertiary sector ... 34
Table 4: Descriptive statistics independent variables ... 35
Table 5: Rotated component matrix ... 36
Table 6: Multicollinearity statistics ... 38
5 Introduction
Due to accelerating globalization pressures companies are increasingly expanding their business abroad in search of new markets and lower operating costs. When going abroad deciding upon the appropriate entry mode is among the most important decision made by companies. A specific entry mode, once selected, is not easily changed or corrected (Pedersen, Petersen & Benito 2002). Therefore, entry mode decisions have long-term consequences for the firm. Additionally, entry mode selection is found to have significant performance implications (Anderson & Gatignon 1986; K.D. Brouthers & Hennart 2007; K.D. Brouthers 2002; K.D. Brouthers, L.E. Brouthers & Werner 2003; Davidson 1982; Kim & Hwang 1992). Recent decades have produced a substantial amount of literature arguing for various factors influencing entry mode decisions. One of these factors is host-country risk experienced by companies operating in foreign markets. Gaining a deeper understanding of how host-country risk influences entry mode strategies suggests that companies can employ this knowledge in order to flourish in environments that are risky but lucrative.
A type of risk that presents a substantial concern for companies is political risk. Therefore, various scholars have been preoccupied with defining, measuring and conceptualising this type of risk. Empirical evidence suggests that FDI inflows in general are sensitive to political risk (Alfaro, Kalemli-Ozcan & Volosovych 2008; Busse & Hefeker 2007; Daude & Stein 2007; Schneider & Frey 1985; Wei 2000). However, research on the relationship between political risk and entry mode decisions is scarce. Exceptions include Henisz (2000) who considers potential expropriation risks for Multinational Enterprises (MNEs) that operate in joint ventures (JVs) and Globerman and Shapiro (2005) who examine institutional distance as a determinant for MNEs using merger and acquisitions (M&A) as entry mode. Slangen and van Tulder (2009) use cultural distance, political risk and governance quality as proxies for the effect of uncertainty of the external environment on entry mode selection and Lee, Bligaiser and Staats (2014) investigate the effects of domestic political institutions on entry mode selection.
In addition to being scarce, the budding literature on the relationship between political risk and entry-mode selection shows a discrepancy. Various scholars have dedicated
6
themselves to the conceptualization and systematic evaluation of the multi-faceted character of political risk. As a result, several frameworks discussing the different types, factors or components of the concept have emerged (Alon & Herbert 2009; Alon & Martin 1998;De La Torre & Neckar 1986; Haner 1979; Robock 1971; Simon 1982). Distinctions are made for example between macro and micro political risk, external and internal sources of risk and between societal, governmental and economic sources of political risk (De La Torre & Neckar 1986; Robock 1971; Simon 1982;). However, these distinctions are missing from the primary research on the relationship between political risk and entry mode selection. Therefore, in order to adequately research this relationship the following research question is central in this thesis:
To what extent do different types of political risk correspond with different entry modes?
In order to adequately answer the research question a quantitative analysis is conducted examining the relationship between government-related, society-related and economy-related types of political and the entrance choice between JVs and Wholly Owned Subsidiaries (WOSs). The study aims to contribute to the budding strand of literature exploring the relationship between political risk and entry mode decisions. In addition, the goal is to bring together two apparently separate strands of literature. The literature focusing on the theoretical exploration of the concept of political risk is applied to entry mode research.
The following structure is applied throughout the study. Chapter 1 addresses the various theoretical perspectives applied to entry mode research. These perspectives include the resource-based view (RBV), transaction costs economics (TCE), institutional theory and Dunning’s (1971) eclectic paradigm. Chapter 2 addresses the various frameworks used to conceptualize political risk. These frameworks contain distinctions between macro and micro political risk, internal and external sources of political risk and lastly the division between government-related, society-related and economy-related political risk that is central in this study. In Chapter 3 the theoretical framework is elaborated upon and hypotheses are developed. Chapter 4 contains the methods section. The results are presented in Chapter 5. The results, their connection with the theory and recommendations for further research is considered in the Discussion. Finally, the study is summarized in the Conclusion.
7 Chapter 1: Literature review of entry mode selection
In this chapter a literary review of entry mode selection is presented. Firstly, entry mode is defined in the context of this study and separated from associated concepts. Secondly, various theoretical perspectives that are applied to the entry mode decisions are set forth. Special attention is paid to how these perspectives approach the influence of risk with regard to the choice between JVs and WOSs. Thirdly, the chapter is concluded with a summary of the reviewed theories and a discussion on the chosen theoretical perspective of this study.
1.1 Defining entry mode
Although no complete consensus on the definition and conceptualization of entry mode exists within the academic field, two substantial distinctions are presented. A first distinction differentiates between entry mode and establishment mode. The entry mode decision refers roughly to the choice between contracts, JVs and WOSs. The establishment mode refers to the choice between an acquisition and a greenfield operation (Divoka & Witteloostuijn 2007). No distinction is made between greenfield operations and full acquisitions in this study.
The second distinction concerns the two main overarching theoretical approaches towards the entry mode decision. The first approach views entry mode selection along a continuum of increasing risk, commitment and control (Anderson & Gatignon 1986; Erramilli & Rao 1990; Hill, Hwang & Kim 1990). For example, firms opt for contracting when they need minimum control, want minimum commitment and minimum risk. This means that along the spectrum of contracts, JVs and WOSs choices are determined by the same variables (K.D. Brouthers & Hennart 2007). In contrast, the second approach distinguishes between non-equity modes, contracting, and equity modes, either JVs or WOSs. This perspective was first articulated by Hennart (1988a; 1989; 2000) who argues that the method chosen to remunerate input providers is the main difference between entry modes. When opting for contracting, the payments to input providers are specified in advance. When opting for a JV or WOS, input providers are paid ex post from the profit of the operation.
8
The focus of this thesis is exclusively limited to the differentiation between equity modes, between JVs and WOSs. As mentioned, no attention is paid to the differentiation between greenfield operations and full acquisitions. Not distinguishing between greenfield ventures and full acquisitions might decrease in the internal validity of the study. A firm’s propensity to opt for a JV or a WOS might be connected to the chosen establishment mode. This connection will be overlooked in the results. However, with regard to the distinction in overarching conceptual approaches towards entry mode decision, focusing on equity modes implies a positive effect on the internal validity of the study. Both approaches enable a comparison between JVs and WOSs alongside a set of the same variables. The effects of political risk can thus be isolated and measured more precise.
1.2 The resource-based view (RBV)
The RBV argues that sustained competitive advantage derives from the resources and capabilities that a firm controls, the firm-specific advantages. In order to be the source of sustained competitive advantage these resources need to be valuable, rare, imperfectly imitable and not easily substitutable (Barney 1991). Expanding abroad presents a possibility to exploit these resources in foreign markets or presents the possibility to acquire new resources held by foreign companies. Within this view JVs can be used to gain access to resources and capabilities hold by the foreign company. A WOS is used to protect firm-specific advantages, especially intangible assets, from imitation (Chang & Rosenzweig 2001).
An early theory on entry mode decisions within the resource-based view is the internationalization theory (also well known as the Uppsala Mode) of Johanson and Vahlne (1977; 1990; 2009). Within this theory experience is an important resource that plays a crucial role in determining the way that MNEs approach their entrance into foreign markets. Internationalization theory argues that firms opt for a gradual involvement in foreign markets as doing business abroad is inherently risky. Therefore, Johannes and Vahlne (1997) argue that firms frequently start with exporting and, as sales grow, will increase their resource commitment gradually, respectively opting for a JV and then a WOS. The placement of entry modes along a continuum of increasing risk, commitment and control has been adopted by subsequent authors within different schools of thought (Anderson & Gatignon 1986; Hill, Hwang & Kim 1990).
9
The resource-based view has limited importance within this study because it is mainly concerned with firm-specific factors and not host-country factors. However in the selection of the sample and the control variables some firm-specific factors are taken into account. These factors are elaborated upon in more detail in chapter 4.
1.3 Transaction cost economics (TCE)
In the late 1980s and early 1990s TCE started to be applied to entry mode decisions. According to K.D. Brouthers and Henart (2007: 400) TCE or Transaction Cost Analysis (TCA) is the most widely used theoretical approach in international entry mode research. Within TCE the boundaries of the firm are determined by internalizing those activities that can be performed at a lower cost while subcontracting will be used if this provides a cost advantage (Anderson & Gatignon 1986; Beamish & Banks 1987; Williamson 1985). In Williamson’s (1985) original work on TCA, three factors are put forth that influence entry decisions by moderating transaction costs. These factors are asset specificity, frequency and uncertainty (both internal and external). Uncertainty and especially external-market specific uncertainty is of special interest to this study because country risk is among the most commonly used constructs to measure external uncertainty (Zhao, Luo & Suh 2004). Moreover it is stated by Gatignon and Anderson (1988: 315) that environmental uncertainties are ‘generally understood to mean the extent to which a country’s political, legal, cultural, and economic environment threatens the stability of a business operation’.
The question how external uncertainty influences the entry mode decision according to TCA is subject to contrasting views. On the one hand it is argued that high environmental uncertainty leads firms to select full integration modes (WOS) because they need to be able to adapt quickly (Williamson 1991). Joint ventures are undesirable in these instances because adaptions would need consent from two (or more) parties involved. According to this type of reasoning environmental uncertainty increases transaction costs thus leading firms to internalize activities and select full hierarchy (WOS) (Robock, Simmonds & Zwick 1989). On the other hand it is argued that JVs actually provide firms with greater flexibility, via lower resource commitment, which is needed to deal with higher environmental uncertainty (Aulakh & Kotabe 1997; Erramilli & Rao 1993; Gatignon & Anderson 1988; Kim & Hwang, 1992). Moreover, Hennart (1988a) considers that joint ventures will be preferred in case of inefficient markets for
10
intermediate inputs such as raw materials, components and knowledge. Joint ventures are thus selected as a reaction to high degrees of environmental uncertainty and risk because these modes involve relatively low resource commitments, increase flexibility and provide a means to access resources that are not easily available in the open market.
1.4 The institution-based view
Institutions are defined by North (1990: 3) as ‘the rules of the game in a society or, more formally, the humanly devised constraints that shape human interaction’. The institution-based view within entry-mode research argues that the institutions present within the host country, the home country and/or the institutional differences between host and home country influence the boundaries of the firm. With regard to the entry-mode decision, a substantial amount of scholars argue for the incorporation of institutional and cultural context variables to supplement transaction cost theory (Kogut & Singh 1988; North 1990; Roberts & Greenwood 1997; Yiu & Makino 2002). A more theoretical perspective on which institutional factors should be selected is developed by Scott (1995). Scott (1995) argues that the institutional environment of a given country rests on three ‘pillars’; the regulatory, normative and cognitive pillar. The regulatory pillar reflects the legal systems, policies and rules present within a country. The normative pillar refers to the moral and ethical systems in place that guide habits and norms. The cognitive pillar reflects cultural systems and underlying values and beliefs. This study is mainly focusing on the formal institutional environment, corresponding with Scott’s (1995) regulatory pillar. However, as discussed in Chapter 4 cultural distance, corresponding to the informal institutional environment is taken into account as a control variable.
How the formal institutional environment influences the choice between JV and WOS has been researched by Meyer, Estrin, Bhaumik and Peng (2009) and Slangen and van Tulder 2009). Meyer et al. (2009) emphasize the importance of institutions in ensuring the efficient functioning of the market and the subsequent effect on the costs of doing business. In a weak institutional environment companies may use JVs as an interface with local partners. Additionally, Meyer et al. (2009) argue, citing Antal-Mokos (1998) and Peng (2008), that entrance via acquisition (WOSs) is especially dependent of the
11
efficiency of the financial market and the market of governance control. A strong institutional environment accommodates the efficiency of the financial markets in a country because it ensures transparency, predictability and contract enforcement (Peng & Heath 1996; Beim & Calomiris 2003 in Meyer et al. 2009).
Slanger and van Tulder (2009) also use the strength of the institutional environment to predict the selection of JV or WOS but refer to it as quality of the governance infrastructure.1 Following Globerman and Shapiro (2003) they define governance
infrastructure as all the public institutions that are created by the government as a framework for legal, economic and social relations. Slangen and van Tulder (2009) reason that lower governance quality result in higher external uncertainty associated with a WOS. They therefore hypothesize and support with empirical evidence that the lower the governance quality in the host country, the higher the likelihood that MNEs choose JVs over WOSs.
1.5 The eclectic paradigm
The well-known eclectic framework of Dunning (1988) is frequently used in entry-mode research (K.D. Brouthers & Hennart 2007). Although not necessarily a theory the eclectic framework or OLI paradigm provides a perspective on entry mode selection that roughly combines the insights the insights of the resource-based view (ownership-advantages), the institution-based view (locational advantages) and transaction cost theory (internalization advantages). The central premise of eclectic explanations of entry mode selection is that a wide variety of factors guide and should guide the entry mode choice (L.E. Brouthers, K.D. Brouthers & Werner 1999). Additionally it can be used as a tool to unravel how different factors that originate from different theories interact with each other. Because this study examines political risk in the host-country, the locational advantages component in the eclectic paradigm is of special interest. Measures of this component generally include investment risk, market potential, cultural distance, market infrastructures and/or the availability of low wage labor or lower production costs in general (Dunning 1993 in Andersen 1997: 34).
1 Slangen and van Tulder (2009) also include political risk as a proxy indicator for environmental
uncertainty and find that political risk has only a very small effect. However, they view political risk as part of the quality of the governance infrastructure while this study views the institutional context as a dimension within the concept of political risk.
12
The central premise of eclectic explanations of entry mode selection is that a variety of factors are influential. L.E. Brouther, K.D. Brouthers and Werner (1999) found that a combination of these factors are not only describing entry mode selection of companies, they are also an adequate explanation firm performance. Even at face value the expectation that a variety of factors are influential is a very promising assumption. Therefore, it should be emphasized that although this study is mainly focused on locational advantages or disadvantages, it is assumed that ultimately a combination of the different components provides a more sufficient explanation of the entry mode choice.
1.6 Conclusion and discussion
A review of the most common theoretical perspectives used to explain the entry mode choice shows that host country risk plays a role in entry mode selection. Internationalization theory sees the choice between JV and WOS as a function of the inescapable risk of doing business abroad. A higher level of risk leads companies to select entry modes that require less resource commitment. However, within internationalization theory risk is mainly moderated by firm-specific factors and especially by experience. The general expectation is that as companies become more internationally experienced they opt for fuller integration modes (Johanson and Vahlne (1977; 1990; 2009). More broadly, the resource-based view also argues that JVs are used to gain access to resources of local firms (Chang & Rosenzweig 2001). As stated, the resource-based school is of limited importance of this thesis because its main focus is on firm-specific factors.
Entry mode research adopting TCA has provided contrasting views on explaining the influence of risk or environmental uncertainty on the choice between JVs and WOSs. On the one hand high environmental uncertainty increases transaction costs leading firms to internalize their activities in search of greater control and opt for WOSs (Williamson 1991; Robock, Simmonds & Zwick 1989). On the other hand environmental uncertainty leads firms to lower their resource commitments as a way to gain flexibility and thus selected JVs (Aulakh & Kotabe 1997; Erramilli & Rao 1993; Gatignon & Anderson 1988; Kim & Hwang, 1992). This study adopts the final expectation, stating that high risk will lead firms to adopts a strategy that encompasses lower resource commitment and thus
13
opt for JVs. Subsequently low risk will lead firms to adopt WOSs. However, this issue is returned to in the discussion.
The institution-based view is characterized by an explicit focus of the host-country environment. The general expectation is that a weak institutional environment present substantial risk to companies operating in that environment. Companies react to a weak institutional context by opting for JVs as a means to moderate negative effects (Meyer et al. 2009; Slangen and van Tulder 2009). In comparison, when facing a strong institutional context, with efficient markets for intermediate inputs, companies opt for WOSs. As is explained in more detail in Chapter 2, the institutional context is closely related to political risk because governments play a crucial role in ensuring institutional quality. Especially the absence of strong governmental actors will increase political risk and its veracity (Jarvis 2008).
Finally, the eclectic paradigm was discussed. This study strongly acknowledges the variety of factors, from different theoretical perspectives, that influence entry mode decisions. The eclectic paradigm is thus used as an envelope framework placing this study in the locational components of the paradigm. The goal is to add to this component with an in-depth exploration of political risk without refuting the explanatory power of the other components.
14 Chapter 2: Literary review of political risk
In this chapter a literary review of the concept of political risk is presented and the frameworks that have been developed are discussed. Firstly, political risk is defined. Secondly, the several frameworks are discussed. These frameworks respectively distinguish between macro and micro political risk, internal and external sources of political risk and lastly government-related, society-related and economy-related political risk. Lastly, the chapter is concluded with a summary of the reviewed frameworks and a discussion of their application in this study.
2.1 Defining political risk
The first studies on political risk appeared in the 1960s against the background of the complex and intertwining processes of Cold War politics and decolonization (Sottilotta 2013). Writings from this period are primarily concerned with drawing attention to the concept and call for a more systematic evaluation of political risk (Aharoni 1966; Basi 1963; Root 1968; Stobaugh 1969). During this inception period political risk is mainly defined in terms of government or sovereign action and focuses on the negative consequences of government interference (Whitman 1965; Zenoff 1969). Although this definition remains implied by later researchers (Ady 1971; Aliber 1975; Dunning 1971), it is also challenged for being ideologically motivated. Kobrin (1979: 69) states that ‘the emphasis on the negative consequences of government intervention entails an implicitly normative assumption that may not be universally valid’. This laissez-faire type view on political risk also forgoes the crucial role played by governments and institutions in ensuring functioning, transparent markets. As noted by Jarvis (2008: 24) ‘rather than the presence of such actors, it is their absence which increases the extensity of political risk and its veracity’. A practical view on this issue is presented by Alon and Herbert (2009) arguing that political risk is a neutral phenomenon because ‘political risk emanates from uncertainty regarding potential outcomes, which may either help or hinder business interests, or prove to be better or worse than expected’ (Alon & Herbert 2009: 130).
A second view regarding the definition of political risk in the literature is presenting it in terms of an environment (Drysdale 1972; Haendel, West & Meadow 1975; Robock 1971; Rummel & Heenan 1978). This is most visible in the operational definition by
15
Robock (1971: 7) stating that ‘political risk in international business exists (1) when discontinuities occur in the business environment (2) when they are difficult to anticipate and (3) when they result from political change.’ Robock makes a further distinction by separating the related concepts of political risk and political instability, the latter being of explicit interest for political scientists. Political instability, referring to unexpected changes in government leadership, may or may not involve political risk for companies. However, defining political instability solely as unexpected changes in government leadership is rather limited. For example, political instability can refer to, among others, the absence of violence, to the existence of a legitimate constitutional regime, the absence of structural change and political stability can be viewed as a multifaceted societal attribute (Hurwitz 1973).
A third type of definition was introduced by Root (1972) and adopted by Brewers (1981) and Jodice (1984). The definition emphasizes that political risk should not be considered in terms of events but rather in terms of the probability of events that might be harmful for the operations of the MNE. This definition is extended by De La Torre and Neckar (1986: 3) who argue that political risk should not be defined in terms of change or instability per se, but in terms of the impact that any externally induced shock may have on the value of its assets. This results in the following definition of political risk as ‘the probability distribution that a real or potential loss will occur due to the exposure of foreign affiliates to a set of contingencies that range from the total seizure of corporate assets to the unprovoked interference of external agents, with or without governmental sanction, with normal operations and performance expected from the affiliate’ (De La Torre and Neckar 1986: 5).
The third type of definition, emphasizing the probability of events occurring instead of the actual events themselves, corresponds best with the objective of this study. Setting up an affiliate abroad is a long-term investment. Therefore, when deciding upon an appropriate entry mode, investors are interested in the future risk their business might be subjected to and the extent of that risk. Moreover, the definition refers to a set of contingencies with or without government sanctions. Thereby the definition abandons the narrow view of political risk as government interference present in earlier definitions and gives room for multiple types of political risk including governmental, societal and economic risk. Lastly, the critique made by Jarvis (2008) stating that the
16
presence rather than absence of governments and institutions that ensure transparent markets decrease rather increase political risk is important. It is important to note that this approach to the role of governments and institutions is present within the business literature on entry mode selection as well. A strong institutional context lowers the cost of doing business and influences the entry mode selection of firms by changing the costs of alternative organizational forms (Bengoa & Sanchez-Robles 2003; Bevan, Estrin & Meyer 2004; Estrin 2002; Williamson 1985). How the strength of the institutional context influences the choice between a JV and a WOS is elaborated upon in Chapter 2.
2.2 Macro and micro political risk
A primary and very influential distinction within the concept of political risk separates macro from micro political risk and was introduced by Robock (1971). Macro political risk affects all or most firms within the host country. Micro political risk is firm specific and effects one firm or a select group of firms or business activities. Macro political risk is undoubtedly more dramatic as it can refer to regime changes, civil wars and large-scale nationalizations. However, micro political risk, which can include discriminatory taxes or import and export restrictions, is more common. This distinction was important for corporate decision makers because it emphasized that they should pay as much attention to small changes in industry conditions as to the probability of catastrophic events like large scale nationalizations (Simon 1982). It is important to note that macro and micro political risk sometimes overlap and share common elements (Alon & Herbert 2009). For example, firms operating in a specific strategic sector are especially susceptible for certain types of macro risk. Although all firms in a host country can face the macro-risk of nationalization or expropriation, firms operating in these strategic sectors are especially vulnerable.
2.3 Internal and external sources of political risk
An additional division within the micro-macro framework concerns the distinction between internal and external sources of political risk. This distinction is introduced by Haner (1979) who develops a (country) risk evaluation consisting of eight potential internal or external causes and two possible symptoms, being either societal conflict or political instability (See figure 1). Internal political risks originate in the host country and refer to fractionalization of the political spectrum, linguistic, ethnic and religious fractionalization, restrictive measures required to retain power and
17
tendencies towards xenophobia and nationalism. External political risks originate outside the host country and can refer to dependence or importance to a major hostile power and negative influences of regional forces. Although the division between the macro and micro dimension is missing from Haner’s framework, the distinction between internal and external causes is adopted repeatedly by subsequent research (Simon 1982; Simon 1984; De La Torre & Neckar 1986; Alon & Martin 1998; Alon and Herbert 2009).
Figure 1: A Framework for Political Risk Evaluation (Haner 1979: 20)
Because Haner (1979) emphasizes the importance for corporate assessment of adopting several variables instead of broadly estimating political risk as a single category, his systems shows similarities with the spirit of this thesis. However, the simultaneous valuation of both causes and symptoms among the same scale is conceptually somewhat problematic. All of the ten variables in Haner’s (1979) forecasting system are rated in two steps for three periods. The three periods are the present period and five and ten years into the future. In the first step the variables are rated on a scale from one to seven for all the periods and in the second step additional weight can be distributed towards specific sources of risk that have a big impact. However, links between causes
18
and the symptoms are not explained and even ignored, as both are rated independent from each other and on the same scale.
2.3 Societal, governmental and economic political risk
Simon (1982) merges the macro-micro dimensions and the internal-external dimension into one framework and adds a third distinction. This distinction divides societal-related risks and government-related risk (See Figure 2). Societal-related risks include various types of internal violence such as revolutions, coup d’états, and civil and factional conflicts among others that can harm businesses. Governmental-related risks refer to events like nationalizations, repatriation restrictions, leadership struggles and regime changes but also to inflation and interests rates. Simon (1982) makes it clear that these dimensions can come to overlap in time. For example, risks originating from societal groups like ethnic conflicts can evolve into macro governmental actions. However, one can argue that factors within categories can overlap as well. For example, ethnic and religious turmoil can result in a civil war and a coup d’état has by definition important consequences on the governmental (state) level.
Figure 2: A General Framework for Political Risk Assessment (Simon 1982: 67)
The distinction between governmental and societal factors is later supplemented with economic-related factors leading to political risk by De La Torre and Neckar (1986) and Alon and Martin (1998). Within the realm of macro political risk, De La Torre and
19
Neckar distinguish between economic and socio-political factors that can either be internally as externally generated. The economic factors include the basic components of the economy of the host nation. The objective is not necessarily an economic analysis but to determine potential problems. The external economic risks refer to the international payment position of a country and thus includes for example external debt.
Alon and Martin (1998) focus exclusively on macro-political risk and distinguish themselves by differentiating between the causes and symptoms of political risk and concentrating on internal and external factors, which shows great resemblance to the work of Haner (1979). However, Alon and Martin further differentiate political risk emanating from governmental, social and economic environments. They view macro political risk as a multidimensional construct and suggest components for each of the six dimensions. For example components of internal governmental causes of risk included degree of elite repression, degree of elite illegitimacy and likelihood that regime change will affect policy. The components of internal societal causes of risk include the degree of fragmentation, potential for social conflict and sense of nationalism, xenophobia, alienation or fundamentalism.
2.4 Conclusion and discussion
The literature review of political risk shows that the concept of political risk has greatly expanded in recent decades. Initial distinctions between macro- and micro political risk and internal and external political risk have been complemented with a division in government-related, society-related and economy-related risk. The distinctions are visualized in figure 3. Because this study examines host country risk specifically, internal macro political risk is the main focus. However, it would interesting for further research to include micro political risk and especially the relationship between micro political and the firm-specific factors that are central in some of the theoretical perspectives applied in entry mode research.
Macro internal political risk has been further divided in government-related, society-related and economy-society-related factors(Alon & Martin 1998; De La Torre & Neckar 1988; Simon 1982). However, two notes have to be made with regard to government-related and society-related risk. Firstly, the indicators used as proxies for these types of risk show great overlap. For example, when are revolutions a societal problem and when are
20
they a governmental problem? For this reason, this study strictly refers to government-related risk as all types of activities undermining the stability of the government in a country. Society-related risk then refers to the underlying structure of society and to what extent this might influence potential conflict. Secondly, missing from the government-related indicators is the general strength of the institutional context in which governments generally play a crucial role. Although Kobrin (1979) and Jarvis (2008) both note the crucial role played by government in ensuring functioning and transparent markets, no measurements of this role are incorporated into the mentioned frameworks. However, as elaborated upon in Chapter 1.4 the institutional context plays an important role in explaining entry mode selection. Therefore, as a synthesis between the two bodies of literature, this study incorporates the quality of the institutional environment into political risk concept and places it into the government-related risk component. An overview of the distinctions within the concept of political risk and the selected focus of this study is visualized in figure 3.
Figure 3: Overview conceptualization political risk
*For a discussion of distinctions within micro political risk see Alon and Herbert (2009).**For a discussion of distinctions within macro political risk originating from external sources see Alon & Martin (1998). Political risk Micro risk* Macro risk External sources** Internal sources Government Society Economy
21 Chapter 3: Theoretical framework
In this chapter government-related, society-related and economy-related political risk is operationalized and connected to expectations regarding the choice between either a JV or a WOS. The general expectation is that as political risk increases, the propensity of a firm to opt for a joint venture increases as a result of the desire to decrease resource commitments and thereby their exposure to risk. Firstly government-related political risk is operationalized in such a way that it includes both the traditional concept of political stability and concepts measuring the strength of the institutional context. Secondly, society-related risk is operationalized. Thirdly, economy-related risk is operationalized including both measurements of market potential and risk. Lastly, the developed hypotheses are represented in a visual model.
3.1 Operationalizing government-related political risk
Government-related political risk in this study refers to both a traditional measure of political stability and to indicators of the strength (health) of the institutional context. In the literature on political risk the focus of government-related political concerns the threat of regime changes, expropriations and wars (Haner 1979; Simon 1982; Alon & Martin 19998). However, governments also play a crucial role as institutions ensuring functioning and transparent markets (Jarvis 2008; Kobrin 1979). This argument is also emphasized in applications of institutional theory on entry mode research especially with regard to the regulatory frameworks in the respective host country (Meyer et al. 2009; Slander and van Tulder 2009).
With regard to the traditional measure of political risk, including the risks of regime changes, wars and revolutions, the H1a is developed. With regard to the institutional context the worldwide governance indicators of the World Bank are used based upon the dimensions developed by Kaufman, Kraay and Mastruzzi (2004). Because of the direction of the indicators then hypotheses are developed in such a way that higher scores imply better governmental results and thus lower risk. The hypotheses are presented below.
H1a: countries experiencing a relatively stable political environment are more likely to be entered through WOSs than through JVs.
22 H1b: countries experiencing relatively high levels of control of corruption are more likely to be entered through WOSs than through JVs.
H2c: countries experiencing relatively high levels of rule of law are more likely to be entered through WOSs than through JVs.
H2d: countries experiencing relatively high levels of government effectiveness are more likely to be entered through WOSs than through JVs.
H2e: countries experiencing relatively high levels of regulatory quality are more likely to be entered through WOSs than through JVs.
3.2 Operationalizing society-related political risk
Because political risk arises from other actors that host-governments as well, society-related risk is taken into account. A substantial amount of scholars refer to society-related risk as risk arising from revolutions, coups d’états, riots, demonstrations and terrorism (Brink 2004; Iankova & Katz 2003; Jeannet & Hennessey 1998; Simon 1982). However, as mentioned in Chapter 2 there is great overlap between this operationalization of societal risk and governmental-related political risk. Moreover, the likelihood of the occurrence of revolutions, coups d’états, riots, demonstrations and terrorism is already captured in the political stability indicator set forth in Chapter 3.2. For reasons cited above, and in accordance with the framework developed by Alon and Martin (1998), society-related risk is operationalized by focusing on the social diversity of society along ethnic, linguistic and religious fragmentation. Is it expected that substantially fragmented societies increase the likelihood of discontent among some groups when their demands are not met (Kennedy 1987 in Alon & Martin 1998). Ethnic, linguistic and religious fragmentation is also mentioned in the framework of Haner (1979) as a contributing factor to political risk. Haner (1979: 19) recalls Eritrea, South Africa, India, Spain, Iran and Northern Ireland as examples that suffer from these types of fractionalization. Based on these observations the following hypotheses are developed:
H2a: countries with a population that is ethnically highly fractionalized are less likely to be entered through WOSs than through JVs.
H2b: countries with a population that is linguistically highly fractionalized are less likely to be entered through WOSs than through JVs.
23 H2c: countries with a population that is religiously highly fragmented are less likely to be entered through WOSs than through JVs.
3.3 Operationalizing economy-related political risk
Economy-related risk is expected to influence the choice between JV and WOS in two distinctive but interrelated ways. Firstly, economic determinants reflect the general attractiveness of the country. For example, formidable economic growth represents a country’s economic health and the general attraction of the investment environment. Secondly, economic development can be a harbinger for future political unrest and change that decreases the instability of the country. As noted by De La Torre and Neckar (1986: 21) ‘the objective is not economic analysis per se, but a search for what one might call the potential for trouble’.
As a consequence of the observations mentioned above, the hypotheses are developed with regard to economy-related political risk. Firstly, countries with high GNI per capita and high GNI per capita growth are expected to have a better investment environment and greater growth potential. These measures reflect the ability of a country to absorb their population growth and are the easiest available measures of a country’s standard of living (Alon & Martin 1998). High scores on these indicators therefore reflect the potential profitability and safety of the investment. The following hypotheses are therefore developed:
H3a: countries with a relatively high GNI per capita are more likely to be entered through WOSs than through JVs.
H3b: countries with relatively high GNI per capita growth are more likely to be entered through WOSs than through JVs.
Secondly, inflation rates, government debt and a country’s current account balance is taken into account. High inflation rates are expected to signal internal economic tension. Additionally, inflation increases can result in price and/demand changes within a country thus adding the environmental uncertainty (K.D. Brouthers, L.D. Brouthers & Werner 2002). Substantial government debt can signal a potential sovereign debt crisis negatively affecting the stability in a country and increasing the like hood of increased government restrictions. Substantial deficits in the balance of payment in a country are expected to be a harbinger for future policy changes. Large deficits suggest that a
24
country is living beyond its means which may become problematic and prompt the government to eventually restrict the movement of free capital (De La Torre & Neckar 1986; Schneider & Frei 1985). Moreover, according to Alon and Martin (1998: 17) balance of payment problems may even signal likelihood of currency inconvertibility. The following hypotheses are developed with regard to the mentioned observations:
H3c: Countries with high inflation rates are less likely to be entered through WOSs than through JVs.
H3d: Countries with high government debt are less likely to be entered through WOSs than trough JVs.
H3e: Countries experiencing a positive account balance are more likely to be entered through WOS than through JVs.
The expected relationships are summarized in figure 3.
Figure 4: Visual representation of the relationship between political risk types and entry mode selection
25 Chapter 4: Methods
4.1 Research Design
The research design of this thesis consists of both exploratory and explanatory elements. The purpose is to examine whether and to what extent various types of political risk influence equity mode selection in a different way. Although these types are identified in the academic debate on political risk they have not yet been connected to different entry modes in the international business literature. Thus, in the absence of guiding theory, the thesis takes an inductive approach aimed at generating knew knowledge. This is the exploratory element. However, after the development of the variables their relationship is tested in order to explain firm’s choices between either a JV or a WOS. This is the explanatory element.
In order to measure the effect of the different types of political risk secondary data has been obtained and subjected to a cross-sectional regression analysis. The usage of secondary data has certain advantages and disadvantages. The disadvantages in this particular thesis include that most of the independent variables are aggregate variables and not designed for this particular study. This has negative consequences for the construct validity of the variables included and especially for their categorization into political risk types. The measurements used are not designed as part of the specific model presented in Chapter 3.
Secondary data also has certain advantages. Firstly, using data from professional organization such as the World Bank and the IMF enables the selection of relatively high quality data. Secondly, limited availability of time and money constrains individual collection of data to a great extent. Subsequently, it would not be possible to collect a sufficient amount of country-data needed for an international comparative analysis, which is the purpose of this study. Similar logic applies to the collection of data on equity mode selection. The usage of secondary data for the dependent variable results in a substantial amount of observations, which has a positive effect on the generalizability of the results. Thirdly, the relative easy accessibility of the databases implies the likelihood that firms, or political risk rating companies on which other firms rely, are using this information as well to assemble risk profiles.
26 4.2 Data collection
4.2.1 Data collection of the dependent variable
In order to collect data for the dependent variable, equity mode selection, the Zephyr database of Bureau van Dijk was consulted. The Zephyr database is an extensive source of company information and includes comprehensive data on Mergers and Acquisitions (M&A), Initial Public Offerings (IPOs), private equity deals and venture capital deals.
Data collection on the dependent variable was done in several steps. Firstly, deals labeled acquisition, joint venture or minority stake were extracted for the years 2007-2011. Only deals of which completion was confirmed were selected. In addition, deals exclusively conducted by an acquirer company employing more than 250 people were included in the initial search. This resulted in a sample consisting solely of large companies.2 Although no consensus exists within the literature it is often argued that firm size influences entry mode selection. It is expected that larger companies are better able to redirect significant resources towards full acquisitions than smaller companies (Agarwal & Ramaswami 1992; Gatignon & Anderson 1988). Moreover, as noted by Lecraw (1984) large companies might enjoy a better bargaining position for greater ownership and control versus governments that impose restrictive investment policies. Composing a database consisting solely of larger firm enabled the isolation of the effects of political risk types, which in turn increased the internal validity of the results.
The sample was designed to include deals of which the acquiring company is either based in the United States or based in a member state of European Union (EU28). Research indicates that companies from emerging economies are more likely to opt for a full acquisition than a joint venture (Ghubb Aulakh, Ray, Sarkar & Chittoor 2009). Acquisitions are used as an important strategic lever by emerging economy companies to facilitate tangible and intangible resources that are both difficult to trade via market transactions and take time to develop. As a consequence these acquisitions generate positive abnormal returns for the acquiring firm’s shareholders (Ghubb et al. 2009). Composing a database consisting solely of acquiring companies from the US and EU
2 The qualification of a large company is established at 250 employees. This threshold is most frequently used in the EU although other countries may maintain different thresholds (OECD SME and
27
firms enabled the isolation of the effects of political risk types, which in turn increased the internal validity of the results. The initial search rendered a result of 14570 observations.
Secondly, in order to retrieve data on international entry mode selection, deals between an acquirer and target company from the same country were filtered out. This resulted in 5307 observations. Subsequently, investments with missing country values and industry information were filtered out. This resulted in 4483 observations. Finally investments with an unknown equity stake, an increase in equity stake and/or an equity stake below 10 percent were deleted from the sample. This resulted in a database consisting of 2604 observations.
4.2.2 Data collection of the independent variables
Data collection on the independent variables included the consultation of various sources. Firstly, in order to collect data on government-related political risk the Worldwide Governance Indicators (WGI) of the World Bank were consulted. The World Bank’s total dataset of the WGI project covers 215 economies over the period 1996-2014. The indicators are composite constructs based on 32 data sources. The WGI draws from four types of data sources: (1) surveys of households and firms (2) commercial business information providers, (3) non-governmental organizations and (4) public sector organizations. The World Bank uses an Unobserved Components Model (UCM) to rescale and combine the data in order to create aggregated indicators. Secondly, in order to collect data on society related political risk the measures of ethnic, linguistic and religious fractionalization constructed by Alesina, Devleesschauwer, Easterly, Kurlat & Wacziarf (2003) are used. Respectively these variables cover 189, 182 and 191 countries.
Thirdly, in order to collect data on economy-related political risk, both the World Development Indicators (WDI) from the World Bank and the World Economic Outlook (WEO) database of the International Monetary Fund (IMF) were consulted. The WDI are compiled from officially recognized international sources. Data is collected for a total of 214 economies. The WEO database includes macroeconomic data series from the statistical appendix of the World Economic Outlook report of the IMF. The database is
28
updated biannually and consists of observations, although varying per year and indicator, of approximately 180 countries.
4.2.3 Composition of final dataset
In order to compose the final dataset the entries from the Zephyr database were linked to the host country specific data. To avoid altercations with missing values during the statistical analysis, only entries into countries of which all data was available were incorporated into the final database. This resulted in a total of 2354 observations.
4.3 Description of the variables 4.3.1 Dependent variable
The dependent variable, equity mode selection, is measured by the equity percentage held by the acquirer firm after the acquisition. Entries labeled as joint venture include both partial acquisitions and joint ventured greenfield operations. Entries labeled as wholly owned subsidiary include both full acquisitions and greenfield acquisitions. Among others, Hennart (1991) considers a 95 percent equity stake as the threshold between a wholly owned subsidiary and a form of collaborative entry. However, the appropriate percentage of equity ownership distinguishing a WOS from a JV is subject to controversy. The majority of empirical research adopts a 95% equity ownership as threshold (Anderson & Gatignon 1986; K.D. Brouthers & Hennart 2007; Hennart 1991; Slangen & van Tulder 2009). However, other researchers have used a threshold of 80% or even 51% (Makino and Beamish 1998; Youssef & Hoshino 2003). Consistent with the majority of empirical studies in entry mode research this study adopts the 95% as appropriate cut-off point differentiating between JV and WOS. However, in order to investigate the influence of political risk further the same analyses are done considering an equity threshold of 80% and 51% as well. Subsequently, three dummy variables were created reflecting a threshold of 95%, 80% and 51%. The variables were coded in such a way that a JV is represented by 0 and a WOS is represented is represented by 1.
4.3.2 Independent variables
The variables included in the operationalization of governmental related political risk, societal related political risk and economic-related political risk were gathered with the help of the standard dataset collection of the Quality of Government Institute
29
associated with the University of Gothenburg (Teorell, Dahlberg, Holmberg, Rothstein, Hartmann & Svensson 2015). The variables are elaborated upon shortly. For a more detailed overview of the variables included see Appendix I.
Government-related risk is incorporated by using five of the Worldwide Governance Indicators of the World Bank. Included are (1) political stability and the absence of
violence, (2) control of corruption, (3) rule of law, (4) government effectiveness and (5) regulatory quality. In the World Bank dataset all the governance estimates are normally
distributed with a mean of zero and a standard deviation of one for each year of measurement. The estimates are ordinal variables. Values range from -2.5 to 2.5 whereas higher values indicate better governance outcomes. Although the indicators are not directly suitable for over-time comparison, because they are standardized, they are useful in broad cross-country comparisons, which is the objective of this study. Societal related risk is represented by the measures developed by Alesina et al. (2003). Included are (1) ethnic fractionalization, (2) linguistic fractionalization and (3) religious
fractionalization.3 The measures reflect the probability that two randomly selected
people from a given country do not share a certain characteristics. The higher the number, the lower the chance of two people sharing that characteristic. High scores thus imply a greater fragmentized society.
Economic risk indicators included are (1) GNI per capita, (2) GNI per capita growth, (3)
inflation, (4) gross government debt and (5) current account balance. GNI per capita is
gross national income divided by midyear population. The variable included is based on purchasing power parity and data is represented in constant 2011 international dollars.
GNI per capita growth reflects the percentage growth rate of GNI per capita based on
constant local currency. Inflation reflects the annual change in consumer prices in percentages. Gross government debt is an indicator from the World Economic Outlook from the IMG and is measured as a percentage of the GDP. The gross debt includes all
3 The ethnic fractionalization indicator is a combination of both racial and linguistic characteristics. It is used alongside linguistic fractionalization because the specific combination accounts for a higher degree of fractionalization than the more commonly used ELF-index (Ethnic-Linguistic Fractionalization) (Teorell et al. 2015). As a result it takes into account the countries where people speak the same language but have different racial backgrounds such as in Latin America.
30
liabilities that require payment or payments of interest and/or principal by the debtor to the creditor at a date or dates in the future. Current account balance reflects the sum of net exports of goods and services, net primary income, and net secondary income. It is measured as percentage of GDP.
4.3.3 Control Variables
Five control variables are included in this study: (1) cultural distance, (2) growth
world output, (3) investment freedom, (4) sector type and (5) sector similarity. Firstly, cultural distance (CD) is taken into account because a substantial amount of studies
argue for CD as a determinant for entry mode selection (Barkema, Bell & Pennings 1996; Erramilli, Agarwal & Kim 1997; Tihanyi, Griffith, & Russell 2005). On the one hand it is argued that high cultural distance leads to MNEs wanting to have greater control in order to minimize transaction costs (Hennart & Reddy 1997). By using wholly owned subsidiaries firms are able to mitigate cultural distance. On the other hand, it is argued that high cultural distance leads to MNEs requiring greater flexibility, smaller resource commitment and subsequently lower risk exposure (Gatignon & Anderson 1988; Kim & Hwang 1992; Grosse & Trevino 1996). In this study the four original cultural dimensions developed by Hofstede were used (1984). These dimensions include Individualism versus Collectivism (IDV), Power Distance (PDI), Uncertainty Avoidance (UAI) and Masculinity versus Femininity (MAS). The variable cultural
distance was constructed by applying Kogut and Singh’s (1988) formula to the updated
version of Hofstede’s dimensions (G. Hofstede & G.J. Hofstede 2010). The formula is as follows:
CDj = 4i=1 {(Iij-Iix)2 /Vi}/4.
CD is the cultural distance between country j and x where country x is the home country.
Iij is the country j’s score on the ith cultural dimension. Iix is the score of the home
country x on the same dimension. Vi is the variance of the score of the respective dimension.
Secondly, the predictor growth world output is included in order to control for a possible effect of the financial crisis of 2007/2008. The expectation is that the financial crisis put severe constrains on the resources available within companies. As resources
31
are already constrained companies might prefer to decrease their resource commitments and opt for JVs instead of WOSs.
The third control variable, investment freedom, scrutinizes a country’s FDI policies. In certain countries governments have formal restrictions limiting total or substantial foreign ownership of operations. In these countries firms are thus obliged to cooperate with local partners resulting in the selection of JV. The country investment freedom indicator of the Heritage Foundation (2014) is used. The indicator ranges between 0 and 100, where higher values represent higher degrees of investment freedom.
As fourth control variable sector type of the acquirer is included because the entry mode literature indicates that especially tertiary sector (service) firms have a greater propensity to select wholly owned entry modes than secondary sector (manufacturing) firms (K.D. Brouthers, L.E. Brouthers & Werner 2002; Li & Guisinger 1994; Sarathy 1994; Nicoulaud 1989). Manufacturing firms often need to invest in a plant and equipment thus resulting in larger resource commitments. As a result these firms experience a greater exposure to risk and prefer less integrated entry modes (Boddewyn, Halbrich & Perry 1986; Gatignon & Anderson 1988). Service firms typically have lower resource commitments and tend to be more people intensive resulting in lower switching costs. If the situation in a country becomes too precarious it is easier to relocate operations (Erramili & Rao 1993 in K.D. Brouthers et al. 2002). Data on sector differentiation was extracted from the Zephyr database. Distribution among the secondary and tertiary sector was carried out based upon the industry classification of the United Nations Statistics Division (see Appendix II). Acquiring companies operating in the secondary sector were coded as 0 and acquiring companies operating in the tertiary sector were coded as 1.
The final control variable, sector similarity, takes into account that companies use JVs in order to access resources not present in the parent company (Hennart 1988a). Acquiring companies may have different preferences for entering into their own sector or into a different sector. When entering into a different sector companies are more likely to need access to resources not (yet) present in the parent company and thus use a JV. When entering into the same sector, this need is less present. Sector similarity was computed by creating a dummy variable based upon the distinction into the 19 sectors
32
displayed in Table. Entries into the same sector are coded as 0 and entries into a different sector are coded as 1.
Table 1: Industry classification Industry Classification Sector
Primary sector Primary Sector (agriculture, mining, etc.)
Secondary sector Chemicals, rubber, plastics, non-metallic products Food, beverages, tobacco
Machinery, equipment, furniture, recycling Metals & metal products
Publishing, printing
Textiles, wearing apparel, leather Wood, cork, paper
Tertiary sector Banks
Construction Education, Health Gas, Water, Electricity Hotels & restaurants Insurance companies Other services
Other services / Education, Health Post and telecommunications Public administration and defence Transport
Wholesale & retail trade
4.4 Data Analysis
The data is subjected to statistical analysis using the Statistical Packages for the Social Sciences (SPSS). Firstly a principal component analysis was conducted examining the underlying structure of the independent variables. Special attention was paid to shared variances between variables that are grouped together in the model specified in Chapter 3 (government-related, society-related and economy-related political risk). Secondly, in order to ensure that correlations among the predictors are not a concern, the variables were subjected to multicollinearity tests. Finally, a multivariate regression analysis was conducted. Due to the nature of the dependent variable a binary logistical regression was applied.
33 Chapter 5: Results
5.1 Descriptive statistics 5.1.1 Describing the sample
The primary descriptive statistics are presented for three different models. The models respectively represent a threshold of 95% (Model 1), 80% (Model 2) and 51% (Model 3) equity to qualify either as a joint venture or a full acquisition. However, for most descriptive statistics represents values associated with Model 1in order to increase the clarity of the presentation.
The sample includes N= 2354 observations representing entries into 73 countries. A list of the countries include is presented in Appendix II. The distribution of WOSs and JVs within the sample is presented in Table 2. In Model 1 the sample consists of N=1973 (76.2%) WOSs and N=561 (23.8%) JVs. In Model 2 the sample consists of N=1836 (78%) WOSs and N=518 (22%) JVs. In Model 3 the sample consists of N=1978 (85%) WOSs and N=376 (16%) JVs. Note that the distribution of the dependent variable is substantially asymmetrical with a relative large amount of WOSs in comparison to a relatively small amount of JVs.
Table 2: Sample distribution of JVs and WOSs among Model 1, 2 and 3
Model 1 (95%) Model 2 (80%) Model 3 (51%) N Percent N Percent N Percent Joint Venture (JV) 561 23.8 183
6
78 197 8
84 Wholly Owned Subsidiary (WOS) 1793 76.2 518 22 376 16 Total 2354 100.0 235
4
100.0 235 4
100.0
The sample includes entries from firms operating in a total of 19 sectors (see Table 2 which were recoded into secondary (manufacturing) firms and tertiary (service) firms. The distribution among these sectors in the sample and the distribution of WOSs and JVs among the entries of the sectors is represented in Table 3. Within the sample 43.1% of the entries were done by firms operating in the secondary sector. Of these entries 20.6% were JVs and 79.4% were WOSs. Subsequently, 56.9% of the entries in the
