The purpose of this study is to identify, based on social network theory, the relationship between the direction of international migration (immigration/emigration) and the international movement of enterprises and their location.
A traditional gravity model and the Tobit estimation method are applied to three groups of countries from three different regions: Latin America, North America and the European Union. The study considers a period from 2001 to 2012.
The main results suggest that the international migration that goes from the European Union and North America to Latin America is related with the firms’ internationalization and their respective location.
Given that migration can be an important and reliable source of information, trust and knowledge, managers should see it as a “bridge” between the home and host countries, which, in turn, can increase their competitive advantage.
Governments can learn how migration and outward foreign direct investment interact. In addition, they could develop political frameworks to accurately and effectively manage international migration (immigration and emigration) and FDI in the best interests of the stakeholders.
This study extends the social network theory by suggesting that networks are not only related with firms’ expansion abroad but as well with their location. This statement could be generalizable as long as emigration/networks (ethnic ties) are considered the links between the home and the host country.
Alcaraz, J. and Salamanca, E. (2018), "Migration and outward FDI: a double direction approach", Review of International Business and Strategy, Vol. 28 No. 2, pp. 240-257. https://doi.org/10.1108/RIBS-12-2017-0114Download as .RIS
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The global labor market has been expanding (Guth, 2009) during the past decades as a consequence of the increase in international migration. More precisely, international migration has grown from 75 million in 1960 to 173 million in 2000, and from 222 million in 2010 to 244 million in 2015 (UNCTAD, 2016). According to the 2015 International Migration Report, such migration increased 41 per cent between 2000 and 2015. In addition, the share of migrants among the world’s population increased from 2.8 per cent in 2000 to 3.3 per cent in 2015. Given these trends and their association with cross-national transference of capitals, different disciplines have identified a relationship between international migration and outward foreign direct investment (OFDI).
Disciplines such as economic geography (Lamin and Livanis, 2013), demography and development studies (Enderwick et al., 2011) have been keenly interested in this particular research topic (Jean et al., 2011). Most of this research has been focused on developed countries. Some examples can be found for the cases of the USA (Kugler and Rapoport, 2007; Javorcik et al., 2011; Foley and Kerr, 2013; Foad, 2012), France, Germany, Italy, Spain and the United Kingdom (Flisi and Murat, 2011) and Japan (Akinori, 2015). As for developing countries, most of the literature is centered on China (Gao, 2003; Tong, 2005; Song, 2011) and India (Zaheer et al., 2009; Anwar and Mughal, 2013). Commonly, research results find a positive relationship between OFDI and migration (Federici and Giannetti, 2010), thus suggesting that they are complementary. In contrast, a negative effect has been identified when migrants’ skill level comes to the equation (Docquier and Lodigiani, 2010; Gheasi et al., 2013).
Regarding the international business literature, the topic has not yet received broadly research attention (Manolova et al., 2010; Musteen et al., 2010; Chung and Tung, 2013; Gao et al., 2013; Kotabe et al., 2013; McHenry and Welch, 2017). In spite of it, it is accepted that the understanding of this particular issue could contribute to this field (Enderwick et al., 2011), mainly because the relevant role played by individuals in the internationalization process (McHenry and Welch, 2017). In fact, the understanding of the links between migration and international flows of capital is essential to comprehend the firms’ internationalization (Gollin and Lange, 2013). Furthermore, the identification of such links and how they influence multinational enterprises (MNEs) location remains an area to be explored (Mesquita, 2016). In this research, we not only propose the existence of a relationship between migration and OFDI, but also suggest that the direction of international migration (immigration/emigration) is related with the international movement of enterprises and their location.
Through the social network theory, firms’ internationalization can be examined in more detail (Zhao and Hsu, 2007) as it explains how networks intervene for the internationalization of enterprises (Zucchella et al., 2007; Chung and Tung, 2013). Migrants act as a source of information, which may decrease the transaction costs inherent to the bilateral movement of capital between countries (Leblang, 2010). Migrants interaction is also related to establishing kinship, professional ties and social and business networks (Bräutigam, 2003), within the context of promoting trust and commitment in cross-national investments (Anwar and Mughal, 2013).
In this sense, networks are highly important for the internationalization of firms (Musteen et al., 2014; Narula and Santangelo, 2012; Ojala, 2015; Zucchella et al., 2007). Moreover, social networks (Aguilera and Jackson, 2003), specifically those related to migrants (Docquier and Lodigiani, 2010), play a primary role in fostering economic activities between countries (Schotter and Abdelzaher, 2013). Thus, migration and their networks (in both the home and host countries) influence the international flows of capital in the global arena in particular (The Economist, 2011; Schotter and Abdelzaher, 2013; Yamin and Kurt, 2018), and the ease of international business (Chung and Tung, 2013) in general.
By using data from three groups of countries from different regions (North America, the European Union and Latin America), it is possible to identify that the international migration that moves from developed countries to developing countries is related with the firms’ internationalization and their respective location but not in the reverse direction. These results were obtained by considering a simultaneous interaction between the immigration that a home country receives and the emigration that leaves the same country (double direction approach).
On the basis of the above, this study contributes to the IBs literature in general and to the firms’ internationalization and firms’ location in particular. It also extends the social network theory in the sense that networks are not only related with firms’ expansion abroad but as well with their location. Moreover, this behavior is not homogeneous, it only happens in a North–South direction, which is also important to highlight given that most of the research has been performed among developed countries, and it has analyzed the case of East Asian countries (mainly China). In the same regard, this study contributes to the understanding of this approach in Latin American countries, which so far remain an under researched region in the context of IBs. The study also allows to state managerial and public policy implications. In the first case, managers should be aware of networks and international migration, in as much as these two may act as a bridge between the home and host countries, which, in turn, can increase the firms’ competitive advantage. As for public policy implications, governments from developed and developing countries could find it useful to develop accurate political frameworks to effectively manage international migration (immigration and emigration) and FDI.
The rest of the article is organized as follows. The literature related to social network theory is reviewed as the main theoretical support of the study. It is also reviewed the previous evidence related to migration and OFDI. Then, hypotheses are stated for two scenarios: immigration and OFDI and emigration and OFDI. The hypotheses are tested and the results discussed. Implications are included at the end of the paper in the conclusions section.
Literature review and hypotheses
Social network theory
Social network theory has been gaining more and more relevance in the understanding of international business (Zhou et al., 2007; Ellis, 2000; Harris and Wheeler, 2005; Yamin and Kurt, 2018; Narula and Santangelo, 2012). More specifically, this theory can be used to explain and understand how firms are affected by social networks (Zhou et al., 2007). In this respect, given the broad relevance of social networks in the literature, it is common to find different terms attempting to describe this concept, including social ties, interpersonal networks, relational networks and personal connections (Zhou et al., 2007; Jean et al., 2011; Eberhard and Craig, 2013).
Moreover, social networks have been defined as networks formed by individuals through social relations, which, in turn, promote information and business exchanges (Björkman and Kock, 1995). Another definition states that social networks represent opportunities transferred from one player to another through their relationship (Burt, 1997). Finally, social networks have been described as determinate groups of players and their relations (Wasserman and Faust, 1994). On the basis of these findings, two key factors can be identified in social networks: individuals and their relationships.
These two factors shape the underlying relevance of the social network theory, that is, knowledge and information transference. They are a source of advantages for the firms when expanding abroad (Jean et al., 2011), resulting in competitive advantages (Dieleman and Sachs, 2008). Following Zhou et al. (2007), there are three benefits from the transfer of information: business opportunities in foreign markets; advice and experiential learning; and referral trust and solidarity. Thus, through the transfer of information, facilitated by social networks, firms are better able to internationalize their productive activities (Ellis, 2000; Ellis and Pecotich, 2001).
An additional point is related to the three dimensions of social networks: relational, cognitive and structural (Nahapiet and Ghoshal, 1998). The relational dimension deals with the emotional connections and trust between the players, whereas the cognitive dimension refers to the same system of meanings shared between the players (i.e. language, codes and narratives). The structural dimension concerns the design and organization of the network (Musteen et al., 2010). These three dimensions play a fundamental role not only in the access of information, but also in the flow/transference of such information through the entire network.
Despite the clear identification of these dimensions and their contributions within the context of international business, the behaviors of the players can differ, owing to the complexity of the phenomenon. In other words, social networks are idiosyncratic, which means that the players could behave differently under similar circumstances (Jean et al., 2011; Yamin and Kurt, 2018). This situation highlights the relevance of using the social network theory to understand the internationalization of firms.
Regarding the study of this latter issue, previous studies have found a positive relationship between networks and the firms’ internationalization. The reason behind this behavior is that networks help to decrease the risks during the firms’ expansion abroad, directly impacting on their competitiveness (Chen and Chen, 1998). In this regard, the presence of networks improves the internationalization process, and they may affect the firms’ early internationalization (Zucchella et al., 2007).
Furthermore, networks facilitate the internationalization not only in terms of time (early internationalization), but as well in terms of taking the decision of going abroad for the first time, which has been observed for the case of new-venture internationalization in Manolova et al. (2010). This study also identifies how important domestic networks are for the firms’ internationalization. Nevertheless, not only are domestic networks important, research results also suggest a positive influence of international networks on firms’ internationalization (Musteen et al., 2010). Ultimately, networks and social network theory are fundamental to advance the understanding of firms’ internationalization (Yamin and Kurt, 2018).
Migration and FDI
The relationship between migration and FDI has been considered as an extension of the traditional trade framework. This neoclassical trade theory treats migration and FDI (as well as trade) as substitutes (De Melo, 1999). In other words, production factors grow and migration decreases; while migration flows in one direction, international capital moves in the opposite way (Foad, 2012).
Under this scenario of substitutability, international capital, on the one hand, flows toward locations with high labor availability, whereas on the other hand, the workforce will move toward areas in which they can obtain the maximum earnings for their services (Kugler and Rapoport, 2011). In this regard, considering that less developed countries offer high returns for invested capital and low salaries, while developed countries offer high salaries and low returns for invested capital, it is expected that the labor force will move in one direction, whereas foreign capital will flow in the other direction (Foad, 2012).
However, labor and foreign capital do not move in opposite directions (Davis and Weinstein, 2002). In fact, evidence suggests that an active and continuous relationship exists between migrants and FDI (Federici and Giannetti, 2010). In this case, these two factors perform as complements, rather than substitutes. There are mechanisms to explain this complementarity effect. That is to say, there are mechanisms through which international migration can promote capital flows in a cross-national context. One premise in this process is that migrants will construct networks between their home and host countries (Flisi and Murat, 2011). In a broad sense, these networks will be the determining factors for counterbalancing information asymmetries, thus effectively overcoming the transaction costs inherent to the founding of subsidiaries abroad (Leblang, 2010).
More specifically, migrants performing in overseas enterprises can act as sources of information, sharing with key actors their knowledge about home market’s structure, such as consumers’ tastes and preferences, local customs, local ethic codes, costs, the availability and skill level of the labor force, among other data useful for identifying investment opportunities (Song, 2011; Zaheer et al., 2009). Migrants also provide access to professional services (e.g. lawyers) and key public servants. According to Kugler and Rapoport (2007), migrants have shown that their presence in a foreign labor market can provide precise features of the labor market in their home country. Other contributions that migrant networks make to ease FDI are related to language and cultural affinity. The former allows for effective communication, which is vital for the success of any project, whereas the latter is an important element that promotes trust between the parties involved (Leblang, 2010; Docquier and Lodigiani, 2010).
Migrant networks also provide information about how to conduct business under their home country’s legal framework and how to deal with specific local issues. It is important to note that legal systems in general, and the rule of law in particular, are “essential factors” that contribute to inward FDI in a particular country (Javorcik et al., 2011). In this sense, the migrants’ knowledge can provide useful information about the application of community sanctions (e.g. contract violations) and the enforcement of contracts (Tong, 2005; Song, 2011). The basic idea above is that investors must ensure that contracts and agreements are enforced (Gao, 2003). This can significantly reduce the uncertainties of unfamiliar contexts, which are more relevant for countries with weak quality institutions, which is the case of emerging markets (Alfaro et al., 2008; Kugler and Rapoport, 2011).
Thus, migrants can be seen and understood as connections between home and host countries that, on the one hand, become a reliable source of information that decreases the risk and uncertainty of cross-national capital transactions; consequently, transaction costs decrease as well (Zaheer et al., 2009). On the other hand, migrants promote the firms’ OFDI (Kugler and Rapoport, 2007; Federici and Giannetti, 2010). Then, the more unfamiliar the home investors with the host country context, the higher the importance of migrant networks (Gao, 2003). In the same regard, the more the involvement of migrants in the OFDI project, the better the chances of success (Bhattacharya and Groznik, 2008).
Finally, one variable that has been frequently considered in the study of migrants’ networks and OFDI is their skill levels. Firms’ investments abroad are activities that, by their nature, involve certain complexities given the wide variety of knowledge and experience that participants must possess related to production, markets, governments, societies and so on (Flisi and Murat, 2011). In this regard, previous studies have indicated that skilled migrants hold a positive relationship with OFDI (El Yaman et al., 2007; Kugler and Rapoport, 2007; Docquier and Rapoport, 2007; Docquier and Lodigiani, 2010; Gheasi et al., 2013; Foley and Kerr, 2013; Flisi and Murat, 2011). However, as unskilled migrants can also promote OFDI (Kugler and Rapoport, 2011; Kugler and Rapoport, 2007; Devadason and Subramaniam, 2016), either skilled or unskilled migrants can influence cross-national flows of capital.
Immigration and outward foreign direct investment
In general, migration and its impact on FDI have been studied in two different directions. The first one takes place when migrants go from the home country to the host country and promote the investments from the host country to the home country, whereas the second one occurs when migrants go from the home country to the host country and the investments flow in the same direction. One of the purposes of the present study is to examine the effects of migrants on FDI in both directions.
In the first case, empirical evidence suggests a positive relationship between the two variables. For example, Kugler and Rapoport (2007) found that migrants in the USA from 55 different countries maintain a positive relationship with OFDI from the USA to the migrants’ home countries. This positive relationship appears in a dynamic perspective, but differs in a contemporaneous context, where both variables show substitutability, that is, a negative effect.
Javorcik et al. (2011) also found that migration promotes direct investments from the USA to the migrants’ countries of origin. Similarly, Bhattacharya and Groznik (2008) found, through their cross-section and panel data analyses, that a positive relationship exists between the variables, whereas Foley and Kerr (2013) indicated a positive relationship between migrants’ innovators based in the USA and direct investments addressed to the ethnic innovators in their home countries. Meanwhile, Buch et al. (2006) showed similar results in which migrants and OFDI act as complements, although their research was limited to Germany. Docquier and Lodigiani (2010) and Federici and Giannetti (2010) found, through numerical simulations, that network externalities as an approach of migrants are positively related with the home country’s FDI. Flisi and Murat (2011) identified that migrants from developed and less developed countries have an impact on OFDI from France, Germany and the UK. However, in the same study, for Italy and Spain, migrants did not have an impact on these countries’ OFDI.
Other two studies that show similar evidence are those developed by Gao (2003) and Tong (2005). However, unlike the previous ones, these have not been conducted in developed countries. More specifically, both studies indicated a positive relationship between the Chinese population abroad and the amount of FDI from the host countries in China. This situation suggests that this phenomenon is not a function of the country’s economic development. On the basis of the above, the following hypothesis is posited:
OFDI from the European Union and North America in Latin America is positively related with the immigration from Latin America in the European Union and North America.
Emigration and outward foreign direct investment
In the second case, the evidence suggests a positive relationship between the variables. For instance, Buch et al. (2006) found a positive relationship between migrants in Germany and FDI coming from their home countries, whereas Foad (2012) obtained similar evidence in the USA. In addition, Flisi and Murat (2011) identified that international migration from Italy and Spain promoted OFDI from these countries to the migrants’ host countries, but not from Germany, France and the UK.
Meanwhile, Gheasi et al. (2013) examined the relationship between migrants in the UK and bilateral FDI, and found no significant influence, although the situation changed into a positive relationship when they considered skilled migrants. There are other cases in which skilled migration seems to have a stronger impact on FDI than unskilled migration. In this regard, Akinori (2015) indicated that migrants in Japan induce FDI from their home countries, whereas Nijkamp et al. (2001) confirmed the positive impact of migrants on inward FDI. In addition, Ivlevs (2006) found a complementary relationship between inward FDI and migration.
Similar to the first case, the majority of previous research has been conducted on developed countries. In this sense and regarding to developing economies, the results obtained by Song (2011) suggest that Chinese migrants living in South Africa have a positive impact on OFDI from China. On the basis of the above findings, the following hypothesis is posited:
OFDI from the European Union and North America in Latin America is positively related with the emigration from the European Union and North America to Latin America.
Migration, networks and MNEs location
Previous studies have found that migrants’ networks have a direct and positive impact on the promotion of bilateral trade (Gould, 1994; Head and Ries, 1998). Moreover, transactions costs and migrant networks can improve the possibility of selecting a better location for allocating a firm’s resources (Rauch and Casella, 2003). This behavior is not exclusive to international trade and migration, it also may occur for FDI.
The relevance of the influence of migration on firms’ location, for this study, is twofold. First, in practical terms, firms’ location decisions are strategic. In this sense, incorrect choices mean that firms are leaving aside other options, perhaps more accurate and potentially successful options, not to mention that incorrect location could be a high-cost decision (Narula and Santangelo, 2012). Second, there has been a recent academic interest to increase the understanding, empirically and theoretically, of this phenomenon.
In this regard, Ojala (2015) states the need to recognize the relevance and the existence of formal as well as informal business and personal ties. In addition, the role of the networks and the actors within the networks influence the making decision process to select a foreign country where to locate a firm. Mesquita (2016) argues that there is a need for more research about firms’ location and internationalization given the requirement for a better understanding of the firms’ country selection for foreign expansion, which becomes more relevant as firms are selecting some countries over others. It is also important to highlight this recent call to take in consideration the role played by individuals in the firm internationalization (McHenry and Welch, 2017), for this specific study, the role played by international migration.
This international migration, or ethnic ties, has a sharp and fundamental role on firms’ location decisions. For instance, Zaherr et al. (2009) found that ethnic ties are associated with firms’ location decisions in India. For Chen and Chen (1998), networks are a major factor for the FDI location choice. They found that network linkages are a determinant element in the case of Taiwanese firms. Moreover, relational networks are important to locate Taiwanese MNEs in Southeast Asia and China but are not important with respect to the USA. For Anwar and Mughal (2013), ethnic networks, both social and business networks, promote foreign investment. They found that Indian international migration in developed countries has a positive effect on Indian OFDI to these migrants’ host countries, mainly those in Asia Pacific although not in the case of European countries. These results show how important networks are for the attraction of foreign capitals and the location of those capitals.
Methodology and data
To test the proposed hypotheses, this study uses a traditional gravity model, which has been widely applied in empirical research to describe the characteristics of trade flows among countries (van Bergeijk and Brakman, 2010; Leblang, 2010). This model has also been used to investigate bilateral flows of FDI, a practice that has been gaining more relevance and acceptance (Blonigen et al., 2007; Garas et al., 2016; Gheasi et al., 2013) given its precision in estimating such phenomena (Navaretti et al., 2004). Bergstrand and Egger (2007) and Head and Ries (1998) are pioneers in applying this type of model to study FDI.
The gravity model was developed by Tinbergen in 1962; nevertheless, it was not until the 90s and the 2000s-decade when the academic interest about this model and, consequently, its use and acceptance, started to increase (van Bergeijk and Brakman, 2010). The gravity model has its foundations on the Newton’s Gravitational Law, which, broadly speaking, states that two objects are attracted by a force, which is directly proportional to their masses but inversely to their distance. In a similar vein, the size of the countries and the distance among them, are playing a significant and fundamental role in the explanation of cross-national economic interactions. That is to say, the interactions among the countries are determined by the size (mass) of the countries and the distance between the countries (Breitenfellner et al., 2008).
In the traditional gravity model, exports and imports vary directly and in proportion to the economic size of the receiving and sending countries. For the present study, the mass is measured by the income level, population and the gross domestic product (GDP; Isard et al., 1998), the “interactions among the countries” are determined by the bilateral OFDI flows, whereas the migration represents a link between the investor home country and the investment location (Leblang, 2010). The general gravity specification model for explaining FDI is as follows:
For the purpose of this study, migration is considered international networks, which include the migrants’ professional and personal interactions (Musteen et al., 2010). The presence, participation and creation of networks is fundamental for firms’ internationalization (Yamin and Kurt, 2018) as well as for firms’ location (Anwar and Mughal, 2013; Chen and Chen, 1998). This behavior is because of the direct effect of migration/networks on the reduction of different costs, such as information and transaction costs (Docquier and Lodigiani, 2010).
It is important to note that the impact of migration on OFDI is not immediate (Madhavan and Iriyama, 2009; Javorcik et al., 2011; Rapoport, 2016; Eberhard and Craig, 2013). In other words, the development of such networks takes some time to have an impact on the internationalization of enterprises (Cuervo-Cazurra et al., 2007). Consequently, the use of longitudinal data becomes more relevant. In this regard, numerous studies on internationalization and networks have been conducted by using cross-sectional data (Eberhard and Craig, 2013). Therefore, the present study considers the aforementioned temporal effect by considering a time lag on the independent variables, except for common language, common colony and distance. This approach, that is, the use of lagged values, also controls and reduces any potential problem related to endogeneity (Zhang and Pezeshkan, 2016; Danis et al., 2011).
Data collection is performed by using diverse sources. In this case, OFDI is obtained from the Bilateral FDI Statistics of the United Nations Conference on Trade and Development (UNCTAD), which provides data from 2001 to 2012, whereas migration is measured by the stock of migrants from the United Nations Global Migration Database. Population and GDPpc (two variables that measure the potential market size) are obtained from the World Development Indicators of the World Bank. For political freedom, the Freedom House database is used, which ranks countries based on their political rights and civil liberties. The political freedom variable in the present study is constructed as the average of political rights and civil liberties (Wang et al., 2013). For common language, common colony and distance, the data for these variables are taken from the Centre d’Études Prospectives et d’Informations Internationales (CEPII). In addition, common language and common colony are dummy variables that take the value of 1 if they share the same language/colony and 0 otherwise. Finally, distance is measured in kilometers.
The sample used in this study consists of two sets of countries: one set for North America and the European Union, and the other set for Latin America. For the latter, UNCTAD classifies 19 countries from this region. However, not all of them were considered for this study, owing to the lack of information. For instance, in regard to Brazil, there was no OFDI information from 2001 to 2005. Thus, the Latin American countries considered in this sample were as follows: Argentina, Belize, Bolivia, Chile, Colombia, Costa Rica, Ecuador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay and Venezuela. From the other set of countries, Greece, Ireland, Malta, Romania, Slovenia and Canada were excluded for the same reason. As a result, the second set of countries included: Austria, Belgium, Cyprus, Denmark, Estonia, Finland, France, Germany, Hungary, Italy, Latvia, Lithuania, Luxemburg, Netherlands, Poland, Portugal, Slovakia, Spain, Sweden, the Czech Republic, the USA and the UK.
It is important to note that when using bilateral transactions among countries, it is common to find zeros in the dependent variable as not all the home countries issue direct investments to all the host countries and not every single year either. For instance, in the case of Helpman et al. (2008), less than 20 per cent of the countries are investing in other countries and for Buch et al. (2006), only 10 per cent of the countries in the sample were receiving capitals from abroad.
There are some technics to address this problem. One of them is to switch all the zeros for a constant and other is just to eliminate all the zeros from the sample (Silva and Tenreyro, 2006). The reason for this is to be able to perform a linear regression with the gravity model (Rose and Spiegel, 2010). Nevertheless, when the elimination of zero values is performed in a non-randomly manner, which is used to be the case, biases are introduced to the estimation model (Brakman et al., 2010). Besides, the very fact that there are countries registering zero inward FDI has meanings for the model, for example, absence of networks (Dolman et al., 2007).
To overcome this classic censored problem, previous studies have suggested the use of the Tobit estimation method (Eaton and Tamura, 1994; Wei, 2000; Greene, 2004; Coniglio et al., 2017). The Tobit estimation also allows to perform non-linear regressions when linear regressions are not suitable to run (Eaton and Kortum, 2001), which is the case for this study.
This section describes the results obtained after the aforementioned data were subjected to the Tobit regression analysis. The variables were taken from the traditional gravity model and applied to explain the OFDI between countries, given the presence of international immigrants and international emigrants. Variables such as population and GDPpc, related with the market size or the potential of the market promote the OFDI among the countries. An institutional variable that measures the political conditions were also included (i.e. political freedom) as it was expected to have a positive impact on the home countries’ capital flows abroad. Other variables that did not vary in time but were helpful for explaining OFDI were common language and colony, especially among the countries that included capital movement activities. An additional non-time variable was the distance between the countries, which was expected to negatively affect OFDI as greater distances between the countries represent higher transaction costs. The results of the OFDI from the European Union and North America in Latin America are presented in Table I.
In the first model, the migrants from countries j in countries i had no significant relationship regarding the issuance of FDI from countries i to countries j. As stated in H1 and as suggested by the literature, a positive and significant relationship was expected. However, a different situation was found in regard to the migrants from i countries to j countries in the second model. These results suggest that, although the migrants from the European Union and North America in Latin America positively influence the direct investments from the European Union and North America in Latin America (as proposed in H2), such an influence did not occur in the reverse direction.
It is possible to observe a similar behavior in model three. In this third model, international migration, both immigration and emigration, is performing simultaneously. The results show a positive relationship between the emigration from the European Union and North America to Latin American countries and the issuance of FDI in the same direction. Even in this model, the immigration from Latin American countries in the European Union and North America does not have a relationship with the OFDI from countries of the European Union and North America to countries from Latin America.
Regarding the variables of population and GDPpc, both of them indicated a positive relationship regarding OFDI. These variables, as measures of market size and market potential, promoted outward FDI from the European Union and North America to Latin America. On the one hand, these variables in the host country became part of the countries’ endowments. On the other hand, these variables indicated the importance of expanding to foreign countries by making direct investments abroad. In general, domestic conditions, especially in terms of GDPpc, had a stronger impact on OFDI than the host country’s conditions for the three models. Furthermore, in the three models, GDPpc had a stronger effect on OFDI than population.
Regarding political freedom, this variable was the average of two indicators (i.e. political rights and civil liberties). In addition, the countries were classified into three categories: free (1.0 to 2.5); partly free (3.0 to 5.5); and not free (5.5 to 7.0). In this sense, a negative value and its significance (<0.001) means that OFDI from the European Union and North America is related to the political freedom of Latin American countries. Thus, the host countries’ institutional environments are vital for the allocation of OFDI from the European Union and from North America.
Two variables that decrease the risks and the transaction costs among countries are common language and common colony. The former, for the three models, showed significant results (<0.01), which indicates that language is important for conducting business among the regions. In other words, a different language will deter OFDI from the European Union and North America to Latin America, whereas the opposite is expected with a similar language. In the case of common colony, the results suggest a positive relationship, which means that a common colony shared by the countries influences OFDI. It is important to note that these two variables are dummies, and they obtain 1 when the countries share the same feature and 0 otherwise. Moreover, common colony, among all the variables considered in the models, had the strongest effect on OFDI.
Finally, the results of the distance between the home and host countries were in line with those of previous studies. More specifically, for all the models considered in this study, there were negative outcomes, indicating that the greater the distance between the countries, the higher the transaction costs and the lower the amount of foreign capital invested in the host countries.
This study examined the effects of migrants on OFDI from developed countries to developing countries. Two different directions regarding international migration were considered: immigration from developing to developed countries, and emigration from developed to developing economies. In both cases, a positive impact of migration on the promotion of OFDI was expected. It was also studied the direction of international migration and how it is related with the international movement of enterprises and their location.
The findings from this study suggest that international migration is positively influencing OFDI. Particularly, this is happening for emigrants from the European Union and North America to Latin America, directly affecting the OFDI in the same direction. These results are in line with previous research (Song, 2011; Foad, 2012; Jayet and Marchal, 2016). On the basis of the social network theory, the emigration from developed countries to developing countries overcomes the disadvantages that the latter countries exhibit, including institutional conditions (i.e. political freedom), language and geographical distance and in general, unfamiliar environments (Zhu et al., 2012).
Specifically, migrants/networks from the European Union and North America in Latin America provide knowledge regarding the host countries, thus increasing experiential learning and trust. In other words, migrants in the host country obtain knowledge about the market, the institutions and the formal and informal practices (Coniglio et al., 2017), which, in turn, promotes OFDI from developed countries to developing countries. This accounts for the relevance that migration networks have on decreasing the barriers between countries and easing economic transactions given the “bridge” between the home and host countries (Rapoport, 2016).
Regarding the other scenario, despite the expected results proposed in H1, the evidence in this study suggests that immigration and OFDI are substitutes. That is, the immigration from Latin American countries in the European Union and North America are deterring the OFDI from the European Union and North America to Latin America. However, this result is not statistically significant. The question is why this result is different.
One possible reason for such a result is that in the first model, education is a factor. According to Gheasi et al. (2013), the migrants’ education level is a determinant factor when a firm makes the decision to invest abroad. Although this factor is not always considered in the case of developing countries (Artuc et al., 2015), empirical evidence suggests a stronger effect of skilled migrants than unskilled migrants given that the former will have a greater ability in acquiring and effectively transferring knowledge (Liu and Giroud, 2016). So perhaps the immigrants from developing countries in developed countries are not skilled enough to promote OFDI from developed countries to developing countries.
Other explanation is the following. It is possible that the relevance of networks regarding the OFDI is higher for the emigration than the immigration. This implies that people from developed countries moving to developing countries and the networks they build are much better (regarding immigration) in the creation of trust and understanding, which facilitates the transference of knowledge and information. Besides, networks in weak institutional countries, as is the case of Latin American countries, may constitute a key factor for firms’ internationalization (Alfaro et al., 2008; Kugler and Rapoport, 2011) from developed countries. Moreover, following Chen and Chen (1998), the above is also related with the firms’ location choice given that networks/emigrants are location specific. This way, emigration could have a positive impact on FDI location (Anwar and Mughal, 2013).
In keeping the same thinking, this behavior should not be specific for a North–South direction. In fact, there is evidence suggesting the same effect for North–North direction (Flisi and Murat, 2011) as well as for South–South direction (Song, 2011) and South–North direction (Anwar and Mughal, 2013); although researchers have not directly looked at the direction of the migration. The general argumentation here is that emigration leaving the home country to establish in a host country could promote the foreign investment as well as contribute to the location decision of that investment. In other words, the direction of the migration is related with MNEs location.
A more recent approach suggests a possible shift from complementarity to substitution, which attends to the newest trends related with anti-globalization phenomena (Brexit, Donald Trump investment and trade ideologies and other nationalist events in Europe; Globerman, 2017). While likely, the complementarity effect will remain when international flows of capitals move from developed to developing countries.
What does it happen with the relationship between emigration and OFDI when the firms are investing in several countries with different level of development? Does the effect of emigration on OFDI change or does it remain the same? If the results are different, are they explained by the home/host country’s features? Are they explained by the networks’ features? Are they explained by the firms’ features? These questions have to be answered in the future.
This study examined the effect of migrants on OFDI following a double direction approach, embracing two different scenarios: the first one in which immigrants from developing countries to developed countries influence the OFDI from developed countries to developing countries, and the second one, in which emigration from developed countries to developing countries influences OFDI in the same direction; that is, from developed to developing countries. The study also analyses how the direction of migration is related with the firms’ location. The basic principle is that migrants and their networks can reduce transaction costs associated with the OFDI between countries, thus promoting the international flows of capital and influencing on the location decision of those capitals.
The findings suggest a positive relationship in only one of the two scenarios proposed: when migrants from the European Union and North America go to Latin America and this migration promotes OFDI in the same direction, which intrinsically suggests the location of the foreign capitals. There is no significant evidence with respect to immigration and OFDI. This difference on the results could be related with the migration skill level, as previous research shows.
Other explanation suggests that the very presence of networks is not enough to promote the movement of FDI neither its location. It is also important to take in consideration the own features and context of the networks. For this study, the emigrants create an enabling environment for the three dimensions of social networks, relational, cognitive and structural, which is not happening with the immigration. More clearly, emigrants from the European Union and North America in Latin America (ethnic networks) are more important for the OFDI promotion and its location than immigrants from Latin America in the European Union and North America.
On the basis of the above, this study contributes to the IBs literature in general and to the firms’ internationalization and firms’ location in particular. It also extends the social network theory in the sense that networks are not only related with firms’ expansion abroad but also with their location. For this study, the previous statement is only valid for the case of emigration from the European Union and North America in Latin America and the OFDI location in countries from the latter region. Nevertheless, the results also allow suggesting that this explanation could be useful not only in a North–South direction but also generalizable for other directions (North–North; South–North; and South–South) as long as emigration/networks (ethnic ties) are considered links between the home and the host countries.
Several implications can be drawn from this study. In terms of managerial implications, managers should be aware about networks and emigration, especially when planning to go abroad. As migration from the home country to the host country can be an important and reliable source of information, trust and knowledge, managers should see these international migration/networks as a “bridge” between the home and host countries, which, in turn, can increase their competitive advantage.
From a public policy perspective, governments from developed and developing countries could find the results of this study particularly useful. First, governments can learn and understand how these two players interact with one another. Second, governmental institutions could develop political frameworks to accurately and effectively manage the binomial constituted by international migration and FDI. In this regard, home governments can promote the income of foreign capital as well as the expansion of domestic firms abroad through OFDI, thus ensuring that the concerted efforts of the local government and the firms are in the best interests of the stakeholders.
In terms of theoretical implications, the social network theory is a fundamental framework for understanding the internationalization and location of multinational firms in particular, and for advancing the international business field in general. On the basis of this theory, it is possible to consider the networks as factors that drive and promote the expansion of domestic firms. In addition, these networks can be considered key location factors for multinational business strategies.
Finally, although this study contributed to the existing literature by indicating that migrants from developed countries have a positive influence on the OFDI from developed to developing countries, this situation has not garnered enough attention from the academic community. Thus, future research should analyze the specific contexts of the countries, particularly information related with migration trends, home and host countries’ features, and networks and firms’ characteristics, to name a few. Contextualizing this phenomenon could be useful for moving forward in the understanding of this phenomenon and for generalizing the research findings.
Results of the analysis of the migrants’ effect on the OFDI based on a gravity model and a Tobit estimation method
|Dependent variable: LogOFDIij|
|Model 1||Model 2||Model 3|
|LogMigjit||−0.021 (0.046)||–||−0.065 (0.050)|
|LogMigijt||–||0.110** (0.048)||0.141*** (0.054)|
|LogPopit||0.716*** (0.076)||0.670*** (0.064)||0.733*** (0.078)|
|LogPopjt||0.808*** (0.061)||0.753*** (0.057)||0.789*** (0.062)|
|LogGDPpcit||2.561*** (0.156)||2.669*** (0.129)||2.791*** (0.163)|
|LogGDPpcjt||1.170*** (0.128)||1.113*** (0.126)||1.093*** (0.130)|
|PolFreejt||−0.465*** (0.080)||−0.399*** (0.076)||−0.408*** (0.077)|
|ComLngij||−3.134** (1.382)||−2.935** (1.298)||−2.900** (1.300)|
|ComColij||6.159*** (1.395)||5.762*** (1.306)||5.853*** (1.311)|
|LogDistij||−1.336*** (0.210)||−1.019*** (0.184)||−1.112*** (0.204)|
|Intercept||−32.333*** (3.573)||−35.302*** (3.380)||−36.955*** (3.554)|
Significance levels: *p < 0.05;
p < 0.01 and;
p < 0.001 in parentheses standard errors
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