Institutional Distance and Brazilian Outward Foreign Direct Investment


Mohamed Amal

Regional University of Blumenau 
(FURB), Brazil              

mohamedamal.amal@gmail.com 

Bruno Thiago Tomio

Regional University of Blumenau 
(FURB), Brazil           

bttomio@furb.br

Institutional Distance and Brazilian
Outward Foreign Direct Investment  

Mohamed Amal � Bruno Thiago Tomio

Abstract. Different studies have discussed the factors that influence outward 
foreign direct investment (OFDI) from developing countries. However, the choice 
of location and the role  of institutional distance are still controversial. The aim of 
the present paper is to  address the determinants of OFDI from Brazil from the 
perspective  of the host countries. Using a panel data model, we have  tested the 
impact of cultural and institutional distances on OFDI, and noted the moderating 
effects of the economic performance of the host country. The results show that 
institutional difference has a positive  effect on OFDI; however, such effect is 
constrained by the size of the host country and the amount of its bilateral trade. 
We have  found no statistically significant relationships between cultural distance 
and OFDI, but in our study geographical proximity had a positive  effect on the 
strategy of Brazilian OFDI. The results pointed to two main implications. First, the 
positive effect of institutional distance on OFDI may be constrained by the 
bilateral trade flows between home and host countries. Second, multinational 
companies from Brazil are more likely to invest in a culturally distant country 
when it delivers better institutional performances, which suggests that there is a 
complementary relationship between cultural and institutional distance. 

! The growing internationalization of multinational companies from emerging 
economies (EMNCs) can be described as a major trend in the current world 
economic scenario. In the last two decades, the  foreign direct investment (FDI) 
outflows from such economies grew at a higher yearly average than those from 
developed  economies. Over 58% of the FDI from emerging countries originates 
in Asia, especially from China and India. Latin America is the second largest 
source of FDI from emerging economies (UNCTAD, 2014). 
! Brazilian outward foreign direct investment (OFDI) was relatively 
insignificant throughout the 1990s. Following economic and institutional reforms 
in the latter half of the decade, Brazilian firms became increasingly 
internationalized, which stimulated the outward FDI as a strategy of entering and 
expanding  business in foreign markets. Thus Brazil became, together with China 
and India, not only one of the main host countries of world FDI, but also one  of 
the leading sources of outward FDI among developing countries. In 2013, FDI 
outflows from developing countries represented 21% of the world total outflows, 
and they registered around 21% of the  total outward stock according to  UNCTAD 
(2009; 2014). 
! Previous empirical studies on the  determinants of FDI have emphasized 
the role of the economic environment and liberalization policies in the host 
country to attract foreign investment. However, very few attempts have been 
made to address the determinant factors of FDI from developing economies. 
Most of the studies focus on the case of Asian multinational companies (MNCs), 
particularly studies that investigate OFDI from China and India (Buckley et al., 

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2007; Cheng & Ma, 2007; De Beule & Van Den Bulcke, 2012; Fung, Garcia-
Herrera, & Siu, 2009; Kolstad & Wiig, 2012; Pradhan, 2011). Other studies are 
more focused on the effect of home  country factors on OFDI and the performance 
of MNCs from emerging economies (Cuervo-Cazurra & Genc, 2011; He & Lin, 
2012; Luo, Xue, & Han, 2009). 
! Egger and Winner (2005) and Cuervo-Cazurra (2006) have shown how 
well firms from developing countries can deal with imperfect institutions in the 
host countries. More recently, some studies have attempted to establish the 
connections between ID and natural resources in the case of FDI from emerging 
economies (Aleksynska & Havrylchyk, 2013), or the  relationship  between 
experience, location choice, and government support (Jinping, 2011; Lu, Liu, 
Wright, & Filatotchev, 2014). However, very few studies have addressed the 
question of how institutional distance  (ID) affects outward FDI from developing 
countries.
! Taking the eclectic paradigm, also called OLI Ownership, Localization and 
Internalization) paradigm as a general framework (Dunning, 1988), the present 
study aims to investigate the importance of the location of the host country on 
OFDI. According to the OLI paradigm, location cannot be restricted to a bundle of 
physical variables, but is instead represented  by the institutions and good 
governance that contribute to economic efficiency and growth (Dunning & 
Lundan, 2008). On the other hand, considering that institutions are dynamic, 
changes over time in the qualitative content of location may significantly influence 
the strategies and performance of MNCs. 
! The results of the empirical studies have shown general trends in the 
behavior of MNCs regarding economic variables like  growth and macroeconomic 
stability. However, the same  studies have also shown more controversial and 
divergent results regarding the role of institutions and  cultural distance (CD) 
between home and host countries. 
! Using Brazil as our case study, we intend to contribute to the debate about 
the determinants of FDI from developing countries by addressing the following 
questions: Do institutional and cultural distances affect OFDI from Brazil? To what 
extent are such effects moderated by economic factors in the host country?
! The main assumption of the study is that MNCs from developing and 
advanced economies are different due to several factors. According to Narula 
(2012), the internationalization of firms follows a similar interaction between 
ownership  assets and location assets regardless of their origin. However, the “O” 
assets may be constrained by the “L” assets of their home countries in different 
ways for developing and advanced economies respectively. We argue that OFDI 
from developing countries is constrained and shaped by the location-specific 
assets of their home countries (Narula, 2012). And since the initial conditions (L 
assets) vary considerably between countries, we expect significant differences in 
the early internationalization of MNCs from different countries, as suggested by 
Narula (2012). We also anticipate that such companies may display different 
behavior and may cope in different ways with ID. However, and  in line with 
different contributions in the international business (IB) literature (Hennart, 2012; 
Narula, 2012; Rugman, Oh, & Lim, 2011), international performance requires 
knowledge assets, and depends on acquiring, maintaining, and developing firm-
specific advantages (FSAs) or ownership-specific (O) assets (Narula, 2012). This 
means that firms from emerging countries with a high level of intangible assets 
are more likely to follow intensive strategies of involvement in foreign developed 
markets, with the  aim to obtain and/or to  enlarge their ownership-specific assets 
and compete globally (Hennart, 2012). The implications of such strategies are 
that firms from emerging economies are more likely to invest in and enlarge their 
foreign assets, no matter what the  cultural and institutional distances might be. 
More specifically, distance does not necessarily have  an adverse effect on firms’ 
operations (Cuervo-Cazurra, 2012). Therefore, distance and foreignness cannot 

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79



be  considered as pure liabilities anymore, but as new strategic opportunities 
which allow access to specific assets “for arbitrage, complementarity or creative 
diversity” (Zaheer, Schomaker, & Nachum, 2012, p. 26).
! In this paper, we will not discuss the role of home country factors; instead, 
we will examine how the economic and non-economic factors in the host country 
influence the choice of location by MNCs. Brazil is an ideal country to illustrate 
and test some general assumptions about OFDI from emerging economies. First, 
it is among the most dynamic economies in an emerging country (the BRIC 
countries). Second, Brazil is the largest Latin American economy, the largest 
recipient of FDI in the region, and one of the leading host countries among the 
developing economies. Finally, yet significantly, although the country started to 
introduce  important market reforms late in the 1990s, it has only been in the  past 
ten years that the  internationalization of Brazilian firms has really been felt. This 
internationalization continues to be an ongoing process, and this study will be a 
reflection of how Brazilian MNCs currently cope with different cultural and 
institutional distances in the host countries, as well as an assessment of the 
challenges of creating value-added activities cross-border.          
! The paper proceeds as follows. In the next section, we will discuss the 
growth and pattern of Brazilian OFDI. Then we will present a literature review and 
the theoretical framework of the study. In the subsequent section we  will present 
the method and the estimate the results of our model. Finally, we will discuss our 
findings and their implications. 

GROWTH OF OFDI FROM BRAZIL

! In the past, the economic and political environment in Brazil has offered 
little incentive for Brazilian companies to internationalize. Since the end of the 
1990s the data shows a growing  tendency towards more international 
involvement from Brazilian firms, not only in terms of import and export activities, 
but also in the acquisition of foreign companies and the building of  new 
subsidiaries in different countries around the world. 
! After a period of different crises that affected Latin America and Brazil in 
the  1980s, most of the emerging countries in the region implemented 
macroeconomic policies designed to stabilize their economies and to create 
better conditions for a climate of investment. The implemented changes aimed at 
liberalizing the economies, removing the barriers on inward  FDI and cross-border 
acquisitions, and creating regulatory processes to remove or at least reduce 
trade barriers among South-South countries.
! Brazilian OFDI was relatively insignificant throughout the 1990s. In the 
second half of the 1990s Brazilian firms became increasingly internationalized, 
and this change  stimulated the outward FDI into foreign markets. In 2002, OFDI 
was estimated at US$ 2.5 billion, and reached its height in 2006, at US$ 28.2 
billion (Unctad, 2014). The amount of OFDI increased particularly after 2002, 
when the Brazilian economy bounced back from the crisis of 2001, and was then 
driven by a relatively long cycle of economic growth in developed and developing 
countries.
! The data of FDI show that the flows of Brazilian investments were relatively 
insignificant during the period between 1990 and 2001, pointing to a low degree 
of internationalization of Brazilian companies. On the other hand, the data shows 
a high degree of fluctuation in the investment flows, suggesting the high 
sensitivity of investments to external changes in the economy. The  expansion of 
Brazilian investment flows correlates strongly with the cycles of inward direct 
investments in the country. The increase of FDI in Brazil has contributed to a 
learning process by Brazilian companies, stimulating and enhancing  their 

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international expansion, and has suggested a tendency towards more 
engagement of Brazilian firms in the world economy. 

Box 1. Brazilian Economic Reforms in the 1990s.

!

! In terms of the  geographic distribution of Brazilian OFDI, the data points 
out two key periods. The first period, which started at the beginning of the 1990s 
and lasted until 2004, showed  that the largest recipients of Brazilian FDI were tax 
haven countries like the British Virgin Islands, the Cayman Islands, and the 
Bahamas, which enjoyed a share of over 70% of the total OFDI. The 
concentration of Brazilian OFDI in tax haven countries can be explained by 
several factors, such as their high levels of regulations and taxes. Thus, moving 
to tax havens used to be a normal outcome of operating in a country with high 
levels of government controls (Stal & Cuervo-Cazurra, 2011). The  year 2005 
registered a shift in the geographic distribution of Brazilian FDI abroad. The 
participation of the tax haven countries decreased  throughout the second half of 
the 2000s, representing in 2010 only 40% of all the Brazilian FDI abroad. 
Meanwhile the second period began, the  share of Europe increased to 42%, and 
it became the largest recipient of Brazilian FDI abroad. The United States also 
increased its participation from 3% in 2001 to 9% in 2012 (BCB, 2014a).

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! To understand the recent growth of Brazilian OFDI and its dynamic, it is 
necessary to go back to the beginning of the 1990s, when most of the 
economic reforms were implemented, and patterns of international 
competitiveness in the country were established. The reforms also created the 
conditions in the domestic market necessary for the internationalization of 
Brazilian firms. The general framework of the reforms was based on three 
fundamental tools: Plano Real, economic openness, and regional integration 
(Cristini & Amal, 2006). The first part of the program was the so-called “Plano 
Real”, which is now regarded as one of the  most important and successful 
strategies in Brazilian history for controlling inflation, reducing the degree of 
external vulnerability, and creating the economic and  political conditions 
needed for long-term stability. The resulting macroeconomic changes in the 
country have created the conditions for a positive investment climate and, 
therefore, for the increased competitiveness of the Brazilian economy. 
! The second part of the plan was a systematic program to facilitate the 
liberalization of foreign trade  and external financial flows. These changes 
provided new opportunities for economic growth but they also exposed the 
country to strong  competition and  new challenges. The third part of the reform 
and stabilization plan was related to the  regional economic integration. The 
creation of the MERCOSUR  contributed significantly to the increase of trade 
among its members, and stimulated  new investments from outside the 
regional bloc. During this period, Brazil went on to establish a basis for the 
expansion of business investment projects of MNCs. Moreover, MERCOSUR, 
which benefited companies through the elimination or reduction of tariff 
barriers, contributed to the international expansion of Brazilian companies. 
This international expansion was not limited to export increase, but also 
manifested itself in a very gradual path through the establishment of sales and 
production subsidiaries.   



THEORETICAL FRAMEWORK AND HYPOTHESES

! In the literature on IB  and management several models have been 
provided to  explain path and internationalization strategies. Dunning (1988) 
combined different perspectives (Buckley & Casson, 1976; Hymer, 1960) from the 
IB  theories into one single framework, which was denoted  as the eclectic 
paradigm. The principal hypothesis on which the  eclectic paradigm of 
international production is predicated is that the  level and structure of a firm’s 
foreign value-adding activities will depend on four conditions being  satisfied. 
These are (Dunning & Lundan, 2008, pp. 99-100): 
! (1) The extent to which it possesses unique and sustainable ownership-
specific (O) advantages vis-à-vis firms of other nationalities in the servicing of 
particular markets or groups of markets. 
! (2) Assuming that condition (1) is satisfied, the extent to  which the 
enterprises perceive it to be in their interest to add to its O  advantages rather 
than to sell them, or their right of use, to independent foreign firms. These 
advantages are called market internalization (I) advantages. 
! (3) Assuming that conditions (1) and  (2) are satisfied, the extent to which 
the global interests of the enterprises are  served by creating, accessing, or 
utilizing their O advantages in a foreign location. 
! (4) Given the configuration of the ownership, location, and internalization 
(OLI) advantages facing a particular firm, the  extent to which a firm believes that 
foreign production is consistent with the long-term objectives of the stakeholders 
and institutions which underpin its managerial and organizational strategy. 
! It is important to mention that while the first, second, and fourth conditions 
are firm-specific determinants of FDI, the third is location-specific and has a 
crucial influence on a host country’s inflows of FDI. In this article, we will limit our 
discussion to  the  issue of location, expressed in terms of the cultural and 
institutional distances between the home and host countries of FDI. Based on the 
different types of advantages, the conceptual framework allows the identification 
of four different types of FDI: resource-seeking, market-seeking, efficiency-
seeking, and strategic-asset seeking (Rugman & Verbeke, 2002).

EMNCS IN THE INTERNATIONAL BUSINESS LITERATURE

! Due to the growing academic and economic importance of MNCs from 
developing countries, several authors have attempted to  document the 
internationalization processes of such firms. Results from these studies reveal 
that incremental behavior is also a feature from the  internationalization of 
emerging markets’ MNCs (EMNCs), and the psychic distance also affects the 
market selection process, even though it does not determine alone, for example, 
the FDI destination (Li,  2003).
! Regarding the extent to which a firm will depend on ownership, 
internalization, and location advantages to internationalize its activities, Li (2003) 
and Lee and Slater (2007) suggest an adaptation for the specific case of EMNCs. 
This can be explained by the fact that these firms often end up  developing 
ownership  advantages over foreign markets, mostly in developed countries, due 
to better access to technology and knowledge. Cuervo-Cazurra (2007) classified 
the MNCs from developing countries as those that seek to develop  ownership 
advantages abroad and those that aim to explore abroad the advantages 
acquired in their domestic market. Those firms that wish to develop  new 
capabilities abroad should choose to establish a foreign subsidiary; either in 
developed  economies, if they seek access to  higher technology; or in developing 
economies, if they aim to obtain access to a country’s abundant resources.

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! The literature on IB  shows that foreign firms face different barriers that 
exist because of different levels of geographic distance, and  psychological, 
cultural, and institutional differences between the  country of origin and host 
countries of their investments (Nachum, 2003; Zaheer, 1995). These barriers are 
often called liability of foreignness (LOF). To overcome the LOF, measured as the 
cost of doing business abroad (Zaheer, 1995) and their disadvantage as 
latecomers, some MNCs can act as a springboard to address firm-specific 
disadvantages via international acquisitions of new assets. 
! In the literature, the main differences in the internationalization patterns 
between developed and developing countries can be  summarized as following: (i) 
EMNCs are based in countries with a low average income per capita and weak 
institutional infrastructure; (ii) EMNCs offer limited ownership  advantages, such 
as technology, brand or other intangibles assets when developing international 
operations; (iii) They are  latecomers (Ramamurti & Singh, 2009) and operate 
differently from MNCs in the countries in which their investments are based; and 
iv) They used to invest in other developing countries, but also in developed 
countries (Sirkin, Hemerling, & Bhattacharya, 2008), acquiring other companies 
as part of their internationalization strategy (Gubbi, Aulakh, Ray, Sarkar, & 
Chittoor, 2010; UNCTAD, 2006).
! Due to the growing academic and economic importance of EMNCs, 
several authors have attempted to establish economic stability, institutional 
change, and culture as key factors that explain the  FDI performance of EMNCs. 
In the present study, we build upon the  existing literature by discussing the  effects 
of institutional and cultural factors on the outward FDI from Brazil. We use the 
concept of CD (Kogut & Singh, 1988) and the six-dimension framework of 
governance developed by Kaufmann, Kraay, and Mastrazzi (2009) to test the 
effect of those factors and their interactions in the host countries on the OFDI 
from Brazil. We investigate the effects of cultural and institutional distance in a 
comprehensive analysis, arguing that OFDI can be constrained by the distance 
from the host countries. However, with the macroeconomic literature in mind, we 
also discuss to what extent such effects can be  moderated by the economic 
performance of host countries.

 EFFECTS OF CULTURE

! The uncertainty associated with conducting business across borders is 
largely represented by the cultural differences between diverse  markets (Doole & 
Lowe, 2008). The psychic distance proposed by the Uppsala model is commonly 
referred to as a factor that constrains the internationalization of firms and makes 
them more likely to enter in to markets that are closer culturally to their home 
market environments. Authors have estimated the effects of culture  on 
international businesses using different indicators. Most of them are related either 
to the CD based on the Hofstede dimensions (Kogut & Singh, 1988), or to 
geographical distance, which is a physical measure mostly used by economists 
to capture the determinants of foreign trade (Angué & Mayrhofer, 2010; 
Ghemawat, 2001).  
! Different studies have  pointed to a positive correlation between CD and 
FDI, as already mentioned by Thomas and Grosse  (2001), which means that the 
higher the  CD between the home and host countries, the more likely it is that 
MNCs will enter into foreign markets through FDI. However, some other studies 
have questioned how relevant the influence of culture really is on FDI flows, 
owing  to the fact that MNCs are usually experienced international firms focusing 
on other advantages besides cultural proximity (Andersson, 2004; Li, 2003; 
Pangarkar & Lim, 2003). Several empirical studies have found strong support for 
a negative correlation between outward FDI from EMNCs and the distance from 
the home market (Cheng & Ma, 2007; Fung et al., 2009), and that outward FDI is 

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positively correlated to cultural proximity between home and host country (like the 
Chinese case; Buckley et al., 2007). However, the relationship  between FDI and 
CD is still controversial.!
! According to the aforementioned literature, it seems that the empirical 
studies are not conclusive  about the effects of the CD on the OFDI, and  even less 
so when regarding the case of emerging MNCs. In the case of emerging  MNCs, 
owing  to their more limited international experience and  limited ownership 
advantages, we  believe that the  correlation between CD and FDI could  be 
positive or negative. However, when one considers that distance may limit the 
competitiveness of firms, and that EMNCs are  seeking to expand their value-
added activities abroad to create and enlarge their ownership  advantages, we 
expect to see a positive correlation between OFDI and CD. Therefore, we will test 
the following two sub-hypotheses:

Hypothesis 1.1 (H.1.1): The  higher the CD between Brazil and host 
country, the higher OFDI will be.

! Ghemawat (2001) suggests that distance between two countries can 
manifest itself along  the cultural, administrative, economic, and geographic 
dimensions. The geographic distance refers to the physical distance existing 
between the countries or geographic spaces in which the partners are operating 
(Angué & Mayrhofer, 2010). However, geographic distance is not simply a 
measurement of the distance between two countries in terms of miles or 
kilometers, but includes other attributes like the physical size  of the country, 
average  within-country distances to borders, access to waterways and the ocean, 
and topography. The geographic distance affects the costs of transportation and 
communications, so it is of particular importance to companies that deal with 
heavy or bulky products, or whose operations require a high degree of 
coordination among highly dispersed people or activities (Ghemawat, 2001). 
Considering that geographic distance can represent specific barriers to  export, 
companies may opt for FDI as an entry mode strategy in foreign countries. 
Therefore, we test the following sub-hypothesis:  

Hypothesis 1.2 (H.1.2): The greater the geographical distance between 
Brazil and host country, the higher OFDI will be.

INSTITUTIONAL DETERMINANTS

! The institutional analysis has evolved significantly in the last two decades, 
particularly its applications in the IB  field. Hotho and  Pedersen (2012) have 
identified three  dominant institutional approaches: New Institutional Economics, 
New Organizational Institutionalism, and Comparative Institutionalism. While 
Organizational Institutionalism mainly focuses on organizational forms and 
organizational practices (Powell & DiMaggio, 1991), New Institutional Economics 
(North, 1990) investigates the implications of the functioning or effectiveness of 
home and host country institutions, and Comparative Institutionalism highlights 
the implications of differences in the  structure and organization of economies for 
multinational companies (Hotho & Pedersen, 2012, p. 237). 
! Since the main objective of the present study is to investigate the 
determinants of Brazilian OFDI and particularly the effect of ID, we limit our 
institutional analysis to a discussion of New Economic Institutionalism. This 
approach asserts that the nature of exchange  processes and the  resulting 
amount of friction are dependent on the institutional context in which they take 
place (Hotho & Pedersen, 2012, p. 240). This implies that the effectiveness or 

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quality of the institutional framework has a direct bearing on the performance  of 
countries and organizations.  
! North (1990, p. 3) defines institutions as “the rules of the game in a society 
or, more formally, [are] the humanly devised constraints that shape human 
interaction”. The author distinguishes between formal institutions, such as rules 
that human beings devise, and  informal constraints, such as conventions and 
codes of behavior. 
! Thus, institutional constraints represent the framework within which human 
interactions take place, and consist of formal written rules as well as unwritten 
codes of conduct that underlie and supplement formal rules (North, 1990, p. 4). 
Formal rules can complement and increase the effectiveness of informal 
constraints. They may lower information, monitoring, and enforcement costs, and 
hence make informal constraints possible solutions to more  complex exchanges. 
Formal rules also may be enacted  to modify, revise, or replace informal 
constraints. 
! For North (1990), the institutions exist to  minimize the  uncertainties present 
in human actions for those who are subject to them. It means that under 
conditions of information asymmetry and limited computational ability, constraints 
reduce the cost of human interaction as compared to a world of no institutions. 
Therefore, the ID measures the  formal and informal institutional constraints that 
shape the decisions MNCs make in order to enter and create value in foreign 
markets. 
! The burgeoning of institutional perspective in the 1990s and 2000s has 
contributed to the understanding not only of the different strategies that firms 
adopt when they go global, but also to the different performances that they 
register in different contexts (Peng, Wang, & Jiang  (2008). One of the  most 
significant contributions in this field is the shift from focusing on cultural 
differences between countries to the broader concept of the ID between the 
home and host countries (Kostova, 1997; Kostova & Zaheer, 1999; Xu & 
Shenkar, 2002). According to Hotho and Pedersen (2012), the introduction of ID 
as a measure  that captures comparative institutional differences offers 
considerable promise in terms of enriching our understanding of how institutions 
affect international business. 
! In the  specific case of our study, we use the world governance indicators of 
the World  Bank (Kaufmann et al., 2009) to measure ID and to assess how much 
the difference between the quality of the institutional environment between the 
home and host countries affects OFDI from Brazil. Therefore, we  conceptualize 
the institutions on a country-wide level, and we understand that institutions are 
constraints that may generate transaction costs and affect the internationalization 
of firms and organizations. In this case, we expect a negative  effect to result from 
the institutional and cultural distances on the multinationality of firms. However, 
such a general prediction is constrained by the dimensions of the institutional 
environment in the home country. For firms from developing countries, distance 
may play a different role in their internationalization.
! The relevance of institutions in the IB  literature began to be noticed when 
the theorists perceived that economic conditions alone could not fully explain the 
competitiveness of a nation’s industry (Amal, Raboch, & Tomio, 2009). Peng et al. 
(2008) consider the institutions as the third leg in the competitive  tripod, in the 
way that a firm’s level of competitiveness is not only a matter of resources 
possessed and industry-specific characteristics, but is also linked to the 
institutional scenario in its home market, which has an influence over local 
business practices. The institution’s role  is related to its ability to improve the 
markets’ structure and efficiency by lessening  transaction and information costs 
and uncertainty and instability levels (Mudambi & Navarra, 2002; North, 1990). 
Bevan, Estrin, and Meyer (2004) understand that both informal institutions and 
government arrangements should affect corporate strategies.

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! Nevertheless, it is important to underline that the  role of institutions also 
has a counter position in the literature; authors like Witt and Lewin (2007) 
understand that poor institutions may drive local firms to international markets in 
order to overcome the  limitations of their domestic barriers. Such behavior can be 
described as institutional escapism (Luo et al., 2009). The authors also accept 
that both approaches co-exist, and that their effects vary between firms and 
industries. While recent studies have indeed  found some  divergent results, the 
institutions mostly have a positive effect on the internationalization of firms from 
developed  countries; but in the case  of developing economies, studies have 
found correlations in the opposite direction (Buckley et al., 2007; Kolstad & Wiig, 
2012).
! In the context of our study, we argue that countries with a high and strong 
quality of governance may positively affect FDI flows, particularly those FDIs from 
countries with weak-institutional performance. According to Cuervo-Cazurra and 
Genc (2008), poor governance makes domestic markets inefficient for business 
activities, due to the instability of the institutional environment. 
! In the case of OFDI from Brazil, which is a country with relatively weak 
institutional performance, we expect to see a positive relationship  between the 
governance quality of the host country and OFDI. 
! Based on the literature review above, we will test the second hypothesis 
that considers the effect of institutional quality and governance in the host country 
on the Brazilian OFDI:

Hypothesis 2 (H.2): Strong institutional host country governance has a 
positive effect on OFDI from Brazil. Therefore, the higher the ID between 
Brazil and the host country, the higher OFDI is.

! To capture the  interaction between institutions and CD, we will test to what 
extent there are complementary or substitutive  relationships in terms of their 
effects on OFDI. A complementary effect means that a higher CD may have a 
positive effect on OFDI if ID is high. A substitutive effect will point to a diverse 
effect on a country’s culture and institutions. Due to the limited expansion of OFDI 
from Brazil, we will consider that their relationship  is more complementary than 
substitutive.    

Hypothesis 3 (H.3): The positive impact of CD on OFDI from Brazil is 
stronger when ID is high.

THE MODERATING EFFECT OF ECONOMIC PERFORMANCE 

! The effects of the economic performance  of the host country have been 
significantly discussed in the literature of FDI determinants (Buckley, 2010; Voss, 
2011). To  report on these effects, authors have used different proxies and 
indicators. Most of the indicators are related to the size  and growth of the 
economy (GDP and GDP per capita), the macroeconomic stability (inflation rate), 
and trade openness (trade flows between the home and host countries, and also 
the exchange rate). GDP is often cited as an indicator of the size and growth 
potential of a country. Several authors have found a significant positive impact of 
GDP over the outward FDI (Amal et al., 2009; Frenkel, Funke, & Stadtmann, 
2004; Kyrkilis & Pantelidis, 2003, 2005). 
! Other authors have also tested the effect of a host country’s GDP on OFDI 
from developing countries. Cheng  and  Ma (2007) pointed out the  positive impact 
of the host country’s GDP on the Chinese FDI. Subramanian, Sachdeva, and 
Morris (2010) studied FDI outflows from India. They found that acquisitions have 
been the predominant mode of entry for Indian firms investing abroad, and that 
seeking new markets has been the primary target of investors. Fung et al. (2009), 

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comparing the case of FDI from China to other Asian MNCs, show that the 
economic performance of the host county was statistically significant. Different 
empirical studies, using  different econometric models, suggest a positive 
correlation between GDP and FDI, which suggests the predominance of the 
market-seeking strategy by MNCs from developing economies.   
! However, other empirical studies come to different conclusions, stating  that 
the GDP’s effects are either positive but not significant (Bae & Hwang, 1997; 
Faria & Mauro, 2009) or negative  (Thomas & Grosse, 2001). According to Faria 
and Mauro (2009), the GDP per capita was more significant than the total GDP. 
Thus the authors regard the per capita income as a better proxy for a nation’s 
aggregated ownership  advantages level due to the  reflex of demand structures of 
the market, since  a higher personal income represents higher levels of demand 
from consumers, which lead firms to offer improved products and services.
! Other macroeconomic variables, such as inflation and interest rates, are 
relevant indicators of the economic stability of countries. High levels for such 
indexes hinder the appeal of investing in a country (Thomas & Grosse, 2001), as 
higher interest rates can reflect a tendency towards higher inflation and, 
therefore, a higher climate of macroeconomic instability, which indicates a 
negative business climate.
! The trade  flows of a nation can be directly related to the local exchange 
rate as both of them have  similar effects on the outward FDI. According to  the 
Uppsala internationalization model (Johanson & Vahlne, 1977), the higher the 
trade flows between two countries, the more likely firms are, through a process of 
learning and knowledge accumulation, to increase their involvements through 
FDI. This implies a positive impact of the economic openness of a country over 
inward FDI. However, authors also suggest that in situations where trade and 
OFDI are negatively correlated, FDI is more likely to replace trade, pointing to 
substitutive relationships (Lim & Moon, 2001). In this article, we  expect to see a 
positive correlation between trade and OFDI, pointing to a strategy of efficiency-
seeking and resource-seeking FDI projects, in which FDI creates an intra-firm 
trade, mostly between raw materials, capital goods, and finished products 
manufactured  under more competitive costs. However, a negative  relationship 
between trade and FDI means that the MNC is more engaged in market-seeking 
projects, since the FDI replaces currently existing exports (Amal & Raboch, 2010; 
Seo & Suh, 2006; Swenson, 2004).
! The impact of the exchange rates also presents a conflicting result in the 
literature regarding  their effects on FDI. In this case, firms can be more or less 
likely to offer FDI depending  on how the  exchange rates affect their goals. Chen, 
Rau, and Lin (2006) argue that firms performing  efficiency-seeking projects might 
wish to invest more abroad in the case of a valued  domestic currency in order to 
reduce production costs. On the other hand, firms willing to conduct market-
seeking projects may rather invest overseas when the domestic currency is 
unvalued, since foreign markets might offer higher profits. Thus, it is reasonable 
to accept that the relationship  between outward FDI, economic openness and the 
exchange rate is dependent upon the FDI’s nature.
! Based on the literature cited above, we  define the economic performance 
of a country as the performance of GDP, GDP per capita, trade openness, and 
real exchange rate variation. We therefore hypothesize: 

Hypothesis 4 (H.4): Brazilian OFDI is positively correlated to the economic 
performance  of the host country, expressed in terms of GDP, GDP per 
capita, trade openness, and exchange  rate variation. The higher the 
economic performance of the host country, the higher FDI flows to that 
country will be, suggesting that Brazilian MNCs show a market-seeking 
strategy.    

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! In the present article, we suppose that host country’s economic 
performance  exerts a moderating effect on OFDI. This implies that the effects of 
ID between the home and host countries are constrained by the size and income 
level of the host country, and also  by the  level of trade relationships between the 
home and host countries. On the other hand, a high CD will exert a negative 
effect on OFDI, no matter the size of the host country.
We therefore suggest the following hypothesis:

Hypothesis 5 (H.5): The positive impact of ID on OFDI from Brazil is 
stronger when the economic performance of the host country is high.

EMPIRICAL MODEL SPECIFICATION AND ESTIMATES

! Based on the literature review above, the outward FDI from Brazil depends 
on the economic performance of the host country, the cultural and  geographical 
distances between the home  and host countries, and  the institutional 
environment in the host country, according to equation 1:

                                 (1)

! Where “c” is the  constant and “ε” is the residual error of the equation. The 
“D”  in the  dependent variable denotes that the  variable is in first differences (see 
Table 4). To add non-linear characteristics on the estimations, every variable is in 
its logarithmic form (denoted by the letter “L”). All variables are represented  by “i”, 
the host country, and “t”, the  time (period). Table 1 presents the main variables, 
with the hypothetical signs and the sources of the collected data.

VARIABLES

Dependent Variable
! The dependent variable is expressed by the stock of outward FDI from 
Brazil to the  host country (for more details on this, see BCB  [2014a]). Data by the 
Central Bank of Brazil is only available  as inward stocks data by host countries. 
We constructed our database using two sources. The first source is the record of 
OFDI in the period from 2001 to 2006 (BCB, 2014b), and the second source 
records the OFDI from 2007 to 2013 (BCB, 2014c).  
! The data is composed of Brazilian annual OFDI ranging from 2002 to 
2012. Our sample is represented by 28 countries in a balanced panel. We 
excluded the tax haven countries, for which we expect that Brazilian MNCs are 
not generating value-added activities (Beugelsdijk, Hennart, Slangen, & Smeets, 
2010). The 28 countries for which we could gather all the data needed to 
estimate the model are mainly located in Latin America, the European Union, 
North America, and China, representing 70% of the main destinations of Brazilian 
OFDI.
! Several authors have  used inward stocks of FDI to record value-adding 
activities of MNE affiliates in the host countries (Dunning & Lundan, 2008; 
Dunning, Fujita, & Yokova, 2007). However, according to Beugelsdijk et al. 
(2010), such measurements may bias the  FDI stocks as a measure of total MNC 
affiliate activity for many reasons. First, since FDI stocks and flows only capture 

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the net financial capital flows (known as the Balance of Payment concept), they 
do  not include all the funds that MNEs raise from host countries with large stock 
and bond markets. On the other hand, not all FDI stocks in the host countries are 
used to  generate affiliate added value in those countries (such as the case of tax 
havens). 
! Despite the restrictions and relative limitations of the  concept of FDI stock 
as a measure, it still captures the capital flows between the home  and host 
countries. On the other hand, since our analysis is based on country-level data, 
the measurement will capture the relative level of involvement of MNEs in the 
host countries.

Independent Variables

Cultural distance. The CD is measured by the index of Kogut and  Singh (1988) 
based on differences in scores for each of the six Hofstede's (1980) cultural 
dimensions (Power Distance [PDI], Individualism versus Collectivism [IDV], 
Masculinity versus Femininity [MAS], Uncertainty Avoidance [UAI], Long-Term 
Orientation [LTO], Indulgence versus Restraint  [IND]) between the country of 
origin of FDI and the host country of FDI, according to the following equation:

                                                                                  (2)
! Where I is the index for one of the six dimensions (i) for the host country (j) 
and (u) for the home country, which is Brazil in this case. The variable V stands 
for the  variance of each dimension of the index. Thus, CD shows the cultural 
difference or distance  between Brazil and the host country of Brazilian FDI. The 
higher the score is, the higher the cultural differences between the two countries.
! This index is calculated by subtracting Brazil’s scores in each of the six 
dimensions from the scores of the recipient country of FDI. The  square  of the 
resulting difference is then divided by the variance of the scores for each 
dimension. The resulting values of the  differences for each dimension are added 
together and then divided by six. The scores were taken from Hofstede’s website 
(www.geert-hofstede.com). However, the cultural effect can also be measured by 
the geographical distance, which is measured using the great circle distance 
between the capital of Brazil and the capitals of the host countries of Brazilian 
OFDI.

Institutional distance. The effect of the institutional framework was calculated 
using the  governance indicators made available annually by the World Bank 
(www.databank.worldbank.org). The indicators are based  on the research by 
Kaufmann et al. (2009), which covered  212 countries and territories and 
measured six dimensions of governance: Voice and  Accountability (VA), 
Regulatory Quality (RG), Rule of Law (RL), Political Stability of Violence/
Terrorism (OS), Government Effectiveness (GE), and Control of Corruption (CC). 
The authors attributed a score of between -2.5and +2.5 to each dimension, with 
higher scores indicating higher levels of the quality of governance.
! Similar to the formula proposed by Kogut and Singh (1988, p. 422), we 
hypothesize that the more institutionally distant the host country is from Brazil, 
the more likely it is that Brazilian MNCs will perform OFDI. Using  Kaufmann et 
al.’s (2009) dimensions of governance, a composite index was formed according 
to the deviation of each of the  six governance dimensions mentioned above of 
each host country from the governance dimensions of Brazil. The deviations were 
corrected for the differences in the variances of each dimension and then 
arithmetically averaged. Thus, in algebraic form, like Kogut and Singh (1988), we 
propose the following index to test the effect of ID on OFDI from Brazil:

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89

http://geert-hofstede.com/dimensions.html
http://geert-hofstede.com/dimensions.html
http://geert-hofstede.com/dimensions.html
http://geert-hofstede.com/dimensions.html
http://geert-hofstede.com/dimensions.html
http://geert-hofstede.com/dimensions.html
http://www.geert-hofstede.com
http://www.geert-hofstede.com
http://www.databank.worldbank.org
http://www.databank.worldbank.org


                                                                        (3)

Where WGIij, stands for the  ithdimensions and jth country, Vi is the variance of 
the index of the ith dimensions, u indicates the home  country of OFDI (Brazil), 
and IDj is the measurement of ID of jth (host country) from Brazil.!

Moderating variables.
! * GDP: Gross Domestic Product refers to  the nominal GDP of the host 
country of Brazilian OFDI and expresses the market size of the host country.
! * GDPPC: GDP per capita refers to the GDP per capita in the host country 
of Brazilian OFDI and is used to assess productivity in the host country.
! * Trade  flows express the bilateral trade between Brazil and the host 
country of Brazilian FDI.
! * Exchange rate refers to  the real exchange rate variation between 
Brazilian currency and  the US dollar. This variable  was calculated using the 
Consumer Price Index (CPIj) and the nominal exchange  rate (NERj) of the host 
country (j), and the  Consumer Price  Index of the United States (CPIUS). The real 
exchange rate equation for the host country is RERj = NERj x (CPIUS / CPIj): 
data from the Brazilian Central Bank (www.bcb.gov.br).
! * To assess the  moderating  effects of economic variables, we calculated ID 
with GDP and bilateral trade flows. 
! Table 1 reports together the variables, hypotheses, expected signs for the 
coefficients, and respective source of the variables.

Table 1. Variables, hypothetical signs, and sourcesTable 1. Variables, hypothetical signs, and sourcesTable 1. Variables, hypothetical signs, and sourcesTable 1. Variables, hypothetical signs, and sources

Variables Hypotheses Hypothetical signs Sources
Outward Flows of Foreign Direct 
Investment (OFDI) Dependent variable

Brazilian Central 
Bank- BCB

Hypothesis H.1 Culture effects
Cultural Distance (CD)1 H.1.1 +/- Hofstede Website

Geographical Distance (GD)2 H.1.2 +/- GlobeFeed

Hypothesis H.2 Institutional Effects
Institutional Distance (ID) H.2: ID effect + World Bank
Hypothesis H.4 Economic Performance Effects
Nominal GDP (GDP) H 4.1 + World Bank

GDP per capita (GDPPC) H.4.2 + World Bank

Trade Flows (TRD) H.4.3 +/- World Bank

Real Exchange Rate (RER) H.4.4 +/- World Bank

Hypothesis H. 3 Interaction 

ID * CD H.3 Complementary or substitutive effects  +/-

Hypothesis H. 5 Interaction 
ID*GDP H.5 +

ID*TRD H.5 +

1. Data for cultural dimensions was obtained from www.geert-hofstede.com/hofstede_dimensions.php  
2. Distance between Brazil (home country of FDI) and the main city (capital or capital’s main airport) 
of the host countries (http://distancecalculator.globefeed.com/).

1. Data for cultural dimensions was obtained from www.geert-hofstede.com/hofstede_dimensions.php  
2. Distance between Brazil (home country of FDI) and the main city (capital or capital’s main airport) 
of the host countries (http://distancecalculator.globefeed.com/).

1. Data for cultural dimensions was obtained from www.geert-hofstede.com/hofstede_dimensions.php  
2. Distance between Brazil (home country of FDI) and the main city (capital or capital’s main airport) 
of the host countries (http://distancecalculator.globefeed.com/).

1. Data for cultural dimensions was obtained from www.geert-hofstede.com/hofstede_dimensions.php  
2. Distance between Brazil (home country of FDI) and the main city (capital or capital’s main airport) 
of the host countries (http://distancecalculator.globefeed.com/).

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http://www.geert-hofstede.com/hofstede_dimensions.php
http://www.geert-hofstede.com/hofstede_dimensions.php
http://distancecalculator.globefeed.com
http://distancecalculator.globefeed.com
http://www.bcb.gov.br
http://www.bcb.gov.br


Table 2. Correlation matrix and descriptive statisticsTable 2. Correlation matrix and descriptive statisticsTable 2. Correlation matrix and descriptive statisticsTable 2. Correlation matrix and descriptive statisticsTable 2. Correlation matrix and descriptive statisticsTable 2. Correlation matrix and descriptive statisticsTable 2. Correlation matrix and descriptive statisticsTable 2. Correlation matrix and descriptive statisticsTable 2. Correlation matrix and descriptive statisticsTable 2. Correlation matrix and descriptive statisticsTable 2. Correlation matrix and descriptive statistics

Variables 1 2 3 4 5 6 7 8 9 10

1. LCD 1

2. LGD 0.68*** 1

3. LID 0.41*** 0.49*** 1

4. LGDP 0.48*** 0.74*** 0.36*** 1

5. LGDPPC 0.40 0.64*** 0.59*** 0.65*** 1

6. LTRD 0.04 0.13** -0.11** 0.64*** 0.13** 1

7. LRER 0.00*** 0.00 -0.02 -0.19*** -0.21*** -0.34*** 1

8. LIDxLCD 0.20*** -0.34*** -0.22*** -0.20*** -0.20*** 0.11* -0.02 1

9. LIDxLGDP 0.40*** 0.48*** 1.00*** 0.35*** 0.58*** -0.11* -0.01 -0.20*** 1

10. LIDxLTRD 0.41*** 0.49*** 1.00*** 0.36*** 0.58*** -0.09 -0.02 -0.23*** 1.00*** 1

Mean -0.13 8.69 0.04 26.32 9.59 21.84 4.59 0.28 1.66 0.75

Median 0.08 8.90 0.28 26.51 10.09 21.99 4.51 0.14 7.35 6.15

Std. Dev. 0.75 0.62 0.93 1.74 1.27 1.41 0.21 0.60 24.54 20.43

Observations 308 308 308 308 308 308 308 308 308 308

* Significant at 10%. ** Significant at 5%. *** Significant at 1%.* Significant at 10%. ** Significant at 5%. *** Significant at 1%.* Significant at 10%. ** Significant at 5%. *** Significant at 1%.* Significant at 10%. ** Significant at 5%. *** Significant at 1%.* Significant at 10%. ** Significant at 5%. *** Significant at 1%.* Significant at 10%. ** Significant at 5%. *** Significant at 1%.* Significant at 10%. ** Significant at 5%. *** Significant at 1%.* Significant at 10%. ** Significant at 5%. *** Significant at 1%.* Significant at 10%. ** Significant at 5%. *** Significant at 1%.* Significant at 10%. ** Significant at 5%. *** Significant at 1%.* Significant at 10%. ** Significant at 5%. *** Significant at 1%.

! Based on country data, we estimated a panel model for Brazil. According 
to Raj and Baltagi (1992), the panel data technique is used when observations in 
cross sections and time series are taken into account simultaneously. The 
advantage of the method, according to Baltagi (1992), is that it allows a level of 
specification that helps to identify an economic model that may offer tighter 
control over individual heterogeneity. On the other hand, in reducing the effects of 
multicollinearity among the independent variables, the panel technique  increases 
the efficacy of the estimations. Table 2 shows the correlation matrix for the 
dataset we used and the descriptive statistics.

!

! The table of the correlation matrix shows a low level of correlation between 
the variables. Only the interaction variables present high correlations. In the 
estimated panel model, we isolated the variables in order to account for 
multicollinearity effects. It is worth noting, as pointed out by Hsiao (2003, p. 311), 
that one of the  main benefits of panel data is that it attenuates the problem of 
multicollinearity by increasing the degrees of freedom, which is possible because 
the panel technique augments the dataset by combining several cross-section 
units and periods (time series). Moreover, the addition of a logarithm has also 
helped, because it seems that the strong interrelations of the variables were 
related to linear relations, which are eliminated by incorporating the nonlinear 

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operations. This means that better data and a bigger dataset is one of the ways 
of eliminating multicollinearity, as noted by Baltagi (2011, p. 76).
! Before discussing the results of the model’s estimation, some preliminary 
issues have to be addressed. First, panel unit root tests were computed to check 
whether the variables are stationary in level or not. The results of these  tests are 
shown in Table 3.

Table 3. Panel unit root testsTable 3. Panel unit root testsTable 3. Panel unit root testsTable 3. Panel unit root testsTable 3. Panel unit root testsTable 3. Panel unit root testsTable 3. Panel unit root testsTable 3. Panel unit root testsTable 3. Panel unit root tests

Tests Levin, Lin & ChuLevin, Lin & Chu Im, Pesaran and ShinIm, Pesaran and Shin ADF - Fisher Chi-squareADF - Fisher Chi-square PP - Fisher Chi-squarePP - Fisher Chi-square

Variables Level First diff. Level First diff. Level First diff. Level First diff.

LOFDI 0.89 -15.45*** 3.50 -10.81*** 31.55 212.00*** 49.33 271.00***

LID -6.27*** -11.90*** -2.50*** -6.04***  82.61*** 137.28*** 69.21*** 142.82***

LGDP -6.58*** -13.48*** -1.65** -6.39*** 80.51** 143.79*** 215.72*** 177.82***

LGDPPC -6.29*** -11.72*** -1.51* -5.99*** 88.20*** 136.19*** 217.33*** 187.14***

LTRD -3.71*** -14.56*** 1.546 -8.71*** 26.07 188.26*** 26.84 259.97***

 LRER -5.54*** -2.99*** 0.90 -2.76*** 36.58 80.19** 73.15 69.34

LIDxLCD -6.27*** -11.90*** -2.50*** -6.04*** 82.61*** 137.28*** 69.21*** 142.82***

LIDxLGDP -6.59*** -11.85*** -2.92*** -5.98*** 87.50*** 136.35*** 70.80* 141.68***

LIDxLTRD -6.86*** -11.66*** -3.13*** -6.01*** 90.92*** 136.72*** 72.39* 139.22***

* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the variables LCD and LGD, it was not possible to 
calculate the panel unit root tests due to their time invariance characteristic.
* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the variables LCD and LGD, it was not possible to 
calculate the panel unit root tests due to their time invariance characteristic.
* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the variables LCD and LGD, it was not possible to 
calculate the panel unit root tests due to their time invariance characteristic.
* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the variables LCD and LGD, it was not possible to 
calculate the panel unit root tests due to their time invariance characteristic.
* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the variables LCD and LGD, it was not possible to 
calculate the panel unit root tests due to their time invariance characteristic.
* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the variables LCD and LGD, it was not possible to 
calculate the panel unit root tests due to their time invariance characteristic.
* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the variables LCD and LGD, it was not possible to 
calculate the panel unit root tests due to their time invariance characteristic.
* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the variables LCD and LGD, it was not possible to 
calculate the panel unit root tests due to their time invariance characteristic.
* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the variables LCD and LGD, it was not possible to 
calculate the panel unit root tests due to their time invariance characteristic.

! Thus, only one variable is non-stationary in level, i.e. LOFDI. The panel 
was estimated through a two-way random-effects model based on the outcomes 
of the Hausman test (see Table 3). This indicates that the null hypothesis of 
consistent estimators for the random-effects model cannot be rejected. In 
addition, the estimation also opted for corrections regarding heteroskedasticity 
and autocorrelation using the White  Period correction, which is used for datasets 
with a large number of cross-sections. 
! On the other hand, we adopted a step-wise technique to run five models to 
test the above-discussed hypotheses. The first model tests only the cultural and 
geographical variables. The second and third models will test, respectively, the 
effects of ID and its interaction with CD. The fourth and fifth models will test, 
respectively, the effects of the interactions, respectively between ID and GDP, 
and ID and trade. We will include the economic performance  variables as 
moderators in all five models. Table 4 reports the model estimations.

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Table 4. Model estimationsTable 4. Model estimationsTable 4. Model estimationsTable 4. Model estimationsTable 4. Model estimationsTable 4. Model estimations
Regression Model I Model II Model III Model IV Model V

Constant 0.8673 0.2808 0.3473 0.2759 0.2777

LCD 0.0546

LGD -0.1519*

LID 0.0557** 0.0591***

LGDP 0.0911** 0.0397 0.0607** 0.0409* 0.0413*

LGDPPC -0.0033 -0.0234 -0.0316 -0.0226 -0.0206

LTRD -0.0885** -0.0501* -0.0714*** -0.0514* -0.0529*

LRER 0.0621 0.0517 0.0292 0.0507 0.0512

Interactions
LIDxLCD 0.1023**

LIDxLGDP 0.0020**

LIDxLTRD 0.0022**

R² 0.0122 0.0115 0.0138 0.0113 0.0109

DW 2.3901 2.3888 2.4040 2.3884 2.3877

Hausman Test (Cross-section and 
period random)

3.8242
(0.2811)

6.4341
(0.1690)

8.9117
(0.1126)

6.4105
(0.1705)

6.0938
(0.1922)

* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the Hausman test, p-value is 
between brackets.

* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the Hausman test, p-value is 
between brackets.

* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the Hausman test, p-value is 
between brackets.

* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the Hausman test, p-value is 
between brackets.

* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the Hausman test, p-value is 
between brackets.

* Significant at 10%. ** Significant at 5%. *** Significant at 1%. For the Hausman test, p-value is 
between brackets.

! The overall results of the estimated models show the relatively stable 
behavior of the variables. Although the values of the coefficient of determination 
R2 are relatively low (but closer in all model alternatives) it seems that Models III 
and IV that include ID, interaction between institutional and cultural distance, and 
the interaction of ID with the  GDP have the best predictive power. This 
assessment is based on the analysis of the coefficient of determination R2, which 
registered a value of 0.0138 and 0.0113, with the values of the F-statistics 
indicating 8.9 and 6.41 respectively, the highest among all other alternatives of 
model regressions.
! According to the results of Model I, it seems that Brazilian OFDI is 
positively correlated to the size of the host country as measured by the GDP, and 
is statistically significant at 5%. The higher the  GDP of the  host country is, the 
higher the flows of Brazilian FDI to that country are. Bilateral trade between Brazil 
and its host countries has been found to be negatively correlated  and is 
statistically significant at 5%, pointing to a more substitutive relationship with FDI.  
The results suggest that the investments of Brazilian MNCs are  more 
concentrated in larger economies, and  that, due to the negative coefficient of 
bilateral trade, the results indicate a substitutive relationship  between trade and 
OFDI. This suggests that in situations where there are some trade barriers, 
Brazilian firms will be quick to  follow a strategy of FDI to enter into those markets. 
The results give some  support for the market-seeking hypothesis, suggesting that 
the investments of Brazilian MNCs are  probably intended to meet a growing 
demand  in the host markets. Exchange rate and GDP per capita were not found 
to be statistically significant.

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! The model estimation has also shown that geographical distance was 
statistically significant at 1% and presented opposite patterns of correlation to the 
Brazilian OFDI. Thus, according to the results of Model I, it seems that Brazilian 
OFDI are more  likely to perform well in geographically closer markets, which 
means that the closer the host country is geographically, the more likely Brazilian 
MNCs will be to enter into those markets through FDI. CD was not found to be 
statistically significant. 
! The results point to two possible implications. The first is that when the 
geographical distance is shorter, the more  likely Brazilian MNCs will be to invest 
in sales and/or production subsidiaries. A shorter geographical distance suggests 
relatively closer cultural behavior, which will make it easier to overcome the 
liability of foreignness. In this case, we expect that Brazilian MNCs may have  the 
knowledge and experience to assume more risks and to commit resources in 
order to develop  a market-seeking strategy. The second  implication is that when 
the distance between the home and host countries is shorter, the higher OFDI to 
that country will be. This result suggests a regional involvement of MNCs that can 
be influenced by the advantages related to geographic proximity.
Thus, the results confirm Hypothesis 1.2, suggesting that Brazilian OFDI is 
positively correlated to geographical proximity. However, we could find  no 
evidence of the  effect of cultural proximity (no support for Hypothesis 1.1). On the 
other hand, the size of the host economy has been found to be positively 
correlated with OFDI, pointing to a more market-seeking strategy of Brazilian 
MNCs, and that bilateral trade and OFDI are substitutive strategies.  
! Model II presents a positive correlation between OFDI and ID. This 
relationship  was found to be statistically significant at 5%. This result suggests 
that the international involvement of Brazilian MNCs is more likely to occur in 
countries that show an improved institutional environment in terms of business 
climate, political stability, existing rules or laws, and government effectiveness. A 
socially and politically stable country which demonstrates transparency and clear 
rules will also reduce  the risks related to cross-border added-value transactions, 
and will therefore  reduce the  impact of the LOF. It means that a positive 
institutional environment positively affects the investment strategies of MNCs 
from Brazil, since tighter regulation leads firms to reduce uncertainty and 
transaction costs, making them more competitive in foreign markets. The results 
of Model II support Hypothesis H.2.
! In Model III we tested the effect of the interaction between CD and ID on 
OFDI. The model estimate has shown a positive correlation, and was statistically 
significant at 5%. The model also  presents a positive correlation between ID and 
OFDI. These results point to  an important implication. Brazilian OFDI is positively 
correlated to ID: the higher ID is, the higher OFDI will be. However, Brazilian 
firms are more likely to invest in culturally distant countries when the institutional 
environment in those countries offers a better level of performance than in the 
home country. Thus, we found positive support for Hypothesis 3 about the impact 
of interaction between ID and CD.  
! Model IV presents a positive and statistically significant (5%) correlation 
between OFDI and the  interaction between the ID and GDP of the host country. 
Model V also shows a positive and statistically significant (5%) correlation 
between OFDI and the interaction between ID and bilateral trade. The results 
suggest that the positive  effect of ID on OFDI is constrained by the economic size 
(GDP) of the host country, and by the importance of bilateral trade between the 
home and host countries. This result gives support for Hypothesis H.5, which 
considers the moderating  effect of the economic performance of the  host country 
on OFDI.

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DISCUSSION AND FINAL REMARKS

! The internationalization of Brazilian MNCs is to a large extent related to the 
market and  institutional reforms that have shaped the country since  the beginning 
of the 1990s. Those reforms and institutional changes positively affected the 
investment climate in the country by attracting MNCs from the  USA and Europe 
to take part in specific projects in the infrastructure and communications 
(privatization), and in developing new greenfield projects in order to meet the 
needs of a growing domestic market. The implication of these changes and the 
engagement of MNCs in Brazil during the 1990s have also stimulated the 
domestic firms to engage in the foreign markets. According to the Investment 
Development Path, the development level of a country encourages FDI inflows 
and stimulates specific ownership  advantages for local firms over time. This 
mechanism can speed up  the internationalization process while fostering the 
development of corporate skills and competencies for managing plants in a 
variety of markets. 
! The home market factors can be  seen as key drivers of the international 
expansion, largely explaining the pattern of the internationalization of Brazilian 
firms. These home market factors have shaped the  form and intensity of how 
EMNCs approach foreign markets, and their own performances in the host 
countries. Thus, studying  the case of Brazilian OFDI represents an important step 
in understanding the development of MNCs from emerging  countries, and the 
extent to which it may present differences to the mainstream theories of IB.    
! In this paper, we have attempted to address the determinants of Brazilian 
OFDI from the perspective of the host countries in order to  assess their economic 
potential. However, we also wanted to establish the impact of cultural and 
institutional distances on OFDI. This represents an important contribution in 
understanding the dynamic of MNCs from emerging countries.
! Our empirical analysis reveals that the economic performance of the host 
country is a significant factor to be considered in the internationalization strategy 
of EMNCs. The economic performance of the host country is connected not only 
to the size and growth of the economy, but more specifically to its trade 
openness, suggesting that MNCs are quicker to develop  substitutive strategies of 
entering  into foreign markets. On the other hand, the estimation of the model, 
different from previous empirical studies (Buckley et al., 2007; Cheng & Ma, 
2007; Fung  et al., 2009), has shown that cultural proximity does not affect the 
internationalization of Brazilian MNCs. But there is a negative correlation 
between geographical distance and OFDI, suggesting that Brazilian OFDI flows 
more easily between countries which are closer geographically, pointing to a 
more regionally oriented internationalization.
! However, unlike previous studies (Buckley et al., 2007; Kolstad & Wiig, 
2012), we found a positive correlation between the institutional environment in 
the host countries and  Brazilian OFDI. This suggests a different pattern of 
investment by Brazilian MNCs compared to Asian MNCs, since it has been 
established that Chinese OFDI are more attracted to countries with poor 
institutions, and  that the limitations of these institutions is moderated by the 
endowment of natural resources in the host country (Aleksynska & Havrylchyk, 
2013), or by the support of the government in selecting the location (Lu et al., 
2014).
! According to  the assumption of Hymer (1960), firms will invest abroad 
because of their ability to compete in other markets, due to the fact that they have 
specific ownership  advantages (Dunning, 1988) which allow them to enter into 
new markets through FDI instead of developing their value-added activities 
through export transactions. In Zaheer’s (1995) approach, firms will be able to 
overcome the transaction costs related to  foreign market entry and therefore they 

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will overcome the LOF because of the specific assets they have developed to 
sustain their global competitiveness. This is a fundamental assumption of MNC 
theory in the IB  literature. It seems, according to the results of our panel model, 
that Brazilian OFDI is also positively correlated  to the CD, but it was not found to 
be  statistically significant. The higher the CD between Brazil and the host country, 
the higher OFDI is to that country. In this way, MNCs from Brazil follow the same 
behavior as MNCs from developed countries. It means, however, that Brazilian 
MNCs hold  specific ownership  advantages to overcome  the LOF, even though 
the restrictions and limitations of competitiveness are  related to  the home 
market’s constraints. This is an important finding that may suggest that despite 
the differences in the level of development in the countries from which the OFDI 
originates, firms in these different countries are all quick to show the  same 
inclination towards international involvement.
! In our model, we also tested the effect of ID on OFDI. In the IB  literature, a 
weak institutional environment represents a high level of market imperfection, 
macroeconomic and political instabilities, and therefore, a high risk for 
investment, and for FDI in particular. Since the end of the 1980s, most countries 
from Eastern Europe as well as other developing countries have set up  important 
economic and political reforms, and have adopted specific measures that create 
an open climate for foreign firms. The economic reforms and political stability in 
the developing  countries stimulated significant FDI inflows, and could contribute 
to the economic growth and development in those countries. In the FDI literature, 
several authors have pointed to a positive correlation between market reforms, 
improved  institutional environments, and inward  FDI (Mudambi & Navarra, 2002). 
However, the relationship  between institutions and FDI is still controversial. 
Authors have suggested assessing this relationship  using ID indicators (Kostova, 
1997). The idea to focus on the distance  has contributed to shifting the 
discussion from macro-level analysis to  firm-level analysis. It means that authors 
have focused much more on how MNCs cope with the ID, and less on their 
effects on FDI projects. 
The growing flows of FDI from developing countries have been an important 
milestone in the world FDI patterns in the 2000s. Several studies (Cuervo-
Cazurra, 2007; Ramamurti & Singh, 2009) have pointed out the differences 
between MNCs from developed and developing countries due to the differences 
in the home countries’ conditions of competitiveness (Cuervo-Cazurra, 2012; 
Hennart, 2012; Narula, 2012). The results of the  studies have suggested that the 
effects of institutional environment in the host country are controversial, and that 
there are differences among  developing  countries as well as in developed  ones. 
Chinese OFDI (Amal & Raboch, 2010; Kolstad & Wiig, 2012) has shown a 
negative correlation with its institutional environment. The weaker the institutional 
environment in the host country, the higher OFDI is, suggesting  that MNCs from 
developing countries are able to cope with unstable institutions due to  the 
knowledge they have accumulated in their home countries about how to deal with 
factors such as corruption, political instability and accountability.
! Moreover, the model points to a positive correlation between OFDI and ID 
(we  used a composite index to  record the relationship). It means that the higher 
the ID between Brazil and the host country, the higher OFDI is to that country. 
Considering that Brazil has a low institutional performance, according to the 
governance indicators of the World Bank, a higher ID means that the host country 
performs better in terms of governance indicators, and therefore, that the 
investment climate is more favorable than in the home  country. The empirical 
finding shows that MNCs from Brazil are quicker to invest in countries that offer 
better institutional environments. However, it seems that such relations between 
ID and OFDI are restrained  by the economic performance of the  host country and 
by the importance of bilateral trade. This may suggest three  important 
conclusions.

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! First, that the FDI strategy of Brazilian MNCs is a more market-seeking 
strategy. Second, that OFDI in an institutionally distant country may be 
constrained by the  flows of trade between the two countries, pointing to a 
relatively gradual process of commitment of Brazilian MNCs as a way to  cope 
with ID. Finally, in the IB  literature, very few attempts have been made  to assess 
the interaction between cultural and institutional distances. Some authors (Xu & 
Shenkar, 2002) consider that ID is a much broader concept, and is therefore 
much more efficient at reflecting the differences among countries than the 
concept of CD. Scott (2001) considers CD as one of the three pillars of 
institutions (cognitive-cultural pillar). In our panel model, we create a variable 
(CDxID) to  assess the interaction between them. The model estimation has 
pointed to a positive correlation between OFDI and CDxID. This finding suggests 
that MNCs from Brazil are quicker to invest in culturally distant countries that 
present better institutional performances than Brazil, which suggests that there is 
a complementary relationship between the two variables (culture and institutions).
Managerial Implications
! What are the main lessons that we draw from the case of Brazil in terms of 
understanding the internationalization patterns of emerging MNCs? The 
estimation of the models has shown that OFDI is highly correlated to the 
economic performance of the host countries, suggesting a more  market-seeking 
strategy, unlike the results of some empirical studies about Asian MNCs, which 
have pointed to more efficiency and resource-seeking  strategies. In this case, we 
suggest that the FDI strategy of Brazilian MNCs is less oriented to create 
capabilities and ownership  advantages in the host markets and is more focused 
on opportunities of growth. The other characteristic of the  Brazilian MNCs 
suggested  by the  results of the panel model is that institutions do matter. This 
finding suggests that Brazilian MNCs, due to their limited global experiences and 
knowledge about foreign markets, prefer to invest in distant countries with a 
positive institutional environment. This feature of Brazilian MNCs is not in line 
with the findings relating to Asian MNCs. 
! Such results point to some differences in the international involvement 
behavior of Brazilian MNCs. First, like MNCs from developed countries, they do 
enter into foreign markets through FDI in culturally distant countries. This pattern 
of international commitment suggests that they also hold specific ownership 
advantages that enable them to overcome the LOF. However, our results suggest 
that such behavior is more likely to occur when culturally distant host countries 
present a better institutional environment than the home country. In other cases, 
distance does matter, and Brazilian MNCs will be more ready to invest in 
geographically closer countries (like Latin American countries). This finding is in 
line with the general assumption that location constrains the ownership  assets of 
MNCs from developing countries, and therefore limits the international 
competitiveness and expansion of Brazilian firms. On the other hand, Brazilian 
MNCs are quick to invest in distant countries when the institutional environment 
in the host countries is better than in Brazil. A better institutional environment will 
reduce the cost of transactions, and therefore will create better conditions for 
firms from developing countries to learn about the foreign markets, and to 
develop or enlarge their ownership advantages through different entry modes.      
! The results of the estimation model present some positive and some 
negative implications. Firstly, the existing theories of IB  can explain many of the 
differences in the  patterns of emerging MNCs. More specifically, the eclectic 
paradigm and institutional theories have been shown to be relevant in terms of 
explaining the role of the  potential host market, and  how institutions affect 
internationalization strategies. On the other hand, due to the very short time 
series of Brazilian OFDI, the results of the estimation models are more 
suggestive, and point more to  tendencies and trends, and not to  differences 
among firms, which is an important limitation of this study. In terms of 

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investigating this issue further, we would suggest conducting a more empirical 
analysis based on firm data, as well as developing comparative studies among 
MNCs from different emerging economies. 

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Mohamed Amal, PhD in economics engeneering,is associate professor of 
International Economics and Business Administration at Regional University of 
Blumenau (FURB), Brazil. 

Bruno Thiago Tomio, Master of Arts in International Economics, is professor of 
Economics at Regional University of Blumenau (FURB), Brazil.  

© The author(s)
www.management-aims.com

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