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UPPSALA UNIVERSITY
Department of Business Studies
Master Thesis
Spring Semester 2012
Swedish FDI in Africa Locational determinants of FDI from the
perspective of the OLI paradigm
Authors: Martin Boman & Christian Hellqvist Supervisor: Philip Kappen
Date of submission: 2012-05-25
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Abstract
The global flows of foreign direct investment (FDI) to Africa have increased steadily in recent
years but the research on what determines the location of these investments is scarce.
Research focusing on FDI flows from small and open economies such as Sweden is even
more uncommon. From the locational factors found in the OLI paradigm we developed a
model that was tested on a dataset of 25 African countries over the period of 2007 to 2010.
The model proved inadequate in explaining the African inward FDI flows from Sweden.
However, it well explains the aggregated inward FDI flows from firms around the world to
Africa. Our results implies that the locational determinants derived from the OLI paradigm
are inadequate in explaining Swedish FDI flows to Africa and maybe even in explaining
flows from a small and open economy to developing countries. The answer to the question of
what locational determinants are important for Swedish companies investing in African
countries should perhaps be sought for elsewhere.
___________________________________________________________________________
Keywords: Africa; Swedish multinational firms; inward FDI; the OLI paradigm; locational
determinants of FDI flows
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Table of contents 1. Introduction ............................................................................................................................ 4
2. Foreign direct investment ....................................................................................................... 6
2.1 Theoretical underplays and rationale ................................................................................ 6
2.1.1 The OLI paradigm ...................................................................................................... 6
2.2 Location-specific determinants of inward FDI in Africa: hypotheses ........................... 10
3. Data and method ................................................................................................................... 14
3.1 Operationalization of independent variables .................................................................. 17
3.2 Control variables ............................................................................................................. 18
3.3 Data considerations and the models ............................................................................... 23
4. Results of the empirical analysis .......................................................................................... 26
5. Discussion of the results ....................................................................................................... 28
5.1 Other approaches ............................................................................................................ 33
6. Conclusion, implications and future research ...................................................................... 35
References ................................................................................................................................ 38
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1. Introduction In a global perspective, six of the ten fastest-growing countries the last decade were African
(Economist, 2011). During the same time there has been a rising inflow of foreign direct
investment (FDI) to the African countries from the entire world (see Figure 1, World Bank,
2011 a). African countries are perceived as the most attractive region for future investments
according to a newly performed global survey (Svd, 2012). FDI to primary sectors, mainly
coal, oil, and gas, is still dominant in Africa and has attracted an increasing amount of Asian
investors, not least Chinese and Indian (Unctad, 2011). FDI inflows to the primary sector in
Africa accounted for 43 % of total inward FDI, and manufacturing accounted for 29 % in
2011 (Unctad, 2011). Foreign firms are however interested in Africa for other reasons as well.
A growing middle class could also be an important factor for the increasing interest of the
region among investors (Svd, 2012).
Figure 1 - FDI Africa 2000-2010, net inflows current US$ (World Bank, 2011 a)
Investments in Africa are made from countries all around the globe and Sweden is no
exception (DN, 2009). Sweden is an economy with a relatively small domestic market and
many Swedish firms must therefore explore and compete on international markets in order to
grow (Swedish Trade Council, 2012). Among the top economies in global FDI outflows,
Sweden ranks number 12 in absolute numbers after countries such as USA, Germany, France,
and China (Unctad, 2011). Swedish companies are still relatively small investors in the
African region but a rising trend is evident (DN, 2009). There are about 100 Swedish
corporate groups with subsidiaries in Africa (Swedish Agency for Growth Analysis, 2012).
The potential of the region for Swedish firms is big and an understanding of the importance of
Africa has been developed among Swedish investors (Government Offices of Sweden, 2011).
However, Africa is a vast continent including many countries with different characteristics
(Handelsbanken, 2012).
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An important question for managers is to decide where in Africa to invest. From a theoretical
viewpoint the locational determinants of FDI has been widely discussed (Luiz &
Charalambous, 2009). One theoretical approach that is highly accepted and relevant is the
OLI paradigm (Luiz & Charalambous, 2009). This is an approach that combines ownership-
specific advantages (O), location-specific advantages (L), and internalization advantages (I)
(Dunning & Lundan, 2008). However, we find that there is an obvious lack of research
applying the OLI paradigm, and especially the L dimension, on the African continent.
Additionally, the research on locational determinants for Swedish companies investing in
Africa is even scarcer. As a result, an important conundrum has been left uncharted, both
from a theoretical and empirical view. To remedy this, we intend to explore the locational
determinants of inward FDI in Africa from, the small and open economy, Sweden. To
understand which determinants are important for Swedish companies is also interesting from
a managerial perspective, since this could help answering the question of where to invest or at
least give a base for discussion when making locational investment decisions. Thus, the
purpose of this study is to explore which factors are important for Swedish firms when
deciding where in Africa to invest. This will be sought for in the OLI paradigm with focus on
the L dimension. This results in the following research question: What are the locational
determinants of inward FDI to African countries from Sweden? The expected contribution of
this paper, from a theoretical perspective, is to start filling the research gap of inward FDI in
Africa from Sweden. The expected practical contribution is to extend the understanding of
what determines the location of Swedish FDI in Africa and thus hopefully give managers a set
of factors to consider when making internationalization decisions regarding this continent.
This paper is organized in the following way. First we review the general theories of FDI with
a focus on the OLI-paradigm including a description of the four types of FDI that can be
derived from this theoretical approach. We continue by describing which locational factors
that can be argued to be the most important determinants for inward FDI in an African
context, and formulate hypotheses on their ability to explain the FDI flows. We develop two
models, one focusing on within-year variation and one that does not. First we test the models
in regression analysis using official data on FDI flows from Sweden to Africa to test the
hypotheses and to see if the models are valid in that specific context. Second, we do the same
regressions but with a dataset of FDI flows from the entire world to test the robustness of the
models. In the regression analysis we also control for variables that may affect FDI but are not
included in the hypotheses. The two models show highly similar results which suggest no
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major problem with between-year variation. In this study, the locational dimension of the OLI
paradigm could not manage to explain Swedish FDI flows to Africa but it well explains FDI
inflows from the world. The results could therefore not support the hypotheses. Swedish firms
seem to be a special case with different preferences than the rest of the world. We conclude
by recommending future researchers to further investigate this matter.
2. Foreign direct investment
2.1 Theoretical underplays and rationale Foreign direct investments are investments with the intent to acquire a lasting management
interest in a firm operating in a country other than that of the investor (World Bank, 2011 j).
This kind of investment behavior by multinational firms is a topic that has been widely
discussed for a long time (Luiz & Charalambous, 2009). From the viewpoint of industrial
organizational theory, Hymer (1976) reasoned that a firm invests abroad when it has a firm-
specific advantage that outweighs the disadvantages that may exist compared to host country
firms. Hymer’s theories mainly tried to answer the question why firms internationalize
(Forsgren, 2008). A more complete view of FDI flows must thus be sought for elsewhere.
Forsgren (2008) states that the research that ended up in what has been called internalization
theory mainly deals with the question “how?”, that is: in which situations does a firm chose to
internalize their operations. He continues by explaining that the answer is developed from
transaction cost theory and claims that a firm internalizes operations due to market pricing
inadequacies rising from uncertainty. When the gains are greater than the costs, a firm will
internalize their abroad facilities and operations (Forsgren, 2008). However, neither of these
theories thoroughly discuss the question of where a firm will internationalize. Luiz and
Charalambous (2009) argue, among others, that one theoretical approach that is established
and relevant when discussing determinants of FDI flows is the OLI paradigm developed by
John H. Dunning. The OLI paradigm is a combination of Hymer’s firm-specific advantages,
internalization advantages, and location-specific advantages (Forsgren, 2008). It constructs a
thorough view of the concept of foreign direct investments (Forsgren, 2008; Luiz &
Charalambous, 2009). This paradigm is explained in the following section.
2.1.1 The OLI paradigm Dunning and Lundan (2008) state that the OLI paradigm seeks to offer a general framework
for determining the pattern of foreign direct investments. It covers various explanations of the
activities of firms engaging in FDI (Dunning & Lundan, 2008). The paradigm has been
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developed and modified since its creation in order to adapt to changing behavioral patterns of
multinational firms (Dunning, 2001). Stoian and Filippaios (2008) state that one problematic
characteristic of the OLI-paradigm is its generality that makes it necessary to apply the
paradigm into a specific context to be able to explain FDI flows. Despite this fact, the OLI-
paradigm has stayed as the most important theoretical approach for empirical studies
regarding FDI determinants during a long period of time (Stoian & Filippaios, 2008). It
consists of three types of advantages that all must be present at the same time in order for FDI
to take place (Dunning & Lundan, 2008):
1). (O) Ownership-specific advantages - A firm must possess ownership-specific advantages
compared to firms in the potential host country (Dunning, 2000; Dunning & Lundan, 2008). It
is critical that these advantages are durable, unique and irreplaceable (Dunning & Narula,
2004). These advantages are often based on intangible assets, shared governance and
coordination of activities across borders that result in value being added to the firm (Dunning
& Lundan, 2008). Examples of ownership-specific advantages are production techniques and
entrepreneurial skills (Twomey, 2002).
2). (L) Location-specific advantages - A firm must be able to create, utilize, or access their
comparative advantages in a foreign country (Dunning & Lundan, 2008). This is dependent
on location-specific advantages such as natural resources and low cost labor (Dunning &
Narula, 2004; Twomey, 2002). Dunning and Narula (2004) state that these factors are
especially important for developing and resource-rich countries. Location-specific advantages
are, as the name implies, tied to a specific location rather than being firm-specific (Dunning &
Lundan, 2008). Countries that possess such resources or advantages will be more attractive
for foreign firms when deciding where to locate their FDI (Dunning & Lundan, 2008).
Political and institutional stability, and access to customers also play important roles in
attracting FDI (Bevan & Estrin, 2004). Other examples of location-specific advantages are
access to technology, and transportation cost and quality (Forsgren, 2008).
3). (I) Internalization incentive advantages - A firm must perceive that it is more value-adding
to internalize their operations in a foreign country rather than to export in order for FDI to
take place (Dunning, 2000; Dunning & Lundan, 2008). Such advantages might demonstrate a
superior efficiency level of a firm or a capability to practice direct power over assets the firm
has in its control (Dunning & Lundan, 2008). In this way the firm receives benefits from
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common governance and advantages in hierarchical control (Dunning & Lundan, 2008). Two
examples of internalization incentive advantages are economies of scale and avoidance of
high costs of external transactions (Dunning, 1988).
The O and I determinants are firm-specific factors of FDI while the L factors are the most
important factors determining where a firm chooses to invest (Dunning & Lundan, 2008; Luiz
& Charalambous, 2009). If a firm possesses competitive O and I advantages and the L
advantages of a country matches these then an investment may take place (Bartels et al.,
2010). Dunning and Lundan (2008) highlight that the paradigm has a dynamic form. The
authors state that changes in FDI for a specific country could be explained in changes in L
advantages relative to other countries. If the L advantages are perceived by foreign firms to be
low the investment will be done elsewhere, and if the L advantages are perceived to be higher
in a specific country than elsewhere this increases the chance of an investment to take place in
that location (Dunning & Lundan, 2008). Four main types of foreign investments can be
derived from the OLI paradigm: a) resource seeking FDI, b) market seeking FDI, c) efficiency
seeking FDI, and d) strategic asset seeking FDI (Behrman & Grosse, 1990; Dunning &
Narula, 2004).
a) Resource seeking FDI is a type of investment where firms seek a particular resource abroad
(Behrman & Grosse, 1990). It is performed in order for a firm to get access to these resources
(Dunning, 2000). The reason to seek a resource abroad can be that the firm can acquire it at a
lower cost or of a higher quality in comparison to its home country (Dunning & Lundan,
2008). It is also a necessary investment when the resource in question is not at all accessible
in the home country (Campos & Kinoshita, 2003). The investing firm is in this way hoping to
increase its profitability and competitiveness in the markets the firm is operating in (Dunning
& Lundan, 2008). One highly wanted resource is physical natural resources such as oil and
minerals (Dunning & Lundan, 2008). Where a resource seeking firm invests will in that case
be determined by a country’s possession of natural resources (Dunning & Narula, 2004).
Regarding FDI in developing countries, the investments are most often resource seeking
(Dunning & Narula, 2004).
b) Market seeking FDI is performed in order for the multinational firm to serve a market or its
neighboring markets with goods or services (Behrman & Grosse, 1990; Dunning, 2000;
Dunning & Lundan, 2008). Dunning and Lundan (2008) state that this kind of FDI can be
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performed either in order to defend or to get access to new markets. It is common that the
investing firm has been serving the market previously by exports but has got incentives to
make a direct investment in order to better reach that market (Dunning & Lundan, 2008). This
kind of FDI occurs when the specific market offers possibilities for production economies of
scale (Dunning & Narula, 2004). Market size and predictions for market growth are important
determinant factors for this kind of investments (Campos & Kinoshita, 2003; Dunning &
Lundan, 2008).
c) Efficiency seeking FDI are generally performed by large and established multinational
firms (Dunning & Lundan, 2008). However, an increasing amount of efficiency seeking
investments is performed by new actors (Dunning & Lundan, 2008). Such investments are
most often associated with relatively high developed countries but do also occur in developing
countries (Dunning & Narula, 2004). Efficiency seeking investments are often performed in
order to organize already established investments and assets (Dunning, 2000; Dunning &
Lundan, 2008). A local investment in production could be integrated internationally, increase
global efficiency, and serve a world market (Behrman & Grosse, 1990). Labor costs,
production incentives, and a favorable environment for business activities are important
determinants factors for efficiency seeking investments (Dunning & Lundan, 2008).
d) Strategic asset seeking FDI are often performed in order for a firm to strengthen or defend
its global competitive standing (Dunning, 2000; Dunning & Lundan, 2008). The firm invests
abroad since it desires to receive a return from a particular asset (Behrman & Grosse, 1990).
Such investments are performed by both large multinational enterprises and firms that are in
the process of becoming global actors and want to acquire competitive advantages in foreign
markets (Dunning & Lundan, 2008). Common objectives for these investors are to create
R&D synergies and to get access to organizational skills and technological assets (Dunning &
Lundan, 2008). Dunning and Narula (2004) state that strategic asset seeking FDI is most often
associated with relatively developed countries. However, since empirical cases show that, for
example, Belgian multinational firms have invested in African countries to acquire
technological capabilities, management or marketing expertise, and organizational skills this
kind of FDI do also occur in developing countries (Dunning & Lundan, 2008).
A conceptual model of the presented literature review is summarized in figure 2 below. It
shows that inward FDI in a country is constituted of four types of FDI. The four types of FDI
10
are derived from the OLI paradigm in which we will focus on the location-specific
advantages, the L dimension, since they are determining where a firm locates its foreign direct
investment.
Figure 2 - Conceptual model of the location-specific advantages relationship to inward FDI (own construct)
2.2 Location-specific determinants of inward FDI in Africa: hypotheses In our study, the multinational firms have already decided to perform a foreign direct
investment. This means that the firms perceive that there are sufficient O and I advantages to
commit in FDI, given that no investment would take place without all three types of
advantages being present at the same time according to Dunning and Lundan (2008). Since
we want to perform our study upon the question where? we will focus on the L factors and
each type of FDI within that group since they are the locational determinants of FDI flows.
After an extensive literature review we have selected the variables that are most commonly
used and show relevance regarding inward FDI in Africa. One location-specific determinant
has been selected for each of the four types of FDI. It is worth to note that a determinant may
fall into several types of FDI and that an investment might be, for example, both resource and
efficiency seeking (Dunning & Lundan, 2008). However, we have chosen the following
categorization since it is distinct, logical and commonly used.
a) Resource seeking FDI
One highly important factor for resource-seeking firms is physical natural resources
(Dunning & Lundan, 2008; Dunning & Narula, 2004). To get access to such resources is
probably one strong reason that Chinese and Indian firms recently have started investing in
Africa (Dunning & Lundan, 2008). Krugell (2005) highlights that an adequate way to develop
the understanding of inward FDI flows in Africa would be to include natural resources as an
important determinant but argue that it has been difficult to find useful data on resource
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exports. Morisset (2000) explains that two of the most successful African countries in
attracting FDI, Nigeria and Angola, are successful because of their comparative advantage in
oil. Onyeiwu and Shrestha, (2004) also argue that it is important to add natural resources as a
determining variable in studies concerning Africa.
We find research on the relationship between FDI and natural resources to be unanimous.
Buckley et al (2007) found a positive relationship between FDI and natural resources in their
study. More specifically, FDI has been positively associated with natural resources in the
context of investments in developing countries (Campos & Kinoshita, 2003). When focusing
on African countries, it has been shown that FDI is positively associated with natural resource
endowments (Asiedu, 2006; Onyeiwu & Shrestha, 2004). This is logical since it can be argued
that ownership control is preferable in exploitation of natural resources (Buckley et al, 2007).
Onyeiwu and Shrestha (2004) argue that FDI will occur in natural resource abundant
countries since firms seek a more stable or a cheaper supply of inputs. From this the following
hypothesis is derived:
H1: African inward FDI from Sweden is positively associated with host country endowments
of natural resources
b) Market seeking FDI
Generally in studies of FDI, the host country’s market size is argued to be a key market
seeking determinant of FDI (Buckley et al., 2007; Zhao & Zhu, 2000). If a multinational firm
seeks a new market, larger market size presents a bigger potential for the firm (Billington,
1999). The relationship between market size and FDI is widely tested in research and
generally accepted as a significant determinant (Chakrabarti, 2001; Krugell, 2005; Stoian &
Fillipaios, 2008). The market size of a country is not less important for the Africa region
where the common perception is that market size is one of the most important determinants of
inward FDI (Asiedu, 2006). Even if the individual buying power can be low in developing
countries, the collective market size can be vast (Prahalad & Hammond, 2002).
When the market size increases, the efficient usage of resources also increases and so does the
scale and scope of the exploitation of the market (Buckley et al. 2007). Stoian and Fillipaios
(2008) argue that economies of scale are more likely to be achieved in the local production of
a large host market but they do not find a significant relationship between market size and
12
FDI. However, market size is most often found to be a positive and significant determinant of
FDI (Bevan & Estrin, 2004; Billington, 1999; Chakrabarti, 2001; Erdal & Tatoglu 2002).
More specifically, large markets have also shown to be positively associated with inward FDI
in the context of African countries (Asiedu, 2006; Luiz & Charalambous, 2009). We therefore
derive the following hypothesis:
H2: African inward FDI from Sweden is positively associated with absolute host market size
c) Efficiency seeking FDI
Dunning and Lundan (2008) describes efficiency seeking FDI as two-folded: firms seeking
economies of product or process specialization, or firms seeking low labor costs and a
favorable business environment. Different labor factors are often described as strong
determinants of inward FDI (Loree & Guisinger, 1995; Villaverde & Maza, 2011). The labor
cost may prove important for FDI decisions, especially for labor-intensive industries (Loree &
Guisinger, 1995). It is generally agreed, on a theoretical level, that cheap labor attract FDI but
there is no unanimity in empirical research (Chakrabarti, 2001). Schneider and Fry (1985)
argue that FDI is more profitable if labor costs are low but it is the labor quality that makes
the investment worthwhile. In an African context, Krugell (2005) discusses that firms are
likely to look for both low labor costs and high levels of labor productivity. Moreover, the
flexibility of the labor market is critical to guarantee that workers are allocated to their most
efficient use (World Economic Forum, 2011). Labor markets must have the flexibility to
move workers from one economic activity to another at a low cost, and to allow for wage
fluctuations (World Economic Forum, 2011). We therefore expect labor market efficiency to
be an important part of a favorable business environment and a highly relevant determinant of
inward efficiency seeking FDI in Africa.
Even if the relationship between labor cost and FDI is obvious, the research results are mixed.
There is evidence that FDI is, in some cases, determined by low wages (Bevan & Estrin,
2004; Sethi et al. 2003). Loree and Guisinger (1995) did not find a significant relationship
between labor cost and FDI. Contrastingly, Zhao and Zhu (2000), found a positive
relationship between labor costs and FDI in a developing country. Focusing instead on labor
quality, Bartels et al. (2010) found a positive significant result between labor quality and FDI
in their study about Sub-Saharan Africa. Labor market efficiency is supposed to capture that
workers are put to the most efficient use in the economy and that they are productive in their
13
work (World Economic Forum, 2011). We argue that, since high labor quality and labor
productivity at a low cost logically should be related to high inward FDI flows, so should a
high labor market efficiency. We thus derive the following hypothesis:
H3: African inward FDI from Sweden is positively associated with the host country’s labor
market efficiency
d) Strategic asset seeking FDI
For a strategic asset seeking multinational firm, technology and organizational assets in which
the firm is deficient are what the firm wants to acquire (Dunning & Lundan, 2008). The
technological level of a country might then be a very important criterion in locational decision
making (Zhao & Zhu, 2000). Access to local technological traditions, know-how, and human
resources is provided by a location with scientific and technological assets (Mariotti &
Piscitello, 1995). It has been argued that firms invest abroad to access location-specific
advantages in the form of foreign proprietary technologies, strategic assets and capabilities
(Buckley et al. 2007). Deng (2003) states that the multinational firms in his study often chose
a location of their FDI where they could access strategic assets such as advanced technology.
This type of investment could also be relevant in African countries since empirical cases show
that firms have invested in Africa to acquire technological capabilities (Dunning & Lundan,
2008). Unctad (2011) describes innovation capacity as an important determinant of country
attractiveness alongside measurements such as patents per million inhabitants and availability
of scientists and engineers.
The relationships between strategic asset seeking motives and FDI are logically explained as
follows; a country’s or its local firms’ technological capabilities lead to location-specific
advantages which attract foreign direct investments of this type (Zhao & Zhu, 2000). Results
are however inconclusive, Zhao and Zhu (2000) found a positive relationship between
strategic asset seeking motives and FDI while Buckley et al. (2007) did not. Nevertheless, if a
firm is seeking important strategic assets such as technologies we believe that the reasonable
relationship with a country’s innovativeness is a positive one. Thus:
H4: African inward FDI from Sweden is positively associated with the host countries capacity
for innovation
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In figure 3 below, the independent variables and their relationship to the dependent variable,
FDI, are summarized. We expect market size, natural resources, labor market efficiency, and
innovation capacity all to be positively related to inward FDI to Africa from Sweden.
Figure 3 - Expected relationship between the independent variables and the dependent variable, inward FDI in
Africa (own construct)
3. Data and method The study is based on data on net FDI flows from Sweden and the entire world to individual
countries in Africa over the period of 2007 to 2010. The number of African countries covered
in the study is 25 for both the Swedish FDI dataset and the World FDI dataset1. To include all
countries in Africa would have been beneficial for our study but access to data has determined
which countries were included. This lack of data could be a shortcoming but was
unfortunately unavoidable. The countries in this study are both small and large countries in
size, countries in different development stages, with different political environments, with
different natural resources endowments, and generally different characteristics (World Bank,
2011 l). We believe that these 25 countries show a nuanced picture of Africa and adequately
mirror the general composition of countries on the continent. The countries that are included
in the two datasets are the same which will reduce the risk of misinterpretation of the outcome
from our analysis. The only difference between the two datasets is the dependent variable
which in the Swedish FDI dataset consists of FDI flows from Sweden and in the World FDI
dataset instead consists of FDI flows from the entire world. The study includes 96 cases for
the dependent variable in the Swedish FDI dataset and 99 cases in the World FDI dataset
which can be regarded as sufficient for our study according to a formula presented by
1 The datasets include: Algeria, Benin, Burkina Faso, Burundi, Côte D’Ivoire, Ethiopia, The Gambia, Kenya, Lesotho, Libya, Morocco, Mozambique, Namibia, Malawi, Mali, Mauritius, Mauritania, Madagascar, Senegal, Tunisia, Tanzania, Uganda, South Africa, Zambia, and Zimbabwe.
15
Tabachnick and Fidell (2007). The authors state that the recommended sample size for
multiple regressions can be calculated by the formula: N > 50 + 8m (where m = number of
variables) and indicates that around 106 cases are sufficient when using seven variables.
Although it could be interesting and relevant to include more variables in our study, the
datasets did not generate a sufficient amount of cases to do this as indicated by the previous
formula.
Relevant data on FDI flows for the Swedish FDI dataset has been collected from Statistics
Sweden and reflects FDI as Swedish net direct investments in foreign countries, covering
investments less disinvestments (Statistics Sweden, 2012 a). The reliability of Statistics
Sweden as a data source for our study is strengthened by the fact that the agency is, to a great
extent, assigned by the Swedish Government (Statistics Sweden, 2012 b). Data on FDI flows
for the World FDI dataset has been retrieved from the Africa Development Indicators (ADI).
This data reflects FDI as net direct investments to African countries from the entire world,
covering investments less disinvestments (World Bank, 2011 g). The World Bank uses the
same definition of net foreign direct investments as Statistics Sweden (Statistics Sweden,
2012 a; World Bank, 2011 g). Data on net flows, rather than gross flows, will hopefully give a
more true indication of which countries that foreign firms tend to be interested in. We argue
that using only investment data might give a skewed picture since it ignores the fact that
companies also disinvest. Moreover, research is usually done on net flows (Li, 2009). All the
indicators in the ADI from the Word Bank are compiled from officially-recognized
international sources, mostly from the African country national statistical systems (The World
Bank, 2011 e). One problem with the ADI is that full comparability cannot be assured.
Different factors, such as conflicts, can affect data availability, comparability, and reliability.
However, we find the ADI to be one of the few extensive longitudinal measures of the
African continent that is available and recognized. The fact that most of the data in this study
was assembled from the same source strengthens our study since we find that the World Bank
most often explores the same countries in different data set. This means that the problem with
missing data is minimized in comparison to a usage of many different sources. Data on all
variables are easily accessible which increases the chance that other studies could reproduce
our analysis. This could otherwise be a problem threatening the reliability of our study
(Saunders et al, 2009).
16
The character of the study with a continuous dependent variable (FDI) and four independent
variables (determinants of FDI) required a statistical technique that enabled us to explore the
relationship of the variables while controlling for the effect of other relevant determinants not
directly connected to one specific type of FDI. Our aim of the study is mainly to assess if the
independent variables, derived from the locational dimension of the OLI paradigm, can
predict where in Africa Swedish firms locate their FDI. To check the robustness of the model
it was also applied on the World dataset. This tested its usefulness in a more general setting
than in the case of Swedish FDI. We were interested in assessing the independent variables
joint possibility to predict the dependent variable as well as assessing each independent
variable. All of this could be accomplished by performing a hierarchical multiple regression
analysis (Pallant, 2010). When analyzing the results we focused on a significance level of 5%,
based on discussion by Pallant (2010), since we find it to be a general accepted level.
This statistical technique makes several assumptions about the data for each variable
regarding multicollinearity and singularity, outliers, normality, linearity, homoscedasticity
and independence of residuals (Pallant, 2010; Saunders et al 2009). We considered each
assumption when exploring our dataset and the procedures and results from testing these
assumptions are presented in section 3.3. However, when using this technique it is important
to be aware of the fact that regression will never reveal causality, only relationships, since
causality is a logical matter to be revealed by theoretical discussion (Tabachnick & Fidell,
2007).
Two different models were used. Model 1 is a pooled ordinary least squares model (POLS),
and model 2 is a fixed effects model (FE). The POLS model gave us an estimation of linear
relationship between the dependent and the independent variable (Newbold et al., 2006). The
reason to also do a FE regression was that by creating dummies for the different years we
could control for biases between the years (Allison, 2005). This means that the FE model only
focused on the within-year variation. In the FE regression a dummy was created for each year
and one of the dummies was dropped in the regression in a method called a least square
dummy variable model (Park, 2009). The variables as determinants of FDI are explained in
the following section and presented in Table 1 (see page 22) that also summarizes the
expected relation of each variable to FDI, measurement of the variable and sources of the
data.
17
3.1 Operationalization of independent variables a) Natural resources
Natural resources could be measured in different ways that changes the scope of the term
natural resources. Onyeiwu and Shrestha (2004) measure fuel exports divided by a country’s
total export. Asiedu (2006) adds minerals to the equation and measures the natural resources
as share of fuel and minerals export of total exports. The World Bank presents, as a part of the
Africa Development Index, a measure of natural resources. The total natural resources rent as
a percentage GDP are here the sum of oil rents, natural gas rents, coal rents, mineral rents, and
forest rents (World Bank, 2011 b). Since the African continent has many different kinds of
natural resources (World Bank, 2011 e), we believe that this measurement will give a more
accurate measurement than using for example only oil rents. Dunning & Lundan (2008) also
discuss that firms that seek physical natural resources might be interested in more than just oil
and minerals. A drawback is that with this broader measurement we will not be able to
pinpoint which kind of natural resource that is most crucial for inward FDI in Africa, but
since that is not our intention this drawback is acceptable.
b) Market size
Host market size is commonly measured by absolute numbers of GDP (Asiedu, 2006;
Buckley et al., 2007). Another way to measure market size can be GDP per capita (Krugell,
2005). According to Eurostat (2011), absolute numbers of GDP is the most frequently used
measure for the overall size of an economy and we will therefore use that measurement in our
study. The data on absolute numbers of GDP is found in the Africa Development Indicators
from the World Bank (World Bank, 2011 n).
c) Labor market efficiency
As discussed in the literature review, we will focus on labor market efficiency rather than
labor costs or labor quality separately. Labor costs are often measured by real wages (Asiedu,
2002), and labor quality is commonly measured through the percentage of the population or
workforce with education on different levels (Cheng & Kwan, 2000; Schneider & Fry, 1985).
These kinds of measures are unfortunately not readily available for developing countries in
Africa (Asiedu, 2002). The Africa Development Indicators presented by the World Bank
includes an index that captures the labor market efficiency. It includes pay and productivity,
flexibility of wage determination, cooperation in labor-employer relations, rigidity of
employment, hiring and firing costs, reliance on professional management, brain drain, and
18
female participation in labor force (World Bank, 2011 f). A problem with this index is that it
captures more than we want to measure and might be a too broad definition. However, data on
labor costs are not available and we find measurements of labor quality to be quite crude. The
labor market efficiency is available for all the countries in our sample and captures many
aspects that are highly relevant for our study.
d) Innovation capacity
Strategic asset seeking FDI can be measured by the rate of patenting in the host country
(Buckley et al., 2007) or by the ratio of technology development expenditure to GDP (Zhao &
Zhu, 2000). The Africa Development Indicators presented by the World Bank do however
feature an index of country innovation. The following aspects are included in that index:
capacity for innovation, utility patents per million population, availability of scientist and
engineers, government procurement of advanced tech products, university-industry
collaboration, quality of scientific research institutions, and company spending on research
and development (World Bank, 2011 i). We find this index as fruitful to use since it includes
more parts of a country's technological potential than, for example, the single measurement of
rate of patenting does.
3.2 Control variables Other highly relevant locational determinants can also explain where a firm chooses to invest
but can be difficult to put into the categories of different FDI types (Campos & Kinoshita,
2003). In order to reduce, or control for, unobserved heterogeneity, we included a number of
control variables in our estimations (Hair et al, 2006). We chose to include political stability,
openness, and infrastructure as control variables since we argue that they are underlying
assumptions or necessities for all the four different types of FDI. This will be further
developed in the sections below.
The political stability in a country is highly relevant since it implies a long-term stable
environment (Dunning & Narula, 2004). Political stability is in that way an underlying
assumption for all other determinants since Dunning and Narula (2004) state that investments
and trade only runs efficiently in a stable and peaceful environment. A more stable political
environment is generally argued to reduce the uncertainty of potential investors and have the
potential to increase the level of inward FDI (Loree & Guisinger, 1995). Political instability
might interfere in economic processes and result in less direct investments (Sethi et al, 2003).
19
The stability of the investment environment is thus often argued to be important for foreign
firms when making an investment decision (Habib & Zurawicki, 2001; Loree & Guisinger,
1995; Luiz & Charalambous, 2009). The variable political stability may be even more
important in FDI decisions regarding African countries than in other parts of the world since
the region is inherently perceived as risky by foreign firms (Asiedu, 2002). Loree and
Guisinger (1995) argue that stability may play a larger role during a time of caution. It is
important to note that even if Africa is often perceived as risky the continent is generally
moving towards political stability and democracy (Cheru, 2012). The results of previous
research on the relationship between political stability and FDI is incoherent (Asiedu, 2002;
Jiménez, 2011). A general direction in research is that there is a positive correlation between
political stability and inward FDI, or put as political risk is negatively correlated with FDI
(Root & Ahmed, 1978; Schneider & Fry, 1985; Sethi et al, 2003). A significant negative
correlation is found between political risk and FDI by Loree and Guisinger (1995) for one
time period in their studies but it is found insignificant in another. Jiménez (2011) even finds
a significant positive relationship between political risk and inward FDI. He explains this by
discussing that firms are searching for a market niche where they can take advantage of their
political capabilities. However, a more stable political environment is generally argued to
reduce the uncertainty of potential investors and have the potential to increase the level of
inward FDI (Loree & Guisinger, 1995).
There are a number of different ways to measure political stability. Berry et al (2010) use
independent institutional actors with veto power while Loree and Guisinger (1995) use the
International Country Risk Guide composite index. Sethi et al (2003) use a composite variable
developed by the Association for Investment Management while Asiedu (2002) uses numbers
of assassinations and revolution. We will turn to the World Bank that in the Worldwide
Governance Indicators (WGI) presents an index of political stability and absence of violence
and terrorism (World Bank, 2011 h). This index captures perceptions of the likelihood that the
government will be overthrown or destabilized by unconstitutional or violent means,
including politically-motivated violence and terrorism (Kaufman et al, 2010). The WGI are
constructed from several hundred of variables collected from multiple databases, covering
governance perceptions as reported by respondents from surveys, non-governmental
organizations, public sector organizations globally and providers of commercial business
information (Kaufmann et al, 2010). The constructors are stating that the indicators can be
useful when making comparisons both across countries and over time (Kaufman et al, 2010).
20
To rely on an indicator that is based and dependent on a large number of other sources can be
a problem since the methodologies of the underlying sources may change over time. The
constructors of the WGI are commenting on this problem by stating that all underlying data
sources have comparable methodologies from one year to another (Kaufmann et al, 2010). We
deem the index of political stability from the WGI to be useful in our study since we find it to
be extensive and reliable.
Campos and Kinoshita (2003) show that a country’s openness is also a consistently important
determinant of inward FDI. Openness describes the competitiveness of a country in the form
of international trade and exposure (Stoian & Filippaios, 2008). Erdal and Tatoglu (2002) also
argue that openness is one of the most important location-specific determinants. They
continue by stating that an economy open to trade makes it easier for foreign firms to fit into
both global trade and production patterns. In a study covering 135 countries ranging from
developing countries in Africa to industrialized countries, Chakrabarti (2001) shows, without
considering the different types of FDI, that openness is one of the most important variables to
take into account. Krugell (2005) discusses openness as a determinant of FDI in Africa and
argue that high level of openness of a country is likely to be important in order to stimulate
growth by attracting FDI. A positive relationship between openness and FDI is found by
many researchers (Erdal & Tatoglu, 2002; Naudé & Krugell, 2007; Nurudeen et al, 2011;
Stoian & Filippaios, 2008). It has been stated that countries with a higher export orientation
will receive larger flows of FDI than countries that are less oriented towards exports (Habib &
Zurawicki, 2001). A positive relationship between openness and FDI flows is commonly
expected since it is the typical found relationship and, as argued by Erdal and Tatoglu (2002)
among others, an open economy eases a fit into global trade patterns.
Trade openness is commonly measured by trade ratio, exports plus imports, to GDP (Asiedu,
2002; Stoian & Filippaios, 2008) or a similar measure like ratio of exports to imports (Erdal &
Tatoglu, 2002). A country’s openness should also mirror involvement in free trade
agreements and customs unions (Bevan & Estrin, 2004). Trade restrictions are the other side
of the coin regarding openness, and could also be a measure of this variable (Asiedu, 2002).
Naudé and Krugell (2007) use a compiled openness indicator that take into account factors
such as tariff rates and black market exchange rates. We find the ratio of export and import to
GDP to be a simple and adequate measurement for our research. A problem could be that it
does not directly take into account free trade areas or trade restrictions. However, we argue
21
that it does so indirectly and the ratio of export and import to GDP is the most commonly
used measure of openness as already described. Data on trade as a percentage of GDP is to be
found in the Africa Development Indicators from the (World Bank, 2011 m).
Infrastructure quality is an additional important variable for foreign investors to consider
since it gives lower communication costs and reduces difficulties in managing business
activities (Chidlow et al., 2009). Campos and Kinoshita (2003) argue that the quality of
infrastructure is an important precondition for all the four types of FDI, determining the
success of an investment. It represents the ease of operations and also allows for easy
transportation of products (Zhao & Zhu, 2000). Erdal and Tatoglu (2002) discuss that foreign
multinational firms prefer a host country with good infrastructure since it facilitates
communication, transportation, and distribution. Infrastructure quality is thus important
regardless of which type of FDI is in question. This is also true for African countries as
Jiménez (2011) discusses, urging African governments to increase the quality of the
countries’ infrastructure to attract FDI. Infrastructure has in many cases showed to have a
positive and significant relationship with FDI inflows (Cheng & Kwan, 2000; Chidlow et al.,
2009; Loree & Guisinger, 1995). However, research made with Africa as case show
ambiguous result. Asiedu (2002) finds significant positive results between infrastructure and
FDI in northern Africa but not in Sub-Saharan Africa. Luiz and Charalambous (2009) showed
in their research, on the other hand, that infrastructure were highly relevant and important for
their researched firms investing in Sub-Saharan Africa. Even if FDI in some cases can be
attracted by low infrastructure quality, as in the case of foreign firms constructing means of
telecommunication (Kirkpatrick et al., 2006), the logical relationship between infrastructure
quality and FDI is a positive one (Billington, 1999).
Infrastructure can be measured in a number of different ways. Erdal and Tatoglu (2002), and
Loree and Guisinger (1995) make a distinction between transportation infrastructure and
communication infrastructure. Transportation infrastructure could be measured by for
example share of transportation expenditures in GDP (Erdal & Tatoglu, 2002) or by total
length of road, paved road, and railway per unit of land mass (Cheng & Kwan, 2000).
Communication infrastructure could be measured by for example communication
expenditures in GDP (Erdal & Tatoglu, 2002) or telephones per 1 000 population (Asiedu,
2002). We believe that measuring only one type of infrastructure may cause problems. When
Asiedu (2002) uses number of telephones per capita as an indicator of infrastructure she finds
22
no significant relationship with FDI. In Africa, cellular phone subscriptions were
approximately 15 times as common as telephone landlines in 2009 (World Bank, 2011 c).
Since cellular phones are much more common in Africa than landline phones, the result could
have been different for Asiedu with a better measurement. We are going to use an index
developed by the World Bank as measurement of infrastructure. The index is part of the
Africa Development Indicators and includes the quality of overall infrastructure, quality of
roads, quality of railroad infrastructure, quality of port infrastructure, quality of air transport
infrastructure, available airline seat kilometers, quality of electricity supply, fixed telephone
lines and, mobile telephone subscriptions (World Bank, 2011 d). This index should paint a
broader and more true picture of a country's infrastructure than a single measurement could
since it includes both transportation and communication infrastructure. A problem that may
arise is that we cannot see what kind of infrastructure that is affecting FDI but since we want
to control for both transportation and communication infrastructure this is not a problem in
this study.
Table 1 - The locational determinants of inward FDI to Africa
23
In table 1, on the previous page, the independent variables and their relationship to the
dependent variables are summarized. The control variables are also included, so are the
theoretical justifications and the data sources used. Important to note is that the variables are
lagged by one year in relation to the dependent variable when possible. This was the case for
all variables except for the labor market efficiency variable since data limitations made this
impossible. Since the data on labor market efficiency does not fluctuate much between
different years we do not think this will pose a major problem. We choose to use lagged data
since Bevan and Estrin (2004), among others, argue that it is likely that the decision and
implementing process of FDI might often be long resulting in that FDI flows might be shown
after some time. This was also confirmed in their study where they revealed that present FDI
flows were more related to lagged information than current information. Krugell (2005),
among others, chose to include lagged variables in his study for the African context which
also supports our decision.
3.3 Data considerations and the models Tabachnick and Fidell (2007) describe the development and testing of a model as a repetitive
process where no model will be perfect in the first run. We have used different tables and
graphs in this process in order to explore our data as recommended by Saunders et al. (2009).
After a preliminary analysis, we conducted several regression analyses to improve the
preconditions for a successful final analysis. This process also included many activities
ensuring that no errors occurred in our datasets which is recommended by Saunders et al.
(2009). An example of one such activity is to check the minimum and maximum values of
each variable (Pallant, 2010).
When conducting the preliminary analysis for both the Swedish FDI dataset and the World
FDI dataset we first transformed all data into logarithms to counteract negative effects on the
results regarding outliers, normality, linearity, and homoscedasticity (Tabachnick & Fidell,
2007). By doing this we follow the recommendation by Tabachnick and Fidell (2007) that
transformations should always be considered. Li (2009) also discusses the advantages of using
logged data, especially when dealing with outlier problems. Transformation of data regarding
similar variables and data characteristics is also common in other studies where Buckley et al
(2007) is a good example. When checking boxplots, histograms, and compared the 5%
trimmed mean to standard mean we found that some variables still had some extreme outliers
that distorted the dataset. Since these cases did not mirror the general characteristics of the
24
African countries we choose to exclude them which is recommended by Tabachnick and
Fidell (2007).
In the Swedish FDI dataset, we excluded four cases from the data on FDI that were regarded
as more extreme in comparison with the other outliers. In the World FDI dataset, we excluded
one case from the data on FDI. Regarding the independent variables in both datasets, we
excluded four cases from the data on natural resources, three cases from the data on market
size, three cases from the data on labor market efficiency, and four cases on the data on
innovation capacity. Among the control variables, eight cases were deleted from the data on
political stability. After these exclusions, the 5 % trimmed mean and the actual mean were
similar and indicated that no further exclusions were needed as Pallant (2007) reasons. With
the outliers deleted and missing values taken into account 96 observations remained for the
dependent variable in the Swedish FDI dataset and 99 observations remained for the
dependent variable in the World FDI dataset. We realize that our data include more missing
values than preferable and this might have an effect on the analysis and our result. However,
we considered if the missing data showed any systematic patterns but the missing data seemed
to occur randomly. Pallant (2010) reasons that there are fewer problems with distortion of the
results if the missing data happens randomly. In the regression analysis, missing data was
excluded pairwise rather than listwise to maximize the number of useful cases (Pallant, 2010).
The pairwise exclusion only excluded cases if data was missing for the requested analysis but
was still included if it had necessary information, which is recommended by Pallant (2010).
Another option could have been to replace missing data with mean values but this option is
very problematic to use especially when the dataset includes a relatively large amount of
missing data since it might bias the results (Pallant, 2010).
We checked all variables to make sure no violations of the assumptions of normal
distribution, linearity, homoscedasticity, and independence of residuals, were made in neither
the Swedish FDI dataset nor the World FDI dataset. After the transformation of the data to
logarithms had been conducted and extreme outliers had been excluded we reached adequate
skewness and kurtosis for all the variables in both datasets. The variable with an arguable too
high kurtosis (K > 1) was the Swedish FDI. However, since the sample resulted in almost 100
cases for this variable we deem this problem as small. The reason for this is that Tabachnick
and Fidell (2007) argue that problems with too high kurtosis disappear with more than 100
cases. The Swedish FDI variable also had some problems showing a straight line in the
25
normal Q-Q plot and showed some clustering in the detrended Q-Q plot indicating a problem
with normal distribution as Pallant (2007) argues. However, when considering the histogram,
skewness and kurtosis, a reasonable normal distribution can be assumed. All other variables
showed good results in the Q-Q plots.
After conducting the preliminary analysis we continued to run tests on both model 1 (POLS)
and on model 2 (FE). The normal probability plot in both models showed a reasonable
straight line for the Swedish FDI dataset and a clear straight line for the World FDI dataset,
which indicates no major deviation from normality according to Pallant (2007). When
examining the scatterplots of the standardized residuals for both models a roughly rectangular
distribution with most scores concentrated along the zero line was found. This implies that the
assumptions of normality, linearity, and homoscedasticity are met (Pallant, 2010). This was
acceptable but rather vague in the Swedish dataset and a clear case for the World FDI dataset.
In the scatterplots for model 1 and in the casewise diagnostics we found only three cases in
the Swedish dataset and no cases in the World dataset with standardized residuals over 3.3 or
less than -3.3. The same was true for model 2 where only two cases were found in the
Swedish dataset and no cases in the World dataset. This low amount makes further actions
against outliers unnecessary (Pallant, 2010). This was also supported by the Mahalonobis
distance since this value was below the critical value for both datasets in the two models
based on the discussion by Pallant (2010). To be sure we also checked this with Cook’s
distance to see if the outliers that still remained had any undue influence on the results for the
datasets. The distances were in both datasets and in both models under the critical value of 1
and thus poses no problem as argued by Pallant (2007).
For the two models we ensured that the assumption of multicollinearity was not violated for
both datasets. If the collinearity between independent variables are higher than 0.7 this
assumption is violated (Tabachnick & Fidell, 2007). No cases of too high collinearity among
the independent variables were present in the two datasets (see table 2 on the next page). As
part of the collinearity diagnostics we ensured that we had acceptable values for tolerance and
VIF in both models. All variables showed tolerance above 0.1 and VIF below 10 which are
the critical values (Pallant, 2010).
26
SFDI_ Lg10
WFDI_ Lg10
PolStab_ Lg10
Open_ Lg10
Infr_ Lg10
NatRes_ Lg10
GDP_ Lg10
LabEf_ Lg10
InCap_ Lg10
SFDI_Lg10 1.000 - WFDI_Lg10 - 1.000 PolStab_Lg10 0.045 0.185 1.000
Open_Lg10 0.034 -0.033 0.195 1.000 Infr_Lg10 -0.106 0.403 0.243 0.227 1.000
NatRes_Lg10 -0.030 0.289 -0.099 0.119 -0.211 1.000 GDP_Lg10 -0.068 0.802 -0.126 -0.216 0.355 0.257 1.000
LabEf_Lg10 0.090 -0.260 0.113 -0.132 0.058 -0.375 -0.348 1.000 InCap_Lg10 -0.062 0.353 0.033 -0.263 0.446 -0.421 0.384 0.210 1.000
Table 2 - Correlations, World FDI and Swedish dataset for both models
4. Results of the empirical analysis The empirical results obtained from model 1 (POLS) and model 2 (FE) are highly similar (see
Table 3 for the Swedish dataset and Table 4 for the World dataset). This implies that the
between-year proportion of variation is small and that the FE regression does not contribute to
the analysis (Allison, 2005). It also strengthens the value of model 1. For this reason we will
only further analyze the results from the POLS regression to keep the discussion simple and
easy to follow.
In the hierarchical multiple regression with Swedish FDI as dataset we assessed the four
independent variables; natural resources, market size, labor market efficiency, and innovation
capacity, after controlling for the influence of political stability, openness, and infrastructure
(see Table 3 on the next page). The control variables were entered in Step 1 but could not
significantly explain the variance of 1.9 % for inward FDI to Africa from Sweden. After entry
of the four independent variables in Step 2 the model as a whole could not significantly
explain the total variance of 3.5 %, F(7, 73) = 0.38 , at a significance level of 5 %. The
variables natural resources, market size, labor market efficiency, and innovation capacity
could not significantly explain more of the variance in FDI, after controlling for political
stability, openness, and infrastructure. R squared change = 0.017, F change (3, 73) = 0.31, at a
significance level of 5 %. None of the independent variables were significant and our
hypotheses could therefore not be supported. None of the control variables could show
significance either.
27
Swedish FDI dataset Model 1- POLS
Model 2 - FE
Step 1 Step 2
Step 2
PolStab_Lg10 0.067 (0.006)
0.064 (0.006)
0.075 (0.006)
Open_Lg10 0.051 (0.007)
0.097 (0.009)
0.111 (0.009)
Infr_Lg10 -0.134 (0.010)
-0.180 (0.013)
-0.199 (0.014)
NatRes_Lg10 (H1) -0.082 (0.003)
-0.071 (0.003)
GDP_Lg10 (H2) 0.116 (0.003)
0.120 (0.003)
LabEf_Lg10 (H3) 0.129 (0.033)
0.132 (0.033)
InCap_Lg10 (H4) -0.064 (0.032)
-0.051 (0.033)
Year dummies included No No Yes Diagnostics
R square 0.019 0.035
0.049 Adjusted R square -0.019 -0.057
-0.087
F 0.493 0.382
0.360 R square change 0.019 0.017
0.030
F change 0.493 0.312 0.316 Notes: In each column the standardized coefficient, beta, is presented. Standard errors are in parentheses.
*Significant at a 5% level
Table 3 - Swedish FDI dataset summary
When checking the robustness of the model by performing a hierarchical multiple regression
with World FDI as dataset, a completely other result was given than when using the Swedish
dataset. We assessed the four independent variables; natural resources, market size, labor
market efficiency, and innovation capacity, after controlling for the influence of political
stability, openness, and infrastructure (see Table 4 on the next page). The three control
variables were entered in Step 1 and could significantly explain 19.1 % of the variance of
inward FDI to Africa from the entire world at a significance level of 5 %. After entry of the
four independent variables, in Step 2, the total variance significantly explained by the model
as a whole was 75.8 %, F(7, 73) = 32.64, at a significance level of 5 %. The natural resources,
market size, labor market efficiency, and innovation capacity significantly explained an
additional 56.7 % of the variance in FDI, after controlling for political stability, openness, and
infrastructure. R squared change = 0.567, F change (3, 73) = 42.70, at a significance level of 5
%. Two of the four independent variables were statistically significant, namely natural
resources and market size. Market size had higher beta value (beta = 0.75, p<0.05) than
28
natural resources (beta = 0.18, p<0.05). One of the control variables was also statistically
significant, namely political stability. These results indicate that firms around the world
investing in Africa seek large markets and natural resources with the underlying assumption
of a stable political environment.
World FDI dataset Model 1- POLS
Model 2 - FE
Step 1 Step 2
Step 2
PolStab_Lg10 0.115 (0.298)
0.268
(0.171)* 0.257
(0.172)*
Open_Lg10 -0.148 (0.392)
0.089 (0.257)
0.099 (0.258)
Infr_Lg10 0.409
(0.537)* 0.027
(0.392) 0.048
(0.400)
NatRes_Lg10 (H1) 0.184
(0.085)* 0.200
(0.085)*
GDP_Lg10 (H2) 0.753
(0.087)* 0.749
(0.086)*
LabEf_Lg10 (H3) 0.022 (0.964)
0.038 (0.960)
InCap_Lg10 (H4) 0.139 (0.955)
0.145 (0.949)
Year dummies included No No Yes Diagnostics
R square 0.191 0.758
0.772 Adjusted R square 0.160 0.735
0.740
F 6.071* 32.640*
23.754* R square change 0.191 0.567
0.581
F change 6.071* 42.702* 25.530* Notes: In each column the standardized coefficient, beta, is presented. Standard errors are in parentheses.
*Significant at a 5% level
Table 4 - World FDI dataset summary
These results imply that the locational determinants derived from the OLI paradigm are useful
in explaining African inward FDI from the entire world and also proves the robustness of the
model. However, the model could interestingly enough not explain FDI flows from Sweden to
Africa and none of the hypotheses were supported. These results will now be discussed in the
following section.
5. Discussion of the results The results suggest that Swedish firms are not investing in Africa with the main reason to
exploit natural resources, to gain access to markets, to use the advantages of labor market
29
efficiency, or to acquire strategic assets. The fact that the control variables were insignificant
suggests that political stability, openness, and the infrastructure are considerations of lesser
importance for the management of Swedish firms when making internationalization decisions
for Africa. In contrast, in this study market seeking and resources seeking FDI showed to be
the main types of FDI in Africa, with the underlying assumption of political stability,
regarding firms from the rest of the world. The L determinants from the OLI paradigm well
explain where in Africa firms from around the world choose to invest. However, in this study,
the L dimension of the paradigm cannot explain the Swedish inward FDI in Africa. Below we
will discuss why we think this was the case for each type of FDI. It is important to note that
since the data on Swedish FDI, after transformations made, still had a high kurtosis. A high
kurtosis could lead to an underestimation of the variance (Pallant, 2010). However, even if the
variance is underestimated in this case we deem it improbable that the model could
significantly explain a higher variance in Swedish FDI since the R square is very low.
a) Resource seeking Swedish FDI in Africa
Since the natural resources variable is insignificant, hypothesis 1 is not supported. We cannot
find support that African inward FDI from Sweden is positively associated with host country
endowments of natural resources. This result for Swedish firms is contrary to what is shown
for firms from the rest of the world. In the world dataset, natural resources was significantly
showed to be the second most important reason for foreign firms investing in Africa. That
international firms are interested in Africa in a large part due to the natural resources of the
continent is expected since resource seeking FDI is one of the most common type of FDI in
developing countries (Dunning & Narula, 2004). Sweden is a country that is not relatively
rich on natural resources in comparison with many countries in the African region (World
Bank, 2011 k). That hypothesis 1 was not supported is therefore surprising since this could act
as a main trigger for Swedish firms to invest in Africa in order to exploit resources that could
not be found in the home country.
There can be several possible explanations for why Swedish firms do not show the expected
investment behavior as firms from other parts of the world do. First, the result could imply
that Swedish companies simply do not invest in Africa with the main interest in exploiting
resources. The firms that have invested in African countries may not be firms interested in
natural resources since they are not operating in that particular industry. Or put in another
30
way, there are too few Swedish firms in the natural resource industry investing in Africa to
make an impact on our results.
Second, it could be the case that Swedish firms are resource seeking in Africa but they are not
investing to gain access to the natural resources investigated in this study. Since we
anticipated this problem and tried to counteract it by using an index including several
different natural resources this is probably not a major problem. However, the index does not
include land or agricultural products. Agricultural products that might attract foreign direct
investments are for example: coffee, bananas, rubber, and tobacco (Dunning & Lundan,
2008). These kinds of foreign direct investment are becoming more common in Africa (UN,
2010).
Third, investments with the intent to gain access to natural resources are often connected to
high capital expenses (Dunning & Lundan, 2008). If a firm makes a large investment one year
to exploit, for example, an African country’s abundance of oil endowments this would show
in our data as a connection between high inward FDI and large endowments of oil. Imagine
then that something unexpected and maybe even unrelated happens the following year; for
example an environmental accident caused by the company in another part of the world, that
forces the company to disinvest the same amount of FDI in the African host country. This
would now show in the data as an abundance in oil is related to a very low amount of inward
FDI or even a negative flow. These two years will now negate each other and no clues of the
relationship between natural resource endowments and inward FDI will be given in the
dataset. For the Swedish dataset an African country’s inward FDI from Sweden might consist
in large part of a few companies’ resource seeking investments which could, as argued, cause
a major problem in evaluating our results. This could be a problem in the data of market-,
efficiency-, and strategic asset seeking FDI as well. South Africa stands for approximately 30
% of the 100 Swedish subsidiaries present in Africa and in most countries there are under 10
Swedish subsidiaries present (Swedish Agency for Growth Analysis, 2012). If a few large
Swedish firms make large disinvestments in a country where only a small number of Swedish
firms are present, it would have a great impact on the inward FDI flows from Sweden. We
believe that this is not likely a major problem in the World FDI dataset where a single
company’s individual actions will probably not have a great impact on the total FDI flows
since this data is based on investments from firms all around the world.
31
b) Market seeking Swedish FDI in Africa
Hypothesis 2 is not supported since the market size variable showed to be insignificant. There
is no support that African inward FDI from Sweden is positively associated with absolute host
market size. This is rather surprising since host country GDP showed to have a strong positive
and significant relation to inward FDI in Africa considering the World dataset. This means
that FDI from various countries all over the world seems to be highly attracted to larger
markets in Africa but this could not be showed considering FDI from Swedish firms.
Although Swedish firms might be aiming to reach new markets when investing in Africa, this
study could not show market seeking motives to be a main driver for such an investment.
Several explanations could be the reason for this. First, it could be argued that Swedish firms
simply do not invest in African countries in order to mainly gain access to new markets and
might not see the possible full potential of the markets. Swedish firms might see African
countries as relatively poor and without business opportunities. Prahalad and Hammond
(2002) discuss that it is not difficult to imagine that some firms might be unwilling to invest
in such areas for that reason. By making this decision, Swedish companies might ignore the
market potential of these regions. A potential that can be larger than expected since these
countries often offer vast populations and many potential future customers (Prahalad &
Hammond, 2002).
Second, we could have included other market seeking variables in our study that could have
led to other results. An example is that Prahalad and Hammond (2002) argue that countries
with relatively low purchasing power of the population, such as African countries, can grow
extremely fast since they are in the first stages of their economic development. By including a
variable of market growth instead of market size regarding market seeking FDI, we could
have reached another result. However, we do not find this likely since market size is generally
viewed as the most common and relevant variable regarding market seeking FDI as discussed
in previous sections.
c) Efficiency seeking Swedish FDI in Africa
Labor market efficiency is an insignificant variable in this study and hypothesis 3 is by that
not supported. We could find no support that African inward FDI from Sweden is positively
associated with the host country’s labor market efficiency. Neither was any significance
showed between labor market efficiency and inward FDI from the rest of the world. Contrary
32
to the resource seeking and market seeking FDI, Swedish firms show in the case of efficiency
seeking FDI the same result as firms from other parts of the world.
We find this reasonable since efficiency seeking investments are usually associated with more
developed countries (Dunning & Lundan, 2008). However, one goal for efficiency seeking
investments could be low labor costs (Dunning & Lundan, 2008) and this is to be found in
Africa which means that the argument above does not fully explain why Swedish FDI do not
seem to engage in efficiency seeking FDI in Africa.
Since efficiency seeking FDI is also related to low labor costs we cannot with certainty
conclude that Swedish firms are not seeking for efficiency advantages in Africa. A second
possible explanation for the insignificant result of this variable is one of focus. The index we
used in order to measure labor market efficiency captured many aspects other than labor costs
which could be regarded as important for foreign firms. However, if it is the case that the
costs are the most important determinant for efficiency seeking FDI this is not fully captured
in our study. Unfortunately, we have not been able to find extensive data on labor costs
covering a good part of the African continent that also covers several years. This lack has also
been pointed out by other researchers (Asiedu, 2002). A third reason for this insignificant
result could possibly be that the data on labor market efficiency was not lagged with one year
as the rest of the data was. The data was unavailable for some years which made lagging
impossible. Since the data did not fluctuate much between years this is probably not the
reason for the insignificance but we cannot rule out the possibility.
d) Strategic asset seeking Swedish FDI in Africa
Since the last independent variable, namely innovation capacity, showed to be insignificant
hypothesis 4 cannot be supported. It is therefore not supported that African inward FDI from
Sweden is positively associated with the host country’s capacity for innovations. The
innovation capacity variable also showed to be insignificant regarding the World FDI dataset.
This is no surprise, since strategic asset seeking FDI is most often focused on developed
countries (Dunning & Lundan, 2008). Since this type of FDI does occur in developing
countries as well and it do exist developed countries in Africa, innovation capacity could have
been proven to be an important locational determinant However, as shown, it was
insignificant for Swedish firms as well as firms from the rest of the world. There is evidence
33
that Swedish firms seek strategic assets in other developed countries (Braunerhjelm &
Svensson, 1996). This fact rules out the possibility that Swedish firms are not engaging in
strategic asset seeking at all. The main part of the African countries are still developing (IMF,
2012) This could be one explanation of why Swedish firms do not engage in this type of FDI
in Africa since developing countries pose no obvious strategic assets.
As discussed in section 2.2, strategic asset seeking companies often perform FDI in order to
acquire technological assets. This is also the general focus in research on strategic asset
seeking FDI (Buckley et al., 2007; Mariotti & Piscitello, 1995). There is a possibility that
such firms might search for other assets such as organizational skills. We do not know if
Swedish firms might invest in Africa to gain access to this other type of strategic assets. A
focus on organizational skills rather than technology might show another result. However,
since strategic assets are generally measured with a kind of technology measurement and we
have not found extensive research on firms seeking organizational skills in Africa we have not
been able to research this question. There are examples of strategic asset seeking FDI in
developing countries (Dunning & Lundan, 2008) which was the reasons that we chose to
include this group of determinants in this study. Still strategic asset seeking FDI, regardless of
focus, is mostly related to developed countries even if there are exceptions. Therefore we do
not see it probable that another result would have been reached with another focus.
5.1 Other approaches None of our hypothesis could be supported when analyzing FDI flows from Sweden to
African countries. This suggests that common and highly relevant FDI determinants based on
the locational dimension of the OLI-paradigm seems to not solely explain where Swedish
companies invest in Africa. If the OLI-paradigm cannot easily explain which the main
locational determinants of Swedish FDI in Africa are, other theoretical approaches might be
necessary to consider. While outside of the scope of the current paper, some speculative notes
can be made.
For example, a network approach towards multinational firms might be more fruitful in the
case of Swedish firms. Chen and Chen (1998) reasons that conventional FDI theories, such as
the OLI-paradigm, in part assumes that the firms are strong in some kind of intangible know-
how and is generally large and strong. They claim that this is not always the case since many
international investors are seemingly small and weak. The explanation could be found in the
34
strategic linkage theory or the network approach since these theories claim that network
linkages may complement or supplant the weakness of firm-specific capabilities and enable
small and weak firms to undertake FDI (Chen & Chen, 1998). The authors show that network
linkages are important determinants of location choice in FDI. If the case is that Swedish
firms investing in Africa are internationally weaker than its counterparts this approach might
explain the locational choice better.
Connected to this is the theoretical approach of psychic distance, aspects that make it
problematic to understand foreign environments, discussed by Johansson and Vahlne (2009).
We believe that this approach might be needed to include in order to understand the
investment behaviors of Swedish firms in Africa. Johansson and Vahlne (2009) state that the
direct importance of psychic distance in the internationalization process of firms has
decreased but can still be a critical factor in order for a firm to be able to create relationships
and networks. A short psychic distance between two countries or regions can make it easier to
create these relationships and networks that are necessary in order for a firm to get access to
opportunities abroad (Johansson & Vahlne, 2009).
To get an understanding of the main determinants of Swedish direct investment in Africa it
could also be necessary to investigate the importance of collaborations between state-owned
and private-owned firms. The Swedish International Development Cooperation Agency (Sida)
describes that Swedish firms are regarded to be an important partner in African countries such
as Kenya and South Africa (Sida, 2011). A concrete example of a state/private firm
collaboration is a case where Swedfund and two Swedish private-owned businesses invested
16.5 million SEK in the building of a hospital in Addis Abeba, Ethiopia (Swedfund, 2010).
There is a possibility that Swedish foreign direct investments in Africa are guided by these
kinds of initiatives.
It is worth to note that different network approaches and even a state/private firm
collaboration could be argued to be part of the OLI paradigm. Dunning (2000) describes
network linkages as a locational strategic asset. We reason that even state/private firm
collaborations could be viewed as a type of network connection and could be squeezed into
the OLI paradigm. Stoian and Fillipaios (2008) mentions that the OLI paradigm has been
criticized to be a shopping list encompassing all thinkable variables. Whether these
35
approaches are to be regarded as a part of OLI or not, we argue that they are not usually
described as a fundamental part of the paradigm.
6. Conclusion, implications and future research Studies on foreign direct investment flows to the African continent from small and open
countries such as Sweden are scarce. The purpose of this study has been to explore what
factors are important for Swedish firms when choosing location of their investments in Africa.
We wanted to both fill the gap in research and to help Swedish managers to further
understand which factors that are usually considered when making investment decisions and
which factors are not.
We build a theoretical framework based on the OLI paradigm with a focus on locational
determinants that are specifically important and relevant in the context of FDI in Africa. From
the L dimension of the OLI paradigm, four hypotheses on Swedish FDI decisions in Africa
were developed and tested by performing a hierarchical multiple regression on official
secondary data. This resulted in two models that included a broad spectrum of main and
control variables that enabled us to explore the research question of this study. One model
focused on within-year variation and one did not, only small differences between the models
were found which suggest no major variation between the years.
The locational determinants extracted from the OLI paradigm poorly explains the variance in
inward FDI flows to Africa from Sweden and none of our hypotheses could be supported. The
model could not help us explain which determinants are important for Swedish firms.
Interestingly enough, the model served well in explaining inward FDI flows to Africa from
the entire world. The model showed that market size and natural resources, with an
underlying assumption of political stability, are significantly important determinants in that
context. This implies that firms from around the world seem to invest in Africa to mainly
access the countries’ markets and natural resources. To our surprise, this is not the case for
Swedish firms.
Why cannot the model explain what locational determinants are important for Swedish firms
when it well explains the same for firms from the rest of the world? We have discussed this
matter and sought answer in mainly three different ways. First, Sweden might not be different
but we have instead simply operationalized and measured the variables inadequately. We do
36
not believe this to be the case since we have been guided by previous research and theory in
choosing the variables and measurement but we cannot rule out the possibility. Second, the
OLI paradigm is a wide theoretical approach encompassing countless determinants. It could
be so that we have looked for the incorrect determinants within the paradigm. However, we
have chosen the most logical and obvious determinants for each type of FDI, for example
market size for explaining market seeking FDI, which should have shown to be a significant
determinant if Swedish firms were conducting market seeking FDI. The third reason, which
we argue is the most relevant, is that the locational determinants derived from the OLI
paradigm are inadequate in explaining African inward FDI from Sweden. Or put in a wider
context, the L dimension of the OLI paradigm might not well explain FDI flows from small
and open countries to developing countries. Although the results of this study help filling the
gap of understanding FDI decisions of firms originating from small and open countries as
Sweden we cannot statistically prove this to be the case for other similar countries. We are
aware of this limitation and can only speculate in the generalizability of this study in the
context of FDI flows from small and open countries to developing countries. However, this
study hints towards the necessity to extend the L dimension of the OLI paradigm with factors
that are important in the context of small and open countries investing in developing countries
or to seek answers beyond the paradigm.
Hopefully, this study also presents a practical use to Swedish managers engaging in location
decisions on the African continent. It should be interesting for them to note that Swedish
firms seem do not generally invest in Africa in order to primarily reach large markets or
natural resources but that these determinants are the strongest determinants for firms around
the world when investing in Africa. This implies that Swedish firms might be missing the
chance of exploiting the natural resources that are present in Africa but could not be found in
Sweden. It could also imply that Swedish firms are not seeing the opportunities that the
African markets present and could start to lag behind international competitors who are
engaging in market seeking FDI in Africa.
The preconditions for this study were overall relatively good regarding the access of suitable
data, characteristics of the data and the number of generated cases from the data. However, it
would have been better if we had managed to include more African countries that could have
generated more cases resulting in a strengthened study. The data on Swedish FDI in Africa
showed a relatively high kurtosis and it cannot be out ruled that this had a misleading effect
37
on our results even though we do not believe that the model could have explained a large part
of the variance since it shows so poor results. The fact that there are still relatively few
Swedish corporate groups that are investing in the African region could also have a
misleading effect on our results since an act of one single firm might have a large impact on
the overall FDI data.
We urge researcher to further test the usefulness of the L dimension of the OLI paradigm
when discussing FDI flows from Sweden to Africa or from another small and open country to
developing countries. By including more years, more cases could have been generated which
might give future researcher a stronger and more nuanced picture of FDI in Africa. We chose
to focus only on the years 2007 to 2010 since these years provide us with the most recent
available data and showed us which determinants that are important for firms in recent years.
Further tests could also be done by using different measurement of the variables, for example
agricultural products could be included when measuring natural resources. However, we
believe that other theoretical approaches can be more useful in this. Future research should
probably focus on network theories, psychic distance and the possibilities of state/private firm
collaborations. Network connections could show to be more important for firms from smaller
countries as Sweden when investing in Africa than, for example, the outlook of a large market
or the abundance of natural resources are. Finally, a qualitative approach could be a good way
to shed light on which determinants are important which could later be followed up by a
quantitative approach to statistically determine these newly found determinants true value.
Acknowledgments
We thank our supervisor Philip Kappen and our opponents at the university for their
commitment, knowledge and constructive criticism.
38
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