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

2

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

3

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

4

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).

5

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

6

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

7

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

8

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

9

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

11

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

14

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

References Allison, P. D. (2005). Fixed Effects Regression Methods for Longitudinal Data. Cary: SAS

Institute Inc

Asiedu, E. (2002). On the Determinants of Foreign Direct Investment to Developing

Countries: Is Africa Different? World Development, 30(1), pp. 107-119.

Asiedu, E. (2006). Foreign Direct Investment in Africa: The Role of Natural Resources,

Market Size, Government Policy, Institutions and Political Instability. World Economy, 29(1),

pp. 67-77.

Bartels, F. L., Sadiq, N., Lederer, A. & Lederer, S. (2010). Foreign Direct Investment in Sub-

Saharan Africa: Motivating Factors and Policy Issues. Journal of African Business, 10 (2), pp.

141-162.

Behrman, J. N., & Grosse, R. (1990). International business and governments: issues and

institutions. Columbia: University of South Carolina press

Berry, H., Guillén, M., F., Zhou, N. (2010). An institutional approach to cross-national

distance. Journal of International Business Studies. 41 (9), pp. 1460-1480.

Bevan, A. A., & Estrin, S. (2004). The determinants of foreign direct investment into

European transition economies. Journal of Comparative Economics. 32 (4), pp. 775 – 787.

Billington, N. (1999). The location of foreign direct investment: An empirical analysis.

Applied Economics. 31 (1), pp. 65-76.

Braunerhjelm, P., Svensson, R. (1996). Host country characteristics and agglomeration in

foreign direct investment, Applied Economics, 28, (7), pp. 833-840.

Buckley, P.J., Clegg, L.J., Cross, A.R., Liu, X., Voss, H., & Zheng, P. (2007). The

determinants of Chinese outward foreign direct investment, Journal of International Business

Studies, 38, (4) pp. 499-518.

39

Campos, N.F., & Kinoshita, Y. (2003). Why does FDI go where it goes? New evidence from

the transition economies. IMF Working Paper, WP/03/228, IMF Institute.

Chakrabarti, A. (2001). The determinants of foreign direct investments: Sensitivity analyses

of cross-country regressions, Kyklos, 54 (1), pp. 89-114.

Chen, H., & Chen, T. (1998). Network Linkages and Location Choice in Foreign Direct

Investment. Journal of International Business Studies, 29(3), pp. 445-467.

Cheng, L. K. & Kwan, Y. K. (2000). What are the determinants of the location of foreign

direct investment? The Chinese experience. Journal of International Economics, 51(2), pp.

379–400.

Cheru, F. (2012) Förändrad maktbalans i Afrika, [Online]. Available at:

http://www.nai.uu.se/index.xml?Language=sv [Retrieved: 2012-04-18]

Chidlow, A., Salciuviene, L. & Young, S. (2009). Regional determinants of inward FDI

distribution in Poland. International Business Review, 18(2), pp. 119–133.

Deng, P. (2003). Foreign Investment by Multinationals from Emerging Countries: The case of

China. Journal of Leadership & Organizational Studies. 10 (2), pp. 113 - 124.

DN (2009). Exportrådet tror på Afrika, [Online]. Available at:

http://www.dn.se/ekonomi/exportradet-tror-pa-afrika [Retrieved: 2012-02-24]

Dunning, J.H. (1988). The eclectic paradigm of international production: a restatement and

some possible extensions. Journal of International Business Studies. 19 (1), pp. 1-31.

Dunning, J.H. (2000). The eclectic paradigm as an envelope for economic and business

theories of MNE activity, International Business Review, 9 (2), pp. 163-190.

Dunning, J.H. (2001). The Eclectic (OLI) Paradigm of International Production: Past, Present

and Future, International Journal of the Economics of Business, 8 (2), pp. 173-190.

40

Dunning, J.H., & Narula, R. (2004). Multinationals and industrial competitiveness: a new

agenda. Cheltenham: Edward Elgar.

Dunning, J.H., & Lundan, S. (2008). Multinational enterprises and the global economy (2nd

ed.). Cheltenham: Edward Elgar.

The Economist (2011). Africa Rising, [Online]. Available at

http://www.economist.com/node/21541015 [Retrieved: 2012-02-22]

Erdal, F. & Tatoglu, E. (2002). Loactional determinants of foreign direct investments in an

emerging market economy: evidence from Turkey. Multinatinational Business Review, 10 (2),

pp. 21-27.

Eurostat (2011). National accounts - GDP, [Online]. Available at:

http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/National_accounts_%E2%80%

9 3_GDP [Retrieved: 2012-03-16]

Forsgren, M. (2008). Theories of the Multinational Firm, Cheltenham: Edward Elgar.

Government Offices of Sweden (2011). Samråd om Afrikas potential inom näringslivet,

[Online]. Available at: http://www.sweden.gov.se/sb/d/14184/a/163463. [Retrieved: 2012-02-

24]

Habib, M., & Zurawicki, L. (2001). Country-level investments and the effect of corruption -

some empirical evidence. International Business Review. 10 (6), pp. 687 - 700

Handelsbanken (2012). Afrikas ekonomier växer snabbt, Video clip, [Online]. Available at:

http://tv.handelsbanken.se/2012/01/24/afrikas-ekonomier-vaxer-snabbt/ [Retrieved: 2012-02-

24]

Hair, J. F., Black, W. C., Babin, B. J., Andersson, R. E., & Tatham, R. L. (2006). Multivariate

data analysis, sixth edition, Upper Saddle River: Pearson Prentice Hall.

Hymer, S. (1976). The international operations of national firms. Massachusettes:

41

Massachusetts Institute of Technology.

IMF (2012). World Economic Outlook, April 2012, [Online]. Available at: http://www.imf.org/external/pubs/ft/weo/2012/01/pdf/text.pdf [Retrieved: 2012-05-02]

Jiménez, A. (2011). Political Risk as a Determinant of Southern European FDI in

neighbouring developing countries. Emerging Markets Finance and Trade , 47 (4), pp. 59-74.

Johanson, J., & Vahlne, J. (2009). The Uppsala internationalization process model revisited:

From liability of foreignness to liability of outsidership. Journal of International Business

Studies, 40 (9), pp. 1411-1431.

Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010). The Worldwide Governance Indicators -

Methodology and Analytical Issues, Policy Research Working Paper 5430, The World Bank.

Kirkpatrick, C., Parker, D., Zhang, Y. (2006). Foreign direct investment in infrastructure in

developing countries: does regulation make a difference? Transnational Corporations, 15 (1),

pp. 143-171.

Krugell, W. (2005). The determinants of foreign direct investment in Africa. In Gilroy, B. M.,

Gries. T., & Naudé, W. A., (eds), Multinational enterprises, foreign direct investment and

growth in Africa: South-African perspectives, Heidelberg: Physica-Verlag, pp. 49-72.

Li, Q. (2009). Outlier, Measurement, and the Democracy-FDI controversy. Quarterly Journal

of Political Science, 4 (2), pp. 167-181.

Loree, D. W. & Guisinger, S. E. (1995). Policy and Non-Policy Determinants of U.S. Equity

Foreign Direct Investment. Journal of International Business Studies, 26(2), pp. 281-299.

Luiz, J.M., & Charalambous, H. (2009). Factors influencing foreign direct investment of

South African financial services firms in Sub-Saharan Africa. International Business Review.

18 (3), pp. 305–317.

Mariotti, S., Piscitello, L. (1995). Information costs and location of FDIs within the host

42

country: empirical evidence from Italy. Journal of International Business Studies. 26 (4), pp.

815-841.

Morisset. (2000). The Foreign Direct Investment in Africa – Policies also matters, Policy

Research Working Paper 2481, The World Bank.

Naudé, W. A. & Krugell, W. F. (2007). Investigating geography and institutions as

determinants of foreign direct investment in Africa using panel data. Applied Economics, 39

(10), pp. 1223-1233.

Newbold, P., Carlson, W., L., Thorne, B. (2006). Statistics for Business and Economics, sixth

edition. Upper Saddle River: Pearson Education Inc.

Nurudeen, A., Wafure, O. G. & Auta, E. M. (2011). Determinants of Foreign Direct

Investment: The Case of Nigeria. IUP Journal of Monetary Economics,, 9 (3), pp. p50-67.

Onyeiwu, S. & Shrestha, H. (2004). Determinants of Foreign Direct Investment in Africa.

Journal of Developing Societies, 20 (1-2), pp. 89-106.

Pallant, J. (2007). SPSS Survival Manual: A Step by Step Guide to Data Analysis using SPSS

Program (3d ed). Berkshire: McGraw-Hill Education.

Pallant, J. (2010). SPSS Survival Manual: A Step by Step Guide to Data Analysis using SPSS

Program (4th ed). Berkshire: McGraw-Hill Education.

Park, H. M., (2009). Linear Regression Models for Panel Data Using SAS, Stata, LIMDEP,

and SPSS. Working Paper. Indiana University.

Prahalad, C., K. & Hammond, A. (2002). Serving the world’s poor. Harvard Business

Review, 80 (9), pp.48-57.

Root, F. R. & Ahmed, A. A. (1978). The Influence of Policy Instruments on Manufacturing

Direct Foreign Investment in. Journal of International Business Studies, 9 (3), pp. 81-93.

43

Saunders, M. et al. (2009). Research methods for business students. (5th ed) Essex: Pearson

Education Limited.

Schneider, F. & Frey, B. S. (1985). Economic and Political Determinants of Foreign Direct

Investment. World Development, 13 (2), pp. 161-175.

Sethi, D., Guisinger, S. E., Phelan, S. E. & Berg, D. M. (2003). Trends in Foreign Direct

Investment Flows: A Theoretical and Empirical Analysis. Journal of International Business

Studies, 34 (4), pp. 315-326.

Sida (2011). Business for Development, [Online]. Available at:

http://www.sida.se/Global/12691_Business_for_Development_memorandum_C4.pdf

[Retrieved 2012-03-11]

Statistics Sweden (2012 a). Direct Investment by country, [Online]. Available at:

http://www.ssd.scb.se/databaser/makro/Visavar.asp?yp=tansss&xu=C9233001&omradekod=

FM&huvudtabell=FM0001T04&omradetext=Financialmarkets&tabelltext=Direct+investmen

t+by+country%2C+net%2C+Annual+data&preskat=O&prodid=FM0001&deltabell=&deltabe

llnamn=Direktinvesteringar%2E+Nettofl%F6de+efter+land%2E+%C5r&innehall=NettoSvm

kr&starttid=1998&stopptid=2011&Fromwhere=M&lang=2&langdb=2. [Retrieved: 2012-02-

22]

Statistics Sweden (2012 b). About us, [Online]. Available at:

http://www.scb.se/Pages/List____250619.aspx [Retrieved: 2012-03-16]

Stoian, C. & Filippaios, F. (2008). Dunning’s eclectic paradigm: A holistic, yet context

specific framework for analysing the determinants of outward FDI Evidence from

international Greek investments. International Business Review, 17 (3), pp. 349-367.

Svd (2012). Investerare: Afrika mest attraktiva tillväxtregionen, [Online]. Available at:

http://www.svd.se/naringsliv/stort-intresse-for-afrika_6799715.svd [Retrieved: 2012-02-24]

Swedfund (2010). Addis Cardiac hospital, [Online]. Available at:

http://www.swedfund.se/?case=addis-cardiac-hospital [Retrieved: 2012-03-29]

44

Swedish Agency for Growth Analysis (2012), Svenska koncerner med dotterbolag i utlandet,

[Online]. Available at:

http://statistikportalen.tillvaxtanalys.se/Pages/CreateReport.aspx?StatisticalAreaID=1

[Retrieved: 2012-03-15]

Swedish Trade Council (2012). Corporate Social Responsibility, [Online]. Available at:

http://www.swedishtrade.se/sv/vara-kontor/europa/portugal/Om-Portugal/CSR/ [Retrieved:

2012-02-24]

Tabachnick, B.G. & Fidell, L.S. (2007). Using multivariate statistics (5th ed). Boston:

Pearson Education.

Twomey, M.J. (2002). A Century of Foreign Investment in the Third World. London:

Routledge.

UN (2010). FDI in Africa, Policy brief No.4, [Online]. Available at:

http://www.un.org/africa/osaa/reports/2010_FDIbrief.pdf [Retrieved: 2012-04-18]

Unctad (2011). World Investment Report 2011, [Online]. Available at:

http://www.unctad.org/Templates/webflyer.asp?docid=15189&intItemID=6018&lang=1&mo

de=downloads [Retrieved: 2012-02-24]

Villaverde, J., & Maza, M. (2011). Foreign direct investment in Spain: Regional distribution

and determinants. International Business Review. Article in press.

The World Bank (2011 a). Africa Development Indicators; FDI net inflows, [Online].

Available at: http://databank.worldbank.org/ddp/home.do [Retrieved: 2012-03-16]

The World Bank (2011 b). Total natural resources rents, [Online]. Available at:

http://data.worldbank.org/indicator/NY.GDP.TOTL.RT.ZS [Retrieved: 2012-02-17]

The World Bank (2011 c). Africa Development Indicators; communications, [Online].

Available at: http://databank.worldbank.org/ddp/home.do [Retrieved:2012-02-24]

45

The World Bank (2011 d). Africa Development Indicators; 2nd pillar infrastructure, notes,

[Online]. Available at: http://databank.worldbank.org/ddp/home.do [Retrieved: 2012-02-15]

The World Bank (2011 e). Africa Development Indicators, [Online]. Available at:

http://data.worldbank.org/sites/default/files/adi_2011-web.pdf [Retrieved:2012-02-19]

The World Bank (2011 f). Africa Development Indicators; 7th pillar labor market efficiency,

notes, [Online]. Available at: http://databank.worldbank.org/ddp/home.do [Retrieved: 2012-

03-01]

The World Bank (2011 g). Africa Development Indicators; FDI net inflows, notes [Online].

Available at: http://databank.worldbank.org/ddp/home.do [Retrieved: 2012-03-06]

The World Bank (2011 h). World Governance Indicators, [Online]. Available at:

http://databank.worldbank.org/ddp/home.do [Retrieved: 2012-02-22]

The World Bank (2011 i). Africa Development Indicators, 12th pillar innovation, notes

[Online]. Available at: http://databank.worldbank.org/ddp/home.do [Retrieved: 2012-03-17]

The World Bank (2011 j). Foreign direct investment, [Online]. Available at:

http://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD [Retrieved: 2012-03-17]

The World Bank (2011 k). World Development Indicators; Total natural resources rents,

[Online]. Available at: http://databank.worldbank.org/ddp/home.do [Retrieved: 2012-04-10]

The World Bank (2011 l). World Data Bank, [Online]. Available at:

http://databank.worldbank.org/ddp/home.do [Retrieved: 2012-03-02]

The World Bank (2011 m). Africa Development Indicators; Trade (% of GDP) [Online].

Available at: http://databank.worldbank.org/ddp/home.do [Retrieved: 2012-03-02]

The World Bank (2011 n). Africa Development Indicators; GDP [Online]. Available at:

http://databank.worldbank.org/ddp/home.do [Retrieved: 2012-03-02]

46

The World Economic Forum (2011). Global Competitiveness Report, 2010-2011, [Online].

Available at: http://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2010-

11.pdf [Retrieved: 2012-03-01]

Zhao, H. & Zhu, G. (2000). Location Factors and Country-of-Origin Differences: An

Empirical Analysis of FDI in China. Multinational Business Review , 8 (1), pp. 60-73.