CHAPTER ONE

INTRODUCTION

1.1 Dissertation Title

The conveyed topic has been formulated into the following research problem:

“Are the determinants of different to China than for the rest of the world?”

1.2 Background

From the time when globalisation has become an essential element of many company strategies in recent times, further and further transnational companies are exploiting new emerging markets for comprehensive economies of scale since the 21st Century. The world's foreign direct investment (FDI) has been increasing at an extraordinary rate. World FDI inflow in 2000 was US $ 1271 billion; by 2008 FDI had grown to an amazing figure of US $ 1.7 trillion, but due to the world economic crisis of 2008-2009, figures are expected to fall to US $1.2 trillion in 2009. A slow growth is expected until 2011 expecting US $ 1.4 trillion in FDI inflows in 2010 and US $ 1.8 trillion in 2011 (World Investment Prospect to 2011 - The Economist, 2007). Inflows of FDI to developing and transitional economies reached their highest level in 2006 with a rise of 21% over 2005. This makes FDI the most significant medium of private capital flows among countries. (UNCTAD, 2001, 2007, 2009)

Emerging markets such as Latin America, Asia and Sub - Saharan Africa have seen rapid investment flows in recent years. Since China has opened its economy, it has attracted huge amount of investment from abroad and has become the world's largest emerging market gaining entry into the World Trade Organisation (WTO). Even though receiving FDI, China is also investing massively in Sub- Saharan Africa. US $ 16.1 billion of FDI are expected in Sub-Saharan African countries by the end of 2009, mainly in Angola, Equatorial Guinea and Nigeria regarding their oil sector. Sub-Saharan Africa FDI inflow in 2006 was only 0.91 % of the World's FDI inflow (National statistics; IMF; UNCTAD, 2007; Economist Intelligence Unit, 2007). Mauritius has received US $ 820 million from China in February 2009 to increase trading between the two countries (Chinadaily, 2009). This study will therefore, analyse what are the determinants of FDI which attract China more than the rest of the world.

The World Investment prospect to 2011 show that SSA had an FDI inflow of US $ 8.4 billions in 1997. The biggest recipients were Nigeria and South Africa as shown in Appendix 3. During the last few years, there has been a significant increase in FDI in SSA leading to an inflow of US $ 16.1 billions in 2009, representing a new record figure.

Harque et al. (1999) argued that:

Investment rating services list Africa as the riskiest region in the world. Indeed, there is some evidence that Africa suffers from being perceived as a ‘bad neighbourhood'. Analysis of the global risk ratings shows that while they are largely explicable in terms of economic fundamentals, Africa as a whole is rated a significantly more risky than is warranted by these fundamentals.

Collier and Gunning (1999, p.20)

Studies by Elbadawi and Mwega (1997), Wilhelms (1998) and Ancharaz (2002) analyse the determinants of FDI in SSA and Mauritius. The Chinese economy has been growing on an average of 10% since 1990's and trade with Africa increased by 700% from 1991 to 2000 (Lyons and Brown, 2009). November 2006 saw a conference between SSA and China following a first ever conference in 2000 between them, to strengthen relationships, increase investment both ways and increase trade. Mauritius saw an increase of Chinese FDI from US $ 10.27 Millions in 2003 to US $ 34.44 millions in 2008. The Chinese government will also investing US $ 750 millions over the next 10 years known as the ‘Jin Fei Project' in Mauritius as a platform to increase trade and investment further in SSA and Africa in general (Oxford Analytica, 2009).

1.3 Problem Definition

The crisis which the governments of each of these countries and local enterprises are facing are the lack of expertise and funds to produce competitive merchandise, development of their industries, exploitation of natural resources and high level of unemployment owing to poor economic expansion and education level which in turn results in less skilled labour. Low output and poor transport system have discouraged investment from abroad, although labour is cheap and resources are available in large amounts. It is a big issue to local governments on how to attract FDI in their country. The question that arises is why China is so focused on investing in this region of the world more rather than other places.

1.4 Rationale

The incentive and personal awareness to the investigator for undertaking this particular study topic arose since studying Economics at A-levels and in previous modules studied at University level about FDI to date. Also being part of the African community, knowing why Eastern Asian countries are showing a huge interest in Sub-Saharan Africa and Mauritius would be very helpful (Adams, 2009). The study is thus aimed at researching the determinants of FDI from Chinese government and private sector to Sub-Saharan Africa and Mauritius mainly as compared to FDI from other countries in the world. In depth study around the subject has given me assurance that there is sufficient research material on the topic chosen. This question was selected as Africa has a lot of under used resources and China has the expertise and funds to exploit them. Various scholars around the world have developed diverse policies and studies to show the presence of China in Africa. However, none of them have in reality, provided a step by step theory on the reasons why FDI from China is increasing so rapidly in Sub-Saharan Africa and Mauritius as compared to other countries involved in OFDI, which this dissertation is aimed at primarily answering.

1.5 Objective of Study

Below is a step-by step plan of how the objective of the study will be achieved:

  • Evaluate the impact of FDI on Mauritius.
  • Compare in which Sub-Saharan African countries FDI have increased massively over the passed few years from China than the rest of the world and the reasons why.
  • Assess the advantages and disadvantages of Mauritius to attract FDI. Economic growth is one of the important aspects gained through FDI and this is what the study will justify.
  • Analyse the determinants which can boost inflow of FDI from China to Mauritius.
  • Discuss what positive outcome can be drawn from FDI in Mauritius which benefited from Chinese FDI as compared to the rest of the world.

1.6 Research Methodology

1.6.1 Data Collection

This study investigates the connection between FDI and important economic indicators, and discusses the inflow of FDI to Sub-Saharan Africa and Mauritius from China as compared to the rest of the world. Secondary data will be used in this dissertation, therefore desk research. Information will be obtained from the sources below:

  • In dept information will be from the library, both written and online, in The University of Northampton in UK through academic journals via Metalib and various other academic journals which can be accessed.
  • Other sources of up-to-date information will be through well respected professional articles such as the Financial Times (FT), British Broadcasting Corporation (BBC) and The Economist.
  • Collection of secondary data will be through United Nations Conference on Trade and Development (UNCTAD), National Bureau of Statistics of China, Republic of Mauritius Central Statistics Office, Bank of Mauritius (BOM), Board of Investment (Mauritius), The State Investment Corporation Ltd (Mauritius), The University of Mauritius (UOM), Southern African Development Community (SADC), The New Partnership for Africa's Development (NEPAD) and Union Economique et Monétaire Ouest Africaine (UEMOA), all through their official website to obtain latest data.

1.6.2 Research Methodologies

This study main research methodology will be as follows:

  • An abstract discussion of the impact of FDI on the economies of the host countries and the factors that encourage inflows of FDI through critical review of literature, providing a theoretical structure for the following of the study.
  • Descriptive study of the allocation of FDI Mauritius, impact of FDI on their economies especially Mauritius and the advantages and disadvantages of attracting FDI.
  • Experiential study of the factors influencing inflow of FDI in Sub-Saharan Africa and Mauritius using regression and correlation analysis, and analysis of the strategies involved to attract FDI to these countries.

1.7 Prior Research

The literature review consists of all the previous publications by authors concerning the subject area, statistical data, and their findings. This will help me increase my knowledge about the subject area and find the gap in the previous literature to explore further into the subject matter.

China has been the recipient of huge amount of FDI inflows since the 21st Century as the government decided to open its economy on the world stage. Since recent years FDI outflow from China has been increasing rapidly. Outflow of FDI (OFDI) was virtually none existent in the 1980's from China, but the latter is now ranked 4th largest developing country involved in OFDI and predictions are that they will soon be the leading country (OECD, 2008). This suggests that Chinese economy is growing very fast and that it is prepared to grab any opportunity, either inflows or outflows of FDI, in various sectors that are available to them.

Source: Ministry of Commerce (MOFCOM) FDI website, www.fdi.gov.cn.

The graph above shows how OFDI from China has increased rapidly over recent years. Although the world incurred a huge economic crisis in 2008, OFDI from China doubled in that year, reaching to US $ 40.7 billion, compared to 2007 (OECD, 2009). Such figures demonstrate the rate of expansion of China on the world stage and that it is not prepared to slow down even during difficult periods.

Relationship between China and Africa dated back from the 1950's. The Bandoeng Conference was organised to link African countries and other countries against the imperialist nations. Following another conference in 2000, China decided to cancel African debt of US $ 10 billion and in 2006 agreed concessional loans of US $ 10 billion to 48 African Countries over the next three years (ECOWAS-SWAC/OECD 2006). Li Xiao Dong, manager of a mineral company in Africa said: “Africa is full of opportunities - it's just like China when we started opening up a few years ago” (BBC News 2007). This shows Chinese interest's in Africa and especially Sub-Saharan Africa where many countries are politically unstable, therefore suggesting under exploitation of resources. This shows that many other countries invest in Africa just for exploiting the resource.

Source: Doing Business, International Finance Corporation

The table above shows the ranking of Sub-Saharan countries and Regions in which it is best to do business in. Although Sub-Saharan Africa was ranked 136th (International Monetary Fund, 2008), China is not afraid to invest there as it knows there is a lot of potential for development.

Mauritius is linked with China since the dawn of the discovery of the island. Chinese community in the island is a major percentage of the whole population of the island as compared to other nations with Chinese community. On the 17 February 2009, during a visit in Mauritius, Chinese President, Hu Jintao, agreed to spend US $ 700 million for the development of houses, offices and factories in the country and to lend US $ 260 million for the expansion of the international airport. Mauritius was one the countries chosen out of five where establishment of special investment zones will be done by China (Oxford Analytica, 2009). It is the only country chosen out of the five that is non-oil producing. This shows that Mauritius is emerging in sectors such as sea-food hub and offshore banking. Moreover, the reform of 2006, aiming at encouraging the economy's world competitiveness had a colossal impact because of policies such as tax cuts, reduction in red tap, new labour law and to come in aid to small and medium entrepreneurs (SMEs).

1.8 Dissertation Outline

The dissertation will be organised into six chapters:

  1. A background of FDI and economic development in different regions of Sub-Saharan Africa and Mauritius, and the problem definition. It will also indicate the objectives and the following methodology.
  2. This literature review which will show the detailed academic structure which will be use to study the issue stated. It will show the effects of FDI on the developing world and the importance of it.
  3. This section will show the distribution of FDI in the different countries involved; the effects on their economies; and discuss the reasons that might limit investment from China. Advantages and disadvantages of attracting FDI from China will also be looked at as compared to other countries.
  4. This part will be the analysis of the different economic variables and FDI through the secondary data gathered.
  5. Investigation of the influential factors of FDI on Sub-Saharan Africa and Mauritius by regression and correlation analysis will be carried out, proving the link between FDI growth and economic growth. This will show the major conditions these countries will have to get together to attract inflows of FDI from China.
  6. Finally, there will be a conclusion and recommendations for the government and companies involved, which will find measures to attract inflows of FDI from China to help increase economic growth.

1.9. Dissertation Plan

The dissertation is due on the 6 May 2010 which is week 18 of The University of Northampton academic calendar. The Gantt chart below illustrates the schedule for my dissertation:

Task

December

January

February

March

April

May

Week Number

49

50

51

52

53

1

2

3

4

5

6

7

8

9

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11

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18

Literature Review

Primary/Secondary data collection

Analysis of secondary data

Initial draft of the Dissertation

Revising draft of the dissertation

Updated Literature Review

Second draft of Dissertation

Revising draft of the dissertation

Submission of Final Dissertation

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

Foreign direct investment (FDI) is increasing globally especially from progressing economies such as China. Sub-Saharan Africa (SSA) is attracting a lot of the inflows of FDI since the turn of the century and China is a major player in this. Investment in SSA was largely influenced by USA, France and UK in the past but this has now changed. This topic has not generated enough academic attention in recent years about the expansion of China in SSA. This paper will therefore analyse what are the determinants of FDI attracting Chinese companies more than the rest of the world, methods used to analyse the different factors affecting FDI and the gaps found in previous studies.

2.2 Definition of FDI

Foreign direct investment arises when an investor located in a country (home country) buys an asset in a different country (host country) and manages the asset (WTO, 1996).This differentiates FDI from portfolio investmentin overseas investment (UNCTAD, 1999). Direct investment includes equity capital, reinvested earnings and other capital (or intercompany debt transactions). FDI companies include supplementary companies (in which non-resident investors own more then 50% of the capital), associates (in which non-resident investors own 50% or less) and branches (totally or mutually owned incorporated companies) (UNCTAD, 1999).

2.3 General trend of FDI

Economic growth, new capital accumulation and aids to government are seen as the positive outcomes of FDI in the host country (UNCTAD, 1992). This is why more and more countries support FDI inflow, particularly developing countries. Inflow of FDI in 1999 was just below US $ 1000 billions rising to over US $ 1.2 trillions in 2009 representing a fall of US $ 0.5 trillions from 2008 because of the world economic crisis (OECD, 2009; World Investment Report 2009). The role of FDI in developing and transitional economies has grown drastically since the 1990's. Total inflow of FDI to developing economies rose from US $ 34 billion in 1990 to just over US $ 1000 billions (see Appendix 1). Sub-Saharan Africa (SSA) is forecasted to comprise of 16.1 % of the world's inflow of FDI in 2009 (World Investment Report to 2011, 2007) (see Appendix 2). Consequently, the issue is about the determinants attracting FDI to SSA and Mauritius especially from Chinese companies than the rest of the world.

2.4 General Determinants of FDI

On theoretical grounds, there are several determining factors of FDI. There is no conclusive evidence of any particular theory of foreign investment. In other words, there is no completely satisfactory theory on foreign investment. Dunning (1981) has classified three reasons as three sets of rewards for a company to go global: ownership, location and internalisation (the so-called OLI theory of eclectic theory). FDI made by multinational companies in a country can depend on these three factors mentioned above and discussed below:

  • Ownership advantage within the company that allows it to develop and diversify more effectively than others at home or overseas (such as proprietary skill, trademark and management knowledge).
  • Location advantage in the host country that makes the country as the best place for the company to produce across countries (such as low-cost labour, increasing market size and tax incentive).
  • Internalisation advantage relates to the company's trade-off between FDI and exporting or licensing (such as transaction costs).

These advantages give a clear indication why companies want to invest overseas although extra costs are involved when a company invest in a country where it is not familiar with the local markets and organisations (South Centre, 1997).

Moreover, many other theories explain FDI in several ways. For instance, Pitelis (1996) analysed the oligopolistic theory which is competition in a market where there are a few large firms dominating the market. Each firm in formulating its pricing and output policies or strategies will be obliged to incorporate the effect of its action on the rival producers and the possible course of action they might in turn pursue. ‘Oligopolistic disequilibrium' took place in recent years in the global economy (South centre, 1997) The reason why FDI is taking place in the form of mergers and acquisitions especially in developed economies is explained by this theory.

Bradley (1999) argued that, if it is believed that a company has great and costless understanding from which benefit taken can be, the company is generally born with a geographical scope limited to an area or a home country, but now this horizon has changed. Internal and external factors are the result of these changes. New product development, rising internal resource and administration devotion are the internal factors; strong competition, government regulations are examples of the external factors. Many companies increase their performance based on access to raw materials, new possible markets and increasing market share. Therefore Dunning (1988) acknowledged 4 stimuli for FDI:

  • Resource-seeking : improving the feature of resources and accessibility of home associates cooperation together to promote knowledge and/or capital-intensive resource use.
  • Market-seeking : enlarged need for existence close to customers and mounting significance of promotional actions by local development organisations.
  • Efficiency-seeking : amplified role of governments in eliminating reshuffling economic action and smooth the progress of improving human resources; an entrepreneurial situation and competitiveness and co-operation amongst companies.
  • Strategic asset seeking : chance presented for swap of ideas and interactive learning; access to diverse customs and traditions, different customer demands and likings.

This theory, i.e. Dunning (1988), suggests that the principle motivation for a company to look for new investment markets are because of the above 4 reasons, for example, low labour costs, easy access to raw materials, increase market share, and a more negligent attention to environment by some governments. Consequently it may be more proficient to produce goods in overseas countries than in the home country. Worldwide investment enables a firm to operate better, ease local resources and diminish risk, find new markets and sway government strategy.

The most common ways to determine the factors affecting FDI in developing countries are as follow (revealed in previous studies):

  1. Human capital and labour costs
  2. Infrastructure
  3. Market size/GDP, openness and export
  4. Natural resources
  5. South-South cooperation
  6. Political stability and corruption level
  7. Government policies, taxes and tariffs

These are discussed further in the next sections, firstly in general terms and then relating to Sub-Saharan Africa and Mauritius in respect to Chinese investment.

2.4.1 Human Capital & Labour Costs and FDI

Authors such as Root and Ahmed (1979), Schneider and Frey (1985), Lucas (1990), Borensztein et al., (1998), Noorbakhsh et al., (2001) and Aseidu (2002) all agreed that human capital and labour, which are both resource-seeking determinants of FDI, have an important role to play in the attraction of investment. Foreign investors require labour to have certain level of education, skills and health status which affects the size of FDI inflows as discussed by Zhang & Markusen (1999). This is because skilled workforce is more productive and can be trained to new technologies easier. Root and Ahmed (1979) use the level of secondary enrolment to calculate the level of FDI in human capital in their study. Coughlin and Segave (2000) study revealed that poor education level can result in low FDI. Investors usually have an overview on the education level by looking at the secondary education enrolment rate (SER) which is available from a country's Central Statistics Office in the labour sector.

The cost of labour has always been a core part of the overall production cost of companies. Variation in wages has been regularly discussed in empirical literature which is a fact in labour-intensive companies where high wage demands would restrict FDI. Nevertheless, high wage demands may occur because the country is receiving high FDI. Investors will therefore also look at the nominal wage rate in a country before considering investing (Wheeler and Mody, 1992).

2.4.2 Infrastructure and FDI

Saggi (2002) suggested that developing countries require a good level of infrastructural facilities (.i.e. resource-seeking determinant of FDI) to be able to attract vast amount of FDI. Road and rail networks, information and telecommunication, harbour, airport, power, gas and water supply all form part of the infrastructure in a country which are available for households, public services and private companies. Aschauer (1988) suggests that investment on infrastructure and FDI are complementary. Nations which devote a large proportion of their gross national product (GNP) to infrastructure enjoys a high level of FDI inflows.

To measure the level of infrastructure, one should take into consideration both the availability and reliability of the latter. Infrastructure is not of much use if it is not reliable, for instance, quality of the phone lines, internet connection or water supply. Availability to foreign investors is also a key factor, for example, will there be internet connection or accessible roads to the location of the company as discussed by Asiedu (2002). To analyse this, Asiedu (2002) using telephone lines per 1000 of population in her study. A good level of infrastructure will decrease the cost of provision of these by foreign investors lowering costs for them (Dupasquier and Osakwe, 2005). To conclude, a country with good quality of infrastructure has potential of attracting more investment.

2.4.3 Market Size/GDP, Openness & Export and FDI

The host country's economic position and possible demand for its production locally and internationally are significant factors to consider before investing in a country. Scaperlanda and Mauer (1969) said “once it reaches a threshold level that is large enough to allow economies of scale and efficient utilisation of resources” (Sawkut et al., 2007, p.7) FDI will flow in. FDI responds in an optimistic manner to important market size as proven by Billington (1999) with his market size hypothesis using cross-sectional sample based on the regression model.

GDP growth is used as a substitute for market demand size (Wei et al., 1999). Real GDP per capita and FDI share a common relationship as confirmed by studies from Kravis and Lipsey (1982), Scheinder and Frey (1985), Wheeler and Moody (1992), Tsai (1994), Lipsey (1999) and Wei (2000). Results from studies by Edwards (1990) and Jaspersen et al. (2000) showed that FDI is inversely related to GDP per capita using the inverse of income per capita as substitute for the return on investment. However some other studies showed a positive relationship between the two, for instance, Schneider and Fry (1985) and Tsai (1994). It is argued that high GDP per capita gives more prospects for inflow of FDI.

Hufbauer et al., (1994) showed that openness encourages FDI but it depends on the type of investment. The ratio of trade to GDP can be used to determine openness of a country. Foreign companies who want to trade in the host country may not wish to setup a branch in the host country because of trade restrictions. Multinational companies will want to seek more open economies if are exporting.

Stern (1997) and Jun and Singh (1996) put forward that FDI and exports work together. FDI in developing countries is more responsive to demands from exports than local demands as shown in Lucas (1993) sample study of South Asian countries. This is because foreign investors have a better experience on the international market leading to an increase the host country's export (Mucchielli and Chedor, 1999). The level of inflation is also considered and may have varied consequences depending on the economic level of the host country (Musila and Singué, 2006).

Market size/GDP, openness and export which are market-seeking determinants of FDI have been used in previous literatures as determinants of FDI such as Kravis and Lipsey (1982), Scheinder and Frey (1985), Wheeler and Moody (1992), Tsai (1994), Lipsey (1999) and Wei (2000) using real GDP per capita as a market indicator for export which has yielded positive results. However, Loree and Guisinger (1995), Hausmann and Fernandez-Aria (2000) did not find it relevant as a determinant of FDI using the same method.

2.4.4 Natural Resources and FDI

As theories suggest, countries blessed by nature with natural resources (.i.e. resource-seeking determinants of FDI) such as oil, minerals and coal are better positioned to attract FDI. Not including this measure from the study especially from countries in Africa will not make it relevant (Asiedu, 2002). Literatures which also took into account natural resources as a determinant of FDI include Warner and Sachs (1999), Asiedu and Esfahani (2001) and the World development Indicators 2009. Dunning (1998) and Caves (1996) both considered the accessibility, expenditure and value of natural resources and their expansion as a major incentive for FDI in a country.

2.4.5 South-South Cooperation and FDI

The South -South Cooperation (SSC) are activities between southern countries which have recently become industrialised and less developed countries in the southern part of the globe. Emerging economies also form part of the SSC including China, Brazil, India and South Africa. This is a strategic asset seeking determinant of FDI. Activities include investments, trade, transfer of technologies resolving crisis in member countries. It was created by the United Nations in the late 1970's (Schmitt, 2007). Member countries have special agreements to promote cooperation amongst them such as very low tariffs to trade amongst them. There is nevertheless a severe lack of academic studies on the SSC as a determinant of FDI. SSC help to expand market size, improve infrastructure and educational level to gain more skilled labour in the future and also help in political and social issues (Lewis, 1980; Antweiler and Trefler, 2002). This promoted FDI inflows in Southern countries to increase from 16% in 2005 to 37% in 1993 (World Bank, 2006). Creation of further smaller co operations groups resulted from this, to encourage larger amount of FDI inflows from the foreign investors, such as in Africa there is the Southern Africa Development Community (SADC) and Common Market for Eastern and Southern Africa (COMESA). This has promoted more confidence in foreign investors to invest in developing countries.

2.4.6 Political Instability & Corruption and FDI

Political instability and corruption, which are strategic asset-seeking determinants of FDI, have an impact of FDI because political issues show a risk of investing a country which creates an environment discouraging FDI (Schneider and Frey, 1985). Risks of investing in a country can be analysed using the political risk rating in the International Country Risk Guide issued every year. Aseidu (2002) and Barro and Lee (1993) used the level of murders and revolutions to measure the level of political instability. Political instability and corruption can be divided into 2 categories: social factors and political factors.

Social factors

consist of the quality of the labour force, crime rate, employee-employer relationship and the level of corruption and transparency (Forontan, 1993). Corruption has an effect of increasing cost of investments. (Ancharaz, 2002). Good governance, stable democratic system and law application are positive political factors that encourage FDI. Foreign investors prefer to invest in a stable political economy as their capital will be safe, not at risk and no fear of production stoppage.

2.4.7 Government Policies, Taxes & Tariffs and FDI

Government can play a crucial role in stimulating foreign investment and is considered an efficiency-seeking determinant of FDI. For instance, increased government grants, investment tax allowances and less time taken to grant permits will be likely to increase investment. Higher corporate tax rates have an adverse effect on FDI and vice-versa approved by Kemsley (1998) and Billington (1999) but Wheeler and Mody (1992) prove tax rates and FDI do not have any relationship. It is argued that the interest rate is the most important determinant of FDI as both are inversely related as interest rate affects the actual cost of capital. This is shown in appendix 4 (Ancharaz, 2002).

Exchange rates have an effect on investment decision from overseas investors as discovered by Harrison & Revenga (1995) and Elbadawi & Mwega (1997). Theoretically, a depreciation of real exchange rate will increase FDI inflows and vice-versa. It can however be contrasted than FDI's main purpose is to create and export from the home country and should not therefore worry the investors. Moreover, it can be argued that a depreciating currency increases import costs and creates a decline in foreign sales profits which are both unfavourable consequences to FDI. All these create conditions determinant to FDI.

2.5 Impact of General Determinants of FDI in SSA from China

Chinese companies and government have invested massively to improve infrastructure in SSA. For instance, the development in infrastructure by China in Angola has a minimum of 30% of local labour force involved as it is a known fact that Chinese firms employ mostly Chinese workers (Donelly, 2005). Cheap labour in Lesotho and its proximity to South Africa is leading to an increase in FDI to access the South African market (Musila and Sigué, 2006). However, cheap labour in China has caused several losses of jobs in some sector, namely textile. For instance, more than 10,000 people lost their jobs in Mauritius because of the low cost of production in china resulting in the closure of several factories (Zafar, 2007).

Generally speaking, SSA countries lack of infrastructure means that overseas investors incur increasing cost of investment. Chinese FDI in SSA was not affected by this because of the Chinese government guarantees and insures every investment in African economies. Chinese investors have, therefore, shown ability to take more risk other foreign investors (World Economic Forum, 2009). Angola is having office building, housing, railway and fibre optic cable put in place by Chinese companies (Donnelly, 2005).

Zafar (2007) concluded that Chinese companies are mainly investing is SSA because of the huge unexploited resources available in those countries such as oil reserves in Angola, Sudan and Nigeria, Copper from Zambia and gold from South-Africa, by conducting a quantitative studies with mixed results. The findings show that China helps in the development of the economies but employs mostly Chinese labour. However, the huge increase in investment in natural resource may result in the Dutch Disease. Also, resource exploitation in SSA from Chinese companies may overlook the development of labour.

In Botswana, good management on the increase in revenue from diamonds suggested a good economic performance increasing GDP and encouraging FDI. This resulted in FDI from China in their mineral mining resources (Iimi, 2006). Nigeria's poor economic growth was reflected in its low FDI but recent increase in economic growth led to increased investment especially from China in its oil sector (Ayanwale, 2007).

The first China-Sub-Saharan Africa Cooperation Forum of 2000, in Beijing, led to more trade and investment between the two. Following on the same note, another forum was held in 2006 to strengthen the relationship with a “no-string attached” trade and investment policies unlike cooperation with other part of the world like the USA. The only request was to support the Beijing “One-China” plan referencing to Taiwan . They also vowed not to meddle with internal affairs in the host country. China is also investing in SSA to gain duty-free access to the US market as African countries enjoy this through the Africa Growth and Opportunity Act (AGOA) (Zafar, 2007).

For African countries, political stability and corruption level are important determinants of FDI. Mali and Mozambique are two countries which were politically unstable a few years ago but managed to change into a suitable environment attracting FDI.

Government policies are considered by Chinese companies before investing in any SSA countries. For instance, stabilisation funds were used by Chad to stimulate FDI inflows resulting in China investing in its energy sector. However, Onyeiwu and Shrestha (2004) concluded that reforms did not increase inflow of FDI to Africa over the last 10 years of their study. They used random and fixed effects model to look at whether the stylised determinants of investment influence inflow of FDI to Africa. Uganda has engaged in a closer monetary and fiscal policy, managing exchange rate on a market approach and opening coffee production and export increasing FDI by US $ 200 millions from 1990 to 2000 (Musila and Sigué, 2006). Since 1994, FDI activities in Mauritius do not require the approval of the Bank of Mauritius (UNCTAD, 2006).

2.6 Inflows of FDI in SSA and Mauritius: China v/s Rest of the World

Since Chinese companies investing in SSA and Mauritius are mostly state owned, they receive Official Development Assistance (ODA) from the China's Ministry of Commerce because of the new cooperation between China and the African continent. Asche and Schüller (2008) analyse the advantage which are subsidised loans, government grant access to information and diplomatic support. Furthermore, the Chinese government provide cheap loans and development the infrastructure in African countries especially those which suffered conflicts in the past. This creates an atmosphere welcoming FDI from China as compared to other non-Chinese firms. On the other hand, other firms engaged in FDI operate individually (Schmitt, 2007).

2.7 Summary

Appendix 5 shows the authors, the type of study, methods use and the results found. This shows that in general, most literatures considered the general determinants of FDI in SSA and only one considered why China is investing in SSA countries. In addition, none of them considered the South-South Cooperation as a determinant of FDI. This is important because of the growing relationship between the African countries and the Chinese government. As a result the gaps in previous literatures are:

  • Lack of focus on why Chinese firms are investing in Africa
  • Omission of analysing the South-South Cooperation as a determinant of FDI in SSA and Mauritius.

Consequently, my study will include these 2 points and give an up-to-date analysis from 2000 to 2009 using 20 SSA countries (i.e. 10 best and 10 worst performing countries) attracting FDI from China compared to the rest of the world. The Multiple Regression Model and correlation analysis will be used in this paper as it is the most common used and provides accurate result. Secondary data will be collected from International Country Risk Guide (ICRG), the World Development Indicators of the World Bank, UNCTAD and Ministry of Commerce of the People's Republic of China as primary sources. The Freedom House and World Resources Institute will be other sources of data.

CHAPTER THREE

RESEARCH METHODS

3.1 Research Plan and Data Collection

For analyse if the determinants affecting SSA and Mauritius different for Chinese companies than the rest of the world, there are several method. These methods will be discussed in this chapter in details.

Researchers tend to use two main methods of analysing data which are quantitative and qualitative analysis (Isadore, 2000).

Quantitative analysis is often referred to as theory testing and can be summarised in the following order:

  • Theory
  • General Hypotheses
  • Data Collection
  • Data Analysis
  • Results
  • Conclusion
  • Theory Confirmation/Revision

Qualitative analysis, on the other hand, is inductive in nature. The use of this analysis is either to test a theory, or to develop and explain it and can be summarised in the following order:

  • Data Collection
  • Data Analysis
  • Conclusions
  • Development of Hypotheses
  • Leading to Theory Development

Considering the nature of the current study, a qualitative approach is needed as this research is not aimed at analysis an existing theory but at developing one. The main strengths of this method is it they help studying cases in depth, provide individual data and enable cross comparison and help to explain complex data. Nevertheless, the main limitations to this method are that data analysis involves a lot of time and results can be influenced by the researcher's personal bias.

3.1.1 Primary Data

Primary data comprises fact and figure findings for an explicit function . This type of data can be collected through: observation, interviews, questionnaires and carrying out tests (Kotler, 1991). Primary information will be gathered to achieve the research's needs. Interviews and questionnaires will help gain more in dept data and will facilitate comparison. However, the major constraint is the time and costs involved. There is limited funding and ability to go to China and to 20 Sub-Saharan African countries to collect first hand data. Therefore, this study will use secondary resources to collect valuable and related information.

3.1.2 Secondary Data

Data which already exist have been gathered for a different reason, and they are called secondary data (Kotler, 1991). The benefits of using secondary information are that gathering them is less expensive, more convenient and less time consuming than gathering primary information.

Although there are many advantages associated with analysing secondary data, disadvantages also occur in this study as shown below:

  • Published information is often one or two years old and recent data, i.e. 2009, are only for the first quarter of the year but very rarely for a complete year. Therefore some figures might be out of date leading to a slight lack for relevance for the study. Furthermore, since there were no studies taking into consideration the South-South Cooperation as a determinant of FDI, this information will have to be calculated.
  • Collecting information regarding inflows of FDI to Sub-Saharan Africa (SSA) is difficult because most studies focus on Africa in general. Data from each individual country is not directly available from the respective governments. Consequently secondary data has to be collected from other source such as the WTO.

Secondary data has been collected from various places and various means. This comprises of international organisations including the World Trade Organisation (WTO), Organisation for Economic Cooperation and Development (OECD) and United Nations Conference on Trade and Development (UNCTAD), World Bank, Bank of Mauritius (BOM), Board of Investment (Mauritius), The State Investment Corporation Ltd (Mauritius), The University of Mauritius (UOM), Southern African Development Community (SADC) and The New Partnership for Africa's Development (NEPAD) mostly. Information of Chinese outward foreign direct investment (FDI) was available from China's Ministry of Foreign Trade and Economic Cooperation (MOFTEC).

3.2 Methodology and Models

Empirical and theoretical works on the various determinants of FDI have already been discussed in the previous chapter and the possible methodology is explained below:

Asiedu (2002) used the

ordinary least square analysis (OLS)

which was discovered by Carl Friedrich Gauss in 1795. This method assumes that the dependent variables are a linear function of the independent variables and the equation as shown below:

Y = x1+x2…+xn

where x1, x2, xn are constants

The aim is to find the best constants to generate the most accurate results. The model is said to be linear because when the constants are plotted on a graph, it forms a straight line (Clockbackward, 2009). When different types of constants are plotted together, for example, four constants, it forms a plane (

Appendix

Its advantages are that it is easy to use, provide a visual results which is easy to understand and can be generated on a computer easily. However, it cannot be applied to this study as it cannot be used with too many variables which is why Asiedu (2002) used only 4. Moreover, in real life, nothing is linear which will make the study theoretical and excessive differences in large and small values of constants make the study inaccurate (Clockbackward, 2009).

Fixed and random effects model

also known as a

mixed model

was used by Onyeiwu and Shrestha (2004). Fixed effects are defined as choosing variables constants variables from a criterion (e.g. tax rate is less than 30%) whereas random effects are choosing random variables (e.g. general tax rate). The formula for this method is a vector equation as shown in

Appendix 7

The major advantage of this model is that the error vector is related to the individual effects of each factor. This method is not suitable for this research as it has no control over other factors making it confusing. Since it involves much computation and the level of south-south cooperation cannot be computed easily, it makes it irrelevant.

Hausman specification test

was used by Sawkut et al. (2007) in their research because it compares fixed (assumes differences in data) and random (explores differences in error variances) effects of data collected under the null hypothesis, where each effects are uncorrelated in the model (Hausman, 1978). It is used to determine the relationship between an efficient variable and an inefficient one and mostly importantly which of the fixed or random effect model should be used for the study and the equation is shown in

Appendix 8

It is useful to determine which model, either fixed or random to use, but nevertheless, the use of large samples and high degree of freedom in variables which leaves the level of difference undetermined and creates an error coefficient which cannot be valued. Another limitation is that is does not change over time which is the main point of the study considering the time period.

For this research, the empirical work of Cleeve (2008) and Billington (1999) is used as a reference to investigate using the

cross sectional multiple regression model

to evaluate the relationship between FDI from China and the rest of the world to the variables in SSA and Mauritius to the period 2005-2008. It is based on 15 countries from the SSA based on their FDI inflows. There will be an improvement of these previous by accounting for South-South Cooperation.

The multiple regressions is used instead of linear regression as makes the analysis more realistic, there is more than one variable, gives a better control over variables and gives a better variance in dependent variables.

Based on the model of scholars such as Cleeve (2008) and Billington (1999), the linear equation can be formulated as:

FDI = f(X1, X2, X3, ….Xi)

Where: i = 1,2,3,…,n

FDI = FDI inflow from China and the rest of the world to host countries in SSA

Xi = Different variable which influence FDI inflows to host countries in SSA

Billington (1999) used this model to analyse the location of FDI in the UK; Cleeve (2008) used the model to discuss how monetary policies by governments in host countries increases FDI and Ancharaz (2002) used cross-country regression analysis to compare the inflows of FDI in SSA to the rest of the world. To obtain successful results, the location and the different factors affecting the countries in the study needs to be considered and the equation does give a guideline for this.

Chen (1997) suggested that this equation needs to be altered to explain the level of inflows of FDI in a host country. Ordinary least square (OLS) is used to evaluate the power of location factors affecting FDI (Zhao and Zhu, 2000). Variables which are dependent and independent have changed to ordinary logarithm. This will considerably decrease the bend of information when plotted on normal scales. This allows the usage of the regression analysis and also improves integrity in the analysis. The adoption of a log-linear form can be useful to change non-linear bonds between FDI and economical factors affecting FDI into linear ones. This will be the basic functional form linking FDI inflows and economical variables in host countries in SSA (Chen, 1997). The equation will consequently change as follows:

InFDIit = β0 + β1InXit + ε

Where: i = cross-section unit or country

t = time

ε = the error term

The current research is based on the determinants affecting FDI from Chinese companies in SSA as compared to the world and therefore this will be the elementary equation used in this study. The following section will describe the determinants of FDI inflows and set up the independent factors.

3.3 Variable Set

Many studies interviewed foreign investors in host countries to show that the numerous economic development factors, .e.g., human capital & labour cost, and FDI policy factors, .e.g. tax incentives, influences directly the target of capital flows across countries borders (Broadman and Sun, 1997).

Previous literatures, FDI inflows from China and the rest of the world differ mainly because of the motives behind the capital outflows. However, there are different attractions of each country which are important determinants of FDI. The variables show the level of economic development and foreign investment policies in each country.

3.3.1 The Dependent Variable

The dependent variable employed in this research is the realised FDI inflows in each country from 2005 to 2009, which will show the difference in the sources and values of FDI inflows.

3.3.2 The Explanatory Variables

1.Human Capital & Labour Cost (HCLC)

A host country influences investors' decisions because of costs and the quality of the labour force (Broadman and Sun, 1997). This enables firms to make use of advanced technology (Zhang, 2001). Skills in labour force can be measured through the level of education in a country. Investment in SSA is largely influence because of the low wage rate in this region causing a negative relationship between labour cost and inflows of FDI (Culem, 1988; Yamawaki, 1991). Some studies, however, show that there also exists a positive relationship because high wage rate implies higher labour skills (Swendenborg, 1979; Dunning 1980). The research expects to investigate whether labour cost is to generate a strong relationship with inflows of FDI form China than from the rest of the world or not. The average annual wage of labour in each selected country is used to measure labour cost at 2009 US dollar price. Education level will be measured by the share of secondary school students in the working population age of each country

2.Infrastructure (INFR)

Infrastructure is often used as a factor to attract FDI inflows and is an important choice in foreign investors' decision (Bartik, 1985; Coughlin et al., 1991). Habours, highways, railways, airports and telecommunication all form part of infrastructure. Since it is impossible to consider all this aspects, the research will use the total length transportation in each country selected as a measure of a country's infrastructure. The railways and highways in each country for 2008 will form part of the calculation. A positive relationship between infrastructure and inflows of FDI is the expected result.

3.Market Size/GDP, Openness and Export (TRADE)

GDP in a country reflects the economic development and is sometimes used as a substitute for the size and growth of market size and demand as it gives a good estimate of market size and demand (Wei et al., 1998). It is important for foreign investors who want to produce and sell in a market (Chen, 1997). Export level gives good indication of a country's openness (Wei et al., 1998). Cross-country studies show positive relationship between market size, GDP, openness, export and FDI (Ray, 1989; Grosse and Trevino, 1996). GDP and export from 2005 to 2009 in each selected country will be used at 2009 US dollar price.

4.Natural Resources (NR)

Countries blessed with natural resources attract more FDI. Very few studies included natural resources in the research except Gastanaga et al. (1998), Morriset (2000) and Noorbakhsh et al. (2001) which can made the studies bias (Asiedu, 2002). To include this in the research, the share of minerals and oil in exports will be used as the natural resource element. Data will be collected from the World Development Indicators 2009 in US dollar.

5.South-South Cooperation (SSC)

South-South cooperation is a gap in previous studies which has not been explored before. This refers to the help between developing countries and is an important element to be considered. It increases the attraction of countries as help from overseas government creates an image of safety for the capital of foreign investors. This will be measured by the level of loans received from the South-South cooperation in each selected countries over the period 2005-2009.

6.Political Stability and Corruption (POL)

Previous literature found mixed results in the correlation of political stability and corruption. It is an important factor in investment decision especially in the case of SSA countries as this factor creates an unfavourable business environment in case of civil wars resulting in lost of workdays. Corruption is related to the red-tapism involved in the laws on a country. Political risk rating will be used for the study which will be available from the International Country Risk Guide of 2009 where the highest value is awarded to the lowest risk country and vice-versa.

7.Government Policies, Taxes and Tariffs (GTAXES)

Government policies, taxes and tariffs are important factors as high corporate tax rates and unfavourable government policies deter inflows of FDI. Kemsley (1998) and Billington (1999) found tax rate to be an important factors, however, Wheeler and Mody (1992) found no such relationship. Tax rate will be available from each country Ministry of Finance and Economic Development for the period.

3.4 Combine Variables and the Model

The discussion above in relation to the determinants of FDI in SSA can be summarised in the table below:

Table: Data Summary

Variable Names

Meaning of Explanatory Variables

Dependent Variable

FDI

Realised FDI at 2009 US dollar price

Independent Variable

HCLC

INFR

TRADE

NR

SSC

POL

GTAXES

Human development index

Telephone line per 100 of Population

Import and export at 2009 US dollar price

Real GDP at 2009 US dollar

Trading amongst selected countries with Brazil, China and India

Political risk rating

Corporate Tax rate from each selected countries

The base equation in the model is combined with the above variables creating a new equation to test the determinants of FDI inflows in SSA as follows:

InFDIjt = β0 + β1InHCLCj,t + β2InINFRj,t + β3InTRADEj,t + β4InNRj,t + β5InSSCj,t

+ β6InPOLj,t + β7InGTAXESj,t + ε

Where:

j = different countries

t = the independent variable

β = the regression parameters

ε = the stochastic disturbance (error)

3.5 Limitations of cross sectional multiple regressions model

This method does have some limitations which cannot be overcome during the study such as variable included can prove insignificant in the middle of the study and cannot be removed. Furthermore relationship between the different variables in this method can only be developed but can never be ascertained. Outliers in the results distort bias the entire findings and should consequently be removed to emerge with an accurate outcome from the study. Moreover to obtain accurate results, the sample size must be large and this is the reason why 15 countries will be selected.

3.6 List of Selected Countries

The list of countries selected for this study has been based on the FDI inflow in 2000 choosing the 5 highest, 5 lowest and 5 random recipients of FDI in SSA, to be able to compare the level of attractiveness, which is as follow:

Countries

General FDI inflows

US $ (Millions)

Chinese FDI inflows

US $ (Millions)

Angola

878.62

0.47

Botswana

57.17

3.69

Congo

162.08

8.11

Congo, Democratic Republic of

23.35

5.07

Equatorial Guinea

111.38

6.35

Eritrea

27.88

-

Ethiopia

134.64

4.93

Liberia

20.80

8.65

Mauritania

40.10

0.36

Mauritius

276.77

2.04

Nigeria

1309.67

53.30

Senegal

62.94

-

South Africa

887.92

47.47

Swaziland

105.80

-

Zambia

121.70

10.09

To fulfil the requirement of this study, the formula will be used twice in respect to in each selected country, firstly, in regards to China inwards FDI to the respectively country and secondly, in regards of FDI from the rest of the world.

3.7 Analysis Method

Since 2 sets of data will be available for each selected country, a method named discriminant analysis will be used. This method will find out whether the data are different in respect to the variables (i.e. determinants of FDI). The results will be grouped forming matrices and the data will generate variances ad co-variances. The matrices will then be compared by conducting a multivariate ‘F test' to determine the difference of the determinants amongst the selected countries. ‘F' represents a value which is assignment to the determinants indicating its importance.

A 2 group discriminant analysis is called ‘Fisher Linear Discriminant Analysis' (Fisher, 1936). The updated equation for this study will be as follows:

InFDIjt = a + β0 + β1InHCLCj,t + β2InINFRj,t + β3InTRADEj,t + β4InNRj,t + β5InSSCj,t

Where a is a constant

To explain the findings better, Statistical Package for the Social Sciences (SPSS) software will be used. This is because this tool will generate graphs, tables, reports and charts by simply entering the data collected in the software.

The following Chapter will show the results of FDI inflows from China and the rest of the world in the selected SSA countries.

CHAPTER FOUR

EMPIRICAL RESULTS

4.1 The Independent and Dependent Variables

The basic statistical data of all the dependent and independent variables in relation to the world's and China's FDI in SSA are shown in table 4.1. The mean and standard deviation of the variables alters significantly from country to country. This suggests that the difference in FDI is because of the different level of the variables in each country.

The average FDI inflow from the world is US $ 1,900 millions. However, there is a huge fluctuation in the inflows of FDI. World's FDI is highest in Nigeria with an inflow of US $ 20,278.50 millions in 2008 and lowest in Congo, Democratic Republic of, with an inflow of US $ -76.03 millions in 2005.

China average outward FDI is US $ 145 millions. The highest was recorded in South Africa, receiving US $ 4,807.86 millions and Angola receiving the lowest with US $ -9.57 millions both in 2008.

Table 4.1: Descriptive Statistics of Variables 2000-2008

N

Minimum

Maximum

Mean

Std. Deviation

World FDI

15

6.62

9755.04

1901.6447

3207.95976

China FDI

15

-1.76

1769.91

145.0961

452.94395

HCLC

INFR

NR

TRADE

SSC

POL

GTAXES

15

15

15

15

15

15

15

.36

.05

.67

-4.51

-28.61

3.00

.15

.80

28.67

234.05

18.37

29532.45

7.00

.40

.5377

3.5938

33.3853

2.3961

5072.5013

5.8667

.2973

.13308

7.32360

64.91653

6.84639

8617.33651

1.55226

.07401

For the variable human capital and labour costs, there is huge difference amongst countries. Mauritius has the highest human development index with 80.2%, while the average is 36%, and also possesses the best infrastructure as compared to the other selected countries with the best ratio of fixed telephone line per 100 inhabitants (i.e. 28.67), the average is 3.5938. Angola has the most open economy with a positive balance of US $ 41.69 billions in 2008. Eritrea has, on the other hand, the worst trade ratio, i.e. 0.12 in 2006, which suggest the economy is relatively close. The political risk index suggests investing in SSA countries is risky with an average of 5.87. The least risky countries are Botswana, Mauritius and South Africa while several other countries are more risky with the highest index of 7 including Ethiopia and Congo.

Table 4.2: Pearson Correlations Matrix

FDI

HCLC

INFR

NR

TRADE

SSC

POL

GTAXES

China

World

FDI

1

HCLC

.255

.027

1

INFR

.204

-.074

.653

1

NR

.901

.713

.197

.121

1

TRADE

-.215

.797

-.024

-.215

.167

1

SSC

.494

.845

.062

-.022

.646

.597

1

POL

-.512

-.154

-.651

-.643

-.452

.140

-.214

1

GTAXES

-.049

.158

-.567

-.587

-.023

.227

.200

.737

1

The correlation between the different variables are summarised in table 4.2. The highest correlation coefficients are between Chinese FDI and NR (0.901) and HCLC and POL (-0.651). The variable POL could have been dropped from the study if all the figures were negative but it has a positive relationship with the variable TRADE and is therefore important.

From the world's point of view, there exist a strong bond between FDI and natural resources (0.713), trade (0.797) and south-south cooperation (0.845). Table 4.2 also shows there is no relationship between FDI and INFR and POL as the figures are negative. HCLC has only a small influence in FDI.

From the Chinese perspective, only natural resources have a strong bond with FDI. TRADE, POL and GTAXES has no influence on FDI from China as the figures are negative.

This suggests that natural resources, trade and SSC are the main variables which influence inflows of FDI. Change in these components will determine the level of inflows of FDI from China and the rest of the world.

4.2 Results of the Regression Model

The cross-section regression results of the World FDI and China FDI in SSA is summarised in table 4.3. The two groups have performed well as the Adjusted R Square is more than 95% suggesting a strong power of the model.

4.3 Cross-section estimate of FDI determinants

Explanatory Variables

World

China

Coefficient

Significance

Coefficient

Significance

Constant

0.026

0.074

HCLC

-0.135

0.003

0.135

0.036

INFR

0.080

0.031

-0.059

0.296

NR

0.555

0.000

0.765

0.000

TRADE

0.647

0.000

-0.518

0.000

SSC

0.133

0.009

0.243

0.008

POL

0.047

0.340

-0.110

0.214

GTAXES

-0.066

0.119

0.160

0.045

R

0.999

0.996

R Square

0.997

0.991

Adjusted R Square

0.994

0.983

F Statistics

358.134

115.610

Std. Error

239.395

59.318

Sig.

0.000

0.000

4.2.1 World Determinant Analysis

The F Statistics of 358.134 and a significant level of 0.000 prove a strong relationship between FDI and the selected variables. A 99.4% variation in FDI is linearly explained by the variation in the 7 variables.

4.3 Other Results in terms of the different countries

4.4 Summary

CHAPTER FIVE

CONCLUSION

5.1 Chapter Introduction

5.2 Key Concepts and Conclusion

5.3 Research Implication

5.4 Recap of Limitation

5.5 Need for Further Study

5.6 Chapter Summary

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