Impact of Chinese foreign direct investment on Sub-Saharan Africa economic growth

Master thesis

Impact of Chinese Foreign Direct Investment on Sub-Saharan

Africa Economic Growth

Student: Andrea Armanetti Student ID: 11246332

Supervisor: Vittoria Scalera Submission date: 22 Mar 2017

Master Dissertation: MSc. Business Administration Track: International Management

Statement of originality

This document is written by Andrea Armanetti who declares to take full

responsibility for the contents of this document.

I declare that the text and the work presented in this document is original

and that no sources other than those mentioned in the text and its

references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the

supervision of completion of the work, not for the contents.

Table&of&contents&

Abstract...4

1. Introduction...5

2. Literature review...10

2.1 Foreign direct investment and economic growth...10

2.2 Infrastructure, human capital and economic growth...13

2.3 Determinants of Chinese foreign direct investment...15

3. Sino-African relationship...19

4. Hypotheses development...24

5. Data and methodology...29

5.1 Sample...29

5.2 Dependent variable...29

5.3 Independent variables...30

5.4 Control variables...31

5.5 Model specification...32

6 Results and analysis...33

6.1 Analysis strategy...33

6.2 Correlations...35

6.3 Linear regression...37

7. Discussion and conclusions...40

7.1 Contribution and limitations...42

Abstract

The need of resources, the growing export markets and the policies made by Chinese government have brought China to become one of the most investors into the Sub-Saharan African (SSA) countries. This paper investigates the impact of the Chinese Foreign Direct investment (CFDI) on the SSA economic growth of the countries. It has been decided to take into consideration 34 SSA countries in the period of time that goes from 2004 to 2012 because of the data availability of the secondary data chosen from the UNCTAD and The World Bank database and because of the fact that during this period there were huge investments of Chinese companies in the SSA countries. The results obtained through the linear regression analysis show an expected positive relationship between CFDI and SSA countries’ economic growth, supporting what the academic literature stated about the modernization theory, according to which FDI is determinant to the inflows of technology and capital into emerging countries. Moreover, this thesis aims at assessing the moderating effect of the infrastructures and human capital on the impact of CFDI on the SSA growth, since the literature suggests that spillover effects deriving from FDI are more visible when the host country presents a minimum level of human capital and infrastructures. What emerges from the analysis is that the effect of both the variables is not significant. This could mean that human capital and infrastructures still have not reached a minimum threshold to guarantee a better absorptive capacity of the huge amount of CFDI by SSA countries.

1. Introduction

The Chinese economy has exponentially grown in the last decades until becoming the second economy in the world in terms of purchasing power parity GDP (Quer et al. 2012). Although this seems to be the result of many decades of developing process, however, the Communist influence over the institutions has brought to a consistent development only since 1970s. Indeed, speaking of the economic field for example, until the 1970s Chinese enterprises were not allowed to undertake Foreign Direct Investment (FDI) and the process of China's entrance into the global market began only with the 'Open Door' policies in 1979. Later on, between the late 1990s and the beginning of 2000, China went a step further by initiating the ‘’Go Global Initiative’’. With this initiative, which aimed to promote the international competitiveness of Chinese firms by reducing or eliminating foreign-exchange-related, fiscal and administrative obstacles to international investment (Sauvant, 2005), the Chinese government encouraged Chinese enterprises to undertake investments abroad (Buckley et al.: 2007). China has grown so fast that by 2004 it became the eighth most important FDI source among developing countries, thanks to its strategy consisting of using the profit deriving from these investments to sustain its economic growth (UNCTAD 2005a). Moreover, as the need for resources has increased as well as the amount of export markets, Chinese enterprises have the necessity to undertake more FDI in countries rich of resources.

An area that China recognized as a major source of natural resources was the region of Sub-Saharan Africa (SSA) and there China invested so much that it became the most important player in Sub-Saharan Africa (Jacoby 2007). Both bilateral trade between China and many SSA countries, and Chinese Foreign Direct investment (CFDI) in Africa have grown rapidly during

the last two decades, together with a considerable inflow of Chinese companies and workers on the SSA land (Foster, 2009). As demonstrate by Javorcik et al. (2011) trade agreements are likely to affect the amount of FDI brought between the countries in the agreement that in turn influence the productivity of local suppliers. More than 2000 Chinese enterprises operate in more than 50 African countries, by covering a broad range of industries, such as oil production, mining, construction, and agricultural production (Foster, 2009). Since the first investment made by China to SSA land, the trade between the two countries has intensified and in 2013 China became Sub-Saharan Africa’s largest export and development partner of this trade. Moreover, also the China’s increasing demand for agricultural products and raw materials and its desire for new export markets, in combination with political and strategic goals, have led the Chinese government to enforce political and economic cooperation with African countries (Asche et al., 2008).

Analyzing the wide surge of Chinese FDI to Africa is interesting for several reasons. Firstly, as previously written, the internationalization of Chinese MNEs is of recent origin. The great wave of FDI, indeed, started after the introduction of the “Go Global” policy. At the same time new business model impacted the way of making business of Chinese companies with a typical transnational corporation mainly due to the financial support from the state (Alden et al, 2006). Secondly, Chinese companies are relatively younger than for instance the Indian ones so it is interesting to see how young companies deal with investments in an emerging country such as SSA and which strategies they use. (Henley et al, 2008). Thirdly, Chinese State-owned enterprises (SOEs), that are the most common outward investors, since investing abroad was illegal for private firms before the 2003 (Buckley et al, 2007), are inclined to have a long-term orientation on their investment compared to the western SOEs companies (Gibbon et al., 2005). Indeed, Chinese SOEs tend to invest strategically in order to acquire needed assets (Alden et al.,

2006). Lastly, the Chinese government has built institutional structures to support the Chinese MNEs in the African continent. In fact, the Chinese state made and continues to make political accords with the states of Africa in order to upgrade the existing weak infrastructure around the continent (Van Dijk, 2009).

The incredible growth of CFDI in SSA countries in the last decades has led to some debate. While some academic studies acclaim the substantial increase of CFDI in SSA land as a potential positive long-term effect in SSA economy, others worry about the motives of these investments and the possible impact on the economic and political development (Brookes, 2007; Wang et al., 2008). For example, some academic scholars concern about the fact that Chinese investment could cause a possible increase of the unemployment rate in the African manufactory industry, since Chinese firms tend to carry their own workers (Cheung et al. 2012). In contrast, other scholars affirm Chinese FDI may guarantee enormous benefits to SSA economy (International Monetary Fund, 2011). Indeed, the history of SSA continent is marked by being underinvested and underserved by international flows of investment. Thus, Chinese role could be positive in terms of productivity, infrastructures and of improving the living standard of Africans and moreover in terms of alternative source of financing (Cheung et al., 2012).

The rapid growth of trades between China and Africa has captured the attention of the research world. However, there have been relatively few studies on the strengthening economic interrelation between China and Africa, which in addition generally lack of knowledge and statistical data. Indeed, most of the studies focus on general theory of CFDI, in terms of location choices and motivations or even if they specifically focus on China's FDI to Africa or Sub-Saharan Africa, they are not based on empirical data (Ajakaiye et al., 2009; Besada et al., 2008). Academic work based on primary research on how China’s interest in Africa may have influenced economic development in African countries, has been relatively scarce. For this reason

the current work aims at increasing the volume of the already existing academic literature regarding the CFDI in SSA countries. Starting from what the existing literature points out regarding the correlation between FDI and economic growth of the host country, I will analyze the case of CFDI in SSA continent in order to shed light on at what extent CFDI actually contributes to the economic growth of SSA territory.

For this purpose it has been taken into consideration the model developed by Borensztein et al. (1998) and partly changed by using CFDI and including infrastructure and human capital as determinant factors on the impact of CFDI on growth. As the existing literature has explained, more advanced technologies require the presence of a sufficient level of human capital and infrastructure in the host economy, which guarantees the absorptive capability of a developing country. As a matter of fact, the more skilled is the human capital of a country, the more successful is the reception of the benefits, the knowledge and the capital provided by the foreign investors in the country. Similarly, the CFDI has a greater impact on the economic growth in those countries where a minimum level of infrastructure and appropriate institutions are available in terms of telecommunication connections, transportation network, insurance and etc. (Borensztein et al. 1998). Therefore, this empirical model will highlight the role of both infrastructures and human capital as the requirement to guarantee the necessary absorptive capability of the advanced technology brought by the CFDI in the SSA countries.

In order to test the hypotheses, it has been created a sample including 34 Sub-Saharian countries for which have been chosen secondary data selected from both UNCTAD and World Development Indicators website between 2004 and 2012 because of the data availability on the database chosen and because of the in this period there were huge investments of Chinese enterprises in the SSA countries. The results of this analysis indicate that CFDI has a positive effect on the SSA economic development, while for what regarding the infrastructure and human

capital effects, we find no impact on the economic growth.

What will be the contribution provided by this thesis? Previous empirical studies on CFDI focus on both examining the determinants of Chinese outward FDI (Duanmu 2012, Kolstad et al. 2012) and how the Chinese institutions, political risk and cultural distance affect FDI behavior (Quer, et al. 2012; Kang et al., 2012). Yet little researching attention has been paid to the details of Sino African relationship and most of the literature regarding this theme is not empirical (Kaplinsky et al., 2009; Kragelund et al., 2009). So the purpose of this work is to increase the work of Borenstein et al. (1998) by providing a new empirical framework in which the main determinants of the economic growth, such as human capital and infrastructure are underlined in order to understand the technology-transfer effect of the spillovers created by CFDI in the specific case of the Sub-Saharan Africa.

This research consists of three main parts. Firstly, an overall of the current literature will be presented by starting with the most relevant literature regarding the impact of FDI on economic growth in general and then by focusing on the specific impact of CFDI on SSA economic development through the analysis of the literature regarding the determinants of CFDI, the impact of infrastructure and human capital on SSA economic growth and the analysis of the Sino-Africa relationship. Secondly, an empirical analysis will be built up. This section will consist of a description of the variables, the research design, the methodology and the model. In conclusion, the work will end by displaying the regression results and analysis. In the last part of this paper, the appropriate conclusion will be highlighted together with the annex limitations of the research and the answer to the main question posed in this introduction will be given.

2. Literature Review

2.1 Foreign direct investment and economic growth

The academic literature furnishes conflicting predictions about the growth effects of FDI (Carkovic et al, 2002). Samuel Adams (2009) identified two main viewpoints to explain the effect of FDI on host countries’ economic growth. These are the modernization and the dependency theories. Modernization theories are founded on the neoclassical and endogenous growth theories, which retain that FDI could support the economic development in emerging countries. Indeed, this theory is based on the fundamental economic assumption that growth requires capital investment. Moreover modernization theories argue that the transfer of technology through FDI plays a crucial role in the developing countries. Indeed developing countries are characterized by a lack of the basic needed infrastructure like, for example, educated population, liberalized markets and strong institutional stability that are a springboard to a future growth (Sánchez-Robles et al, 2002). Not only capital and technology stem from FDI but also competences. Kumar and Pradhan (2002) observe that FDI brings to knowledge spillovers

Fig.!1!Conflicting!predictions! !

in terms of organization and managerial competences, strategic know-how and skilled workforce. Furthermore, Javorcik et al. (2008) recognize differences on the level of the spillover effect depending on the local participation. Indeed they observe that wholly owned foreign affiliates affect more the knowledge spillovers than joint ventures and foreign ownership. Indeed, MNEs devote more resources to technology transfer to their wholly owned subsidiary than to joint venture or non-equity modes of investments (Ramachandaram, 1993). Overall FDI contributes to host country growth in two ways: capital accumulation and increasing productivity factors.

Contrary to the modernization aspects, dependency theorists claim that FDI has a negative effect on the economic growth of the host countries and on the distribution of income. They argue their thesis underlying the fact that FDI in developing countries may create an economic framework in the host countries where monopolies are prevalent and so an inefficient use of the local productive force may occur (Bornschier et al, 1985). The reasons are explicable on the basis that an economy controlled by foreigners would not evolve in a homogeneous manner because of different goals among the stakeholders and different interests involved (Amin, 1974). For example this could bring to a consistent growth of one industry, but in the same time neglect the growth of another industry and thereby leading to stagnant growth in the emerging countries (Pigato, 2000). Moreover, FDI seems to have a negative impact on the host firms’ performance inside the same industry. Aitken et al. (1999) demonstrate that producers lose part of their market share when MNEs entering in a developing country and thus bring to split their fixed costs over a smaller volume of production. Other authors suggest the negative competition effect deriving from FDI from foreign MNEs overcome the positive effect of spillover effects in emerging countries (Djankov et al., 2000; Konings, 2001).

Differently, other authors have questioned the causality effect of the FDI, namely if the FDI causes growth or if the growth attracts FDI (Hansen et al., 2006). Zhang (2011) tests the

relationship indicating the presence of a strong long-run Granger-causal relationship between FDI and GDP growth. De Mello (1999) finds heterogeneous results of the long-run effect of FDI among 32 countries. Nair-Reichert et al. (2001) find the same results of De Mello, therefore they use the mixed fixed and random (MFR) coefficient finding out a significant causal impact of FDI on growth. Lastly the Basu et al. (2003) emphasizes exports and imports as a fundamental determinant for the effect of FDI on growth, since they find two-direction causality both in long-term and short-long-term when the host countries have open economies, whilst causality is unidirectional only in the long-term when the host economies are closed.

Given the conflicting theoretical perspectives, the academic literature have empirically analyzed the FDI effect on economic growth and obtained different results. Balasumbramanyam et al. (1996) examine the role of FDI on the growth of 46 developing countries in the period from 1970 to 1985. This empirical research shows that FDI has a positive and relevant effect on economic development and also finds out that the effect is more evident in countries where exports are promoted (i.e. Singapore, Malaysia and Chile) than in countries where import substitution policies are assumed (i.e. Peru and Bangladesh). Similar results are carried out by Borenzstein et al. (1998). In his cross-country regression framework Borenzstein et al. test 69 developing countries in the period from 1970 to 1989 finding out that FDI is an important tool for transmitting of technology and it affects the economic growth more than domestic investment. Zhang (2001) examines the causal relationship between FDI and economic growth of 11 emerging economies in East Asia and Latin America. His results show that FDI contributes to economic growth, but the effect depends on country-specific features, for example host countries in which there are liberalized trade policies, high education standards and encouragement of export-oriented FDI.

FDI facilitates economic growth only if there is an active and evolved financial market. Their work consists in an analysis of the link of FDI, financial markets and economic growth using data from 1975 and 1995. The results show that only in countries where evolved financial market occur the effect of FDI is evident, but in general the effect of FDI alone is ambiguous. By analyzing 80 countries in the period between 1979 and 1998, Durham (2004) suggests that the FDI impact on economic growth is contingent on the financial and institutional framework and in general on the absorptive capacity of the host country. Moreover Blomstrom (1994) identifies a positive effect of FDI only when the minimum threshold of wealth per capita is reached.

Another possible negative effect of FDI on the host country econonomy is the crowding-out effect (Razafimahefa et al. 2007, Borensztein et al.1998). The crowding-out effect may occur when domestic firms have a lack of resources, organizational skills to compete against the foreign firms. This situation can bring to an exit of domestic firm from the local market because if foreign MNEs are able to produce local goods and factor markets more efficiently than the domestic firm, then the crowding-out effect occur by forcing a swap to activities of the existing local firm or an exit from the market.

To conclude, from the literature emerge that there are four necessary conditions to guarantee a positive effect of FDI on economic growth. First, the level of development of the financial markets (Alfaro et. al., 2004); second, the level of infrastructure and education in the labor force (Borensztein 1998); third, the trade openness, in terms of export and imports (Balasubramayan et al. (1996) and lastly when a country is adequately rich in terms of per capita income (Blomstrom, 1994).

2.2 Infrastructure, human capital and economic growth

is a key factor for economic growth (Calderòn et al. 2008). SSA countries are ranked historically at the bottom of all the charts registering countries in terms of infrastructure performance and academics state that this very low infrastructure level is one of the main problems for the economic development of the region (Foster, 2009). In Sub-Saharan Africa a large number of countries are landlocked and this geographical problem is an enormous disadvantage that these countries face since it can only be overcome through a consistent transport network that they still lack of. In addition to the natural barriers to trade that somehow block the economic growth, the literature underlies other aspects that limit the development and the absorptive capacity of FDI in SSA countries. Reinikka et al. (1999) use data from Uganda’s industrial companies to test the consequences of an inadequate supply of electricity. Indeed, they point out that weak supply electricity limits the amount of inflow of FDI. In addition, Estache et al. (2007) argue that a precarious power generation capacity limits economic development in Ghana, while Lumbila (2005) finds out that deficient infrastructure may impede FDI in Africa. Moreover, Estache, Speciale and Veredas (2005) analyze a wide range of infrastructure indicators in their analysis and conclude that only roads, power and telecommunications infrastructure affect the long-run growth in Africa, whereas water and sanitation do not.

Several studies prove how much the human capital plays a fundamental role in a country’s economic growth, even though, depending on institutions’ policies, job markets and education level, each country has its own results so as to render difficult determining on average how human capital as education enrollment levels affects a country’s economic growth (Barro, 2001; Benhabib et al. 1994; Barro, 2013). Other studies consider the quality of schooling instead of the quantity because they consider it more relevant. For example, Erik Hanushek and Dennis Kimko (2000) report that scores on examination (indicators of schooling quality) matter more than years of enrollment for the achievement of higher level of economic growth. Speaking of education, it

is really important for two aspects: firstly, it allows ranking workers in the job market (high, medium and low skilled workers) according to their education level and secondly a higher level of education facilitates education spillovers.

Indeed spillovers take place when local firms learn about new technologies, marketing or management techniques by observing foreign MNEs behaviors, so it seems obvious that higher level of education brings to higher capability to absorb information. Moreover, Javorcik et al. (2008) claim that MNEs have a propensity for to transfer knowledge to local firm since they can improve the performance of intermediate suppliers. There are other studies that demonstrate FDI being an important engine for knowledge transfer. Findlay (1978) supports this effect by saying that in case of FDI, domestic firms have more likelihood to observe advanced technology used by MNEs bringing to raise the domestic level of technology. Wang (1990) adds that an increase of the amount of FDI provoke more investment in human capital, which improve the absorptive capability of the host country. Walz (1997) affirms that FDI in poor countries causes knowledge spillovers in the R&D sector with consequent positive effect on the economic growth. Further Glass and Saggi (1998 and 2002) argue that the presence of FDI facilitate lowers the cost of imitation when the foreign-invested firm produce products within the LDC country.

Even though some studies (Youssef et al. 2001) considered human capital as an important and positive determinant to attract FDI in emerging countries, the specific case of SSA lacks of academic analysis. To conclude this is why the aim of this work is to analyze all the infrastructural parts, both intangible (human capital) and tangible (infrastructure), in order to understand their impact on the CFDI and on the SSA economic growth.

2.3 Determinants of Chinese Foreign Direct Investment

since the beginning of this century, China’s outward foreign direct investment has increased over ten times. According to Deng (2007) outward investment was a necessary stage of growth for Chinese companies and a precondition to affirm the competitiveness of the country in global markets. The process of China's entrance into the global market began with the 'Open Door' policies of the late 1970s. In fact, the outward investment liberalization and growth can be traced particularly from Deng Xiaoping's 'Go Global' initiative, effective since 1999, which aimed to promote the international competitiveness of Chinese firms by reducing or eliminating foreign-exchange-related, fiscal and administrative obstacles to international investment (Sauvant, 2005). Moreover, this process of globalization and openness towards the rest of the world was accelerated after joining the World Trade Organization (WTO) in 2001. Studies of this process generally examine China in terms of its position in global trade flows (e.g., Lall and Albaladejo, 2004), its comparative advantage as a manufacturing location (e.g., Rowen, 2003) and in the volume, distribution and impacts of inbound FDI (e.g., Buckley et al., 2002). Since 1979, when FDI was formally allowed under the 'Open Door' policies, the internationalization of Chinese firms has been severely controlled by national and provincial government, both directly, by administrative decree, and indirectly, via economic policy and other measures designed to advance the economic development agenda (Buckley et al., 2006). However, recently administrative controls have been relaxed and approval processes and procedures simplified (Sauvant, 2005).

Foreign direct investment is a widely researched topic in the existing literature. The general principles of the theory of FDI are basically two: (1) firms internalize missing or imperfect external markets until the benefits overcome the costs of further internalization; and (2) firms choose locations for their constituent activities that reduce the total costs of their operations (Buckley et al. , 1976). In the case of emerging economy Multinational Enterprises (MNEs), such

as the Chinese ones, there are some imperfections in home country capital markets that may require special applications of the theory (Buckley et al 2007). As included in Dunning's eclectic paradigm, the location choice is rooted on four main motivations (Dunning, 1977, 1993):

• Foreign market seeking FDI, • Efficiency seeking FDI, • Resource seeking FDI , • Strategic-asset seeking FDI.

However, the general theory derives from analysis of developed countries and that unavoidably creates some gaps if the theory is applied to emerging countries. Market seeking FDI will be undertaken by emerging economy firms for traditional trade by supporting reasons such as accessing distribution networks, facilitating the exports of domestic producers, and enhancing exports from the host country to other large and rapidly growing markets. Efficiency seeking FDI will occur when outward investors look for lower cost locations for activities and in particular in the search for lower cost labor. Resource seeking FDI from emerging economy MNEs, such as those presenting in China, occurs in order to acquire the supply of raw materials and energy sources in short supply at home (Dunning, 2001).

Bucley et al. (2007) identify a “special” theory to clarify the determinants of FDI from emerging countries and in particular FDI from China. Following this theory, there are three main factors that push Chinese MNEs to invest abroad:

1. Capital market imperfections, 2. Special ownership advantages, 3. Institutional factors.

determinate period of time. This would create disequilibrium in the market in which potential investors can exploit (Buckley, 2007). This disequilibrium facilitates the capital procurement of state-owned firms, because they can have capital in the form of soft budget constraints (Scott, 2002). By considering that Chinese outward investors could be considered as being state-owned, since private firms were legally prohibited from investing abroad prior to 2003 (Buckley et al 2007), there is a good reason for believing that this imperfection exists in China. Other imperfections of capital market that exist in China are the inefficient banking system (Warner et al, 2004), the conglomerate firms that may operate an inefficient internal capital market (Liu, 2006) and the huge presence of family-owned firms that may have access to cheap capital from the family components (Child et al., 2003).

The ownership advantages of Chinese firms can include flexibility (Wells, 1983), minimizing the use of assets, benefits from the host country and the capabilities of creating a positive network between firms and other stakeholders in order to have access to more resources (Dunning, 2002). The latter may create a long-term advantage inasmuch firms can obtain relevant information about the most appropriate investment opportunities and institute commercial relationship in order to facilitate market entry and development (Zhan, 1995).

The institutional framework influences the tendency of domestic firm to invest abroad. The emerging literature on this field underlies that domestic institutions make “the rule of the game” by influencing the formal and informal norms regarding investment (North, 1990). High levels of government support (i.e. low cost capital, subsidies, and access to inputs) create fundamental benefits for emerging firms that permit to them to overcome the disadvantages that may exist (i.e. liability of foreignness and lack of assets) (Zaheer, 1995). The Chinese government has characterized in promoting FDI during the last decades. For example, the Chinese institutions support FDI through introducing tax reductions, foreign exchange assistance and financial

supports (Wong et al., 2003).

Cheung and Qian (2008) analyze data on Chinese FDI to 31 countries in the period from 1991 to 2005. In their analysis, they discover that country risks do not influence the choice of Chinese MNEs, whereas natural resources significantly do. Moreover, they find that host country GDP and low wages attract Chinese FDI, while GDP per capita do not influence them.

Lastly, Cheng and Ma (2008) conduct an analysis on data of 90 host countries for the period of time from 2003 to 2006. Their specification does not include institutions or natural resources, but they find that GDP, culture proximity and a common border with China attract Chinese FDI. In sum, Chinese firms, given that are from an emerging country, have different reason to go abroad compared to firms from developed countries. The academic literature suggests that Chinese institutions in the last decade have pushed home country firms to invest abroad. The pushed factors are mainly tax benefits, cheap capital and financial supports. In regard to host country, the literature finds out that poor institutions and natural resources either facilitate or do not matter for FDI.

3. Sino-African relationship

The main factors that have contributed to a fast increase of CFDI to Africa in the recent years are the willingness of Chinese institutions to internationalize their firms, the global pooling of resources, the increasingly growing economic rates of African countries in the last decade and the fact that nowadays Africa is seen as a continent of financial opportunity (Ouma, 2012). The relative improvement in the economic performance of various African countries is a result of the

change in policy framework since the mid-1990s (Fischer et al., 1998). Indeed, improvements in economic policies were needed in order to enhance macroeconomic performance and attained the minimum growth rate required to meet the Millennium Development Goals set by the United Nations. This factor helped to make African countries attracting towards foreign investors. Therefore since the 1990’s, an increasing number of Chinese companies have developed connections in Africa with the purpose of increasing trade between China and Africa. Later on, thanks to the “Go Global” policy of 1999, since the early 2000 the Chinese investments in SSA countries have gradually increased until they arrived to an incredible surge in 2008 as we can see in Fig. 2, which shows the trend of Chinese FDI from the 2003 to 2012 and their outflows have increased or decreased during each year.

Fig 2. Amount of CFDI in SSA. Data by UNCTAD

Moreover, since during the last decade the necessity of resources and export markets to sustain the growth of the nation had increased, Chinese enterprises had to undertake more FDI in

countries rich of resources just like those located in Sub-Saharan Africa (SSA). Particularly, the incredible Chinese economic development required always more energy, even more if the growth is based on manufacturing. In 2014, China has contributed by 13.39% on the global GDP, by using 23% of the global energy. It is the biggest producer, consumer and importer of coal in the world and in 2014, China has consumed 50,6% of the coal used in the entire globe. China is also the second oil importer, after US, and the third importer of natural gas. Furthermore, in 2014, China consumed 13.2% of the total nuclear energy consume, 27.4% of hydroelectric power, 22.4% of wind power, 15.7% of solar power, 16.7% of other renewable energies (data from Statistical Review of World Energy, 2015).

SSA land is rich of power sources that China needs and it is also an alternative to the dependence with the Middle East. Moreover the mining processes are cheaper in SSA than in China, by considering the low local wages. The Chinese interest does not include only oil, but also other minerals and metals. Indeed overall SSA countries possesses more than 60% of the global platinum, 55% of cobalt and 45% of aluminum (Weisbrod et al., 2011).

As shown in Fig. 3, the African countries have several kinds of natural resources and even more natural resources are in the SSA and they are: Niger (uranium, gold and coal), Namibia (uranium, lead, zinc, sulfur, salt, copper), Democratic Republic of Congo (copper, diamond, cobalt, oil, gold, tin), Zambia (copper, cobalt, emerald), South Africa (iron, platinum, diamond, gold), Tanzania (gold, diamond, silver), Ghana (gold, diamond, bauxite, manganese, salt, oil, silver), Botswana (diamond, coal, copper, nickel, sodium carbonate).

Since the first years of the Sino-African cooperation, China has invested a lot in infrastructures, for example building roads or railway stations in Ethiopia, Rwanda, Sudan, or rebuilding Angolan buildings after they have been destroyed during the civil war. Other examples are in Tanzania and Zambia, where the railway that links the two countries was built in order to contrast the Russian influence in the East Africa. For the same reason China built stadiums in Gambia, Mali, Sierra Leon; government palace in Mozambique, Angola, Uganda; roads in Kenya and Ethiopia; dams in Ghana and Ethiopia. In 2006, the Chinese premier Wen Jiaboa planned some projects to guarantee economic and social development in Africa. For example, he offered

scholarships to 18,000 students of 50 African countries for studying in China and 16,000 doctors in 47 African countries. Moreover he offered training to 10,000 African people in civilian and security areas, he employed 700 teachers to work in rural schools. He also bestowed medical donations, built 30 hospitals, with a value of 37.5 billion, in order to fight malaria. Furthermore, he has sent 100 agricultural experts in Africa and it has built 100 rural schools (Naidu et al., 2008). Wen Jiaboa have also brought modernization in the military, through introduction of helicopters, software and mines; for example during the war between Ethiopia and Eritrea has sold weapons to both sides. By 2009 China has deleted debts of 32 countries and in 2015 during the New York summit has renewed its goals of deleting debts of the Least Developing Countries (LDCs) (UNCTAD, 2014). Announced during the Fourth Ministerial Conference of the China-Africa Cooperation Forum (FOCAC) by Wen Jiabao, the “special loans for the development of small African companies” is one of the eight recent measures that the Chinese government will make in order to strengthen the Sino-African relationship. The main goal consists in helping African countries in stabilize the employment rate and the economic development of the local market. For the same reason the China-Africa Development Fund (CADF) has been instituted for a value of 5 billion in order to assist entrepreneurs willing to invest in Africa.

Beside the fact that SSA has represented an interesting investment opportunity for China, it has also developed itself as a good export market for cheap Chinese exports. So, it seems that China has different reasons for investing in SSA: to get the resources it needs to grow but also to provide for an export market for its own products. (Zafar, 2007, Ali et al., 2012). Graph 2 shows the different typologies of CFDI by sector (IOSC, 2013) The Fig. 4 below shows the percentage of Chinese FDI by sector.

To conclude, China represents both an opportunity for Africa to reduce its marginalization from the global economy and a challenge to effectively exploit the flow of resources in order to promote poverty-reducing economic development at home (Zafar, 2007) and the main purpose of this thesis is to accomplish empirically to which extent CFDI has had an impact on the SSA economic growth

4. Hypotheses development

The purpose of this work is to answer to this main research question:

RQ: How does CFDI impact the economic development of Sub-Saharan Africa?

The beginning of 2000 showed a radical change in the Chinese foreign economy since the “Go Global” entered into force and through that the Chinese government started to encourage the Chinese enterprises to undertake FDI (Sauvant, 2005 and Buckley et al., 2007). The Chinese

internationalization was so fast that it became one of the most important FDI source among developing countries permitting to sustain its economic growth by using the profit deriving from these FDI (UNCTAD, 2005). Further, the necessity to acquire more resources and the size of the export market has brought to undertake huge investment in SSA where China found to be the ideal source of natural resources and a potential growing market in which promoting commodities exports (Jacoby, 2007). In this context, we saw incredible inflows of Chinese companies, workers and products on the SSA land (Foster, 2009). Financial Times in a database including 156 Chinese greenfield projects in SSA occurred in the period between 2003 and 2014, provides an idea of the number of job positions created by China. The manufacturing industry, comprehending 77 projects, created the highest amount of jobs: over 39’000 out of the total 64’000 positions, more than the half created by those 156 projects. The outcome results to be positive, mostly because capital investments in the manufacturing industry are lower than in other industries. Indeed, construction and extraction industries receive the highest amount of money, constituting respectively the second and the third most important source of jobs. In the sample analyzed by Financial Times the ten companies with higher values of investments (equivalent to 39% of the total amount of invested capital), create 38% of jobs; the first 4 companies are: China National Petroleum, China Nonferreous Metals Mining, Beiqi Foton Motor, Huawei. Further, in the previous section we have seen some example of what Chinese institution and companies have built on the SSA countries in order to permit the regular execution of the companies’ activities, such as railways buildings, hospitals and bridges. Chinese MNEs have the necessity to facilitate the distribution of products all around the host country and so they improve the network of transport and communication contributing to the economic growth of the host country. For example the construction of a bridge by MNEs can improve the overall productivity of the host nation (Bengoa et al., 2003). Not only infrastructures are built, but there are also made policies

by Chinese institutions in order to guarantee social development in the poor areas of the SSA countries and for this reasons, for example, doctors and teachers were sent in order to promote and legitimate the presence of Chinese companies in the SSA land.

However, the literature identifies two conflicting results regarding the effect of the FDI on the economic development of host countries. On one hand, FDI can reduce the gap between emerging economies and developed ones through the capital, technology, competences and infrastructures brought in the developing country (Adams, 2009; Sánchez-Robles et al. 2002; Kumar et al. 2002). In the long-term, in order to take advantage of the CFDI to support their own growth, SSA countries necessitate of policies that foster the competitiveness of their industries, so that China can in turn take competitive advantage of SSA countries resources. On the other hand, FDI can be also “market stealing”, when only investors reach benefits because of the fact that they substitute local workers with the ones from their home country and so the local economy does not benefit from technological spillovers (Lederman et al, 2013).

Some SSA economies have benefit from the Chinese fast internationalization growth, while others have suffered the competition with the low-price products of the Asian country (Ajakaiye et al. 2009; Ademola et al. 2009). However, CFDI facilitates the SSA integration into the global economy thanks to a “learning by doing “ approach they improve work skills and raise their productivity. The acquisition of new skills and new technologies are a springboard to compete in the global market. Thus CFDI can be considered to be a driving force for the economic development of the SSA countries and from the test of the first hypothesis we expect CFDI has a positive effect on the SSA economic development sustaining what the modernization theory affirms (Adams 2009). The first hyphotesis deals with the main research question, indeed it aims at testing linearly if CFDI actually contributes positively to the economic growth of SSA countries.

H1: Chinese FDI has a positive effect on the SSA economic growth

In each of the other hypotheses a moderator will be introduced: the level of infrastructure and the level of human capital. Indeed, in SSA, domestic firms can take advantage from technological spillover effects that are identifiable in outflows of knowledge and acquisition of new infrastructure from Chinese firms. The literature argues that more advanced technologies require sufficient levels of human capital and infrastructures in order to guarantee the maximum positive effect deriving from the FDI in the host developing countries. Indeed, it is easy to understand that higher are the skills in the human capital of a country, the more successful is the acquisition of knowledge and capital brought by FDI (Barro 1997; Bernhabib et al. 1994; Barro, 2013). In turn the wider is the amount of people having access to knowledge, the higher is the likelihood to implement the absorptive capacity of knowledge by the host country (Durham, 2004). Likewise, an adequate level of infrastructure permits a higher impact of FDI on the economic growth, since it can provide telecommunication connections and transportation network (Borenzstein, 1998). Indeed high-developed infrastructures foster communication and connections, which are important features to lower the transaction costs (e.g. transport and logistic costs). In fact, facilitated communication channels can favor the technological spillovers from the foreign investors to the local entities, so as to contribute to the long-term economic growth of the latter. Since the biggest part of SSA countries has low-skilled labor force, low level of infrastructural indicators (Foster et al. 2002), few access to the sea that is not balanced out by a consistent transport network and few investments in R&D into the local firm, only technological spillovers can improve the local productivity. Lederman et al. (2010) verified the spillover effects on SADC countries and they found that the effects are positive and relevant: by increasing by 10% the host ownership of firms, the local productivity goes up by 2%. Thus, CFDI will have a greater impact

Fig.!5!Framework!Model!

on growth in countries where a minimum of infrastructure and human capital are available. Hence, the following hypotheses aim at understanding if the capital, both infrastructural and related to human skills, influences the effect of CFDI in SSA.

Thus for accomplishing the purpose of this thesis, the following two moderation hypotheses are presented in order to investigate the effect of infrastructures and human capital on the FDI and SSA growth. What we expect at the end of our analysis is to find a higher impact of the CFDI on growth when the infrastructural parts are more advanced.

H2: The higher is the level of infrastructure, the more positive is the effect of Chinese FDI on the

SSA economic growth

H3: The higher is the level of human capital, the more positive is the effect of Chinese FDI on the

SSA economic growth

To summarize the hypotheses presented in this thesis, the Fig.5 below shows the relationships this model wants to analyze and the expected signs of the relationship in brackets.

(+)

CFDI! Economic!SSA!

Growth!

Infrastructure!

Human!Capital! (+)!

5. Data and methodology

5.1 Sample

This section describes the data used in the regression analysis as measure of economic growth. The data used was collected from both UNCTAD and World Development Indicators website. Therefore all data are secondary. Although the data sources are two of the most reliable and complete in this area, a complete dataset regarding both China and SSA countries is difficult to obtain because of the recent openness of China and the underdevelopment of SSA. Nevertheless, the sample is quite wide to create a sample that permits to achieve the goals of this work. It consists of 34 Sub-Saharan Africa countries chosen depending on the secondary data availability. The Sub-Saharan Africa countries are listed in the table 1.

The designed period in which I collected my data ranges from 2003 to 2012. Here again, the choice is due to the data availability.

5.2 Dependent variable

The dependent variable in this research is the growth rate. The growth rate is measured as the

Angola Botswana+ Cameroon Congo+

Cote+D'+Ivorie Equatorial+Guinea Eritrea Ethiopia Gabon Ghana

Guinea Kenya Liberia Madagascar Malawi Mali

Mauritania Mauritius Mozambique Namibia Niger Nigeria

Rwanda Senegal+ Seychelles Sierra+Leone South+Africa Sudan

Tanzania Togo Uganda Zambia Zimbabwe

Democratic+Republic+of+Congo

34#Sus'Saharan# Africa#Countries

average annual growth of GDP per capita as many authors in FDI growth studies have done (Borensztein et al., 1998; Kumar et al., 2002; Makki et al., 2004; Nath, 2005). The GDP per capita data derive from the World Development Indicators website and it is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current international dollars based on 2011 (data.worldbank.com).

Since the effect of the independent and control variables on the dependent ones does not occur immediately, but in a following period of time, the variable Growth has been lagged by one year (t+1).

5.3 Independent variables

All the independent variables have been collected from the website data.worldbank.org. By considering the hypotheses made, the independent variables are the CFDI, human capital and the level of infrastructure. CFDI is calculated as the ratio between the amounts of Chinese inward FDI and the total worldwide inward FDI in each SSA country. The reason of this choice derives from the fact that the intention of this work is to analyze the only effect of Chinese investment on Sub-Saharan Africa economic growth, therefore the total amount of FDI in each Sub-Saharan Africa has to be taken into consideration in order to remove the effect of no-Chinese FDI in Africa. CFDI data derive from the UNCTAD Bilateral FDI Statistics 2014 where data are in principle collected from national sources. To collect this, the data about bilateral Chinese outflows investment in each Sub-Saharan Africa country has been gathered. The total amount of FDI in each SSA country refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Differently from

Borensztein et al. (1998) model, development is measured as the gross enrolment in primary education, expressed as a percentage of the population of official primary education age. The reason of this is that education limits the absorptive capacity of the host country to deploy benefits from FDI and it is universally recognized to be the most accurate indicator for understanding the degree of human capital. To measure the level of infrastructure development, it will be used a single-sector approach following the work of Easterly (2001) and Loayza, Fajnzylber and Calderón (2004). “Infrastructure is a multi-dimensional concept, comprising services that range from transport to clean water” (Calderón and Servén, p.8, 2008). They use telephone density indicator to evaluate the effect of infrastructure on growth. So, data from data.worldbank.org about the number of cellular subscriptions is collected. “Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provides access to the PSTN using cellular technology; the indicator includes (and is spit into) the number of postpaid subscriptions and the number of active prepaid accounts)” (International Telecommunication Union; World Telecommunication/ICT Development Report and database).

5.4 Control variables

The controlling variables used in the analysis are bilateral investment treaties, aids per capita, Openness to international trade, government consumption, the population, inflation and the total amount of inward investment FDI. A bilateral investment treaty (BIT) is an agreement between two countries regarding promotion and protection of investments made by investors from respective countries in each other’s territory. When the value of the BIT is “1”, it will mean that the BIT exists and is in force; otherwise the value will be “0”. Aid per capita includes the net official development assistance (ODA). It “consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the

members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients; and is calculated by dividing net ODA received by the midyear population estimate” (data.worldbank.org). It has taken in account since many SSA countries are poor and dependent on it. Openness to international trade is captured by the ratio of the sum of exports plus imports to total output (GDP). Government consumption is the ratio of central government expenditure to GDP. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. Inflation, measured as the percentage change in the consumer price index, is used as a proxy for macroeconomic instability. Finally, since the percentage of the CFDI on the total of inward investments, the total amount of inward FDI in SSA has to be controlled.

5.5 Model specification

A test of the effect of CFDI on SSA economic growth is performed in a framework of cross-country regression utilizing data on CFDI flows in 34 SSA countries for the period 2003-2012. For doing that, the growth equation I use is that adopted by many authors in FDI-growth studies:

Yit = β0 +β1Xit +β2Zit +εit

Where “i” identifies each of the 34 SSA countries and “t” the year in exam. Y is the real GDP per capita annual growth rate; β0 is the constant term, βis are the coefficients that will be estimated. X is a vector of independent variables including CFDI, Infrastructure and Human Capital. Z is a set of control variables that comprehends Treaties, Aid, Openness, Institution, Population, Inflation and IFDI. ε is the standard error. The name variables and their description

are summarized in table 2.

In order to investigate the impact of the independent variables on the dependent variable a linear regression was chosen because the dependent variable is a continuous variable and so the best model to analyze the relationships a dependent continuous variable and several independent variables.

For this thesis 4 models will be tested. The first one only including the control variables, the second adding our independent variables and the last two model testing the effect of the moderator variables I chose.

6. Results and analysis

6.1 Analysis Strategy

First of all, I used the lag function for the variable GROWTH in order to analyze the effect of Name Description Growth Average,annual,growth,of,GDP,per,capita,(%) CFDI Chinese,foreign,direct,investment IFDI Foreign,direct,investment,in,SSA, Human,capital Gross,enrolment,ratio,,primary,,both,sexes,(%) Infrastructure Mobile,cellular,subscriptions,(per,100,people) Treaties Bilateral,investment,treaties,(0/1) Opennes Sum,of,exports,and,imports,of,goods,and,services,(%,of,GDP) Aid Net,Official,Development,Assistance,(ODA),received,per,capita,(current,US$) Inflation Inflation,,consumer,prices,(annual,%) Institution General,government,final,consumption,expenditure,(%,of,GDP) Population Population,,total Table!2:!Variables!names!and!descriptions!

Table!3:!The!Outliers!

independent and control variables at t-1 time. The logic of this choice is due to the fact that in the analysis independent and control variables do not affect the dependent variable (GROWTH) immediately, but more likely one year later. Actually, the effect could be t-2 or t-3 but for the sake of simplicity we refer only to t-1 (Beck, 2001).

Moreover, I checked the frequencies to examine if there were any errors in the data. There were no errors found. Then, a control of the missing value occurred. Only the variable Human capital presents a significant amount of missing data (>10%). I dealt with this by excluding cases listwise that means that only cases that had no missing data in any variable were analyzed.

After this, outliers were checked through a descriptive analysis. Indeed by standardizing the scores of the variables the possible outliers were checked when z>|3| cases happened, the outliers were deleted. The number of outliers is shown in the following table 3.

Excluding cases listwise function treated the missing data deriving from the outliers deleted. Screening the data in this way allowed me to check the normality. Even though I eliminated outliers, the sample still presents values that are not normally distributed, so I decided to transform some variables. The variables Growth, CFDI, Human Capital, Institution, IFDI,

Variables Outliers Growth 5 Human-Capital 0 Infrastructure 6 CFDI 14 Treaties 0 IFDI 20 Aid 7 Institution 5 Openness 6 Population 9 Inflation 4

Openness, Aid and Population had been transformed as showed in the table 4:

Variables)

Transformations)

Growth' LogGrowth=LOG10(Growth)' CFDI' LogCFDI=LOG10(CFDI)' Human' capital' LogHuman'Capital=LOG10(Human'Capital)' Institution' LogInstitutionl=LOG10(Institution)' IFDI' LogIFDI=LOG10(IFDI)' Openness' LogOpenness=LOG10(Openness)' Aid' LogAid=LOG10(Aid)' Population' LogPopulation=LOG10(population)'

Table 4: Variables transformations

6.2 Correlations

Once I have checked the normality of the transformed variables, I analyzed the correlation between my variables. The table 5 below shows the bivariate correlations coefficients between the variables I used. The Pearson correlation indicates the linear dependence (correlation) between two variables. The matrix shows that the biggest part of the correlations is significant. There is a positive significant correlation (p=0.039) between the dependent variable (Growth) and the independent variable Human capital (0.147), which means that an increase of the variable Human capital brings to an increase on the growth of the SSA countries. This correlation follows what I predicted in the hypothesis 3, namely an increase in the Human capital would lead to higher level of growth. Other important significant positive correlations are between Growth and respectively IFDI, Aid and Treaties whilst Population and Inflation are significant negative correlate with Growth. However, the overall analysis does not show levels of correlation above 0.7 that means that there are not any multicollinearity problems because 0.7 has been recognized as the maximum threshold under which the multicollinearity effect does not occur.

! 36 ! Table!5:!the!regression!analysis! Variables M SD 1 2 3 4 5 6 7 8 9 10 1.7Growth .4680 .41630 2.7Human7Capital 19889 .10220 .147* 3.7Infrastructure 37.697 30.985 G.011 .130* 4.7CFDI .4861 .58540 .006 G.070 .006 5.7Treaties .28 .452 .233** G.079 .173** .024 6.7IFDI 24771 .57116 .165* .071 .345** G.345** .217** 7.7Aid 16730 .30131 .154* .143* .199** G.067 G.118* .025 8.7Institution 11208 .18585 .018 G.065 .229** .024 G.143* G.113 .150* 9.7Openness 18659 .19165 .095 .199** .410** G.148* .048 .197** .184** .023 10.7Population 69669 .62555 G.011 G.074 G.322** .147* .157** .157** G.373** G.040 G.634** 11.7Inflation 8.524 7.602 .051 .076 G.106 .057 .137* G.147* G.105 G.099 .007 .041 *7Correlation7is7significant7at7the70.057level7(2Gtailed). **7Correlation7is7significant7at7the70.017level7(2Gtailed).

6.3 Linear regression

In order to test the three hypotheses 4 regression analyses were conducted. Table 6 presents the results for the regression with Growth as dependent variable. For each hypothesis a model was run. Model 2 tests the hypothesis 1, model 3 the hypothesis 2, model 4 the hypothesis 3. Beta and p-value are reported in all the models. Beta shows the effect of the variable in the whole model, whilst p-value shows the significance and the reliability and it is the final test to check if the Hypotheses are supported (Field, 2009).

R2 _{is also provided. Model 1 reports R}2 _{= 0.121, namely that 12% of the variance is explained by}

the model. In model 2, where the independent variables were added, the 26% of the variance is explained, in model 3 the 26% and model 4 the 27%.

Model 1 comprehends the control variables Population, Inflation, Treaties, Institution, Aid, IFDI, Openness and the dependent variable Growth. The ANOVA measures if the model you use is significantly good at predicting the result. By looking at the first analysis we observe that the model is significant as p=0.001.

The control variable Treaties is significantly correlated with the Growth (p=0.002), with b*=0.213 and t=2.978. Thus if a bilateral investment treaty between China and Sub-Saharan countries exists the GDP Growth will increase by 0.213%.

AID variable is positive significantly correlated with Growth as well since p=0.004 and B=0.402. The other results are not statistically significant. On the basis of these results it can be concluded that bilateral investment Treaties between China and SSA countries have a positive impact on the economic growth of the latter. Furthermore the NET official development assistance contributes positively to the economic development of the SSA countries.

Model 2 aims at tested the first hypothesis, namely whether there is a relationship between CFDI and economic growth of SSA countries. We expected to have a positive relationship between the

two variables: when SSA countries have more CFDI, the economic development increases. It analyzes the effect of the independent variable CFDI, Human Capital and Infrastructure on the Growth controlling for Population, Inflation, Treaties, Institution, Aid, IFDI, Openness. The ANOVA test presents sig. = 0.000. Interesting results emerge since CFDI, Infrastructure, Human capital, IFDI, Treaties, Aid and Institution are significantly correlated with Growth. CFDI is significantly positive correlated in this model since B= 0.095 and p=0.043, so we can affirm that they have a certain degree of impact on the SSA economic growth. Indeed, as the CFDI grows by 1%, the Growth increases by 0.095%. Therefore the hypothesis 1 is supported. Moreover by looking at the Infrastructure and the Human capital variables, what emerges is that former has a significantly negative impact on the Growth (B=-0.004 and p=0.008), whilst the latter has a significantly positive linear interaction on the dependent variable (B=1.140 and p=0.002). Although this model shows a negative effect of Infrastructure on Growth, the impact is not so highly affecting since the value of Beta is -0.004. Indeed when the Infrastructure increases by 1% the Growth will decrease by 0.004%. This result was not expected, since it is hard to believe that higher level of Infrastructure, in this case represented by the amount of cellular subscriptions, does not contribute to the spillover effect that foreign direct investment usually brings to the host countries. Human capital has B=1.140 and p=0.002, thus the model can be said to show a positive correlation between Growth and Human capital, indeed as we can see by looking at the table that if the Human capital grows by one point, the Growth increases by 1.101. Through this result we can partially support the hypothesis 3 by saying that Human capital affects positively the SSA economic growth. Other results that can be highlighted from this model are the total amount of Inflow foreign direct investment in SSA is significantly positively correlated with SSA economic growth (B=0.191 and p=0.029). Furthermore, bilateral investment treaties between China and SSA countries have a positive impact on the economic growth of the latter (B=0.215 and

p=0.004). Moreover, NET official development assistance contributes positively to the economic development of the SSA countries (B=0.410 and p=0.004). Lastly, the general government final consumption expenditure has a positive effect on the economic development of SSA countries (B=0.569 and p=0.022).

For the second and third Hypothesis we include moderators of the relationships. Two moderators are included, one for each model. Hypothesis 2 affirmed that the relationship between CFDI and SSA economic growth is positively moderated by the level of infrastructure. In other words, when the level of infrastructure is higher, the impact of CFDI on SSA growth should be higher. By giving a look at Model 3, in table 6, we see that Beta od the independent variable CFDI has grown from 0.131 to 0.133, supporting Hypothesis 2. However the coefficient of the moderation is not significant, therefore Hypothesis 2 is not supported by the analysis.

The same outputs occur in Model 4, where the human capital was tested as moderator: the coefficient of the independent variable increase from 0.131 to 0.150. So, the more advanced is the level of Human capital the stronger is the impact of CFDI. However the moderator is not significant and so hypotheses 3 is completely rejected. Therefore, since the moderator effects are not significant, we can conclude that there is no evidence of moderation and so the hypotheses 2 and 3 are completely rejected.

The tables above show the results from the moderation analyses fulfilled for testing hypotheses 1, 2 and 3.

7. Discussion and conclusions

This study shows that CFDI has a positive effect on the SSA economic growth, supporting what the academic literature stated about the modernization theory (Sánchez-Robles et al. 2002), according to which FDI is determinant to the transfer of technology and capital to developing countries. In particular, CFDI has brought huge amounts of money in order to support the resource seeking business, which is the main reason why China has invested so much in the SSA countries rather than in other countries (Cheung et al., 2008). Moreover as the academic literature

Dependent'variable:'LogGrowth

Control'variables Beta Sig. Beta Sig. Beta Sig. Beta Sig. LogAid' .402† .004 .410† .004 .416† .005 .417 .004 LogIFDI .049 .432 .191** .029 .187** .036 .195 .026 Treaties .213† .002 .215† .004 .218† .004 .216 .004 LogInstitution .146 .467 .569** .022 .567** .023 .626 .012 LogOpenness .044 .850 .110 .673 .118 .656 .076 .772 LogPopulation .068 .398 .021 .842 .025 .818 .008 .944 Inflation .000 .914 N.007 .222 N.007 .220 N.006 .281 Indipendent(variables LogCFDI .131** .043 .133** .044 .150** .024 Infrastructure N.004† .008 N.004† .008 N.005† .002 LogHuman'Capital 1.140† .002 1.168† .003 1.122† .004 Moderators MOD'Human'capital N.008 .847 MOD'Infrastructure .021 .625 N 186 117 117 116 R2 .121 .259 .259 .273 F5test 3.535 .001 3.737 .000 3.370 .001 3.590 .000 *p<0.1 **p<0.05 †p<0.01 H2 Model'3 H3 Controls Model'1 H1 Model'2 Table!6:!Regression!analysis!

has affirmed (Naidu et al. 2008), China has represented an opportunity for Africa in order to promote the reduction of poverty in the SSA countries. This was possible not only thanks to the huge investments made by China, but also because of the policies issued by Chinese institutions in collaboration with the SSA governments, which aimed at promote the development through the construction of railways, roads, buildings, hospitals, the provision of high-skilled doctors, qualified teachers and the offer of scholarships. On one hand for what concerns Human capital variable, this analysis permits to affirm that the gross primary enrolment ratio has a direct positive effect on the development of SSA economy. However, when Human capital is used as moderator, the variable is not significant and this renders this model unable to demonstrate that the higher is the level of Human capital, the higher is the effect of CFDI on the SSA economic growth. This could be explained by referring to what Lumbila (2005) stated, namely education enhances the impact of FDI on growth only in countries where the human capital is more developed. Since the analysis takes into consideration the primary education and as the literature says, in less developed countries such as SSA ones, the level of education is quite low, we can agree with what Lumbila (2005) affirmed and so stating that the impact of CFDI on growth could not be positively moderated by low level of human capital. Moreover several studies after having analyzed this impact, have stated that it is more productive if there is the preexistence of a minimum threshold stock of human capital (Borensztein et al., 1998; Azariadis et al., 1990; Anderson et al., 1965).

On the other hand, speaking about infrastructure, examined as cellular subscriptions, they do not impact positively on the economic growth of the SSA country. This can mean that poor countries such as those of SSA area are still not have the necessary means to exploit the productivity deriving from the facilitations of infrastructures (in this case telephone subscriptions) create (Estache et al., 2002). Indeed, until a network of connections around all the countries is not