african development review, vol. 22, no. s1, 2010

African Development Review, Vol. 22, No. S1, 2010, 599–614

Chinese FDI to Africa: What Is the Nexus with ForeignEconomic Cooperation?∗

Marco Sanfilippo∗∗

Abstract: China, once a major recipient of foreign direct investment (FDI), has recently become one of the main ‘emerging’investors, especially in developing countries. Chinese Outward Foreign Direct Investment (OFDI) plays a very prominent role ineconomic interaction with many African countries. This paper empirically investigates the determinants of Chinese OFDI versus41 African countries over the period 1998–2007. The analysis is novel because it provides empirical support to the existing, so farpurely anecdotic, evidence describing Chinese FDI to Africa as driven by natural resources endowments and market potential. Theeconometric analysis highlights strong interrelationships between Chinese FDI and economic cooperation, which make standardmodels of investments unfit when assessing the role of China in Africa. It also suggests some new lines of research, exploitingthe strong links between these different sources of financing.

1. Introduction

China’s enormous growth over the last three decades has now jumped to a new, significant, stage. Official statistics from themost recent years highlight how the country, from being the first recipient of foreign direct investment (FDI) among developingcountries, is also becoming a major source of FDI. This follows a new trend, which consists of a more prominent role of developingand transition economies as increasing source of FDI (UNCTAD, 2006).

Recently, given the country’s need to fuel its growth with natural resources, many of the African economies have been targetedas the main destination of Chinese capital flows, part of which are in the form of FDI.

Though Chinese outward FDI (OFDI)1 is the object of a growing strand of literature, little is known about the factors drivingthe decision of Chinese multinational enterprises (MNEs) to invest abroad. This is true especially for Chinese OFDI to Africa,whereas the existing evidence shows that it is difficult to discern FDI flows from other capital flows, especially when Chinese stateowned enterprises (SOEs) are involved in huge deals with recipient governments in key sectors such as mining, infrastructuresand telecommunications. This paper tries to clarify the picture, using official data from the Chinese Ministry of Commerce inorder to analyse empirically the determinants of Chinese OFDI to 41 African countries.

The paper proceeds as follows. Section 2 summarizes the theoretical background. Section 3 presents a review of the literaturebased on the existing evidence on Chinese OFDI to Africa, analysing specifically the nexus with international economiccooperation projects. Section 4 presents the interpretative model and reports the main hypotheses. Results of the empiricalanalyses are presented in Section 5. Section 6 concludes.

2. Theoretical Background

The most influential approach to study the international activities of MNEs is represented by the ‘eclectic paradigm’ originallyproposed by Dunning (1993).2

With regard to the opportunity of explaining the recent rise of developing country MNEs, the main criticism that has beenmoved to the eclectic paradigm is that such firms might not possess the same competitive advantages of developed country MNEs

∗I am grateful to Giorgia Giovannetti and Mario Biggeri for their helpful suggestions. I have benefited from comments made by participants at theEuropean Report on Development conference, ‘Financial markets, adverse shocks and policy responses in fragile countries’ in Accra, May 2009, andat the seminar ‘China’s increasing engagement in Africa in the aftermath of the financial crisis’, organized by the African Development Bank in Tunis,March 2010.∗∗Robert Schuman Centre for Advanced Studies, European University Institute, Florence, Italy; e-mail: [email protected].

C© 2010 The Author. African Development Review C© 2010 African Development Bank. Published by Blackwell Publishing Ltd,9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. 599

600 M. Sanfilippo

and, thus, they do not invest abroad on the basis of their unique O-advantages. As a consequence, internationalization may beseen, in many cases, as a strategy that aims to enhance the firms themselves toward the accumulation of resources previously notavailable (Amighini et al., 2009).

Mathews is the only author who has introduced an ad hoc theoretical framework entirely based on the observation of latecomerMNEs from emerging countries (Mathews, 2002, 2006).

Mathews (2002) has proposed an alternative framework: the so-called Linkage, Leverage and Learning (LLL) framework.Linkages (such as joint ventures or other forms of collaboration into global value chains) with foreign companies represent thesafest way to get access to the resources they lack internally. Once linked, the crucial point is how firms are able to gain the accessto the resources they need. This depends on the leverage potential of the resources, that is, how accessible they are. A continuedinteraction of linkage and leverage may conduce to the final outcome, that is, learning, which is the opportunity for the firms tobetter understand how to operate internationally and to allow them to be competitive in the global markets.

The LLL framework has been criticized for its focus on firms originating from the rapidly growing countries in the AsiaPacific region (Narula, 2006), as well as for the fact that some latecomer firms might indeed possess certain unique competitiveadvantages (Dunning, 2006). Recently, Dunning et al. (2008) have highlighted the importance of country-specific ownershipadvantages in determining emerging country outward FDI. Cuervo-Cazurra and Genc (2008), for instance, show that MNEsenjoy a competitive advantage compared to developed country MNEs in more difficult institutional environments, such as thosecharacterizing other developing countries. In this case, developing country MNEs are able to turn their relative disadvantage ofcoming from countries with poor institutions to an advantage, consisting of moving easily into more difficult contexts.

Another weakness of the Mathews’ paradigm is that, as it stands, it is more likely to explain south–north FDI flows, providingno real prescriptions on south–south FDI. On this regards, Aykut and Goldstein (2006) suggest that differences between south–south and south–north FDI are better emphasized when this ‘new’ paradigm combines with other theoretical approaches such asinstitutional theory and global value chain analysis.

3. Chinese OFDI

Chinese investments abroad have increased significantly during the last years and their stock in 2008, according to the data of theMinistry of Commerce (MOFCOM, 2009), has jumped to $42 billion.

Geographically, the distribution of Chinese OFDI is skewed towards developing countries. Asia is by far the preferreddestination, with Hong Kong making the lion’s share when single countries are concerned. Latin America follows with 25 percent of the stock. In this case, however, two offshore centres — Cayman and Virgin Islands — account for about 90 per cent ofthe total investments in the region. Africa represents the third continent, attracting 4.2 per cent of total stock. Considering theflows only, however, Africa’s performance has been substantial between 2004 and 2008, when the continent has attracted around10 per cent of Chinese OFDI.

Sectorally, about 88 per cent of the final stock of Chinese OFDI is shared among six branches. That of business services is theleading sector, followed by trade, finance, mining, manufacturing and transports.

3.1 Chinese OFDI to Africa

Although Chinese OFDI to Africa is still low, the rate of growth over the last years has been impressive (Figure 1). Available dataon approved investments per country of destination show that the pattern of Chinese investment in Africa has been historically

Figure 1: Chinese OFDI to Africa (real US$million, 2005)

0

1000

2000

3000

4000

5000

6000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Source: Author’s elaboration on China Statistical Yearbook and MOFCOM (2009).

C© 2010 The Author. African Development Review C© 2010 African Development Bank

Chinese FDI to Africa 601

represented by a large number of enterprises and, consequently, small amounts on average. Indeed, there is evidence that themajority of Chinese firms operating in Africa are small (UNCTAD, 2007; Wang, 2007; Henley et al., 2008). Together with thesesmall enterprises, which are mainly private and operate in the manufacturing sector and in retail trade (Gu, 2009), Kaplinsky andMorris (2009) identify two additional types of Chinese investors in Africa. The first includes SOEs involved in big projects in thenatural resources sector and in the infrastructures, which are often linked to aid packages and concessional loans offered by thesame Chinese Government (Cai, 1999). The second type of Chinese investors in Africa is represented by medium-sized privatecompanies who generally operate in the manufacturing, telecommunications and wholesale trade.

Sectoral data on China’s FDI flows to Africa at the aggregate level are only available for the period 1979–2000 (UNCTAD,2007). At that time, manufacturing (especially textiles) and resource extraction were the two most relevant sectors (amounting to74 per cent of the total value of FDI). The tertiary sector is nowadays becoming much relevant in absolute terms, with increasinginvestments in telecommunications and the construction sector which historically hold a prominent position in Chinese investmentin Africa.3

Resource-seeking and market-seeking investments are recognized as the main motivations for Chinese firms that invest inAfrica. As a matter of fact, most of the Chinese OFDI in Africa comes from SOEs searching for unexplored reserves, often tyingFDI to government aid programmes, especially in politically risky domestic environments. With regard to the market-seekingmotivation, it has been often argued that Chinese OFDI is strictly linked to overall trade with Africa (World Bank, 2004). Anecdoticevidence is rich in examples of Chinese companies entering Africa with the aim of placing their low cost products. Indeed, resultsfrom a survey on 80 Chinese firms investing in Africa show that the access to the local market ranks first among the motivationsof their investment (Gu, 2009). On the other hand, Henley et al. (2008), using data from a UNIDO survey of 2005 (UNIDO, 2007),have shown that Chinese investors in sub-Saharan Africa (SSA) are, together with other Asian investors, relatively more exportoriented and that the export flows are mainly directed back to the home country. An additional type of market-seeking investmentis targeted to serve third markets. This is the case of global value chains that use African countries to overcome trade barriers andto take advantage of preferential market access to industrialized countries (Besada et al., 2008; Kaplinsky and Morris, 2009).4

Domestic sources may come to help in order to get access to more detailed information on Chinese FDI to individual Africancountries. Very recently, many country case studies have appeared with some interesting new information on Chinese investmentsto Africa (Table 1).

The evidence summarized in Table 1 provides additional information on the nature of Chinese investments in Africa. Concerningthe sectoral distribution, it is interesting to notice the prevalence of the manufacturing that, together with the construction andtrade-related services, represents the most common industry where Chinese firms operate all over the African continent. Thesesectors are also a source of local employment, especially low skilled.5 Data on employment are available only for a small numberof cases. Nonetheless, it is possible to observe that in some circumstances the contribution of Chinese investments is substantial.In the case of Madagascar, for instance, Razafindrevonona et al. (2008) calculate that Chinese firms contributed to 10 per cent oftotal employment created by foreign MNEs during the period 2000–2006. Onjala (2008) reports that, in Kenya, local employmenthas represented 96 per cent of the total new employment generated by Chinese FDI. Still, in a risky context such as Zimbabwe,Edinger and Burke (2008) report that the Chinese SOE Baosteel — one of the world’s top players in the steel industry — hasinvested around $300 million in the mining industry, creating about 2,000 new jobs.

FDI-Economic Cooperation Nexus

As it is possible to see from Table 1, investments in natural resources are often not included in the data provided by local authorities,although they represent the bulk of Chinese OFDI in the continent. Beyond any methodological concern,6 this is due to the natureof the Chinese investment itself. Much of Chinese investment in the natural resource sector is made, as Ogunkola et al. (2008)document for the Nigerian case, by public enterprises forming joint ventures with local companies. This has generated muchconfusion in the literature, since it is difficult to distinguish whether Chinese firms operate on their own initiative or if they areinvolved in government deals with host countries that are often part of China’s international aid or concessional loan projects.This, in the opinion of many, is mainly an outcome of the outward looking policies adopted by the Central Government at thebeginning of the new millennium:

As the ‘going out’ of Chinese enterprises has become an official goal of the government, China’s OFDI in Africa has been activelyfacilitated by the government aid programmes: for example, construction projects to build politically important public buildingsand basic infrastructure are offered under the aid programme arguably in the expectation of winning political support for largenatural resources-related FDI deals between African governments and Chinese SOEs. (OECD, 2008, p. 114)


602 M. Sanfilippo

Table 1: Chinese OFDI to selected African countries

Source Country Period N. of firms Sector Employment

Campos and Vines (2008);Corkin (2008)

Angola 2005–2007 51 Construction (56%); Industry (29%); Transport(10%); Fisheries (2%) and Commerce (1.6%)

n.a.

CCS (2008) Ethiopia 1980–2007 95 50% goes to Manufacturing (including steel,chemicals, pharmaceutical, textiles,machinery, paper and glass)

n.a.

Tsikata et al. (2008) Ghana 2001–2006 249 General trade (51%); Tourism (33%);Manufacturing (29%)

n.a.

Onjala (2008) Kenya 2000–2006 70 Manufacturing (90%) and Services (10%) 5097Razafindrevonona et al.

(2008)Madagascar 2000–2006 146 In 2006 Chinese FDI were concentrated in

telecommunications and financial services.Numerically, the bulk of Chinese firms goesto wholesale trade

6041

Sanogo (2008) Mali 2006 5 Chemicals, agro-food industry, manufacturing 148Ogunkola et al. (2008) Nigeria 1999–2006 30 Agro-allied industry; Manufacturing and

communications (Private FDI); Construction,Oil&Gas (Public FDI)

n.a.

Boungou Bazika (2008) Rep. of Congo 1995–2005 n.a. Energy (59%); Infrastructures (18%); Health(10%); Manufacturing (5%);Telecommunications (4%)

n.a.

Maglad (2008) Sudan 2006–2007 31 Construction (26%); Plastic products (19%);Machinery and electrical appliances (16%)

n.a.

Moshi and Mtui (2008) Tanzania 1990–2006 140 Manufacturing (79%); Agriculture (10%);Construction (3%)

n.a.

Obwona et al. (2007) Uganda 1991–June 2007 118 Manufacturing (63%); Electricity, gas and water(13%); Construction (5.65); Financialservices (5%); Agriculture (4.56%)

n.a.

Kragelund (2009) Zambia up to 2007 184 Manufacturing (44%); Services (20%);Construction (16%); Agriculture (12%);Mining (5%)

12334

Infrastructural projects, together with natural resources, are the emblem of this comprehensive approach in Africa, whichoften includes also the provision of aid and technical assistance to the same countries. The bulk of Chinese investment in theinfrastructures is directed to the construction of transport routes for the export of natural resources (Kaplinsky and Morris, 2009).As reported by Naidu and Davies (2006, p. 80), Chinese entry into Africa is characterized by a sort of ‘coalition investment’strategy in a way that can include tying energy acquisitions to funding for infrastructure development. In some circumstances,huge infrastructural projects are made in return to the access to natural resources. Chinese contractors, however, do not qualifyas foreign investors given that they do not risk their own equity capital but are indirectly financed by the government, and, inaddition, they do not gain control of any foreign affiliate (World Bank, 2008).

As a final remark to this descriptive part, thus, it is really hard to analyse the motivations of Chinese FDI in Africa withoutconsidering the possible synergies arising with the aid strategies of the Chinese Government (Biggeri and Sanfilippo, 2009).

Economic cooperation projects, a rough proxy for Chinese aid,7 can be thought to be strictly intertwined with FDI for at leasttwo reasons. The first is that, as part of its grand strategy in Africa, the Chinese Government strategically uses its aid flows inorder to foster recipients to use the funds to attract Chinese investors. The second reason is methodological. Several analysesbased on a detailed screening of the official definition of Chinese economic cooperation and on its modalities of provision agreein concluding that it shares by no means some of the characteristics of FDI (World Bank, 2008; Centre for Chinese Studies, 2008;Bhaumik and Yap Co, 2009).

A causality test based on F statistics (Granger, 1969) is performed on data of Chinese outflows of FDI and economic cooperationaggregated for the whole African continent. As it stands, causality tests should be cautiously considered as a source of informationon the direction of causality in the absence of other variables (Greene, 2003). Rather than causality, this kind of test determineswhich of the two variables follows the other and, thus, ‘precedence’ is considered a more appropriate term to describe what aGranger test effectively captures (Mukherjee et al., 1998).

Data on Chinese OFDI and economic cooperation flows cover the period 1998–2007. Table 2 reports the test’s results. Thenull hypothesis of non-causality is refused when testing the effects of economic cooperation on both OFDI flows and stock.



Table 2: Granger causality test (1998–2007)

Economic cooperation does not Granger-cause OFDI flowlag1 Chi2 25.83

p>chi2 0.0000Economic cooperation does not Granger-cause OFDI stocklag1 Chi2 9.78

p>chi2 0.0018OFDI flow does not Granger-cause economic cooperationlag1 Chi2 0.89

p>chi2 0.3444OFDI stock does not Granger-cause economic cooperationlag1 Chi2 2.18

p>chi2 0.1401

Conversely, when reversing the order of the variables, the null hypothesis of non-causality is confirmed, suggesting the existenceof a unidirectional pattern. Huge economic cooperation projects supported by the Chinese Government are found to precedeinvestment decisions of Chinese companies. The ultimate question to be answered is whether this ‘grand strategy’ is somehowaffected by the same factors, given the nature of economic cooperation projects, often very close to that of direct investments.

4. Empirical Analysis

4.1 The Model

There is still limited empirical literature on the foreign activities of Chinese MNEs (see, among others, Buckley et al., 2007;Cheung and Qian, 2008; Kolstad and Wiig, 2009). Also empirical studies on FDI inflows to Africa are limited (Asiedu, 2002,2006; Morriset, 2000; Ajayi, 2006). In addition, no empirical work — to the author’s knowledge — has so far investigatedempirically Chinese FDI to Africa.

The remaining part of this paper intends to fill these gaps, analysing, by means of diverse econometric estimation techniques,the determinants of Chinese FDI to Africa.

Determinants of Chinese FDI to Africa

As remarked above, market seeking is a relevant motivation of Chinese overseas investments. The same, in general, is true in thecase of foreign investments to Africa (see Section 3.1 and Morriset, 2000). Already in 1993, China’s Ministry of Foreign Tradeand Economic Cooperation (now MOFCOM) identified Africa as a ‘key’ market for Chinese consumer products (Shelton, 2007).Indeed, preliminary evidence say that Chinese investments to African countries are motivated by the search for new, relevant,markets for exporting Chinese low-cost manufacturing (Broadman, 2007; Wang, 2007; Gu, 2009).

Following the empirical literature, market size and attractiveness is proxied by the total income level of the host country, which,on average, has a positive influence on FDI (Dunning, 1993; Buckley et al., 2007; Asiedu, 2006).

H1a: Market-seeking — Chinese OFDI investors to Africa are attracted by countries with higher market potential.

In addition, market-seeking investments can be motivated by the need to overcome external trade barriers. More specifically,FDI can be positively affected by trade openness or by the participation of the host country to a generalized system of preferenceswith third markets (Asiedu, 2002). This is notably what happened in the textile sector before the expiration of the Multi FiberArrangement (MFA). Chinese companies have strongly invested in African least developed countries that enjoyed special ruleson their exports in the manufacturing sector (Broadman, 2007), especially under the provisions of the African Growth andOpportunity Act (AGOA) (Besada et al., 2008; Kaplinsky and Morris, 2009).

Though the share of trade on GDP is usually adopted in the literature as a proxy to determine the openness of a given economy(Bende-Nabende, 2002; Morisset, 2000), the inclusion of a dummy indicating the participation of African countries to AGOA is


604 M. Sanfilippo

deemed more useful to check whether Chinese companies have invested into Africa in order to take advantage of tariff exemptionsin third markets.

H1b: Market-seeking — Chinese OFDI investors are motivated by the need to establish export platforms to serve third countriesproviding preferential treatments to African countries.

As the literature on China-Africa relations has widely affirmed that the need to get a secure access to natural resources isthe main motivation driving Chinese MNEs in the continent, a second hypothesis has to do with the characteristics of the hostcountry in terms of natural resource endowments. Natural resource endowments are proxied by the quantity of oil produced byeach individual country.

H2: Resource-seeking — Chinese investors in Africa are motivated by the search for long-term access to natural resources.

Another group of variables is used to evaluate the country — political and economic — risk, which represents by no means themajor impediment to attract FDI in the region (Ajaye, 2006; Asiedu, 2006; UNIDO, 2007; Broadman, 2007). Inflation is used asa standard indicator of macroeconomic instability (Nnadozie, 2000; Asiedu, 2002; Buckley et al., 2007). Similarly, a high levelof international debt indicates a relatively unfavourable environment for foreign investment (Nnadozie, 2000).

Political risk is estimated through two different variables. A first variable is taken from the Uppsala Conflicts Database and hasbeen constructed attributing a value of 2 in the presence of a major conflict, 1 in the presence of a minor conflict and 0 with noarmed conflicts.8

A second variable indicates civil liberties and comes from Freedom House. The index rates civil liberties on a scale of 1 to 7,with 1 representing the most free and 7 the least free situation. There is evidence that developing country MNEs are relativelyindifferent to the institutional weaknesses of the host countries (Cuervo-Cazurra and Genc, 2008). A recent work by Kolstad andWiig (2009), for instance, finds that Chinese OFDI are directed to countries with poor institutions, especially if they are endowedwith natural resources.

H3: Risk aversion — Following anecdotic evidence, Chinese MNEs — especially SOEs — appear relatively less risk adversecompared to traditional investors in the continent.

A last set of variables is used to control for other relevant factors that can influence the decision of Chinese firms to invest inAfrica. The number of telephone mainlines is used to proxy the availability of infrastructures and communications facilities inthe country, both regarded by foreign companies as important prerequisites for their investments (Khadaroo and Seetanah, 2007;Calderon and Serven, 2008). The level of human capital is measured by the adult literacy rate. Although the presence of skilledhuman capital is usually felt as a relevant pull factor for foreign MNEs, there is evidence that Chinese companies tend to rely ontheir own nationals for managerial positions (Chen et al., 2007). Finally, we also check whether Chinese companies follow thepattern of the other investors in the continent or, in the alternative, if they follow a pattern of their own. Based on a comparisonamong correlation coefficients between 2003 and 2007, Berthelemy (2009) has showed that the geographical distribution ofChinese and non-Chinese OFDI to Africa has become progressively more similar.

FDI-Trade Nexus

The model also investigates the relation between FDI and trade. With specific regards to the recent rise of MNEs from emergingeconomies, it is believed that their investments are ‘triggered by trade-related variables, which facilitate and necessitate outwardFDI’ (Banga, 2009, p. 202). On China, Cheung and Qian (2008) add that the complementarity between FDI and exports hasincreased after the launch of the ‘Going Out’ strategy and that it is stronger for investments directed towards developing countries.

Therefore, looking at the impact of trade on FDI, it is possible to assume that more exports on the one hand may require animprovement in trade supporting services and, on the other hand — providing knowledge on external markets — may reducetransaction costs of the investments, encouraging FDI. Imports — providing an indication of the importance of the products(mostly natural resources) transferred — may spur firms to internalize these strategic flows by means of OFDI.

H4: FDI-Trade nexus — Chinese OFDI to Africa is associated positively with bilateral trade.



Table 3: Summary statistics

Variable Obs Mean Std. Dev. Min Max

lnOFDI 430 15.81 1.81 9.90 20.22lnGNI 422 22.21 1.49 18.99 26.11OIL 430 0.19 0.47 0 2.62CONF 430 0.32 0.63 0 2CIVIL 430 4.38 1.41 1 7INFL 429 18.18 70.08 −17.79 1016.68TEL 428 3.09 5.05 0.02 28.75LIT 430 60.80 18.62 14.30 93.00lnFDI 409 17.96 3.80 −10.77 22.88AGOA 430 0.38 0.49 0 1lnDEBT 410 21.92 1.30 18.19 24.24lnCNEXP 417 18.03 1.84 12.54 22.58lnCNIMP 379 16.91 2.82 8.01 23.13lnCOOP 387 16.48 4.22 −10.77 21.13

Chinese Overall Strategy into Africa: The Nexus between FDI and Economic Cooperation

The last hypothesis aims to capture the supposed strong relationship between FDI and Economic Cooperation and, thus, theoverall strategy of China in Africa. In other words, at least in the China-Africa case, Chinese OFDI and international cooperationare expected to be complementary and, as the Granger causality test performed in Section 3.1 suggests, it is expected that largecooperation projects reinforce FDI, through a successive commitment of Chinese MNEs.

To some extent, it is possible to proxy this variable, in the absence of other official data, with bilateral aid. Since there isempirical evidence that investment decisions follow often bilateral aid patterns (Yasin, 2005), the expected sign for this variableis positive.

H5a: Overall approach into Africa — OFDI to Africa are part of a more comprehensive strategy, supervised by the CentralGovernment, of engagement into Africa that include as a preliminary step the provision of international economiccooperation.

An additional issue to consider is that the variable international economic cooperation might be generated by the same modelof FDI. As remarked above, in some cases it can be methodologically difficult to distinguish between OFDI and economiccooperation. Therefore, to the extent that economic cooperation projects might be considered as an alternative modality to provideFDI, it is reasonable to think that the factors affecting FDI decisions are similar to those affecting the decision to provide economiccooperation projects (Bhaumik and Yap Co, 2009). This, in addition, can provide a more efficient measure of the overall Chinesemove into Africa (Biggeri and Sanfilippo, 2009).

H5b: Overall approach into Africa — Chinese FDI activities and international economic cooperation projects are likely to beaffected by the same set of determinants and the supposed interrelations between the two variables can be more easily caughtin a system of equations model.

4.2 Data and Methodology

Following the analysis of Section 4.1, the main model can be analysed using the following functional relationship:9

OFDIit = f (GNIit−1; OILit−1; Inflit−1; LITit−1; Telit−1; CONFit−1; FDIit−1;

Civilit−1; Debtit−1; AGOAit−1; CH EXPit; CH IMPit; COOPit−1) (1)

where OFDIit, the dependent variable, is the stock of OFDI received at time t (t = 1,. . .T) from China by country i (i = 1,. . .N).The data set used for the empirical analysis consists of annual data from 1998 to 2007 for 41 African countries. Table 3 reportsthe summary statistics of the data, while Tables A1 and A2 in the Appendix report, respectively, the description of the variablesand the correlation matrix.


606 M. Sanfilippo

A Chow test on the joint significance of country fixed effects is first performed (Baltagi, 2005).10 The test confronts the pooledestimator with a least squares dummy variables (LSDV) model, pointing to a major efficiency of the latter. Panel analysis is thusapplied to produce consistent estimates. In order to choose the correct specification to estimate the coefficients, the Hausmanspecification test is performed. Looking at the test’s results, the fixed effect model is preferred.

Fixed effects models control for country specific characteristics, which usually improve the whole efficiency of the model.Nonetheless, in some instances it may be relevant to control also for time-specific effects that are not included in the regression(Baltagi, 2005). In the China-Africa case, there are different reasons to include such variables.11 The joint F test that all the timeeffects are equal to zero suggests that they should be included in a properly specified model. The final model adopted is thereforea two-way fixed effects error component model that can be specified as follows:

lnOFDIit = β1 lnGNIit−1 + β2OILit−1 + β3CONFit−1 + β4Civilit−1 + β5INFit−1 + β6TELit−1

+β7LITit−1 + β8 lnFDIit−1 + β9AGOAit−1 + β10 lnDEBTit−1 + εit + μi + λt (2)

which becomes the following when including trade at time t for testing hypothesis 4:

lnOFDIit = β1 lnGNIit−1 + β2OILit−1 + β3CONFit−1 + β4Civilit−1 + β5INFit−1 + β6TELit−1 + β7LITit−1 + β8 lnFDIit−1

+β9AGOAit−1 + β10 lnDEBTit−1 + β11 lnCH EXPit + β12 lnCH IMPit + εit + μi + λt (3)

and the following when testing hypothesis 5a:

lnOFDIit = β1 lnGNIit−1 + β2OILit−1 + β3CONFit−1 + β4Civilit−1 + β5INFit−1 + β6TELit−1 + β7LITit−1 + β8 lnFDIit−1

+β9AGOAit−1 + β10 lnDEBTit−1 + β11 lnCOOPit−1 + εit + μi + λt (4)

where ε it is the stochastic disturbance, μi represents the country specific slopes and λt the time fixed effects.Natural logarithms are used to transform the variables expressed in monetary terms. This, in turn, should reduce the risk related

to heteroscedasticity, which is nonetheless common in cross-country analyses. In a panel context with fixed effects, Baum (2001)suggests computing a modified Wald statistic to test groupwise heteroscedasticity in the residuals. The test statistics, distributedChi-squared with 41 degrees of freedom, rejects the null of homoscedasticity, pointing to the need to adopt robust standard errorsto correct for heteroscedasticity (Baltagi, 2005).

Testing a System of Equations

Although a panel estimator represents an efficient instrument to estimate Equation 2 in its general form, when testing hypothesis5 the inclusion of the variable on Chinese international cooperation might generate some methodological questions.

Moving to hypothesis 5b, then, the choice to estimate a system of structural equations arises from the need to test theprovisions coming from theoretical and descriptive interpretations that see this comprehensive ‘move’ into Africa — eitherthrough international economic cooperation projects and outward direct investments — by Chinese companies as affected by asimilar set of determinants.

It is therefore possible to assume that international economic cooperation projects could be explained by the same set ofvariables that explain FDI, according to the following system of equations:

lnOFDIit = β1 lnGNIit−1 + β2OILit−1 + β3CONFit−1 + β4Civilit−1 + β5INFit−1 + β6TELit−1 + β7LITit−1

+β8 lnFDIit−1 + β9AGOAit−1 + εit + μi + λt (5)

lnCOOPit = θ1 lnGNIit−1 + θ2OILit−1 + θ3CONFit−1 + θ4Civilit−1 + θ5INFit−1 + θ6TELit−1

+ θ7LITit−1 + θ8AGOAit−1 + θ9 lnDEBTit−1 + uit + μi + λt (6)

In principle, according to the econometric literature, 5 and 6 can be defined as structural equations given that they are bothderived from theory (Greene, 2003).

Nonetheless, even if literature suggests that single equations estimation can be considered a consistent method in such a system,it cannot be a fully efficient one given that it could provide only limited information in the presence of either cross equationsrestrictions on the parameters or a possible cross-correlation between the error terms in different equations (Mukherjee et al.,1998; Wooldridge, 2002). To put it differently, there might be some common factors influencing the disturbances in both theequations that have not been specified explicitly in the matrices of explanatory variables.

In these cases, system approaches adopting a generalized least square estimator (GLS), such as Zellner’s seemingly unrelatedregression (SUR), guarantee an increase in the whole efficiency of the estimation (Wooldridge, 2002; Greene, 2003).



Table 4: Correlation matrix of residuals∗

lnOFDI lnCOOPT

lnOFDI 1.0000 0.1828lnCOOPT 0.1828 1.0000

∗Breusch-Pagan test of independence: chi2(1) = 12.993, Pr = 0.0003.

SUR is a two-stage estimator in which the residuals are calculated from OLS in order to apply GLS. The SUR assumescontemporaneous correlation in the error term of the equations. In this case a GLS (which more often transforms in a feasibleGLS given that the covariance matrix of the disturbances is generally unknown) provides more efficient estimators of thecoefficients (Greene, 2003).

There are two cases in which single equation estimation by OLS is equivalent to SUR (Greene, 2003, p. 257): (1) when theregressors in each equation are identical; (2) when no cross correlation of the disturbance is found.

In order to overcome point (1), the variable lnDEBT has been omitted from Equation 5, while the variable lnFDI has beenomitted from Equation 6.12 Concerning point (2), results of the Breusch–Pagan test for independent equations (reported in Table 4)reject the null of independence between FDI and economic cooperation, showing that there is a statistically significant correlationbetween the error terms of the two equations and that a system estimator is asymptotically more efficient than estimating equationby equation via OLS.

This conforms to the a priori expectations that the two variables may have underlying similar determinants. At the same time,however, the cross equations correlation is not particularly strong, showing that the efficiency gains of SUR, though existent, arenot that much larger compared to single equations methods.

5. Results and Interpretations

5.1 Standard Specification

Table 5 reports results from estimation of Equation 2. Different specifications are presented, showing an interesting stability ofthe parameters estimated.

Beginning with the first specification, results in column (I) strongly confirm many of the a priori expectations. In particular,hypothesis 1a is confirmed. Results show that market attractiveness presents a strong positive and significant coefficient (anincrease of 1 per cent in the GNI generates a 1.2 per cent increase in Chinese OFDI).

Hypothesis 1b is also confirmed by the regression’s results. Following the existing evidence (Broadman, 2007), FDI are usedby Chinese companies to target third country markets that guarantee preferential trade agreements to African least developedcountries. SSA countries that are members of the AGOA are in fact most likely to attract Chinese FDI compared to other countries.

Natural resource endowments of most of the African countries in the sample represent, as expected, a significant pull factorfor Chinese MNEs, thus confirming hypothesis 2. This means that, other things being equal, oil producers are more attractive forChinese FDI compared to others and that an increase of 1 per cent in oil production causes a corresponding rise in Chinese OFDIof about 3 per cent.

Concerning hypothesis 3, results point to the existence of contrasting findings. Inflation results positively related to Chineseinvestment decisions (though with a very small coefficient) consistently with the findings of Buckley et al. (2007). Conversely,the high level of international debt determines an unfavourable environment for Chinese investment, though the coefficient is notsignificant.

The results on the variables indicating the political instability of the host countries are controversial. On the one hand, conflictintensity has a negative impact on investment decisions by Chinese companies. On the other hand, the variable reporting thescores for civil liberties has a positive and significant sign. This means that Chinese decision makers do not seem to refrain frominvesting in those countries where political effectiveness is weak.

With regard to the group of controlling variables, the level of human capital has a positive and not significant impact on ChineseOFDI, while the presence of infrastructures has a positive and significant impact on investment decisions. Interestingly enough,results show the existence of a small agglomeration effect, given that Chinese FDI concentrates in those countries that receiveinvestments from other countries too, confirming thus the findings of Berthelemy (2009).


608 M. Sanfilippo

Table 5: Results of Models 2–4

(I) (II) (III)lnOFDI lnOFDI lnOFDI

lnGNI 1.176 0.875 1.078(2.61)∗∗∗ (1.91)∗ (2.37)∗∗

OIL 3.019 3.143 3.053(2.64)∗∗∗ (2.74)∗∗∗ (2.56)∗∗

CONF −0.27 −0.221 −0.226(1.79)∗ (1.25) (1.41)

CIVIL 0.173 0.136 0.178(1.76)∗ (1.33) (1.65)∗

INFL 0.003 0.004 0.004(2.89)∗∗∗ (2.87)∗∗∗ (3.06)∗∗∗

TEL 0.07 0.112 0.107(1.78)∗ (2.32)∗∗ (2.55)∗∗

LIT 0.015 0.026 0.018(1.32) (1.98)∗∗ (1.44)

lnFDI 0.024 0.005 0.033(1.73)∗ (0.38) (2.05)∗∗

AGOA 0.632 0.691 0.652(2.79)∗∗∗ (2.82)∗∗∗ (2.67)∗∗∗

lnDEBT −0.249 0.085 −0.154(1.23) (0.38) (0.77)

lnCOOP 0.074(3.77)∗∗∗

lnCN_EXP 0.26(1.77)∗

lnCN_IMP 0.035(0.94)

Constant −14.576 −21.496 −15.933(1.22) (1.66)∗ (1.37)

Number of countries 41 41 41Observations 389 342 352R-squared 0.78 0.79 0.79Country effects Yes Yes YesTime effects Yes Yes YesBreusch-Pagan (Chi2) 383.73 (0.0000)Chow test (F-test) 21.38 (0.0000)Hausman test 65.96 (0.0000)F-test on time effects 5.07 (0.0000)Modified Wald test of heteroskedasticity 9930 .00 (0.0000)

Notes: Robust t statistics in parentheses.∗ significant at 10%; ∗∗ significant at 5%; ∗∗∗ significant at 1%.

Also, the coefficients of the years’ fixed effects, not reported in Table 6, provide interesting results. Coefficient values areespecially high for the years 2000 (0.67) and 2001 (0.55). Not surprisingly the highest coefficient (1.06) is recorded for 2006, theyear of the third Forum on China-Africa Cooperation (FOCAC) and the Chinese ‘year of Africa’.

As far as trade intensity with China is concerned (H4), results (reported in column II) are mixed. As expected, Chineseexports are found to affect OFDI, with a positive coefficient of about 0.3. This result is consistent with traditional theories onthe internationalization of firms (Vernon, 1966), affirming that FDI is fostered by exports. In addition, as outlined by Buckleyet al. (2007), this reinforces the view that Chinese OFDI are strongly export oriented, supporting once more the market-seekingstrategy. Conversely, the sign of Chinese imports is not significant. This makes it difficult to understand whether or not the aimof Chinese investors is to substitute for imports to China.

Finally, hypothesis 5a is tested in column III. Economic cooperation has a positive impact on FDI, though much smaller thanexpected. A 1 per cent increase in economic cooperation causes a 0.07 per cent increase in Chinese OFDI. This confirms the resultof the Granger causality test, showing that government-led economic cooperation projects provides significant opportunities forChinese companies to move into African countries. This relation deserves much attention, and it will be discussed in the nextparagraph.



Table 6: Results of system Equations 5–6

lnOFDI lnCOOP

lnGNI 1.159 2.455(4.02)∗∗∗ (3.22)∗∗∗

OIL 3.247 6.1(3.82)∗∗∗ (2.71)∗∗∗

CONF −0.26 0.147(2.56)∗∗ (0.55)

CIVIL 0.159 0.378(1.95)∗ (1.76)∗

INFL 0.003 −0.004(2.49)∗∗ (1.19)

TEL 0.063 −0.106(1.38) (0.87)

LIT 0.014 −0.108(1.59) (4.54)∗∗∗

lnFDI 0.023(2.26)∗∗

AGOA 0.643 −0.104(3.92)∗∗∗ (0.24)

lnDEBT 1.76(4.62)∗∗∗

Constant −20.244 −87.661(2.97)∗∗∗ (4.32)∗∗∗

Observations 389 389Number of countries 41 41R-Squared 0.9972 0.9827Country fixed effects Yes YesYear fixed effects Yes YesWald test(chi2)a 118.13 (0.0000)

Notes: Robust z statistics in parentheses.aTest that the coefficients on variables that are common to both equations are jointly 0.∗ Significant at 10%; ∗∗ significant at 5%; ∗∗∗ significant at 1%.

5.2 Results from a System of Equations

Additional results from Table 6 report system estimation of models (5) and (6).13

Equation 5 has no substantial differences with results presented in Table 6. On the other hand, the sign and the magnitude ofmost of the variables of Equation 6 correspond to the main equation, confirming that the strategic drivers of entry into Africa arecommon. Once more, market size and the possession of natural resources are found to be the main drivers of the Chinese moveinto Africa. Nonetheless, the magnitude of both the coefficients is two times larger in the case of economic cooperation comparedto OFDI. The natural resource-seeking motivation is straightforward, since it confirms that Chinese economic cooperation, whichconsists often in the provision of infrastructures in exchange for natural resources, is largely directed to oil producers.

Very interesting results emerge when testing hypothesis 3. The sign and the significance of the variables related to the economicrisk are reversed compared to the case of OFDI. The coefficient of the variable external debt shows that Chinese internationalcooperation projects flood to those countries that are most seriously indebted. In many cases, in fact, the needs of some Africancountries for new sources of external finance have represented an opportunity for China to start a profitable relationship.

On the other hand, variables indicating political risk converge. While the variable conflict presents a (positive) non-significantsign, the variable reporting the scores for civil liberties has a positive and significant one. This means that, when huge projectsinvolving the economic commitment of the Central Government are at stake, Chinese decision makers do not seem to refrainfrom investing in those countries where political risk is high. Considering that also in Equation 5 civil liberties reports a positiveand significant sign, this result is of great relevance in understanding the overall approach of China in Africa, since it confirmsthe view of many observers that see Chinese investors relatively less risk adverse compared to their counterparts from developedcountries, especially when investing in strategic sectors such as natural resources and the infrastructures.

Finally, concerning the other variables, it is noteworthy to observe that the sign of the variable adult literacy rate now turnsnegative, confirming the view that — especially in large investment projects financed by aid or concessional loans — there islittle or no recourse to local recruitment (Tjonneland et al., 2006; Chen et al., 2007).


610 M. Sanfilippo

6. Conclusions

This paper has examined Chinese OFDI to Africa, going through the main hypotheses that have emerged from the analysis of theliterature. The main findings, which show that Chinese OFDI to Africa have both conventional and idiosyncratic dimensions, canbe summarized as follows:

• Chinese OFDI to Africa is pushed by the need to satisfy a growing internal demand for natural resources. Africa is in factbecoming one of the main suppliers to China of crude petroleum and other natural resources and this trend is growing as Chinabecomes a stronger political partner of many African countries.

• Another consistent part of Chinese OFDI can be explained by the fact that China sees in some African countries a good marketpotential to place its low cost production in excess. Countries with the highest level of income appear to be the most attractivefor Chinese OFDI. In addition, the empirical analysis shows that Chinese MNEs have invested in some African countries totake advantage from special provisions on exports of manufacturing products guaranteed to African least developed countries.

• Chinese investors in Africa keep an ambiguous approach towards risk. If, in fact, on the one side they seem to not pay majorattention to the macroeconomic instability and to the weak political conditions of the host countries, on the other they refrainfrom investing in heavily indebted countries and in conflict affected ones. When the decision to invest is analysed in a systemof equations, emphasizing the existing relations with the decision to provide economic cooperation (aid) to African countries,the hypothesis of low risk aversion of Chinese companies is reinforced by more robust results.

• Finally, results of a system of equations have showed that roughly the same set of factors affects the decisions of Chinese actorsto invest abroad and to provide aid in the form of economic cooperation. This reinforces once more the idea that the ‘GoingOut’ strategy launched by the Chinese Central Government is fostered by internal political and economic factors as well as bythe characteristics of the receiving countries in terms of natural resource endowments and market potential.

Notes

1. According to the definition provided by the Chinese National Bureau of Statistics, outward foreign investment refers to‘enterprises set up or bought by domestic investors in foreign countries and in Hong Kong, Macao and Taiwan, and theeconomic activities centring on operation and management of those enterprises are under the control of domestic investors.’

2. According to the eclectic paradigm, the decision of firms to invest abroad depends on the possession of three kinds ofadvantages: the O (Ownership)-advantages, which represent the ownership of firm specific resources to exploit externally;the L (Location)-advantages, that depend on the characteristics of the host countries; and the I (Internalization)-advantages,that depend on the opportunity to internalize firm specific advantages rather than to exploit them through arm’s-lengthtransactions.

3. Also the financial sector has recently witnessed progress, with the $5 billion acquisition in 2007 by the state-controlledIndustrial and Commercial Bank of China of a 20 per cent stake in the South African Standard Bank.

4. Henley et al. (2008), using data from the UNIDO survey, report that Asian (including Chinese) investors in SSA quotedthe existence of international preferential agreements such as the African Growth and Opportunity Act (AGOA) and theEverything But Arms (EBA) as having significantly affected their decision to invest in the manufacturing sector in Africa.

5. In the case of Zambia, half of the employment recorded is attributed to the manufacturing sector (Kragelund, 2009), while inMadagascar in 2006, 60 per cent of employment has been generated by investment in the construction sector.

6. In the case of Angola, for instance, ANIP — the national agency of investment promotion — does not record investments inthe oil and the diamond sectors (Campos and Vines, 2008).

7. Though criticized because it also takes into account contracts won by Chinese firms and financed by other institutions(Brautigam, 2009), a recent publication of the OECD has used data on economic cooperation to proxy the distribution ofChinese aid among African countries affirming that ‘[t]his figure is inclusive of China’s ODA programmes as well as othercontracted projects undertaken by Chinese contractors in each African country. Hence, the figure is likely to be correlatedwith China’s ODA in each country, but is an over-estimate’ (OECD, 2008, p. 125). Also Berthelemy (2009) has used thisfigure as a proxy of the aid expenditure of China, while Bhaumik and Yap Co (2009) use this variable as a proxy of Chinese‘soft power’.



8. A conflict is defined active when there are at least 25 battle-related deaths per year. A minor conflict is defined as onerecording 25–999 battle related causalities, while a major conflict is one with more than 1,000 causalities.

9. Following the existing literature, which assume that the decision to invest depends on information available concerning themost recent period (Nnadozie, 2000; Cheung and Qian, 2008), the independent variables are lagged (one year).

10. All tests’ results are reported in Table 6.

11. In the year 2000 there was the official launch of FOCAC. Other FOCAC official meetings were hold in 2003 and 2006. Still,the year 2001 corresponds to the official launch of the Going Out strategy by the Chinese Government.

12. This choice follows theoretical motivations. External debt is meant to be more relevant for Chinese aid programmes thatare mainly provided through loans and grants especially to those countries that are heavily indebted and that, for differentreasons, do not get money from international financial institutions. FDI from other countries than China are obviously morerelevant for Equation 5 in order to test the hypothesis of agglomeration.

13. In order to address heteroscedasticity, standard errors of Equations 5 and 6 have been transformed using a robust varianceestimator obtained through the _robust command in Stata (version 10.1).

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Statistical Appendix

Table A1: Description of variables

Variables Description Source

OFDI Approved overseas Chinese direct investment China Commerce YearbookGNI Gross National Income, Atlas Method WDIOIL Production of Crude Oil (millions of barrels per day) International Energy Annual (IEA)CONF Conflicts Uppsala Conflict DatabaseCIVIL Civil Liberties Index (from 1 to 7, 1 indicates the most free, 7 the least free) Freedom HouseINFL Inflation, consumer prices (annual %) IMF World Economic OutlookTEL Telephone mainlines (per 100 people) WDILIT Literacy rate, adult total (% of people aged 15 and above) WDI and Human Development Report, UNDPFDI Foreign Direct Investment from the rest of the world WDIAGOA Dummy, 1 if AGOA member AGOA webpageDEBT External debt, total WDICNEXP Chinese Exports to Africa China Statistical YearbookCNIMP Chinese Imports from Africa China Statistical YearbookCOOPT Economic Cooperation with Foreign Countries or Regions, Total China Statistical Yearbook

Table A2: Correlation matrix

lnOFDI lnGNI OIL CONF CIVIL INFL TEL LIT lnFDI AGOA lnDEBT lnCNEXP lnCNIMP lnCOOP

lnOFDI 1lnGNI 0.4017 1OIL 0.1707 0.4578 1CONF −0.1909 0.0148 0.1533 1CIVIL −0.1721 −0.097 0.1165 0.4393 1INFL 0.0426 0.0009 0.0273 0.2143 0.2188 1TEL 0.1095 0.3786 0.0637 −0.1556 −0.3233 −0.0837 1LIT 0.2161 0.2898 0.1106 −0.1619 −0.0396 0.0788 0.3843 1lnFDI 0.1818 0.4338 0.3342 0.0743 −0.0284 0.0099 0.1647 0.1628 1AGOA 0.2146 −0.0172 −0.0769 −0.2115 −0.4254 −0.1511 −0.0537 −0.0161 −0.0219 1lnDEBT 0.2727 0.806 0.4199 0.1676 0.0936 0.1041 0.1606 0.0952 0.3246 −0.1469 1lnCNEXP 0.4562 0.6953 0.369 −0.1264 −0.2245 −0.0754 0.3003 0.1272 0.3717 0.1019 0.6103 1lnCNIMP 0.3768 0.5103 0.3403 0.008 0.1373 0.095 0.1025 0.32 0.392 0.0777 0.4812 0.4878 1lnCOOP 0.4471 0.2439 0.1536 −0.0382 −0.0894 0.0007 0.1327 0.2173 0.0447 −0.0025 0.2146 0.419 0.2065 1


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