Master Thesis Political Science
China’s Second Agenda:
The political considerations behind China’s trade relations with Africa
Gündoğdu Demirtürkoğlu – S2081482 Supervisor: Dr. Jojo Nem Singh
Second reader: Dr. F.G.J. Meijerink June 2018, Leiden
List of abbreviations
BM EU
Beijing Model European Union
FOCAC Forum for China-Africa Cooperation
GDP Gross Domestic Product
IMF International Monetary Fund
ODI Outward Direct Investment
OLS Ordinary Least Squares
PRS Political Risk Services
SC Security Council
UN United Nations
UNGA United Nations General Assembly
US United States
WC Washington Consensus
TABLE OF CONTENTS
1. INTRODUCTION... 1
1.1 RESEARCH QUESTION AND OBJECTIVES ... 2
2. LITERATURE REVIEW ... 3
2.1 SCHOOLS OF THOUGHT ... 3
2.2 CHINA’S APPROACH TO AFRICA ... 4
2.3 CHINA’S DRIVING FORCES ... 5
3. ANALYTICAL FRAMEWORK ... 8
4. RESEARCH DESIGN & METHODOLOGY ... 11
4.1 DESIGN AND CASE SELECTION ... 11
4.2 THE STATISTICAL MODEL ... 12
4.3 OPERATIONALIZATION ... 13
5. RESULTS & ANALYSIS ... 15
5.1 THE REGRESSION MODELS ... 17
5.2 CORRELATION ANALYSIS ... 22
6. DISCUSSION ... 23
6.1 CHANGING INFLUENCES ... 23
6.2 CHINA’S DRIVING FORCES ... 24
6.3 EXPLANATORY THEORIES ... 26
6.4 COMPARING THE RESULTS ... 27
7. CONCLUSION ... 28
REFERENCES ... 32
Abstract
This thesis examines the driving forces behind China’s trade investments in Africa, especially those with political intent. The aim of the thesis is therefore, to show which political considerations, host-country characteristics and economic determinants play an influential role in the trade relations between China and Africa. Furthermore, the study also shows the differentiations in changing influences of political factors over a period of time. Therefore, three different years (1999, 2006 and 2016) are analysed using OLS regression and correlation analysis to find the influential political factors for each year. The research is based on a quantitative research design to conduct a large-N analysis and includes 52 African countries. The results show that there is certainly a change in the influential political factors over time. China has changed its scope in its trade relations with Africa. In 1999, they were more interested in the domestic possibilities of the African countries. However, in addition to that, in 2016, they paid more attention to the international role of the African countries. Other important findings are that China trades significantly more with African countries who have large trade relations with the US and an abundance of natural resources. These results contribute to the literature in the field of political economy, in particular to the literature on China’s growing global power.
1. Introduction
The rise of China as a global political and economic power is one of the most important developments of the twenty-first century (Campbell, 2008). In this century, China entered political, military and economic deals with countries in different regions of the world. In line with this policy, they entered a strategic partnership with Africa at the Forum for China-Africa Cooperation (FOCAC) in 2006. This partnership was an indication that the Chinese relations with Africa would go beyond development aid alone. On top of that, it included a comprehensive package of trading opportunities, soft loans for infrastructure projects and direct investments (Campbell, 2008; Hackenesch, 2011). Ten years after the FOCAC, in 2016, became China the second largest trade partner of Africa.
The rise of China in Africa attracted a lot of attention from both political and academic world. They sought to know why China is increasing its engagement in Africa, and what political and economic motives they had in doing this. To find this out, several scholars studied the Chinese motives for investing in Africa. Cheung et al. (2012a) found that China’s outward direct investments (ODIs) were driven by the market-seeking, risk-avoiding and the resources-seeking motive. In another paper (2012b) they found that in the 1990s, political factors such as being a political ally of China and some host-country characteristics (stability and regime type) were decisive when making decisions about which countries should receive Chinees ODIs. In addition to that, Dreher and Fuchs (2011) found that political considerations were also relevant determinants for Chinese aid. The countries who voted more in line with China in the United Nations (UN) and did not recognize the independence of Taiwan received more aid.
Although the above mentioned studies found some influential political factors, none of them did examine whether those factors also influenced the trade relations between the two sides. For that reason, the aim of this thesis is to examine the driving forces behind China’s trade relations with Africa. This will be done by creating a statistical model which includes
political and economic factors. This model will be analysed using two different methods of analysis: a regression and a correlation analysis. The analysis will show which political and economic factors have a significant influence on the Chinese-African trade relations and which not.
1.1 Research question and objectives
The Chinese engagement in Africa was a new phenomenon for Western political observers. They questioned the driving forces behind China’s large scale investments. This led to the establishment of different approaches to China-Africa relations (Brautigam, 2009). The main arguments made were that China went to Africa to have access to African oil and other natural resources, to weaken the position of the West and its organizations (Chin, 2012) and to challenge the global hegemony of the United States (US) (Campbell, 2008). In essence, Western observers believe that the driving forces behind China’s engagement in Africa are not only economic but also political. This thesis examines to what extent these claim are right, and whether political factors significantly influence the trade relations between the two sides. Therefore, the following research question is formulated: which and to what extent, do political
considerations influence the trade relations between China and Africa?
In contrast to earlier studies this study focuses on trade relations between the two sides, instead of ODIs or aid. The aforementioned studies used data that was recent at the time but is now considered as outdated. This study makes use of the most recent datasets that are available. Furthermore, the study contributes to the body of knowledge in three ways. Firstly, by presenting the significant and insignificant political determinants of China for trading with African countries. As second, by showing the differentiations in changing influences of political factors over a period of time. Finally, this study makes it possible to compare the determinants for trade with the determinants of other Chinese investments/projects in Africa, to observe whether China have different considerations in choosing their trade partners.
2. Literature Review
2.1 Schools of thought
Asongu (2016) reviewed about 100 articles on China-Africa relations and classified the literature into three dominant schools of thought, namely: the neo-colonial or pessimistic, balance-development or optimistic and the accommodation school. One of the dominant themes that lies at the basis of the debate is the one about development theories: the Washington Consensus (WC) vs. the Beijing Model (BM). The main difference between the two is that WC promotes liberal democracy, private capitalism and priority in political rights, while the BM emphasize deemphasized democracy, state capitalism and priority in economic rights.
The pessimistic school is critical towards the BM. Their main argument is that Chinese investments in Africa do not promote ‘good governance’ because its lack of conditionalities. Another motivation of this school is that China invests especially in resource-rich countries with authoritarian regimes. Also, they do not hire Africans to work in their projects but import workers from China. Western politicians described this attitude of China as neo-colonialism and business without morals. Scholars supporting this school are Giovannetti and Sanfilippo (2009) and Askouri (2007). The former argues that Chinese trade is crowding-out domestic industries and make them vulnerable because of the existence of a displacement effect. Askouri writes that China is destroying local communities in Sudan by expropriating their land and appropriating their natural resources.
The second school has an optimistic narrative with respect to the Chinese-African relations and forms an antithesis to the pessimistic school. They argue that the Chinese approach of non-interference and less political conditionality, is more favourable for the African countries than the Western one. They also find it misleading to describe the Chinese policies in Africa as ‘neo-colonialism’, since it was the West that used foreign aid to influence domestic politics in Africa. Thus the Western approach to Africa, according to this school, is more in line
with the colonial mentality than the Chinese approach. Literature supporting this school concluded that Chinese investments are also beneficial in non-resource rich countries such as Mauritius (Ancharaz, 2009) and that African politics and institutions play an important role in these relations, China is not the only driving force (Mohan & Lampert, 2013).
The accommodation school argues that the relations between China and Africa is neither an issue of pessimism nor one of optimism. This school argues that “the nexus is simply a chain of ineluctable evolving globalization and economic relational processes to which African nations should accommodate” (Asongu, 2016, p.366). Other literature from this school claims that China has the same market-seeking and resource-seeking motivations as the West (Drogendijk & Blomkvist, 2013). China and the West (especially the US) have both neo-colonial ambitions, however, Africa have no other alternatives and is forced to trade with both.
2.2 China’s approach to Africa
China’s approach to Africa is different than those of the Western donor agencies and international financial institutions. This different approach helped China to increase its influence in the region. After 2006, China was increasing its financing in Africa. In line with this policy, it provided African governments with financing to help develop their economies and modernize their countries. In contrast to the World Bank and other traditional donors, the Chinese banks had less loan conditionalities. This made China an attractive trade partner for the African countries. Due to low interest rates for concessional loans, less conditionalities and flexible repayment schedules, China became an alternative partner to low-income countries. African countries were not satisfied with assistance from traditional donors because of their political conditionalities. Examples of those conditionalities are liberalisation of the economy, good governance and more democratisation (Naidu & Mbazima, 2008; Chin 2012).
According to Davies (2010), China is different from the traditional Western donors because it is more answerable to political stakeholders than to private ones. Another important
difference in approach is that the Chinese discourse maintain the principle of non-interference. This means that they do not require political and economic reform in return for their assistance. They are not interested in building institutions or changing governmental structures within a country. Instead, they propose a comprehensive package of trading opportunities and economic cooperation that is based on a “win-win cooperation” (Hackenesch, 2011, p. 10). This approach of China is highly welcomed in African countries. However, this is not always the case. Not all Chinese investments are without conditionalities, especially the concessional loans. According to Brautigam (2011), only large projects that involve considerable use of Chinese goods (at least 50 per cent) and services (Chinese construction firms as contractors) are funded with concessional loans. Moreover, there is also criticism on the ‘win-win model’. Several scholars, especially from the pessimistic school, argue that Chinese investments are destroying the local economies and that not Africa but China benefit from these investments. The African economy is still characterized by low levels of diversification and small productive capacities (Asongu, 2016).
2.3 China’s driving forces
Cheung et al. (2012, 2014) categorize China’s driving forces to invest in Africa into three categories: economic, political and host-country characteristics. They conducted two studies (2012b, 2014) to examine which factors were significant determinants within these categories. Their findings are discussed in in this section together with driving forces presented by other scholars.
Cheung et al. (2014) found in the two different studies that one of the economic determinants of China is to find new markets. They had significantly more ODIs and engineering projects in countries with larger markets and higher GDP per capita. Another economic determinant is natural resources. It is important for China to secure a stable supply of oil and other resources. For this reason, they invest more in oil-producing African countries.
Brautigam (2009) mentioned that some Chinese companies in Africa even get paid in natural resources. Subsequently, Foster et al. (2009) suggested that in kind payment is a deliberate strategy of China to get access to natural resources. A third economic determinant is the risk level in the host country. Cheung et al. (2012a) found in their study that the risk factor plays an significant role. High-risk countries received less ODIs than the other ones.
The second category consist of the political considerations of China to invest in Africa. An important factor in this category is the voting behaviour of African countries in the UN. Cheung et al. (2014) found that the more countries vote in line with China in the UN, the more China invest in these countries. Being a political ally of China is thus a relevant determinant. The alliance with Africa is highly appreciated by China. They see a greater role for Africa in future world politics. For this reason, they constantly claimed that China and Africa share similar opinions on major international affairs and have common interests (Taylor, 2006). According to Yu (2015), China has the same strategy in Latin-America. They use their economic relations to develop stronger political relations to obtain political support from Latin-American countries in the world arena.
China’s political interest goes beyond gaining support in the UN. They have also military interests in Africa. The most important indication of this is the military base they built in Djibouti in 2015. That was the first time that China built an overseas military facility. The fact that they built it Djibouti is interesting because this country is also home to the largest US military base in Africa. Another way that China is engaged in military action in Africa is through UN Peacekeeping missions. They are the biggest contributor of troops for the missions in Africa among the permanent five (P5) member states of the Security Council. For instance, in South Sudan, an oil-rich country, China has contributed a 1030-troop infantry battalion to the UN mission. In the Democratic Republic of Congo, one the largest copper producing countries in the world, China has contributed 218 troops (Downs, Becker & DeGategno, 2017;
“Troop and police,” 2018).
Another political factor is that of recognizing the independence of Taiwan. When a country recognize Taiwan as an independent state, China is less intended to invest in that country (Dreher & Fuchs, 2011). This finding is also supported by Yu, who states that severing the official relations with Taiwan and switching to China was the breakthrough in the Chinese-Latin American relations. He adds that Chinese leaders view their involvement in Chinese- Latin-America as an effective way to block the recognition of Taiwan. China has beside material also political goals in Africa and marginalizing Taiwan’s engagement in this continent is one of them. As a result, Liberia (2003), Senegal (2005), Chad (2006) and Malawi (2008) have cut their diplomatic relations with Taiwan to switch to China (Dzinesa & Masters, 2009).
There are also arguments that China is trying to counter US influence by building up networks in different continents, including Africa. One way to do this, is to decrease the influence of US-led international organizations such as the World Bank and IMF. China does this by offering better loan conditionalities to borrowing countries (Campbell, 2008; Chin 2012). Also remarkable was that Cheung et al. (2012b) found in their statistical model that the countries who voted structural in line with the US in the UN, received significantly less Chinese investments. In addition to this, Yu writes that there is a general agreement in Chinese politics and IR narratives that economic investments must be the basis for restoration of the Chinese soft and hard power. The exercise of soft power should lead to more strategic benefits in order to elevate China’s status in the global power hierarchy, they believe. The Chinese IR narratives argue that economic cooperation is the first step to gain more soft power. The benefits of such cooperation for other nations will ensure that they see China’s rise not as a threat but an opportunity to gain more wealth. This will help China to gain more soft power and rise more quickly in the global power hierarchy. Therefore, they see Latin-America and Africa as strategic partners in their competition with the US, who is their biggest competitor in this area.
The final category is the host-country characteristics. The most debated topic in this category is whether China prefers to invest in countries with authoritarian and dictatorial regimes. China’s support for the controversial regimes in Sudan and Zimbabwe lies at the basis of this assertion. The US and EU sought to impose UN sanctions on the Sudanese regime for their support for a genocide in Darfur, however, China vetoed it. The same happened when the UN wanted to sanction president Mugabe of Zimbabwe for abusing human rights (Brookes, 2007). Further, Cheung et al. (2012b, 2014) found that countries with less military participation and high political stability receive more investment. An interesting finding was that China invests in African countries with a high level of corruption. Cuervo-Cazurra (2006) explained this by arguing that countries who have corruption in their own system will not be deterred by corruption in foreign countries because of the similarities in the conditions of the institutional environment.
3. Analytical Framework
The main concepts in this thesis that are relevant to understand and explain the political considerations in the Sino-African trade relations are: trade, economic determinants, political considerations and host-country characteristics. Trade refers in this thesis to the total amount of money (in US dollar) earned by export and spend on import between the African countries and China. The money earned on export and spend on import are summed up to calculate to total trade. Normally, import is deducted from export to calculate the balance of trade, however, this thesis in not interested in the profits and losses China made from trading with Africa but in the total trade volume.
Economic determinants refer to the standard economic factors which make countries more (or less) attractive to invest in. This concept consists of five determinants. The first one is the market potential of a country. As the literature shows, a high market potential have positive influence on Chinese investments. The second one is the abundance of natural
resources which also leads to more Chinese investments. The other three are: level of industrial development, labour participation and the amount of taxes on goods and services. These are added as economic factors because, according to the World Bank, these three factors are important indicators of the development level of a country. For this reason, they are considered as relevant determinants that could affect China’s decision to invest in a country. The economic factors will be used as control variables in the statistical model.
The second concept is political considerations and focuses on political factors that influence the trade relations. The factors are divided into three groups. The first group of factors are those related to foreign policy and international politics: being a political ally to China, recognizing Taiwan as an independent state, competition with the US and the role of African countries in international organizations. Earlier studies showed that countries received more aid and ODIs when they vote the same as China in the UN and did not recognize the independence of Taiwan. Also, it is mentioned in the literature that China tries to compete with the US for more influence in Africa. The final factor in this group is the role of African countries on the international arena. China believes that Africa will take a key position in future world politics. For this reason, it is considered that a higher power share for an African country in an international organization could lead to more trade between China and the country concerned.
Another group of factors in this concept are the military-political factor(s). China’s military engagement with Africa consist of bilateral military exchanges, support for UN peacekeeping missions and arms trade. China is one of the largest contributors of troops for UN missions in Africa. Arms trade, in particular, has been an important feature of China’s military relations with Africa. They are one of the largest weapons exporter to this continent (Dzinesa & Masters, 2009).
The last factor in this concept is the resource factor. China’s interest in natural resources, especially in oil, is multiple times mentioned in the literature by different authors (Taylor,
2006). It is expected that China will trade more with oil producing countries than non-oil countries. The influence of all these mentioned political factors on trade will be examined through testing hypotheses 1-7.
The final concept is the host-country characteristics, referring to political and institutional factors in the host countries. In fact, the host-country characteristics are also political factors but they are categorised separately to have a distinction between the domestic political factors and those that are related to foreign policy and international politics. This distinction will help to find the type of African countries China is interested in without influence of political factors from international political context (competition with US) and Chinese domestic politics (recognizing Taiwan). This concept is build-up from the following determinants: authoritarianism, military participation, corruption and political stability. Existing literature showed, as presented above, that these factors influence China’s investments in Africa. To test the influence of these factors on the trade relations, the hypotheses 8-11 will be tested.
The combination of these economic, political and host-country factors in the same model, offer, in contrast to other studies (including qualitative ones), a better overview of the significant factors. This approach offers a more ‘complete picture’ of the China-Africa trade relations. There are eleven different political factors involved in this study, which is more than in any other study done before on this subject. This means that the most possible explanatory political factors, which are measurable with existing data, are involved.
Table 1: Hypotheses
Hypotheses: China trade more with African countries… H1 …that are political allies.
H2 …who do not recognize the independence of Taiwan. H3 …who have small trade relations with the US.
H4 …who have Chinese troops in their country for an UN mission. H5 …who buy weapons from China.
H6 …who are producing oil.
H7 …who play an important role in international organizations. H8 …with authoritarian and dictatorial regimes.
H9 …with less military participation in politics. H10 …that are corrupt.
H11 …that are politically stable.
4. Research Design & Methodology
4.1 Design and case selection
This study uses a quantitative research design to conduct a large-N statistical analysis. This design fits the research question because a large-N analysis makes it possible to test multiple factors at the same time through a statistical model, which will show the political factors that are significant in the China-Africa trade relations. The analysis is not limited to a number of cases but includes all 52 African countries. The databases include all the countries, so it is not needed to exclude countries from the analysis.
As mentioned before, this study aims to show the differentiations in changing influences of political factors over a period of time. In order to achieve this aim, three different years will be analysed, namely: 1999, 2006 and 2016. There was a rapid development in China-Africa relations in the 1990s, on both political and economic areas. Between 1990 and 1999, 68 African leaders visited China to promote the relations. Throughout this period, Chinese investments in Africa accelerated. The trade volume increased from 900 million US$ in 1990
to more than 6 billion US$ in 1999 (Li et al., 2012). To examine which political factors played a significant role in this period, the year 1999 will be analysed. Another important year in the relationship is 2006. In this year, China entered a strategic partnership with Africa at the FOCAC meeting. The analysis for 2006 will show what effect the FOCAC had on the significance of political factors. The third year is 2016. It is chosen because the most recent data for trade volume and most other variables are from this year. The biggest advantage of this is that the analysis for 2016 will deliver the most recent results on China-Africa trade relations. Additionally, it makes it possible to compare the most recent results with older ones to show the differentiations.
4.2 The statistical model
The research strategy of this thesis draws inspiration from the three-category model of Cheung et al. (2014). However, it is an econometric model which cannot be applied exactly in the same form to a political economy study. Therefore, the model is adapted and improved by adding more political factors and host-country characteristics, found in the existing literature (presented in chapter 3). The model that will be used is an OLS multiple linear regression model. It includes six political variables and four host-country variables, together with five control variables. These are the independent variables of the model. The dependent variable is the ‘China-Africa trade volume’.
The model is build up as follow: The first model includes the foreign policy and international factors. In the model column the military factors are added. The resource factor, oil, is added in the third model. In the fourth model, the host-country variables are added. This model is also the one that includes all political factors. It shows which political factors are significant when tested for all together. Model 4 also show which host-country characteristics have significant influence on trade. At last, in the fifth model (or full model), the economic control variables are added. The full model shows whether the significant political and
host-country factors in the previous column still remain significant after adding the control variables. The variables that are still significant in the full model are the ones that significantly influence the trade relations between China and Africa.
In addition to the regression analysis, also a bivariate correlation analysis will be conducted. The advantage of a correlation analysis is that it measures the strength of the association between two variables, in contrast to a regression analysis where multiple variables are included in the analysis. The correlation tests will show whether there is significant correlation between the political factor and the trade volume without the influence of other factors. For instance, it could be the case that factors which are not significant in the regression analysis, turn out to be significant in the correlation analysis. In such a case, it means that these factors are affected by the others and lost their significance while they play a significant role in the trade relations. Conducting a correlation analysis could confirm the results of the regression analysis or give additional information about the factors.
4.3 Operationalization
The aforementioned factors are measured by using different datasets from various sources. The data for China Africa trade volume is from the SAIS China Africa Research Initiative (CARI) based at the Johns Hopkins University School of Advanced International Studies. This is the most comprehensive and methodologically rigorous available dataset. Also, it has complete data for the 52 African countries, in contrast to the World Bank data on China-Africa trade which has huge gaps.
The political factor of being an ally to China, is measured using a dataset that is compiled and published by Professor Erik Voeten of Georgetown University: The Affinity of Nations-Similarity of State Voting Positions in the UNGA. This dataset shows the affinity in voting behaviour between China and the African countries. Further, data on which countries recognize the independence of Taiwan is found on the official website of the Taiwanese
government and in the report of Dzinesa and Masters (2009). The ‘competition with US’ factor is measured with data on trade between the US and African countries, again data is delivered from SAIS-CARI. This data will show how trading with the US affects China’s investments. It will answer the question whether China is more or less interested in countries who have large trade relations with the US. The next factor that needs to be operationalized is the international power of African countries. This is done by looking whether or not they have a seat in the UN Security Council (or had in 1999 and 2006).
Data on Chinese troop contribution to UN missions in Africa is taken from the International Peace Institute Peacekeeping Database and from the UN peacekeeping website. This variable includes not only military personal but also police experts and staff officers. Data on weapons-export is from the Stockholm International Peace Research Institute (SIPRI). The SIPRI Arms Transfers Database contains information on all transfers of major conventional weapons. This variable is measured with the total value of weapons exported from China to African countries.
Next is the operationalization of oil. There are different ways to measure the influence of oil. One can choose to measure how much oil a country exports or produce. Another way is measuring the oil revenues as percentage of the GDP. The oil factor in this study is operationalized by using a dummy variable. This means that within this variable the countries are divided in two categories: oil producing and no oil countries. This categorization is based on the oil revenues of countries. Countries with more than 5% oil revenue as percentage of their GDP are categorized as oil-countries. The consequence of this choice is that the analysis will show whether being an oil producing country leads to more trade with China, however, it will not show whether producing more oil also leads to more trade. Thus the difference between the oil countries cannot be observed.
The host-country characteristics are operationalized as follow: The degree of authoritarianism is measured with the level of democracy index from the Polity IV Project data. Military participation is measured with data from the Political Risk Services International Country Risk Guide (PRS). Political stability and corruption are measured with data from the World Bank’s World Development Indicators (WDI). A positive relation with trade, in the regressions, means that more democracy, more stability, less military participation and less corruption leads to more trade. In case of a negative relation, the opposite applies.
Finally, the data for all five control variables is from World Bank’s WDI. The market potential is measured with the GDP per capita in US$. Natural resources is operationalized using the total rents earned from natural resources of percentage of GDP. Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents and forest rents. The next control variable, taxes, is measured with net taxes on products. Net product taxes are those taxes paid by producers that relate to the production, sale, purchase or use of the goods and services. The level of industry is measured with the value added in mining, manufacturing, construction, electricity, water, and gas. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. Data for both taxes are industry are in US$. The last control variable is labour force participation and is measured with the labour participation rate That is the proportion of the population ages 15 and older that is economically active.
5. Results & Analysis
In this chapter, the results of the regression and correlation analysis will be discussed. The results are presented in the tables below. Subsequently, the formulated hypotheses will be confirmed or rejected on basis of these results.
Before discussing the influence of the political factors, a mixed-design ANNOVA is used to show the significant increase in the trade volume between China and Africa. Mauchly's
Test of Sphericity (Appendix table 1) indicated that the assumption of sphericity had been violated (χ²(2) = 33.05, p < .001), therefore degrees of freedom were corrected using Greenhouse-Geisser estimates of sphericity (ε = .671). The results show that there is a significant increase in trade volume between China and Africa over time, F (1.34, 67.10) = 10.95, p = .001 (Appendix table 2), which is shown in figure 1.
Furthermore, the 52 African countries are divided over five regions to observe the differences between regions in trade with China. The results are shown in figure 2. The figure shows that each region increased its trade volume with China. A remarkable finding is that the North Africa region is China’s largest trade partner, and the southern region’s upswing from the smallest trade partner in 1999 to the second largest in 2016.
Among the countries is South Africa the largest African trade partner of China and Angola the second largest. That is an interesting development for Angola since they were one the smallest trade partners of China in 1999.
Figure 2: Trade between China and different regions in Africa
5.1 The regression models
Table 2 presents results of the regression analysis for the year 1999. The results provide some support for the role of political factors in the Chinese-African trade relations in 1999. First of all, the most remarkable finding is that the ‘trade with US’ is a significant variable in all models. Model 4 shows that also oil and political stability are significant. This means that in 1999 China traded more with countries who had large trade relations with the US, produced oil and were political stable. However, when the control variables are added in model 5, oil and stability lose their significance whereas trade with US remains significant. Other significant variables in the full model (5) are troops in Africa, industry level and taxes (negative relation). According to the full model for 1999, China traded significantly more with African countries who: were a large trade partner of the US, had Chinese troops in their country for a UN mission, had low product taxes and had a developed industry.
These results support hypothesis 4 as China traded significantly more with countries who have Chinese troops in their country. Also, hypotheses 6 and 11 are partly supported because these are significant when tested for political factors only (model 4) but lose it when the control variables are added (model 5).
The results for 2006 are presented in table 3. Trading with US and being a member of the United Nations Security Council are the only significant political factors in the first three models. However, when all the political factors are included in model 4, the significant variables are Security Council membership and political stability. Adding the control variables leads to a very interesting situation where there are no significant political factors that affect the trade relations in 2006. The only significant variable in the full model is industry level. The results show that in 2006 China traded significantly more with countries with a high level of industry. To make the picture more complete for this year, it must be said that Security Council membership and political stability played also some role. Although, political stability has a negative effect which means that China traded more with the less stable countries.
The full model provides no support for one of the hypotheses. Nonetheless, it should be mentioned that hypothesis 7 is supported partly as it is significant in model 4. This is not the case for hypothesis 11, even though political stability is significant in model 4. The hypothesis expect this variables to have positive effect on the Chinese-African trade but its effect in this analysis is negative.
Finally, the results for 2016 are presented in table 4. Like in the previous regressions, trading with US is a significant political factor. Interestingly, it is also the only significant factor in the first four models. That is a remarkable finding, because it is the first time that there is only one significant factor in model 4, where all the political variables are included. The full model on the other hand, contains four significant variables. According to these results, China traded in 2016 significantly more with African countries who were a member of the Security
Council and significantly less with countries who bought weapons from China. Further, there was significantly more trade between China and countries with natural resources abundance and high taxes. This means that the results only support hypothesis 7.
Table 2: Regression analysis for 1999
1 2 3 4 5
UN voting affinity with China -.001 -.015 -.190 -.183 -.157 Recognition of Taiwan -.393 -.410 -.308 -.099 -.173 Trade with US .642** .623** .455** .541** 1.475** SC membership -.143 -.150 -.062 .095 -.073 Arms trade -.121 -.046 -.043 -.027 Troops in Africa .122 .222 .207 .417* Oil .529** .483* .273 Democracy level -.193 -.135 Military participation -.206 -.144 Political stability .489* .074 Corruption -.364 -.027 Real GDP -.080 Natural resources -.298 Taxes -1.673** Industry level .744*
Labour force participation .024
Observations 51 51 51 51 51
R square .490 .522 .709 .800 .949
Dependent Variable: China-Africa trade volume
Table 3: Regression analysis for 2006
1 2 3 4 5
UN voting affinity with China .101 .129 .129 .147 .030 Recognition of Taiwan -.084 -.097 -.085 .009 -.030 Trade with US .240* .268* .269* .124 -.194 SC membership .759** .737** .743** .795** .085 Arms trade -.065 -.068 -.010 -.011 Troops in Africa -.062 -.092 -.197 -.036 Oil .078 .164 -.094 Democracy level -.041 -.016 Military participation .126 -.145 Political stability -.415* -.064 Corruption .114 -.075 Real GDP .149 Natural resources -.067 Taxes .629 Industry level .449*
Labour force participation .023
Observations 51 51 51 51 51
R square .829 .834 .839 .898 .957
Dependent Variable: China-Africa trade volume * p < 0.05, ** p < 0.01
Table 4: Regression analysis for 2016
1 2 3 4 5
UN voting affinity with China .008 .021 .047 .050 -.119 Recognition of Taiwan -.044 -.045 -.043 -.062 -.076 Trade with US .936** .966** .949** .909** -.115 SC membership .086 .080 .063 .090 .251* Arms trade -.081 -.116 -.121 -.194* Troops in Africa .034 .010 -.051 -.117 Oil .091 .060 -.331 Democracy level .022 .001 Military participation .105 .218 Political stability -.148 -.243 Corruption -.062 -.201 Real GDP .348 Natural resources .241* Taxes .293* Industry level .974
Labour force participation .153
Observations 52 52 52 52 52
R square .917 .923 .929 .946 .977
Dependent Variable: China-Africa trade volume * p < 0.05, ** p < 0.01
5.2 Correlation analysis
In addition to the regression analysis, table 5 presents the bivariate correlation analysis between the dependent variable of this study: ‘China-Africa trade volume’ and the political factors (independent variables).
Table 5: Results bivariate correlation analysis for 1999, 2006 and 2016
Variables China-Africa trade volume
1999 2006 2016
UN voting affinity with China .125 .043 .235
Recognition of Taiwan -.260 -.152 -.120 Trade with US .559** .745** .914** SC membership .041 .729** .454** Arms trade -.032 .030 .238 Troops in Africa .169 -.048 -.056 Oil .634** .308* .304* Democracy level -.149 -.044 .031 Military participation -.090 -.036 .124 Political stability -.001 -.040 -.095 Corruption -.160 -.061 -.041 * p < 0.05, ** p < 0.01
As shown in table 5, there are some similarities and differences with the results in the regressions. The correlation analysis shows that trading with US turns out to be a significant political factor in trade relations between China and Africa, like in the regression analysis. Another similarity is that ‘being a Security Council member’ is a significant political factor in both analysis. The biggest difference between the regression and correlation analyses is about the role of oil. Oil was only significant in the regression of 1999. Even there, it was not significant in the full model but in the models 3 and 4. In contrast to those results, is oil a
significant political factor in the correlation analysis for all three years. Another difference is that, troops in Africa and arms trade were significant factors in respectively 1999 and 2016. However, both are not significant in the correlation analysis.
The results of provide support for hypotheses 6 and 7. This means that China, according to the correlation analysis, trades significantly more with three type countries: oil producing countries, countries with international power (Security Council membership) and those who have large trade relations with the US.
6. Discussion
6.1 Changing influences
Three regression analysis are conducted to investigate which political and economic factors played an influential role in the Chinese-African trade relations. An overview of the significant variables is presented in table 6. The table also shows the differentiations in changing influences of political factors over a period of time.
The most noticeable finding is that no political or economic factor is significant in all three years. All factors that were significant in 1999, lose their significance in 2006. The only exception is the level of industry which is significant both in 1999 and 2006. These results indicate that the influences of political and economic factors have changed over time. The domestic characteristics of the African countries – such as the amount of Chinese troops in an African country, oil, industry level and taxes – played a more important role in 1999 than in the other years. The influence of these factors decreased in 2006. In 2016, there was more variety in factors that were influential on the trade relations: foreign policy and international factors (SC membership, trade with US), military factors (troops in Africa) and economic factors (natural resources, taxes).
Table 6: Overview of significant variables over period of time Significant factors 1999 2006 2016 Trade with US + – ± SC membership – ± + Arms trade – – + Troops in Africa + – – Oil + – – Political stability ± ± – Natural resources – – + Taxes + – + Industry level + + –
+ = significant in full model, – = no significance, ± = significant in model 4
6.2 China’s driving forces
A large part of the existing literature emphasized the role of three specific political factors in the Chinese-African relations, namely: countering the influence of the US in Africa, getting access to oil and other natural resources and supporting authoritarian regimes. Their argument was that these are the main political considerations behind China’s relations with Africa. The results from the regression analysis (1999 and 2016) and correlation analysis provide support for their first claim about countering the US. China traded significantly more with countries who also had large trade relations with the US. The fact that China invests in the same African countries as the US, also supports the thoughts of the accommodation school. They argue that both China and the US have the same market-seeking and resource-seeking motivations. The results support them to the point that China has indeed resource-seeking motivations and, at the same time, is interested in the same countries as the US. This all together highly suggests that
China has the same motivations as the US to invest in Africa, as argued by the accommodation school.
The role of oil and other natural resources is the most discussed factor regarding this topic. Natural resources, specifically oil, play a political as well as an economic role. China need good political relations with the African countries to secure a stable supply of oil and other natural resources for its economy. For this reason, oil is seen as the most important driving force behind China’s investments in Africa. However, according to the regression analysis, is oil only a significant factor in 1999. But in the bivariate correlation analysis, it is significant for all three years. The natural resources variable on the other hand, is only significant in 2016. These results show that oil and other natural resources undoubtedly play a relevant role for China in their relations with Africa. Still, it cannot be determined from the results whether oil is ‘the most’ important factor for China, as asserted by some authors in the existing literature. The regression analysis for 2016 show that oil is an influential factor together with natural gas, coal, mineral and forest (natural resources variables, significant in 2016, includes them all). China is thus interested in more natural resources than oil. The claims that oil is the main factor behind the investments could be slightly correct for 1999, but it does not hold for 2016.
Another often mentioned claim was that China is supporting authoritarian and dictatorial regimes in Africa. The main argument here is that these regimes gains support because they are the same kind of political regimes as in China. There is only one finding that supports this claim. In the regression analysis for 2006, the political stability variable has a negative significant effect, meaning that China traded more with unstable countries. But the significance of this variable disappears in the full model of the same year. In contrast to this finding, the same variable has in 1999 a positive effect on the trade relations. Further, all the other variables that are used to measure the type of regime – level of democracy, military participation in politics and corruption – have no significant effect in any of the analysis. This leads to the indication
that the type of a regime is not a significant political factor that influence the trade relations between the two sides.
6.3 Explanatory theories
As shown in section 6.1, China has in 2016 much more attention for international and foreign policy factors than before. This finding leads to the question of what motivated China to do so? There are two theories in international politics that help to understand and explain this phenomenon. These are the globalization theory and the theory about the foreign policy consequences of trade.
The results show that China trades significantly more with Security Council members and countries with a high voting share in the World Bank. The political consideration behind this could be that China wants to increase its influence in world politics through making African allies who have international influence. The reason for this is the globalization of world politics in the past decades. An important dimension of globalization has been the establishment of international regimes and institutions. These became more and more important in the globalized world (politics) (Baylis, Smith, Owens, 2017). Decisions on major international affairs are made in these institutions by a majority voting system, especially in the UN and its organs. Hence, it is important for China to have as many countries as possible to vote in the same line as they do. On this way, they can increase their influence on the decisions making processes in international institutions.
Hirschman’s (1945) theory, about the foreign policy consequences of trade, states that trading could be an effective way to build alliances, which lead to more affinity in voting behaviour in international institutions. He asserted that trade dependence between states produce foreign policy convergence. According to Hirschman: states have incentives to converge their foreign policy, because of the fear that foreign policy disputes could interfere with the benefits of trade. However, there is a difference when one state is more dependent on
trade relations than its partner. The less dependent side can afford to stop the trade with little costs to its economy, while the other side would suffer more if trade was disrupted. Seeking to maintain the trade relations, the state that is more dependent should be more willing to grant political concessions than the less dependent state. Such disparities in trade dependence are, in words of Hirschman, ‘an effective weapon in the struggle for power’ (Flores-Macías & Kreps, 2013). Since China has the second largest economy in the world, they are the less dependent side in their trade relations with Africa. This means that they can use their dominance in the trade relations to converge the foreign policy of the African states in their favour to gain more influence in world politics.
Finally, the results for 2016 show that arms trade have a significant negative effect on the trade relations. This means that China trade less with countries who buy large quantities of weapons from them. A possible explanation for this could be that China do not want to invest in countries with armed conflicts or a high possibility for an armed conflict. Because buying large quantities of weapons could be interpreted as a sign for a possible future conflict. In addition to this, the results already showed that political stability plays a relevant role in these trade relations. China is more interested in politically stable countries.
6.4 Comparing the results
As mentioned in the introduction, Cheung et al. (2012b & 2014) did two studies to investigate which political and economic determinants were significant for China’s engineering projects and ODIs in Africa. Although their statistical model differs from the one used in this study, there are some same variables in the models that make it possible to compare the results.
Cheung et al. (2014) found that Chinese engineering projects and ODIs are both attracted to African countries who have a large market potential (measured with GDP) and are political allies with China (UN voting). Both findings are not supported by the results of this study. Market potential and being a political ally of China did not influence the trade relations
in any of the three investigated years. Chinese ODIs, on the other hand, were attracted to countries where was less corruption, a high degree of political stability and an abundance of natural resources. Similarly, natural resources were an influential factor for trade in 2016 and political stability was a significant factor for the trade relations in 1999 and 2006. Another similarity between the studies is that the recognition of Taiwan had no influence in any of the studies.
This comparison shows that although there are some common determinants for China’s ODIs, contracted engineering projects and trade in Africa, the driving forces for trade are not the same as those of the other two. Engineering projects and trade are more motivated by different (from each other) economic and political factors whereas ODI’s are more influenced by host-country characteristics.
7. Conclusion
The aim of this study was to study the political driving forces behind China’s trade relations with Africa and, in addition to this, show the differentiations in changing influences of political factors over a period of time. This aim is realised by analysing the political factors, host-country characteristics and economic determinants that China takes into account in these trade relations. Two different methods of analysis are used to carry out this analysis: a multiple linear regression analysis and a bivariate correlation analysis. The studied years are 1999, 2006 and 2016. The first two years are chosen because of the peak in the Chinese-African relations in those years. 2016 is chosen for the reason that the most recent data for most variables was from this year. The research question was as follows: which and to what extent, do political considerations influence the trade relations between China and the African countries? To find the influential factors and answer this question, 11 hypotheses are tested.
The results are presented in table 7. The hypotheses are tested for three different years, therefore, there a three different results. Hypotheses 1, 2, 3, 4, 8, 9,10 are all rejected for all
three years. No one of these factors was significant in the regression and correlation analysis and thus have no influence on the trade relations. The results for hypotheses 5, 6, 7, and 11 differentiate over the years. They are rejected in one year and confirmed or partly confirmed in another one. This shows the differentiations in changing influences of political factors over a period of time. Further, the table shows that some hypotheses are ‘partly’ confirmed in some years. In case of hypotheses 6 and 7, oil and international role, it means that the factor was significant in the correlation but not in the regression analysis. And in case of political stability (H11) does it means that the factor was significant in in model 4 of the regression analysis, but lost its significance in the full model when control variables are added.
Table 7: Results hypotheses tests Hypotheses:
China trade more with African countries…
Results
1999 2006 2016
H1 …that are political allies. Rejected Rejected Rejected H2 …who do not recognize the independence of Taiwan. Rejected Rejected Rejected H3 …who have small trade relations with the US. Rejected* Rejected* Rejected* H4 …who have Chinese troops in their country for an UN mission. Confirmed Rejected Rejected H5 …who buy weapons from China. Rejected Rejected Rejected* H6 …who are producing oil. Confirmed Partly con. Partly con. H7 …who play an important role in international organizations. Rejected Partly con. Confirmed H8 …with authoritarian and dictatorial regimes. Rejected Rejected Rejected H9 …with less military participation in politics. Rejected Rejected Rejected
H10 …that are corrupt. Rejected Rejected Rejected
H11 …that are politically stable. Partly con. Rejected Rejected
Notes:* Opposite effect found to be significant. Thus: China traded significantly more with countries who had large trade relations with the US and significantly less with those who bought weapons from China.
Given the results of the study as presented in table 7, an answer on the research question can be formulated as follows: Over the years, there have been several political factors that have
were oil, trade with US, troops in Africa and, to a lesser extent, political stability. Against the expectations, China traded in this year significantly more with countries who had large trade relations with the US. In addition to that, China traded more with oil producing and political
stable countries and those who had Chinese troops in their country for an UN mission. The influential political factors in 2006 were: role in international organizations and again, oil
and trade with US. Remarkable is that both international role and oil factor are only partly confirmed by the results. The economic and US factor(s) were more influential in this year. Arms trade, trade with US, international role and to a lesser extent oil, were the significant political factors in 2016. In that year, China traded more with more with African countries who had an important role in international organizations, large trade relations with the US and produced oil. On the other hand, they traded significantly less with countries who bought large quantities of weapons from them.
Beside political also economic factors played an influential role in the Chinese-African trade relations. These were the level of industry, natural resources and product taxes. China invested in 1999 and 2006 significantly more in countries with a developed industry. In 2016, countries with more abundance of natural resources received more Chinese trade investments. A notable finding was that although China traded more with countries who had low product taxes in 1999, they did the opposite in 2016. In that year they traded more with countries with high taxes on products.
In conclusion, there are both political and economic factors that have influenced the trade relations between China and the African countries. Competition with the US and oil were the only political factors that played a significant role in all three years that have been studied. The differentiations in changing influences of political factors over a period of time showed that China has changed its scope and political consideration in its trade relations with Africa. There focus lies not anymore only on host-country characteristics such as stability and oil, as it
was in 1999. In 2016, they were also interested in the international role the African countries play or could play. Moreover, they had more interest in other natural resources than oil, such as gas, coal and mineral.
The findings of this study fits best with the accommodation school of thought. As the results showed, China has both political and economic considerations to trade with Africa. The situation is thus not completely pessimistic or optimistic as argued by the other two schools. The fact that China trade more with the African countries the US trade with, also supports the thoughts of the accommodation school. They argued that both China and the US had the same market-seeking and resource-seeking motivations. This claim isstrengthened by the results of this study which clearly showed the resource-seeking motivations of China.
Subsequently, further research is needed to investigate how China is challenging the US influence on the field and in which countries they are successful and which not. There is need for studies from the perspective of the African countries. Studies that show how the African countries deal with Chinese influence and how they balance that with the already existing Western influence.
References
Ancharaz, V. (2009). David v. Goliath: Mauritius facing up to China. The European Journal of
Development Research, 21(4), 622-643.
Askouri, A. (2007). China’s investment in Sudan: displacing villages and destroying communities. African perspectives on China in Africa, 71-86.
Asongu, S. A. (2016). Sino‐African Relations: A Review and Reconciliation of Dominant Schools of Thought. Politics & Policy, 44(2), 351-383.
Baylis, J., Smith, S., & Owens, P. (Eds.). (2017). The globalization of world politics: an
introduction to international relations. Oxford University Press.
Brautigam, D. (2009). The dragon's gift: the real story of China in Africa. Oxford University Press.
Brautigam, D. (2011). Chinese development aid in Africa: What, where, why, and how much?. Brookes, P. (2007). Into Africa: China’s grab for influence and oil. Heritage lectures, 1006,
1-5.
Campbell, H. (2008). China in Africa: challenging US global hegemony. Third World
Quarterly, 29(1), 89-105.
Carmody, P. R., & Owusu, F. Y. (2007). Competing hegemons? Chinese versus American geo economic strategies in Africa. Political Geography, 26(5), 504-524.
Cheung, Yin-Wong, Jakob de Haan, XingWang Qian and Shu Yu, “China’s Outward Direct Investment in Africa,” Review of International Economics 20 (2012a): 201–20. ———, “China’s Investments in Africa,” in Y-W. Cheung and J. de Haan (eds), The Evolving
Cheung, Y. W., Haan, J., Qian, X., & Yu, S. (2014). The Missing link: China's Contracted engineering projects in Africa. Review of Development Economics, 18(3), 564-580.
Chin, G. (2012). Two-way socialization: China, the World Bank, and hegemonic weakening.
The Brown Journal of World Affairs, 19(1), 211-230.
Cuervo-Cazurra, A. (2006). Who cares about corruption?. Journal of International Business
Studies, 37(6), 807-822.
Davies, M. (2010). How China is Influencing Africa's Development: Background Paper for the
Perspectives on Global Development 2010: Shifting Wealth. Organisation for economic
co-operation and development (OECD). Development centre.
Downs, E., Becker, J., & DeGategno, P. (2017). China's Military Support Facility in Djibouti:
The Economic and Security Dimensions of China's First Overseas Base. Center for
Naval Analyses Arlington United States.
Dreher, A., & Fuchs, A. (2011). Rogue aid? The determinants of China’s aid allocation.
Drogendijk, R., & Blomkvist, K. (2013). Drivers and motives for Chinese outward foreign direct investments in Africa. Journal of African Business, 14(2), 75-84.
Dzinesa, G., & Masters, L. (2009). Taming the Dragon? Defining Africa's Interests at the Forum on China Africa Co-operation (FOCAC).
Flores-Macías, G. A., & Kreps, S. E. (2013). The foreign policy consequences of trade: China’s commercial relations with Africa and Latin America, 1992–2006. The Journal of
Politics, 75(2), 357-371.
Giovannetti, G., & Sanfilippo, M. (2016). Do Chinese Exports Crowd-out African Goods? An Econometric Analysis by Country and Sector. In The Power of the Chinese Dragon (pp. 10-41). Palgrave Macmillan, London.
Hackenesch, C. (2011). Competing for development: the European Union and China in Ethiopia.
Hirschman, A. (1945). National Power and the Structure of Foreign Trade. Berkeley, CA: University of California Press.
Hudson, L. A., & Ozanne, J. L. (1988). Alternative ways of seeking knowledge in consumer research. Journal of consumer research, 14(4), 508-521.
Li, A., Liu, H., Pan, H., Zeng, A., & He, W. (2012). FOCAC twelve years later: Achievements,
challenges and the way forward. Nordiska Afrikainstitutet.
Mohan, G., & Lampert, B. (2013). Negotiating China: Reinserting African Agency into China Africa Relations. African Affairs, 112(446), 92-110.
Naidu, S., & Mbazima, D. (2008). China–African relations: A new impulse in a changing continental landscape. Futures, 40(8), 748-761.
Sautman, B., & Hairong, Y. (2009). African perspectives on China–Africa links. The China
Quarterly, 199, 728-759.
Stiglitz, J. E. (2002). Globalization and its Discontents (Vol. 500). Norton: New York.
Stockholm International Peace Research Institute (SIPRI), SIPRI Arms Transfers Database. Retrieved from https://www.sipri.org/databases/armstransfers
Taylor, I. (2006). China's oil diplomacy in Africa. International affairs, 82(5), 937-959.
Troop and police contributors Peacekeeping. (2018). Retrieved from
https://peacekeeping.un.org/en/troop-and-police-contributors
Yu, L. (2015). China's strategic partnership with Latin America: a fulcrum in China's rise.
APPENDIX
Table 1: Mauchly’s Test of Sphericity
Table 2: Test of Within-Subjects Effects
Mauchly's Test of Sphericitya
Measure: MEASURE_1
Within Subjects Effect Mauchly's W Approx. Chi-Square df Sig.
Epsilonb
Greenhouse-Geisser Huynh-Feldt Lower-bound
time ,509 33,045 2 ,000 ,671 ,683 ,500
Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proporti onal to an identity matrix. a. Design: Intercept
Within Subjects Design: time
b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table.
Tests of Within-Subjects Effects
Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
time Sphericity Assumed 150939341,600 2 75469670,780 10,945 ,000
Greenhouse-Geisser 150939341,600 1,342 112489702,900 10,945 ,001
Huynh-Feldt 150939341,600 1,365 110555529,100 10,945 ,000
Lower-bound 150939341,600 1,000 150939341,600 10,945 ,002
Error(time) Sphericity Assumed 689543796,600 100 6895437,966
Greenhouse-Geisser 689543796,600 67,090 10277847,510
Huynh-Feldt 689543796,600 68,264 10101127,840
Table 3: Model Summary Regression analysis 1999
Table 5: Model Summary Regression analysis 2006
Table 8: Model Summary Regression analysis 2016
