China-Angola Investment Model

Abstract

In the aftermath of Angola’s civil war, strong economic relations developed between the country and the People’s Republic of China. Our study addresses China’s investment risks in Angola, considering an infrastructure-for-petroleum partnership between these two countries. The main working hypothesis is that the recovery of Chinese investments made in Angola is has translated into thousands of barrels of petroleum being imported daily from Angola. We analyzed the main economic, social, and political indicators that describe the situation in Angola that could impact the recovery of Chinese loans in the form of oil exports. Data processing implied involved regression-based imputation, MinMax data normalization, the use of the Analytical Hierarchy Process (AHP), and econometric analysis, next to the construction of a composite risk indicator. The results of the econometric analysis highlighted that an increase in the composite risk indicator of 1% leads to a decrease in the quantity of petroleum exported by almost 6377 barrels per day. Because, at least in the short run, the economic diversification in Angola is weak, and the most important asset is its oil, the partnership with China will continue to exist. This cooperation model represents a source of economic growth and infrastructure development for Angola and a source of energy that fuels China—one of the most powerful economies in the world.

Full text

sustainability
Article
China-Angola Investment Model
Liviu Stelian Begu 1, Maria Denisa Vasilescu 1, 2, *, Larisa Stanila 2and Roxana Clodnitchi 3
1
Faculty of Economic Cybernetics, Statistics and Informatics, The Bucharest University of Economic Studies,
010374 Bucharest, Romania; liviu.begu@csie.ase.ro
2The National Scientific Research Institute for Labour and Social Protection, 010643 Bucharest, Romania;
lari.stanila@gmail.com
3The Faculty for Business Administration in Foreign Languages, The Bucharest University of Economic
Studies, 010374 Bucharest, Romania; roxana.clodnitchi@fabiz.ase.ro
*Correspondence: mariadenisa.vasilescu@gmail.com; Tel.: +40-721-924-441
Received: 7 July 2018; Accepted: 14 August 2018; Published: 18 August 2018


Abstract:
In the aftermath of Angola’s civil war, strong economic relations developed between
the country and the People’s Republic of China. Our study addresses China’s investment risks
in Angola, considering an infrastructure-for-petroleum partnership between these two countries.
The main working hypothesis is that the recovery of Chinese investments made in Angola is has
translated into thousands of barrels of petroleum being imported daily from Angola. We analyzed
the main economic, social, and political indicators that describe the situation in Angola that could
impact the recovery of Chinese loans in the form of oil exports. Data processing implied involved
regression-based imputation, MinMax data normalization, the use of the Analytical Hierarchy Process
(AHP), and econometric analysis, next to the construction of a composite risk indicator. The results of
the econometric analysis highlighted that an increase in the composite risk indicator of 1% leads to
a decrease in the quantity of petroleum exported by almost 6377 barrels per day. Because, at least
in the short run, the economic diversification in Angola is weak, and the most important asset is its
oil, the partnership with China will continue to exist. This cooperation model represents a source
of economic growth and infrastructure development for Angola and a source of energy that fuels
China—one of the most powerful economies in the world.
Keywords: Angola; PRC; China; investment risk; risk; Africa
1. Introduction
Currently, Africa is perceived by the world’s powers as a huge distribution market with good
development potential. Also, the rich resources of the African continent have increased the interest of
the world’s major economies in this region.
China has become the most important economic partner of the African states after the massive
investment it has made in many countries on the continent. China’s main economic partners in
Africa are Angola, South Africa, Nigeria, and Egypt [
1
]. Through bilateral and multilateral relations
with these countries, China seeks to make vital raw materials available to sustain its own economy.
At present, economic growth in China is facing development constraints to their energy supply [
2
].
Among the energy resources, the supply of petroleum is a major concern. China became the world’s
largest importer of petroleum in September 2013, surpassing the United States, as depicted in Figure 1.
China also surpassed the United States in annual gross crude oil imports in 2017, importing 8.4 million
barrels per day (b/d) compared with 7.9 million b/d for the United States [2].
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Sustainability 2018,10, 2936 2 of 17
Sustainability 2018, 10, x FOR PEER REVIEW 2 of 18
Figure 1. Net imports of petroleum and other liquid fuels for China and the United States. Data
source. EIA [3], authors’ own calculation and representation.
At present, China is the world's largest importer of petroleum; therefore, the substantial
petroleum demand has driven China to engage in trade relations with many countries and to import
huge quantities from various sources. China has implemented an oil import diversification strategy
that can meet the short-term demands of the domestic market, but this does not reduce the risk of oil
importing, making it difficult for China to guarantee its long-term oil import demand [4].
Within this paper, we focus on the relations between China and Angola with the objective of
analyzing the evolution of China's investment risk in Angola. Angola is the first petroleum exporter
to China after Saudi Arabia, with more than half of the petroleum exported by Angola in 2016 being
imported by China, accounting for 12% of China’s total petroleum imports. In this way, in recent
years, the Chinese investments made in the industry and infrastructure of Angola, destroyed in the
aftermath of the 27-year civil war, are being recovered.
Investment risk analysis takes into account those factors that could have an impact on the
recovery of the investment. These factors represent economic, social, and political indicators of
Angola, the evolution of which correlates with the export of petroleum to China.
The strategic partnership between China and Angola relies heavily on oil cooperation. Both
countries are interested in maintaining and developing this partnership by developing trade and
investment relationships with multiple benefits in key areas such as industry, infrastructure
development, urban construction, energy and mineral resources exploitation [5].
Although Angola is currently one of Africa's richest countries, this does not apply to the
population as well. Thus, despite its natural resources, Angola’s GDP per capita remains one of the
lowest, and subsistence farming is one of the most important economic activities with approximately
85% of the population involved. Given that the economy of Angola is closely linked to petroleum
exports and implicitly to its price, a decrease in petroleum prices could have a strong impact on
government expenditure. In this context, the importance of the strategic relations with China
becomes even more obvious, as China provides financial resources for the development of
construction and infrastructure projects.
The Chinese-Angolan Partnership has been debated in many articles and publications, most of
the time doubting its morality (for example, [6], [7], [8] and [9]). This strategic link has been seen by
some analysts as a marriage of convenience [10]. China's interest is centered on petroleum and other
mineral resources, and Angola's plan is to rebuild the country which was destroyed by civil war. The
question that arises in this context is to what extent Chinese investments really aid in the
development of Angola [11]. Some economists are optimistic and state that the development of a
state is based on a profitable primary sector, and that exports are currently a way to pay for projects
to improve infrastructure and investments in human capital to sustain this growth in the future.
Figure 1.
Net imports of petroleum and other liquid fuels for China and the United States. Data source.
EIA [3], authors’ own calculation and representation.
At present, China is the world’s largest importer of petroleum; therefore, the substantial petroleum
demand has driven China to engage in trade relations with many countries and to import huge
quantities from various sources. China has implemented an oil import diversification strategy that can
meet the short-term demands of the domestic market, but this does not reduce the risk of oil importing,
making it difficult for China to guarantee its long-term oil import demand [4].
Within this paper, we focus on the relations between China and Angola with the objective of
analyzing the evolution of China’s investment risk in Angola. Angola is the first petroleum exporter
to China after Saudi Arabia, with more than half of the petroleum exported by Angola in 2016 being
imported by China, accounting for 12% of China’s total petroleum imports. In this way, in recent years,
the Chinese investments made in the industry and infrastructure of Angola, destroyed in the aftermath
of the 27-year civil war, are being recovered.
Investment risk analysis takes into account those factors that could have an impact on the
recovery of the investment. These factors represent economic, social, and political indicators of Angola,
the evolution of which correlates with the export of petroleum to China.
The strategic partnership between China and Angola relies heavily on oil cooperation. Both
countries are interested in maintaining and developing this partnership by developing trade
and investment relationships with multiple benefits in key areas such as industry, infrastructure
development, urban construction, energy and mineral resources exploitation [5].
Although Angola is currently one of Africa’s richest countries, this does not apply to the
population as well. Thus, despite its natural resources, Angola’s GDP per capita remains one of the
lowest, and subsistence farming is one of the most important economic activities with approximately
85% of the population involved. Given that the economy of Angola is closely linked to petroleum
exports and implicitly to its price, a decrease in petroleum prices could have a strong impact on
government expenditure. In this context, the importance of the strategic relations with China becomes
even more obvious, as China provides financial resources for the development of construction and
infrastructure projects.
The Chinese-Angolan Partnership has been debated in many articles and publications, most
of the time doubting its morality (for example, [
6
–
9
]). This strategic link has been seen by some
analysts as a marriage of convenience [
10
]. China’s interest is centered on petroleum and other
mineral resources, and Angola’s plan is to rebuild the country which was destroyed by civil war.
The question that arises in this context is to what extent Chinese investments really aid in the
development of Angola [
11
]. Some economists are optimistic and state that the development of

Sustainability 2018,10, 2936 3 of 17
a state is based on a profitable primary sector, and that exports are currently a way to pay for projects
to improve infrastructure and investments in human capital to sustain this growth in the future.
Habiyaremnye [
12
] argues that resources-for-infrastructure swap deals enabled African countries to
increase their diversification capacity.
Christina Wolf also argues that many China-related effects work in favor of manufacturing
development [
13
] and advocates for the positive role that China played in Angola’s domestic-market
formation and general economic development. The process was, according to her, supported by
the economic engagement with China, on one hand due to the Chinese contracted projects which
increased demand for building materials. Her study addresses the increasing volumes of foreign and
domestic investment in food and beverages production but also in the export-oriented sectors like
light manufacturing, even if these remain in the shadow of the much larger mining sector [14].
Other experts, however, believe that this trade linkage, on the contrary, prevents the
industrialization process, does not include other branches of the economy and does not create jobs for
the Angolans. Tkachenko [
15
], for instance, stresses the huge increase in the workforce from China in
Africa, especially in Algeria and Angola. He observes that they reached record-breaking numbers for
the past 15 years and intensive growth is also expected for the future. Schmitz [16] also speaks about
an estimate of 250,000 Chinese migrants in Angola.
Much of the money borrowed by China was intended for health, education, transport, social
communication, and public works. For example, in 2004, nearly 32% of the Exim Bank’s loans in Angola
supported education and health projects, and in 2007 more than half of the allocated funds went to such
investments. Chinese enterprises renovated, expanded, and constructed hospitals, and financed the
purchase of ambulances. Also, they renovated and built dozens of schools. China–Angola cooperation
in social development extended even to higher education through scholarships offered by the Chinese
government [17].
However, studies have shown that Chinese investment did not lead to significant social
development, and among the main criticisms is the lack of adaptability to local needs [
18
].
An interesting example is the so-called “ghost towns” built by the Chinese from scratch. These
are not just residential districts but whole cities where hospitals, schools and other such institutions
have been built and where access to drinking water is ensured. An example of this is the city of
Kilamba, located near Luanda, built in a record time of only 3 years. Although the initial prices were
subsequently reduced following a decision by President Dos Santos, an extremely small percentage of
the local population can afford to purchase an apartment in Kilamba for $70,000–$190,000, with most
of the population living below the poverty line of $1.25 per day. Cain [
19
] names the private sector,
both international and local, as the major beneficiary of these state construction subsidies. The private
sector involved in such projects, however, has been reluctant to provide its own financing. It avoided
investing in real estate due to weak land tenure and the lack of legislative reforms to create a functional
real estate market.
These government measures have increased the dissatisfaction of the population that cannot
benefit from the outcomes of the Chinese investments and who see the efforts made to pay for these
investments through huge petroleum exports.
Zafar [
7
] argues that “China poses a challenge to good governance and macroeconomic
management in Africa because of the potential Dutch disease implications of commodity booms”. Not
only are other sectors late to develop, but a strong kwanza also makes them less competitive on the
international markets.
The relationship between the level of taxation and people’s involvement in governmental
decision-making processes (the level of democratic control and accountability) is not very strong in
oil-dependent states like Angola. Governments often use petroleum revenues instead of tax revenues
to ensure a minimal level of social welfare therefore reducing popular unrest in order to avoid citizens’
influence in policy making [
20
]. Critiques of China’s petroleum diplomacy also center on its alleged
disregard for transparency and human rights. Bader and Daxecker [
21
] performed a study in 2015

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which shows that petroleum producing states dependent on exports to the USA exhibit lower human
rights performance than those exporting to China. They argue that, contrary to conventional wisdom,
petroleum export dependence on the USA affects human rights more negatively than dependence
on China. This difference is given by the timing of market entry, since human rights played a less
important role in the past than they do today. Hackenesch [
22
] performed an empirical analysis
regarding the accused undermining of the US and the EU’s possibilities to set material incentives for
reforms in Angola due to Chinese support policies with “no political strings attached”, which notably
suggests that the level of challenge to regime survival that the government of Angola faces, rather than
the relation with China, influences the government’s willingness to engage with the EU and US.
From a legal perspective, Siu notes that financing agreements are neither backed by a treaty
between states, nor by a commercial contract between a host state and a purely private investor.
They rely on “a hybrid sovereign-commercial legal regime from the doctrines and mechanisms
of public international law, international commercial law and public law”. The author concludes
that while this kind of arrangement may deliver meaningful benefits to the African state involved,
“the underdevelopment of legal approaches and of self-regulatory organizations will likely vitiate the
long-term sustainability of such arrangements for either country” [23].
The paper is organized as follows: Section 2presents the data used in the analysis and the
methods employed. Section 3presents the results, while Section 4discusses the empirical results and
their implications.
2. Materials and Methods
2.1. Data Used in the Analysis
Investment decisions are part of the category of complex economic actions and depend on regional
considerations [
24
]. Any investment project also involves a quantitative analysis of the risk that the
investor is exposed to and financial risks related issues [
25
]. Therefore, the assessment of China’s
investment risk in Angola implies a quantitative analysis of the main economic, social and political
indicators that describe the situation in Angola. Starting from the three dimensions—economic, social,
and political—this study proposes the construction of a composite risk indicator that includes the most
important indicators related to these three aspects. Thus, depending on the availability and relevance
of data, the following indicators (Table 1) were selected for the period 2000–2012:
Table 1. Indicators’ description and data source
Indicator Description Source
Petroleum exports to
China Billions of dollars
US Energy Information
Administration (EIA) [3]
The Organization of
Petroleum Exporting
Countries (OPEC) [26]
Net imports of
petroleum Millions of barrels per day US Energy Information
Administration (EIA) [3]
Economic component
Gross domestic
product
Millions of dollars; values are obtained by converting the local
currency to US dollars using the official exchange rates for
each year.
World Bank [27]
Inflation World Bank [27]
Share of imports in
the gross domestic
product
The cost of imports includes, in addition to the value of the
products traded, the transportation costs associated with the
purchase of goods, production stocks and services provided
by third parties.
World Bank [27]

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Table 1. Cont.
Indicator Description Source
Share of exports in
gross domestic
product
The value of exports also includes the costs associated with
the export of products and services in addition to their actual
value.
World Bank [27]
Exchange rate
Reflects the purchase price of a dollar in the local currency of
the country; the indicator is calculated annually as an average
of the official exchange rates recorded monthly.
World Bank [27]
Share of population
occupied in total
population
World Bank [27]
Social component
Human Development
Index
Composite index that holds information on life expectancy,
literacy, education, and living standards; to complete the
missing values for this indicator, the life expectancy
indicator—a synthetic indicator regarding the health status of
the population measured as the number of years that
newborns will live if they live the rest of their lives according
to the mortality by age levels of that country—is also taken
into account.
United Nations
Development Program
[28]
Fertility rate World Bank [27]
Population share
between 0 and 14
years in the total
population
World Bank [27]
Share of people who
have access to
drinking water in
total population
Measure for the risk of illness of the population as well as for
the degree of poverty. World Bank [27]
Political component
Corruption
perception index
Aspects of corruption are assessed by experts in each country;
a score close to 0 indicates a high level of corruption and
a score close to 1 the reduction in corruption levels in the
public sector.
Transparency
International [29]
Governance
efficiency
Reflects the perception of the quality of public services,
the lack of political pressure and the degree of confidence in
government measures and policies; this indicator takes values
in the range [−2.5,2.5], where −2.5 indicates an inefficient
government to the detriment of the population, and 2.5
efficient governance.
World Bank [27]
Voice and
accountability
Reflects the extent to which the vote of the population is taken
into account as well as aspects of freedom of expression and
the freedom of the media; this indicator takes values in the
range [−2.5,2.5], where −2.5 suggests an extremist political
regime where the vote of the population is not taken into
account, and 2.5 a political regime in which the state
leadership is elected by democratic vote.
World Bank [27]
Political stability and
lack of
violence/terrorism
Reflects the extent to which the rule of a state can be
destabilized or replaced by unconstitutional means, coup
d’états, acts of violence or terrorist attacks; this indicator takes
values in the range [−2.5,2.5], where −2.5 suggests a high
degree of instability and 2.5 political stability.
World Bank [27]
Source: authors’ representation.

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In order to determine the extent to which the investment risk affects the amount of petroleum
exported to China, we use a single factor regression model, with the dependent variable being the
indicator of the quantity of exported petroleum, measured in thousands of barrels per day.
2.2. Imputation of Missing Data: The Regression-Based Imputation Method
After selecting the variables, the next step was the imputation of the missing data, because there
are intermediate years for which few data are missing. For example, for the Human Development
Index in Angola there is no information available for the period 2001-2004, although the values for
both 2000 and the period 2005–2012 are public. In this case, objective imputation was used to estimate
missing data.
Objective imputation implies generating a regression equation based on the data set containing
complete records of the variable to be subjected to the imputation process [
30
]. The equation may take
the form
y= β0+β1x1+β2x2+ . . . + βkxk, (1)
where
y represents the variable to be imputed for the given values of variables xi, i = 1,2, ..., k, and
xirepresents the explanatory variables correlated with the explained variable y.
A similar situation was also encountered in the case of political indicators, the efficiency of
government, the participation of the population in the political decisions, the political stability and the
lack of violence / terrorism, where data are missing only for 2001. A linear dependence was considered
either with the Gross Domestic Product, or with the inflation and the perceived corruption index.
2.3. Data Normalization: Min-Max Method
Normalization is the scaling of all data to a default range. This transformation must be done
before the aggregation of the indicators, as association between variables measured on different scales
is likely to affect the consistency and accuracy of the results. In practice, a number of normalization
methods are used. One of the most used methods is the min-max method. This involves scaling the
data within a preset interval, for example the range (0.1; or 0%,100%). The method usually applies
when the minimum and maximum values of a variable are known because the first step is to assign
the value 0 to the minimum or maximum value. The other intermediate values are converted by
the formula
v0
=
v−minA
maxA−minA
(2)
where
v0represents the normalized value of indicator A
vrepresents the initial value of indicator A
minArepresents the minimum value of indicator A in the original dataset
maxArepresents the maximum value of indicator A in the original dataset
Similarly, all the indicators selected for analysis were normalized after their impact on investment
had been studied. Since it is very difficult to assess Chinese investments in infrastructure projects,
the export of petroleum (1000 barrels per day) to China was used, as this represents the main purpose
of Chinese investments in Angola. In order to see the link between each economic, social, or political
indicator and the investment, it was therefore necessary to study the correlation between them and the
export of petroleum (1000 barrels per day) to China.
2.4. Weighing and Aggregation: The Analytical Hierarchy Process
The analytical hierarchy process (AHP) was the method used for weighting and ranking the
analyzed criteria. This method conceived by Thomas L. Saaty [
31
] has proven to be one of the most

Sustainability 2018,10, 2936 7 of 17
applied methods of multicriterial analysis and is mentioned in most manuals and guides on decisional
processes [32].
This method is essentially an interactive one, the input data being answers to questions such as
“How important is the C
1
criterion compared to C
n
?”. Thus, ‘pair comparisons’ are achieved, with
scores or weights being obtained. The method comes largely from the theories of human behavior,
including those related to thinking, logic, intuition, experience, and learning theories.
The first step of AHP is to build a comparison matrix for the criteria (Figure 2).
Sustainability 2018, 10, x FOR PEER REVIEW 7 of 18
The first step of AHP is to build a comparison matrix for the criteria (Figure 2).
Figure 2. AHP—criterion comparison matrix. Source: Authors’ representation based on Saaty’s
methodology [31].
The purpose of this approach is to determine the contribution of these criteria to the overall
objective. If the criteria are broken down into a number of sub-criteria, the pair comparisons can be
repeated for each level of the hierarchy [33]. To make these comparisons, Saaty has also developed a
scale of measurement of ‘importance intensity’ in which the criteria are evaluated against each other,
as presented in Table 2.
Table 2. AHP— fundamental scale of the AHP importance intensity value.
Qualitative variables
Quantitative variables
Equal importance
1
Moderate importance
3
Strong importance
5
Very strong importance
7
Extreme importance
9
Source: Authors’ representation based on Saaty’s methodology [31].
Intermediate values can be used to define nuances between the five basic formulations. Of
course, if Cn is considered to be of very strong importance as compared to C1, then the mutual is also
valid, so C1 is associated with 1/7 relative to Cn. On the main diagonal, the completed values will be 1
because a criterion is equally important in relation to itself.
After completing the matrix, the next step was to calculate the geometric means. These are
calculated for each line, summed up and rescaled so that the results were weights that together
added up to 100%.
This Analytical Hierarchy process was applied to each set of indicators as used by other authors
[34]. Thus, each economic, social, or political indicator was assigned a weight to reflect its
importance within the group to which it belongs.
The basis for the "pair comparisons" and the determination of the level of importance were
studies performed by experts from "The Economist"[35], BBC [36], CSIS (Center for Strategic and
International Studies) [37], and other publications on geopolitical issues. Moreover, the hierarchy of
indicators could also be made according to the intensity of the links between them and the export of
petroleum to China, as this was the benchmark in the investment risk analysis.
After determining the weights for each of the 14 selected indicators, the next step is to aggregate
them at a group level. Thus, out of the 14 indicators, there were 3 aggregated indicators
corresponding to the economic, social and political dimensions respectively, as presented in Tables
3–5.
Figure 2.
AHP—criterion comparison matrix. Source: Authors’ representation based on Saaty’s
methodology [31].
The purpose of this approach is to determine the contribution of these criteria to the overall
objective. If the criteria are broken down into a number of sub-criteria, the pair comparisons can be
repeated for each level of the hierarchy [
33
]. To make these comparisons, Saaty has also developed
a scale of measurement of ‘importance intensity’ in which the criteria are evaluated against each other,
as presented in Table 2.
Table 2. AHP— fundamental scale of the AHP importance intensity value.
Qualitative Variables Quantitative Variables
Equal importance 1
Moderate importance 3
Strong importance 5
Very strong importance 7
Extreme importance 9
Source: Authors’ representation based on Saaty’s methodology [31].
Intermediate values can be used to define nuances between the five basic formulations. Of course,
if C
n
is considered to be of very strong importance as compared to C
1
, then the mutual is also valid,
so C
1
is associated with 1/7 relative to C
n
. On the main diagonal, the completed values will be 1
because a criterion is equally important in relation to itself.
After completing the matrix, the next step was to calculate the geometric means. These are
calculated for each line, summed up and rescaled so that the results were weights that together added
up to 100%.
This Analytical Hierarchy process was applied to each set of indicators as used by other
authors [
34
]. Thus, each economic, social, or political indicator was assigned a weight to reflect
its importance within the group to which it belongs.
The basis for the “pair comparisons” and the determination of the level of importance were
studies performed by experts from “The Economist” [
35
], BBC [
36
], CSIS (Center for Strategic and
International Studies) [
37
], and other publications on geopolitical issues. Moreover, the hierarchy of
indicators could also be made according to the intensity of the links between them and the export of
petroleum to China, as this was the benchmark in the investment risk analysis.
After determining the weights for each of the 14 selected indicators, the next step is to aggregate
them at a group level. Thus, out of the 14 indicators, there were 3 aggregated indicators corresponding
to the economic, social and political dimensions respectively, as presented in Tables 3–5.

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Table 3. Weight associated with each economic indicator.
Economic
Indicators
GDP
(Mil $)
Exchange
Rate
Imports
(% of GDP)
Exports
(% of GDP)
Employed
Population
(% pop)
Inflation Geometric
Mean Weights
GDP (mil $) 1 5 5 6 6 7 4.3 49%
Exchange rate 0.2 1 2 3 3 5 1.62 19%
Imports
(% of GDP) 0.2 0.5 1 3 3 5 1.28 15%
Exports
(% of GDP) 0.17 0.33 0.33 1 2 3 0.69 8%
Employed
population
(% pop)
0.17 0.33 0.33 0.5 1 3 0.55 6%
Inflation 0.14 0.2 0.2 0.33 0.33 1 0.29 3%
Total 8.74 100%
Source: Authors’ own calculations.
Table 4. Weight associated with each social indicator.
Social Indicators Access to Drinking
Water (% pop)
Fertility
Rate HDI Population Aged
0–14 Years (% pop)
Geometric
Mean Weights
Access to drinking
water (% pop) 1 1 1 9 1.73 32%
Fertility rate 1 1 1 9 1.73 32%
HDI 1 1 1 9 1.73 32%
Population aged
0–14 years (% pop) 0.11 0.11 0.11 1 0.19 4%
Total 5.39 100%
Source: Authors’ own calculations.
Table 5. Weight associated with each political indicator.
Political Indicators
Political Stability
and Absence of
Violence
Voice and
Accountability
Government
Effectiveness CPI Geometric
Mean Weights
Political Stability and
Absence of Violence 1 3 5 6 3.08 56%
Voice and
Accountability 0.33 1 3 5 1.5 27%
Government
Effectiveness 0.2 0.33 1 2 0.6 11%
CPI 0.17 0.2 0.5 1 0.36 6%
Total 5.54 100%
Source: Authors’ own calculations.
For the aggregated indicators, weights were also determined applying the analytical hierarchy
process in order to obtain a single indicator that encompasses all of the information of an economic,
social, and political nature, as depicted in Table 6.
Table 6. Weight associated with each aggregated indicator (social, economic, and political)
Aggregated Indicators Social Economic Political Geometric Mean Weights
Social 1 3 5 2.47 64%
Economic 0.33 1 3 1 26%
Political 0.2 0.33 1 0.41 10%
Total 3.87 100%
Source: Authors’ own calculations.

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2.5. Econometric Analysis
The single factor regression model describes how an endogenous quantitative variable (the
dependent variable Y, in our study the crude oil exported to China, expressed in thousands of barrels
per day) is influenced by an exogenous quantitative factor (variable X, in this study the ‘Global Risk
Indicator’) and by residual factors (
ε
). The general, synthetic form of the single factor regression
model is
y = f(x) + ε, (3)
where f is the linear function describing the dependence
In addition to explaining the model in mathematical form, regression model analysis involves
estimating regression parameters, testing the significance of the model using the t-test, testing its
validity, and checking the classical assumptions of a regression model [38].
3. Results
3.1. Chinese Investment Model in Angola
Although diplomatic relations between China and Angola date back to 1983, with the
establishment of peace in 2002, a strategic relationship has been created, and so important investments
have been made by Chinese companies in order to restore Angola, which was destroyed in the
aftermath of the 27-year war. The fundamental assumption in our proposed China-Angola model
is a win-win situation, with China interested in Angola’s oil resources and Angola in the important
amount of money China invested and continues to invest mostly in its infrastructure projects.
This theory reflects macroeconomic complementarities between Angola and China: China has one of
the world’s largest and most competitive construction industries and few natural resources, while
Angola has an infrastructure deficit and a wealth of resources [39].
China is extremely interested in importing oil from Angola because the Angolan crude oil
is suited to the needs of Chinese refineries which are configured to process internally extracted
medium-sour crude oil [
40
]. On the other hand, Angola considers China as an advantageous alternative
to conventional financing sources because it provides loans without political conditionality and at
a much lower interest rate than any international financial institution [41].
The Angolan government uses Chinese credit facilities backed by petroleum-based guarantees to
finance investments [
19
]. However, these investments do not represent amounts of money delivered
directly to the Angolan government, but rather the provision of the necessary funds for Chinese
public enterprises to develop infrastructure and industrial projects in exchange for petroleum and
minerals [
17
]. According to figures published by the Finance Ministry of Angola [
42
] at the end of 2011,
the amount invested by China through the Exim Bank, the Development Bank, and the Commercial
Bank was estimated to be $14.5 billion. Chinese investments are recovered from extracted and imported
petroleum. The diagram below (Figure 3) describes the investment model applied in Angola and
its flow.

Sustainability 2018,10, 2936 10 of 17
Sustainability 2018, 10, x FOR PEER REVIEW 10 of 18
Figure 3. Chinese investment model in Angola. Source: Authors’ representation.
Since this funding model is atypical and complex, it is difficult to determine the amounts
invested over time and their recovery. China's investment risk in Angola could be assessed to the
extent that petroleum exports from Angola to China are affected. Figure 4 shows the evolution of
petroleum exports from Angola to China between 2000 and 2016. Since 2010, almost half of Angola's
exported petroleum has been imported by China which accounts for 12–17% of China's total
petroleum imports.
Figure 4. Angola—the evolution of petroleum exports between 2000 and 2016. Data source:
Observatory of Economic Complexity [43], authors’ own calculation and representation.
3.2. Impact of Social, Economic, and Political Risk Indicators on Petroleum Exported
Using the assumption that Chinese investments are recovered from extracted and imported
petroleum, the investment risk factors are the same as factors that would reduce the amount of
petroleum exported to China. These factors depend on the destination of the investments, and thus
on the situation in Angola. To describe the overall situation of Angola, data were gathered for the 14
indicators (economic, social, and political), as described in the previous chapter. We have considered
these three thematic areas because most of the country profiles are being built taking into account
the economic, social, and political contexts [27]. The strength of their relationships with petroleum
33.4%
13%
16.5%
27.3%
41.1%
32.8%
37.8%
34.9%
34.9%
39.6%
46.6%
42.9%
52.3%
49.8%
48.6%
45.5%
54.4%
0
10
20
30
40
50
60
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Billions $
Total Exports of crude oil
Crude oil exports to China
Figure 3. Chinese investment model in Angola. Source: Authors’ representation.
Since this funding model is atypical and complex, it is difficult to determine the amounts invested
over time and their recovery. China’s investment risk in Angola could be assessed to the extent that
petroleum exports from Angola to China are affected. Figure 4shows the evolution of petroleum
exports from Angola to China between 2000 and 2016. Since 2010, almost half of Angola’s exported
petroleum has been imported by China which accounts for 12–17% of China’s total petroleum imports.
Sustainability 2018, 10, x FOR PEER REVIEW 10 of 18
Figure 3. Chinese investment model in Angola. Source: Authors’ representation.
Since this funding model is atypical and complex, it is difficult to determine the amounts
invested over time and their recovery. China's investment risk in Angola could be assessed to the
extent that petroleum exports from Angola to China are affected. Figure 4 shows the evolution of
petroleum exports from Angola to China between 2000 and 2016. Since 2010, almost half of Angola's
exported petroleum has been imported by China which accounts for 12–17% of China's total
petroleum imports.
Figure 4. Angola—the evolution of petroleum exports between 2000 and 2016. Data source:
Observatory of Economic Complexity [43], authors’ own calculation and representation.
3.2. Impact of Social, Economic, and Political Risk Indicators on Petroleum Exported
Using the assumption that Chinese investments are recovered from extracted and imported
petroleum, the investment risk factors are the same as factors that would reduce the amount of
petroleum exported to China. These factors depend on the destination of the investments, and thus
on the situation in Angola. To describe the overall situation of Angola, data were gathered for the 14
indicators (economic, social, and political), as described in the previous chapter. We have considered
these three thematic areas because most of the country profiles are being built taking into account
the economic, social, and political contexts [27]. The strength of their relationships with petroleum
33.4%
13%
16.5%
27.3%
41.1%
32.8%
37.8%
34.9%
34.9%
39.6%
46.6%
42.9%
52.3%
49.8%
48.6%
45.5%
54.4%
0
10
20
30
40
50
60
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Billions $
Total Exports of crude oil
Crude oil exports to China
Figure 4.
Angola—the evolution of petroleum exports between 2000 and 2016. Data source:
Observatory of Economic Complexity [43], authors’ own calculation and representation.
3.2. Impact of Social, Economic, and Political Risk Indicators on Petroleum Exported
Using the assumption that Chinese investments are recovered from extracted and imported
petroleum, the investment risk factors are the same as factors that would reduce the amount of
petroleum exported to China. These factors depend on the destination of the investments, and thus
on the situation in Angola. To describe the overall situation of Angola, data were gathered for the 14
indicators (economic, social, and political), as described in the previous chapter. We have considered
these three thematic areas because most of the country profiles are being built taking into account the
economic, social, and political contexts [
27
]. The strength of their relationships with petroleum exports,
as well as the direction of these relationships, can be determined by analyzing Pearson’s correlation

Sustainability 2018,10, 2936 11 of 17
coefficients. Correlations between the Angolan indicators and the petroleum export to China index are
shown in Table 7.
Thus, among the economic indicators, only Angola’s GDP and the exchange rate have a strong
positive correlation with the crude oil exports to China. Given that the economy of Angola is based on
petroleum, the relationship between GDP and the crude oil exports is well known. With regard to the
exchange rate, because repayments of Chinese loans are related to the price of oil at the time of their
negotiation, Angola has to export more oil to China when its value depreciates.
Table 7. Correlations between Angola’s indicators and the amount of petroleum exported to China.
INDICATORS Crude Oil Export to China
Economic Indicators
GDP (mil $) 0.882 **
Exchange rate 0.732 **
Imports (% of GDP) −0.699 **
Exports (% of GDP) −0.673 *
The employed population (% pop) −0.630 *
Inflation −0.604 *
Social indicators
Access to drinking water (% pop) 0.935 **
Fertility rate −0.934 **
HDI 0.915 **
Population aged 0–14 years (% pop) 0.189
Political indicators
Political stability and absence of violence 0.817 **
Voice and accountability 0.716 **
Government effectiveness 0.571 *
CPI 0.546
* Correlation is significant at the 0.05 level; ** Correlation is significant at the 0.01 level. Source: Authors’ calculations.
Both imports and exports (as% of GDP) are negatively correlated with crude oil export to China.
A large percentage of imports in the GDP is an alarm signal indicating a decrease in production and
increasing dependence on the country from which it is imported. The share of export in the GDP could
be a risk factor as China is not the only trading partner of Angola. The structure of petroleum exports
in 2016 is presented in Figure 5which shows the percentage exported to each destination.
Sustainability 2018, 10, x FOR PEER REVIEW 12 of 18
Figure 5. Angola—structure of crude oil exports depending on the destination (2016). Data source:
Observatory of Economic Complexity [43], author’s own calculation and representation.
An increase in the share of the employed population in the total population would be a sign
that Angola's developing economy is expanding thus creating jobs. As any development process
involves resource consumption, this consumption could affect the amount of petroleum intended for
China by using this amount for its own benefit in order to sustain economic growth. Thus, increasing
the share of the employed population may be considered a risk factor. In this case, it is also worth
mentioning that Chinese construction companies build infrastructure projects with their own
employees. China does not create jobs for Angolans, with evoked reasons being the language
barrier, unskilled labor, and the long time it takes to train foreign employees.
Another macroeconomic factor that could influence the China-Angola relationship is inflation.
A widespread increase in prices may affect exports, as inflation has a direct impact on production
costs, which can be labor-related costs, material expenditures, etc. Thus, inflation may negatively
affect petroleum exports to China, resulting in an increased investment risk.
Access to drinking water is one of the social factors that are highly positively correlated with
the export of crude oil to China. At present, only half of Angola's population has access to drinking
water, which is one of the country's major health-related problems and without an improvement in
the well-being of the population there can be no sustainable development, so this factor was taken
into account in the investment risk analysis. Also the Human Development Index is positively
correlated with the export of petroleum to China. In Angola, HDI has steadily increased in recent
years, but it is still at a low level. In assessing the evolution of a country, people are extremely
important – their health status influences productivity and their skills are essential to the
development of a country, so HDI is an indicator that cannot be neglected in the investment risk
analysis.
The fertility rate in Angola is very high, with six live births per woman being reported in 2012,
which is negatively correlated with the petroleum exports. As a rule, the registration of a high rate of
fertility in a country is associated with a low level of the Human Development Index and indicates a
low development level of that state. Angola is a country with a very young population. Almost half
of the country's population, 47.58% in 2012, is younger than 14 years old. Calculations show that the
share of the population aged 0 to 14 years is not correlated with the export of petroleum.
Although China does not intervene in the domestic politics of any African state with which it
has commercial relations, there is a fear of these former colonies being, once again, under the
domination of a superpower [44]. In Angola, a possible threat could come from the population as
well [45]. Considering the four centuries under Portuguese rule (1575–1975), a period followed by a
54.0%
23.8%
10.0%
7.6%
6.7%
5.6%
0.9%
Where does Angola export Crude Petroleum to?
China
EU
USA
Other Asia
India
South Africa
Uruguay
Figure 5.
Angola—structure of crude oil exports depending on the destination (2016). Data source:
Observatory of Economic Complexity [43], author’s own calculation and representation.

Sustainability 2018,10, 2936 12 of 17
An increase in the share of the employed population in the total population would be a sign that
Angola’s developing economy is expanding thus creating jobs. As any development process involves
resource consumption, this consumption could affect the amount of petroleum intended for China by
using this amount for its own benefit in order to sustain economic growth. Thus, increasing the share
of the employed population may be considered a risk factor. In this case, it is also worth mentioning
that Chinese construction companies build infrastructure projects with their own employees. China
does not create jobs for Angolans, with evoked reasons being the language barrier, unskilled labor,
and the long time it takes to train foreign employees.
Another macroeconomic factor that could influence the China-Angola relationship is inflation.
A widespread increase in prices may affect exports, as inflation has a direct impact on production
costs, which can be labor-related costs, material expenditures, etc. Thus, inflation may negatively affect
petroleum exports to China, resulting in an increased investment risk.
Access to drinking water is one of the social factors that are highly positively correlated with
the export of crude oil to China. At present, only half of Angola’s population has access to drinking
water, which is one of the country’s major health-related problems and without an improvement in the
well-being of the population there can be no sustainable development, so this factor was taken into
account in the investment risk analysis. Also the Human Development Index is positively correlated
with the export of petroleum to China. In Angola, HDI has steadily increased in recent years, but it
is still at a low level. In assessing the evolution of a country, people are extremely important—their
health status influences productivity and their skills are essential to the development of a country,
so HDI is an indicator that cannot be neglected in the investment risk analysis.
The fertility rate in Angola is very high, with six live births per woman being reported in 2012,
which is negatively correlated with the petroleum exports. As a rule, the registration of a high rate of
fertility in a country is associated with a low level of the Human Development Index and indicates
a low development level of that state. Angola is a country with a very young population. Almost half
of the country’s population, 47.58% in 2012, is younger than 14 years old. Calculations show that the
share of the population aged 0 to 14 years is not correlated with the export of petroleum.
Although China does not intervene in the domestic politics of any African state with which it has
commercial relations, there is a fear of these former colonies being, once again, under the domination
of a superpower [
44
]. In Angola, a possible threat could come from the population as well [
45
].
Considering the four centuries under Portuguese rule (1575–1975), a period followed by a civil war,
state independence is still a sensitive issue. The influence of a super power like China has awakened
a sense of mistrust and uncertainty among the population, but the memory of the war discourages
them from expressing their discontent. The political stability of Angola is an extremely important
factor that can influence trade relations with China. China has already had a similar experience with
Sudan, one of its major partners until 2012, where the partnership ended due to internal conflicts for
the resources between Sudan and South Sudan [3].
Thus, the specific political aspects of Angola are directly related to the export of petroleum to
China, with any political tension in the population being a risk factor. From Figure 6, it is visible that
since the end of the war, the risk has had a downward trend so far, a sign that political tensions have
diminished and the country is trying to rebuild by attracting investments.

Sustainability 2018,10, 2936 13 of 17
Sustainability 2018, 10, x FOR PEER REVIEW 13 of 18
civil war, state independence is still a sensitive issue. The influence of a super power like China has
awakened a sense of mistrust and uncertainty among the population, but the memory of the war
discourages them from expressing their discontent. The political stability of Angola is an extremely
important factor that can influence trade relations with China. China has already had a similar
experience with Sudan, one of its major partners until 2012, where the partnership ended due to
internal conflicts for the resources between Sudan and South Sudan [3].
Thus, the specific political aspects of Angola are directly related to the export of petroleum to
China, with any political tension in the population being a risk factor. From Figure 6, it is visible that
since the end of the war, the risk has had a downward trend so far, a sign that political tensions have
diminished and the country is trying to rebuild by attracting investments.
Figure 6. Evolution of China's investment risk in Angola in the period 2000–2012. Source: Authors’
own calculation and representation.
3.3. Interpreting the Global Risk Indicator and Its Impact on Recovery of Investments Made in Angola
According to the investment model depicted in Figure 3, China recovers its investments made
in Angola's industry and infrastructure through the petroleum it imports from this country. After
having highlighted the direction and the relationship between the amount of exported petroleum
and each economic, social, and political factor that could constitute an investment risk factor, it is
important to also analyze to what extent the increase in the Global Risk Indicator (GRI) influences
the amount of petroleum exported.
As a result of processing on the data, a reverse linear relationship between the amount of crude
oil exported to China and the level of investment risk was identified. The linear dependence is
presented in Figure 7, and the explicit mathematical model has the following form
Petroleum exported = –6,3768 × GRI + 743,9
(4)
Figure 6.
Evolution of China’s investment risk in Angola in the period 2000–2012. Source: Authors’
own calculation and representation.
3.3. Interpreting the Global Risk Indicator and Its Impact on Recovery of Investments Made in Angola
According to the investment model depicted in Figure 3, China recovers its investments made in
Angola’s industry and infrastructure through the petroleum it imports from this country. After having
highlighted the direction and the relationship between the amount of exported petroleum and each
economic, social, and political factor that could constitute an investment risk factor, it is important to
also analyze to what extent the increase in the Global Risk Indicator (GRI) influences the amount of
petroleum exported.
As a result of processing on the data, a reverse linear relationship between the amount of crude oil
exported to China and the level of investment risk was identified. The linear dependence is presented
in Figure 7, and the explicit mathematical model has the following form
Petroleum exported = −6.3768 ×GRI + 743.9 (4)
Sustainability 2018, 10, x FOR PEER REVIEW 14 of 18
Figure 7. Linear dependence of petroleum exported to China and the Global Risk Indicator (GRI).
Source: Authors’ calculation and representation.
This model is guaranteed with a 95% probability and is statistically valid. Therefore, a 1%
increase in risk results in a drop in the amount of petroleum exported by almost 6377 barrels per
day. Otherwise, the risk variation explains 86% of the petroleum exported to China.
Furthermore, the hypotheses of the linear single factor regression model were then tested to
confirm the model. To test the significance of the model, we turned to the t-test, assuming that the
parameter tested is statistically insignificant [38]. In the present case, the value of the t-test indicated
the rejection of the null hypothesis and the acceptance of the alternative, according to which all the
model parameters are statistically significant.
To check the validity of the regression model, we performed the Fisher test. The value obtained
also indicates the rejection of the null hypothesis and acceptance of the alternative, according to
which the model is valid.
Tests were then required to verify the hypotheses of homoscedasticity, normality and
autocorrelation. The homoscedasticity hypothesis (Var(εi) = σ2) refers to the variance of the model
and involves the constant dispersion of the errors. If errors do not have this property, then they are
supposed to be heteroscedastic, a situation that needs to be highlighted and corrected. The White
test is a statistical test that starts by explaining the errors observed with one or more exogenous
variables. The results of this test indicated that the errors are homoscedastic (Appendix A, Table 1).
Regarding the autocorrelation hypothesis, the Durbin–Watson test was used, which has as a
null hypothesis the fact that the errors are not autocorrelated. The result obtained led to the
acceptance of this hypothesis (DW statistics was 1.824). Also, for the normality test, we used the
Jarque–Bera test, which is based on the property of the normal distributions to be characterized only
by the first two moments (mean and standard deviation), with all other moments being expressed
according to these. Thus, any normal distribution is symmetrical (skewness = 0 and kurtosis = 3). The
result of the test indicated that the model errors have a normal distribution (Appendix A, Table 1).
It can be concluded that the single factor regression model complies with all the assumptions of
a statistically valid model. The link between the investment risk and the quantity of petroleum
exported from Angola to China can be described by a linear equation. Thus, an increase in
investment risk of 1% leads to a decrease in the amount of petroleum exported by 6377 barrels per
day.
4. Discussion
China is among the major economic partners of the African states and grants massive
investments, infrastructure projects, and loans to them. The interest in the African continent is
nourished, on the one hand, by its new potential markets and on the other hand, by its wealth of
natural resources. China's substantial need for natural and energy resources is explained by its
Figure 7.
Linear dependence of petroleum exported to China and the Global Risk Indicator (GRI).
Source: Authors’ calculation and representation.

Sustainability 2018,10, 2936 14 of 17
This model is guaranteed with a 95% probability and is statistically valid. Therefore, a 1% increase
in risk results in a drop in the amount of petroleum exported by almost 6377 barrels per day. Otherwise,
the risk variation explains 86% of the petroleum exported to China.
Furthermore, the hypotheses of the linear single factor regression model were then tested to
confirm the model. To test the significance of the model, we turned to the t-test, assuming that the
parameter tested is statistically insignificant [
38
]. In the present case, the value of the t-test indicated
the rejection of the null hypothesis and the acceptance of the alternative, according to which all the
model parameters are statistically significant.
To check the validity of the regression model, we performed the Fisher test. The value obtained
also indicates the rejection of the null hypothesis and acceptance of the alternative, according to which
the model is valid.
Tests were then required to verify the hypotheses of homoscedasticity, normality and
autocorrelation. The homoscedasticity hypothesis (Var(
εi
) =
σ2
) refers to the variance of the model
and involves the constant dispersion of the errors. If errors do not have this property, then they are
supposed to be heteroscedastic, a situation that needs to be highlighted and corrected. The White test
is a statistical test that starts by explaining the errors observed with one or more exogenous variables.
The results of this test indicated that the errors are homoscedastic (Appendix A, Table A1).
Regarding the autocorrelation hypothesis, the Durbin–Watson test was used, which has as a null
hypothesis the fact that the errors are not autocorrelated. The result obtained led to the acceptance of
this hypothesis (DW statistics was 1.824). Also, for the normality test, we used the Jarque–Bera test,
which is based on the property of the normal distributions to be characterized only by the first two
moments (mean and standard deviation), with all other moments being expressed according to these.
Thus, any normal distribution is symmetrical (skewness = 0 and kurtosis = 3). The result of the test
indicated that the model errors have a normal distribution (Appendix A, Table A1).
It can be concluded that the single factor regression model complies with all the assumptions
of a statistically valid model. The link between the investment risk and the quantity of petroleum
exported from Angola to China can be described by a linear equation. Thus, an increase in investment
risk of 1% leads to a decrease in the amount of petroleum exported by 6377 barrels per day.
4. Discussion
China is among the major economic partners of the African states and grants massive investments,
infrastructure projects, and loans to them. The interest in the African continent is nourished, on the
one hand, by its new potential markets and on the other hand, by its wealth of natural resources.
China’s substantial need for natural and energy resources is explained by its spectacular economic
growth. It is also the world’s largest consumer of energy, and as its consumption cannot be covered
by its own production, China has to import raw materials from many sources. China became the
worldwide largest petroleum importer in September 2013, surpassing the United States that had been
the petroleum import world leader since the 1970s.
The strategic partnership between China and Angola is based, in particular, on oil cooperation,
simplified as a bilateral barter relationship “infrastructure for petroleum”, as China provided significant
financial resources, guaranteed by future oil exports, for infrastructure projects meant to create the
conditions for economic growth and sustainable development. The analysis of China’s investment
model in Angola is based on the assumption that China recovers its investments by importing
petroleum and that the risk factors for the investments are the same as the factors that would reduce the
amount of petroleum exported by Angola to China. These factors depend on the situation in Angola.
In the analysis of the correlations between the economic, social, and political indicators of Angola
and the export of petroleum to China, the social risk factor was shown to be the most risky at 64%,
followed by the economic (26%) and the political (10%) factors. Although the Human Development
Index has steadily increased since 2000, its value is low compared to other countries on the African
continent. Also, the increased fertility rate of 6 live births to a woman is often associated with weak

Sustainability 2018,10, 2936 15 of 17
state development. However, the social factor that most discourages investment is access to drinking
water. In Angola, almost half of the population has no access to clean drinking water, and this has
a major impact on health.
Regarding the economic component, Angola’s GDP is by far the most important factor in the
analysis of investment risk. GDP growth diminishes the risk of not recovering investments. Indeed,
GDP growth in Angola is based on petroleum production and exports.
Based on the 2000–2012 data, the political factor has a less important influence on the investment
risk analysis. Although Angola is one of the most corrupt states in the world (among the top
25 countries), this aspect does not seem to discourage investment. This is explained by the fact
that China’s partnership with Angola does not imply intervention in the domestic policy. Diplomatic
relations between the two countries are very tight, with private Chinese investments being increasingly
diversified in sectors such as agriculture, industry, human resources and health, all of which strengthen
the sustainable partnership between the two countries. On the other hand, a risk factor is given by the
political tensions still felt among the population. They disagree with the China–Angola partnership
because they associate this relationship with those of the colonial period. However, as the population
benefits from Chinese investment and the economy will be on a rising trend, with obvious positive
effects for the well-being of the population, these concerns will fade away.
All these economic, social, and political Angolan issues whose dynamics could impact the
evolution of oil export to China are summarized by the newly built GRI (Global Risk Indicator)
indicator. Its evolution in the period 2000–2012 follows a linear downward trend. This indicates
a reduction in investment risk. Consequently, China’s investments in Angola are safer, and the
relationship between the two states is getting stronger.
Starting from the assumption that the recovery of investments made in Angola is translated
into thousands of barrels of petroleum imported daily from Angola, the second part of the analysis
highlighted the risk impact of the amount of petroleum imported by China. The links between these
variables were described by a single-factor regression model, a model that was proven to be valid
(hypotheses of homoscedasticity, autocorrelation, and error normality were verified). The variation
of global risk indicator explains 86% of the variation in the amount of petroleum exported to China.
An increase in risk of 1% would lead to a decrease in the quantity of petroleum exported by almost 6377
barrels per day. Our study has, of course, some limitations—due to the small number of observations,
the causal relationship obtained in our model should be viewed with caution.
Therefore, the evolution of the China–Angola partnership is difficult to predict, being influenced
by many aspects. It may depend on other factors that were not included in the model, such as
exogenous factors of political or decisional nature. One example might be Angola’s desire to diminish
its dependence on China by trying to find other sources of funding from other states. On the other
hand, China could try to streamline its resource consumption or reconsider its investment in many
countries around the world. It can be said, however, that this partnership will continue as long as
China continues to invest in Angola, all the more so as petroleum is the only currency currently
available to Angola.
Author Contributions:
Conceptualization, all authors; Methodology, L.S.B.; Software, M.D.V.; Validation, L.S.;
Formal Analysis, M.D.V.; Investigation, all authors; Resources, L.S. and R.C.; Data Curation, L.S.B. and M.D.V.;
Writing—Original Draft Preparation, L.S. and R.C.; Writing—Review and Editing, all authors; Visualization, L.S.
and R.C.; Supervision, L.S.B. and M.D.V.; Project Administration, L.S.B.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.

Sustainability 2018,10, 2936 16 of 17
Appendix A
Table A1. Results of the White and Jarque–Bera tests.
Test Name Test Value Probability
White heteroscedasticity test 1.70 0.42
Jarque–Bera test 0.17 0.91
References
1. Van de Looy, J. Africa and China: A Strategic Partnership? ASC Work. Pap. 2006,67, 1–28.
2. Deng, C.; Jiang, Z.; Sun, C. Estimating the Efficiency and Impacts of Petroleum Product Pricing Reforms in
China. Sustainability 2018,10, 1080. [CrossRef]
3.
EIA. China International Analysis U.S. Energy Information Administration (EIA). 2015. Available online:
https://www.eia.gov/beta/international/analysis.php?iso=CHN (accessed on 16 August 2018).
4.
Xu, J.; Zhang, J.-S.; Yao, Q.; Zhang, W. Is It Feasible for China to Optimize Oil Import Source Diversification?
Sustainability 2014,6, 8329–8341. [CrossRef]
5.
Mo, C. Energy ties and the China–Angola strategic partnership. Newsletter
2012
,62, 29. Available online:
https://iias.asia/sites/default/files/IIAS_NL62_29.pdf (accessed on 26 July 2018).
6. Taylor, I. Mainland China–Angola relations: Moving from debacle to détente. Issues Stud. 1997,33, 64–81.
7.
Zafar, A. The growing relationship between China and Sub-Saharan Africa: Macroeconomic, trade,
investment, and aid links. World Bank Res. Obs. 2007,22, 103–130. [CrossRef]
8.
Aguilar, R.; Goldstein, A. The Chinisation of Africa: The Case of Angola. World Econ.
2009
,32, 1543–1562.
[CrossRef]
9.
Alves, A.C. The Petroleum Factor in Sino–Angolan Relations at the Start of the 21st Century, Occasional
paper. China Afr. Proj. 2010,2, 55.
10. Corkin, L. China and Angola. Strategic partnership or marriage of convenience. Angola Br. 2011,1, 1.
11.
Power, M. Angola 2025: The Future of the “World’s Richest Poor Country” as Seen through a Chinese
Rear-View Mirror. Antipode 2012,44, 993–1014. [CrossRef]
12.
Habiyaremye, A. Is Sino-African trade exacerbating resource dependence in Africa? Struct. Change Econ. Dyn.
2016,37, 1–12. [CrossRef]
13.
Wolf, C. China and Latecomer Industrialization Processes in Sub-Saharan Africa. A Case of Combined and
Uneven Development. World Rev. Political Econ. 2016,7, 249–284. [CrossRef]
14.
Wolf, C. Industrialization in Times of China: Domestic-Market Formation in Angola. Afr. Aff.
2017
,116,
435–461. [CrossRef]
15. Tkachenko, K.A. Chinese Infrastructure Projects in Africa. Aziya I Afr. Segodnya. 2018,2, 41–44.
16.
Schmitz, C.M.-T. Performing “China in Africa” for the West: Chinese migrant discourses in Angola. Asian Pac.
Migr. J. 2018,27, 9–27. [CrossRef]
17.
Kiala, C. China–Angola aid relations: strategic cooperation for development? S. Afr. J. Int. Aff.
2010
,17,
313–331. [CrossRef]
18.
Murgui, M.V. FDI and Development. Desk-Study on the Impact of Chinese Foreign Direct Investment (FDI)
Has in Angola’s Socio-Economic Development. 2004. Available online: https://ddd.uab.cat/pub/tfg/2014/
119109/TFG_mvargasmurgui.pdf (accessed on 07 August 2018).
19.
Cain, A. Alternatives to African commodity-backed urbanization: The case of China in Angola. Oxf. Rev.
Econ. Policy. 2017,33, 478–495. [CrossRef]
20. Shaxson, N. Oil, corruption and the resource curse. Int. Aff. 2007,83, 1123–1140. [CrossRef]
21.
Bader, J.; Daxecker, U.A. Chinese resource curse? The human rights effects of petroleum export dependence
on China versus the United States. J. Peace Res. 2015,52, 774–790. [CrossRef]
22.
Hackenesch, C. Not as bad as it seems: EU and US democracy promotion faces China in Africa.
Democratization 2015,22, 419–437. [CrossRef]
23.
Siu, S. The Sovereign-Commercial Hybrid: Chinese Minerals for Infrastructure Financing in the Democratic
Republic of the Congo. Columbia J. Transnatl. Law. 2010,48, 599–653.

Sustainability 2018,10, 2936 17 of 17
24.
Miron, D.; Dima, M.A.; Vasilache, S. Models Of The Intra-Regional Trade Influence On Economic Sustainable
Development In Romania. Amfiteatru Econ. J. 2010,12, 27–35.
25.
Dima, M.A.; Vasilache, S. Credit Risk modeling for Companies Default Prediction using Neural Networks.
J. Econ. Forecast. 2016,XIX, 127–143.
26.
The Organization of Petroleum Exporting Countries (OPEC). Available online: http://www.opec.org/opec_
web/en/about_us/24.htm (accessed on 18 February 2018).
27.
The World Bank Group. Available online: https://www.worldbank.org/en/country/angola/overview
(accessed on 26 July 2018).
28.
The United Nations Development Programme (UNDP). Available online: http://www.undp.org/content/
undp/en/home.html (accessed on 18 February 2018).
29.
Transparency International. Available online: https://www.transparency.org/country/AGO (accessed on
18 February 2018).
30.
McDonough, I.K.; Millimet, D.L. Missing Data, Imputation, and Endogeneity. J. Econ.
2017
,199, 141–155.
[CrossRef]
31.
Saaty, T.L. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation; McGraw-Hill Press:
New York, NY, USA, 1980.
32.
Agoston, S.; Dima, A.M. Modeling Intellectual Capital Using Analytic Hierarchy Process (AHP).
In Proceedings of the 4th European Conference on Intellectual Capital: Arcada University of Applied
Sciences, Helsinki, Finland, 23–24 April 2012.
33.
Hadad, S. Analytic hierarchy process analysis for choosing a corporate social entrepreneurship strategy.
Manag. Mark. 2015,10, 185–207.
34.
Pop, I.; Chicu, N.; Rădu
t
,
u, A. Non-performing loans decision making in the Romanian banking system.
Manag. Mark. 2018,13, 761–776.
35. The Economist. Available online: https://www.economist.com/ (accessed on 18 February 2018).
36.
British Broadcasting Company (BBC). Available online: https://www.bbc.com/news (accessed on
18 February 2018).
37.
Center for Strategic and International Studies (CSIS). Available online: https://www.csis.org/ (accessed on
18 February 2018).
38.
Wooldridge, J.M. Introductory Econometrics–A Modern Approach, 2nd ed.; South-Western: Nashville, TN,
USA, 2002.
39.
Zongwe, D. On the Road to Post Conflict Reconstruction by Contract: The Angola Model. Available
online: https://ssrn.com/abstract=1730442 or http://dx.doi.org/10.2139/ssrn.1730442 (accessed on
23 December 2010).
40.
Meidan, M. China’s Loans for Oil: Asset or Liability? Available online: https://www.oxfordenergy.org/
wpcms/wp-content/uploads/2016/12/Chinas-loans-for-oil-WPM-70.pdf (accessed on 18 February 2018).
41.
Aberg, J. Chinese Financial Assistance in Angola: Promise, Curse or an Uncertain Venture? Department
of Political Science, Lund University, 2010. Available online: https://lup.lub.lu.se/luur/download?func=
downloadFile&recordOId=1666736&fileOId=1666737 (accessed on 07 August 2018).
42.
Finance Ministry of Angola. Available online: http://www.minfin.gov.ao/PortalMinfin/#!/ (accessed on
18 February 2018).
43.
The Observatory of Economic Complexity. Available online: https://atlas.media.mit.edu/en/ (accessed on
27 July 2018).
44.
Szunomár, Á. China’s relations with the developing world: A new type of colonialism or a fruitful cooperation.
In China’s Changing Competitiveness: Shaking up or Waking up the European Union? McCaleb, A., Ed.; Warsaw
School of Economics Press: Warsaw, Poland, 2013; pp. 85–110.
45.
Vines, A.; Weimer, M. Angola—Assessing Risks to Stability; Center for Strategic and International Studies:
Washington, GL, USA, 2011.
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