Conventional explanatory variables and bilateral trade for African countries 2000 and 2005

Discover the world at Leiden UniversityDiscover the world at Leiden University
Conventional explanatory variables
and bilateral trade for African
countries 2000 and 2005
Omegal Mahabeer (1657461) | The Hague
Course: Configuring the world project tutorial
Group: 116
Tutor: Sebastian van Rijswijk, mPhil.

Discover the world at Leiden University
Study structure
2
1
1.Introduction
The following key points are
discussed in the introduction:
• Present presentation
structure
• Introduce the topic
• Discuss relevance of study
• Present research question
2
2.Literature
(4 slides)
The literature reviews scientific
research on bilateral trade and
explanatory factors. The
following research is reviewed:
• Griffiths
• Melitz
• Barro and Mcleary,
Iannacone (Anderson
(gravity method)
3
3.Methodology
(5 slides)
discussed in the methodology:
• Fundamental techniques
• Examined factors
• Hypothesis
• Sample set
4
4.Empirical results
(16 slides)
The following topics are
discussed in the empirical
results:
• Selection bias
• Trends
• Country profiles
• Scatterplots
• Regression results
5
5. Conclusion
(5 slides)
discussed :
• GDP
• Population
• Linguistic diversity
• Religious diversity
• Final conclusion
1. Introduction 2. Literature 3. Methodology 4. Empirical results 5. Conclusion

Introduction
3
In 2015, horrible xenophobic attacks terrorized South Africa. These
attacks exposed the interconnectedness within the African continents,
as in retaliation other African countries started boycotting and even
attacking South Africans in other African countries.
These examples show, besides interconnectedness, the effect of
fragmentation on bilateral trade. Namely, the presence of foreign
nationals in South Africa fragments, which results in various (positive
and negative) externalities. This essay aims to study the effect of these
fragmentations on bilateral trade, in order to find explanatory
variables for bilateral trade. This could provide insights into the
dynamics of contemporary African continental bilateral trade and the
intracontinental dependency of African countries. Previous research
has not focused on the African continent, but the world as a whole,
therefore possibly foregoing any continent specific dynamic and are
outdated.
Based on scientific research, conventional explanatory variables will
be identified and through empirical analysis these explanatory
variables will be regressed against bilateral trade for 20 countries for
the years 2000 and 2005.
The research question is then derived as “Which scientifically
identified explanatory variables have a significant impact on bilateral
trade in selected African countries in 2000 – 2005?”, followed by the
sub questions: “Which variables did scientific research find for
bilateral trade?”, “Which methodology can be used to analyze the
impact of an variable on bilateral trade?” and “What were the
descriptive statistics for the African bilateral trade in 2000 and
2005?”. The main thesis argument is that conventional explanatory
factors are unable to significantly predict bilateral trade, proving that
African bilateral trade follows continent specific dynamics.
Xenophobic attacks
exposes
interconnectedness
In response to Nigeria calling the
South African ambassador to explain,
the South African government
released a statement that “South
Africa remains committed to a strong
bond of friendship and bilateral
relations with Nigeria” (Allison).
In Mozambique the xenophobic
attacks in South Africa against
Mozabicans resulted in tension
against South Africans in Mozabique,
causing Sasol, a South African
chemical company, to temporary
evacuate its South African workers
from Mozambique (Smith).
Aim to study effects of
fragmentation on
bilateral trade
This could provide insight into
contemporary African bilateral trade.
23
23 African countries, in
2000 and 2005
Explanatory variables will be applied to
test for significance in bilateral trade
from 2000 to 2005. The years 2000
and 2005 the most recent data
available, the 23 countries were also
selected on data availability
?
Research question, main
thesis statement
The research question is: “Do
general scientifically identified
explanatory variables have a
significant impact on bilateral
trade in selected African
countries in 2000 – 2005?”.
The main thesis argument is
that conventional explanatory
factors are unable to
significantly predict bilateral
trade, proving that African
bilateral trade follows continent
specific dynamics.

Literature (1/4)
4
Griffiths
Key observations
In the configuring the world book, various
explanatory variables are discussed. Griffith
describes and criticizes fragmentation
measures based on ethnicity, language and
religion. Furthermore the book describes an
relation between fragmentation, trust,
governance and (economic) growth which
could be applied to describe how
fragmentation is conducive to bilateral
trade. Finally, it data accuracy of indicators
such as GDP for poor countries is
questioned. Unfortunately, no specific
reference is made to bilateral trade or
African countries. Below the most relevant
key findings:
• Inequality and homogeneity are linked
through trust
• Since concept and definition are
debatable, ranking of Alesina et al. carry
less significance (Griffiths, 211 - 212).
• Ethnicity is not relevant in all cases
(Griffiths 210)
• Language may offer a means for
fragmentation, as it is easier to measure
(Griffiths 210)
• There is a scarcity of religious reliable
fragmentation data (Griffiths 214)
• GDP for poor countries could be
inaccurate (Griffiths 109)
Melitz
Barro and
McCleary,
Iannacone
Anderson and
van Wincoop
Configuring the world by Griffiths (2015)
Linking fragmentation to growth through trust and governance
𝟏
𝒙
Fragmentation
- Language
- Ethnicity
- Religion
- Income
Trust
Governance
Prosperity
Regular causation
Lower fragmentation creates trust
which allows for effective
governance and then prosperity.
Applied to bilateral trade could
show a different relationship.
Fragmentation such as language,
introduces the possibility of
commonality of language with
another country. This increases the
trust between countries, which
allows for the necessary
infrastructure to be build and
bilateral trade to increase.
Reverse causation
Higher prosperity allows for better
governance which allows for
increased trust which allows for
fragmentation to exist. Applying
this to bilateral trade, increased
bilateral trade (prosperity) allows
for better infrastructure increasing
trust and therefore fragmentation
in a society to continue to exist.
Source: Configuring the World; A critical Political Economy approach
Fig 1. The benefits of trust model

Literature (2/4)
5
Melitz Key observations
Melitz finds that having a common language
increases bilateral trade (BLT). To arrive to
this conclusion the scholar regresses GDP,
distance, adjacency, language variables,
currency union, common country, free trade,
ex colony, common colonizer, population,
country area size, landlocked, lingual
diversity, literacy and a soviet country
dummy on bilateral trade for the period
1970 – 1995. Below the most relevant
observations on this paper:
• Uses gravity model
• Positive relationship language and BLT
• Positive relationship GDP and BLT
• Negative relationship distance and BLT
• This paper is that it is outdated, for
example it uses a soviet dummy variable,
which is irrelevant now.
• Does not specifically focus on African
countries (where different dynamics
could exists).
Griffiths
Barro and
McCleary,
Iannacone
Anderson and
van Wincoop
Language and foreign trade by Melitz (2015)
The gravity model
The gravity model refers to a model in which the
bilateral trade between countries is determined by
the size of the countries. More specifically this means
that 2 countries with larger GDP’s are more prone to
engage in bilateral trade than 2 countries with
smaller GDP’s. The same theory is used by Melitz for
the distance and size of the country variable.

Literature (3/4)
6
Key observations
Barro and McCleary investigate the relation
between religiosity and economic growth.
Religiosity refers to the dedication to the
religion. Religiosity was captured though
variables such as church attendance, belief
in heaven and hell, which was measured by
surveys in 59 countries from 59 countries
from 1981 – 1999. Iannaccone concluded
that based on the World Religious Database
and applying religion market dynamics
proposed in Adam Smith’s Wealth of the
Nations , the relationship between religiosity
and fragmentation of the religion are
country specific (Iannacconne 168 – 169).
Combining these two researches the
following observations are made:
• Barro and McCleary find a relationship
between religiosity and economic
growth.
• Ianncconne states that the relationship
between religiosity and religious
fragmentation is idiosyncratic
• Combining Barro and McCleary and
Iannaconne, that there is probability of a
relationship between economic growth
and fragmentation, but this relationship
would be country specific
• Criticism on both papers is that it is
outdated
• Criticism Iannaccone paper is that it
does not include African countries
• Criticism Iannaccone is that it used the
World Christian Encyclopedia, this
Encyclopedia is notorious for the bias
towards keeping groups in their
Christian Encyclopedia, or including
villages which are not even Christian but
close to Christian villages
Griffiths
Anderson
Religion and economic growth by Barro and McCleary (2003) and The
consequences of religious market structure by Iannacone (1991)
Barro and McCleary,
Iannacone
Melitz Linking fragmentation to economic growth through
religiosity
This slide tries to find
literature which links
religious fragmentation to
economic growth, literature
fails to find a consistent
relationship
Fig 2. Religious fragmentation, religiosity and economic growth
hypothesis

Literature (4/4)
7
Key observations
Anderson explains the theoretical
background behind the gravity model using
variables GDP, population and distance to
explain bilateral trade. Anderson refers to
other scholars to state that these variables
have been extensively proven through
empirical research (Anderson 108). These
are the key findings:
• The gravity model using GDP,
population and distance between
countries as explanatory variables have
significant impact on bilateral trade, as
proven through theoretical justification
as well as empirical research.
• Distance impacts bilateral trade as it
represents a proxy for transportation
costs. Therefore the greater the distance
between countries, the higher the
transportation costs and therefore less
bilateral trade.
• Size impacts bilateral trade as it
represents a supply proxy. Therefore the
greater the country the lower the price
and the lower the value of the bilateral
trade.
• Rearranging the Cobb – Douglas
function proves how GDP affects
bilateral trade.
• Criticism on the Anderson paper is that
the explains the gravity model strictly
based from a supply side perspective.
Increase pf country size could perhaps
also mean greater target market, which
could increase bilateral trade.
Griffiths A theoretical foundation for the Gravity Equation by Anderson (1979)
Melitz
𝒇(𝒙)
Anderson’s theoretical explanation of the gravity model on
bilateral trade and GDP
Anderson
Barro and
McCleary,
Iannacone
Assuming each country is completely specialized in production of 1 particular good, and isolating
the effect of GDP on bilateral trade. The consumption of good x in country a , with GDP noted as
Y the part of GDP spend on this good is c, the production named P and assuming equilibrium the
function becomes:
𝑃xa = 𝑐 𝑥 𝑌𝑎
Assuming that people on spend their entire income on product, the following formula is derived:
𝑃a = 𝑐 𝑥 𝑌𝑎
Introducing another country b into this equation and solving for 𝑐 𝑥 Anderson (106) derives the
following formula:
𝑃ab =
𝑌𝑎 𝑌𝑏
𝑌𝑎
This represents the gravity model. The greater the GDP of two countries, the higher the
production and demand (therefore trade) of this product. This theoretical explanation is subject
to unrealistic assumptions (for example, transportation are never 0), however empirical research
according to Anderson does support this relationship (Anderson 106)

Methodology (1/5)
8
Empirical analysis based on
normal distribution
The underlying assumption of all the empirical
analysis is that the data used behaves according
to the normal distribution. The normal
distribution assumes that 68% of all observation
fall within one standard deviation of the mean.
Furthermore it assumes a kurtosis of 3
(indicating the length of the observations) and
skewness of 0 (indicating no bias to positive or
negative observations in the distribution). To
generate normalized scores bilateral trade, GDP,
distance and population are log normalized.
y
=  +
𝒂𝒙 𝟏 + ⋯ +
𝒂𝒙 𝒏 + 
Multivariate linear regressions
based on Ordinary Least Squares
The technique used to derive to linear regression
uses the Ordinary Least Squares technique. This
technique derives the regression by
minimalizing the squared distance between the
independent variables (GDP, distance,
population, language and religion) and
dependent variable bilateral trade. This
technique assumes normality and
homogenous behavior of the residuals .
Homogenous behavior of the residuals will prove
to be an important assumption later on.
Fundamental techniques used in the empirical analysis
HHI
Diversity indices based on
Herfindahl Herschman Index
The diversity indices for religion and language
are derived from the Herfindahl Herschman
Index which is based on the following formula:
𝐹𝑅𝐴𝐶𝑇𝑗 = 1 − 𝑠𝑖𝑗
2
𝑁
𝑖=1
where,
𝐹𝑅𝐴𝐶𝑇𝑗 = diversity index score of country j
𝑠𝑖𝑗
2
= squared share of group i in country j
The higher 𝐹𝑅𝐴𝐶𝑇𝑗 the higher diversity index

Methodology (2/5)
9
GDP data from
World Bank database
The GDP data for the sample is derived
from the World Bank database using the
indicator GDP (current US$) (indicator
code: NY.GDP.MKTP.CD) for the years
2000 and 2005. Henceforth this
factor will be reffered to as GDP
and refered to as bilateral trade in
current billion US$. This variable will
be calculated as the log normal of the
gravity formula as described by Anderson
(see literature; Anderson).
Population data from
World Bank database
The population data for the sample is
derived from the World Bank database
using the indicator Population, total
(indicator code: SP.POP.TOTL) for the
years 2000 and 2005. The population
data serves as an size proxy, however
accuracy of the actual count of number of
people in African countries could be
questioned. Henceforth this factor
will be reffered to as POP and
refered to population in millions.
This variable will be calculated as the log
normal of the gravity formula as
described by Anderson (see literature;
Anderson).
Bilateral trade data, GDP, distance and population, language diversity variables 2000 and 2005
Linguisitic diversity index
data from
Ethnologue
The linguistic diversity index is derived
from the Gordon and Grimes datasets
from 2000 and 2005, which is based on
the Ethnologue dataset. Critisim of this
dataset is the accuracy in capturing the
actual linguistic diversity is questionable.
African countries could be questioned.
Henceforth this factor will be
reffered to as LDI and reflected as a
ratio between 0 and 1.
Bilateral trade data from
OECD database
The bilateral trade data for the sample is
derived from the OECD database using
the indicator Total trade in goods in
thousands of US dollars between the
selected countries for the years 2000 and
2005. Henceforth this factor will be
reffered to as BLT and refered to as
bilateral trade in billion US$.
dependent variable independent variables

Methodology (3/5)
10
Religious diversity variable 2000 and 2005
Religious diversity data
from World Religion
database
The religious diversity index is which
is based on the World Religion
dataset. The values for 2005 is
inferred from a monte Carlo
simulation. Criticism of this dataset is
the accuracy in capturing the actual
religious diversity is questionable.
African countries could be questioned.
Henceforth this factor will be
referred to as RDI and reflected
as a ratio between 0 and 1.
Monte Carlo simulation for the 2005 religious
diversity index
The religious diversity index for 2005 is derived from a monte
Carlo simulation, since there are only values available for 2000
and 2015. Firstly linear growth is assumed between 2000 and
2015 for the diversity index score for each country. Using 5 year
growth rates the 5 year standard deviation is calculated using data
from 1900 to 2015. Using the normal distribution a random
number is generated which utilizes the parameter 5 year growth
between 2000 and 2015 and the standard deviation. This random
number is then generated 10,000 times. The mean of these
10,000 simulations is used multiplied by the value in the year
2000 to get to the value of the religious diversity index for 2005
independent variable

Methodology (4/5)
11
20 African countries, slight bias towards
western African countries 2000 and 2005
Countries examined
Table 1 shows the entire sample of African countires. The countries
were selected on data availability of countries in the African continent.
In the Microsoft Bing map Excel created visualization in the round
icon, the red dots indicate countries in the sample.
Time frame examined
The years examined were the years for which data was available and
which were the most recent. By examining the most recent data, it is
possible to detect contemporary dynamics of the variables examined.
Algeria Guinea
Benin Kenya
Botswana Madagascar
Burkina Faso Mali
Cameroon Mauritania
Central African
Republic Mauritius
Cote d'Ivoire (Ivory
Coast) Morocco
Ethiopia Mozambique
Gabon Namibia
Ghana South Africa
Table 1: Sample set of African countries
Source: Microsoft Bing Maps App in Microsoft Excel
Fig 3. Dispersion of sample set in Africa

Methodology (5/5)
12
𝑯 𝟎
Challenging conventional research
Conventional research suggests that GDP, POP, LDI and RDI have a positive significant impact on BLT.
Contrary to conventional research the hypotheses will state the following:
𝑯 𝟎 : 𝑮𝑫𝑷, 𝑷𝑶𝑷, 𝑳𝑫𝑰 𝒂𝒏𝒅 𝑹𝑫𝑰 𝒂𝒓𝒆 𝒊𝒏𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕 𝒆𝒙𝒑𝒍𝒂𝒏𝒂𝒕𝒐𝒓𝒚 𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝒇𝒐𝒓 𝑩𝑳𝑻
𝒇𝒐𝒓 𝒕𝒉𝒆 𝒔𝒆𝒍𝒆𝒄𝒕𝒆𝒅 𝑨𝒇𝒓𝒊𝒄𝒂𝒏 𝒄𝒐𝒖𝒏𝒕𝒓𝒊𝒆𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒔𝒆𝒍𝒆𝒄𝒕𝒆𝒅 𝒕𝒊𝒎𝒆 𝒔𝒆𝒓𝒊𝒆𝒔
𝑯 𝟏: 𝑮𝑫𝑷, 𝑷𝑶𝑷, 𝑳𝑫𝑰 𝒂𝒏𝒅 𝑹𝑫𝑰 𝒂𝒓𝒆 𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕 𝒆𝒙𝒑𝒍𝒂𝒏𝒂𝒕𝒐𝒓𝒚 𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝒇𝒐𝒓 𝑩𝑳𝑻

Empirical results (1/16)
13
Selection bias
Trends
Countries
Scatterplots
10%
35%
25%
15%
15%
Fig 4. Sample regional distribution of
African countries selected
Northern Africa
Western Africa
Eastern Africa
Southern Africa
Middle Africa
12%
29%
34%
9%
16%
Fig 5. Regional distribution of
all African countries
Northern Africa
Western Africa
Eastern Africa
Southern Africa
Middle Africa
Sample displays slight bias towards western African countries
The left chart displays the relative weight of each African region in the sample. The right chart shows the relative weight
of each African region (not excluding any countries). It is observed that the sample displays a slight bias towards
Western African countries and Southern African countries. This could indicate some selection bias, which could affect
the results from the regression analysis.
Source: United Nations, United Nation Statistics Division (2015)
Regression

14
Countries
Scatterplots
00
200
400
600
800
1,000
1,200
CurrentbillionUS$
Year
Fig 6. GDP in current US$ for sample African countries 1986 - 2014
Northern Africa Western Africa Southern Africa Middle Africa Eastern Africa
Source: World Bank, World Development Indicators (2015); United Nations, United Nation Statistics Division (2015)
Explosive growth in GDP in sample
set Africa from 1986 - 2014
The chart on the right shows the growth in GDP in
current US dollar from the World Bank database. After
the relative stagnant years 1986 – 2000, the period
2000 – 2014 shows an significant increase in growth,
particularly in Southern Africa and Northern Africa. The
total growth in the sample was equal to 346% over the
period 1986 – 2014. The years 2000 and 2005 are
excellent case studies as they present a pre boom and
boom year.
Trends
Selection
bias
Regression

15
1. Introduction 2. Literature 3. Methodology 4. Empirical results
5. Conclusion
Non normal distribution in static diversity indices
The above charts show the standardized scores of the sample countries. This standardization is achieved
by the following formula Z = (X- μ) / σ, where Z is the standardized score. The distributions of both
diversity indices are non normal, and appear the static for 2000 and 2005. The average value for the
religious diversity index in 2000 and 2005 was 0.42 and 0.43 with standard deviation of 0.23 in both
years. The average value for the linguistic diversity index in 2000 and 2005 was 0.66 with standard
deviation of 0.26 in both years, confirming static behavior of the indices.
Countries
Scatterplots
Trends
Selection
bias
0
2
4
6
8
10
-3 -2.5 -2 -1.5 -1 0 1 1.5 2 2.5 3
Observations
Z-score
Fig 7. Distribution Z scores Religious Diversity Sample
Africa 2000 and 2005
Z score religious diversity 2000 Z score religious diversity 2005
0
2
4
6
8
10
-3 -2.5 -2 -1.5 -1 0 1 1.5 2 2.5 3
Observations
Z-score
Fig. 8 Distribution Z scores Linguistic Diversity
Sample Africa 2000 and 2005
Z score linguistic diversity 2000 Z score linguistic diversity 2005
5. Conclusion
Regression
Source: World Religion Database (2008) Source: Grimes (2000) and Gordon (2005)

16
Countries
Scatterplots
Trends
Selection
bias
Benin
3%
Burkina Faso
4%
Botswana
0%
Central African Republic
1%
Cote d'Ivoire
5%
Cameroon
5%
Algeria
7%
Ethiopia
24%
Gabon
0%
Ghana
6%
Guinea
3%
Kenya
11%
Morocco
5%
Madagascar
6%
Mali
4%
Mozambique
6%
Mauritania
1%
Mauritius
0%
Namibia
1%
South Africa
10%
Fig. 10 Contribution to African sample population
growth 1986 - 2014
0%
20%
40%
60%
80%
100%
120%
200
250
300
350
400
450
500
Cumulativepercentagegrowthpopulation
Populationinmillions
Year
Fig. 9 Population growth African sample 1986 - 2014
Population in absolute terms [L]
Cumulative growth population since 1986 [R]
Population more than doubled from 1986 - 2005
The left chart displays growth of the total of the African sample countries. The left axis displays
the absolute number, starting at 230 million in 1986 and ending at 580 million in 2014, more
than doubling the number of people in these countries. The right chart displays the contribution
of each country to the growth of population. Ethiopia, South Africa, Kenya’s and Algeria
contributed over 50% of the growth, indicating concentrated population growth.
105%
5. Conclusion
Regression
Source: World Bank, World Development Indicators (2015) Source: World Bank, World Development Indicators (2015)

17
5. Conclusion
Bilateral trade between African sample countries shows growth
The left chart displays the absolute bilateral trade volume between the countries in the sample
billion US dollar . The growth has been especially strong since 2002, as is clarified in the right
chart showing that the year over year growth in bilateral trade since 2002 was higher than any of
the other years before 2002. The total cumulative growth of bilateral trade in the period 1996 –
2005 for the selected African countries was 221%.
Source: OECD, OECD.Stat (2015)
0
2
4
6
8
10
BillionUS$
Year
Fig. 11 Bilateral Trade volume sample African
countries 1996 - 2005
Source: OECD, OECD.Stat (2015)
0%
50%
100%
150%
200%
250%
-10%
0%
10%
20%
30%
40%
Cumulativepercentagegrowth
Yearlypercentagegrowth
Year
Fig. 12 Percentage growth Bilateral Trade volume
sample African countries 1996 - 2005
Yearly percentage growth bilateral trade [L]
Cumulative percentage growth bilateral trade since 1996 [R]
221%
Countries
Scatterplots
Trends
Selection
bias
5. Conclusion
Regression

18
Source: OECD, OECD.Stat; Microsoft Bing Maps App in Microsoft Excel
5. Conclusion
20 African countries, few dominate
bilateral trade volume
This chart shows the size of the bilateral trade volume per country in
the sample set. South Africa is clearly the largest by bilateral trade
volume. Moreover, in instances (especially visible in the wester
African countries) where the blue circle dominates the brown circle,
there has been a contraction of bilateral trade from 2000 observation
compared to the 2005 observation.
Fig. 13 Bilateral trade volume per country in 2000 and 2005
Scatterplots
Selection
bias
Countries
Trends
Regression

19
Scatterplots
Selection
bias
Source: World Bank, World Development
Indicators (2015)
Countries
Trends
5. Conclusion
Country profile South Africa 2000 and 2005
00
50
100
150
200
250
300
2000 2005
CurrentbillionUS$
Year
Fig 14. South Africa
GDP 2000 and 2005
42
43
44
45
46
47
48
2000 2005
Populationinmillion
Year
Fig. 15 South Africa
Population 2000 and
2005
0
1
2
3
4
5
6
2000 2005
billionUS$
Year
total Bilateral Trade
2000 and 2005
0.24
0.26
0.28
0.30
0.32
0.34
2000 2005
Year
Fig 17. South Africa
Language Diversity
Index 2000 and 2005
0.28
0.30
0.32
0.34
0.36
0.38
2000 2005
Year
Religious Diversity
Index 2000 and 2005
Regression
South Africa boasts the largest bilateral trade volume of all of the countries. Below charts show the
characteristics of each factor in 2000 and 2005. There could be an indication that growth in China
spurred GDP growth in commodity rich South Africa. The GDP and bilateral trade have moved in
concert, whereas the change in the other factors display small change. In particular the diversity
indices appear inert. With only three languages identified it scores low on the Language diversity,
although this observation is highly debatable, as the official languages succeed three languages.
Indicators (2015)
Source: OECD, OECD.Stat (2015) Source: World Religion Database (2008)Source: Grimes (2000) and Gordon (2005)

20
Country profile Algeria 2000 and 2005
00
20
40
60
80
100
120
2000 2005
CurrentbillionUS$
Year
Fig 19. Algeria GDP
2000 and 2005
30
31
32
33
34
2000 2005
Populationinmillion
Year
Fig 20. Algeria
Population 2000 and
2005
0.0
0.1
0.2
0.3
0.4
0.5
0.6
2000 2005
billionUS$
Year
Fig. 21 Algeria total
Bilateral Trade 2000
and 2005
0.00
0.02
0.04
0.06
0.08
0.10
2000 2005
Year
Fig. 22 Algeria
Language Diversity
Index 2000 and 2005
0.00
0.02
0.04
0.06
0.08
0.10
2000 2005
Year
Fig 23. Algeria
Religious Diversity
Index 2000 and 2005
Algeria provides a test case wherein relatively high GDP is combined with low bilateral trade. Below
charts show the characteristics of each factor in 2000 and 2005. The GDP and Bilateral trade were
correlated although the magnitude of the bilateral trade compared to South Africa has been marginal.
Algeria displays low scores on the diversity scores, indicating a homogenous country. With only 2
languages spoken and a vast majority of Muslims the diversity scores are unsuprising.
Scatterplots
Selection
bias
Countries
Trends
Regression
Indicators (2015)
Indicators (2015)

21
Country profile Benin 2000 and 2005
0
1
2
3
4
5
2000 2005
CurrentbillionUS$
Year
Fig. 24 Benin GDP
2000 and 2005
6.0
6.5
7.0
7.5
8.0
8.5
2000 2005
Populationinmillion
Year
Fig. 25 Benin
Population 2000 and
2005
0.00
0.05
0.10
0.15
0.20
0.25
0.30
2000 2005
billionUS$
Year
Fig. 26 Benin total
Bilateral Trade 2000
and 2005
0.00
0.20
0.40
0.60
0.80
2000 2005
Year
Fig. 27 Benin Language
Diversity Index 2000
and 2005
0.60
0.62
0.64
0.66
0.68
0.70
2000 2005
Year
Fig. 28 Benin Religious
and 2005
Benin is a small countries by any of the gravity measures. Below charts show the characteristics of each
factor in 2000 and 2005. The GDP and Bilateral trade were correlated although the magnitude of the
bilateral trade compared to South Africa has been marginal. Benin has four observed languages in
2000 and 6 in 2005, explaining the increase in language diversity. The division of Christians, Muslims
and indigenous religions, spread evenly causes a high score on religious diversity.
Scatterplots
Selection
bias
Countries
Trends
Regression
Indicators (2015)
Indicators (2015)

22
Country profile Central African Republic 2000 and 2005
0.0
0.5
1.0
1.5
2000 2005
CurrentbillionUS$
Year
Fig. 29 Central African
Republic GDP 2000
and 2005
3.5
3.6
3.7
3.8
3.9
4.0
4.1
2000 2005
Populationinmillion
Year
Republic Population
2000 and 2005
0.00
0.01
0.02
0.03
0.04
2000 2005
billionUS$
Year
Republic total Bilateral
Trade 2000 and 2005
0.20
0.40
0.60
0.80
1.00
2000 2005
Year
Republic Language
and 2005
0.49
0.50
0.51
0.52
0.53
0.54
2000 2005
Year
Republic Religious
and 2005
Central African Republic displays a similar profile as Benin on the GDP, population and bilateral trade.
It also displays a consistently high measure of religious diversity, having 13 observed languages. The
Religious Diversity lags and it is observed that the country has become more religious homogenous.
One can not observe theses measures and not consider the catastrophic influence of war on these
variables, especially considering the effect of Muslim militia fighting Christian militia (Council on
Foreign Relations)Scatterplots
Selection
bias
Countries
Trends
Regression
Indicators (2015)
Indicators (2015)

23
Selection
bias
Trends
5. Conclusion
Countries
Regression
Scatterplots
y = 1E-09x + 0.9128
R² = 0.0145
0
1.1
2.2
3.3
4.4
5.5
0.0 0.2 0.4 0.6 0.8 1.0
GDP
BLT
Fig. 34 Correlation African sample GDP and BLT 2000 and 2005No correlation GDP and Bilateral
trade
The chart on the right shows the gravity measurement of
GDP and Bilateral trade. The observations are dispersed
incoherently. Most of the observations lie at the origin of
the x axis, indicating that most of the bilateral trade
observations were less than 100 million US dollar. The
observed regression reflects the inconsistent behavior of
the observation in its low explanatory value with the R
square lower than 2%.
Source: World Bank, World Development Indicators (2015), OECD, OECD.Stat (2015)

24
Selection
bias
Trends
5. Conclusion
Countries
Regression
Scatterplots
y = 3E-10x + 0.0475
R² = 0.0431
0
0.3
0.6
0.9
1.2
1.5
0.0 0.2 0.4 0.6 0.8 1.0
POP
BLT
Fig. 35 Correlation African sample POP and BLT 2000 and 2005No correlation Population and
Bilateral trade
The chart on the right shows the gravity measurement of
Population and Bilateral trade. The observations are
also dispersed incoherently. The observed regression
reflects the inconsistent behavior of the observation in
its low explanatory value with the R square lower than
5%.
Source: World Bank, World Development Indicators (2015), OECD, OECD.Stat (2015)

25
Selection
bias
Trends
5. Conclusion
Countries
Regression
Scatterplots
y = 1E-10x + 0.6469
R² = 0.0002
0.0
1.0
2.0
3.0
4.0
5.0
0.0 0.2 0.4 0.6 0.8 1.0
LDI
BLT
Fig 36. Correlation African sample LDI and BLT 2000 and 2005No correlation Linguistic diversity
and Bilateral trade
Similarly to the previous scatterplots, the pattern
displayed on the right chart shows no obvious relation
between Linguistic diversity and Bilateral trade, with the
R square essentially at 0%.
Source: Grimes (2000) and Gordon (2005); OECD, OECD.Stat (2015)

26
Selection
bias
Trends
5. Conclusion
Countries
Regression
Scatterplots
y = -7E-10x + 1.0617
R² = 0.003
0.0
1.0
2.0
3.0
4.0
5.0
0.0 0.2 0.4 0.6 0.8 1.0
RDI
BLT
Fig. 37 Correlation African sample RDI and BLT 2000 and 2005No correlation Religious diversity
and Bilateral trade
The scatterplots are finished with the last independent
variable, religious diversity, which consistent with all
the other three scatterplots show no correlation with the
dependent factor bilateral trade. This indicates that a
gravity model explaining bilateral using independent
GDP, Population, Linguistic Diversity and Religious
diversity should perform poorly.
Source: World Religion Database (2008); OECD, OECD.Stat (2015)

27
Selection
bias
Trends
5. Conclusion
Countries
Regression
Scatterplots
𝒇(𝒙) Regression analysis
Coefficients
Standard
Error t Stat P-value
Intercept -9,991,976 7,827,547 -1.28 0.20
GDP 17,803,025 3,785,920 4.70 0.00
POP 187,687,977 30,734,148 6.11 0.00
LDI 7,045,728 4,994,013 1.41 0.16
RDI -1,636,782 3,436,250 -0.48 0.63
Multiple R 0.29041743
R Square 0.08434229
Adjusted R Square 0.07755964
Standard Error 82139097.4
Observations 545
The regression analysis displays some remarkable results. The following formula is extracted
from the data set:
BLT = -9,991,976 + 17,803,025GDP + 187,687,977POP + 7,045,728LDI -1,636,782RDI
The GDP shows a positive significant relation, similar to the population variable. Fort the
diversity variables the Language variable is insignificantly positive, whereas the Religious
variable is insignificantly negative. The low R Square indicates the low fit of the variables.
Furthermore, based on the previous scatterplot, it is highly probable that the residuals are
heterogeneous, underlying the low value one should assign to the linear regression.
Table 2: Variable statistics Table 3: Model statistics

28
Selection
bias
Trends
5. Conclusion
Countries
Regression
Scatterplots
𝒇(𝒙) Heteroskedacity indication
Heteroskedacity refers to the fact that residuals in the regression behave heterogeneously, opposed the
homogenous behavior assumed in ordinary least squares linear regression. Below charts display this
behavior as residuals that fall outside the fit are vastly dispersed.
0.0
0.2
0.4
0.6
0.8
1.0
0.0 1.2 2.4 3.6 4.8 6.0
BLT
GDP
Fig. 38 GDP Line Fit Plot BLT
BLT Predicted BLT
0.0
0.2
0.4
0.6
0.8
1.0
0 0.3 0.6 0.9 1.2 1.5
BLT
POP
Fig. 39 POP Line Fit Plot BLT
BLT Predicted BLT
0.0
0.2
0.4
0.6
0.8
1.0
0 1.2 2.4 3.6 4.8 6
BLT
LDI
Fig. 40 LDI Line Fit Plot BLT
BLT Predicted BLT
0.0
0.2
0.4
0.6
0.8
1.0
0 1.2 2.4 3.6 4.8
BLT
RDI
Fig. 41 RDI Line Fit Plot BLT
BLT Predicted BLT
Source: World Bank, World Development Indicators (2015);
OECD, OECD.Stat (2015)
Source: World Bank, World Development Indicators (2015);
OECD, OECD.Stat (2015)
Source: World Religion Database (2008); OECD, OECD.Stat
(2015)
Source: Grimes (2000) and Gordon (2005); OECD, OECD.Stat
(2015)

Conclusion (1/5)
29
GDP
Population
GDP explanatory value on bilateral trade
Conventional scientific research finds that GDP using the gravity model has a significant positive impact
on bilateral trade.
Empirical research shows that for the selected African countries, GDP has a significant positive impact on
bilateral trade. This relationship is affected by heteroskedacity.
It is concluded that GDP has an positive impact on bilateral trade, however further research needs to be
done to establish a positive impact of GDP on bilateral trade for African countries. Empirical research
indicates a possible unconventional impact of GDP on bilateral trade.
Linguistic
diversity
Religious
diversity
Final
conclusion

Conclusion (2/5)
30
Linguistic
diversity
Population explanatory value on bilateral trade
Conventional scientific research finds that size using the gravity model has a significant positive impact
on bilateral trade.
Population is used as proxy for size. Empirical research shows that for the selected African countries,
Population has a significant positive impact on bilateral trade. This relationship is affected by
heteroskedacity.
It is concluded that population has an positive impact on bilateral trade, however further research needs
to be done to establish a positive impact of population on bilateral trade for African countries. Empirical
research indicates a possible unconventional impact of size on bilateral trade.
Population
GDP
Religious
diversity
Final
conclusion

Conclusion (3/5)
31
Religious
diversity
Linguistic diversity explanatory value on bilateral trade
Conventional scientific research finds that linguistic diversity could increase trust between actors
consequently positively impact bilateral trade, however no accurate and robust measurement of linguistic
diversity exists.
The linguistic diversity index from Ethnologue is used as proxy for linguistic diversity. Empirical research
shows that for the selected African countries, linguistic diversity has a insignificant positive impact on
bilateral trade. This relationship is affected by heteroskedacity and measurement is poor.
It is concluded that linguistic diversity does not impact bilateral trade and the Ethnologue dataset is an
inaccurate tool.
GDP
Linguistic
diversity
Population
Final
conclusion

Conclusion (4/5)
32
Final
conclusion
Religious diversity explanatory value on bilateral trade
Conventional scientific research finds that religious diversity could increase trust between actors
consequently positively impact bilateral trade, however no accurate and robust measurement of religious
diversity exists.
The religious diversity index from World Religion Database is used as proxy for linguistic diversity. Empirical
research shows that for the selected African countries, linguistic diversity has a insignificant negative impact on
bilateral trade. This relationship is affected by heteroskedacity and measurement is poor.
It is concluded that religious diversity does not impact bilateral trade and the World Religion Database
dataset is an inaccurate tool.
GDP
Population
Religious
diversity
Linguistic
diversity

Conclusion (5/5)
33
GDP
Population
Linguistic
diversity
Final
conclusion
Religious
diversity
Idiosyncratic behavior of African bilateral trade
Conventional research suggests that GDP, POP, LDI and RDI have a positive significant impact on BLT.
Contrary to conventional research the hypotheses will state the following:
𝑯 𝟎 : 𝑮𝑫𝑷, 𝑷𝑶𝑷, 𝑳𝑫𝑰 𝒂𝒏𝒅 𝑹𝑫𝑰 𝒂𝒓𝒆 𝒊𝒏𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕 𝒆𝒙𝒑𝒍𝒂𝒏𝒂𝒕𝒐𝒓𝒚 𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝒇𝒐𝒓 𝑩𝑳𝑻
𝑯 𝟏: 𝑮𝑫𝑷, 𝑷𝑶𝑷, 𝑳𝑫𝑰 𝒂𝒏𝒅 𝑹𝑫𝑰 𝒂𝒓𝒆 𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕 𝒆𝒙𝒑𝒍𝒂𝒏𝒂𝒕𝒐𝒓𝒚 𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝒇𝒐𝒓 𝑩𝑳𝑻
Although the GDP and POP indicated significant positive impact, the model suffers from heteroskedacity. The diversity measures LDI, RDI were
insignificant. Therefore 𝑯 𝟎 is rejected.
The research question is: “Do general scientifically identified explanatory variables have a significant impact on bilateral trade in
selected African countries in 2000 – 2005?”.
Literature found that possible explanatory variables GDP, population, religious diversity and linguistic diversity. Empirical research found significant
impact for most of these variables, however the model showed heteroskedacity. This heteroskedacity indicates that conventional explanatory factors
are unable to significantly capture bilateral trade, suggesting that African bilateral trade follows continent specific dynamics.
The findings in the empirical research are restricted by the limited data availability, exposing it to selection bias on the African countries selected as
well as time period by selecting only two years (2000 and 2005). Further research should be done over longer time spans to generate a more robust
conclusion. Furthermore, one can not entirely exclude the influence the impact of the world economy. For example, it is difficult to exclude the impact
of Chinese growth and the influence this could have had on the commodity rich countries South Africa and Algeria and therefore its bilateral trade
behavior with other African countries. Further research could expand more on the factor of commodity prices and bilateral trade, therefore further
investigate unconventional explanatory variables for bilateral trade in African countries.

Works cited (1/2)
34
Anderson, James E. "A Theoretical Foundation for the Gravity Equation." The
American Economic Review (1979): 106 - 116. Print.
Barro, Robert J. and Rachel Mcleary. Religion and Economic Growth. Working Paper.
Cambridge: NBER, 2003. Print.
Griffiths, Richard T. Configuring the World: A critical political economy approach.
Leiden: Leiden University, 2015. Book.
Iannaccone, Laurence R. "The Consequences of Religious Market Structure; Adam
Smith and the Economics of Religion." Rationality and Society (1991): 156 - 177. Print.
Melitz, Jacques. "Language and foreign trade." European Economic Review (2007):
667 - 699. Print.
Literature
cited

Works cited (2/2)
35
OECD. OECD.Stat. Web. 29 Nov. 2015
http://stats.oecd.org/Index.aspx?DataSetCode=BTDIXE_I4
Gordon. Ethnologue 15th Edition (2005)
Grimes, B.F. Ehnologue 14th Edition (200)
http://terralingua.org/our-work/linguistic-diversity/
Todd M. Johnson and Brian J. Grim, eds. World Religion Database (Leiden/Boston:
Brill, 2008)
World Bank. World Development Indicators. Web. 29 Nov. 2015.
http://data.worldbank.org/indicator/NY.GDP.MKTP.CD
http://data.worldbank.org/indicator/SP.POP.TOTL
Databases
cited

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