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)

The following key points are 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)

The following key points are 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on the African continent, but the world as a whole, therefore possibly foregoing any continent specific dynamic and are outdated.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


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)


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


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 = 𝑐𝑥𝑌𝑎

Assuming that people on spend their entire income on product, the following formula is derived:

𝑃a = 𝑐𝑥𝑌𝑎

Introducing another country b into this equation and solving for 𝑐𝑥 Anderson (106) derives the following formula:

𝑃ab = 𝑌𝑎𝑌𝑏 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 = +𝒂𝒙𝟏 +⋯+𝒂𝒙𝒏 +

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 − 𝑠𝑖𝑗2

𝑁

𝑖=1

where, 𝐹𝑅𝐴𝐶𝑇𝑗 = diversity index score of country j

𝑠𝑖𝑗2 = 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


𝑯𝟎

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:

𝑯𝟎 : 𝑮𝑫𝑷,𝑷𝑶𝑷, 𝑳𝑫𝑰 𝒂𝒏𝒅 𝑹𝑫𝑰 𝒂𝒓𝒆 𝒊𝒏𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕 𝒆𝒙𝒑𝒍𝒂𝒏𝒂𝒕𝒐𝒓𝒚 𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝒇𝒐𝒓 𝑩𝑳𝑻

𝒇𝒐𝒓 𝒕𝒉𝒆 𝒔𝒆𝒍𝒆𝒄𝒕𝒆𝒅 𝑨𝒇𝒓𝒊𝒄𝒂𝒏 𝒄𝒐𝒖𝒏𝒕𝒓𝒊𝒆𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒔𝒆𝒍𝒆𝒄𝒕𝒆𝒅 𝒕𝒊𝒎𝒆 𝒔𝒆𝒓𝒊𝒆𝒔

𝑯𝟏: 𝑮𝑫𝑷,𝑷𝑶𝑷, 𝑳𝑫𝑰 𝒂𝒏𝒅 𝑹𝑫𝑰 𝒂𝒓𝒆 𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕 𝒆𝒙𝒑𝒍𝒂𝒏𝒂𝒕𝒐𝒓𝒚 𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝒇𝒐𝒓 𝑩𝑳𝑻 𝒇𝒐𝒓 𝒕𝒉𝒆 𝒔𝒆𝒍𝒆𝒄𝒕𝒆𝒅 𝑨𝒇𝒓𝒊𝒄𝒂𝒏 𝒄𝒐𝒖𝒏𝒕𝒓𝒊𝒆𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒔𝒆𝒍𝒆𝒄𝒕𝒆𝒅 𝒕𝒊𝒎𝒆 𝒔𝒆𝒓𝒊𝒆𝒔


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

Cu

rren

t b

illio

n U

S$

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

Ob

serv

ati

on

s

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

Ob

serv

ati

on

s

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

Cu

mu

lati

ve

per

cen

tag

e g

row

th p

op

ula

tio

n

Po

pu

lati

on

in

mil

lio

ns

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

10B

illi

on

US

$

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%

Cu

mu

lati

ve

per

cen

tag

e g

row

th

Yea

rly

per

cen

tag

e g

row

th

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

Cu

rren

t b

illi

on

US

$

Year

Fig 14. South Africa GDP 2000 and 2005

42

43

44

45

46

47

48

2000 2005Po

pu

lati

on

in

mil

lio

n

Year

Fig. 15 South Africa Population 2000 and

2005

0

1

2

3

4

5

6

2000 2005

bil

lio

n U

S$

Year

Fig. 16 South Africa 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

Fig. 18 South Africa 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.


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

Cu

rren

t b

illi

on

US

$

Year

Fig 19. Algeria GDP 2000 and 2005

30

31

32

33

34

2000 2005Po

pu

lati

on

in

mil

lio

n

Year

Fig 20. Algeria Population 2000 and

2005

0.0

0.1

0.2

0.3

0.4

0.5

0.6

2000 2005

bil

lio

n U

S$

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






21


Country profile Benin 2000 and 2005

0

1

2

3

4

5

2000 2005

Cu

rren

t b

illi

on

US

$

Year

Fig. 24 Benin GDP 2000 and 2005

6.0

6.5

7.0

7.5

8.0

8.5

2000 2005Po

pu

lati

on

in

mil

lio

n

Year

Fig. 25 Benin Population 2000 and

2005

0.00

0.05

0.10

0.15

0.20

0.25

0.30

2000 2005

bil

lio

n U

S$

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 Diversity Index 2000

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






22


Country profile Central African Republic 2000 and 2005

0.0

0.5

1.0

1.5

2000 2005Cu

rren

t b

illi

on

US

$

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 2005Po

pu

lati

on

in

mil

lio

n

Year

Fig. 30 Central African Republic Population

2000 and 2005

0.00

0.01

0.02

0.03

0.04

2000 2005

bil

lio

n U

S$

Year

Fig. 31 Central African Republic total Bilateral Trade 2000 and 2005

0.20

0.40

0.60

0.80

1.00

2000 2005

Year

Fig. 32 Central African Republic Language

Diversity Index 2000 and 2005

0.49

0.50

0.51

0.52

0.53

0.54

2000 2005

Year

Fig. 33 Central African Republic Religious

Diversity Index 2000 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






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

GD

P

BLT

Fig. 34 Correlation African sample GDP and BLT 2000 and 2005 No 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

PO

P

BLT

Fig. 35 Correlation African sample POP and BLT 2000 and 2005 No 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

LD

I

BLT

Fig 36. Correlation African sample LDI and BLT 2000 and 2005 No 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

RD

I

BLT

Fig. 37 Correlation African sample RDI and BLT 2000 and 2005 No 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

BL

T

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

BL

T

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

BL

T

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

BL

T

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



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


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



Conclusion (5/5)

33


GDP

Population


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:

𝑯𝟎 : 𝑮𝑫𝑷, 𝑷𝑶𝑷, 𝑳𝑫𝑰 𝒂𝒏𝒅 𝑹𝑫𝑰 𝒂𝒓𝒆 𝒊𝒏𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕 𝒆𝒙𝒑𝒍𝒂𝒏𝒂𝒕𝒐𝒓𝒚 𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝒇𝒐𝒓 𝑩𝑳𝑻 𝒇𝒐𝒓 𝒕𝒉𝒆 𝒔𝒆𝒍𝒆𝒄𝒕𝒆𝒅 𝑨𝒇𝒓𝒊𝒄𝒂𝒏 𝒄𝒐𝒖𝒏𝒕𝒓𝒊𝒆𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒔𝒆𝒍𝒆𝒄𝒕𝒆𝒅 𝒕𝒊𝒎𝒆 𝒔𝒆𝒓𝒊𝒆𝒔

𝑯𝟏: 𝑮𝑫𝑷, 𝑷𝑶𝑷, 𝑳𝑫𝑰 𝒂𝒏𝒅 𝑹𝑫𝑰 𝒂𝒓𝒆 𝒔𝒊𝒈𝒏𝒊𝒇𝒊𝒄𝒂𝒏𝒕 𝒆𝒙𝒑𝒍𝒂𝒏𝒂𝒕𝒐𝒓𝒚 𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔 𝒇𝒐𝒓 𝑩𝑳𝑻

𝒇𝒐𝒓 𝒕𝒉𝒆 𝒔𝒆𝒍𝒆𝒄𝒕𝒆𝒅 𝑨𝒇𝒓𝒊𝒄𝒂𝒏 𝒄𝒐𝒖𝒏𝒕𝒓𝒊𝒆𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒔𝒆𝒍𝒆𝒄𝒕𝒆𝒅 𝒕𝒊𝒎𝒆 𝒔𝒆𝒓𝒊𝒆𝒔

Although the GDP and POP indicated significant positive impact, the model suffers from heteroskedacity. The diversity measures LDI, RDI were insignificant. Therefore 𝑯𝟎 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

http://stats.oecd.org/Index.aspx?DataSetCode=BTDIXE_I4



















http://data.worldbank.org/indicator/NY.GDP.MKTP.CD














http://data.worldbank.org/indicator/SP.POP.TOTL












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

Documents

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