BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
London Metropolitan Business School
Economics Subject Group
“AN INVESTIGATION INTO THE
EFFECT OF AID ON GROWTH IN
SUB-SAHARAN AFRICA”
Basil Osman El-Khawad
April 2011
This project is submitted in part fulfilment of the requirement for a BA Economics
degree at the London Metropolitan University. This work is the sole responsibility of
the candidate.
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BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
Contents Page
1.0 Chapter One: Introduction
Overview
Background
1.1 Economic Aid
1.2 Macroeconomic Policy
1.3 Sub-Saharan Africa
1.4 Conclusion to Background
1.5 Project Aim
1.6 Project Objectives
1.7 Research Area
1.8 Justification for the Research
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2.0 Chapter Two: The Literature Review 11
Introduction
2.1 Aid and Economic Growth
2.2 Aid, Policies and Growth
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3.0 Chapter Three: Methodology
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Introduction
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3.1 Panel data Method
3.2 Pooling Assumption
3.3 Advantages of Panel data models
3.4 Linear Panel data model
3.5 Estimation of the model
3.6 Resources needed to undertake methodology
3.7 Empirical Model 1
3.8 Empirical Model 2
3.9 Data
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3.10 Methodological and data challenges
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4.0 Chapter Four: Results and Analysis
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Introduction
4.1Results: Eview estimation output: Model 1
4.2 Results: Eview estimation output: Model 2
4.3 Diagnostic tests: Model 1
4.4 Diagnostic tests: Model 2
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BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
4.5 T-tests
4.6 T-tests: Model 1
4.7 T-tests: Model 2
4.8 Analysis
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5.0 Chapter Five: Conclusions &
Recommendations
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Introduction
5.1 Research Objectives: Objective 1
5.2 Research Objectives: Objective 2
5.3 Research Objectives: Objective 3
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6.0 Appendices
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6.1 Appendix 1
6.2 Appendix 2
6.3 Appendix 3
6.4 Appendix 4
6.5 Appendix 5
6.6 Appendix 6
6.7 Appendix 7
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7.0 References
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6.0 Bibliography
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Word count: 8,775
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BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
Acknowledgements
I would like to thank the Economics Project Coordinator and my supervisor, Dr Helen
Solomon for all her assistance with my project. I would also like to thank my personal
academic advisor Valerio Lintner, I hope that I have lived up to his expectations of me. I also
want to express my deep respect for William Easterly, Professor of Economics at New York
University, whose work inspired me to carry out this project. I am indebted to Economist
Dambisa Moyo, a fellow African whose work provided me with the background knowledge
necessary to carry out this research.
My supervisor, Dr Helen Solomon, has been an asset to me, giving me her invaluable time
and effort to guide me through this maze. Thanks to her patience I have reached the end.
Finally, thanks to all my family and friends who have supported me and kept me sane along
the way. Most importantly, I would like to thank my mother and father, my role models, who
have always pushed me to work harder and who constantly reassure me that I can do anything
I set my mind to, no matter how many hardships I encounter.
I dedicate this project to Africa. May your turn come next.
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BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
1.0 Chapter One: Introduction
Overview
20,000 children died today of hunger related causes; most of those deaths were preventable.
Imagine if 20,000 human beings were killed in a terrorist attack. The world would be shocked
and would be taking measures either through policy or direct action to make sure it never
happened again. The story would be on the front of every newspaper and television
programmes would be interrupted to broadcast the breaking news. The world would come to
a standstill. Nevertheless, 20,000 children die every day and hardly anyone in the world even
realises it. Disproportionately those children are in Sub-Saharan Africa. Since 1970 the
continent has received over $300 Billion in development assistance in the form of aid.
Despite this, the region is engulfed in a continuous loop of disease, corruption, poverty, and
instability to name a few of its ills but mainly it suffers from sluggish economic.
“Why is it that Africa, alone among the continents of the world, seems to be locked into a
cycle of dysfunction? Why is it that out of all the continents in the world Africa seems unable
to convincingly get its foot on the economic ladder? Why in a recent survey did seven out of
the top ten ‘failed states’ hail from that continent? Are Africa’s people universally more
incapable? Are its leaders genetically more venal, more ruthless, more corrupt? Its policy
makers more innately feckless? What is it about Africa that holds it back, that seems to
render it incapable of joining the rest of the globe in the twenty-first century? The answer has
its roots in aid.”(Moyo 2009, P. 6).
The main aim of this project is to examine the effect aid has on growth in Sub-Saharan
Africa. There is an old Texan saying that states that “the man with the gold makes the rules”.
However, in academia it is the evidence that glitters. Is aid the disease or is it the cure?
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BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
Background
1.1 Economic Aid
Economic aid is defined as, “Economic assistance from one country to another, the recipient
typically being a less developed country. Aid is usually intended either to provide
humanitarian relief in emergencies, to promote economic development, or to finance military
relief in emergencies.”(Black, Hashimzade, Myles 2009, P. 9).
This project is not concerned with emergency support aid, but the huge amounts of transfers
to African governments that are usually concessional loans or grants from rich countries and
international institutions such as the IMF. The loans are borrowed at low interest rates and
tend to be below the market interest rates and have quite long lending life spans.
Development aid in the past was an insignificant concern for governments and was
administered through small departments within governments and by NGOs. To compare in
today’s context, development issues, in particular aid, has become a central policy of world
leaders and has been given priority in UN summit and G8 discussions. Some may argue this
is due to the vast flows that make their way into the developing world. The revival of aid has
given way to old questions that surrounded the debate on development aid.
What leads policy makers to assume that the higher volumes of aid packages today will bring
about the economic growth yesterday’s flows failed to achieve? Is it merely a question of
larger packages that will have the desired impact? Do developing economies have the
competence and discipline to absorb more aid without facing the same traps they fell into
over the last half century? On the other side of the fence, recipients are asking if they can rely
on the promises and prospects of poverty reduction.
It has been recognised that aid should be increased and that more assistance in the form of
grants rather than loans should be administered by industrial countries. In March 2002 at the
International Conference of Financing Development, 50 heads of state made a pledge to
increase foreign assistance to 0.7 % of their GDP, however this has yet to become a reality.
There has been mixed feeling in the support for aid over the last 50 years. Aid has been
repackaged and resold after a series of uncertainties, suspicions and fears over its nature. Its
goals have been changed to counter the scrutiny that surrounds it.
“Towards the end of the Cold War aid levels reached their lowest for four decades” (Riddell
2007, P. 2). At the turn of the millennium aid levels rose again and today it seems set for a
revival. The growing fascination of today’s politicians with ‘poverty reduction’ has resulted
in aid being given a new standing on the international development arena. Another example
is the June 2005 G8 summit where the world’s leading economies pledged to increase aid in
order to achieve the UN Millennium Development Goals. In that year alone overall aid
provided by those nations reached $100bn.
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The legend of aid began in July 1944 at a meeting held in Bretton Woods, New Hampshire,
USA. Within the setting of the Second World War leaders from 44 countries decided to
establish a global system of financial and monetary management. “Post-war aid can be
broken down into seven broad categories: its birth at Bretton Woods in the 1940s; the era of
the Marshall Plan in the 1950s; the decade of industrialisation of the 1960s; the shift towards
aid as an answer to poverty in the 1970s; aid as the tool for stabilization and structural
adjustment in the 1980s; aid as a buttress of democracy and governance in the 1990s;
culminating in the present-day obsession with aid as the only solution to Africa’s myriad of
problems.” (Moyo 2009, P. 10). However, there were attempts before such as the 1929
Colonial Development Act where the British Government administered grants to its former
subjects to aid them in infrastructure projects.
In the 1944 Bretton Woods meeting it was decided that for Europe to re-establish its social,
political and economic might, aid must be thrown in its direction. However, Europe was not
to be born out of this assistance; it was only to be aided to recovery for its already standing
physical, legal and social infrastructure that purely needed fixing. The World Bank and the
International Monetary Fund were the organizations that were born out of the Bretton Woods
agreement. These two giants would later take centre stage in the international development
dialogue. The World Bank was designed to aid capital for reconstruction and the IMF to
supervise the global financial system.
The transfer of Aid had began mainly from the US to Europe. In 1947 a radical proposal was
made by the US that it would provide $100 billion (in today’s terms) to a devastated Europe.
This was known as “the Marshal Plan”, as in return for the money, Europe had to draw up an
economic revival plan. This huge inflow of capital not only put Europe back on track but
provided the United States of America with a strong role in any future multilateralism.
From 1940 onwards 1 trillion US Dollars has been transferred in the form of aid (grants and
loans) from rich countries to African countries. Why hasn’t this enormous amount in
development assistance made African people better off? Foreign aid is pivotal in
development policy across the globe. The main aim of this project is to access the effect of
aid on economic growth and investigate whether sound macroeconomic policies boost its
impact.
1.2 Macroeconomic Policy
The term ‘macro’ denotes the ‘aggregate’, and in this case ‘macroeconomic’ is the aggregate
economic behaviour. This could be the levels of growth, inflation and unemployment. In the
case of this project the term Policy will refer to fiscal, monetary and trade policies. These
parameters make up what will be referred to as ‘good policies’. But in laymen terms they
represent whether a country is living within its means.
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1.3 Sub-Saharan Africa
Sub-Saharan Africa is a term that describes a geographical location on the African continent.
In general, it describes the region that lies south of the Sahara desert. It excludes the North
African region which is deemed to be part of the Arab world. Although sharing the same
continent, North African countries have had different experiences than Sub-Saharan Africa
since independence. However, Africa as a whole has showed through past and present
economic experience that plenty of land and natural resources endowment may not guarantee
long term economic growth and prosperity. Despite its rich history, Sub-Saharan Africa is
today without a shadow of a doubt the poorest region on the planet. After a succession of
European interventions was dismantled and left to fend for itself in the wake of its
distruction. During the last half century of European colonialist rule, Africa as a whole
experienced faster economic growth than Asia. As the mid 20th century dawned,
independence was on the horizon and self determination was no longer a thing of the past.
Between 1960-73 GDP growth was even greater than during colonialism but during the 70s it
is safe to say that “everything collapsed”. Both economic and political matters buckled.
Military coup d’états were as widespread as malaria across Sub-Saharan Africa. Democratic
‘nation states’ disappeared and were replaced by dictatorships and autocracies. Despite the
presence of oil booms in the 70s, countries like Nigeria could not help but move away from
the path of economic growth and development. The story since has been that of torment at the
hands of economic mismanagement, corruption and conflict as well as external implications.
Since the 1980s GDP per capita in Sub-Saharan Africa has been declining at an average rate
of 1% per year across 32 nations.
1.4 Conclusion to Background
Injecting an infinite flow of capital such as aid into an economy is not a simple mission.
Many diverse considerations and evaluations need to be made before such a choice is made.
The very first aspect to be considered is whether or not an actual need for it exists. The
lowest income region in the world, Sub-Saharan Africa (according to the World Bank) has
always been viewed as a needy region. The second is to test whether the donation will have
the desirable effect on growth. The third is to test whether the donation will have the
desirable effect on growth in the presence of sound macroeconomic policies. Many
researchers have in fact found that Foreign Aid has not produced the growth that donors
profess will result even in a ‘good policy’ environment.
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1.5 The Project Aim
Therefore the research aim of this project is to:
“Investigate the impact of aid on growth in Sub-Saharan Africa” and in turn provide
any recommendations.
1.6 Project Objectives
Objective 1: To examine the “effectiveness” of the claim that aid increases GDP growth.
Objective 2: To investigate the possibility that the presence of ‘good policies’ boosts the
impact of aid in a recipient country.
Objective 3: To establish some recommendations for development strategy with regards to
aid.
1.7 Research Area
This study will be linked to development economics and its different theories.
1.8 Justification for the Research
After the research has been conducted, the answers to the technical merit of aid as an
instrument to engineer a solution to poverty will be evaluated and in turn aid agencies may
then prepare to form a plan and strategy for the future of financial assistance as a solution to
underdevelopment. It is crucial today, especially in a time where governments are making
spending cuts in order to close their deficits, to look deeply into the question whether
increasing foreign aid is the means to the end of poverty.
The need to test the relationship between foreign aid and growth (GDP) is essential in this
day and age because of the ongoing debate on the issue. There has been certainty since the
1950s that countries need an aid-financed push to propel them to a takeoff into self-sustained
growth and there on in, aid will not be needed. “The idea that ‘aid buys growth’ is an integral
part of the founding myth and ongoing mission of the aid bureaucracies.”(Meir, Rauch 2005,
P. 315). The figure below may point in that direction.
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2.0 Chapter Two: The Literature Review
Introduction
This chapter will examine the literature that is appropriate to the research topic. During the
second half of the 20th century there was much questionable literature on the links between
aid and economic growth. This was due to the limited availability of data. There was also a
lot of confusion about the different ways aid would affect growth. Therefore findings were
not as robust as they could have been.
In the last few years there has been a growing consensus emerging stating that foreign aid
does work. It is the case that empirical investigations have proved that foreign aid does in
fact have a positive effect on growth. On the other hand, a debate has always been present
due to the evidence that aid is not effective everywhere. An important question is what causes
these disparities in the results of the returns to aid? In terms of policy making this is a key
concern as it would influence the distribution and allocation of foreign aid across regions.
2.1 Aid and Economic Growth:
During my research I found no real theoretical model that shows how aid influences growth.
This is why there is no real sound empirical condition of the aid-growth relationship. Chenery
and Strout (1966) “two-gap” model could be said to be the first attempt at explaining the
relationship. The model goes on to show that foreign aid fills two gaps in an economy and
boosts growth. The first gap is between domestic savings and the level of investment needed
to bring about a certain rate of GDP per capita growth. The second gap is between required
level of imports to attain a level of production and foreign exchange earnings. Chenery and
Strout were inspired by the early work of Walt Rostow (1960) who boasted that aid that
finances investment would propel economies into achieving self sustained growth.
This is where the idea that “aid buys growth” comes from. The suggestion that by financing a
gap in an economy to increase investment will in turn increase economic growth is the model
that stands strong till this day. Institutions such as the World Bank use this model to justify
aid policy. Easterly (2001) tested the model that aid develops investment and as a result
growth. He found that the theoretical and empirical foundations were questionable. The
economist, Boone (1996), found that aid financed consumption as opposed to investment.
This was significant because increasing the consumption of a small proportion of the
population is not the desired outcome of aid donors who seek an increase in investment that
would boost GDP per capita growth.
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Some donors are now giving aid only to countries that do well in particular parameters of
their policy making. This new movement towards aid allocation has been inspired by the
work of economists such as Burnside and Dollar (2000) and Collier and Dollar (2001). Their
research pointed them in the direction to conclude that foreign aid works best in countries
that have ‘good policies’. It is important to maximise the effectiveness of tax financed
international assistance for reasons such as efficiency. In today’s context where governments
are tightening their purse strings in order to close their budget deficits this is important. On
the other hand, if the effectiveness of aid in the long run is not brought about through ‘good
policies’ then a different rule would have to be adopted to maximise the returns to aid.
2.2 Aid, Policies and Growth:
Economists, Burnside and Dollar produced a paper that was publicised in the ‘American
Economic Review. The two authors investigated the relationship between GDP per capita
growth, economic policy and foreign aid. They found that foreign aid and economic policy
had a positive relationship. In the paper they state, (p.847) “We find that aid has a positive
impact on growth in developing countries with good fiscal, monetary, and trade policies but
has little effect in the presence of poor policies.”
Their research met high academic standards and has formed a foundation for future research
and expansion on their findings. Nevertheless, their research was used to justify policy
changes to increase foreign aid to countries that had a good policy environment. This was
done without further testing their thesis by expanding the dataset or including different
parameters to see their effects on GDP per capita growth. International aid agencies used the
results of their findings and even published them to justify new aid strategies, e.g. the World
Bank’s Assessing Aid (1998) and the British Department for International Development
(2000). These organisations insisted that development assistance can bring about poverty
reduction where governments practise “good policies”. Burnside and Dollar’s research was
used as a strong body of evidence in debates on the usefulness of foreign aid boosts growth
when regions have sound economic policies (represented by low budget deficits, low
inflation and free trade).
As stated before, their hypothesis was that aid affects growth. However its impact was
conditional to policies that affect growth. “Poor countries with sound economic policies
benefit directly from the policies, and in this environment aid accelerates growth. In highly
distorted economies, however, aid is dissipated in unproductive government expenditure.”
(Burnside, Dollar 2000, P. 847) . They used the framework of neoclassical growth models
and interpreted foreign aid as an income transfer that may produce growth depending on how
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BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
it is used. This is dependent on whether it is invested or consumed. If it is invested, then it
would be effective. On the other hand, if it is consumed, it would be useless.
“A lump sum gift of aid should have a positive effect on growth, which would be transitory
if there were diminishing returns to capital. If there were policies that affected growth,
however, they would also affect the extent to which a gift of aid is used productively. Hence,
if aid is added to the growth equation, it should be interacted with policies.” (Burnside, Dollar
2000, P. 849). They rationalised that the incentive to invest aid is influenced by “policy
distortions”. Therefore, Burnside and Dollar went on to develop that a country’s growth rates
will depend on income, policy distortions, aid, and aid interacted with distortions.
To undertake the empirical investigation of their hypothesis they used a dataset on foreign aid
from the World Bank, formed a policy index and pooled the data with regards to 56 countries
over a period of 24 years. The evidence from their estimation proved their hypothesis. There
were many studies that were just as scientific as Burnside and Dollar’s. However, the policy
community chose their findings as the body of evidence that would work in favour of aid
agencies.
Many papers have been published to investigate Burnside and Dollar’s (2000) findings. Some
of these include Hansen and Tarp (2001), Dalgaard and Hansen (2001), Collier and Dehn
(2001), Collier and Dollar (2002), Lensink and White (2001) and Guillamont and Chauvet
(2001). These particular research papers undertook different variations on the Burnside and
Dollar approach but also introduced other parameters such as terms of trade. Some of these
papers coincided with Burnside and Dollar’s results. However, some found that when adding
other parameters the interaction between aid and policy becomes insignificant.
Easterly, Levine and Roodman (2003) used the exact same approach as Burnside and Dollar
(2000). However, they added more data to the dataset and undertook the same regression.
Their findings led them to conclude that aid does not work in a “good policy” environment
and in fact the coefficient of the critical interaction term between aid and policy was
statistically insignificant.
Instead of discussing each and every one of the different approaches and studies; I will focus
on the Burnside and Dollar (2000) approach. I will undertake a similar regression but I will
focus on the region of Sub-Saharan Africa.
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3.0 Chapter Three: Methodology
Introduction
In tackling my research problem I plan to use econometric analysis. Econometrics allows
economists to undertake the quantification and evaluation of economic hypothesis and
theories. A theory may suggest that there is a relationship between variables that cause
feedback to an outcome, econometrics provides the ability and means to proving a theory is a
real one through evidence using actual data using statistical and mathematical techniques.
The starting point is a theory from which an econometric model is constructed. The next step
is to collect data for the variables in the model and estimate the equation for analysis. Due to
the nature of my data sample I will be using two panel data models.
3.1 Panel data Method
Panel data is an efficient analytical method in handling data similar to my sample. It allows
data for N cross sections and T time periods, including time series for every cross section
value in a data set, by pooling data into one data set and setting common variables across
them. It is popular among social scientists as it allows one to undertake a number of
estimation methods, and observe the effects of cross sectional data and their developments
over time.
3.2 Pooling Assumption
The main assumption of panel data analysis is that individual relationships have the same
parameters by pooling all observations together and enforcing common parameters for them.
This is known as the pooling assumption.
3.3 Advantages of Panel data models
a. A sample size can be increased significantly resulting in better estimates of parameters.
b. Issues with biased estimates in a single regression can be eliminated by using panel data.
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BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
3.4 Linear panel data model
𝑌𝑖𝑡 = 𝑎𝑖 + 𝛽𝑋𝑖𝑡 + 𝑢𝑖𝑡
Key:
Y and X ; have i = 1,2,....,N sections and t = 1,2,.....,T time periods.
αi ; differs for each cross section in the sample.
β ; will be the same for all units and for all years. (This is the main assumption of the panel
data estimation).
3.5 Estimation of the model
There are three methods1. The common constant method
2. The fixed effects method
3. The random effects method
1. The common constant method (pooled OLS method):
This method estimates a common intercept term (α) for all cross section values. It is useful
when the data set is a-priori homogeneous. An example of this is when we are dealing with
region or classification specific data).
2. The fixed effect method:
Also known as the least-squares dummy variables estimator, this method the constant or
intercept term is group specific for cross section data, so section values can be grouped and
allocated a different constant. A dummy variable is included for each group in order to allow
various constants for each group.
This method allows us to analyse the effects specific to each group depending on geographic
location or any other category.
3. The random effects method:
This is similar to the fixed effect method but it handles the constants (α) for each cross
section as a random variable. In other words the random effect model assumes each cross
section has a different error term;
𝛼𝑖 = 𝛼 + 𝑣𝑖
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BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
3.6 Resources needed to undertake methodology
Software: Eviews (available on campus provided by London Metropolitan Business
School). Eviews is generally used for statistical and econometric analyses, such as crosssection, panel data and time analysis.
3.7 Empirical Model 1
The empirical investigation will attempt to answer the following question:

What is the nature of an effect of aid on growth?
𝑔
𝑔𝑖𝑡 = 𝛽0 + 𝑎𝑖𝑡 𝛽𝑎 + ∈𝑖𝑡
Key:
i
: indexes countries
t : indexes time
𝑔𝑖𝑡 : GDP per capita growth
𝛽0
: Intercept term
𝑎𝑖𝑡 : Aid receipts relative to GDP
𝑔
∈𝑖𝑡 : Error term
The model above is to find the impact of foreign aid on growth. My equation includes 𝑎𝑖𝑡 ,
being the level of aid in proportion to GDP received by country i in time period t and it also
includes 𝑔𝑖𝑡 , which is GDP per capita growth by country i during the time period t expressed
as a percentage.
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3.8 Empirical Model 2
The empirical investigation will attempt to answer the following question:

What is the nature of an effect of aid on growth in the presence of good policies?
𝑔
𝑔𝑖𝑡 = 𝛽0 + 𝑎𝑖𝑡 𝛽𝑎 + 𝑝𝑖𝑡 𝛽𝑝 + 𝑎(𝑝𝑖𝑡 ) 𝛽𝑎𝑝 + ∈𝑖𝑡
Key:
i
: indexes countries
t : indexes time
𝑔𝑖𝑡 : GDP per capita growth
𝛽0 : Intercept term
𝑎𝑖𝑡 : Aid receipts relative to GDP
𝑝𝑖𝑡 : Policies that affect growth
𝑎(𝑝𝑖𝑡 ) : an interaction term between aid and policy
𝑔
∈𝑖𝑡 : Error term
The model above is similar to Burnside and Dollar (2000) growth equation. Since I seek to
find the impact of foreign aid on growth in the presence of ‘good policies’ my equation
includes 𝑎𝑖𝑡 , being the level of aid in proportion to GDP received by country i in time period
t. To test the theory that macroeconomic policies affect growth therefore I included 𝑝𝑖𝑡 which
is in actual fact a macroeconomic management rating (1=low to 6=high).
To estimate the equations above (1) (2) I will use ordinary least squares (OLS), the method to
estimate the unknown parameters in the above linear regression model. This regression is an
approach to modelling the relationship of one or more variables so that the model depends
linearly on the parameters that need to be estimated from the data. As this is panel data it will
be the common constant method (pooled OLS method). This is due to the fact that I will be
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using priori homogeneous data set; this is where there is no difference between the estimated
cross sections because our sample consists of Sub-Saharan African countries.
In general, a linear regression model can be stated as:
𝑌𝑖𝑡 = 𝑎𝑖 + 𝛽𝑋𝑖𝑡 + 𝑢𝑖𝑡
In this model there is assumption there is feedback from X to Y, which is that Y changes and
X does not. We also assume that the relationship between the parameters is linear. We
assume that X values are independent of the error (disturbance term).
In the case of this assignment we are trying to find the relationship between aid,
macroeconomic policy, and GDP per capita growth. When constructing models firstly we
must find the dependent variable & independent variable that is the regressant and regressors
or endogeneous and exogenous variable respectively. In these models GDP growth is
dependent and the right side of the equation is independent.
𝑔
𝑔𝑖𝑡 = 𝛽0 + 𝑎𝑖𝑡 𝛽𝑎 + ∈𝑖𝑡
𝑔𝑖𝑡 = 𝛽0 + 𝑎𝑖𝑡 𝛽𝑎 + 𝑝𝑖𝑡 𝛽𝑝 + 𝑎(𝑝𝑖𝑡 ) 𝛽𝑎𝑝 + 𝑢𝑖𝑡
(equation 2)
3.9 Data
I will be using secondary data. My data will be collected from the World Bank Statistics
online. (http://databank.worldbank.org)
I will be looking at the time period of 2005-2009 that is a total of 5 years. My Cross sectional
values will strictly look at a sample of Sub-Saharan Africa according to the World Bank’s
geographic classifications. I will be analysing a total of 37 countries from the region (see
appendix 1). There are 48 countries in the region according to the classification but due to
missing data, 11 countries were excluded from the dataset. I will be using the following
variables; aid receipts as a percentage of GDP, GDP growth per capita %, (see appendix 2),
Aid as a percentage of GDP (see appendix 3) and a policy variable (see appendix 4); this is a
combination of CPIA economic management cluster average and CPIA macroeconomic
management rating. These both capture a rating or index of monetary and fiscal policy. All of
the data is collected from the World Bank databank. These variables will enable me to
construct and estimate a model similar to Burnside and Dollar’s. My dataset has a total of 555
observations. (See appendix for data tables.)
17
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
3.10 Methodological and data challenges
The investigation into the effect of aid on growth rates is only as good as the data which it is
based upon. The problems that may arise are due to the accuracy of data.
With regards to aid there are big problems with the accuracy of data on levels of aid. “There
are serious questions about the accuracy of some official aid data, and usually significant
differences between amounts of ODA recorded by donors and the amounts of aid that
recipient governments receive.”(Riddel 2007, P. 166). It can also be noted that aid figures do
not include emergency aid and NGO activities funded by private sources. It is also important
to reason that the impact of aid is not an instantaneous result and may take a significant time
period before any real results become apparent.
There are many shortcomings when it comes to the measure of GDP growth; there are
different methods and generally it is accepted that the figures are always inaccurate. This is
the case in industrialised countries let alone in developing nations where there are very poor
levels of national account statistics. Even a parameter such as a policy rating is not accurate
at all and is based on many assumptions and hearsay.
These types of shortcomings in data are the reason why no economist can make any bold
statement about the effect of aid on growth especially from a cross-country point of view.
18
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
4.0 Chapter Four: Results and Analysis
Introduction
This chapter will present the research findings with regards to both econometric models that
were constructed to complete the projects objectives (1) and (2):
Objective 1: To examine the “effectiveness” of the claim that aid increases GDP growth.
Objective 2: To investigate the possibility that the presence of ‘good policies’ boost the
impact of aid in a recipient country.
The first section will present the findings of the econometric estimation for both models,
followed by diagnostic tests of the models and finally an analysis of the results will follow.
Note: The results of the panel data method of estimation of my model was carried out using
Eviews as stated before. I estimated equation (1) (2) using the method of pooled least
squares (The common constant method). As stated before I justify using this method because
I used a priori homogeneous data set. The output of the estimation is as below.
19
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
Results:
4.1 Eviews Estimation output: Model 1
Dependent Variable: Y_?
Method: Pooled Least Squares
Date: 03/17/11 Time: 12:50
Sample: 2005 2008
Included observations: 4
Cross-sections included: 37
Total pool (balanced) observations: 148
White period standard errors & covariance (d.f. corrected)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
A_?
-3.980294
6.028126
-0.660287
0.5101
C
3.168534
1.103555
2.871206
0.0047
R-squared
0.006331
Mean dependent var
2.678686
Adjusted R-squared
-0.000475
S.D. dependent var
4.454867
S.E. of regression
4.455924
Akaike info criterion
5.839767
Sum squared resid
2898.868
Schwarz criterion
5.880270
Log likelihood
-430.1428
Hannan-Quinn criter.
5.856224
F-statistic
0.930250
Durbin-Watson stat
0.919923
Prob(F-statistic)
0.336393
20
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
4.2 Eviews Estimation output: Model 2
Dependent Variable: Y_?
Method: Pooled Least Squares
Date: 03/19/11 Time: 15:44
Sample: 2005 2008
Included observations: 4
Cross-sections included: 37
Total pool (balanced) observations: 148
White period standard errors & covariance (d.f. corrected)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
A_?
-0.335023
19.17969
-0.017468
0.9861
P_?
2.158869
1.138318
1.896543
0.0599
C
-4.660746
4.320438
-1.078767
0.2825
A_?*P_?
-0.064329
5.867656
-0.010963
0.9913
R-squared
0.160477
Mean dependent var
2.678686
Adjusted R-squared
0.142987
S.D. dependent var
4.454867
S.E. of regression
4.124093
Akaike info criterion
5.698224
Sum squared resid
2449.172
Schwarz criterion
5.779230
Log likelihood
-417.6686
Hannan-Quinn criter.
5.731137
F-statistic
9.175325
Durbin-Watson stat
1.045477
Prob(F-statistic)
0.000014
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BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
Diagnostic tests
4.3 Model 1
From the results the estimated model (equation 1) is as follows.
𝑔𝑖𝑡 = 3.168534 − 3.980294𝑎𝑖𝑡 + 𝑢𝑖𝑡
t :
(2.87)
Prob :
(0.0047)
(-0.66)
(0.51)
𝑅2 = 0.16
4.4 Model 2
From the results the estimated model (equation 2) is as follows.
𝑔𝑖𝑡 = − 4.660746 − 0.335023𝑎𝑖𝑡 + 2.158869 𝑝𝑖𝑡 − 0.064329𝑎(𝑝𝑖𝑡 ) + 𝑢𝑖𝑡
t :
Prob :
(-1.08)
(-0.02)
(1.90)
(0.01)
(0.28)
(0.99)
(0.06)
(0.99)
𝑅2 = 0.16
4.5 T- tests:
By using t- tests we can conclude some things from the estimated model (equation 1). A ttest is a statistical hypothesis where we test a hypothesis and if it is true. It is used when the
test statistic follows a normal distribution. The t statistic is defined mathematically as
𝛽~− 𝛽
t= 𝑠.𝑒.(𝛽)
note: s.e. being the standard error.
Using Eviews I deducted the P value to help me make conclusions on the tests. The P value
gives the significance level at which a null hypothesis is accepted or rejected.
I wish to state beforehand that the level of significance I use is 5% throughout the testing.
Therefore I intend to reject Null Hypothesis and accept alternative if P value is less than the
statistic I test.
22
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
4.6 T- tests: Model 1
t-test for (aid) βa: Level of significance 5% (0.05)
Is Aid influencing output?
P value= 0.51 (Eviews estimation)
𝐻0 : β1 = 0 This would Imply that aid βa has no consequence on GDP growth
𝐻1 : β1 ≠ 0 This would Imply that aid βa has a consequence on GDP growth
We can now conclude as shown above the P value is more than the 0.05 therefore we accept
𝐻0 and reject 𝐻1 .
4.7 T- tests: Model 2
t-test for (α) β0: Level of significance 5% (0.05)
Is the constant influencing output?
P value= 0.28 (Eviews estimation)
𝐻0 : β0 = 0 This would Imply that the constant β0 has no consequence on GDP growth
𝐻1 : β0 ≠ 0 This would Imply that the constant β0 has a consequence on GDP growth
We can now conclude as shown above the P value is more than the 0.05 therefore we accept
𝐻0 and reject 𝐻1 .
t-test for (aid) βa: Level of significance 5% (0.05)
Is Aid influencing output?
P value= 0.99 (Eviews estimation)
𝐻0 : β1 = 0 This would Imply that aid βa has no consequence on GDP growth
𝐻1 : β1 ≠ 0 This would Imply that aid βa has a consequence on GDP growth
23
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
We can now conclude as shown above the P value is more than the 0.05 therefore we accept
𝐻0 and reject 𝐻1 .
t-test for (Policy distortion) βp: Level of significance 5% (0.05)
Is Policy influencing output?
P value = 0.06 (Eviews estimation)
𝐻0 : β2 = 0 This would Imply that policy βp has no consequence on GDP growth
𝐻1 : β2 ≠ 0 This would Imply that policy βp has a consequence on GDP growth
We can now conclude as shown above the P value is more than the 0.05 therefore we accept
𝐻0 and reject 𝐻1 .
t-test for (aid and policy interaction) βap: Level of significance 5% (0.05)
Is the interaction between aid and policy influencing output?
P value = 0.99 (Eviews estimation)
𝐻0 : β2 = 0 This would Imply that aid x policy βap has no consequence on GDP growth
𝐻1 : β2 ≠ 0 This would Imply that aid x policy βap has a consequence on GDP growth
We can now conclude as shown above the P value is more than the 0.05 therefore we accept
𝐻0 and reject 𝐻1 .
24
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
4.8 Analysis
The purpose of this assignment was to try and find if there is relationship between aid,
macroeconomic policy, and GDP per capita growth in Sub-Saharan Africa.
Objective 1: To examine the “effectiveness” of the claim that aid increases GDP growth.
𝑔𝑖𝑡 = 3.168534 − 3.980294𝑎𝑖𝑡 + 𝑢𝑖𝑡
(Eq. 1)
Equation (1) tackles objective (1) .The results from the estimation of equation (1) show that
aid has a negative effect on GDP per capita growth
Objective 2: To investigate the possibility that the presence of ‘good policies’ boost the
impact of aid in a recipient country.
𝑔𝑖𝑡 = − 4.660746 − 0.335023𝑎𝑖𝑡 + 2.158869 𝑝𝑖𝑡 − 0.064329𝑎(𝑝𝑖𝑡 ) + 𝑢𝑖𝑡
(Eq.2)
Equation (1) tackles objective (1) .The results from the estimation of equation (1) show that
the interaction between aid and policy has less of a detrimental impact on GDP in the
presence of good policies however it is still negative. The estimation also shows that
macroeconomic policy has a positive impact on growth.
The t-tests show that all parameters in the simplified version of the Burnside and Dollar
model (equation 2) are statistically insignificant. This is similar to Easterly, Levine and
Rooadman (2003) findings. They used the exact same approach as Burnside and Dollar
(2000) and found that in fact the coefficient of the critical interaction term between aid and
policy was statistically insignificant.
25
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
5.0 Chapter Five: Conclusions & Recommendations
Introduction
This chapter will present the core findings of the project by stating whether or not the
objectives set out at the beginning of this project were fulfilled and tackled. The final
objective will be considered and fulfilled by presenting recommendations in light of what the
study has illustrated.
Research objectives
5.1 Objective 1: To examine the “effectiveness” of the claim that aid increases GDP growth.
The “effectiveness” of this claim, was examined by running a panel data regression of a
sample of Sub-Saharan African countries estimating how aid influences GDP per capita
growth over a 5 year period 2005-2009. The results show that aid had a negative effect on
growth in Sub-Saharan Africa during this period. (See Appendix 7 for general graphical
representation).
So why is aid not working? Many researchers have come to the conclusion that aid is not
working and have attributed it to many different reasons. William Easterly is probably the
leading researcher in the argument against aid. “A big problem with foreign aid has been its
aspiration to a utopian blueprint to fix the world’s complex problems.”(Easterly 2006, P 321).
The main argument against aids failure is that it is caused by systematic and institutional
factors. These are the structural and institutional constraints which hinder the ‘effectiveness’
of aid. “Large amounts of aid are needed by, and given to, countries which have weak
institution and capacities, and weak governance, which constrains their ability to use it
effectively.”(Riddel 2007, P. 358). This belief leads on to the claim that aid works well in
countries who’s governments have pledged to use it well through their capacities.
Transparent and accountable institutions, and policies and strategies set on macroeconomic
stability are said to be the ideal conditions for aid to have the desirable effect on economic
growth. This leads us onto the second objective of this project.
26
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
5.2 Objective 2: To investigate the possibility that the presence of ‘good policies’ boosts the
impact of aid in a recipient country.
The effect of good policies on a recipient country’s growth rate was examined by running a
panel data regression of a sample of Sub-Saharan African countries estimating how aid
interacted with policies, influenced GDP per capita growth over a 5 year period 2005-2009.
The results show that aid interacted with policies had a negative effect on growth in SubSaharan Africa during this period. However, the effect was less unfavourable if a country was
ranked higher in terms of macroeconomic management policies. The estimation showed that
policies have a positive effect on growth in relation to aid. Nevertheless if aid was increased
then GDP growth would decrease especially if policies didn’t improve.
Easterly, Levine and Rooadman (2003) findings were identical. They used the exact same
approach as Burnside and Dollar (2000); but increased the data set and found that aid does
not work in a “good policy” environment. It didn’t matter how strong the evidence was that
aid doesn’t work in a ‘good policy’ environment, aid agencies have been selective in their
academic backing to support their prior beliefs. This is known as “confirmation bias”.
By applying a different data set to the original model the same outcome should be reached.
This is crucial in testing a model for its robustness. Easterly, Levine and Roodman (2003)
preformed this on the Burnside and Dollar model and came to the same conclusion as this
project.
Development aid is provided to countries as a result of their poverty. One may reason that
poverty is not only indicated by income, but also by the very policies that are said to boost
aid effectiveness. Weak governments with interlinked systematic problems and limited
abilities could be one reason why poverty proliferates. In summary the idea that aid works
well in ‘good policy’ environments is useless because generally aid is most needed in ‘bad
policy’ environments, therefore to base the allocation of aid on policy and concentrate it
towards countries with ‘good’ ones is futile.
27
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
5.3 Objective 3: To establish some recommendations for development strategy with regards
to aid.
The presence of ‘good policies’ should not be the benchmark for decision making on aid
allocation. There are more dimensions to macroeconomic policy that should be taken into
account such as unemployment. “To most economists, inflation is not so much an end in
itself, but a means to an end: it is because excessively high inflation often leads to low
growth, and low growth leads to high unemployment, that inflation is so frowned upon.... A
country like Argentina can get an ‘A’ grade, even if it has double digit unemployment for
years, so long as its budget seems in balance and its inflation seems in control.” (Stiglitz
2002, P. 27).
The IMF has already taken this different approach to international assistance. It reviews a
recipient’s macroeconomic situation and is mostly concerned with inflation. It has minimum
standards that it sets and if a country doesn’t maintain them its assistance is suspended. This
‘one size fits all’ approach is damaging as the theory of aid, growth and policy is flawed,
proven by this and other studies the reasoning behind administering aid to countries based on
parameters such as inflation does not have the desirable effect.
28
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
6.0 Chapter Six: Appendices
6.1 Appendix 1
Sample of Sub-Saharan African Countries
Country Name
Angola
Benin
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African
Republic
Chad
Comoros
Congo, Dem. Rep.
Congo, Rep.
Cote d'Ivoire
Eritrea
Ethiopia
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Madagascar
Malawi
Mali
Mauritania
Mozambique
Niger
Nigeria
Rwanda
Sao Tome and
Principe
Senegal
Sierra Leone
Sudan
Tanzania
Togo
Uganda
Zambia
Zimbabwe
Country
Code
AGO
BEN
BFA
BDI
CMR
CPV
CAF
TCD
COM
ZAR
COG
CIV
ERI
ETH
GMB
GHA
GIN
GNB
KEN
LSO
MDG
MWI
MLI
MRT
MOZ
NER
NGA
RWA
STP
SEN
SLE
SDN
TZA
TGO
UGA
ZMB
ZWE
29
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
6.2 Appendix 2
GDP per capita growth
AGO
BEN
BFA
BDI
CMR
CPV
CAF
TCD
COM
ZAR
COG
CIV
ERI
ETH
GMB
GHA
GIN
GNB
KEN
LSO
MDG
MWI
MLI
MRT
MOZ
NER
NGA
RWA
STP
SEN
SLE
SDN
TZA
TGO
UGA
ZMB
ZWE
2004
17.1139
-0.45992
2.817436
-2.05455
-0.03739
10.15311
0.580911
13.57133
2.052245
3.328143
5.414656
-0.87719
-1.24556
8.951254
2.018833
3.580944
0.984063
2.56325
3.16734
0.109979
1.734901
-0.23264
3.563448
2.70876
5.66291
0.783628
2.904898
7.203836
3.935759
2.894545
3.378548
4.134077
4.456402
-1.31287
2.922625
2.814724
-5.98942
2005
15.29194
0.762658
1.963931
2.009157
0.876026
8.500467
1.900809
-2.78495
-0.922
2.106163
4.023911
-1.50922
-4.34434
7.989847
3.515032
4.128017
0.415398
-0.09784
3.55808
5.523453
2.173461
4.723894
2.821248
16.3952
6.040651
1.896352
3.701218
6.616939
4.953101
-0.14139
3.946502
8.906732
3.792419
1.316085
7.226094
3.716748
-3.21038
2006
17.09027
1.298366
0.10077
0.500887
1.158984
7.090075
1.764133
-2.59873
-1.87638
3.328424
-3.3922
-0.55683
-1.85832
8.595528
3.368421
4.236474
-0.39271
-1.90476
4.220849
1.52037
3.391573
2.891278
1.856417
-8.01009
4.750924
-0.61449
3.964689
2.771293
4.311526
2.119143
3.504259
7.745236
4.14669
-0.61651
4.92409
3.660459
-3.58257
2007
10.39051
1.836691
1.467921
1.441987
0.590365
4.965916
0.272002
-3.05339
-1.40384
3.331805
3.699694
-0.11185
-12.4676
7.940846
3.243953
6.201795
2.610689
1.228092
-1.09417
3.559043
4.288675
5.625087
2.448661
1.211695
4.291634
5.295288
3.553217
8.155259
4.124228
0.637763
2.887047
4.475809
4.388758
-0.69212
5.208535
3.254542
-17.3449
2008
-1.93703
0.629663
0.064345
0.644595
-0.26377
1.382001
0.475019
-4.16828
-0.58811
-0.04746
5.61258
1.217001
0.610366
5.94521
1.838903
2.522447
-2.6189
0.737551
-0.08002
0.002801
-6.18586
4.658687
1.859107
-3.33734
3.957426
-2.86944
3.199951
1.216575
2.350748
-0.41273
1.518515
2.233777
2.984443
0.022016
3.616164
3.810692
5.187948
30
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
6.3 Appendix 3
Aid/GDP
AGO
BEN
BFA
BDI
CMR
CPV
CAF
TCD
COM
ZAR
COG
CIV
ERI
ETH
GMB
GHA
GIN
GNB
KEN
LSO
MDG
MWI
MLI
MRT
MOZ
NER
NGA
RWA
STP
SEN
SLE
SDN
TZA
TGO
UGA
ZMB
ZWE
2004
1.35%
8.09%
12.77%
45.73%
2.49%
16.23%
6.56%
7.17%
5.88%
26.49%
23.42%
0.56%
29.81%
15.69%
13.09%
10.72%
6.75%
11.18%
4.02%
5.13%
18.12%
20.81%
13.27%
10.07%
19.72%
15.27%
5.71%
22.36%
28.50%
8.02%
27.41%
6.66%
10.60%
3.90%
13.24%
16.38%
6.68%
2005
0.36%
7.98%
15.15%
46.88%
9.49%
12.50%
9.04%
4.72%
7.60%
25.72%
3.34%
1.42%
9.82%
13.09%
14.39%
5.95%
6.01%
14.58%
4.19%
4.98%
13.73%
22.40%
14.16%
7.69%
22.63%
14.43%
7.78%
18.93%
18.41%
8.94%
24.43%
5.62%
12.83%
3.56%
15.66%
13.57%
5.35%
2006
0.42%
8.55%
14.06%
48.51%
9.31%
12.41%
10.33%
5.10%
9.57%
13.59%
1.42%
0.86%
11.52%
13.44%
11.24%
4.72%
5.42%
17.68%
4.87%
8.17%
12.18%
21.51%
14.27%
12.20%
22.14%
12.76%
1.18%
19.31%
24.83%
7.69%
32.78%
4.54%
16.76%
4.85%
14.61%
8.84%
9.54%
2007
0.44%
9.60%
12.44%
43.50%
2.31%
14.48%
12.90%
5.01%
7.03%
15.26%
4.12%
2.66%
8.68%
12.85%
11.42%
4.57%
8.67%
15.54%
4.54%
9.02%
8.94%
22.67%
11.05%
8.91%
20.23%
11.33%
0.62%
19.89%
27.25%
8.08%
18.76%
4.11%
11.25%
11.37%
11.37%
7.76%
14.42%
2008
0.32%
10.26%
13.31%
41.42%
2.93%
12.65%
11.81%
8.21%
9.45%
22.25%
2.95%
10.15%
7.73%
13.39%
17.46%
6.05%
5.23%
17.39%
6.05%
7.79%
5.19%
16.34%
10.95%
9.48%
20.56%
8.73%
0.96%
17.91%
16.12%
7.94%
22.52%
4.19%
13.73%
17.48%
11.13%
9.91%
13.10%
31
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
6.4 Appendix 4
Policy Rating
AGO
BEN
BFA
BDI
CMR
CPV
CAF
TCD
COM
ZAR
COG
CIV
ERI
ETH
GMB
GHA
GIN
GNB
KEN
LSO
MDG
MWI
MLI
MRT
MOZ
NER
NGA
RWA
STP
SEN
SLE
SDN
TZA
TGO
UGA
ZMB
ZWE
2004
2.75
4.25
4.5
3.416667
3.666667
4.333333
2.75
3.666667
2.666667
3.333333
3.25
2.25
2.083333
3.583333
3.25
4.083333
2.583333
2.416667
4.333333
4
3.416667
3
4.416667
2.416667
4.083333
3.416667
3.916667
3.75
2.916667
4.333333
3.833333
3.166667
4.75
2.25
4.5
3.416667
1
2005
2.833333
4.25
4.416667
3.333333
3.75
4.416667
2.75
3.25
2.25
3.333333
3.166667
2.166667
2.083333
3.25
3.25
4.083333
2.583333
2
4.333333
4
3.75
3.333333
4.416667
3.166667
4.083333
3.833333
4
3.916667
2.916667
4
3.85
3.1
4.75
2.25
4.5
3.833333
1
2006
3
4.25
4.416667
3.333333
3.833333
4.5
3.166667
2.833333
2.25
3.333333
2.75
2.666667
2.083333
3.25
3.666667
4
3
2
4.333333
4
3.833333
3.416667
4.416667
3.5
4.083333
3.833333
4.166667
3.916667
2.916667
4.333333
3.833333
3.083333
4.416667
2.333333
4.5
3.833333
1
2007
3
4.25
4.4
3.4
3.85
4.5
3.15
2.6
2.25
3.35
3.15
2.75
2.1
2.9
3.75
3.6
3
1.9
4
4
3.9
3.4
4.4
3.5
4.4
3.85
4.15
3.9
2.9
3.9
3.85
3.1
4.4
2.85
4.5
3.85
1
2008
3
3.833333
4.416667
3.416667
3.833333
4.5
3.25
2.5
2.666667
3.333333
3.25
3.166667
1.916667
3.583333
3.75
3.583333
2.416667
2.333333
4.333333
4
3.833333
3.083333
4.416667
3.333333
4.5
3.916667
4.166667
3.916667
2.916667
4
3.833333
3.083333
4.416667
2.916667
4.5
3.75
1.833333
32
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
6.5 Appendix 5
Eviews Estimation output: Model 1
Dependent Variable: Y_?
Method: Pooled Least Squares
Date: 03/17/11 Time: 12:50
Sample: 2005 2008
Included observations: 4
Cross-sections included: 37
Total pool (balanced) observations: 148
White period standard errors & covariance (d.f. corrected)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
A_?
-3.980294
6.028126
-0.660287
0.5101
C
3.168534
1.103555
2.871206
0.0047
R-squared
0.006331
Mean dependent var
2.678686
Adjusted R-squared
-0.000475
S.D. dependent var
4.454867
S.E. of regression
4.455924
Akaike info criterion
5.839767
Sum squared resid
2898.868
Schwarz criterion
5.880270
Log likelihood
-430.1428
Hannan-Quinn criter.
5.856224
F-statistic
0.930250
Durbin-Watson stat
0.919923
Prob(F-statistic)
0.336393
33
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
6.6 Appendix 6
Eviews Estimation output: Model 2
Dependent Variable: Y_?
Method: Pooled Least Squares
Date: 03/19/11 Time: 15:44
Sample: 2005 2008
Included observations: 4
Cross-sections included: 37
Total pool (balanced) observations: 148
White period standard errors & covariance (d.f. corrected)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
A_?
-0.335023
19.17969
-0.017468
0.9861
P_?
2.158869
1.138318
1.896543
0.0599
C
-4.660746
4.320438
-1.078767
0.2825
A_?*P_?
-0.064329
5.867656
-0.010963
0.9913
R-squared
0.160477
Mean dependent var
2.678686
Adjusted R-squared
0.142987
S.D. dependent var
4.454867
S.E. of regression
4.124093
Akaike info criterion
5.698224
Sum squared resid
2449.172
Schwarz criterion
5.779230
Log likelihood
-417.6686
Hannan-Quinn criter.
5.731137
F-statistic
9.175325
Durbin-Watson stat
1.045477
Prob(F-statistic)
0.000014
34
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
6.7 Appendix 7
35
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
7.0 Chapter Seven: Bibliography
Books
Asteriou, D. Hall, S.G. 2007. Applied Econometrics. Basingstoke: Palgrave Macmillan.
Black, J. Hashimzade, N. Myles, G. 2009. Oxford Dictionary of Economics. 3rd ed. New
York: Oxford University Press.
Collier, P. 2008. The Bottom Billion. New York: Oxford University Press.
Easterly, W. 2006. The White Man's Burden. New York: Oxford University Press.
Froyen. R.T. 2009. Macroeconomics: Theories and Policies. 9th ed. London: Pearson
Prentice Hall
Gujarati, D.N. Porter, D.C. 2009. Basic Econometrics. 5th ed. New York: McGrawHill/Irwin.
Kenwood, A.G. Loughead, A.L. 1999. The growth of the international economy 1820-2000.
4th ed. Abingdon: Routledge.
Meir, G.M. Rauch, J.E. 2005. Leading Issues in Economic Development. 8th ed. New York:
Oxford University Press.
Moyo, D. 2009. Dead Aid. London: Penguin Books Ltd.
Riddle, R.C., 2007. Does Aid Really Work? New York: Oxford University Press.
Sen, A. 1999. Development As Freedom. New York: Oxford University Press.
Stglitz, J.E. 2002. Globalization And Its Discontents. London: Penguin Books Ltd.
Stiglitz, J.E. 2007. Making Globalization Work . London: Penguin Books Ltd.
Journals
Boone, P. 1995. The Impact of Foreign Aid on Savings an Growth. Working paper, London
School of Economics, November.
-1996. Politics and the Effectiveness of Foreign Aid. European Economic Review, February
40(2), pp. 289-329.
36
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
Burnside, C. Dollar, D. 2000. The American Economic Review, Vol. 90, September, (4) , pp.
847-868.
Easterly, W.R. Levine, R. 1997. Africa's Growth Tragedy: Policies and Ethnic Divisions.
Quarterly Journal of Economics, November, 112(4), pp. 1203-50.
Easterly, W.R. Rebelo, S.T. 1993. Fiscal Policy and Economic Growth: An Empirical
Investigation. Journal of Monetary Economics, December, 32(3), pp. 417-58.
Website:
World Bank, 2011. Data Bank. [online], Available at:
<http://databank.worldbank.org/ddp/home.do?Step=2&id=4&hActiveDimensionId
=WDI_Series >[ Accessed 10 March 2011]
37
BASIL EL-KHAWAD : AN INVESTIGATION INTO THE EFFECT OF AID ON GROWTH IN SUBSAHARAN AFRICA
8.0 Chapter Eight: References
Books
Black, J. Hashimzade, N. Myles, G. 2009. Oxford Dictionary of Economics. 3rd ed. New
York: Oxford University Press.
Easterly, W. 2006. The White Man's Burden. New York: Oxford University Press.
Moyo, D. 2009. Dead Aid. London: Penguin Books Ltd.
Riddle, R.C., 2007. Does Aid Really Work? New York: Oxford University Press.
Stglitz, J.E. 2002. Globalization And Its Discontents. London: Penguin Books Ltd.
Journals
Burnside, C. Dollar, D. 2000. The American Economic Review, Vol. 90, September, (4) , pp.
847-868.
38