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Migbaru Alamirew Workneh
Master of Development Evaluation and Management
Supervisor: Prof. Dr. Nathalie Francken
Academic year 2014-2015
UNIVERSITY OF ANTWERP INSTITUTE OF DEVELOPMENT POLICY
AND MANAGEMENT
Dissertation
Impact of Foreign Aid on Domestic Savings in Sub-Saharan Africa (Panel Data Analysis)
Migbaru Alamirew Workneh
Master of Development Evaluation and Management
Supervisor: Prof. Dr. Nathalie Francken
Academic year 2014-2015
UNIVERSITY OF ANTWERP INSTITUTE OF DEVELOPMENT POLICY
AND MANAGEMENT
Dissertation
Impact of Foreign Aid on Domestic Savings in Sub-Saharan Africa (Panel Data Analysis)
I
PREFACE
This dissertation is submitted in partial fulfillment of the requirements for the award of the
Master of Development Evaluation and Management of the Institute of Development Policy and
Management (IOB) of the University of Antwerp.
The selection of my topic was motivated by two main factors; firstly because of my personal
interest and experiences in the area of foreign aid to developing countries as I was working in
Bilateral Cooperation Department, Ministry of Finance and Economic Development of Ethiopia.
Secondly, to see the tremendous impact of the flow of huge amounts of money starting from the
last more than five decades as a form of official development assistance on gross domestic
savings of developing countries especially to Sub-Saharan Africa, which is the dominant aid
receiver region.
In doing this research and in my study here, I have received invaluable help from many people.
Above this, I have learnt a lot of things in an effort to accomplish this research. Getting
ideas/information for this study and seeing to it to achieve the objective of the study has not been
an easy task. First and for most, I am grateful to Almighty God for giving me grace, mercy and
strength in all my endeavors. My special thanks and gratitude extends to my supervisor Prof. Dr.
Nathalie Francken, from whom I get a lot, for her invaluable help and advice and also
constructive comments which helped me to bring this research to what it is. My heartfelt thanks
also to my sponsors the VLIR-UOS Scholarship for their financial support throughout the whole
my study and stay.
I am also very grateful to my beloved family; my father Mr. Alamirew Workneh, my Mother
Wubalech Admasie, my brothers and sisters, and also my girlfriend D. A. for their support and
encouragement throughout my stay away from home. Thanks to all my friends and fellow
students for your unforgettable and memorable friendship and help.
II
Table of Contents PREFACE ............................................................................................................................................................... I
List of Tables ...................................................................................................................................................... III
EXCUTIVE SUMMARY................................................................................................................................. IV
INTRODUTION ................................................................................................................................................. 1
CHAPTER ONE ................................................................................................................................................. 3
THEORETICAL AND EMPIRICAL LITERATURE REVIEW ...................................................................... 3
1.1. Theoretical Literature .................................................................................................................................. 3
1.1.1. Definition of Foreign Aid ......................................................................................................................... 3
1.1.2. The Macroeconomic Rationale for Aid .................................................................................................... 4
1.1.2.1. Harrod-Domar Growth Model ............................................................................................................... 4
1.1.2.2 The Two Gap Growth Model ................................................................................................................. 6
1.2. Empirical Studies on Foreign Aid and Domestic Saving ............................................................................ 7
1.2.1. Brief Summary of some articles on Domestic Saving and Foreign Aid ................................................. 12
CHAPTER TWO .............................................................................................................................................. 15
Description of Variables, Methodology and Empirical Model Specification................................................... 15
2.1. Variables of Interest .............................................................................................................................. 15
2.2. Hypotheses ........................................................................................................................................... 17
2.3. Methodology and Empirical Model Specification ................................................................................ 19
2.4. Scope and Limitation of the Study ....................................................................................................... 24
CHAPTER THREE .......................................................................................................................................... 25
EMPIRICAL DATA ANALYSIS .................................................................................................................... 25
3.1. Estimation Results ..................................................................................................................................... 25
3.2. Interpretation of Estimation Results .......................................................................................................... 28
3.2.1. Interpretation of the Aggregate Estimation Results ................................................................................ 28
3.2.2. Interpretation of the Disaggregated Estimation Results ......................................................................... 29
CHAPTER FOUR ............................................................................................................................................ 32
CONDLUDING REMARKS ........................................................................................................................... 32
References ........................................................................................................................................................ 35
Annex I .............................................................................................................................................................. 43
Annex II ............................................................................................................................................................. 52
Annex III ............................................................................................................................................................ 53
III
List of Tables
Table 1: Brief Summary of scientific articles on domestic saving and Foreign Aid ..................... 12
Table 2: Variable used in the panel data estimation analysis and their symbols ........................... 24
Table 3: Summary of Estimation Results for the impact of aggregate official development
assistance on gross domestic savings ................................................................................. 25
Table 4: Summary of Estimation Results for the impact of disaggregated official development
assistance on gross domestic savings ................................................................................. 26
Table 5: Descriptive Statistics of Variables Used .......................................................................... 52
IV
EXCUTIVE SUMMARY
This paper tried to address the impact of foreign aid on gross domestic savings in forty Sub-
Saharan African countries in aggregate and by disaggregating foreign aid into bilateral aid from
DAC member countries, including the European Union and multilateral aid from UN agencies,
World Bank, IMF and African Development Bank. Using annual panel data from 2002 to 2013
for twelve years in the sample countries, Simple Panel data analysis with fixed effects and
without fixed effects is done, and also Hausman test, Breusch-Pagan LM test and time fixed
effect tests are applied. The study seeks to determine whether the direction of the impact of
foreign aid on gross domestic savings is different based on aid modalities (bilateral and
multilateral aid). Based on the results from the random effect model estimation, and other
diagnostic tests, the impact of bilateral aid and multilateral aid is the same with the aggregate
effect of net official development assistance on gross domestic saving, even if multilateral aid is
insignificant. The absence of Good governance in Sub-Saharan Africa, as an institutional factor,
is also affected gross domestic savings negatively. The estimation result is in favor of those
researchers who claim about the negative impact of foreign aid flow on gross domestic savings
based on their research, but the result may depend on the variables and the methodology used.
Hence, to see the different arguments of researchers on foreign aid and domestic savings and to
know the real impact of foreign aid in more broad and detailed concept, macro-economic policy
soundness as an institutional factor and the role of aid beyond growth, which may have a
potential influence on gross domestic savings in developing countries, may play an important role
in the statistical estimation in addition to good governance and the disaggregation of official
development assistance.
Keywords: Sub-Saharan Africa, Panel Data Analysis, Gross Domestic Savings, Foreign Aid
(Official Development Assistance), Bilateral Aid, Multilateral Aid
1
INTRODUTION
Africa has reached a turning point in 2000s and starts to play a more significant role in the global
economy since its economy has experienced high and continuous economic growth in the past
decade (UNCTAD, 2014). The economic growth of Africa during 2000s is impressive and higher
than during the 1990s and 1980s as the average gross domestic product (GDP) grows more than
double from just above 2% during the 1980s and 1990s to above 5% between 2001 and 2014
(AEO, 2015). This Economic growth varies across Africa, which reflects the factors, such as
differences in income levels, availability of natural resources, macroeconomic policies, and
political and social stability, that affects the growth of the economy which are different in
different regions of Africa. As AEO (2015) indicates the economic growth remains highest in the
East, West and Central Africa, 7%, 6% and 5.6%, respectively, and lowest in North and Southern
Africa, 1.7% and 3% respectively in 2014. In North Africa, except Mauritania, almost all
countries of the region’s experienced very low economic growth and even negative growth of
Libya’s economy due to Arab spring which results the political unrest and civil war. In sub-
Saharan Africa, the region which contains more than 47 countries of Africa, the average
economic growth was 5.2% in 2014 (AEO, 2015).
Despite the rapid economic growth in Africa, specifically in Sub-Saharan Africa in the last
decade, many countries in the region are struggling with several development challenges like
self-insufficiency in food supply (food security problem), poverty and inequality, low economic
infrastructure, environmental degradation and low regional and global economic integration
(UNCTAD, 2014). These challenges influence the investment and domestic saving in the region,
which are the main drivers of sustained and transformative economic growth. In addition, the
domestic saving and investment can be influenced by foreign aid, the growth of gross national
product per capita in each country, the productivity of agriculture measured in value added in
agriculture, and unemployment may also affect the investment and savings.
The flow of Official Development Assistance (ODA), which is the most common foreign aid
transfer, received by Sub-Saharan African countries increases for the last 53 years and in 2013 it
was around 46.77 billion USD which is 78 times more than the amount in 1960 (597 million
2
USD) (as shown in figure 1 in the Annex III). This implies that, developed countries invest their
huge amount of money in these developing countries for the last more than five decades for the
sake of economic growth (whatever the reason was behind) and their interest increases time to
time as the graph shows. But regarding the effectiveness of Official Development aid transfer,
there are opposite arguments made by researchers on the area. Some researchers like Kalyvitis
(2007) and Moyo (2009) shows a negative impact of foreign aid on gross domestic savings, while
other researchers like Balde (2011), Irandoust and Ericsson (2005) and Shields (2007) observes a
positive impact of aid. Not only at a glance, rather foreign aid based on its sources (bilateral or
multilateral aid), may affect gross domestic savings differently.
In addition to foreign aid, the Gross National Product per capita (GDP per capita), value added
agriculture, sound administration and unemployment may cause a possible impact on gross
domestic savings and investment, and on economic growth in world-wide. In developing
countries, especially in Africa, domestic saving is very low, even if it is the main source of
funding for domestic investment and economic growth (World Bank, 2015). These major foreign
aid receiver countries are less developed and hence, domestic saving in these countries may
affected by different factors, including foreign aid, per capita GDP growth, value added
agriculture and good governance. In favor of this, the main research question of this paper is:
what will be the impact of foreign aid, at a glance and also in disaggregation of bilateral and
multilateral aid, on gross domestic savings in Sub-Saharan African countries? In addition, the
impact of good governance, value added agriculture, unemployment and per capita GDP growth
on gross domestic savings in the region will statistically tested and analyzed. To answer these
questions, the paper has four Chapters and it organized as follows: Chapter one provides a review
of theoretical and empirical studies on the relationship mainly between gross domestic savings
and foreign aid in the general and in Sub-Saharan Africa in particular. Chapter two provides the
model specification, variables of interest and hypotheses. Chapter three discusses the empirical
analysis of the study, which mainly focused on fixed effect and random effect model estimations
using secondary data from World Development Indicators and World Wide Governance
Indicators. The Hausman test and F-test are discussed in this chapter. Finally, chapter four
concludes the study and provides concluding remarks.
3
CHAPTER ONE
THEORETICAL AND EMPIRICAL LITERATURE REVIEW1
1.1. Theoretical Literature
1.1.1. Definition of Foreign Aid
Foreign aid can be in-kind like physical goods, skills and technical know-how, or it can be in
cash and/or noncash financial support like grants and loans at concessional rates transferred from
donors to aid recipient developing countries. The Development Assistance Committee (DAC) of
the Organization for Economic Cooperation and Development (OECD) defines aid as Official
Development Assistance (ODA). According to the DAC, aid qualifies as ODA when the
following three criteria are met: it has given by official agencies; based on the main objective of
economic development and welfare promotion and twenty five or more percent of the aid should
be grant. Project aid, humanitarian aid including food aid, technical assistance and programme
aid (balance of payments support and budget support) are most common modalities that ODA
provided to recipient countries. Also, in addition to the official development assistance, the Non-
Governmental Organizations (NGOs) provide aid in support of poverty reduction activities and
emergency relief in developing countries.
Aid can be from individual governments through a bilateral agreement and negotiations between
the donor and the recipient country, bilateral aid; from multilateral organizations like the World
Bank, the United Nations, the International Monetary Fund, and regional development banks,
including the African Development Bank and Asian Development Bank, multilateral aid; or from
non-governmental organizations (NGOs) such as World Vision, Red Cross Society, and Oxfam,
non-governmental aid. This study uses the DAC definition of foreign aid.
1 For the development of my literature review, I used the first End of Module Paper as a base.
4
1.1.2. The Macroeconomic Rationale for Aid
The macroeconomic rationale for aid, which is based on the growth model of Harrod-Domar, is
about how aid can substitute and increase domestic savings, foreign exchange and government
revenue for economic growth. In the Harrod-Domar growth model, which assumes physical
capital formation drives growth, investment rate and productivity of investment are the factors
that affect output. Tradition considering physical capital formation as a central driving force of
economic growth is not only in the Harrod-Domar model but also in the 1950s and 1960s gap
models (Hjertholm, Laursen and White, 2005). The total saving of countries is generated from
domestic sources (domestic saving) and from foreign sources (foreign savings) in an open
economy and these savings are the main sources of investment. Hjertholm, Laursen and White
(2005) argue that when countries saving from domestic sources are not sufficient to finance their
investment to attain the planned economic growth, a savings gap will occur; and trade gap or the
foreign exchange gap will occur when the revenue from exports are not enough to import the
desired level of capital and services, based on the assumption that not all goods and services are
produced domestically. This argument of Hjertholm, Laursen and White (2005) make sense and
strongly based on the idea that, there should be a clear distinction between the desired and actual
investment and domestic savings (savings gap); and also between the desired and actual import-
export (trade gap) in a given an exogenously determined planned growth rate. The difference
between these two actual and desired gaps become large, it affects the investment and economic
growth if there is no any other option like foreign aid to finance the large gap and the desired
growth rate will not be attained finally. If foreign aid is allocated in the desired and appropriate
way, it can fill both gaps simultaneously (by paying for imported capital equipment, a single aid
dollar relaxes both the savings and the foreign exchange constraint). The Harrod-Domar growth
model and The Two Gap model are discussed below:
1.1.2.1. Harrod-Domar Growth Model
An econometric growth model of Harrod-Domar, which is an influential and very handy
applicable growth model in modern aid theory, assumes that capital is the most crucial factor for
enhancing the growth rate of the economy (Pankaj, 2005). According to the Harrod-Domar
5
model, output depends on the productivity and the rate of investment in which saving (sum of
domestic and foreign savings in open economy) is the main source of finance. This model, which
explains economic growth in terms of a savings ratio and capital-output coefficient, (as cited in
Kabet, C. N., 2008: 19) is expressed as;
g = (I/Y) /μ ……….. (2.1) and
I/Y= A/Y + S/Y …………. (2.2)
where I is required investments, Y is output; g is the target GDP growth, A is aid, S is domestic
saving and μ the incremental capital-output ratio (ICOR). The ICOR, which is the ratio of
investment rate to the growth rate, gives the amount of additional capital units required to yield a
unit of additional output. When the value of the incremental capital-output ratio (ICOR), which is
mostly range between 2 and 5, is high, it is an indication of poor quality of investment which
implies, to attain a very low economic growth rate, huge amount of investment should undertake.
By using the idea of ICOR, the Harrod-Domar model was the base for the national development
plans in developing countries and even now a day it is mostly used by researchers and some
policy makers (de Silver, 1984 cited by Kabet, 2008). As of Sheilds (2007), the simple version of
the Harrod-Domar growth model is the base for the most famous models which claim that aid
induces growth when growth is determined by the saving rate where the growth rate of per capita
income (g) is given by:
g = s/v – n ……………..2.3
Where s is the marginal saving rate, v is the incremental capital-output ratio and n is the population
growth rate. In this model, saving is equal to the investment and anything which increases the
marginal saving rate (s), decreases the ICOR (v), or decreases the population growth rate (n) and if n
is less than g will increase the growth rate of per capita income (g) and aid is taken as either
augmented savings or improving technology. Hence, from the above arguments since savings is the
sum of domestic and foreign saving (like foreign aid) in an open economy, it is possible to say
that foreign aid can influence the savings and economic growth rate and can fill the saving-
investment gap to achieve a target growth rate. Despite this argument, savings, especially
domestic savings are the main source of investment and hence can play the most imperative role
in the economic growth of countries. Thus, for those aid recipient countries, to minimize their
dependence on foreign aid and also the amount of aid flows from donors may decrease due to
different factors like financial and economic crises like Ireland and Italy did for Ethiopia in 2008
6
and 2009, they need to increase their capacity of generating domestic saving, which will increase
the domestic revenue to finance investment.
1.1.2.2 The Two Gap Growth Model
The first and standard model, even still the most influential growth model, which is used to
justify the role of foreign aid to allow countries in achieving the desired investment and economic
growth rate, was the ‘two gap model’ of Chenery and Strout (1966) (Ahmad and Ahmed, 2002;
Kabet, 2008 and Serieux, 2009). This growth model is based on two assumptions; linear and
stable relationship between investment and growth, and aid finances investment. The saving gap
and foreign exchange gap (trade gap) are the two gaps considered in this growth model. The
inflow of foreign resource from the outside world like foreign aid can enable developing
countries to fill their saving gaps and foreign exchange (trade gap) by providing the needed funds
and foreign exchange (Serieux, 2009). This filling of the two gaps by foreign aid will hold true
only if the only constraint on investment is a shortage of fund that is a liquidity problem, not
another problem like lack of incentives, and also the ‘two gap model’ supports the investment-
limited growth assumption of Harrod- Domar growth model that assumes a specific amount of
investment to increase growth (Kabet, 2008). This is in a sense that if poor incentives are the
cause for low investment, aid will not fill those gaps and not increase investments rather it will
finance consumption or other reverse flows. In addition, Easterly (2001) and Bender and
Lowenstien (2005) also criticizes the two assumptions of two-gap model as; the linearity of
investment and foreign aid relationship may not happen, i.e. the production function may allow
substitution of capital by labor and hence if non-substitutable assumption fails, the model fails to
see how the foreign aid allocate and what was the role of this resource. Foreign aid may also use
to finance consumption, and even to finance reverse flows like debt repayment as Serieux (2011)
argue. The effectiveness of foreign aid in filling the two gaps may also determine by the
productivity of the investment itself (White, 1992 cited by Kabet, 2008). In line with the
effectiveness of aid in this model, the saving gap and foreign exchange gap may not be at the
same time. As Serieux (2009) argue, the saving gap is the binding constraint in the early stages of
growth and as the economy develops, since the saving is expected to increase due to increase in
income, the saving-investment gap will be covered by domestic savings; while when the
7
economy grows, it will come with higher demand of investment and hence higher demand for
imported intermediate and capital goods, and this may exceed the revenue from exports to
finance it and thus the foreign exchange gap (trade gap) will become the binding constraint on
investment and sustained economic growth.
1.2. Empirical Studies on Foreign Aid and Domestic Saving
Even if the researchers cannot reach at the same argument in common about the impact of foreign
aid on domestic saving and economic growth, the area is widely studied and is still more
investigations are going on. From those literatures which examine the impact of foreign aid on
domestic savings and economic growth in recipient countries, some studies find evidence of a
positive effect, while other studies find evidence of a negative effect. For instance, an influential
study by Burnside and Dollar (2000) founds that foreign aid can be effective, and only increases
economic growth in developing countries when the good macroeconomic policy environment
exists, but Hansen and Trap (2001) show that foreign aid still can play an important role in the
economic growth of those recipient countries even without sound macroeconomic policy
conditionality. The necessity of sound macroeconomic policy and management for the
effectiveness of foreign aid on economic growth of developing countries by increasing domestic
saving and filling the foreign exchange gap is also highly recommended by Tassew (2011) and
Girma (2015). Moreira (2005), Hatemi-J and Irandoust (2005), Adamu (2013) and Basnet (2013)
also argue that the role of foreign aid in the economic growth of developing countries is
significant and foreign aid transfer is necessary to run out of poverty.
The impact of foreign aid on investment and economic growth can be through domestic saving,
which is the main determinant of economic growth and main source of fund for investment
(Hansen and Tarp, 2000) or through income. Foreign aid can enhance the main source of fund for
investment, savings, and also foreign aid can influence investment through an income effect (i.e.
transfer of purchasing power) (Hansen and Tarp, 2000). The importance of sound
macroeconomic policies and management for the effectiveness of foreign aid is not only essential
for the economic growth but also to accelerate the growth of domestic saving which is an
important prerequisite and determinant for capital formation (additions to capital stock) and
8
increasing aid to Sub-Saharan Africa is one way to achieve the Millennium Development Goals
as Armah and Nelson (2008) and Freytag and Voll (2013) argue. In addition to sound
macroeconomic policy environment and management, income level, levels of aid allocation and
geographical location of recipient countries also determine the positive impacts of foreign aid on
economic growth (Durbarry, Gemmel and Greenway, 1998).
The flow of foreign aid from the developed world can have a significant positive impact on
domestic saving and hence promotes investment in the major aid recipient region, Sub-Saharan
Africa, as Balde (2011) argue on his study of foreign aid and domestic saving using ordinary least
squares and instrumental variables estimation method. The results of other studies done in this
area, like “aid effectiveness in Africa” by Loxley and Sackey (2008) and “aid and investment in
least developed countries” by Gyimah-Brempong and Racine (2010) also show that the major
transmission-mechanism in the aid-growth relationship, investment rate, significantly and
positively influenced by foreign aid. This implies that, since investment is equal to saving in the
Harrod-Domar growth model theory and also based on the two-gap model which states that
foreign aid has a potential to fill the saving-investment gap and the foreign exchange gap, foreign
aid also have a positive and significant impact on domestic saving. In addition to filling the two
gaps, foreign aid can play a crucial role in the growth of the country’s economy by creating
access to modern technology and managerial skills, and by allowing easier access to foreign
markets, which have a potential to affect domestic savings directly and indirectly (Irandoust and
Ericsson, 2005).
In his study in 119 aid recipient countries, Michael P. Shields (2007) also tried to see the
crowding out effect between foreign aid and domestic saving by adding value added in
agriculture as a percentage of Gross Domestic Product and labor force as additional control
variables and he confirm that there is a positive relationship between foreign aid and domestic
saving which is in favor of the above arguments that foreign aid can increase domestic saving and
investment. Tolessa (2001), on his study of “Impact of foreign aid on domestic saving,
investment and economic growth”, argue that the influence of foreign aid on domestic saving and
investment is not only at a glance, but also its impact may depend on the type of aid modalities,
and hence, foreign grant has a negative effect while loan has a positive impact on domestic
saving and investment.
9
In the estimation of foreign aid and domestic saving, important variables like investment rate,
which is the main transmission mechanism to the economic growth, are often didn’t used and
hence, the estimated coefficients of aid generated from the statistical estimations may suffer from
omitted variable bias. Through this transmission mechanism, investment, which is equal to saving
under Harrod-Domar growth theory, foreign aid has been beneficial to African countries’
economic growth through that saving and investment even if more investigations are necessary to
ensure that these benefits lead to sustainable growth since economic growth is a result of growth
of different indicators (Girma, Gomannee and Morrissey, 2005). Based on their statistical
estimation using ordinary least square regression with an autoregressive model, Eregha and
Irugha (2009) argue that the role of foreign aid for the growth of aggregate domestic savings was
very important in the long run and short run in Nigeria even if debt service payment have a
negative impact.
In sharp contrast with the argument about foreign aid effectiveness and its role in increasing
domestic saving and promoting growth, whether under sound macroeconomic policy and
management or not, some researchers found negative impact of foreign aid flow on domestic
saving which influence economic growth. Easterly, Levine, and Roodman (2003) found that there
is no real evidence to support the argument given by Burnside and Dollar (2000) since the results
obtained are not robust when different measures of foreign aid, policies, and growth are used.
Kalyvitis (2007) also strengthens their idea that foreign aid may become the main source of fund
for those rent seeker governments of developing countries and will hurt economic growth by
distorting individual incentives and reducing domestic savings since those rent seeker
governments are incapable and irresponsible to mobilize domestic resources. Rather than playing
an important role in the economic growth of recipient countries, the money around $1 trillion
transferred from developed countries to developing countries for the last more than 60 years to
finance development related activities has trapped many African nations in corruption and it
slows down the economic growth, and cutting of the aid flows would be more beneficial than
continuing its flow (Moyo, 2009).
In particular for bilateral aid, Moyo (2009) is totally against it since government to government
aid only makes the developing country's government not to be responsible for their citizens,
10
rather being loyal for donors, and leads to stagnant poor economic performance and aid
dependent. This implies that those governments will not generate domestic resources rather being
dependent on external resources, and hence the domestic saving (particularly public saving) will
decrease. Moyo (2009) also argues, only humanitarian aid should continue and the aid for NGOs
should be for a short period of time and in a strong control for specific objectives. In addition to
corruption, effectiveness of foreign aid in Africa is also determined by conflict, fractionalized
society and dependence on primary commodities (Collier, 2006). He recommended those donors
that in addition to increasing their aid flows, they should also focus on security, good governance,
temporary trade preferences, like AGOA (African growth and Opportunity Act), and conditioning
aid on good governance rather than policies.
The ineffectiveness of foreign aid in Sub-Saharan African countries for the last more than thirty
years is because of diversion of the aid flows to reverse flows (debt service payment, finance
capital flight, accumulates reserves), and this makes foreign aid flow ineffective and lack
appropriate response for the desired investment and economic growth by filling the domestic
savings-investment gap (Serieux, 2011). As Serieux (2011) argue, from 1980-2006 nearly 50 %
of the aid flows spent to finance reverse flows in that undeveloped region. This unrecognized and
unacceptable way of foreign aid spending limits the impact of the incremental foreign aid flows
on domestic saving and investment (Serieux, 2009). The study done by Boyce and Ndikumana
(2012) in the thirty three Sub-Saharan African countries supports the arguments made by Serieux
(2009) that Sub-Saharan Africa is the source of largest capital flight even now during relatively
high economic growth and for the last forty years (from 1970 to 2010) thirty three countries from
the region lost 814 billion USD which exceeds the external liabilities of this group of countries.
Dutch disease, which is the appreciation of real exchange rate due to the flow of foreign aid, is
the other problem faced by those aid recipient countries, and this decreases country's
competitiveness in the international market (Rajan and Subramanian, 2011). In his study of
foreign aid, domestic saving and growth in South Asia, Basnet (2013) argue that even if foreign
aid has positive impact on growth during the study period (1960-2008), in the very long run, it
has a negative impact on domestic saving and hence it offsets the positive impact on growth in
the study period. Depending on theory, which says the investment capacity of developing
countries is limited by the entrepreneurial stock, Taslim and Weliwita (2000) argue that even if
11
there is a huge amount of aid flow to developing countries, since those countries lack sufficient
entrepreneurial skill to invest, that huge aid flow will not spend in the right way for the right
purpose, and hence the relationship between foreign aid and domestic saving is inverse.
The effectiveness of foreign aid, in its role in increasing and promoting domestic saving and
investment, may also depend on the aid composition and source, whether from bilateral sources
or multilateral. As of the cross-country estimation on foreign aid (bilateral and multilateral aid)
and domestic saving done by Nushiwat (2007), the impact of bilateral aid on domestic saving is
positive and significant, but there is a negative impact from multilateral aid. As Nushiwat (2007)
argue, in most cases, multilateral organizations come to deliver their aid during poor economic
and political conditions, natural disasters, civil wars, and low saving, and at that stage, economic
and saving growth is not expected. The argument of McGillivray (2009) is also in favor of
Nushiwat (2007) that, the multilateral aid is more sensitive to be fungible and results in a
decrease of domestic tax generation and public sector savings since the aid recipient government,
especially in least developing countries like Sub-Saharan Africa, may depend on external
sources and will not concern about its citizens. The inflow of foreign aid to developing countries,
specifically in Sub-Saharan Africa, where the study was done, can be a substitute for domestic
saving rather than being an addition when the government lack fiscal discipline and use
international resources as a source of revenue and expenditure. This decreases the ability of the
government to generate domestic revenue and even contribute to fiscal deficit and decreases the
domestic saving (Mallik, 2008). Foreign aid has a significant positive impact for the economic
growth of Pakistan by increasing saving and investment in a sound macro-economic policies and
institutions and when disaggregated the aid in two bilateral and bilateral, bilateral aid is
significantly positive in the short run and multilateral aid is insignificant (Javid and Qayyum,
2011).
On the contrary, Alvi and Senbeta (2012) argue that, since mostly the flow of multilateral aid is
less vulnerable to political pressures and focused on poverty reduction developmental strategies
and goals, multilateral aid and grant aid do better and more effective than bilateral aid and loan
to increase domestic saving and investment and hence to reduce poverty in developing countries.
McGillivray et al. (2004) also argue since multilateral aid has a greater focus on the property than
12
bilateral aid and if the situations that makes foreign aid fungible reduced through monitoring or
other controlling mechanisms, multilateral aid is more effective than bilateral aid for increasing
domestic saving and hence to increase investment. This shows that the inconsistency of results
and disagreement between scholars about the impact of foreign aid on domestic savings goes to
not only foreign aid at a glance but also on the source and composition of foreign aid.
1.2.1. Brief Summary of some articles on Domestic Saving and Foreign Aid
Table 2.1 below shows the authors, the research topic, the methodology they used, the area of the
research and the results obtained by the researchers to summarize the above mentioned scientific
articles on the relationship between foreign aid and domestic saving in aid recipient countries
since 2000.
Table 1: Brief Summary of scientific articles on domestic saving and Foreign Aid
No. Author (year) Research Topic Methodology Area of Research Result
1 Basnet (2013) Foreign aid, Domestic
savings and Economic
Growth
Simultaneous
Equation System
(Growth and Saving
Equations)
South Asia
(Bangladesh, India,
Nepal, Pakistan and
Sri Lanka)
Foreign Aid Affects
Economic growth Positively
but Domestic Saving
negatively
2 Alvi and Senbeta (2012) Does Foreign Aid Reduce
Poverty?
Dynamic Panel
Data Estimation
techniques
100 developing
countries
Multilateral aid is more
significant for domestic
saving and economic
growth since it face less
political pressure and it
mostly focuses on Poverty
reduction strategies than
bilateral aid
3 Balde (2011) The Impact of Remittances
and Foreign aid on
Savings/Investment
Ordinary Least
Square (OLS) and
Instrumental
Variables (2SLS)
Sub-Saharan Africa Foreign Aid has Positive
and Significant impact on
Saving and Investment
4 Javid and Qayyum (2011) Foreign Aid and Growth
Nexus in Pakistan: The Role
of Macroeconomic Policies
ARDL
cointegration
Approach,
Pakistan Foreign aid has a positive
impact for growth through
investment and saving
under sound
macroeconomic policies
5 Serieux (2011) Aid and Resource
Mobilizations: The Role of
reverse flows
Pooled Mean Group
(PMG) estimator
Sub-Saharan Africa Aid flow spent for financing
of reverse flow (debt
service payment, finance
capital flight and
accumulate reserves)
6 Gyimah-Brempong and
Racine (2010)
Aid and Investment in
LDCS: A Robust Approach
Panel Data and
Local Linear Kernel
Estimator (LLKE)
Least Developed
Countries
Foreign aid has a positive
impact on physical
investment
7 Eregha and Irugha(2009) An empirical Analysis of Time series Both in short run and long
13
the long run and short run
impacts of Aid on Domestic
Saving
Analysis (OLS with
an autoregressive
model)
Nigeria run foreign aid affects
domestic saving positively
8 McGillivray (2009) Aid, Economic Reform, and
Public Sector Fiscal
Behavior in Developing
Countries
Fiscal response
model
Philippines Multilateral aid is more
sensitive to be fungible and
has no significant impact
rather bilateral aid is better
9 Serieux (2009) Aid and Savings in Sub-
Saharan Africa: should we
worry about rising aid
levels?
Panel Data Analysis 29 Sub-Saharan
Africa countries
Aid flow spent for the
finance of reverse flow and
consumption and hence
decrease saving
10 Loxley and Sackey (2008) Aid Effectiveness in Africa Panel (Fixed Effect
growth model
estimation) data
analysis
40 AU member
countries
Aid increases the major
transmission mechanism in
aid-growth relationship-
Investment
11 Mallik (2008) Foreign Aid and Economic
Growth: A Cointegration
Analysis of the Six Poorest
African Countries
A Cointegration
Analysis
Six Poorest African
Countries
Foreign aid can be
substituted for domestic
saving rather than
increasing it and reduces the
ability of domestic resource
mobilization.
12 Nushiwat (2007) Foreign Aid to Developing
Countries: Does it crowd
out the recipient countries
Domestic Saving?
Multivariate
regression
Developing countries Impact of Aid may depend
on its sources and hence
bilateral aid has a positive
impact while multilateral
aid has negative impact on
domestic saving
13 Shields (2007)
Foreign Aid and Domestic
Saving: Crowding-out
Effect
Ordinary Least
Squares regression
119 aid recipient
countries
Foreign aid is beneficial for
domestic saving and
investment, and crowding
out effect does not appear
as a common problem
14 Girma, Gomannee and
Morrissey (2005)
Aid and Growth in Sub-
Saharan Africa: Accounting
for Transmission
Mechanisms
Panel Data Analysis 25 Sub-Saharan Foreign aid increases
economic growth through
transmission mechanism
(Investment)
15 Irandoust and Ericsson
(2005)
Foreign Aid, Domestic
Saving and Growth in LDCs
Likelihood based
Panel Co-
integration
African Countries Foreign aid can supplement
domestic saving and fill the
exchange gap, to foster
economic growth
16 McGillivray, Feeny, and
White (2004)
Multilateral Development
Assistance: Good, Bad and
Just Plain Ugly
Statistical
Description about
Multilateral Aid
Developing countries Under sound monitoring
and control to reduce
fungibility, Multilateral aid
is more significant for
developing countries
17 Tolessa (2001) Impact of Foreign aid on
domestic saving, investment
and growth
Times series
analysis
Ethiopia Loan has positive impacts
and the grant has negative
impact on domestic saving
18 Taslim and Weliwita (2000) The inverse relation
between saving and aid: An
Alternative Explanation
Co-integration
Analysis using time
series data
Bangladesh Inverse relation between aid
and domestic saving
14
The reviewed literatures on the topic verify that there is disagreement among the researchers on
how the impact of foreign aid on domestic saving and economic growth looks like and there is
also inconsistency of results, as one can see from the above table. The political, economical and
social difference between sample countries, availability of data and difference in the use of
control variables (like per-capita income, agricultural value added, dependency ratio, financial
development) are may be the sources of the difference in the arguments of researchers in the area.
The political, economical and social situation of Sub-Saharan African countries is very different
and even the features of those countries may differ from other countries in another region like
Asian or Caribbean countries. The quality of macroeconomic policy and economic institutions,
which may have a potential to influence the effectiveness of foreign aid in increasing the
domestic saving and investment to promote economic growth, also differ from country to country
and from region to region. The use of different estimation methodologies and different additional
control variables may be the second possible reason for the inconsistency of results and
disagreement. The estimation results from Ordinary Least Squares regression may not be the
same with other more advanced econometric models OLS takes into account different
assumptions like; no hetroschedasticity and endogeniety problem, and even the time frame that
researchers used may also affect the result since as the time frame increases, the quality of the
estimation may also increase especially in time series data analysis.
15
CHAPTER TWO
Description of Variables, Methodology and Empirical Model
Specification
The data set includes 40 Sub-Saharan African countries over the period 2002-2013. The sample
countries are selected based on the availability of consistent data within the period for the
variables of interest. The data set starts from 2002 since the sample countries have full data in
each variable starting from 2002, and some countries in the region (including Eritrea, South
Sudan, Somalia, Djibouti, Zambia, and Equatorial Guinea) are excluded because of data
inconsistency.
2.1. Variables of Interest
The dependent variable is the share of gross domestic savings to Gross National Product (GDP),
for which the data are taken from the World Development Indicators (WDI) (World Bank, 2015).
Foreign aid, which is considered as one of the determinants of domestic saving (Serieux, 2011),
can have a positive or negative impact on gross domestic savings. The existing literature points
out that the effect of foreign aid on gross domestic savings is inconsistent. Foreign aid may have
a positive impact on domestic saving and promotes investment in Sub-Saharan Africa, the major
aid recipient region (Balde, 2011; Loxley and Sackey, 2008). On the negative side, aid can be
fungible when it is misused/ misallocated and may generate wasteful rent seeking activities by
empowering irresponsible politicians. Hence, the impact of foreign aid can be negative or
positive, and the basic objectives of this paper is to see the impact of foreign aid, at a glance and
by disaggregating foreign aid into bilateral and multilateral aid, on domestic saving in the region
(Sub-Saharan Africa). The data for net official development assistance (Net ODA) as a share of
GDP (both at a glance, total net ODA, and in disagregation, bilateral and multilateral aid) are
taken from the World Development Indicators (WDI) (World Bank, 2015). The data for
multilateral aid is calculated by summing up the aid from UN agencies, World Bank, IMF and
African Development Bank in the sample period and for sample countries.
16
Since the growth of Sub-Saharan Africa gross domestic savings is not only determined by official
development assistance, some additional explanatory variables, which can have a potential
impact on gross domestic saving in the region, are taken into consideration in the statistical
estimation. Out of those factors, some of them can be: agricultural value added, the growth rate of
per capita GDP, good governance and unemployment. Most of Sub-Saharan African countries
economy is mostly depend on agriculture, and as Shields (2007) and Tiffin and Irz (2006) also
emphasized the importance of agriculture and being as engine for economic growth in developing
countries; Value added agriculture is necessary to move the economy forward since it enables to
maintain food security, and it provides raw material, capital and foreign exchange (Tiffin and Irz,
2006), and also it tend to enhance domestic saving and hence increase investment and economic
growth (Shields, 2007). Hence, to see the impact of value added agriculture, which is the net
output of agricultural outputs from all sub-sectors of agriculture including natural resources
(World Bank, 2015), on domestic savings in Sub-Saharan Africa, it is taken as an additional
explanatory variable in the estimation. The data for share of value added agriculture to GDP are
taken from the world development indicators (WDI) (World Bank, 2015).
The quality of governance (good governance) and unemployment are also other possible
determinants of gross domestic savings considered in this paper. The quality of good governance
can be measured by six broad dimensions of governance indicators; government effectiveness,
corruption, rule of law, political stability and absence of violence and regulation quality (WGI,
2015), and these indicators may influence the growth of domestic saving and investment. In most
of developing countries, especially in Sub-Saharan Africa and Middle East, governments are
ineffective, arbitrary, irresponsible and autocratic, and such kind of political underdevelopment is
a major cause of low level of domestic resource mobilization (domestic savings) and poverty in
those countries (Moore, 2001). Weak fiscal and financial policies, macroeconomic instability,
low level of financial infrastructure development, corruption, weak institutional capacity,
including ineffective and incapable tax administration, lack of property rights, and also capital
flight, which are the main features of lack of good governance, are among the obstacles which
adversely affect domestic savings (domestic resource mobilization) in Sub-Saharan Africa (UN,
2005; Mubiru, 2010; Culpeper, 2010). This lack of good governance, which may exist due to
Irresponsible and unaccountable aid dependent governments in Sub-Saharan Africa countries,
17
may reduce domestic savings (particularly public saving) and hence appropriate public services
and investments will not delivered to not only for the current generation but also for next
generation since good governance is essential for sustainable economic growth and development
by enabling the countries to mobilize their own domestic resources (Clark, 2012). The data for
good governance is calculated as the average of the six broad indicators of good governance
based on the data form Worldwide Governance Indicators (WGI) (World Bank, 2015).
High and persistent unemployment is a negative phenomenon in any human society since it
affects the economy and society in different dimensions and directions (Al-habees and Abu
Rumman, 2012), and hence it will decrease the saving and investment since consumption will
increase more than the income generation due to high unemployment. The negative consequence
of high unemployment rate is not only in the economic wellbeing of individuals but also on the
federal budget of the government and hence, it has a potential to affect the level of public savings
and also investment (Levine, 2013). The data for unemployment (as percentage of total labor
force) are taken from the world development indicators (WDI) (World Bank, 2015).
The other variable of interest as an explanatory variable is per capita GDP growth. As of Mohan
(2006) and Mousavi and Monjazeb (2014) the growth of per capita GDP is one of the
determinants of gross domestic savings and it has a positive impact on gross domestic savings
and increases the growth of investment in developing countries. This is in a sense that, when the
economy of countries grows, their GDP also grows and hence the per capita GDP growth rate
also increases if the midyear population doesn’t change or the increase is less than the increase of
GDP. Hence the income of citizens will also increase, which is the main source for the domestic
saving. The data for Gross National Product Per capita growth (annual growth rate) is taken from
World Development indicators (WDI) (World Bank, 2015).
2.2. Hypotheses
Since there is no consistent implication on the impact of official development assistance, as a
general and in disaggregation, impact of multilateral and bilateral foreign aid on gross domestic
savings, as existing literatures imply, there are three main hypotheses to be statistically tested
based on the given data and given methodology;
18
Hypothesis 1: The role of net official development assistance (ODA) for the growth of gross
domestic savings in the aid recipient countries is significant and crucial to alleviate poverty, as of
Shields (2007), Loxley and Sackey (2008), Balde (2011) and other pro-aid researchers’ argument.
In contrast with this argument, some researchers like Moyo (2009), Serieux (2009) and Serieux
(2011), flow of official development assistance to developing countries, particularly to Sub-
Saharan Africa countries, has been spent for financing reverse flows and creates irresponsible and
unaccountable governments for their citizens, and hence, it slows the growth of their economy
rather than promoting development and being a catalyst for growth by enable countries to fulfill
their saving-investment and foreign exchange gap. This implies that flow of official development
assistance will decrease gross domestic savings by deteriorating domestic resource mobilization
capacity of recipient governments. Thus, the null and alternative hypotheses in this case are:
H0: β >0; and Ha: β < 0, where β is the coefficient of total net official development assistance
received as a share of GDP.
Hypothesis 2: Net Official Development Assistance from DAC member countries and the
European Union (Bilateral aid) affects the gross domestic savings positively in Sub-Saharan
Africa countries based on the argument of Javid and Qayyum (2011), McGillivray (2009) and
Nushiwat (2007) that bilateral aid is more effective and has a positive significant impact on
domestic saving and hence increase investment. Hence, the null hypothesis is:
H0: β >0; where β is the coefficient of total net official development assistance from bilateral
sources as a share of GDP.
The alternative hypothesis is the opposite of the null hypothesis that bilateral aid may have a
negative or insignificant impact on gross domestic saving since it is less focused on property and
poverty oriented developmental strategies and highly determined by the political situation of
countries (Alvi and Senbeta, 2012; Mallik, 2008). The alternative hypothesis is:
Ha: β < 0
Hypothesis 3: The impact of multilateral aid from international organizations (mostly from
United Nation Agencies, World Bank, International Monetary Fund and African Development
Bank) on gross domestic savings can be positive since it is more focused on poverty reduction
19
strategies and has a greater focus on property and less political pressure than bilateral aid, as Alvi
and Senbeta (2012) argue. McGillivray et al. (2004) also support the positive and more
significant impact of multilateral aid on domestic saving than bilateral aid under a condition of
controlling and monitoring the implementation of projects and programmes, and existence of
sound economic policies.
On the contrary to the argument about the positive impact of multilateral aid on gross domestic
savings, Nushiwat (2007) and McGillivray (2009) argue that multilateral aid is less effective and
may have even negative impact since it is more sensitive and vulnerable to fungbility and
corruption than bilateral aid since the political pressure and control in developing countries is
less. Hence, the null hypothesis for the impact of multilateral aid on gross domestic saving is:
H0: γ > 0; and the alternative hypothesis is; Ha: γ < 0, where γ is the coefficient of total net
official development assistance from multilateral sources as a share of GDP.
2.3. Methodology and Empirical Model Specification
To see the impact of net official development assistance (ODA) as a total and by disaggregated
into bilateral and multilateral aid, value added agriculture, good governance, unemployment and
per capita GDP growth in forty countries of Sub-Saharan Africa, based on the data obtained from
World development indicators (WDI) and Worldwide Governance Indicators (WGI) (World
Bank, 2015), Simple Panel data analysis is used. The statistical estimation test is undertaken with
fixed effects and without fixed effects (i.e. random effects) model specifications. Fixed effect
estimation, which can be entity fixed effect or time fixed effect or both, is used to investigate the
relationship between the dependent variable and the explanatory variables within an entity
(country, person, etc.) (Torres-Reyna, 2007). This estimation technique assumes two basic things,
as (Torres-Reyna, 2007) argue; the first assumption is that time-invariant variables for each
individual are unique and are not correlated with other characteristics of individuals, and the
second one is non-correlation of error term and the constant (which captures the individual
characteristics) with others. Fixed effect model estimation used to control the omitted variable
bias and also to control the effects of time-invariant variables with time-invariant effects
(Williams, 2015). Since the variables may differ from country to country or from time to time,
20
the fixed effect can be due to the time-invariant country specific fixed effects or can be from
time-invariant time fixed effects (over time effects). To see these country specific and time
specific fixed effects, there are two separate model specifications; country fixed effects, and
country and time fixed effects models. The equation for the country fixed effects model becomes:
Yit = βiXit + αi + uit-----------------------------------------------------------------------------2.1
Where
– αi (i=1….n) is the unknown intercept for each entity (n entity-specific intercepts) and called
country specific fixed effects, Yit is the dependent variable (DV) where i = entity and t = time, Xit
represents an independent variable, βi is the coefficient for that independent variable, uit is the
error term.
To capture the entity and time fixed effects together, as explained by Subhayu et al. (2014) and
Torres-Rayna (2007), the equation 1.1 will be:
Yit = αi + βiXit + ɳi+κt+ uit -------------------------------------------------------------------------------2.2
Where
αi are the intercepts for each entity, βi are coefficients for independent variables, Xit are the
independent variables which contain observable variables that can change over time but not over
entities or can change over entities but not over time or change over time and entity in both, ɳis
are the unobservable time-invariant country specific fixed effects, κts are unobservable year-
specific effects, which changes over time but not over countries, and uit are error terms.
Based on Equation (2.1) and (2.2), the simple panel data model to estimate the effect of net
official development assistance in total, per capita GDP growth, quality of good governance,
unemployment and value added agriculture on gross domestic savings in Sub-Saharan Africa
takes the following form:
For country specific fixed effect estimation;
DSit = αi + βODAit +ѱGPGit + δGGit + ωURit + θVAit + uit ------------------------------------------2.3
And for country and time specific fixed effects together;
21
DSit = αi + βODAit +ѱGPGit + δGGit + ωURit + θVAit + ɳi+κt+ uit-----------------------------------2.4
Where
In Equation (2.3, and 2.4) i refer to the country, t stands for time (year), DS expressed as the
Gross Domestic Saving as share of GDP, ODA expressed as the Net Official development
Assistance received, as share of GDP, GPG articulated as the annual growth rate of GDP per
capita, GG stands for quality of Good Governance, measured as average of the six broad
governance indicators from Worldwide Governance Indicators (WDI, 2015), UR stands for
Unemployment (percent of total labor force), VA is Value Added Agriculture as share of GDP,
αis denote intercepts, ɳis denote time-invariant, country specific fixed effects which absorb the
influence of any unobservable factors on gross domestic savings like dependency ratio, and
deposit saving rate, since these potential determinants of domestic saving are specific and
different from one country to another country, κts are year-specific effects which account for any
time-varying common shocks, and uit is the usual disturbance term. The other letters are
parameters, which are coefficients for the explanatory variables, to be determined in the statistical
test. Specifying equation (2.3 and 2.4) in natural logarithm form, which is used to see the impacts
of those explanatory variables in percentage changes in the estimation, and also used as a
variance stabilizing and normality transformation (Wicklin, 2011), is as follows in equation 2.5
and 2.6 respectively;
LDSit = αi + βLODAit +ѱLGPGit + δLGGit + ωLURit + θLVA it + uit-------------------------------2.5
LDSit = αi + βLODAit +ѱLGPGit + δLGGit + ωLURit + θLVA it + ɳi+κt+ uit----------------------2.6
To see the disaggregated impact of official development assistance on gross domestic savings,
equation 2.3 and 2.4 are reformulated as follows by disaggregating net official development
assistance in to net official development assistance from bilateral sources, including the European
Union and net official development assistance from multilateral sources (from UN agencies,
World Bank, IMF, and African Development Bank).
22
For country specific fixed effect estimation;
DSit = αi + βBAit + γMA + ѱGPGit + δGGit + ωURit + θVAit + uit ----------------------------------2.7
And for country and time specific fixed effects together;
DSit = αi + βBAit + γMA + ѱGPGit + δGGit + ωURit + θVAit + ɳi+κt+ uit---------------------------2.8
Where, BA stands for net Official development assistance from bilateral sources, including the
European Union, as a share of GDP, MA is the net official development assistance from
multilateral sources, as a share of GDP and others are as explained in equations (2.3 and 2.4). The
specification of equations (2.7 and 2.8) in natural logarithm form for country specific fixed effect
and country and time fixed effect is the following in equations (2.9 and 2.10) respectively:
LDSit = αi + βLBAit + γLMA + ѱLGPGit + δLGGit + ωLURit + θLVAit + uit--------------------2.9
LDSit = αi + βLBAit + γLMA + ѱLGPGit + δLGGit + ωLURit + θLVAit + ɳi+κt+ uit-----------2.10
The random effect estimation technique is used to estimate the without fixed effect estimators.
Random effect estimation assumes that individual effect is not correlated with any
regressors/explanatory variables and it is random, and estimates the error variance specific to
groups or times and it is not constant (Park, 2011). As of Park (2011), the role of dummy
variables, in which it is part of the intercept in the fixed effects estimation but considered as
random and captured with the error term in the random effect estimation, is the main difference
between fixed effects and random effects. The other differences between fixed and random
effects comes from the intercept and error variance; intercept is not constant and vary across the
group and time, and the error variance is constant under fixed effect estimation, but in the random
effect estimation, it is the opposite and thus the intercept is constant and the error variance is not
constant and randomly distributed across the group or time.
Based on the argument of Park (2011) and Torres-Reyna (2007), the model for random effect
estimation, which allows to include time invariant variables as explanatory variables for the
estimation, is:
Yit = βXit + α + uit + εit …………………………….............…........................................……2.11
23
Where i stand for countries, t refers time, Y is the dependent variable, X is the explanatory
variable, u is between-entity effect, ε is with-in entity effect and others are the parameters to be
estimated. Hence, the random effect estimation model to see the impact of total net official
development assistance, value added agriculture, unemployment, good governance and per capita
GDP growth on gross domestic savings is:
DSit = αi + βODAit +ѱGPGit + δGGit + ωURit + θVA it + uit + εit……………….……………2.12
Where, u stands for unobserved variables between-countries which affect domestic saving, ε
refers with-in country effect which mean those unobserved variables with-in each country which
may have an impact on domestic saving of that country, and others are as explained in the fixed
effect model specification above. The natural logarithm form of equation (2.12) is:
LDSit = αi + βLODAit +ѱLGPGit + δLGGit + ωLURit + θLVA it + uit + εit …………………2.13
The equation which shows the disaggregation impact of official development assistance in the
random effect estimation is:
DSit = αi + βBAit + γMA + ѱGPGit + δGGit + ωURit + θVAit + uit + εit ---------------------------2.14
And the natural logarithm form of equation (2.14) is:
LDSit = αi + βLBAit + γLMA + ѱLGPGit + δLGGit + ωLURit + θLVAit + uit + εit --------------2.15
The following table shows the variables used in the panel data analysis, their symbol and data
source that from where the data generated. Note that, the statistical estimation is done using the
logarithm of these variables. To handle the negative values of data during log transformation, as
of Wicklin (2011), the most common technique is to add a constant value to the data before the
log transformation. This constant value should make the minimum value of a variable, which has
negative values, very small positive number. Hence, for the log transformation of this data, the
same method is used.
24
Table 2: Variable used in the panel data estimation analysis and their symbols
Variable Name Symbol Data Source
Gross Domestic Savings (% of GDP) DS World Development Indicators
Net Official Development Received (% of GDP) ODA World Development Indicators
Bilateral Aid from DAC members and European
Union (% of GDP)
BA World Development Indicators
Multilateral Aid from UN, WB, IMF and AfDB
(% of GDP)
MA Calculation based on data from
World Development Indicators
Value Added Agriculture (% of GDP) VA World Development Indicators
Good Governance (Average of Six Indicators) GG World Wide Governance Indicators
GDP Per capita Growth (annual rate) GPG World Development Indicators
Unemployment (percent of total labor force) UR World Development Indicators
2.4. Scope and Limitation of the Study
Data used for this study are for the period from 2002 to 2013 for the last twelve years in forty
Sub-Saharan African countries excluding some countries; Equatorial Guinea, Djibouti, Eretria,
Guinea-Bissau, Somalia, South Sudan, Zambia, in which getting consistent data is difficult. The
use of simple panel data analysis rather than more sophisticated panel data models like Dynamic
panel data analysis, which may give better results, using only good governance as institutional
factor, value added agriculture, unemployment and per capita GDP growth as additional factors
of domestic saving and ignoring other variables like inflation which may have a potential to
affect the domestic saving, and also a short period of time are major limitations of this study.
25
CHAPTER THREE
EMPIRICAL DATA ANALYSIS
3.1. Estimation Results
As the two tables below designates, I report the results of two different estimations done to see the
effect of net official development assistance both in aggregate and by disaggregated into bilateral
and multilateral aid, value added agriculture, good governance, unemployment rate and growth of
per capita GDP on gross domestic savings in forty Sub-Saharan African countries for twelve years.
The first estimation is done using the logarithmic form of the share of gross domestic savings to
GDP as a dependent variable and the logarithmic form of net official development assistance as a
share of GDP, the share of Valued added agriculture to GDP, good governance, unemployment (as
percent of total labor force) and growth of per capita GDP as explanatory variables as table 3
shows. In the second estimation, substitution of the aggregate net official development assistance
by the disaggregated, i.e. substituting the total net official development assistance as a share of GDP
by two separate explanatory variables (bilateral aid from DAC countries and the European Union,
and multilateral aid from UN agencies, IMF, World Bank and African Development Bank) as a
share of GDP is done by including those explanatory variables used in the first estimation as table 4
shows. In both tables, the second column, in the tables below, is results from country fixed effects
estimation, the third column is the results from country and time fixed effects estimation together
and the fourth column is results from random effect estimation (estimation without fixed effects).
Table 3: Summary of Estimation Results for the impact of aggregate official development assistance
on gross domestic savings
Country Fixed Effect Country and Time Fixed Effect Random Effect
Share of Net Official
Development Assistance Received
to GDP
-0.0349272
(0.0232395)
-0.0364838
(0.0239777)
-0.0570023 **
(0.0199821)
Share of Value Added Agriculture
to GDP
-0.1603933*
(0.083546)
-0.1724925*
(0.0922251)
-0.1505102 ***
(0.039899)
Good Governance -0.0867033***
(0.1373726)
-0.5113968 ***
(0.1380343)
-0.2402366**
(0.0911938)
Unemployment (% of total labor -0.065542 -0.0799196 -0.0396367
26
force) (0.0970794) (0.0983534) (0.0392494)
Per Capita GDP Growth -0.0001549 (0.0231341) 0.0054539
(0.023687)
-0.0006477
(0.022762)
Prob > F /Prob > chi2 0.0011 0.0144 0.0001
F-test Prob > F = 0.0000 P>F = 0.0000
The test for time fixed effects Prob > F = 0.4633
Hausman test Prob>chi2 = 0.0581 Prob>chi2 = 0.0581
Breusch-Pagan LM test Prob > chi2 = 0.000
N (= n*T) 478 478 478
Table 4: Summary of Estimation Results for the impact of disaggregated official development
assistance on gross domestic savings
Country Fixed Effect Country and Time Fixed Effect Random Effect
Share of Net Official
Development Assistance from
Bilateral sources to GDP
-0.030326
(0.0188562)
-0.0269635
(0.019272)
-0.0465413**
(0.0173525)
Share of Net Official
Development Assistance from
Multilateral sources to GDP
-0.0078341
(0.0167482)
-0.0181322
(0.0171234)
-0.0055756
(0.0166143)
Share of Value Added Agriculture
to GDP
-0.133978 *
(0.0802324)
-.1493246*
(0.0877606)
-0.1109133**
(0.0411787)
Good Governance -0.5333989***
(0.135588)
-0.5253629***
(0.1362313)
-.2559548**
(0.0895417)
Unemployment (% of total labor
force)
-0.0602796
(0.0976425)
-0.0681781
(0.0987557)
-0.0365657
(0.0388266)
Per Capita GDP Growth -0.00191
(0.0231381)
0.0023763
(0.0236175)
-0.0027447
(0.0228047)
Prob > F /Prob > chi2 0.0020 0.0154 0.0004
F-test Prob > F = 0.0000 P>F = 0.0000
The test for time fixed effects Prob > F = 0.4185
Hausman test Prob>chi2 = 0.0948 Prob>chi2 = 0.0948
Breusch-Pagan LM test Prob > chi2 = 0.000
N (= n*T) 478 478 478
NB: ***, **, * stands for coefficients significant at the 1, 5 and 10 significance level respectively in
both tables, and the numbers in parenthesis are standard errors of each coefficient.
27
To see whether the fixed effect or random effect is appropriate in both estimations, Hausman test is
used, in which the null hypothesis is “fixed effect is not efficient”, for both country fixed effect and
country and time fixed effects estimations. Hence, in both cases I fail to reject the null hypothesis
since the p-value is greater than 0.05 (as shown in the above tables and in the annex I also). So,
based on this, the random effect estimation is preferable than fixed effect estimation model (both in
the country specific fixed effects estimation and country and time specific fixed effects estimation)
in both estimations. This implies that the error variance is not constant and varies across the group
or time, and the parameter estimate of dummy variables in the random effect estimations is part of
the error term rather than being part of the intercept and it is random. In the Huasman test result,
even if the p-value is small to choose random effect in the above estimation, as Torres-Reyna
(2007) argue, it makes sense when the difference between countries like good governance may
expect to have a potential to affect the dependent variable, and in such cases random effect
estimation is applicable. Based on the value given by the Breusch-Pagan Lagrange multiplier (LM)
test, which is used to decide to use whether OLS or random effect estimation for panel data
estimation is used, I reject the null hypothesis since the p-values are less than 0.05 (p-value=0.00) in
both estimations (as shown in the tables above and in the annex I). Hence, random effect estimation
is preferable than OLS and fixed effect estimations.
The Prob > F /Prob > chi2 in the above tables is used to see whether the model specification is
appropriate both in the fixed effects and random effect estimations in the two estimations. Hence,
based on the value given from the estimated result, the model specification for all model
specifications, country fixed effect estimation model, country and time fixed effects estimation
model and random effect estimation model in both cases, is appropriate (i.e. The model is ok) since
the value of prob > F/Prob > chi2 has been less than 0.05 in all estimations (as shown in the above
tables and in the annex I).
To decide whether time fixed effect is needed or not when running fixed effect model estimation, I
test for a time fixed effects, which is a test used to see whether all the dummies for all years are
equal to zero; that is the null hypothesis to the test is “all dummies for all years are zero”, and hence
no need to time fixed effects. Based on this test, I fail to reject the null hypothesis that no need of
time fixed effects since the p-value of the test is greater than 0.05 (p-value= 0.4633 in the aggregate
estimation and p-value= 0.4185 in the disaggregate estimation) as seen from the above tables and
28
from Annex I. So, for the fixed effect estimation, time fixed effects are not necessary and most of
them are insignificant as one can see from Annex I.
The estimation of random effect with time dummies also does not affect the level of significance
and the amount of coefficients for other explanatory variables, and all the coefficients of time
dummies are insignificant except one year dummy (as shown in Annex I). This implies the use of
time effects in both fixed effect and random effect estimations is not needed here. Other tests like a
test for cross-sectional dependence or contemporaneous correlation, a test which used to see
whether the residuals across entities are correlated or not, and serial correlation tests are applied for
macro level panels with long time series (over 20-30 years) and cross-sectional dependence and
serial correlation are not a problem for micro level panels and for macro level panels with short time
series, as Torres-Reyna (2007) and Williams (2015) argue. Hence, for the above fixed and random
effect model estimations there will not be a problem of cross-sectional dependence and serial
correlation since the time period is below 20 years (12 years).
3.2. Interpretation of Estimation Results
Since random effect model estimation is preferable to see the effect of official development
assistance, in aggregate and in disaggregated into bilateral and multilateral aid on gross domestic
savings based on Hausman test and Breusch-Pagan Lagrange multiplier (LM) test, the interpretation
for the estimation results given in the above two tables (table 3 and 4) is as follows based on the
given three hypotheses in section 2.2.
3.2.1. Interpretation of the Aggregate Estimation Results
Hypothesis 1: Based on the results from the first estimation, the coefficient of net official
development assistance (% of GDP) is significant at the 5 % level of significance since the p-value
is less than 0.05 (p-value=0. 004) and its value is less than zero (since β= -0.0570023) as shown in
table 3 above and in the annex I. Hence, under hypothesis one, I reject the null hypothesis in favor
of the alternative hypothesis, and thus, official development assistance has a negative impact on
gross domestic savings in Sub-Saharan Africa. This argument is in line with the argument given by
some researchers like Moyo (2009), Kalyvitis (2007), Basnet (2013), and Serieux (2009) that the
29
flow of official development assistance to developing countries, particularly in Sub-Saharan African
countries, has no significant impact on domestic saving for the growth of their economy rather it
may deteriorate and sluggish it.
Out of the control variables taken as additional explanatory variables in the first estimation, both per
capita GDP growth and unemployment are insignificant since their p-values are greater than 10%
level of significance (0.977 and 0.313 for per capita GDP growth and unemployment respectively).
The other thing is that, even if it is significant at the 1 % level of significance (p-value=0. 000), the
sign of the coefficient of value added agriculture is unexpected, it is negative. The coefficient for
Good governance, which is significant at the 5 % level of significance (p-value=0. 008), is less than
zero. This may imply the absence of good governance and its negative impact on the ability and
willingness of governments in the region (Sub-Saharan Africa) to mobilize domestic resources to
increase their gross domestic savings. Even if good governance is considered as a catalyst for
economic growth, most of the governments of Sub-Saharan African Countries are either non-
democrat dictators, or autocratic governments and quality of good governance is very poor. This
may affect the overall economy starting from design of national level macroeconomic policies to
the implementation of those policies on the ground, delivery of public services, domestic resource
mobilization and investment, and hence it may decrease gross domestic savings.
3.2.2. Interpretation of the Disaggregated Estimation Results
Hypothesis 2: In the second estimation, in which official development assistance disaggregated into
bilateral and multilateral official development assistance, the coefficient of Net bilateral official
development assistance from DAC member countries and the European Union in Sub-Saharan
African countries is significant at the 5 % significance level since the p-value is less than 0.05 (p-
value = 0.007), as the table 4 above and the annex I shows. In this case, the value of β is less than
zero (β = -0.0465413), and hence I reject the null hypothesis in favor of the alternative hypothesis.
Hence, based on this estimation results, the net bilateral official development assistance from DAC
member countries and the European Union to Sub-Saharan African countries has a negative impact
on gross domestic savings of those developing countries. This argument is related with the
argument of Moyo (2009), Basnet (2013), Serieux (2011) and other researchers, who don’t support
the flow of foreign aid to developing countries, that the flow of bilateral aid (government to
30
government aid) has no any significant positive impact and it lucks appropriate response on the
factors like domestic savings which influence the economic growth of recipient countries, rather it
may use to strengthen irresponsible and corrupted governments. Thus, this leads to a negative
impact on investment and economic growth through a decrease in domestic savings since
irresponsible and corrupted governments will not have the capacity to mobilize their domestic
resources. This negative impact of bilateral aid may happen also due to the reason give by Alvi and
Senbeta (2012) and Mallik (2008) that, since bilateral aid from individual governments of
developed countries is mainly face the political pressure starting from the beginning, to which
countries the aid should given, to the implementation level (how to implement and in which area), it
may fail to be succeed and in some cases donors may support irresponsible and unaccountable
governments simply for their political relation and support, as Moyo (2009) also argue.
Hypothesis 3: Based on the estimation results under random effect model estimation by
disaggregated aid data, the coefficient of multilateral aid from international financial institutions
and banks (UN agencies, World Bank, IMF and African Development Bank) to Sub-Saharan
African countries is negative but insignificant since the p-value is greater than 0.1 (p-value =
0.737). The insignificant impact of multilateral aid on gross domestic savings may happen when the
aid from multilateral agencies, especially from the World Bank and IMF, which have a condition to
give the aid (structural adjustment program, or economic reform), is spent based on conditions
without considering the country specific contexts. As McGillivray (2009) argues, the
implementation of structural adjustment and economic reform by aid recipient countries based on
the IMF and World Bank involvement is done without considering the contexts, and the aid from
these multilateral agencies results a reduction in public fixed capital accumulation, domestic
resource mobilization and domestic savings and after some years the impact will be insignificant
since those reforms and structural changes will fail. Unless the structural adjustments and economic
reforms are based on country context, the effectiveness of aid will be in question and it may be
insignificant after some years, as Grindle (2011) also argues, and considering country specific
contexts, like macro-economic policy, political system and governance, is a critical issue for aid
effectiveness.
In Similar with the first estimation, the per capita GDP growth and unemployment are insignificant
(p-value of 0.904 and 0.346 respectively) in the second estimation, and the sign of value added
31
agriculture is also unexpected here, it is negative, even if it is significant at the 5 % level of
significance (p-value of 0.007) as shown in table 4 above and in the annex I. Even if the unexpected
sign of value added agriculture in both estimations makes sense since most of the agricultural
dominated economy of Sub-Saharan African countries are in their early stages of development, this
unexpected effect is not captured by GDP per capita growth since it is insignificance which is
incredible. Even this insignificance of GDP per capita growth will not happen because of
multicollinearity since there is no a problem of multicollinearity in the model as the results shows in
Annex I.
The quality of good governance has also same significant negative coefficient with 5% level of
significance (p-value of 0.004), and this may imply that the absence of good governance in Sub-
Saharan Africa deteriorates the growth of the gross domestic savings, particularly public savings
since those governments of aid recipient countries of the region are not responsible and accountable
for their citizen when they lack good governance, and this makes them inefficient to generate
domestic resources and to play their vital role in the economic growth through investment and
public service delivery. Even if fixed effect estimation (both country specific and country and time
fixed effects) is not supported by Hausman test, net official development assistance, bilateral aid,
multilateral aid, unemployment and per capita GDP growth are insignificant in the aggregate and
also disaggregate estimations, and value added agriculture and good governance are significant even
if the sign of value added agriculture is unexpected here also.
32
CHAPTER FOUR
CONDLUDING REMARKS
Those reviewed literatures which are done in the area of the foreign aid (as an aggregate and
disaggregated into bilateral and multilateral aid) and domestic savings in developing countries,
particularly in Sub-Saharan African countries to which huge amount of foreign aid goes every
year, assures that there is no agreement between researchers on the impact of foreign aid (official
development aid) on gross domestic savings (public and private investments), and the results are
inconsistent. In one way, some researchers argue that foreign aid can contribute for increasing in
gross domestic savings of aid recipient countries based on the empirical evidence done by using
different econometrical estimation methods and also based on Harrod-Domar growth model and
Two-Gap growth model; while in another way, some researchers are in favor of the negative
impact of foreign aid (official development assistance) on gross domestic savings and rather than
being a catalyst for the economic growth through increase in gross domestic savings, it leads to a
decrease in domestic resource mobilization, irresponsible governments for their citizens and aid
dependency by creating rent seeker and corrupted, donor accountable government officials.
By using these two different arguments as the benchmark, this paper investigates the impact of
Official Development Assistance (ODA), as an aggregate and by disaggregating into bilateral aid
from DAC member countries, including the European Union and multilateral official
development aid from international multilateral organizations (UN agencies, IMF, World Bank
and African Regional Development Bank), on the gross domestic savings in 40 Sub-Saharan
African countries. Simple panel data analysis is used based on the data from those sample
countries within a period of 2002-2013 for twelve years time period. The statistical test is
undertaken with and without fixed effect/ random effect estimation models, and to distinguish
whether fixed effect estimation or random effect estimation is preferable, Hausman test was
applied, and random effect is chosen based on the result of the test. In addition to Hausman test,
Breusch-Pagan Lagrange multiplier (LM) test is used to assure that random effect estimation is
33
preferable than Ordinary Least Square (OLS) regression. The necessity of time fixed effects is
also tested using the time fixed estimation test.
Based on the results obtained from the random effect estimation model, Net Official
Development Assistance (ODA) as an aggregate has significant negative impact on gross
domestic savings in Sub-Saharan African countries under the estimation by using total net official
development aid without disaggregation. In the estimation of disaggregated official development
aid into bilateral and multilateral official development aid, the official development aid from
bilateral DAC member donor countries and the European Union has significant negative impact
on gross domestic savings, based on the results from the estimation, but official development aid
from multilateral organizations is insignificant. Regarding the control variables, only value added
agriculture and good governance are significant in both estimations, even if the sign of value
added agriculture is unexpected. But the significant impact of lack of good governance on gross
domestic savings is, as I think, shown in both estimations as the results indicate.
Even if the result of the statistical estimation is aligned with the argument of some researchers,
who argue that flow of developmental aid has no significant positive impact on growth of gross
domestic savings in aid recipient countries, including Sub-Saharan African countries, the result is
opposite to other researchers who argue that foreign aid flow is essential for developing countries
to get out of poverty by filling the saving-investment gap and foreign exchange (trade) gap. This
implies that the inconsistency of estimation results and the disagreement between researchers on
the impact of foreign aid on domestic savings, in developing country's economy, may not only
due to the variables used and the time frame but also may be due to different methodologies used
by different researchers.
Even though a lot of research is done by many researchers and scholars in this area, the
inconsistency and disagreement is still there, and hence more investigation by using additional
explanatory variables which may have a potential to affect the gross domestic savings in the
region (like the soundness of macro-economic policy) and more advanced panel data analysis
methods like Dynamic Panel Data Analysis may help to get better results. The other important
thing in addition to the macro-economic policy, which has a potential to affect the effectiveness
of foreign aid in developing countries as some scholars argue, is the role of official development
assistance beyond economic growth. The impact of official development assistance beyond
34
growth is the most missed role of foreign aid, especially from multilateral organizations in most
of the studies done in the area of foreign aid and gross domestic savings including this paper. The
importance of official development assistance beyond growth includes investment on health
(fighting on child mortality, maternal mortality, HIV/ADIS and others), education and income
equality, which has a potential to affect the productivity of citizens directly and also domestic
saving and investment indirectly. Thus, in my view, in addition to macro-economic policy and
additional explanatory variables and more sophisticated and appropriate model estimations, these
uncovered roles of foreign aid may change the estimation results and arguments of researchers if
we used in the statistical analysis of foreign aid and domestic savings.
35
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43
Annex I
Statistical Results of the Data from STATA
Country Fixed Effect and Random Effect
1. Aggregate Estimation
delta: 1 unit
time variable: year, 2002 to 2013
panel variable: country1 (strongly balanced)
. xtset country1 year
. estimate store fe
F test that all u_i=0: F(39, 433) = 8.31 Prob > F = 0.0000
rho .51565342 (fraction of variance due to u_i)
sigma_e .24336523
sigma_u .25110731
_cons 5.579111 .3311116 16.85 0.000 4.928325 6.229897
lnUR -.065542 .0970794 -0.68 0.500 -.2563474 .1252635
lnGPG -.0001549 .0231341 -0.01 0.995 -.045624 .0453142
lnGG -.5213059 .1373726 -3.79 0.000 -.791306 -.2513058
lnVA -.1603933 .083546 -1.92 0.056 -.3245995 .0038128
lnODA -.0349272 .0232395 -1.50 0.134 -.0806035 .010749
lnDS Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = -0.4053 Prob > F = 0.0011
F(5,433) = 4.12
overall = 0.0430 max = 12
between = 0.0597 avg = 11.9
R-sq: within = 0.0454 Obs per group: min = 11
Group variable: country1 Number of groups = 40
Fixed-effects (within) regression Number of obs = 478
. xtreg lnDS lnODA lnVA lnGG lnGPG lnUR, fe
44
. estimate store re
rho .39568918 (fraction of variance due to u_i)
sigma_e .24336523
sigma_u .19692707
_cons 5.416755 .2015518 26.88 0.000 5.021721 5.811789
lnUR -.0396367 .0392494 -1.01 0.313 -.1165641 .0372908
lnGPG -.0006477 .022762 -0.03 0.977 -.0452605 .0439651
lnGG -.2402366 .0911938 -2.63 0.008 -.4189732 -.0615
lnVA -.1505102 .039899 -3.77 0.000 -.2287107 -.0723097
lnODA -.0570023 .0199821 -2.85 0.004 -.0961664 -.0178382
lnDS Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0001
Wald chi2(5) = 25.04
overall = 0.1120 max = 12
between = 0.1972 avg = 11.9
R-sq: within = 0.0359 Obs per group: min = 11
Group variable: country1 Number of groups = 40
Random-effects GLS regression Number of obs = 478
. xtreg lnDS lnODA lnVA lnGG lnGPG lnUR, re
45
Breusch-Pagan Lagrange multiplier (LM) Test
Time fixed effect test
lnUR -.0799196 .0983534 -0.81 0.417 -.273243 .1134039
lnGPG .0054539 .023687 0.23 0.818 -.0411052 .0520131
lnGG -.5113968 .1380343 -3.70 0.000 -.7827171 -.2400764
lnVA -.1724925 .0922251 -1.87 0.062 -.3537704 .0087853
lnODA -.0364838 .0239777 -1.52 0.129 -.0836144 .0106469
lnDS Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = -0.3918 Prob > F = 0.0144
F(16,422) = 1.96
overall = 0.0560 max = 12
between = 0.0682 avg = 11.9
R-sq: within = 0.0692 Obs per group: min = 11
Group variable: country1 Number of groups = 40
Fixed-effects (within) regression Number of obs = 478
. xtreg lnDS lnODA lnVA lnGG lnGPG lnUR i.year, fe
46
F test that all u_i=0: F(39, 422) = 8.29 Prob > F = 0.0000
rho .51190004 (fraction of variance due to u_i)
sigma_e .24342548
sigma_u .24928964
_cons 5.648444 .3721458 15.18 0.000 4.916954 6.379935
2013 -.0138405 .0581737 -0.24 0.812 -.1281868 .1005058
2012 -.0114251 .0578835 -0.20 0.844 -.1252009 .1023507
2011 .0049458 .0570887 0.09 0.931 -.1072679 .1171596
2010 -.0038106 .0563482 -0.07 0.946 -.1145686 .1069475
2009 -.0256116 .0550735 -0.47 0.642 -.1338641 .0826408
2008 -.0530019 .0555711 -0.95 0.341 -.1622325 .0562288
2007 -.0227493 .0564306 -0.40 0.687 -.1336693 .0881706
2006 -.1270292 .056115 -2.26 0.024 -.2373289 -.0167295
2005 -.0105644 .0552966 -0.19 0.849 -.1192554 .0981266
2004 -.0061554 .0551735 -0.11 0.911 -.1146045 .1022937
2003 .0250562 .0553545 0.45 0.651 -.0837487 .1338611
year
Prob > F = 0.4633
F( 11, 422) = 0.98
(11) 2013.year = 0
(10) 2012.year = 0
( 9) 2011.year = 0
( 8) 2010.year = 0
( 7) 2009.year = 0
( 6) 2008.year = 0
( 5) 2007.year = 0
( 4) 2006.year = 0
( 3) 2005.year = 0
( 2) 2004.year = 0
( 1) 2003.year = 0
. testparm i.year
47
2. Disaggregation Estimation
. estimate store fe
F test that all u_i=0: F(39, 434) = 8.53 Prob > F = 0.0000
rho .52109443 (fraction of variance due to u_i)
sigma_e .24296977
sigma_u .25344604
_cons 5.441294 .3123389 17.42 0.000 4.827409 6.055179
lnUR -.0602796 .0976425 -0.62 0.537 -.2521906 .1316315
lnGPG -.00191 .0231381 -0.08 0.934 -.0473866 .0435666
lnGG -.5333989 .135588 -3.93 0.000 -.7998896 -.2669082
lnVA -.133978 .0802324 -1.67 0.096 -.2916704 .0237145
lnMA -.0078341 .0167482 -0.47 0.640 -.0407518 .0250837
lnba -.030326 .0188562 -1.61 0.109 -.0673869 .0067349
lnDS Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = -0.4132 Prob > F = 0.0020
F(6,434) = 3.54
overall = 0.0396 max = 12
between = 0.0523 avg = 12.0
R-sq: within = 0.0466 Obs per group: min = 12
Group variable: country1 Number of groups = 40
Fixed-effects (within) regression Number of obs = 480
. xtreg lnDS lnba lnMA lnVA lnGG lnGPG lnUR, fe
. estimate store re
rho .38670703 (fraction of variance due to u_i)
sigma_e .24296977
sigma_u .19293423
_cons 5.191593 .1897585 27.36 0.000 4.819673 5.563513
lnUR -.0365657 .0388266 -0.94 0.346 -.1126644 .0395331
lnGPG -.0027447 .0228047 -0.12 0.904 -.0474411 .0419516
lnGG -.2559548 .0895417 -2.86 0.004 -.4314534 -.0804562
lnVA -.1109133 .0411787 -2.69 0.007 -.191622 -.0302045
lnMA -.0055756 .0166143 -0.34 0.737 -.0381391 .0269879
lnba -.0465413 .0173525 -2.68 0.007 -.0805516 -.012531
lnDS Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0004
Wald chi2(6) = 24.46
overall = 0.1021 max = 12
between = 0.1731 avg = 12.0
R-sq: within = 0.0370 Obs per group: min = 12
Group variable: country1 Number of groups = 40
Random-effects GLS regression Number of obs = 480
. xtreg lnDS lnba lnMA lnVA lnGG lnGPG lnUR, re
48
Breusch-Pagan Lagrange multiplier (LM) Test
Prob>chi2 = 0.0948
= 10.80
chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
lnUR -.0602796 -.0365657 -.0237139 .0895911
lnGPG -.00191 -.0027447 .0008347 .0039136
lnGG -.5333989 -.2559548 -.2774441 .1018154
lnVA -.133978 -.1109133 -.0230647 .068859
lnMA -.0078341 -.0055756 -.0022584 .0021136
lnba -.030326 -.0465413 .0162153 .0073789
fe re Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
. hausman fe re
Prob > chibar2 = 0.0000
chibar2(01) = 318.65
Test: Var(u) = 0
u .0372236 .1929342
e .0590343 .2429698
lnDS .1098905 .3314973
Var sd = sqrt(Var)
Estimated results:
lnDS[country1,t] = Xb + u[country1] + e[country1,t]
Breusch and Pagan Lagrangian multiplier test for random effects
. xttest0
49
Time fixed effect test
F test that all u_i=0: F(39, 423) = 8.57 Prob > F = 0.0000
rho .51839763 (fraction of variance due to u_i)
sigma_e .24287732
sigma_u .2519847
_cons 5.516973 .3464746 15.92 0.000 4.835947 6.197999
2013 -.0138481 .0577925 -0.24 0.811 -.1274444 .0997481
2012 -.0107493 .0574872 -0.19 0.852 -.1237454 .1022468
2011 .0052121 .0568251 0.09 0.927 -.1064827 .1169069
2010 -.0045091 .0562465 -0.08 0.936 -.1150666 .1060483
2009 -.0267177 .0549949 -0.49 0.627 -.1348151 .0813797
2008 -.0545573 .0555079 -0.98 0.326 -.163663 .0545483
2007 -.023554 .0563311 -0.42 0.676 -.1342777 .0871698
2006 -.132512 .0566936 -2.34 0.020 -.2439483 -.0210758
2005 -.0093484 .0551757 -0.17 0.866 -.1178011 .0991042
2004 -.0039066 .0550369 -0.07 0.943 -.1120866 .1042733
2003 .0227196 .0546009 0.42 0.678 -.0846033 .1300424
year
lnUR -.0681781 .0987557 -0.69 0.490 -.2622911 .1259349
lnGPG .0023763 .0236175 0.10 0.920 -.0440459 .0487986
lnGG -.5253629 .1362313 -3.86 0.000 -.7931375 -.2575882
lnVA -.1493246 .0877606 -1.70 0.090 -.3218258 .0231766
lnMA -.0181322 .0171234 -1.06 0.290 -.0517898 .0155253
lnba -.0269635 .019272 -1.40 0.163 -.0648443 .0109173
lnDS Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = -0.4021 Prob > F = 0.0154
F(17,423) = 1.92
overall = 0.0527 max = 12
between = 0.0607 avg = 12.0
R-sq: within = 0.0715 Obs per group: min = 12
Group variable: country1 Number of groups = 40
Fixed-effects (within) regression Number of obs = 480
. xtreg lnDS lnba lnMA lnVA lnGG lnGPG lnUR i.year, fe
Prob > F = 0.4185
F( 11, 423) = 1.03
(11) 2013.year = 0
(10) 2012.year = 0
( 9) 2011.year = 0
( 8) 2010.year = 0
( 7) 2009.year = 0
( 6) 2008.year = 0
( 5) 2007.year = 0
( 4) 2006.year = 0
( 3) 2005.year = 0
( 2) 2004.year = 0
( 1) 2003.year = 0
. testparm i.year
50
Multicolinearity Test
Random Effects Estimation with Time dummies
_cons -0.3191 -0.8180 -0.4121 -0.2429 -0.5936 1.0000
lnUR 0.0254 0.3444 0.0305 0.0267 1.0000
lnGPG -0.0735 0.0156 -0.0778 1.0000
lnGG -0.1562 0.3730 1.0000
lnVA 0.0450 1.0000
lnODA 1.0000
e(V) lnODA lnVA lnGG lnGPG lnUR _cons
Correlation matrix of coefficients of xtreg model
. vce, corr
rho .40356169 (fraction of variance due to u_i)
sigma_e .24342548
sigma_u .20023419
_cons 5.432938 .205887 26.39 0.000 5.029407 5.836469
y13 0 (omitted)
y12 .0025786 .0547227 0.05 0.962 -.1046759 .1098331
y11 .0175786 .0547273 0.32 0.748 -.089685 .1248421
y10 .0133199 .0550188 0.24 0.809 -.094515 .1211548
y09 -.0117883 .0552764 -0.21 0.831 -.1201281 .0965515
y08 -.0366251 .0549886 -0.67 0.505 -.1444009 .0711507
y07 -.006941 .054882 -0.13 0.899 -.1145078 .1006258
y06 -.1140645 .0549405 -2.08 0.038 -.2217459 -.006383
y05 .0107654 .055058 0.20 0.845 -.0971464 .1186772
y04 .0124645 .0551382 0.23 0.821 -.0956044 .1205333
y03 .0376434 .0562445 0.67 0.503 -.0725938 .1478805
y02 .0164561 .0557929 0.29 0.768 -.092896 .1258081
lnUR -.0437272 .040073 -1.09 0.275 -.1222689 .0348144
lnGPG .00508 .0233586 0.22 0.828 -.040702 .0508621
lnGG -.2405373 .0920321 -2.61 0.009 -.4209168 -.0601578
lnVA -.1554305 .0412768 -3.77 0.000 -.2363316 -.0745294
lnODA -.0577408 .0202312 -2.85 0.004 -.0973933 -.0180883
lnDS Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0030
Wald chi2(16) = 35.87
overall = 0.1250 max = 12
between = 0.1988 avg = 11.9
R-sq: within = 0.0601 Obs per group: min = 11
Group variable: country1 Number of groups = 40
Random-effects GLS regression Number of obs = 478
note: y13 omitted because of collinearity
. xtreg lnDS lnODA lnVA lnGG lnGPG lnUR y02 y03 y04 y05 y06 y07 y08 y09 y10 y11 y12 y13 , re
51
rho .3869114 (fraction of variance due to u_i)
sigma_e .24287732
sigma_u .19294392
_cons 5.207572 .1927502 27.02 0.000 4.829788 5.585355
y13 0 (omitted)
y12 .0033049 .0548638 0.06 0.952 -.1042261 .1108359
y11 .0178334 .0548842 0.32 0.745 -.0897377 .1254045
y10 .0112621 .0551228 0.20 0.838 -.0967767 .1193009
y09 -.0142912 .0553594 -0.26 0.796 -.1227936 .0942112
y08 -.0396506 .0550728 -0.72 0.472 -.1475913 .0682901
y07 -.0083937 .0550343 -0.15 0.879 -.1162589 .0994715
y06 -.1184678 .0555343 -2.13 0.033 -.2273129 -.0096226
y05 .01121 .055211 0.20 0.839 -.0970015 .1194216
y04 .0139972 .0553289 0.25 0.800 -.0944454 .1224397
y03 .0300881 .0555398 0.54 0.588 -.0787678 .138944
y02 .0137532 .0558584 0.25 0.806 -.0957272 .1232337
lnUR -.0384544 .0391999 -0.98 0.327 -.1152849 .038376
lnGPG .0015442 .0233673 0.07 0.947 -.044255 .0473433
lnGG -.251417 .0898744 -2.80 0.005 -.4275676 -.0752665
lnVA -.1139594 .0420247 -2.71 0.007 -.1963263 -.0315925
lnMA -.015637 .016947 -0.92 0.356 -.0488526 .0175785
lnba -.0432147 .0175718 -2.46 0.014 -.0776547 -.0087746
lnDS Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0054
Wald chi2(17) = 35.48
overall = 0.1131 max = 12
between = 0.1704 avg = 12.0
R-sq: within = 0.0618 Obs per group: min = 12
Group variable: country1 Number of groups = 40
Random-effects GLS regression Number of obs = 480
note: y13 omitted because of collinearity
. xtreg lnDS lnba lnMA lnVA lnGG lnGPG lnUR y02 y03 y04 y05 y06 y07 y08 y09 y10 y11 y12 y13 , re
52
Annex II Table 5: Descriptive Statistics of Variables Used
Where:
ODA=Net Official development Assistance (%GDP)
BA=Net Official Development Assistance from Bilateral Sources including European Union
(%GDP)
MA=Net Official Development Assistance from Multilateral Sources (%GDP)
VA=Value Added Agriculture (%GDP)
DS=Gross Domestic Savings (%of GDP)
GPG= per capita GDP Growth (annual)
GG=Good Governance
UR=Unemployment (%of total labor force)
NB: The Number of Sample Countries for the estimation are 40.
53
Annex III Flow of Official Development Assistance to Sub-Saharan Africa for the last more than five
decades
Fig 1.1: Flow of Net ODA for Sub-Saharan African Countries for the last 53 years (1960-2013)
Source: World Development Indicators, World Bank (2015)
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
1950 1960 1970 1980 1990 2000 2010 2020
Net ODA Recieved (in M USD)
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