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)