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AN EMPIRICAL INVESTIGATION OF FOREIGN AID EFFECTIVE NESS IN REDUCING POVERTY IN SOME SELECTED SADC COUNTRIES: 2 00
A DISSERTATION SUBMITTED IN PARTIAL FULFILMENT
REQUIREMENTS FOR THE DEGREE OF
1
AN EMPIRICAL INVESTIGATION OF FOREIGN AID EFFECTIVE NESS IN REDUCING POVERTY IN SOME SELECTED SADC COUNTRIES: 2 00
By
KLERY CHIKWEDE
A DISSERTATION SUBMITTED IN PARTIAL FULFILMENT
REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
IN
ECONOMICS
Department of Economics
Faculty of Social Studies
University Of Zimbabwe
APRIL 2016
AN EMPIRICAL INVESTIGATION OF FOREIGN AID EFFECTIVE NESS IN REDUCING POVERTY IN SOME SELECTED SADC COUNTRIES: 2 005-2013
A DISSERTATION SUBMITTED IN PARTIAL FULFILMENT OF THE
i
ABSTRACT
Historically, aid flows from the developed to developing countries have been economically
justified for reducing poverty either through directly targeting the poor or indirectly via
economic growth. This present study investigates whether or not aid has produced the
anticipated results in 12selected SADC countries using panel data analysis covering a period
of nine years (2005-2013). The variable of choice for measuring aid effectiveness in reducing
poverty in this present study is the human development index (HDI), a non-monetary poverty
measure. Overally, the study finds thataid has a negative and no significant impact on poverty
reduction, supporting the works of the public choice hypothesis. The negative and
insignificant results could beexplained by aid misallocation, misuse and lack of absorptive
capacity by recipient countries. Secondly for the analysis of how aid can be made more
effective in reducing poverty, empirical evidence suggests that institutional quality, control of
corruption and trade openness are vital for aid effectiveness. Economic growth and trade
openness have been found to be necessary conditions for poverty reduction.
ii
ACKNOWLEDGEMENTS
I would like to express my deepest and most sincere gratitude to my supervisor, Dr. T.
Mumvuma for his reliable guidance, clarification of issues, support and patience in particular,
which enabled me to develop and understand the subject. I am whole heartedly thankful to
Dr.Makochekanwa, Mr Muhoyi and Mr Zivengwa who provided useful information and
insights as I was doing this research.Dr. W. G. Bonga, my friend, special thanks for the
invaluable comments and proofreading of this project. Over the past three years I benefited
from the experience and knowledge on major economic issues, of my lecturers at the
University of Zimbabwe,Dr. P. Kadenge, Dr. T. Mumvuma, Dr. A. Makochekanwa, Dr. H.
Zhou, Mr Hazvina, Mr Mavesere, and Mr Pindiriri, may they be pleased to receive my
sincere gratitude. I am also grateful to all other lecturers and staff in the Economics
Department for the support during the entire programme.
Special acknowledgements go to all my colleagues for the company and support during the
course of this programme. Betty, Taguma, Godwin, Tatenda, Sukho, Precious,to mention but
a few, it would have been much harder without you.Special thanksalso go to my bosses, Ms
C. S. J. Murewi, Mr T. G. Mashonganyika, and my work colleagues Mrs A. Mafuratidze,O.
Kudzurungaand Mr T. Dzitirofor all the support and encouragement, not forgetting all those
who supported me in any respects from the onset up to the end of programme.
My grateful thanks are also extended to ZEPARU and USAID-SERA for providing the
financial support when I most needed it. All the staff at ZEPARUand USAID-SERA, thank
you for providing all the assistance, above all, you inspired me to complete the programme.
Finally, I am deeply indebted to my husband, Tonderai BrianChatira (T.B.C.) who has been
the motivational force in my life; the patience, understanding and invaluable support is
greatly appreciated. Wadza, Brenda, Gwen, Ruth, Ringi and Herminahmy God-given sisters,
I greatly appreciate your spiritual and moral support. My siblings Munya, Lloyd and Lucky,
my cousins, in-laws and the church of God, “Glad Tidings Fellowship” I owe you, there are
so many things you had to face on your own during my absence, I really have missed our
social time.
iii
DEDICATION
AwesomeGod Almighty, had it not been for your favour, this dissertation would not have
made it through, thank you for your sufficient grace. This work is also to the loving memory
of my late mother, Mrs KettyChikwede and a dedication to my father, Mr Nathan
BenChikwede for laying the foundation of better education to a girl-child.
iv
CONTENTS
Abstract ……………………………………………………………………… i
Acknowledgement ……………………………………………………………………… ii
Dedication …………………………………………………………………….... iii
Contents ………………………………………………………………………iv
List of Tables ……………………………………………………………………….vi
List of Figures ………………………………………………………………………vii
List of Abbreviation …………………………………………………………………….....viii
Chapter 1: Introduction .......................................................................................................... 1
1.0 Introduction .......................................................................................................................... 1
1.1. Background ......................................................................................................................... 2
1.1.1 Foreign aid and poverty: an overview of global context ...................................... 3
1.1.2 Foreign aid and poverty: the African region context ............................................ 5
1.1.3 Foreign aid and poverty in SADC ........................................................................ 6
1.2 Statement of the problem ..................................................................................................... 8
1.3 Objectives of the study......................................................................................................... 9
1.4 Research questions ............................................................................................................... 9
1.5 Justification of the study ...................................................................................................... 9
1.6 Organisation of the study ................................................................................................... 10
Chapter 2: Literature Review ............................................................................................... 11
2.0 Introduction ........................................................................................................................ 11
2.1 Theoretical Literature Review ........................................................................................... 11
2.2 Empirical Literature Review .............................................................................................. 21
2.3 Conclusion ......................................................................................................................... 31
Chapter 3: Methodology........................................................................................................ 33
3.0 Introduction ........................................................................................................................ 33
3.1 Model Specification ........................................................................................................... 33
3.2 Panel data Methodology .................................................................................................... 34
3.3 Estimation Procedure ......................................................................................................... 35
3.3.1 Model Specification Tests................................................................................... 35
v
3.3.2 Parameter and Misspecification Tests ................................................................ 37
3.4 Definition, Measurement and Justification of Variables ................................................... 38
3.4.1 Dependent variable ............................................................................................. 38
3.4.2 Explanatory variables .......................................................................................... 39
3.5 Data and Data Sources ....................................................................................................... 45
3.6 Conclusion ......................................................................................................................... 45
Chapter 4: Estimation, Results Presentation and Interpretation ..................................... 47
4.0 Introduction ........................................................................................................................ 47
4.1 Descriptive Statistics .......................................................................................................... 47
4.2 Econometric Tests .............................................................................................................. 48
4.2.1 Testing for Model Specification ......................................................................... 49
4.2.2 Parameter Tests ................................................................................................... 51
4.3 Model Estimation ............................................................................................................... 51
4.3.1 Presentation of Results ........................................................................................ 51
4.3.2 Discussion of Results .......................................................................................... 54
4.4. Conclusion ........................................................................................................................ 60
Chapter 5: Conclusion and Policy Recommendations ....................................................... 61
5.0 Introduction ........................................................................................................................ 61
5.1 Summary and Conclusion .................................................................................................. 61
5.2 Policy Recommendations................................................................................................... 63
5.3 Areas of further Research .................................................................................................. 67
References ............................................................................................................................... 69
Appendix 1 Descriptive Statistics ............................................................................................ 76
Appendix 2 Multicollinearity tests results ............................................................................... 77
Appendix 3 Summary of model specification tests ................................................................. 78
Appendix 4 Summary of regression results ............................................................................. 79
Appendix 5 Regression results................................................................................................. 81
vi
LIST OF TABLES
Table 1.1 Total aid flows, extreme poverty and intensity of poverty in Africa .................. 5
Table 1.2 Measures of multidimensional poverty by country from 2005-2014 .................. 7
Table 4.1 Summary of Descriptive Statistics ....................................................................... 47
Table 4.2(b) Correlation matrix ........................................................................................... 49
Table 4.2.1 Summary of model specification tests .............................................................. 50
Table 4.3.1 Summary of regression results for model 1 ..................................................... 52
Table 4.3.2 Summary of regression results for model 2 ..................................................... 52
Table 4.3.3 Summary of regression results for model 3 ..................................................... 53
vii
LIST OF FIGURES
Fig 1.1 Global share of poverty among developing regions in developing regions ........... 3
Fig 1.2 Extreme poverty by region using share of population below US$1.25/day ........... 4
Fig 1.3 Regional share of official aid disbursements 1990 - 2012 ........................................ 4
Fig 1.4 Foreign aid trends received in SADC 2005 -2013 ..................................................... 6
Fig 1.5 Human Development Index Trends for 12 selected SADC countries..................... 7
viii
LIST OF ABBREVIATIONS
2SLS Two Stages Least Squares
CPIA Country Policy and Institutional Assessment
DAC Development Assistance Committee
DRC Democratic Republic of Congo
EFWI Economic Freedom of the World Index
FDI Foreign Direct Investment
FEM Fixed Effects Model
FTS Financial Tracking Services
GDI Gender Inequality Index
GDP Gross Domestic Product
GMM Generalised Method of Moments
GNI Gross National Income
GNP Gross National Product
HDI Human Development Index
ICRG International Country Risk Guide,
IMF International Monetary Fund
WEO World Economic Outlook
LM Lagrange Multiplier
MDGs Millennium Development Goals
MPI Multidimensional Poverty Index
NGOs Non-Governmental Organisations
NODA Net Official Development Assistance
OA Official Aid
ODA Official Development Assistance
OECD Organisation of Economic Cooperation Development
OLS Ordinary Least Squares
PDF Probability Distributed Function
PFI Political Freedom Index
PPE Pro-Poor Expenditure
REM Random Effects Model
SADC Southern Africa Development Community
ix
SAPs Structural Adjustment Programs
SDGs Sustainable Development Goals
SSA Sub-Sahara Africa
UN United Nations
UNDP United Nations Development Programme
VIF Variance Inflation Factor
WB World Bank
WDI World Development Indicators
1
CHAPTER ONE
INTRODUCTION
1.0Introduction
For the past six decades or so, the most outstanding relationship of the African states with the
outside world has been the aid relationship. Aid has been used by developed countries to
stimulate growth, alleviate poverty and consequently reduce income disparity in developing
countries. In assessing aid effectiveness, most studies have focused on aid’s macroeconomic
impact on economic growth, international trade, investment, savings and public consumption
but reported mixed outcomes.There has not been much research done to investigate the
impact of aid flows on poverty reduction. This is surprising because for the past two decades,
the international communities have given high priority to using aid resources to reduce
poverty, for example, through the attainment of the Millennium Developing Goals (MDGs).
From 6-8 September, 2000, 191 Heads of State and Government met at the United Nations
Headquarters in New York to shape a broad vision to fight poverty in all its dimensions. They
signed a Millennium Declaration, a pledge “to free our fellow men, women and children from
the abject and dehumanizing conditions of extreme poverty” 1which gave birth to eight
MDGs2which were set to be achieved by 2015. One of the top priority targets was “to halve,
between 1990 and 2015, the proportion of the world’s people whose income is less than a
dollar a day and the proportion of people who suffer from hunger”3.Most of the people
livingin extreme poverty facesome of the hardest conditions imaginable, hunger, epidemic
diseases, illiteracy, poor sanitation, unclean drinking water and lack of education. The UN
MDG resolutions of 2000 resolved to give more generous aidto poverty plagued developing
economies as one of the strategies which was to be employed to eradicate poverty4.
With this recent change of focus on the priority of using aid resources from economic
growthto poverty reductionand since we have reached the end of Millennium Development
Agenda period, it is timely to investigate whether the foreign aid received had been effective
1United Nations General Assembly, 2000, 55th session Agenda item 60 (b), page 4-5) 2 The MDGs are: 1. Eradicate extreme poverty and hunger 4. Reduce child mortality 7. Ensure environmental sustainability 2. Achieve universal primary education 5. Improve maternal health 8. Global partnership for development 3. Promote gender equality and empower women 6. Combat HIV/AIDS, malaria and other diseases 3United Nations General Assembly, 2000, 55th session Agenda item 60 (b), page 5 4United Nations General Assembly, 2000, 55th session Agenda item 60 (b), page 4
2
in reducing poverty. In this regard, this study empirically tests whether foreign aid flows have
been effective in lubricating the process of poverty reduction in some selected SADC
countries5 and if not, investigate why aid is failing and how it can be made more effective.
1.1 Background
Poverty is not a unique case for one region; almost all societies have some of their citizens
living in poverty. However, even though poverty is everywhere, the kind of poverty in the
Sub Saharan African region is of great magnitude both in its spread, depth and severity.
Thisphenomenon has attracted the international community andforeign aid has been hailed as
one of the answers to solve the poverty-related problems6. However, the reality is that aid is
not eliminating poverty in Sub Saharan African regiondespite the large sums of aid being
received annually (Randel, et al, 2004).
Although, the aid-poverty debate on one hand focus on key ways in which the quality of
foreign aid can be improved in order to effectively reduce poverty, it needs to be understood
that on the other hand, the issue on the adequacy of the quantity of aid being received in
developing regions, Sub Sahara Africa in particular,has also been debated for long. The
Monterrey Consensus7 of March 2001 in Mexico at the International Conference on
Financing for Development recognizes that, although, the governments of poor countries
have the main responsibility to accelerate development by putting in place appropriate policy
and institutional frameworks, they cannot achieve it without the cooperation and assistance
ofthe international community in areas such as trade, investment, debt relief and official
development assistance.
Following this consensus,donors officially committed to increase the quantity of aid to 0.7%
of donor gross national income (GNI), a target that had been in place since the mid-1960s
(UN, 1970). However, the global aid flows to the least developed Sub Saharan African
countries and in-deed SADCcountries do not corroborate the pledges made at the
international summits and conferences. For instance, as of 2013 and 2014, aid levels stood at
0.3% of the total GNI for the 28 OECD Development Assistance Committee (DAC) member
5SADC has a membership of 15 member states namely Angola, Botswana, DRC, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe. In this study Mauritius, Seychelles and Botswana are excluded because during the period under investigation foreign aid (both humanitarian and budgetary support) to these countries has been erratic and very marginal. These are also in the high development category in terms of aggregate welfare as measured by the HDI. 6United Nations General Assembly, 2000, 55th session Agenda item 60 (b), section VII page 7-8; Pfutze and Easterly 2008 7 The text of the Monterrey Consensus can be found at http://www.un.org/esa/ffd/0302finalMonterreyConsensus.pdf
countries which is less than half of the agreed target
Most donor countries have failed to donate 0.7% of their GNI.By
DAC countries, only seven countries namely United Kingdom, Sweden, Norway,
Netherlands, Luxembourg, Finland and Denmark
Therefore, the inability of aid to alleviate poverty
the aid resources that reaches the Sub Saharan Africa
meeting the poverty needs and there is a growing gap between Africa’s aid needs and the aid
provided. Pekka (2005) argue that this is
target by donor countries and
and services from donor countries
1.1.1 Foreign aid and poverty: an
Globally,according to the MDG Report of 20
extreme poverty declined from 1.9 billion in 1990 to 836 million in 2015
world as a whole, it also declined
Report, 2015). Most of the progress
the developed world amounting to
past 50 years (Easterly and Pfutze, 2008)
met, progress has been uneven across regions
poverty of 41.7%, followed by
1.1.
Figure 1.1 Global share of pover
Source: Chandy and Hami, 2014
8OECD, 2016- Statistics on resource flows to developing countries as at 22 December 20159 Borger and Denny of the Guardian (UK) (cited in Shah, aid among the DAC member countries, it has the worst record for spending its aid budget itself. According to them, 70% of US on US goods and services with more than half spent in the Middle East. Only $3 billion goes to South Asia and Subcountries where aid is mostly needed.
East Asia and
3
less than half of the agreed target of 0.7% of total GNI
donor countries have failed to donate 0.7% of their GNI.By 2013/2014
only seven countries namely United Kingdom, Sweden, Norway,
bourg, Finland and Denmark donateclose to 0.7% of their GNI
Therefore, the inability of aid to alleviate poverty canalso be attributed to the inadequacy of
ces that reaches the Sub Saharan Africancountries. Aid levels are not based on
meeting the poverty needs and there is a growing gap between Africa’s aid needs and the aid
Pekka (2005) argue that this is due to the low commitment level
and that most of the aid resources are wasted on overpriced goods
and services from donor countries hence too little aid reaches the developing
Foreign aid and poverty: an overview of the global context
Globally,according to the MDG Report of 2015, the number of people who were
declined from 1.9 billion in 1990 to 836 million in 2015
declined significantly from 47% in 1990 to 14% in 2015
. Most of the progress is attributed to the increased inflow of
the developed world amounting to US$103 billion in 2006 and over US$2.3 trillion over the
(Easterly and Pfutze, 2008). While globally the target to halve
uneven across regions.By 2010 South Asia had the largest share of
poverty of 41.7%, followed by Sub-Saharan Africa with a share of 34.1% as shown in fi
Global share of poverty (%) among developing regions in 2010
Source: Chandy and Hami, 2014
Statistics on resource flows to developing countries as at 22 December 2015
Borger and Denny of the Guardian (UK) (cited in Shah, 2005), observed that although the US remains a big player in the disbursement of aid among the DAC member countries, it has the worst record for spending its aid budget itself. According to them, 70% of US
an half spent in the Middle East. Only $3 billion goes to South Asia and Sub
Sub Saharan
Africa
34.1%
Middle East and
South Asia
41.7%
East Asia and
Pacific
20.7%
Latin America and
the Caribbean
2.7%
Europe and
Central Asia
0.3%
GNI (OECD, 2016).
2013/2014, out of the 28
only seven countries namely United Kingdom, Sweden, Norway,
0.7% of their GNI or more8.
to the inadequacy of
Aid levels are not based on
meeting the poverty needs and there is a growing gap between Africa’s aid needs and the aid
level to 0.7% of GNI
wasted on overpriced goods
developing countries9.
who were living in
declined from 1.9 billion in 1990 to 836 million in 2015. In the developing
from 47% in 1990 to 14% in 2015 (MDG
attributed to the increased inflow of foreign aid from
over US$2.3 trillion over the
lve poverty has been
South Asia had the largest share of
with a share of 34.1% as shown in figure
2005), observed that although the US remains a big player in the disbursement of aid among the DAC member countries, it has the worst record for spending its aid budget itself. According to them, 70% of US aid is spent
an half spent in the Middle East. Only $3 billion goes to South Asia and Sub-Saharan African
Sub Saharan
Africa
34.1%
Middle East and
North Africa
0.7%
4
But by, 2015, all developing regions except Sub Saharan Africa had met the target of halving
poverty (Global Monitoring Report of 2014/2015) as shown in figure 1.1.
Figure 1.2 – Extreme poverty by region using share of population below US$1.25/ day (2005 PPP)
Source: Global Monitoring Report 2014/2015
As shown from the figure 1.2, East Asia and the Pacific had made the most significant
progress in reducing poverty i.e. by 54.1 points from 58.2% in 1990 to 4.1% in 2015,
followed by South Asia by 28.7 points from 53.2% in 1990 to 24.5% in 2015. Sub-Sahara
Africa(SSA) marginally reduced poverty by 15.7 points from 56.6% in 1990 to 40.9% in
2015. SSA had the largest number of its population(40.9%) in extreme poverty by 2015
followed by South Asia with 24.5% and the rest of the other sub-regions had marginal
poverty levels.Of all the developing regions, Sub-Saharan Africa has made the slowest
progress in meaningfully reducing poverty yet ithas received the bulk of aidover the period as
shown in figure 1.3.
Figure 1.3- Regional Share of Official Aid (ODA) disbursements 1990-2012
Source: Global Monitoring Report, 2014/2015
Eastern Europe and Central Asia
Middle East and North
Africa
Latin America and the
Caribean
East Asia and Pacific
South AsiaSub-Saharan
Africa
1990 1.5 5.8 12 58.2 53.2 56.6
2005 1.3 3 7.4 16.7 39.3 52.8
2011 0.5 1.7 4.6 7.9 29 46.8
2015 0.3 2 4.3 4.1 24.5 40.9
010203040506070
Pov
erty
hea
dcou
nt %
5
Figure 1.3shows that Sub Saharan Africa received the largest regional allocation of the total
ODA disbursements on incremental basis from 37% in 1990 to 46% in 2012 followed by
South Asia from 10% in 1990 to 21% in 2012. The rest of the sub-regions’ aid allocations
decreased from levels above 15% in 1990 to levels below 10% in 2012yetthey have the
lowest shares of global poverty compared to SSA and South Asia.
1.1.2 Foreign and poverty: the African region context
Africa lagged behind other regions of the developing world in its attempt to reduce the
intensity of poverty despite the significant amounts of foreign aid received. Sachs and Ayittey
(2009) observed that more than US$450 billion had been pumped to Africa since 1960 with
negligible results in reducing poverty.Easterly and Pfutze (2008) noted that regardless of
efforts by G8 countries to write off more than US$40 billion in debts and doubling aid to
US$50 billion in 2010, Africa is failing to register the intended results in poverty reduction.
From 1990 to 2010 the intensity of poverty in Africa only reduced by 2%that is from 13% to
11% while developing regions as whole reduced by at least 9%(MDG report, 2015).
Withinthe African region, performance varies by sub-region. North Africa, by 2011 had
managed to halveits poverty despite receiving smaller amounts of aid compared to Sub-
Saharan Africa which receives more aid (Global Monitoring Report 2014/2015). The
intensity of poverty in Sub-Saharan Africa surpasses that of its counterpart, North Africa,that
is, 19.2% and 0.4% respectively in 2011 (see table 1.1).
Table 1.1 Total official aid flows, extreme poverty &intensity of poverty in SSA & North Africa Sub-region Year Aid flows US$
million (ODA+OOF) % of people in extreme poverty
Intensity of poverty %
Sub-Sahara Africa (SSA)
1990 13 259.27 56.6 25.5 2005 22 649.73 52.8 22.4 2011 27 184.77 46.8 19.2
North Africa 1990 3 124.69 5.8 1.1 2005 798.97 3 0.6 2011 2 307.24 1.7 0.4
Source: Poverty data- WDI, PovcalNet& Aid flows –OECD. Stats, 2016
In 1990 Sub Saharan Africa received official aid flows amounting to US$13 259.27 million
and by 2011 the aid flows had doubled to US$27 184.77 million (OECD. Stats, 2016) butthe
percentage of people living in extreme poverty only reduced marginally from 56.6% in 1990
to 46.8% in 2011 (WDI-Povcalnet, 2016). By the same period, official aid flows received by
6
North Africa had reduced from US$3124.69 million to US$2307.24 million yet poverty had
been halved from 5.8% to 1.7% as shown in table 1.1 in previous leaf.
Easterly (2006) argues that despite the astronomical sums of aid that have been spent on Sub
Saharan Africa, there is very little to show for it in terms of poverty reduction.Magnon (2012)
and Ijaiya G.T. &IjaiyaM.A. (2004) confirmed Easterly’s assertion. However, they did not
consider how foreign aid’s impact may differ across Sub Saharan Africa’s regional economic
communities due to institutional differences. So, does Easterly’s assertion also apply to the
Southern Africa Development Community?The study intends to answer this question.
1.1.3 Foreign aid and poverty in Southern Africa Development Community
From 2005 to 2013 SADC receiveda substantial amount of aid totalling
US$118,834,06110billion.Total aid had been increasing since 2005, though between 2011
and2012 it declined butgenerally,it followed an upward trend. On each year, the largest
amount of foreign aid received in SADC was in the form of budget support (NODA + official
aid). Foreign aid in the form of humanitarian assistance had been increasing from 2005 to
2009 and from 2009 to 2013 it took a downward trend as shown in figure 1.4.
Figure 1.4 Foreign aid trends received in SADC 2005 - 2013
Sources: World Bank, World Development Indicators, 2014 & UN Relief web, 2015
Out of the 12 selected countries in SADC, when using the HDI to measure poverty reduction,
from 2005 to 2013 Namibia and South Africa remained in medium human development
category while the rest of the countries remained in low human development category
10Total for both ODA and humanitarian (Source - World Bank, World Development Indicators, 2015).
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
18,000,000
2005 2006 2007 2008 2009 2010 2011 2012 2013
Fore
ign
aid
US
$ '
00
0(b
illi
on
s)
Humanitarian aid NODA & official aid (budgetary support) Total Aid
Total aidNODA & Official aid
Humanitarian aid
7
(UNDP Report, 2014). There is insignificant change in poverty reduction despite the large
sums of aid received as shown in fig 1.5.
Figure 1.5: Human Development IndexTrends for 12 selected SADC Countries
Source: UNDP, 2016
Although substantial amounts of foreign aid have been received in SADC from 2005 to 2013,
the progress towards poverty reduction has been very slow. The intensity of poverty in SADC
remainssevere compounded by growing income inequalitiesand persistent gender inequalities
(Gender Inequality Index of 0.538 in2013). In most SADC countries more than 50% of the
population live below their national poverty lines of which the majority of the poor live in
rural areas as shown in table 1.2 (UNDP Report, 2014).On average the intensity of
deprivation in each country is very high above 45%in most instances.
Table 1.2– Measures of multidimensional poverty by country from 2005-2014 Country (2005-2014)
Multidimensional Poverty Index 11
Population below national poverty line (%)
Intensity of deprivation (%) 12
Population in severe poverty (%) 13
Pop poor rural %
Pop poor urban%
DRC 0.401 63.6 50.8 36.7 88.2 48.6 Tanzania 0.335 28.2 50.4 32.1 74.7 34.6 Lesotho 0.227 57.1 45.9 18.2 43.3 9.7
11 Multidimensional Poverty Index is the percentage of the population that is multi-dimensionally poor adjusted by the intensity of the deprivations in education, health and standards of living. 12 Intensity of deprivation is the average percentage of deprivation experienced by people in multidimensional poverty. 13Population in severe poverty is the percentage of the population in severe multidimensional poverty—that is, those with a deprivation score of 50 percent or more.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
2005 2006 2007 2008 2009 2010 2011 2012 2013
HD
I v
alu
e
Angola DRC Lesotho Madagascar
Malawi Mozambique Namibia South Africa
Swaziland Tanzania Zambia Zimbabwe
DRC
Mozambique
Malawi
South Africa
Namibia
Swaziland
Zambia
Tanzania
Madagascar
Angola Lesotho Zimbabwe
8
Madagascar 0.420 75.3 54.6 48.0 73.7 24.9 Malawi 0.332 50.7 49.8 29.8 72.0 39.7 Mozambique 0.390 54.7 55.6 44.1 83.9 38.2 Namibia 0.205 28.7 45.5 13.4 56.0 15.3 Zambia 0.264 60.5 48.6 22.5 80.6 34.4 Zimbabwe 0.128 72.3 44.1 7.8 51.1 11.4 Swaziland 0.113 63.0 43.5 7.4 24.6 6.5 South Africa 0.041 53.8 39.6 1.3 19.8 5.4 Source: UNDP website 2015
On a scale of 0 to 1, Madagascar had the highest multidimensional poverty index (MPI) of
0.42 indicating that about 42% of its total population was multi-dimensionally poor between
2005 and 2014, followed by DRC with 0.40 and South Africa had the least index of 0.041. In
terms of the intensity of poverty, Mozambique has the highest depth of poverty of 55.6%
followed byMadagascar with 54.6% and DRC with 50.8%. South Africa has the least depth14
of poverty of 39.6%. The greatest percentage of the people in severe poverty is in
Madagascar with 48% followed by Mozambique and DRC with 44, 1% and 36.7%
respectively.
1.2 Statement of the problem
Despite, the renewed commitment to poverty reduction as the core objective of aid
disbursement over the past decade, progress to this end in SADC countries, like the rest of
Sub-Saharan Africa remains disappointing.From the discussions above, it is clear that during
the period under investigation SADC countries, like the rest of Sub Saharan Africa,
receivedsubstantial amounts of aid but there is little to show for it in terms of poverty
reduction. These aid flows receivedhave not yielded meaningful reduction in poverty as was
expected. As aid is increasing, the corresponding reduction in poverty isvery marginal. The
reality is that aid has failed to register the intended results ofimproving the welfare of the
SADC countries’ population.This inadequate progress raises questions on the effectiveness of
the aid strategy that have been adopted to achieve poverty reduction andalso raises questions
on why SADC countriesare failing to uplift its people out of povertydespite the large sums of
different types of aid being received. Therefore, the purpose of this study is to investigate
why foreign aid is not having sustainable impact on poverty reductionand explain the
conditions under which foreign aid can be made effective if it is to uplift SADC economies
from the “poverty trap”.
14Depth of poverty describes how far off households are from the poverty line
9
1.3Objectives of the study
The main objective of thisstudy is to investigatewhyaidis failing to reduce poverty in some
selected SADC countries.
The specific objectives are;
a. To determine whether foreign aid has been effective in reducing poverty in some
poverty plagued aid recipient SADC countries.
b. To determine which type of foreign aid is more beneficial in reducing poverty.
c. To investigate what is derailing progress in reducing poverty given the amount of aid
flows to SADC region over the period under investigation.
d. To explain the conditions under which aid can be made more effective in reducing
poverty inSADC countries basing on the empirical results.
e. To determine which otherforms of economic activities other than the aid strategy can
be employed to effectively reduce poverty in SADC region.
1.4 Research questions
The questions which this study seeks to address are as follows:
a. Has foreign aid been effective in reducing poverty in SADC?
b. Which type of aid is more beneficial in reducing poverty in SADC?
c. What is derailing progress in reducing poverty in SADCi.e., is it its misuse or
misallocation or other factors?
d. Under what conditions can aid be made effective in fighting poverty in SADC
countries?
e. What other forms of economic activities other than the aid strategy can effectively
reduce poverty in SADC countries?
1.5 Justification of the study
The study seeks to assess if the aid strategy has been effective in reducing poverty in SADC.
Therefore,the findings of this study will inform central governments of aid recipient countries
on what mechanisms could be adopted to harness aid resources to reduce poverty in SADC
aid recipient countries. Furthermore, examining the effectiveness of aid on poverty could
10
provide crucial policy options to donor countries and multilateral agencies on the impact of
aid.
One of the priority goals adopted under the new Sustainable Development Goals (SDG)
Agendaon 25 to 27 September 2015 is to ensure thatpoverty and hunger in all its forms
everywhere is put to an end by 203015. In order to achieve this goal in the next 15 years there
is need to adopt effective strategies to eradicate poverty hence the findings of this study will
inform on the relative importance of other forms of economic activitiescompared with foreign
aid strategy in a bid to reduce poverty in SADC economies.
1.6 Organisation of the study
The rest of the study is organised as follows:Chapter 2 provides a review of both the
theoretical and empirical literature. Chapter 3 outlines the methodology that will be used for
the study. Chapter fourpresents a discussion and assessment of the estimation procedure and
the interpretation of the results. Chapter five will provide the conclusion of the study, the
policy recommendations and areas of further research.
15Sustainable Development Goals Final Proposal of OWG on 19 July 2015
11
CHAPTER TWO
LITERATURE REVIEW
2.0 Introduction
A number of studies have been done on the effectivenessof foreign aid inreducing poverty in
several countries. The studieshave however produced two contrasting views on the
effectiveness of aid on poverty reduction. The public interest view argues that aid is effective
in reducing poverty and should be used in the development process (Sachs, 2005;Sen, 1999).
The public choice view argues that foreign aid is ineffective in poverty reduction (Easterly,
2001; Bauer, 2000).This chapter therefore, reviews some of these schools of thought and the
other theories of aid in relation to poverty reduction. The other theories reviewed in section
2.1 include the big push model, vicious cycle of poverty, stages of growth theory, two gap
model, recipient needs model, donor interest model,principal agent theory, theory of
incentives, rent seeking model and gift exchange game theoretical model. Section 2.2 reviews
the empirical literature.
2.1 Theoretical literature review
To set the context straight, since the 1950s, the Marshal Plan era in which Europe was rebuilt
through development aid, underdevelopment was thought to be a product of capital shortage
hence aid was channelled through capital transfers and investment projects in the 1960s
(decade of industrialisation). Following failure of growth orientation of 1960s, in the 1970s
aid was then channelled through anti-poverty programs. In 1980s, the diagnosis of aid
effectiveness problems turned to policy failures, the solution to which lay ‘aid with
conditions’ programs such as stabilisation and Structural Adjustment Programs (SAPs).
Following failure of the SAPs, the international community identified poor governance,
institutional failure and corruption as factors militating against aid effectiveness and needs to
be tackled (Moyo, 2009; Paul, 1996). The purpose of this section is to review the insights
provided by economic theory in relation to why developing countries need foreign aid, how
donors allocate aid and what factors militate against aid effectiveness.
The Big Push Model
The Big Push model was propounded by Rosenstein Rodan in 1943. The theory states that
developing countries are caught up in a low income equilibrium trap which prevents self-
12
sustaining growth hence are poor. He advocated for a critical mass of simultaneous large
scale investments and other supportive initiatives such as corresponding infrastructure
provision and institutional developmentas the only feasible way for poor countries to escape
from this trap. He viewed the ‘big push’ as a necessary initial condition for growth that will
allow these poor countries to escape from their low incomes. The primary policy implication
of the model is that the needed large scale investment resources could be met through foreign
aid as the big pushto accelerate the take-off into a self-sustained growth by generating new
domestic investment and ultimately reducing poverty since growth is viewed as the primary
driver of poverty (Appolinari, 2009; Easterly, 2005;Waterson 1965; Chenery, 1960).
According to Hirschman (1958) the big push model is heavily criticised for ignoring the
agriculture sector yet in most of the low income economies particularly Sub Saharan Africa
and indeed the SADC region it is the agriculture sector which is large. Therefore, foreign aid
resources could also be invested in agriculture so that it goes hand in hand with those in
industry to stimulate the industrial sector because if agriculture is neglected it would be
difficult to meet the food requirements of the nation and the food shortages may impose
inflationary pressures perpetuating poverty. In addition, the big push model overlooks that
massive industrialisation programmes may be constrained by inadequate resources,
ineffective disbursement of resources, macroeconomic problems, weak institutionsand
volatile foreign aid flows which are common features in low developing sub-regions like
SADC.
Sachs (2005) in agreement with the big push model argues that large infusions of foreign aid
can break the low income equilibrium trap by facilitating investment in business,
infrastructure, natural, public institutional and knowledge and human, capital. The big push
model is useful in this study as it provides the underlying principles for bothcurrent aid
policies advocating for more aid to AfricaDevarajanet al 2002 at the WB; Commission for
Africa (2005) andregression specifications in the aid-poverty empirical literatureMasud and
Yontcheva (2005)by recognising that aid is growth enhancing and in turn growth is a
necessary, though not sufficient, condition for poverty alleviation.
The vicious circle of poverty Nurkse (1953) observed that underdeveloped countries were caught up in two interconnected
vicious circles of poverty both from the demand and supply side that lock them in a low
13
income equilibrium trap. On the demand side, demand is low due to very low incomes and
limited market hence less incentive to make private investments and capital formation and
accumulation remain at very low levels. As a result no real productivity improvements occur
and therefore incomes remain very low. On the supply side, the low incomes result in a
reduced capacity to save reflected in lack of capital and low productivity. The final outcome
is stagnant economic growth and the reproduction of mass poverty. The preconditions for
breaking out of these poverty circles were according to Nurkse, the creation of strong
incentives to invest along with increased mobilisation of investible funds particularly on the
domestic front through a significant expansion of the market andsimultaneous massive capital
investments in industrial sectors.The implication of the model is that since most developing
countries are capital constrained and have low ability to save, the much needed investible
funds could also be met through foreign aid.
The vicious circle of poverty theory is useful to this study because to a greater extent it
outlines the causes and effects of poverty trapswhich are more applicable in most of the
SADC countries. The theory also identifies capital formation and foreign aid as necessary
though not sufficient conditionsfor economic growth thus to some extent the international
community has a vital role to play in the development process of low developing countries by
providing ideas, models and necessary funding.However, of greater importance according to
the vicious circle of poverty,there is need for SADC countries to mobilise investible funds
particularly on the domestic frontthrough significant market expansion (international trade) to
allow them to break out of the poverty traps.
Stages of growth theory
Rostow (1960) proposed that all countries sooner or later during their development process
will pass through the same sequence of the five stages of growth which are the traditional
society, transitional (preconditions for take-off), take-off, drive to maturity and high mass of
consumption (Todaro and Smith, 2012). In the drive to maturity and high mass consumption
stages, nations achieve stable conditions for self-sustaining growth and wealth creation and
ultimately poverty reduction. The traditional society stage is characterised by high poverty,
subsistence production and retrogressive traditional values and systems. The critical stage is
the take-off stage whereby investment rate tends to increasesharply, leading economic sectors
tends to create investment opportunities in other parts of the economy and there is
establishment of political and social institutional frameworks to ensure self-sustained growth.
14
The model purports that during the transitional phase, the preconditions for investment (take-
off) are identified as the ability to mobilise domestic and foreign savings, willingness of
people to lend risk, innovate and be entrepreneurs and willingness of society to operate
economic systems based on capitalist principles. However, since most poor countries
especially those in SADCregion, have relatively low levels of new capital formationand
cannot save enough,developed countries in this transitional stage can then assist through
foreign aid and making industrial investments in order to pull out of poverty millions of poor
people in these developing countries (Todaro and Smith, 2012).
The mechanisms of development embodied in Rostow’s stages of growth theory do not
always work because even though savings and investmentsare necessary conditions for
accelerated economic growth rates, they are not sufficient conditions. Countries receiving aid
need to possess the necessary structural, institutional and attitudinal conditions. Rostow
assumesthat these conditions exist in developing countries yet in most countries, those in
SADC in particular, they are lacking, making aid fail to achieve growth and poverty
reduction.
The Two Gap Model
The two gap models developed by Chenery& Bruno (1962) and Chenery&Strout (1966)
purports that in developing countries, Africa in particular, investment and economic growth
are restricted by the level of domestic saving or import purchase capacity which are termed
the two gaps. The saving-investment gap (domestic resource gap) is whereby given the
import purchasing power of the economy and the level of other resources, domestic savings
are inadequate to support the level of growth. The import-export gap (external resource gap)
is whereby the import purchasing power conferred by the value of exports plus capital
transfers are inadequate to support the level of growth permitted by the level of domestic
savings. According to this model, foreign aid is viewed as a tool for overcoming these
financing gaps in developing countries. The main argument is that foreign aid is growth-
enhancing hence it is expected to promote economic growth by augmenting foreign exchange
needed in production hence ultimately reduce poverty.
Displacement theorists (Leff, 1969; Griffin, 1970; Weisskopf, 1972) criticised the two gap
model for being capital oriented and that it heavily depends on aid that can adversely affect
economic growth by substituting for domestic savings on two accounts. Firstly, aid may
15
encourage the recipient government to ease its revenue generation efforts so that consumption
expenditures increase or imports are liberalized. Secondly, savings may fall as foreign
investment crowd out domestic investment (Mahmood, 1997). Furthermore, aid may depress
the growth rates of recipient countries and result in inefficiencies due to inappropriate
technology and management styles. In such instances aid may indirectly fail to reduce
poverty through failing to increase economic growth. Moreso, capital and foreign exchange
are not the only constraints for development, there are some factors such as corruption and
weak institutions which are not considered in the two gap model.
The model however, provides support for poverty targeting aid policies as a basis for both the
administration of foreign aid programs and estimation of global aid requirements (Mikeselet
al, 1982). Another advantage of the model is that it allows for disaggregation and rapid
identification of fundamental inconsistencies in the economy that need to be corrected. For
instance, it fully recognises the need for governmental policies that would promote
productivity, savings, and the allocation of resources to productive investments which would
then accelerate growth and in turn reduce poverty.
The model is useful in this study as it identifies growth as a necessary condition for poverty
reduction. However, economic growth alone cannot solve poverty issues (Haveman and
Schwabish, 2000). For the theory to be more applicable in this study, the model needs to be
extended to include other missing gaps in the SADC region which are also hindering
development. These include the infrastructure gap, technological gap and human capital skills
gap.Where there is poor infrastructure set up and inadequate infrastructure linkages as widely
evidenced in SADC, foreign aid can be channelled to develop the infrastructure in order to
reduce market failures which are increasing the prevalence of poverty in SADC countries.
Where there are human capital skills gaps and technological gaps to permit a level of
investment sufficient to achieve sustained growth, foreign aid that could be in form of
technical assistanceserves to increase the capacity of a country to employ capital
productively(Mikeselet al, 1982). Transfers of knowledge, skills and technology are desirable
for the aid recipient countries so that human poverty can be easily addressed.
The recipient needs model
Kostadinova(2009) argues that the recipients needs model is derived from the assumption of
the West’s moral obligations to help those in need, arguing that the economic, political and
16
social needs of the receiving countries drive the amount of aid they receive over time. Thus
the greater needs translate to higher levels of assistance. The needs could be in a variety of
ways which include income levels, poverty levels, infant mortality rates, levels of human and
political development and population size. The basic proposition of the model is that
countries that are lacking in the areas supported by foreign aid and those with large
population sizes would receive more assistance than countries that are better off in these
areas.
Although on one hand, aid may release governments from binding revenue constraints, it may
also create dependencyon the other hand.One weakness of the recipient needs model is thatit
encourages aid dependency in the sense that if donors announce that in future they will
disburse aid according to the needs of the poor, potential recipient countries will have less
incentive to introduce policies that would reduce poverty now. Thus potential recipient
countries will be reluctant to develop their capacities or perform some of the core functions
of the government such as maintenance of existing infrastructure and or delivery of basic
public services as witnessed in most SADC countriescreating a dependency syndrome which
exacerbates poverty (Brautigam and Knack, 2004).
The model is relevant to this study as it manages to explain how donors may decidewhich
countries to allocateaid.From the model, we derive that aid dependenceis strongly correlated
to povertywhich explains why aid may not be effective in some instances. Literature
highlights that aid dependence cannot be directly measured hence a proxy that reflects ‘aid
intensity’ can be used (Brautigam and Knack, 2004). These are thenetaid flows as a
percentage of GDP and aid as a percentage of government expenditure.
Some donor countriesin allocating aid as observed in SADC countriesseem to also take into
consideration the merits of recipient countries such as past performance as measured by the
quality of institutions and policies and government effectiveness. Therefore, to increase the
relevance of the recipient model to this study it can be extended to recipient needs and merits
modelso that it captures the merits of recipient countries as well. According to Collier and
Dollar (2002) the number of people pulled out of poverty can be maximised if aid is allocated
to countries where the aid needs are high but also their policies and institutions are of good
quality and the level of controlling corruption is high. Aid effectiveness is likely to be
increased if donors move to a ‘need and merit’ based aid allocation.
17
The donor interests’ model
Kostadinova(2009) asserts that the donor interests’ model sees foreign development
assistance as driven by the strategic and economic considerations of the donor countries.
Thus in distributing foreign aid, donors are driven by their own geo-political and strategic
interests to advance their own political and military positions.The majority of aid allocation
decisions are done in the best interest of the donor for instance donors may give more aid to
those countries which tend to vote with them at the United Nations sessions or their former
colonial possessions (Alesina and Dollar, 2000). Other donor interest factors include; security
alliance (Schraeder et al, 1998),oil reserves in the recipient country (Breuning and Ishiyama,
1999), stocks of private direct investments, promotion of international trade and image-
building of the donor in the international arena (Cooray, 2005) and availability of strategic
raw materials(Maizels and Nissanke, 1984).
In assessing the impact of foreign aid on poverty reduction, it is important to consider the
motivations of donor countries when allocating aid as recognised by the donor’s interest
model. Scholars have argued that aid levels in Africa, SADC in particular, are not based on
meeting the needs of the poor hence there is a growing gap between Africa’s aid needs and
the aid provided which may explain why foreign aid has been unsuccessful in fostering
sustainable impact on poverty reduction (Riddel, 1999).Though the model downplays the
importance of economic indicators of the recipient countries, the model helps to explain the
disappointing record of foreign aid in reducing poverty. The model identifies low quantityand
unpredictable flows of aid being receivedas some of the factors that militate against aid
effectiveness.
Sometimes when serving their own interests, donors tend to turn aid into a business and
propose for tied aid. In his book, ‘Lords of Poverty’, Graham Hancock (1989) argues that
sometimes one can get quite rich attending the poor in the business of transferring aid
resources in the sense that with tied aid about 80% of the overall expenditures of the various
UN bodies engaged in relief and development goes towards personnel and related costs and
overpriced goods and services from the donor countries. Therefore, only a smaller percentage
18
reaches the needy poor countries hence there will be no sustainable impact on poverty
reduction16.
The principal-agent theory
The principal agency theory studies the delegation problem in an environment of information
asymmetry, uncertainty and risks. According toAzom and Laffont (2003) the model
assumesthat foreign aid is a contract in which donor countriescan make a transfer of aid
resources to the needy recipient countries in return for poverty alleviation.Therepresentative
citizen in the North who wishes to attain high level of the international public good
(consumption of the poor in the South)is the principaland the agent is the government in the
South who controls the level of the international public good through its redistribution
policy.However, there are principal-agency problems that emerge when there is both a
divergence of interests between the agents and the principals and asymmetric information
between the two parties (Paul, 2006). These principal agency problems adversely affect aid
effectiveness.
There are principal-agency problems that arise as a result of the existence of multiple
principals and objectives (Martel et al 2001). The government as the aid agency has multiple
objectives suchas building schools, hospitals, roads and financing small enterprises
andprivatisation programmes. It is also characterised by joint delegation of tasks for instance,
from politicians and parliamentarians. These multiple principals rarely sharethe same
objectives. For instance, while one parliamentarian prefers to allocate more aid resources to
road construction because he has a construction company in his constituency, another may
want research in prevention and cure of diseases to be prioritised because he has a medical
research laboratory in his constituency.In public administrations of aid there are no clearly
defined or measurable trade-offs between the multiple options which mayresult in potential
inconsistencies and contradictions and inefficient aid allocation. Also, multiple principals and
objectives result in procedural bias in the aid delivery system which keeps ownership of
decisions in the hands of politicians giving rise to lack of transparency and accountability in
the use of aid resources which fuel up corruption hence compromising aid efficiency in
reducing poverty.
16Lords of Poverty: The Power, Prestige, and Corruption of the International Aid Business. New York: Atlantic Monthly Press, 1989,
19
In addition, the principal-agency problem could also be as a result of the existence of a
broken information feedback loop (Martel et al 2001). This is due to the geographical and
political separationbetween aid beneficiaries and taxpayers from whom the aid resources are
obtained which increases the cost of obtaining information to the aid suppliers while
reducingthe benefits of information to the aid beneficiaries. Beneficiaries may observe
performance of aid agencies but cannot modulate payments. Though donors would want to
see that their funds are well spent it is difficult for them to do so and there is no obvious
mechanism for transmitting the beneficiaries’ view to the sponsors.This broken information
feedback loop due to lack of information and accompanying required institutions to mitigate
it, induces lack of transparency and accountability which compromises the performance of
aid.
The principal agency model overlooks some of the complex principal agency relationships
that exist in the current aid delivery systems particularly in the SADC region. Paul (2006)
argues that the aid delivery channels can also include other actors such as subcontractors. In
some cases, there exists double principal aid relationship whereby the recipient government
may be viewed as the agent of the donor (political principal) on one hand and the agent of the
citizens on the other hand. In other cases there exist multiple types of donors each with
differing objectives within the same aid recipient country. If the model is extended to capture
these complex aid relationships which exist within SADC region, the model will be able to
explainsome other problems which compromise aid effectiveness such as inequity, aid
coordination failures among donors, lack of recipient ownership over aid projects and
programs, lack of coherence between the programs and policies of recipient governments.
This model is very useful in this study as it identifies factors that hinder aid effectiveness
which include lack of commitment and capacity of recipient governments to put aid to best
use. In addition, institutional and policy weaknesses within aid recipient countries such as
weak national leadership of the development agenda, ineffective public institutions and
public financial management systems can lead to inefficiency in the use of aid and lack of
sustainability in the results of aid. These highlighted risks are high in Africa and in deed
SADC has a particularly high proportion of such countries. The model has also highlighted
that poor practices on the part of donors, fragmented project assistance andparallel reporting
requirements also reduces aid effectiveness. The model also highlights that it is the key role
20
of parliaments to ensure government accountability in aid use. The Paris Declaration’sfive
basic principles17 for aid effectiveness attempts to address these problems (OECD, 2005).
The theory of incentives
This theory propounds that third parties to the donor recipient relationship e.g. companies
from donor countries may also influence how aid is disbursed bycreating incentives such as
institutional and individual incentives not to halt aid after non-compliance. Incentives
problems may also stem from the aid delivery system or even the donor’s own incentives. For
example the Samaritan’s dilemma which arises when the donor cannot commit not to assist
those in need and the aid recipient governments anticipate this softness and choose its policy
accordingly (Torsvik, 2005). Therefore, announcing that aid will be allocated on the basis of
poverty, aid may be counterproductive if the recipient government can adjust in order to
qualify for aid. Conditional aid contracts to influence domestic policy may solve this problem
(Paul, 2006). However, aid conditionality has also been heavily criticised for having negative
effects on the ownership of the aid programme and political environment of the recipient
countries hence aid programmes may fail to achieve the intended purpose which is poverty
reduction in this case.
Rent seeking models
The models stress that poverty in developing countries may be partly caused by political
regimes that are dominated by rent seeking culture and corruption. Svensson (2000) in the
rent seeking model argues that an increase in unrestricted aid in countries with different and
competing social groups may result in an increase in rent seeking which in turn results in low
provision of public goods thus limiting aid effectiveness. The forms of rent seeking include
the directly unproductive type which involves withdrawing resources from productive
activities to less productive activities and the corrupt transfers’ type in which aid resources
are transferred to political decision-makers (resource leakages). Thus, the model is useful in
this study by showing that aid can also affect the equilibrium outcome in a less direct way
17 The Paris Declaration principles are:
• National ownership or leadership of the formulation and implementation of development strategies, • Donors’ alignment with these strategies and use of country systems accompanied by strengthening of public financial
management capacity and improved predictability of aid commitments and disbursements, • Harmonization through donors’ using common arrangements (for planning, funding, disbursement, monitoring,
evaluation) and avoiding practices that undermine national capacity, • Managing for results including strengthening linkages between national development strategies and budget process, • Mutual accountability: strengthening parliaments’ oversight of development strategy and budgets in aid recipient
countries and improved provision of information on aid flows by donors.
21
through the mechanism that enforces the controlof rent dissipation in the economy so that it
increase rent seeking and be detrimental to the poor. Thus according to the rent seeking
model, if the control of corruption is high, aid may be more effective and poverty is more
likely to be reduced.
The gift exchange game theoretical model
Donor agencies are also subject to political influences. In this model, Lundborg (1998) argue
that on one hand, aid donors giveaid to developing countries in order to reach their foreign
policy goals. On the other hand the aid recipient countries in turn give political support to
donor countries in exchange for the aid. Political factors on the donor’s side, particularly non-
economic factors play a central role in explaining the failure of foreign assistance in SADC
countries like the rest of Africacountries (Alesina& Dollar, 2000). These political factors
often lead to the granting of strategic aid which is not aligned to poverty reduction objective
hence aid becomes less effective in achieving sustainable impact on poverty reduction.
2.3 Empirical literature review
There is an intense debate on the role of foreign aid in the bid to reduce poverty around the
world. Empirical literature pertaining to the effectiveness of aid in poverty reduction can be
categorised into three different strands. The first strand supporting the public interest view
purport that foreign aid is effective in reducing poverty with some of them arguing that aid is
only effective in reducing poverty under certain initial conditions. The second strand
supporting the public choice view, purport that aid is ineffective in reducing poverty and the
third category argue that aid and poverty reduction have no relationship at all hence they
advocate for complete stoppage on the usage of aid. This section reviews various studies on
the impact of aid on poverty reduction.
Economists like Sachs, Stiglitz and Stern argue that aid has supported poverty reduction and
improved human welfare in some countries and prevented worse performance in others
(Pollen, 2013). However, some studies argue that foreign aid decreases poverty given some
certain initial conditions like in the presence of good policies and institutions (Collier and
Dollar, 2001; 2002). Some researchers in their studies reveal that the effect of aid on poverty
reduction is region specific as shown in the study by Arvin and Barillas (2002) which
revealed that though foreign aid helped to reduce poverty in East Asia, it worsened poverty in
22
low income countries. Sachs and Ayittey (2009) argue that another source of debate on the
effect of aid on poverty is shaped by disagreements on the types of aid that are most
beneficial in combating poverty. Sachs and McArthur (2001) believe that it is the sector
specific targeted aid that can help eradicate poverty in developing countries.
There are also major controversies surrounding the aid - poverty debate in relation to whether
aid to developing countries should be increased. On one hand, Jeffrey Sachs (2005) in his
book18 argued for a much more expansionary foreign aid policy. On the other hand, William
Easterly (2006) in his book19argued that foreign aid in the past had done little to reduce
poverty in developing nations hence there is no need to suppose that a dramatic expansion of
aid is likely to have a larger and sustainable impact in the future. DambisoMoyo (2009)has
also joined in the aid effectiveness debate. In her book20she took a critical view of aid in
Africa and suggests that foreign aid has undermined development and worsened poverty in
developing countries hence advocate for complete stoppage on the use of aid in the
development process.
Simplice (2014) empirically examined whether initial levels in GDP growth, GDP per capita
growth and inequality adjusted human development index (HDI) matter in the impact of aid
on development in 22 African countries for the period 1996 to 2009. The study used panel
quantile regression technique where the error term and the dependent variable need not be
normally distributed. Panel quantile regression technique enables investigation on whether
aid development relationship differs throughout various distributions of development
dynamics. The study found that firstly aid-GDP growth nexus is positive with increasing
magnitudes across the distribution thus in terms of general economic growth, high growth
countries are more likely to benefit from aid than their low growth counterparts, secondly
there is positive aid-GDP per capita income relationship and the aid-human development
index (HDI) nexus is negative and almost similar in magnitudes across distributions and
specification. The policy implication is that to balance the impact of aid, the low growth
countries needs more aid than their counterparts, the high growth countries. He argues that
the negative aid-HDI relationship is attributed to the misappropriation of aid funds and
overgeneralisation on the constituents of HDI which is limited to GDP per capita, education
18The End of Poverty:Economic Possibilities of our Time 19The White Man’s Burden: Why the West’s Efforts to Aid the Rest Have Done So Much Ill and So Little Good 20Dead Aid: Why Aid Is Not Working And How There Is A Better Way For Africa
23
and life expectancy. Simplice (2014) also assert that research now needs to focus on the third
finding because the first and second are well established in the literature.
Collier and Dollar (2001) in their study argued that foreign aid reduces poverty by increasing
economic growth and therefore estimated aid’s impact on income per capita for 59 countries
from 1974 to 1997 on four year averages using OLS. The dependent variable used is the
growth rate per capita GNP and the independent variables used are policy (CPIA),
institutional quality (ICRG), regional dummies and period dummies to account for world
business cycles. The data for the variables was drawn from World Bank database. The study
concluded that aid is effective in promoting economic growth in countries with pro-growth
macroeconomic policies. They then developed a theoretical model to determine a poverty
efficient aid allocation rule which maximises poverty reduction given a certain level of aid.
The Collier-Dollar model found that the impact of aid on poverty depends on the initial level
of poverty, its elasticity of poverty with respect to income and its macroeconomic policies.
They argue that poverty efficient aid allocation rule illustrate that aid should be redirected to
countries with good economic policies and higher poverty rates until the marginal
productivity of aid in decreasing poverty is equalised across countries. They assert that if aid
is allocated this way about 9.1 million could be lifted out of poverty.
Unlike previous studies by Burnside and Dollar (2000) which confined their measurement of
policies to three macroeconomic indicators covering only 275 observations for 56 countries,
Collier-Dollar model used CPIA which has 20 different equally weighted components
covering broad spectrum of policies. According to Collier and Dollar (2001) these policies
include structural policies, macroeconomic issues, policies for social inclusion and public
sector management. The studyalso used 375 observations which is a larger number than
Burnside and Dollar’s. The main weakness of the study is the simplifying assumption that
donors cannot directly target particular households but can only help the poor by increasing
aggregate income. This meant that aid’s impact on growth is the only channel through which
aid impacts on poverty. However, while development aid may spur poverty alleviation by
promoting economic growth, others argue that aid can impact the level of poverty within a
country through various direct channels other than growth. Gomaneeet al(2005a) identified
three direct channels through which aid can reduce poverty. Firstly is through direct project
funding by donors in social sectors such as health, education and sanitation. Secondly aid can
reduce poverty by directly targeting skill acquisition, and provision of capital. Thirdly aid can
24
reduce poverty through government spending targeting social sectors which contribute more
to human welfare such as primary education, primary health,and provision of more water
sources,training farmers and construction of rural roads.
Mosley, Hudson and Verschoor (2004) argue that aid can impact directly on poverty for
instance through projects aimed at raising the incomes of individuals living below poverty
line and through other channels of growth like through influencing the elasticity of poverty
with respect to growth. Due to these various mechanisms by which aid impacts on poverty,
Mosley et al (2004) therefore investigated the impact of aid on poverty from 1990 to 1999 for
various regions arguing that the total impact of aid on poverty is a combination of its direct
effect, its effect on growth (GNP per capita), plus its effect on policy. They treated aid,
poverty21 and pro-poor expenditure (PPE) as endogenous using the GMM22 technique to
simultaneously estimate the three equations for the endogenous variables. The dependent
variables used for the poverty equation arepoverty head count ratio and infant mortality rate
and the explanatory variables are the income per capita, corruption, inequality and public
spending indicators. For the aid equation, the dependant variable is the share of ODA in GNP
and the explanatory variables are the population size, income per capita, variables indicating
donor’s interest and policy variables.The explanatory variables for the policy equation are
aid, income per capita and control vector k. The data used was extracted from World Bank
Monitoring Database and World Development Indicators.
From the model they found that aid has a significant and negative impact on poverty and that
the elasticity of poverty with respect to income across all countries receiving aid is 0.48
which is lower than the elasticity of 2 assumed by Collier and Dollar (2001). Unlike the
Collier and Dollar study, this study investigated on what other factors that militate against aid
effectiveness and found that corruption, inequality and the composition of public expenditure
are strongly associated with aid effectiveness.
BahmaniOskooee and Oyolola (2009) used pooled time series and cross sectional data from
49 developing countries to empirically investigate the impact of foreign aid on poverty for the
period 1981 to 2002 in a panel framework focusing on the direct channel between aid and
poverty reduction. The dependent variable is the poverty measure which in their study is
21Poverty as measured by the headcount index of the number of people living on less than US$1 22 GMM- generalised method of moments
25
proxied by poverty headcount index. Explanatory variables of poverty used are GDP per
capita, institutional quality, income inequality, social programs and aid. The data was
collected from World Bank Database, OECD CD Rom and World Bank World Development
Indicators. The study estimated a fixed effects model to control for endogeneity and reduce
the severity of heterogeneity by including country specific factor. Two-stage-least square
(2SLS) estimation was used to remedy the problems of OLS which include failure to take
into account country-specific effects and time-specific effects. They found out that foreign
aid is effective in reducing poverty in aid recipient developing countries supporting the public
interest view. However, the impact is not as robust as the impact of inequality and growth.
Inequality was found to be harmful to poverty reduction and growth was found to be a
necessary condition for poverty reduction. The study only covered 46 countries hence the
overall results may not be reflective of the impact of aid on poverty reduction in different
regions of the developing world. There is need to disaggregate the developing region to fully
analyse the effect.
Other studies have questioned whether altering definitions of the variables used in the
Collier-Dollar model and Mosley et al (2004)changes the conclusion that aid is effective in
reducing poverty. By switching more to a comprehensive broader data set, altering the
definitions of foreign aid and the measures of poverty and using the direct link channel
between aid and poverty, Chong, Gradstein and Calderon (2009) empirically examined the
effect on foreign aid on inequality and poverty in developing countries during 1972 to 2002.
They questioned the effectiveness of foreign aid using two econometric techniques. The cross
sectional analysis had 94 observations and they consecutively run inequality, various
measures of poverty23, various types of aid24, corruption, schooling, the share of agriculture
and industry in the total output and income per capita. The data used come from United
Nations 2008 database, Povcalnet 2010, OECD 2010, World Bank Development Indicators
(2010), International Country Risk Guide (2009) and Alesina et al (2003). They find that
even in the presence of good institutions and low corruption foreign aid does not help reduce
poverty. However, since the cross country findings could be biased due to simultaneity and
reverse causation between foreign aid and poverty & income inequality, panel data method
with 465 observations was then used to tackle these potential endogeneity and persistence
23The measures of poverty employed are the headcount index, the poverty gap index and the squared poverty gap index. 24 The measures of foreign aid used are the official development assistance (ODA), effective development assistance (EDA) and commitment aid.
26
issues. They still uncovered thatforeign aid insignificantly affects poverty and income
inequality even when corruption is low,supporting the public choice view.
The lack of association between foreign aid and both poverty and inequality may be
explained by misallocation of aid resources by the donor countries which often stipulate that
the recipient countries should contract with firms and consultants from the donor countries
(Chong et al, 2009). Furthermore, Chong et al (2009) argue that policy makers in the giving
country may have preferences which are not consistent with reducing poverty and inequality
in developing countries. Although Chong et al 2009, controlled for endogeneity and
considered various definitions of aid and poverty, they failed to consider how foreign aid’s
impact on poverty differs across regions. Aid could be less effective or have more positive
effects in other regions than others. They constrained their model to be the same across
regions hence there is need for disaggregation.
Magnon (2012) considered the possibility that aid effectiveness on poverty reduction could
be region specific, hence empirically examined if the impact of foreign aid on inequality and
poverty differs in Sub Saharan Africa for the period 1972 to 2008. The emphasis on Sub-
Saharan Africa was because of its particular characteristics including its singularity, the
greater number of illegitimate states and the highest level of ethnic fractionalisation
and(Englebert, 2000). The researcher used cross sectional analysis and panel data analysis
following Chong et al (2009)’s methodological approach. The variables and the data sources
are the same as those used by Chong et al (2009). They found that there is no strong
evidence that foreign aid affects income disparity and poverty differently in Sub Saharan
Africa compared to the rest of the developing world. The cross sectional results indicated that
foreign aid has no significant relationship with poverty. The panel data analysis indicated that
ODA does have a negative and statistically significant association with poverty in Sub-
Saharan Africa but this finding fails to hold when using alternative definitions of aid. Their
main findings coincided with the main conclusions of Chong et al (2009). One of the
weaknesses of this study and others reviewed above is that they focused on the income
measures of poverty only which do not consider the non-monetary aspects of being poor.
Ijaiya G.T and Ijaiya M.A. (2004) examined the aid-poverty relationship in Sub Saharan
Africa using cross country data for 1997 and a multi regression analysis. The variables used
as explanatory variables are aid and social and political variables proxied by dummies. The
27
dependant variable was proxiedby the number of people who were not poor during that
period (poverty reduction). They obtained that foreign aid has no significant influence on
poverty reduction. They linked the insignificant relationship to the countries’ weak economic
management evidenced by high levels of corruption, bad governance, political instability and
economic instability. However, cross country multi-regression analysis is not the best
estimation method to investigate the impact of aid on poverty as results could be biased since
it does not control for endogeneity between foreign aid and poverty variables. In addition,
this study is limited to its time coverage thus entire study contains data set for one year hence
results could be more flawed due to its incredibly narrow data set.
Since poverty is a multidimensional phenomenon and data income measures of poverty is
relatively sparse, several studies have attempted to use the non-income measures of poverty
such as HDI, literacy rates or infant mortality rates which considers non pecuniary factors of
poverty and thus providing a better measure of overall poverty. Gomanee, Girma and
Morrissey (2003) empirically assessed the effect of development aid, pro-poor expenditure
and military spending on HDI and infant mortality rates using quantile regression rather than
OLS in order to determine if the impact differs basing on a country’s initial level of welfare.
They find that foreign aid and pro-poor expenditure are more effective at improving both
measures of welfare in countries with low initial levels of aggregate welfare. However, the
study did not control for country specific effects and the sample was smaller.
Using a larger sample of 104 countries and controlling for country specific effects using fixed
effects estimator tool, Gomanee, Morrissey, Mosley and Verschoor (2004) tested the same
hypothesis with aggregate welfare as the dependant variable while the explanatory variables
are aid and pro-poor expenditure. Estimation was based on unbalanced panel over the period
1980 to 2000 on four year period averages for sub samples of middle income and low income
countries.To address potential endogeneity and also to allow for the fact that it takes some
time for aid to impact on HDI, lagged aid was used in the regression as an instrumental
variable. They find that aid contributes significantly to aggregate welfare and the
effectiveness of aid is greater in low income countries. The result is robust for HDI but
weaker for infant mortality. For the same period and sample Morrissey, Mosley and
Verschoor (2005) after controlling for the level of pro-poor expenditure (PPE) used ordinary
least square (OLS) estimation to regress the aid and infant mortality against the PPE index,
per capita income and government military expenditure. They find that though the PPE index
28
does not significantly impact on any of the two measures of welfare, aid itself directly
influences the HDI and infant mortality rates. However, all these studies incorporated both
low income and middle income nations which may need to be disaggregated according to
level of development to give informative findings as institutional factors may differ.
Kumler (2007) examined the impact of foreign aid on aggregate welfare as measured by HDI
in 87 developing countries for the period 1980 to 2000 following the empirical models used
by Morriseyet al (2003;2005). The study sought to determine if macroeconomic policies
influence the impact of foreign aid on aggregate welfare by including a policy index as well
as an interaction term between aid and policy. The study used two stages least squares (2SLS)
estimation to control for endogeneity. The study finds that for the entire sample, higher levels
of foreign aid decrease HDI which contradicts the results of Morriseyet al (2004,) and
Gomaneeet al (2004; 2005) who found a positive relationship. The negative relationship also
holds when looking at low income countries only and aid has an insignificant impact on HDI
in countries with medium human development. The study also finds that macroeconomic
policies such as inflation, trade openness and budget surpluses do not impact on a country’s
level of human development when controlling for real per capita income and pro-poor
expenditures.
The finding by Kumler (2007) about the negative relationship between foreign aid and HDI
for the entire sample for nations with low human development presents an unexpected result
considering the positive relationship found by other previous researchers. If greater aid does
cause HDI to decrease in countries with low human development, it would suggest that aid
should be stopped to developing countries as it perpetuates poverty. However, before this
conclusion is adopted, further research should be conducted to further investigate the aid-
poverty25 relationship.Omission of theoretically significant variables such as corruption,
institutional quality and inequality may have impacted the relationship. It could also be that
limited data availability may have impacted the relationship between aid and HDI for
example data was averaged across five periods from 1980 to 2000 due to missing data. Also
lack of regional disaggregation to take into account differences in institutional factors may
have impacted on the results.
25Poverty measure used was the HDI
29
Nakamura and McPherson (2005) investigated several questions regarding the impact of
foreign aid on poverty reduction on eight four year time periods from 1970 to 1973 until 1998
to 2001 in Sub Saharan Africa using data for a panel of 49 countries. The dependant variable,
poverty indexwas proxied by life expectancy, infant mortality, primary school enrolment or
headcount index. The explanatory variables used are per capita real income, aid and a set of
control variables (inflation, openness index, budget balance, institutional quality, financial
depth and landlocked, conflict, tropics & East Asia dummies. The data on aid was obtained
from OECD data base and was categorised into humanitarian, short and long aid. The rest of
the data was from World Development Indicators, International Development Statistics and
research papers reviewed by the study. Both the two stage least squares (2SLS) and general
moment methods (GMM) were used as estimating techniques. To control for endogeneity
between aid and poverty variables they used instrumental variables, lagged policy variable
and lagged aid. The study found that aid had not had a robust and significant impact on
several poverty indexes regardless of the decomposition of aid. The study highlighted that
this could be because aid is misallocated, misused and or aid recipients have a lack of
absorptive capacity. The most significant and robust determinant of poverty is real per capita
income. Nakamura and McPherson argued that when aid is not used effectively due to
administrative bottlenecks and corruption in the recipient country aid disbursed through
NGOs and private bodies or humanitarian assistance might be more productive. There is still
need to disaggregate the Sub-Sahara African region to fully analyse the impact of aid on
poverty.
The studies reviewed above were focusing on the general measure of aid that is official aid
which is disbursed through government to government transfers. However, the type of aid
may also have an impact on its effectiveness. To this end, Masud and Yontcheva (2005)
analysed the impact of different types of aid on literacy rates and infant mortality using least
square estimationsand also analysed whether foreign aid reduces government efforts in
achieving developmental goals using GMM.They considered two different sources of aid,
bilateral aid and aid donated by European NGOs to determine if these two have similar
impacts on infant mortality and literacy rates. The explanatory variables used were GDP per
capita, aid, government’s effort in promoting human development and control vector Z of
factors that might affect human development indicators. The data was obtained from World
Development Indicators, IMF database, OECD database and European Commission budget.
30
They used unbalanced panel regression of varying number of countries (50-76) from 1990 to
2001 depending on data availability. They find that neither type of aid influence literacy rates
but humanitarian aid disbursed through NGO significantly decreases infant mortality in
recipient countries and does so more effectively than official bilateral aid. However, the
measure of official aid may not be appropriate indicator as it covers all types of projects and
programs. Also the study lacks regional disaggregation.
Some scholars have contributed to the debate on aid effectiveness by arguing that sectoral
allocation of aid has greater impact on poverty related issues. To that end, Williamson (2008)
empirically tested the hypothesis that human welfare can be increased through targeted aid by
examining the impact of health sector specific aid on various health indicators in 208
developing countries for the period 1973 to 2004. The data for health aid was obtained from
OECD’s Credit Reporting System and the health indicators (infant mortality, life expectancy,
death rate and immunisations) & control variables (percentage urban population, number of
physicians, GDP, Fraser Freedom Index and Political Freedom Index) were obtained from
World Development Indicators, 2006. A fixed effects model using 5 year averages was
developed and used to test for the impact after controlling for reverse causality. The results
indicated that health aid is ineffective at increasing overall health thus targeted aid is an
unsuccessful human development tool supporting the public choice view. The study also
replaced health aid with overall foreign aid and used lagged aid as an explanatory variable to
investigate the impact of aid on poverty and the results suggested that international aid is not
one of the most powerful weapons against poverty. However, the results may not be
reflective of the impact of health aid on health since there is no regional disaggregation to
fully reflect the regional institutional factors. More so, the reason health aid might not be
having an impact may be that the amount given to the sector is not large enough for instance
accounting for 7% of all foreign aid to the countries in question during the study period.
Therefore, to investigate on another sector Ndikumana and Pickbourn (2015) empirically
tested whether targeting foreign aid in the water and sanitation sector can help achieve the
goal of expanding access to water and sanitation services in the Sub Saharan region. The
analysis was based on panel data estimation techniques to allow for controlling for potential
endogeneity of regressors and country specific effects. The findings of the studysupported the
public interest view thus increases in the allocation of foreign aid to water and sanitation
infrastructure are associated with increased access to improved sanitation facilities and clean
31
drinking water in the rural areas of the Sub Saharan African countries.The study suggests that
in addition to scaling up of aid disbursements, donors also need to increase aid allocation
specifically to water and sanitation as well as others sectors where Sub-Saharan African
countries, lags behind.
2.3 Conclusion
The chapter has reviewed theoretical and empirical aspects of the quest for aid effectiveness
in poverty reduction.The theoretical survey has focused on the interrelationship between aid
and economic policies in the theories of development and displacement while the empirical
survey has examined the empirical framework employed to estimate the aid-poverty nexus in
various models. Theoretically, to some extent aid is said to be effective in reducing poverty if
an increase in aid raises economic growth via increasing savings, investment and export
earnings26. The prototype models in this indirect channel of impact are the two gap model,
the big push model, stages of growth theoryand the vicious circles of poverty theory. The
direct channel of impact to which aid can also effectively reduce poverty according to
Gomaneeet al (2005) involves spending aid resources through direct projects funding, skill
acquisition and provision of capital and government spending on social sectors such as
agriculture, health and education.
The question to why aid may not always have positive impact on poverty reduction can be
answered from both the theoretical and empirical points of view. The recipient needs model
and the donor interest model have revealed that in aid allocations donors may consider, the
poverty needs or merits (past performance) of a recipient country or may be driven by their
own strategic interests in both cases which may have an adverse effect on the effectiveness of
aid in reducing poverty. The other factors that can make aid less effective as outlined by
theories such as principal agency theory, theory of incentives, rent seeking model and game
theoretical model include aid fungibility, recipient country’s policy mismanagement, weak
institutions and high corruption. The theories are however inconclusive and biased towards
the actions of the recipient countries overlooking the actions of the donors which may also
militate against aid effectiveness in reducing poverty with factors such as aid conditionality,
unpredictable aid flows, insignificant aid flows and financing modalities.
26indirect channel of impact or the trickle down approach
32
The empirical literature on aid-poverty nexus also still remains far from conclusive.As can be
seen, empirical literature on aid effectiveness has resulted in mixed results. This could be due
to the heterogeneity of aid motives, heterogeneity of aid recipient sub-regions, heterogeneous
nature of aid, the limitations of the tools of analysis and the complex causality chain linking
external aid to final outcomes. While Collier and Dollar’s model finds that aid can reduce
poverty through increasing economic growth (indirect channel), studies that come after
Collier and Dollar (2001, 2002) have found a wide array of results ranging from aid being
ineffective to aid being effective. Some researchers find that foreign aid has a positive effect
on poverty reduction (Bahmani-Oskoee and Oyolola, 2009; Gomanee et al, 2003; 2004;
Morrisey et al 2005; Ndikumana and Pickbourn, 2015). Another group of studies find that aid
has a negative and significant impact on poverty reduction (Mosley, Hudson and Verschoor,
2004; Kumler, 2007; Simplice, 2014) while another set of group find that aid has a negative
and insignificant effect on poverty reduction (Chong et al, 2009; Magnon, 2012; Ijaiya G.T
and Ijaiya M. A. 2004;Kumler, 2007; Nakamura and McPherson, 2005; Williamson, 2008).
Most of the studies reviewed in this study found a negative relationship between aid and
poverty reduction. However, generalisations cannot be made for all other sub-regions because
results may differ due to the heterogeneity of aid recipient sub-regions. Therefore this study
seeks to close this gap by investigating whether aid is effective in reducing poverty in SADC
since none of the studies has considered this kind of analysis in the SADC region. More so,
none of the above studies have investigated on what other forms of economic activities other
than the aid strategy that SADC can employ in order to effectively reduce poverty. Only one
study has decomposed aid to consider whether the impact differs with the type of aid but has
however used an income measure of poverty. To close these gaps this study considers the
impact of humanitarian and budget support aid using the non-income measure of poverty and
analyses the relative importance of international trade compared to aid strategy in reducing
poverty. Institutional indices, control of corruption index and trade openness are considered
to control for the institutional and policy environment. These indices are interacted with
foreign aid to determine if institutional environment that is free of corruption and is open to
trade has an impact of the effectiveness of foreign in poverty reduction. This research
therefore seeks to add into literature on aid effectiveness, stimulate economic debate and
guide policies meant to improve aid effectiveness in poverty reduction in the SADC region.
CHAPTER THREE
33
METHODOLOGY
3.0 Introduction
The chapter discusses the empirical model used to investigate foreign aid effectiveness in
reducing poverty in the SADC region as guided by the theoretical and empirical models
discussed in the previous chapter. Adjustments are made to the theoretical models after taking
into account empirical considerationsto fit the SADC context.Specifically the chapter
encompasses the model to be adopted, estimation techniques, definition of variables, their
proxies, data types and sources. The chapter also justifies why panel data is to be used instead
of ordinary time series or cross section models.
3.1 Model Specification
The study employs a similar model with Bahmani-Oskooee and Oyolola (2009) and adds
other variables identified from the reviewed theoretical and empirical literature to empirically
investigate aid effectiveness in reducing poverty in SADC. The study by Bahmani-Oskooee
and Oyolola (2009) is in line with economic theory which identifies, economic growth,
income inequality, foreign aid, social programs on poverty and quality of institutions as the
determinants of poverty. The present study extendsthe model by disaggregating institutions
into economic institutions and political institutions and disaggregating aid along the lines of
Clemens et al (2004) but only using two of the ways: a) budget support aid proxied by net
ODA and other official aid and b) humanitarian aid. The model also adds other variables such
as trade openness, foreign direct investment, control of corruption and infant mortality rate.
The model also includesthe interactiveterms(aid &institutions, aid &control of corruption and
aid & trade openness) to establish if aid effectiveness in reducing poverty is dependent on the
quality of institutions, degree of controlling corruption and openness to trade. The dependent
variable is also adjusted by replacing the poverty headcount index they used with the human
development index. The specific empirical model to be estimated is as follows:
����� = �� + 1(�� ��)�� + 2(���)�� + 3(�����)�� + 4( ����)�� + 5(����� ∗ ���)��+ 6( ���� ∗ ���)�� + 7(��)�� + 8(�� ∗ ���)�� + 9(!!)�� + 10(!!∗ ���)�� + 11(#��) + 12(��#�$)�� + 13(���/���)�� + Ɛ��
Where; ��is the individual country specific effect and 1 to 13 are constants to be estimated.
��� = Human Development Index, a proxy measure for poverty
34
�� �� = per capita real incomein constant dollars
��� = Foreign aid disaggregated as:
a) budget support - net ODA + official aid ('���� it)
b) humanitarian aid(���� it)
����� = quality of economic institutions
���� = quality of political institutions
(����� ∗ ���)= interactive term between quality of economic institutionsand aid
( ���� ∗ ���= interactive term between quality of political institutions and aid
(��)= trade openness
(�� ∗ ���)= interactive term between trade openness and aid
(!!)= control of corruption
(!! ∗ ���)= interactive term between control of corruption and aid
(#��)= foreign direct investment
(��#�$)= infant mortality rate
(���/�� )it= proxy for aid dependence
Ɛ�� = error term (�� + (� + )��) �; � = countries 1,2…12 and time (years) 1,2…9
All the other variables are in natural logarithms except for foreign direct investment which
has negative values and the economic freedom index and political freedom index which since
they are already indices.
3.2 Panel data methodology
The study is a regional study where cross sectional units (selected countries as listed on
footnote 5 page 2) are studied over time hence panel data methodology has been chosen. The
choice of this method is based on the weight of its advantages relative to pure time series or
cross sectional data procedures. Panel data allows the study to control for individual
differences (heterogeneity) thus it admits that countries are not homogenous unless the
homogeneity is tested. Countries in the SADC region exhibit individual specific variables
such as income per capita levels, corruption levels, institutional qualities, government
effectiveness, policies etc. Period specific variables can also not be over-ruled.
Panel data or longitudinal data is defined as a data set which follows a given sample of
individuals over time and thus provides multiple observations on each of the individuals in
35
the entire sample (Hsiao, 1996). Cameron and Trivedi (2005) defined panel data as repeated
observations on the same cross section, of individuals or firms in microeconomics
applications observed for several time periods.
Kennedy (1985) highlighted that the advantages of panel data over other estimation
techniques are that it gives, more variability, more informative data,less collinearity among
variables and more degrees of freedom to increase reliability. According to Islam (1995)
panel data takes into account some potentially important factors that cannot be measured such
as any unobservable country specific effects which may bias coefficients and cannot be done
by a pure cross country instrumental variable regression. Gujarati (2004) states that the use of
panel data enhance the quality and quantity of data in ways that would be impossible when
using time series only or cross sections only. Cameron and Trivedi (2005) summed up the
major advantage of panel data as the increased precision in estimation which arises as a result
of the increase in the number of observations owing to combining several time periods for
each individual (country, in this case). Furthermore, panel data models are used to study
dynamics of adjustment (Baltagi, 2005 and Hsiao, 1996).
3.3 Estimation Procedure
3.3.1 Model specification tests
When estimating the panel model, a choice has to be made on the most appropriate model,
either pooled OLS,fixed effects model (FEM) or random effects model (REM). Kennedy
(1985) suggests that the choice depends on the context of the data. If the data exhausts the
population,FEM should be used but if data is drawn on observations from a large population
and inferences is to be made about the other members of that population then REM is
suitable. According to Kennedy (1985), the pooled model leads straight to a classical linear
regressionformulation for which OLS produce consistent and efficient estimates.
Specification tests shall be carried out to guide on the model that best fits the available data
and these are the fixed effects (Chow) tests, Hausman test andBreusch and Pagan Lagrange
multiplier test.
The Fixed Effects Test/ Chow Test
36
To test whether the pooled or fixed effects model is appropriate, the F-test is used. The
hypothesis for the test is as follows:
H0: the pooled (restricted) model is suitable
H1: the fixed effects (unrestricted) model is suitable
The F-statistic is calculated as follows:
# = ,--../0-..1/2 3, 0-..14/1/53
~#[(N-1), (NT-N-K)]
Where RRSS is the residual sum of squares for restricted (pooled) model, URSS is residual
sum of squares for unrestricted (fixed effects) model, N is number of countries, T is time
period and K is number of parameters. If the null hypothesis is rejected then the study adopts
an unrestricted model that is either the fixed effects or the random effects model. If the F-
statistic is significant it implies that there are significant individual effects hence the pooled
OLS would be inappropriate (Baum, 2006).
The Breusch and Pagan Lagrange Multiplier test (1979)
To decide whether to use the pooled model or the random effects model, the Lagrange
Multiplier (LM) test may be performed. The hypothesis is as follows:
H0: σµ2 = 0
H1:σµ2≠ 0
Where the LM statistic 7 = , 89:(9;<)3 =∑ (∑ ?��9
@A<8BA< )2- 1], is asymptotically distributed as a
chi-square distribution with one degree of freedom and?itis the initial least squares residuals
from regressing C��on ���. Rejecting the null hypothesis implies that REM is appropriate
hence pooling the data will give biased results. However, the Lagrange Multiplier has lower
power hence it is suggested that Chow test for fixed effects model against pooled model be
done even if the random effects model is suspected to be the correct model.
The Hausman test
The Hausman test (1978) is used to determine the suitability of either the fixed effects model
(FEM) or random effects model (REM). If N, (number of countries) is large and T, the time
frame is small and if the assumptions of the REM hold, then REM is more efficient and is
then used to estimate the equation.If the errors are correlated with the observations then FEM
would be appropriate. The FEM is consistent under both the null and the alternative
37
hypothesis while the REM is consistent under the null and inconsistent under the alternative.
The hypothesis to be tested is as follows:
H0: Cor(E�, ��� = 0) (REM)
H1: Cor(E�, ��� ≠ 0) (FEM)
Rejecting the null hypothesis implies that FEM is suitable. We can test the suitability using
the probability values. If the p value of the chi square is less than 0.1 then according to Baum
(2006) individual effects appear to be correlated with the regressors and hence FEM is more
appropriate than the REM.
Redundant Likelihood Fixed effects test
TheF-test will be used to determine the significance of time effects, country effects and both
effects. Rejecting the null hypotheses that both the cross section and time effects are
redundant impliesthat there is no homogeneity in the countries and periods hence the effects
are not redundant hence should be included in the model.
Significance of the whole model
The F-test will also be performed to determine the significance of the whole model. The F-
statistic will be compared with the critical value. The decision criteria will be to reject the
null hypothesis that none of the variables explain poverty reduction when the F-statistic is
greater than the critical value.
3.3.2 Parameter and Misspecification tests
Parameter tests
The tests bring about significance to the used variables. They are captured in the statistical
package to be used (Eviews 9). If efficiency is to be improved the General to Specific Model
Approach which involves dropping of highly insignificant variables is to be followed.
Misspecifications tests
These will also be carried out to ensure that statistical assumptions are not violated. The tests
are also captured automatically in the econometric package.
Heteroscedasticity tests
These will be carried out to ensure that consistent though not efficient estimates are yielded.
In the presence of heteroscedasticity standard errors will be biased and robust standard errors
38
(panel corrected standard errors) will be computed to correct possible presence of
heteroscedasticity. According to Cameron and Trivedi (2005), a way to check for
heteroscedasticity is to compute the correlation between the explanatory variables and the
error term. If it is near zero then there is homoscedasticity hence no need to use robust
standard errors.
Multicollinearity test
The study will perform this test. According to Gujarati (2004) this is a situation where there
exists a perfect or exact linear relationship among some or all of the explanatory variables
hence it becomes difficult to separate the effect of one explanatory variable on the dependent
variable from the other. The effect can be detected by looking at the correlation matrix for a
relationship that exceeds 0.80(Cameroni and Trivedi, 2005). According to Bruderl (2000) a
test statistic, Mean VIF can also be computed and if the computed value exceeds 4 it implies
a strong multicollinearity. To correct for multicollinearity, the model would need to be
correctly specified which can be done so by simply dropping one of the correlated variables
in the regressions.
3.4 Definition, Measurement And Justification Of Variables
3.4.1 Dependent variable
Human Development Index [HDIit]
The studies reviewed in the previous chapter used a variety of measures both monetary and
non-monetary to capture the impact of aid on poverty reduction. The current and most
frequently utilised proxy for poverty reduction is the HDI (Gomaneeet al, 2003; 2005;
Mosley et al, 2004) and Kumler, 2007). The human development index (HDI) is a composite
index measuring average achievement in three basic dimensions of quality of life, that is, a
long and healthy life, knowledge and a decent standard of living (UNDP, 2016). The
longevity or health dimension is measured by life expectancy at birth, the education
dimension is measured by mean years of schooling for adults aged 25 years and more and
expected years of schooling for children of school entering age and the standard of living
dimension is measured by gross national income (GNI) per capita. The score of the three
dimension indices are then aggregated into a composite index using geometric mean.
39
The income measures such as the headcount index, poverty gap or poverty gap squared could
be utilised to quantify extreme poverty but the cross country data is not widely available over
time and is often incomparable (Kumler, 2007). One general merit of the HDI is that it is an
aggregate measure of quality of life calculated on a consistent basis for a large sample of
countries (Morrissey et al, 2004). For instance for the present study the data is available on
annual basis from 2005 to 2013 unlike the previous studies where it was averaged on either 5
or 3 year intervals. Unlike the monetary measures of poverty which ignore the non-monetary
aspects of being poor, HDI considers non-pecuniary factors of poverty thus providing a better
measure of overall poverty. Although difficulties may exist when comparing a country’s total
welfare with that of its poorest citizens, the inclusion of real per capita GDP in purchasing
power parity dollars in the HDI index suggests that the HDI will be inversely correlated with
income measures of poverty to the extent that countries with higher real GDP have lower
poverty. Thus measures aimed at increasing HDI are likely to improve the livelihood of those
living in poverty. Therefore this present study proxies poverty, the dependent variable, with
the HDI.
3.4.2 Explanatory variables
Initial income [GDP per capita, PPP, constant $, 2005 -(GHIJKit)]
GDP per capita, is the gross domestic product divided by midyear population (WDI, 2016).
Some studies use GDP, GDP per capita (constant or current),GNP, GDP per capita, PPP
(current or constant $) to reflect initial income. OECD(2015)highlights that GDP per capita,
PPP (current $)captures changes in both volumes and relative prices and is the appropriate
tool when looking at the country’s GDP per capita position given the set of international
prices of the year considered whileGDP per capita, PPP (constant $) in which a base year is
fixed, captures volume changes only and is the appropriate toolwhen looking at how relative
the position of a country’s GDP per capita has changed over time given its measured growth
performance.However the problem with the GDP per capita measure adjusted using the
purchasing power parity is that it is highly correlated with HDI and most of the explanatory
variables. In line with Bahmani-Oskooee and Oyolola (2009) and Chong et al (2009) who
used GDP per capita (constant $2000), this present study also uses GDP per capita (constant
$, 2005).
40
Like the models employed by most of the studies previously reviewed�� ��it is included in
the model in order to control for initial income (economic growth). In theories like two gap,
big push, stages of growth and vicious circle of poverty, economic growth is widely viewed
as a necessary though not sufficient condition for poverty reduction. By considering income
per capita in the year preceding the start of the time period, the model controls for the effect
of GDP on HDI since any aid disbursement could increase GDP in the current time period.
Because an increase in per capita income directly increases the quality of life thus a direct
reduction in poverty in SADC countries,�� ��it is expected to have a positiveimpact on HDI.
Foreign aid [(LMH)it]
Foreign aid is the key variable of interestin the study. It is defined as the source of foreign
capital inflow that involves the transfer of resources from rich countries to poorer countries in
the form of financial (grants, export credit and concessional loans) and non-financial
assistance (project and non-project assistance, food, medical and technical
assistance(WorldBank, 1997). According to Sachs and Ayittey (2009), one of the main
disagreements which shape debates on foreign aid effectiveness is which type of foreign aid
is most beneficial in combating poverty regardless of motivation. In orderto investigate which
of the two is most beneficial in reducing poverty, in this present study foreign aid is
disaggregated in two waysfollowing Clemens et al(2004)that is, budget support aid and
humanitarian aid. HDI is to be regressed on aid in three different models27 i.e. [1] on budget
support[2] humanitarian aid [3] both budget support and humanitarian aid.
• Budget support aid[(NOLMH)it]
Practitioner’s forum on budget support (2005) has defined budget support as an aid modality
in which aid money is given directly to recipient country’s government from donor country’s
government thus through government to government transfers to support a recipient country’s
own development programs. The main focus will be on increasing economic growth,
reducing poverty, strengthening institutions, and fiscal adjustment in budgetary processes.
Net official development assistance and official aid received (current US$)is used in this
model as a proxy for budget support aid because this is the aid which is given to developing
countries with the aim of spurring their economies.According to WDI (2016), net official
27This serves to investigate on which of the two types of aid modalities is more beneficial in poverty reduction. Humanitarian aid usually avoids misallocation and misuse while budget support is more prone to the principal agency problems.
41
development assistance (NODA) consists of disbursements of loans made on concessional
terms (net of repayments of principal) and grants by official agencies of the members of the
Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC
countries to promote economic development and welfare in countries and territories in the
DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent
(calculated at a rate of discount of 10 percent). Net official aid refers to aid flows (net of
repayments) from other official donors.The expected sign of the impact of budget support aid
is either negative or positive. It is expected to have a positive impact on HDI if aid resources
are properly used. However a negative sign is expected if there is either misallocation or
misuse of aid resources or the recipient country lacks absorptive capacity.
• Humanitarian aid (PLMHit)
Humanitarian aid comprise of emergency aid which is mobilized and dispensed in response
to catastrophes & calamities and charity based aid which is disbursed to institutions or people
on the groundby charitable organizations (Moyo, 2009).This variable has been chosen
because the data is readily available. Also this variablehas been chosen because it represents
aid that goes directly at the grassroots to meet the needs of the poor and therehas been a
growing number of non-governmental organisations, the channel through which this type of
aid is being disbursed in most of the SADC countries. According to the principal agency
theory, this type of aid is expected to avoid two pitfalls of misallocation and misuse
commonly attributed to budget support aid or is less subjected to these pitfalls.Therefore, it is
expected to have a positive impact on HDI.
Institutional environment [economic institutions - (QRSit) and political institutions (IRMit)
Increases in economic and political freedom have been shown to be positively related to
economic and human development (Gwartney, Lawson, and Holcombe 1999; Acemoglu,
Johnson, and Robinson 2001, 2002). Thus, a country's institutional environment may
influence poverty reductionas well and should be included in the analysis. Therefore, to
control for the institutionalenvironment, some studies have used Country Policy and
Institutional Assessment - CPIA (Abubakar, 2015), International Country Risk Guide – IRCG
(Chong et al. 2009), Fraser’s Economic Freedom of the World Index and Freedom House
Political Index. The model include the economic freedom index and the political freedom
42
index in the regression to capture the quality of economic institutions and political
institutions respectively since the data is readily available.
• Economic Freedom of the World Index [�#T�it]
The Fraser Institute’s economic freedom of the world (EFWI)index is a proxy to capture the
quality of economic institutions, The EFWI index is scaled from 1 to 10 with 10 representing
the highest level of freedom and 1 the lowest. According to Gwartneyet al 2013, this index
measures the degree of economic freedom present in five major areas which are [a] Size of
Government; [b] Legal System and Property Rights; [c] Sound Money; [d] Freedom to Trade
Internationally and [e] Regulation. Williamson (2008) used the EFW index to capture quality
of economic institutions. A positive sign is predicted
• Political Freedom Index [ #�it]
The Freedom House Organisation’s political freedom (PF) index captures the political
institutions.The political freedom index is scaledfrom 1 to 7, with 1 representing the highest
level of freedom and 7 the lowest. This index averages scores from an index on political
rights and an index on civil liberties to calculate one comprehensive measure of political
freedom. Anegative relationship between the political institutional variable and HDI is
expected. Since a bigger number represents the lowest degree of political freedom while a
smaller number represent highest degree of political freedom the expected sign becomes
negative. This would mean that countries with weak political institutions (lower degree of
political freedom) worsen poverty reduction efforts.
The interactive terms between the institutional environment and foreign aid
Interactive terms between the economic institutional environment and foreign aid [(�#T ∗���)it] and political institutional environment and foreign aid [( #� ∗ ���)it] are included in
this present study to allow us to check the effect of institutional environment on aid
effectiveness. These variables will determine if the political and economic institutions have
abearing on aid effectiveness. These variables have been chosen due to the availability of
data and the inclusion of policy factors. The recipient needs and merit model purports that aid
is allocated to developing countries, SADC in particular, on the basis of their merits such as
past performance as measured by the quality of institutions and policies to ensure that aid is
made more effective.Brautigam and Knack (2004) argue thataid has failed to reduce poverty
in Sub-Saharan Africa and in-deed SADC due to weak institutions. According to the principal
agency theory these weak institutions give rise to the principal-agency problemswhich
43
ultimately result in aid misallocation or misuse. Collier and Dollar (2001; 2002) point out that
the number of people pulled out of poverty is maximised if aid is allocated to countries whose
institutions and policies are of good quality.We therefore expect a positive relationship of
(�#T ∗ ���)itwith HDI and a negative relationship of ( #� ∗ ���)itwith HDI.
Trade Openness [(UV)it]
Trade openness is another key variable in the study and in this study it has been proxied by
the summation of the exportand the import to GDP ratio.The exports and imports in this case
measure the value of all goods and services provided to and received from the rest of the
world respectively (WDI, 2016). They both include the value of transport, insurance
merchandise, travel, freight, transport,royalties, license fees, and other services, such as
construction,communication, financial, business, information, personal, and government
services but exclude factor services and transfer payments (WDI, 2016).Trade flows do
reflect the level and extent of trade openness; hence giving weight to the choice of the proxy.
According to Sachs and Ayittey (2009) one of the disagreements on which the debate of aid
effectiveness in reducing poverty rests on is the issue of the relative importance of foreign aid
as compared to other forms of economic activities such as international trade in reducing
poverty. According to the vicious circle of poverty theory, the preconditions for breaking out
of poverty circles include increased mobilisation of investible funds particularly on the
domestic front through a significant expansion of the market. Therefore,the variable has been
included to check the effect of trade openness on the poverty reduction. A positive sign is
expected between the trade openness variable and HDI.
The interactive term between trade openness and foreign aid [(UV ∗ LMH)WX]
The interactive term between trade openness and foreign aid is the variable that we get after
multiplying the trade openness variable with the aid variable. Dare (2012) has used this
variable to investigate the joint effect of trade openness and aid on economic growth and
found it to be significant but negative. This interactive variable of trade openness and foreign
aid has been considered inorder to investigate and establish the joint effect of trade openness
and foreign aid on poverty reduction. Trade openness is one of the macroeconomic policies
hence inclusion of its interaction will enable us to determine if aid effectiveness in reducing
poverty can be improved in an economy that is open to trade. We expect a positive
relationship with HDI.
44
Foreign direct investmentnet inflows [(RHM)it]
Apart from foreign aid, foreign direct investment (FDI) is another source of foreign capital
flows hence FDI is one of the control variables in this present study. According to the World
Development Indicators, 2016, foreign direct investment, represent the net inflows of
investment required to acquire a lasting management interest of 10 percent or more of voting
stock in an enterprise operating in an economy other than that of the investor. It is calculated
as the sum of reinvestment of earnings,equity capital,other long-term capital, and short-term
capital as shown in the balance of payments. FDI, net inflows (% of GDP) is the proxy
variable chosen as it gives new investment inflows less disinvestment made in the reporting
economy from foreign investors divided by GDP.The ongoing financial and economic crises
have reawakened the debate on the importance of foreign direct investment for poverty
reduction as opposed to foreign aid especially in Africa and in deed SADC. Many economists
agree on the fact that the levels of poverty may have been increased by current financial
crises due to the potential reduction in foreign capital flows (Gohou, 2009). Foreign direct
investment is expected to have a positive impact on HDI.
Infant mortality rate [(MRVY)it ]
Infant mortality rate according to WDI (2016) is the number of infants dying before reaching
one year of age per 1000 live births given in a year.Following BahmaniOskooee and Oyolola
(2009), this variable is included in the study as a control variable. According to the
recipients’ model, it is one of the factors that determine aid allocation. Aid is disbursed
according to the needs of the receiving country such as high levels of infant mortality rate
which is believed to exacerbate poverty. Thus high infant mortality rate has nefarious effects
on HDI. Therefore we expect a negative relationship between infant mortality rate and HDI.
Control of corruption Index [(ZZ)it ]
Control of corruption index shows the perceptions of the degree to which public power is
exercised for private gain, including both petty and grand forms of corruption, and also
“capture” of the state by elites and private interests (Worldwide Governance Indicators,
2016).The index is ranges from -2.5(minimum) to 2.5(maximum) control of corruption. High
degree of control on corruption should transform to higher quality of life as it would mean
that resources are properly used and accounted for. Therefore a positive sign is expected.
The interactive term between control of corruption and foreign aid [(ZZ ∗ LMH)WX]
45
The interactive term is obtained after multiplying control of corruption variable with foreign
aid variable. This interactive variable has been considered in order to investigate and
establish if there is a conditional relationship between control of corruptionand foreign aid on
poverty reduction. Chong et al (2009) also used this variable in their study. It enables us to
determine if aid effectiveness in reducing poverty can be improved in an economy that has
higher degree of controlling corruption. We expect a positive relationship with HDI.
Aid dependence [(LMH/G[M)WX] Aid dependence according to Roger Riddell (1999) is a problematic condition by which
continued provision of aid makes no significant contribution to the achievement of self-
sustaining development. This is so because according to the recipient needs model whereby
aid is allocated on the basis of poverty levels, recipient countries may become so reluctant to
develop their own capacities such as introducing self-sustaining ways to reduce poverty
knowing that they will continue to receive aid. Heavy dependence on aid may encourage
recipient governments to ease their revenue generation efforts and savings may also fall as
foreign investment crowd out domestic investment thereby depressing growth rates which in
turn increase poverty (Mahmood, 1997). Aid dependency cannot be directly measured so we
use a proxy that reflects aid intensity thus net aid flows as a percentage of GDP [(���/�� )it]
and aid as a percentage of government spending [(���/��� )it]. However, data for [(���/
��� )it] is not available for the entire period under study for all countries in the study.
Therefore [(���/�� )it] is used in this study as data is readily available. This variable has
been included because there is a strong correlation between aid dependence and poverty thus
high aid dependency exacerbates poverty therefore aid dependence variable is expected to
have a negative impact on HDI.
3.5 Data and data sources
The study considers data for period 2005 to 2013 following the availability of data for all the
variables included in the study. The data set is a balanced panel data where each country has
the same number of observations studied over time. Data was compiled on 14 main variables
which are HDI, GDP per capita,budget support, humanitarian aid,aid dependence,economic
freedom of the world index, political freedom index, FDI, trade openness, infant mortality
rate, control of corruption and interactive terms28 between aid and quality of economic
28The other three variables used in the study which are the interactive terms are owner calculated.
46
institutions, quality of political institutions and control of corruption. Data for all these
variables were collected from various secondary data sources. The data for HDI was collected
from the United Nations Development Programme Report for 2014, GDP per capita data was
collected from IMF World Economic Outlook (WEO) database for 2015 and Humanitarian
aid data was drawn from Financial Tracking Services (FTS), UN Relief Web. The data for
Economic Freedom of the World Index was collected from the Fraser Institute’s EFW reports
and Political Freedom Index was drawn from Freedom House Organisation’s 2015 annual
report. FDI inflows (% of GDP), infant mortality rate and trade openness data (exports and
imports as % of GDP) was collected from the World Bank’s World Development Indicators
(2016). Control of corruption index used for robustness check was drawn from World Bank’s
Worldwide Governance Indicators.
3.6 Conclusion
This chapter has outlined the model specification,estimation procedure and data sources used
for the study. The study methodology that is the panel data analysis has also been discussed,
including econometric tests that are necessary for such a panel data analysis. The empirical
model to be estimated has HDI, a proxy for poverty reduction, as the dependent variableand
GDP per capita, budget support aid, humanitarian aid, trade openness, economic and political
institutions, FDI, infant mortality rate and control of corruption as the major explanatory
variables.
CHAPTER FOUR
ESTIMATION, RESULTS PRESENTATION AND INTERPRETATION
47
4.0 Introduction
This chapter focuses on estimation of the model as guided by the methodology in the
previous chapter and presentation and interpretation of final results.Section 4.1 presents the
descriptive statistics of the data. The subsequent sections present the results, discussion and
interpretation of the specification tests results as well asthe panel regression model results.
The tests carried outinclude parameter test, multicollinearity, fixed effects (country effects,
period effects and validity of the model),Lagrange Multiplier, and Hausman.
4.1 Descriptive Statistics
The study used 108 observations for all variables in all the selected countries (footnote 5 page
2) in SADC hence it is a balanced panel. The variables are given in logarithms except forFDI,
HDI, EFWI, PFI29. Transforming data into logarithms gives coefficient estimates as
elasticities hence they will be easier to interpret and it also improves the data as it is purified
towards normal distribution. Table 4.1 below gives the summary statistics, mean, standard
deviation, minimum, maximum values and kurtosis of the variables to check for some
outliers.
Table 4.1: Summary of Descriptive Statistics
Variable Mean Max Min Std. Dev. Kurtosis Obs
HDI 0.475093 0.66 0.29 0.088861 2.538129 108
BSAID 20.27556 22.42 17.5 1.136857 2.391508 108
HAID 8.875278 13.52 0 3.019656 4.529612 108
AID 20.27574 22.42 17.5 1.136927 2.391273 108
AID/GNI 1.502407 3.41 -1.66 1.388035 2.696973 108
EFWI 5.965648 7.31 3 0.955627 3.681027 108
PFI 4.037037 6.5 1.5 1.509632 1.679004 108
CC -0.510741 0.58 -1.48 0.565156 2.007597 108
EFWI*AID 120.8698 153.39 60.1 19.96506 3.827745 108
PFI*AID 81.64278 134.54 30.79 30.04223 1.895383 108
CC*AID -10.54009 11.92 -32.14 11.58193 2.028525 108
XM 4.430463 5.18 3.67 0.355587 2.513042 108
XM*AID 1786.337 3315.5 832.7 580.5471 3.011451 108
FDI 4.35963 41.81 -5.5 6.84998 16.40649 108
INFMR 4.098889 4.8 3.54 0.328905 2.15127 108 29Some of the foreign direct investment (FDI) values were negative hence could not be transformed to logarithms. For easy interpretation of summary statistics for all the indices, the values were not transformed into logarithms.
48
GDPpc 7.821944 9.46 6.12 0.972615 1.791515 108 Source: Author’s compilation
The variables GDP per capita (GDPpc), budgetary support aid (BSAID), humanitarian aid
(HAID), total aid (AID), trade openness (XM), infant mortality rate (INFMR) and control of
corruption (CC) have small ranges between the minimum and the maximum values.
However, the range for foreign direct investment (FDI)and the interactive terms of aid
withcontrol of corruption, the proxy for political institutions (Political Freedom Index and
proxy for economic institutions (Economic Freedom of the World Index) and trade
opennessare huge30. As a result there seem to be greater variability in these three variables as
shown by their large standard deviations which seem to be outliers in the data range. The
HDI has a mean of 0.475093 indicating that on average most countries in SADC are still poor
despite having received huge amounts of aid as shown by mean of 20.27574 for BSAID and
8.875278 for HAID. EFWI, HAID, FDI, and (EFWI*AID)have excess kurtosis as their
computed kurtosis is greater than three,thus their Probability Distributed Function (PDF) is
leptokurtic (slim or long-tailed). On the other hand, the PDF for the rest of the variables is
less than three, meaning that their PDF is platykurtic, that is, fat or short-tailed.
4.2 Econometric Tests
The model is a short panel model in the sense that number of observations (N=12) is greater
than Time (T=9), ruling out possibilities of non-stationarity of data.Baltagi (2005) argue that
in order to get sensible results for panel cointegration and unit root tests the panel should
have more than 30 time periods. Therefore, for this study there is no need for performing
stationarity tests.Model misspecification is automatically corrected by the statistical package
used.
Multicollinearity test
Multicollinearity occurs when two or more independent variables or combinations of
independent variables are highly (but not perfectly) correlated with each other (Gujarati,
2004). This result in high R2 and low t statistics hence this test is carried out to investigate the
30For FDI it is because the variable could not be transformed to logarithms because of negative values and for the interactive terms it could be due to the way they were transformed into logarithms to ensure that the basic rules of logarithms and correct functional forms are maintained. Therefore these variables are not to be adjusted as adjusting them could disrupt panel tests.
49
presence of such in the data and correct if need be. The rule of thumb is that multicollinearity
exists among explanatory variables when correlation coefficients are above 0.8. There is
multicollinearity in the data with budgetary support being highly correlated with total aid
(AID) and aid dependence (AID/GNI) being highly correlated to GDP per capita and the
interactive terms being highly correlated with the other explanatory variables [see Appendix
2 table 4. 2(a)]. Therefore, to correct for multicollinearity aid dependence, total aid and all
interactive variables are droppedhenceto estimate their impact on HDI they will be regressed
separately and not included in same equations.
Table 4.2(b) below shows the new correlation matrixto check if the remaining variables to be
incorporated in the model are not seriously correlated. The correlation matrix shows that
there is no more multicollinearity between the series.
Table4.2b: Correlation Matrix
GDPPC BSAID HAID EFWI PFI XM FDI INFMR
GDPPC 1
BSAID -0.54136 1
HAID -0.64128 0.32084 1
EFWI 0.39356 -0.08152 -0.5755 1
PFI -0.27763 -0.12493 0.45205 -0.61574 1
XM 0.22195 -0.75562 -0.0119 -0.10527 0.143862 1
FDI -0.27692 0.25979 0.01952 0.16232 -0.18473 -0.03975 1
INFMR -0.31022 -0.13918 0.30486 -0.53995 0.478832 0.33127 -0.17981 1
The variance inflation factor (VIF) was computed to also check for multicollinearity. The
results shown in Appendix 2 table 4.2 (c) shows that the centered VIF for all the variables are
below 4 which means there is no strong multicollinearity confirming the results of the
correlations matrix shown above hence this allows us to include all the variables in the
regressions (Bruderl, 2000).
4.2.1 Testing for Model Specification
The study estimates 3 models in which the human development index is regressed on budget
support aid, humanitarian aid and both types of foreign aid. The model specifications tests
present necessary tests toguide on the appropriate model which suits the data being used in
50
the study hence will be performed on all the three models. The table below shows the
summary results of Breusch Pagan LM, fixed effects and Hausman tests on all the models.
Table 4.2.2Summary of model specification tests Model 1 Test Critical value P-value Decision Implication Regression of HDI on budget support aid
Breusch Pagan LM test (Pooled vs random) H0: no random effects
cross section effects LM = 309.5788
0.0000 Reject H0 Random effects model (REM) is more appropriate
time effects LM = 3.177281
0.0747 Fail to reject H0at 5% level of significance
Both effects LM = 312.7561
0.0000 Reject H0
Fixed Effects test H0: Fixed effects are redundant
Cross section effects F(11.81) = 129.380471
0.0000 Reject H0 Fixed effects are not redundant. However they will be of importance if fixed effect model is chosen by Hausman test
Period effects F (8.81) = 4.486223
0.0002 Reject H0
Both effects F(19.81) = 76.791411
0.0000 Reject H0
Hausman test (REM vs FEM) H0: random effects is consistent)
Chi-Sq.(7) = 2.659929 0.9146 Fail to reject H0
Random effects model (REM) is more appropriate
Model 2 Test Critical value P-value Decision Implication Regression of HDI on humanitarian aid
Breusch Pagan LM test (Pooled vs random) H0: no random effects
cross section effects LM = 328.9916
0.0000 Reject H0 Random effects model (REM) is more appropriate
time effects LM = 3.8990
0.0483 Reject H0
both effects LM = 181.24
0.0000 Reject H0
Redundant Likelihood Fixed Effects test H0: Fixed effects are redundant
cross section effects F(11.81) = 136.66987
0.0000 Reject H0 Fixed effects are not redundant. However they will be of importance if fixed effect model is chosen by Hausman test
time effects F(8.81) = 4.432293
0.0002 Reject H0
Both effects F (19.81) =79.89773
0.0000 Reject H0
Hausman test (REM vs FEM) H0: random effects is consistent)
Chi-Sq. (7) = 1.609875 0.9783 Accept H0 Random effects model (REM) is more appropriate
Model 3 Test Critical value P-value Decision Implication Regression of HDI on both budget support and humanitarian aid
Breusch Pagan LM test (Pooled vs random) H0: no random effects
cross section effects LM = 310.1764
0.0000 Reject H0 Random effects model (REM) is more appropriate
time effects LM = 3.0261
0.0819 Fail to reject H0 at 5% level of significance
Both effects LM = 269.66
0.0000 Reject H0
Redundant Likelihood Fixed Effects test H0: Fixed effects are redundant
cross section effects F(11.80) = 129.36418
0.0000 Reject H0 Fixed effects are not redundant. However they will be of importance if fixed effect model is chosen by Hausman test
period effects F(8.80) = 4.60395
0.0001 Reject H0
both effects F(19.80) = 76.97345
0.0000 Reject H0
Hausman test (REM vs FEM) H0: random effects is consistent)
Chi Sq. (8) = 2.14680 0.9762 Accept H0 Random effects model (REM) is more appropriate
51
From the specification tests carried out we find that for all the three models to be estimated,
the random effects model (REM) with cross section effects is appropriate for this present
study. Therefore, the fixed effects model (FEM) and the pooled OLS cannot be used in this
study as they will give inconsistent and biased results as indicated by the above tests.The
random effectsmodel, assumes that there is no correlation between the unobserved
heterogeneity and the regressors (Bahmani-Oskooee and Oyolola, 2009).
4.2.2 Parameter Tests
Serial correlation or autocorrelation is the correlation between the error terms. The effect of
autocorrelation is incorrect standard errors. The test for autocorrelation has been carried out
using the DW statistic and it has recorded values of 1.251886, 1.273090and 1.278593for
models 1, 2, 3 respectively as shown in regression results in section 4.4. This shows that there
is no serious autocorrelation hence no need to correct for that.
4.3 Model Estimation
4.3.1 Presentation of results
Table 4.3.1 shows the results for model 1 for the impact of budget support aid on HDI, table
4.3.2shows the impact of humanitarian aid on HDI and table 4.3.3 shows the results for the
impact on HDI of including both humanitarian and budget support aid in the same model. In
all the tables, Column A shows the initial regression after having controlled for the economic
and institutional environment, column B shows the inclusion of institutional quality indices
interacted with aid to determine if institutional quality does matter on the effectiveness of aid
in reducing poverty. The institutional variables could not be included in the same regression
model with the interactive terms since there will be multicollinearity which may cause
inefficiency of parameter estimators. Columns C, D and E present results for robustness
checks exercises conducted to investigate the strength of the basic findings. In this study the
robustness checks account for additional variables that are theoretically and empirically
relevant in the aid poverty-nexus. These variablesinclude the interactive term of trade
openness and aid, control of corruption and interactive term of corruption and aid.
Table 4.3.1: Summary of regression results for model 1
52
HDI is the dependent variable
Variable A B
Robustness checks (Additional variables)
C D E
GDPpc 0.161939 (0.0000)***
0.161412 (0.0000)***
0.162644 (0.0000)***
0.1590965 (0.0000)***
0.151602 (0.0000)***
BSAID 0.000701
(0.8958 -0.006555
(0.2229) -0.000619
(0.9074) -0.000379
(0.9429) -0.000363
(0.9455)
EFWI 0.030819 (0.0000)***
0.030527 (0.0000)***
0.032105 (0.0000)***
0.032223 (0.0000)***
PFI -0.008042
(0.0935)* -0.008090
(0.0883)* -0.004484
(0.3772) -0.004765
(0.3512)
XM 0.028095 (0.0097)***
0.028426 (0.0097)***
0.032105 (0.0042)***
0.031009 (0.0046)***
FDI -0.000635
(0.0652)* -0.000649
(0.046)** -0.000694 (0.0286)**
-0.000654 (0.0355)**
-0.000652 (0.0368)**
INFMR -0.118060 (0.0000)***
-0.117720 (0.0000)***
-0.122070 (0.0000)***
-0.132464 (0.0000)***
-0.131247 (0.0000)***
EFWI*AID 0.001527 (0.0000)***
PFI*AID -0.000380
(0.1130)*
XM*AID 0.0000173 (0.0044)***
CC 0.022837 (0.0674)*
CC*AID 0.001033 (0.0899)*
C -1.831459 (0.0000)***
-1.685107 (0.0000)***
-1.698045 (0.0000)***
-1.688309 (0.0000)***
-1.713250 (0.0000)***
R-squared 0.899873 0.897953 0.901315 0.903358 0.902806 Adjusted R-
squared 0.892864 0.890809 0.894407 0.895549 0.894952
F-Statistic 128.3898
(0.000000)*** 125.7054
(0.000000)*** 130.4754
(0.000000)*** 115.6750
(0.000000)*** 114.9477
(0.000000)***
The p values which indicate significance of variables are in parenthesis and asterisks * denotes statistical significance at 10%, ** at 5% and *** at1% level
Table 4.3.2: Summary of regression results for model 2 HDI is the dependent variable
Variable A B
Robustness Checks
C D E
GDPpc 0.164677 (0.0000)***
0.164379 (0.0000)***
0.165306 (0.0000)***
0.152960 (0.0000)***
0.153555 (0.0000)***
HAID 0.000619
(0.4390) 0.000375
(0.6463) 0.000545
(0.4929) 0.000782
(0.3229) 0.000789
(0.3208)
EFWI 0.030926 (0.0000)***
0.030743 (0.0000)***
0.032537 (0.0000)***
0.032620 (0.0000)***
PFI -0.008708
(0.0683)* -0.008464
(0.0745)* -0.004928
(0.3297) -0.005335
(0.2924)
XM 0.025940 (0.0199)**
0.026791 (0.0187)**
0.028681 (0.0097)***
0.028549 (0.0105)***
FDI -0.000616 (0.0488)**
-0.000583 (0.0680)*
-0.000664 (0.0342)**
-0.000618 (0.0443)**
-0.000623 (0.0434)**
53
INFMR -0.116600 (0.0000)***
-0.109792 (0.0000)***
-0.118846 (0.0000)***
-0.130758 (0.0000)***
-0.130565 (0.0000)***
EFWI*AID -0.001477 (0.0000)***
PFI*AID -0.000397
(0.1068)*
XM*AID -0.0000162
(0.0096)***
CC 0.024541 (0.0504)**
CC*AID 0.001127 (0.0676)**
C -1.955970 (0.0000)***
-1.862654 (0.0000)***
-1.747350 (0.0000)***
-1.714863 (0.0000)***
-1.719272 (0.0000)***
R-squared 0.900285 0.896399 0.901620 0.904247 0.903672 Adjusted R-
squared 0.893305 0.889147 0.894734 0.896510 0.895888
F-Statistic 128.9799
(0.000000)*** 123.6059
(0.000000)*** 130.9243
(0.000000)*** 116.8644
(0.000000)*** 116.0928
(0.000000)***
The p values which indicate significance of variables are in parenthesis and asterisks * denotes statistical significance at 10%, ** at 5% and *** at1% level
Table 4.3.3: Summary of regression results for model 3 HDI is the dependent variable
Variable A B Robustness Checks
C D E
GDPpc 0.165089 (0.0000)***
0.164208 (0.0000)***
0.165723 (0.0000)***
0.152885 (0.0000)***
0.153620 (0.0000)***
BSAID -0.0000649
(0.9906) -0.007110
(0.1959) -0.001268
(0.8161 -0.001592
(0.7710) -0.000736
(0.8928)
HAID 0.000621
(0.4477) 0.000538
(0.5140) 0.000576
(0.4772) 0.000829
(0.4477) 0.000810
(0.3196)
EFWI 0.030938 (0.0000)***
0.030659 (0.0000)***
0.032504 (0.0000)***
0.032593 (0.0000)***
PFI -0.008722
(0.0785)* -0.008709
(0.0757)* -0.005198
(0.3148) -0.005481
(0.2922)
XM 0.025896 (0.0212)**
0.026534 (0.0196)**
0.028521 (0.0107)**
0.028460 (0.0113)**
FDI -0.000616
(0.0521)* -0.000633 (0.0488)**
-0.000676 (0.0344)**
-0.000631 (0.0433)**
-0.000629 (0.0447)**
INFMR -0.116245 (0.0000)***
-0.116038 (0.0000)***
-0.119963 (0.0000)***
-0.132859 (0.0000)***
-0.132859 (0.0000)***
EFWI*AID -0.001531 (0.0000)**
PFI*AID -0.000406
(0.1003)*
XM*AID 0.0000162 (0.0099)***
CC 0.025235 (0.0483)**
CC*AID 0.001135 (0.0681)*
C -1.841822 (0.0000)***
-1.697501 (0.0000)***
-1.719082 (0.0000)***
-1.671400 (0.0000)***
-1.700232 (0.0000)***
R-squared 0.900216 0.898096 0.901620 0.904307 0.903673
54
Adjusted R-squared 0.892152 0.889862 0.893670 0.895519 0.894827
F-Statistic 111.6427
(0.000000)*** 109.0630
(0.000000)*** 113.4125
(0.000000)*** 102.9008
(0.000000)*** 102.1521
(0.000000)***
The p values which indicate significance of variables are in parenthesis and asterisks * denotes statistical significance at 10%, ** at 5% and *** at1% level 4.4.2 Discussion of Results
The F statistics for all the three models using random effects model (REM) as shown above
have a p value of [(0.000000)*** ] showing that the models are correctly specified and that the
null hypothesis is rejected at the 1% level of significance. We therefore conclude that in each
of the models summarized in the above tables, at least one of the variables in each model
explains poverty reduction in SADC economies. The $2of 0.8998, 0.9003 and 0.9002using
REM imply that 89.98%, 90.03% and 90.02% variation in poverty reduction is explained by
the independent variables in model 1, 2, 3 respectively.
The coefficient of GDP per capita has a positive sign and significant at 1% level of
significance in all the three models indicating that GDP per capita is positively related to
HDI. A 1 percent increase in GDP per capita results in about 0.16% increase in HDI. As was
expected, it implies that economic growth positively impacts on poverty reduction. This
finding corroborates the widespread belief that economic growth isa necessary though not
sufficient condition for poverty reduction (Ravallion, 2004). This concurs with economic
theories and previous empirical findings. The big push thesis, two gap model, stages of
growth theory purports that aid is given to developing countries to stimulate economic
growth which in turn reduces poverty (trickle down approach or indirect channel of impact).
A study bySimplice (2014) predicted a positive HDI-GDP per capita income nexuswhich is
similar to the finding of this present study and otherstudies BahmaniOskooee and Oyolola
(2009), Gomanee et al (2003, 2005) and Kumler (2007) reviewed in chapter 2 despite the
measure of poverty and the channel of impact used also founds similar results.
Budget support aid proxied by net official development assistance plus official aid in model 1
in which HDI is regressed on budget support aid only, it has positive coefficient but
insignificant. In model 3 budget support aid has a negative andinsignificant impact on HDI.
Ourfinding contradicts the empirical results of Mosley et al (2004),Gomanee et al (2004;
2005) who finds a positive and significant relationshipbut is similar to Kumler (2007) who
finds that a negative relationship between budget support aid and poverty reduction holds
55
when looking only at countries with lowhuman development and also similar to the findings
of Ijaiya G. T. and Ijaiya M.A. (2004). Increasing foreign aid, thus, appears to worsen
poverty or the quality of life in SADC countries. The negative and insignificant result can be
explained by weak economic management evidenced by high levels of corruption, bad
governance, institutional failures and macroeconomic instability. If foreign aid is being
misallocated and misused to finance non-development related tasks (such as arms
expenditure or as payoffs for corrupt officials) or if recipients lack absorptive capacity then
increased aid could theoretically have no impact on poverty reduction and this is in line with
the principal agent theory and rent seeking model. This may be the case for SADC where
there is governance crisis.Easterly (2009) argue that the developmental approach used by
donors to tackle poverty in Sub Saharan Africa and in deed SADC allows for repetition of
previous errors and the top down reforms imposed in order to be eligible for aid are not
necessarily conducive to lower poverty which might have led to the ineffectiveness of several
aid projects .
The coefficient of humanitarian aid exhibits a positive impact on HDI but the coefficient is
insignificant in models 2 and 3. Our finding contradicts the findings of Nakamura et al (2005)
who finds a positive and significant impact of humanitarian aid on HDI. Though the
coefficient is insignificant the positive sign conforms to our expectation which is in line with
the principal-agent theory which highlights that humanitarian aid and the disbursement
modality which is used for this type of aid often avoid two pitfalls of misallocation and
misuse. This type of aid usually directly reaches the intended beneficiaries. However, a
possible reason that could have made it insignificant is that it usually comes in small
quantities intending to address the specific emergency or disaster that could have risen in the
recipient country. From chapter 2, it is clear that the amount of humanitarian aid received in
SADC during the period under study compared to budget support is marginal and erratic
which could have made it insignificant in determining poverty reduction in
SADC.Furthermore, the insignificant result could be due to the fact a lot of aid is wasted on
overpriced goods and services from donor countries hence too little aid reaches the poor who
desperately need it (Pekka, 2005). More so, lack of coordination between various aid
agencies operating through NGOs and local governments may make results of humanitarian
aid programs seem insignificant since some of the results are not recorded (van de Walle,
2003).
56
The economic freedom of the world index is positive and significant at 1% level of
significance in all the three models implying that strong economic institutions would be more
effective in reducing poverty.A one percent improvement in the quality of economic
institutions results in 0.03% increase in HDI thus good economic institutions contribute
positively to poverty reduction. Economic institutions involve the rules that define allocation
of economic resources, production, distribution and processing of good and services, type of
legal system and the enforcement of property rights. These economic institutions include
Central Bank, Auditor General’s Office, Ministry of Finance and the judiciary system and
theyplay an important role in poverty reduction by ensuring efficient allocation of economic
resources and putting constraints on those in positions of power so that they cannot
expropriate the resources of an economy for their own benefit at the expense of others. Our
finding corroborates the findings of Nakamura et al (2005) and Williamson (2008) who finds
a positive and significant relationship betweeneconomic institutions and poverty reduction.
However, our finding contradicts that of BahmaniOskooee and Oyolola (2009) who finds that
the quality of institutions has nefarious effects on poverty reduction.
The interactive term between aid and economic institutions is positive and significant at 1%
level of significance in all the three models as was expected. This implies that aid is made
more effective in a good economic institutional and policy environment. In all the three
models a 1% increase in aid in the presence of good economic institutions increases HDI by
0.001%. This suggests that aid is more effective in reducing poverty in the presence of strong
economic institutions such that aid resources directly reach the poor when they are allocated
and distributed.
Political freedom index is negative and significant at 10% level of significance in all the three
models.The significant coefficient suggests that the quality of political institutions is
important in determining poverty reduction and is in line with Boone (1996) who argues that
countries with more liberal political regimes tend to have better conditions for the poor as
they tend to empower them by providing more basic services. Political institutions include
electoral rules, type of political system, measures of checks and balances, constitutions and
political stability and these include bodies such as the Parliament and Electoral Commissions.
Our results are in contrary to the findings of Williamson (2008) who finds that political
freedom index is insignificant in determining the overall quality of health, a proxy for
poverty reduction.The negative sign for the political freedom index in our findingsis in line
57
with our expectation because the scale for political freedom index has a bigger digit
representing lower degree of freedom while a smaller digit represents highest degree of
freedom. Therefore the negative sign suggests that countries with lower degree of political
freedom (relatively weak political institutions)find it difficult to reduce poverty. Thus poor
quality of political institutions militates against aid effectiveness. The less accountable
political elites could be diverting disbursed foreign aid due to the absence of democratic and
credible political institutions such that projects are unable to lower poverty (Boone, 1996).
The interactive term between aid and political institutions is negative and insignificant at all
levels of significance. The results are contraryto our expectation. The insignificant coefficient
may suggest that the interaction of aid and political institutions is not important in
determining aid effectiveness in poverty reduction. This suggestion is in line with the
findings of the Freedom House Organisation 2015 Report, that Middle East and North Africa
had the worst ratings of political freedom in the world yet they have the least poverty rates in
the world. However this contradicts what we see in most developing countries whereby due
to lack of constraints on the actions of the political elites, lack of democracy and rampant
corruption, political institutions tend to determine aid allocation thereby increasing misuse
and misallocation of aid which negatively impacts on aid effectiveness. The insignificance of
this variable could be due to the controversy surrounding the measure of political institutions
hence further research on this issue is recommended using alternative measures of political
institutions.
The coefficient of trade openness is positive and significant at either 1% or 5% level of
significance in all models. A 1% increase in trade openness increases HDI by 0.02%. This
concurs with our expectation that international trade is a relatively important economic
activity in reducing poverty compared tosolely depending on foreign aid. This corroborates
the postulations of the vicious circle of poverty theory that in order for developing countries
to break out of poverty, they need to produce more and expand their markets even beyond
borders. Policies to expand international trade in SADC countries will be a welcome move in
order to reduce poverty. Our finding contradicts the finding of Nakamura (2005) who finds a
positive but insignificant relationship maybe due to the fact that the two studies consider two
different sub-regions of the developing world with different institutional set ups.
58
Foreign direct investment (FDI) is negative and significant. Thus the results suggest that FDI
is an important determinant of poverty reduction but is worsening HDI.Our finding
contradicts the empirical finding of Gohou (2009) who finds a positive and significant impact
of FDI on poverty reduction. The negative signwhich indicates that FDI worsens poverty
could be explained by the financial crises around the world or the increase in the cost and
ease of doing business in most of the SADC countries or lack of policies to attract FDI.This
could also be indicating that foreign direct investment is a necessary but not sufficient
condition for poverty reduction. It could also be that channeling of these FDI inflows into
investments that benefit the poor is missing within the SADC region. From the perspective of
the vicious circle of poverty theory, SADC countries should concentrate more on mobilizing
investible resources on the domestic front if it is to uplift its people from poverty.
Infant mortality rate is negative and significant at 1% level of significance. A one percent
increase in infant mortality rate reduces HDI by 0.1%. This is in line with our expectation
that higher infant mortality rates are positively correlated with high poverty rates hence
reduces HDI (slackening poverty reduction). This validates the recipient needs model on how
aid is allocated thus countries with high infant mortality rates tend to receive more aid.
Robustness Checks
To ensure the validity of the previous results robustness checks are provided.Following
Williamson (2008) the robustness checks allow for the inclusion of other additional control
variables that are theoretically and empirically relevant in the analysis of aid effectiveness in
reducing poverty. The additional control variables included are control of corruption,
interactive term between trade openness and aid, interactive term between aid and control of
corruption. Summary of the results are presented in columns C through E in tables 4.4.1,
4.4.2 and 4.4.3 for models 1, 2 and 3 respectively.
In column C where trade openness is replaced by the interaction between aid and trade
openess (�� ∗ ���)the original results for all other variables are confirmed. This new variable
is positive and significant at 1% level of significance.The results indicate that a1 percent
increase in aid in the presence of trade openess may reduce poverty by 0.00002%. This new
variable is in line with our prediction that the joint effect of aid and international trade will
give positive results on poverty reduction. In other words aid is made more effective in a
good policy environment which permits international trade. Investing aid resources in
59
massive industrialisation, agricultural production and value addition and beneficiation entail
increased returns through exportswhich in turn increases economic growth and reduce
poverty.
In column D31, in which the control of corruption is added to the three models, the results for
all other variables conform to the original results except for the political freedom index that
becomes insignificant but mantains the negative sign. The coefficient of control of corruption
is positive and significant in all the three models. A 1% increase in the control of corruption
index will result in about 0.02% increase in HDI. This suggest that controlling for corruption
has a positive impact on poverty reduction and is very critical.
In column E, in which the interactive terms between control of corruption and aid is added to
the models, the results corroborates our original findingsfor all other variables except for the
political freedom index which then becomes insignificant compared to the initial results of
column A. This new variable (!! ∗ ���)ispositive and significant as is expected thus aid is
made more effective in reducing poverty in the absence of corruption. Having higher degree
of control of corruption ensure that aid resources that are supposed to reach the poorest are
not wasted and diverted to the less poor.This finding is similar to Chong et al (2009) who
finds that aid is effective in the absence of corruption though their finding was not robust.
In model F (see Annexure 5), HDI total aid is regresed on total aid and same variables as in
other models to establish the effect of aid dependence on poverty reduction we regress aid in
the absence of GDP per capita. The coefficient of aid dependency is negative and significant
at 1 percent level of significance. A 1 percent increase in aid dependence is reducing HDI by
0.04%. This is in line with our prediction that aid dependence increases poverty. Our finding
corrobates the conclusion by Moyo (2009) that the culture of heavily depending on aid has
left Africa more debt laden, prone to inflation and not attractive to investment. Recipient
countries that are are heavily dependent on aid tend to be reluctanct to develop their
capacities to engage in economic activities that are more productive to ensure that millions of
their people are uplifted from poverty (thus aid dependence has negative impact on domestic
resource mobilisation). In SADC, Botswana is one country that managed to use the aid
31According to Allison (2012) multicollinearity can safely be ignored when the variables with high VIFs are control variables and the variables of interest do not. Therefore control of corruption though it has a correlation coefficient of 0.7 with PFI and EFWI they can still be included in the same regression equation and the performance of the variables are not impaired.
60
resources effectively to invest and build their economy and then cut itself from being an aid
dependent economy and their economy currently is fairly doing well (Moyo, 2009).
4.4 Conclusion
The chapter has estimated and interpreted the regression results which have indicated that the
impacts of foreign aid on poverty reduction is insubstantial and weak stastistically despite
efforts to use the non income measure of poverty (HDI) and to decompose aid. Our major
findings seem to reject the hypothesis that aid has been effective in reducing poverty in
SADC during the period under study.Aid has been found to be negative and insignificant in
poverty reduction, thus it seem to be failing to reduce poverty in SADC countries but rather
worsen. In examiningwhy aid is failing to reduce poverty we found the causes to be the
presence of weak institutions, poor control of corruption and increased aid dependence.
Another hypothesis answered by the study concern the relative importance of international
trade compared to foreign aid in reducing poverty.
CHAPTER FIVE
CONCLUSION AND POLICY RECOMMENDATION
61
5.0 Introduction
This chapter contains a detailed conclusion to the study. It presents a brief summary of the
results of the study and the conclusions drawn from the results of the previous chapter. This
will be followed by policy recommendations. The last subsection will give recommendations
for further study.
5.1 Summary and conclusions
The study investigated several questions regarding the impact of foreign aid in reducing
poverty using a panel of 12 countries in SADC region and 9 time periods, 2005 to 2013. The
aim of the study was to: [1] investigate whether foreign aid has been effective in reducing
poverty in some selected SADC countries, [2] determine which type of foreign aid is more
beneficial in reducing poverty, [3] explain what is derailing progress in reducing poverty
given the astronomical aid flows to SADC over the period under investigation, [4] explain the
conditions under which aid can be made more effective in reducing poverty inSADCcountries
basing on the empirical results and [5] explain other forms of economic activities other than
the aid strategy, that can alternatively be employed to reduce poverty sustainably basing on
the empirical results.
The analysis was done in several steps. Following part of Clemens et al (2004)’s
classification, aid was disaggregated and two types of aid were chosen for the study,
humanitarian aid and budget support aid. Three panel regression models were set up in which
HDI was regressed on each type of aid under the same control conditions and the last one in
which both types of aid are included. Panel regression allows controlling for both country and
time effects. Model specification tests were carried out and Random Effects Model (REM)
with cross country effects was found to be the most appropriate compared to the fixed effects
and pooled OLS models. The equations were then estimated and three robustness checks
were made. Robustness checks were made by adding additional control variables to the
models estimated using the random effects model to check if there will be consistency in the
results.
The finding from the study is that aid has not had a significant impact on poverty reduction in
SADC regardless of using the non-income measure of poverty,the human development index
and decomposing aid. The study finds that budget support aid has a negative impact on
62
poverty reduction and is statistically insignificant while humanitarian aid has a positive
impact on poverty reductionbut isalso statistically insignificant. The absence of statistically
significant coefficientsin both types of aid does not mean that aid is irrelevant for poverty
reduction in SADC countries but this could be explained by several reasons. It could be that
the poverty is so high in SADC that the amount of foreign aid disbursed seems
insignificant.Also the ability of aid to effectively lower poverty can also be diluted because
donors may be motivated by other considerations like strategic and economic interests which
benefit them only at the expense of the aid recipient country (Allesina and Collier, 2000). In
addition, lack of significant results may also be due to lack of systematic evaluation and
feedback on aid programs, lack of transparency of donors and the negligence of local officials
while conceiving aid.More so foreign aid may not have a lasting effect on poverty reduction
since the absorptive capacity of the population in SADC to maintain infrastructure is very
low. Therefore, it can be deduced that aid resources can be made more effective in reducing
poverty under certain conditions.
The study examined why aid is failing to reduce poverty in SADC by including some
institutional and policyvariables and other factors that militate against aid effectiveness from
the theoretical point of view. The study finds that the quality of institutions matter in
determining aid effectiveness in reducing poverty. Both economic and political institutions
have been found to be robust and significant in reducing poverty thus aid becomes more
effective in reducing poverty in the presence of both strong economic and political
institutions. The interaction terms between aid and control of corruption have been found to
be positively linked to poverty reduction andstatistically significant. Thissuggests that aid
works better in an environment where the control of corruption is high.
GDP per capita income remains a necessary though not sufficient condition for poverty
reduction. The empirical result of GDP-HDI is robust and statistically significant at all levels.
More so, higher levels of aid have been found to be creating aid dependence which is found
to be negatively related to HDI thus exacerbating poverty. This suggests that aid dependence
worsens the quality of life thus it negatively impacts on poverty reduction efforts. Trade
opennesshas been found to be positive and statistically significant. The interactive term
between trade opennessand foreign aid has also been found to be having a positive and
significantimpact on poverty reduction. This indicates that aid can be made more effective in
reducing poverty under good macroeconomic policy environment such as trade openness
63
which permits expansion of markets even beyond borders. The study also finds that foreign
direct investment has detrimental effect on HDI which supports the proposal of the vicious
circle of poverty theory which favoursmobibilisation of investible funds on the domestic
front.
5.2 Policy Recommendations
Given the aforementioned results, it is clear that aid is failing to achieve its intended purpose
of helping the poordue to aid misallocation, misuse, lack of absorptive capacity by the
recipient countries and the insignificants amounts being received vis-à-vis SADC’ s poverty
needs.However banning all aid is not the answer, evidence from the study has shown that
there are major concerns that need to be addressed and appropriate measures be put in place
in order to increase aid effectiveness in reducing poverty.
One concern is the recognition of the importance of improving the quality of aid in order to
increase its effectiveness. Evidence from the study has revealed that the effectiveness of aid
depends on the commitments and capacity of recipient government to put aid to the best use.
The failure of many aid funded projects in SADC in most cases has nothing to do with the aid
itself, but the way the projects are implemented and the way the aid is used.World
Governance Indicators show that SADC has governance crisis, on a scale of -2.5 to 2.5;
control of corruption has a mean of -0.63.Therefore, there is need for governments in SADC
countries to address the issue of bad governance and corruption that has eaten so deep in the
sub-region in orderto minimise pitfalls such asaid misuse and misallocation.With good
governance the people of SADC would be involved in management and use of foreign aid
meant for social service delivery and governments’responsibility to ensure accountability,
openness and transparency in the management and use of foreign aid would have maximum
impact in uplifting people from poverty. The fight against corruption should thus be
intensified in order to guard against the diversion of foreign aid. Such measures include
putting up and strengthening of anticorruption bodies and the judiciary system that apprehend
corrupt officials and ensure that there are punitive punishment for all those caught on the
wrong side.
Weak institutional frameworks and policies within developing countries,such as weak
leadership of the development agenda, ineffective public administrations and financial
management systems,lead to inefficiency in the use of aid resources. It also leads to lack of
64
sustainability in the results of aid. These risks are high in SADC countries hence having the
strong institutions that can manage expenditure and revenue generation in a sustainable
manner and institutions that are not politicized would in the long run be able to create the
required political buy in and will, mobilise, manage the aid resources and deliver the services
provided by foreign aid.Putting in place policies that protect individual and property rights
offer swiftest hope if poverty is to be meaningfully reduced.Conducive political environment
would also guarantee strong institutional and absorptive capacity for the use of foreign aid for
poverty reduction and economic development at large
Another concern is on financing modalities. When aid is not being used effectively due
toadministrative bottlenecks and corruption in the recipient country, aid throughNGOs and
private bodies might be more productive (Nakamura and McPherson, 2005). There is need
for adopting an aid modality that is consistent with poverty reduction strategies in the region.
For example, humanitarian aid programmes that prioritise the development of infrastructure
such as construction of new roads in addition to maintenance of existing markets to ensure
that small scale producers face lower transportation costs, have improved communication
systems and have greater opportunity to get their products to market and raise their rural
incomes and Provision of a clean water supply must take a higher priority in donor aid
programmes. Securing a clean water supply to a greater proportion of the population can be
expected to have knock-on effects leading to improvements in health indicators.
The study has also shown that aid effectiveness in reducing poverty also depends on the
actions of donors. Therefore another concern is on the quantity of aid. There is need to
significantly scale up aid to improve its effectiveness in poverty reduction. In SADC like the
rest of Sub-Sahara Africa poverty needs far outweighs the amount of aid being received
hence no sustainable impact on poverty reduction. Furthermore, unpredictable aid flows
undermine government’s efforts at medium and long term planning, while large flows of off-
budget aid compromise rational resource allocation and the role of parliaments in ensuring
government accountability.
Another concern is the need for aid recipient governments to adopt a comprehensive view of
povertyand associated problems and developslong-term strategies. One of the strategies as
revealed in the study is the promotion of international trade. Thus an alternative to aid is
trade, on one hand upon receiving aid, the aid resources should be channelled to building of
65
industries to promote value addition so that exports are increased. On the other hand,
according to Smith (2002), rather than giving money that can be embezzled and mismanaged,
the best alternative that donor countries can do is to build industries directly for poor
countries where the only profit to be made is in production, value addition and beneficiation.
The study has also shown that persistence in poverty in SADC can be linked to lack of
sustainable growth which is essential to reduce poverty. The gaps which SADC is facing as
outlined by the study are the savings gap, low-capital threshold gap and infrastructure gap
which makes it fail to register sustainable growth need to be addressed. Thus Stable
macroeconomic policy environment that guarantee price stability, exchange rate stability, and
economic growth is essential for high rate of returns to be achieved by anti-poverty projects
financed with foreign aid.
Furthermore, having the right kind of absorptive capacity most especially human capital to
manage and use foreign aid is also essential since a country with a highly skilled and
educated labour force is expected to have high returns from investments that are financed by
foreign aid. Thus SADC countries need to invest in its people to give them the edge to escape
poverty and improve their individual development particularly on the rural area front where
majority of the poor reside.
5.3 Areas for Further Research and limitation of the study
This study provides a preliminary step towards further research on aid effectiveness in
poverty reduction. To address some of the limitations of this study, further research needs to
be conducted. For instance, the study focused on 12 countries in SADC region due to lack of
long time series data for some variables for each country we were unable to estimate models
at country level. Further studies can be done at country level since the countries are not
homogenous.In addition, lack of complete and full data sets resulted in the study dropping
some variables such as inequality (both gender and income) which negatively impacts on
HDI. Also pro-poor expenditure and comprehensive policy variables such as CPIA could not
be included as the data was unavailable and further research is recommended to include these
variables to be able to conclude on the issue of poverty and the effectiveness of foreign aid in
a good policy environment respectively.
66
The study focused on two broad categories of aid. Further studies can also be done to test the
effectiveness of aid specifically to sectors such as health, sanitation, education. Before
concluding that aid is not the most powerful weapons against poverty. Williamson (2008) has
attempted to test the effectiveness of aid in the health sector and found it to be insignificant
but the results were for a sample from various developing sub-regions. Further research could
be done for SADC region.
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Appendix 1: Summary Statistics
Table 4.1 Descriptive statistics
74
Mean
Median Max Min Std. Dev. Skewness Kurtosis
Jarque-
Bera
Probability Sum
Sum
Sq.Dev Obs
HDI (not
in logs) 0.475093 0.48 0.66 0.29 0.088861 0.031186 2.538129 0.977469 0.613402 51.31 0.844899 108
HDI -0.761389 -0.73 -0.42 -1.24 0.194131 -0.425476 2.743881 3.55373 0.169168
-82.23 4.032492 108
BSAID 20.27556 20.53 22.42 17.5 1.136857 -0.476394 2.391508 5.7513 0.056379
2189.8 138.2915 108
HAID 8.875278 9.33 13.52 0 3.019656 -1.023922 4.529612 29.4002 0
958.53 975.6605 108
AID 20.27574 20.53 22.42 17.5 1.136927 -0.476718 2.391273 5.75815 0.056187
2189.8 138.3086 108
AID/GNI 1.502407 1.965 3.41 -1.66 1.388035 -0.889042 2.696973 14.6403 0.000662
162.26 206.1506 108
EFWI 5.965648 6.2 7.31 3 0.955627 -1.058598 3.681027 22.2584 0.000015
644.29 97.71485 108
PFI 4.037037 3.5 6.5 1.5 1.509632 0.180531 1.679004 8.43929 0.014704
436 243.8519 108
CC -0.510741 -0.455 0.58 -1.48 0.565156 -0.217233 2.007597 5.28131 0.071315
-55.16 34.17594 108
EFW*IAID 120.8698 124.1 153.39 60.1 19.96506 -0.911539 3.827745 18.0395 0.000121
13054 42650.57 108
PFI*AID 81.64278 75.035 134.54 30.79 30.04223 0.148467 1.895383 5.88757 0.052666
8817.4 96571.32 108
CC*AID -10.54009 -9.48 11.92 -32.14 11.58193 -0.233897 2.028525 5.23168 0.073106
-1138.3 14353.1 108
XM 4.430463 4.4 5.18 3.67 0.355587 0.220136 2.513042 1.93936 0.379205
478.49 13.52928 108
XM*AID 1786.337 1639.88 3315.5 832.7 580.5471 0.805284 3.011451 11.6733 0.002919
192924 36062739 108
FDI 4.35963 2.835 41.81 -5.5 6.84998 3.189961 16.40649 991.968 0
470.84 5020.679 108
INFMR 4.098889 4.075 4.8 3.54 0.328905 0.255188 2.15127 4.41373 0.110045
442.68 11.57507 108
GDPpc 7.821944 7.65 9.46 6.12 0.972615 0.146477 1.791515 6.95816 0.030836
844.77 101.2199 108
Kurtosis
Measures the peakness or flatness of the distribution of the searies.The measure of Kurtosis is given by the formula below;
∑=
−=N
i
i yy
NK
1
4_
1
σ
N represents the number of observations in the current sample.y represents the series whilst _
y is the mean of the series.
σ is based on the biased estimator for the variance.
Skewness
Is a measure of asymmetry of the distribution of the series around its mean.Measure of skewness is given by the formula
below;
∑=
−=N
i
a
i yy
NS
1
_
1
σ
Jaque -Bera
Is a test statisticc for testing whether the series is normally distributed. Jaque –Bera is a test statistic given by the formula
below;
( )
−+−= 22 34
1
6KS
kNJB
S is the skewness, K is the kurtosis, k represents the number of estmated coefficients used to create the series.
H0: Residuals are normally distributed.
75
JB is disttributed as 2χ with two degrees of freedom, i.e. JB ~
2χ
Reject H0 if p ≤ 0.05
Appendix 2:Multicollinearity tests
Table 4.2(a) Correlation Matrix
BSAID HAID AID AIDGNI EFWI PFI CC
EFWIAI
D PFIAID CCAID XM XMAID FDI INFMR
GDP
PC
BSAID 1
HAID 0.3208 1
AID 1 0.3209 1
AIDGNI 0.4491 0.4837 0.4492 1
EFWI -0.0815 -0.5755 -0.0815 -0.09846 1
PFI -0.1249 0.452 -0.125 0.09514 -0.6157 1
CC -0.2954 -0.5987 -0.2953 -0.15485 0.731 -0.7953 1
EFWIAID 0.27 -0.4507 0.2699 0.0477 0.9369 -0.6467 0.6018 1
PFIAID 0.0421 0.529 0.042 0.18534 -0.647 0.9844 -0.856 -0.6193 1
CCAID -0.3394 -0.6166 -0.3393 -0.19397 0.7194 -0.7807 0.9978 0.5766 -0.8507 1
XM -0.7556 -0.0119 -0.7555 -0.14406 -0.1053 0.1439 0.132 -0.3708 0.032 0.156 1
XMAID -0.6777 0.0196 -0.6776 -0.07443 -0.0986 0.0947 0.1373 -0.3378 -0.0023 0.155 0.97913 1
FDI 0.2598 0.0195 0.26 0.33925 0.1623 -0.1847 0.1097 0.2462 -0.1494 0.096 -0.0397 -0.01937 1
INFMR -0.1392 0.3049 -0.1393 0.0087 -0.54 0.4788 -0.533 -0.5717 0.4688 -0.52 0.33127 0.362046 -0.1798 1
GDPPC -0.5414 -0.6413 -0.5414 -0.83749 0.3936 -0.2776 0.4631 0.2027 -0.3898 0.496 0.22195 0.151969 -0.2769 -0.3102 1
Table 4.2(b): Correlation Matrix
GDPPC BSAID HAID EFWI PFI XM FDI INFMR
GDPPC 1
BSAID -0.54136 1
HAID -0.64128 0.32084 1
EFWI 0.39356 -0.08152 -0.5755 1
PFI -0.27763 -0.12493 0.45205 -0.61574 1
XM 0.22195 -0.75562 -0.0119 -0.10527 0.143862 1
FDI -0.27692 0.25979 0.01952 0.16232 -0.18473 -0.03975 1
INFMR -0.31022 -0.13918 0.30486 -0.53995 0.478832 0.33127 -0.17981 1
Table 4.2 (c): Variance inflation Factor (VIF)
Variance Inflation Factors
Date: 04/29/16 Time: 00:51
Sample: 2005 2013
Included observations: 108
76
Coefficient Uncentered Centered
Variable Variance VIF VIF
C 0.069808 109.5656 NA
GDPPC 0.000323 34.39849 3.398962
BSAID 2.97E-05 20.51644 1.322012
HAID 6.51E-07 1.239878 1.159380
EFWI 2.17E-05 2.531419 1.319819
PFI 2.65E-05 2.024010 1.347140
XM 0.000120 5.018182 1.320333
FDI 9.49E-08 1.275969 1.273138
CC 0.000159 1.566940 1.501722
INFMR 0.000525 17.37213 3.525485
Appendix 3: Summary of Model Specification tests
Table 4.3 Summary of model specification tests Model 1 Test Critical value P-value Decision Implication Regression of HDI on budget support aid
Breusch Pagan LM test (Pooled vs random) H0: no random effects
cross section effects LM = 309.5788
0.0000 Reject H0 Random effects model (REM) is more appropriate time effects
LM = 3.177281 0.0747 Fail to reject
H0at 5% level of significance
Both effects LM = 312.7561
0.0000 Reject H0
Fixed Effects test H0: Fixed effects are redundant
Cross section effects F(11.81) = 129.380471
0.0000 Reject H0 Fixed effects are not redundant. However they will be of importance if fixed effect model is chosen by Hausman test
Period effects F (8.81) = 4.486223
0.0002 Reject H0
Both effects F (19.81) = 76.791411
0.0000 Reject H0
Hausman test (REM vs FEM) H0: random effects is consistent)
Chi-Sq. (7) = 2.659929 0.9146 Fail to reject H0
Random effects model (REM) is more appropriate
Model 2 Test Critical value P-value Decision Implication Regression of HDI on humanitarian aid
Breusch Pagan LM test (Pooled vs random) H0: no random effects
cross section effects LM = 328.9916
0.0000 Reject H0 Random effects model (REM) is more appropriate time effects
LM = 3.8990 0.0483 Reject H0
both effects LM = 181.24
0.0000 Reject H0
Redundant Likelihood Fixed Effects test H0: Fixed effects are redundant
cross section effects F(11.81) = 136.66987
0.0000 Reject H0 Fixed effects are not redundant. However they will be of importance if fixed effect model is chosen by Hausman test
time effects F(8.81) = 4.432293
0.0002 Reject H0
Both effects F (19.81) = 79.89773
0.0000 Reject H0
Hausman test (REM vs FEM) H0: random effects is consistent)
Chi-Sq. (7) = 1.609875 0.9783 Accept H0 Random effects model (REM) is more appropriate
Model 3 Test Critical value P-value Decision Implication Regression of HDI on
Breusch Pagan LM test (Pooled vs random)
cross section effects LM = 310.1764
0.0000 Reject H0 Random effects model (REM) is more
77
both budget support and humanitarian aid
H0: no random effects time effects LM = 3.0261
0.0819 Fail to reject H0 at 5% level of significance
appropriate
Both effects LM = 269.66
0.0000 Reject H0
Redundant Likelihood Fixed Effects test H0: Fixed effects are redundant
cross section effects F(11.80) = 129.36418
0.0000 Reject H0 Fixed effects are not redundant. However they will be of importance if fixed effect model is chosen by Hausman test
period effects F (8.80) = 4.60395
0.0001 Reject H0
both effects F(19.80) = 76.97345
0.0000 Reject H0
Hausman test (REM vs FEM) H0: random effects is consistent)
Chi Sq. (8) = 2.14680 0.9762 Accept H0 Random effects model (REM) is more appropriate
Appendix 4: Summary of regression results
Table 4.4.1: Summary of regression results for model 1 HDI is the dependent variable
Variable A B
Robustness checks (Additional variables)
C D E
GDPpc 0.161939 (0.0000)***
0.161412 (0.0000)***
0.162644 (0.0000)***
0.1590965 (0.0000)***
0.151602 (0.0000)***
BSAID 0.000701
(0.8958 -0.006555
(0.2229) -0.000619
(0.9074) -0.000379
(0.9429) -0.000363
(0.9455)
EFWI 0.030819 (0.0000)***
0.030527 (0.0000)***
0.032105 (0.0000)***
0.032223 (0.0000)***
PFI -0.008042
(0.0935)* -0.008090
(0.0883)* -0.004484
(0.3772) -0.004765
(0.3512)
XM 0.028095 (0.0097)***
0.028426 (0.0097)***
0.032105 (0.0042)***
0.031009 (0.0046)***
FDI -0.000635
(0.0652)* -0.000649
(0.046)** -0.000694 (0.0286)**
-0.000654 (0.0355)**
-0.000652 (0.0368)**
INFMR -0.118060 (0.0000)***
-0.117720 (0.0000)***
-0.122070 (0.0000)***
-0.132464 (0.0000)***
-0.131247 (0.0000)***
EFWI*AID 0.001527 (0.0000)***
PFI*AID -0.000380
(0.1130)*
XM*AID 0.0000173 (0.0044)***
CC 0.022837 (0.0674)*
CC*AID 0.001033 (0.0899)*
C -1.831459 (0.0000)***
-1.685107 (0.0000)***
-1.698045 (0.0000)***
-1.688309 (0.0000)***
-1.713250 (0.0000)***
R-squared 0.899873 0.897953 0.901315 0.903358 0.902806 Adjusted R-
squared 0.892864 0.890809 0.894407 0.895549 0.894952 F-Statistic 128.3898 125.7054 130.4754 115.6750 114.9477
78
(0.000000)*** (0.000000)*** (0.000000)*** (0.000000)*** (0.000000)***
The p values which indicate significance of variables are in parenthesis and asterisks * denotes statistical significance at 10%, ** at 5% and *** at1% level
Table 4.4.2: Summary of regression results for model 2 HDI is the dependent variable
Variable A B
Robustness Checks
C D E
GDPpc 0.164677 (0.0000)***
0.164379 (0.0000)***
0.165306 (0.0000)***
0.152960 (0.0000)***
0.153555 (0.0000)***
HAID 0.000619
(0.4390) 0.000375
(0.6463) 0.000545
(0.4929) 0.000782
(0.3229) 0.000789
(0.3208)
EFWI 0.030926 (0.0000)***
0.030743 (0.0000)***
0.032537 (0.0000)***
0.032620 (0.0000)***
PFI -0.008708
(0.0683)* -0.008464
(0.0745)* -0.004928
(0.3297) -0.005335
(0.2924)
XM 0.025940 (0.0199)**
0.026791 (0.0187)**
0.028681 (0.0097)***
0.028549 (0.0105)***
FDI -0.000616 (0.0488)**
-0.000583 (0.0680)*
-0.000664 (0.0342)**
-0.000618 (0.0443)**
-0.000623 (0.0434)**
INFMR -0.116600 (0.0000)***
-0.109792 (0.0000)***
-0.118846 (0.0000)***
-0.130758 (0.0000)***
-0.130565 (0.0000)***
EFWI*AID -0.001477 (0.0000)***
PFI*AID -0.000397
(0.1068)*
XM*AID -0.0000162
(0.0096)***
CC 0.024541 (0.0504)**
CC*AID 0.001127 (0.0676)**
C -1.955970 (0.0000)***
-1.862654 (0.0000)***
-1.747350 (0.0000)***
-1.714863 (0.0000)***
-1.719272 (0.0000)***
R-squared 0.900285 0.896399 0.901620 0.904247 0.903672 Adjusted R-
squared 0.893305 0.889147 0.894734 0.896510 0.895888
F-Statistic 128.9799
(0.000000)*** 123.6059
(0.000000)*** 130.9243
(0.000000)*** 116.8644
(0.000000)*** 116.0928
(0.000000)***
The p values which indicate significance of variables are in parenthesis and asterisks * denotes statistical significance at 10%, ** at 5% and *** at1% level
Table 4.4.3: Summary of regression results for model 3 HDI is the dependent variable
Variable A B
Robustness Checks
C D E
79
GDPpc 0.165089 (0.0000)***
0.164208 (0.0000)***
0.165723 (0.0000)***
0.152885 (0.0000)***
0.153620 (0.0000)***
BSAID -0.0000649
(0.9906) -0.007110
(0.1959) -0.001268
(0.8161 -0.001592
(0.7710) -0.000736
(0.8928)
HAID 0.000621
(0.4477) 0.000538
(0.5140) 0.000576
(0.4772) 0.000829
(0.4477) 0.000810
(0.3196)
EFWI 0.030938 (0.0000)***
0.030659 (0.0000)***
0.032504 (0.0000)***
0.032593 (0.0000)***
PFI -0.008722
(0.0785)* -0.008709
(0.0757)* -0.005198
(0.3148)* -0.005481
(0.2922)
XM 0.025896 (0.0212)**
0.026534 (0.0196)**
0.028521 (0.0107)**
0.028460 (0.0113)**
FDI -0.000616
(0.0521)* -0.000633 (0.0488)**
-0.000676 (0.0344)**
-0.000631 (0.0433)**
-0.000629 (0.0447)**
INFMR -0.116245 (0.0000)***
-0.116038 (0.0000)***
-0.119963 (0.0000)***
-0.132859 (0.0000)***
-0.132859 (0.0000)***
EFWI*AID -0.001531 (0.0000)**
PFI*AID -0.000406
(0.1003)*
XM*AID 0.0000162 (0.0099)***
CC 0.025235 (0.0483)**
CC*AID 0.001135 (0.0681)*
C -1.841822 (0.0000)***
-1.697501 (0.0000)***
-1.719082 (0.0000)***
-1.671400 (0.0000)***
-1.700232 (0.0000)***
R-squared 0.900216 0.898096 0.901620 0.904307 0.903673 Adjusted R-
squared 0.892152 0.889862 0.893670 0.895519 0.894827
F-Statistic 111.6427
(0.000000)*** 109.0630
(0.000000)*** 113.4125
(0.000000)*** 102.9008
(0.000000)*** 102.1521
(0.000000)***
The p values which indicate significance of variables are in parenthesis and asterisks * denotes statistical significance at 10%, ** at 5% and *** at1% level
a
Appendix 5: Regression Results
MODEL 1: BUDGET SUPPORT AID
Lagrange Multiplier Tests for Random Effects (REM vs Pooled OLS)
Null hypotheses: No effects
Alternative hypotheses: Two-sided (Breusch-Pagan) and one-sided
(all others) alternatives
Test Hypothesis
Cross-section Time Both
Breusch-Pagan 309.5788 3.177281 312.7561
(0.0000) (0.0747) (0.0000)
Honda 17.59485 -1.782493 11.18103
(0.0000) -- (0.0000)
King-Wu 17.59485 -1.782493 10.06077
(0.0000) -- (0.0000)
Standardized Honda 25.51588 -1.625027 10.53917
(0.0000) -- (0.0000)
Standardized King-Wu 25.51588 -1.625027 8.966050
(0.0000) -- (0.0000)
Gourierioux, et al.* -- -- 309.5788
(< 0.01)
*Mixed chi-square asymptotic critical values:
1% 7.289
5% 4.321
10% 2.952
Correlated Random Effects - Hausman Test (REM vs FEM)
Equation: Untitled
Test cross-section random effects
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 2.659929 7 0.9146
Cross-section random effects test comparisons:
Variable Fixed Random Var(Diff.) Prob.
GDPPC 0.170884 0.161939 0.000190 0.5161
BSAID 0.000422 0.000701 0.000003 0.8645
EFWI 0.030922 0.030819 0.000001 0.9273
PFI -0.007938 -0.008042 0.000003 0.9516
XM 0.027486 0.028095 0.000004 0.7579
FDI -0.000642 -0.000635 0.000000 0.8815
INFMR -0.109234 -0.118060 0.000135 0.4474
Residual Cross-Section Dependence Test
Null hypothesis: No cross-section dependence (correlation) in residuals
Equation: Untitled
Periods included: 9
Cross-sections included: 12
Redundant Likelihood Fixed Effects Tests
Equation: Untitled
Test cross-section and period fixed effects
Effects Test Statistic d.f. Prob.
Cross-section F 129.380471 (11,81) 0.0000
Cross-section Chi-square 315.528206 11 0.0000
Period F 4.486223 (8,81) 0.0002
Period Chi-square 39.612491 8 0.0000
Cross-Section/Period F 76.791411 (19,81) 0.0000
Cross-Section/Period Chi-square 318.072144 19 0.0000
b
Total panel observations: 108
Note: non-zero cross-section means detected in data
Cross-section means were removed during computation of correlations
Test Statistic d.f. Prob.
Breusch-Pagan LM 96.09972 66 0.0092
Pesaran scaled LM 1.575378 0.1152
Pesaran CD 1.501750 0.1332
Model 1 results
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/27/16 Time: 15:24
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.831459 0.246011 -7.444626 0.0000
GDPPC 0.161939 0.015870 10.20439 0.0000
BSAID 0.000701 0.005337 0.131355 0.8958
EFWI 0.030819 0.004636 6.647718 0.0000
PFI -0.008042 0.004749 -1.693533 0.0935
XM 0.028095 0.010656 2.636597 0.0097
FDI -0.000635 0.000311 -2.041310 0.0439
INFMR -0.118060 0.021003 -5.621145 0.0000
Effects Specification
S.D. Rho
Cross-section random 0.074743 0.9602
Idiosyncratic random 0.015221 0.0398
Weighted Statistics
R-squared 0.899873 Mean dependent var -0.051565
Adjusted R-squared 0.892864 S.D. dependent var 0.045482
S.E. of regression 0.014887 Sum squared resid 0.022162
F-statistic 128.3898 Durbin-Watson stat 1.251886
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.914153 Mean dependent var -0.761389
Sum squared resid 0.346178 Durbin-Watson stat 0.080145
MODEL 2: HUMANITARIAN AID
Lagrange Multiplier Tests for Random Effects (REM vs Pooled OLS)
Null hypotheses: No effects
Alternative hypotheses: Two-sided (Breusch-Pagan) and one-sided
(all others) alternatives
Test Hypothesis
Cross-section Time Both
Breusch-Pagan 328.9916 3.899046 332.8907
(0.0000) (0.0483) (0.0000)
Honda 18.13813 -1.974600 11.42934
(0.0000) -- (0.0000)
King-Wu 18.13813 -1.974600 10.26712
(0.0000) -- (0.0000)
Standardized Honda 25.85614 -1.847861 10.64297
(0.0000) -- (0.0000)
Standardized King-Wu 25.85614 -1.847861 9.039827
(0.0000) -- (0.0000)
Gourierioux, et al.* -- -- 328.9916
(< 0.01)
*Mixed chi-square asymptotic critical values:
1% 7.289
5% 4.321
10% 2.952
c
Redundant Likelihood Fixed Effects Tests
Equation: Untitled
Test cross-section and period fixed effects
Effects Test Statistic d.f. Prob.
Cross-section F 136.669874 (11,81) 0.0000
Cross-section Chi-square 321.137147 11 0.0000
Period F 4.432293 (8,81) 0.0002
Period Chi-square 39.213123 8 0.0000
Cross-Section/Period F 79.897727 (19,81) 0.0000
Cross-Section/Period Chi-square 322.133769 19 0.0000
Correlated Random Effects - Hausman Test (REM vs FEM)
Equation: Untitled
Test cross-section random effects
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 1.609875 7 0.9783
Cross-section random effects test comparisons:
Variable Fixed Random Var(Diff.) Prob.
GDPPC 0.173222 0.164677 0.000167 0.5084
HAID 0.000630 0.000619 0.000000 0.8058
EFWI 0.030965 0.030926 0.000001 0.9684
PFI -0.008576 -0.008708 0.000003 0.9339
XM 0.025334 0.025940 0.000004 0.7527
FDI -0.000620 -0.000616 0.000000 0.8690
INFMR -0.108059 -0.116600 0.000141 0.4716
Residual Cross-Section Dependence Test
Null hypothesis: No cross-section dependence (correlation) in residuals
Equation: Untitled
Periods included: 9
Cross-sections included: 12
Total panel observations: 108
Note: non-zero cross-section means detected in data
Cross-section means were removed during computation of correlations
Test Statistic d.f. Prob.
Breusch-Pagan LM 93.22885 66 0.0153
Pesaran scaled LM 1.325501 0.1850
Pesaran CD 1.632553 0.1026
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/27/16 Time: 15:55
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.838629 0.202109 -9.097228 0.0000
GDPPC 0.164677 0.016585 9.929332 0.0000
HAID 0.000619 0.000796 0.777058 0.4390
EFWI 0.030926 0.004617 6.699026 0.0000
PFI -0.008708 0.004725 -1.843145 0.0683
XM 0.025940 0.010966 2.365530 0.0199
FDI -0.000616 0.000309 -1.994911 0.0488
INFMR -0.116600 0.020226 -5.764883 0.0000
Effects Specification
S.D. Rho
Cross-section random 0.081943 0.9669
Idiosyncratic random 0.015168 0.0331
Weighted Statistics
d
R-squared 0.900285 Mean dependent var -0.046890
Adjusted R-squared 0.893305 S.D. dependent var 0.045168
S.E. of regression 0.014754 Sum squared resid 0.021768
F-statistic 128.9799 Durbin-Watson stat 1.273090
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.913410 Mean dependent var -0.761389
Sum squared resid 0.349172 Durbin-Watson stat 0.079366
MODEL 3: BOTH BUDGET SUPPORT AND HUMANITARIAN AID
Lagrange Multiplier Tests for Random Effects (REM vs Pooled OLS)
Null hypotheses: No effects
Alternative hypotheses: Two-sided (Breusch-Pagan) and one-sided (all others)
Test Hypothesis
Cross-section Time Both
Breusch-Pagan 310.1764 3.026111 313.2025
(0.0000) (0.0819) (0.0000)
Honda 17.61182 -1.739572 11.22338
(0.0000) -- (0.0000)
King-Wu 17.61182 -1.739572 10.10444
(0.0000) -- (0.0000)
Standardized Honda 25.60628 -1.579003 10.62771
(0.0000) -- (0.0000)
Standardized King-Wu 25.60628 -1.579003 9.052019
(0.0000) -- (0.0000)
Gourierioux, et al.* -- -- 310.1764
(< 0.01)
*Mixed chi-square asymptotic critical values:
1% 7.289
5% 4.321
10% 2.952
Redundant Fixed Effects Tests
Equation: Untitled
Test cross-section and period fixed effects
Effects Test Statistic d.f. Prob.
Cross-section F 129.364179 (11,80) 0.0000
Cross-section Chi-square 316.785138 11 0.0000
Period F 4.603953 (8,80) 0.0001
Period Chi-square 40.900369 8 0.0000
Cross-Section/Period F 76.973543 (19,80) 0.0000
Cross-Section/Period Chi-square 319.586189 19 0.0000
Correlated Random Effects - Hausman Test
Equation: Untitled
Test cross-section random effects
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 2.146802 8 0.9762
Cross-section random effects test comparisons:
Variable Fixed Random Var(Diff.) Prob.
GDPPC 0.173425 0.165089 0.000168 0.5197
BSAID -0.000438 -0.000065 0.000002 0.8094
HAID 0.000642 0.000621 0.000000 0.7502
EFWI 0.030919 0.030938 0.000001 0.9858
PFI -0.008664 -0.008722 0.000002 0.9702
XM 0.025280 0.025896 0.000003 0.7413
FDI -0.000624 -0.000616 0.000000 0.8352
INFMR -0.108431 -0.116245 0.000115 0.4672
Regression results for model 3
Dependent Variable: HDI Method: Panel EGLS (Cross-section random effects)
Periods included: 9
e
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.841822 0.252427 -7.296440 0.0000
GDPPC 0.165089 0.016921 9.756501 0.0000
BSAID -6.49E-05 0.005487 -0.011823 0.9906
HAID 0.000621 0.000815 0.762224 0.4477
EFWI 0.030938 0.004670 6.625278 0.0000
PFI -0.008722 0.004905 -1.778066 0.0785
XM 0.025896 0.011062 2.340976 0.0212
FDI -0.000616 0.000313 -1.966095 0.0521
INFMR -0.116245 0.021544 -5.395785 0.0000
Effects Specification
S.D. Rho
Cross-section random 0.085724 0.9693
Idiosyncratic random 0.015254 0.0307
Weighted Statistics
R-squared 0.900216 Mean dependent var -0.045081
Adjusted R-squared 0.892152 S.D. dependent var 0.045055
S.E. of regression 0.014796 Sum squared resid 0.021673
F-statistic 111.6427 Durbin-Watson stat 1.278593
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.913107 Mean dependent var -0.761389
Sum squared resid 0.350396 Durbin-Watson stat 0.079085
Residual Cross-Section Dependence Test
Null hypothesis: No cross-section dependence (correlation) in residuals
Equation: Untitled
Periods included: 9
Cross-sections included: 12
Total panel observations: 108
Note: non-zero cross-section means detected in data
Cross-section means were removed during computation of correlations
Test Statistic d.f. Prob.
Breusch-Pagan LM 93.37585 66 0.0149
Pesaran scaled LM 1.338296 0.1808
Pesaran CD 1.643815 0.1002
Interactive variables: institutions and aid Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/27/16 Time: 16:49
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.862654 0.208277 -8.943152 0.0000
GDPPC 0.164379 0.016866 9.746130 0.0000
HAID 0.000375 0.000815 0.460366 0.6463
EFWIAID 0.001477 0.000234 6.307671 0.0000
PFIAID -0.000397 0.000244 -1.627365 0.1068
XM 0.026791 0.011207 2.390631 0.0187
FDI -0.000583 0.000316 -1.845082 0.0680
INFMR -0.109792 0.021052 -5.215349 0.0000
Effects Specification
S.D. Rho
f
Cross-section random 0.081468 0.9651
Idiosyncratic random 0.015501 0.0349
Weighted Statistics
R-squared 0.896399 Mean dependent var -0.048193
Adjusted R-squared 0.889147 S.D. dependent var 0.045253
S.E. of regression 0.015067 Sum squared resid 0.022701
F-statistic 123.6059 Durbin-Watson stat 1.236976
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.914656 Mean dependent var -0.761389
Sum squared resid 0.344149 Durbin-Watson stat 0.081593
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/27/16 Time: 16:53
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.685107 0.242519 -6.948360 0.0000
GDPPC 0.161412 0.016195 9.966541 0.0000
BSAID -0.006556 0.005345 -1.226547 0.2229
EFWIAID 0.001527 0.000235 6.486860 0.0000
PFIAID -0.000380 0.000238 -1.598960 0.1130
XM 0.028426 0.010774 2.638484 0.0097
FDI -0.000649 0.000315 -2.064063 0.0416
INFMR -0.117720 0.021330 -5.519015 0.0000
Effects Specification
S.D. Rho
Cross-section random 0.076144 0.9608
Idiosyncratic random 0.015390 0.0392
Weighted Statistics
R-squared 0.897953 Mean dependent var -0.051179
Adjusted R-squared 0.890809 S.D. dependent var 0.045455
S.E. of regression 0.015020 Sum squared resid 0.022560
F-statistic 125.7054 Durbin-Watson stat 1.237535
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.916014 Mean dependent var -0.761389
Sum squared resid 0.338673 Durbin-Watson stat 0.082437
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/27/16 Time: 17:00
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.697501 0.249015 -6.816854 0.0000
GDPPC 0.164208 0.017216 9.538106 0.0000
BSAID -0.007110 0.005460 -1.302061 0.1959
HAID 0.000538 0.000822 0.654977 0.5140
EFWIAID 0.001531 0.000237 6.453226 0.0000
PFIAID -0.000406 0.000245 -1.658775 0.1003
XM 0.026534 0.011181 2.373234 0.0196
FDI -0.000633 0.000317 -1.995019 0.0488
g
INFMR -0.116038 0.021864 -5.307269 0.0000
Effects Specification
S.D. Rho
Cross-section random 0.086497 0.9691
Idiosyncratic random 0.015437 0.0309
Weighted Statistics
R-squared 0.898096 Mean dependent var -0.045214
Adjusted R-squared 0.889862 S.D. dependent var 0.045063
S.E. of regression 0.014955 Sum squared resid 0.022142
F-statistic 109.0630 Durbin-Watson stat 1.259466
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.915158 Mean dependent var -0.761389
Sum squared resid 0.342124 Durbin-Watson stat 0.081510
ROBUSTNESS CHECKS: ADDITIONAL CONTROL VARIABLES Trade openness and aid interactive
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/28/16 Time: 01:57
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.698045 0.243562 -6.971722 0.0000
GDPPC 0.162644 0.015762 10.31884 0.0000
BSAID -0.000619 0.005307 -0.116599 0.9074
EFWI 0.030527 0.004595 6.643104 0.0000
PFI -0.008090 0.004701 -1.721085 0.0883
XMAID 1.73E-05 5.94E-06 2.913419 0.0044
FDI -0.000694 0.000313 -2.221632 0.0286
INFMR -0.122070 0.021001 -5.812485 0.0000
Effects Specification
S.D. Rho
Cross-section random 0.075052 0.9611
Idiosyncratic random 0.015092 0.0389
Weighted Statistics
R-squared 0.901315 Mean dependent var -0.050919
Adjusted R-squared 0.894407 S.D. dependent var 0.045437
S.E. of regression 0.014765 Sum squared resid 0.021800
F-statistic 130.4754 Durbin-Watson stat 1.279988
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.913231 Mean dependent var -0.761389
Sum squared resid 0.349896 Durbin-Watson stat 0.079747
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/28/16 Time: 02:09
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.747350 0.201397 -8.676139 0.0000
h
GDPPC 0.165306 0.016488 10.02607 0.0000
HAID 0.000545 0.000792 0.688183 0.4929
EFWI 0.030743 0.004582 6.709535 0.0000
PFI -0.008464 0.004696 -1.802421 0.0745
XMAID 1.62E-05 6.12E-06 2.642387 0.0096
FDI -0.000664 0.000309 -2.146692 0.0342
INFMR -0.118846 0.020228 -5.875442 0.0000
Effects Specification
S.D. Rho
Cross-section random 0.082698 0.9679
Idiosyncratic random 0.015053 0.0321
Weighted Statistics
R-squared 0.901620 Mean dependent var -0.046113
Adjusted R-squared 0.894734 S.D. dependent var 0.045119
S.E. of regression 0.014639 Sum squared resid 0.021429
F-statistic 130.9243 Durbin-Watson stat 1.303502
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.912512 Mean dependent var -0.761389
Sum squared resid 0.352795 Durbin-Watson stat 0.079176
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/28/16 Time: 02:04
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.719082 0.250295 -6.868234 0.0000
GDPPC 0.165723 0.016818 9.853624 0.0000
BSAID -0.001268 0.005440 -0.233166 0.8161
HAID 0.000576 0.000807 0.713515 0.4772
EFWI 0.030659 0.004632 6.619306 0.0000
PFI -0.008709 0.004852 -1.794905 0.0757
XMAID 1.62E-05 6.16E-06 2.629655 0.0099
FDI -0.000676 0.000315 -2.144723 0.0344
INFMR -0.119963 0.021569 -5.561900 0.0000
Effects Specification
S.D. Rho
Cross-section random 0.086620 0.9704
Idiosyncratic random 0.015131 0.0296
Weighted Statistics
R-squared 0.901620 Mean dependent var -0.044258
Adjusted R-squared 0.893670 S.D. dependent var 0.045004
S.E. of regression 0.014675 Sum squared resid 0.021320
F-statistic 113.4125 Durbin-Watson stat 1.307658
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.912033 Mean dependent var -0.761389
Sum squared resid 0.354726 Durbin-Watson stat 0.078595
Control of corruption
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/28/16 Time: 02:19
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
i
Variable Coefficient Std. Error t-Statistic Prob.
C -1.688309 0.253932 -6.648661 0.0000
GDPPC 0.150965 0.016664 9.059551 0.0000
BSAID -0.000379 0.005287 -0.071768 0.9429
EFWI 0.032105 0.004618 6.952177 0.0000
PFI -0.004484 0.005054 -0.887101 0.3772
XM 0.031107 0.010618 2.929605 0.0042
FDI -0.000654 0.000307 -2.131735 0.0355
INFMR -0.132464 0.022065 -6.003460 0.0000
CC 0.022837 0.012349 1.849251 0.0674
Effects Specification
S.D. Rho
Cross-section random 0.073105 0.9596
Idiosyncratic random 0.014992 0.0404
Weighted Statistics
R-squared 0.903358 Mean dependent var -0.051924
Adjusted R-squared 0.895549 S.D. dependent var 0.045507
S.E. of regression 0.014707 Sum squared resid 0.021414
F-statistic 115.6750 Durbin-Watson stat 1.279775
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.909436 Mean dependent var -0.761389
Sum squared resid 0.365199 Durbin-Watson stat 0.075043
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/28/16 Time: 02:16
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.714863 0.210222 -8.157374 0.0000
GDPPC 0.152960 0.017520 8.730527 0.0000
HAID 0.000782 0.000787 0.993464 0.3229
EFWI 0.032537 0.004612 7.054380 0.0000
PFI -0.004928 0.005031 -0.979625 0.3297
XM 0.028681 0.010875 2.637329 0.0097
FDI -0.000618 0.000303 -2.037234 0.0443
INFMR -0.130758 0.021267 -6.148357 0.0000
CC 0.024541 0.012392 1.980319 0.0504
Effects Specification
S.D. Rho
Cross-section random 0.082495 0.9684
Idiosyncratic random 0.014908 0.0316
Weighted Statistics
R-squared 0.904247 Mean dependent var -0.045783
Adjusted R-squared 0.896510 S.D. dependent var 0.045098
S.E. of regression 0.014508 Sum squared resid 0.020838
F-statistic 116.8644 Durbin-Watson stat 1.327674
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.908737 Mean dependent var -0.761389
Sum squared resid 0.368016 Durbin-Watson stat 0.075175
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/28/16 Time: 02:24
Sample: 2005 2013
j
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.671400 0.264211 -6.325993 0.0000
GDPPC 0.152885 0.017967 8.509163 0.0000
BSAID -0.001592 0.005454 -0.291865 0.7710
HAID 0.000829 0.000807 1.027602 0.3067
EFWI 0.032504 0.004657 6.979059 0.0000
PFI -0.005198 0.005144 -1.010441 0.3148
XM 0.028521 0.010956 2.603309 0.0107
FDI -0.000631 0.000308 -2.047183 0.0433
INFMR -0.132859 0.022915 -5.797891 0.0000
CC 0.025235 0.012621 1.999450 0.0483
Effects Specification
S.D. Rho
Cross-section random 0.087296 0.9714
Idiosyncratic random 0.014982 0.0286
Weighted Statistics
R-squared 0.904307 Mean dependent var -0.043486
Adjusted R-squared 0.895519 S.D. dependent var 0.044958
S.E. of regression 0.014532 Sum squared resid 0.020695
F-statistic 102.9008 Durbin-Watson stat 1.335668
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.907929 Mean dependent var -0.761389
Sum squared resid 0.371276 Durbin-Watson stat 0.074451
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/28/16 Time: 03:36
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.713250 0.255063 -6.716969 0.0000
GDPPC 0.151602 0.017104 8.863569 0.0000
BSAID -0.000363 0.005293 0.068552 0.9455
EFWI 0.032223 0.004660 6.915391 0.0000
PFI -0.004765 0.005087 -0.936731 0.3512
XM 0.031009 0.010685 2.902177 0.0046
FDI -0.000652 0.000308 -2.117113 0.0368
INFMR -0.131247 0.022355 -5.871017 0.0000
CCAID 0.001033 0.000603 1.712723 0.0899
Effects Specification
S.D. Rho
Cross-section random 0.076641 0.9629
Idiosyncratic random 0.015046 0.0371
Weighted Statistics
R-squared 0.902806 Mean dependent var -0.049720
Adjusted R-squared 0.894952 S.D. dependent var 0.045355
S.E. of regression 0.014700 Sum squared resid 0.021393
F-statistic 114.9477 Durbin-Watson stat 1.288665
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.910899 Mean dependent var -0.761389
Sum squared resid 0.359297 Durbin-Watson stat 0.076728
k
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/28/16 Time: 04:52
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.719272 0.217219 -7.914941 0.0000
GDPPC 0.153555 0.018121 8.474020 0.0000
HAID 0.000789 0.000791 0.997907 0.3208
EFWI 0.032620 0.004654 7.009431 0.0000
PFI -0.005335 0.005041 -1.058439 0.2924
CCAID 0.001127 0.000610 1.847955 0.0676
XM 0.028549 0.010939 2.609788 0.0105
FDI -0.000623 0.000305 -2.046205 0.0434
INFMR -0.130565 0.021887 -5.965348 0.0000
Effects Specification
S.D. Rho
Cross-section random 0.088696 0.9723
Idiosyncratic random 0.014961 0.0277
Weighted Statistics
R-squared 0.903672 Mean dependent var -0.042741
Adjusted R-squared 0.895888 S.D. dependent var 0.044914
S.E. of regression 0.014492 Sum squared resid 0.020792
F-statistic 116.0928 Durbin-Watson stat 1.337350
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.910018 Mean dependent var -0.761389
Sum squared resid 0.362850 Durbin-Watson stat 0.076631
Control of corruption and aid interactive term
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/28/16 Time: 04:58
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -1.700232 0.264912 -6.418092 0.0000
GDPPC 0.153620 0.018386 8.355396 0.0000
BSAID -0.000736 0.005447 -0.135104 0.8928
HAID 0.000810 0.000810 1.000380 0.3196
EFWI 0.032593 0.004701 6.933274 0.0000
PFI -0.005481 0.005176 -1.058933 0.2922
XM 0.028460 0.011023 2.581947 0.0113
FDI -0.000629 0.000309 -2.033338 0.0447
INFMR -0.131431 0.023188 -5.667974 0.0000
CCAID 0.001135 0.000615 1.844877 0.0681
Effects Specification
S.D. Rho
Cross-section random 0.091480 0.9737
Idiosyncratic random 0.015043 0.0263
Weighted Statistics
R-squared 0.903673 Mean dependent var -0.041672
Adjusted R-squared 0.894827 S.D. dependent var 0.044851
S.E. of regression 0.014546 Sum squared resid 0.020734
F-statistic 102.1521 Durbin-Watson stat 1.340496
l
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.909703 Mean dependent var -0.761389
Sum squared resid 0.364120 Durbin-Watson stat 0.076332
Model F: AID DEPENDENCY
Dependent Variable: HDI
Method: Panel EGLS (Cross-section random effects)
Date: 04/28/16 Time: 06:41
Sample: 2005 2013
Periods included: 9
Cross-sections included: 12
Total panel (balanced) observations: 108
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C -2.252614 0.254902 -8.837175 0.0000
GDPPC 0.117508 0.016427 7.153267 0.0000
AID 0.041053 0.010230 4.013011 0.0001
AIDGNI -0.041393 0.009113 -4.542134 0.0000
EFWI 0.023471 0.004460 5.262246 0.0000
PFI -0.004455 0.004414 -1.009342 0.3153
CC 0.026063 0.010809 2.411170 0.0178
XM 0.035603 0.009317 3.821379 0.0002
FDI -0.000630 0.000268 -2.355787 0.0205
INFMR -0.112670 0.019826 -5.683042 0.0000
Effects Specification
S.D. Rho
Cross-section random 0.065268 0.9614
Idiosyncratic random 0.013069 0.0386
Weighted Statistics
R-squared 0.919298 Mean dependent var -0.050707
Adjusted R-squared 0.911886 S.D. dependent var 0.045422
S.E. of regression 0.013483 Sum squared resid 0.017816
F-statistic 124.0377 Durbin-Watson stat 1.330355
Prob(F-statistic) 0.000000
Unweighted Statistics
R-squared 0.871498 Mean dependent var -0.761389
Sum squared resid 0.518185 Durbin-Watson stat 0.045739