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