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12-2009
Three Essays on Foreign Aid, Poverty and Growth.
Aberra Senbeta
Western Michigan University
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THREE ESSAYS ON FOREIGN AID, POVERTY AND GROWTH
by
Aberra Senbeta
A Dissertation
Submitted to the
Faculty of The Graduate College
in partial fulfillment of the
requirements for the
Degree of Doctor of Philosophy
Department of Economics
Advisor: Eskander Alvi, Ph.D.
Western Michigan University
Kalamazoo, Michigan
December 2009
THREE ESSAYS ON FOREIGN AID, POVERTY AND GROWTH
Aberra Senbeta, Ph.D.
Western Michigan University, 2009
This dissertation studies how foreign aid impacts poverty and economic
growth and addresses three interrelated issues: whether aid reduces poverty, how aid
affects economic growth, and whether the poverty mitigating role of aid depends on
the levels of poverty.
The first essay, "Does Foreign Aid Reduce Poverty?" examines the direct
effects of foreign aid on poverty in developing countries using dynamic panel
estimation techniques. The results suggest that aid has a significant poverty reducing
effect even after controlling for average income. The findings are robust to model
specification and estimation techniques. The policy implication is that poverty
reduction can be enhanced by concentrating on the direct channels via which aid
alleviates poverty. We also find that the composition of aid matters—multilateral aid
reduces poverty whereas bilateral aid does not, and grants do better in reducing
poverty compared to loans.
The second essay, titled "Foreign Aid and the Sources of Growth," evaluates
the effects of aid on sources of growth; capital accumulation and total factor
productivity (TFP). This approach is different in that it focuses on efficiency changes
resulting after the disbursement of aid. Interestingly, we find contradictory effects of
aid; while aid boosts investments, it adversely affects TFP. These findings suggest
that despite the strong positive association between aid and investment, the efficiency
losses resulting from aid undermine the overall effects of aid on growth.
In the third essay, "Foreign Aid, Growth and Poverty Relationship: Quantile
Regression Approach," we extend the analysis of the aid-growth-poverty relationship
by using quantile regression, which enables us to estimate the impact of growth and
growth enhancing policies at different points in the distribution of poverty. We find
that the response of poverty reduction to growth in average income and other growth
enhancing policies decreases at higher levels of poverty. These results are robust to
the consideration of endogeneity of aid and use of alternative measures of poverty,
poverty gap and squared poverty gap.
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Aberra Senbeta
2009
ACKNOWLEDGMENTS
I would like to thank the members of my dissertation committee: Dr. Eskander
Alvi, Dr. Debasri Mukherjee and Dr. Ajay Gupta for their advice, support and
encouragement. My sincere appreciation goes to Dr. Eskander Alvi, my committee
chairperson, for guiding me through with insightful suggestions and comments which
make the completion of this dissertation possible. Our discussions have made a profound
impact on my professional development. I have learned a whole lot from him, but most
of all, patience and kindness. I am also thankful to Dr. Sisay Asefa and Dr. Wei-Chiao
Huang for their advice and support.
A special "thank you" goes to my best friend Dr. Dawit Senbet for encouraging
me and for his support all along. I am also grateful to my friends Sintayehu Bedasa and
Melesse Tashu who over many years supported me in all my endeavors. I also extend my
gratitude to my friends and classmates in the Department of Economics especially
Tamrat Gashaw and his wife, Sintayehu Bezabeh, who were always helpful. Financial
support from Western Michigan University is gratefully acknowledged.
Finally, I would like to dedicate this dissertation to my parents, Senbeta Bedane
and Ehtabezaw Welde Hawariyat for their love and sacrifices. I am here today because of
their aspirations and sacrifices. If this dissertation is an accomplishment, it is more theirs
than it is mine.
Aberra Senbeta
ii
TABLE OF CONTENTS
ACKNOWLEDGMENTS
ii
LIST OF TABLES
vii
LIST OF FIGURES
viii
CHAPTER
I. INTRODUCTION
1
II. DOES FOREIGN AID REDUCE POVERTY?
5
2.1 Introduction
5
2.2 Review of Related Literature
9
2.2.1 Aid Effectiveness Literature
2.2.2 Growth-Poverty Reduction Literature
2.3 Data Description and Descriptive Statistics
9
14
16
2.3.1 Measurement of Poverty
17
2.3.2 Constructing an Income Measure of Poverty
19
2.3.3 Data Source and Coverage
20
2.3.4 Descriptive Statistics
22
2.4 Model Specification and Econometric Methodology
25
2.4.1 Model Specification
25
2.4.2 Econometric Methodology
27
2.5 Empirical Results
28
2.5.1 The Effect of Aggregate Aid
29
2.5.2 Unequal Impact of Aid by Source and Type
32
iii
Table of Contents—continued
CHAPTER
2.6 Summary and Concluding Remarks
37
REFERENCES
39
APPENDICES
43
A. Constructing Income per Capita of the Poor
43
B. Variable Definitions and Sources
44
C. Sample Countries
45
D. OLS Estimation Results
46
III.
FOREIGN AID AND THE SOURCES OF GROWTH
48
3.1 Introduction
48
3.2 Review of Related Literature
52
3.2.1 The Sources of Growth
52
3.2.2 Aid Effectiveness Literature
54
3.3 Data Description and Descriptive Statistics
56
3.3.1 Data Source and Coverage
56
3.3.2 Descriptive Statistics
59
3.4 Model Specification and Econometric Methodology
62
3.4.1 Capital Accumulation
62
3.4.2 TFP Growth
63
3.4.3 Econometric Methodology
64
IV
Table of Contents—continued
CHAPTER
3.5 Empirical Results
65
3.5.1 Capital Accumulation
65
3.5.2 TFP Growth
70
3.6 Summary and Concluding Remarks
77
REFERENCES
80
APPENDICES
84
A. Estimation Results with the Level of TFP
84
B. Sample Countries
87
C. Variable Definitions and Sources
88
IV.
FOREIGN AID, GROWTH AND POVERTY RELATIONSHIP:
QUANTILE REGRESSION APPROACH
89
4.1 Introduction
89
4.2 Review of Related Literature
92
4.3 Data, Model Specification and Econometric Methodology
96
4.3.1 Data
96
4.3.2 Model Specification
96
4.3.3 Econometric Methodology
97
4.4 Empirical Results
99
4.5 Robustness of the Results
105
4.5.1 Endogeneity of Foreign Aid
v
106
Table of Contents—continued
CHAPTER
4.5.2 Using Alternative Measures of Poverty
107
4.6 Summary and Concluding Remarks
REFERENCES
110
.
APPENDICES
Ill
113
A. Quantile Regression Results with Poverty Gap
113
B. Quantile Regression Results with Squared Poverty Gap
114
V..
OVERALL CONCLUSIONS
115
VI
LIST OF TABLES
2.1. Summary Statistics and Correlations
24
2.2. System GMM Estimation: Aggregate Aid
30
2.3. System GMM Estimation: Bilateral and Multilateral Aid
35
2.4. System GMM Estimation: Grants and Loans
36
3.1. Summary Statistics and Correlations
61
3.2. The Effect of Aid on Physical Capital Accumulation: Total Aid
67
3.3. The Effect of Aid on Physical Capital Accumulation: Bilateral versus
Multilateral Aid
69
3.4. The Effect of Aid on Physical Capital Accumulation: Grants and Loans
70
3.5. The Effect of Aid on TFP Growth: Total Aid
73
3.6. The Effects of Aid on TFP Growth: Bilateral versus Multilateral Aid
76
3.7. The Effect of Aid on TFP Growth: Grants versus Loans
77
4.1. Quantile and OLS Regression Estimates
101
4.2. F-test for Equality of Coefficients at Conditional Quantiles
105
4.3.2SLS and Instrumental Variable Quantile Regression Estimates
108
4.4. F-test for Equality of Coefficients at Conditional Quantiles
109
vn
LIST OF FIGURES
2.1. Income of the Poor and Mean Household Income
25
3.1. Aid and TFP Growth (1970 - 2004)
62
4.1. Growth Poverty Relationship
100
4.2. Quantile Regression and OLS Coefficients
102
4.3.2SLS and IV Quantile Regression Coefficients
109
viii
CHAPTER I
INTRODUCTION
The goal of poverty reduction has seen significant progress at the global level in
the last couple of decades. Despite that achievement there are notable failures and
frustrations in different regions of the developing world. Specifically, while East Asia and
the Pacific have experienced a substantial decline in the number of poor other regions
specifically Sub-Saharan Africa has witnessed the reverse. Overall, poverty reduction
remains one of the biggest challenges facing the 21 st century.
Historically, foreign aid has been regarded as an important tool in fighting
poverty. The channel via which aid is believed to address poverty is by facilitating faster
and sustained economic growth. The rationale for international aid derives from the belief
that aid money is used to finance consumption and public infrastructure projects that
would otherwise see too little investment. Nevertheless, the effectiveness of aid in
enhancing economic growth has been debated for decades without any clear evidence
about its effectiveness. Critics argue that poor economic performance and disappointing
poverty reduction experience in major aid recipient countries shows lack of a positive role
of aid, especially when contrasted with nations that have managed to achieve significant
progress without foreign aid.
Despite the lack of robust positive association between aid and growth, which led
to a drop in the volume of international aid going to the developing world during the
1990s, recently foreign aid has regained its standing in the international arena. With the
Millennium Declaration, foreign aid has received greater attention as a vital tool in
achieving the Millennium Development Goals (MDGs), which include halving poverty
1
rate by 2015. As a result Official Development Assistance (ODA) flow to developing
regions has doubled over the period 2000-2007.
The aid effectiveness literature has indicated that in the past problems from both
the donors' and recipients' sides have undermined the effectiveness of aid. As part of the
new commitment to make foreign aid more effective, more than ever, poverty reduction
has become the primary focus of international aid. The Millennium Villages Project is
one example of such commitment. It is also a departure from the old model of
international aid which intended rounded economic transformation. According to
Konecky & Palm (2008), the Millennium Villages Project is a new approach that focuses
on community based investments and fights poverty by targeting investments that directly
benefit the poor. The renewed interest in aid and the poor economic performance in
regions such as Sub-Saharan Africa have sparked a new debate if foreign aid would be an
effective means in achieving the new MDGs.
This dissertation examines the aid-growth-poverty relationship to answer three
interrelated questions relating to the effectiveness of foreign aid in enhancing economic
growth and reducing poverty. The first essay answers the question: does foreign aid
reduce poverty directly besides its effects through growth in average income? This
question is particularly important because of the growing focus of international aid on
poverty reduction and lack of robust growth enhancing effects of aid. The indirect effects
of aid on poverty channeled through growth in average incomes assumes that aid boosts
economic growth and that aid induced growth in average income translates to poverty
reduction. The long and still ongoing debate about the effectiveness of aid in enhancing
economic growth casts doubt on the aid, growth, poverty reduction sequence which is
perceived to be the primary channel via which aid helps reduce poverty. The strong
2
positive effect of aid documented in micro studies and lack of such indirect effects
transmitted through growth in average income highlights the importance of direct effects
of aid on poverty. Therefore, using a large dataset from 79 developing countries and
employing dynamic panel data technique we explore the direct effects of aid on poverty
after controlling for average income and income distribution. The data on poverty,
inequality and foreign aid are obtained from the World Bank-PovcalNet, Organization
from Economic Cooperation and Development (OECD) and the World Bank-World
Development Indicators.
The second essay explores the aid-growth relationship in a way different from the
standard aid-growth literature. The standard aid-growth literature assumes that the aidinvestment relationship is the primary transmission mechanism in which aid spurs
growth. This essay builds on aid-investment studies by examining how aid impacts
efficiency or productivity of resources. Ignoring the efficiency effects, a positive aidinvestment association is often referred to as evidence of positive effects of aid on
growth. Considering the possible efficiency diluting effects of aid, even strong positive
effects of aid on investment may not guarantee overall growth effects. The importance of
factor productivity in explaining cross-country differences in growth and level of income
documented in the growth literature also suggests that the effect of aid on Total Factor
Productivity (TFP) growth could significantly influence the growth outcome. Therefore,
we examine the effects of aid on the sources of growth: capital accumulation and TFP
using a large dataset from sixty two developing countries employing dynamic panel data
estimation technique.
In the third essay we test for non-linear effects of growth in average income and
growth enhancing policies (including foreign aid) on poverty using quantile regression
3
techniques. Cross-country and country case studies have shown that sustained economic
growth is the primary factor behind observed reduction in poverty. Whereas economic
growth in general has proved to be vital for poverty reduction, countries that register
comparable levels of growth could experience differences in poverty reduction. Studies
have showed that sectoral composition and location of growth could significantly affect
the poverty outcome. Using quantile regression we examine whether the level of poverty
in a country affects the poverty response to changes in average income and growth
enhancing policies. In other words, we test for possible heterogeneous response of
poverty to growth in average income by estimating poverty response at different quantiles
in the conditional distribution of the dependent variable. Chapter five concludes the
dissertation by drawing major conclusions and policy implications.
4
CHAPTER II
DOES FOREIGN AID REDUCE POVERTY?
2.1 Introduction
In this chapter we examine the role of foreign aid in reducing poverty in recipient
developing countries. Historically, foreign aid has been a favored means of enhancing
economic growth and alleviating poverty in developing countries. Ironically, the regions
receiving the highest foreign aid happen to experience an increase in the number of poor
while substantial progress has been made in countries where the contribution of aid was
limited (Easterly, 2006; 2006a; 2007). In spite of such questionable record in the past,
foreign aid has regained its appeal in recent years taking center stage in reducing poverty
and achieving the Millennium Development Goals (MDGs) (Easterly, 2006). Aid is often
perceived to raise average incomes via overall economic growth which then through the
"filter down" effect is supposed to reduce poverty. Whereas this is a likely channel, the
evidence is mixed and controversial. Whether aid helps overall income growth, and if
such overall growth translates to an increase in income for the poor—are two difficult
questions without clear-cut answers. Answering either in the negative implies that the
aid-growth-poverty reduction sequence does not work. Instead, we examine if aid directly
reduces poverty, separately from the average income enhancement effect that may also
reduce poverty. The evidence suggests that aid does reduce poverty in receiving countries,
but that multilateral aid and grants are more effective in reducing poverty.
The recently available estimates from the World Bank show a declining trend in
the number of people living below the poverty line over the last two decades. The same
5
estimates show that in 2004 close to 1 billion people lived on less than one dollar a day
and about 2.5 billion people lived on less than two dollars a day (Chen & Ravallion,
2007). Despite the general overall trend of decline, some regions of the developing world
have experienced an increase in the number of poor (Chen & Ravallion, 2007).
The conventional wisdom is that aid reduces poverty by raising the average
income in recipient countries. In general, growth and poverty reduction go hand-in-hand
and empirical studies also document that faster and sustained growth is a major factor
behind poverty reduction (Ravallion & Chen, 1997; Besley & Burgess, 2003; Dollar &
Kraay, 2002; 2004). For many growth-enhancing policies such as openness to
international trade and quality of institutions, the impact on poverty is clear because of
their robust effect on average income. The relationship between aid and growth lacks
such robustness, however.
The aid-growth literature has long debated whether foreign aid is effective in
enhancing growth in receiving countries, but the conclusions are somewhat mixed and on
balance show lack of growth effects (Easterly, Levine, & Roodman, 2004; Rajan &
Subramanian, 2005; 2005a; 2007). New data and sophisticated estimation techniques
haven't resolved the controversy and no consensus exists as to whether aid works and
under what conditions. As a result, the role of foreign aid in poverty reduction, which
relies heavily on how aid spurs growth, is also dubious. The idea that growth in average
income is the sole channel through which aid reduces poverty is criticized by Lensink &
White (2000), Mosley et al. (2004) and Mosley & Suleiman (2007) because aid can
impact poverty in ways other than raising average income. Besides the ambiguous aidgrowth relationship, the impact of aid on poverty, working via average income, also
6
depends on how the growth in average income translates to growth in the income of the
poor.
In this study we assess the direct impact of aid on poverty i.e., we examine if aid
helps reduce poverty after controlling for average income. The pursuit of a direct effect of
aid on poverty is important for two reasons. First, even when aid enhances growth, there
are questions if the poor benefit from aid. Second, poverty reduction is often a primary
goal of donors—an objective that has become more pertinent with the new MDGs and the
past failure of aid in bringing overall development in many regions. With the growing
focus on poverty reduction and the delivery of more targeted aid it is possible that aid
affects the living conditions of the poor directly without having substantial effects on
average income. For instance, the Millennium Village Projects that target a particular
locality can have an impact on the living conditions of the poor with little effect on
national income, at least in the short term. Hence, we examine the impact of aid on
poverty by estimating the relationship between foreign aid and changes in poverty levels.
This study is different from the conventional aid-growth studies which look at the
effect of aid on growth in per capita income. We test the effect of aid on poverty within
the framework of growth-inequality-poverty literature, where changes in poverty are
decomposed into changes in average income and changes in income distribution (Datt &
Ravallion, 1992; Ravallion & Chen, 1997; Besley & Burgess, 2003; Kraay, 2006;
Oyolola, 2007). We then estimate the aid-poverty relationship controlling for both
average income and income distribution. Compared to the aid effectiveness literature, this
study is closer to Boone (1996) and Masud & Yontcheva (2005) who assess the effect of
aid on health and education indicators. Unlike Boone (1996) and others who use health
and education indicators as a proxy for living conditions of the poor, we use the widely
7
known Foster, Greer, & Thorbecke (1984) measures of absolute poverty: headcount
index, poverty gap index, and squared poverty gap index at the $1 a day poverty line. In
addition to total aid, we also disaggregate aid by source and type to check if there are any
systematic differences between the impacts of different categories of aid.
The empirical analyses yield three interrelated findings. First, we find a consistent
negative association between exogenous components of aid and the chosen poverty
measures after controlling for average income. This indicates that despite the
inconclusive effect of aid on growth, there is a direct poverty reducing effect of aid. This
result is robust to the use of different measures of poverty, exclusion of outliers, inclusion
of other control variables and different estimation techniques. Second, we find that
different
components
of aid have different
impacts on
poverty—highlighting
heterogeneity of the effects of aid. Disaggregating aid by source, we find strong evidence
that aid from multilateral sources have better poverty reducing effects compared to aid
from bilateral sources. We also find that grants reduce poverty whereas loans do not.
Third, we also find a strong negative effect of financial development on poverty, which
suggests that countries with a well developed financial system experience a faster
reduction in poverty.
This study contributes to both the aid effectiveness literature and the growthinequality-poverty literature. Regardless of the disagreement over whether aid enhances
growth and how successfully it translates to poverty reduction, we will first test the direct
effect of aid on poverty by examining whether the poor benefit from aid programs. The
findings of this study fit into the "micro-macro paradox" of aid effectiveness—positive
returns to aid-financed projects at the micro level but lack of robust positive effects of aid
at macro level. Second, working within the framework of the growth-poverty literature
8
we also examine the relative importance of growth in average income, income
distribution and other growth enhancing policies and institutions that lead to poverty
reduction in a large sample of developing countries.
The layout of the chapter is as follows. In Section 2.2 we briefly review the
literature on aid effectiveness and the related growth-poverty reduction literature. In
Section 2.3 and 2.4 we describe the data and discuss the methodological issues. In
Section 2.5 we present the empirical results and discuss policy implications and Section
2.6 concludes the chapter.
2.2 Review of Related Literature
In this section we review the literature on empirical aid effectiveness and growthpoverty reduction. We start by reviewing the aid effectiveness literature, their main
findings and how they relate to this study. Then we review the growth-poverty reduction
literature and the framework within which we examine the impact of aid on poverty.
2.2.1 Aid Effectiveness Literature
The key issues in aid effectiveness literature are whether foreign aid enhances
growth and improves the living conditions of people in recipient countries. Despite
widespread skepticism about effectiveness, foreign aid has recently regained momentum
and became the centerpiece of international development programs that aim to reduce
poverty and achieve the MDGs. The debate over the effectiveness of aid however
continues. Easterly (2006a), on the one end, argues that historically aid has been
ineffective and questions the revival of aid optimism, while Sachs (2005) argues to the
9
contrary that poor countries are stuck in a poverty trap which they cannot escape without
further external assistance.
There is empirical support for both sides of the argument. Proponents argue that
foreign aid is an important source of finance that helps developing countries to achieve
self-sustained growth. One specific view is that aid works in a good policy and
institutional environment; another view is that aid works irrespective of the policy
environment but only with diminishing returns (Burnside & Dollar, 2000; Collier &
Dollar, 2001; 2002; Dalgaard, Hansen, & Tarp, 2004; Karras, 2006; Dovern &
Nunnenkamp, 2007; Alvi, Mukherjee, & Shukralla, 2008). The positive association
between aid and growth these studies document suggests that aid can have a role in
poverty reduction through an increase in average income.1
Collier and Dollar (2002), in addition to discussing aid effectiveness, also show
how aid via growth in average income can translate to poverty reduction. First, they
estimate the effect of aid on income per capita growth; they then use the growth elasticity
of poverty (how growth reduces poverty) to show how aid translates to poverty reduction.
Using the World Bank's CPIA index as a policy performance measure they find support
for the Burnside and Dollar (2000) result that aid is productive in a good policy
environment. Once they estimate the marginal effect of aid on growth, they use poverty
elasticity with respect to mean income—negative 2 for the headcount index from
Even within this camp, however, there is considerable disagreement over conditions in which aid works,
whether the relationship between aid and growth is linear or nonlinear, and the presence of an absorptive
capacity in recipient countries are far from settled. Particularly, the findings of Burnside and Dollar (2000)
that aid works in a good policy environment as measured by low inflation, low budget deficit and greater
openness to international trade has attracted both support and criticism. For instance, Dalgaard, Hansen, &
Tarp (2004) find weak evidence for aid-policy interaction, but a strong evidence that aid is less effective in
the tropics. Alvi, Mukherjee, & Shukralla (2008) address the nonlinearity issue in aid-policy-growth
relationship by estimating a semiparametric specification. They find that aid is growth enhancing when
policy is above a certrain threshold but aid effectiveness is not higher with better policy once the policy
threshold is reached, which offers partial support for Burnside and Dollar (2000) results.
10
•J
Ravallion & Chen (1997)—to calculate poverty-efficient allocation of aid.
Comparing
the actual and their calculated poverty-efficient allocation of aid, they suggest that current
allocation of aid is geared more towards inducing policy reforms rather than reducing
poverty. They also argue that there is a huge difference between actual and povertyefficient allocation of aid and that the number of poor lifted out of poverty could be
doubled by following the poverty-efficient allocation of aid.
The critics of aid, on the other hand, are skeptical of the productivity of foreign
aid. This camp argues that aid is detrimental to growth because it displaces domestic
savings, finances consumption and leads to overvaluation of real exchange rate (Boone
1996; Rajan & Subramanian, 2005a; 2007). The adverse incentive effects of aid and
weakening of recipient country institutions are also sighted as aspects of foreign aid that
offset the positive effect of resource transfer (Boone, 1996; Svensson, 2000; Brautigam &
Knack, 2004; Easterly, Levine, & Roodman, 2004; Rajan & Subramanian, 2005a; Rajan
& Subramanian, 2007; Easterly 2003; 2006; 2006a; and 2007).
The inconclusive and conflicting findings of the empirical studies are often
attributed to differences in data, specification and econometric techniques used. The aid
effectiveness literature implicitly assumes that aid can help sustain growth in recipient
countries. Economic growth literature, on the other hand, shows that growth is a
multifaceted process which is affected by a wide range of factors: economic, institutional
and governance (Rodrik et al. 2004; Acemoglu et al., 2005). For instance, Rodrik et al,
(2004) show that the quality of institutions is the prime factor that explains growth. They
also show that the significance of other factors such as geography and trade evaporate
2
Poverty-efficient allocation of aid is an allocation rule for which the marginal impact of an additional
million dollars in aid is equalized across aid receiving countries (Collier & Dollar, 2002).
11
once the quality of institutions is controlled for. The complexity of growth and the
difficult issues surrounding aid such as donors' motives, quality of aid and inappropriate
pooling of different types of aid suggest that even when aid is used to finance an
investment it may not show up as an important factor that explains growth. This raises
doubts as to whether growth in per capita income is an appropriate criterion in judging
effectiveness of aid.
Dovern & Nunnenkamp (2007) argue that the search for the effect of aid on per
capita income growth may be a demanding criterion and assesses its effectiveness within
the framework of growth acceleration proposed by Hausmann, Pritchett, & Rodrik
(2005). Using data from 124 countries over the period of 1960-1994 they examine
effectiveness of aid using a less stringent criterion—whether aid raises the probability of
growth acceleration. Their cross-country analysis shows that aid indeed increases the
probability of growth acceleration, though the effect is modest. The complexity of
economic growth makes this analysis interesting, because what causes growth and what
sustains it could well be different. Acknowledging this, in its recent report World Bank
(2005) argues for "country-specific and institutionally-sensitive policies" instead of the
one-policy-fits-all approach.
An alternative criterion by which aid effectiveness can be assessed is how it
affects the lives of the poor. This criterion is particularly important because of the
growing shift in donor communities attempting to make poverty reduction the centerpiece
of their development projects and assistance. The MDGs which list reducing the
proportion of the poor by half in 2015, is one such example. Additionally, understanding
the effect of aid on poverty is important for both donors and policy makers.
12
In the past due to lack of reliable internationally comparable poverty data, many
studies used health and education indicators such as infant mortality and illiteracy rates as
proxies for poverty. Departing from the conventional literature which looks at the
relationship between aid and growth in per capita income, Boone (1996) examined the
impact of aid on investment and human development indicators using data from 97
developing countries. The main findings are that aid has no significant impact on infant
mortality, primary schooling ratio and life expectancy. Boone argues that aid is mainly
used for consumption purposes which tend to benefit the political elite but not the poor.
Following this work, health and education indicators have been used widely as proxy for
economic conditions of the poor.
Masud & Yontcheva (2005) examine the effect of foreign aid on poverty as
measured by the infant mortality and illiteracy rate, using panel data from 58 countries
over the period 1990-2001. They looked at two distinct categories of aid: bilateral aid and
Non-Governmental Organization (NGO) aid. They find that NGO aid reduces infant
mortality but find no evidence that bilateral aid helps reduce infant mortality and illiteracy
rates.
Kosack (2003) examines the impact of foreign aid on quality of life using the
Human Development Index (HDI) as a proxy. The cross-country analysis shows that the
impact of aid depends on the quality of institutions in recipient countries. Kosack finds
that aid is effective in improving quality of life in democracies but has no effect in
autocracies. Arvin & Barillas (2002) test the causal relationship between aid, democracy
and poverty using data from 118 countries. Their trivariate granger causality test suggests
that, conditional on the state of democracy, there is no significant causal relationship
between aid and income per capita.
13
Like per capita income growth, the use of average health and education indicators
assumes away the distributional aspects of aid. Even when there is an increase in per
capita income or reduction in infant mortality due to aid, the question of whether the poor
are really the beneficiaries of development assistance remains unanswered. Although
there isn't much work done on the effect of aid on income distribution, related theoretical
and empirical studies in the aid effectiveness literature suggest that foreign aid increases
the size of the government and rent-seeking activities which benefit only a few (Boone,
1996; and Svensson, 2000).
In this study we take advantage of the newly available, internationally
comparable, time series poverty data to assess the direct effect of aid on poverty. The
modeling framework for the empirical analysis is what is commonly used in the growthinequality-poverty literature. In this setup changes in poverty are decomposed into two
sources: growth and distributional components (Datt & Ravallion, 1992; Besley &
Burgess, 2003; Kraay, 2006; Perry et al. 2006 pp. 60). In the next sub-section, we briefly
review some of the growth-poverty studies.
2.2.2 Growth-Poverty Reduction Literature
In general, growth and poverty reduction go hand-in-hand and empirical studies
show that economic growth is the key to poverty reduction (see Ravallion & Chen, 1997;
Dollar & Kraay, 2002; 2004; Besley & Burgess, 2003; Kraay, 2006). Using data from a
sample of 92 developed and developing countries over four decades, Dollar & Kraay
(2002) show that average income of the poor rise proportionately with national average
income. On the other hand, they find no evidence that many growth enhancing policies
and institutional factors significantly affect income of the poor after controlling for
14
average income. In a follow up paper, Dollar & Kraay (2004) examine the effects of trade
on poverty and inequality, using the average income of the bottom 20 percent and the
Gini coefficient. Their findings show that there is no systematic relationship between
changes in trade volume and poverty and inequality.
Unlike Dollar & Kraay (2002 & 2004), Ravallion & Chen (1997), Besley &
Burgess (2003) and Kraay (2006) use the absolute measure of poverty to examine the
effect of changes in average income and income distribution on poverty. The highly
significant negative elasticity of poverty with respect to average income obtained in these
studies confirms the findings from case studies and cross-country analyses that faster and
sustained growth is the driving force behind successful poverty reduction (Ravallion &
Chen, 1997; Chen & Ravallion, 2008). Even with the significant poverty elasticities, there
are notable regional differences. Besley & Burgess (2003) estimate that Sub-Saharan
Africa has the lowest elasticity of poverty with respect to income per capita, while
Eastern Europe and Central Asia have the highest (more than twice the elasticity in SubSaharan Africa) elasticity.
Kraay (2006) tested the relative importance of growth versus change in income
distribution for changes in absolute poverty using data from developing countries over the
1980s and 1990s. Similar to the findings of earlier studies, this decomposition exercise
also underscores the importance of growth in average incomes for poverty reduction, that
is, "growth is good for the poor."
Kraay's findings also suggest that much of the
variation in poverty reduction across countries is explained by differences in growth
performance.
In this study we assess the effect of aid on poverty after controlling for income
and redistribution—two main components of poverty. Calderon, Chong, & Gradstein
15
(2006) and Oyolola (2007) are two closely related studies that assess the impact of aid on
poverty in a similar setup. Oyolola's (2007) analysis was limited to 49 countries and finds
that aid in general has no direct effect on poverty. Calderon, Chong, & Gradstein (2006)
on the other hand, examine the effects of foreign aid on poverty and income inequality
over the period 1971-2002 using inequality and poverty data from UN World Income
Inequality Database and World Bank PovcalNet, respectively. They use the three
measures of poverty: headcount index, poverty gap index and poverty gap square index,
and also the Gini coefficient as a measure of income inequality in a country. Both their
cross-country and the dynamic panel estimation results show little evidence that aid helps
reduce poverty. For the distributional effect of aid, however, they find evidence that aid
improves income inequality in good institutional environments, as measured by lack of
corruption.
Although similar in nature, this study is different from Calderon, Chong, &
Gradstein (2006) and Oyolola's (2007) in some important aspects. First, following the
growth-poverty literature, we assess the role of aid in poverty reduction after controlling
for income and redistribution components of changes in poverty. Second, in addition to
total aid we examine the impact of different categories of aid on poverty. We distinguish
between aid from different sources: bilateral and multilateral, and different types of aid:
grants and loans.
2.3 Data Description and Descriptive Statistics
This section describes the key variables, provides sources of data and descriptive
statistics. The poverty data we use in this study is obtained from the World Bank, poverty
16
and inequality dataset—PovcalNet. The poverty and inequality measures in this dataset
are compiled by Chen & Ravallion from nationally representative living standard
household surveys. The newly revised version of the dataset provides triannual estimates
of the measures of absolute poverty over the period 1981-2004. The new estimates are
based on over 500 household surveys covering 100 developing countries (Chen &
Ravallion, 2007). Average income or consumption per capita in each household survey is
converted to 1993 Purchasing Power Parity (PPP). We use the international poverty line
of "$1 a day" which in 1993 dollars is $1.08 a day.4
2.3.1 Measurement of Poverty
We use the class of Foster, Greer, & Thorbecke's (1984) (FGT) measure of
poverty which is expressed as follows
1 N" fa V
P
a=-U—\
•
2.1
Where a is a measure of sensitivity of the index to poverty, NP is the number of
poor and Z is the poverty line. Gj is the poverty gap (Gj = Z - Xj), where Xj is per capita
income or expenditure, and N is the population size. When a = 0, the expression
corresponds to headcount index, a = 1 corresponds to poverty gap index, and a = 2
corresponds to squared poverty gap index. The definition of each measure of poverty
given below is as used in the PovcalNet database.
3
The data is available every three years from 1981 to 2002, and the latest available is for 2004. For detail
on the dataset, see Chen & Ravallion (2007) or http://iresearch.worldbank.org/PovcalNet/jsp/index.isp.
4
In this paper for the sake of convenience we use the terminology of $1 a day and $2 a day while in our
empirical analysis we use the corresponding poverty lines of $1.08 a day in 1993 PPP. The $1 a day
international poverty line was first introduced in 1990 and the difference between $1 a day in 1985 PPP and
$1.08 a day in 1993 PPP is an adjustment due to changes in purchasing power. For the revised international
poverty line in 2005 PPP see Ravallion, Chen, & Sangraula (2008).
17
The headcount index (Po), also known as poverty rate is a measure of the
proportion of the population living in a household with consumption or income per
person below the poverty line. This is the most popular measure of poverty because of its
simplicity. The deficiency of this measure is that it ignores the distribution of the poor
because no weight is attached to the relative distance of income of the poor from the
poverty line. In other words, everybody living below the poverty line is counted as poor
without any distinction on how far an individual is from the line.
The second measure of poverty is poverty gap index (Pi), also known as poverty
depth. It measures the mean income shortfall as a proportion of the poverty line. Hence,
this measure of poverty expresses the average income needed to bring the poor to the
poverty line expressed as a ratio to the poverty line.5 This measure of poverty takes into
account the distribution of the poor.
The third measure of poverty is squared poverty gap index (P2), also known as
poverty severity index. Unlike the poverty gap measure which gives equal weight to the
income shortfall of the poor, squaring the shortfall in the poverty severity index gives
more weight to the very poor. Hence, this is a measure which magnifies the state of the
poorest of the poor.
The three measures described above yield absolute measures of poverty when the
international poverty line of $ 1 a day is used, because the poverty line is anchored to the
cost of basic needs or nutritional requirements (Ravallion, Chen, & Sangraula, 2008).
Specifically, the international poverty line of $1 a day is believed to reflect the cost of
basic needs in low-income countries.
It is important to note that the gap is considered to be zero for everyone else who earns above the poverty
line.
18
Though the three measures of absolute poverty we discussed above are the best
available measures, they are far from perfect. A decrease in the proportion of people
living below the poverty line (poverty rate) could be misleading if the actual number of
people living below the poverty line remains the same or increases. An example of such
divergence between the poverty rate and the number of poor can be cited from the
experience of Sub-Saharan Africa. In this region the poverty rate, measured at $1 a day,
has dropped from 42.3 percent in 1981 to 41.1 percent in 2004, while the actual number
of people living below the poverty line has increased from 167.5 million to 298.3 million.
Hence, the use of these two measures of poverty can lead to different conclusions. The
other two measures of poverty: poverty gap and squared poverty gap index, however, are
more of an income measure of poverty and are less ambiguous.
As discussed in section one, the effect of aid on poverty through an increase in
average income depends on a robust positive relationship between aid and growth, and
how growth translates to increase in income of the poor. To get a sense of how average
income and income of the poor are related, we construct an income measure of poverty
that can be used to assess the association between the two.
2.3.2 Constructing an Income Measure of Poverty
The income measure of poverty we construct here is per capita income of the
poor. It is constructed using information on poverty rate, poverty gap and the poverty line.
Starting with the poverty gap index and manipulating we arrive at an expression for
income per capita of the poor, which is given in equation 2. For derivation refer
Appendix A.
19
YP=Z
£o
f_
2.2
It is important to note that the income measure of poverty we construct here is
different from the income of the poorest 20 percent of the population which is widely
used by previous studies including Dollar & Kraay (2002;2004), and Beck et al. (2007).
While our measure is an absolute measure of poverty based on a dollar a day, the income
of the poorest 20 percent of the population is a relative measure of poverty.
2.3.3 Data Source and Coverage
Foreign aid data are obtained from the World Bank, World Development
Indicators, and OECD-DAC. We use the standard definition of aid—the ratio of Net
Official Development Assistance (ODA) to Gross National Income (GNI). The following
are official definitions of different types of aid obtained from the OECD-DAC website.
ODA consists of grants and loans (that consists of at least a 2 5 % grant element) from
both bilateral and multilateral sources. 6 This measure of aid excludes grants and loans for
military purposes. In addition to examining the effect of total aid on poverty we also
assess if different categories of aid have a different impact on poverty. To do this we
disaggregate aid into different forms. First, we consider aid by source. This classification
gives us two categories of aid, from bilateral and multilateral sources. Bilateral aid is a
direct transfer from a donor country to a recipient country, while multilateral aid is
channeled via an international organization. Second, we consider aid by type: grants
versus loans. Grants are transfers made in cash, goods or services for which no repayment
6
For definition of different types of aid see
http://vvww.oecd.Org/glossary/0.3414.en_2649_33721_1965693 l_l_l_l,00.html# 1965586
20
is required. Loans are transfers for which repayment is required but with longer maturity
and lower rates.
The other variables we use include real GDP per capita, trade, financial
development, inflation, and age dependency ratio, obtained from the World Bank, World
Development Indicators. Institutional variables, political rights and civil liberties are
obtained from Freedom House, while the corruption and overall political risk rating index
are from the International Country Risk Guide (ICRG). Democracy score is from Polity
IV Project. Details of variable definitions and sources of data are available in Appendix
B.
Although we initially obtained poverty data for 96 developing countries spanning
1981-2004, excluding 12 East European countries and others mainly due to concerns
about data reliability leaves us with 79 countries. It is a fairly balanced dataset, but
depending on the control variables used, the number of observations varies across
specifications. It is also important to note that our sample doesn't include China, the
country that has registered a significant reduction in poverty over the last two decades,
because of lack of poverty data at the national level. Besides, as Easterly (2006) argues,
China is one of the countries that have made great progress against poverty and that is an
achievment less associated with aid. The list of countries and regional groups are given in
Appendix C. Since the dependent variable—measures of poverty—is available every
three years all the explanatory variables are measured as averages over three years. For
instance, the value of foreign aid that corresponds to year 1990 is an average of aid flows
over 1988-1990.
21
2.3.4 Descriptive Statistics
Table 2.1 presents descriptive statistics and correlations between poverty and the
key variables that we use in the empirical analysis. The summary statistics show a
sizeable variation in poverty levels across sample countries. For instance, the proportion
of people living below the $1 a day poverty line ranges from zero to 90.3 percent. The
size of aid flows also varies significantly among sample countries ranging from -0.4
percent to 62.1percent.7
Average income, trade and finance are negatively correlated with poverty, while
aid and Gini coefficient are positively and significantly correlated with poverty. The
negative association between poverty, financial system development and trade indicates
that countries with more developed financial systems and open to trade experience a
reduction in poverty. The positive correlation between aid and poverty is misleading since
it suggests that aid flows tend to increase poverty. As we see later, accounting for
endogeneity and other econometric issues, aid tends to reduce poverty. We investigate the
poverty-aid relation controlling for country and time specific effects, endogeneity of aid
and taking into account the persistent nature of poverty.
Before we discuss the model specification we briefly look at the relationship
between average income and income of the poor in our sample—another factor that
influences how aid impacts poverty when channeled through growth in average income.
Figure 1 below shows the relationship between average income and income per capita of
the poor. Contrary to the findings of Dollar & Kraay (2002; 2004), who find a one-to-one
7
The highest aid recipient in our sample was Mozambique in 1993. During the sample years, 1981-2004,
Mozambique had an average poverty rate of 41.6 percent, the aid to GNI ratio of 28.7 percent, and GDP per
capita was $450.1 in 2005 PPP. The highest poverty rate in our sample corresponds to Uganda in year
1993. Over the reference period, 1981-2004, Uganda had an average poverty rate of 86.4 percent, an aid to
GNI ratio of 11.8 percent, and GDP per capita of $634.5 in 2005 PPP.
22
relationship between average income and income of the poor, we observe a weak
relationship in our sample. In our sample, a one percent increase in mean household
income raises the income of the poor only by 0.15 percent. Such a weak association
between the two suggests skepticism about the role of aid in reducing poverty via
increased average income. It is important to note that Dollar & Kraay (2002; 2004) use
the income of the bottom 20 percent of the population, which is a relative measure of
poverty, while we use an absolute measure of poverty—per capita income of the poor.
23
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2.4 Model Specification and Econometric Methodology
2.4.1 Model Specification
In order to empirically examine the relationship between aid and poverty, we
estimate variants of the following specification. The model we use is a basic specification
for growth-poverty relationship used by Datt & Ravallion (1992); Ravallion & Chen
(1997); Besley & Burgess (2003); Perry et al. (2006) and others to test the relative roles
o
of growth and income distribution for poverty reduction.
log Pu=a0+ # log Yit + J32 log Gu + v, + su
8
2.3
The basic specification for poverty analysis from Datt & Ravallion (1992) is Pt = P(Z I ]Ll t ,Lt), where
Pt is the poverty measure, Z is the poverty line, / / ( i s the mean income, and Lt is Lorenz curve.
Therefore, changes in poverty are decomposed into growth and redistribution components.
25
Where i and t index countries and years, respectively, Plt is the measure of poverty
in country i at time t. Yjt and G/(are real per capita income and Gini coefficient for
country i at time t, respectively. vi is unobserved country specific effects, /?, is the growth
elasticity of poverty, denoting the percentage change in poverty due a change in average
income, and su is idiosyncratic errors.
We augment equation 2.3 by including aid flows to country i at time t, AIDU as an
additional variable that explains changes in poverty. To account for the persistent nature
of poverty we also introduce the lagged dependent variable as an additional variable that
explains the level of poverty.9
log P„=a log />,_, + /?, log Y„ + p2 log G„ + p3AIDH + o, + eu
2.4
We further extend equation 2.3 to include policies and institutions that are
identified in the growth literature as determinants of growth. Hence, XH is a set of policies
and institutional variables that affect poverty.
logi>, =alogP^
+/?, l o g ^ + p2 logG„ +P3AIDU +X„ '6 + vi +eit
2.5
The key parameter of interest is /?3, which measures the impact of aid on poverty
after controlling for changes in average income. In other words, /?3, measures the direct
effect of aid on poverty after we control for income and distribution components of
changes in poverty. The total impact of aid on poverty, of course, depends on both the
direct and indirect effects. As Mosley, Hudson, & Verschoor (2004) and Mosley &
Suleiman (2007) note the indirect effects of aid on poverty could be channeled through
growth and changes in policy and institutions—policy reforms.
9
Due to the persistent nature of poverty past levels of poverty explain a great deal of the present and future
levels.
26
A value of /?3 = 0 indicates that foreign aid has no direct effect on poverty over
and above its impact on income per capita, i.e., foreign aid only affects poverty in the
recipient country by possibly raising aggregate income.10 A value of /?3 greater (less) than
zero indicates that aid increases (decreases) poverty. Likewise the estimates of 6 capture
the direct effect of the variables in X on poverty.
The other parameter of interest is /?,, which is the elasticity of poverty with
respect to average income. This parameter measures the efficiency of growth in poverty
reduction; i.e., how growth in average income enables the poor to exit poverty.
2.4.2 Econometric Methodology
This section describes the econometric techniques we use for estimation purposes.
The presence of the lagged dependent variable as an explanatory variable poses
econometric issues that need to be addressed properly. Even if the lagged dependent
variable and the error terms are not correlated the introduction of a lagged dependent
variable makes random and fixed effects estimates inconsistent because the lagged
dependent variable would be correlated with the transformed error terms (Baltagi, 2005).
To overcome this problem we employ the system GMM estimator—first introduced by
Arellano & Bover (1995) and developed by Blundell & Bond (1998), which accounts for
the bias introduced by the lagged dependent variable. This estimator improves on the
Arellano & Bond (1991) difference GMM estimator where only lagged levels are used as
instruments for difference equation. The Arellano-Bover/Blundell-Bond system GMM
estimator uses more moment conditions—lagged differences are used as instruments for
10
Although it is unrealstic to assume that donors can target a particular household, many aid programmes
do target the poor and aid does benefit the poor in particular areas.
27
level equation and lagged levels are used as instruments for the difference equation
(Arellano & Bover, 1995, and Blundell & Bond, 1998).
The consistency of the system GMM estimator depends on the validity of
instruments and the absence of second order serial autocorrelation. To illustrate the
moment conditions, let's consider a dynamic panel-data model of the form:
Yu = aYu_x + Xu' P +1>. + su where (p-=vt+ eu
2.6
The first-difference of equation 6 is:
AY^aAY^+AX.'P
+ As,
2.7
The moment conditions in the first difference equation are E(Yit_sAejt) = 0for
lagged dependent variable, and E(Xil_sAsjl) = 0 V / > 3,...,T and s > 2 for the covariates.
The moment conditions in the level equation are £(A^,_, #>,.,) = 0 for the lagged dependent
variable, and E(AXn_x(pu) = 0 \ft >3,...,T for the covariates11. Finally, the condition for
no second order serial autocorrelation is E(AsltAsj (_,) = 0 for t = 2.
2.5 Empirical Results
In this section we present the system GMM estimation results of equations 2.4 and
2.5. The benchmark OLS estimation results are reported in Appendix D. For each
measure of poverty we report results corresponding to the specifications in equations 2.4
and 2.5. The standard errors reported in the parenthesis are robust standard errors.
The validity of the instruments and the moment conditions are tested using the
Sargan test of overidentifying restrictions and the Arellano-Bond first and second order
It is important to note that predetermined and endogenous variables are handled the same way as the
lagged dependent variable.
28
serial autocorrelation tests, respectively. In all the cases, we do not reject the null that the
instruments are valid and there is no second order autocorrelation.
2.5.1 The Effect of Aggregate Aid
In this section we present the results when aggregate aid measure is used. Total
aid includes aid from multilateral and bilateral sources in the form of grants and loans. In
the specification corresponding to equation 2.5 among the covariates, aid and finance
1 "K
variables are treated endogenous.
In Table 2.2 we present results when total aid is used. Columns (1), (3) and (5)
report results when only aid is added to the basic specification and in columns (2), (4),
and (6) we report results when aid and a set of covariates identified as growth-enhancing
policies and institutions are included.
For all measures of poverty, in both specifications, the variable of interest—
foreign aid—enters negatively and significantly. The results indicate that aid plays a
positive role in reducing poverty. The finding that aid has poverty reducing effects even
after controlling for average income is noteworthy and has important policy implications.
Despite the controversy surrounding the aid-growth relation, the direct poverty reducing
effect of aid suggests that aid indeed helps reduce poverty even though its effects on
growth remain uncertain. When we use the headcount index, a one percentage point
12
As Arellano & Bond (1991) note, the one-step Sargan test tends to over-reject the null in the presence of
heteroskedasticity. Hence, the two-step Sargan test statistics which is consistent in case of heteroskedasticity
is reported.
13
In system GMM estimation the number of instruments quickly grow with the number of time periods and
covariates. To control for the proliferation of instruments we limit the maximum number of lags of the
dependent variable used as instruments to two and number of lags for the endogenous variables (used as
instruments) to three.
29
increase in aid to a recipient country reduces the proportion of people living below the
poverty line by 0.018 percent. This result is consistent with the findings of Mosley &
Suleiman (2007) who find that aid reduces headcount poverty by a similar magnituide in
cross-country analysis of 49 countries. Hence, taking into account this direct effect of aid
on poverty, poverty-efficient allocation of aid calculated by Collier & Dollar (2002) could
have an even stronger poverty reducing effect.
Table 2.2. System GMM Estimation: Aggregate Aid
(2)
(1)
Poverty Rate
Dependent Variables:
Aid
-0.018**
(0.008)
Finance
Log GDP per capita
Log Gini
-0.666***
(0.201)
2.345***
(0.635)
Openness
Age dependency ratio
Democracy score
Lagged Poverty Rate
0.651***
(0.099)
-0.022**
(0.011)
-0.192*
(0.103)
-0.530**
(0.216)
2.442***
(0.615)
-0.010
(0.210)
0.752
(0.756)
-0.014
(0.015)
0.648***
(0.097)
Lagged Poverty Gap Index
(3)
(4)
Poverty Gap
-0.028**
(0.011)
-0.763***
(0.256)
2.361***
(0.776)
0.715***
(0.097)
-0.019*
(0.011)
-0.198*
(0.114)
-0.510**
(0.207)
2.799***
(0.726)
-0.246
(0.207)
1.002
(0.694)
-0.008
(0.019)
-0.026***
(0.010)
-0.821***
(0.219)
2 751***
(0.703)
-2.694
(2.209)
-4.067*
(2.121)
-2.490
(3.125)
-5.281*
(2.758)
0.739***
(0.074)
-3.826
(2.553)
569
79
0.31
0.63
524
75
0.77
0.93
565
79
0.48
0.37
520
75
0.58
0.24
555
79
0.49
0.64
Observations
Number of panel
Sargan test
2nd order autocorrelation test
Robust standard errors in parentheses
***p<0.01,**p<0.05, *p<0.1
30
-0.015*
(0.009)
-0.242*
(0.129)
-0.532***
(0.204)
3.208***
(0.687)
-0.265
(0.218)
0.674
(0.802)
-0.011
(0.019)
0.613***
(0.089)
Lagged Squared Poverty Gap Index
Constant
(6)
(5)
Squared Poverty Gap
0.662***
(0.077)
-6.612**
(2.628)
511
75
0.62
0.59
The negative and significant estimates of the aid parameter in columns (3) through
(6) suggest that aid helps even the poorest of the poor who survive on a dollar a day or
less. The estimate in column (3) indicates that a one percentage point increase in aid
reduces the average income shortfall of the poor by 3 percent.
Another interesting finding is the effect of finance on poverty. We find
consistently negative and signficant estimates for finance, suggesting a prominent role
that financial sector development can play in poverty reduction. This finding is inline
with that of Beck et al. (2007) who argue that financial development raises incomes of the
poor more that proportionately. Like the effect of aid on poverty, the observed strong
poverty reducing effects of finance appear after controlling for average income.
According to Beck et al. (2000) the exogenous components of financial development
have strong positive impact on growth mainly through improvements in productivity and
by easing credit constraints facing the poor (Beck et al., 2007). Hence, the strong direct
and indirect effects of finance make it an important factor in poverty reduction strategies.
The economic significance of these results can be assessed by calculating how an
increase in aid and improved financial sector development would translate to poverty
reduction. For instance, for Kenya over the period 1981-2004, had the average aid to GNI
ratio been doubled to 14.2 percent, the average poverty rate would have fallen to 19.6
percent instead of its actual value of 23.4. Such an increase would have freed close to 1
million people from poverty. The economic significance of financial development is even
more substantial. Over the period under consideration Colombia and Zambia had
domestic credit to private sector as percentage of GDP of 31 and 11.3 percent,
respectively. Had Zambia's domestic credit to private sector ratio GDP been the same as
Colombia's, which is 178 percent higher, the average poverty rate would have fallen from
31
57.7 percent to 12.1 percent, freeing close to four million people from poverty. Financial
development appears to have a more potent effect on poverty reduction than foreign aid.
The estimates of income per capita and the Gini coefficient enter with the
expected signs and are consistent with the findings of earlier studies by Ravallion & Chen
(1997), Besley & Burgess (2003) and Oyolola (2007). But our growth elasticity of
poverty estimates from system GMM, which takes into account the persistent nature of
poverty and other control variables, are lower compared to the estimates of Ravallion &
Chen (1997) and Besley & Burgess (2003) who examine the relationship using the fixed
effects model. In all specifications and for all three measures of poverty the lagged
dependent variable enters positively and significantly suggesting the persistence of
poverty. Trade, age dependency ratio, and democracy do not enter significantly,
indicating a lack of any direct effect of these variables on poverty once we control for
average income.
The lack of significant poverty reducing effects of growth-enhancing
policies and institutions such as trade and democracy confirms the findings of Dollar &
Kraay (2002; 2004).
2.5.2 Unequal Impact of Aid by Source and Type
The aid effectiveness literature shows that different types of aid have different
impacts on growth. We also examine if such a result holds for the effect of aid on
poverty. In this section we present results when using different categories of aid rather
than just aggregate aid. This is captured by using the same specification as before but
with sub-categories instead of aggregate aid. In this part of the study to control for the
32
proliferation of instruments we only treat the variable of interest—disaggregated aid, as
endogenous.
The results presented in Table 2.3 apply when aid is disaggregated by source:
bilateral and mulitilateral. The results indicate that aid from multilateral sources reduces
poverty while aid from bilateral sources do not. There are two interesting findings worth
mentioning here. First, aid from mulitilateral sources enter negatively and significantly
with coefficients bigger than the one obtained for aggregate aid in Table 2.2. This
indicates that aid from multilateral sources reduces poverty by more than aggregate aid.
Second, the estimates of bilateral aid are not only insignificant but enter positively when,
squared poverty gap index measure of poverty is used.
As can be seen from Table 2.1, aid from bilateral sources, which is less productive
in poverty reduction, accounts for 62 percent of total aid. No wonder, then, the estimates
of total aid in Table 2.2 is smaller in magnitude than the estimates of multilateral aid in
Table 2.3.
These results are consistent with the idea that multilateral aid is less political
while bilateral aid is motivated by "geopolitical and strategic" interests of donors.
Empirical aid effectiveness studies also show that aid from multilateral sources has
growth enhancing and poverty reducing effects compared to bilateral aid (Headey, 2008).
For instance, Masud & Yontcheva (2005) find that NGO aid reduces infant mortality
while bilateral aid does not. Unlike our results in Table 2.2 where finance is treated as
endogenous, financial development is insignificant throughout. The other variables:
We tested to see if the effect of aid varies with the state of democracy by including an interaction term
between aid and democracy. We do not find any evidence to support that aid works differently in more
democratic environments.
33
lagged dependent variable, average income and the Gini coefficient enter with the
expected signs.
In.Table 2.4, we present the results for the two types of aid: grants and loans. The
results indicate that grants have poverty reducing effects compared to loans. For
specifications that use the poverty gap and squared poverty gap index as the dependent
variable, grant enters negatively and significantly when other growth enhancing policies
and instituions are not included. Similar to bilateral aid, loans have no effect on poverty.
One reason for this outcome is that grants have no repayment condition and therefore may
be used for poverty reduction purposes, while loans must be used for financially
productive projects since repayment is important. In this part, also the significance of
financial sector development fully disappears, while average income, the Gini coefficient
and the lagged dependent variables enter significantly with the expected signs.
34
Table 2.3. System GMM Estimation: Bilateral and Multilateral Aid
(2)
(1)
Poverty Rate
Dependent Variables:
Bilateral aid
Multilateral aid
Log GDP per capita
Log Gini
-0.004
(0.005)
-0.029**
(0.012)
-0.429***
(0.138)
1.636***
(0.534)
Finance
Openness
Age dependency ratio
Democracy score
Lagged Poverty Rate
0.789***
(0.094)
-0.008
(0.007)
-0.028*
(0.015)
-0.347**
(0.175)
1.634***
(0.583)
-0.064
(0.083)
-0.123
(0.171)
0.691
(0.610)
-0.013
(0.014)
0.758***
(0.100)
Lagged Poverty Gap Index
(4)
(3)
Poverty Gap
-0.000
(0.006)
-0.051***
(0.017)
-0.513***
(0.146)
1.964***
(0.736)
-0.001
(0.007)
-0.033*
(0.019)
-0.360**
(0.173)
2.103**
(0.819)
-0.061
(0.096)
-0.273
(0.185)
0.631
(0.643)
-0.014
(0.018)
0.813***
(0.079)
0.728***
(0.090)
/
Lagged Squared Poverty Gap Index
Constant
Observations
Number of panel
Sargan test
2nd order autocorrelation test
(5)
(6)
Squared Poverty Gap
0.004
(0.008)
-0.055***
(0.018)
-0.589***
(0.161)
2.554***
(0.726)
0.006
(0.008)
-0.033*
(0.019)
-0.428**
(0.192)
2.813***
(0.791)
-0.004
(0.104)
-0.425**
(0.186)
0.810
(0.833)
-0.017
(0.019)
0.720***
(0.083)
-6.171*
(3.181)
506
74
0.60
0.53
-2.125
(2.137)
-2.549
(2.240)
-2.944
(2.973)
-3.982
(3.171)
0.814***
(0.069)
-4.727*
(2.512)
559
78
0.57
0.83
518
74
0.76
0.77
556
78
0.38
0.40
515
74
0.55
0.26
546
78
0.73
0.66
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
35
Table 2.4. System GMM Estimation: Grants and Loans
(2)
(1)
Poverty Rate
Dependent Variables:
Grants
Loans
Log GDP per capita
Log Gini
-0.012*
(0.007)
-0.029
(0.018)
-0.479***
(0.144)
1.312**
(0.513)
Finance
Openness
Age dependency ratio
Democracy score
Lagged Poverty Rate
0 797***
(0.098)
-0.019**
(0.009)
-0.012
(0.016)
-0.350**
(0.178)
1.298**
(0.593)
-0.032
(0.092)
-0.027
(0.167)
0.866
(0.623)
-0.008
(0.016)
0.808***
(0.093)
Lagged Poverty Gap Index
(3)
(4)
Poverty Gap
-0.017**
(0.008)
-0.034
(0.024)
-0.497***
(0.159)
1.352**
(0.670)
-0.013
(0.010)
-0.011
(0.021)
-0.326**
(0.162)
1.393*
(0.790)
0.011
(0.108)
-0.195
(0.181)
0.772
(0.639)
0.000
(0.020)
0.851***
(0.078)
0.808***
(0.087)
Lagged Squared Poverty Gap Index
Constant
Observations
Number of panel
Sargan test
2nd order autocorrelation test
(5)
(6)
Squared Poverty Gap
-0.023**
(0.011)
-0.010
(0.021)
-0.542***
(0.166)
] 774***
(0.640)
-0.016
(0.012)
0.008
(0.020)
-0.410**
(0.191)
2.058***
(0.685)
0.068
(0.115)
-0.288
(0.201)
0.746
(0.844)
-0.003
(0.020)
0.815***
(0.078)
-4.273
(2.763)
502
74
0.84
0.92
-0.508
(2.047)
-2.121
(2.204)
-0.768
(2.933)
-2.355
(3.242)
0.871***
(0.060)
-2.447
(2.261)
554
78
0.39
0.86
514
74
0.67
0.73
551
78
0.70
0.47
511
74
0.80
0.32
541
78
0.57
0.78
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
To summarize, this chapter uses a new time series poverty and inequality dataset
for developing countries and analyzes the relationship between poverty and aid after
controlling for income and distribution components of changes in poverty. Overall the
empirical analyses produce some interesting results on the role of aid in poverty reduction
with important policy implications. First, we find a negative and significant effect of aid
on poverty, indicating that aid has a direct poverty reducing effect and the result is robust
to the use of different measures of poverty, inclusion of control variables, exclusion of
36
outliers and different estimation techniques. Desipte doubts as to whether aid spurs
growth, the findings of this study indicate that aid plays a significant role in poverty
reduction. The policy implication is that the poverty impact of aid can be improved by
exploiting its direct effect—that is, by targeting the poor directly rather than hoping that
increased average incomes following aid lowers poverty indirectly.
Second, we find that different components of aid have an unequal impact on
poverty: (a) multilateral aid has a strong poverty reducing effect, while there is no
evidence that bilateral aid helps reduce poverty, (b) comparing grants and loans, we find
that grants reduce poverty while loans do not.
Third, we find that financial sector development has significant poverty reducing
effects with sizeable potential for poverty reduction.
Fourth, we find that average income and income distribution, measured by Gini
coefficient, are consistently significant and enter with the expected signs indicating that
growth and redistribution of income play important roles in poverty reduction. These
findings corroborate the results of earlier studies by Ravallion & Chen (1997), Besley &
Burgess (2003), Kraay (2006) and others that growth and redistribution of income play a
central role in poverty reduction.
2.6 Summary and Concluding Remarks
The effectiveness of aid has been debated for decades. Depite much skepticism,
foreign aid has recently regained much attention in the international development arena.
Increased official development assistance is the basis for an ambitious plan of cutting the
37
proportion of people living in absolute poverty by half by 2015, and there are other grand
goals included in MDGs.
Conventional wisdom says that aid improves the living conditions of people in
recipient countries by raising the average income. The effect of aid on poverty that works
through an increase in average income depends on two things. First, aid has to enhance
growth and second, that has to translate to an increase in incomes of the poor. Emipircal
studies show no robust relationship between aid and growth; even when aid spurs growth
in average income, it does not necessarily translate to reduced levels of poverty. The lack
of a robust positive relationship between aid and growth and the weak association
between average income and income of the poor jointly make the role of aid in poverty
reduction through increased average income dubious.
In this study, we examine the relationship between aid and the three measures of
poverty: headcount index, poverty gap index and squared poverty gap index. We find
strong evidence that aid reduces poverty after controlling for average income and income
distribution. The poverty reducing effect of aid is robust to changes in specifications,
exclusion of outliers and estimation techniques. In addition to size, we also find that the
composition of aid matters—multilateral aid and grants reduce poverty, while bilateral
aid and loans do not. In addition to aid, we also find that financial sector development has
a significant poverty reducing effect.
This implies that the direct effect of aid on poverty could be the most important
channel through which aid reduces poverty. The policy implications are clear: the role of
aid in poverty reduction can be improved by strengthening the direct effect of aid on
poverty through more targeted interventions and by creating opportunities for the poor
rather than pursuing the elusive goal of spurring growth.
38
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42
Appendix A
Constructing Income per Capita of the Poor
We started with the poverty gap index, where Gj is the poverty gap (the shortfall), Z is the
poverty line, and N is population size.
1 N" ( G\
Multiplying N p / Np, where
NNp Pis&(GA
the number of poor in the country.
1
= —x
>
yZJ
0
MP Z-ll
N"
g
P0 is poverty rate.
I(G,)
P
[2]
Z
Np
The second term in equation 2 is the average shortfall of income of the poor. Rearranging
equation 2, we derive the express for average shortfall
= ^ x '=1
Np
E(3)
=^z
[3]
Nf
Lastly, we arrive at per capita income of the poor by deducting the average shortfall from
the poverty line and that gives us the expression in equation 4.
P
YP=Z—LZ
;=i
v
[4]
p
43
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loans as share to GDP
o
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T3
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s s
td
o
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B
E
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-i—»
c
03
m bilateral
4—
c/T
emo cracy score (from 0 to 10)
Offici al Devel opment Assistance
share to GDP
from mu ltilateral sources as sha:
o
Povc
Povc
Povc
ts
u
Indi
£ £
vT
u
o
grants as share to GDP
IT)
-fcj
Bilateral
_"£
i-H
Democra y score
<u
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erty
c
u
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Povc
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ivelop
13
o
•£
^
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O
;-H
poverty line
f povei
s share
Q Q Q
T3
T3
2
:velop
c/T
Worl
c/f
ivelop
O
WorL
a s s
c
T3
ogo f proportion of people living bel
ogo f the avei rage income shortfalias
ogo f the squ;ared average incom<
ogo f the Gin i coefficient
ogo f the mean annual household
rvic
£ £ £ £
O
Indi
Indi
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Appendix D
OLS Estimation Results
Panel A. Aggregate Aid and Poverty Measures
(3)
(4)
(1)
(2)
Poverty Gap
Dependent Variables:
Poverty Rate
Aid
-0.016**
-0.014**
-0.017**
-0.013*
(0.007)
(0.008)
(0.008)
(0.007)
Log GDP per capita
-1.331*** -0.940*** -1.576*** -1.027***
(0.086)
(0.105)
(0.072)
(0.091)
Log Gini
2.866***
2.482***
4.251***
3.848***
(0.252)
(0.218)
(0.275)
(0.231)
Openness
-0.245***
-0.260**
(0.103)
(0.088)
Finance
-0.150**
-0.296***
(0.063)
(0.073)
Age dependency ratio
3 275***
3.930***
(0.422)
(0.364)
Democracy score
0.055***
0.052***
(0.014)
(0.016)
Constant
2.030**
-0.940
-2.589**
-6.748***
(0.905)
(1.064)
(1.048)
(0.893)
Observations
503
473
501
471
Adjusted R-squared
0.52
0.65
0.54
0.68
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(5)
(6)
Poverty Gap Square
-0.016*
-0.011
(0.009)
(0.008)
-1.646***
-0.994***
(0.096)
(0.114)
5.000***
4.596***
(0.304)
(0.273)
-0.233**
(0.111)
-0.424***
(0.080)
4.260***
(0.458)
0.048***
(0.017)
-5 739***
-10.691***
(1.134)
(1.171)
496
466
0.53
0.69
Panel B. Bilateral and Multilateral Aid and Poverty Measures
(1)
(2)
(3)
(4)
Dependent Variables:
Poverty Rate
Poverty Gap
Bilateral aid
0.004
0.004
0.006
0.012
(0.012)
(0.012)
(0.014)
(0.015)
Multilateral aid
-0.040**
-0.039**
-0.045**
-0.051**
(0.018)
(0.017)
(0.022)
(0.020)
Log GDP per capita
-1.366*** -1.068*** -1.608*** -1.154***
(0.065)
(0.081)
(0.094)
(0.078)
Log Gini
3.035***
2.760***
4 332***
4.046***
(0.217)
(0.215)
(0.260)
(0.247)
Openness
-0.205**
-0.193**
(0.082)
(0.095)
Finance
-0.251***
-0.428***
(0.060)
(0.069)
Age dependency ratio
2.638***
3 373***
(0.356)
(0.410)
Democracy score
0.064***
0.067***
(0.015)
(0.013)
Constant
1.589*
-0.445
-2 731*** -6.074***
(0.841)
(0.882)
(1.004)
(1.014)
Observations
624
576
621
573
Adjusted R-squared
0.64
0.53
0.54
0.67
Standard errors in parentisjses
***p<0.01, ** p<0.05, '*p<0.1
(5)
(6)
Poverty Gap Square
0.012
0.023
(0.016)
(0.015)
-0.050**
-0.064***
(0.024)
(0.021)
-1.686*** -1.124***
(0.087)
(0.102)
5.015***
4.697***
(0.289)
(0.268)
-0.121
(0.103)
-0.574***
(0.075)
3.872***
(0.445)
0.068***
(0.017)
-5.581*** -9.922***
(1.114)
(1.100)
615
567
0.53
0.68
46
Panel C. Grants and Loans and Poverty Measures
_
(1)
(2)
Dependent Variables:
Poverty Rate
Grants
Loans
Log GDP per capita
Log Gini
-0.018**
(0.009)
-0.011
(0.021)
-1 371***
(0.068)
3.008***
(0.224)
Openness
Finance
Age dependency ratio
Democracy score
Constant
1.773**
(0.869)
Observations
613
0.52
Adjusted R-squared
Standard errors in parentheses
(3)
(4)
Poverty Gap
(5)
(6)
Poverty Gap Square
-0.022**
(0.009)
0.006
(0.020)
-1.053***
(0.084)
2.736***
(0.218)
-0.210**
(0.083)
-0.236***
(0.059)
2.867***
(0.352)
0.067***
(0.013)
-0.673
(0.893)
-0.019*
(0.010)
-0.008
(0.026)
-1.612***
(0.082)
4.328***
(0.267)
-2.643**
(1.039)
-0.022**
(0.010)
0.008
(0.023)
-1.125***
(0.096)
4.015***
(0.251)
-0.212**
(0.096)
-0.406***
(0.069)
3.674***
(0.406)
0.070***
(0.015)
.6.404***
(1.029)
569
611
567
605
561
0.64
0.53
0.67
*** pO.Ol, ** p<0.05, * p<0.1
0.52
0.67
47
-0.017
(0.011)
-0.005
(0.028)
-1 677***
(0.091)
5.013***
(0.297)
-5.600***
(1.153)
-0.018*
(0.011)
0.009
(0.024)
-1.074***
(0.105)
4.644***
(0.272)
-0.155
(0.105)
-0.542***
(0.075)
4.236***
(0.442)
0.071***
(0.017)
-10.352***
(1.118)
CHAPTER III
FOREIGN AID AND THE SOURCES OF GROWTH
3.1 Introduction
Effectiveness of foreign aid in enhancing economic growth and reducing poverty
is a highly debated topic in development economics. The controversy surrounding the
impact of foreign aid on economic growth and how that growth translates to poverty
reduction is far from over. Renewed interest in foreign aid to achieve the Millennium
Development Goals (MDGs) and donors' promise to double assistance for developing
countries has revived the debate once again. Over the period 2000-2007 alone, Official
Development Assistance (ODA) flow to developing countries has grown by more than
double (from $49.9 billion to $105.3 billion).
The vast empirical aid effectiveness literature has produced mixed results and is
inconclusive on the effectiveness of aid and the conditions under which it is effective.
Skeptics of the positive association between aid and economic growth argue that aid
could hurt growth because it displaces domestic saving, finances consumption, leads to
overvaluation of real exchange rate and weakens the recipient country institutions (see
Boone, 1996; Ovaska, 2003; Easterly et al., 2004; Rajan & Subramanian, 2005a; 2007;
Svensson, 2000; Brautigam & Knack, 2004; Easterly 2003: 2006a: 2006, and 2007).
These studies show lack of a robust positive relationship between aid and economic
growth, and some even show a negative relationship. On the other hand, Burnside &
Dollar (2000), Loxley & Sackey (2008), Collier & Dollar, (2001:2002), Dalgaard et al.
(2004), Karras, (2006); Dovern & Nunnenkamp, (2007), Alvi et al. (2008) and others
48
show that aid spurs growth in good policy environment with the possibility of
diminishing returns to aid.
Theoretically, the two and three-gap models which form the basis of the aidgrowth relationship emphasize the aid-investment link as the primary channel through
growth is materialized. Recent studies, however, have explored other possible
transmission mechanisms in explaining aid-growth relationship, which include the effect
of aid on incentives, economic policies and quality of institutions. Two of the latest
contributions on aid-quality of institutions relationship include Economides et al. (2008)
and Djankov et al. (2008). The bulk of the evidence in this literature suggests that aid has
a negative effect on the quality of institutions. Djankov et al. (2008) for instance, show
that foreign aid like natural resources induces rent seeking activities and has a detrimental
impact on the quality of institutions. Economides et al. (2008) also find similar negative
association between aid and quality of institutions.
Despite the search for other possible transmission mechanisms from aid to
growth, the aid-investment-growth link remains the most popular among researchers.
Whereas this is a likely channel, the overall effect of aid on growth depends both on the
aid-investment relationship and the productivity of investments financed by aid, that is,
its efficiency. Thus, the positive association between aid and investment alone does not
guarantee economic growth. Exclusive emphasis on the aid-investment relationship in the
literature has ignored the efficiency channel via which aid can affect economic
performance.
Cross-country growth accounting studies reveal the importance of Total Factor
Productivity (TFP) in explaining differences in both the level and growth of income
across nations (see Easterly & Levine, 2001; Caselli, 2005 and Baier et al., 2006).
49
Easterly & Levine (2001), for instance, argue that TFP rather than capital accumulation
accounts for most of the cross-country differences in the level and growth of income.
Caselli (2005) also argue that the role of TFP is robust to considerations for the quality of
physical and human capital.
In this chapter, we focus on both the aid-investment and aid-productivity
relationships to assess the effects of aid on the two prominent sources of growth.
Specifically, we test how foreign aid affects the sources of growth: capital accumulation
and TFP growth. Such a study helps us better understand aid effectiveness, and also
explains the weak and sometime perverse effects of aid on growth.
Although the aid-investment relationship is widely studied, despite its importance
the effect of aid on factor productivity has been largely ignored. To the best of our
knowledge this study is the first attempt to empirically test the effect of aid on factor
productivity. The fact that TFP growth explains the bulk of cross-country differences in
both the level and growth of income makes this exercise particularly worthwhile.
Somewhat closely related studies include those that examine the impact of aid on
governance and quality of institutions. The implication of the aid-governance studies is
that the perverse effects of aid on governance and quality of institutions in recipient
countries reduce the productivity of factors via reduced efficiency. Our attempt to link aid
and TFP is similar to Beck et al. (2000) which examine the effects of financial system
development on productivity and overall growth.
This chapter makes several contributions to the literature. First, this is possibly the
first study to explicitly investigate the effect of aid on factor productivity, which explains
the bulk of cross-country differences in both the level and growth of income across
countries. Second, our assessment of the aid-productivity relation suggests factors that are
50
likely to improve or worsen the perverse effects of aid. From the policy perspective it is
clearly useful to know which factors to promote and which to minimize, where possible,
in the presence of foreign aid. Third, in addition to aggregate aid, we also test the impact
of multilateral and bilateral aid, and loans versus grants to assess the heterogeneous
effects of aid on growth. Accordingly, we empirically investigate the effects of aid flows
on the two sources of growth: capital accumulation—as measured by investment rate, and
TFP growth, using a large panel dataset from sixty two developing countries over the
period 1970-2004.
Our results suggest that foreign aid has a perverse effect on TFP growth, although
the effect on capital accumulation is positive and statistically significant. The conflicting
effects of aid on the two key sources of growth are an indication that the net effect on
growth could be either positive or negative, depending on the strength of each of these
effects. Our results, therefore, tend to suggest why earlier studies often find no robust
positive relationship between aid and growth. On the perverse effects on TFP, one
possible explanation could be aid induced the distortions that weaken domestic
institutions. For example, if aid is associated with weak governance and increased rent
seeking activities it might reduce efficiency and profitability of investment that would
ultimately limit growth (Raj an & Subramanian, 2007).
The rest of the chapter is organized as follows. Section 3.2 presents a brief review
of the related literature, and Section 3.3 describes the data and Section 3.4 discusses
model specifications and the empirical strategy. Estimation results and interpretations are
in Section 3.5, while Section 3.6 concludes the chapter.
51
3.2 Review of Related Literature
In this section we briefly review related literature on the sources of growth, aidinvestment and aid-governance (quality of institutions) relationships. We start by
reviewing the empirical cross-country growth accounting studies that look at the role of
factor accumulation and TFP growth in explaining cross-country differences in the level
and growth of income. Then we proceed with the aid effectiveness literature with
emphasis on investment and institutions. Of course, our contribution to the literature may
be viewed as an integration of the two strands to assess the effects of aid on the two
sources of growth: capital accumulation and TFP growth.
3.2.1 The Sources of Growth
The relative importance of factor accumulation and total factor productivity in
explaining cross-country differences in both the level and growth of income has generated
much debate for decades. While the exact explanatory power of TFP differs from one
study to another, there is consensus that TFP plays an important role in explaining crosscountry differences in income. Caselli (2005) for instance, argues that TFP growth is at
least as important as capital accumulation and the importance of productivity is robust to
attempts to account for the quality of human capital—schooling and health of the
workforce and age composition of the capital stock.15 Using data from 145 countries,
15
The significance of TFP growth in explaining cross-country variations in the level and growth of income
have been questioned by some studies which find no significant role for TFP growth. For instance, Young
(1995) show that factor accumulation was key to the growth miracle of some East Asian countries. AbuQarn & Abu-Bader (2007) also examined the sources of growth in ten Middle East and North African
countries and find that the explanatory power of variation in TFP growth is negligible.
52
Baier et al. (2006) also show that while for the whole sample countries TFP growth
accounts for small fraction (14 percent) of the average growth in output per worker, they
observe a clear regional pattern in the importance of TFP growth. The authors argue that
while growth in aggregate input and TFP are equally important for Western and Southern
Europe, the growth of TFP is more important in regions where institutional changes and
armed conflicts are involved. Using data from large sample of countries over the period
1980-92, Easterly & Levine (2001) also show that capital growth accounts for less than
half of output growth. They, therefore, argue that TFP rather than capital accumulation
accounts for the bulk of the difference in income across countries.
The observed strong explanatory power of variations in TFP growth has attracted
researchers' attention to find out the factors that could explain differences in the level and
growth of TFP. See for example Beck et al. (2000); Miller & Upadhyay, (2000); Rioja &
Valev (2004); Arestis et al. (2006: 2006a) and Bonfiglioli (2008). Pioneered by Beck et
al. (2000), most of these studies investigate the effects of the level of financial system
development on factor productivity. Using data from over seventy developed and
developing countries, Beck et al. (2000) show that the level of financial development in a
country has a positive effect on TFP growth. The implication of their results is that the
level of financial development helps growth mainly by improving the efficient allocation
of resources rather than through factor accumulation.
Bonfiglioli (2008) examines the effects of financial liberalization on productivity
and capital accumulation and finds that financial liberalization has positive effect on
productivity but negligible effect on investment, results that substantiate the findings of
Beck et al. (2000). Using data from 83 countries over the period 1960-1989, Miller &
Upadhyay (2000) also examine the effect of openness, trade orientation and human
53
capital on the level TFP. The authors find that openness, outward trade orientation and
human capital all have a positive effect on TFP. In this chapter, we examine the effect of
aid on the sources of growth following the framework used by Beck et al. (2000),
Bonfiglioli (2008) and others.
3.2.1 Aid Effectiveness Literature
The theoretical model behind the aid-growth relationship assumes that aid spurs
growth by financing investment which in turn is assumed to boost economic growth.
Dollar & Easterly (1999) call this underlying model an aid-financed investment
development strategy. In this setup, the effectiveness of aid depends on the extent to
which aid is used to finance investment and on the productivity of that investment, which
is not robust. For instance, the diversion of aid money from investment to consumption,
the crowding-out effect of aid on domestic savings and the Dutch Disease effect of aid all undermine the aid-investment-growth linkage. Even worse, any positive effect of aid
on growth that transmits through increased investment could be mitigated by adverse
effects of aid on incentives and quality of institutions. Several recent empirical studies
such as Boone (1996), Dollar & Easterly (1999), Ovaska, (2003), Doucouliagos &
Paldam (2006) and Raj an & Subramanian (2007) are among the few that question the
robust positive association between aid, investment and growth.
Lavy (1987) examines the impact of aid on investment using cross-country data
from 39 developing countries over the period 1960-1980. The author finds that aid has a
positive impact on investment. Although the results are based on a simple specification
where investment rate is a function of domestic savings and aid flows, the author finds
that the coefficient of aid is close to one which indicates a one-to-one relationship
54
between aid flows and investment. Recent contributions by Lensink & Morrissey (2000);
Payne & Kumazawa (2005) and Kasuga (2007) also find a significant positive effect of
aid on investment. Lensink & Morrissey (2000) find the positive significant impact of aid
on investment after controlling for instability (volatility) of aid, which has a significant
negative impact.
Boone (1996), however, shows that aid doesn't contribute to investment or
growth; it rather increases public consumption. In addition to its negligible effect on
investment, the author argues that aid also reduces politicians' incentives to reform
distortionary policies. Using African data, Dollar & Easterly (1999) also show that the
aid-investment-growth linkage is very weak. After reviewing 24 aid-saving studies and 29
aid-investment studies Doucouliagos & Paldam (2006) also conclude that only about a
quarter of aid is invested, which casts doubt on the favored theoretical aid-investmentgrowth framework.
A recent paper by Agenor et al. (2008) uses a dynamic macroeconomic model
which links foreign aid, public investment, growth and poverty to assess the effects of aid
on poverty. Simulating their model for Ethiopia, the authors argue that the positive effects
of aid on public investment is the primary channel through which aid in the long-run
impacts the supply side of the economy. Nevertheless, a lower positive effect of aid on
public investment often makes the perverse demand side effect of aid—the Dutch Disease
effect—more persistent; thereby further diminishing the positive arguments for aid.
Similar to the aid-growth relation, even though the aid-investment studies also
produce mixed results, on balance studies find that aid has a positive effect on
investment. While a positive association between aid and investment is important it isn't
sufficient to guarantee the desired boost in economic growth. As mentioned before, the
55
growth effect of aid depends not only on the positive association between aid and
investment but also on the productivity of aid-financed investments and other deleterious
effect of aid flows. One such harmful effect is the incentive effect of aid i.e., aid
encouraging rent seeking activities and the weakening of domestic institutions, which
lessen any positive gains from aid.
In addition to the aid-saving/investment relationships discussed above, recently
both theoretical and empirical studies have emphasized the possible incentive effects of
aid. The adverse incentive effects of aid and weakening of recipient country institutions
are some aspects of foreign aid that could offset the positive effect of aid (Boone, 1996;
Svensson, 2000; Brautigam & Knack, 2004; Rajan & Subramanian, 2005a, 2007;
Economides et al., 2008 and Djankov et al., 2008). Using data from 75 developing
countries Economides et al. (2008), for instance, show that while aid has a positive direct
effect on growth, there is a strong negative indirect effect due to the inducement of rent
seeking activities.
3.3 Data Description and Descriptive Statistics
This section describes the key variables, provide the data sources and presents
descriptive statistics.
3.3.1 Data Source and Coverage
In this chapter we use a large panel dataset from sixty two countries over the
period 1970 - 2004. The list of countries and regional groups are given in Appendix B.
Following the empirical literature, we averaged the data over five-year intervals: 1970-74,
56
J
1975-79 and so on, which gives us a total of seven observations per country. Depending
on the control variables used, the number of observations varies across specification. The
major sources of our data are Perm-World Tables, Organization for Economic
Cooperation and Development (OECD) and the World Bank-World Development
Indictors.
To assess the impact of aid on the sources of growth we have two dependent
variables: capital accumulation and TFP growth.
Capital accumulation: Gross capital formation to GDP ratio from the World
Bank-World Development Indicators is used as a measure for physical capital
accumulation. TFP growth is derived from the aggregate production function as follows.
Let the aggregate production function be
Y = AK^H^L1-*-?
3.1
where Y is real GDP, K is physical capital stock, H is stock of human capital, L is
the labor force, and A is the level of total factor productivity. The production function
expressed in per-worker terms is
y
=AkshfS
From this intensive-form production function we can compute the level of total factor
productivity as
In A = In y — a In k - /? In .h
3.2
Average annual growth rate of TFP is computed by taking the log difference of
the level of TFP between two time periods.16 Following earlier studies, we assume that
16
Average annual growth rate of TFP is computed as TF?arslvzh = ihiAv - iw:4f_1] * 100/5
57
physical capital's share of income, a is 0.3. Subsequent to Mankiw et al. (1992), we
assume that human capital's share ,S is also 0.3.
Following Caselli (2005) we construct human capital data from the Barro and Lee
(2005) dataset of average years of schooling in the population over 25 years old.
h = e"''-~J
where s is the average years of schooling and the 0 (s) is a piecewise linear function with
slope 0.13 for s < 4,0.10/"or 4 < s < 8, and 0.O7 for 8 < s.
The slope values
represent return to education.
Physical capital stock: the physical capital per work data is from the Perm World
Tables 6.1. Foreign Aid: Foreign aid data are obtained from OECD-DAC. We use the
standard definition of aid—the ratio of Net Official Development Assistance (ODA) to
Gross National Income (GNI). In addition to examining the effect of total aid, we also
assess if different categories of aid have different impact on the sources of growth. To do
this we disaggregate aid into different forms. First, we consider aid by source. This
classification gives us two categories of aid: bilateral and multilateral. Bilateral aid is a
direct transfer from a donor country to a recipient country, while multilateral aid is
channeled via an international organization. Second, we consider aid by type: grants
versus loans. Grants are transfers made in cash, goods or services for which no repayment
is required. Loans are transfers for which repayment is required but with longer maturity
and lower rates.
The control variables we use include openness to international trade, financial
system development, government consumption expenditure and inflation. These data are
from the World Bank-World Development Indicators. Institutional variables, political
risk rating index and the composite democracy index are from International Country Risk
58
Guide (ICRG) and Polity IV Project, respectively. Because corruption and rent-seeking
behaviors possibly distort resource allocation leading to inefficiency,
following
Economides et al. (2008) we construct an index for rent-seeking activities from ICRG
dataset using five components of political risk rating indicators: government stability,
corruption, law and order, democratic accountability and bureaucracy quality. For all the
indicators higher values indicate a better risk rating, hence for the higher composite
indicator to measure rent-seeking activities we multiply the composite indicator by minus
one. Details of variable definitions and sources of data are available in Appendix C.
3.3.2 Descriptive Statistics
Table 3.1 presents descriptive statistics and correlations among key variables. The
summary statistics show a sizeable variation in both investment rate and TFP growth.
While the investment rate ranges from 4.8 percent to 58.9 percent, the average investment
rate in the sample is 21.4 percent. TFP growth rates also show significant variation
ranging from -12 to 10.2 percent. The average of 6.2 percent aid to GDP ratio also
signifies the importance of foreign aid in sample countries. While a simple correlation
between the variables could be misleading, the correlation matrix in Panel B Table 1
shows strong correlation between the sources of growth and growth in real GDP per
capita. More importantly there is a strong positive association between TFP growth and
GDP per capita growth. We also observe a negative association between foreign aid and
TFP growth. The correlation matrix also shows a negative association between aid and
investment.
Figure 3.1 also depicts a negative association between average annual TFP growth
and aid flows in the period 1970-2004. In Section 3.5, we systematically investigate the
59
effects of aid on the sources of growth using econometric technique and controlling for
other factors, country and time specific effects and possible endogeneity of aid.
60
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Figure 3.1. Aid and TFP Growth (1970 - 2004)
3.4 Model Specification and Econometric Methodology
In this subsection we present and discuss the specifications of the models and the
econometric techniques used.
3.4.1 Capital Accumulation
To evaluate the effect of aid on investment rate we estimate the following
specification based on investment-savings relationship used by Feldstein & Horioka
(1980). Equation 3.3 expresses investment rate as a function of financial sources—
domestic saving, foreign direct investment flows and foreign aid, economic conditions—
62
30
economic growth, openness to trade, government consumption, financial development
and inflation, and political stability.
fix
fS\
fFDl\
/Aids,
[?)„=a< +M?),, + ""> hr),j^
(—)„ + *•* - •«
Where Y is GDP, I is gross capital formation, S is gross domestic saving, FDI is
foreign direct investment inflows, and Aid is foreign aid and ? is the error term. Xit is a
set of economic, policies and institutional variables that could affect investment. The
subscripts I and t indicate country and time period, respectively. The key parameter of
interest here is 0Aid, which measures the impact of aid on capital accumulation. Likewise
the estimates y and other 8, also capture the effect of the respective variables on capital
accumulation.
3.4.2 TFP Growth
To examine the impact of aid on TFP growth, we follow Beck et al. (2000) and
estimate the following equation.
TFPit =
ai
TFPit_t - ZL8 f vl + sit
3A
Expressing equation 3.4 in growth rate
dTFPit = (at - 1)?FR t _ ± + Z^B + vi + ^ t
3..4a
TFP growth is a function of the initial level of TFP, aid and other control
variables. The other variables include financial system development, openness to
international trade, government consumption and institutional quality.
63
3.4.3 Econometric Methodology
We estimate equation 3.3 and 3.4a using panel data of seven five-year averages
between 1970 and 2004 using system GMM estimator proposed by Blundell and Bond
(1998). The system GMM estimator which is widely used in aid effectiveness literature
enables us to control for unobserved country specific effects and possible endogeneity of
aid. The system GMM estimator, first introduced by Arellano & Bover (1995) and
developed by Blundell & Bond (1998), improves on the Arellano & Bond (1991)
difference GMM estimator where only lagged levels are used as instruments for the
difference equation. The Arellano-Bover/Blundell-Bond system GMM estimator uses
more moment conditions—lagged differences are used as instruments for the level
equation and lagged levels are used as instruments for the difference equation (Arellano
& Bover, 1995, and Blundell & Bond, 1998). We also estimate investment equation using
fixed effects instrumental variable (IV) estimator which uses two-stage least-squares
within estimator. Unlike system GMM, which uses all available lagged values as
instruments, fixed effects IV estimator uses external exogenous variables as instruments.
Following the aid literature we use income per capita, population
size and regional
dummy variables as external instruments for foreign aid.
The consistency of system GMM estimator depends on the validity of instruments
and the absence of second order serial autocorrelation. To illustrate the moment
conditions let's consider a dynamic panel-data model of the form
Yit =
CCY^-L
-f Xitfi + vt + sir
3.5
Where <p = vt + sit
The first-difference of equation 5 is
64
AYie = OAKJ-.J +Mitl3
+ Asit
3.6
The moment conditions in the first difference equation are E (Yit_ sAs it) = 0 for
lagged dependent variable, and E(Xit_,A£it)
:
= 0 V t > 3, ...,T an.ds > 2 for the
covariates. The moment conditions in the level equation are ECAYit._tcpit) = 0 for the
lagged dependent variable, and E'(&Xit-i<p;t) = 0 V t >3,..,,T or the covariates.
Finally,
the
EiAs^E.^
condition
for
no
second
order
serial
autocorrelation
is
= 0 for t = 2.
3.5 Empirical Results
This section presents the estimation results of the effects of aid on physical capital
accumulation and TFP growth. To assess the robustness of our results we use various
estimation techniques with and without the set of control variables.
3.5.1 Capital Accumulation
In Table 3.2, we present results for the effect of aid on capital accumulation, as
measured by investment rate. Column 1 reports pooled OLS estimation result whereas
column 2 and 3 report panel fixed effect and panel instrumental variable (IV) fixed effect
results, respectively. Column 4 reports consistent and unbiased system GMM estimation
results. The fixed effects model is chosen based on Hausman test17. All our system GMM
Hausman test for random effects versus fixed effects model (with null hypothesis of random effects
model) we reject the null with X 2 (U) = 44.42 (0.0000).
65
models pass the Sargan/Hansen test of overidentifying restrictions and a test for the
absence of second order serial correlation AR(2) in the error terms.
The pooled OLS regression reported in column 1 reveals that aid has a positive
effect on investment rate. The panel fixed effects reported in column 2 also shows the
same positive impact of aid on investment. Since the coefficients from OLS and panel
fixed effects regressions may suffer from endogeneity bias we present panel IV and
system GMM results in column 3 and 4. The panel IV and system GMM results also
confirm that the exogenous components of aid have investment enhancing effects. The
results are consistent with the findings of earlier studies and our estimates for the
coefficient of aid are also comparable to the coefficients of aid in Payne & Kumazawa
(2005) and Kasuga (2007).
66
Table 3.2. The Effect of Aid on Physical Capital Accumulation: Total Aid
Dependent Variable: Gross capital formation to GDP ratio
Fixed Effects
OLS
Variable
(2)
(1)
Aid
0.186***
0.215***
(0.044)
(0.065)
Gross domestic savings
0.313***
0.332***
(0.037)
(0.062)
Economic growth rate
0.470***
0.177*
(0.114)
(0.105)
Openness to trade
2.796***
3.581**
(0.677)
(1.503)
Finance
0.307
2.275***
(0.360)
(0.807)
Foreign direct investment
0.141
O.494***
(0.194)
(0.188)
Government consumption
0.028
0.094
(0.072)
(0.126)
Political stability
0.009
0.122**
(0.040)
(0.048)
Inflation
0.148
0.732**
(0.280)
(0.295)
East Asia and Pacific
0.247
0.000
(1.088)
(0.000)
Sub-Saharan Africa
-3 119***
0.000
(0.874)
(0.000)
Lagged gross capital formation
Constant
R-squared
AR (2) Test
Hansen's J Test
Number of countries
Observations
1.242
(2.864)
0.60
-16.912***
(5.964)
0.42
58
222
222
IV Fixed Effects
(3)
0.394*
(0.236)
0.359***
(0.070)
0.138
(0.117)
4.042**
(1.632)
1.945**
(0.909)
0.431**
(0.206)
0.071
(0.132)
0.142***
(0.054)
0.793**
(0.312)
0.000
(0.000)
0.000
(0.000)
-22.378***
(7.672)
0.46
58
222
System GMM
(4)
0.121*
(0.073)
0.213***
(0.053)
0.316**
(0.132)
1.735*
(1.018)
0.254
(0.527)
0.380**
(0.167)
-0.103
(0.077)
0.008
(0.034)
-0.291
(0.244)
-1.715
(1.664)
-1.193
(1.104)
0.410**
(0.174)
0.600
(3.266)
0.69
0.29
58
221
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Table 3.3 and 3.4 present results from estimations when we use disaggregated aid
to assess whether the effect of aid on investment varies by sources and type of aid. Hence,
we estimate equation 3.3 by replacing total aid flow by aid from bilateral and multilateral
67
sources, and grants and loans, respectively. Estimates in Table 3.3 show that aid from
bilateral and multilateral sources have different impact on investment. It appears that aid
from multilateral sources affect investment positively while aid from bilateral sources has
no significant impact on investment. As Alesina & Dollar (2000) suggest aid from
bilateral sources is driven by donor's political and strategic consideration while aid from
multilateral sources often tend to reflect the recipient country's economic needs.
Therefore, it is not surprising to observe such sharp contrast between the effect of aid
from bilateral and multilateral sources.
Estimates in Table 3.4 also show that grants and loans affect investment
differently. It appears that aid in the form of loans tend to raise investment while grants
have no significant effect on investment. The difference between the two types of aid
could be explained by the incentive effects of grants and loans which could in turn affect
the way the resource is used. The repayment obligations—both the principal and interest,
associated with loans could induce the recipient country government to use the resource
to finance investment as opposed to consumption. In the case of grants—a free resource
which the recipient government is not expected to repay, there could be an incentive to
use the resources for consumption.
In general, the results in Tables 3.2-3.4 show that foreign aid impacts investment
rate positively. Our results also show that among the control variables domestic saving
and openness to international trade proved to have strong positive impact on investment.
Economic growth, financial system development and political stability also have a
positive effect on investment though the results are not robust to estimation techniques.
68
Table 3.3. The Effect of Aid on Physical Capital Accumulation: Bilateral versus
Multilateral Aid
Dependent Variable: Gross capital formation to GDP ratio
OLS
Fixed Effects
\ / n-**i fi n 1 £±
v anaoie
(2)
(1)
0.170
Bilateral Aid
0.043
(0.108)
(0.116)
Multilateral Aid
0.289*
0.461**
(0.164)
(0.192)
Gross domestic savings
0.315***
0.325***
(0.038)
(0.063)
Economic growth rate
0.489***
0.176
(0.116)
(0.107)
Openness to trade
2.992***
4.057**
(0.698)
(1.581)
Finance
0.401
2 377***
(0.367)
(0.849)
Foreign direct investment
0.194
0.535***
(0.199)
(0.191)
Government consumption
0.041
0.078
(0.074)
(0.132)
Political stability
-0.006
0.105**
(0.042)
(0.051)
Inflation
0.296
0.746**
(0.297)
(0.332)
East Asian and Pacific
0.054
0.000
(1.103)
(0.000)
Sub-Saharan Africa
-3.139***
0.000
(0.890)
(0.000)
Lagged gross capital formation
Constant
R-squared
AR (2) Test
Hansen's J Test
Number of countries
Observations
0.085
(3.021)
0.61
-19 772***
(6.581)
0.43
58
216
216
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
69
IV Fixed Effects
(3)
-0.018
(1.546)
1.123
(1.860)
0.334***
(0.110)
0.131
(0.139)
5.018**
(2.481)
2.117
(1.749)
0.475**
(0.215)
-0.004
(0.247)
0.113
(0.115)
0.811
(0.508)
0.000
(0.000)
0.000
(0.000)
-23.870***
(8.109)
0.43
58
216
System GMM
(4)
-0.032
(0.177)
0.409*
(0.230)
0.235***
(0.041)
0.340***
(0.118)
1.872
(1.241)
0.385
(0.613)
0.376*
(0.194)
-0.069
(0.076)
-0.013
(0.037)
-0.047
(0.322)
-0.446
(2.009)
-1.812*
(0.961)
0.281***
(0.107)
2.580
(4.864)
0.82
0.50
58
215
Table 3.4. The Effect of Aid on Physical Capital Accumulation: Grants and Loans
Dependent Variable: Gross capital formation to GDP ratio
Fixed Effects
OLS
Variable
(2)
(1)
0.124
Grants
0.126*
(0.073)
(0.088)
0.324**
0.462***
Loans
(0.146)
(0.168)
Gross domestic savings
0.325***
0.309***
(0.062)
(0.037)
Economic growth rate
0.475***
0.179*
(0.113)
(0.105)
2 7j j * * *
Openness to trade
3.450**
(0.677)
(1.507)
Finance
0.301
2.199***
(0.358)
(0.810)
Foreign direct investment
0.169
0.500***
(0.194)
(0.188)
Government consumption
0.035
0.093
(0.126)
(0.072)
0.123**
Political stability
0.007
(0.048)
(0.040)
0.724**
Inflation
0.119
(0.280)
(0.295)
East Asian and Pacific
0.216
0.000
(1.085)
(0.000)
Sub-Saharan Africa
-3.022***
0.000
(0.874)
(0.000)
Lagged gross capital formation
Constant
R-squared
AR (2) Test
Hansen's J Test
Number of countries
Observations
1.578
(2.863)
0.61
-16.097***
(6.012)
0.43
58
222
222
IV Fixed Effects
(3)
0.331
(0.448)
0.724
(1.981)
0.354***
(0.077)
0.131
(0.128)
3.915**
(1.833)
1.700
(1.730)
0.426**
(0.213)
0.063
(0.143)
0.150**
(0.071)
0.794**
(0.319)
0.000
(0.000)
0.000
(0.000)
-19.833**
(8.257)
0.45
58
222
System GMM
(4)
0.052
(0.117)
0.462**
(0.218)
0.224***
(0.066)
0.185
(0.138)
1.711
(1.224)
-0.154
(1.136)
0.519*
(0.284)
-0.167
(0.164)
0.118**
(0.053)
0.211
(0.384)
-2.136
(4.001)
-2.787
(2.947)
0.324***
(0.110)
-2.206
(4.380)
0.75
0.32
58
221
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
3.5.2 TFP Growth
Table 3.5 presents results for the effect of total aid on TFP growth. Similar to the
correlation coefficient in Table 3.1 and Figure 3.1, pooled OLS estimation result in
70
column 1 shows a negative effect of aid on TFP growth. The estimation results from
system GMM in columns 2, 3 and 5 also confirm the negative impact of aid flow on TFP
growth.18 The parameter estimates of aid from system GMM are even bigger than those
from pooled OLS. The fact that system GMM estimates takes into account unobserved
country specific effects and potential endogeneity of aid show that the observed strong
negative association between aid and TFP growth is not driven by the endogeneity bias.
Given the importance of TFP growth in explaining cross-country differences in
both the level and growth of income, the observed negative effect of aid on TFP growth
could be a big drag on the effectiveness of aid in boosting economic growth.
One possible explanation for the negative effect of aid on productivity growth
could be the adverse effect of aid on incentives and quality of institutions. Previous
studies have indicated that aid has a distortionary effect on incentives and institutions. For
instance, Economides et al. (2008) show that foreign aid is associated with an increase in
rent-seeking activities and weaker domestic institutions which reduce efficient use of
resources. Svensson (2000) also shows that foreign aid and other windfall incomes tend
to raise corruption, especially when there are competing social groups, which is true in
many aid dependent countries. As Mo (2001) noted corruption and rent-seeking activities
on the other hand, adversely affects economic performance through political instability,
reducing human capital and private investment (Mo, 2001).
Consistent with the above claim we find that rent-seeking activities do have a
statistically significant negative effect on TFP growth. As can be seen in column 3,
In all the cases, we do not reject the null that the instruments are valid and there is no second order
autocorrelation.
71
including rent-seeking activities as an additional control variable has reduced the
coefficient of aid though aid continues to have a negative significant effect. The
persistence negative effect of aid on productivity growth even after controlling for rentseeking activities shows that there are other channels beyond inducing rent-seeking
activities through which aid adversely affects productivity.
Among the other control variables, financial system development and inflation
rate appear to be have statistically significant effect on productivity growth. As Beck et
al. (2000), Levine et al. (2000) and others argue financial system development boost
economic growth mainly by improving efficient allocation resources by coordinating
savings and investment. Inflation rate as a proxy for macroeconomic instability appears to
negatively affect TFP growth also.
To capture the channels via which aid leads to efficiency loss other than by
encouraging rent-seeking activities, in columns 4 and 6 we introduce an interaction term
between aid and financial system development. This interaction term is expected to
capture how aid flows affect the efficiency of resource allocation in the country, since
financial intermediaries play a primary role in disbursing resources. The interaction term
turns out to be negative and highly significant which suggests that aid flows compromise
the financial sector's efficiency—their ability to provide resources where they are best
used, screening good from bad projects and monitoring managerial effort. That the
efficiency reducing effects increases with aid possibly arises from governments' direct or
indirect control and/or ownership of domestic financial institutions, which face relaxed
resource constraints following the receipt of aid. Weakening of incentives in financial
institutions would be one explanation of the efficiency loss.
72
Table 3.5. The Effect of Aid on TFP Growth: Total Aid
Dependent Variable: TFP Growth
OLS
Variable
0)
Initial level of productivity
Aid
Rent seeking
Finance
Openness to trade
Government consumption
Inflation
-1 742***
(0.319)
-0.049**
(0.020)
-0.188***
(0.038)
0.306**
(0.138)
-0.154
(0.257)
-0.441
(0.383)
-0.464***
(0.122)
(2)
-2.555***
(0.782)
-0.120**
(0.052)
System GMM
(3)
(4)
-2.789***
-2.740***
(0.748)
(0.686)
0.115
-0.075**
(0.094)
(0.034)
-0 199***
-0.165***
(0.063)
(0.045)
0.833***
0.413**
(0.265)
(0.181)
0.275
0.481
(0.357)
(0.329)
Aid X Finance
Constant
10.271***
(2.505)
0.31
R-squared
AR (2) Test
Hansen's J Test
Number of countries
Observations
231
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
16.210***
(4.953)
11.012**
(5.310)
-0.073***
(0.027)
9.121**
(4.434)
0.13
0.34
62
360
0.22
0.28
50
242
0.56
0.93
50
242
(5)
-3.614***
(0.708)
-0.087**
(0.037)
-0.166***
(0.055)
0.639***
(0.201)
-0.134
(0.302)
-0.506
(0.592)
-0.379***
(0.069)
19.868***
(5.061)
(6)
-2.532***
(0.749)
0.039
(0.065)
-0.166***
(0.050)
0.644***
(0.214)
-0.130
(0.281)
-0.421
(0.515)
-0.434***
(0.090)
-0.040**
(0.018)
13.024**
(5.416)
0.31
0.49
50
231
0.36
0.94
50
231
In Tables 3.6 and 3.7 we present results which test whether different components
of aid have varying effects on TFP growth. The results in Table 3.6 show that there is a
clear distinction between aid from bilateral versus multilateral sources. While aid from
bilateral sources has no significant impact on productivity growth, aid from multilateral
sources has a negative impact. As we discussed earlier, aid from multilateral sources,
which is mainly given based on recipient country's economic needs, could be used
differently from bilateral aid which is often given for non-economic considerations. Our
results are consistent with the findings of Headey (2008) that bilateral aid driven by
73
geopolitical motivations of donors has no significant impact on growth but its
effectiveness has improved during the post-cold war era.
While aid from bilateral sources has no impact on investment and TFP growth,
aid from multilateral sources has conflicting impacts on the sources of growth.
Multilateral aid, while it helps capital accumulation, has a detrimental effect on factor
productivity. Similar to multilateral aid, total aid also has conflicting effects on the
sources of growth. Such conflicting effects reduce the net effect of aid on growth in
income per capita. The other control variables, financial system development, inflation,
institutional quality (as measured by rent-seeking activities) significantly
affect
efficiency. The interaction terms between multilateral aid and financial system
development also supports the result reported in Table 3.5 that is aid flows reduce
efficient allocation of resources.
As can be seen from Table 3.7, when aid is disaggregated by type between grants
and loans, we see some differences between the effects of the two components of aid on
TFP growth. As indicated earlier, the incentive effects associated with repayment
obligation could affect the way aid is used and consequently impact TFP growth
differently. Due to absence of repayment obligations, grants may be used to finance
projects that create distortions and hence affect TFP growth negatively. Consistent with
the findings by Beck et al. (2000), Rioja & Valev (2004) and others, we find that financial
system development improves factor productivity while macroeconomic instability (as
measured by inflation rate) negatively impact factor producivity.
Overall, we find that aid is positively and significantly correlated with capital
accumulation while it has a significant negative impact on productivity growth. Second,
we find that aid from bilateral and multilateral sources have different impact on capital
74
accumulation and TFP growth. Specifically, we find that aid from bilateral sources has no
significant effect on either capital accumulation or TFP growth, while aid from
multilateral sources raises investment rate but exerts a negative impact on efficiency.
Comparing aid by type, we find that loans contribute positively to capital accumulation
but grants have no significant impact. On their impact on TFP growth, grants impact
negatively while loans are neutral—have no impact at all. We check the robustness of our
results by estimating the effects of aid on the level of TFP. The results using the level of
TFP reported in Appendix A confirm the findings reported in Tables 3.5-3.7.
75
Table 3.6. The Effects of Aid on TFP Growth: Bilateral versus Multilateral Aid
Dependent Variable: TFP Growth
Variable
Initial level of productivity
Bilateral aid
Multilateral aid
Rent seeking
Finance
Openness to trade
Government consumption
Inflation
OLS
(1)
-1 757***
(0.343)
-0.001
(0.045)
-0.128
(0.078)
-0.227***
(0.043)
0.235
(0.143)
-0.288
(0.273)
-0.629
(0.409)
-0.484***
(0.133)
(2)
-3.167***
(0.862)
0.127*
(0.076)
-0.573***
(0.159)
(3)
-3.633***
(0.766)
0.043
(0.077)
-0.334**
(0.170)
-0.203***
(0.061)
0.471**
(0.190)
0.184
(0.400)
19.964***
(5.428)
16.358***
(5.754)
0.27
0.59
62
322
0.36
0.81
50
224
Bilateral X Finance
Multilateral X Finance
Constant
9.984***
(2.712)
0.31
R-squared
AR (2) Test
Hansen's J Test
Number of countries
Observations
213
Robust standard errors ini parentheses
*** p<0.01, ** p<0.05, * p<0.1
76
System GMM
(5)
(4)
-3.787**
-3.292***
(0.674)
(1.490)
0.300
0.051
(0.207)
(0.088)
-0.179
-0.285
(0.338)
(0.184)
-0.176**
-0.220***
(0.086)
(0.066)
1.046**
0.539***
(0.474)
(0.196)
-0.006
-0.400
(0.632)
(0.342)
-0.942
(0.583)
-0.482***
(0.092)
0.037
(0.091)
-0.282**
(0.140)
16.462*
19.668***
(9.938)
(5.837)
0.41
0.21
50
224
0.47
0.98
50
213
(6)
-5.687***
(1.784)
0.209
(0.248)
-0.381**
(0.187)
-0.206**
(0.081)
0.936**
(0.421)
-1.204**
(0.610)
-1.354
(0.935)
-0.416***
(0.086)
0.090
(0.087)
-0.257**
(0.118)
37.614***
(12.277)
0.84
0.61
50
213
Table 3.7. The Effect of Aid on TFP Growth: Grants versus Loans
Dependent Variable: TFP Growth
Variable
Initial level of productivity
Grants
Loans
Rent seeking
Finance
Openness to trade
Government consumption
Inflation
OLS
(1)
-1.788***
(0.320)
-0.014
(0.032)
-0.171*
(0.088)
-0.184***
(0.038)
0.316**
(0.137)
-0.130
(0.257)
-0.483
(0.384)
-0.462***
(0.122)
(2)
-1.716**
(0.790)
-0.086*
(0.050)
-0.186
(0.190)
(3)
-2.813***
(0.754)
-0.100**
(0.041)
-0.026
(0.166)
-0.167***
(0.049)
0.498**
(0.204)
0.239
(0.352)
Grants X Finance
11.170**
(5.009)
11.958**
(4.858)
-0.192***
(0.046)
-0.012
(0.179)
-0.084
(10.612)
0.22
0.83
58
337
0.22
0.91
50
242
0.94
0.33
50
242
Loans X Finance
Constant
10.719***
(2.518)
0.31
R-squared
AR (2) Test
Hansen's J Test
Number of countries
Observations
231
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
System GMM
(4)
-1.547
(1.867)
0.450***
(0.153)
0.033
(0.475)
-0.106*
(0.055)
1.272**
(0.603)
0.784**
(0.382)
(5)
-3 395***
(0.836)
-0.074
(0.059)
-0.106
(0.207)
-0.191***
(0.059)
0.658***
(0.229)
-0.434
(0.323)
-0.833
(0.513)
-0.475***
(0.102)
20.355***
(5.339)
(6)
-5.214***
(1.653)
0.131
(0.170)
-0.239
(0.522)
-0.147***
(0.057)
1.287***
(0.398)
-0.102
(0.576)
-0.918
(0.640)
-0.263**
(0.102)
-0.083
(0.070)
0.062
(0.184)
28.578***
(9.685)
0.32
0.98
50
231
0.53
0.52
50
231
3.6 Summary and Concluding Remarks
The aid effectiveness literature is dominated by studies that assess the relationship
between aid and growth. Empirical results from such studies are mixed however. To
better understand the effect of aid on growth it is important to investigate how aid affects
the sources of growth because cross-country growth accounting studies show that
particularly variations in TFP explain the bulk of cross-country differences in both the
77
level and growth of income. Given the primacy of capital accumulation and TFP growth
in explaining cross-country differences in both the level and growth of income, it is vital
to understand how aid affects these two sources of growth.
Using large panel dataset from sixty two developing countries we examine the
effect of aid on capital accumulation and TFP growth. After controlling for potential
endogeneity of aid and unobserved country specific effects we find that foreign aid has a
significant positive effect on capital accumulation, while it has a significant negative
effect on total factor productivity growth. Therefore, even when aid is used to finance
investment, the negative effect of aid on efficiency could offset any gains acquired
through capital accumulation. These conflicting effects could well explain aid's poor
performance in facilitating economic growth. The strong negative impact of aid on
productivity growth is similar to the adverse effects of aid on recipient country domestic
institutions and governance documented in the literature, which in turn reduce efficiency.
While perverse effects on institutions and corruption could be a possible explanation for
the negative effect of aid on productivity, aid flows also appear to interfere with financial
intermediaries' ability to ensure efficient allocation of resources.
We also find heterogeneous impact of different components of aid on both capital
accumulation and factor productivity growth. Similar to total aid, multilateral aid affects
capital accumulation positively but it affects TFP growth negatively. Bilateral aid,
however, has no significant impact on both capital accumulation and factor productivity.
With regard to the impact of the two types of aid, we find that loans affect capital
accumulation positively but have no significant impact on efficiency. On the other hand,
grants have no impact on investment but affect efficiency negatively. Among the control
78
variables we find that rent-seeking activities and inflation affect efficiency negatively
while financial system development boost factor productivity.
79
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83
Appendix A
Estimation Results with the Level of TFP
Al. The Effect of Aid on level of TFP: Total Aid
Dependent Variable: TFP level
•
Variable
Initial level of productivity
Aid
(1)
0.930***
(0.014)
-0.004***
(0.001)
Rent seeking
Finance
Openness to trade
OLS
(2)
0.894***
(0.015)
-0.004***
(0.001)
-0.009***
(0.002)
0.017**
(0.007)
0.017
(0.012)
Government consumption
Inflation
Constant
0.436***
(0.088)
0.95
R-squared
AR (2) Test
Hansen's J Test
Number of countries
Observations
360
Robust standard errors in parenthieses
0.393***
(0.101)
0.97
242
*** p<0.01, ** p<0.05, * p<0.1
84
(3)
0 9]3***
(0.016)
-0.002**
(0.001)
-0.009***
(0.002)
0.015**
(0.007)
-0.008
(0.013)
-0.022
(0.019)
-0.023***
(0.006)
0.514***
(0.125)
0.97
231
System GMIVI
(6>
(5)
0.861*** 0.819***
(0.035)
(0.037)
-0.004**
-0.004**
(0.002)
(0.002)
-0.010*** -0.008***
(0.003)
(0.003)
0.021**
0.032***
(0.010)
(0.009)
0.014
-0.007
(0.018)
(0.015)
-0.025
(0.030)
-0.019***
(0.003)
0.810*** 0.551**
0.993***
(0.248)
(0.265)
(0.253)
(4)
0.872***
(0.039)
-0.006**
(0.003)
0.13
0.34
62
360
0.22
0.28
50
242
0.31
0.50
50
231
A2. The Effect of Aid on level of TFP: Bilateral versus Multilateral Aid
Dependent Variable: TFP level
Variable
Initial level of productivity
Bilateral aid
Multilateral aid
(1)
0.914***
(0.015)
0.000
(0.002)
-0.013***
(0.004)
Rent seeking
Finance
Openness to trade
OLS
(2)
0.891***
(0.017)
-0.002
(0.002)
-0.006
(0.004)
-0.010***
(0.002)
0.015**
(0.007)
0.013
(0.013)
Government consumption
Inflation
Constant
R-squared
AR (2) Test
Hansen's J Test
Number of countries
Observations
0.519***
(0.094)
0.95
322
0.376***
(0.114)
0.96
224
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
85
(3)
0.912***
(0.017)
-0.000
(0.002)
-0.006
(0.004)
-0.011***
(0.002)
0.012
(0.007)
-0.014
(0.014)
-0.031
(0.020)
-0.024***
(0.007)
0.499***
(0.136)
0.97
213
System GMM
(5)
(4)
(6)
0.818***
0.842***
0.835***
(0.043)
(0.038)
(0.034)
0.006*
0.002
0.003
(0.004)
(0.004)
(0.004)
-0.029***
-0.017**
-0.014
(0.008)
(0.009)
(0.008)
-0.010*** -0.011***
(0.003)
(0.003)
0.024**
0.027***
(0.010)
(0.010)
0.009
-0.020
(0.020)
(0.017)
-0.047
(0.029)
-0.024***
(0.005)
0.998***
0.818***
0.983***
(0.292)
(0.271)
(0.288)
0.27
0.59
62
322
0.36
0.82
50
224
0.47
0.98
50
213
A3. The Effect of Aid on level of TFP Grants versus Loans
Dependent Variable: TFP level
Variable
Initial level of productivity
Grants
Loans
(1)
0.932***
(0.014)
-0.002*
(0.001)
-0.011**
(0.004)
Rent seeking
Finance
Openness to trade
OLS
(2
0.894***
(0.015)
-0.002*
(0.001)
-0.009**
(0.004)
-0.009***
(0.002)
0.016**
(0.007)
0.019
(0.012)
Government consumption
Inflation
Constant
R-squared
AR (2) Test
Hansen's J Test
Number of countries
Observations
0.445***
(0.087)
0.95
337
0.398***
(0.101)
0.97
242
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
86
(3)
0
gj|***
(0.016)
-0.001
(0.002)
-0.009*
(0.004)
-0.009***
(0.002)
0.016**
(0.007)
-0.007
(0.013)
-0.024
(0.019)
-0.023***
(0.006)
0.536***
(0.126)
0.97
231
System GMM
(4)
(5)
(6)
0.914*** 0.859***
0.830***
(0.039)
(0.038)
(0.042)
-0.004*
-0.005**
-0.004
(0.002)
(0.003)
(0.003)
-0.009
-0.001
-0.005
(0.010)
(0.008)
(0.010)
-0.008*** -0.010***
(0.002)
(0.003)
0.025**
0.033***
(0.010)
(0.011)
0.012
-0.022
(0.018)
(0.016)
-0.042
(0.026)
-0.024***
(0.005)
0.559**
0.598**
1.018***
(0.243)
(0.267)
(0.250)
0.22
0.83
58
337
0.22
0.91
50
242
0.32
0.98
50
231
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CHAPTER IV
FOREIGN AID, GROWTH AND POVERTY RELATIONSHIP:
QUANTILE REGRESSION APPROACH
4.1 Introduction
Global poverty has dropped substantially over the past two decades (1981-2004).
Yet World Bank estimates show that in year 2004 close to 1 billion people lived below
the poverty line of $1 a day per person. Despite the remarkable progress made at the
global level there are noticeable regional disparities in the progress on poverty reduction
(Chen & Ravallion, 2007). Among the developing regions of the world, East Asia and
Pacific have experienced the sharpest decline in both the number of poor and the poverty
rate. Other regions have experienced a modest fall in poverty rate which in some cases
were not strong enough to bring down the number of poor. At the other end of the poverty
spectrum, Sub-Saharan Africa, for instance, has experienced a signficant increase in the
number of poor over the same time period. The mixed experience across different regions
makes poverty reduction a major challenge for the future.
Many cross-country studies and case studies show that sustained economic growth
is the major driving force behind poverty mitigation (see Dollar and Kraay, 2002:2004;
Tsai & Huang, 2007; Chambers, Wu, & Yao, 2008 and Kraay, 2007). For instance, Dollar
& Kraay (2002) find that there is a one-to-one relationship between income of the poorest
quintile and average income, which signifies the importance of economic growth for
poverty reduction.
In addition to economic growth, recent emprical studies also show that growth
enhancing policies such as the level of financial system development, openness to trade
89
and remittances can systematically affect the poor. In their recent paper Beck et al.,
(2007) show that financial development is positively related to income of the lowest
quintile and it also affects income inequality negatively. These effects of financial
development on income of the lowest quintile and distribution of income show that
financial development plays a positive role in poverty reduction, well beyond its indirect
effect through growth of average income. The impact of openness to international trade
on poverty, however, remains a controvertional topic in the growth-poverty literature.
Controling for growth in average income, Dollar and Kraay (2002; 2004) find that trade
has no significant impact on poverty though case studies by Tsai & Huang (2007) and
others show that openness to trade has important role for poverty reduction.
One common feature of the empirical growth-poverty relationship studies is that
the results are based on least squares estimation which implicitly assumes that growth and
other factors that affect poverty have identical impact in all countries. A nonlinear
relationship between growth in average income, or other factors, and poverty could arise
for different reasons. The level of development, sectoral composition of growth and the
prevalence of poverty in a country could affect the impact that growth and growth
enhancing policies have on poverty. Loayza & Raddatz (2006), for instance, find that
labor intensity affects the impact of growth, showing that growth in unskilled-laborintensive sectors contribute more toward poverty reduction. Chambers et al. (2008) also
argue that a nonlinear effect of growth on poverty can emerge due to the Kuznet
hypothesis, which states that income inequality first rises with income and falls later.
The posssibility that growth and growth enhancing policies have varying effects
on poverty could have significant policy implications for poverty reduction. From the
policy perspective it is important to answer the following two interrelated questions. First,
90
how does growth and growth enhancing policies affect poverty and second, whether the
effect of each of these explanatory variables on poverty is nonlinear.
In this chapter we take a different approach and revisit the relationship between
average income and other determinants of growth on poverty using quantile regression.
The least squares estimation results from previous studies which provides a point
estimate—the average effect of growth and growth enhancing policies on poverty, could
be misleading if the real effect is nonlinear. Contrary to least squares estimation, quantile
regression estimates a whole set of coefficients at different points in the distribution of
the dependent variable (Koenker & Hallock, 2001).19 Using quantile regression we can
therefore show where in the distribution growth in average income is more effective in
reducing poverty, and test whether foreign aid is more or less effective in countries with
widespread poverty. This adds valuable information because depending on the
relationships the policy implications for poverty reduction could be very different for
countries at different points in the distribution. That poverty response to growth and
policies is heterogeneous can influence the sequence of reforms for effective and faster
reduction of poverty.
The purpose of this study is to extend the analysis in Chapter II to test whether
poverty responses to changes in average income and policies vary at different points in
the distribution i.e., whether the impacts are the different, say in countries where poverty
is widespread versus where poverty is low. In the analysis we use absolute measures of
poverty based on the international poverty line of $1 a day per person.
19
Quantile regression also has the advantage that the estimates are robust to outliers compared to least
squares regression.
91
We find some evidence for heterogeneous impact of growth and policies on
poverty. First, we find that growth in average income has strong and significant impact on
poverty. This result corroborates the findings of earlier studies that sustained economic
growth is the principal driving force for poverty reduction. Second, distribution of income
also explains poverty, where the more unequal distribution of income increases poverty.
Third, we also find strong poverty reducing effects of a set of explanatory variables that
empirical growth literature has identified as determinants of growth such as level of
financial system development, foreign aid and remittances. The significance of these
growth enhancing policies suggests that these variables affect poverty directly as well as
indirectly through growth in average income. Finally, while our findings underscore the
importance of growth in average income and growth enhancing policies for poverty
reduction we also find that the impacts are nonlinear.
The rest of the chapter is organized as follow. Section 4.2 provides a brief review
of the empirical literature on growth-inequality-poverty relationship. Section 4.3
discusses the data, model specification and estimation methodology. Estimation results
and interpretations are presented in Section 4.4; and Section 4.5 concludes the chapter.
4.2 Review of Related Literature
The primacy of sustained economic growth as a key factor in poverty reduction
has been shown in both cross-country and case studies (see Ravallion & Chen, 1997;
Dollar & Kraay, 2002:2004; Besley & Burgess, 2003; Kraay, 2006). The debate over why
poverty responses to growth vary, and the quality and composition of pro-poor growth,
however, is far from over. Whether growth enhancing policies and institutions help the
92
poor beyond the positive impact via average income and improvement in income
distribution is also an interesting question. Specifically, the impact of globalization,
economic and political freedom on poverty has recently attracted the attention of
researchers.
Dollar & Kraay (2002) examine the effect of growth in average income on the
income of the poorest quintile using data from 92 countries. They find that there is a oneto-one relationship between average income and income of the poorest quintile.
Controlling for average income and distribution of income, however, they find that the
traditional determinants of growth such as trade policies have no impact on income of the
poor. This result suggests that policies and institutions that explain growth affect the poor
only through their effect on average income and there is no systematic relationship
between these policies and poverty. Decomposing the change in poverty, Kraay (2006)
also finds that changes in poverty are mainly due to growth in average incomes. The
author finds that growth in average incomes account for 70 percent of the variance in the
short run and 97 percent of the variance in the long run, stressing the centrality of growth
in poverty reduction.
Numerous case studies also find support for the role of growth while also
highliting different factors that contribute to poverty reduction in different countries.
Using time series data from 1964-2003, Tsai & Huang (2007) examined the role of
economic growth, openness to international trade and foreign direct investment (FDI) for
poverty reduction in Taiwan. Similar to Dollar and Kraay (2002), they also find a roughly
one-to-one relationship between average income and income of the poorest quintile which
underscores the importance of sustained economic growth for poverty reduction in
Taiwan. Contrary to the findings of Dollar and Kraay (2002;2004), however, they find
93
that openness to interantional trade has strong poverty reducing effects, by raising the
income share of the poorest quintile beyond its indirect contribution through growth in
average income. Using micro and macro data Ravallion (2006), however, finds a much
weaker poverty reducing effect of trade. Contrary to the claim that FDI expands
employment opportunity in the host country, Tsai & Huang (2007) find that FDI inflow
has no signficant effect on income of the poor.
Chambers et al. (2008) examine the impact of growth on poverty in Chinese
provinces using panel data over the period 1986-2000. While their overall findings also
emphasize the importance of sustained economic growth for poverty redcution they find
an inverted-U shaped relationship between income and poverty i.e., economic growth in
the short run raises poverty while long-run growth reduces poverty. Initially, growth in
average income which reduces poverty also raises income inequality which tends to
reduce the growth impact of poverty. After some threshold level of income is reached,
however, growth in average income reduces poverty and the associated counteracting
effect through increase in income inequality also weakens. They also find that other
factors such as government expenditure, expenditure on education and agriculture do not
have a significant effect on poverty. Studying the evolution of poverty in China over
1981-2004, Ravallion & Chen (2007) identify that growth in agriculture has played a
leading role in poverty reduction compared to the other sectors of the economy. Likewise
Suryahadi et al. (2009) also examine the effect of location and sectoral composition of
growth on poverty in Indonesia and find that growth in rural agriculture and services in
both urban and rural areas significantly contribute to poverty reduction. Examining the
evolution of poverty, inequality and growth in selected Middle East and North African
countries over the period 1980-2000, Adams & Page (2003) also find international
94
remittances and public sector employment as factors that have contributed to poverty
reduction in the region. Specifically they find that international remitances have strong
poverty reducing effects in the region.
Another important factor that is considered to have a role in poverty reduction is
access to credit. Many empirical case studies show that microfinance plays a crucial role
in poverty reduction by easing the credit constraints facing the poor. Beck et al. (2007) for
instance, examine the effect of financial development on poverty by estimating its impact
on income of the lowest quintile and changes in distribution of income. They find that
financial development not only positively affects income of the poorest quintile but also
reduces income inequality. These two results coupled with growth enhancing effects of
financial development substantiate the vital role financial development plays in poverty
reduction. These findings are consistent with the claim that financial development
disproportinately benefits the poor, the group for which the credit constraint could be
binding, by relaxing credit restrictions.
The main contribution of this study thus is to analyze the role of growth in
average income and growth enhancing policies using the quantile regression technique
which allows us to test for possible nonlinear effects of a set of explanatory variables on
poverty. To the best of our knowledge this is the first study to use quantile regression
techniques to test whether poverty response to growth and growth enhancing policies,
including foreign aid, varies across countries with different levels of poverty.
95
4.3 Data, Model Specification and Econometric Methodology
4.3.1 Data
In this chapter we use the same dataset we used in chapter II. For details on the
data refer to Section 2.3.3. Our sample consists of a cross-section of 79 developing
countries over the period 1981-2004. Data on poverty and measure of income inequality,
the gini coefficient, are obtained from the World Bank's poverty and inequality dataset—
PovcalNet. The other variables we use include real GDP per capita, trade, financial
development, inflation, age dependency ratio, foreign direct investment flows,
international remittances and government consumption expenditure. These data are
obtained from the World Bank, World Development Indicators. Institutional variables,
political rights and civil liberties are obtained from Freedom House, while the corruption
and overall political risk rating index are from the International Country Risk Guide
(ICRG). Foreign aid data is obtained from OECD-DAC and democracy score is from
Polity IV Project.
4.3.2 Model Specification
We use the basic specification for growth-poverty relationship used by Datt &
Ravallion (1992); Ravallion & Chen (1997); Besley & Burgess (2003); Perry et al. (2006)
and others to test the relative role of growth and income distribution for poverty
reduction. To test the effect of growth enhancing policies on poverty besides their effect
through growth in average income and income inequality we augment the basic
specification to include these factors that growth literature identifies as determinants of
growth, such as openness to international trade, financial development, government
96
consumption, quality of institutions and international capital flows—foreign aid, FDI
inflows, and international remittances. The augmented growth-poverty relationship is
given as follow.20
P.: = aT + j£L yj + y^G; + X\ 8T + sTi
4.1
where R is headcount index measure of poverty, Y« is average income, Gt is the
gini coefficient, X is vector containing explanatory variables and parameters such as j3T
are parameters associated with the rf;iquantile and sTi is unknown error term.
In the analysis, we are interested not only in signs and statistical significance of
the parameters which tell us how these variables affect poverty, but also whether the
effects of the explanatory variables vary at different points in the conditional distribution.
Statistically significant difference between fir at 10th and 90th percentile, for example,
means that poverty responds to changes in average incomes differently at these two points
in the conditional distribution of poverty.
4.3.3 Econometric Methodology
We use quantile regression introduced by Koenker & Bassett (1978).21 Quantile
regression provides a thorough description of the relationship between the dependent
variable and a set of regressors at different percentiles (like the 10th and 95th percentiles)
of the dependent variable. For recent applications of quantile regression, see Barreto &
Hughes (2004); Gomanee et al. (2005) and Osborne (2006).
A concise representation of equation 1 is
21
OLS provides a single estimate of the effect of right hand side variable on poverty, the average for the
whole population; while quantile regression allows an assessment of these important potential differences at
different quantiles of the conditional distribution.
97
To present the quantile regression in relation to least squares estimator consider for
following linear model
i = 1, .... n
y, = x. J T Uj
If Eij&Ax,) = 0 then the conditional mean of y, with respect to x. is
E(yi\x) =x-P
The parameter {3 is estimated using least squares estimators by
Minfi ) (y, -xfp) 2
Similarly, quantile regression is also based on conditional quantiles, the r "conditional
quantile of y; is
.- t j ' ! jXi.J = S i p T
Compared to the least squares estimator which minimizes the sum of squared errors,
quantile regression coefficients are estimated by minimizing the sum of absolute values
of errors (Koenker & Bassett, 1978; Koenker & Hallock, 2001; Cameron & Trivedi,
2009).22 Hence, the zth quantile regression coefficient /3r is estimated by minimizing
The coefficient estimates in a quantile regression capture the marginal impact of a
change in the explanatory variable observed at the rth quantile of the dependent variable
(Arias et al., 2001; Osborne, 2006; Cameron & Trivedi, 2009). This technique is
appealing because of its suitability to the case at hand where growth and growth
98
enhancing policies are likely to have different impacts on poverty in different countries.
Furthermore, quantile regression also has other advantages such as robustness to outliers
(Cameron & Trivedi, 2009).
4.4 Empirical Results
In Figure 4.1 we plot the relationship between economic growth and poverty
reduction. We plot average annual decrease in headcount poverty over 1981-2004 (the
vertical axis) against the average annual growth in GDP per capita (the horizontal axis).
Figure 4.1 shows that there is a strong positive association between the two variables
which stresses the importance of economic growth for poverty reduction. In addition to
uneven growth experience, Figure 4.1 also illustrates heterogeneous poverty reduction
experiences among countries with comparable levels of growth. In this section we explore
this relationship between growth and poverty further using econometric techniques and
controlling for other variables that affect poverty.
22
The sum of absolute error values is minimized relative to a particular point in the sample distribution of
the dependent variable. The regression line is fitted through various quantiles of the sample distribution of
the dependent variable, but all observations are used to compute the coefficients at each point.
99
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The benchmark model is estimated using OLS. Then we employ quantile
regression to estimate the coefficients at eight quantiles, namely the 10th, 25th, 40th, 50th,
60th, 75th, 80th and 90th quantiles. Table 4.1 reports the regression results of equation 4.1
using logarithm of poverty rate as the dependent variable. Column 1 reports OLS
estimates, the marginal effect of explanatory variables on the conditional mean of
headcount poverty index. The conditional quantile estimates reported in columns 2-9
show the coefficients of each explanatory variable at different quantiles of the distribution
of the dependent variable. The heterogeneous effects of growth and other variables on
poverty at different points in the distribution are reflected by the size, sign and
significance of the estimated coefficients. Hence, we compare whether coefficient
estimates vary at different points in the distribution. Figure 4.2 plots quantile regression
and OLS coefficients of four key variables: average income, distribution of income,
foreign aid and remittances against various quantiles and shows the 95 percent confidence
interval around the estimates computed using 400 bootstrap replications.
100
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As expected the two important variables that explain poverty—average income
and income distribution, are strongly significant with the expected signs. The other
explanatory variables foreign aid, financial development, dependency ratio and
international remittances are all significant with expected signs.
The coefficients of average income are consistently negative and significant at all
quantiles reported (see also Figure 4.2, Fig.2a). Our results provide strong evidence of the
importance of economic growth for poverty reduction. Nevertheless, it is worth noting
that the magnitude of the coefficients of average income—growth elasticity of poverty,
drops significantly as we move up along the distribution of the dependent variable—
which is associated with higher rates of poverty. For instance, the coefficient of average
102
income at 90 quantile is a little over one-third of the coefficient estimate at the 10
quantile of the distribution. The observed decline in the coefficient of growth in average
income after controlling for distribution of income and other determinants of poverty
indicates that the level of poverty in a country could be one of the factors that explain the
heterogeneous poverty response to economic growth documented in the literature.
The heterogeneous poverty response to growth implies that an effective poverty
reduction strategy requires more than just the achievement of higher growth. The policy
implication is that average income growth delivers less in terms of poverty reduction in
the poorest countries—a recognition that would imply that resources directed to overall
growth enhancement be reallocated to improve poverty reduction outcomes.
Gini coefficient, which measures the extent to which income distribution is far
from the perfectly equal distribution of income, has positive and significant coefficients.
This result suggests that other things equal, unequal distribution of income raises poverty
in a country. As expected the poverty increasing effects of income inequality decreases
with the prevalence of poverty.
The coefficient of foreign aid is consistently significant and negative up to the 60th
quantile. Beyond the 601 quantile foreign aid has no significant impact on poverty which
can clearly be seen from Figure 4.2. There are two interesting results worth discussing
here. First, foreign aid has a direct poverty reducing effect past its indirect impact through
growth in average income. Second, foreign aid has stronger direct poverty reducing effect
toward the bottom the distribution where poverty rate is low.
Financial system development has a consistently significant negative coefficient
which shows that it has strong direct poverty reducing effects which go beyond its
indirect effect through growth in average income. This result is consistent with the
103
findings of earlier studies such as Beck et al. (2007) who find that financial development
has a systematic effect on income of the poorest quintile. Unlike financial development,
there is no evidence that openness to international trade can help or hurt the poor.
Openness has a significant negative coefficient only at the 10l , 40l and 50l quantile.
OLS result in column 1 also shows that openness to trade has no significant impact on
poverty. As expected age dependency ratio also has a positive significant coefficient
which indicates that higher age dependency ratio is associated with higher rates of
poverty. We also find evidence that international workers' remittances has poverty
reducing effects which is consistent with the findings of earlier studies such as Adams &
Page (2003), who find that remittances play an important poverty reducing role in the
Middle East and North African countries. Government consumption expenditure also has
a consistently negative coefficient up to the 60th quantile which suggests that government
expenditure has some poverty reducing elements. The quality of institutions as measured
by democracy scores and FDI flows have no impact on poverty.
Finally we test whether the observed differences in the coefficients of the
explanatory variables at different conditional quantiles are statistically significant. In
other words, we test for the equality of regression coefficients at different conditional
quantiles. Table 4.2 presents test results for equality of coefficients of the explanatory
variables for the eight conditional quantiles reported in Table 4.1, namely the 10th, 25th,
40th, 50th, 60th, 75th, 80th and 90th quantiles. The null hypothesis is that coefficient
estimates at the quantiles specified above are equal. The test result reveals that the
differences in the parameter estimates of four variables; average income, gini coefficient,
foreign aid and openness are statistically significant at least at the 10 percent level of
104
significance. This result confirms the heterogeneous impacts of growth and other growth
enhancing policies on poverty in countries with different levels of poverty.
Table 4.2. F-test for Equality of Coefficients at Conditional Quantiles
Log GDP per capita
Log Gini
Aid
Finance
Openness
Foreign direct investment
Remittances
Government consumption
F-statistics and p-value
F( 7, 477)= 3.62
Prob > F = 0.0008
F( 7, 477)= 4.63
Prob > F = 0.0001
F( 7, 477)= 1.78
Prob > F = 0.0883
F( 7, 477)= 0.88
P r o b > F = 0.5199
F( 7, 477)= 4.15
Prob > F = 0.0002
F( 7, 477)= 0.52
P r o b > F = 0.8187
F( 7, 477)= 1.21
Prob > F = 0.2940
F( 7, 477)= 0.86
Prob > F = 0.5376
4.5 Robustness of the Results
This Section presents robustness checks of our main results conducted on two
fronts. First, we check the robustness of the impact of foreign aid by addressing the
possible endogeneity of foreign aid, and second we estimate the model using alternative
measures of poverty: poverty gap and squared poverty gap.
The null hypothesis is that coefficient estimates at the quantiles specified above are equal.
Null Hypothesis:
j3ql(i = pq2s. = A,4Q = A?so = i W = Aj?s. = Asc = Aso
105
4.5.1 Endogeneity of Foreign Aid
As we mentioned in Section 4.1, in addition to testing for the possible
heterogeneous effects of average income and growth enhancing policies, we are
particularly interested in the effect of foreign aid on poverty. The result in Table 4.1 (see
also Figure 4.2, Fig.2c) show that foreign aid has stronger direct poverty reducing impact
especially when the level of poverty in the country is low.
In this Section we address endogeneity of foreign aid using the instrumental
variable quantile regression technique proposed by Arias, Hallock, & Sosa-Escudero
(2001).24 Similar to the results in Table 4.1 we estimate the coefficients at eight quantiles,
namely the 10th, 25th, 40th, 50th, 60th, 75th, 80th and 90th conditional quantiles. Table 4.3
presents the regression results using 2SLS and IV quantile regression. Column 1 reports
2SLS and columns 2-9 report IV quantile regression estimates where foreign aid is treated
as endogenous. We use instruments of aid that are commonly used in the literature such
as population, income and regional dummy variables. Both the 2SLS and IV quantile
regression results show that the exogenous components of foreign aid have strong direct
poverty reducing effects. Figure 4.3 plots the coefficients of 2SLS and IV quantile
regression for four key variables including the exogenous components of foreign aid.
Compared the results reported in Table 4.1, here the effects of aid on poverty are more
pronounced. Unlike the result in Table 4.1 where impact of aid on poverty is limited only
toward the lower end of the distribution, here we find that poverty reducing effects of aid
According to Arias Hallock, & Sosa-Escudero (2001), instrumental variables quantile regression is
analogous to 2SLS where the endogenous variable is replaced by its first stage OLS predicted value and the
second stage performs quantile regression. For X2 instrumental variables, for the structural equation
where Yi is endogenous and X, is exogenous. For X2 instrumental variables the corresponding reduced
form equations Y and Y, are
Y =.?-; +• v and iv = x- + v
106
work throughout the distribution but with stronger effect when poverty rate is low. The
coefficients equality test reported in Table 4.4 shows that unlike the earlier result there is
no statistical difference between coefficients of aid at different quantiles. Despite some
changes to the magnitude of the coefficients of some of the explanatory variables, in
general the results we find here are comparable to the one reported in Table 4.1.
4.5.2 Using Alternative Measures of Poverty
In this Section we check for robustness of our results using alternative measures
of poverty, namely poverty gap and squared poverty gap. The results are reported in
Appendix. The results confirm the findings reported in Tables 4.1 and 4.2 which show
that growth in average income, distribution of income, foreign aid and financial
development have the expected effects on poverty.
where z=.[s\,>:.]
107
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Table 4.4. F-test for Equality of Coefficients at Conditional Quantiles25
One
Log GDP per capita
Log Gini
Aid
Finance
Openness
Foreign direct investment
Remittances
Government consumption
F( 7, 477)=
Prob > F =
F( 7, 477)=
Prob > F =
F( 7, 477)=
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Prob > F =
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0.67
0.6958
The null hypothesis is that coefficient estimates at the quantiles specified above are equal.
Null Hypothesis;
J ? ? l 0 = / 3 q 2 5 = jffo40 = jffoS0 = pa60 = fi^m = fiqS0 = ^ q 5 0
109
4.6 Summary and Concluding Remarks
In this study we investigate the relationship between growth and poverty using
quantile regression that allows us to simultaneously test the role of growth and growth
enhancing policies in poverty reduction and whether the effects vary between countries
with different levels of poverty. Using data from eight four developing countries over
1981-2004, our results from quantile regression provide evidence that growth in average
incomes and growth enhancing policies have heterogeneous impact on poverty depending
on the level of poverty.
The main finding is that poverty responds to growth in average income and
foreign aid strongly when the prevalence of poverty is low. In other words, high incidence
of poverty in a country significantly reduces poverty response to the effects of growth and
a whole set of explanatory variables, including foreign aid. We also find that foreign aid,
financial development and international remittances have strong direct poverty reducing
effects even after controlling for average income and distribution of income.
The heterogeneous impact of growth and other factors on poverty has important
policy implication for an effective poverty reduction strategy. Simply targeting higher
growth of income, as is often suggested by cross-country studies, may not go very far in
reducing poverty especially in countries beset by pervasive poverty. Other factors that
facilitate poverty reduction, such as specifically targeting employment generation in lowskill manufacturing and agriculture, providing access to consumption and production
credit and access to healthcare facilities may achieve better poverty reduction outcomes
when combined with an overall pro-growth agenda.
110
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112
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xi 7
O c^
CHAPTER IV
OVERALL CONCLUSIONS
In this dissertation we examine the effectiveness of foreign aid in enhancing
economic growth and reducing poverty. We explore the aid-growth-poverty relationship
in ways that are different from standard aid effectiveness studies by examining the direct
effects of aid on poverty and the effects of aid on the sources of growth: capital
accumulation and factor productivity. We find three interesting and interrelated results
that have important policy implications.
In the first essay we examine the direct effect of aid on poverty, separate from the
indirect effects that are transmitted through growth in average income. The total effect of
aid on poverty is composed of the direct and indirect effects. While the indirect effects of
aid on poverty transmitted through growth in average income is the commonly referred to
channel through which aid contributes to poverty reduction, lack of robust positive effect
of aid on growth is troublesome. This is the case because the aid-growth-poverty
sequence depends on two conditions: a robust positive association between aid and
growth in average income, and strong poverty reducing effects of the aid-induced increase
in average income. The dubious indirect effects of aid on poverty, on the one hand, and
the growing focus of international aid on poverty, on the other hand, makes studying the
direct effect of aid on poverty interesting. Even after controlling for average income and
income distribution we find that foreign aid has poverty reducing effects. This direct
effect of aid has important policy implication in enhancing the role of aid in poverty
115
reduction. Further exploration also suggests heterogeneous impacts of different types of
aid and aid from different sources. Multilateral aid tends to have strong direct poverty
reducing impact compared to aid from bilateral sources and grants have better outcome
than loans. The results are robust to the consideration of endogeneity of aid and use of
alternative measures of poverty.
In the second essay we extend the aid-investment studies to assess the impact on
total factor productivity, since the net effect of aid on growth depends on how aid affect
capital accumulation and productivity. We find that aid boosts investment, but it
adversely affects TFP or efficiency. These conflicting effects of aid on investment and
efficiency mean that the net effect of aid on growth depends on the balance of these two
effects. The efficiency reducing effects of aid could be one of the reasons for lack of
robust growth effects of aid in spite of the observed positive association between aid and
investment.
In the third essay we examine the effects of growth in average income and polices,
including foreign aid, on poverty. Using quantile regression techniques which allow us to
test for heterogeneous impact of growth and policies on poverty we test whether poverty
response to growth varies across countries with different levels of poverty. We find that
growth and growth enhancing policies including aid have heterogeneous impact on
poverty. Specifically, we find that high incidence of poverty reduces the effect of growth
on poverty.
116