Wageningen University- Department of Social Sciences
MSC Thesis: Development Economics Group
EFFECTIVENESS OF AID ON SECTORIAL GROWTH: EVIDENCE
FROM PANEL DATA FROM AID RECIPIENT DEVELOPING COUNTRIES
Tigist Mekuria
May, 2014
i
Effectiveness of aid on sectorial Growth: Evidence from panel
data from aid recipient developing countries
Tigist Mekuria
Registration number: 861110555110
Supervisor: Jereon Klomp (PhD)
A thesis submitted in partial fulfilment of the degree of Masters of Science at
Wageningen University and Research Center,
Netherlands
May, 2014
Wageningen, Netherlands
Thesis Code: Dec-80433
Wageningen University and Reserch Center
Program:
Management,
Economics
ii
and
Consumer
studies
(MME)
Acknowledgement
First and foremost praises and thanks to the almighty God for granting me the capability to
proceed successfully by giving me the power to believe in my passion and pursue my dreams.
I am very grateful to the governments of the Netherlands for giving me the opportunity to
study in Wageningen University. I would like to express my deepest gratitude to my research
supervisor Jeroen Klomp (PhD), for his valuable and constructive comments and suggestions
which enabled me to develop a better understanding of the subject. This research could not
have been accomplished without his willingness to give his time, patient guidance,
enthusiastic encouragement and useful critiques. My special thanks are also extended to my
supportive husband, Asefa Teferi, for his help and encouragement during the development of
this work. Your encouragement and moral support when the times got difficult are much
appreciated. I also wish to thank my parents and entire families for their unconditional support
and encouragement throughout my study. I cannot find words to thank my sister, Asegedech
Shiferaw, who let me study abroad by taking all the responsibilities by the time she deserves
my support. Finally, I would like to offer my special acknowledgement to my friends for their
moral encouragement and much more appreciation to Abebayew Girma for technical support.
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Acronyms
DAC: Development Assistance Committee
GDP: Growth Domestic Product
GMM: Generalized Methods of Moments
IDA: International Development Association
IMF: International Monetary Fund
LDC: least developed countries
MDGs: Millennium development goal
ODA: Official Development Assistance
OECD: Organisation for Economic Co-operation and Development
OLS: Ordinary Least square
UNDP: United Nations Development Programme
WB: World Bank
WDI: World Development Indicator
2SLS: Two stage least square
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Table of Contents
Acronyms ..................................................................................................................................................ii
Table of Contents ....................................................................................................................................iii
List of Tables .............................................................................................................................................v
Abstract ................................................................................................................................................... vi
Chapter One: Introduction ....................................................................................................................... 1
1.1 Background ................................................................................................................................... 1
1.2 Problem statement........................................................................................................................ 4
1.3 Objective of the study.................................................................................................................... 6
1.5 Scope of the study ......................................................................................................................... 6
1.6 Organization of the thesis .............................................................................................................. 7
Chapter two: Literature review ............................................................................................................... 8
2.1 Introduction ................................................................................................................................... 8
2.1.1 Definition and typologies of Foreign aid ................................................................................ 8
2.1.2 History of Foreign aid .......................................................................................................... 10
2.1.3 What is/not included in ODA? ............................................................................................. 12
2.2 Official Development Assistance and economic growth............................................................. 13
2.2.1 Direct effect of ODA on economic growth .......................................................................... 13
2.2.2 Indirect effect of ODA through saving & investment .......................................................... 16
2.3 Official Development Assistance and Sectorial growth .............................................................. 16
2.3.1 ODA and Manufacturing sector ........................................................................................... 17
2.3.2 ODA and Service sector ....................................................................................................... 19
2.3.3 ODA and Agricultural sector................................................................................................ 20
2.4 Aid and Policy Environment ....................................................................................................... 21
2.4.1 Good policy model ............................................................................................................... 22
2.4.2 Institutional model ................................................................................................................ 23
2.4.3 Medicine model .................................................................................................................... 24
Chapter Three: Methodology ............................................................................................................... 25
3.1 Data and data source.................................................................................................................... 25
3.2 Description of variables............................................................................................................... 25
3.2.1 Why control variables? ......................................................................................................... 26
3.2.2 Which and Why interaction terms? ...................................................................................... 30
3.3 Econometrics Techniques............................................................................................................ 31
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3.4 Instrumental Variable Estimation Method .................................................................................. 33
3.5 How to check endogeniety .......................................................................................................... 34
3.6 Specification of the Model .......................................................................................................... 35
Chapter Four: Results ............................................................................................................................ 37
Descriptive Statistics ............................................................................................................................. 37
4. Econometrics Results .................................................................................................................... 40
4.1 Result of OLS Estimator ............................................................................................................. 41
4.1.1 The effect of ODA on economic growth .............................................................................. 41
4.1.2 The effectiveness of ODA and the growth of manufacturing sector .................................... 46
4.1.3 The effectiveness of ODA and the growth of the service sector .......................................... 50
4.1.4 The effectiveness of ODA and the growth of agriculture sector .......................................... 54
4.2 Instrumental variable Estimation result ....................................................................................... 58
4.2.1 The aggregate effect of ODA on economic growth.............................................................. 61
4.2.2 The aggregate effect of ODA on manufacturing sector........................................................ 62
4.2.3 The aggregate effect of ODA on the growth of Service sector ............................................ 63
4.2.4 The aggregate effect of ODA on Agricultural sector ........................................................... 65
4.3 Comparison of the effect of ODA across sectors and estimators ................................................ 66
4.4 The effectiveness of ODA in low income countries.................................................................... 68
Chapter five: DISCUSSION ................................................................................................................ 71
5.1 ODA and economic growth ......................................................................................................... 71
5.2 ODA and sectorial growth ........................................................................................................... 75
5.2.1 ODA and Manufacturing sector ........................................................................................... 75
5.2.2 ODA and service sector ........................................................................................................ 76
5.2.3 ODA and Agricultural sector................................................................................................ 77
Chapter six: Conclusions and recommendations ................................................................................... 80
6.1 Concluding remarks .................................................................................................................... 80
6.2 Recommendations and Policy implications................................................................................. 82
6.3 Direction for further research ...................................................................................................... 82
References ............................................................................................................................................. 93
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List of Tables
Table 1: Result Of The Effect Of ODA On The Growth Of Real GDP per Capita ................. 44
Table 2: Result Of The Effect Of ODA On The Growth Of Manufacturing sector ................ 48
Table 3: Result Of The Effect Of ODA Aid On The Growth Of Service Sector..................... 52
Table 4: Result Of The Effect Of ODA On The Growth Of Agricultural Sector .................... 56
Table5. Comparison Of The Effect Of ODA Across Sectors .................................................. 67
Table 6. Result Of Regional Dummies Interacted With ODA ................................................. 69
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Effectiveness of Foreign Aid on Sectorial Growth: Evidence from
panel data from aid recipient developing countries
Abstract
Drawing on panel data covering 91 aid recipient developing countries over the period of
1985-2011, we empirically examined the effect of Official Development Assistance (ODA) on
the economic growth and sectorial growth. This thesis addressed three main research
questions: what is the effect of ODA on the overall economic growth? What is the effect of
ODA on the growth of manufacturing, service and agricultural sector? And finally what are
the conditions that make ODA effective? To address these research questions, we run a
regression analysis on a fixed effect model employing both OLS and 2SLS estimators. We
employed 2SLS to account for endogenous nature of ODA. ODA is found to be endogenous in
the aggregate and manufacturing sector while it is turned to be exogenous in the service and
agricultural sector. This evidenced that omitted variable bias is the cause for endogenous
nature of ODA in the model. In the first part of the analysis we run a regression of growth
rate of GDP per capita on ODA and other appropriate explanatory variables. In the second
part we run regression of the sectorial growth rate on ODA and the same explanatory
variables. We also tested the hypothesis that ODA is more effective in those countries with
good monetary policies and good level of democracy by including aid interaction with the
conditioning variables as explanatory variables. Based on the regression result, our finding
shows that a one percent increase in ODA will increase the growth rate of per capita GDP by
1.85 percent. Our result on the effect of aid on sectorial growth also suggested that ODA
could be pretty effective in bringing growth in all the three sectors. Moreover, our results on
aid conditionality also confirmed the empirical support for the hypothesis that aid is
conditioned by the policy environment of aid recipient countries. In all specification, except
agricultural sector where only democracy matters for aid effectiveness, the effectiveness of
ODA is significantly less in countries with high level of inflation.
Key Words: Effectiveness of ODA; Sectorial growth; Aid conditionality; Aid recipient
developing countries
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Chapter One: Introduction
1.1 Background
More than one billion people live under absolute poverty and deprivation (Temple, 2010). In
the period 2011-2013, 842 million people (12% of world population) were suffering from
chronic hunger from which 827 million of them live in developing regions (FAO, 2013).
Noticing the problems that existed in developing countries more international financial flows
shifted their emphasis towards supporting poverty reduction schemes in recent periods. A
large flow of foreign aid to developing countries have been supported in recent years as a way
of escaping poverty traps and promote development in developing countries (Doucouliagos &
Paldam, 2008; Selaya & Thiele, 2010).
Foreign aid began in the 19th and early 20th century when the western nations considered
supporting the economies of their colonies and other poor countries (Kanbur, 2006). Foreign
aid as an institution begun in 1947 with Marshall Plan and expanded in 1960’s to developing
countries (Bräutigam & Knack, 2004). According to World Bank (WB) report, the
International Monetary Fund and the International Bank for Reconstruction and Development
were established in 1945 that would help with debt relief and economic development of
developing countries. After 1960’s the International Development Association (IDA) was
opened and Regional Development Banks were started in Asia, Africa and Latin America
focusing on poverty alleviation and improving social services (Kanbur, 2006).
Among foreign financial flows to developing countries, Official Development Assistance
(ODA) is one which accounts approximately for seven percent of total international financial
flows (ODA, 2012). According to OECD report, ODA is foreign aid that flows from
multilateral agencies and bilateral donors to developing countries. ODA , being one of a
number of international resource flows available to developing countries, is one of the
program focusing on promoting development and welfare in the developing countries (ODA,
2012). Its primary objective is to grant sustainable development assistance to countries that
make effort to reduce poverty (Rajan & Subramanian, 2008; Temple, 2010). Annually more
than US$ 60 billion development aid is flowing to poor countries to generate economic
growth and, on average, recipient countries receive about 7.5% of their GDP (Doucouliagos
& Paldam, 2008). ODA has grown, from around US $40 billion a year in the 1960s to over
1
US $125 billion today where least developed countries (LDCs) now account for 45% of ODA
disbursements and where Sub-Saharan Africa receives more ODA than any other region with
volumes reaching US $45 billion in 2010 (ODA, 2012).
Despite this large flow of aid as a treatment given to poor countries in order to stimulate
economic growth (Doucouliagos & Paldam, 2008), poverty still remains to be a major
challenge as millions are still living in chronic poverty. According to Collier and Dollar (2001)
half of the world population lives on less than 2$ per day. If we compare Latin America with
Sub Saharan Africa in terms of aid received and economic growth achieved: in Sub Saharan
Africa, ODA constitute more than 10% of GDP and 25% of government expenditures while
the share of aid for Latin American countries (e.g. Brazil) is less than 0.03 % of GDP
(Azarnert, 2008; Bräutigam & Knack, 2004; Burnside & Dollar, 2000). Nevertheless,
comparatively speaking, Latin American countries recorded good progress in economic
growth while the performance is still weak in sub-Saharan countries where per capita income
declines during 1980-2000 (Herdt, 2010; Temple, 2010). Moreover, 45 percent of population
in sub Saharan Africa lives under poverty and the region is characterized by food problems,
least educated people and bad health condition (Herdt, 2010; Jayne et al., 2003).
In general, in spite of all the efforts made the economic growth gained by developing nations
is still disappointing and the contribution of foreign aid to the economic growth has been
evaluated more doubtfully (Bourguignon & Sundberg, 2007; Walt, Pavignani, Gilson, & Buse,
1999). So, whether foreign aid helps poor countries to grow remains an important question for
many scholars, governments and donors. This makes foreign aid an important topic given its
implications for economic growth in developing countries.
A number of studies have been undertaken to examine the effectiveness of aid on economic
growth with the results being inconclusive. Some of the researchers found insignificant effect
while others found significantly positive and negative effect of aid on economic growth. For
example, Selaya and Thiele (2010) and Dalgaard, Hansen, and Tarp (2004) found that foreign
aid have positive effects on economic growth while Rajan and Subramanian (2008) found
none or weak negative relationships as evidence for the effectiveness of foreign aid.
Furthermore, more studies revealed that aid is detrimental to economic growth through
reducing competitiveness of the export sector (Rajan & Subramanian, 2011; Ram, 1987). On
the other hand, Selaya and Thiele (2010) found an evidence for the absence of “Dutch disease”
2
because of idle capacity in developing nations which prevent an increase in price of nontraded goods.
Yet other researchers explained that the effectiveness of aid could depend on the other third
factors, where effectiveness of foreign aid is found to be conditional, as its effect might be
explained based on the situation of recipient country’s circumstances. So, it means that even if
the effect of aid on economic growth is insignificant in general, it could have positive or
negative effect depending on the situation of the recipient countries. The magnitude as well
as the sign of the impact might depend on macroeconomic policies, amount of aid,
institutional quality, and climate circumstances, among others and many researchers tested
these conditionality (Bräutigam & Knack, 2004; Dalgaard et al., 2004; Hansen & Tarp, 2001;
Hudson & Mosley, 2001; Tezanos, Quiñones, & Guijarro, 2013). For example, Burnside and
Dollar (2000); Collier and Dollar (2001) and Collier and Dollar (2002) reported positive
effect of aid on economic growth for those countries which have a good policy environment
while Hansen and Tarp (2001) explains a positive effect of aid on economic growth through
diminishing returns which implies that aid only works if it is given moderately. Weak and
insufficient institutional arrangement, corruption and bureaucratic inefficiency were also cited
as reasons for aid ineffectiveness (Azarnert, 2008; Tezanos et al., 2013). Foreign aid in most
cases is found to be more sensitive to political conditions than economic realities of the
recipient countries (Alesina & Dollar, 2000), because the allocative efficiency of foreign aid
to the economic pillars requires political commitment and responsibility. This implies that the
effectiveness of aid depends on multiple factors including, but not limited to, the quality of
governance in recipient countries.
Moreover, the lack of coordination between donor countries and recipient countries was found
to be (partly) the reason for the ineffectiveness of aid in developing countries in 1980’s and
1990’s (Walt et al., 1999).Yet the result on effectiveness of aid is far from being conclusive,
and Kaya, Kaya, and Gunter (2013) mentioned three reasons for this controversial results
including
inconsistent definition and poor quality of data, lack of understanding the
determinants of economic growth and problem of modelling aid effectiveness.
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1.2 Problem statement
Although many studies were conducted to examine the effectiveness of foreign aid on
economic growth few of them consider whether the effectiveness of aid might differ among
sectors (Herdt, 2010). This study attempts to investigate the impact of aid both on aggregate
economic performance and sectorial growth rate where these sectors include manufacturing,
service and agriculture. This is because, the effect of aid might be felt differently by different
sectors (agriculture, manufacturing and service) no matter what the result might be when seen
as a whole.
The relationship between sector and foreign aid could be seen in different dimensions. First,
foreign aid might increases agricultural production, build agricultural institutions and promote
economic growth by increasing the demand for food which will increase the price of
agricultural products and then expand agricultural production (Herdt, 2010; Kaya et al., 2013).
The money that comes from foreign aid can be used for financing agricultural research and
development, potential inputs like machineries, irrigation, fertilizer and chemicals, for
instance. WB lent about US $ 31 billion for irrigation purpose from 1950 to 1993 where 67%
of those projects were rated as satisfactory, generating an average rate of return of 15% which
shows an increase in the productivity of the sector, because of availability of inputs, leading
eventually to expansion of the agricultural output. Herdt (2010) further explained many ways
in which aid could contribute to the growth of agricultural sector. He stated (on Page 3257),
that:
“Aid might provide inputs to be used in agricultural production free or at a
subsidized cost; aid might help improve production efficiency by improving marketing and
information flow; and aid might have helped to create technology with higher inherent
productivity so that available inputs produce more output”.
This shows how aid might bring growth in the sector. The growth in productivity of this
sector can be used as a tool in poverty reduction in developing countries to bring economic
growth (Kaya, Kaya, & Gunter, 2012). Therefore, as most of the poor countries generate their
income mainly from agricultural sector Kaya et al. (2012), aid might have significant effect
on economic growth of developing countries by boosting the growth of this sector. Beyond
the growth of agricultural sector the non-agriculture sector will be benefited as the surplus
from agriculture is an input for other sectors and thus leading to (the spill over effect) overall
economic growth (Herdt, 2010). As agricultural sector is viewed as “the engine of economic
4
growth” in the early stage of development the growth in this sector could help bring economic
growth for developing countries (Kaya et al., 2012).
Second, ODA could have impact on the service sector. Development aid can provide income
to pay for basic needs like schooling, health and housing (Herdt, 2010). He stated (on page
3297) that:
“To the extent that agricultural development assistance contributes to accelerating
agricultural production, it contributes to development and the ability to pay for health,
education, housing, and food and over the longer run to pay for secondary and higher
education, more healthcare ... and knowledge”
The health budget for many low income countries is highly dependent on foreign aid and
donor institutions are active in helping the health sectors of developing countries (Buse &
Walt, 1996). World Bank lending for health increased significantly in the 1990s as health care
needs are increasing with population expansion and aging which shows more foreign aid is
spent on this sector (Walt et al., 1999). Aid can also improve human capital since it may bring
know how to the countries which shows the indirect link between aid and economic growth
through human capital (Rajan & Subramanian, 2008). Easterly (2003) mentioned a case
where aid given to water projects in Ethiopia saved life and let children go to school.
Third, aid might also affect manufacturing sector through different channels. On one hand aid
can affect the sector positively through human capital accumulation, and technological
innovations. Foreign aid can partly spent on infrastructures like ports, reliable power supplies
and roads that could lower costs of production leading to growth of the sector (Temple, 2010).
On the other hand, aid might affect the growth of the sector negatively through exchange rate.
Aid will increase national income which in turn rises consumption of goods which is mostly
spent on non-traded goods like construction, health care and education (Rajan & Subramanian,
2011). This will put upward pressure on the prices of goods (non-traded) because the price of
traded goods is determined by the world market while the price of non-traded goods is
determined by domestic market. So an increase in consumption (especially non-traded goods)
will directly raise the relative price of non-tradable. This will lead to real exchange rate
appreciation (an increase in relative price of non-traded goods to traded goods) which reduces
competitiveness of exporting sector.
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Therefore investigating aid effectiveness at sectorial level is important to see the channels
through which aid might affect the overall economic growth. Besides, investigating the
effectiveness of aid at sectorial level would allow for the identification of sectors which
strongly responds (be positively or negatively) to foreign aid flows. So, both sector wide as
well as aggregate analysis of the relationship between aid and economic growth should be
undertaken.
1.3 Objective of the study
The general objective of this thesis is to investigate the effectiveness of foreign aid on
economic growth of the developing countries. To address this objective we have investigated
different specific objectives. Firstly, we have examined the effect that aid would have on
sectorial growth like that the effect of aid on the growth rate of valued added of the sectors
(agricultural, manufacturing and service). Secondly, we have re-examined the conditionality
of aid effectiveness on democracy (as a measure of good governance) and inflation (as a
measure of good monetary policy).To reach the above mentioned objectives we have a
number of research questions which is presented as follows: What is the effect of aid across
different economic sectors? What are the conditions that make aid effective and do these
conditions differ across different economic sectors?
1.5 Scope of the study
Foreign assistance might take different forms like financial aid, humanitarian aid, debt relief,
technical assistance, food aid and so on, targeted either for emergencies, military objectives,
or poverty reduction and economic development. However, the scope of this paper is limited
to the analysis of only ODA which has a primary objective of welfare and economic
development of developing countries (ODA, 2012). This is because, aid given for
humanitarian purposes to address an emergency or political and strategic considerations of the
donors should not be expected to bring economic growth in developing countries (Kaya et al.,
2012). ODA embraces loans and grants with concessional terms (includes loans and grants
but contains grant element of at least 25%) from both bilateral donors and multilateral
agencies which are given for the purpose of promoting economic development and welfare in
developing countries (ODA, 2012). “ODA is ‘official’ funding, provided by governments and
official agencies in the 23 countries that are members of the Organisation for Economic
Cooperation and Development’s Development Assistance Committee (OECD DAC) plus the
European Commission”(ODA, 2012) p.1). Finally the study covers a period over 1985 to
6
2011 based on data availability and to focus on recent nexus between aid and economic
growth.
1.6 Organization of the thesis
This thesis will consist six chapters. The first chapter introduces the topic which explains
background information, statement of the problem, the objective, and research questions.
The second section reviews the existing literature regarding the relationship between ODA
and economic progress; ODA and sectorial growth and policy environment. The third chapter
is devoted to methodologies and econometric techniques which describes in detail the
methods employed and the variables used. The forth chapter will present the findings of the
thesis and chapter five discusses the results comparing with previous studies. The final
chapter draws conclusions and gives possible policy recommendations and direction for future
studies.
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Chapter two: Literature review
2.1 Introduction
2.1.1 Definition and typologies of Foreign aid
Official Development Assistance (ODA) which is channelled through multilateral or bilateral
agencies has become the important financing source for low and middle income countries
(Wall, 1995). Since the late 1990s, ODA has been widely acknowledged as prominent
economic policy to support low-income countries to alleviate poverty (Nkusu, 2004). ODA
encompasses a wide range of financial transfers including loans and grants based on
concessional terms by multilateral and bilateral sources to promote economic development
and welfare (Wall, 1995). According to the Organization for economic commission and
development (OECD) and WB, ODA includes grants and loans to developing countries that
fulfils (i) the actions should undertake by the official sector of the donor country, (ii) the
promotion of economic development and welfare in the recipient country being the main
objective and (iii) at the concessional financial terms which means that if a loan is involved it
has to have at least 25% grant (Kanbur, 2006).
Regardless of the definitions used for describing ODA, some authors looked at the presence
of any predefined purposes assigned to each transfer. For instance Reisen, Soto, and
Weithöner (2004) classified ODA as (i) free transfer where the decision on what to do with
the transfer is up to the recipient country (ii) earmarked transfer where the donor wants to
ensure that maximum effect is reached per ODA dollar by earmarking. Another way of
looking at aid typologies is based on the channels of the transfer. As such foreign aid or
international aid could also be seen as bilateral or multilateral aid.
Moreover, others are assertions that international transfer has undergone a paradigm shift
based on the purposes of the transfer hence distinguishing between traditional ODA and the
global public goods (GPGs) (Raffer, 1999). According to Raffer (1999) traditional ODA
functions are best characterized by the dual gap theory which means closing the gaps of both
savings and foreign exchange in developing countries. According to Harrod growth model
saving gap is the difference between the rate of capital accumulation1 and the proportion of
national income saved. As saving rate is low in most of developing countries, foreign aid
1
The rate of capital accumulation is determined by the target or planned output defined by the output capital
ratio of a country
8
could raise national income per capita which supposed to increase the proportion of income
saved and then fill the saving gap as of the traditional ODA. However, other papers claimed
that large fraction of aid is used to increase consumption than saving (Griffin, 1970).
The foreign exchange gap proposed by economists argued that the challenge of many under
developed country is not only from inability to save but also from inability to get sufficient
foreign exchange to support imports of important raw materials for production. According to
Griffin (1970), the supply of foreign exchange obtained from export of goods and service may
be in short of financing the demand for foreign currency to support imports. So, in this case
the idea of traditional ODA is that foreign exchange rate might help to fill the gap of foreign
currency to bring the desired level of economic growth.
On the other hand, the new shift towards GPGs focussed on helping the international
community to manage global challenges and problems such as concerns for sustainability,
global environmental policies and AIDS (Raffer, 1999). Sagasti (2009) also describes the
provision of public goods as follows: both donor and recipient nations require the provision of
public goods to counter international externalities such as global and regional environmental
threats, addressing global population growth and health threats and supporting international
cooperation initiatives.
Raffer (1999) further argues that these new activities are less developmental in the traditional
sense of ODA, but rather tasks of ensuring the global common interest and remedial action
against international externalities. These make the OECD definition of ODA ambiguous in the
strict developmental sense as the definition and scope of what falls under the ODA changes
with circumstances as long as the term ‘welfare’ is involved. This does not only make the
definition of ODA confusing but also makes the effectiveness of ODA misleading for it might
be granted and allocated for different purposes than economically viable sectors.
On the other hand, Clemens, Radelet, and Bhavnani (2004) treats aid in three categories:
emergency and humanitarian aid, long impact aid and short impact aid. Humanitarian aid is a
support given after a natural disaster or for reconstruction of an area after a period of conflict
(Clemens et al., 2004; Selaya &Thiele,2010, 2012). According to Sagasti (2009) humanitarian
aid is grouped under ‘international solidarity and religious motivation’ for giving aid. As per
of international solidarity and religious motives: altruism, ethical and humanitarian concerns,
highlighting the moral obligation of donor countries to assist the poor in developing countries.
These includes alleviation of human sufferings and expression of solidarity with fellow
9
human sufferings, help to cope with natural and man-made disasters through humanitarian
and emergency relief, and build local capacities to undertake initiatives for improving living
standards. According to Clemens et al. (2004), long impact aid is foreign aid that affects
growth only over long period of time and a support given to democracy, environmental
protection, health or education are grouped under this type of aid. On the other hand foreign
aid that could stimulate growth in short time (within four year), for example a support given
to budget balance, investment infrastructure, economic sectors are grouped under short impact
aid (Clemens et al., 2004).
Selaya and Thiele (2012), on the other hand, distinguish between program and project aid.
According to OECD-DAC, programme aid is a support given to general budget support,
development food aid and action related to debt while project aid is a support given to
investment in social and economic infrastructure, and production sector (Selaya & Thiele,
2012). Furthermore, Selaya and Thiele (2012) have indicated that if program aid is given as a
budget support, it could undermine governance quality. This implies that governments of aid
recipient might allocate budget support to furthering their political power and maintain the
status-quo instead of implementing economically viable projects. Hence, the category of a
particular transfer and the discretion entitled to the recipient regarding its allocation has far
reaching consequences in either furthering or hampering the purpose of the aid.
2.1.2 History of Foreign aid
Official development assistance dates back to over six decades ago against the backdrop of
the Marshall Plan’s success in rebuilding the war damaged Europe (Sagasti, 2009).
Development paradigms and ideas on how to organize development cooperation and
particularly ODA have changed and evolved overtime. Sixty years ago a handful of
institutions evolved with the assumption that development would come to poorer regions
through the provision of capital and technical know-how by countries of the developed world.
As this assumption was proved to be far too simplistic than the complex reality, many more
organizations evolved over the past years and recently it is composed of the World Bank, IMF
and more than 20 regional Development Banks, over 40 bilateral development agencies and
the UN family of organizations, thousands of local and international NGOs as well as private
foundations (Sagasti, 2009). Since the late 1940s, development thinking and practice were
based on concepts of self-sustaining economic growth by primarily investing in human capital
10
and through import substitution industrialization that would lead to a take-off into selfsustained growth (Mikesell, 1970).
The latest formulation of the framework for development thinking and practice that has
supplanted the Washington Consensus is the ‘Millennium Development Goals’ (MDGs). The
MDGs is one of the development programs with a goal of reducing extreme poverty, hunger,
illiteracy, environmental degradation and gender discrimination, among others to address
development challenges by setting a time bound and a measurable objectives (Summit).
MDGs saw a return by directly addressing health, education and housing problems in poor
countries (Herdt, 2010). The MDGs coupled with nationally owned poverty reduction
programs, sensible macroeconomic policies, effective public expenditure management and
new forms of conditionality related to good governance appear to be the main organizing
principles for ODA. Moreover, international security considerations primarily linked to war
against terrorism have intruded and are affecting ODA thinking and practice (Sagasti, 2009).
Moreover, Sagasti (2009) was explained ‘narrow and enlightened self-interest’ as rationales
for giving development assistance as follows. This consists strategic and security interest,
political interest and economic and commercial interest of donor countries to provide aid. The
strategic and security interest responds to geopolitical and security consideration of donor
countries while political interest focuses on obtaining political support for foreign and
domestic policies. The economic and commercial interest emphasizes on direct commercial
and financial benefits to the donor country.
Although the realities and needs in the low-income countries justify their urgent need for
injecting capital to breakout the poverty trap, donors also had their own motivations
influencing the amount and direction of their aid flows in both temporal and spatial
dimensions. These motivations have been changing overtime with changing developmental
themes as well as prevailing international concerns. According to Sagasti (2009) providing
ODA particularly when channelled through bilateral agencies, is an instrument of foreign
policy for donors and is usually aligned with their strategic objectives and interests.
Regardless of the variations overtime and among donors in terms of the whole mix of aid
motivations, Sagasti (2009) summarizes three main sets of rationales for development
assistance in the 21st century as: international solidarity and religious motivations, narrow and
enlightened self-interest and provision of international public goods.
11
2.1.3 What is/not included in ODA?
All foreign aid that flows to developing countries will not fall under ODA. According to ODA
report, the following transfers are some of the non- ODA transfers (ODA, 2012);
Peace keeping: Funding that comes from DAC donor governments that falls for the
enforcement aspects of peace keeping is not included.
Aid flows from non-member DAC: ODA does not include aid from governments that are
not members of the DAC, nor does it include the money given by the public to NGOs or
appeals or the funding provided by foundations.
Exclusion of military aid: The supply of military equipment and services, and the
forgiveness of debts incurred for military purposes, are not reportable as ODA. On the other
hand, additional costs incurred for the use of the donor’s military forces to deliver
humanitarian aid or perform development services are ODA-eligible.
Social and cultural programmes the promotion of museums, libraries, art and music schools,
and sports training facilities and venues counts as ODA, whereas sponsoring concert tours or
athletes’ travel costs does not. Cultural programmes in developing countries whose main
purpose is to promote the culture or values of the donor are not reportable as ODA.
Research - Only research directly and primarily relevant to the problems of developing
countries may be counted as ODA. This includes research into tropical diseases and
developing crops designed for developing country conditions. The costs may still be counted
as ODA if the research is carried out in a developed country.
Anti-Terrorism - Activities combating terrorism are not reportable as ODA, as they
generally target perceived threats to donor, as much as to recipient countries, rather than
focusing on the economic and social development of the recipient.
12
2.2 Official Development Assistance and economic growth
2.2.1 Direct effect of ODA on economic growth
The empirical literature on the effects of aid on macroeconomic management has revealed
mixed results. Some authors claims that the effect of aid on economic growth is negligible or
even negative (Easterly, 2007). On the other hand, others argue that aid had positively
affected economic growth of recipient countries (Hudson & Mosley, 2001; Roodman, 2007;
Selaya & Thiele, 2010) . These diverging and often inconclusive results on the effect of aid
could be attributed to the heterogeneity of recipients, aid motives and even the methodologies
used to assess the effect (Selaya & Thiele, 2010).
Rajan and Subramanian (2005) examined the effect of aid on economic growth using both
cross-sectional and panel data model. They used instrumental variable estimation technique
and Generalized Method of Moments (GMM) to tackle for endogenous nature of aid 2 .
However, their instrumentation strategy is totally different from other works in that their
instrumentation is donor related rather than recipient countries, with the assumption that noneconomically motivated aid is unlikely to be driven by economic outcomes. This means that
their instrumental variable was based on ‘what drive the donor country to give aid’ rather than
on the characteristics of the recipient countries. This is to satisfy the criteria for the
instrumental variables as they should not be correlated with the dependent variable (economic
growth). They based their instrumentation on historical link3 and influence4 where the first
one is captured by ‘colonial links and common language’ and the second is captured by
‘relative size of donor and recipient’ and the interaction between relative size and colonial
links.
They reported a negative effect of aid on economic growth based on both cross sectional and
paned data specification and also in different time period. Rajan and Subramanian further
indicated no evidence for the conditionality of aid on better policy or institutional
environment. They also constructed an interaction term between aid and geography to see if
2
Endogeneity of aid implies that aid itself is determined in the model which is not taken as something given
independent of the income level. Aid disbursements are obviously determined to some extent by the recipient
country’s growth process itself. Developed nation may give aid for those who have success history while others
may give aid to those who are poor or experience natural disaster (Rajan & Subramanian, 2008).
3 Assuming that the more historical relation between donor and recipient country the more likely that a donor
will allocate more aid
4 Assuming donors allocate more aid if they expect they have an influence over the recipient countries
13
aid effectiveness depends on the geographical location. However conflicting findings were
also documented on the conditionality of aid on geography in this paper. In cross-sectional
specification, the interaction term between aid and geography has been found to be
insignificant while it turns to be significant in panel data specification and the coefficient is
positive when estimated using GMM ARELLANO-BOND (AB) procedure and negative
when estimated by GMM BLUNDELL_BOND (BB) procedure5. This shows how the nexus
between aid and economic growth is partly affected by estimation techniques used. However,
Rajan and Subramanian (2005) refuted on the importance of policy conclusion from the
effectiveness of aid based on better geographical location since aid and its effectiveness itself
is more important inside tropics where most of the poor countries are located. On the other
hand this implies that those countries which are located in the tropics receive more aid than
others.
On the other hand Dalgaard, Hansen, & Tarp (2004) found a positive effect of aid by
stimulating long-run productivity. The coefficient of aid is significantly positive for all
specifications though the magnitude is different across the techniques and aid is found to be
effective outside the tropics (Dalgaard et al., 2004; Selaya & Thiele, 2010). However, similar
to Rajan and Subramanian (2005), Dalgaard et al. (2004) also did not draw any policy
conclusion from the result on the effectiveness of aid outside tropics but opening a new
window for further research understanding the importance of aid in the tropics.
Moreover, Hansen and Tarp (2001) provides an empirical result on the effectiveness of aid
on economic growth making use of a cross country panel data model. They also revisited the
issue of endogeinity of aid in the regression in the sense that they did not accept the test result
of the Durbin –WU-Hausman which showed that OLS estimates does not deviate significantly
from IV estimates in the regression. Instead they interpreted this result as neither OLS nor IV
estimation technique is consistent. They also raised the issue of endogeniety for other
explanatory variables used in the regression model, other than aid. So, they used an estimation
technique which is consistent in the presence of endogenous explanatory variables and
5 The similarity if AB and BB is that both estimators use the lagged values of the endogenous variables as an
instrument while the difference is that in AB estimator the lagged values are used to instrument for the
differenced right hand side variable while in BB estimator the estimated system comprises the difference
equation instrumented with lagged levels which is estimated using lagged differences as instruments(Rajan &
Subramanian, 2005).
14
country fixed effect which is a GMM estimator. They found that in all cases aid is found to
have positive effect on economic growth. However, Hansen and Tarp claimed that aid is
effective if given moderately not by policy environment. They reach this conclusion by taking
aid square and aid interaction with policy index. The coefficient for aid square is negative and
significant while the coefficient of aid-policy interaction is not significant.
The effectiveness of aid could also depend on the set of control variables used in the
regression model other than the type of estimators. Hansen and Tarp (2001) found that if
investment and human capital are controlled for in the regression, no positive effect of aid on
economic growth showing the channel in which aid affects economic growth. So, they
concluded that aid affects economic growth via investment and capital accumulation. They
also indicated that aid might had marginal negative effect for those aid dependent countries
though this effect has been over weighted by the positive side effect of aid through
stimulating investment.
Burnside and Dollar (2000), also investigated the relationship between foreign aid, economic
policies and GDP growth rate. Their main focus was i) to analyse whether the effect of aid is
conditional on economic policies, ii) to investigate if donor countries allocate more aid for
those who pursue good economic policies, and iii) to investigate other factors that determine
aid flows. They used a set of panel data averaged over 4 years for low and middle income
countries both together and separately. They also constructed policy index by making a
regression analyses on
GDP growth rate using initial GDP, ethnic fractionalization,
assassinations, financial depth and regional dummies (sub-Sahara Africa and East Asia)
including the main policy variables which are inflation, budget surplus and trade openness. So,
using the coefficient of these policy variables they constructed a policy index to address their
objectives. Regarding the estimation techniques, they used both OLS and 2SLS and the test
for endogenity of aid showed that there is no correlation between aid and the error term
evidencing for exogeneity of aid in the model.
Their result indicated insignificant effect of aid on economic growth. However, when they
include the interaction term between aid and policy index they found a significant positive
effect of aid on economic growth while a negative significant coefficient emerged from the
quadratic term (aid square) from the OLS result. Their result implied that the effect of aid on
economic growth is positive for those countries that follow good policies and aid is found to
work at diminishing return. However, the result from the 2SLS lacks significant coefficient
15
though the magnitude is the same as OLS and their reason for such difference was inrelevance of the instrumental variables. To test for the robustness of their results, they
dropped middle income countries from the regression and the result remains the same.
Regarding the allocation of aid the coefficient for initial GDP and population is negative and
significant showing that small and poor countries get more aid. The other variables including
the policy index had the expected sign though they lack significance. On the other hand,
multilateral aid is largely a function of income, population and good policy (Burnside &
Dollar, 2000).
In general, they stressed on the importance of policy state and distortions to aid and
acknowledged that government consumption or expenditure increases especially when aid is
channelled through bilateral sources and this partly explains why aid does not work in some
countries.
2.2.2 Indirect effect of ODA through saving & investment
“Theoretically the main role of aid in stimulating economic growth is to supplement
domestic sources of finance such as saving, thus increasing the amount of investment
and capital stock” (Hudson & Mosley, 2001) p.1024. However, studies revealed mixed
results as elsewhere where some reported no positive effect of aid on saving (Boone, 1996).
Selaya and Thiele (2012), also asserted that aid provided as budget support could undermine
the quality of governance and that such type of aid might lead to low saving and investment.
Others place importance on the policy priorities and available resources at the disposal of aid
recipient countries when it comes to harmonizing between budgetary consumption and
investments in projects (Cordella & Dell'Ariccia, 2007). In their extensive review, Hansen
and Tarp (2000), concluded that aid had a positive effect on saving and thus on economic
growth as revealed in the majority of the cases reviewed while only one study reported a
negative correlation. These findings revealed that the effect of aid on saving and investment
depends to a certain extent on the type of aid itself, the policy environment and the channel of
aid inflows i.e., bilateral or multilateral.
2.3 Official Development Assistance and Sectorial growth
In the above section we have seen different literatures on the direct relationship between aid
and economic growth in the developing aid recipient countries. Yet the result remains
inconclusive. The reasons behind this inconclusive nature of aid and economic growth have
16
been also studied by many scholars. Poor quality of data, different in econometrics tool
employed, heterogeneity of aid recipient countries, aid motives, mode of delivery, types of aid,
etc. were among the reasons for the inconclusive nature of the relationship between aid and
economic growth. On the other hand, rather than focusing only on the aggregate effect of aid
on economic growth it is worthwhile to see the channels in which aid might affect the overall
economic growth of developing countries. These channels could be the different economic
sectors that have significant impact on the level of economic growth of any countries.
However, many studies have been dealt with the issue at aggregate level, except few studies,
and neglect the different effect aid might have on the sectorial growth (Herdt, 2010). This is
because; the effect of aid might come though different sectors such as agriculture,
manufacturing or service among others no matter what the result might be when seen as a
whole. The existing literature regarding the effect of foreign aid on the different economic
sectors will be discussed in detail in the following sections.
2.3.1 ODA and Manufacturing sector
The effect of aid on the manufacturing sector is ambitious from previous papers. Some of the
papers found positive effect while others found negative effect of aid on the sector. According
to the previous work, foreign aid affects this sector through two channels. The first one is
through increasing domestic inflation rate while the second is by increasing nominal exchange
rate. Aid will increase national income which in turn rise consumption of goods which is
mostly spent on non-traded goods like construction, health care and education and this will
put upward pressure on the domestic inflation rate (Rajan & Subramanian, 2011; Selaya &
Thiele, 2010). An increase in consumption or demand for goods directly increases the price of
non-traded goods because the price of traded goods is determined by the world market while
the price of non-traded goods is determined by domestic market. The second channel is the so
called Dutch disease 6 . The Dutch disease predicts that the inflow of foreign aid to the
economy which produce both tradable and non-tradable goods, puts upward pressure on the
nominal exchange rate and make the domestic currency more strong (Selaya & Thiele, 2010).
6
Dutch Disease: is the impact of aid on recipient countries, because of inflow of capital,
which causes real exchange rate appreciation and reduce the competitiveness of the
exportable sector in the developing aid recipient countries.
17
The combined effect of the above two channels will end up in the appreciation of the real
exchange rate (Rajan & Subramanian, 2011; Selaya & Thiele, 2010).
Aid
increase consumption
raise the price of non-traded goods
domestic inflation
will increase
Aid
availability of more foreign currency
raise the value of domestic currency
nominal exchange
rate overvaluation
However, whether the above problem could happen or whether the problem has a negative or
positive effect on economic growth of recipient developing countries are doubtful. On one
hand, Rajan and Subramanian (2011) argued the persistence of the above problem because of
aid inflow and that the growth of the traded sector is negatively affected by the problem. He
also stressed the importance of the traded sector for any countries as the manufacturing sector
is the engine for growth take-off for most of fast growing developing nations. On the other
hand, Selaya and Thiele (2010) stressed on the absence and even positive impact of the above
problem for the growth of manufacturing sector. Their reason for the absence of the problem
was availability of idle capacity in the developing countries while the fact that most of aid
recipient countries are net importer could make the above problem even positive. Their
arguments are explained in detail as follows:
Rajan and Subramanian (2011) in their extensive study on the effect of aid on manufacturing
sector concluded that aid adversely affected the growth of the sector and these effects are felt
through exchange rate overvaluation induced by aid. These authors also reminded that many
fast growing countries experienced this problem when they first embarked on development
and advised to create conditions that generate competitive exchange rate. They widely
acknowledged that the growth and competitiveness of the manufacturing sector for most of
the developed nations and countries in transition helped in the take-off of their economy.
Moreover, Ram (1987) and Rajan and Subramanian (2011) stated that the export sector
stimulates economic growth by bringing
know how from abroad while competition
stimulates productivity gain and trade may entails technology transfer. This makes aid
detrimental to economic growth by reducing the competitiveness of the recipient countries.
On the other hand, Selaya and Thiele (2010) also empirically examined the linkage between
foreign aid and sectorial growth. They decomposed the sectors into tradable and non-tradable
to investigate whether the effect of aid is different among these economic sectors. They
18
proxies the tradable sector with the non-service GDP and sum of agricultural sector and
industrial sector while the proxy for non-tradable sector is the value added in the service
sector. Their result indicated positive effect of aid both at aggregate level (though at
diminishing return) and sectorial decomposition and the result are equally strong for both
sectors. Contrary to Rajan and Subramanian (2011), Selaya and Thiele (2010) found the
evidence for the absence of Dutch type disease.
Selaya and Thiele (2010) have discussed two reasons for the absence of the negative impact
of aid on the growth of the traded sector. The first reason focuses on the absence of
overvaluation of real exchange rate while the second reason stressed on the importance of real
exchange rate overvaluation itself. The absence of overvalued exchange rate was because of
the availability of idle capacity in the developing countries. This implies that an increase in
the aggregate demand because of foreign aid inflow will be followed by the expansion of
aggregate supply induced by the idle capacity. Though the stimulated demand because of aid
put upward pressure on domestic inflation, on the other hand, an increase in aggregate supply
will reduce the upward pressure on the level of inflation and then prevent the RER
overvaluation. The importance of real exchange rate overvaluation comes from the fact that
developing country’s production function is mainly dependent on imported input. As
production in most of aid recipient countries is dependent on imported goods, overvaluation
of RER implies reduction of cost of production for those countries (Selaya & Thiele, 2010).
This means aid is not detrimental for the growth of traded sectors given the idle capacity and
the dependency of production function on imported inputs in the developing aid recipient
countries.
2.3.2 ODA and Service sector
Aid could be helpful in attaining basic needs like schooling and health services by financing
social sectors. Gomanee, Girma, and Morrissey (2005) on page 300 wrote that:
“…aid might not have a direct impact on welfare...however if aid affects the amount of
public expenditure directed at areas that enhance welfare (health, education, water and
sanitation), then aid can indirectly contribute to levels of welfare.”
This shows how aid might affect welfare or economic development through affecting those
sectors which produces domestically produced goods and services like education and health
19
which directly impact human development and then bring desired change. The health budget
for many low income countries is highly dependent on foreign aid and donor institutions are
more active in helping the health sectors of developing countries (Buse & Walt, 1996). WB
lending for health increased significantly in the 1990s as health care needs are increasing with
population expansion and aging (Walt et al., 1999). Aid can also improve human capital
since it may bring know how which is important for economic growth (Hudson & Mosley,
2001; Rajan & Subramanian, 2008). Easterly (2003) also mentioned a case where aid given to
water projects in Ethiopia saved life and let children’s go to school which might be of
negligible economic benefits in the short run.
According to Gomanee et al. (2005), foreign aid has a significant effect on human
development index by lower infant mortality rate and the effect is more strong for those with
lower levels of human development indicators and higher values of infant mortality rate. They
stressed that aid can finance expenditures which help to get universal access to primary
education and health care without affecting the income level of aid recipient nations. Though
the indirect effect of aid, through financing social sector, is well explained, they did not take
the problem of endogeneity into account while they already confirmed that ‘aid tends to
decline as income rises’.
On the other hand, Williamson (2008) investigated the effect of development aid on the health
sector and the result showed that aid is not significantly improved overall health in developing
aid recipient countries. The paper employed a fixed effect model with instrumental variable to
control for endogeneity of aid in the model. The instrumental variable used in the paper was
two and three years lagged value of foreign aid to health sector. He showed the robust of the
result to different specification, different indicators of health, additional control variables and
replacement of foreign aid to health with general foreign aid.
2.3.3 ODA and Agricultural sector
Agricultural production in developing regions underwent growth by two and half times since
the 1960s with Asia increasing its products by around four folds (Herdt, 2010). Herdt (2010),
asserts that development aid could have contributed to agricultural growth in different ways:
(i) Aid might provide inputs for agricultural production either for free or at subsidized rate
(moving along the production function), (ii) help improve production efficiency by improving
20
marketing and information flow (shifting a production function) 7, and (iii) aid might have
helped to create technology with higher inherent productivity so that available inputs produce
more outputs (a completely new, higher production function). It is thus essential to see the
trend of ODA to support the agricultural sector over the years. Beyond the growth of
agricultural sector the non-agriculture sector will be benefited as the surplus from agriculture
is an input for other sectors and thus leading to overall economic growth (Herdt, 2010).
According to the Food and Agricultural Organization of the United Nations (FAO, 2007), a
sustained reduction in hunger, one of the MDGs, is only achieved by placing especial
emphasis on agricultural and rural development. Nevertheless, despite the acknowledged link
between agricultural sector growth, sustainable economic growth and poverty reduction, the
total share of ODA allocated to the agricultural sector has sharply dropped from 1980 to 2008
(Herdt, 2010; Kaya et al., 2012). Aid to agriculture decreased by two-thirds in real terms
between those years with the steepest decline in the late 1980s and 1990s being 17% to 8%, in
the same order. In the 2008, aid to the sector amounted to only 4% of total ODA while the
total ODA by all donors nearly tripled between 1980s and 2008 (Kaya et al., 2012).
According to Gupta, Pattillo, and Wagh (2006), roughly one-fourth of ODA was provided in
the form of technical cooperation which saw a hike for social infrastructures while those to
economic infrastructure agriculture, and industry has dropped from 25 to 16% for subSaharan Africa.
2.4 Aid and Policy Environment
Bourguignon and Sundberg (2007), have discussed the links from aid to its final outcome.
They identified three links of aid, through policies, to the final outcome. The first one is the
link between donor organization, being governmental or non-governmental, and policy
makers. These shows how donor organizations influence policy makers either through aid
conditionality or technical assistance. The second is the link from policy makers to policies:
this is how the local government leads to good governance, bureaucratic efficiency and
institutional quality. The third linkage between aid and final outcome is the link from policies
to outcomes which focuses on how policies affect economic growth. Below the conditionality
issue of the relationship between aid and economic growth will be explained in detail.
7
An increase in production efficiency will make the inputs more productive so that more output can be
produced from the same amount of inputs or the same amount of output can be produced using less input
making the production function to shift upward.
21
Conditionality of aid
Despite the initial optimism that dominated the emergence of development aid to unleash
economic growth, the internal drivers of growth depends to a great extent on the internal
conditions of the individual countries (Sagasti, 2009). Accordingly many authors attributed
aid ineffectiveness to prevalence of corruption in the recipient country and binding policy
environment (Djankov, Garcia-Montalvo, & Reynal-Querol, 2006; Svensson, 2000a, 2000b;
Tezanos et al., 2013), weak institutional framework (Shirley, 2005), problem of fungiblity of
aid (Chatterjee, Giuliano, & Kaya, 2007; Hudson & Mosley, 2001), geographical disadvantageous (Dalgaard et al., 2004; Rajan & Subramanian, 2005; Selaya & Thiele, 2010).
Burnside and Dollar (2000), also reported that bilateral aid in particular increased government
consumption confirming the fungible nature of aid.
Burnside and Dollar (2000) found that the performance of aid is better under less distortive
policies and that aid had affected policies and that aid had a positive effect on growth. This is
because of the importance of those variables for economic growth: Bourguignon and
Sundberg (2007) on page 278 explained that “...countries with good track records of
macroeconomic management...as well as good governance do better at development, whether
measured as growth, literacy or infant mortality”. In general, there are three models which
explain the conditionality of aid. These models are good policy model, institutional model and
medicine model and each of them will be explained in the subsequent sections:
2.4.1 Good policy model
This model emphasizes on the importance of good policy for the effectiveness of aid. Better
macroeconomic policies like fiscal, monetary and trade policies are needed to make aid more
effective. According to this model aid works if the recipient country pursues good polices and
is harmful in countries pursuing bad policies (Collier & Dollar, 2002). Having good
macroeconomic policy environment could help to overcome the negative part of aid like
Dutch disease. Better monetary policy would grant aid flow to be spent primarily on private
investment rather than consumption and hence reducing the Dutch disease problem (Younger,
1992). Some studies found that the effectiveness of aid on economic growth is positively
affected by good macroeconomic policies (Burnside & Dollar, 2000; Collier & Dollar, 2001).
On the other hand, this model has been strongly criticized, since no evidence for the
conditionality of aid on better macroeconomic policy environment was found (Gomanee et al.,
22
2005; Hansen & Tarp, 2001; Hudson & Mosley, 2001; Rajan & Subramanian, 2005; Selaya &
Thiele, 2010).
For example, according to Selaya & Thiele (2010), the effectiveness of aid is not affected by
the quality of macroeconomic policy environment but limited in those countries where the
climate conditions are dis-advantageous. Hence, geographical factors were found more
important than the policy environments. However, the definition of “good policy” is different
in some cases, for example Hudson and Mosley (2001) restricted the definition of good policy
to free market policies while others used trade openness, inflation and budget balance to
construct a policy index. According to Hudson and Mosley (2001), good policy should first
address the root causes of aid ineffectiveness like fungibility 8 and distortions to make aid
effective and they focused on the importance of good governance and rule of law than
macroeconomic policy environment.
2.4.2 Institutional model
This model asserted that aid is effective if the institutional context of aid recipient country is
good. Good governance is the main drivers of economic growth. Studies revealed that good
governance and democracy have indirect positive effect on human capital by increasing
income (Klomp & de Haan, 2013). So, this shows how far institutional quality is important
for economic growth via human capital accumulation which is the most determinants of
economic growth. Understanding the importance of governance to bring economic growth,
many organizations like IMF, WB and bilateral donors have been imposed governance as a
requirement to give a fund (Bräutigam & Knack, 2004). If the institutional quality of the
country is poor aid might be detrimental to economic growth. For example, Azarnert (2008)
identifies insufficient institutional development and bureaucratic inefficiency as a reason for
aid ineffectiveness. Tezanos et al. (2013) also found that aid is more effective in less
corrupted countries while other studies stressed on marginal importance of institutional
quality for the effectiveness of aid (Collier & Dollar, 2001).
8
“Fungibility implies how well aid gets translated into growth may not depend on the specific purpose aid is
given for, or the intent behind it, but how well the recipient translates all expenditure to growth” (Rajan &
Subramanian, 2005) p.655
23
2.4.3 Medicine model
This model emphasizes on the non-linear relationship between aid and economic growth
implying that aid is good if given moderately and harms if taken in excess, just like most
medicines. Despite the rhetoric on international development forums on developed world’s
commitment to lift the low-income countries from poverty trap through increased aid,
evidences show the trending down in the ODA per capita as well as percent of gross national
income (GNI) of recipient countries (Nkusu, 2004), and yet many countries rely on such
assistance to finance major projects. On the other hand, increased allocation of aid was
blamed to induce macroeconomic management problems for those countries whose ODA saw
increment over the years that raise the concerns for “Dutch Disease” which is the long term
undermining effect of “too much aid”. A significant result of non-linearity of aid and
economic growth was reported by (Hansen & Tarp, 2001). Some authors also stressed the disadvantageous of high aid as it creates dependencies, corruption and deteriorations of
governance (Bräutigam & Knack, 2004; Hudson & Mosley, 2001; Moyo, 2009). Kourtellos,
Tan, and Zhang (2007), on the other hand, found no evidence for non-linearity of aid and
economic growth. So, the effectiveness of too much aid has been debated due to mixed
empirical evidences from cross country analysis.
24
Chapter Three: Methodology
3.1 Data and data source
We use a panel data collected from aid recipient developing countries for which data are
available over the period of 1985 to 20119 based on data availability. Accordingly the sample
covers a group of 91 countries for the aggregate equation, 87 for the manufacturing sector and
88 for both service sector and agricultural sector. The list of sample countries included in the
analysis is given in Appendix E. All recipients of ODA and for which data is available are
taken into account to tackle sample selection problem. The period is selected to analyse the
recent impacts of aid based on availability of data. All the data used in this paper is secondary
data extracted from online databases including the World Development Indicator (WDI),
Barro and Lee dataset and polity IV database. All data except human capital and the level of
democracy are extracted from WDI. Data on human capital is extracted from Barro and Lee
dataset while the level of democracy is obtained from polity IV database which contains
annual information on regime and authority characteristics. Temperature and precipitation
data are extracted from Dell et al.(2009). All data are expressed as a share of recipient’s
country’s GDP.
3.2 Description of variables
The dependent variables in this thesis are the annual growth rate of real GDP per capita for
the aggregate equation and growth rate of sectorial value added in each sector for the sectorial
equations. The growth rate of real GDP per capita which measures the annual growth rate of
per capita income is taken as a dependent variable since it represent the overall economic
growth (Williamson, 2008). In the sectorial decomposition equations, the dependent variables
9
The sample does not include 2012 because many of the variables including the interest variable (ODA) are not
updated for the year.
25
measure the growth rate of value added in each sector to GDP. The WB defined valued added
as the net output of a sector after adding up all outputs and subtracting intermediate inputs. As
of WB, the service sector includes value added in hotels and restaurants; transport; education;
health care; and real estate service be it governmental or personal service. The agricultural
sector includes “production of primary commodities including cultivation of crops, livestock
production, forestry, hunting and fishing” (World Bank, 2014).
The variable of interest in this thesis is ODA, which covers donors’ bilateral and multilateral
aid flows to developing countries. ODA is measured as a share of GDP of recipient countries.
ODA embraces loans and grants with concessional terms (includes grants and loans with the
later containing grant element of at least 25%) from both bilateral donors and multilateral
agencies which are given for the purpose of promoting economic development and welfare in
developing countries (ODA, 2012).
Beyond the variable of interest there are a number of explanatory variables included in the
model. These explanatory variables are the determinants of economic growth which help to
control for the differences in economic growth in the sampled countries and help to avoid
omitted variable bias in the estimation technique. Economic theory and recently published
papers are used to select the most important control variables. This comprises: initial GDP;
governance quality (IQ); human capital; trade openness; monetary policy; government
expenditure; life expectancy; gross investment; financial depth; agricultural land as a share of
total land; share of rural population as a percentage of total population and regional dummies.
These variables are explained in the subsequent sections.
3.2.1 Why control variables?
The above variables are taken as control variables since they are the determinants of
economic growth in many theories. In his extensive study for a panel of 100 countries Barro
(1996b), concluded that economic growth of any economy is positively affected by human
capital, life expectancy, better rule of law and trade openness given the initial real per capita
GDP while economic growth is negatively related with the initial level of real per capita GDP
and government consumption. Each of the control variables that are included in the model are
motivated in detail as follows:
26
Initial GDP per capita: Initial GDP per capita with five years lag is taken into account to
control for conditional convergence10 among the sample countries. The idea of conditional
convergence according to Barro (1996b), comes from the extended version of neoclassical
growth model where the growth rate depends on the relationship between the initial level of
output and the target/long run output. Hence the lower the initial per capita GDP in relation to
its target or long-run position the higher the growth rate of GDP per capita will be. So, for a
given level of target output the growth rate and the initial level of output is negatively related
to each other (Barro, 1991, 1996b). This conditional convergence has been tested and found to
be significant in almost all empirical studies on economic growth. However, the conditional
convergence of sectorial growth lacks theoretical justification and we include initial GDP per
capita in the sectorial equation assuming that the sectorial growth rates is supposed to come
from differences in initial per capita incomes (Selaya & Thiele, 2010). The natural logarithm
of initial GDP per capita is taken to normalize and interpret the coefficients in terms of
percentages.
Democracy: is taken as a proxy for governance quality. The index of democracy is taken
from polity IV database as a proxy for political institution which captures the level of
democracy in each country. The index assigns a value that ranges from -10 to +10 with the
higher values showing better democracies based on institutional characteristics such as
openness of elections or constraints on the executive (Fulginiti, 2010). It indicates the quality
of governance institutions which creates better environment for economic growth. According
to Rivera‐Batiz (2002) and Rodrik (2008), governance has a strong effect on economic
growth by reducing corruption which in turn stimulates technological change, increase capital
inflow, increase domestic rates of return to capital and finally spur economic growth.
Moreover Barro (1996a), stressed that democracy has a favourable effect on economic growth
through maintaining the rule of law, reducing fertility rate by stimulating human capital 11and
reducing government consumption by limiting government expenditure on unproductive
investments.
Human Capital: is captured by secondary school enrolment rate. According to Herdt (2010)
education is usually identified as the level of human capital. Secondary school enrolment
10
The idea of convergence is derived from diminishing return to capital in neoclassical model where countries
that have less capital per worker relative to their long run target output tends to grow faster because of
higher rate of return. And this convergence is conditional since the long-run target income per capita is a
function of saving, population growth rate and position of production function that varies across countries
(Barro, 1996b).
11
More political right encourages female education which reduces fertility rate.
27
measures the number of students enrolled in the secondary school relative to the total
population of the corresponding age group (Barro, 1991). Human capital is the most important
factor of economic growth in history of economic development by increasing the ability to
innovate new ideas; adopt foreign technologies; increase physical investment and reduce
fertility rate 12 (Barro, 1991, 1992, 1996b; Edwards, 1993; Herdt, 2010; Mincer, 1984;
Petrakos & Arvanitidis, 2008). Educated people are more responsive to changes and adapt it.
Moreover, Barro (1996b) explained how human capital, through learning by doing, brings
discoveries that immediately spilled to the entire economy since knowledge is non-rival.
Therefore, as human capital is one of the most important variable to bring economic growthit should be controlled to identify the effectiveness of aid.
Trade openness: This is the share of export and import to GDP. It captures the relationship of
a country with the international market or the openness of the economy to the other nations.
Countries that are more open to the rest of the world have greater ability to absorb
technological advances from developed nations and use resources more efficiently because of
competition (Barro, 1996b; Edwards, 1993, 1998; Yanikkaya, 2003). Moreover, Trade helps
open countries to enjoy absolute/comparative advantage through the import of goods and
services that are otherwise costly for the economy and export of goods and services that is
cheaper for the economy to produce them domestically. In addition, open economy are able to
absorb negative shocks than closed economy through trade balance. For example, an economy
that experiences a sudden natural disaster which reduces domestic production would simply
balance the demand and supply side by importing from the rest of the world.
Monetary Policy: is captured by the inflation rate. Inflation can be considered as the best
single indicator of monetary policies which have a negative impact on economic growth and
indicates poor government performance to regulate the economy (Barro, 1996b; Fischer, 1993;
Rodrik, 2008; Tezanos et al., 2013). It is measured by the natural logarithm of one (1) plus the
inflation rate13.
Government consumption: Ram (1986) theoretically discussed two point of view on the
relationship between economic growth and government consumption as follows. On one hand,
large government consumption decreases economic growth because i) government operations
12
Barro (1991)p.p 409 concluded that “Human capital is more productive in producing goods and additional
human capital rather than more children” and Becker, Murphy, and Tamura (1994) also explained where high
stock of human capital reduces the demand for more children because of its high opportunity cost.
13
Because log of zero is undefined in case inflation rate is zero
28
are often conducted inefficiently ii) the regulatory process imposes excessive burdens and
costs on economic system, and iii) many government policies tend to distort economic
incentives and lower productivity (Rodrik, 2008). On the other hand others assign the
importance of government consumption in stimulating economic growth by harmonizing
conflicts between private and social interests; prevention of exploitation of the country by
foreigners and securing an increase in productive investment (Ram, 1986). However, Ram
(1986) in his investigation argued on the importance of government consumption to foster
economic growth specially in developing countries. On the other hand, negative effect of
government consumption by financing unproductive investment is also reported (Barro, 1991,
1996b; Landau, 1983) .
Life expectancy: is used to proxy health status. Life expectancy, being the proxies for health
status and quality of human capital, has a positive effect on economic growth by increasing
the productive capacity of labour force (Barro, 1996b; Sachs & Warner, 1997).
Financial depth: This variable captures the financial sector (size of banks and other financial
institutions and markets in a country) relative to the economy. Private credit relative to GDP
is a proxy variable of financial depth which is defined as domestic private credit to the real
sector by deposit money banks as percentage of local currency GDP (World Bank, 2013).
Real exchange rate: as the exchange rate is one of the most important determinants of the
manufacturing sector of any economy, it is used to control for the differences in the growth of
this sector across the sample countries. High real exchange rate (undervaluation of currency)
tends to foster economic growth, independent of global economic environment, in developing
countries by stimulating the tradable sector (Rodrik, 2008). Since the entire sample countries
in this thesis are from developing countries, the coefficient of this variable is expected to be
positive. On page 366 Rodrik (2008) argued that “Overvalued currency are associated with
foreign currency shortages, rent seeking and corruption, unsustainably large current account
deficits, balance of payment crises and stop and go macroeconomic cycles all of which are
damaging to growth”. However, including this variable in the regression model will reduce
the sample countries by half (from 91 to 45) since the data for this variable is missing for
many countries. Despite its importance, because of missing data, this variable is dropped from
the regression model.
Agricultural land as a share of total land and Rural Population share: this is taken to
control for the differences in agricultural sector across the sample countries.
29
Regional dummies: East Asian dummies and sub-Saharan African dummies are also
included in the regression model. There is an observed evidence for surprisingly low
performance in sub-Saharan African countries and high economic growth in East Asian
countries (Kaya et al., 2012). Therefore, this is to control for the regional differences among
the sample countries.
3.2.2 Which and Why interaction terms?
Institutional quality and inflation rate is taken as interaction term with aid to test for
conditionality of aid on better institutional environment and monetary policy, respectively.
This is because of the assumption that aid could be more effective in countries with better
policies and institutional environment. As already discussed under both introduction and
literature review part, many papers explained why aid is effective in some countries and why
not in other countries. Conditionality of aid, being one reason for aid ineffectiveness in some
regions, is a hot issue discussed by many recent papers. The general theoretical argument
behind this proposition is that aid effectiveness is explained based on the situation of recipient
countries’ circumstances. Among these circumstances macroeconomic policy, governance
structure and climate conditions were the most proposed variables that affect the aid
effectiveness. However, previous papers focused on the policy environment where they
construct a policy index using trade openness, inflation and budget balance altogether. But, it
is also important to see the effect of these policy variables on the effectiveness of aid
separately since it helps both donors and recipient countries to know to which these policy
variables aid might respond well. So, in this thesis, the focus is on monetary policy and the
level of democracy. Better monetary policy helps to make aid flow spent primarily on
investment rather than consumption. Governance quality is also very important since the
allocative efficiency of foreign aid to the economic pillars requires political commitment and
responsibility as aid is directly given to government officials. Moreover, the aid square term is
also included in the regression model to control for the non-linearity of aid. This shows
whether the effectiveness of aid in bringing economic growth depends on the amount of aid
received or not.
30
3.3 Econometrics Techniques
The model is estimated using econometric software STATA. The estimation technique
applied is a regression analysis on panel data to empirically test the aid-growth relationship.
Accordingly a regression analysis is performed to see the effect of aid on economic growth at
aggregate level and sectorial growth in the sectorial decomposition.
According to Verbeek (2008), a panel data is a repeated observation on a set of crosssectional units, in our case countries. This methodology has a number of advantages over
cross sectional and time series data. Firstly, a panel data model has a power of addressing the
problem of unobservable heterogeneity through including country fixed effects (Hansen &
Tarp, 2001; Rajan & Subramanian, 2005, 2008; Verbeek, 2008). Moreover, potential missing
variable and multi-collinearity problem is smaller, and also the estimates are usually more
efficient due to many observations (Verbeek, 2008).
Panel data model
In a panel data we have two models, random effect (RE) and fixed effect (FE) which are
described as follows:
.............................................1
.......................................2
Where,
is a K-dimensional vector that include all explanatory variables, i denotes unit
(country), t denotes time,
are unit specific intercept and
is the mean intercept.
The fixed effect model (equation 1) is a linear regression model in which the intercept terms
( ) vary over the individual units (in this case countries). The random model (equation 2) the
intercept term is a random walk which is independently and identically distributed over
countries and thus included in the error term (Verbeek, 2008). Therefore, in the model
specified under equation 2, the total residual consists of a unit- specific time invariant random
term (αi) and the common error term (
). As RE model assumes that αi is part of the error
term, αi should not be correlated with the other explanatory variables in the model (Verbeek,
2008). Therefore, we need different estimation technique for FE and RE. If the explanatory
variables are uncorrelated with the country fixed effect, both FE & RE are consistent while
31
RE is more efficient (because of both within and between exploitation). If the country fixed
effect is correlated with the error term of the model FE is consistent while RE is not and in
this case one should go for FE. In order to know the correct model, we need to run the
Hausman specification-test in which under the null hypothesis both estimator will not differ
much but RE is more efficient. If the null hypothesis gets rejected one should go with fixed
effect since RE is inconsistent.
Which estimators?
Foreign aid disbursements are obviously determined to some extent by the recipient country’s
growth process itself. Developed nation may give aid for those who have success history
while others may give aid to those who are poor or experience natural disaster (Rajan &
Subramanian, 2008). This might show high correlation (positive/negative) between aid and
economic growth but it never shows a causal relationship between aid and growth rate.
Hansen and Tarp (2001) stated the difficulty of taking foreign aid as it is given independent of
the income level of recipient countries. This could make the aid variable endogenous
regressor in the model. Endogeneity might be caused by omitted variables, wrong direction,
simultaneity and autocorrelation with lagged dependent variable.
Kaya et al. (2012),
mentioned two major reasons for the endogeneity of aid variable. These are possible ‘reverse
causation’ where the amount of aid received by the recipient is determined by past
performance and ‘simultaneous causation’ where an omitted variable may affect both aid and
growth. This brings problem of endogeniety of aid where aid itself is determined in the model.
In the presence of the above problem, ordinary least square (OLS) estimator will be
inconsistent and interpreting the model in-terms of causal relationship will be invalid
(Verbeek, 2008). Therefore, other estimators that can be consistent in presence of such
problem should be applied. Durbin-WU-Hausman test will be used to test this problem. If the
problem of endogeneity existed, Instrumental Variables (IV) estimator which accounts for
possible endogenous nature of aid could be a solution for the problem. However, in this paper
both OLS and IV estimator will be used to investigate also the difference that might come
from different estimators. However if the problem of endogeneity is occurred in the model,
one should rely on the estimates of the IV estimator as the OLS estimates are biased.
32
3.4 Instrumental Variable Estimation Method
If one of the explanatory variable in the model is endogenous, the unknown parameters in the
model will not be identified, so that we need additional moment condition which is derived
from instrumental variables (Verbeek, 2008). IV estimator allows consistent estimates when
the explanatory variable is correlated with the error term in the regression model. According
to Verbeek (2008), to apply IV estimations we need to have instrumental variables that should
satisfy two things. The first is that the instrumental variable must be sufficiently correlated
with the endogenous explanatory variables (strong vs. weak) once we control for the
independent variables in the model which help identify the parameters.
The second criterion is that the instrumental variable should not correlate with the error term
in the model (valid vs invalid). This means the instrumental variable should not be correlated
with the dependent variable in the model. If the instruments are weak, the IV estimation will
cause large variance relative to OLS while invalidity of instruments will cause the estimates
to be inconsistent. Recently published papers were used to select these instrumental variables.
Selected Instrumental variables
The following variables are used as instrumental variables: lag temperature; lag precipitation;
lag Ethnic fractionalization times precipitation; lag Ethnic fractionalization times temperature;
lag temperature times square log of population and lag log population times initial GDP
The reason behind using temperature and precipitation as instrumental variable is that most of
poorest countries are located either in highly tropical areas, or in places that are considerably
far from the tropical lands and obviously more aid is given for poor countries (Rajan &
Subramanian, 2005, 2008; Selaya & Thiele, 2010). Guillaumont and Chauvet (2001) also
found that aid allocation had been influenced by environmental conditions. On the other hand,
donors prefer to send money to smallest countries (by population size) for strategic purpose or
to get more influence over recipient countries (Rajan & Subramanian, 2005; Selaya & Thiele,
2010).
Once these variables are selected, the test for relevance (strong vs weak) and validity of the
selected instrumental variables should be done. If instrumental variables are not valid
meaning if they are correlated with the error term in the model the estimators are biased. On
33
the other hand irrelevance of instrumental variable, if weakly correlated with the variable they
are instrumenting, would lead to large variance.
Validity of instrumental Variables
The validity of instruments is tested by the Sargan-Hansen test of over-identifying restriction.
The joint null hypothesis is that the instruments are valid instruments (uncorrelated with the
error term in the model). The test statistics is distributed as chi-square with degree of freedom
equal to number of excluded instruments minus the number of specified endogenous
regressors. For 2SLS estimator (like in this case) the test statistics is Sargan’s statistics and
with robust Hansen’s J statistics will be reported. If the instrumental variables used are valid
the null hypothesis should not be rejected. This test is reported under 2SLS estimator result
table.
Relevance of instrumental variables
The relevance of instruments can be checked by many ways. First, Staiger & Stock (1997)
provide a rule of thumb for the case of a single endogenous variable. According to this rule
the instruments are strong if the value of the F statistic from the regression of the endogenous
variable (ODA) on the set of selected instruments is greater than 10. The second approach is
a test of Stock and Yogo (2005) which is reported under 2SLS result. The null hypothesis of
this test is that instruments can cause large biases relative to OLS estimator. If the error terms
are identically and normally distributed Cragg-Donald F-statistics otherwise KleinbergenPaap F-statistic will be reported. Rejection of null hypothesis (if corresponding F-statistics is
greater than the critical value) indicates instrumental variables are looking good.
3.5 How to check endogeniety
Using 2SLS estimator, a test for endogeneity of one or more endogenous regressors can be
tested by entering a command endog (var.endogenous) option. Under the null hypothesis the
specified endogenous regressors can be treated as exogenous variable and the test statistics is
distributed as chi-square with degree of freedom equal to number of variable tested. Another
option to test endogeniety of endogenous regressor is to use Durbin-WU-Hausman test and
the procedure is as follows,
Durbin-WU-Hausman test
34
Ho: cov (AID, ui) = 0
Aid is exogenous
Ha: cov (AID, ui) ≠0
Aid is endogenous
Step 1. Regress the AID variable on the instrumental variables and get the residuals, εt
step 2. Estimate the original model adding the residual term εt
step 3. Test whether the parameter for εt is significantly different from zero using standard ttest.
step 4. Reject Ho, if the parameter εt differ significantly from zero.
3.6 Specification of the Model:
Our model has four specifications. The first two specifications address the direct effect of aid
on the growth rate while the remaining specifications address the sectorial objectives. The
sectorial decomposition includes manufacturing, agricultural and service sectors assuming the
overall production in the economy comes from these three sectors (Selaya & Thiele, 2010). A
regression analysis of the growth rate of real GDP per capita (
) and sectoral growth on
aid, aid-conditioning variables (inflation and democracy), and other determinants of economic
growth will be applied in the first and third specifications. In the second and fourth
specification, in addition to explanatory variables, an interaction terms between aid and
conditioning variables are included for both aggregate and sectorial equations. Each of the
specifications is modelled as follows:
Equation 1: This equation is the main equation for the aggregate effect of aid on economic
growth. It attempts to answer the first research questions (the effect of aid on the growth of
real GDP per capita).
...................................1
Equation 2: This equation is also an aggregate equation but in this case aid interaction terms
are included in the model. Each interaction terms is included in a separate model. It attempts
to answer whether the effectiveness of aid to bring economic growth is conditional on
inflation rate and the level of democracy. Besides it also investigates the hypothesis of
medicine model.
35
................2
Equation 3: This equation is the same with the first equation in a sense that all explanatory
variables included are the same. The difference between the first and this equation is that in
this case the dependent variables are the sectorial growths. So, this equation attempts to
answer the third research question.
...............................................3
Equation 4: This is also the same with the second equation with respect to the explanatory
variables included in the model. The only difference is again the dependent variables, where
in this case the dependent variables are the growth rate of sectorial growth. This model
attempts to answer whether the effectiveness of aid in the sectorial growth rate is conditional
on the conditioning variables mentioned above.
..........4
Where,
is the annual growth rate of real GDP per capita in country i at t time period,
is growth rate of value added of sector K to real GDP,
is the log of initial GDP per capita (five years lag),
is ODA as a share of GDP,
is a vector of control variables, all initial values
.. is a vector of conditioning variables namely aid square, inflation rate and the level of
democracy
αi and
i
shows country specific constant (since the model is estimated using fixed effect
model), and εit= is white noise.
Interpretation of coefficients (equation 1)
0:
shows the conditional convergence among the sample countries given that it is negative.
1:
responsiveness of growth to aid
: measures the extent to which the effectiveness of aid depends on the conditioning variables
36
λz indicates the joint effect of other variables that determine long run economic growth.
Note: the coefficients of the second equation have the same interpretations as the first
equation (except for the initial GDP per capita) although in the second equation the dependent
variables are the growth rate of value added of each sectors rather than the growth rate of real
GDP per capita.
Chapter Four: Results
Descriptive Statistics
Appendix 1 summarizes the mean and standard deviations for all variables used in the
regression model. From the table one can observe the overall mean variations in the growth
rate of real GDP per capita and sectorial value added across different regions.
The average growth rate of real GDP per capita for all countries included in the sample is
obtained to be 1.66% annually. Further investigation of the average growth rate of real GDP
per capita regionally shows that the average real per capita GDP growth rate is high for East
Asian countries (5.21%) while sub-Saharan African countries reported the least average GDP
per capita growth rate (0.92%). The average growth rate of real GDP per capita in Latin
America, East Europe and central Asia and Middle East & North Africa is obtained to be 1.57,
1.94 and 1.45, respectively. From this one can see that the average growth of real GDP per
capita for East Asia and sub-Saharan African countries is quite different from other regions
while the average growth rate in other regions is somehow similar. These result confirmed the
recent high economic performance in East Asian countries and the low performance of
economic growth in sub-Saharan African countries in terms of the growth rate of real GDP
per capita they have registered.
The summary statistics also shows that the average growth rate of manufacturing sector value
added is 4.09% while the average growth of the service sector value added is 4.15% annually
for all countries included in the analysis. Comparatively speaking, the average growth rate of
the value added in the manufacturing and service sector is almost similar while the average
growth rate for agricultural sector value added is lowered by half (2.42%). The average
sectorial growth rate is higher than the average growth rate of GDP per capita since the
growth for sectors is not in per capita terms.
37
If the average growth rate of the value added in each sector is seen regionally, the average
growth rate of both manufacturing and service sector is high for East Asian countries which
confirmed the recent high performance of the region. Moreover this shows that the East Asian
countries are more of manufacturing and service based economy while the growth of
agricultural sector value added for the region (2%) is lowest following East Europe and
central Asia (1.3%). The average growth rate of agricultural sector is high for Middle East and
North Africa (4.36%) followed by Sub-Saharan African countries (2.82%). On the other hand,
the result also confirmed the fact that the economy of sub-Saharan African countries is
agricultural based following the Middle East and North Africa where the average growth rate
in Agricultural sector is the highest. The growth rate of the manufacturing sector is also
registered to be high in the sub-Saharan African countries. The average growth rate of the
value added in the three sectors is almost similar for the Middle East and North Africa
showing, at least, similar growth rate of the sectors in the region. In the case of East Europe
and central Asia, the growth rate in the service sector is high following East Asia while the
growth rate in the agricultural sector is the lowest.
Regarding the interest variable, on average, ODA accounts for 8.7% of GDP of 155 aid
recipient countries. Sub-Saharan Africa is the largest recipient of ODA where ODA
constitutes 13.37% of their GDP followed by East Europe and Central Asia (4.45%), Latin
America (3.38%) and Middle East and North Africa (2.93%).
Partial correlation between the dependent variables and the interest variable (ODA)
Appendix C summarizes the partial correlation coefficient between the dependent and interest
variables. As can be seen from the appendix the partial correlation coefficient between ODA
and real GDP growth rate is 0.013 but it is not statistically significant. This indicated
insignificant weak positive partial correlation between the growth rate of GDP per capita and
ODA. The partial correlation coefficient between ODA and the sectorial growth rate is almost
similar. The same with the above result, there is a weak and positive partial relationship
between ODA and sectorial growth rate except for the growth rate in the service sector value
added. The partial correlation between the growth of the service sector and ODA is 0.03
which is also statistically significant. The sign of partial correlation coefficient implies that
countries that receive large amount of foreign aid tend to grow faster than those countries
which receive less foreign aid though the relationship is very weak and statistically
insignificant. In general, the partial correlation between the dependent variables and the
38
interest variable are almost the same, both in terms of magnitude and sign, with a weak
positive relationship. The scatter plot of dependent variables and ODA also confirms these
results (see figure below) which showed a very weak positive relationship. From the graph
we can observe that most of the observations are scattered around the same points with
exception of some outliers. As can be seen from the graph the growth rates are highly
exaggerated which raises doubt on the data. These scatter plots are based on all the sample
countries we have choice to include in the regression analysis. However, in the econometrics
results we already dropped out the outliers because of missing data for those countries. The
summary statistics and the scatter plot for those observations included in the analysis can be
found in Appendix A2. Both the partial correlation coefficient and the scatter plot only show
the relationship between the two variables, so that we cannot derive any conclusions from
200
these correlation coefficients regarding the direction of causality.
100
150
Tajikistan
Liberia
Kyrgyz Republic
Trinidad and Tobago
Mongolia
Sudan
Mozambique
Gabon
Albania
Cote
d'Ivoire
Nepal
Botswana
Mozambique
Cambodia
Sudan
Cambodia
Albania
Iran,
Islamic
Mongolia
Lesotho
Fiji
Togo
Botswana
LesothoCongo, Dem. Rep.
Mongolia
Costa
RicaRep.
Togo
Nepal
Jordan
Congo,
Rep.
Rwanda
Mali
Iran,
Islamic
Rep.
Kyrgyz
Republic
Zimbabwe
Malaysia
Mauritius
Sudan
Botswana
Cambodia
Uganda
Fiji
Jordan
Cote
d'Ivoire
Malawi
Rwanda
Tunisia
Liberia
China
China
Peru
Mozambique
Singapore
Malaysia
Malaysia
Zambia
Thailand
Kyrgyz
Republic
Liberia
Gambia,
Cambodia
Nepal
The
Jordan
Cambodia
Trinidad
Malaysia
and
Congo,
Tobago
Ghana
Rep.
Jordan
Togo
Malawi
Mozambique
China
Peru
Uganda
Trinidad
and
Tobago
Lesotho
Thailand
Thailand
Thailand
Cambodia
Thailand
Cameroon
Papua
Lesotho
New
Guinea
China
India
Pakistan
Papua
New
Guinea
Malaysia
Mauritius
Nepal
Cambodia
Lao
Mozambique
Rwanda
PDR
China
Botswana
Sierra
Jordan
Leone
Malaysia
Ukraine
Gabon
Mauritius
Zimbabwe
Mongolia
Lesotho
Mozambique
Uruguay
India
Uganda
Cambodia
Uganda
Saudi
Arabia
Trinidad
Gabon
Trinidad
and
Tobago
Malaysia
and
Pakistan
Tobago
Lesotho
Saudi
Arabia
Kazakhstan
Peru
Panama
Kazakhstan
Vietnam
Lao
Lao
Togo
PDR
PDR
Malaysia
Peru
Vietnam
Congo,
Uganda
Rep.
Rwanda
Gambia,
The
China
Lesotho
Guyana
Lao
Mozambique
PDR
Singapore
Ukraine
Dominican
Fiji
Republic
Benin
China
Indonesia
Fiji
Gabon
Vietnam
Mongolia
Nepal
Cambodia
Tajikistan
Rwanda
Lesotho
Liberia
Iran,
Islamic
Rep.
Indonesia
Indonesia
Fiji
Lao
Zambia
PDR
Korea,
Rep.
Malaysia
Uruguay
Iran,
Islamic
Thailand
Thailand
Rep.
Thailand
Botswana
Malaysia
Dominican
Indonesia
Dominican
Tunisia
Gabon
Republic
Republic
Cameroon
Vietnam
Zimbabwe
Vietnam
Iran,
Islamic
Malaysia
Rep.
Malaysia
India
Philippines
China
Peru
Thailand
Indonesia
Congo,
Jordan
Senegal
Dem.
Cote
Malawi
Rep.
d'Ivoire
Mexico
Costa
Iran,
Islamic
Rica
India
Rep.
Costa
Rica
Jordan
Mauritania
Guyana
Saudi
Arabia
Costa
Rica
Thailand
Indonesia
Bangladesh
Togo
Bangladesh
India
Panama
Fiji
Vietnam
Lao
PDR
Lao
PDR
Saudi
Arabia
Mexico
Brazil
Singapore
Mexico
Singapore
Iran,
India
China
Islamic
Uruguay
Rep.
Kazakhstan
Botswana
Indonesia
Congo,
Pakistan
Rep.
Tanzania
Rwanda
Gambia,
The
Singapore
Slovenia
India
China
Singapore
Ecuador
Thailand
Indonesia
Bangladesh
Egypt
Vietnam
Jordan
Tajikistan
Cambodia
Tanzania
Nepal
China
Iran,
China
Malaysia
Islamic
Rep.
Thailand
India
Indonesia
Dominican
Pakistan
Bangladesh
Philippines
Republic
Sri
Uganda
Tajikistan
Tanzania
Lanka
Uganda
Guyana
Korea,
Korea,
China
Rep.
Rep.
China
Malaysia
Peru
Ecuador
Ukraine
Sri
Panama
Lanka
Congo,
Sri
Jordan
Dominican
Sri
Lanka
Sri
Lanka
Lao
Nepal
Benin
Lanka
Rep.
Tanzania
Kyrgyz
Nicaragua
Sierra
PDR
Republic
Republic
Leone
China
India
Pakistan
Gabon
Papua
Cambodia
Benin
Sri
Tanzania
Lanka
New
Mozambique
Zambia
Guinea
Brazil
Singapore
China
Trinidad
Algeria
Dominican
India
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otswana
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50
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20
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-40
Congo, Dem. Rep.
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-5
0
5
ODA %GDP_lag
Services annual growth rate
ODA %GDP
Fitted values
A
Figure 1: partial correlation between dependent variables and ODA
39
w
F
4. Econometrics Results
Outline of the results
In this section we will present the econometrics results of the effect of aid on the aggregate
economic and sectorial growth. In section 4.1, we will present the result of OLS estimator and
section 4.2 will present the result of 2SLS for both aggregate and sectorial impact. In section
4.3 we will compare the results in terms of estimators (OLS vs. 2SLS) and the effect of ODA
across sectors. Section 4.3 briefly describes why the estimates of OLS and 2SLS are different.
This section also discusses why the impact of aid across sectors is different. Moreover, this
section will discuss which estimator (OLS or 2SLS) is better for which specification. In
section 4.4 the effect of aid in different regions based on income level will be presented to
check for robustness of our result. Finally, more detail discussion of the results and
comparison of our finding with previous result will be discussed in discussion part (chapter 5).
The estimated effect of aid on the aggregate growth and sectorial growth are displayed from
table 1 through 4. All explanatory variables are lagged one period to allow time for those
variables to bring the expected change except GDP per capita. GDP per capita is lagged 5
years to tackle for simultaneity problem as aid can affect both GDP per capita and growth rate
if taken in the same period. Moreover, initial GDP per capita, ODA, inflation, financial depth,
trade openness, capital formation and government consumption are transformed to
logarithmic form to make sure that the samples are more normally distributed.
Table 1 shows the direct effect of aid on the growth rate of real GDP per capita while the
remaining tables (2, 3 and 4) present the effect of aid on the sectorial growth. The second
table reveals the effect of ODA on the growth of value added in manufacturing sector. Table 3
displays the effect of ODA on the service sector growth rate while the fourth table shows the
effect that aid might have on the growth of agricultural sector value added.
40
Each table has 8 columns and all explanatory variables are included in all columns. The
regression results of the first four columns are estimated using OLS estimator while the last
four columns are estimated using 2SLS estimator. The main difference between each column
is that in the first and fifth columns there are no aid interaction terms included while in the
rest columns at least one aid interaction terms are included. From table 1 through 4 aid square
term is included in the regression model in column 2 and 6, aid interaction with inflation is
included in the third and seventh columns while the interaction between aid and the level of
democracy is included in the fourth and eighth columns.
Pre-estimation tests
Before running a regression, it is important to check different tests or assumptions that are
necessary for the estimation technique. To check for the constant variance assumption we
tested whether the error term is homoscedastic or not. The Modified Wald test is used to test
this assumption in which under the null hypothesis the variance is constant or homoscedastic.
For all equation, the null hypothesis is rejected and the result indicated violence of this
assumption. We corrected for this violation by taking the robust standard errors. We also
checked for multicollinearity of independent variables in the model using a variance inflation
factor (VIF). The general rule of thumb for this test is that a VIF of 10 and above indicated a
multicollinearity problem. In our case, the result indicated absence of this problem since the
value of mean VIF is 1.76 (see appendix D). We will come to the endogenous nature of the
aid under the result of 2SLS (section 3.2) and for a moment we assumed aid is strictly
exogenous.
4.1 Result of OLS Estimator
4.1.1 The effect of ODA on economic growth
The aggregate effect of ODA on the growth of real GDP per capita is presented in table 1
from column 1 through 4. In column 1, the coefficient of aid is significantly positive
indicating a positive effect of ODA on the growth rate of real GDP per capita. The point
estimates indicates, a one percent increase in ODA disbursement have raised the real GDP per
capita growth rate by 0.76 percent keeping all other explanatory variables constant. The result
suggests a positive effect of ODA in stimulating economic growth. All explanatory variables
have their expected effect at least in their sign except financial depth which enters
significantly but with wrong direction (see discussion part for further explanation). The results
suggest that economic growth is positively determined by trade openness, democracy and life
41
expectancy while economic growth is negatively affected by initial GDP per capita, inflation
and financial depth. The results show importance of trade openness, good level of democracy
and life expectancy in furthering economic growth. The point estimates indicate, on average,
a one unit increase in democracy and life expectancy has increased the growth rate by around
0.07 and 0.21 percent, respectively. Moreover, the growth rate of real GDP per capita will
increase by 2.14 percent as trade openness increases by one percent indicating that those
countries which are open to international markets are economically grow faster than closed
economy.
On the other hand, initial GDP per capita, inflation, and financial depth have a significantly
negative effect on the growth rate of real GDP per capita. The negative sign of the coefficient
of inflation rate suggests negative impact of bad monetary policy for economic growth. The
coefficient of initial GDP per capita which is significantly negative shows the presence of
conditional convergence among the sample countries included in the regression model.
Accordingly, a one percent increase in initial GDP per capita, inflation and financial depth is
associated with a 0.92, 0.53, and 0.65 percent reduction in the growth rate of real GDP per
capita, respectively.
The coefficient of human capital, capital formation, government and share of rural population
are not statistically significant. This shows absence of direct relationship between economic
growth and the above variables. However, those variables could affect the growth rate of GDP
per capita indirectly through affecting other variables. In general the results indicate those
countries with high level of democracy, life expectancy, good monetary policy, low level of
initial income per capita and open to international markets will grow faster than others.
From column 2 through 4 on the same table, the aid interaction terms, namely aid square, aid
interacted with inflation and aid interacted with the level of democracy, are included in the
regression model to test for aid conditionality. These interaction terms show whether the
effect of a change in aid on the growth rate of GDP per capita depends on the amount of aid,
inflation rate, and the level of democracy.
A square aid term is included to capture the non-linear relationship between growth and aid
where aid might be subject to diminishing returns while aid interacted with inflation is added
to examine dependency of aid effectiveness on monetary policy. However, the coefficients of
aid square and aid interacted with inflation rate are statistically insignificant (see column 2
42
and 3). These results suggest the absence of diminishing effect of aid and un-conditionality of
aid on monetary policy. On the other hand, the interaction between aid and democracy is
turned to be significantly positive implying for presence of institutional model. To interpret
this coefficient, we have taken the average democracy level (2.03) of those countries included
in the analysis and multiply by the coefficient of the interaction term (0.03). Accordingly the
result suggests that for those countries with average level of democracy the effect of a one
percent increase in aid on economic growth will be increased by 0.06 (2.03*0.03) percent.
This means a one percent increase in aid flow will increase the growth rate by 0.77 (0.06
+0.71) for those countries with average level of democracy. In general, the results of OLS
estimator showed that aid has a positive and direct effect in stimulating economic
performance without diminishing returns. And that the monetary policy environment would
not affect the impact of aid on economic performance. However, aid is found to be more
effective in those countries where the level of democracy is high which indicates importance
of good governance for aid to bring the expected impact. So the result indicates that being
democratic or having good governance will stimulate the effect of aid in achieving the desired
impact on economic performance.
43
Table 1: regression result of the effect of aid on the growth of real GDP per capita
Dependent Variable: Average growth of real GDP per capita
OLS
OLS
OLS
OLS
2SLS
2SLS
2SLS
2SLS
var.
1
2
3
4
5
6
7
8
_________________________________________________________________________________________________________
Aid
0.76***
0.82***
0.87***
0.71***
1.85***
1.79***
3.09***
1.82
(0.20)
(0.25)
(0.25)
(0.21)
(0.57)
(0.56)
(0.97)
(0.57)
Aid2
0.05
0.09*
(0.05)
(0.05)
Aid_inf
-0.05
-0.41**
(0.07)
(0.16)
Aid_democ
0.03*
0.02
(0.02)
(0.02)
Initial
-0.92**
0.92**
-0.88**
-0.84**
-0.40
-0.49
0.17
-0.33
GDP
(0.42)
(0.42)
(0.43)
(0.40)
(0.52)
(0.51)
(0.61)
(0.52)
Human
0.03
0.03
0.03
0.03
0.05*
0.05*
0.07**
0.05*
Capital
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
Infli_
-0.53***
-0.53***
-0.50***
-0.53***
-0.60***
-0.59***
-0.31*
-0.60***
lation
(0.16)
(0.16)
(0.17)
(0.16)
(0.12)
(0.12)
(0.17)
(0.12)
Trade
2.14**
2.04***
2.19***
2.14***
2.35***
2.19***
2.73**
2.36**
(0.63)
(0.62)
(0.65)
(0.63)
(0.65)
(0.65)
(0.65)
(0.65)
Democ
0.07*
0.07*
0.07*
0.03
0.05
0.05
0.05
0.03
0.04)
(0.04)
(0.04)
(0.04)
(0.03)
(0.03)
(0.03)
(0.04)
Life_
0.21***
0.21***
0.21***
0.21***
0.22**
0.22***
0.23***
0.22
expect
(0.06)
(0.06)
(0.06)
(0.06)
(0.09)
(0.09)
(0.09)
(0.09)
finan_
-0.65**
-0.63*
-0.69**
-0.70**
-0.60*
-0.60*
-0.87***
-0.63*
depth
(0.32)
(0.32)
(0.32)
(0.32)
(0.33)
(0.33)
(0.32)
(0.33)
capital_
-0.28
-0.34
-0.30
-0.29
-0.7
-0.75
0.99
-0.72
formation
(0.69)
(0.69)
(0.69)
(0.68)
(0.58)
(0.59)
(0.63)
(0.58)
governm_
-0.86
0.93
-0.83
-0.87
-1.5***
-1.56**
-1.30*
-1.49*
consump
(1.06)
(1.03)
(1.06)
(1.05)
(0.77)
(0.76)
(0.75)
(0.77)
agic_land
-0.05
-0.05
-0.05
-0.07
-0.06
-0.06
0.04
-0.07
(0.04)
(0.04)
(0.05)
(0.04)
(0.05)
(0.04)
(0.05)
(0.05)
Rural_POP
-0.06
-0.07
-0.06
-0.07
-0.08*
-0.08*
-0.08*
-0.08*
44
_cons
Number of
Countries
Number of
Observation
(0.04)
-3.27
(6.7)
91
1775
Hausman_
Specification
p-value
0.0000***
Hansen Jp-value
Specif_tes
Endogeneitytest
(0.05)
-2.93
(6.76)
(0.04)
-3.94
(6.82)
(0.04)
-2.91
(6.70)
(0.04)
(0.04)
(0.04)
91
91
91
86
86
86
1775
1775
1775
1598
1598
1598
0.213
0.169
0.040**
0.0580*
(0.04)
86
1598
0.0000***
The robust standard errors are in brackets. The ***,
10%, respectively.
0.438
0.0135**
0.264
0.035**
** and * shows statistical significance level at 1%, 5% and
45
4.1.2 The effectiveness of ODA and the growth of manufacturing sector
The estimated effect of aid on manufacturing sector is presented from column 1 through 4 of
table 2. In column 1, one can observe that aid has a significant positive effect on the growth of
manufacturing sector. A one percent increase in the aid flow resulted in a 0.97 percent
increase in the growth of manufacturing sector value added keeping all other variables
constant. This shows importance of foreign aid in expanding the growth of manufacturing
sector. Moreover, manufacturing sector is positively and significantly affected by life
expectancy and rural population whereas inflation rate, financial depth and government
consumption revealed a significantly negative effect on the growth of the sector. These show
that that life expectancy, rural population share, inflation rate, financial depth and government
consumption are important factor for the growth of the sector.
The coefficient indicates that if life expectancy increases by one unit the growth rate of the
sector will go up by 0.38 and a one percentage point increase in rural population will
increases the growth rate of the sector by 0.20 percent. These show that, on average, in
countries with high rural population, low inflation rate, high life expectancy and low
government consumption the growth of the manufacturing sector will be higher compared to
others. The point estimates also indicate a one percent rise in inflation rate, financial depth
and government consumption will reduce the growth of the sector by 0.83, 1.69 and 4.02
percent, respectively keeping all other variables constant. The result, however, showed
insignificant direct role of human capital, capital formation, level of democracy and initial
GDP per capital in explaining the growth of manufacturing sector since the coefficients of
these variables are not statistically significant. As a summary, from OLS estimator, one can
conclude that an increase in life expectancy, rural population and a decrease in inflation rate,
financial depth and government consumption are all important factors that directly foster the
growth of the manufacturing sector.
The interaction terms are included from column 2 through column 4 of the same table to test
if aid effectiveness in the manufacturing sector depends on the proposed conditioning
variables. Inclusion of these variables in the regression leaves the significance of other
variables unchanged. The same with column 1, the result indicated positive effect of aid on
the growth of manufacturing sector and the effect works without diminishing returns.
Furthermore, the effect of aid does depend neither on monetary policy nor on the level of
democracy since the coefficients of the interaction terms are not statistically significant. These
46
indicate no direct link between aid effectiveness and good monetary policy, and governance
level for the growth of manufacturing sector.
47
Table 2: regression result of the effect of aid on the growth of Manufacturing value added
Dependent Variable:
Average growth rate of manufacturing value added
OLS
OLS
OLS
OLS
2SLS
2SLS
2SLS
2SLS
var.
1
2
3
4
5
6
7
8
_________________________________________________________________________________________________________
Aid
0.97**
1.03**
1.10**
0.89**
4.35***
4.34***
6.35***
4.40*
(0.37)
(0.43)
(0.51)
(0.38)
(1.17)
(1.14)
(1.88)
(1.17)
2
**
Aid
0.05
0.21
(0.08)
(0.11)
Aid_inf
-0.06
-0.92***
(0.14)
(0.33)
Aid_democr
0.03
-0.02
(0.03)
(0.04)
Initial
0.55
0.55
0.59
0.59
1.6
1.42
2.27
1.56
GDP
(1.32)
(1.32)
(1.34)
(1.30)
(1.26)
(1.25)
(1.44)
(1.24)
Human
0.02
0.02
0.02
0.02
0.09
0.10
0.11*
0.09
Capital
(0.06)
(0.06)
(0.06)
(0.06)
(0.06)
(0.06)
(0.07)
(0.06)
Infli_
-0.83***
-0.83***
-0.79***
-0.83**
-0.93***
-0.92***
-0.26
-0.94***
lation
(0.32)
(0.32)
(0.30)
(0.32)
(0.29)
(0.30)
(0.34)
(0.30)
**
**
***
Trade
2.35
2.25
2.42
2.37
3.47
3.00
4.64
3.45**
(1.82)
(1.84)
(1.82)
(1.81)
(1.47)
(1.51)
(1.51)
(1.47)
Democ
-0.03
-0.03
-0.03
-0.07
-0.05
-0.05
-0.04
-0.02
(0.08)
(0.08)
(0.08)
(0.07)
(0.07)
0.07)
(0.07)
(0.08)
Life_
0.36**
0.37**
0.36**
0.37**
0.34***
0.35***
0.35***
0.33**
expect
(0.17)
(0.17)
(0.17)
(0.17)
(0.13)
(0.13)
(0.13)
(0.13)
finan_
-1.69**
1.68**
-1.73**
-1.73
-1.26*
-1.25*
-1.83***
-1.25*
depth
(0.72)
(0.72)
(0.72)
(0.71)
(0.69)
(0.69)
(0.68)
(0.70)
**
**
capital_
-1.64
-1.70
-1.66
-1.65
-3.06
-3.22
-3.25
-3.04**
formation
(1.77)
(1.77)
(1.78)
(1.78)
(1.48)
(1.48)
(1.48)
(1.47)
governm_
-4.02
-4.06**
-4.01
-4.04
-5.09***
-5.19*
-4.94***
-5.08*
consump
(1.83)
(1.86
(1.83)
(1.83)
(1.87)
(1.88)
(1.87)
(1.86)
agic_land
0.01
0.004
0.01
-0.01
-0.07
-0.08
-0.03
-0.06
(0.09)
0.09)
(0.09)
(0.10)
(0.09)
(0.09)
(0.09)
(0.09)
48
Rural_POP
_cons
0.21**
(0.09)
-23.06
(12.63)
Number of
Countries
87
Number of
Observation
1597
Hausman_
Specification
p-value
0.0001***
Hansen Jp-value
Specif_tes
Endogeneitytest
0.21**
(0.09)
-22.71*
(12.53)
87
1597
0.21**
(0.09)
-23.72
(12.83)
0.21**
(0.09)
-22.63
(12.67)
0.10
(0.10)
-0.09
(0.10)
-0.12
(0.11)
87
87
80
80
80
80
1597
1597
1438
1438
1464
1464
0.442
0.452
0.227
0.468
0.0005***
0.0008
0.0013
0.0003
0.10
(0.10)
0.0005***
The robust standard errors are in brackets. The ***,** and * shows statistical significance level at 1%, 5% and 10%,
respectively.
49
4.1.3 The effectiveness of ODA and the growth of the service sector
Table 3, from column 1 through 4, presented the estimated effect of aid on the service sector
using OLS estimator. Like the previous result, the effect of aid turned out to be significantly
positive on the growth of the service sector which indicates positive effect of aid on the
growth of the sector. A one percent increase in ODA disbursement has raised the growth rate
of the service sector by 0.63 percent keeping all explanatory variables unchanged. The
coefficients of all other variables were as expected and almost similar with the aggregate
regression of table one. As expected, human capital, trade openness, and life expectancy are
found to affect the growth of the sector positively. On average, a one percentage point
increase in human capital and a unit increase in life expectancy have increased the growth rate
of the value added in the service sector by around 0.07 and 0.20 percent, respectively. This
suggests, on average, those countries with high level of human capital, trade openness and life
expectancy will register high growth of the service sector compared to others. Moreover, a
one percent rise of trade openness will increase the growth rate of the sector by 1.66 percent
indicating advantageous of being an open economy for the growth of the sector.
On the contrary, inflation, initial GDP per capita and government consumption revealed
significantly negative coefficients affecting the growth rate of the sector adversely. A one
percentage rise in inflation rate, government consumption and initial GDP per capita will
resulted in a reduction of the growth rate of the service sector by 0.89, 1.78 and 1.94 percent,
respectively. The remaining variables that are included in the regression model lack statistical
power in explaining the growth of the service sector directly.
From column 2 through column 4 of table 3, aid interaction terms are included in the
regression. As can be seen from these columns the coefficient of ODA remained statistically
significant in all specifications. This indicates that controlling for all the proposed
conditionality of aid, the growth of the sector is still positively affected by foreign aid. The
coefficient of aid square term is statistically insignificant indicating absence of medicine
model. This shows that the effectiveness of aid would not limited by the amount of aid
received. On the other hand, the interaction term between aid and inflation is turned out to be
statistically significant (see columns 2 and 3). This shows high impact of aid in stimulating
the growth of the sector in those countries with good monetary level. To interpret the
coefficient of the interaction term we have taken the average inflation rate (2.16) of those
countries included in the analysis. Accordingly, the coefficient indicates that for those
50
countries with average inflation rate, the effect of a one percent increase in aid flow on the
growth rate of the service sector will be reduced by 0.28 (0.13*2.16) percent. Meaning a one
percent increase in aid flow is associated with a 0.64 (0.92- 0.28) percent increase in growth
of the sector for those countries with average inflation rate. This shows that having good
monetary policy positively contributes to the impact that aid could have on the growth of the
sector. To test the conditionality of aid on the level of democracy, we have included an
interaction term between aid and the level of democracy. However, the coefficient of the
interaction term is not statistically significant showing unimportance direct role of the level of
democracy
for
aid
effectiveness
51
in
the
sector.
Table 3: regression result of the effect of aid on the growth of Service sector value added
Dependent Variable:
Average growth rate of Service value added
OLS
OLS
OLS
OLS
2SLS
2SLS
2SLS
2SLS
1
2
3
4
5
6
7
8
_____________________________________________________________________________________________________________
Aid
0.63***
0.65**
0.92***
0.60**
0.95
0.90
1.83*
0.95
(0.22)
(0.25)
(0.29)
(0.24)
(0.64)
(0.63)
(1.01)
(0.62)
Aid2
0.01
0.02
(0.04)
(0.05)
Aid_inf
-0.13*
-0.28*
(0.07)
(0.15)
Aid_democr
0.02
0.02
(0.02)
(0.02)
Initial
-1.94***
-1.94***
-1.84***
-1.91***
-1.82***
-1.87***
-1.41*
-1.75***
GDP
(0.60)
(0.60)
(0.61)
(0.59)
(0.67)
(0.66)
(0.75)
(0.65)
***
**
**
**
**
**
***
Human
0.07
0.07
0.08
0.07
0.09
0.09
0.10
0.09***
Capital
(0.04)
(0.04)
(0.03)
(0.67)
(0.03)
(0.03)
(0.04)
(0.03)
Infli_
-0.89***
-0.89***
-0.80***
-0.89***
-0.95***
-0.95***
-0.77***
-0.95***
lation
(0.27)
(0.27)
(0.25)
(0.27)
(0.18)
(0.18)
(0.19)
(0.18)
*
*
*
*
*
*
**
Trade
1.66
1.64
1.81
1.67
1.5
1.47
1.79
1.51*
(0.91)
(0.91)
(0.94)
(0.91)
(0.82)
(0.82)
(0.83)
(0.82)
Democ
0.06
0.06
0.06
0.04
0.07
0.07
0.07
0.05
(0.04)
(0.04)
(0.04)
(0.05)
(0.04)
(0.04)
(0.04)
(0.05)
Life_
0.2**
0.19**
0.19**
0.20**
0.21***
0.21***
0.21***
0.22***
expect
(0.09)
(0.09)
(0.09)
(0.09)
(0.06)
(0.06)
(0.06)
(0.06)
finan_
-0.06
-0.06
-0.15
-0.09
-0.1
-0.11
-0.28
-0.12
depth
(0.52)
(0.52)
(0.51)
(0.52)
(0.43)
(0.43)
(0.42)
(0.43)
capital_
0.92
0.91
0.88
0.91
0.83
0.82
0.65
0.81
formation
(0.66)
(0.66)
(0.66)
(0.67)
(0.68)
(0.68)
(0.70)
(0.68)
governm_
-1.78*
-1.79*
-1.69*
-1.78
-1.76*
-1.77*
-1.63*
-1.77*
consump
(0.97)
(0.98)
(0.94)
(0.97)
(0.95)
(0.96)
(0.92)
(0.95)
agic_land
0.01
0.004
0.01
-0.005
0.01
0.004
0.02
-0.01
(0.05)
(0.05)
(0.05)
(0.05)
(0.05)
(0.05)
(0.05)
(0.05)
var.
52
Rural_POP
_cons
-0.02
(0.05)
1.47
(8.26)
Number of
Countries
88
Number of
Observation
1626
Hausman_
Specification
p-value
0.0049***
Hansen Jp-value
Specif_tes
Endogeneitytest
-0.03
(0.05)
1.54
(8.30)
88
1626
-0.02
(0.05)
0.20
(8.16)
88
1626
-0.03
(0.05)
1.48
(8.27)
88
1626
-0.01
(0.06)
-0.01
(0.06)
-0.01
(0.06)
-0.01
(0.06)
80
80
80
80
1464
1464
1464
1464
0.739
0.725
0.829
0.769
0.689
0.734
0.0002***
0.482
0.595
The standard errors are in brackets. The ***, ** and * shows statistical significance level at 1%, 5% and 10%,
respectively.
53
4.1.4 The effectiveness of ODA and the growth of agriculture sector
The effect of aid on the growth of agricultural sector is displayed in table 4 from column 1
through 4. In column 1 the direct effect of aid on the growth rate of agricultural sector is
significantly positive. This suggests that aid has a strong effect in stimulating the growth of
the agricultural sector. Keeping all independent variables constant, the result showed that the
growth of the agricultural sector is increased by 0.70 percent as aid increases by one percent.
So the result indicates strong positive contribution of aid in stimulating the growth of
agricultural sector. Regarding the explanatory variables, the coefficients of most of the
variables in this case enter statistically insignificant with the exception of such variable as
initial GDP, trade openness, and agricultural land (see discussion part for this explanation).
Initial GDP per capita and trade openness have positive effect while the effect of agricultural
land as a share of total land has a negative effect on the growth of the sector with the latter a
puzzling result. Although the share of agricultural land is expected to increase the growth of
the sector, the result indicated negative effect. On the other hand, the growth of agricultural
sector is positively affected by initial GDP and trade openness while the economic
performance of the sector is negatively affected by the share of agricultural land. A one
percent increase of trade openness and initial GDP per capita will increase the growth rate of
agricultural sector by 1.26 and 3.22 percent while the growth rate of the sector is reduced by
0.22 percent as a share of agricultural land to total land increases by one percentage points.
From column 2 through 4 of the same table, aid interaction terms are included in the
regression model. In column 2, aid square term is included in the regression though the
coefficient lacks statistical significance while the main effect (coefficient of aid) is
statistically significant. Therefore, the result shows positive and significant effect of aid on the
growth of the agricultural sector without diminishing returns. This indicates that the
effectiveness of aid in the sector is not constrained by the amount of aid received. In column 3
and 4, aid interacted with inflation rate and aid interacted with the level of democracy are
included in the regressions. As can be seen from the table, the main effect of aid turned out to
be statistically insignificant while the interaction term between aid and democracy turned to
be positively significant. As before, we have taken the average level of democracy to interpret
this result. Accordingly, the coefficient of the interaction term indicates that, for average
countries, a one percentage increase in aid flow will increase the impact of aid on the growth
rate of the sector by 0.14 (0.07* 2.02) percent. Unlike the service and manufacturing sector,
54
the aid-inflation term is not statistically significant showing independence of aid effectiveness
on the monetary policy (see discussion part for the explanation). The statistical significance of
all other variables is the same with column 1 except that, in case where aid square and aiddemocracy terms are included in the regression, the coefficient of life expectancy became
statistically significant with the right direction.
55
Table 4: regression result of the effect of aid on the growth of Agricultural sector value added
Dependent Variable:
Average growth rate of Agricultural value added
Dependent
OLS
OLS
OLS
OLS
2SLS
2SLS
2SLS
2SLS
var.
1
2
3
4
5
6
7
8
___________________________________________________________________________________________________________________
Aid
0.7**
(0.34)
Aid2
0.81**
(0.39)
0.01
(0.08)
Aid_inf
0.37
(0.42)
Democ
Life_
expect
finan_
depth
capital_
formation
governm_
consump
agic_land
1.79*
(1.04)
1.71*
(1.02)
0.11
(0.08)
0.15
(0.11)
Aid_democr
Initial
GDP
Human
Capital
Infli_
lation
Trade
0.52
(0.33)
1.26*
(0.71)
-0.07
(0.05)
-0.25
(0.23)
3.22***
(1.12)
0.0005
(0.05)
0.16
(0.10)
-0.59
(0.48)
-0.93
(1.16)
0.91
(1.63)
-0.22***
1.27*
(0.71)
-0.62
(0.05)
-0.25
(0.23)
3.00***
(1.10)
0.0005
(0.05)
0.17*
(0.10)
-0.56
(0.49)
-1.06
(1.16)
0.78
(1.56)
-0.23***
1.15
(0.75)
-0.07
(0.05)
-0.35
(0.25)
3.03***
(1.11)
0.0003
(0.05)
0.16
(0.10)
-0.49
(0.49)
-0.88
(1.18)
0.81
(1.62)
-0.23***
2.64
(1.72)
1.75*
(1.01)
-0.17
(0.28)
0.07***
(0.03)
1.42**
(0.70)
-0.06
(0.04)
-0.24
(0.23)
3.23***
(1.11)
-0.07
(0.06)
0.18**
(0.09)
-0.71
(0.47)
-0.97
(1.13)
0.89
(1.62)
-0.27***
1.92*
(1.03)
-0.05
(0.06)
-0.25
(0.23)
2.87**
(1.27)
-0.02
(0.08)
0.17
(0.11)
-0.26
(0.64)
-0.76
(0.96)
-0.21
(1.34)
-0.26***
56
1.78*
(1.01)
-0.05
(0.06)
-0.24
(0.23)
2.64**
(1.27)
-0.12
(0.08)
0.18
(0.11)
-0.26
(0.64)
-0.84
(0.97)
-0.30
(1.33)
0.26***
2.39*
(1.24)
-0.03
(0.07)
-0.16
(0.29)
2.99**
(1.30)
-0.02
(0.08)
0.17
(0.11)
-0.32
(0.66)
-0.94
(0.99)
-0.24
(1.35)
-0.26***
0.06*
(0.03)
2.17**
(1.02)
-0.04
(0.06)
-0.24
(0.23)
2.87*
(1.26)
-0.10
(0.07)
0.19*
(0.11)
-0.32
(0.64)
-0.81
(0.96)
-0.24
(1.33)
-0.31***
Rural_POP
_cons
(0.08)
0.06
(0.08)
-19.20
(11.6)
Number of
Countries
88
Number of
Observation
1668
Hausman_
Specification
p-value
0.0041***
Hansen Jp-value
Specif_tes
Endogeneitytest
(0.08)
0.06
(0.08)
-18.31
(11.54)
(0.08)
0.06
(0.08)
-17.19
(11.76)
88
88
1668
1668
(0.09)
0.04
(0.08)
-18.34
(11.54)
(0.08)
-0.01
(0.1)
88
1668
(0.08)
-0.01
(0.1)
81
81
1502
1502
(0.08)
-0.03
(0.1)
(0.08)
-0.03
(0.1)
81
81
1502
1502
0.0006***
0.167
0.572
0.1402
0.691
0.186
0.270
0.241
0.388
The standard errors are in brackets. The ***,** and * shows statistical significance at level 1%, 5% and 10%,
respectively.
57
4.2 Instrumental variable Estimation result
The regression result of 2SLS estimation is presented from column 5 through 8 of table 1
through 4. These tables contain the estimated effect of aid on the aggregate growth, sectorial
growth and also conditionality of aid. Everything presented here is the same with the previous
tables in terms of the outline and sequence except that in this case the estimation technique is
based on 2SLS instead of OLS estimator. Before presenting the results of 2SLS regression,
the choice of instrumental variables and the result of different test that are necessary for 2SLS
estimation will be presented.
Choice of instrumental variables
We used, lag temperature; lag precipitation; lag Ethnic fractionalization times precipitation;
lag Ethnic fractionalization times temperature; lag temperature times square log of population
and lag log population times initial GDP as instrumental variables.
The reason behind using temperature and precipitation as instrumental variable is that most of
poorest countries are located either in highly tropical areas, or in places that are considerably
far from the tropical lands and obviously more aid is given for poor countries (Rajan &
Subramanian, 2005, 2008; Selaya & Thiele, 2010). Guillaumont and Chauvet (2001), also
found that countries that affected by a poor environment has been received more aid. And also
in most cases donors prefer to send money to smallest countries (by population size) for
strategic purpose or to get more influence over recipient countries (Rajan & Subramanian,
2005; Selaya & Thiele, 2010). Moreover, more aid is also allocated for those countries which
experience civil war because of ethnic fractionalization (Radelet, 2006).
Test for relevance and validity of instrumental variables
The general rule of thumb as proposed by Staiger & Stock (1997), only in the case of single
endogenous variable, is used to test if instrumental variables are strong or not. According to
this rule, instrumental variables are weak if the value of F-statistics from the regression of
endogenous variable on the set of all instrumental variables is less than 10 to insure that the
maximum bias in IV estimators to be less than 10%. Accordingly ODA was regressed on the
exogenous independent variables and a set of instrumental variables (listed above) and the Fstatistics from this regression is 65.61 which show the relevance of the instrumental variables.
58
Moreover, under 2SLS result, the test of weak identification and under identification is
reported. For all the three sectors both tests rejected the null hypothesis evidencing for the
relevance of the set of instrumental variables which indicated that the instruments were
sufficiently correlated with the endogenous regressors they are instrumenting.
There is no absolute test for the validity, COV (instrumental variable, error term ≠ 0) of
instrumental variables as the error term in the model is unobservable. However, Hansen’s J
statistics proposed a test of over identifying restrictions which is used as the test for the
validity of instrumental variables. Hansen J statistics is reported at the bottom of each table.
Hansen’s J statistics is a test of over identifying restrictions and it is used to test the null
hypothesis of no correlation between the instrumental variables and the error term.
Accordingly, the null hypothesis of validity of instrumental variables could not be rejected
which showed that the set of instrumental variables selected were not correlated with the error
term in the model evidencing for validity of instruments. A rejection of null hypothesis would
have cast doubt on the validity of the instrumental variables used.
Test for endogenous nature of foreign aid
Our main concern in aid-growth relationship is endogeneity. As already mentioned, taking aid
as something given independent of the income level of the recipient countries have its own
limitations. Therefore, we have tested it and the test for endogeneity of ODA is reported at the
bottom of each table. Accordingly, ODA become endogenous in the aggregate and
manufacturing sector specifications while for the service sector and agricultural sector
specifications it turned out to be exogenous (see the explanation for this in the example below)
and the result remained the same for all specifications. Therefore, for the aggregate and
manufacturing sector specification one should rely on the estimates of 2SLS as OLS estimator
is inconsistent and biased. On the other hand, both OLS and 2SLS estimators are consistent
for the agricultural and service sector specifications while the estimates are more efficient in
OLS estimator.
The fact that ODA is endogenous in the aggregate and manufacturing sector while it turned to
be exogenous in the service and agricultural sector seems that the reason behind the
endogenous nature of ODA comes from omitted variable bias. This means that our model
suffers from omitted variable problem where this omitted variable affect simultaneously both
the dependent variables and ODA. This will make the aid variable endogenous in the model
which makes OLS estimates biased and inconsistent.
59
How omitted variable bias causes endogeneity?
An omitted variable bias can cause endogeneity of one of the independent variable if the
omitted variable is correlated with that independent variable.
Assumption:
1. The covariance between ODA and the omitted variable is non zero.
Example:
...................1
With the assumption of : E(vi, t-1|ODA,zi,xi) =0
Suppose that the first model is the true model while we run the following misspecified model,
omitting the variable z from the regression.
....................................2
So in the second equation, the error term contains both the random error term and the omitted
variable, like that εi,t-1=β2zi,t-1 +vi,t-1. In the second or in the misspecified model it holds that:
COV(ODAi,t-1, εi,t-1 )= COV(ODAi,t-1, β2zi,t-1 +vi,t-1 )= β2cov(ODAi,t-1, zi,t-1 )
If the COV (ODAi,t-1, εi,t-1) = β2cov(ODAi,t-1, zi,t-1 )= 0, no problem otherwise ODA will be
endogenous in the estimated model. ODA will be exogenous if and only if β2= 0 because we
already assumed that COV (ODAi,t-1, zi,t-1 )≠0. In our model the variable ODA is similar in all
specifications so that the COV (ODAi,t-1, zi,t-1 )≠0 holds for all specifications. So, the
difference in the endogenous nature of ODA across the specification will depend on the value
of β2. It could be possible that the omitted variable might have a significant effect on the
aggregate and manufacturing sector while do not affect the growth of the service and
agricultural sectors. Therefore, as long as the effect of the omitted variable on the dependent
variable is not significantly different from zero (β2 is not significantly different from 0),
meaning no omitted variable bias, ODA is exogenous in the model. In general, given omitted
variable bias, ODA is endogenous in the estimated model as long as the omitted variable is
correlated with ODA.
60
If ODA is exogenous, both OLS and 2SLS estimators are consistent otherwise the OLS
estimates is inconsistent. Therefore, in the presence of endogenous variable in the regression
model, one should rely on the results of 2SLS estimators.
Therefore, once the important tests are presented the estimation result of 2SLS estimator
would be subsequently presented. The explanation of all columns is the same with previous
table where the result of OLS estimator is presented.
4.2.1 The aggregate effect of ODA on economic growth
The estimated effect of ODA on the growth of real GDP per capita is presented from column
5 through 8 of table 1. As can be seen from column 5 the coefficient of aid is positive and
statistically significant. This result is similar with what is reported under OLS result in terms
of statistical significance. However, in all regression, the point estimates of 2SLS are more
than doubled compared to the point estimates of OLS (see section 3.3 for explanation). The
result indicated that on average, a one percent increase in aid flow will raise the real GDP per
capita by 1.85 percent. So, the result indicated high contribution of foreign aid in stimulating
economic growth of the aid recipient countries. Unlike the result of OLS estimator, human
capital and government consumption are found to affect the economic performance of an
economy while the level of democracy and initial GDP per capita lack statistical power in
explaining the growth rate.
Human capital, inflation rate, trade openness, life expectancy, and government consumption
are found to affect the growth rate positively. Financial depth with wrong direction showed a
significant effect on the growth of real GDP per capita. Meaning, the result indicates negative
effect of financial depth on the growth rate. The coefficient of initial GDP per capita is also
negative as expected but not statistically significant showing absence of conditional
convergence among the sampled countries included in the regression model.
From column 6 through 8 aid interaction terms are included in the model. In all columns the
effect of aid is still positive and significant. In column 6 the medicine model or diminishing
returns to aid is investigated. To test this proposition aid square term is included as
independent variable in the regression. Unlike OLS result, the coefficient of aid square is
significantly positive evidencing for the presence of increasing return to aid. This suggests
that the more the amount of aid is the high its effect will be on the economic growth.
Moreover, to test the conditionality of aid on better monetary policy, we have interacted aid
61
with inflation rate in the regression (see column 7 of the same table). Adding aid-inflation
term in the regression model resulted in a sizeable change in the magnitude of aid coefficient
in the IV regression result. This shows that once we control for conditionality of aid
effectiveness on monetary policy in the regression, the impact of aid is higher in stimulating
economic growth showing disadvantageous of bad monetary policy for aid effectiveness. The
coefficient of the aid-inflation interaction term is significantly negative indicating the higher
the inflation rate, the lower the effect of aid on the growth rate will be. This indicated that, for
average countries in terms of inflation rate, a one percentage increase in aid reduces the
impact of aid on economic growth by 0.89 (0.41 * 2.16) percent. We can interpret this result
as, a one percent increase in aid flow increases the growth rate of an economy by 2.2 (3.090.89) percent for those countries with average level of inflation. This result confirms the
disadvantageous of bad monetary policy for aid effectiveness.
To test if the effectiveness of aid depends on better governance, aid interacted with the level
of democracy is also included in the regression (see column 8 of the same table). Contrary to
OLS result, the interaction between aid and democracy, however, lacked statistical
significance. The result showed insignificant role of the level of democracy for aid
effectiveness in stimulating economic performance.
4.2.2 The aggregate effect of ODA on manufacturing sector
The estimated effect of ODA on the growth of manufacturing sector is presented from column
5 through 8 of table 2. One can see from column 5 that, similar to OLS result, the coefficient
of ODA enters statistically significant showing positive contribution of ODA for the growth
rate of manufacturing sector. The point estimates shows that if aid is increased by one percent,
the growth rate of the sector will increase by 4.35 percent which is much higher compared to
OLS estimates (see discussion part for explanation of this result). Human capital, trade
openness and life expectancy has significant and positive effect on the growth of the sector
though human capital lacks direct impact in explaining the growth of the sector. A one
percentage increase in trade openness will resulted in a 3.47 percent rise in the growth of the
sector while the growth of the sector is increased by 0.34 percent as life expectancy increases
by a unit. As expected, inflation and government consumption are negatively and significantly
affected the growth of the sector. The growth rate of the sector is reduced by 0.93 and 5.09
percentage as inflation rate and government consumption are increased by one percent in the
same order. However, contrary to expectation, financial depth and capital formation have
62
significantly negative effect on the growth of the manufacturing sector (for explanation see
discussion part). A one percent increase in financial depth and capital formation will reduce
the growth rate of the sector by 1.25 and 3.22 percent, respectively.
From column 5 through 8, the aid interaction terms are included in the specification to
examine the conditionality of aid in bringing the expected effect on the growth of
manufacturing sector. The same with column 5 of table 1 the coefficients of aid square and
aid-inflation terms become significantly positive and negative, respectively. The coefficient of
aid square term indicates increasing return to scale meaning the more aid is given the more its
effect will be. The coefficient of aid-inflation term indicates that for those countries with
average inflation rate, the effect of a one percent increase in aid flow on the growth rate of the
sector will be reduced by 1.99 (0.92*2.16) percent. This can be interpreted as, a one percent
increase in aid flow increases the growth rate of the manufacturing sector by 4.36 (6.35-1.99)
percent for those countries with average level of inflation.
In the final column of table 2 aid interacted with the level of democracy is included in the
model though the term lacks statistical significance revealing unimportance of the level of
democracy in explaining aid effectiveness in the sector. The significance of all explanatory
variables’ coefficients is the same with basic specification (column 5) except for human
capital. In this case the coefficient of human capital revealed statistically significant with
positive sign. In general the result remained the same with column 7 of table 1 indicating
importance of good monetary policy for the effectiveness of aid in the sector.
4.2.3 The aggregate effect of ODA on the growth of Service sector
The estimated effect of ODA on the growth of the service sector is presented from column 5
through 8 of table 3. As can be seen from column 5, unlike OLS result (see column 1 of the
same table), the effect of ODA on the growth of the service sector became statistically
insignificant though the sign is positive. Human capital, trade openness, and life expectancy,
as expected, showed significantly positive effect while initial GDP, inflation rate and
government consumption revealed statistically significant negative coefficients.
In column 6, the aid square term is included in the specification to investigate the
conditionality of aid effectiveness on the amount of aid itself. The result indicated no
significant effect of aid as well as absence of diminishing returns to aid. In column 7 aidinflation term is included in the model. Now the effect of aid on the growth of the service
63
sector turned out to be significantly positive and that this effect depends on the monetary
policy. The same with regression result of the aggregate and manufacturing equation, the
effectiveness of aid is found to be conditional on the monetary policy of the recipient
countries similar to OLS result. The coefficient of aid-inflation term indicated that the effect
of aid on the growth of the sector is reduced by 0.61 (0.28*2.16) percent for those countries
which have average level of inflation rate. This indicates that a one percent increase in aid
flow will increase the growth of the sector by 1.22 (1.83-0.61) percent for average countries.
To test the conditionality of the effectiveness of aid on the level of democracy, the aiddemocracy term is also included in the model (see the last column of table 3). Like the results
of OLS, the interaction between aid and the level of democracy is positive although it did not
yield statistically significant coefficient. However, including the interaction terms changed the
significance of the interest variable as can be seen from column 3, 4 and 5 of the same table.
The coefficient of ODA was statistically significant only in column 4 where the interaction
between aid and inflation rate is included in the model. Regarding the significance of other
explanatory variables, inclusion of the interaction terms in the regression model leaves the
others the same as in the basic specification (column 5) with exception of minor changes in
the magnitude of the coefficients.
64
4.2.4 The aggregate effect of ODA on Agricultural sector
The regression result of the effect of ODA on the growth of the agricultural sector is
presented in table 4 from column 5 through 8. The same with manufacturing and aggregate
growth equation the effect of aid on the growth of agricultural sector is positive and
significant (see column 5). The point estimates indicates that, on average, a one percent
increase in aid flow increases the growth rate of the sector by 1.79 percent. So similar to OLS
result, though the magnitude in this case is more than doubled, the result indicates important
contribution of foreign aid for expanding the growth of agricultural sector. Like the result of
OLS estimator initial GDP and trade openness have statistically significant positive effect on
the growth of the agricultural sector while the share of agricultural land to total land have a
significant negative effect on the growth of the sector.
From column 6 through 8 of the same table, aid interaction terms are included in the
specification to identify the conditions that make aid effective in the agricultural sector. In
column 6 adding the aid square term in the model leave the statistical significance of the
coefficient of aid as it is while the coefficient of aid square term lacks statistical significance.
This indicated positive effect of aid on the growth of the sector without diminishing returns.
Contrary to the other equations, the interaction between aid and inflation term becomes
statistically insignificant (see column 7). However, the interaction term between aid and
democracy is found to be positive and significant showing that aid effectiveness in the
agricultural sector is conditioned by governance quality not by monetary policy or amount of
money. These results are similar with the results reported under OLS estimator where the
level of democracy is found to be important for aid effectiveness in the agricultural sector (see
discussion part for why democracy matters). The only difference between OLS and 2SLS
result is that, in the latter case the coefficient of aid is turned out to be significant once the
aid-inflation term and aid-democracy term are controlled in the regression.
The point
estimate of aid-democracy term indicates that the effect of a one percent increase in aid on the
growth of the sector will be increased by 0.06 for those countries with average level of
democracy.
Regarding the significance and sign of all other variables’, all the parameters are found to be
the same with the regression result of column 5 of the same table except minor changes in the
magnitude of the coefficients which shows the robustness of our result.
65
4.3 Comparison of the effect of ODA across sectors and estimators
Table below summarizes the effect of ODA on the aggregate and sectorial growth rate for
both OLS and 2SLS estimators. As already discussed, in the aggregate and manufacturing
sector ODA is found to be endogenous while for both service and agricultural sector
specification ODA can be treated as exogenous. Therefore, in the specification where ODA is
found to be endogenous, one should rely on the estimates of 2SLS as OLS is biased and
inconsistent. However using instrumental variable estimator while ODA is actually exogenous
is costly in terms of precision (tend to make t-stat smaller) because the variance of IV is
always bigger. This could be the reason for insignificant of the effect of ODA in the service
sector. So, for both service and agricultural sector relying on the estimates of OLS would be
warranted.
Why the magnitudes of 2SLS estimates are larger than OLS estimates?
Let us have a simple example where we have only one single regressor, ODA, in the model
and this example used http://cameron.econ.ucdavis.edu/e240a/ch04iv.pdf: as a reference.
...
In a standard regression of OLS estimator, there is an assumption that the regressor is
uncorrelated with the error term in the model; therefore the only effect of ODA on
the direct effect through
will be
. But what if ODA is endogenous in the model? In this case the
above assumption is no longer be valid like that the correlation between ODA and the error
term is non-zero. So that the effect of ODA on
will be:
So, whether OLS underestimates or overestimates the effect of ODA will depend on the
direction of correlation between ODA and the error term. In our case OLS is found to
underestimate the effect of ODA on both aggregate and sectorial growth which means that the
correlation between the endogenous regressor, ODA, and the error tem is negative. Therefore,
the reason behind smaller estimates of OLS could be the negative correlation between ODA
and the error term. The coefficient of all other control variables is almost similar in both OLS
and 2SLS estimators which can confirm our reasoning.
66
In all regression the effect of ODA on the growth rate of real GDP per capita is smaller than
sectorial growth and this should not be a surprise. The potential reason for this result is that in
the former case we measure the growth rate in per capita terms while in the latter case we take
the growth rate of the value added in each sector to GDP not in per capita terms. That is why
the point estimates of the sector is larger than the point estimate of the aggregate equation. If
we compare the impact of ODA across the sectors, though it is difficult to compare the result
as the estimators are different, the effect of ODA on the service sector seems smaller14 than
others and the possible reason for the result could be that in most cases the expected effect of
aid on the service sector could take longer time as Kaya et al. (2012) already discussed.
Regarding the conditionality of aid, in all specifications monetary policy is found to matter for
aid effectiveness. However, in agricultural sector the level of democracy is found to be the
determinants of aid effectiveness than monetary policy. One possible justification for this
result could be that the channels through which aid impact the growth of the agricultural
sector are mostly intervened by government officials (see the discussion part for further
explanation).
Table 5. OLS & 2SLS regression result: comparison of the effect of ODA across sectors
OLS
2SLS
Dependent
GDP Manuf Servi.
Agric.
GDP
Manuf. Ser
Agr
var.
1
2
3
4
5
6
7
8
___________________________________________________________________________
____________________________
aid
0.76*** 0.97** 0.63**
0.7**
1.85*** 4.35***
0.95
1.79*
(0.20) (0.37) (0.22)
(0.34)
(0.57) (1.17)
(0.64) (1.04)
Aid2
0.05
(0.05)
0.05
0.01
(0.08) (0.04)
0.01
(0.08)
0.09*
(0.05)
0.21**
(0.11)
Aid_inf
-0.05
(0.07)
-0.06 -0.13*
(0.14) (0.07)
0.15
(0.11)
-0.41**
(0.16)
-0.92*** -0.28* -0.17
(0.33) (0.15) (0.28)
Aid_democ
0.03*
(0.02)
0.03
0.02
(0.03) (0.02)
0.07***
(0.03)
0.02
(0.02)
-0.02
(0.04)
14
0.02
0.11
(0.05) (0.08)
0.02
(0.02)
0.06*
(0.03)
Note that we do not have any statistical evidence whether the impact of aid across sectors is significantly
different or not.
67
4.4 The effectiveness of ODA in low income countries
To investigate whether the effectiveness of ODA is same or different in different regions, we
classified the countries as low income; lower middle income and upper middle income based
on their income level. The classification of the countries is based on the level of income
which is taken from WB development indicator. According to WB, low income countries are
those with income level of $1,035 or less; lower middle income $1,036 - $4,085; and upper
middle income, $4,086 - $12,615. Accordingly, we have taken ODA interacted with low
income countries to see if the effectiveness of ODA in low income countries is different from
the rest of the countries that are included in the analysis (see table 6). The significance and
sign of the point estimates remain the same with the previous results. The coefficient of ODA
interacted with low income countries never show up significant, except for manufacturing
sector for OLS estimator, while the main effect of ODA is positively significant. We cannot
conclude anything regarding the significance of ODA interacted with low income countries in
the manufacturing sector specification since ODA is endogenous making OLS estimates
inconsistent. The result indicates significantly positive effect of ODA both on the aggregate
and sectorial growth rate and that the result did not show any significant difference between
low income countries and lower/upper middle income countries that are included in the
regression model. This regression result also confirmed the robustness of the results we did so
far.
68
Table 6. regression result of Regional dummies interacted with ODA: both aggregate and sectorial
OLS
2SLS
Dependent
GDP
Manuf.
Servi.
Agric.
GDP
Manufac.
Service
Agriculture
var.
1
2
3
4
5
6
7
8
_________________________________________________________________________________________________________
aid
Initial
GDP
Human
Capital
Infli_
lation
Trade
Democ
Life_
expect
finan_
depth
capital
formation
governm_
consump
agic_land
Rural_POP
0.61***
(0.17)
-0.91**
(0.41)
0.3
(0.03)
-0.52***
(0.15)
1.96***
(0.60)
0.07
(0.04)
0.22***
(0.06)
0.74**
(0.33)
0.55
(1.31)
0.01
(0.06)
-0.82***
(0.30)
2.06***
(1.82)
-0.03
(0.08)
0.38**
(0.17)
0.52***
(0.19)
-1.94***
(0.59)
0.07*
(0.03)
-0.88***
(0.26)
1.51*
(0.89)
0.06
(0.04)
0.20**
(0.08)
0.58*
(0.34)
1.27*
(0.71)
-0.07
(0.05)
-0.24
(0.24)
3.07***
(1.10)
-0.00
(0.05)
0.17*
(0.10)
1.82***
(0.66)
-0.39
(0.52)
0.05
(0.03)
-0.59***
(0.12)
2.30***
(0.61)
0.05
(0.03)
0.22***
(0.09)
4.35***
(1.40)
1.55
(1.29)
0.09
(0.07)
-0.93***
(0.30)
3.61***
(1.50)
0.05**
(0.07)
0.33**
(0.13)
0.84
(0.74)
-1.84***
(0.67)
0.08**
(0.04)
-0.95***
(0.18)
1.37***
(0.82)
0.07
(0.04)
0.21***
(0.06)
-0.67**
(0.31)
-0.50
(0.69)
-1.00
(1.00)
-0.05
(0.04)
-0.07
(0.04)
-1.71**
(0.69)
-2.01
(1.78)
-4.22***
(1.85)
0.01
(0.09)
0.21**
(0.09)
-0.07
(0.51)
0.74
(0.67)
-1.88*
(0.98)
0.004
(0.05)
-0.03
(0.05)
-0.60
(0.48)
-1.12
(1.06)
0.81
(1.53)
-0.22*
(0.08)
-0.06
(0.08)
-0.61*
(0.33)
-0.76
(0.57)
-1.52***
(0.73)
-0.05
(0.05)
-0.08*
(0.04)
-1.26*
(0.71)
-2.93**
(1.45)
-4.99***
(1.83)
-0.08
(0.09)
0.10
(0.10)
-0.12
(0.43)
-0.72
(0.70)
-1.83*
(0.95)
0.01
(0.04)
-0.01
(0.06)
69
1.97
(1.25)
2.01*
(1.06)
-0.05
(0.07)
-0.26
(0.23)
2.91*
(1.26)
-0.02
(0.08)
-0.17
(0.11)
-0.24
(0.65)
-0.73
(0.98)
-0.22
(1.32)
-0.27***
(0.08)
-0.02
(0.10)
ODA_low income 1.33
Countries
(0.83)
Number of
countries
91
Number of
1775
Observation
Hansen Jp-value
Specif_tes
Endogeneity-
1.91*
(1.12)
0.92
(0.67)
87
1597
88
1626
0.94
(1.19)
0.28
(0.89)
-0.63
(1.81)
0.66
(1.03)
86
1598
80
1438
80
1464
0.211
0.433
0.736
0.051
0.0014
0.758
88
1668
The robust standard errors are in brackets. The ***, ** and * shows statistical significance level at 1%, 5% and 10%, respectively.
70
-0.31
(1.54)
81
1502
0.165
0.497
Chapter five: DISCUSSION
5.1 ODA and economic growth
Based on the analysis of the effect of aid on economic growth (table 1), the coefficient of aid
is significantly positive for both OLS and IV estimation technique though the magnitude is
larger for the latter estimator. The difference in both estimation techniques comes from the
fact that aid is endogenous in the model. This makes the estimate of OLS biased and
inconsistent as the expectation of the error term conditional on independent variable is no
longer be zero (Verbeek, 2008). For example, Hansen and Tarp (2001) and Rajan and
Subramanian (2008) argued that aid to some extent is determined by the condition of the
recipient countries income. Developed nation may give aid for those who have success
history while others may give aid to those who are poor or experience natural disaster (Rajan
& Subramanian, 2008). This might show high correlation (positive/negative) between aid and
economic growth but it never shows a causal relationship between aid and growth rate.
Therefore if the value of aid is determined within the model, it could be invalid to talk about
causality issues. On the other hand, the endogeneity of aid could come from simultaneous
causation where omitted variable may affect both the dependent and aid in the model (Kaya et
al., 2012). In our case the endogenous nature of aid also seems as it comes from simultaneous
causation as the result is different across the sector which is consistent with the previous
argument.
In all regressions of 2SLS, ODA is found to have a significantly positive impact on the rate of
economic growth. From this it can be concluded that aid is in general effective in furthering
growth without any indication of diminishing returns. This result is consistent with the
findings of others Dalgaard et al. (2004); Hansen and Tarp (2000); Hudson and Mosley
(2001); Minoiu and Reddy (2010); Roodman (2007) and Selaya and Thiele (2010) where they
revealed significantly positive effect of aid on the rate of growth. Our result is particularly
consistent with what Dalgaard et al. (2004) reported, where the coefficient of aid was found
to be positively significant across different specifications though the magnitude of the
coefficient was different across the techniques. However the result showed positive and
significance of the aid square term indicating increasing return to aid as opposed to (Dalgaard
et al., 2004; Hansen & Tarp, 2001). If the coefficient of aid square term were negative and
71
significant, it would imply diminishing returns as in the finding of (Dalgaard et al., 2004;
Hansen & Tarp, 2001).
The result is also consistent with the findings of Hansen and Tarp (2001),where aid was found
to have a significant positive effect on economic growth though the technique used was GMM
estimator in their cases. However the result of Hansen and Tarp (2001) also showed that aid
works with diminishing return which is contrary to findings of this study as the coefficient of
aid square term never negative and significant in any regression in this case. Moreover, unlike
the result of this study, Hansen and Tarp (2001) claimed that if investment and human capital
were controlled for in the regression no significant positive effect of aid on economic growth
would be observed which showed the channels through which aid impact economic growth:
human capital accumulation and investment in physical capita. In this study, however, even if
human capital and investment are controlled for, the coefficient of aid remained significantly
positive evidencing at least the presence of other channels, other than what was proposed by
Hansen and Tarp (2001), through which aid could impact the overall economic growth. One
possible explanation for this difference could be exclusion of all sources of both human and
physical capital accumulation from the regressions. For example, in our thesis a proxy for
human capital is secondary school enrolment rate, but primary and tertiary school enrolment
could still be the channels through which aid might affect human capital.
On the other hand, the findings from this study contradicts with what Burnside and Dollar
(2000); Easterly (2007) and Rajan and Subramanian (2005) found where they reported a
negative/no significant effect of aid on economic growth from both panel and cross sectional
model. The econometrics technique employed in this study and that of Burnside and Dollar
(2000) was somehow similar in that both used panel data model where both OLS and IV
estimation techniques were employed though the instrumental variables used were different.
However, in the study of Burnside and Dollar (2000) the aid variable was found to be
exogenous while in case of this study it turned out to be endogenous. The reason why aid
turned out to be exogenous in the former case might come from the fact that their instrumental
variables were weak as they already confirmed when they noticed different estimates from
OLS and 2SLS estimators. As already discussed more in detail under literature review part,
the methodology employed (both estimation technique and instrumental variables), by Rajan
and Subramanian (2005) is totally different from our case.
72
Therefore, the diverging results on the effectiveness of aid could be attributed to the
differences in methodologies used to investigate the effect (Selaya & Thiele, 2010). Rajan and
Subramanian (2005) also confirmed that differences in econometrics technique used
significantly changed the result as their result on cross-sectional and panel model was totally
different for the aid interacted with geographic variable. Moreover, the difference in sampled
countries and sampled period could also partly explain the reason behind the differences in
the results.
The findings from this study also confirmed the empirical support for the hypothesis that aid
is conditioned by the policy environment of aid recipient countries as the interaction between
aid and inflation was statistically significant. The importance of better macroeconomic policy
environment for aid effectiveness has been given attention by many scholars, for example,
(Burnside & Dollar, 2000; Collier & Dollar, 2001, 2002). In all regression, except for
agricultural sector specification, inflation appeared to matter in stimulating economic growth
and also it does appear to impact aid effectiveness significantly. For example, Barro (1995)
found that an increase in average inflation rate by 10 percentage points per year reduces real
GDP per capita growth rate by 0.2- 0.3 percentage points and decrease the ratio of investment
to GDP by 0.4-0.6 percentage points. Aid in all specifications seemed to be less effective in
countries with bad monetary policy because high inflation rate increases the opportunity cost
of holding money which discourages the channels through aid impact growth: saving and
investment. Better monetary policy granted long-term planning which helps to make aid flow
spent primarily on private investment rather than consumption. As such this result confirmed
the importance of good monetary policies for both growth rate and aid effectiveness. This
result backs up the thesis of previous studies that claimed the importance of good policy
environment for the effectiveness of aid (Burnside & Dollar, 2000; Collier & Dollar, 2001).
On the other hand, the result is in disagreement with what was reported by Hansen and Tarp
(2001) and Rajan and Subramanian (2008) where no evidence was found for the
conditionality of aid on better policy environment. Moreover, our result contradicts the
findings of Guillaumont and Chauvet (2001) which reported even a positive effect of aid for
those countries that experience bad environment and subsequently concluding that improved
policy does not contribute for aid effectiveness. However, unlike the previous studies which
focussed on macro policy in general by taking inflation, trade openness and budget balance as
an indicator for macroeconomic policy, this paper investigated the conditionality of aid on
monetary policy as measured by a single indicator-inflation rate. Therefore, the diverging
73
result of the aid conditionality on better policy environment should not come as a surprise as
the indicator is different in our case.
In addition to the macroeconomic policy environment, weak and insufficient institutional
arrangements, corruption and bureaucratic inefficiency have been raised as one of the reasons
behind aid ineffectiveness in some regions (Alesina & Dollar, 2000; Azarnert, 2008; Tezanos
et al., 2013). The result of OLS showed that the level of democracy matters for both growth
rate and effectiveness of aid implying that aid is more effective in those countries with high
level of democracy. Democracy is associated with more respect for citizens, less abuse and
more civil and political freedom which is important to secure property right that directly
support both physical and human investment (Fulginiti, 2010). However, the result of 2SLS
showed insignificant role of the level of democracy for aid effectiveness in the aggregate
equation, manufacturing and service sector. Of course, the reason behind diverging result of
OLS and 2SLS result, as already mentioned, is endogeneity of aid. Our finding from 2SLS
estimator is consistent with the findings of Collier and Dollar (2001); Rajan and Subramanian
(2008) where the quality of governance appeared to be insignificant for aid effectiveness
while it contradicts with the findings of (Azarnert, 2008; Tezanos et al., 2013).
As regards to other explanatory variables, human capital, trade openness, and life expectancy
exerts a positive and statistically significant effect on the overall rate of economic growth.
This result is consistent with the theory of economic development as the effect of important
variables turned out to be significant in explaining the growth rate. Human capital, trade
openness and life expectancy as expected have positive effect on economic growth by
increasing the ability to innovate new ideas, adopt foreign technologies and absorb
technological advances; while life expectancy increase the productive capacity of labour force
(Barro, 1991, 1992, 1996b; Edwards, 1993; Herdt, 2010; Mincer, 1984; Sachs & Warner,
1997; Yanikkaya, 2003).
The analysis also showed that, initial GDP, inflation, financial depth, government
consumption exerted a significantly negative effect on the growth rate. However the
coefficient of initial GDP per capita lacks statistical significance in IV estimation although it
showed negative sign as expected evidencing the absence of conditional convergence among
the sampled countries included in the regression. The significance of initial GDP per capita in
the OLS estimator showed the conditional convergence among the sampled countries included
in the regression model though the estimates of OLS is inconsistent. This finding is consistent
74
with the result of previous studies (Barro, 1991, 1996b). In all regression and across estimator
the coefficient of inflation rate is negative and significant. This result confirmed the negative
impact of inflation rate on the growth rate of the economy through discouraging investment
and saving (Barro, 1995). This in turn damages the pace of growth and this result supports
similar findings from previous studies, (Fischer, 1993; Rodrik, 2008; Tezanos et al., 2013),
for example.
Government consumption also has adverse effect on the rate of growth. This is mainly
because government consumption finances unproductive investment and many government
spending could prevent innovation because of absence of competition which puts downward
pressure on productivity (Barro, 1991; Landau, 1983; Rodrik, 2008). However, the
significantly negative effect of financial depth on the growth rate is a puzzling result as
financial depth is expected to have a positive effect on the growth rate. However, previous
studies also found similar effects and this could be due to omitted variable bias as financial
depth might be correlated with other sources of foreign capital such as foreign bonds or
external debt (Selaya & Thiele, 2010).
5.2 ODA and sectorial growth
5.2.1 ODA and Manufacturing sector
A further sector-wise analysis of the effect of aid is performed and the effect for
manufacturing sector is presented in table 2. Our main results showed significant positive
effects of aid on the growth of manufacturing sector without diminishing returns. The result is
robust to estimation technique (both OLS and IV), the conditioning variables and endogenous
nature of aid variable. In both OLS and IV estimation techniques the coefficient of aid is
significantly positive. Moreover, adding the conditioning variable in the regression model did
not change the effect of the variable. This result is consistent with what Selaya and Thiele
(2010) reported which is a significantly positive effect of aid on the growth of industrial
sector without diminishing returns to aid. However, this finding contradicts with what was
found by (Arellano, Bulíř, Lane, & Lipschitz, 2009; Lane, Bulir, Arellano, & Lipschitz, 2005;
Rajan & Subramanian, 2011). The core argument for those who found a significantly negative
effect of aid on the growth of manufacturing sector was the so called “Dutch disease”-the
adverse effect of natural resource revenues on the manufacturing sector via a real exchange
rate appreciation (Arellano et al., 2009). Nevertheless, Temple (2010) already argued that the
real exchange rate appreciation would be less than expected as some of the aid is spent partly
75
on imports and some of aid is given in terms of technical assistance. Moreover, Guillaumont
and Chauvet (2001) had stated that aid that support imports can avoid the negative effects that
have lasting effects. These could limit the increase in the demand for non-traded goods which
is the root cause behind the real exchange rate appreciation. Moreover, the effect of aid flow
on the exportable sector could be offset by: appropriate subsidies to that sector; other policy
instruments and spending aid partly on infrastructures like ports, reliable power supplies and
roads which could lower costs of production for the traded sector (Temple, 2010). Therefore,
in these cases aid will transfer to higher growth of the sector than labelled as a ‘disease’. On
the other hand, aid can be used to invest on both physical and human capital accumulation
through building schools, infrastructures such as roads, electricity, water supply etc. that
positively impact the growth of the sector.
5.2.2 ODA and service sector
The estimated effect of aid on the growth of service sector is also significantly positive in all
regression of OLS estimator. However, in 2SLS regression result the impact of aid on the
growth of the sector is significant if and only if aid-inflation interaction term is included in the
model (see table 3). In both estimators, however, the effectiveness of aid is limited in those
countries with high inflation rate. In general we can conclude that aid has a significantly
positive effect on the growth of the sector without diminishing returns and this is consistent
with previous studies on aid-sectorial growth relationship (Selaya & Thiele, 2010).
Aid can affect service sectors such as health and education both directly and indirectly.
Foreign aid helps countries by providing more income which help to pay for basic health,
education, housing, and basic food (Herdt, 2010). According to Levine (2004), aid can
increase worker productivity by providing access for better health services through providing
new medicines and other health technologies. The same source indicated that aid finances
programs that can potentially contribute to eradication of small pox, polio and other diseases.
Reduction of these diseases implies good health status which will increase the labour
productivity. Moreover, Gomanee et al. (2005) argued that foreign aid has a significant effect
on human development index by lowering infant mortality rate specially for those countries
with lower levels of human development indicators and higher values of infant mortality rate.
This indicated high importance of aid for developing countries as the indicator for human
development is very low in these countries.
76
On the other hand, aid can also positively affect the education sector by financing primary and
secondary schools and also by improving learning environment through building schools and
supplying teaching materials (Dreher, Nunnenkamp, & Thiele, 2008; Herdt, 2010). Dreher et
al. (2008), analysed the effect of aid on education sector and found that a one percent
increase in aid increases the primary school enrolment by 0.29 percent for the sampled
countries in the sample period of time.
However, Williamson (2008) investigated the effect of development aid on the health sector
and the result showed that aid does not significantly improve the overall health in developing
aid recipient countries. Similar to our case, the paper employed a fixed effect model with
instrumental variable to tackle for endogeneity of aid in the model although the instrumental
variables used are different. The instrumental variables they used were two and three years
lagged value of foreign aid. This finding can be criticized by two points. On one hand, the use
of lagged value as an instrument might raise doubt on the validity and relevance of
instrumental variables since these variable might be significantly related to the dependent
variable as aid might even take some time to bring changes in the sector. On the other hand,
Kaya et al. (2012) already stated that aid given to service sector can take longer time to have
the expected impact. As such expecting the effect of current aid on the health sector in the
same year might be too early to witness the effect and justify the lack of significant effect.
5.2.3 ODA and Agricultural sector
Similar to the manufacturing sector, aid has a significantly positive effect on the growth of
agricultural sector both in OLS and IV estimation techniques. The sector could benefit from
the aid inflow through many channels. Aid can be used for financing important inputs like
fertilizer, pesticides, machineries which are important production factor for the sector.
Moreover, aid can be used to finance potential agricultural research that could create new
technologies and seeds which in turn increase outputs by increasing productivity and
improving production efficiency. For example, Herdt (2010) reported where aid given by WB
to finance irrigation purpose increased agricultural production by increasing the productivity
of the sector because of availability of inputs. (Kaya et al. (2012); Radelet (2006)), also
argued where aid have been used as instruments for ‘green revolution’ which provided
transfer of technologies through import of capital goods, technical assistance or through direct
transfer of technologies such as the introduction of new seeds and fertilizers. Aid can also
increase worker productivity through impacting the health sector which can indirectly affect
77
the growth of agricultural sector as most of agricultural sector particularly in developing
countries is labour intensive. These all would be transferred to high growth of the sector by
increasing productive capacity of the sector. Moreover, the growth of agricultural sector will
benefit the overall economic growth as this sector is an engine and important for growth and
poverty reduction particularly in developing countries as it is the source of livelihood
particularly for the poor (Fulginiti, 2010; Kaya et al., 2012). The growth of agricultural sector
production can also benefit the growth of other sectors since the surplus from the agricultural
sector can be used as inputs for other sectors which stimulate the overall economic growth
(Fulginiti, 2010; Herdt, 2010). In addition, Gollin, Parente, and Rogerson (2002) have
discussed the importance of an increase in agricultural productivity for income per capita by
supporting the industrial sector.
Although the level of democracy do not matter for the growth of the sector directly, from both
OLS and IV estimation result one can conclude that the level of democracy indirectly affects
the growth of the sector through affecting aid effectiveness. Aid effectiveness in the
agriculture sector is found to be dependent on the level of democracy. This confirms the
importance of political commitment for aid effectiveness in the agricultural sector and the
result is in accordance with what was reported in previous studies where the quality of
governance was found to be a factor for aid effectiveness in some regions (Azarnert, 2008;
Tezanos et al., 2013). One possible justification for this result could be that the channels
through aid impact the growth of the sector is mostly intervened by government officials and
that most of the worker in the sector are uneducated particularly in developing countries.
Birner and Palaniswamy (2006) indicated that democratic reform gives farmers more
right/voice in agricultural policy-making which gives them more possibilities to ask for more
budget share for the sector. So, presence of democracy could grant allocation of foreign aid
for the agricultural sector. The empirical finding of Zerfu (2007) also indicated that good
governance increases agricultural productivity through facilitating credit market, input market
and extension packages since farmers depend on the government for provision of these inputs.
Therefore, given that the impact of aid on the growth of the agricultural sector is through
financing important inputs and these inputs are provided mostly by government, then good
democracy can positively contributes to aid effectiveness.
Regarding other explanatory variables, the coefficients of most of the variables in this case
enter statistically insignificant with the exception of such variables as initial GDP, trade
openness, and agricultural land. Having an open economy and high initial level of GDP per
78
capita are associated with high growth of the sector. But, contrary to expectations, the share of
agricultural land exerted adverse effect on the growth of the sector. This result could partly be
affected by omitted variable bias as most of the variables that were expected to affect
agricultural sector are not included in the regression. Other factors, besides the included
variables, may of course determine the sectorial growth rates. However, those variables are
not included in the regression model because of absence of panel dataset.
79
Chapter six: Conclusions and recommendations
6.1 Concluding remarks
Official development assistance (ODA) is one of a number of international resource flows
available to developing countries with primary objectives of promoting economic growth in
the developing countries. Annually more than US$ 60 billion development aid is flowing to
poor countries to generate economic growth and, on average, recipient countries receive
about 7.5% of their GDP (Doucouliagos & Paldam, 2008). However, the relationship between
economic growth and foreign aid has been a hot debate in the history of economic
development. Whether foreign aid helps poor countries to grow remains an important question
for many scholars and governments of donors and recipient countries. Therefore, the
effectiveness of foreign aid becomes an active research area given its implication for
economic growth and its inconclusive nature.
This paper tries to contribute something for aid-economic relationship literature. Foreign
assistance might take different forms like financial aid, humanitarian aid, debt relief, technical
assistance, food aid and so on, targeted either for emergencies, military objectives, and
economic development. However, the scope of this paper is limited to the analysis of only
ODA which has a primary objective of welfare and economic development of developing
countries. The effects of aid for economic growth are assessed almost for 91 aid recipient
countries over the period of 27 years (from 1985 to 2011). A regression analysis were done
using a fixed effect panel data model using both OLS and 2SLS estimators to empirically test
the aid-growth relationship. Furthermore, to take into account aid allocation issues, we used
two stage instrumental variable (2SLS) estimators to tackle for the endogenous nature of aid.
As most of previous studies on aid effectiveness focused only on the direct effect of aid on the
aggregate economic growth with inconclusive result, our paper attempted to investigate the
effect of aid at a sectorial level assuming disaggregation of the effect of aid is more warranted.
This is because, the effect of aid on overall economic growth might come through different
sectors such as agriculture, manufacturing or service among others no matter what the result
might be when seen at aggregate level. We also re-examined the effect of ODA on the overall
economic growth.
The proposition that aid is more effective in some regions while it is not effective in other
region has been also a hotly debated issue frequently raised by many scholars and donors
which is still with inconclusive result. Conditionality of aid is the main reason for aid
80
ineffectiveness in some regions which was discussed by many recent papers. The general
theoretical argument behind conditionality of aid is that aid effectiveness is explained based
on the situation of recipient countries’ circumstances. So our paper also tested the hypothesis
that aid is more effective in those countries with good policies and governance quality by
including interaction terms of aid with inflation and the level of democracy. Moreover, the
medicine model which emphasized on diminishing returns of aid or non-linear relationship
between aid and economic growth was tested by including the square of aid in the regression
model.
Our findings from all regressions results indicated that ODA has a significantly positive effect
on economic growth without diminishing return. The result indicated that on average, a one
percentage increase in aid inflow will raise the real GDP per capita by 1.85 percent. Our result
also confirmed the empirical support for the hypothesis that aid is conditioned by the policy
environment of aid recipient countries. In all regressions, except for agricultural sector
specification, inflation appeared to matter in stimulating economic growth and also it does
appear to impact aid effectiveness significantly. High inflation rate increases the opportunity
cost of holding money which discourages the channels through which aid impact growth:
saving and investment. Therefore the combination of good monetary policy and aid increases
the impact that aid has on the growth rate. This is because better monetary policy helps to
make aid flow spent primarily on private investment rather than consumption since it granted
long-term planning. However, the importance of good level of democracy for aid
effectiveness lacks empirical justification except for agricultural sector. Our findings for the
effect of other control variables, except for conditional convergence which lacks statistical
significance, on economic growth were also consistent with theories and other empirical
results as the results are robust across the specifications.
The regression result of the effect of aid on sectorial growth points out three relevant results.
Firstly, we found that aid can pretty effective in bringing growth in all the three sectors.
Secondly, the analysis suggest that the effectiveness of aid in the service and manufacturing
sector is limited by monetary policy of the recipient countries as in the aggregate regression
while the effectiveness of aid in the agricultural sector is not affected by the monetary policy.
Thirdly, the effectiveness of aid in the agricultural sector seems to be stronger in countries
with better level of democracy.
81
In general, our finding suggests positive effect of aid on overall economic growth and
sectorial growth and that this effect is stronger in countries with good monetary policy.
Regarding other determinants of economic growth, our findings suggest that trade openness;
inflation and government consumption should be an important factor to be taken into account
when planning for development strategies.
6.2 Recommendations and Policy implications
Based on the findings of our thesis, the following recommendations and policy implication
are given:
Based on the findings of our study, to enhance economic growth and sectorial growth, one of
the most important things that the government of aid recipient countries should do is to
provide good monetary policy environment where the impact of aid will be well achieved.
Since high inflation rate in all regression and across different estimators adversely affect both
the rate of economic growth and aid effectiveness, the government should take this seriously.
Moreover, in order to enhance the effectiveness of aid the government should have to play its
role in attaining good level of democracy particularly in agricultural based economies. We
also recommended the donor governments that in addition to helping developing countries by
granting aid, emphasis should be put also on encouraging the recipient countries governments
on how to strengthen good monetary policy and governance. Our findings also suggested that
trade openness; inflation and government consumption should be an important factor to be
taken into account when planning for development strategies. As regards to the role of
financial depth, one should be cautious in drawing out policy implications from our empirical
finding as the result could be possibly affected by omitted variable bias.
6.3 Direction for further research
Several further improvements should make to better understand the aid-growth causalrelationship. Firstly, further research should take place for better understanding of the
interaction between aid and policy environment. To give a potential direction, our study has
focussed only on the role of monetary policy and the level of democracy for aid effectiveness.
Therefore, possible extension could explore the possible importance of various indicators of
policy environment, like that of trade policy and fiscal policy. These could significantly
improve our understanding on the conditionality of aid on policy environment. Secondly, the
causal-relationship between aid and real exchange rate could be a subject worth further
82
research which we could not did because of absence of panel data information. Therefore,
while we recommend availability of this variable for the concerned body as the same time we
recommend a further research on the topic. Finally, the endogenous nature of foreign aid only
in the aggregate and manufacturing sector could also be a subject that demands further
research.
83
Appendix
84
Appendix A. A. Overall Summary Table
Variable
mean
Std. Dev.
Min
Max
Observations
GDP per capita growth rate
1.66
6.44
-50.24
91.67
4321
Manufacturing sector value-
4.09
14.87
-78.17
575.26
3431
4.15
6.99
-57.35
73.7
3682
2.4
9.73
-52.73
78.01
3741
ODA as a share of GDP
8.73
13.13
-2.48
241.70
4205
GDP per capita
3916.95
7590.51
64.81
93605.75
Human Capital
26.75
18.63
inflation
43.65
97.19
openness
84.32
51.09
added_growth rate
Services sector valueadded_growth rate
Agriculture sector valueadded_growth rate
democracy
.12
6.78
0
4511
86.4
3422
-17.64
23773.13
3740
6.32
446.05
4242
-10
10
3726
life expectancy
63.56
9.98
26.77
85.16
4780
financial depth
33.10
30.27
.56
294.79
4015
exchange rate
2910.05
98788.55
19.53
4342911
1964
agricultural_land
38.52
22.08035
.44
91.16044
4678
capital_formation
22.06
9.486732
-2.42
154.80
4008
85
government_cons
15.82
7.31
1.375188
76.22212
4019
Appendix A2: descriptive statistics for only those countries included in the final econometrics result
. summ
ODA gdppercapitagrowthrate if
Variable
Obs
Mean
ODA
gdppercapi~e
1766
1775
6.247049
2.025105
. summ
ODA
Obs
Mean
ODA
gdppercapi~e
1590
1598
6.33149
1.913611
ODA
Obs
Mean
ODA
manufactur~h
1436
1441
6.145514
4.162722
ODA
9.706688
4.373211
Obs
Mean
ODA
manufactur~h
1591
1597
6.084945
4.362155
Max
-.6820857
-47.31423
147.1683
22.72784
Std. Dev.
9.626386
4.468879
Min
Max
-.6820857
-47.31423
147.1683
22.72784
_est_predictedd==1
Std. Dev.
9.735271
8.371605
manufacturingannualgrowth if
Variable
Min
_est_predictedd==1
manufacturingannualgrowth if
Variable
. summ
Std. Dev.
gdppercapitagrowthrate if
Variable
. summ
_est_pred==1
Min
Max
-.6820857
-54.00703
147.1683
82.81618
_est_pre==1
Std. Dev.
9.858575
9.180545
Min
Max
-.6820857
-54.00703
147.1683
177.3157
86
. summ
ODA
servicesannualgrowth if
Variable
Obs
Mean
ODA
servicesan~h
1662
1626
6.236154
4.32602
. summ
ODA
Obs
Mean
ODA
servicesan~h
1620
1626
6.191655
4.32602
ODA
Obs
Mean
ODA
agricultur~h
1662
1668
6.236154
2.912325
ODA
9.911429
5.058799
Obs
Mean
ODA
agricultur~h
1499
1504
6.319897
2.902052
Max
147.1683
32.55473
Std. Dev.
9.753495
5.058799
Min
Max
-.6820857
-33.53941
147.1683
32.55473
_est_predict==1
Std. Dev.
9.911429
8.311105
agricultureannualgrowth if
Variable
Min
-.6820857
-33.53941
_est_predictdddd==1
agricultureannualgrowth if
Variable
. summ
Std. Dev.
servicesannualgrowth if
Variable
. summ
_est_predict==1
Min
Max
-.6820857
-43.9491
147.1683
78.00708
_est_pred==1
Std. Dev.
9.81893
8.453143
Min
Max
-.6820857
-43.9491
147.1683
78.00708
87
200
100
150
Tajikistan
Liberia
Kyrgyz Republic
Trinidad and Tobago
Mongolia
Sudan
Mozambique
Gabon
Albania
Cote
d'Ivoire
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Botswana
Mozambique
Cambodia
Sudan
Cambodia
Albania
Iran,
Islamic
Mongolia
Lesotho
Fiji
Togo
Botswana
LesothoCongo, Dem. Rep.
Mongolia
Costa
RicaRep.
Togo
Nepal
Jordan
Congo,
Rep.
Rwanda
Mali
Iran,
Islamic
Rep.
Kyrgyz
Republic
Zimbabwe
Malaysia
Mauritius
Sudan
Botswana
Cambodia
Uganda
Fiji
Jordan
Cote
d'Ivoire
Malawi
Rwanda
Tunisia
Liberia
China
China
Peru
Mozambique
Singapore
Malaysia
Malaysia
Zambia
Thailand
Kyrgyz
Republic
Liberia
Gambia,
Cambodia
Nepal
The
Jordan
Cambodia
Trinidad
Malaysia
and
Congo,
Tobago
Ghana
Rep.
Jordan
Togo
Malawi
Mozambique
China
Peru
Uganda
Trinidad
and
Tobago
Lesotho
Thailand
Thailand
Thailand
Cambodia
Thailand
Cameroon
Papua
Lesotho
New
Guinea
China
India
Pakistan
Papua
New
Guinea
Malaysia
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Cambodia
Lao
Mozambique
Rwanda
PDR
China
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Sierra
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Leone
Malaysia
Ukraine
Gabon
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Zimbabwe
Mongolia
Lesotho
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Saudi
Arabia
Trinidad
Gabon
Trinidad
and
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and
Pakistan
Tobago
Lesotho
Saudi
Arabia
Kazakhstan
Peru
Panama
Kazakhstan
Vietnam
Lao
Lao
Togo
PDR
PDR
Malaysia
Peru
Vietnam
Congo,
Uganda
Rep.
Rwanda
Gambia,
The
China
Lesotho
Guyana
Lao
Mozambique
PDR
Singapore
Ukraine
Dominican
Fiji
Republic
Benin
China
Indonesia
Fiji
Gabon
Vietnam
Mongolia
Nepal
Cambodia
Tajikistan
Rwanda
Lesotho
Liberia
Iran,
Islamic
Rep.
Indonesia
Indonesia
Fiji
Lao
Zambia
PDR
Korea,
Rep.
Malaysia
Uruguay
Iran,
Islamic
Thailand
Thailand
Rep.
Thailand
Botswana
Malaysia
Dominican
Indonesia
Dominican
Tunisia
Gabon
Republic
Republic
Cameroon
Vietnam
Zimbabwe
Vietnam
Iran,
Islamic
Malaysia
Rep.
Malaysia
India
Philippines
China
Peru
Thailand
Indonesia
Congo,
Jordan
Senegal
Dem.
Cote
Malawi
Rep.
d'Ivoire
Mexico
Costa
Iran,
Islamic
Rica
India
Rep.
Costa
Rica
Jordan
Mauritania
Guyana
Saudi
Arabia
Costa
Rica
Thailand
Indonesia
Bangladesh
Togo
Bangladesh
Sri
Lanka
India
Panama
Fiji
Vietnam
Lao
PDR
Lao
PDR
Saudi
Arabia
Mexico
Brazil
Singapore
Mexico
Singapore
Iran,
India
China
Islamic
Uruguay
Rep.
Kazakhstan
Botswana
Indonesia
Congo,
Pakistan
Rep.
Tanzania
Rwanda
Gambia,
The
Singapore
Slovenia
India
China
Singapore
Ecuador
Thailand
Indonesia
Bangladesh
Egypt
Vietnam
Jordan
Tajikistan
Cambodia
Tanzania
Nepal
China
Iran,
China
Malaysia
Islamic
Rep.
Thailand
India
Indonesia
Dominican
Pakistan
Bangladesh
Philippines
Republic
Jordan
Sri
Uganda
Tajikistan
Tanzania
Lanka
Uganda
Guyana
Korea,
Korea,
China
Rep.
Rep.
China
Malaysia
Peru
Ecuador
Ukraine
Panama
Congo,
Sri
Jordan
Dominican
Sri
Lanka
Lanka
Lao
Nepal
Benin
Rep.
Tanzania
Kyrgyz
Nicaragua
Sierra
PDR
Republic
Republic
Leone
China
India
Gabon
Papua
Cambodia
Benin
Sri
Lanka
New
Mozambique
Zambia
Guinea
Brazil
Singapore
China
Trinidad
Algeria
Dominican
India
India
and
Tunisia
Namibia
Congo,
Bangladesh
Tobago
Republic
Egypt
Vietnam
Lesotho
Togo
Bangladesh
Rep.
Sri
Papua
Tanzania
Nicaragua
Lanka
Rwanda
Malawi
Lesotho
New
Guinea
Brazil
Iran,
Uruguay
Islamic
Uruguay
Iran,
China
Islamic
Rep.
South
Ukraine
Egypt
Ecuador
Africa
Rep.
Pakistan
Pakistan
Congo,
Bangladesh
Costa
Botswana
Bangladesh
Togo
Mongolia
Bolivia
Botswana
Rep.
Rica
Benin
Senegal
China
Mexico
Kazakhstan
Kazakhstan
Colombia
Mauritius
Sri
Sudan
Egypt
Yemen,
Lanka
Egypt
El
Mauritius
Vietnam
Salvador
Sudan
Rep.
Honduras
Zambia
Bolivia
Congo,
Tanzania
Tanzania
Kyrgyz
Malawi
Mauritania
Congo,
Lesotho
Rep.
Republic
Mozambique
Rep.
Saudi
Arabia
Chile
Kazakhstan
Uruguay
Kazakhstan
Slovenia
Croatia
Peru
Tunisia
Peru
Kazakhstan
Malaysia
Egypt
Sudan
Pakistan
Vietnam
Namibia
Mauritius
Pakistan
Jamaica
Egypt
Ghana
Bangladesh
Senegal
Cote
Tanzania
Central
Zambia
d'Ivoire
Liberia
African
Republic
Malaysia
Mexico
India
Thailand
Dominican
India
Colombia
Tunisia
Republic
Sri
Bangladesh
Lanka
Pakistan
Jordan
Bangladesh
Nepal
Nicaragua
Togo
Uganda
Mali
Uganda
Mexico
Saudi
Saudi
Arabia
Arabia
Iran,
Costa
Islamic
India
Rica
Malaysia
Kazakhstan
Tunisia
Rep.
Panama
Congo,
Pakistan
Fiji
Morocco
Togo
Fiji
El
Cameroon
Rep.
Cameroon
Salvador
Costa
Nepal
Nicaragua
Rica
Mexico
Trinidad
Colombia
Thailand
Gabon
and
Tunisia
Cyprus
Philippines
Tobago
Bangladesh
Yemen,
Mauritius
El
Bangladesh
Salvador
Egypt
Sri
Congo,
Lanka
Honduras
Rep.
Sri
Sri
Uganda
Lanka
Lanka
Dem.
Rep.
Korea,
Rep.
Croatia
South
India
Dominican
Dominican
Ecuador
Mauritius
Africa
China
Indonesia
Paraguay
Bangladesh
Costa
Republic
Republic
Sri
Panama
Tunisia
Bangladesh
Zimbabwe
Lanka
Kenya
Kenya
Rica
Honduras
Congo,
Bangladesh
Albania
Honduras
Kyrgyz
Benin
Nicaragua
Tajikistan
Nepal
Uganda
Rep.
Gambia,
Burundi
Republic
The
Mexico
Colombia
Trinidad
Algeria
South
Dominican
Botswana
Trinidad
Botswana
and
Africa
Thailand
Tunisia
Egypt
Ecuador
Tobago
and
Indonesia
Republic
Sudan
Sri
Tobago
Kenya
Sudan
Sudan
Lanka
Sudan
Kenya
Bolivia
Mongolia
Lesotho
Togo
Honduras
Tanzania
Uganda
Brazil
Panama
Algeria
Mauritius
Dominican
Peru
Tunisia
Botswana
Philippines
Republic
Congo,
Costa
Zimbabwe
Pakistan
Congo,
Kenya
Jamaica
Central
Rica
Lao
Rep.
Kenya
Mauritania
Nicaragua
Nepal
Dem.
Guyana
Egypt
Zambia
Lao
Zambia
PDR
Central
Nicaragua
African
Congo,
PDR
Rep.
African
Dem.
Liberia
Republic
Rep.
Brazil
Saudi
Arabia
Colombia
Colombia
Slovenia
Slovenia
India
South
Trinidad
Egypt
Peru
India
Indonesia
Peru
India
Philippines
Africa
Sudan
Philippines
Panama
Peru
Yemen,
and
Pakistan
Botswana
Philippines
Tobago
Congo,
Zimbabwe
Cameroon
Sri
Jamaica
Rep.
Lanka
Honduras
Senegal
Benin
Bolivia
Uganda
Bolivia
Honduras
Lesotho
Congo,
Malawi
Zambia
Rep.
Thailand
Malaysia
South
Colombia
Indonesia
Philippines
Botswana
Slovenia
Africa
Philippines
Yemen,
Paraguay
Fiji
Philippines
Bangladesh
Sri
Lanka
Rep.
Papua
Sri
Lanka
Kenya
Mali
Zambia
Mali
Mali
New
Congo,
Guinea
Dem.
Rep.
Malaysia
Saudi
Arabia
Mexico
Brazil
Uruguay
Uruguay
Indonesia
Ecuador
Philippines
Ecuador
India
Ecuador
Cote
Yemen,
Guatemala
Morocco
Pakistan
Sri
Fiji
d'Ivoire
Tunisia
Mauritius
Lanka
Cote
Bolivia
Costa
Rep.
Zambia
Honduras
Papua
Senegal
d'Ivoire
Bolivia
Tanzania
Rica
Bolivia
Bolivia
Mauritania
Tanzania
Tanzania
New
Mozambique
Mozambique
Guinea
Saudi
Arabia
Indonesia
Colombia
Indonesia
Colombia
Mauritius
Ecuador
Tunisia
Botswana
Indonesia
Morocco
Botswana
Egypt
Pakistan
Dominican
Yemen,
Pakistan
Sri
Kenya
Lanka
Sudan
Kenya
Yemen,
Kenya
Sri
Gambia,
Rep.
Sri
Rwanda
Honduras
Uganda
Lanka
Republic
Mali
Senegal
Lanka
Togo
Zambia
Rep.
Rwanda
The
Mexico
Saudi
Korea,
Arabia
Malaysia
Rep.
Mexico
Trinidad
India
Panama
Slovenia
Croatia
and
Ecuador
Philippines
Croatia
Croatia
Dominican
Costa
Tobago
Panama
Egypt
Philippines
Egypt
El
Morocco
Rica
Salvador
Republic
Mauritius
Honduras
Egypt
Honduras
Honduras
Gambia,
Zambia
Benin
Tajikistan
Benin
Rwanda
Dem.
Benin
Malawi
Kyrgyz
Zambia
Mali
Rep.
The
Republic
Venezuela,
Mexico
Mexico
RB
Costa
Panama
Gabon
Costa
Uruguay
Rica
Mauritius
Philippines
Rica
Uruguay
Mauritius
Morocco
Cyprus
Indonesia
Mauritius
Sri
Yemen,
Tunisia
Botswana
Tunisia
Morocco
Honduras
Tunisia
Lanka
Jordan
Yemen,
Honduras
Jamaica
Cameroon
Rep.
Honduras
Benin
Senegal
Bolivia
Kenya
Senegal
Papua
Gambia,
Gambia,
Rep.
Kenya
New
The
The
Guinea
Liberia
Saudi
Mexico
Arabia
Saudi
Arabia
Indonesia
Algeria
Philippines
Philippines
Dominican
Guatemala
Costa
Morocco
Pakistan
Guatemala
Morocco
Cote
Rica
Pakistan
Bolivia
Bolivia
Ghana
Kenya
Uganda
Republic
d'Ivoire
Cameroon
Sudan
Sudan
Nepal
Lao
Congo,
Kenya
Uganda
PDR
Lesotho
Zambia
Rep.
Lesotho
Costa
Korea,
Rica
Rep.
Mexico
Iran,
Mexico
Islamic
Croatia
Iran,
Dominican
Croatia
Colombia
Costa
Botswana
Panama
Rep.
Islamic
Panama
India
Paraguay
Tunisia
Peru
Guatemala
Indonesia
Rica
Tunisia
Peru
Morocco
Republic
Paraguay
Rep.
Guatemala
Morocco
Sri
Guatemala
Bangladesh
Lanka
Botswana
Cote
Botswana
Honduras
Nepal
Senegal
d'Ivoire
Jordan
Senegal
Malawi
Malawi
Brazil
Malaysia
Uruguay
Colombia
Mexico
Colombia
Croatia
Algeria
India
Cyprus
Philippines
Mauritius
India
Morocco
Ecuador
Morocco
Guatemala
Pakistan
El
Cote
Guatemala
Salvador
Costa
Yemen,
Botswana
Zimbabwe
Zimbabwe
d'Ivoire
Sudan
Morocco
Nepal
Bolivia
Cambodia
Rica
Egypt
Senegal
Senegal
Rep.
Zambia
Mozambique
Zambia
Malawi
Mozambique
Malawi
Malawi
Burundi
Singapore
India
Slovenia
South
South
Paraguay
South
Dominican
Ecuador
Algeria
Algeria
South
Tunisia
Ecuador
Africa
Cyprus
Africa
Cyprus
Africa
Guatemala
Africa
Ecuador
Cyprus
Egypt
El
Dominican
Republic
Namibia
Philippines
Salvador
Vietnam
Cameroon
Bolivia
Morocco
Mongolia
Bolivia
Jordan
Honduras
Bangladesh
Senegal
Papua
Republic
Tajikistan
Togo
Zambia
Togo
Papua
Congo,
Togo
New
New
Dem.
Guinea
Guinea
Rep.
Saudi
Arabia
Brazil
Brazil
Dominican
Croatia
Slovenia
India
Mauritius
Dominican
El
Slovenia
Paraguay
China
Salvador
Ecuador
Republic
Cote
El
Guatemala
Salvador
El
Republic
Cameroon
d'Ivoire
Salvador
Tunisia
Guatemala
Guatemala
Pakistan
Costa
Nepal
Congo,
Nepal
Papua
B
Gambia,
Benin
Nicaragua
Nepal
olivia
Rica
Guyana
Lesotho
Guyana
Dem.
New
Guyana
The
Guinea
Rep.
Gambia,
The
Singapore
Iran,
Islamic
Trinidad
Panama
Algeria
Indonesia
Colombia
Rep.
Panama
and
Cyprus
Jamaica
Mauritius
Tobago
Morocco
Mauritius
El
Guatemala
Ecuador
Morocco
Yemen,
Cameroon
Salvador
Sri
Cameroon
Namibia
Guatemala
Jordan
Fiji
Jordan
Guatemala
Togo
Lanka
Gambia,
Lesotho
Rep.
Cote
Nepal
Zambia
Lesotho
Malawi
Rwanda
Kyrgyz
Bolivia
d'Ivoire
The
Nicaragua
Mongolia
Mozambique
Gambia,
Republic
The
Mexico
Brazil
Jamaica
Colombia
Colombia
Philippines
Jamaica
Paraguay
Guatemala
Egypt
Philippines
Paraguay
Tunisia
El
Salvador
Gambia,
Bolivia
Jamaica
Central
Zambia
Kenya
Nepal
Gambia,
Honduras
Zambia
Kenya
Mozambique
The
African
Gambia,
The
Republic
The
Uruguay
Thailand
Colombia
Thailand
Peru
Algeria
Pakistan
Jamaica
Yemen,
Gambia,
Sudan
Central
Malawi
Rep.
Egypt
Congo,
The
Congo,
Tanzania
Sierra
African
Dem.
Dem.
Leone
Republic
Rep.
Rep.
Panama
Algeria
Algeria
Croatia
Jamaica
Trinidad
Tunisia
Tunisia
Guatemala
and
Costa
Tobago
Zimbabwe
Bolivia
Congo,
Gabon
Kenya
Morocco
Jordan
Honduras
Rica
Kenya
Nicaragua
Rep.
Senegal
Brazil
Malaysia
Panama
Dominican
Tunisia
Fiji
Paraguay
Sudan
Republic
Gabon
Philippines
Yemen,
Cote
Cote
Tajikistan
Papua
Mauritania
Mali
Gambia,
d'Ivoire
Rep.
Togo
Malawi
d'Ivoire
Malawi
Congo,
New
Burundi
The
Guinea
Dem.
Rep.
Uruguay
Colombia
Costa
Croatia
Colombia
South
Rica
Peru
Guatemala
Paraguay
Africa
Panama
Mauritius
Ecuador
Morocco
Sudan
Cameroon
Kenya
Cameroon
Congo,
Papua
Honduras
Bangladesh
Benin
Jamaica
Senegal
New
Rep.
Tanzania
Guinea
Saudi
Brazil
Arabia
Cyprus
Panama
Ecuador
Ecuador
Morocco
Gabon
Cyprus
Fiji
Namibia
Kenya
Central
Gambia,
Bolivia
Gambia,
African
The
Mauritania
Republic
The
Brazil
Iran,
Croatia
Islamic
India
Mauritius
Syrian
Paraguay
Rep.
Cote
Pakistan
Jamaica
Arab
d'Ivoire
Kenya
Fiji
Republic
Kenya
Sudan
Albania
Cote
Albania
Papua
Togo
Central
Zambia
d'Ivoire
Malawi
Rwanda
New
Lesotho
African
Guinea
Republic
Colombia
Jamaica
Croatia
Algeria
South
Uruguay
Africa
Ecuador
Mauritius
Philippines
Senegal
Zambia
Mexico
Mexico
Saudi
Arabia
Mexico
Panama
Jamaica
Algeria
Uruguay
Guatemala
Paraguay
Peru
Paraguay
Dominican
Bolivia
Republic
Zambia
Central
Malawi
African
Republic
Uruguay
Egypt
Paraguay
Guatemala
Fiji
Tunisia
Togo
Fiji
Morocco
Zimbabwe
Nepal
Nepal
Nicaragua
Burundi
Dominican
Panama
Algeria
Philippines
Thailand
Republic
Jamaica
Cote
Ghana
Ghana
d'Ivoire
Senegal
Albania
Trinidad
Uruguay
and
Tobago
Algeria
Ecuador
Uruguay
South
Paraguay
Algeria
Gabon
Paraguay
Africa
Cameroon
Senegal
Jordan
Brazil
Dominican
Jamaica
Republic
Philippines
Yemen,
Senegal
Rwanda
Honduras
Rep.
Iran,
Islamic
Rep.
Paraguay
Fiji
Fiji
Kenya
Sudan
Benin
Congo,
Mauritania
Dem.
Rep.
Kuwait
Venezuela,
RB
Cyprus
India
Mauritius
Jamaica
Peru
Kenya
Senegal
Central
African
Republic
Panama
Kazakhstan
Croatia
Uruguay
Cote
Cote
Congo,
d'Ivoire
d'Ivoire
Dem.
Mauritania
Rep.
Kazakhstan
El
Botswana
Salvador
Botswana
Zimbabwe
Guyana
Malawi
Trinidad
Panama
and
Botswana
Peru
Tunisia
Tobago
Peru
Gabon
Zimbabwe
Mongolia
Central
Rwanda
Congo,
Gambia,
African
Dem.
The
Rep.
Republic
Uruguay
Uruguay
Algeria
Pakistan
Benin
Mongolia
Mauritania
Iran,
Costa
Islamic
Rica
Jamaica
Malaysia
Jamaica
Rep.
Congo,
Cameroon
Jordan
Mali
Dem.
Rep.
Brazil
Malaysia
Colombia
Sri
Dominican
Lanka
Cameroon
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0
20
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Jordan
Republic
Jamaica
Fiji
Sri
Lanka
Guatemala
Jordan
Bangladesh
Zambia
Sri
Bolivia
Senegal
Papua
Lanka
New
Guinea
Saudi
Arabia
South
Croatia
Panama
Africa
Ukraine
Cote
Guatemala
Guatemala
El
d'Ivoire
Salvador
El
Honduras
Salvador
Gambia,
Congo,
Bolivia
Zimbabwe
Bangladesh
Benin
Tanzania
Mongolia
Rep.
Malawi
The
Gabon
Trinidad
South
Peru
Ecuador
Paraguay
Pakistan
Africa
Guatemala
Peru
El
and
Guatemala
Sudan
Salvador
Jamaica
Tobago
Nepal
Jordan
Lesotho
Brazil
Peru
Algeria
Ecuador
Honduras
Egypt
Egypt
Senegal
Bolivia
Gambia,
Kenya
Bolivia
Mali
The
Saudi Arabia
Brazil
Uruguay
Ecuador
Paraguay
Panama
Egypt
Zimbabwe
Nepal
Burundi
Malawi
Dem.
Rep.
Uruguay
Iran,
Cyprus
South
Islamic
Africa
Algeria
Ecuador
Rep.
Mauritius
Syrian
Paraguay
Syrian
Zimbabwe
Arab
Cameroon
Dominican
Arab
Cote
Lesotho
Republic
Republic
Congo,
Burundi
Nicaragua
d'Ivoire
Tanzania
Republic
Gambia,
Dem.
Rep.
The
Saudi
Arabia
Malaysia
Panama
Dominican
Cote
Peru
Pakistan
Republic
Fiji
d'Ivoire
Kenya
Kenya
Cote
Sri
Egypt
Papua
Lanka
d'Ivoire
New
Guinea
Dominican
Cote
d'Ivoire
Morocco
Paraguay
El
Republic
Salvador
Mauritius
Benin
Lesotho
Kyrgyz
Republic
Mexico
Croatia
Colombia
South
Colombia
Peru
Africa
Ecuador
Nicaragua
Malawi
Iran, Mexico
Islamic
Brazil
Rep.
Panama
Thailand
Cyprus
Panama
Fiji
Morocco
Philippines
Cote
Zimbabwe
d'Ivoire
Senegal
Congo,
Rep.
Trinidad
Colombia
Slovenia
Croatia
and
Tobago
Ecuador
El
Salvador
Paraguay
Jordan
Kenya
Vietnam
Jordan
Cambodia
Honduras
Jordan
Benin
Burundi
Burundi
Gambia,
The
Brazil
Colombia
Jamaica
Algeria
Paraguay
Ecuador
Philippines
Panama
Zimbabwe
Costa
Central
Sri
Papua
Kenya
Lanka
Rica
Egypt
Mongolia
African
New
Guinea
Republic
Brazil
Singapore
Algeria
Panama
Iran,
Trinidad
Algeria
Islamic
and
Ecuador
Rep.
El
Tobago
Salvador
Kenya
Costa
Bolivia
Rica
Jordan
Mozambique
South
Cyprus
Panama
Africa
Dominican
Fiji
Bolivia
Republic
Mali
Brazil
Croatia
China
Ecuador
Jamaica
Kenya
Namibia
Fiji
Syrian
Bolivia
Guyana
Arab
Gambia,
Republic
The
Brazil
Uruguay
Paraguay
Morocco
Papua
New
Guinea
Saudi
Brazil
Arabia
Brazil
Jamaica
Colombia
Panama
Uruguay
Uruguay
Pakistan
Tunisia
Kenya
Sri
Benin
Lanka
Togo
Algeria
Ecuador
Pakistan
Togo
Guatemala
Fiji
Bolivia
Burundi
Mexico
Algeria
Zimbabwe
Guyana
Mexico
Mexico
Cote
Zimbabwe
d'Ivoire
Jamaica
Guyana
Saudi
Mexico
Arabia
Saudi
Arabia
Colombia
Costa
Croatia
Algeria
Rica
Jamaica
Fiji
El
Salvador
Lesotho
Togo
Honduras
Malawi
Costa
Rica
Ecuador
El
Gabon
Salvador
Tunisia
Mongolia
Lesotho
Colombia
Kazakhstan
Peru
Jamaica
Sudan
Senegal
Mauritania
South
El
Salvador
Peru
Paraguay
Africa
Fiji
Philippines
Morocco
Senegal
Guyana
Brazil
Fiji
Honduras
Gambia,
The
Trinidad
Trinidad
and
and
Tobago
Tobago
Mongolia
Mauritania
Peru
Dominican
Sudan
Republic
Zimbabwe
Kyrgyz
Mali
Republic
Saudi
Arabia
Togo
Honduras
Papua
New
Guinea
Trinidad
and
Tobago
Ukraine
Cote
d'Ivoire
Zimbabwe
Jamaica
Algeria
Thailand
Kazakhstan
Jamaica
Syrian
Arab
Republic
Kazakhstan
Philippines
Tanzania
Malawi
Cote
d'Ivoire
Benin
Morocco
Papua
Togo
New
Guinea
Algeria
Jamaica
Fiji
Bolivia
Senegal
Senegal
Mali
Venezuela,
Saudi
Arabia
RB
Peru
Kenya
Uganda
Congo,
Cote
Rep.
d'Ivoire
Uruguay
Guatemala
Togo
Lesotho
Mongolia
Fiji
Gambia,
The
Uruguay
Malaysia
Morocco
Sri
Zimbabwe
Lanka
Burundi
Mauritania
Algeria
Paraguay
Cote
Mali
Togo
d'Ivoire
Mali
Croatia
Gambia,
The
Mali
Gambia,
The
Gabon
Kazakhstan
Cote
Cameroon
d'Ivoire
Cote
Guyana
d'Ivoire
Croatia
Peru
Guatemala
Syrian
Zimbabwe
Arab
Honduras
Republic
Honduras
Paraguay
Serbia
Cameroon
Papua
New
Guinea
Indonesia
Gambia,
The
Uruguay
Thailand
Jamaica
Congo,
Dem.
Burundi
Rep.
Cote
Morocco
d'Ivoire
Jamaica
Cote
d'Ivoire
Tanzania
Trinidad
and
Tobago
Malawi
Colombia
Jamaica
Paraguay
Fiji
Sudan
Congo,
Dem.
Rep.
Brazil
Gabon
Uganda
Trinidad
and
Tobago
Papua
Gambia,
New
Guinea
The
South
Uruguay
Africa
Saudi Arabia
Trinidad
Algeria
and
Algeria
Cote
Philippines
Tobago
d'Ivoire
Cote
d'Ivoire
Mexico
Paraguay
Indonesia
Zimbabwe
Nicaragua
Uruguay
Cameroon
Senegal
Congo,
Dem.
Malawi
Rep.
Central
African
Republic
Congo,
Nepal
Zimbabwe
Cote
d'Ivoire
Rep.
Algeria
Jamaica
Cameroon
Central
African
Malawi
Republic
Trinidad
and
Tobago
Cameroon
Burundi
Jordan
Nicaragua
Panama
Fiji
Congo,
Rep.
Zambia
Gabon
El
Salvador
Bolivia
Sudan
Mauritania
Malawi
Congo,
Dem.
Burundi
Rep.
Gambia,
The
Fiji
Albania
Albania
Togo
Dominican
Republic
Zambia
Congo,
Dem.
Egypt
Rep.
CroatiaGabon
Congo,
Dem.
Rep.
Fiji
Mozambique
Mexico
Gabon
Liberia
Zimbabwe
Malawi
Peru
Cameroon
Gabon
Central
African
Republic
Syrian
Togo
Arab
Republic
Zimbabwe
Malawi
Togo
Mexico
Papua
New
Guinea
Central
African
Republic
Iran,
IslamicUruguay
Rep. Egypt Malaysia
Tunisia
Central
African
Republic
Central
African
Republic
Zambia
Syrian
Arab
Republic
Papua
New
Guinea
Togo
Peru
Cameroon
Mongolia
Syrian
Arab
Bahrain
Republic
Togo
Congo,
Rep.
Ecuador
Zimbabwe
Papua
Burundi
New
Guinea
Iran, Islamic Rep.
Zambia
Central
African
Republic
Syrian
Arab
Republic
Cameroon
Thailand
Congo,
Dem.
Rep.Republic
Panama
Congo,
Dem.
Rep.
Togo
Zambia
Trinidad
and
Cameroon
Tobago
Papua
Papua
New
New
Guinea
Guinea
Central
African
Congo,
Dem.
Rep.
Peru
Zimbabwe
Cote
d'Ivoire
Sudan
Uruguay
Iran, Islamic Rep.
Congo,
Dem.
Rep.
Congo,
Rep.
Ukraine
Jordan
TajikistanBurundi
Indonesia
Trinidad and Tobago
Gabon Jamaica
Togo
-40
-50
Congo, Dem. Rep.
-10
-5
0
5
-10
Services annual growth rate
-5
0
5
ODA %GDP_lag
ODA %GDP_lag
Agriculture annual growth rate
Fitted values
88
Fitted values
Figure 2: Partial correlation between dependent variables and ODA for those countries included in the analysis
Appendix B. Summary Table of regions separately
Region
Mean of dependent and interest variables for each region
overall
Sub
East
Latin
Middle East and
Saharan Asia
America North Africa
African
1.66
0.92
5.21 1.57
1.45
GDP growth
rate
Manufacturing 4.09
growth rate
Service
growth rate
Agricultural
growth rate
ODA as
percentage of
real GDP
East Europe
& central
Asia
1.94
4.45
7.19
2.63
4.13
2.51
4.15
3.80
7.37
3.25
4.31
4.64
2.4
2.82
2.00
1.92
4.36
1.3
8.7
13.37
2.28
3.38
2.93
4.45
89
C: Partial Correlation Coefficient between the dependent variables and the interest varible
Partial
Correlation
ODA
GDP per
capita_
growth
rate
GDP per
capita_
growth rate
Manufacturin
g sector
value added_
growth rate
Service
sector value
added_
growth rate
Agricultural
sector value
added_
growth rate
0.013
1.00
. pwcorr
gdppercapi~e
1.0000
aid
0.0130
0.4252
manufa~h
manufactur~h
1.0000
aid
0.0166
0.3573
. pwcorr
servic~h
servicesan~h
1.0000
aid
0.0323
0.0635
. pwcorr
agricu~h
agricultur~h
1.0000
aid
0.0159
0.3556
15
3306
1.00
aid, sig
1.0000
aid, sig
aid
1.0000
aid, sig
aid
1.0000
agricultureannualgrowth
Observati
on
3095
1.00
aid
servicesannualgrowth
Agricultura
l sector
value
added_
growth rate
1.00
0.016
manufacturingannualgrowth
Service
sector
value
added_
growth
rate
3793
0.032*15
gdppercapitagrowthrate
gdpper~e
. pwcorr
0.017
Manufac
turing
sector
value
added_
growth
rate
aid, sig
aid
1.0000
Significant at 10 percent level of significance
90
3362
Appendix D: Premilinary test
xttest3: test for constant variance (Modified Wald test for groupwise heteroskedasticity in fixed effect regression
model)
Aggregate equation
H0: sigma(i)^2 = sigma^2 for all i
chi2 (91) = 25525.59
Prob>chi2 =
0.0000
Manufacturing sector
H0: sigma(i)^2 = sigma^2 for all i
chi2 (87) = 49802.45
Prob>chi2 =
0.0000
Service sector
H0: sigma(i)^2 = sigma^2 for all i
chi2 (88) = 1.3e+05
Prob>chi2 =
0.0000
Agricultural sector
H0: sigma(i)^2 = sigma^2 for all i
chi2 (88) = 64287.34
Prob>chi2 =
0.0000
Multiollinearity Diagnostics:
Collin var. list (test command)
Variables
Initial GDP per capita
ODA
Human capital
Inflation rate
Trade openness
Capital formation
Level of Democracy
Life expectancy
Financial depth
Rural population
Agricultural land
Government consumption
VIF
2.47
2.66
1.48
1.17
1.65
1.39
1.15
2.70
1.79
2.17
1.09
1.34
Indicator
Tolerance
0.4045
0.3759
0.3754
0.6762
0.8576
0.6076
0.7218
0.8670
0.3701
0.5586
0.4599
0.7452
91
Mean VIF
1.76
Appendix E: List of sample countries included in the analysis
1. Albania
2. Algeria
3. Bahrain
4. Bangladesh
5. Benin
6. Bolivia
7. Botswana
8. Brazil
9. Burundi
10.
Cambodia
11.
Cameroon
12.
Central African
Republic
13.
China
14.
Chile
15.
Colombia
16.
Congo, Dem. Rep.
17.
Congo, Rep.
18.
Costa Rica
19.
Cote d'Ivoire
20.
Croatia
21.
Cyprus
22.
Dominican Rep
23.
Ecuador
24.
Egypt
25.
El Salvador
26.
Fiji
27.
Gabon
28.
Gambia, The
16
17
29.Ghana
30.Guatemala
31.Guyana
32.Honduras
33.India
34.Indonesia
35.Iran
36.Israel*** 16
37.Jamaica
38.Jordan
39.Kazakhstan
40.Kenya
41.Korea, Rep.
42.Kuwait
43.Kyrgyz Republic
44.Lao PDR
45.Lesoto
46.Liberia
47.Libya***
48.Malawi
49.Malaysia
50.Mali
51.Mauritania
52.Mauritius
53.Mexico
54.Mongolia
55.Morocco
56.Mozambique
57.Namibia
58.Nepal
59.Nicaragua
60.Niger***
Israel, Libya, and Niger are not included in the three sectorial analysis
Serbia is not included in the manufacturing sector regression
92
61.Pakistan
62.Panama
63.Papua New Guinea
64.Paraguay
65.Peru
66.Philippines
67.Rwanda
68.Saudi Arabia
69.Senegal
70.Serbia17
71.Sierra Leone
72.Singapore
73.Slovenia
74.South Africa
75.Sri Lanka
76.Sudan
77.Syrian Arab
78.Tajikistan
79.Tanzania
80.Thailand
81.Togo
82.Trinidad and
Tobago
83.Tunisia
84.Uganda
85.Ukraine
86.Uruguay
87.Venezuela, RB
88.Vietnam
89.Yemen, Rep.
90.Zambia
91.Zimbabwe
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