Foreign Aid, Donor Fragmentation, and
Economic Growth
Kurt Annen∗
Stephen Kosempel†
November 2007
Abstract
This paper analyzes the impact of foreign aid on growth. It differs from the previous literature in three ways. First, we present a
theoretical model of the aid-growth relationship that is capable of
pinning down the specification needed for empirical analysis. Second,
we differentiate between foreign aid as “technical assistance” (TA)
and “non-technical assistance” (NTA). Third, we test the hypothesis
that the effectiveness of aid depends on its level of fragmentation. To
preview our empirical results: NTA has no statistically significant impact on growth; but TA has a positive and significant impact, except
in countries where it is highly fragmented.
Keywords: Foreign Aid, Technical Assistance, Donor Fragmentation,
Development.
JEL: F35, F43, F34, O11.
∗
Department of Economics, Guelph, ON N1G 2W1, Canada.
Email: kannen@uoguelph.ca
†
Department of Economics, Guelph, ON N1G 2W1, Canada. Email: kosempel@uoguelph.ca
1
1
Introduction
In the heated debate about the impact of foreign aid on economic growth Jefferey Sachs (2005) and William Easterly (2006) have some things in common:
both report in their books to have travelled extensively in the developing
world; both have down-to-earth knowledge of the problem of development;
and both sympathize with the poor in these countries. Despite these similarities, the policy suggestions of the two are in stark contrast. While Sachs
asks for much more foreign aid, Easterly warns that aid may be harmful so
that simply increasing it is counterproductive. In the background of this
debate is a large empirical literature on the impact of foreign aid on growth
which concludes that “. . . at best there appears to be a small, positive, but
insignificant, impact of aid on growth.” (Bourguignon and Sundberg 2007)
This paper argues that it is premature to draw strong policy conclusions
on the basis of this literature for at least three reasons. First, most of the
current literature uses aid measures as an aggregate of many different forms
of aid including emergency and food aid, and debt forgiveness of loans that
were not originally intended for development projects (i.e. debtforgiveness of
non-ODA loans).1 These aid measures are not specific enough. It is not surprising that such macro measures yield inconclusive results. In this paper we
focus on foreign aid in the form of technical assistance (TA) and in the form
of non-technical assistance (NTA), excluding emergency and food aid and
focusing on gross disbursements (i.e. ignoring debt service on concessional
loans as a negative aid flow). Figure 1 depicts a 5 year moving average of
the fraction of TA to total gross aid disbursements of all recipient countries
included in the DAC reporting system as provided by the OECD. This figure
shows that TA is a substantial part of total new aid money flowing into the
developing world, and there seems to be a positive trend. The average ratio
increased from 35% in 1970 to 52% in 2005. In average, developing countries received 39$ of technical aid per capita (PPP adjusted) in the last few
years. However, some countries received substantial higher amounts of TA.
For example, Tonga and Solomon Islands received about an average of 300$
per person per year in TA in the last four years.
Second, the literature takes it for granted that foreign aid should enter
as a fraction of total GDP into a regression equation. To use aid-GDP ratios
1
Two exceptions are Clemens et al. (2004) and Minoiu and Reddy (2007). They observe
that disaggregating aid measures is key for a better understanding of the impact of aid on
growth.
2
70
60
50
40
30
1970
1980
1990
Year
B. tech. aid/B. Gaid
2000
2010
Donor fragmentation
Figure 1: Technical aid Ratio and Donor Fragmentation
seems customary, and to our knowledge there is no explicit economic debate
of why aid as a fraction of GDP is the appropriate measure. On the basis of a
neoclassical growth model for a small open economy we show that aid should
be measured in per effective units of labor (i.e. divided by population and
technology) if aid changes the resource constraint of an economy. However,
if aid directly affects the production function of an economy – such as TA –,
then we believe that aid per capita is the correct specification. Furthermore,
if the true economic relationship is one between aid per capita and growth,
to measure aid as a fraction of GDP introduces a bias against finding an
impact of foreign aid on growth. In this case, the partial impact of aid per
capita for two countries with the same aid per capita measure should be the
same. However, if one country grows faster than the other, measuring aid as
a fraction of GDP means that less aid will be associated with more growth,
thus introducing a bias against finding an impact of aid on growth.
Third, one dollar of aid may not equal one dollar of additional financial
resources. The disbursement of foreign aid is costly. If the technology used
for aid disbursements exhibits increasing returns to scale, then the effectiveness of a given amount of aid depends on how fragmented this amount of
aid is. This is particularly true for TA. Donor countries that provide TA
3
run local offices in recipient countries, or hire NGOs to run them on their
behalf. To run these offices involves fixed costs in the form of maintaining
offices typically in the fancier part of the capitals of developing countries,
living expenses for foreign engineers and consultants, etc. This may suggest that the effectiveness of TA increases with the scale of the operation.
There may be an interaction effect between TA and donor fragmentation.
Figure 1 shows the 5 year moving average of donor fragmentation for TA
among all DAC donor countries. This figure reveals that donor fragmentation has increased substantially between 1970 and 2005, namely from 40%
to 62%.2 In 2005, countries like Vietnam (86%), Nicaragua (86%), Gambia
(85%), and Bangladesh (82%) had the highest donor fragmentation; whereas
countries like Comoros (7.3%) and Solomon Islands (7.7%) had the lowest
fragmentation, with France and Australia as the major donors respectively.
We estimate the impact of technical- and non-technical assistance on economic growth using a fixed effect estimator and the GMM system estimator
developed by Arellano and Bond (1991) and Blundell and Bond (1998). The
advantage of these estimators is that we do not need to deal with country
specific effects such as institutional and cultural variables that are difficult
to measure.3 We include policy measures that control for a changing policy
environment across time. This paper finds a strong positive impact of TA
per capita on growth rates. For the average developing country, a 25% increase in technical assistance per capita leads to about a quarter percentage
point increase in the yearly growth rates. We believe the predicted impact is
in the range on what can be reasonably expected. Note, furthermore, that
an increase in TA of 25% is modest when recognizing that donor countries
promised to give 0.7 percent of their GNP as official development assistance
in the Monterrey Consensus, which means that aid would more than triplicate from 0.2 percent of GNP in 2002. In addition, when interacting TA with
donor fragmentation, the interaction term is negative as expected. In this
case, the partial impact is zero for aid that is highly fragmented (above 65%),
2
When calculating the fragmentation index we follow Knack and Rahman (2007). The
fragmentation index is constructed by calculating an Herfindahl index of donor concentration, which ranges from 0 to 1. This index is then subtracted from 1 and multiplied
by 100. The value of donor fragmentation of zero indicates completely unfragmented aid
(i.e. one donor in a given country). The fragmentation measure increases in the number
of donors or with equality of aid shares.
3
See Glaeser et al. (2004) for a critical discussion of the institutional variables typically
used in the literature.
4
while the partial impact of TA is positive for lower levels of fragmentation.
When using OLS or IV estimation with a specification that is close to the
one used by Burnside and Dollar (2000), the result is similar. In this specification, TA may actually have a negative impact when highly fragmented
(i.e. above 84%).
The papers most closely related to the empirical component of this paper are Miniou and Reddy (2007) and Clemens, Radelet, and Bhavnani
(2004). Both papers disaggregate the ODA measure into different forms
of aid. Miniou and Reddy (2007) focus on aid which is “developmental” and
“non-developmental”. They refer to aid that was given for geopolitical reason
as “non-developmental” while the remaining aid is called “developmental.”
They use an aid allocation regression to obtain their measures. Using similar
estimation techniques than here, they show that “developmental” aid has a
significant positive impact on growth in the long run. Clemens et al. (2004)
divide aid into three categories: humanitarian, long-run impact, and shortrun impact aid. They find strong positive causal relationship between “shortimpact” aid and growth. These two papers suggest that disaggregating aid
measures is a promising way of getting a better understanding of aid on
growth. Our paper follows these approaches by distinguishing between aid
in form of technical assistance (TA) and non-technical assistance (NTA).
Our work is also related to a recent paper by Knack and Rahman (2007).
They analyze the impact of donor fragmentation on recipient countries. They
show that donor fragmentation is inversely related to bureaucratic quality of
recipient countries. However, they do not link donor fragmentation to aid
effectiveness. We study the impact of donor fragmentation regarding TA on
aid effectiveness.
Our paper also has a theoretical component. The papers that most closely
resemble the theoretical component of this paper are Dalgaard et al. (2004),
Hodler (2007), Chatterjee et al. (2003) and Chatterjee and Turnovsky (2007).
The common feature in all of these earlier papers is that they study the relationship between foreign transfers and economic growth using an intertemporal framework with optimizing agents. Dalgaard et al. adopted a 2-period
Diamond (1965) model as the basic framework for their analysis, but they
only considered non-technical aid. Next, Hodler adopted the Barro (1990)
endogenous growth model as his underlying framework, but there are no
transitional dynamics in this model, and this is a feature that will not pass
our empirical tests. Finally, the models constructed by Chatterjee et al.
(2003) and Chatterjee and Turnovsky (2007) also have endogenous growth
5
(via investments in public infrastructure), but (unlike Hodler’s model) their
models have transitional dynamics. However, the downside of adopting such
an endogenous growth model, is that within this framework it is difficult to
pin down the empirical specification of an aid-growth relationship.4 Given
the complexity of their models, their analysis had to be conducted numerically - by calibration and simulation5 ; whereas we chose estimation, that is,
we want to confront our theory with empirical evidence. In our analysis the
underlying theoretical framework is the small open economy version of Solow
(1956)-Swan (1956) growth model. Unlike the earlier papers that studied the
aid-growth relationship, there is no optimization in our model. However, we
have found that this model provides an excellent foundation for our empirical
work, because (i) it provides enough tractability to identify an appropriate
specification for an aid-growth regression; and (ii) most of the variables and
parameters in the model are observable, which means that data exists for
them.
The rest of the paper is organized as follows. Section 2 describes our aid
measures. Section 3 outlines the theoretical model. Section 4 presents the
empirical results. Section 5 repeats the analysis in Section 4 by using aid as
a fraction of GDP instead of aid per capita. Finally, Section 6 concludes.
2
Measuring Aid
Resource transfers from developed to developing countries happen for many
reasons. Transfers intended to foster economic development is only a fraction of all transfers flowing from rich to poor. Rich countries help poorer
countries because of food shortage and emergencies due to climate and war
disasters. The ODA measure typically used in the aid effectiveness literature
includes food and emergency aid. Rich countries also transfer resources for
military reasons. The ODA measure includes military transfers and other
transfers not intended for development purposes when initially given in the
form of a loan that is subsequently forgiven (debtforgiveness of non-ODA
loans). Roodman (2006) provides a very useful discussion of how the ODA
measure is derived. The intensely debated paper by Burnside and Dollar
4
Easterly (2003) observes the lack of theoretical models on the aid growth relationship
which help to pin down the exact specification needed for empirical analysis.
5
The calibration and simulation approach has also been applied recently by Agénor et
al. (2007) to identify the effects that foreign aid has on the Ethiopian economy.
6
(2000) uses an aid measure which is close to the ODA measure. They use
the measure called “Effective Development Assistance” (EDA) which measures the grant element of aid, excluding the loan component of “concessional
loans.” To our knowledge, this measure includes food and emergency aid, and
debtforgiveness of non-ODA loans.
In this paper we will distinguish between TA and NTA. TA is clearly intended to foster economic development. Technical assistance is recorded by
the OECD under “technical cooperation.” Technical cooperation is defined
as follows: “Technical co-operation is defined as activities whose primary
purpose is to augment the level of knowledge, skills, technical know-how
or productive aptitudes of the population of developing countries, i.e., increasing their stock of human intellectual capital, or their capacity for more
effective use of their existing factor endowment.” Technical assistance is intended to fill skills and knowledge gaps in developing countries. TA comes
in two forms: It is either linked to other aid projects, providing the technical component and know-how for these aid projects, or TA constitutes
free-standing initiatives focusing on training and skills transfer (Ridell 2007,
p. 203). In the assessment of the successfulness of TA, Ridell (2007) refers to
“a growing consensus – not least among many leading official donors – that
TA, as traditionally given, has largely been a failure.” However, Ridell also
notes that surprisingly enough there is little evidence of studies that have
assessed the results and impact of TA. The assessment that TA has mainly
been a failure stems from two kinds of observations among others: First,
the costs of providing TA have always been very high. Ridell reports that
for example the yearly costs of employing a foreign consultant amounted to
about $150,000 in the early 80s. Today, this amount is probably the double.
A second problem associated with TA is duplication of activities which has
arisen because donors failed to consult with each other. For example, Ridell
(2007, p. 205) claims that ”it was not uncommon in the past, and still not
unknown today, for long-term consultants, working in a ministry to discover
other consultants, located even along the same corridor, funded by other
donors to do work overlapping with their own.” We address the possibility of
overlap by including donor fragmentation of TA into our regression analysis.
We find that an increasing donor fragmentation is associated with a lower
partial impact of TA on growth.
The theoretical framework introduced below models TA as factor-augmenting
technology. Here, we focus on bilateral TA which includes TA from all DAC
7
−100
−50
0
50
100
countries.6 Most of the TA is bilateral. Multilateral institutions spent a lesser
fraction of their total budget on TA. The ratio of bilateral technical aid to
total bilateral gross aid (excluding food and emergency aid) is larger than the
same ratio for all aid (i.e. including bilateral and multilateral aid). Figure 2
plots this comparison between 1970 and 2004. TA is to our knowledge mainly
disbursed by the donor countries themselves or by NGOs working for donor
countries. Thus, the view that aid money is in the form of a check that the
government of a recipient country can spend at its own discretion does not
apply to TA.7 We believe that TA is a better measure for resources intended
for development than the ODA measure typically used in the literature.
1970
1980
1990
Year
B. tech. aid/B. Gaid
All tech. aid/All Gaid
2000
2010
B. tech. aid/B. ODA
All tech. aid/All ODA
Figure 2: Technical Aid Ratios
As indicated in the introduction, about half of total aid is NTA (Figure
1). Table 2a in the DAC database reports total aid disbursements of each
donor-recipient pair between 1966 and 2005.8 We follow Roodman (2006) by
focusing on current and actual transfers so we exclude all debt forgiveness
6
The countries included are: Australia, Austria, Belgium, Canada, Denmark, Finland,
France, Germany, Greece, Ireland, Italy, Japan, Luxemburg, Netherlands, New Zealand,
Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States.
7
TA is a form of tied aid as opposed to un-tied aid. See Amegashi et al. (2007) for an
economic model explaining why donor countries may use tied- as opposed to un-tied aid.
8
See the OECD database web-site at http://stats.oecd.org/wbos/.
8
grants and capitalized interest, none of which involves actual movement of
money. In addition, we exclude food and emergency aid from our measure
since we believe that these forms of aid are not directly intended for “development.” We call this measure Gaid (for gross aid).9 The ODA measure
used in the literature subtracts amortization of concessional loans, i.e. it is
a net aid measure. Thus, it treats repayments of loans as a reduction in
capital similar to the capital flow concept of net foreign direct investments.
Similarly, Roodman’s (2006) measure of “net aid transfers” (NAT) is net of
any transfer from recipient to donor country be it as an interest payment
or down-payment of the principal. This procedure is reasonable if new aid
money is used to service old debts. Then, only aid money net of repayments
may be invested productively. Unfortunately, there is no data on whether
new aid money is used to service old debts. Birdsall et al. (2002) found
evidence for Sub-saharan Africa that higher debt levels lead to higher net
transfers from bi- and multilateral donors. They show that this is particularly true for multilateral debt. However, since we focus on bilateral debt,
using gross aid instead of net aid may be more appropriate. Furthermore, if
we use net aid and subtract food-, emergency aid, and TA, then in the year
2000, 30 out of 156 recipient countries would have a negative per capita aid
measure.10 This does not make any sense. In the absence of data on whether
new aid money is used to service old debts, a measure of non-technical aid is
not perfect, whether one uses gross or net aid. In order to avoid the problem
of negative aid numbers, we use gross aid (Gaid) in this paper. Our measure
for non-technical aid then equals gross aid (Gaid) minus TA.
Figure 2 plots total and bilateral TA as a fraction of gross aid (Gaid) and
ODA (which is a net aid measure). Technical aid increases smoothly when
taken as a fraction of gross aid, but fluctuates dramatically when taken as a
fraction of ODA. This ratio becomes actually negative which happens because
the ODA measure is negative for some countries and smaller in absolute term
than TA which produces this large negative average ratio. This suggests that
ODA may not be a very good measure of foreign aid. In addition, ODA
fluctuates quite dramatically which may explain why results based on the
use of ODA are no very robust. Figure 2 also shows that the fraction which
9
We denote our aid measure “Gross aid” derived from aid data reported by the
OECD as follows: Gaid= Total Grants-Debt Forgiveness Grants+ODA loans extendedReorganized Debt-Emergency Aid-Development Food Aid.
10
Depending on the year, the number of countries with negative aid numbers varies
around thirty.
9
includes multilateral and bilateral aid is strictly lower than the fraction that
only includes bilateral aid. This implies that most of the TA is bilateral.
3
Foreign Aid and Growth: Theoretical Analysis
Another measurement issue is whether aid – be it technical or non-technical
aid – should enter as a fraction of GDP into a regression equation or whether
it should enter in some other form (per capita or total aid). What specification is the true economic specification is a question that needs to be answered
by theory. Our theoretical analysis is based on a small open economy version of the Solow (1956)-Swan (1956) growth model.11 The decision to study
foreign aid in an open economy, as opposed to a closed, is three fold. First,
most of the economies that receive foreign aid must reasonable be considered small and open. Second, to the extent that international credit markets
are imperfect, some forms of foreign aid can have a positive impact on the
poor. Third, in our empirical work we provide statistical evidence to suggest
that greater international openness and access to credit stimulates economic
growth. As such, the open economy model helps to establish a close link
between the theory and the empirics.
Consider a Cobb-Douglas production function of a given recipient country
of foreign aid as follows:
Y (t) = K(t)α (E(t)L(t))1−α ,
(1)
where Y (t), E(t), K(t), and L(t) denote GDP, the level of total factor productivity, the level of capital, and population at date t respectively.
The country receives foreign aid in form of technical and non-technical
assistance. We assume that foreign aid in the form of TA affects growth
because it affects the production function, while foreign aid in the form of
NTA affects growth because it changes the resource constraint. We assume
that TA affects the production technology in the following way:
·
¸
T A(t)
E(t) = 1 +
X(t),
(2)
L(t)
11
A version of this model without foreign aid is presented by Escot and Galindo (2000).
10
where X(t) is the level of labor augmenting technology. TA is not essential
for production. When TA is zero, the country is still able to produce. The
idea to include TA as a separate argument of the production function is that
private inputs, represented by K, are not close substitutes for the inputs
provided through TA. Note moreover that TA enters in per capita terms into
the production function because we think of TA as being rival in its nature.
Thus, we assume that foreign TA is not a pure public good.12 TA enters
into the model in way that corresponds closely to the OECD definition of
“technical cooperation.”
Non-technical aid affects the resource constraint, which is given by
Y (t) = C(t) + I(t) + N X(t) − N T A(t),
(3)
where C(t) denotes consumption expenditures, I(t) is capital investment
expenditures, and N X(t) is net exports.
As in the standard Solow-Swan model we assume that the population
and technology grow at the exogenous rates n and g respectively, where
L(t) = L(0)ent and X(t) = X(0)egt .
The law of motion for capital is given by
K̇(t) = I(t) − δK(t),
(4)
where δ denotes the rate of capital depreciation.
In addition to domestic capital assets, the economy may acquire foreign
financial assets. The law of motion for net foreign asset holdings, A(t), is
given by
Ȧ(t) = N X(t) + rA(t),
(5)
where r is the exogenous world real interest rate.
One important open economy feature to consider is the difference between
output (GDP or Y ) and income (Z). The relationship between these two
variables is given by
Z(t) = Y (t) + rA(t) + N T A(t).
(6)
Like the standard Solow-Swan model, the current model incorporates a constant and exogenous savings rate, s. As such, consumption and national
12
Note that TA essentially enters into the production function similar to government
infrastructure as analyzed in Barro (1990). Like the service flow from infrastructure,
foreign TA is a service that will increase production capabilities.
11
savings, S(t), satisfy the following equations:
C(t) = (1 − s)Z(t),
S(t) = sZ(t).
(7)
(8)
Savings can be allocated to acquire more capital, or more foreign assets (or
equivalently to pay down foreign debt), that is, S(t) = I(t) + Ȧ(t). Savings
will be allocated to the asset with the highest return.
A second important open economy feature to consider is the relationship
between the rates of return on foreign and domestic assets. If there are no
international credit constraints, then in equilibrium there can be no arbitrage
opportunities between capital and the foreign asset, and therefore rates of
return on these assets must be equalized. As a result, in the model the countries that are initially poorly endowed with capital will finance their capital
investments by borrowing from abroad at the low world interest rate, and
this would lead to instantaneous convergence of output and capital to their
long-run growth paths. Following Escot and Galindo (2000), we reconcile
these predictions with the evidence of gradual convergence by incorporating
international credit restrictions into the model. Like Escot and Gallendo, we
assume that only a fraction, θ, of the capital stock owned by the residents
can serve as collateral to international debt. Formally,
A(t) ≥ −θK(t),
with 0 ≤ θ ≤ 1.13
(9)
Equation (9) may or may not be binding, depending on how initial conditions compare to long-run outcomes. If K ∗ (t) − K(0) ≤ θK ∗ (t) + A(0),
then the credit constraint is not binding and the economy converges instantaneously to its long-run outcome. However, if K ∗ (t) − K(0) > θK ∗ (t) + A(0),
then the credit constraint is binding, and this restriction will lead to gradual
convergence. We therefore focus on this situation. When an economy faces
a binding borrowing constraint it will mortgage all of its allowable physical
capital,
A(t) = −θK(t),
(10)
13
In the model the parameter θ can be interpreted as reflecting a country’s access to
credit or its degree of openness. For example, if θ = 0, then the economy would not be
permitted to borrow from abroad - it would be a ‘closed’ economy. As such, the closed
economy Solow-Swan model is a special case of the more general model presented here.
On the other hand, if θ ≈ 1, then the economy can borrow and import as much as it wants
to satisfy its demand for capital.
12
and it will then use these funds to acquire even more capital.
In order to describe the model’s dynamics, it will be convenient to rewrite
the system in terms of variables that remain constant in the long-run. The
transformation involves dividing all variables by the number of effective units
of labor (XL); for example, y ≡ Y /(XL) and k ≡ K/(XL).14 The transformed system is given by:
y(t) = T AL(t)1−α k(t)α ,
(11)
y(t) = c(t) + i(t) + nx(t) − nta(t),
(12)
k̇ = i(t) − (δ + n + g)k(t),
(13)
ȧ(t) = nx(t) + (r − n − g)a(t),
(14)
z(t) = y(t) + ra(t) + nta(t),
(15)
c(t) = (1 − s)z(t),
(16)
where T AL(t) denotes one plus the level of foreign TA per capita. Finally,
for an economy that faces a binding credit restriction,
a(t) = −θk(t).
(17)
The information in these equations can be combined, and the system can
be simplified to a single differential equation. The fundamental differential
equation of the model is given by,
·
¸
k̇(t)
s
δ − θsr
s nta(t)
1−α
α−1
=
T AL(t) k(t)
− n+g+
+
. (18)
k(t)
1−θ
1−θ
1 − θ k(t)
In order to solve the model it will be necessary to make some assumptions about the dynamics of foreign aid. First, we assume aid is temporary,
and therefore in the long-run aid levels are zero: T A∗ = 0 and N T A∗ = 0.
Second, it is assumed that the aid variables T AL(t) and nta(t) converge to
their steady-state values at the constant rates µ and λ, respectively. These
assumptions allow the level of aid to change over time, but in a deterministic
way. The fact that the model is non-stochastic helps to ease the computational burden of the calculations.
14
The convention throughout the paper is to use upper case letters to denote aggregate
variables, lower case letters to denote variables that have been transformed into effective
labor units, and a star superscript (∗ ) to denote a long-run or steady-state value.
13
Under these conditions, equation (18) implies that k(t) converges to the
steady-state value
1
µ
¶ 1−α
s
∗
k =
.
(19)
δ + (1 − θ)(n + g) − θsr
A comparison of equations (18) and (19) reveals that foreign aid affects the
rate of economic growth in the short-run, but not in the long-run. Since aid
is assumed to be temporary, it will have no long-term effects in the model.
In the steady-state, both capital and income per capita grow at the rate of
technological change (g).
The differential equation (18) is highly non-linear and as a result it has
no closed form analytical solution. However, an approximate solution can be
obtained. Following the convention in the literature, we simplify the problem
by log-linearizing the model around the steady-state position. A log-linear
approximation of equation (18) around the steady-state yields
k̇(t)
s
= β ln T AL(t) − β[ln k(t) − ln k ∗ ] +
nta(t),
k(t)
(1 − θ)k ∗
(20)
β ≡ (1 − α)[n + g + δ/(1 − θ) − θsr/(1 − θ)].
(21)
where
We impose the restriction that β > 0, since this will be true for any reasonable
parameterization.15 The solution to (20) is given by
ln k(t) − ln k(0) = (1 − η1 )[ln k ∗ − ln k(0)] + η2 ln T AL(0) + η3 ∗ nta(0), (22)
where
η1 ≡ e−βt > 0,
µ
¶
¡ −βt
¢
1
η2 ≡ β
e
− e−µt > 0,
µ−β
µ
¶
¡ −βt
¢
s
1
−λt
η3 ≡
e
−
e
> 0.
(1 − θ)k ∗ λ − β
(23)
(24)
(25)
Equations (11) and (22) can be combined to generate an expression for
per capita income; which is given by
ln
Y (t)
Y (0)
Y (0)
− ln
= (1 − η1 ) ln X(0) + gt + (1 − η1 ) ln y ∗ − (1 − η1 ) ln
+
L(t)
L(0)
L(0)
+η4 ln T AL(0) + αη3 ∗ nta(0),
(26)
15
In the empirical part of the paper we find also β > 0.
14
where
α
{ln(s) − ln[(1 − θ)(n + g) + δ − θsr]},
£1 − α
¤
≡ (1 − α)(e−µt − e−βt ) + αη2 R 0.
ln y ∗ =
η4
(27)
(28)
Equation (26) is the base equation for the empirical part of the paper.
Inspecting (26) reveals that the transitional growth rate of income per capita
depends positively on the savings rate, access to credit, initial technology
level, and initial NTA per effective labor unit; and it depends negatively on
initial income per capita and the population growth rate. In the model, the
initial level of foreign TA per capita has an ambiguous effect on the growth
rate of income per capita at date t. The intuition for this is as follows:
According to the model, foreign TA has both a direct effect on the production
of output and and indirect effect through the capital stock. The indirect effect
of T AL(0) on the rate of economic growth is positive, because according to
(22) when the initial level of TA is high, there are more resources to produce
output during the period, and this leads to more investment, and therefore
higher levels of capital and output at the end of the period. However, the
direct effect of T AL(0) on the rate of economic growth is negative, and this
is because we have assumed that during the period the level of foreign aid
falls as it approaches its steady-state value (zero). As such, by assumption
there will be less foreign assistance at the end of the period compared to the
beginning. In our empirical work we expect to find that the net effect of
T AL(0) on the rate of economic growth is positive, and this is because the
worldwide level of foreign aid shows very little in the way of a discernible
trend. This observation suggests that convergence rates for foreign aid are
slow, and therefore differences between the end and beginning of period levels
of aid will be small. Under these circumstances the direct effect will be small
and will be dominated by the indirect effect.
4
Foreign Aid and Growth: Empirical Analysis
In order to estimate the impact of technical and non-technical aid on economic growth we use panel data as done in the extensive literature on aid
effectiveness. Data sources are explained in Table 5 in the Appendix. The
sample includes 95 countries which are listed in Table 4 in the Appendix. We
15
use data between 1970 and 2004 and divide this length into eight 4-year and
one 3-year period indexed by t ∈ {1, . . . , 9}. We use 4-year periods because
they have been used elsewhere (for example, Burnside and Dollar 2000). We
use period averages to estimate the impact of foreign aid on growth. This
procedure is done to account for business cycle movements.
The model developed in the previous section identified equation (26) as
the equation to be estimated. Based on (26), we estimate the following
equation:
yi,t − yi,t−1 = b0 + b1 yi,t−1 + b2 T Ai,t + b3 N T Ai,t
+ b4 X 0i,t + φi + ϕt + ²i,t ,
(29)
where i indexes countries, and t indexes periods. The variable yi,t is the
logarithm of real per capita GDP in the last year of period t, T Ait is the
logarithm of one plus the average of per capita TA. N T Ai,t is the average
of non-technical aid receipts per capita during period t divided by GDP per
capita in 1970.16 The vector X it is a vector of control variables that affect the
steady state of country i such as the logarithm of the investment rate, population growth, and the credit constraint parameter θ. We use openness and
M2/GDP lagged as proxies for the credit constraint of a country. Money
divided by GDP proxies for the development of the financial system, and
because of concerns of endogeneity we lag it by one period (Burnside and
Dollar 2000, p. 850). We include two additional control variables for policy which are inflation and budget surplus, thereby following Burnside and
Dollar (2000).17 Finally φi and ϕt are country and time fixed effects. We
use a country fixed effect to properly control for unobserved time invariant
variables that may be correlated with some of the independent variables. In
particular, we do not need to control for institutions, where only imperfect
measures are available. Glaeser et al. (2004) provides a discussion of the
institutional measures used in the growth literature which highlights their
16
Equations (26) revealed that NTA should enter on the right-hand side of the regression
equation in per effective units of labor (i.e. aid divided by the population and technology).
Because there are no satisfying measures of technology, one may use aid divided by GDP
as a proxy. However, in order to avoid issues of endogeneity we divide all per capita NTA
observations by GDP per capita in 1970. This procedure is accurate if technology growth
is assumed to be constant and equal across countries. This assumption is maintained in
most growth regressions in the literature.
17
In that these variables pick up government consumption and savings decisions, they
may also reflect access to credit.
16
limitations. By using fixed effects, we avoid these limitations. Furthermore,
fixed effects will also control for other permanent factors such as culture and
initial level of technology.
Using a fixed effect estimator (FE) for equation (29), however, raised
econometric issues. First, a fixed effect estimator yields consistent estimates
if the so called “strict exogeneity” assumption holds. This assumption states
that ei,t is not correlated with any other future, current, and past righthand side variable in (29). In a model with a lagged depended variable,
the strict exogeneity assumption is necessarily violated (Woolbridge, 2002,
p. 255). Thus, estimates using a fixed-effect estimator in this setting are
biased. Monte Carlo simulations, however, show that the bias on the lagged
dependent variable can be significant, while it tends to be minor for other
right-hand side variables (Judson and Owen 1996). This would be true for
foreign aid. There are estimators that are able to address the problem of
unobserved country fixed effects which do not require the strict exogeneity assumption. Building on Arellano and Bond (1991), Blundell and Bond
(1998) developed a system estimator (AB-BB) that allows ei,t to be correlated with future values of the other right-hand side variables in (29) (but
not with current and past values) which is consistent with the generalized
method-of-moments (GMM). This estimator uses lagged level variables as instruments for first differences and lagged differences as instruments for level
variables. We estimate (29) using the FE and AB-BB estimators as our
preferred estimation modes. We, however, include OLS and 2SLS estimations for a comparison. In these regressions, we include additional country
specific controls such as ethnic fractionalization, institutional quality, and
geographical dummies thereby following the aid effectiveness literature.
Table 1 reports the estimation results. In Columns (1) and (2) technical
aid per capita has a positive and significant impact on growth, whereas there
is no significant relationship between NTA and growth. There seems to be
no bias in the FE estimations regarding TA since the point estimates for
technical aid are essentially the same for the two estimation methods. The
interpretation of the point estimate is that increasing TA per capita by 25%
increases per capita incomes by 1 percent over a four year period. Or in
other words, increasing TA by about 25% increases the yearly growth rate
by 0.25 percentage points. Note that an increase of TA by 25% is modest
when recognizing that donor countries promised to give 0.7 percent of their
GNP as official development assistance in the Monterrey Consensus, which
implies that aid would more than triplicate from 0.2 percent of GNP in 2002.
17
Table 1: Foreign Aid and Growth
(1)
(2)
(3)
(4)
Estimation method
FE
AB-BB
OLS
2SLS
Initial GDP pc
-.24∗∗∗
-.02∗∗∗
-.07∗∗∗
-.07∗∗∗
(.03)
(.02)
(.02)
(.02)
Investment/GDP
Population growth
Openness
M2/GDP (lagged)
Inflation
Budget surplus
∗
∗∗∗
.05
.11
(.03)
(.02)
.04
-.03
(.02)
(.02)
.04∗∗
(.02)
∗
-.003
∗∗
-.05
(.02)
∗∗∗
(.02)
∗∗
.0005
.0004
.0004
(.0003)
(.0004)
(.0002)
∗∗∗
∗∗∗
.003
.004
(.0008)
(.001)
∗∗∗
.002
(.0002)
∗∗∗
.002∗∗∗
(.0007)
∗∗
-.0000511
(.0006)
∗∗∗
-.0000511∗∗∗
-.0000376
(.0000137)
(.0000173)
(.0000143)
(.0000137)
.0003
.0002
.16
.15
(.0004)
(.0006)
(.14)
(.14)
-.02
-.03
(.03)
(.03)
Institutional quality
.02
∗∗∗
(.005)
Sub-Saharan Africa dummy
-.08
∗∗∗
(.02)
East Asia dummy
NTA pc/GDP70
.0005∗∗
-.0000467
Ethnic fractionalization
TA pc
-.05∗∗∗
(.02)
.05
∗∗∗
∗∗∗
∗∗
.02∗∗∗
(.005)
-.08∗∗∗
(.02)
.05∗∗
(.02)
(.02)
.04
.05
.003
-.002
(.01)
(.02)
(.008)
(.01)
-.24
-.22
.22
.19
(.28)
(.29)
(.28)
(.46)
Obs.
566
566
433
432
Overid test p-value
0.56
0.69
Cragg-Donald F-stat
123
AB(1) p-value
0
AB(2) p-value
0.52
Dependent Variable in all equations is the log difference of final GDP pc across periods.
Significance levels :
∗ : 10%
∗∗ : 5%
∗ ∗ ∗ : 1%. Robust standard errors are in
parenthesis. Constant term, time -, and country fixed effects are not reported. In equation (2) aid variables are endogenous variables and investment/GDP is a predetermined
variable and instrumented for by all their lags. Sargan test of overidentifying restrictions
is performed without robust standard errors. AB(1) and AB(2) refers to the ArellanoBond test for first and second order zero autocorrelation respectively. In equation (4) aid
variables are endogenous and are instrumented for by the one period-lag of aid, years as
a colony, and the civil liberty status.
18
The estimation results for the OLS and 2SLS regressions results are reported in Columns (3) and (4) in Table 1 respectively. In the 2SLS, we
instrument for the aid variables by taking one period lags of aid, the “Civil
Liberty” index provided by Freedom House, and the number of years of being
a colony in the 20th century. The choice of the instruments is guided by the
work of Alesina and Dollar (2000) who show that colonial past and “politics”
are a good predictor of foreign aid. The presence of civil liberties does not
imply that a country implements pro growth policies, however, it matters
for the “politics of foreign aid.” In this sense, these political variables are
more symbolic in their nature. The instruments have substantial explanatory power. The Shea partial R-squared for TA and NTA equals 0.77 and
0.55 respectively. We fail to reject the null that instruments are exogenous
by using the Hansen J test for overidentifying restrictions. In contrast to the
previous results, TA has no impact on growth using either OLS or 2SLS. The
point estimates for TA in these two columns are not significant. The next
table which includes donor fragmentation will give an explanation for this
finding.
NTA, in contrast, has no impact on growth no matter the estimation technique. The point estimates in all columns are not significant. There may be
at least two reasons for this finding: First, NTA affects GDP growth through
the accumulation of capital, and according to equation (20) the magnitude
of this effect will depend on the savings rate. If households and governments
invest little from the resources provided by NTA, then the partial impact of
NTA on growth will be zero. Boone (1996) provides evidence that suggests
that most of foreign aid is consumed rather than invested. Burnside and
Dollar (2000) find that when NTA is interacted with a policy variable, then
aid can have a positive and significant impact on growth for sufficiently high
policy levels.18 An interaction term may help to identify the extent to which
aid is being invested instead of being consumed. When we run a regression
with a policy interaction term for NTA following the approach derived by
Burnside and Dollar, the interaction term is positive. However, the partial
impact conditional on policy is not statistically significant.19 The second
reason for the finding may be that NTA is still a residual measure of aid. It
18
This results, however, seems not to be that robust. See Easterly et al. (2004).
For the 2SLS regressions, the policy interaction term with NTA is significant at the
10% level. However, the partial impact of NTA conditional on policy is not significant
for any policy level, not even at the 10% level. Note also that adding a policy interaction
term does not affect our results regarding TA.
19
19
may include many forms of aid so that a further disaggregation of this aid
measure is needed in order to get a better understanding of the impact of
non-technical assistance on growth.
Table 1 furthermore confirms other predictions of the growth model introduced earlier. The countries in the sample exhibit conditional convergence
(since the point estimates of initial GDP are negative), with convergence rates
ranging between 0.5% and 6.9% depending on the estimation technique.20 All
of these estimates are well within the rates observed in the literature.21 We
also find that the investment rate positively affects economic growth. The
estimate is significant in each column, although only at the 10% level in the
FE estimation. Population growth has the expected sign and is statistically
significant in all columns but Column (1). Regarding the policy variables,
inflation and the M2-GDP ratio significantly affect growth rates. Openness
and the budget surplus have the expected sign but are not significant (except for openness in Columns (3) and (4)). Finally, the additional controls
included in Columns (3) and (4) all have the expected sign. As in many
growth regressions, institutional quality is strongly correlated with economic
growth.
The estimations reported in Table 1 are done under the assumption that
one aid dollar equals one aid dollar. We argued in the introduction that if the
technology of the disbursement for technical aid exhibits increasing returns
to scale, less fragmented technical aid will have a larger impact on growth
rates than more fragmented technical aid. Furthermore, we indicated that
donor duplication is a well known problem associated with technical assistance (Ridell 2007). In order to test the impact of donor fragmentation on aid
effectiveness, we include an interaction term of TA and donor fragmentation.
More specifically, we estimate the following variant of equation (29):
yi,t − yi,t−1 = b0 + b1 yi,t−1 + b2 T Ai,t + b3 N T Ai,t + b4 X 0i,t
+ b5 Fi,t + b6 Fi,t × T Ai,t + φi + ϕt + ²i,t ,
(30)
where Fi,t measures the average donor fragmentation of recipient country i
in period t. In (30), the partial impact of technical aid on growth now equals
b2 + b6 × F . In addition, the standard error of interest now is given by
q
σ̂ ∂y = var(b̂2 ) + F var(b̂6 ) + 2F cov(b̂2 , b̂6 ).
(31)
∂T A
20
Note this is expected since the fixed effect estimator introduces a strong bias with
respect to the lagged dependent variable.
21
See Barro and Sala-i-Martin (2004) for a survey of the empirical growth literature.
20
The point estimate and the standard error now depend on the level of donor
fragmentation, F . In particular, note that in case the estimates of b2 and
b6 are not statistically significant separately, they may nevertheless be significant jointly, since cov(b̂2 , b̂6 ) can be negative. It is also important to
recognize that in case the interaction term b6 is significant by itself, there
is no straightforward interpretation of this result since F is continuous. If
F is a dummy variable (either zero or one), then a significant interaction
term means that the estimates for the two groups 1 and zero are significantly
different from each other. However, this interpretation cannot be used if F
is continuous. For two values of F that are close to each other, differences
in point estimates will not be statistically different from each other.22 We
hypothesize that b6 is negative so that the effectiveness of aid decreases as
donor fragmentation increases.
Table 2 reports the estimation results with donor fragmentation. Column
(1) and (2) show the results using FE and AB-BB. For a comparison, again
we add the OLS and 2SLS results in the last two columns of Table 2. In
the 2SLS regression we instrument for aid and the interaction term of aid by
using lagged aid, the log of population, the measure of “Civil Liberties” (CL)
produced by Freedom House, and the number of years of being a colony of
one of the donor countries in the 20th century. Table 2 shows in all columns
a positive and significant impact on TA on growth. As before, the point
estimate for NTA is not significant. This estimate changes its sign from
negative in Columns (1) and (2) to positive in the last two columns. In
contrast, the point estimate for TA is positive and significant no matter the
estimation technique. The interaction term is negative as predicted and it is
significant at the 10% in Column (2) and it is significant at the 1% and 5%
level in Columns (3) and (4) respectively. The estimates on all remaining
variables are all very similar to the ones reported in Table 1.
As discussed earlier, results with an interaction term need to be interpreted with care. Figure 3 plots a 95% confidence interval for the partial
impact of TA on growth for fragmentation levels between 0 and 100 using
the estimation results reported in Column (1) and (2) in Table 2. The dashed
vertical line in in the two graphs show the average level of fragmentation observed in the sample. This figure confirms the finding reported in Table 1
22
Most of the critical discussion of the policy interaction term in Burnside and Dollar
(2000) focusses on the significance of the interaction term itself (see for example Easterly
(2003) and Easterly et al. (2004)). We believe this is misleading. One needs to focus on
the point estimates and standard errors pointed out above.
21
Table 2: Foreign Aid, Donor Fragmentation, and Growth
(1)
(2)
(3)
(4)
Estimation method
FE
AB-BB
OLS
2SLS
Initial GDP pc
-.24∗∗∗
-.04∗∗∗
-.07∗∗∗
-.07∗∗∗
(.03)
(.02)
(.01)
(.01)
Investment/GDP
Population growth
Openness
M2/GDP (lagged)
Inflation
Budget surplus
∗
∗∗∗
.05
.10
(.03)
(.03)
.05
∗∗∗
(.02)
∗∗∗
.0008
-.02
-.05
(.02)
(.02)
(.02)
(.02)
.0006
.0004
(.0004)
(.0004)
(.0002)
∗∗∗
.003
.003
(.0008)
(.001)
∗∗∗
-.0000463
-.0000436
(.0000133)
(.0000218)
.003
∗∗
(.0000145)
∗
.25
(.13)
-.05
.01
∗∗∗
TA pc
TA pc × frag.
NTA pc/GDP70
Donor Fragmentation
∗∗
.08
.11
(.03)
(.05)
.09
(.005)
-.06∗∗
.04∗∗
(.02)
∗∗∗
(.03)
∗
.01∗∗
(.02)
∗∗
(.02)
∗∗
(.0000142)
(.03)
(.02)
.05
-.0000414∗∗∗
-.07∗∗
∗∗∗
-.07
(.0008)
∗∗∗
(.16)
∗
(.005)
East Asia dummy
.003∗∗∗
.33∗∗
(.03)
Sub-Saharan Africa dummy
(.0002)
∗∗∗
-.0000468
(.0004)
Institutional quality
.0004∗∗
(.0008)
.0004
Ethnic fractionalization
-.04∗∗
∗∗
.0004
∗∗∗
.06∗∗∗
(.02)
.16∗∗
(.07)
∗∗∗
-.003∗∗
-.0007
-.001
-.001
(.0005)
(.0007)
(.0004)
(.001)
-.19
-.08
.28
.40
(.29)
(.29)
(.26)
(.42)
∗∗∗
.002
.004
.004
.008∗∗
(.002)
(.002)
(.001)
(.003)
Obs.
566
566
433
432
Overid test p-value
0.24
0.64
Cragg-Donald F-stat
10.33
AB (1) p-value
0
AB (2)p-value
0.5
Dependent Variable in all equations is the log difference of final GDP pc across periods.
Significance levels :
∗ : 10%
∗∗ : 5%
∗ ∗ ∗ : 1%. Robust standard errors are in
parenthesis. Constant term, time -, and country fixed effects are not reported. In equation (2) aid variables are endogenous variables and investment/GDP is a predetermined
variable and instrumented for by all their lags. Sargan test of overidentifying restrictions
is performed without robust standard errors.
22 AB(1) and AB(2) refers to the ArellanoBond test for first and second order zero autocorrelation respectively. In equation (4) aid
variables are endogenous and are instrumented for by the one period-lag of aid, the log of
population, years as a colony, and the civil liberty status.
.2
.15
.1
.1
0
.05
0
−.1
−.05
0
20
40
60
80
Donor Fragmentation
100
Partial Impact from Column (1), Table 2
0
20
40
60
80
Donor Fragmentation
100
Partial Impact from Column (2), Table 2
Figure 3: 5% Confidence Interval of Partial Impact of Tech. Aid
that the coefficient of TA is around 0.04 for a country with an average level
of fragmentation, and it is significant at the 5% level. This figure also shows
that the coefficient decreases with an increasing level of fragmentation. In
particular, the partial impact of TA on growth is no longer significant for
a fragmentation level of 73% with FE and 64% with AB-BB. For countries
with a low level of donor fragmentation, the partial impact of TA is large
and significant at the 5% level. The figures also show that the difference of
the point estimates of a country with fragmentation of zero and one with
fragmentation of one hundred is not statistically significant since interval
estimates overlap.
Figure 4 shows the same graphs for the OLS and 2SLS regressions reported in Columns (3) and (4) in Table 2. This figure differs from Figure
3 in two ways: First, there is no significant impact of TA on growth for a
country with an average level of donor fragmentation. For example, in the
2SLS equation, the point estimate for a country with an average fragmentation level (dashed vertical line) equals 0.004 which again is close to the
point estimate reported in Table 1. Second, there is a significant negative
relationship between TA and growth if donor fragmentation is above 84%
and 72% in the left- and right-hand side panel of Figure 4 respectively.
23
.2
.3
.15
.1
.1
0
.05
−.1
0
−.2
−.05
0
20
40
60
80
Donor Fragmentation
100
Partial Impact from Column (3), Table 2
0
20
40
60
80
Donor Fragmentation
100
Partial Impact from Column (4), Table 2
Figure 4: 5% Confidence Interval of Partial Impact of Tech. Aid
To conclude, donor fragmentation matters. For a donor fragmentation
below 40% we find a positive and statistically significant impact of TA on
economic growth no matter which estimation technique is used. For levels
of donor fragmentation exceeding 73% there is a non-positive impact of TA
on growth no matter the estimation technique. The FE and AB-BB regressions suggest that for the average country there is a positive and statistically
significant impact of TA on growth.
5
Aid as a Fraction of GDP
In this section we analyze the impact of aid on growth when aid is expressed
as a fraction of GDP as done in virtually all of the growth effectiveness literature. Based on the theoretical results presented earlier, we do not believe
this is the correct specification of the aid-growth relationship. However, we
presents these results anyway to facilitate a comparison with the existing
literature. Table 3 reports the estimates on foreign aid variables when aid
is measured as a fraction of GDP. Otherwise this table uses the exact same
specification than the one reported in Table 2. The main insight generated
in this table is that estimation results on TA vary substantially with the
24
Table 3: Foreign Aid, Donor Fragmentation, and Growth
Estimation method
TA/GDP
TA/GDP × frag.
NTA/GDP
Donor Fragmentation
(1)
(2)
(3)
(4)
(5)
FE
AB-BB
OLS
2SLS
2SLS
3.47
13.15∗∗∗
.62
-11.75
51.49∗∗
(3.48)
(3.52)
(3.99)
(23.59)
(25.09)
∗∗∗
-.04
-.20
-.01
.13
-.70∗∗
(.05)
(.05)
(.06)
(.33)
(.34)
-.68
.03
-.37
.46
-1.71
(.46)
(.49)
(.48)
(1.27)
(1.53)
∗∗
-1.82e-06
.002
.0008
-.001
.008∗∗
(.0008)
(.0009)
(.0007)
(.004)
(.004)
Obs.
566
566
433
432
433
Sargan test p-value
.43
.29
0.43
Cragg-Donald F-stat
1.63
1.8
AB (1) p-value
0
AB (2)p-value
.59
Dependent Variable in all equations is the log difference of final GDP pc across periods.
Significance levels :
∗ : 10%
∗∗ : 5%
∗ ∗ ∗ : 1%. Robust standard errors are in
parenthesis. Constant term, time -, and country fixed effects are not reported. In equation (2) aid variables are endogenous variables and investment/GDP is a predetermined
variable and instrumented for by all their lags. Sargan test of overidentifying restrictions
is performed without robust standard errors. AB(1) and AB(2) refers to the ArellanoBond test for first and second order zero autocorrelation respectively. In equation (4) aid
variables are endogenous and are instrumented for by the one period-lag of aid, the log of
population, years as a colony, and the civil liberty status.
estimation technique used. The FE and OLS regressions report no impact
of aid on growth. The same is true for 2SLS if one period lags of aid/GDP,
population, civil liberty, and colony are used as instruments. In contrast,
the AB-BB regression and the 2SLS regression where a one period lag of
total aid, population, civil liberty, and colony are used as instruments show
a positive and significant impact of TA on growth.
In order to understand the results reported in Table 3, consider the following simplified empirical model
yi,t = a0 + a1 yi,t−1 + a2 (
T Ai,t
) + φi + ²i,t .
yi,t
(32)
Inspecting (32) reveals that if T A is exogenous, then OLS and FE estimates
25
are biased since ²i,t and yi,t are correlated.23 Furthermore, the introduced bias
is negative since high growth will be associated with a low level of T A/y and
vice versa. Then, countries with a large growth rate end up with a low aid
measure and countries with a low growth rate end up with high aid measure,
thus, introducing a bias against finding an impact of aid on growth. Using
instrumental variables can correct for the bias which explains the result in
Table 3. The AB-BB and the 2SLS estimator use instrumental variables to
instrument for foreign aid. Table 3 confirms the prediction of a negative bias
since the point estimate in the FE regression is considerably smaller than
the one in the AB-BB regression. The only difference between Columns (4)
and (5) is that in Column (5) we include total aid lagged by one period as
an instrument instead of aid divided by GDP lagged by one period. As one
can see in Table 3, this one change changes the result substantially. The first
stage in the 2SLS regression in Column (4) of Table 3 regresses something
similar to
T Ai,t
T Ai,t−1
= c0 + c1 yi,t−1 + c2
+ φi + ui,t .
(33)
yi,t
yi,t−1
Both, the second and third term of this equation include yi,t−1 so that his
equation may suffer from multicollinearity. Using one period lags of total aid
instead of aid divided by income per capita takes care of this problem.
Figure 5 shows the 95% confidence interval of the partial impact of TA
on growth as estimated in Column (2) and (5) in Table 3. These figures
show a significant positive impact of TA on growth if aid is not too highly
fragmented similar to the results presented in our main table, Table 2.
The theoretical model introduced earlier suggests that TA per capita is
the appropriate measure if aid enters directly into the production function.
The analysis in this section makes clear that there are also econometric reasons to use aid per capita instead of aid as a fraction of GDP. Using aid as
a fraction of GDP produces an endogeneity bias which is difficult to control. For example, the explanatory power of the instruments in the 2SLS
regressions in Table 2 and Table 3 vary substantially. The Cragg-Donald Fstatistics equals 10.33 in Table 2 but equals only 1.8 in Column (5) in Table
3. This suggests that the instruments in this regression are weak. Using aid
per capita is not only appropriate from a theoretical point of few it is also
preferable from an econometric point of view.
23
The estimates are not biased if donor countries commit to give a constant fraction of
GDP in foreign aid. However, the data does no support this hypothesis.
26
100
20
50
10
0
0
−50
−10
0
20
40
60
80
Donor Fragmentation
100
Partial Impact from Column (2), Table 3
0
20
40
60
80
Donor Fragmentation
100
Partial Impact from Column (5), Table 3
Figure 5: 5% Confidence Interval of Partial Impact of Tech. Aid
6
Conclusions
In this paper we analyzed the impact of foreign aid on economic growth. Our
analysis departed from the previous literature in at least three important
ways.
First, we presented a theoretical growth model for a small open economy
that was capable of identifying the appropriate specification required for an
aid-growth regression. According to our theory, aid in the form of nontechnical assistance should enter into the regression equation in per effective
labor units; whereas technical assistance should enter in per capita units. Aid
should not enter as a fraction of GDP, as is done in virtually all of the growth
effectiveness literature. In fact, our econometric results also demonstrated
that to measure aid as a fraction of GDP introduces a bias against finding an
impact of foreign aid on growth when not addressing properly the problem
of endogeneity.
Second, we differentiated between foreign aid as technical assistance and
non-technical assistance. When aid takes the form of NTA - as is presumed
in the existing empirical literature - then it affects the resource constraint of
the economy. Theoretically NTA should provide more resources with which
27
to finance capital investments, and a larger capital stock will lead to more
output. However, like the existing literature, in our empirical work we did
not find a statistically significant relationship between NTA and growth. A
possible explanation for this result is that the savings rate applied to NTA is
low, and therefore most of these resources are used to finance consumption
instead of investment. Following the work of Burnside and Dollar (2000),
we attempted to improve the statistical relationship observed between NTA
and growth by interacting it with some of our other explanatory variables.
The idea here is that aid will only be used for investment in economies with
good policy environments. Although the policy interaction term for NTA
was found to be positive, as expected; the partial impact of NTA conditional
on policy was found not to be statistically significant for any policy level.
Perhaps disaggregating this aid measure further will reveal additional insights
into how these funds are used.
In comparison, in our model TA enters as a productive input. Since TA
affects the production function directly, (theoretically) it will have a positive
effect on GDP even for economies with very low savings rates and poor economic policies. Therefore, unlike NTA, in a regression with GDP growth as
the dependent variable it should not be necessary to interact TA with other
explanatory variables (perhaps with the exception of fragmentation) in order
to establish a positive effect. Our empirical results provide evidence to support this prediction. That is, when aid takes the form of TA our results show
that it has a strong positive and statistically significant impact on economic
performance. Specifically, our estimates show that for the average developing
economy a 25% increase in TA will lead to about a quarter percentage point
increase in its yearly growth rate.
Third, our analysis differed from what has been done in the existing aid
effectiveness literature in that we interacted aid with the level of fragmentation. We tested the hypothesis that less fragmented aid will have a larger
impact on growth than more fragmented aid. As expected, when interacting
TA with donor fragmentation, we found the interaction term to be negative.
In fact, our estimates indicate that when the level of fragmentation is high
- above 73%, the partial impact of TA on growth is zero or even negative,
depending on the estimation procedure.
According to growth accounting studies, differences in levels of GDP per
person between the wealthiest and poorest countries are due primarily to
differences in the production function residual; whereas only a small amount
is accounted for by differences in physical capital intensity (see Hall and Jones
28
1999). As such, one would think that the policies that will be most effective
in reducing international income disparities will be the ones that help reduce
the productivity gap, and this is exactly what technical assistance is intended
to do. This paper demonstrated that when foreign aid takes the form of
technical assistance it can have important effects on improving economic
conditions in poor countries, at least when it is administered efficiently.
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31
Appendix
Table 4: Countries in the Sample
Algeria
Argentina
Bahamas, The
Bahrain
Barbados
Belize
Bhutan
Bolivia
Botswana
Brazil
Burkina Faso
Burundi
Cambodia
Cameroon
Chad
Chile
China
Colombia
Congo, Dem. Rep.
Congo, Rep.
Costa Rica
Cote d’Ivoire
Cyprus
Dominica
Dominican Republic
Ecuador
Egypt, Arab Rep.
Ethiopia
Fiji
Gabon
Gambia, The
Ghana
Grenada
Guatemala
Guinea-Bissau
Haiti
Honduras
India
Indonesia
Iran, Islamic Rep.
Jamaica
Jordan
Kenya
Korea, Rep.
Kuwait
Lesotho
Macao, China
Madagascar
Malawi
Malaysia
Maldives
Mali
Malta
Mauritania
Mauritius
Mexico
Mongolia
Morocco
Nepal
Netherlands Antilles
Nicaragua
Niger
Nigeria
Oman
32
Pakistan
Papua New Guinea
Paraguay
Peru
Philippines
Rwanda
Saudi Arabia
Senegal
Sierra Leone
Singapore
Solomon Islands
South Africa
Sri Lanka
St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Syrian Arab Republic
Tanzania
Thailand
Togo
Tonga
Trinidad and Tobago
Tunisia
Turkey
Uganda
Uruguay
Venezuela, RB
Zambia
Zimbabwe
Table 5: Data Source
Variable
Definition
Source
2-3 Initial GDP pc
GDP per capita PPP adj.
in $2000.
Investment share of real
GDP in $2000.
Population Growth
Exports and Imports divided
by real GDP ($2000)
M2 dividid by GDP both
in local current currency
Yearly consumer price
inflation
Budget surplus divided by
GDP both in local current
currency or US dollars
Ethnic fractionaliation
Institutional Quality (ICRG)
Technical Cooperation (PPP adj.)
divided by population
See discussion in text
Penn World Tables 6.2
Investment/GDP
Population Growth
Openness
M2/GDP (lagged)
Inflation
Budget surplus
Ethnic fractionaliation
Institutional Quality
TA pc
NTA
Instruments
Population
Civil Liberties
Colony
Civil Liberties
Years of being a colony
in 20th century
33
Penn World Tables 6.2
Penn World Tables 6.2
Penn World Tables 6.2
World Development Ind.
World Development Ind.
Financial Statistics IMF
Easterly et al. (2004)
Easterly et al. (2004)
OECD.stat
OECD.stat
Penn World Tables 6.2
Freedom House
CIA Facts Book