Modern Economy, 2013, 4, 681-695
Published Online November 2013 (http://www.scirp.org/journal/me)
http://dx.doi.org/10.4236/me.2013.411074
Open Access ME
Threshold Effects in the Foreign Aid-Economic Growth
Relationship: The Role of Institutional Quality and
Macroeconomic Policy Environment
Daniel Komlan Fiodendji1*, Kodjo Evlo2
1Departement of Economics, University of Ottawa, Ottawa, Canada
2Faculté des Sciences Economiques et de Gestion (FASEG), Université de Lomé, Lomé, Togo
Received August 31, 2013; revised September 30, 2013; accepted October 10, 2013
Copyright © 2013 Daniel Komlan Fiodendji, Kodjo Evlo. This is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
ABSTRACT
Since the influential paper of [1], the issue relating to the conditions in recipient countries has become central in the
foreign aid debate. Scholars and policymakers alike are interested in identifying the conditions which make foreign aid
more effective. To contribute to this growing debate, this paper investigates the role of macroeconomic policy environ-
ment, institutional policy and a combination of these two previous variables in aid-growth relationship. The empirical
analysis is based on a panel data set including 13 ECOWAS1 countries during the period from 1984 to 2010. Using a
modified panel threshold model, the evidence strongly supports the view that the relationship between aid and economic
growth is nonlinear with a unique threshold. The paper finds that a stable macroeconomic environment and better insti-
tutional quality are sine qua non for the effective contribution of aid to sustainable growth in ECOWAS countries. Fur-
thermore, we find that institutional quality is an important determinant condition which allows aid affects economic
growth. One of main contributions of this paper is to successfully identify the conditions under which the aid has a posi-
tive impact on economic growth. It is desirable to keep the combination condition in States II and IV (the macroeco-
nomic policy environment is below or above and institutional quality above their threshold respectively) because it may
be helpful for the achievement of sustainable economic growth. The results seem to indicate that bad institutional qual-
ity may have detrimental effects on economic growth. This will be an important result for the policymakers and interna-
tional financial institutions, which increasingly favour conditionality and selectivity in the allocation of aid resources.
The major policy implication of our results is not a call for a reduction of foreign aid but rather a call for rethinking
strategies for international assistance and redesigning existing aid programs.
Keywords: Foreign Aid; Economic Growth; Threshold Effects; Institutional Quality; Economic Policy
1. Introduction
The impact of foreign aid on economic growth in devel-
oping countries has been emphasized in the literature
over the past few decades. The importance of this topic
stems from its policy-relevance, given the focus African
countries and their financial and technical partners put on
poverty reduction in the conduct of development policy.
Several studies have tried to capture the effectiveness of
aid on economic growth and poverty reduction. Previous
empirical studies on foreign aid and economic growth
come to contradictory results. There are three possible
results regarding the impact of foreign aid on growth: 1)
foreign aid has no effect on economic growth; 2) foreign
aid has a positive impact on growth, but with diminishing
returns; and 3) foreign aid has a conditional relationship
with economic growth, helping to accelerate growth only
under certain circumstances.
The negative or, at best, insignificant growth effect of
aid supported by the majority of studies lies in the central
assumption that the relationship between aid and growth
is uniform across countries. [2] did not find evidence on
*Corresponding author.
1Economic Community of West African States (Benin, Burkina Faso,
Côte d’Ivoire, Gambia, Ghana, Guinea, Guinea Bissau, Mali, Niger,
N
igeria, Senegal, Sierra Leone and Togo). 13 ECOWAS countries were
selected in a panel regression due to data availability.

D. K. FIODENDJI, K. EVLO 682
the relationship between aid and growth rate in develop-
ing countries. [3] finds that aid has no impact on growth
or investment. However, [4,5] report a positive effect of
aid on economic growth, although aid is shown to have
diminishing returns. The recent literature has tried to es-
tablish that aid works under certain conditions. Various
scholars have argued that aid is indeed effective in good
policy environments (see [6,7]).
Since the influential paper of [1], the issue relating to
the conditions in recipient countries has become central
in the foreign aid debate. Scholars and policymakers
alike are interested in identifying the conditions which
make foreign aid more effective. Does aid effectiveness
vary with the recipient country or with domestic regime
type? Is foreign aid more effective at promoting growth
in good macroeconomic environments or in settings
where institutional quality is good?
Motivated by these questions, the crucial goal of this
paper is to contribute to the inconclusive debate on aid
and growth relationship. More specifically, this study
empirically examines the link among aid and growth
conditioned to the role of macroeconomic policy envi-
ronment and institutional quality in ECOWAS countries.
Developing countries are in fact specified by the vari-
ous economic problems these countries are often faced.
For example, a low level of income, a high level of un-
employment, a very low industrial capacity utilization,
and a high poverty level. In addressing these problems,
foreign aid has been suggested as a veritable option for
augmenting the insufficient domestic resources. While
some countries that have benefited from foreign assis-
tance at one time or the other have grown such that they
have become aid donors (South Korea, China etc.), ma-
jority of countries in Africa have remained backward.
ECOWAS countries have continued to benefit from all
sorts of foreign assistance and in fact still collect at least
as much as the amount collected in the early 1980s, yet
socio-economic development has remained dismal.
Whilst there could be so many determinants explaining
these unfavourable trends, the incessant socio-political
crisis, policy inconsistencies, macroeconomic instability
and bad institutional quality evident in many ECOWAS
countries which are indeed indicators of poor policy
framework, should give one a pause. On the contrary,
however, [8] suggested that empirically, aid is effective
everywhere, even in bad policy environments.
To address these issues, this paper applies an alterna-
tive modelling approach on which uses a threshold vari-
able to investigate whether the relationship between aid
and growth is different in each sample grouped on the
basis of certain thresholds. Threshold models are simple
yet efficient methods to capture nonlinearities in cross
section and time series models. They split the sample
into classes based on the value of observed variables ac-
cording to threshold values. Indeed, there are various
ways to identify the presence of a threshold in an eco-
nomic relationship, depending on the criteria used to de-
termine how to split the sample. [9] applies the technique
of exogenously imposed data splits as a straightforward
technique to select sub-sample. In order to determine the
existence of threshold effects between two variables is
different from the traditional approach in which the
threshold level is determined exogenously. However,
under this approach both the number of regimes and the
location of sample splits are arbitrarily selected and not
based on prior economic guidance. Another limitation of
this approach is that it is not possible to derive confi-
dence intervals for the location of the threshold. The ro-
bustness of the results from the conventional approach is
likely to be sensitive to the level of the threshold. The
econometric estimator generated on the basis of exoge-
nous sample splitting may also generate serious inferen-
tial problems (for further details, see [10,11]). Threshold
models have some popularity in current applied econo-
metric practice. The model splits the sample into classes
based on the value of an observed variable-whether or
not it exceeds some threshold. When the threshold is
unknown (as is typical in practice) it needs to be esti-
mated, and this increases the complexity of the econo-
metric problem. A theory of estimation and inference is
fairly well developed for linear models with exogenous
regressors, including [10-13].
These papers explicitly exclude the presence of en-
dogenous variables, and this has been an obstacle to em-
pirical application, including panel models. Advantages
of the endogenous threshold regression technique over
the traditional approach are that: 1) it does not require
any specified functional form of non-linearity, and the
number and location of thresholds are endogenously de-
termined by the data; and 2) asymptotic theory applies,
which can be used to construct appropriate confidence
intervals. A bootstrap method to assess the statistical si-
gnificance of the threshold effect, in order to test the null
hypothesis of a linear formulation against a threshold al-
ternative, is also available. This approach is supposed to
eliminate multicollinearity problems among some of the
regressors, in order to be able to identify the partial ef-
fects of these variables on the dependent variable. For this
purpose we used a sample of 13 ECOWAS countries co-
vering the period 1984-2010.
For the purpose of presentation, the rest of this paper is
structured as follows: Sections 2 describes the evolution
of aid in ECOWAS countries, Section 3 provides econo-
metrics methodology, Section 4 sets out our empirical ana-
lysis and interpretation of our results, and Section 5 pro-
vides concluding remarks.
2. Foreign Aid in ECOWAS Countries
Figure 1 depicts the evolution of the ratio of total foreign
id to GDP. For the whole ECOWAS countries, the a
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D. K. FIODENDJI, K. EVLO
Open Access ME
683
Figure 1. Trends in Aid to ECOWAS countries, 1984-2010.
average variations seem to stable around 10%.
In Guinea Bissau, aid has reached its highest levels in
the 1990s, with a peak of 51% in 1996. Three countries
have received relatively small amounts of aid. These
countries are Côte d’Ivoire, Guinea and Nigeria. Fur-
thermore in the latter country, the ratio of aid was around
1% during 1990s. After that, it became almost nil from
1995 to 2005 before rising slightly and making a jump to
8% in 2007. The trend is stable at our countries sample
level. However, for Sierra Leone, there is a change
sawtooth. For example, the ratio of aid is around 5% in
1990 except for 2003 (22%) and 2008 (21%). In general
case, we have been observed some convergence of ratio
of aid for most of ECOWAS countries from the 2000s. In
fact the ratio of aid is between 1% and 10%.
3. Econometric Methodo logy
Threshold models are simple yet efficient methods to
capture nonlinearities in cross section and time series
models. The main purpose of this paper is to use a thres-
hold variable to investigate whether the relationship be-
tween Aid and economic growth through the policy en-
vironment and institutional quality is different in each
sample grouped on the basis of certain thresholds. The
endogenous determination of threshold effects between
variables is different from the traditional approach in
which the threshold level is determined exogenously. If
the threshold level is chosen arbitrarily, or is not deter-
mined within an empirical model, it is not possible to
derive confidence intervals for the chosen threshold. The
robustness of the results from the conventional approach
is likely to be sensitive to the level of the threshold. The
econometric estimator generated on the basis of exoge-
nous sample splitting may also pose serious inferential
problems (for further details, see [10,11]).
3.1. Econometric Framework: Panel Threshold
Models
[10] developed the econometric techniques appropriate
for threshold regression with panel data. Allowing for
fixed individual effects, the panel threshold model di-
vides the observations into two or more regimes, depen-
ding on whether each observation is above or below the
threshold level. The general specification threshold mo-
del takes the following form:


1
11
0
11
K
itik itkitk
k
K
it KitKit
yxIq
xI q
 






 


 
 (1)
where subscripts i stands for the cross-sections with


1iN and t indexes times . i

1tT


is the
countries-specific fixed effect and the error term it

is
independent and identically distributed (iid) with mean
zero and finite variance 2


. I(.) is the indicator function
indicating the regime defined by the threshold variable
it the threshold parameter γ. it is dependent variable
and the vector of explanatory variables. 0
q
it
x
y
,



1K



 . Equation (1) allows for K threshold values
and, thus, (K+1) regimes. In each regime, the marginal
effect of


it
xk

on may differ.
it
Following the modified version of [10] panel threshold
model proposed by [14], we consider a discriminator
constant which is not individual specific but captures a
common effect for all cross-sections. According to these
authors, ignoring regime dependent intercepts
y


k

can
lead to biased estimates of both the thresholds and the
corresponding marginal impacts.


1
11
0
11
K
itikk it kitk
k
Kit KitKit
yxI
xI q
 
 

 


 



1
q
(2)
This formulation assumes that the difference in the re-
gime intercepts, represented by

k

, is not individual
specific but the same for all cross-sections. According to
[14], omission of any variable correlated with at least one
regressor and the dependent variable causes biased esti-
mates, but regime intercepts are a particularly interesting

D. K. FIODENDJI, K. EVLO 684
case. First, the bias can be clearly interpreted. Second,
availability of regime intercepts as regressors is not an
issue since they are as easily constructed as the re-
gime-dependent exogenous regressors for a given thresh-
old.
3.2. Estimation and Test Strategy
3.2.1. Estimation Method
Estimation of the panel threshold model involves several
stages. First, estimation of the parameters model requires
eliminating the individual effects i

by removing indi-
vidual-specific means and then applying the least squares
sequential procedure (see [10] for more details). Indeed,
the individual specific effects are eliminated using the
standard fixed-effects transformation implying for the
identification of k

and 1k


that the elements of it
x
are neither time-invariant nor adding up to a vector of
ones. This case applies to regime intercepts which are
usually included in each regime in threshold models in
pure cross-sectional or time-series contexts. For example,
in the case of two regimes, even in the presence of fixed
effects it is possible to control for differences in the re-
gime intercepts by including them in all but one regime
as in the extension of the following equation2:
  
11 2it iitititit
yxIqxIq
it

   
(3)
The seminal contribution of [11] allows us to estimate
and make valid statistical inferences on the threshold.
There are three statistical issues that need to be addressed
in a threshold model: 1) how to jointly estimate the
threshold value

and the slope parameters; 2) how to
test the hypothesis that a threshold exists and; 3) how to
construct confidence intervals for

and β. We briefly
discuss each in turn. [11] recommends obtaining the least
squares estimate ˆ

as the value that minimizes the
concentrated sum of squared errors,

1
S

. The sum of
the squared error function depends on

only through
the indicator function. Hence, the minimization problem
is a step procedure where each step occurs at distinct
values of the observed threshold variable

it
qfter the
threshold value

. A

is estimated, it is important to deter-
mine whether the threshold effect is statistically signifi-
cant. In order to test the statistical significance of a
threshold effect typically we would want to test the null
hypothesis of no threshold effect, 01 2 :H

 . How-
ever, since

is only identified under the alternative
11 2

:H


 , the distribution of classical test statistics,
such as the Wald and Likelihood ratio tests, are not as-
ymptotically Chi-squared. In essence this is because the
likelihood surface is flat with respect to

, consequently
the information matrix becomes singular and standard
asymptotic arguments no longer apply. There are meth-
ods for handling hypothesis testing within these contexts.
In some instances, we are able to bind the asymptotic dis-
tribution of likelihood ratio statistics ([15,16]); alter-
natively their asymptotic distribution must be derived by
bootstrap methods (see [11]). The appropriate test statis-
tic is


01
12
ˆ
ˆ
SS
F



 where and are, respect-
0
S1 S
tively, the residual sum of squares under the null hy-
pothesis 0
H
and the alternative 1
H
with 2
ˆ

the re-
sidual variance under the alternative hypothesis. Once
the threshold effect exists, the next question is whether or
not the threshold value can be known. The null hypothe-
sis of the threshold value is 00 :H

 , and the likely-
hood ratio statistics is
 


11
12
ˆ
ˆ
SS
LR




 where


1
S

and


1ˆ
S

are the residual sum of squares from
Equation (3) given the true and estimated value, respec-
tively. The null hypothesis is rejected for large value of
1. The asymptotic distribution of LR

10
LR

can be
used to form valid asymptotic confidence interval about
the estimated threshold values. The statistics of


1
LR 0

are not normally distributed and [11] com-
puted their no-rejection region,

c

with

the
given asymptotic level. He proves that the distribution
function has the inverse


2ln 11c

  from
which it is easy to compute the critical values. The test
rejects the null hypothesis at the asymptotic level

if


1
LR 0

exceeds


c

. The asymptotic

1



con-
fidence interval for

is set of values of

such that




10
LR c

 .
3.2.2. Regime Intercepts
The role of regime intercepts will be discussed in the
context of a single threshold model, though it is straight-
forward to introduce them in a model with multiple
thresholds. The elimination of the individual specific
effect in Equation (3) with the standard fixed-effects
transformation implies for the identification of slope co-
efficients 1

and 2

that the elements of it
x
are nei-
ther time-invariant nor adding up to a vector of ones.
This latter case applies to regime intercepts which are
usually included in each regime in threshold models in
pure cross-sectional or time-series contexts. Even in the
presence of fixed-effects it is possible to control for dif-
ferences in the regime intercepts by including them in all
but one regime as in the following extension of Equation
(3):

 


112it iititititit
yxIq IqxIq

 


(4)
2There is no reason to limit our analysis to just two regimes. Hence, the
estimation approach proposed by [10] and extended by [14] allows a
more general specification with K thresholds (i.e. K + 1 regimes).
This formulation assumes that the difference in the re-
gime intercepts, represented by 1

, is not individual
Open Access ME

D. K. FIODENDJI, K. EVLO 685
specific but the same for all cross-sections. Since Equa-
tion (4) has neither been considered by [10] nor any of
the numerous studies, e.g. [17,18] or [19], applying his
methodology, it seems worthwhile to briefly discuss the
role of regime intercepts for the estimation results in the
[10] framework.
In case a regime intercept is included, as in specifica-
tion (4), the slope estimates for each regime are identical
to those from a regression using only observations from
the respective regime which reflects the orthogonality of
the regressors

im
I
xx and

ii m

x
Ix x. Omission
of any variable correlated with at least one regressor and
the dependent variable causes biased estimates, but re-
gime intercepts are a particularly interesting case. First,
the bias can be clearly interpreted. Estimating Equation
(3) in the presence of a regime intercept in the data gen-
erating process results in a bias proportional to 1
ˆ

be-
cause the orthogonality of the regressors is not preserved
anymore. Second, availability of regime intercepts as
regressors is not an issue since they are as easily con-
structed as the regime-dependent exogenous regressors
for a given threshold.
Biased estimates of the regression slopes have further
consequences in the panel threshold model because the
threshold estimates are also obtained by least squares.
Only by coincidence, these estimates will be the same for
specifications (3) and (4) if a regime intercept is present
in the data generating process. Moreover, unbiased esti-
mates of 1

and 2

are crucial for the test of the sig-
nificance of a threshold which is based on the null hy-
pothesis of equality of the two coefficients.
Eventually, the setup in [10] has to be extended to al-
low for regime intercepts as in Equation (4). First, the
null hypothesis to test for the significance of the thresh-
old has to be extended by 10


. Second, the derivation
of the asymptotic distribution of the threshold estimate
now relies on the additional technical assumption that
10

 as . It means that the difference in the
intercepts between the two regimes is ‘small’ relative to
sample size which is completely analogous to the assum-
ption regarding the slope coefficients. Third, the proof in
the appendix in [10] now relies on the following two
expressions taking the regim
regressor into account:
N
e

intercept as an additional


21 1

 

 and
.

it it
zx



C1
4. Empirical Analysis
4.1. The Variables
The set of explanatory variables that constitute the vector
it
x
include; foreign aid as a percentage of GDP, policy
index, the institutional quality index, investment as a
percentage of GDP and human capital as a measured of
secondary enrollment schools and initial income.
The policy variables are openness, inflation and fiscal
policy. Openness, a measure of international trade, is
believed to affect growth through several channels, such
as access to technology from abroad, greater access to a
variety of inputs for production and access to broader
markets that raise the efficiency of domestic production
through increased specialization. There are various meas-
ures of openness but in this paper, we use ratio of total
trade to GDP. As suggested by [1], budget surplus as a
percentage of GDP is included as a measure of fiscal
policy. The budget surplus is believed to be an indicator
of the stabilizing role of government. In line with [20]
inflation is taken as a measure of monetary policy.
012 3
Policyfispolicy opensinf
itititit it
www w


 (5)
where 0 the constant term is the country’s predicted
growth rate for given values of budget surplus (fish-
policy), trade openness
w


opens and the inflation rate


inf assuming that it had the mean values of all other
characteristics. The weights 12 and 3 are ob-
tained from OLS regression of these variables on growth.
The intuition is that the policy index should weight the
policies according to their impact on growth.
,ww w
The policy index is a measure of the quality of eco-
nomic policy; the higher the index the higher the quality
of economic policy. Obviously, countries with good eco-
nomic policies tend to grow faster than countries with
bad economic policy.
The institutional quality index (ircg) is not a weighted
average of the institutional variables3, but is obtained
from OLS regression of corruption, ethnic tension, so-
cioeconomic conditions, law and order, profile invest-
ment and government stability.
01 234
56
icrgcorr ethnicssocioeclaw
proinvgovstab
itititit it
itit it
kk kkk
kk

 
 (6)
The institutional settings within which economic poli-
cies are formulated are of crucial importance, because the
quality of these institutions can be a primary source of
the differences in economic growth among nations. Coun-
tries with good institutions such as lower level of confis-
cation of private properties, lower level of governmental
corruption, lower ethnic tensions, and efficient profile
investment are expected to grow faster than countries
with bad institutions. Poor institutions interfere with
economic growth by inducing economic agents to engage
in redistributive politics rather economic activity with
lower economic returns. The coefficient of institutional
quality is expected to be positive.
Another control variable included in equation is in-
vestment and human capital. The level of investment is
also included as a control variable. The investment/GDP
ratio


invest is used as a proxy for the growth rate of
3See Ali et al. (2009) for more details.
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D. K. FIODENDJI, K. EVLO
Open Access ME
686

Financial Statistics and the International Country Risk
Guide (ICRG).
the capital stock. Since the investment/GDP ratio is not
reported for the majority of the ECOWAS countries,
gross fixed capital formation as a share of GDP is used to
represent investment/GDP ratio. The higher the invest-
ment is, the higher the growth rate is. Therefore, we ex-
pect a positive sign for the coefficient of investment.
Human capital

is Secondary School Enroll-
ment Rate measures the percentage of school age popula-
tion that was enrolled in secondary schools. Thus, the
GDP growth rate is a positive function of education. We
expect a positive sign for the coefficient of this variable.
human

We are able to identify the regime of the economy
with respect to the macroeconomic policy environment
and institutional quality which depend on the estimate of
the policy index and institutional quality thresholds. Thus
we can also investigate all combinations of those regimes.
So we can distinguish between four different states as
shown in Figure 2.
Figure 2 displays the four states the donors can face
when deciding about the aid in recipient countries.
We have to use the threshold estimated
p
olicy

and
institutions

to determine the regime. We are able to dis-
tinguish with this approach between a situation where the
macroeconomic policy environment and institutional qua-
lity are below
p
olicy institutions

(State I), the macroeco-
nomic policy environment is below and institutional
quality above
p
olicy institutions

and vice versa (States II
and III), and a situation where both are above
p
olicy institutions

(State IV). We can therefore estimate
for each case the aid impact on economic growth and
compare those to each other.
The key independent variable and the variable of in-
terest is which is foreign aid expressed as per-
centage of GDP. The aid variable used is the Effective
Development Assistance (EDA), which measures official
aid flows as the sum of grants and the grant equivalent of
official loans. The grant equivalent of a financial inflow
is the amount that, at the time of its commitment, is not
expected to be repaid, i.e., the amount subsidized through
below-market terms at the time of commitment.

aid
The initial income level (initial) measured as GDP per
capita is included to verify the convergence hypothesis.
The convergence hypothesis and the steady-state theory
predicted in the neoclassical growth theory rests on the
premise that countries are similar except for their starting
GDP level. Therefore, poor countries are predicted to
grow faster than rich countries. If this is true, we expect a
negative sign for the coefficient of this variable.
However, some differences are of special economic
growth. Since when comparing States I and II it becomes
obvious that only the sign of the institutional quality has
changed while the macroeconomic policy environment
remains negative (below the threshold value
p
olicy

) in
both cases. The same holds for the States III and IV where
again only the macroeconomic policy environment re-
mains positive (above the threshold value
p
olicy

). The
same argumentation applies when comparing States I and
III with respect the negative sign of institutional quality
(below the threshold value institutions

) or positive (above
the threshold value institutions

). According to our analysis,
we expect that the aid negatively affects growth in States
I and III and has positive impact in States II and IV.
4.2. Data and Preliminary Analysis
In this paper, we consider annual data from the
ECOWAS countries which are collected from various
sources and covered the period 1984 to 2010. Data are
collected from the Penn World Table 6.1 and 6.2, World
Development Indicators (WDI), the IMF’s International
policy index
State III
State I State II
State IV
Institutional quality
policy

nsinstitutio

Figure 2. The four states of the economy.

D. K. FIODENDJI, K. EVLO 687
Having constructed the data we can now separate them
into the four states by simply introducing the threshold
measures explained in Figure 2.
The summary statistics of the different states together
with those for each threshold and linear relationship be-
tween aid and growth are given in Table 1. Several in-
teresting insights can be drawn from Table 1. First, fol-
lowing [10], each regime contains at least 5% of all ob-
servations. So we have enough data points for each re-
gime in order to get consistent estimates. Furthermore,
for their combination given by the four states the same
conclusion can be drawn. Second, the descriptive statis-
tics show that the aid is lower if the policy environment
and institutional quality are above their threshold values.
This suggests that a stable macroeconomic policy envi-
ronment and better institutional quality allow a little aid
to improve economic grow. However, the aid is higher
when the policy environment and institutional quality are
below their threshold values. This implies that even they
have more quantitative aid; its impact on growth is un-
clear. Following, the four states, our statistics show that
economic is highly efficient if the institutional quality
achieves optimal value.
Before conducting the regression investigation as pro-
posed in the recent panel data econometric literature, we
tested for possible unit roots in the panels. [10] panel
Table 1. Descriptive statistics.
Aid Policy index Institutional quality
Linear <0.087 >=0.087 <1.880 >=1.880 <0.571 >=0.571
grow 0.007 0.005 0.010 0.007 0.004 0.000 0.017
grow
 0.044 0.037 0.059 0.046 0.035 0.048 0.035
max
grow 0.226 0.122 0.226 0.226 0.080 0.122 0.226
min
grow −0.296 −0.191 −0.296 −0.296 −0.170 −0.296 −0.067
policy 1.794 1.811 1.750 1.758 1.934 1.768 1.839
p
olicy
 0.898 0.658 1.294 0.862 0.264 1.039 0.466
max
policy 2.056 2.004 2.056 1.880 2.056 2.007 2.056
min
policy 0.040 0.858 0.040 0.040 1.882 0.040 1.363
institutions 0.539 0.550 0.511 0.536 0.554 0.496 0.615
institutions
 0.074 0.072 0.074 0.076 0.066 0.054 0.032
max
institutions 0.712 0.712 0.673 0.698 0.712 0.570 0.712
min
institutions 0.325 0.325 0.353 0.325 0.395 0.325 0.571
aid 0.079 0.048 0.157 0.083 0.063 0.087 0.065
aid

0.068 0.024 0.080 0.069 0.061 0.077 0.045
max
aid 0.525 0.086 0.525 0.525 0.319 0.525 0.241
min
aid 0.001 0.001 0.087 0.001 0.001 0.001 0.001
invest 0.178 0.173 0.190 0.179 0.172 0.171 0.190
invest
 0.062 0.052 0.081 0.064 0.049 0.065 0.054
max
invest 0.484 0.310 0.848 0.484 0.310 0.484 0.310
min
invest 0.035 0.035 0.067 0.035 0.087 0.035 0.067
income 5.898 6.023 5.579 5.840 6.139 5.809 6.053
income
 0.492 0.479 0.364 0.476 0.484 0.496 0.444
max
income 7.222 7.222 6.648 7.157 7.222 7.222 7.157
min
income 4.878 4.878 4.935 4.878 5.352 4.878 5.217
human 0.220 0.247 0.149 0.207 0.271 0.188 0.275
human
 0.119 0.121 0.082 0.118 0.109 0.099 0.132
max
human 0.590 0.590 0.560 0.590 0.560 0.440 0.590
min
human 0.030 0.030 0.050 0.030 0.040 0.030 0.060
N 351 252 99 283 68 223 128
Open Access ME

D. K. FIODENDJI, K. EVLO 688
Continued
State 1 State II State III State IV
grow −0.003 0.021 0.004 0.004
grow
 0.049 0.037 0.043 0.019
max
grow 0.122 0.226 0.080 0.038
min
grow −0.296 −0.067 −0.170 −0.036
policy 1.726 1.813 1.940 1.924
p
olicy
 0.990 0.377 0.234 0.293
max
policy 1.876 1.880 2.007 2.056
min
policy 0.040 1.363 1.882 1.885
institutions 0.493 0.614 0.508 0.620
institutions
 0.057 0.030 0.035 0.038
max
institutions 0.570 0.698 0.571 0.712
min
institutions 0.325 0.571 0.395 0.579
aid 0.091 0.068 0.068 0.054
aid

0.078 0.045 0.070 0.046
max
aid 0.525 0.241 0.319 0.179
min
aid 0.001 0.001 0.001 0.015
invest. 0.171 0.193 0.168 0.179
invest.
 0.069 0.053 0.046 0.053
max
invest. 0.848 0.309 0.282 0.310
min
invest. 0.035 0.067 0.087 0.096
income 5.731 6.041 6.168 6.097
income
 0.443 0.472 0.572 0.326
max
income 7.109 7.157 7.222 6.734
min
income 4.878 5.217 5.352 5.601
human 0.179 0.259 0.230 0.330
human
 0.102 0.130 0.071 0.127
max
human 0.440 0.590 0.390 0.560
min
human 0.030 0.060 0.040 0.180
N 183 100 40 28
Notes:
x
stands for the mean of the respective variable, max
x
and min
x
for the maximum and minimum realization, while
x

is the
standard deviation, N = number of observations.
threshold regression model is an extension of the tradi-
tional least squared estimation method, in fact. It requires
that variables considered in the model need to be station-
ary in order to avoid the so-called spurious regression4.
Since the stationarity properties of the variables are stud-
ied, i.e. the examination of whether or not the variables
appear to contain panel unit roots. Non-stationary panels
have become extremely popular and have attracted much
attention in both theoretical and empirical research over
the last decade. A number of panel unit root tests have
been proposed in the literature, in this research, we use
[21-24] all based on a null hypothesis that a unit root
exists in the panels. Indeed, the [21,22] panel unit root
tests assume a homogeneous autoregressive unit root
under the alternative hypothesis whereas [23] allows for
a heterogeneous autoregressive unit root under the alter-
native hypothesis. Fundamentally, the [23] test averages
the individual augmented Dickey-Fuller (ADF) test sta-
tistics. Both the [21,23] tests suffer from a dramatic loss
of power when individual specific trends are included,
which is due to the bias correction. However, the [22]
4Spurious regression is argued in Granger and Newbold (1974) that the
estimation of the relationship among non-stationary series is easily
getting higher R2 and t statistics.
Open Access ME

D. K. FIODENDJI, K. EVLO 689
panel unit root test does not rely on bias correction fac-
tors. Monte Carlo experiments showed that the [22] test
yields substantially higher power and smallest size dis-
tortions compared to [21,23]. [24,25] suggest comparable
unit root tests to be performed using the non-parametric
Fisher statistic.
Table 2 displays the results of panel unit root tests in
levels for all the variables. All tests reject the null hy-
pothesis of a unit root in the examined series. As regards
to institutional quality and investment, the tests failed to
reject the null hypothesis of unit root. According to [26],
this result may be due to the fact that the tests have a low
power against nonlinear stationary process. From the
nonlinear unit root test, we can conclude that all the vari-
ables in the paper are stationarity. It was deemed safe to
continue with the panel data estimates of the above
econometric specification.
Suspecting strong collinearity between some regres-
sors, Ta ble 3 reports the pairwise correlation coefficients
between all the candidate variables of the models. As can
be seen, our results suggest that the inclusion of all these
variables in the same model pose none problem of mul-
ticollinearity. Indeed, coefficients of correlation appear
quite low on the whole.
Table 2. Panel unit root test results.
Aid Policy Institutions Investment Initial income Human capital
Intercept
Levin, Lin and Chin −2.928a −2.242b −1.244 1.714 −4.851a −2.247b
(0.002) (0.013) (0.101) (0.957) (0.000) (0.012)
Breitung −2.543a −1.761b 0.559 −0.379 −3.711a −3.913a
(0.006) (0.039) (0.712) (0.352) (0.000) (0.000)
Im, Pesaran and Shin −2.779a −1.745b −0.497 0.668 −4.251a −2.554a
(0.003) (0.041) (0.310) (0.748) (0.000) (0.005)
Fisher-ADF 49.383a 41.953b 30.353 24.192 68.092a 69.023a
(0.004) (0.025) (0.253) (0.565) (0.000) (0.000)
Fisher-PP 72.120a 76.062a 35.575c 37.930c 174.617a 148.824a
(0.000) (0.000) (0.090) (0.062) (0.000) (0.000)
Intercept + trend
Levin, Lin and Chin −3.499a −3.265a 1.282 1.278 −8.500a −5.813a
(0.000) (0.000) (0.900) (0.899) (0.000) (0.000)
Breitung −2.562a −0.450 −2.185b 3.096 −7.400a −4.259a
(0.005) (0.326) (0.014) (0.999) (0.000) (0.000)
Im, Pesaran and Shin −2.678a −1.879b 1.237 1.087 −6.352a −6.011a
(0.004) (0.029) (0.897) (0.861) (0.000) (0.000)
Fisher-ADF 48.013a 46.674a 18.472 21.340 92.614a 94.777a
(0.005) (0.008) (0.858) (0.724) (0.000) (0.000)
Fisher-PP 67.405a 86.773a 20.553 40.996b 151.377a 166.730a
(0.000) (0.000) (0.765) (0.031) (0.000) (0.000)
Notes: Figures in square brackets are probability values. a, b, and c represent significance at 1%, 5%, and 10% respectively. The maximum
number of lags is set to be four. MAIC is used to select the lag length. The bandwidth is selected using the Newey-West method. Barlett is
used as the spectral estimation method.
Table 3. Correlation matrix of the variables include in the model.
Aid Growth Policy InstitutionsInvestmentInitial income Human capital
Aid 1.000
Growth 0.055 1.000
Policy −0.311 0.018 1.000
Institutions −0.224 0.170 0.381 1.000
Investment 0.321 0.188 0.018 0.249 1.000
Initial income −0.419 0.066 0.387 0.360 0.058 1.000
Human capital −0.369 0.112 0.174 0.300 0.201 0.429 1.000
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D. K. FIODENDJI, K. EVLO 690
In fact, several reasons might explain the low correla-
tion between aid and economic growth outcomes. One of
them is the phenomenon of aid fungibility, i.e. aid could
be redirected by the recipient country toward sectors
other than those originally provided in the commitments.
In order to test the presence of non-linear effect with
respect to aid, institutional quality and the policy index
we apply the Hansen’s test described above, with 1000
bootstrap replication to compute the p-value of the F-test
statistic.
The estimated threshold and the p-value of the F-test
for the null of no threshold are reported in Table 4. The
results show that the linearity hypothesis is strongly re-
jected in favour of threshold regression for both three
variables. This confirms the presence of nonlinearities in
aid–growth relationship. Once the presence of threshold
effect is confirmed the next step is to estimate the thresh-
old regression following the procedure as discussed in
the methodology section.
4.3. Aid Thresholds and Economic Performance
Let us now apply the modified panel threshold model to
the analysis of the impact of aid on economic growth in
ECOWAS countries. To that aim, consider the following
threshold model of the aid-growth relationship:


11
21
345
growAid AidAid
AidAidinvestinitial
humanpolicy ircg
it iititit
it ititit
ititit it
II
I
 


 



2
(7)
where

Aidit I

 and


Aidit I

 are indicator
functions which take the value of one if the term between
parentheses is true, and are zero otherwise. This model
specifies the effects of Aid with two coefficients: of 1

and 2

. 1

denotes the effect of Aid below the thresh-
old level

, and 2

denotes the effect of Aid exceed-
ing the threshold level

.
Table 5 presents the estimation results obtained of
Equation (7) and includes two parts. The first part of the
table displays the regime-dependent coefficients of aid
on growth. Specifically,


12
ˆˆ

 denotes the marginal
effect of aid on growth in the low (high) aid regime, i.e.
when aid is below (above) the estimated threshold value.
The coefficients of the control variables are presented in
the second part of the table. Our results reveal that the
coefficients of aid have different signs and significances
across the low and high aid regimes. When aid is above
the threshold value


ˆ0.087
 , our results indicate that
foreign aid have positive but insignificant impact on
economic growth. However, when aid is below the
threshold value, there are negative relationship between
aid and growth and aid marginal effect is significant.
This negative impact can be explained by the simple fact
that a permanent rise in aid reduces long term capital
accumulation and labour supply and by extension re-
duces the rate of economic growth [27]. These findings
suggest that foreign aid perpetuates poor economic poli-
cies and postpone reform; limited absorptive capacity in
the recipient country reduces the effectiveness of aid and
aid reduces both domestic private and public saving (see
[28,29]). Moreover, our results have shown how devel-
opment assistance leads to distortion and disruption in
the domestic economy.
Regarding the control variables, we notice that inves-
tment and institutional quality and human capital have
positive impact on growth, while the initial income is ne-
gatively and significantly correlated with economic growth.
This result confirms the conditional convergence hypo-
thesis of [30-32].
Table 4. F-test of null of no threshold

01 2
:H

 .
Aid Policy index
Institutional
quality
Estimated threshold0.087 1.880 0.571
Confidence Interval[0.004 0.210] [1.432 1.952] [0.545 0.632]
LM-test 25.435 17.952 37.930
p-value 0.015 0.037 0.008
critical values
10% 16.370 13.946 20.790
5% 20.617 16.747 24.724
1% 26.674 21.648 34.230
Table 5. Aid-growth threshold regressions using Aid as a threshold.
Regime-dependent coefficients Regime-independent coefficients
1

2

initial
income invest. Human capital Policy index Institutional quality 1
ˆ

−0.186a 0.017 −0.010a 0.097a 0.014c −0.002 0.099a 0.014b
(0.059) (0.039) (0.002) (0.025) (0.008) (0.002) (0.015) (0.007)
R2 = 0.344
F-stat = 24.748
p-value = 0.000
Notes: Standard errors are given in parentheses. a, b and c indicate significance at the 1%, 5% and 10% level.
Open Access ME

D. K. FIODENDJI, K. EVLO 691
4.4. Aid Impact Conditional to Policy
Environment
Following [33] argument that aid positively influences
long term growth in countries with good policy environ-
ment, we use panel threshold model to investigate impact
of aid conditional to stable macroeconomic policy envi-
ronment. With regard to aid, variable that holds the in-
terest of this research, it is expected that the relationship
is positive growth in normal regime, i.e., when policy
index is greater or equal to an endogenous threshold
value (good policy environment). We assume that the aid
impact on economic growth depends on a level of policy
index. Thus our nonlinear model specification is as fol-
lows:


11
21
34
growAid PolicyPolicy
AidPolicyinvestinitial
humanircg
it iititit
it ititit
itit it
II
I

2
 




 


(8)
where

Policyit I

 and

Policyit I


are indica-
tor functions which take the value of one if the term be-
tween parentheses is true, and are zero otherwise. This
model specifies the effects of Aid with two coefficients:
of 1

and 2

. 1

denotes the effect of Aid below the
threshold level

, and 2

denotes the effect of Aid
exceeding the threshold level

.
To examine the affect of aid on growth in the presence
of good policy environment, we estimate the Equation 8
the results are reported in Table 6. Our investigation
shows that, on unstable macroeconomic policy environ-
ment (low policy regime) foreign aid has a positive effect
on the economic growth rate; however, this positive rela-
tionship is not statistically significant. This result is con-
sistent with [7,8,34-36] analysis which states that mac-
roeconomic environment has no significant influence on
the link between aid and economic growth. On the other
side, in high policy regime, the marginal impact of aid on
economic performance is positive and statistically sig-
nificant. These findings indicate that foreign aid does
have some positive impact on economic performance,
conditional on stable macroeconomic policy environment
(when policy index is above 1.880). The result is similar
to that found by [33]. It shows that the effectiveness of
aid in the growth process depends on the level and qual-
ity of economic policies. In addition, when country size
is included the growth model, the impact of aid is posi-
tive, larger and significant (see [33,1,37]). These results
imply aid effectiveness depends upon macroeconomic
policies. There are two possible justifications for the po-
sitive effect of aid on growth in the presence of good po-
licy. Stable macroeconomic indicators are more attrac-
tive for the investor. High inflation and high budget defi-
cit may cause the macroeconomic instability which dis-
courages the investment. High non developing expendi-
tures cause the high budget deficit. In case of high budget
deficit, foreign aid may be used for government con-
sumption instead of investment purpose. All of the con-
trol variables (the regime independent regressors) have
expected sign and are statistically significant.
Our finding suggests that sound economic manage-
ment policy in terms of low inflation, trade openness and
low budget deficit is crucial for aid effectiveness. There
is need to implement appropriate policy measure, in or-
der to achieve the positive impact of foreign aid on eco-
nomic growth through minimizing budgetary deficits,
lower the inflation rate and to achieve trade openness.
4.5. Aid Impact Conditional to Institutional
Quality
Let us now use the panel threshold model specification to
the investigation of the effect of aid on economic growth
conditional to institutional quality in ECOWAS countries.
To that aim, consider the following threshold model of
the Aid-growth nexus:



11
21
23 4
growAid ircgircg
Aidircginvest
initial humanPolicy
it iititit
it itit
ititit it
II
I


 






 

(9)
where


ircgit I


and

ircg it I


 are indicator
functions which take the value of one if the term between
parentheses is true, and are zero otherwise.
Table 6. Aid-growth threshold reg ressions using a condi tional variable (policy index) as a threshold.
Regime-dependent coefficients Regime-independent coefficients
1

2

Initial
income invest. Human capital Institutional quality 1
ˆ

0.027 0.125a −0.006b 0.073a 0.025a 0.103a −0.007c
(0.036) (0.037) (0.003) (0.026) (0.009) (0.016) (0.004)
R2 = 0.362
F-stat = 26.726
p-value = 0.000
Notes: Standard errors are given in parentheses. a, b and c indicate significance at the 1%, 5% and 10% level.
Open Access ME

D. K. FIODENDJI, K. EVLO 692
Table 7 indicates the results obtained with respect to
the institutional quality conditioned in aid-growth nexus.
Our findings suggest that for the low-institutional quality
regime (in which the institutional quality is below 0.571),
the marginal impact of aid on economic growth is nega-
tive and strongly significant. In the better institutions
regime, our results show a positive impact of aid on
growth and this impact is statistically significant. Strong-
ly positive and significant coefficient of aid in aid-
growth relationship implies that impact of aid on growth
is function of institutional quality. An interesting finding
is that the marginal impacts of aid on growth when we
take institutional quality as condition variable are more
important than to consider macroeconomic policy envi-
ronment as condition variable. Therefore, controlling low
institutional quality regime should be the main goal for
policymakers in ECOWAS zone since in this regime
more aid is detrimental to economic growth.
From the previous results, it is clear that in the midst
of current efforts to achieve the Millennium Develop-
ment Goals (MDGs) in ECOWAS zone, the need for
foreign assistance is inevitable. However, no amount of
foreign assistance will promote sustainable growth and
development in ECOWAS countries if the problem of
unstable macroeconomic environment and bad institutio-
nal quality persists. It is, therefore, crucial for govern-
ments in the ECOWAS area to improve institutional qua-
lity and to pursue economic policies that are conducive to,
among others, low inflation, productive budgetary bal-
ance and a competitive environment, and that attend to
the incessant corruption and political instability.
4.6. Aid Impact Conditional to Combination of
Two Indexes
From this econometric approach, we identify four states
of the economy consistent with the results of the growth
framework. Using these four states, we are able to esti-
mate the relation between aid and economic growth in
nonlinear fashion in each state based on the deviation
from macroeconomic policy environment and the institu-
tional quality. Our model specification is:





11
23
4123
growAidPolicy ;ircgPolicy ;ircg
AidPolicy ;ircgAidPolicy ;ircg
Policy ;ircginvestinitialhuman
itiititit itit
it it itit it it
ititititit it
II
II
I







 
  


 
(10)
where

Policy ;ircg
it it

 

y ;ircg
it it
indicates state1,
Polic

 

y ;ircg
it it
state 2,
Polic

 

y ;ircg
it it
state 3 and

Polic

  represents state 4. This model
specifies the effects of Aid with four coefficients: of 1

,
2

, 3

and 4

.
j

denotes the effect of Aid in state
j (j =1, 2, 3, 4).
The estimation results of Equation 10 are presented in
Table 8. Using the combination terms which signal the
state of the economy, we find that the impact of aid on
growth is negative in the States I and III (situation where
institutional quality is below its threshold value and
macroeconomic policy environment is above or below its
threshold value). The marginal impact of aid is statisti-
cally insignificant in state I but strongly significant in
state III. This negative relationship between aid and
economic growth strength the idea that resources transfer
from donors to ECOWAS countries are oriented towards
their own economic and strategic interest instead of
needs of the recipient countries. The negative effect of
aid on growth in these countries can be justified on the
following arguments. First, aid may be used to invest
either in less productive sector or to increase government
consumption. This is consistent with finding of [27] that
aid leakage (outflow) into non-productive expenditures
in the public sector may be the cause of negative rela-
recent book, [38,39] argues that not only billions of dol-
lars of aid spent have not significantly improved the
well-being of Africans, but rather they have worsened the
situation. Second, unstable aid volatile macroeconomic
environment and bad institutional quality have spoiled
the favorable effect of aid on economic growth. Third,
aid into ECOWAS countries is used to substitute gov-
ernment’s inability to tax its own citizens because of po-
litical pressure from elite groups.
In contrast to States I and III, o
tionship between aid and economic growth. In fact, in a
ur results show the
positive and statistically significant relationship between
aid and growth in States II and IV. However, the mar-
ginal effect of aid on growth is more consistent in terms of
magnitude in state II


20.206
  against

40.120
 
in state IV. Our resuat foreign
ates economic growth by supplementing domestic capital
formation.
Research
lts suggest th aid acceler-
er highlights some key issues which may un-
de
economy. These reasons are institutional quality in
rmine the impact of foreign aid on economic growth.
These include donors conditionality attached to aid in-
flow, stable macroeconomic environment in aid recipient
country, institutional quality, governance issues; donors
tide the some portion of aid and donors strategic motives
for the allocation of aid. Among these two reasons are
highly concerned in the management of aid inflow into
ECOWAS countries and its contribution for ECOWAS
Open Access ME

D. K. FIODENDJI, K. EVLO 693
Table 7. Aid-growth threshol d reg ressions using a condional variable (institutional quality) as a threshol d. ti
Regime-dependent coefficients Regime-independent coefficients
initial
income inv Human Policy
est. capital index 1
ˆ

1

2

−0.096a 0.20 a 0.00 b 1−0.006b 0.139a 0.018c −0.001 8
(0.033) (0.064) (0.023) (0.004)
2 = 0.350
p-
(0.003) (0.010) (0.002)
R
F-stat = 21.701
value = 0.000
Notes: Stan in parentheses. a, b and c indicate significancet the 1%, 5% avel.
the economy.
ndard errors are give and 10% le
Table 8. Estimation results of Aid-growth thre shold depending on the state of
Regime-dependent coefficients Regime-independent coefficients
initial
income Human
invest. capital 1
ˆ

1

2

3

4

−0.0 0.20a −0.096a 0.12 b −0.014a 22 60−0.007b 0.098a 0.023b
(0.033) (0.048) (0.033) (0.054) (0.022) (0.003)
= 0.42ald-test
-stat = 30.855 -stat = 18.011
p-0 p-
(0.003) (0.009)
R29 W
F F
value = 0.00 value = 0.000
Notes: Standard errors arses. a, b and c indicate significancevel.
ECOW
is area.
has positive impact on economic growth of
EC
ign aid helps to promote sustainable
proves the welfare in developing
nd institutional quality in
ECOWAS countries. For this purpose, we have estimated
e given in parenthe at the 1%, 5% and 10% le
AS zone and macroeconomic policy instability in economic policy environment a
th
The major point emerging from this study is that for-
eign aid
OWAS countries conditional on sound macroeco-
nomic policies and better institutional quality. Based on
the empirical results we find that foreign aid and growth
has negative relationship in States I and III while this
relation has positive and significant in States II and IV.
The interesting results emerge in state II, i.e. if macro-
economic policy environment is below its threshold
value and institutional quality is above its threshold value.
Our finding suggests that better institutional quality in
terms of lower risk of contract repudiation, lower level of
governmental corruption, efficient government stability
and lower ethnic tensions is crucial for aid effectiveness.
Therefore, it is desirable for ECOWAS policymakers to
target state II and good institutional quality regime
should be the main goal for these countries.
5. Conclusions
The belief that fore
economic growth and im
countries is debatable issue since its start. A large body
of literature now is available on aid effectiveness but the
issue regarding its contribution for growth and welfare
remains controversy. The aim of our paper is to investi-
gate whether aid effectiveness depends on the macro-
the impact of foreign aid on economic growth by consid-
ering the macroeconomic policy environment, institu-
tional quality and the combination of two latter indexes.
Therefore, we use [14] the approach based on the panel
of 13 ECOWAS countries covering the period from 1984
to 2010. According to our econometric results, the null
hypothesis of linearity against the alternative of a non-
linear specification is rejected by the data. Hence, the
relationship between aid and growth can be better mod-
eled as a nonlinear model. The paper finds that aid into
ECOWAS area will be effectively conditional on a stable
macroeconomic policy environment and better institu-
tional quality. In other words, the increasing flows of aid
into ECOWAS countries have not promoted meaningful
development due to the unstable macroeconomic envi-
ronment and bad institutional quality. Most countries are
characterized by policy inconsistencies, the poor institu-
tional framework, the high level of corruption, incessant
political crises and ethnic tension. This will be an impor-
tant result for the policymakers and international finan-
cial institutions, which increasingly favour conditionality
and selectivity in the allocation of aid resources. The
major policy implication of our results is not a call for a
reduction of foreign aid but rather a call for rethinking
strategies for international assistance and redesigning
existing aid programs.
Open Access ME

D. K. FIODENDJI, K. EVLO 694
From a policy perspective, the present research offers
three interesting insights. First, increasing transfer with-
out any conditions may not only be ineffective but may
strongly hurt economic performance of ECOWAS co
tri
cies, and Growth:
Working Paper
No. 3251, The 04.
http://dx.doi.o 51
un-
es. According to our investigation, no amount of for-
eign assistance will promote sustainable growth and de-
velopment in ECOWAS countries if the problem of un-
stable macroeconomic environment and bad institutional
quality persists. The second insight is that institutional
quality is a sine qua non condition for aid to promote
economic performance. Hence, States II and IV are iden-
tified as determinant regimes for the effective contribu-
tion of aid to sustainable growth and improve the welfare
in ECOWAS countries. Finally, from the two conditional
indexes, institutional quality is more important condition
through which aid positively affects economic growth.
Making access to better institutional quality may be a
way to spur economic growth even in a bad macroeco-
nomic policy environment. It is, therefore, crucial for
governments in the ECOWAS area to improve institu-
tional quality and to pursue economic policies that are
conducive to, among others, low inflation, productive
budgetary balance and a competitive environment, and
that attend to the incessant corruption and political insta-
bility. Unless such measures are taken, the problem of
slow growth will remain unabated. Our results also ad-
vocate the development of alternative mechanisms for
aid, as aid flows are shown to have an uncertain effect on
the growth performance of the recipients. Therefore, it is
worth investigating how the two instruments, foreign aid
and institutional quality, work together.
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