Foreign Aid Boosts Growth in African Nations

Economic Modelling 22 (2005) 616 – 627
www.elsevier.com/locate/econbase

Foreign aid, domestic savings, and growth in LDCs:
An application of likelihood-based
panel cointegration
Manuchehr Irandousta,T, Johan Ericssonb,1
a ¨ ¨
Department of Economics (ESI), University of Orebro, SE-701 82, Orebro, Sweden
b
Department of Economics Statistics, Stockholm School of Economics, P.O. Box 6501,
SE-113 83 Stockholm, Sweden
Accepted 17 March 2004

Abstract

The foreign aid, domestic saving, and economic growth relationships are investigated for a panel
of African countries over the period 1965–2000. The departure from earlier studies of the role of
foreign aid on economic growth is in the asymptotic theory of likelihood-based panel cointegration
allowing for multiple cointegrating vectors. The results reveal that the variables contain a panel unit
root and they cointegrate in a panel perspective. The findings show that foreign aid and domestic
saving enhance economic growth for all countries in the sample.
D 2005 Elsevier B.V. All rights reserved.

JEL classification: F35; C32; 011

Keywords: Foreign aid; Domestic savings; Economic growth; Rank tests; Panel unit root tests; Panel
cointegration

T Corresponding author. Tel.: +46 19 30 33 98; fax: +46 19 33 25 46.
E-mail addresses: manuchehr.irandoust@esi.oru.se (M. Irandoust)8 johan.ericsson@hhs.se (J. Ericsson).
1
Tel.: +46 8 73 69 247; fax: +46 8 34 81 61.

0264-9993/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.econmod.2004.03.004

M. Irandoust, J. Ericsson / Economic Modelling 22 (2005) 616–627 617

1. Introduction

Foreign aid is a major source of economic growth to developing countries,
especially in Africa, where it averages 12.5% of gross domestic product and establishes
by far the important source of foreign capital (Pallage and Robe, 2001). Thus, foreign
aid has a potential to play a key role in boosting developing countries’ economic
growth. There are two strands of literature on the role of foreign aid on economic
growth. The first studies claim that foreign capital inflow is necessary and sufficient for
economic growth in the less developed countries. They assert that there is a positive
relationship between aid and economic growth because it not only augments domestic
resources, but also supplements domestic savings, assists to close the foreign exchange
gap, creates access to modern technology and managerial skills, and allows easier
access to foreign market (Chenery and Strout, 1966; Papanek, 1973; Gulati, 1975;
Gupta, 1975; Over, 1975; Levy, 1988; Islam, 1992; Dalgaard et al., 2001; Hatemi and
Irandoust, in press).
The second studies assert that external capital has significant negative effects on
the economic growth of recipient countries. According to this view, foreign aid is
fully consumed, substitutes rather than compliments domestic resources, assists
import of inappropriate technology, distorts domestic income distribution, and en-
courages a bigger, inefficient and corrupt government in developing countries (Grif-
fin, 1970; Griffin and Enos, 1970; Weisskoff, 1972a,b; Boone, 1994, 1996; Easterly,
1999).
Furthermore, a series of studies finds that the negative relation that might exist
between foreign aid flows and economic growth could be related to factors such as
economic policies, state intervention, business cycles, and stability of foreign aid flows
in the recipient countries. Singh (1985) takes government regulatory activities into
consideration. He suggests that state intervention in the economy has a negative impact
on economic growth and causes the aid-growth relationship statistically non significant.
Burnside and Dollar (2000) argue that the relationship between foreign aid and
economic growth may depend on the recipient’s having adopted sound economic
policies. Lensink and Morrissey (2000) examine the impact of aid uncertainty on
economic growth in developing countries. They reveal that the effect of foreign aid on
economic growth is a function, not only of aid levels, but also of the stability of aid
flows.
Finally, Pallage and Robe (2001) document empirical regularities in the foreign aid
flows to developing countries. They find that for the vast majority of recipients, aid flows
are a major source of income that is highly volatile and, most important, overwhelmingly
procyclical. This implies that, even if foreign aid were meant solely to assist foster
economic growth, serious concerns would nonetheless result from the fact that aid
disbursement patterns contribute to the volatility of developing countries’ disposable
income and, this in turn, affects growth negatively.
However, reviewing the existing literature on the aid, savings, and growth relationship
shows that the results are mixed and elusive. Furthermore, the models that were used in
most literature to explain the relationship between foreign aid and economic growth were
estimated using obsolete and inappropriate techniques ignoring non-stationarity and

618 M. Irandoust, J. Ericsson / Economic Modelling 22 (2005) 616–627

endogeneity problems.2 The purpose of this study is to examine the effect of foreign aid
and domestic saving on the economic growth in developing countries. The sample
countries are: Niger, Nigeria, Rwanda, Senegal, and Togo, and the sample period is 1965–
2000. We have restricted the sample to this period due to the availability of the data. The
reason for the choice of these countries is that they are important recipients of foreign aid
in Africa.
The departure from earlier studies of the role of foreign aid flows and domestic saving
on economic growth is in the methodology used to examine the interaction between
variables. Here we make use of panel unit root tests suggested by Levin et al. (2002) and
Im et al. (2003) who argue that adding a cross-sectional dimension to the data could
resolve well-known low power problems of conventional, pure time series unit root tests.
The methodology for panel cointegration used here is an extension of the Johansen
(1988, 1991, 1995) multivariate maximum likelihood developed by Larsson and Lyhagen
(1999) and Larsson et al. (2001). They have developed a likelihood-based panel test of the
cointegrating rank and a general likelihood-based framework for inference in panel-VAR
models with cointegration restriction, allowing for multiple cointegrating vectors. By
using this method, the assumption of a unique cointegrating vector and the problem of
normalization are relaxed. This is not the case with the usual residual-based tests of
cointegration developed by Kao (1999), Kao and Chiang (1999), and Pedroni (1995, 1997,
1999).3
Recently, Moon and Perron (2003) proposed a method to estimate seemingly unrelated
linear models with integrated regressors and stationary errors. The procedure in Moon and
Perron is rather simple compared to the one used in this paper. They exclude the possibility
that there exists a cointegrating relation among the regressors which is not the case in our
study. However, this study is probably the first attempt to test the impact of foreign aid on
the economic growth in the developing countries using panel cointegration techniques
based on likelihood inference of cointegrating vectors.4
The remainder of this paper is organized as follows: Section 2 introduces our model,
data, and methodology. Section 3, the empirical evidence is presented. Finally, Section 4
offers conclusions and policy implications.

2. Model, data, and methodology

In the light of the existing literature, we use the following two models:
PCYit ¼ c0i þ c1i SYit þ c2i PCAIDit þ eit ; i ¼ 1; N ; N ; t ¼ 1; N T ; ð1Þ
and
logðPCYit Þ ¼ c4 þ c4 logðPCAIDit Þ þ e4 ;
0i 1i it i ¼ 1; N N ; t ¼ 1; N ; T ; ð2Þ

2
One exception here is the study by Hatemi and Irandoust (in press) who apply residual-based panel
cointegration tests to examine the relationship between foreign aid and economic growth.
3
Phillips and Moon (2000) and Baltagi (2001) provide concise survey on recent developments in this field.
4
Ericsson and Irandoust (2004) use likelihood-based panel cointegration approach to test the PPP hypothesis.


where PCY is per capita real GDP, SY is the savings-GDP ratio, PCAID is per capita real
foreign aid, e t and e* are well-behaved disturbance terms. All variables are in terms of US
t
dollars, annual, and they cover the period 1965–2000. Domestic saving is used as a proxy
for investment from domestic sources. Since domestic saving is negative in some cases,
we cannot take log of this variable. The countries in the panel are Togo, Senegal, Niger,
Nigeria, and Rwanda. Data are collected from World Development Indicators (WDI)
published by the World Bank (2001). Tables A1–A3 in Appendix A show descriptive
statistics of the data used.
As a pre-test for the cointegration analysis, we first investigate panel non-stationarity of
the variables in Eqs. (1) and (2). Here two types of panel unit root tests are employed. The
t-bar test proposed by Im et al. (2003, IPS) and panel unit root test proposed by Levin et al.
(2002, LL). The motivation for using two tests for stationarity is due to the different
alternative hypotheses in the tests. Thus, suppose that the stochastic process y it is
generated by the following process:
Dyit ¼ bi yi; tÀ1 þ ami dmt þ uit ; i ¼ 1 N ; N ; t ¼ 1; N T ; ð3Þ
where d mt contains deterministic variables; d 1t ={}, d 2t ={1}, d 3t ={1,t} and u it are
independently and normally distributed random variables with zero means and finite
(possible) heterogeneous variances. The null hypothesis of unit roots in the IPS test is:
H0 : bi ¼ 0; i ¼ 1; N ; N ; ð4Þ
against the alternative:
H1 : bi b0; i ¼ 1; N ; N1 ; bi ¼ 0; i ¼ 1; N ; N2 : ð5Þ
Hence, the alternative allows for b i to differ across groups. The IPS t-bar statistics is
based on the mean of the individual Dickey–Fuller t-statistics of each unit in the panel.
Lags of the dependent variable may be introduced to allow for serial correlation in the
errors. The exact critical values of the t-bar statistic are given in IPS. After transformation
by factors provided in the paper, the C¯ statistic is distributed standard normal under the
t
null hypothesis of non-stationarity.
The LL test assumes that each individual unit in the panel shares the same AR(1)
coefficient, that is b i =b. Hence we have the null hypothesis:

H0 : b ¼ 0; ð6Þ

H1 : bb0: ð7Þ
Lags of the dependent variable may be introduced to allow for serial correlation in the
errors. The test may be viewed as a pooled Dickey–Fuller test, or an Augmented Dickey–
Fuller (ADF) test when lags are included, with the null hypothesis that of non-stationarity
(I(1) behavior). After transformation by factors provided by LL, the t* statistic is
distributed standard normal under the null hypothesis of non-stationarity.
Therefore, for a panel data in question, if both tests accept the null, that implies a strong
evidence of panel non-stationarity. On the other hand, if the former (IPS) rejects and the


latter (LL) accept the null, that gives rise to a mixed result. Assuming that the variables are
non-stationary and at most I(1), we continue with the panel cointegration analysis.
Let the p-vector of variables for group i at time t be given by yit y i1t , y i2t ,. . ., y ipt ]V
V=[
and define Yt =[y1t, y2t,. . ., yV ]V as the Np-vector of the panel of observations available at
V V NT
time t on the p variables for the N groups. Following Larsson and Lyhagen (1999) and
Larsson et al. (2001), we can write:
2 3 2 32 3
Dy1t P11 P12 N P1N y1tÀ1
6 Dy 7 6 P P2N 76 y2tÀ1 7
6 2t 7 6 21 P22 76 7
6 7¼6 76 7
4v 5 4v O v 54 v 5
DyN t PN 1 PN 2 N PN N yN tÀ1
2 32 3 2 3
C11; k C12; k N C1N ; k Dy1tÀk e1t
mÀ1 6
X 76 Dy 7 6e 7
6 C21; k C22; k C2N ; k 76 2tÀk 7 6 2t 7
þ 6 76 7þ6 7; ð8Þ
k¼1
4v O v 54 v 5 4v 5
CN 1; k CN2; k N CN N ; k DyN tÀk eN t
or more compactly written as:
X
mÀ1
DYt ¼ PYtÀ1 þ Ck DYtÀk þ et ; ð9Þ
k¼1
where qt is assumed to be multivariate normally distributed with mean zero and covariance
matrix W={X ij }. Then Pconsider the reduced rank specification of the panel model, where
the matrix C is of rank r i , 0Vr i Vp, specified as P=ABV, where the matrices A and B are
P
both of order Np Â r i given by
È É
A ¼ aij ; ð10Þ
and
È É
B ¼ bij ; ð11Þ
such that A contains the short-run coefficients and B the long-run coefficients.
This model makes possible for simultaneous modelling of the long-run relations
between several variables for a panel of groups allowing for heterogeneous long-run
cointegration relations within each group. Cointegrating relations are only allowed for
within each of the N countries but the model allows for important short-run dependence
between the panel groups.
Based on the above panel model, we are interested in two different hypotheses. The first
hypothesis considers the rank of the panel group-specific matrices P i . The null hypothesis:
H0 : rankðPi Þ ¼ ri Vr; 8i ¼ 1; N ; N ; ð12Þ
is tested against the alternative:
H1 : rankðPi Þ ¼ p; 8i ¼ 1; N ; N : ð13Þ
using the likelihood ratio test. The test and its asymptotic distribution is presented in Larsson
and Lyhagen (1999).


Table 1
Panel unit root tests
Variable IPS test LL test
¯
t C¯
t p-value tT p-value
PCY À2.171 À1.766 0.039 À1.838 0.033
SY À2.006 À1.373 0.085 À1.593 0.055
PCAID À1.621 À0.459 0.323 À0.959 0.168
log(PCY) À2.017 À1.398 0.081 À1.556 0.059
log(PCAID) À1.604 À0.417 0.338 À0.830 0.203
Critical values for the ¯ test statistic are À2.020 and À2.160 using 10% and 5% significance level, respectively.
t

Given the assumption of equal rank, it is of interest to test the null hypothesis:
H0 : b1 ¼ b2 ¼ N ¼ bN ; ð14Þ
H1 : bi pbj for some i pj: ð15Þ
The test statistic is again a likelihood ratio test statistics and is asymptotically v 2
distributed with (NÀ1)r( pÀr) degrees of freedom.
Finally, we check if the underlying assumptions are satisfied, i.e., if the residuals are
normal distributed. The normality test is a multivariate extension of the Bowman–Shenton
test developed by Doornik and Hansen (1994). Furthermore, we test for autocorrelation by
using the Ljung–Box test statistics.

3. Empirical results

As a pre-test for the cointegration analysis, we first investigate panel non-stationarity
among the variables. The results of IPS tests and LL tests are listed in Table 1. After
inspection of the data, we only include a constant term and no trend. When applying the
Schwartz criterion to decide the optimal lag length, the common lag length was set to four.
Using the IPS test, the p-values are larger than 5 for all variables, except for real GDP per
capita (PCY). The p-values for PCY are 3.9% and 3.3% for the IPS test and LL test,
respectively.5 Hence, the PCY series would be stationary on a 5% significance level.
However, since the p-values are close to 5% our overall judgment is that all variables seem
to support the null hypothesis of panel non-stationarity. Furthermore, note that our
approach does not exclude the possibility of including stationary variables. The effect of
one stationary variable in the system is that the rank order increases with one.
Turning to the cointegration analysis, we first consider the relationship between the log
of real GDP per capita (log(PCY)) and the log of real AID per capita (log(PCAID)). The
likelihood ratio tests are given in Table 2. The Bartlett corrected critical values are gained
by using the estimated model as data generating process when calculating the sample
mean. Using the Bartlett corrected critical values, the test rejects the null of 0 cointegrating
5
We prefer the IPS test because Monte Carlo simulations conducted by Karlsson and Lothgren (2000) show the
¨
better performance of the IPS test than other panel unit root tests regarding power properties.


Table 2
Test for cointegrating rank
H0 As.crita B.ricb À2logQ T
R=0 216.56 251.84 260.04
RV1 83.15 145.37 133.19
a
The asymptotic critical values at 5% significance level.
b
Bartlett corrected critical values at 5% significance level.

rank but accepts the null of 1 cointegrating vector. Note that if we use the asymptotic
critical values, the estimated rank is 2.
The estimated cointegrating vectors are presented in Table 3, normalized with respect to
log(PCY). All coefficients on log(PCAID) are positive. This indicates that foreign aid has
a positive impact on the real GDP per capita for the sample countries. Furthermore, when
testing for a common cointegrating vector by using the likelihood ratio test, we obtain a
test value of 10.282 with the corresponding p-value of 3.6%. The rejection of common
cointegrating vector at 5% significance level is probably due to Nigeria, which differs
from the rest of the countries. Redoing the analysis with Nigeria excluded yields a p-value
of 5.1% and accepts the common cointegrating vector (À1.000, 0.752).
The tests for cointegrating rank for model (1) are presented in Table 4. Since we accept
the null of 1 cointegrating rank when using the Bartlett corrected critical values we
proceed and estimate the cointegrating vectors. These are displayed in Table 5.
According to Table 5, we can assert that foreign aid and domestic savings are positively
associated with economic growth for almost all countries in the sample. Exception is
Nigeria. In Nigeria, the coefficients for domestic saving and aid are negative and positive,
respectively. Since, the test for a common cointegrating vector has a very low p-value the
estimated common cointegrating vector is not reported. Once again, it seems that Nigeria
is different from the rest four countries. However, excluding Nigeria from the sample does
not improve the test for common cointegrating vector.6
In Table B1 in Appendix B, the results from the diagnostic tests are reported. It seems
that we do not have any problem with autocorrelation since all p-values are very high.
However, the null hypothesis of normality is rejected for Eq. (1) but not for Eq. (2). The
problem of normality could not be solved by using more lags.

4. Summary and conclusions

The models that have used in most literature to explain the relationship between foreign
aid and economic growth have estimated using obsolete and inappropriate techniques
ignoring non-stationarity and endogeneity problems. In other words, the previous aid-
growth work has estimated where savings behavior has been assumed to be exogenous and
no attentions have been paid to the time-series characteristics of the data employed. Under
these conditions, the fungibility of aid whereby aid is diverted from investment to
consumption is regarded as an exogenous phenomenon and, moreover, the application of
6
Obviously, Nigeria differs from the rest four countries. It seems that the power of homogeneity test must be
low so that these differences cannot be rejected due to small sample.


Table 3
Cointegrating vectors, normalized on log(PCY)
Togo Senegal Niger Nigeria Rwanda Common b
log(PCY) À1.000 À1.000 À1.000 À1.000 À1.000 À1.000
log(PCAID) 0.625 0.663 0.661 0.035 0.689 0.571

conventional econometric techniques to non-stationary (integrated) time-series can give
rise to misleading results and erroneous inferences. These problems cause analytical works
based on this approach vulnerable to the Lucas-critique. However, technical issues about
the choice of functional forms and econometric techniques are quite important in the
empirical studies of the effectiveness of foreign aid.
Thus, this study applies the new developments in the filed of likelihood-based panel
cointegration analysis to examine the long-run relationship between foreign aid, domestic
saving, and economic growth. The countries in the panel are: Niger, Nigeria, Rwanda,
Senegal, and Togo, and the sample period is 1965–2000. The departure from earlier
studies of the role of foreign aid on economic growth is in the asymptotic theory of panel
cointegration used to test the hypotheses. The tests are based on the Johansen (1988, 1991,
1995) multivariate likelihood-based inference in cointegrated VAR models and on a new
cointegration rank test for panel models proposed by Larsson and Lyhagen (1999) and
Larsson et al. (2001). This method allows for multiple cointegrating vectors, which is not
the case with the usual residual-based tests of panel cointegration developed by Kao
(1999), Kao and Chiang (1999), and Pedroni (1999). Hence, the assumption of a unique
cointegrating vector and the problem of normalization are relaxed.
The methodology used here is based on two steps: first, panel unit root tests developed by
Im et al. (2003) and Levin et al. (2002), and second, a panel cointegration approach
developed by Larsson and Lyhagen (1999) and Larsson et al. (2001). The estimation results
show that the variables are characterized by one panel unit root and the tests for panel
cointegration indicate one cointegrating vector. However, the findings show that foreign aid
and domestic savings are positively associated with economic growth in all countries in the
sample. This result is consistent with the economic theory of foreign aid which asserts that
overseas development assistance accelerates economic growth by supplementing the
domestic capital formation (Chenery and Strout, 1966; Papanek, 1973; Dalgaard et al.,
2001). In other words, foreign aid and domestic resources contribute to economic growth.
The economic implication of our results is that foreign aid has a potential to play a key
role in boosting developing countries’ economic growth. That is, there is a positive
relationship between aid and economic growth because it not only augments domestic
resources, but also supplements domestic savings, assists to close the foreign exchange

Table 4
Test for cointegrating rank
H0 As.crita B.ricb À2logQ T
R=0 469.85 633.53 661.24
RV1 260.27 508.66 396.37
RV2 106.07 274.30 169.76
a
The asymptotic critical values at 5% significance level.
b
Bartlett corrected critical values at 5% significance level.


Table 5
Cointegrating vectors, normalized on PCY
Togo Senegal Niger Nigeria Rwanda
PCY À1.000 À1.000 À1.000 À1.000 À1.000
SY 7.008 11.838 5.887 À4.565 19.107
PCAID 6.831 6.691 5.268 24.671 5.638

gap, creates access to modern technology and managerial skills, and allows easier access
to foreign markets.

Acknowledgements

Valuable comments of two anonymous referees are greatly appreciated. Of course, we
alone remain responsible for any errors.

Appendix A. The descriptive statistics

Table A1
Growth (%) of real GDP per capita
Mean 0.0315 À0.0824 À2.1004 0.4154 0.9049
S.D. 5.9369 4.5713 6.1776 8.0819 10.1008
Kurtosis 3.7190 3.0571 4.6833 4.3101 10.5225
Skewness À0.3252 0.0180 À0.9327 0.3984 À1.1033

Correlation matrix
Togo 1.0000 À0.2370 0.0569 0.0743 À0.0638
Senegal 1.0000 0.2839 0.0588 0.1213
Niger 1.0000 0.1269 0.0803
Nigeria 1.0000 0.0994
Rwanda 1.0000

Table A2
Growth (%) of real foreign aid per capita
Mean 6.4871 7.4793 8.3845 5.0441 12.4372
S.D. 30.0242 31.0778 30.3416 39.8457 33.2343
Kurtosis 2.3865 3.7422 3.5636 11.8618 9.0431
Skewness 0.0627 1.1142 0.9599 2.6268 1.8067

Correlation matrix
Togo 1.0000 0.4233 0.2092 0.0776 0.2190
Senegal 1.0000 0.2926 0.1089 0.2545
Niger 1.0000 À0.1931 0.0997
Nigeria 1.0000 À0.2065
Rwanda 1.0000


Table A3
Growth (%) of savings-GDP ratio
Mean 174.0696 À2.4259 136.5267 7.3823 71.3528
S.D. 869.3388 121.6608 431.3733 30.7008 1452.3648
Kurtosis 29.5399 9.8570 13.2874 3.5027 18.3199
Skewness 5.2372 À2.0039 3.3478 0.9170 1.5425

Correlation matrix
Togo 1.0000 0.1739 À0.1152 À0.0576 À0.6075
Senegal 1.0000 0.2137 0.0575 0.0986
Niger 1.0000 0.2741 0.1354
Nigeria 1.0000 0.1879
Rwanda 1.0000

Appendix B. Diagnostic tests

Table B1
Diagnostic testsa
Normalityb Autocorrelationc
Model (1) PCYit =c 0i +c li SYit +c 2i PCAIDit +e it ,
0.0489 0.4066

Model (2) log(PCYit )=c * +c *log(PCAIDit )+e *,
0i li it
0.1608 0.3143
a
The table reports the p-values.
b
The test is a multivariate extension of the Bowman–Shenton
test developed by Doornik and Hansen (1994).
c
This is the Ljung–Box test statistics for autocorrelation.

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