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This article was downloaded by: [118.137.232.37]On: 04 December 2014, At: 04:57Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number:1072954 Registered office: Mortimer House, 37-41 Mortimer Street,London W1T 3JH, UK
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Regime changes, economicpolicies and the effect of aid
on growthMuhammed N. Islam
Published online: 24 Jan 2007.
To cite this article:Muhammed N. Islam (2005) Regime changes, economic policies
and the effect of aid on growth, The Journal of Development Studies, 41:8,
1467-1492, DOI: 10.1080/00220380500187828
To link to this article: http://dx.doi.org/10.1080/00220380500187828
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Regime Changes, Economic Policies
and the Effect of Aid on Growth
MUHAMMED N. ISLAM
This study finds that on average aid has little impact on economic
growth, although a robust finding is that aid promotes growth only
in a politically stable environment irrespective of the quality of the
countrys economic policies. Aid is ineffective in an unstable
environment even in the presence of good policies. The results,
however, indicate that policy is more effective in promoting growth
when supported by increased aid flows rather than aid being more
effective in good policy environment. The empirical results also
provide some tentative support for the presence of an aid Laffer
curve in the politically stable countries. The allocation of aid is
found to be influenced by the country size and its state of
development, rather than the quality of policy.
I . I N T R O D U C T I O N
Recent studies on the effects of foreign aid1 on economic growth indicate that
aid has a negative effect, or no significant relationship with growth [see
Cassen et al., 1994; Griffin and McKinley, 1994; Boone, 1996]. Burnside and
Dollar [2000], however, argue that aid has a positive impact on growth in
developing countries with good economic policies, but it has very little effect
in the presence of poor policies. Their conclusion is based on a positive
significant coefficient on an aid6policy interaction term in a growthequation.
There are some difficulties with this conclusion. First, the positive
significant estimate of the interaction coefficient can be interpreted to mean
that policies are more effective when supported by aid inflows [see Lensink
and White, 1999].2 Policies can also be positively influenced by aid through
its conditionality, or negatively if aid can be considered as a substitute for
Muhammed N. Islam, Associate Professor, Department of Economics, Concordia University,Montreal, Canada H3G 1M8, [email protected]. The author is grateful for helpfulcomments from Saud Choudhry, Stanley Winer, and two anonymous referees.
The Journal of Development Studies, Vol.41, No.8, November 2005, pp.1467 1492ISSN 0022-0388 print/1743-9140 onlineDOI: 10.1080/00220380500187828 2005 Taylor & Francis
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public effort [Guillaumont and Chauvet, 2001]. Second, the estimated effect
of aid is found to be sensitive to the choice of estimator and the set of control
variables [Hansen and Tarp, 2001]. It is also found that aid promotes growth
regardless of the policy environment [Dalgaard and Hansen, 2001]. In this
paper, I show that aid does not necessarily promote growth unless the countryhas a stable political system.
Although a good policy environment is a necessary condition for growth,
in itself it does not provide a sufficient condition for aid to stimulate growth.
Sufficiency is insured only if good policies are coupled with political
stability, offering an environment free from uncertainty in the marketplace.
A stable political system allows agents to utilise aid funds effectively
without any interruption, thereby achieving more growth. If the political
system becomes unstable, aid is more likely to be dissipated in unproductive
consumption rather than being invested even in the presence of good
policies.
With a high degree of uncertainty, the supply of complementary factors
(capital goods, investment funds from domestic/foreign sources, for example)
can decline. Political instability, resulting from strikes, violence, sabotage
and the like, can impose a constraint on the productive capacity of aid-using
enterprises, thereby reducing the effectiveness of aid. This paper identifies
political instability as an important factor that underlies the inverse
relationship between aid and growth. My basic hypothesis is that aid canstimulate growth in a developing country only if it has a stable political
system and this positive impact of aid is not conditional on the quality of the
countrys economic policies.
Political instability refers to irregular changes in the political system,
which can be associated with social unrest and political violence. One can
view political instability in two different ways. First, it can involve frequent
changes in government through electoral process. This can result in some
economic adjustments in accordance with market conditions, with minor
effects on economic growth. The high frequency of such change ofgovernment can, however, be associated with policy uncertainty and some
threats to property rights.
The second one focuses on more radical changes in the existing political
system, which can be a change in regime from, say, democracy to
dictatorship through coups detat. A change in regime can occur through
political violence (riots, strikes, assassinations, and revolutions) against the
ruling government or through violence within the regime which includes
coups detat. It is not the type of regime, democracy or not, per se rather the
frequency of regime change over time that makes a country stable orunstable. A stable nation, democracy or dictatorship, is one with no change in
its regime type during a specified period, which of course implies a zero level
1468 T H E J O U R N A L O F D E V E L O P M E N T S T U D I E S
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of incidence of political violence and civil unrest. Alesina and Perotti [1996],
however, note that for the same level of political violence, dictatorships are
more likely to be overthrown by extremists than a stable democracy. A
regime change can bring about alterations in laws, regulations and property
rights and create uncertainty in the marketplace, with a negative effect ongrowth.
Which one of these two approaches is preferable is not clear a priori and
may depend upon the purpose of inquiry. Given that the main focus of this
study is to investigate the impact of aid on growth controlling for changes in
political system, the second approach seems more appropriate. Any measure
of political instability should, however, capture the essence of uncertainty
created by social unrest and political violence leading to the change in the
system. The rest of the paper is organised as follows. Section II describes the
specification of a four-equation system and discusses the identifying
restrictions. An operational measure of political instability is also
constructed. The basic hypothesis is tested on data for 65 developing
countries over the period 196897. A description of the data and the main
results are given in Section III. Section IV contains a sensitivity analysis and
tests of robustness of the results. The final section concludes.
I I . M O D E L S P E C I F I C A T I O N
My empirical work attempts to investigate whether political instability
reduces growth and further aggravates the decline in growth by reducing the
effectiveness of aid. Broadly speaking, political instability reduces aid-
effectiveness through two main channels. First, it increases uncertainty in the
economy, thereby inducing investors to postpone aid-using projects and
divert aid funds to unproductive consumption. Second, it can cause disruption
of productive activities and thus a fall in the productive capacity of aid-using
enterprises, as noted earlier.
To capture these links between political instability and the effectiveness ofaid, I employ a parsimonious specification of an estimating growth
regression. The processes determining the aid growth link are undoubtedly
complicated, and it is likely that other factors are involved besides those
included in the regression. I therefore subject it to an extensive sensitivity and
robustness analysis, by adding other relevant exogenous variables used in
previous studies, including identifying restrictions on parameters, and so on.
The basic specification of the growth regression for the ith country at time
period t can be formulated as follows:
Yit b0AitbaA2itba2 PI
0itbpi
i1;2;...;N; t1;2;...;T;
AitPI0itb1Z
0itbze
yit; 1
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where yit is the per capita real GDP growth, Ait is aid receipts relative to
GDP, (PI) is a P61 vector of political instability variables, Zit is a K61
vector of other exogenous variables including economic policies, institutional
factors, time-and-country fixed effects that might affect growth, and eyit is a
N61 vector of random errors with mean zero.I include both (PI) and (A6PI) to capture the direct effect of political
instabilities and their indirect effects via aid. A2itis included to account for the
non-linearity of aid growth relationship [Hansen and Tarp, 2000]. Non-
linearity in the aid growth relationship can be attributed to inappropriate
technology [Lensink and White, 1999] and absorptive capacity constraints
[Hadjimichael, et al., 1995]. As indicators of political instability, I include
five variables (assassinations, riots, strikes, revolutions and coups detat) as
elements of the vector (PI)it. The recent literature on the aidgrowth link
provides some guidelines to select the variables included in the vector Zit. It
includes three policy variables: budget surplus, inflation rate, and trade
openness, a dummy variable developed by Sachs and Warner [1995]. These
variables are used as indicators of the recipient countries fiscal, monetary
[see Fischer 1993], and trade policies.
Several studies used M2/GDP (lagged) as a proxy for the development of
the financial system and therefore it is included in eq.(1). Ethno-linguistic
fractionalisation index, a time-invariant [1980] institutional variable, has also
been used in a number of recent studies. This variable is likely to becorrelated with political instability, which reduces growth. I therefore
exclude it from eq. (1) and include it in the political instability function.
Initial GDP per capita is included to capture convergence effects, as is
standard in the empirical growth literature. The vector Zitalso includes time
dummies to capture the impact of global business cycles and two regional
dummies (sub-Saharan Africa and east Asia) for fixed -country effects. To
avoid the problem of collinearity among various political instability variables
and also among the policy variables, I construct a composite index of
political instability and an index of economic policy, as explained later.Previous studies [Boone, 1996; Alesina and Dollar, 2000; Burnside and
Dollar 2000; Dalgaard and Hansen; 2001] indicate that the aid variable may
be endogenous. These studies examine the endogeneity of aid using Durbin-
Wu-Hausman (DWH) test. Their results show that the OLS and instrumental
variable estimates are not significantly different from each other and thus
they treat aid as exogenous. The main reason for this finding, as Hansen and
Tarp [2001] argue, is that they did not consider the existence of country
specific effects. These effects can render the instrumental variable estimates
inconsistent because of their negative relationship with the initial level ofincome. The endogeneity of the political instability index was also taken into
account in a recent study [Alesina and Perotti, 1996]. Burnside and Dollar
1470 T H E J O U R N A L O F D E V E L O P M E N T S T U D I E S
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[2000] treated the policy index as exogenous but it was considered as
endogenous in Guillaumont and Chauvet [2001]. These three variables may
be interdependent and depend on the growth rate of GDP. This problem of
simultaneity can be solved by using appropriate instruments for them, which
should be highly correlated with growth but independent of growth residuals,eit.It is then possible to extract the exogenous component of each of them and
to examine whether this component is correlated with economic growth.
My strategy is to specify a simultaneous equation model, with economic
growth, aid, political instability, and policy as endogenous. I follow standard
specification in recent empirical aid growth literature and use relevant
exclusion restrictions to achieve identification of the system. For identifica-
tion of an equation in the system, the aid equation for example, I include at
least one exogenous variable that affects aid only and not the growth rate.
The details of specification of these equations in the system and the exclusion
restrictions are discussed in the following subsections.
The Aid Equation
The allocation of aid among developing countries depends, as is well known,
on (i) donors strategic interests or the preferential political links with the
main donors; (ii) the prevailing socio-economic conditions; (iii) structural
vulnerability; and (iv) economic policy. To capture strategic interests and
political links, I instrument aid, as in Boone [1996], by dummy variables forcentral American countries (in the sphere of American influence), Egypt (an
American ally), the Franc zone (getting preferential treatment from France),
and sub-Saharan Africa (receiving the bulk of European aid). The socio-
economic conditions are represented by two variables, log of initial GDP per
capita (a measure of initial economic performance of the country) and infant
mortality rate as an indicator of poverty level and human development [see
Boone 1996].
Aid can help a country withstand structural vulnerability resulting from an
exposure to various external shocks such as trade shocks. Trade shocks canarise whenever the real value of exports fluctuates, with depletion in the
countrys foreign exchange reserves. Large countries, as Guillaumont and
Chauvet [2001] note, are less vulnerable to trade shocks than small ones. I
include an index of export instability to represent trade shocks and use
logarithm of population as a proxy for structural exposure to this shock. The
economic policy index (lagged) is included to explore whether aid is allocated
in favour of good policy. The lagged values of the aid variable (A71and A72)
are included to account for the effect of previous aid policy on the current
period allocation decision. The aid equation can now be written as:
Ait a0Z0itaze
ait; 2
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where the vector Zit includes log of initial GDP per capita, infant mortality
rate, log of population, export instability index, lagged policy index, lagged
aid variables, and four dummy variables for central American countries,
Egypt, Franc zone, and sub-Saharan Africa. eit is an N61 vector of random
errors with mean zero.
Political Instability
A. Constructing a composite index of political instability. Several alterna-
tive techniques are available to construct an index of political instability [see
Gupta, 1990; Alesina and Perotti, 1996; and Guillaumont and Chauvet,
2001]. Guillaumont and Chauvet [2001] have used an index of political
instability defined as the weighted sum of the number of revolutions per year
and of the number of assassinations per million inhabitants per year, withequal weights for both. The omission of other important factors that may
create political instability and the use of equal weights in the construction of
the index are questionable. The other two studies have included an extended
set of factors, such as deaths/executions from political violence, coups
detats, strikes, riots, and regime types. In the construction of the index Gupta
[1990] has used a discriminant analysis, but Alesina and Perotti [1996] have
used the method of principal components.
I define the index as a linear combination of five variables (assassinations,
coups detats, revolutions, riots, and strikes). The basic reason for choosingthese variables is to summarise important phenomena of social unrest and
political violence that can create uncertainty in the marketplace. The
technique is similar to the one used in Burnside and Dollar [2000] for
the policy index. The advantage of this method is that the weights assigned to
the various phenomena of instability are not subjective, but rather they are
determined according to their impact on growth, a feature which is not found
in principal components method or discriminant analysis.
To determine the weights, I use a two-step procedure. First, the growth
equation (1), excluding the aid variable, is estimated by ordinary least square(OLS) method. Second, I use the statistically significant estimates of bpi(the coefficients of the instability factors) to define the index of political
instability:
PI PI0itbpi 3
where b^pi are the OLS estimates of the parameters bpi, which are not
constrained to be equal.
B. The political instability equation. Political instability can differ among
nations depending upon a number of factors, such as the level of education,
1472 T H E J O U R N A L O F D E V E L O P M E N T S T U D I E S
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government economic performance, party fractionalisation, regime type, and
socio-cultural diversity among countries. A higher level of education may
lead to improvement in legal and political rights and this may reduce the
incidence of political violence. To capture this link, I include in the instability
equation a variable SCPR (the primary school enrolment as a percentage ofadult population). The positive growth rate of GDP (Y), indicating good
economic performance, is included to test whether rapidly growing
economies tend to be more stable. Rapid economic growth provides more
prosperity, less dissatisfaction and possibly less instability. One can, of
course, argue, as Alesina and Perotti [1996] note that rising economic growth
may lead to social disruptions and economic transformations, with an
increase in political instability.
I add in the equation another variable PFI (party fractionalisation index)
that captures the degree of disharmony among members in the legislature. An
increase in fractionalisation may make it difficult for smooth functioning of
the legislature, create dissatisfaction among members, and may lead to
successful or unsuccessful coups detats. A country with a higher level of PFI
is likely to be more unstable than others. I include ethno-linguistic
fractionalisation index EF on the assumption that more homogeneous
societies are likely to be more politically stable, all else equal. The equation
also includes lagged values of the instability index (PI71 and PI72), two
dummy variables, REG (regime type) and DLA (Latin America), and laggedPI index. Given the same level of political violence, a dictatorship is more
likely to be overthrown by extremists than a stable democracy, as noted
earlier. I include DLA, as in Alesina and Perotti [1996], on the assumption
that Latin American countries tend to be more unstable than other developing
countries. The instability equation can thus be written as:
PIit g0Z0itgze
piit 4
where the vector Zit includes the exogenous variables SCPR, Y , PFI, EF,
PI71, PI72, REG, and DLA . epiit is an N61 vector of random errors withmean zero.
The Policy Equation
Following Burnside and Dollar [2000], I construct a policy index as a linear
combination of the three main indicators of macroeconomic policy, budget
surplus, inflation, and trade openness. They are weighted by their effect on
economic growth. The specification of the policy equation builds on
previous studies. Guillaumont and Chauvet [2001] included aid plus severalenvironmental/structural vulnerability variables in a policy equation. The
initial level of human capital (education) and some vulnerability variables
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are found to be statistically significant. The structural vulnerability of a
country can result from several kinds of shocks, climatic, ecological, and
trade shocks, for example. Only trade shocks are found, in the same study,
to have significant negative effect on growth, possibly through policy
changes.I therefore instrument policy by the initial level of human capital (EDU)
and trade shocks, measured by the instability of the real value of exports
(EXI). Besides these instruments, I also include the initial GDP per capita, the
lagged value of the policy index, the lagged values of the aid variable and a
regional dummy variable DASfor east Asian countries, because of the relative
success of their economic policies in the recent years. The policy equation
can therefore be written as:
POLit d0Z0itdzepolit 5
where the vector Zit includes the exogenous variables, Y0, EDU, EXI,
POL71, and DAS e
polit
is an N61 vector of random errors with mean zero.
Identification of the Model
I now have a system of four equations, (1), (2), (4) and (5). The vector (PI)
and the three policy variables (budget surplus, inflation, and trade openness)
in the growth equation (1) are now replaced by the corresponding indices.The system contains 26 exogenous variables and seven endogenous
variables (including aid squared, an interaction of aid with the political
instability index and as well as the policy index). Equation (1) has six right-
hand side endogenous variables and a total of 19 excluded exogenous
variables and thus the equation is overidentified. The number of excluded
exogenous variables in the other three equations in the system exceeds the
number of right-hand side endogenous variables, thereby meeting the order
condition of identification. Notice that the system also meets the rank
condition of identification.In order to estimate the growth equation, I use a two-stage least square
(2SLS) procedure, with simultaneous instrumentation of aid, political
instability, and economic policy. To check that the instruments are not
correlated with the growth residual, I use Hausman test of overidentification
[Hausman, 1983].
I I I . T H E D A T A A N D E S T I M A T I O N R E S U L T S
The DataI perform cross-section regressions to examine the relationship between aid
and growth using a sample of 65 countries for the period 1968 97. Although
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the published data on aid cover a large number of countries (111 countries in
Lensink and White [2001], for example), the data on other variables used in
this study are not available for many countries. I was able to collect the
requisite information for a sample of 65 countries. The dependent variable is
the average annual growth rate of real GDP per capita over six five-yearperiods, starting with 196872 and ending with 199397. In case of missing
data (Bangladesh 196872, for example), the average value of existing
observations are used to represent the average for the specific time period.
Table 1.1 provides the definitions and sources of data used, along with their
summary statistics (means, standard deviations and the correlations between
the growth rate and the explanatory variables).
Table 1.2 highlights simple correlations between the independent
variables. The correlation matrix shows that political instability is highly
correlated with coups detat (0.92), assassinations (0.36) and revolutions
(0.35). It has a very low correlation (0.09) with aid flows and a negative
correlation with per capita GDP growth. The latter is positively correlated
with primary schooling, financial depth and economic policy and it is
negatively correlated with ethno-linguistic fractionalisation, population size,
and export instability.
To construct the political instability index, eq. (1), excluding any of the
terms involving aid, is estimated by ordinary least square (OLS) method.
The results are presented in Table 2 column (1). Of the five variables used inthe definition of political instability index, assassination, coup detat, and
revolution have negative significant effect on growth. The other two
variables, riots and strikes, do not have intuitive sign and are not significant.
The political instability index is thus formed by using the estimated
coefficients of assassination, coup detat, and revolution as their respective
weights:
PI0:02550:2282Assassinations0:4218Coup detat
0:0406Revolutions 6
where 0.0255 is the average growth rate for all countries during 196897.
Consistent with a numerically large coefficient in the growth regression,
coup detat has a large impact on the instability index. A high value of
instability in terms of assassinations, coup detat, and revolution, leads to a
small value of the index. In other words, a small value of the index is an
indication of more political instability and vice versa. Hence the effect on
growth is expected to be negative.The policy variables, budget surplus, inflation, and trade openness, are all
found to have significant effect on growth (see Table 2 column 1). The policy
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TA
BLE1.1
DEFIN
ITIONSOFVARIABLES
ANDTHEIRSUMMARYS
TATISTICS
Variables
Definitionsanddatasources*
Mean(Standarderror)
Correlation
b
Growthrate:(Y)
Thegrowthratesarecalculated
fromannualrealGDP
percapitainconstantdollars
(internationalprices,
baseyear1985)andthenaveragedoverfiveyear
intervals,startingwith196872andendingwith199397.
0.0255(0.0155)
InitialGD
P(Y0)
ThelogarithmofrealGDPinthelastyearprecedingthe
periodforwhichthegrowth
rateiscalculated.
6.9081(0.0165)
0.1256
Instbl.in
GDP(CV)
InstabilityinGDPismeasured
bythecoefficientofvariation
ofrealGDPpercapita.
0.0042(0.0100)
70.0800
Positiveg
rowth(Y
)
Themeanvalueofonlypositiv
egrowthratesofrealpercapita
GDPduringaspecifiedtime
period,0otherwise
0.0600(0.0200)
0.6520
Aid/GDP
(A)
ThenetODAdisbursementsas
apercentageofGDP.ODA
includesdirectgrantsandco
ncessionalloansforwhichthe
grantcomponentexceeds25
percenta
5.613(0.0620)
70.1390
Source:OECD,GeographicalD
istributionofFinancial
FlowstoLDCs.
Primarys
chooling(SCRP)
Averageenrolmentsinprimary
schoolsasapercentage
ofadultpopulation.
78.9730(3.5360)
0.1857
Secondaryschooling(SCH)
Averageenrolmentsinseconda
ryschoolsasapercentage
ofadultpopulation.
34.1100(2.8470)
0.0981
Humancapital(HC)
Meanschoolyearsofeducationattheprimaryandsecondary
levels[Nehruet.al.,
1995].
56.5420(2.6730)
0.1476
Education
(EDU)
Thelogarithmoftheinitiallevelofhumancapital.
0.0760
Population(POP)
Population,total(million)expressedin
logarithms.
2.2363(0.1410)
70.1789
Mortality
(MORT)
Mortalityrate,infant(per1,000livebirths).
28.590(2.1230)
70.1052
Inflation(INF)
Consumerprices(annual%).
0.1503(0.2950)
70.2034
Budgetsu
rplus(BS)
Governmentoverallsurplus/deficit,excludingcapitalgrants,
asapercentageofGDP.
0.4123(0.0120)
0.1367
(continued)
1476 T H E J O U R N A L O F D E V E L O P M E N T S T U D I E S
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TA
BLE1.1
(Continued)
Variables
Definitionsanddatasources*
Mean(Standarderror)
Correlation
b
Tradeopenness(OPN)
SachsandWarner[1995]index
,0foreconomiesthathave
averagetariffsonimportsof
intermediateandcapitalgoods
above40percentorblackm
arketexchangepremiumsabove
20percentorahighcoverageofquotasonmachineryand
materials,and
1foropenness.Thedatafortheperiod
199397arebasedontheinformationcollectedfromEasterly
andYu[1999].
0.4539(0.0490)
0.3845
Financial
depth(M2/GDP)
Moneyandquasi-money(M2)asapercentageofGDP.
25.8860(1.4720)
0.6012
Policy(POL)
Indexofeconomicpolicy,constructedaslinearcombination
ofBS,OPN,andINF,weigh
tedbytheireffectson
economicgrowth.
0.2729(0.0730)
0.2231
Assassina
tions(ASS)
Thenumberofanypoliticallymotivatedmurdersorattempted
murdersofhighgovernment
officialsorpoliticiansper
100,000ofpopulation.
0.3026(0.1001)
70.1534
Coupsdetat(COP)
Thenumberofsuccessfulcoup
speryear.
0.0308
70.2567
Revolutio
ns(REV)
Thenumberofforcedchanges
ingovernmentalelite,orany
successfulorunsuccessfular
medrebellionsperyear,aimed
atachievingindependence.
0.1503(0.0122)
70.0670
Riots(RT
S)
Thenumberofviolentdemonstrationsorclashesperyearof
atleast100people,involvingtheuseofphysicalforce.
0.4123(0.0124)
70.0978
Strikes(S
TK)
Thenumberofstrikesperyear
of1,000ormoreworkers
ofatleastoneorganisation,aimedatgovernmentpolicy.
0.1339(0.0560)
70.0895
Regime(REG)
Adummyvariable,1forciviliangovernment,
2
formilitary-civilian,3formilitary,and
4forothers.
1.0538(0.0670)
0.0934
Politicalinstability(PI)
Indexofpoliticalinstability,co
nstructedasalinearcombinatio
n
ofASS,COP,andREVweightedbytheireffectson
economicgrowth.
0.0207(0.0020)
70.2789
(continued)
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TA
BLE1.1
(Continued)
Variables
Definitionsanddatasources*
Mean(Standarderror)
Correlation
b
Freedom
(FR)
FR
(14-CL-PR)/12;CL,PRareGastilindicesofcivilliberties
and
politicalrights.
Source:Free
domintheWorld(variousissue
s).
0.4934(0.3120)
70.3010
Govtcons.(Gc)
Governmentconsumptionexpe
nditure/GDP.
0.2926(0.8900)
0.0311
Partyfrac
tionalisation(PFI)
Anindex
(1t);tthepropo
rtionofmembersassociatedwith
the
ithpartyinthelowerhouse
ofthelegislature.
0.1881(0.0260)
70.0766
Exportin
stability(EXI)
Anindexofinstabilityofthere
alvalueofexports,measuredby
the
coefficientofvariationinatimeinterval,startingwith196872
andendingwith199397
0.0313(0.0135)
70.1745
Trade(TRD)
TheratioofthesumofrealvaluesofexportsandimportstoG
DP.
0.2826(1.1200)
0.0911
Ethno-linguisticfragmentation(EF)
Afixedfactorshowingthenum
berofdivisionsofthesocietyin
the
year1980intermsoflinguisticandethnicoriginsofpeople.
46.4620(3.037)
70.1987
Timedum
my(Dti)
Adummyfortheithfive-year
sub-period.i1,2,......,5.
Reg.dum
my(Di)
Adummyvariablefortheithg
eographicalarea,iAS(Asia),AF
(Africa),LA(LatinAmerica
),FZ(FrancZone),CA(Central
America),andEGP(Egypt.)
IncomeD
um.
Dinc
1foramiddle-incomecountry,
0foralow-income
country.
0.2453(0.4300)
70.3600
Notes:AlldataarefromEasterly,W.and
H.Yu[1999],GlobalDevelopmentNetworkGrowthDatabase,WorldBankorunlessotherwiseindicated.
aODAratherthanEDA(efficientdevelopmentassistance),asusedinBurn
sideandDollar[2000]isused,b
ecausethedifferencebetweenth
em,asDalgaard
andHansen[2001]note,isasimpletransformationwithacorrelationof
0.98betweenthem.
bThecorr
elationbetweenthedependentv
ariable,thegrowthrate,andthe
explanatoryvariables.
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index is therefore constructed by using these variables, weighted by their
estimated impact on growth:
POL 0:025530:0329Budget surplus
0:0742Openness0:1974Inflation 7
where 0.02553 is the average growth rate of real GDP per capita for all
countries in the sample over the period, 196897. A good policy, in terms of
budget surplus, trade openness, and low inflation, leads to a high value of the
index and thus it is expected to have a positive effect on growth. The other
most significant variable in the regression is the dummy for sub-Saharan
Africa, which appears with a negative significant coefficient. The initial GDP
per capita Y0,the dummy variable for east Asia, and M2/GDP (lagged) have
intuitive signs but they are never significant. The regression also includesfour time dummies and none of them are significant.
Table A1 presents a classification of the countries in the sample by
political instability. In the absence of a compelling theory of classification of
countries in terms of political instability, I use here an ad hoc procedure. I
define a country as stable, at least moderately, if its instability index is greater
than or equal to the overall average growth of GDP per capita minus one
standard deviation of the average. The difference between the average
growth and one standard deviation of growth is 0.01 (0.02550.0155). The
other countries can be defined as politically unstable. Based on thisclassification, there are 20 relatively stable and 45 unstable countries in the
sample.
T A B L E 1 . 2
C O R R E L A T I O N M A T R I X
Y0 A SCPR SCH POL M2 PI COP ASS REV EF
Y0 1.00
A 70.45 1.00SCPR 0.71 70.32 1.00SCH 0.58 70.25 0.67 1.00POL 0.22 70.01 0.38 0.45 1.00M2 0.31 0.001 0.18 0.28 0.39 1.00PI 70.21 0.09 70.43 70.19 0.01 70.13 1.00COP 70.32 70.15 70.23 70.28 70.18 0.05 0.92 1.00ASS 70.09 0.04 70.37 70.18 70.07 0.02 0.36 0.09 1.00REV 70.08 0.02 0.08 0.01 70.001 0.04 0 .35 0.15 0.10 1.00EF 70.41 0.05 70.38 70.40 70.06 70.20 0.12 0.04 0.08 0.06 1.00POP 70.08 0.48 70.37 70.25 70.12 70.22 0.02 0.03 0.12 0.05 0.26
Note: See table 1.1 for definitions of variables.
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TABLE2
GROWTH
REGRESSIONS
(t-ratiosinparentheses)
Estimationmethod
Regressio
n
OLS(1)
2SLS(2)
2SLS(3)
2SLS(4)
2SLS(5)
2SLS(6)
2SLS(7)
Y0
(InitialGDP)
70.0399(71.15)
70.5439(72.57)
70.9
520(74.16)
70.2561(70.42)
70.5598
(72.34)
70.5358
(71.23)
70.7732
(72.57)
ASS(Ass
assinations)
70.2282(72.83)
COP(Coupdetat)
70.4218(71.72)
STK(Strikes)
0.0677(0.39
)
REV(Revolutions)
70.0406(71.69)
RTS(Rio
ts)
0.0794(71.2
5)
PI(Politicalinstab.
index)
73.1513(72.63)
A(Aid/GDP)
70.0175(70.75)
0.1197(1.90)
70.0066
(70.22)
0.0997
(2.05)
70.0094
(70.31)
A1
(Aid/G
DPsquared)
70.0002(70.70)
70.0103(71.98)
0.0004(3.36)
70.0003
(71.96)
0.0003
(2.42)
A2
(Aid/G
DP6Pol.
instab.)
70.0751(73.07)
INF(Inflation)
70.1974(71.88)
BS(Budg
etsurplus)
0.0329(1.68
)
OPN(Openness)
0.0742(1.65
)
POL(Policyindex)
1.4695(0.05)
0.4
935(2.22)
70.0070(70.22)
0.0145(0.43)
0.0105
(0.82)
0.0209
(0.58)
A3
(Aid/G
DP6Policy)
1.0032(3.27)
0.1
491(3.70)
A5(Aid/GDP6Pol.
Inst.6
Policy)
0.1792(0.42)
M2/GDP(Lagged)
0.0026(0.69
)
0.1342(0.72)
0.0
178(1.70)
0.0031(0.14)
0.0345(0.41)
0.0272
(1.24)
70.0307
(71.95)
(continued)
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TABLE2
(Co
ntinued)
Estimationmethod
Regressio
n
OLS(1)
2SLS(2)
2SLS(3)
2SLS(4)
2SLS(5)
2SLS(6)
2SLS(7)
Daf(Afric
a)
70.5762(73.34)
70.3971(74.94)
70.2
186(72.43)
70.6739(73.98)
70.4472
(74.52)
70.4277
(72.57)
70.6088
(75.71)
Das(East
Asia)
0.2499(0.85
)
0.1982(0.19)
0.0
424(0.04)
0.4525(2.23)
0.9335(1.39)
0.6791
(2.43)
0.8973
(1.01)
Constant
73.2984(74.31)
76.5751(71.07)
75.7
020(71.11)
72.5821(70.51)
7.2219(2.21)
7.0341
(12.29)
7.6697
(7.76)
Observations
325
260
260
80
180
76
168
JB(Norm
alitytest)
56.84
52.67
51.90
11.17
26.01
3.59
4.81
Adjusted
R2
0.49
0.43
0.37
0.39
0.36
0.32
0.34
w2(Overidentification
test)
3.90
3.67
1.89
2.34
1.98
3.52
Notes:Timedummiesareincludedinallregressions.Theyaregenerallyfoundtobestatisticallyinsignificant.JB:Jarque-Berateststatistic
isdistributedas
w2with2
degreesoffreedom.Thecritica
lw2with2degreesoffreedom
at5percentlevelofsignificanceis5.99.
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Estimation Results
I now treat aid, political instability and economic policy as endogenous and
estimate the growth regression by 2SLS method. The results (see Table 2,
column 2) indicate that the coefficient on aid/GDP is negative andstatistically insignificant; the coefficient on aid squared is also insignificant.
This implies that aid is ineffective in promoting growth in the recipient
countries, which conforms to Dalgaard and Hansen [2001].
There are two aspects of the insignificant marginal effect of aid on growth
with which I am particularly concerned. The first one is whether aid becomes
ineffective in the presence of political instability and the other is whether the
impact of instability is influenced in any way by the quality of economic
policy. The results show that the coefficients on political instability and its
interaction with the aid variable A2are both negative significant. This implies
that political instability leads to a significant decline in GDP growth by
reducing the effectiveness of aid. The coefficient on the policy variable is
positive insignificant, although the interaction of policy with aid appears with
a positive significant coefficient. The coefficient on the interaction term A5(aid6political instability6policy) is positive insignificant (see column 2).
This implies that the incremental marginal effect of policy on growth due to
increased aid flows becomes quite insignificant in the presence of political
instability. In other words, a comparison of the coefficient on A5with that onA2 suggests that political instability reduces the effectiveness of aid even in
the presence of good economic policies.
Burnside and Dollar [2000] interpret the positive coefficient on the
interaction term A3 (aid6policy) as aid being more effective in countries
with good policies than in others. But I suspect that this positive significant
coefficient on the interaction term may imply that policy is more effective
when supported by inflows of aid rather than aid being more effective in a
good policy environment. To test this proposition I re-estimate the growth
regression excluding the aid variable. The results (see column 3) show thatpolicy has a positive impact on growth and this positive marginal effect is
higher in countries with increased aid flows, as indicated by the positive
significant coefficient on A3. The initial GDP per capita appears with a
negative significant coefficient, capturing the conditional convergence effect.
The other significant variable in the regression is the dummy for sub-Saharan
Africa; the coefficient is negative conforming to previous results.
As my main focus is on the growth regression, I briefly discuss the
estimates of the other equations (aid, political instability and policy) in the
model. The variables that have positive significant effect on aid allocationinclude infant mortality rates, and the Franc zone dummy (Table 3 column 1).
Population size appears with a negative significant coefficient, implying that
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TABLE3
AI
D,POLITICALINSTABIL
ITYANDPOLICYREGRE
SSIONS
(t-ratiosinparentheses:samplesize
325)
Thedependentvariable
independentvariable
Aid/GD
P(1)
Political
instabilityindex(2)
Policy
index(3)
Y0
(InitialGDP)
72.2618
(72.70)
Y
(Positivegrowth)
0.0024(1.06)
SCPR(Primaryschooling)
70.2100(72.44)
0.0003(2.36)
MORT(M
ortality)
0.1194
(1.93)
EXI(Exp
ortinstability)
0.3585
(0.39)
70.0092(70.34)
POP(Pop
ulation)
70.5980
(76.10)
A71
(Aid/GDPlagged)
0.1200(74.21)
70.1748(73.82)
A72
(Aid/GDPlaggedtwoperiods)
70.0246
(72.70)
70.1022(72.87)
PI71
(Pol.instab.lagged)
0.4109(7.13)
PI2
(Laggedtwoperiods)
0.0211
(2.83)
REG(Regimetype)
70.0076(71.32)
EF(Ethnic-fractionalisation)
0.0005(0.31)
PFI(Partyfractionalisation)
0.0095(0.62)
DFZ
(Fran
czonedummy)
0.5598
(15.94)
DCA
(Cen
tralAmericandummy)
70.8046
(70.36)
DEGYPT(Egyptdummy)
0.6120
(0.61)
DAF
(SubSaharadummy)
0.2653
(0.87)
DLA
(LatinAmericandummy)
0.0137(2.56)
Das(East
Asiandummy)
0.0397(2.82)
POL71(P
olicyindexlagged)
70.0856
(70.97)
0.0406(0.70)
Constant
0.5954
(8.73)
0.1711(9.38)
0.2319(9.62)
Observations
325
3
25
325
Adjusted
R2
0.54
91
0.6
495
0.4895
w2
(Overidentif.test)
3.9
1
2.98
2.87
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smaller countries tend to receive more aid than larger ones. Initial GDP per
capita and the previous aid shares also have negative significant impact on aid
flows. Policy lagged, export instability and the other donor interest dummy
variables do not affect aid significantly. The results in general conform to
some previous studies, Guillaumont and Chauvet [2001] for example.Political instability seems to persist over time; PI
71and PI72appear with
positive significant coefficients. A country with a higher level of education is
found to experience less instability than others. The Latin American countries
are also found to have more political instability than other developing
countries in the sample. The coefficients on other variables in the regression,
such as positive growth, party fractionalisation, ethnic fractionalisation, and
regime type, have expected signs, but they are never significant (see Table 3
column 2).
The most significant variables in the policy regression (see Table 3 column
3) are the level of education, lagged aid shares and the dummy for east Asian
countries. The level of education seems to improve the quality of a countrys
economic policy. Countries with a higher level of education are found to have
better economic policies than others. The results also indicate that east Asian
countries have better economic policies than others. The aid shares (lagged)
appear with negative significant coefficients. Aid allocations are often
conditional on better economic policies and thus countries receiving more aid
in the past tend to have fewer changes in current policies than others. Table 3also includes overidentification test results, measured by w2. The calculated
values of w2 are all less than their critical values, which implies that the
instruments chosen for aid, political instability and policy are not correlated
with the growth residuals and thus the instruments can be considered as
appropriate. R2 in 2SLS is not well-defined and therefore I have included
adjusted squared-correlation between the observed and predicted dependent
variable.
I V . R O B U S T N E S S A N D S E N S I T I V I T Y A N A L Y S I S
I now examine how sensitive the results are when the model is tested on
the data for stable and unstable countries separately. It is expected that the
coefficient on aid would be positive for stable countries and negative for
the unstable ones. There is likely to be little change in the impact of aid on
growth when the estimation is done controlling for policy. It will be
interesting to investigate the sensitivity of the results when possible outliers/
potentially influential observations are deleted and an alternative measure of
political instability is used. It is also important to examine, as an anonymousreferee has suggested, whether the results still hold if a fixed effects model
had been estimated.
1484 T H E J O U R N A L O F D E V E L O P M E N T S T U D I E S
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When the model is estimated on separate samples of stable and unstable
countries, an interesting story emerges. The coefficient on aid/GDP is now
positive significant for politically stable countries and negative insignif-
icant for the others (see Table 2 columns 45). One percentage point
increase in aid/GDP ratio in a stable country leads to roughly 0.12percentage point increase in the growth rate, when the effect is evaluated
at the mean value of the aid variable. This finding conforms to Hansen
and Tarp [2001]. The positive marginal effect, however, tends to decline,
as indicated by the negative coefficient on aid/GDP squared. The turning
point3 for which the marginal effect is negative corresponds to an aid/GDP
ratio of 5.8 per cent. The implied turning points in previous studies vary
from 3.7 per cent [Collier and Dollar 1999] to 10 per cent [Lensink and
White 2001], when PPP-values for GDP are used in measuring aid/
GDP ratio.
Although the coefficient on the aid variable is negative insignificant for the
unstable countries, aid squared appears with a positive significant coefficient
and this implies increasing returns to aid. The coefficient on the policy
variable is now statistically insignificant, with a negative sign for stable
countries and positive for others. It is also found that when this variable is
deleted from the regression for stable countries, the coefficient estimate of the
aid variable becomes more efficient; there is little change in the estimate for
the unstable countries. The other coefficient estimates remain more or lessunaffected. Thus, the impact of aid on growth does not seem to be influenced
by policy.
Table 2 includes Jarque-Bera test of normality of residuals. The computed
test statistics for residuals in eqs. (1)(5) exceed their critical values,
indicating that the residuals are not normally distributed and there is a
likelihood of extreme outliers in the sample. Dalgaard and Hansen [2001]
detected nine outliers4 and five leverage5 points in a sample of 56 developing
countries. The outliers include: Cameroon (197881, 199093), Egypt
(198285), Ethiopia (198285), Gabon (197477, 197881), Nicaragua(197881), Nigeria (197073), and Syria (197477). The leverages are
Argentina (197477), Gambia (198689, 199093), Guyana (199093), and
Nicaragua (199093). The sample in the present study includes nine
additional countries which show two more outliers (Liberia, 199397, and
Uganda, 199397).
I exclude two outliers (Gabon, 197377, 197882) and two leverages
(Gambia, 198387, 198892) from the sample of stable countries. Note that
the time intervals are slightly changed to match with the five yearly-intervals
used in this study. The sample of unstable countries excludes the remainingnine outliers and three leverages. Regressions (6) and (7) show the changes
in the parameter estimates for stable and unstable countries respectively.
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The exclusion of outliers/leverages leads to a more efficient estimate of the
coefficient on aid; the t-ratios are considerably higher in both regressions. Aid
squared appears with significant coefficients, without any change in sign. It
can be noted that the coefficients on aid and aid squared in the regression
estimated on the combined sample of stable and unstable countries, excludingthe outliers/leverages, are both positive and statistically insignificant (the
results are not reported). Overall, the results are sensitive to the outliers but
the main finding that aid promotes growth only in a politically stable
environment seems to be a robust result.
I investigate the sensitivity of the results when the instability index is
measured alternatively by the weighted sum of the number of revolutions per
year and of the number assassinations per million inhabitants per year, as
used in Guillaumont and Chauvet [2001]. The coefficients on both aid and aid
squared are now positive and significant at 10 per cent level. The coefficient
on the instability index is negative insignificant, but the coefficient on the
interaction term (aid6instability) is negative significant. There are only
minor changes in the other coefficient estimates. If political instability is
measured by the ethnic-fractionalisation index, the coefficients on aid and the
interaction term are both insignificant (the results are not reported). The
results are thus sensitive to different measures of political instability and
therefore it is important to use a proper definition of the index, as explained
earlier.
Fixed Effects Estimation and Sensitivity of the Results
I now estimate the growth regressions (2), (4)(7) in Table 2 by fixed effects
method, where an individual constant is entered for each country. Following
Barro [2000], I exclude the two time-invariant regional dummies, one for
sub-Saharan Africa and the other for east Asia, from each of these
regressions. This is intended to check whether it is sufficient to only include
regional dummies for fixed country effects or instead use a panel estimation
technique (fixed effects method). Table 4 shows the results of a fixed effectsestimation of these regressions.
The estimated coefficients on aid/GDP are still statistically insignificant
for unstable countries (and in the full sample), but significant for stable
countries. However, the sizes of these coefficients are now considerably
lower than their 2SLS estimates. The fixed effects specification allows only
for contemporaneous relations between aid and economic growth and thus
the coefficient estimates would pick up a short-run link between them. But
in the 2SLS estimation using pooled data, the estimates reflect both time-
wise and cross-sectional variations, and thus the estimates pick up longerrun aspects of the relationship between aid and growth. Note that the fixed
effects estimates of the coefficient on aid/GDP for a stable country reflects
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TABLE4
FI
XEDEFFECTSESTIMATIONOFGROWTHREGRESSIONS
(t-ratiosinparentheses)
Fullsamp
le
Stable
Unstable
Stable
Unstable
Includingoutliers
Excludingoutliers
CountryR
egressions
(2)
(4)
(5)
(6)
(7)
A(Aid/GDP)
0.01124(0.44)
0.0729(2.30)
70.0301(70.84)
0.0897(2.20)
70.0194(70.52)
A1
(Aid/G
DPsquared)
0.0056(0.19)
70.0021(71.66)
0.0022(2.45)
70.0035(71.76)
0.0021(2.43)
POL(Policyindex)
0.7307(0.69)
70.0731(70.31)
0.8706(0.79)
70.0751(70.74)
0.6953(0.62)
M2/GDP(Lagged)
70.00002(7
0.002)
0.0232(1.34)
70.0031(70.19)
0.0229(1.01)
70.0124(70.82)
Constant
70.0070(7
0.07)
0.1119(0.69)
70.0213(70.18)
0.1389(0.74)
70.0195(70.17)
Observations
325
80
245
70
195
Notes:Th
eregressions(2),(4)(7)corre
spondtothe2SLSregressionspresentedinTable2.Theconstanttermrepresentstheaverage
oftheestimated
intercepts
forallcountriesincludedinthe
regression.
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roughly 60 per cent of the variations in growth due to changes in aid share,
all else equal. There is hardly any change in the coefficients on aid/GDP
squared, except that its sign is changed in the full sample, but it is still
insignificant. Thus, the main finding that aid promotes growth only in a
stable environment still seems to be a robust result. There is also no changein the robustness of the other coefficient estimates with a fixed effects
estimation.
An Alternative Stability Test
Levine and Renelt [1992] and Sala-i-Martin [1997] suggest a more
systematic procedure for testing robustness (or stability) of parameter
estimates in regression analysis. The procedure involves the following
steps: (a) estimate the coefficient on the variable of interest, aid/GDP ratio
in this case, from a growth regression, using a set of independent variables
which in the past were found to have important influence on growth across
countries, and (b) augment this base regression with linear combinations of
up to three additional variables which have explanatory values (a
Q-vector) and find an estimate of the coefficient on aid/GDP. If the sign
of this estimated coefficient remains the same and its level of significance
does not change too greatly, the aidgrowth relationship can be considered
as robust.
I include the following six variables in the vector Q: trade (ratio of exportsplus imports to GDP), civil liberties, political rights, the share of government
consumption in GDP, economic instability and a dummy variable ( 1 for amiddle income country, 0 for a low-income country). The first threevariables are added as they are included in Islam [2003] and Lensink and
White [2001]. The justification for including the other variables can also be
found in previous studies. Barro [1991] has found a negative significant
relationship between government consumption share and growth. Economic
instability, measured by the coefficient of variation in per capita real GDP,
has also a negative significant effect on growth [Islam and Winer, 2004]. Theincome dummy is added because aid does not necessarily have same effect on
growth in countries with different levels of per capita income [see Burnside
and Dollar, 2000].
Table 5 shows the coefficient estimate of aid/GDP based on the base
growth regression and its augmented forms, using alternative linear
combinations of the Q-variables. For the full sample the coefficient estimate
of aid/GDP remains statistically insignificant, but its sign changes and
therefore the coefficient estimate is not robust. In case of the stable (unstable)
countries, the coefficient estimate retains its positive (negative) signand statistical significance (insignificance) and thus the corresponding
aidgrowth relationships can be considered as robust.
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V . C O N C L U S I O N S
In this study I have investigated several questions regarding the interactions
among aid, political instability, economic policies and growth. Consistent
with other studies, I found that on average aid has an insignificant impact on
growth, although a robust finding was that aid promotes growth only in apolitically stable environment irrespective of the quality of countries
economic policies. The empirical results also provide some tentative support
for the presence of an aid Laffer curve in the politically stable countries. The
returns to aid become negative at higher levels of aid inflows, in particular,
beyond an aid/GDP ratio of 5.8 per cent. This finding underlines the
importance of incorporating political instability in an aid growth regression.
The data from different developing countries should not be pooled without
allowing coefficients to vary with political instability, which places interesting
restrictions on the signs of the coefficients of aid in explaining economicgrowth.
The allocation of aid is found to depend on the size of a country and its
state of human development, and partly on the strategic interests of the
donors. The quality of economic policy did not influence aid allocation.
A country with a higher level of education is found to have less political
instability than others. Consistent with past history, the Latin American
countries are found to be more unstable than other developing countries.
A country with a higher level of education and also an east Asian country are
found to have better economic policies than others.
Final version received August 2004
T A B L E 5
S E N S I T I V I T Y T E S T R E S U L T S F O R T H E E F F E C T S O F A I D O N G R O W T H
CountryClassification Regression
CoefficientAid/GDP t-ratio R 2 Q-variables
Robust/fragile
All countries High 0.0208 0.78 0.33 TRD, FR, Dinc FragileBase 0.0175 0.75 0.37 Low 70.0007 70.02 0.30 FR, Dinc
Stable countries High 0.1287 1.98 0.39 TRD, FR, Dinc RobustBase 0.0997 2.05 0.32 Low 0.0309 1.99 0.33 TRD, FR, Gc
Unstable countries High 70.0098 70.32 0.33 FR RobustBase 70.0094 70.31 0.34 Low 70.0636 71.87 0.43 FR, Dinc
Notes: TRD Trade, FR (14-CL-PR)/12, Gc Government consumption/GDP, an Dinc 1
for a middle-income country, and 0 for a low-income country. High and Low refer to theaugmented regressions yielding the highest and lowest values of 2SLS estimate of the coefficienton aid/GDP.
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N O T E S
1. Foreign aid or technically called official development assistance (ODA) includes grants andconcessionary loans from government(s) to government(s). They contain a grant element of atleast 25 per cent; the grant element is the difference between the face value of a loan and its
present value of amortisation including interest payments. Roughly 65 per cent of aid funds goto projects, the rest being divided among programme assistance (6 per cent), food aid (3 percent), and others (26 per cent). ODA does not include non-concessionary loans, grants andloans for defence purposes.
2. Consider the following growth model:
gt b0 b1At b2Pt b3APt ut 1
where gt growth rate of GDP, At aid share in GDP, and Pt a policy index.The model can be written as:
gt b0 b1 b3PtAt b2Pt ut 2
Or
gt b0 b2 b3AtPt b1At ut 3
The coefficient of At, in eq. (2), implies that the effect of aid on growth depends on the level ofpolicy as in Burnside and Dollar [2000], while the coefficient of Pt in eq. (3), implies thatpolicies work better if supported by aid inflows. Thus Burnside and Dollar results can have adifferent interpretation.
3. The turning point is the share of aid in GDP above which more aid has a negative marginalimpact on growth. The partial derivative of the growth rate with respect to the aid share (inregression 4, Table 2) is equal to 0.1197 2(0.0103) A/GDP.
Equating this to zero and solving for A/GDP, the turning point is found as: A/GDP 5.81or approximately 5.8 per cent.
4. An outlier is an extreme observation, usually generated by some unusual factors. Theinformation it conveys is quite different from the rest of the observations and as a result it
produces substantial changes in the estimated regression equation. An observation isconsidered as an outlier if the corresponding studentised residual exceeds plus or minus 2.A better technique to detect outliers/influential observations is to use DFFITS, which is astandardised measure of the changes in the fitted value of the dependent variable due to
deleting the particular observation.5. A leverage point has an above-average influence on fitted values of the dependent variable.
R E F E R E N C E S
Alesina, A. and D. Dollar, 2000, Who Gives Foreign Aid to Whom and Why?, Journal of Economic Growth, Vol.5, pp.3363.
Alesina, A. and R. Perotti, 1996, Income Distribution, Political Instability, and Investment,European Economic Review, Vol.40, pp.120328.
Barro, R.J., 1991, Economic Growth in a Cross Section of Countries, The Quarterly Journal ofEconomics, Vol.106, pp.40743.
Barro, R.J., 2000, Inequality and Growth in a Panel of Countries, Journal of Economic Growth,Vol.5, pp.532.
Boone, P., 1996, Politics and the Effectiveness of Foreign Aid, European Economic Review,40(2), pp.289330.
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Burnside, C. and D. Dollar, 2000, Aid, Policies and Growth, The American Economic Review,90(4), pp.84768.
Cassen R. and Associates, 1994, Does Aid Work? Second edition, New York: Oxford UniversityPress.
Collier, P. and D. Dollar, 1999, Aid Allocation and Poverty Reduction, Policy Research
Working Paper 2041, World Bank, Washington, DC.Dalgaard, C.J. and H. Hansen, 2001, On Aid, Growth, and Good Policies, Journal of Development Studies, 37(6), pp.1741.
Easterly, W. and H. Yu, 1999, Global Development Network Growth Database, World Bank,Washington DC.
Fischer, S., 1993, The Role of Macroeconomic Factors in Growth, Journal of MonetaryEconomics, 32(3), pp.485512.
Griffin, K. and T. McKinley, 1994, A New Framework for Development Cooperation,Occasional Papers, Human Development Report Office, UNDP, New York.
Guillaumont, P. and L. Chauvet, 2001, Aid and Performance: A re-Assessment, Journal of Development Studies, 32(3), pp.6692.
Gupta, D.K., 1990, The Economics of Political Violence, New York: Praeger.
Hadjimichael, M.T., Ghura, D., Muhleisen, M., Nord, R., and E.M. Ucer, 1995, Sub-SaharanAfrica: Growth, Savings, and Investment, 19861993, Occasional Papers 118, InternationalMonetary Fund, Washington, DC.
Hansen, H., and F. Tarp, 2000, Aid Effectiveness Disputed, Journal of InternationalDevelopment, 12(3), pp.37598.
Hansen, H., and F. Tarp, 2001, Aid and Growth Regressions, Journal of DevelopmentEconomics, Vol.64, pp.54770.
Hausman, J.A., 1983, Specification and Estimation of Simultaneous Equation Models, inZ. Grilliches and M.D. Intriligator (eds.), Handbook of Econometrics 1, Amsterdam: NorthHolland, Ch. 7.
Islam, M.N., 2003, Political Regimes and the Effects of Foreign Aid on Economic Growth, TheJournal of Developing Areas, 37(1), pp.3552.
Islam, M.N., and S.L. Winer, 2004, Tinpots, Totalitarians (and Democrats): An empiricalInvestigation of the Effects of Economic Growth on Civil Liberties and Political Rights,
Public Choice, 118(3), pp.145.Lensink, R., and H. White, 1999, Assessing Aid: A Manifesto for the 21st Century, A Sida
evaluation reports 99/17:13, Stockholm, Sweden.Lensink, R., and H. White, 2000, Aid Allocation, Poverty Reduction and the Assessing Aid
Report,Journal of International Development, Vol.12, pp.399412.Lensink, R., and H. White, 2001, Are there Negative Returns to Aid?, Journal of Development
Studies, 32(3), pp.4265.Levine, R., and D. Renelt, 1992, A Sensitivity Analysis of Cross-country Growth Regressions,
The American Economic Review, Vol.82, pp.94263.
Nehru, V., Swanson, E., and A. Dubey, 1995, A New Database on Human Capital Stock inDeveloping and Industrial Countries: sources, methodology, and results, Journal of Development Economics, Vol.46, pp.379401.
Sachs, J.D., and A.M. Warner, 1995, Economic Reform and the Process of Global Integration,Brookings Papers on Economic Activity, Vol.1, pp.1118.
Sala-i-Martin, X., 1997, I Just Ran Two Million Regressions, The American Economic Review,Vol.87, pp.17883.
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T A B L E A 1
C L A S S I F I C A T I O N O F C O U N T R I E S B Y P O L I T I C A L I N S T A B I L I T Y
I N D E X ( P I )
Stable countries
Country PI Country PI Country PI
Mauritius 0.0218 Paraguay 0.0179 Indonesia 0.0152Malaysia 0.0179 Singapore 0.0162 Nepal 0.0228Malawi 0.0114 Zaire 0.0179 Cote dIvoire 0.0225Dom. Repub. 0.0152 Costa Rica 0.0255 Tri. & Tobago 0.0242Botswana 0.0255 Gabon 0.0255 Gambia 0.0242Madagascar 0.0139 Mali 0.0288 Tanzania 0.0179Tunisia 0.0255 Zambia 0.0242
Unstable countries
Country PI Country PI Country PIJamaica 70.0049 Argentina 70.5010 Ecuador 0.0006Uruguay 70.0125 Venezuela 0.0014 Algeria 70.0228Cameroon 0.0098 Egypt 70.0255 Ghana 70.0275Kenya 0.0027 Liberia 0.0049 Morocco 70.0242
Nger 0.0047 Nigeria 70.0364 Senegal 70.0283Sierra Leone 70.0156 Sudan 70.0334 Swaziland 70.0166Togo 70.0051 Uganda 70.2969 Zimbabwe 70.0374El Salvador 70.4084 Guatemala 70.7100 Honduras 70.1348Mexico 70.0353 Nicaragua 70.0485 Bolivia 70.1175Brazil 70.0429 Columbia 70.3056 Peru 70.2098
India 7
0.1907 S.Korea 0.022 Pakistan 7
0.0984Philippines 70.2265 Sri Lanka 70.0743 Thailand 70.0102Haiti 70.0578 Chile 70.1486 Burk Faso 70.0337Ethiopa 70.1199 Somalia 70.0046 Syria 70.1673Turkey 70.2094 Bangladesh 70.0589 Guyana 70.0125
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