jods-regime changes, economic policies and the effct of aid on growth

8/10/2019 JoDS-Regime Changes, Economic Policies and the Effct of Aid on Growth

1/28

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

The Journal of Development

StudiesPublication details, including instructions for

authors and subscription information:

http://www.tandfonline.com/loi/fjds20

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

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of allthe information (the Content) contained in the publications on ourplatform. However, Taylor & Francis, our agents, and our licensorsmake no representations or warranties whatsoever as to the accuracy,completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of

the authors, and are not the views of or endorsed by Taylor & Francis.The accuracy of the Content should not be relied upon and should beindependently verified with primary sources of information. Taylor andFrancis shall not be liable for any losses, actions, claims, proceedings,demands, costs, expenses, damages, and other liabilities whatsoever
http://dx.doi.org/10.1080/00220380500187828http://www.tandfonline.com/action/showCitFormats?doi=10.1080/00220380500187828http://www.tandfonline.com/loi/fjds20


2/28

or howsoever caused arising directly or indirectly in connection with, inrelation to or arising out of the use of the Content.

This article may be used for research, teaching, and private studypurposes. Any substantial or systematic reproduction, redistribution,reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access

and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Dow

nloadedby[118.137.232.37]at04:5704December2014
http://www.tandfonline.com/page/terms-and-conditionshttp://www.tandfonline.com/page/terms-and-conditions


3/28

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

Dow



4/28

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

Dow



5/28

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

E F F E C T O F A I D O N G R O W T H 1469

Dow



6/28

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


Dow



7/28

[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


Dow



8/28

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,


Dow



9/28

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


Dow



10/28

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


Dow



11/28

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


Dow



12/28

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)


Dow



13/28

TA

BLE1.1

(Continued)

Variables


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)


Dow



14/28

TA

BLE1.1

(Continued)

Variables


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.


Dow



15/28

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.


Dow



16/28

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)


Dow



17/28

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.


Dow



18/28

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


Dow



19/28

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


Dow



20/28

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.


Dow



21/28

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.


Dow



22/28

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


Dow



23/28

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.


Dow



24/28

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.


Dow



25/28

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.


Dow



26/28

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.


Dow



27/28

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.


Dow



28/28

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


Dow


jods-regime changes, economic policies and the effct of aid on growth

Documents