Heterogeneity of the Effects of Aid on Economic Growth in Sub-Saharan Africa: Comparative Evidence from Stable and Post-

Conflict Countries

No. 179 – September 2013

Douzounet Mallaye & Yogo Urbain Thierry

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Correct citation: Mallaye, Douzounet and Yogo Urbain, Thierry (2013), Heterogeneity of the Effects of

Aid on Economic Growth in Sub-Saharan Africa: Comparative Evidence from Stable and Post-Conflict

Countries, Working Paper Series N° 179 African Development Bank, Tunis, Tunisia.

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Salami, Adeleke

Heterogeneity of the Effects of Aid on Economic Growth in Sub-

Saharan Africa: Comparative Evidence from Stable and Post-Conflict Countries

Douzounet Mallaye & Yogo Urbain Thierry1

Office of the Chief Economist

1 CEREG-Université de Yaoundé II

Douzounet Mallaye::douzounetmallaye@yahoo.fr

Yogo Urbain Thierry: yogout@gmail.com

AFRICAN DEVELOPMENT BANK GROUP

Working Paper No. 179

September 2013

Abstract: This study provides evidence of

the effectiveness of aid through the use of a

sample of 34 sub-Saharan African

countries over the period 1990-2010. After

considering the endogeneity of aid, the

results of the estimation reveal that aid has

a positive effect on growth only when the

estimation is controlled for governance.

The comparative analysis shows in turn

that governance and education are the

main channels through which aid is

transmitted to growth in a stable

environment. In a post-conflict

environment, on the other hand, aid affects

growth via investment in public capital

(infrastructure). Finally, the Oaxaca-

Blinder decomposition approach shows

that the difference in terms of amounts of

aid received does not explain the

differences in growth observed between

stable countries and countries in post-

conflict situations. Based on those results,

the economic policy implications are

discussed in the conclusion.

Keywords: ODA; economic growth; post conflict; panel data; Africa

JEL Classification: C33, D72, E62, F35, F43.

5

1. Introduction

Sub-Saharan Africa is the world’s leading beneficiary of external aid. Since 1960, the

international community has devoted over US$568 billion to the development of sub-Saharan

Africa (SSA), representing roughly 15% of the continent’s GDP or proportionally four times

the Marshall plan that restarted the European economies after the Second World War (United

Nations Economic Commission for Africa, 2010). However, growth in SSA has not followed

the trend of official development assistance (ODA) inflows, but has remained low despite the

recovery of economic activity in the early 2000s (2.37% on average).

The relationship between ODA and growth in SSA has been addressed in empirical studies

since its advent in 1950. Recent work has been dominated by the idea that ODA is ineffective

to support growth (Bauer, 2000; Easterly, 2003; Williamson, 2009). Yet most of the studies

supporting this view have numerous limitations. First, they fail to take account of specific

characteristics of the beneficiary countries (post-conflict situation, vulnerability to external

shocks, etc.). Second, they overlook the indirect effects of aid on growth. Finally, few studies

rigorously and concomitantly examine the positive effects of ODA in samples including both

stable and post-conflict countries.2 This study proposes to compensate for these limitations

through an approach that combines a theoretical analysis with an empirical analysis based on

econometric tests.

Accordingly, the objective of this study is to demonstrate the effects of ODA on growth and

determine the channels of transmission in SSA. In reference to the existing literature

(Gomanee et al., 2005, Tarp et al., 2011), we test several essential channels of transmission,

including investment in capital (human and public) and governance (public revenue

management and democracy). Indeed, the effectiveness of aid is contingent the improvement

of governance (democracy and public revenue management) and social services (education,

infrastructure, etc.) in the beneficiary countries (Burnside and Dollar, 1997; Doucouliagos and

Paldam, 2009; Temple, 2010). The concept of governance is also central to the new

development model recommended by the Bretton Woods institutions in the 1990s when

adjustment policies proved inadequate to promote growth in SSA. In addition to the direct and

indirect effects, the study also uses a methodological approach based on the Oaxaca-Blinder

decomposition, which is rarely used in macroeconomics, to analyze the contribution of aid in

explaining differences in growth.

2 Following Collier and Hoeffler (2002), we consider post-conflict countries to be those countries that have

experienced civil war in the previous two decades and still encounter pockets of rebellion.

6

The remainder of this study is organized as follows. Section 2 presents a review of the

literature. Section 3 presents the methodology of the study. Section 4 presents the results of

the regressions. Finally, section 5 concludes the study and discusses recommendations.

2. Review of literature

The empirical analysis of the effectiveness of development assistance, already explored in the

1970s, received renewed attention in the early 1990s. As aid faced a crisis of legitimacy, the

study by Burnside and Dollar (1997) provided a response to the detractors of development

assistance. The authors showed that the effectiveness of aid is contingent on improved

governance in the beneficiary countries. Despite the reservations prompted by several studies

(Easterly, 2003, Boone, 1994; Temple, 2010), the conclusions of Burnside and Dollar (1997)

would be adopted and defended by the World Bank (1998), Dalgaard and Hansen (2001),

Lensink and White (2000), with important implications for economic policy. This review

continues that discussion, focusing on work based on SSA countries.

From this perspective, the central issue for the relationship between aid and growth is whether

the allocation of aid to countries structurally characterized by weak governance can be

considered wasteful. A tentative answer is provided by Gomanee et al. (2005), who

emphasize the direct and indirect effects of ODA on growth in SSA. The authors utilize panel

data for 25 SSA countries from the period 1970-1997. The results of their regressions show

that aid has a positive direct and indirect effect on growth. Indirectly, aggregate aid effects

growth in SSA via public investment. This result corroborates the findings of Hansen and

Tarp (2001). This optimistic vision is likewise shared by Tarp et al. (2003), who find that aid

is effective even when macroeconomic conditions are poor. This assertion is also consistent

with the study by Guillaumont and Chauvet (2001), which finds that returns on aide are

higher in countries vulnerable to macroeconomic shocks.

However, this optimistic view is not shared by Rajan and Subramanian (2008, 2010), who

estimate a neoclassical production function to evaluate the effect of ODA on growth over a

period of 30 years. They observe no significant effect of aid in a sample of developing

countries including African countries. In their 2010 article, moreover, they observe that

increased aid may lead to appreciation of the exchange rate, thereby producing a negative

effect on growth.

7

More recently, Tarp et al. (2011), in the same vein as Radelet et al. (2004), observe that most

of the studies use cross-section or panel data with a short time dimension, demonstrating only

the cyclical effects of aid. In order to offset this limitation, they conduct a study of a sample

of 38 SSA countries over the period 1960-2007. Their article is distinguished by the

methodological choice of the error correction model used to capture the long-term dynamic of

the relationship between aid and growth. They arrive at the result that aid directly and

indirectly promotes economic growth even in an unfavorable macroeconomic context. In

particular, the observed effect is transmitted through investment in public capital. In the same

line of thought, the work of Hadjimichael et al. (1995), Mc Gillivray and Ouattara (2003),

Kene et al. (2008), Brempong and Asiedu (2008), Arndt et al. (2010) and Dietrich and Wright

(2012) identify multiple channels through which aid is transmitted, notably investment,

government expenditure, imports, institutions, a policy variable (a linear combination of

inflation, budget surplus and openness to trade), taxes and education.

In short, the studies addressing the indirect relationship between aid and growth in Africa

arrive at conflicting results. In addition, while the macroeconomic context is identified as a

controlling factor for effectiveness, few studies consider the political environment, much less

the heterogeneity of channels through which the effect of that environment is transmitted.

In light of the foregoing, the contribution of this study is to identify the macroeconomic

effects of aid on growth and determine the channels through which those effects occur in

SSA. It continues the line of inquiry pursued by Morrissey (2001), Gomanee et al. (2005) and

de Tarp et al. (2011). Unlike those studies, however, it conducts a comparative analysis

between SSA countries in a post-conflict environment and those in a stable environment. In

this regard, it uses the Oaxaca-Blinder decomposition approach to explain differences in

growth. The objective of this study is to show that ODA, when controlled by governance,

contributes directly or indirectly to explaining growth in SSA. It endeavors to show that ODA

indirectly influences economic growth in SSA via investment in human capital (education)

and in public capital (infrastructure). The channels of transmission vary according to the

political environment in the recipient countries.

In the following section we present the methodology used to verify this postulate and the

results obtained from our analysis.

8

3. Methodology

To demonstrate the influence of ODA on growth, we draw on the work of Gomanee et al

(2005), Rajan and Subramanian (2008) and Tarp et al. (2011).

The methodology utilized is distinguished by two essential points: (i) in addition to analyzing

the direct effects of aid, the methodology identifies the channels through which aid is

transmitted to growth; and (ii) it makes use of the Oaxaca-Blinder decomposition to evaluate

the contribution of aggregate aid in explaining differences observed in terms of growth

between stable countries and countries in post-conflict situations.

3.1. Direct and indirect affects of aid on growth in sub-Saharan Africa

The basic specification is the following:

(1)

In equation (1), represents the growth rate of country for period t;

represents ODA, as a percentage of GDP, received by country i during period t; X is a group

of control variables. The main variable of interest is ODA. The matrix of control variables

includes education measured by the primary school enrollment rate, gross fixed capital

formation, inflation captured by the change in the consumer price index, openness to trade

measured by total imports and exports divided by GDP, foreign direct investment, petroleum

revenue as a percentage of GDP and governance.3 The data relating to these variables are

from the World Bank (2011). The analysis is conducted on a sample of 34 SSA countries over

the period 1990-2010. The descriptive statistics for the variables used are presented in Table

1.

[Table 1 here]

Equation (1) may be estimated by the ordinary least squares (OLS) technique. However, the

principal limitation of the OLS technique is that it leads to biased estimators if the variable

measuring aid is correlated to unobservable components that could affect growth. For

example, if a country receives an increasing amount of aid in response to a decline in growth

3 We calulate a composite index of governance using principal component analysis (PCA) based on six

aggregate indicators of governance. The indicator is then standardized using the following formula:

. The results of the PCA are presented in Table 6.

9

and regression of the principal social indicators, the beneficial effect of aid could be

underestimated (Rajan and Subramanian, 2005). Another potential source of bias relates to the

measurement error for aid. In effect, from the time the aid data are recorded by the donors,

any measurement error is susceptible of being correlated to the characteristics of the recipient

country. This would imply that any beneficial effect of aid would be underestimated (Tarp et

al., 2011). Thus the OLS estimator would be biased towards zero. In order to address the

endogeneity bias, the instrumental variables technique is applied. Two instruments are used,

following the instrumentation procedure proposed by Tavarez (2003) and recently revisited by

Brun et al. (2006), Chauvet et al. (2008) and Ebeke and Drabo (2011): total grants weighted

by the inverse of the distance between the donor country and the aid recipient country and the

donor country’s conventional deficit weighted by the inverse of the distance between the

donor country and the recipient country. The concept underlying this procedure is that the

amount of aid received by a given country from one of its principal donors is highly

dependent on geographic and cultural proximity between donor country and recipient country

(Ebeke and Drabo, 2011). In order to ensure that the instruments utilized are not weak, we use

the Stock and Yogo (2005) weak instrument test. The results are reported in each table of

results.

It is difficult to formulate an initial hypothesis as to the effect of aid on economic growth. The

findings in the literature are mixed. While Gomanee et al. (2005), Tarp et al. (2011) observe a

positive and significant effect of aid on growth, Rajan and Subramanian (2008; 2011)

question such an effect. Apart from this controversy, which is difficult to resolve in light of

the diverse methods used and specific country characteristics (Williamson, 2009), the

contribution of this article also lies in identifying the channels of transmission of aid to

growth. To this end, we rely on the work of Karlson et al. (2010). The indirect effect of aid is

measured as the difference between the total effect and the direct effect of aid. The direct

effect is that obtained by controlling for the broad range of variables that could explain

growth. The total effect, in turn, is obtained through a simple regression of growth on aid.

3.2. Contribution of aid to explaining the differences between stable countries and

countries in post-conflict situations in terms of growth

This subsection is based on the postulate that aid could be more effective in countries in post-

conflict situations (Collier and Hoeffler, 2002; Collier et al., 2010). The reasoning

10

underpinning this assertion is that during the initial years of peace, the aid absorption capacity

represents twice the levels usually received. Moreover, as noted by Collier et al. (2010), aid

stabilizes the post-conflict environment, thereby increasing the likelihood of success of

projects financed by the aid.

Based on this reasoning, we will investigate whether the differences between stable countries

and post-conflict countries in terms of amounts of aid received contribute to explaining

differences in growth. Recall that countries in post-conflict situations are defined as countries

that have experienced a civil war during the previous two decades and in which pockets of

rebellion persist. For purposes of simplification, we adopt the general presentation of Yun

(2005), i.e., a gross variable that is a linear combination of independent variables such that we

have:

( )cr F V (2)

F is a function that may or may not be linear; cr is growth; V is the K N matrix of

independent variables, one of which is aid. We assume the existence of two groups A (stable

countries) and B (countries in post-conflict situations). The average difference between A and

B is given as follows:

ˆ ˆ ˆ ˆ( ) ( ) ( ) (A B A A B A B A B Bcr cr F V F V F V F V (3)

Where ̂ is the vector of estimated coefficients of equation (2). The first component in

parentheses measures the differences in the observable characteristics (dependent

component), and the second component measures the difference of the coefficients

(independent component).

Following Even and Macpherson (1990; 1993), Yun (2005), the contribution of a variable k to

the explanation of differences in growth is given as follows:

ˆ( )ˆ ˆ( ) ( )

ˆ( )

k k k

A B Ak A B B B

A B A

V VC F V F V

V V

(4)

Where k

gV is the average of observations of variable k in group g : A, B. ˆ k

g is the estimated

coefficient of variable k in group g .

11

4. Results

In this section, we present and discuss the statistical and econometric results.

4.1. Several correlations between aid and growth in sub-Saharan Africa

Before turning to the results of the econometric estimations, we examine the evolution of aid

and growth in SSA from a statistical perspective over the period 1990-2010. We then examine

the econometric relationship between aid and growth.

Figure 1 presents aid as a percentage of GDP (shown in red) compared to per capita GDP

growth (shown in blue) between 1990 and 2010.

The figure shows that the respective trends in economic growth and aid do not appear to be

correlated over the period under review. Indeed, while a marked increase in growth can be

observed between 1995 and 2000, aid appears to move in the opposite direction. At the same

time, we observe a coordinated trend in both variables between 2004 and 2008. Over the

remainder of the period, the trends are fairly erratic. However, this global perspective

conceals certain specific characteristics illustrated by Figure 2.

Figure 2 presents a combined view of the respective correlations between aid and growth over

the entire sample, in stable countries and in post-conflict countries.

12

Figure 1: Overall aid and growth trends (1990-2010)

Source: World Bank raw data (2011)

Figure 2: Correlations between aid and growth in sub-Saharan Africa (1990-2010)

Source: World Bank raw data (2011)

-50

51

01

5

1990 1995 2000 2005 2010

Années

Croissance Aide%PIB

AGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGO

BENBENBENBENBENBENBENBENBENBENBEN

BWABWABWABWABWABWABWABWABWABWABWABFABFABFABFABFABFABFABFABFABFABFA

BDIBDIBDIBDIBDIBDIBDIBDIBDIBDIBDICAFCAFCAFCAFCAFCAFCAFCAFCAFCAFCAF

TCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCD

CODCODCODCODCODCODCODCOD

CIVCIVCIVCIVCIVCIVCIVCIVCIVCIVCIV

ETHETHETHETHETHETHETHETHETHETHETH

GABGABGABGABGABGABGABGABGABGABGAB

GHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHA

GINGINGINGINGINGINGINGINGINGINGIN

GNBGNBGNBGNBGNBGNBGNBGNBGNBGNBGNB LBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBR

MWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWINAMNAMNAMNAMNAMNAMNAMNAMNAMNAMNAM

NERNERNERNERNERNERNERNERNERNERNER

RWARWARWARWARWARWARWARWARWARWARWA STPSTPSTPSTPSTPSTPSTPSTPSTPSTPSTPSLESLESLESLESLESLESLESLESLESLESLE

SDNSDNSDNSDNSDNSDNSDNSDNSDNSDNSDN

SWZSWZSWZSWZSWZSWZSWZSWZSWZSWZSWZ

TZATZATZATZATZATZATZATZATZATZATZA

TGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGO

ZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMB

ZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWE

-50

51

0

Cro

issan

ce

0 10 20 30Aide%Pib

Aide et croissance

BeninBeninBeninBeninBeninBeninBeninBeninBeninBeninBenin

BotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBotswanaBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina FasoBurkina Faso

GabonGabonGabonGabonGabonGabonGabonGabonGabonGabonGabon

GhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhanaGhana

GuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaGuineaMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawiMalawi

NamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibiaNamibia

NigerNigerNigerNigerNigerNigerNigerNigerNigerNigerNiger

Sao Tome and PrincipeSao Tome and PrincipeSao Tome and PrincipeSao Tome and PrincipeSao Tome and PrincipeSao Tome and PrincipeSao Tome and PrincipeSao Tome and PrincipeSao Tome and PrincipeSao Tome and PrincipeSao Tome and Principe

SwazilandSwazilandSwazilandSwazilandSwazilandSwazilandSwazilandSwazilandSwazilandSwazilandSwaziland

TanzaniaTanzaniaTanzaniaTanzaniaTanzaniaTanzaniaTanzaniaTanzaniaTanzaniaTanzaniaTanzania

TogoTogoTogoTogoTogoTogoTogoTogoTogoTogoTogo

ZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambiaZambia

ZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabweZimbabwe-4-2

02

4

Cro

issan

ce

0 5 10 15 20

Aide%Pib

Aide et croissance, pays stables

AngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngolaAngola

BurundiBurundiBurundiBurundiBurundiBurundiBurundiBurundiBurundiBurundiBurundiCentral African RepublicCentral African RepublicCentral African RepublicCentral African RepublicCentral African RepublicCentral African RepublicCentral African RepublicCentral African RepublicCentral African RepublicCentral African RepublicCentral African Republic

ChadChadChadChadChadChadChadChadChadChadChad

Cote d'IvoireCote d'IvoireCote d'IvoireCote d'IvoireCote d'IvoireCote d'IvoireCote d'IvoireCote d'IvoireCote d'IvoireCote d'IvoireCote d'Ivoire

EthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopiaEthiopia

Guinea-BissauGuinea-BissauGuinea-BissauGuinea-BissauGuinea-BissauGuinea-BissauGuinea-BissauGuinea-BissauGuinea-BissauGuinea-BissauGuinea-BissauLiberiaLiberiaLiberiaLiberiaLiberiaLiberiaLiberiaLiberiaLiberiaLiberiaLiberia

RwandaRwandaRwandaRwandaRwandaRwandaRwandaRwandaRwandaRwandaRwanda

Sierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra LeoneSierra Leone

SudanSudanSudanSudanSudanSudanSudanSudanSudanSudanSudan

-20

24

68

Cro

issan

ce

0 10 20 30Aide%Pib

Aide et croissance, pays en post conflit

13

The first graph in the upper left corner shows almost no correlation between aid and growth

over the entire sample. It also shows a country like Liberia receiving a very large amount of

aid (close to 30% of GDP) but with growth below the average of the distribution. In contrast,

a country like Angloa receives very little aid but has fairly high growth.

The second and third graphs distinguish between stable countries and countries in post-

conflict situations. The observation that can be made is that in stable countries, a positive

correlation appears to exist between aid and growth, while the opposite appears to hold true in

post-conflict countries. We also observe that in the sample of stable countries, the country in

which aid was most effective is São Tomé and Príncipe.

It should be noted, however, the comparison must be put in perspective insofar the ratio of aid

to GDP depends not only on the amount of aid but also on the level of GDP. Thus it is

possible that a high ratio can be explained solely by low GDP.4

4.2. Aid and growth in sub-Saharan Africa: econometric results

The main objective of this subsection is to present and discuss the results of the econometric

estimation. Specifically, we will discuss the direct and indirect effects of aid on growth.

4.2.1. Is there a direct effect of aid on growth in sub-Saharan African countries?

Table 2 presents the results of the estimation by double least squares of the effect of aid on

growth. Columns (1) and (2) of the table present the estimation results without considering

governance. They show that aid has a negative effect on economic growth. Specifically, a

one-point increase in aid produces a 0.26-point decline in growth (see column 1).5 According

to Rajan and Subramanian (2011), this counterintuitive result can be explained by the fact that

inflows of aid could lead to an appreciation of the real exchange rate and reduced

competitiveness of the tradables sector. Columns (3) through (8) present the results obtained

by introducing the index of governance and various components of governance in the model.

Those columns show that the addition of governance, regardless of the dimension, produces a

change in the sign of the aid coefficient. In other words, a one-point increase in aid produces

an effect of between 0.99 and 0.73 points on growth.

4 The same comment applies to the other graphs.

5 Column (2) includes democracy and taxes in the model to take account of a crowd-out or plausible lever effect.

14

[Table 2 here]

To illustrate the significance of the change in sign, Figure 3 presents the distribution of

growth variables, aid and governance among stable countries and countries in post-conflict

situations. Three factors may be observed. First, the countries in post-conflict situations

receive more aid than stable countries. Secondly, those countries have lower indices of

governance. Third, the countries in post-conflict situations have lower economic growth.

These three facts suggest that aid is less effective in countries in post-conflict situations where

good governance is not in place. Yet as soon as we take governance into account in the

estimation, the overall effect of aid becomes positive. In addition, the instability of results

observed in theory (Rajan and Subramanian, 2005) can be explained by the fact that the

relationship between aid and growth not only differs depending on whether the countries in

question are stable or in post-conflict situations but also depends on governance.

Figure 3: Distribution of aid, governance and growth between stable countries and countries

in post-conflict situations.

Source: World Bank raw data (2011)

To refine the analysis, a comparison is made between countries in post-conflict situations and

stable countries. As indicated previously, this choice is justified by the idea that aid may be

02

46

81

0

Aid

e %

Pib

Stable Post conflit

Distribution de l'aide, pays stables/pays post conflit

0.2

.4.6

Ind

ice

de g

ouvern

ance

Stable Post conflit

distribution de la gouvernance, pays stables/ pays post conflit

0.5

11

.5

Cro

issan

ce

Stable Post conflit

Distribution de la croissance, pays stables/pays post conflit

15

more effective in post-conflict countries. Table 3 presents the results of the Oaxaca-Blinder

decomposition.

On the contrary, the differences relating to governance appear to be the principal explanation

for the differences in terms of growth observed between stable countries and countries in

post-conflict situations over the period under review.

The very first comment that can be made at this juncture is that the differences in terms of the

amount of aid received do not explain the differences in growth observed between stable

countries and countries in post-conflict situations.6

[Table 3 here]

4.2.2. Is there an indirect effect of aid on growth in sub-Saharan African countries?

Table 4 presents the results of estimation of the indirect effects of aid on growth. In reference

to the existing literature (Gomanee et al., 2005, Tarp et al., 2011), we test several essential

channels of transmission: education, investment in public capital, tax revenue, governance

and democracy. The results show that in countries in post-conflict situations, investment in

public capital seems to be the valid channel through which aid is transmitted to growth. This

result is consistent with the findings of Arndt et al. (2011) over a larger, heterogeneous

sample. For stable countries, education and governance are established as the channels par

excellence by which aid is transmitted to growth.

[Table 4 here]

5. Conclusion

The question of the effectiveness of ODA is part of an ongoing debate spreading through

economic, political and social circles. Notwithstanding the profusion of work on the subject,

the controversy remains open and unresolved.

6 For purposes of interpretation, note that the column of interest is “Characteristics,” which reflects the

differences due to characteristics (column 2).

16

This article supplements the existing literature by providing evidence of the effectiveness of

aid through the use of a sample of 34 SSA countries over the period 1990-2010. Its principal

contribution lies first in illuminating the direct and indirect effects of aid on growth within a

unique methodological framework. Secondly, the article uses the Oaxaca-Blinder

decomposition approach, which is rarely used in macroeconomics, to analyze the contribution

of aid to explaining differences in growth between stable countries and countries in post-

conflict situations.

The results observed suggest that the effect of aid on growth varies depending on whether

governance is incorporated in the model. If governance is not considered, the effect of aid on

growth is negative. This result can be explained by the appreciation of the real exchange rate,

which results in a loss of competitiveness and growth. On the other hand, if governance is

taken into consideration, regardless of the indicator, the effect of aid becomes positive. In that

case, a one-point increase in aid results in an increase of between 0.73 and 0.99 points in

growth. At the same time, the results obtained show that the channels of transmission of aid to

growth vary depending on whether the country concerned is stable or in a post-conflict

situation. Specifically, in the case of a stable country, aid is transmitted to growth through the

channels of education and governance. In a post-conflict country, by contrast, the sole channel

through which aid is transmitted to growth is investment in public infrastructures. Finally, the

analyses show that the difference in terms of amounts of aid received does not explain the

differences in growth observed between stable countries and countries in post-conflict

situations. Rather, the distinctive element appears to be governance. In other words, improved

governance in countries in post-conflict situations could serve to reduce the gaps in growth

observed between those countries and stable countries.

Based on those results, an economic policy recommendation could be to direct the aid

allocations to financing education and improved governance in stable countries. In countries

emerging from conflicts, on the other hand, aid should be required to be used to finance

infrastructures.

In this regard, future studies could analyze the respective effects of aid to education, aid to

infrastructures and aid to governance on growth.

17

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20

ANNEXES

Table 1: Descriptive statistics

Variable Obs Average

Standard deviation

Min Max

Growth 307 1.605596 4.723405 -29.48342 29.10352

Aid % of GDP 307 6.528935 7.435935 -.4584045 88.3

Primary school enrollment rate 307 86.56725 26.11164 27.84674 155.7863

Gross fixed capital formation 307 18.432 7.121535 2.062013 59.72307

Inflation CPI 307 35.68957 244.436 -100 4145.107

Openness to trade 307 70.18488 36.16919 17.85861 209.4103

Work force 307 53.26655 3.554967 48.55053 69.1262

Oil revenue % GDP 307 5.776258 15.5517 0 72.48502

Foreign direct investment 307 3.263606 5.86656 -8.589392 46.48782

Governance index 307 .4802915 0.1582793 0.0836404 .8897291

Conflict 307 .3485342 0.4772841 0 1

21

Table 2: Direct effects of aid on growth

[1] [2] [3] [4] [5] [6] [7] [8]

Dependent variable Growth Growth Growth Growth Growth Growth Growth Growth

Aid % of GDP -0.268* -0.485*** 0.997** 0.960* 0.761** 0.790** 0.788** 0.733**

(0.134) (0.154) (0.472) (0.472) (0.335) (0.340) (0.339) (0.349)

Primary school enrollment rate 0.0505** 0.0287 0.0186 0.0174 0.0195 0.0231 0.0240 0.0209

(0.0242) (0.0367) (0.0237) (0.0245) (0.0216) (0.0220) (0.0224) (0.0200)

Gross fixed capital formation 0.114 0.249** 0.0185 0.0227 0.0321 0.0410 0.0331 0.0299

(0.0901) (0.116) (0.105) (0.112) (0.0810) (0.0747) (0.0791) (0.0835)

Inflation CPI -0.000340*** -0.000404*** 0.00130 0.00124 0.000868 0.000801 0.000854 0.001000

(7.90e-05) (9.46e-05) (0.00103) (0.00135) (0.00102) (0.000889) (0.000941) (0.000915)

Openness to trade -0.00637 0.0459 -0.00151 0.00104 0.0224 0.0165 0.0146 0.0194

(0.0348) (0.0470) (0.0645) (0.0634) (0.0440) (0.0467) (0.0487) (0.0413)

Work force 0.0814 -0.0463 -0.848 -0.796 -0.713 -0.754 -0.766 -0.754

(0.276) (0.357) (0.691) (0.682) (0.491) (0.511) (0.521) (0.461)

Oil revenue % GDP 0.290*** 0.0530 0.258*** 0.254*** 0.276*** 0.277*** 0.273*** 0.280***

(0.0555) (0.107) (0.0854) (0.0853) (0.0764) (0.0778) (0.0746) (0.0763)

Foreign direct investment 0.0221 0.305 -0.0401 -0.0419 -0.0233 -0.0256 -0.0190 -0.00531

(0.0505) (0.242) (0.139) (0.142) (0.128) (0.129) (0.128) (0.119)

Tax revenue

-0.0923

(0.105)

Democracy index

-0.0204

(0.0152)

Governance index

-5.261

(13.39)

Control of corruption

-6.129

(8.842)

Rule of law (transparency)

1.475

22

(6.989)

Effectiveness of governance

-3.303

(6.914)

Quality of regulation

-2.928

(7.936)

Political stability

3.061

(5.370)

Observations 510 347 307 307 321 321 321 321

Number of countries 34 23 34 34 34 34 34 34

Number of instruments 2 2 2 2 2 2 2 2

F-stat instrument 17.71 17.71 3.95 3.42 5.39 5.47 5.52 4.72

Hansen OID test-p-value 0.74 0.49 0.54 0.58 0.51 0.39 0.63 0.42

Robust standard deviations between parentheses. *** p<0.01, ** p<0.05, * p<0.1. The estimation method used is the double least squares method

Double least squares with fixed effect.

23

Table 3:Contribution of aid to differences in growth between post-conflict countries and stable countries

(1) (2) (3) (4)

VARIABLE Total Characteristics Coefficients Interaction

Aid % of GDP

0.0225 1.051* -0.237

(0.107) (0.637) (0.278)

Primary school enrollment rate

0.0358 -0.841 -0.0945

(0.144) (1.409) (0.178)

Gross fixed capital formation

0.610 -1.224 -0.232

(0.389) (1.345) (0.285)

Inflation CPI

0.0156 -0.00991 -0.00922

(0.0483) (0.0401) (0.0413)

Openness to trade

-0.531 3.605** 0.547

(0.727) (1.760) (0.755)

Work force

-0.484 14.37 0.485

(0.629) (17.32) (0.661)

Oil revenue % GDP

-1.544 -2.370* 1.927

(1.089) (1.327) (1.308)

Foreign direct investment

0.161 0.476 -0.200

(0.209) (0.587) (0.271)

Governance index

3.238*** -2.404 -1.547

(1.167) (2.218) (1.446)

Stable countries 1.820***

(0.394)

Countries in post-conflict situations 2.258**

(0.959)

Difference -0.439

(1.037)

Characteristics 1.523

(1.195)

Coefficients -2.601**

(1.314)

Interaction 0.640

(1.409)

Constant

-15.25

(16.13)

Observations 393 393 393 393

Standard deviations in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

24

Table 4: Indirect effects of aid, comparison of post-conflict and stable countries

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Conflict Stable Conflict Stable Conflict Stable Conflict Stable Conflict Stable Conflict Stable

Dependent variable Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth Growth

Total effect -0.0870 -0.151*** -0.0758 -0.182*** -0.128** -0.0582 0.0163 0.105 0.0357 0.0721 -0.0984* -0.157***

(0.0587) (0.0543) (0.0575) (0.0563) (0.0545) (0.0986) (0.0566) (0.0841) (0.0592) (0.0839) (0.0572) (0.0547)

Direct effect -0.102* -0.121** -0.110* -0.182*** -0.125** -0.0913 0.0238 0.0494 0.0435 0.0722 -0.102* -0.161***

(0.0577) (0.0565) (0.0566) (0.0563) (0.0545) (0.0991) (0.0563) (0.0835) (0.0587) (0.0842) (0.0572) (0.0545)

Indirect effects 0.0146 -0.0307** 0.0342** -0.000186 -0.00343 0.0331 -0.00755 0.0553* -0.00784 -0.000114 0.00364 0.00356

(0.0136) (0.0144) (0.0165) (0.00264) (0.0118) (0.0207) (0.0319) (0.0305) (0.0147) (0.00388) (0.00921) (0.00392)

Transmission channel Education Education Investment Investment Tax revenue Tax revenue Governance Governance Corruption Corruption Democracy Democracy

Observations 225 427 225 397 155 295 140 274 140 274 225 427

25

Table 5: List of countries

Post-conflict countries

Stable countries

Angola Benin Mauritius

Burundi Botswana Mozambique

Central African Republic Burkina Faso Namibia

Chad Cameroon Niger

Congo, Dem. Rep. Gabon Senegal

Congo, Rep. Gambia Swaziland

Cote d’Ivoire Ghana Tanzania

Ethiopia Kenya Togo

Guinea Bissau Madagascar Zambia

Rwanda Malawi Zimbabwe

Sudan Mali Uganda Mauritania

Table 6: Composite governance index

Dimensions of governance Index of quality of governance

Control of corruption 0.4217

(0.89)

Rule of law 0.4367

(0.9295)

Quality of regulation 0.4055

(0.86)

Effectiveness of governance 0.4284

(0.90)

Political stability 0.3672

(0.79)

Transparency and Voice 0.3856

(0.84)

Note: The correlation between each dimension of governance and the composite index is indicated in

parentheses. Index=0.42*Corruption+0.43*Rule of law +0.40*Effectiveness of governance

+0.36*Political stability +0.38*Transparency

26

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178 2013

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Duru

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What should Africa Do?

177 2013 Daniel Zerfu Gurara A macroeconomic model for Rwanda

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173 2013 Kjell Hausken and Mthuli Ncube Production and Conflict in Risky Elections

172 2013 Kjell Hausken and Mthuli Ncube Political Economy of Service Delivery: Monitoring versus

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171 2013 Therese F. Azeng & Thierry U. Yogo

Youth Unemployment and Political Instability in Selected

Developing Countries

170 2013 Alli D. Mukasa, Emelly Mutambatsere,

Yannis Arvanitis, and Thouraya Triki Development of Wind Energy in Africa

169 2013 Mthuli Ncube and Eliphas Ndou Monetary Policy and Exchange Rate Shocks on South African Trade

Balance