ISSN 1347-9156

JBICIWorking Paper

Complementarities between
Grants and Loans
Atsushi IIMI, Yasuhisa OJIMA

NO. 20

May 2005
JBIC Institute (JBICI)ISSN 1347-9156

The JBICI Working Papers are based on the research done by staffs ofJapan Bank for International Cooperation (JBIC) and published by the
JBIC Institute. The views expressed in this paper are those of theauthor and do not necessarily represent the official position of JBIC.

0Complementarities between Grants and Loans(May 2005)
Atsushi IIMI*†, Yasuhisa OJIMA***

Japan Bank for International Cooperation (JBIC) and International Monetary Fund
700 19th Street N.W., Washington D.C. 20431
Tel: (202) 623-6931; Fax: (202) 623-5322; E-mail: aiimi@imf.org**

Japan Bank for International Cooperation (JBIC)
1-4-1 Ohtemachi Chiyoda-ku, Tokyo 100-8144 Japan
Tel: +81-3-5218-3145; Fax: +81-3-5218-3969; E-mail: y-ojima@jbic.go.jp

Abstract
A debate has emerged over the optimal delivery of official development assistance (ODA)to developing countries: through grants or through concessional loans. The argument
remains inconclusive theoretically as well as empirically. This paper presents an
endogenous growth model with a good governance factor, in which public spending isfinanced through grants and loans and the donor community is able to influence a recipient
country’s growth path by choosing a combination of these two aid instruments. The model
indicates that in terms of enhancing growth, grants and loans are complementary.
Empirically, it is found that while an increase in the concessionality attached to ODA loans
can facilitate recipient countries’ economic development, grants may not be useful instimulating growth. It is also found that grants and loans are in fact complementary with an
optimal grant-loan ratio of 1 to 0.4.

Keywords: foreign aid, economic development, concessional loans, grant element, crosscountry IV regression

JEL classification: C11, F35, O11†

Corresponding author.

11. Introduction
A debate has emerged concerning the utility of grants and loans in the delivery of official
development assistance (ODA), but the relative value of each delivery instrument is stillinconclusive in both theoretical and empirical contexts. Theoretically, the response of recipient
countries to foreign aid as well as hold-up problems faced by donors may be the key to determining
whether grants or concessional loans are more conducive to economic growth. Empirical study on
this topic is lacking and the research conducted tends to be affected by specifications and the scopeof the data. As such, this paper constructs an analytical model relating foreign aid to growth,through which donors can maximize a recipient country’s economic growth by selecting a specificcombination of grants and loans from a dynamic perspective. In addition, several of the analyticalmodel’s predicted implications are tested empirically by performing instrumental variable (IV)regressions.
Globally, 40-50 billion U.S. dollars of bilateral and multilateral official resources, including
grants, technical assistance and loans flow into developing countries every year (Figure 1). These
official resource inflows, though surpassed by private sector inflows such as foreign direct
investment, continue to be crucial sources of external financing for developing countries, typically
being used to finance economically and socially essential public spending. The benefit of theseofficial resource inflows cannot be underestimated, even if the amount of such aid has continued tolevel off over the past decade.
As emphasized by Klein and Harford (2005), an important policy question that is attracting
increased attention in the international donor community is how to deliver large amounts of aid todeveloping countries most effectively. In particular, whether it makes any difference to provide
donor funding through pure grants or through concessional loans, in cases where the grantequivalence is the same, is a question that must be addressed as there seems to be different
perspectives among aid-providing countries. Figure 2 shows the breakdown in the provision of
grants and official loans (including other long-term capital) for OECD member countries from 1999
to 2003.1 While some countries such as Japan and Spain tend to provide aid in the form ofconcessional loans, other countries such as Netherlands and Sweden put more emphasis on theprovision of grants.

1

Data are on a commitment basis. For analytical purposes, the other forms of foreign aid are ignored.

2

Theoretically, there would be no difference in the effectiveness of grants and loans if capital
markets were perfect since recipient governments can always borrow additional funds withreasonable conditions on the market if the grant equivalence of donors’ funding is not enough tofinance a development project. In reality, however, capital markets are never perfect and mostdeveloping countries have little access to international capital markets. According to Moody’s
ratings list, two-thirds of the bonds listed by low and medium-income countries’ governments are
rated “Ba” or below, reflecting the speculative element of these bonds.
Capital market imperfection fundamentally differentiates grants from concessional loans,
thereby creating different economic growth implications for each of the two aid instruments. First,
unlike grants, ODA loans, amplifying a small amount of grant equivalence through preferable
lending conditions such as low interest rates and long repayment and grace periods, can financelarge-scale development projects, requiring a large initial investment. A typical example is a
network infrastructure project such as electricity and water distribution utilities. This idea of loans
being a more aid-effective instrument may be supported by Murphy et al.’s (2000) big push theory.
Grants, on the other hand, may support relatively small-scale projects such as the provision of
medical equipment to hospitals and the establishment of primary schools. This difference can beinterpreted to mean that as long as there is a wide spectrum of development projects in terms ofscale and scope, there is a need for both loans and grants in development assistance. In that sense,
the focus should be grants and loans rather than grants versus loans. Thus, the analytical model andempirical work presented in this paper cast a light on the degree of complementarities between
grants and loans.
Second, ODA loans must of necessity involve a long-term relationship between recipient
governments and donors, thereby creating an incentive issue for recipients. While it may bepresumed that grants merely finance domestic tax reductions, loans tend to motivate recipient
governments to increase their revenues and strengthen budgetary discipline. This perspective isrelated closely to the foreign aid fungibility argument (e.g., Devarajan and Swaroop, 1998). If
grants are more fungible than loans and a recipient’s public expenditure management is poor, suchgrant assistance may not be cost effective. Gupta et al. (2003), in addressing the effect of aid
decomposition from this fungibility viewpoint, find that concessional loans are associated withhigher domestic revenue mobilization but that grants are completely offset by a decline in revenue.
This implies that loans are more effective in terms of a recipient governments’ fiscal response.
Finally, the long-term recipient-donor relationship inherent in ODA loans may induce a moral
hazard problem for the former and a hold-up problem for the latter. Once loan assistance is3committed, the donor has little effective external leverage to ensure that loan repayments are madein the future (Bulow and Rogoff, 2005). In fact, recipient countries can always default on the loan’s
repayment. Obviously, on that basis, grants are superior to loans for avoiding future default risks.
Cordella and Ulku (2004), in modeling a recipient country’s efforts and incentive to make its
foreign debt repayments, show that the impact of concessionality on growth is negative in countries
with good policies and positive in those with bad policies. This implies that loan assistance is moresuitable for developing countries with sound policies in place, for the underlying reason thatrecipient countries with better policies are able to achieve structural adjustment at lower costs.
Defaults, thus, are less attractive to them.
Unfortunately, however, Cordella and Ulku’s model is very static, and not necessarilystraightforward enough to apply in empirical research. In addition, Cordella and Ulku interpret thedifference between grants and loans as one of differing degrees of concessionality. Therefore,
grants and loans are placed on a continuum of aid concessionality, also referred to as the grantelement. If a loan is attached to 100 percent concessionality, it should be equivalent to grants.
Nevertheless, as shown in Figure 3, even though it is theoretically true, the degree ofconcessionality does not appear continuous, particularly for a grant element of over 80 percent.
Figure 3 depicts the frequency of the average grant element among developing countries for the past
decade. No country received official loans with more than an 80 percent grant element on average.
This is a natural consequence of the practical differentials between grants and loans, as mentioned
above.
This paper considers a more dynamic analytical model based on the endogenous growth
literature, in which grants and loans are defined as completely different development assistanceinstruments. A degree of concessionality attached to loans is also incorporated in this model.
Moreover, the model takes into account some elements of recipient governments’ policy aspects, ormore precisely corruption.
This reference to corruption follows the recent development in the literature that examines therelationship among policies, aid and growth. One of the pioneer studies in this area is Burnside andDollar (2000), which has influenced the international donor community to a large extent. It has also
provoked some academic criticism in terms of their estimation method (e.g., Easterly et al., 2004).2
In examining the growth impact of policies on developing counties with a panel of 56 countries
2

Easterly et al. (2004), using the same specification as Burnside and Dollar (2000), but simply adding moredata through 1997, show that the significance of the key interactive variable coefficient between aid and good
policy is lost. This implies that regardless of public policies, foreign aid is not effective in stimulating
economic development.

4

from 1970 to 1993, Burnside and Dollar find that aid has a positive impact on growth when good
policies exist. Following this, Sawada et al. (2004) also investigate aid and policy effects on growthby decomposing aid inflows into grants and concessional loans. Their finding that loans areassociated with higher growth rates in countries with good policies is interesting and consistent withthe above-mentioned study by Gupta et al. (2003).
Nevertheless, as Easterly (2003) notes, the lack of clear theoretical models relating aid andpolicies to growth hampers solid empirical analysis, and empirical definitions of aid, good policies
and growth remain open to debate. The model presented in this paper clearly indicates that
economic growth is affected by the recipient government’s consumption, including corrupt
spending, but not directly affected by corruption per se. Moreover, donors can influence the growth
path by selecting a certain assistance package combining both grants and loans. Importantly, thesubsequent empirical work maintains a direct and consistent connection with the theoretical
implications of how foreign aid is related to recipient countries’ policies and economic growth.
The following sections are organized as follows: In Section 2, an analytical framework based on
the endogenous growth model with foreign aid is presented, and its implications are examined. In
Section 3, data and econometric issues are described. Finally, in Section 4, the estimation results
are presented and several policy implications are discussed.

2. Analytical framework
The model is based on a simple endogenous model with exogenous foreign aid and a factor of
public sector corruption included. Suppose that a government that receives official grants and loans
maximizes the following utility function:∞⎧⎪⎡ c g 1−φb1−φ ⎤ ⎫⎪ − ρt+ (1 − λ ) ⎢ µ+ (1 − µ )⎥ ⎬e dt
0
1 − φ ⎥⎦ ⎪⎢⎣ 1 − φ⎪⎩ 1 − σ
⎭

∫ ⎨λ

cp

1−σ

(1)

under the conditions:k& = (1 − τ ) y − c p − c g − b − nk(2)

y = kα g βlγ(3)τy = (1 − θ )l .

(4)5

c p and c g are private and government consumption per capita, respectively. b is public corrupt
spending per capita. ρ is a time discount factor, and σ and φ determine the elasticities ofsubstitution between consumption at any two points in time, which are constant and equal to

1

φ

1

σ

and

for private and public consumption, respectively. Since it is empirically well known that theinter-temporal elasticity of private consumption is very low, it may be reasonable to assume that σ
is large enough so that σ > φ .
The utility function shown in Equation (1) implies that this government is also a representative
consumer as well. The weight of private and public consumption is determined by a parameter
denoted as λ , which may reflect a degree of political preferences for governmental activities,
including citizens’ ideological orientation toward a small government. If λ is unity, the economy
is purely private-sector driven, not leaving any ordinary activities for the government. On thecontrary, if λ is zero, the government completely crowds out private consumption like that in asocialist economy. In other words, ( 1 − λ ) can be interpreted as the extent of governmental
distortions of markets and private business decisions. µ represents a degree of good governance on
the part of the government. Given a certain level of government size associated with λ , if µ is
equal to unity, there is no corruption; the government is absolutely efficient. On the contrary, if µ
is zero, government spending is totally unproductive and wasteful, perhaps due to extra
remuneration stemming from inefficient and dawdling public work and the additional cost of public
procurement caused by corruption.
As usual, y denotes GDP per capita, and n is the population growth rate. k, g, and l are private
capital, public investment associated with grant assistance, and public spending financed byconcessional loans. Equation (3) implies that presumably grants and loans are both productive.
ODA loans are characterized by two parameters: gross loan amount, l , and concessionality, θ , anda grant equivalent of the loans can be written by θl . θ indicates a degree of concessionality
attached to the loans, which is defined between zero and one, and the government has to repay afraction of loans ( 1 − θ ) by domestic taxation, of which the average rate is denoted by τ . The
fraction depends on the granted concessionality. If θ is zero, the recipient country must repay infull. On the other hand, if θ is large enough, the government’s repayment would be ignorable. It is6noteworthy, however, that the model clearly differentiates grants from very concessional loans atleast on the expenditure side, even though the concessionality is close to unity.
For simplicity, in the above model, interest payments associated with foreign loans are ignored,
and government spending financed by domestic tax revenue is not taken into account.
Then, a steady state of growth rate is given by:
1−α
⎡
⎤
β
⎛ α ⎞⎛ τ ⎞ αy& ⎛ s p 1 − s p ⎞ ⎢⎟⎟ (1 − τ )⎜⎜⎟⎟⎜= ⎜⎜ +⎟ ( gl ) α − n − ρ ⎥
⎥
φ ⎠⎢
y ⎝σ
⎝ 1 − γ ⎠⎝ 1 − θ ⎠⎢⎣⎥⎦where s p is the private consumption share to total consumption, that is s p =(5)cpc p + cg + b. Thisgrowth equation indicates that economic growth is essentially a function of the average tax rate,
grants and loans received from the donor community, concessionality attached to the loans,
population growth, and the share of private consumption. A variety of implications of therelationship between aid, policies and growth can be derived from this equation.
Firstly, economic development is affected by the average tax rate ( τ ). If the tax rate is highenough, additional taxation slows growth. From Equation (5), more precisely, if τ > 1 − α , then

∂ y& y< 0 . This is the same effect as expected in a simple endogenous growth model where a∂τpermanent increase in government taxes and purchases reduces economic growth (e.g., Romer,
1996).
Secondly, Equation (5) implies that official grants and loans are both conducive to growth, thatis

∂ y& y∂ y& y> 0 and> 0 . Moreover, an increase in the degree of concessionality also results in∂g∂lhigher growth since∂ y& y> 0.
∂θFinally, it is of particular interest that economic growth does not depend on the degree of therecipient’s good governance ( µ ) or corruption ( 1 − µ ) per se, but does rely on the share of private
consumption to total domestic consumption ( s p ). This might explain the reason for the lack of adirect relationship between policies and growth in the existing literature (e.g., Easterly et al., 2004).
By Equation (5), it can be shown that if σ > φ , an increase in private consumption would impedeeconomic growth, that is∂ y& y< 0 . The interpretation of this is very straightforward; the increased∂s p7

private-sector consumption reduces the amount of national resources that could otherwise beinvested in capital to achieve further future growth, no matter if government consumption isjustifiable or wasteful.
Now, given a steady state of growth in Equation (5), the development assistance strategy of thedonor community is investigated. Suppose that the donor community maximizes the growth rate bychoosing a package of grants and loans, given a fixed total amount of money to be used for foreign
aid denoted by A .3 That is:
1−α
⎡
⎤
β
⎛ s p 1 − s p ⎞⎢⎛ α ⎞⎛ τ ⎞ α⎟⎟ (1 − τ )⎜⎜⎟⎟⎜max g ,l ,θ ⎜⎜ +⎟ ( gl )α − n − ρ ⎥
⎢
⎥
−
−
σ
φ
γ
θ
1
1
⎝
⎠
⎝
⎠
⎝
⎠⎢
⎣
⎦⎥

(6)

subject tog + θl ≤ A .

(7)

Equation (7) merely means that the sum of grants and a grant equivalence of loans is less than the
total available resources of the donor community.
Then, the optimal amounts of grants and gross loans are g * =A(1 − α + β ) A
and l * =,22βrespectively. Equivalently, the optimal combination can be written as:l* =(1 − α + β )

β

g* .

(8)

Proposition: If the donor community maximizes the recipient country’s economic growth, grants⎛1−α + β ⎞⎟⎟ -ratio as the aid resources increase.
and gross loans must increase at a one- ⎜⎜β⎝⎠Apparently, the proposition implies that grants and concessional loans are complementary in orderto achieve optimal growth. From a policy viewpoint, importantly, it can be interpreted to mean that3

One may think that the foreign aid objective is not necessarily to maximize the recipient countries’
economic growth. As shown by Alesina and Dollar (2000), there may be significant differences in the
behavior of different donors. Foreign aid flows tend to be determined by national security problems,international politics, and historical relationships with developing countries. Nevertheless, there is broad
agreement within the donor community that the primary objective of foreign aid is to assist developing
countries’ economic growth.

8there is the need for coordination among multilateral and bilateral donors to satisfying this condition
as a whole.
To investigate empirically the relationship among grants, loans, good governance and growth aspredicted above, two linear regression models are considered. The first is a growth equation
associated with Equation (5):y&= κ 0 + κ1 g + κ 2l + κ 3θ + κ 4τ + κ 5 s p + κ 6 n + X 'κ 7 + ε 1y(9)where X is a set of exogenous variables to control for heterogeneity across countries. The second isa grant-loan equation derived from Equation (8):l = π 0 + π 1 g + X 'π 2 + ε 2 .

(10)

It is noteworthy that no interaction term between grants and loans is included in Equation (9) by
way of a theoretical prediction, and their complementarities must be captured only by the grant-loanequation.

3. Data and econometric issues
The current analysis uses data from 61 countries, for which the latest macroeconomic statistics
including fiscal data is available. A list of sample countries is shown in Table 1. It includes 24 low
income countries, 24 lower-middle income countries, and 13 upper-middle countries.
A critical problem in using cross-country data is the difficultly in quantifying enormous culturaland institutional differences among countries. In order to partly accommodate this problem,
income-group and region-specific fixed effects are incorporated in the empirical models. Obviously,
regardless of foreign aid, it is commonly agreed that Asian developing countries exhibit distinctive
growth performance. Sub-Saharan Africa tends to be in strong contrast to the situation in Asia.
Moreover, while middle income countries may have a relatively low growth rate, low income
countries sometimes attain marked economic growth. Thus, these income-group and regionaldummy variables are useful to controlling for certain heterogeneity across countries.
For the growth equation, the dependent variable is the average growth rate of real GDP per
capita from 1998 to 2002, and for the grant-loan equation, it is the average gross official loans per
capita for the same period. Taking the average values aims to avoid measurement errors due toshort-term economic fluctuations. In the growth equation regression, six endogenous variables are9taken into account. Firstly, grants are defined as the average amount of annual official grants per
capita in U.S. dollar terms for the period. Secondly, loans are similarly defined as the averageamount of annual gross official loans per capita. Taking gross data is consistent with the above
theoretical framework. Thirdly, the concessionality attached to the loans is measured by the period
average of the so-called grant element, which is defined by the weighted average of grant equivalentassociated with public loans during the year. Fourthly, the average tax rate is measured by total tax
revenues divided by GDP in a particular year during the period since the time-series data on
government finance is limited. Fifthly, the share of private consumption comes from the national
accounts data and is defined as a period average from 1998 to 2002. Finally, the average population
growth rate for the period is used for the population growth n.
The selection of exogenous control variables in X follows the standard growth literature, such
as Mankiw et al. (1992), and Barrow and Sala-i-Martin (1995). Although any aggregated variables
are potentially considered to be endogenous in this type of analysis, it is economically reasonable toassume that at least initial economic conditions are exogenous. To control for the differences ininitial state conditions across countries, this paper employs the initial accumulation of humancapital and GDP per capita just before the sampling period. The initial human capital is measured
by the percentage of gross secondary school enrolment to the official school age population in
1997,4 and GDP per capita is in U.S. dollar terms for 1997.5 In fact, according to the Hausman
exogeneity test, the hypothesis that these two variables (i.e., initial human capital and initial GDP)are exogenous cannot be rejected.6
All the macroeconomic data used for this study were taken from the International Monetary
Fund’s International Finance Statistics and Government Finance Statistics and the World Bank’s
World Development Indicators and Global Development Finance.

Table 2 shows the summary statistics. The average growth rate of the sample countries is about
1%. The average grants per capita vary largely, from US$0.68 to over US$150 with a mean of
US$24, and the gross loans per capita also have a significant variation with a mean of about US$13.
The attached grant element is on average 48% with a maximum of 81% and a minimum of 16%.
The mean of the average tax rate is 16%. Population growth is commonly moderate across the4

For several countries, the human capital variable in 1996 is used due to a lack of available data.
The secondary school enrollment ratio is the number of children of official school age (as defined by thenational education system) enrolled in school to the population of the corresponding official school age.

5

In the Hausman test, the χ statistics are estimated at -0.89 for the growth model and -0.22 for the grantloan equation. These negative test statistics can be interpreted as strong evidence that the null hypothesis
cannot be rejected.
6

2

10

sample countries, but several countries have a high rate of population growth measuring more than
3%. The initial conditions in terms of human capital and national income have considerable
variations as well.
The most important econometric issue in estimating Equations (9) and (10) with aggregatedcross-country data is how to deal with biases caused by measurement errors and endogeneity.
Particularly, in the current analysis, while growth is affected by grants and loans, these foreignassistance instruments are simultaneously determined so that they are provided according to themacroeconomic performance and policy efforts of recipient governments. Consequently, theindependent variables may be contemporaneously correlated with the error term. In the following
analysis, the five-year lagged values of the independent variables are used as a set of instrumental
variables, since there is usually no correlation between the disturbance and the lagged values.7 For
the original variables for the period from 1998 to 2002, the lagged equivalents from 1992 to 1996
are used as instruments.

4. Estimation results and policy implications
Both growth and grant-loan equations are estimated by the Ordinary Least Squares (OLS) and
Instrumental Variables (IV) techniques and the results are shown in Table 3. In the growth
regression, the OLS estimates indicate that grants tend to be negatively associated with growth,
while gross official loans have a positive impact on growth.8 The results are similar to those of
Gupta et al. (2003). However, the significance of these coefficients considerably drops in the IV
estimation, implying that foreign aid and economic growth are likely to be determined
endogenously and raising the possibility of bias within the OLS estimates. This evidence seems tobe somewhat consistent with the previous literature, such as Easterly et al. (2004), that foreign aid
does not enhance recipient countries’ growth.
On the other hand, one important positive finding is that regardless of the lack of solid growth
influence associated with grants and gross loans, the estimation results clearly show a positivecoefficient of the grant element. This result is robust in both OLS and IV regressions. Therefore, in
7

In fact, it has been found that the correlation of the residuals in the growth regressions at two periods is not
crucially high; the simple correlation is 0.40 for the growth equation. For the grant-loan equation, similarly,
the correlation is estimated at 0.39. Thus, the lagged values are considered as valid instruments in thefollowing analysis.
8
The negative effects of grants on growth may be interpreted as a lack of incentive mechanisms fordeveloping countries with abundant grants to engage in structural adjustment necessary for faster economic
growth.

11

addition to the evidence above, the implication is that while the gross amount of ODA loans may
not matter in aid effectiveness, the concessionality attached to the loans plays an important role infacilitating the recipient countries’ economic development. At the operational level, the grantelement can be raised either by lowering interest rates or by extending repayment and grace
periods.9
It is also of particular interest that while an increase in the concessionality of loans leads tohigher growth, grants are not an effective instrument for development assistance. This is the exact
point that this paper’s theoretical model takes into consideration. Even if loan concessionality ( θ )approaches unity, it does not necessary mean that loans and grants are identical. It may be useful torecall that many developing countries have no access to international capital markets. Therefore, an
important policy implication of foreign aid related to growth is this: ODA loans with a high degreeof concessionality are more effective than grants in assisting recipient countries’ economic growth.
This may be concluded by the fact that the incentives and commitment of the recipient governments
matter, and their policy efforts to accelerate growth, mobilize domestic revenue and stabilize
macroeconomic conditions for ensuring repayments can actually result in faster economic
development.
Regarding other explanatory variables in the growth equation, the share of private consumption
has a significant negative coefficient, thus implying that a large amount of private sector
consumption results in a reduction of the national resources available for future investment. Thismay contradict existing evidence that a greater volume of government consumption leads to a lower
growth rate (e.g., Barro, 1997). Nevertheless, there are measurement error issues. While the above
empirical model specifies the relative size of government in the economy as the share ofgovernment consumption to total domestic consumption, Barro’s model defines it by the ratio toGDP, adding that government consumption is nonproductive. First, it is not always true that all
government consumption is unproductive since certain current expenditures are needed for
productive capital investment to enable future growth acceleration. Rather, our theoretical model
reveals that regardless of the productivity of government consumption, larger private consumption
tends to slow economic growth. Second, as far as the relative extent of government distortions in

9From the broader viewpoint, one might think that a certain combination of grants and commercial loans –loans with zero concessionality – would be substitutable for concessional loans on the country level. The
average concessionality of grants and commercial loans could have the same effects as concessional loans.
However, it is important to recall that grants may not be an effective aid tool, as shown, and the governmentsof developing countries may not have full access to commercial credit. Therefore, these may limit thesubstitutability of combined grants and commercial loans for concessional loans.

12the national economy is concerned, it should be measured by the level of government consumption
relative to private consumption, as in this paper. Otherwise, capital investment might exaggerate
the degree of market distortions.10
The tax rate is negatively associated with economic growth, just as earlier growth studies suchas Davoodi and Zou (1998) indicate. This supports the conventional theoretical prediction that anincrease in public purchases slows overall economic development. While the education level does
not explain economic growth so much, the heterogeneity in income levels at the initial stages
strongly influences subsequent economic development. The negative coefficients associated withinitial GDP per capita imply that a certain convergence in national incomes may be realized.
Concurrently, however, the estimation results also indicate that low income countries tend to record
lower GDP growth rates. This means that even though the difference in initial per capita income
levels is controlled, there is a systematic difference in growth paths between low and middle income
countries. Low income countries may face institutional and socioeconomic obstacles in achieving
high economic development.
Finally, the estimated grant-loan equation indicates that loans and grants are complementary to
one another. The coefficients associated with grants per capita are significantly positive at 0.3 to
0.4 in both OLS and IV regressions. This therefore implies that under the assumption that the donor
community maximizes the aid recipient countries’ economic growth by choosing aid decomposition,
an optimal ratio of grants to loans is about 1 to 0.4. However, one may claim that this is notrealistic and that there is sometimes a considerable lack of donor coordination in development
assistance, and as such the estimated ratio may not be optimal. Nevertheless, it is important torecall that theoretically, there must be an optimal combination between grants and loans, and therecent coordination efforts within the donor community can be understood as one of the processes
to achieve the optimal aid composition.

10

This point can be verified easily by considering the following example. Suppose that one country has a
private consumption-government consumption-investment ratio of 70-30-0, while another country has a ratioof 30-30-40. Under Barro’s measurement, these two countries have the same amount of government(unproductive) activities in the economy. However, that is not the case. The former country instead has
fewer distortions resulting from government spending. Thus, the ratio of government consumption to GDP
may not be a good proxy in this context.

13

5. Conclusion
There has been a debate on how to deliver foreign development assistance to developing
countries: through grants or through concessional loans. The argument remains inconclusive
theoretically as well as empirically. This paper presents an endogenous growth model with agovernance factor, in which public spending is financed through grants and loans and the donor
community can influence a recipient country’s growth path by choosing a grant-loan package. The
model indicates that economic growth is a function of the average tax rate, grants and gross loans
received from donors, the concessionality attached to the loans, population growth and the share ofprivate consumption, but it does not depend on a good governance or corruption parameter.
Moreover, the model demonstrates that there exists an optimal combination of grants and loans toenhance growth, indicating their complementarity.
Empirically, it is indeed found that while an increase in the concessionality attached to ODA
loans can enhance a recipient countries’ economic growth, grants may not be useful in stimulating
growth. This can be interpreted as evidence that concessional loans are superior to grants in termsof recipient countries’ efforts to achieve economic growth, fiscal discipline and domestic revenue
mobilization. Importantly, this paper also finds that grants and loans are actually complementarywith an optimal grant-loan ratio of 1 to 0.4. Hence, this implies that the donor community needs toconsult with one another beyond differences in foreign aid strategy to ensure the best combinationof grants and loans for each recipient country. In this regard, the importance of foreign aid
coordination cannot be overemphasized.

14

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Growth, 5(1), pp. 33-63.
Barro, R. 1997. Determinants of Economic Growth: A Cross-Country Empirical Study. MIT Press,
Massachusetts.
Barro, R., Sala-i-Marin, X. 1995. Economic Growth, McGraw-Hill, New York.
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Economic Association Meetings Paper.
Burnside, C., Dollar, D. 2000. Aid, policies and growth. The American Economic Review, 90, pp.
847-868.
Cordella, T., Ulku, H. 2004. Grants versus loans. IMF Working Paper WP/04/161, International
Monetary Fund.
Davoodi, H., Zou, H. 1998. Fiscal decentralization and economic growth: a cross-country study,
Journal of Urban Economics, 43, pp. 244-257.
Devarajan, S., Swarrop, V. 1998. The implications of foreign aid fungibility for development
assistance. Policy Research Working Paper 2022, World Bank.
Easterly, W. 2003. Can foreign aid buy growth? Journal of Economic Perspectives, 17(3), pp. 2348.
Easterly, W., Levine, R., Roodman, D. 2004. Aid, policies, and growth: comment. The American
Economic Review, 94(3), pp. 774-784.
Gupta, S., Clements, B., Pivovarsky, A., Tiongson, E. 2003. Foreign aid and revenue response:
does the composition of aid matter? IMF Working Paper WP/03/176, International Monetary
Fund.
Klein, M., Harford, T. 2005. Grants or loans? Public Policy for the Private Sector, Note Number
287, World Bank.
Mankiw, G., Romer, D., Weil, D. 1992. A contribution to the empirics of economic growth,
Quarterly Journal of Economics, 107, pp. 407-437.
Murphy, K., Shleifer, A., Vishny, R. 2000. Industrialization and the big push, in Readings in
Development Microeconomics Volume 1 (Micro-theory), pp. 171-196, MIT Press,
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Sawada, Y., Kohama, H., Kono, H. 2004. Aid, policies, and growth: a further comment.
Mimeograph.

15

Table 1: Sample Countries
Period average (1998-2002)

Albania
Algeria
Argentina
Azerbaijan
BelizeBolivia
BrazilBurundiCameroon
Chile
China
Colombia
Republic of Congo
Costa Rica
Cote d'Ivoire
Croatia
Dominican Republic
Egypt
El Salvador
Ethiopia
GuatemalaGuineaIndia
Indonesia
Iran
Jamaica
JordanKenya
Kyrgyz Republic
LebanonLesotho

Period average (1998-2002)

GDP per capita
growth rate (%)

Grants per
capita (US$)

Gross official
loans per capita
(US$)

10.54
1.93
-4.01
8.88
2.33
0.13
0.36
-0.01
2.36
1.11
6.82
-1.29
0.05
2.49
-1.84
2.68
4.20
2.54
0.94
1.91
0.73
1.55
3.63
-1.38
2.85
-0.12
0.85
-1.24
2.04
-0.22
-0.18

71.53
5.30
2.99
11.32
94.21
61.68
2.06
15.19
24.49
5.33
0.68
8.19
24.81
15.05
30.45
19.32
14.13
23.89
30.91
8.88
22.48
28.39
0.85
3.48
1.74
34.99
91.61
12.21
18.45
40.85
25.67

37.07
5.09
1.95
13.83
70.46
29.25
1.02
4.22
15.43
1.12
1.37
0.82
2.61
4.30
18.46
1.88
8.55
4.25
8.35
0.05
3.87
14.83
1.80
6.40
0.60
14.22
21.04
6.96
28.15
24.65
13.08

GDP per capita
Gross official
Grants per
growth rateloans per capita
capita (US$)
(%)
(US$)
Madagascar
Malaysia
Maldives
Mauritius
MexicoMongolia
Morocco
Nepal
Nicaragua
Niger
Nigeria
Oman
Pakistan
PanamaParaguay
Peru
SenegalSierra Leone
Sri Lanka
Thailand
Togo
Tunisia
TurkeyUgandaUruguay
Vanuatu
Venezuela
Vietnam
Republic of Yemen
Zimbabwe

-1.67
0.01
3.59
4.14
1.71
1.63
1.92
1.15
2.89
0.36
-0.59
1.01
0.72
1.85
-2.21
0.06
1.89
-0.74
2.06
0.50
-2.44
3.17
-0.48
3.05
-3.34
-2.54
-3.64
4.91
1.80
-5.00

Table 2: Summary Statistics
GDP per capita growth rate
Grants per capita
Gross loans per capita
Grant element
Average tax rate
Share of private consumption
Population growth rate
Initial human capital
Initial GDP per capita
Regional dummy
East Asia & PacificSouth Asia
Sub-Saharan Africa
Income group dummy
Low income countries
Lower middle income countries
Higher middle income countries

Mean
Std. Dev.
1.09
2.76
24.13
28.32
12.83
13.57
47.69
21.20
15.69
10.15
82.55
7.01
1.83
0.76
48.66
22.29
1808.30
1804.99

Max
-5.00
0.68
0.05
15.88
0.00
60.00
0.02
6.64
113.00Mini
10.54
151.91
70.46
81.03
56.60
93.10
3.28
88.87
8447.00

0.11
0.08
0.28

0.32
0.28
0.45

0.00
0.00
0.00

1.00
1.00
1.00

0.39
0.39
0.21

0.49
0.49
0.41

0.00
0.00
0.00

1.00
1.00
1.00

16

18.00
3.85
73.09
25.18
1.64
53.35
16.33
12.18
93.59
18.02
1.20
14.86
3.27
12.95
13.81
14.79
40.56
31.64
10.32
4.01
15.42
18.85
3.90
25.27
8.01
151.91
1.93
6.09
10.52
16.40

10.42
5.40
35.69
20.64
0.14
38.25
9.91
6.81
47.57
8.16
0.65
9.56
8.49
5.16
8.12
7.82
20.57
15.20
21.38
13.03
7.39
27.99
4.25
10.37
0.70
39.60
0.60
12.45
15.97
4.44

Table 3: Estimation Results
Growth equation
Grants per capita
Gross loans per capita
Grant element
Average tax rate
Share of private consumption
Population growth
Initial human capital
Initial GDP per capita
East Asia & PacificSouth Asia
Sub-Saharan Africa
Low income countries
Lower middle income countries
Constant

Grant-loan equation

OLS

IV

OLS

IV

-0.0334 *-0.0257

0.3910 ***0.3665 ***(0.0187)(0.0453)(0.0728)(0.0727)

0.0638**0.0072(0.0273)(0.0994)

0.0690 **0.1003 *(0.0305)(0.0508)-0.0366 *-0.0893 *(0.0216)(0.0451)-0.0740 *-0.1522 **(0.0436)(0.0699)-1.5191 ***-1.8543 **(0.5550)(0.8283)-0.0292-0.0456

0.1280 *0.1172 *(0.0290)(0.0335)(0.0681)(0.0669)-0.0011 ***-0.0013 ***-0.0018 *-0.0020 *(0.0003)(0.0004)(0.0010)(0.0010)-1.6138-2.2284 *0.15730.2381(1.2698)(1.2712)(3.8123)(3.5744)-0.2750-0.7759

0.8432

0.6353(1.1119)(1.0895)(3.9791)(4.1331)-2.2786-3.3179 *-3.4824-3.8388(1.4756)(1.7090)(3.7156)(3.8522)-5.8139 ***-7.3446 ***-0.4856-1.1768(2.0705)(2.4711)(6.7180)(7.0684)-2.5972 **-3.5200 **-5.6111-5.9291(1.2799)(1.5370)(5.7127)(5.9466)

14.8511 ***24.2547 ***3.7431

5.6337(5.5216)(8.4576)(7.8174)(8.3651)
Obs.
61
61
61
61
F statistics
3.33
3.20
7.98
6.95
R-squared
0.49150.40040.70290.7007
Note: The dependent variables are GDP per capita growth for the growth equation model and the amount of grossloans per capita for the grant-loan equation model, respectively. The White-heterooscedasticity consistent
standard errors are shown in parentheses; * 10% level significance; ** 5% level significance; *** 1% levelsignificance.

17

Figure 1: External Financing for Developing Countries
450

Workers' remittances
Other private net flows
Portfolio equity inflows
Net foreign direct investment inflows
Other official net flows
GrantsTechnical cooperation grants

400
350

(Billion US$)

300
250
200
150
100
50
0
1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

Source: Global Development Finance 2004

Figure 2: Composition of Official Development Assistance by Donor
GrantsLoans and other long-term capital

New Zealand
Luxembourg
Ireland
Australia
Netherlands
SwedenCanadaGreeceNorwayUnited States
Belgium
Switzerland
Finland
Portugal
Denmark
United Kingdom
Austria
Italy
FranceGermany
All donors,Total
Spain
Japan
0.0

0.1

0.2

0.3

0.4

0.5

Source: OECD DAC, International Development Statistics.

18

0.6

0.7

0.8

0.9

1.0

Figure 3: Average Grant Element of Official Development Assistance (Period Average: 1992-2001)
0.35

0.30

Frequency

0.25

0.20

0.15

0.10

0.05

0.00
0-10%

10-20%

20-30%

30-40%

40-50%

Source: Global Development Finance 2004.

1950-60%

60-70%

70-80%

80-90%

90-100%