motivation for bilateral aid allocation

European Journal of Political Economy 24 (2008) 661–674

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European Journal of Political Economy

j ourna l homepage: www.e lsev ie r.com/ locate /e jpe

Motivation for bilateral aid allocation: Altruism or trade benefits

Javed Younas⁎Department of Economics, Central Michigan University, Mount Pleasant, MI 48858, USA

a r t i c l e i n f o

⁎ Tel.: +1 989 774 2969; fax: +1 989 774 2040.E-mail address: [email protected].

1 ODA/aid does not include loans, grants, and credit2 For example, see DAC guideline for poverty reduct3 UNDP (2005) p. 75.

0176-2680/$ – see front matter © 2008 Elsevier B.V. Adoi:10.1016/j.ejpoleco.2008.05.003

a b s t r a c t

Article history:Received 11 July 2007Received in revised form 15 May 2008Accepted 26 May 2008Available online 6 June 2008

This paper argues that OECD countries allocatemore aid to recipient nations who import goods inwhich donor nations have a comparative advantage in production. The estimates indicate that asubstantially larger amount of aid is provided to recipients who import capital goods, whileimports by other category groups have no significant effects. Given that developed donor nationsaremajor producers and exporters of capital goods, this result at least partially supports their tradebenefits motive. Donors also appear to be more concerned about alleviating physical miseries(infant mortality) and rewarding good human rights conditions, but less towards reducingeconomic hardships (poverty).Moreover, the usual political and strategic considerations of donorscontinue to be the major determinants of aid allocation even in the Post Cold War era.

© 2008 Elsevier B.V. All rights reserved.

JEL classification:F35O1

Keywords:Bilateral aidImportsCapital goodsComparative advantage

1. Introduction

Official Development Assistance (ODA), commonly known as foreign aid, includes loans, grants and technical assistanceon concessional financial terms with the objectives of reducing poverty and promoting economic development in developingcountries.1,2 However, despite its continued use, the role of aid for reducing poverty and enhancingwell-being remains controversial.

An influential past literature on aid allocation concludes that political, economic and strategic interests of donors rather thanthe development objectives play a dominant role in their aid allocation decision (McKinlay and Little, 1977, 1979; Maizels andNissanke, 1984; Dowling and Hiemenz, 1985; Svensson, 1999; Neumayer, 2003a,b). More recent studies find that colonial ties andcountries supporting donor countries in the U.N. voting receive more aid (Alesina and Dollar, 2000; Kuziemko and Werker, 2006).Examining whether less corrupt government are rewarded with increase in bilateral aid, Alesina and Weder (2002) find thatcorrupt governments receive asmuch aid as less corrupt governments. Concerning the question of allocation of aid and good policyenvironment, Burnside and Dollar (2000) find no significant effect of later on the former. In retrospect to the aid literature, thefollowing statement in the Human Development Report of United Nations Development Program (2005) is also worth noting:

‘International aid is one of the most powerful weapons in the war against poverty. Today that weapon is underused andbadly targeted. There is too little aid and too much of what is provided is weakly linked to human development.’3

Existing studies also demonstrate that a higher total exports of donor countries to the recipient countries results in greater aidallocation (Dudley and Montmarquette, 1976; Neumayer, 2003a). However, to our knowledge no study provides a systematicanalysis of whether recipient nations’ imports, aggregated as well as disaggregated, have an impact on the flow of bilateral aid.

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Table 1Export of major products under machinery and transportation equipment category by the developed countries

Machinery and transportation equipment (SITC 7) Export share in world (%)

1990 1995 2000 2003

Engines and motors 97 93 96 93Agriculture machinery (excluding tractors) 91 94 93 91Tractors (non-road) 84 92 91 87Civil engineering equipment 87 87 85 82Paper etc mill machinery 94 93 93 92Print and book binding machinery 97 96 94 91Food machinery (non-domestic) 92 92 90 92Machinery for special industries 88 89 87 85Metal working machinery 87 90 86 83Pumps for liquids, etc 95 92 90 89Mechanical handling equipment 92 89 89 88Non-electric machinery 95 92 89 87Office machines 83 71 66 58Telecom 76 68 67 54Electro-medical equipment 98 97 93 93Passenger motor vehicles (excluding bus) 95 92 88 89Lorries 94 88 82 79Railway vehicles 75 88 85 86Aircrafts, etc. 94 94 92 91Ships, boats, etc. 78 71 64 63

Note: Data was taken from Handbook of Statistics, UNCTAD (2005).

662 J. Younas / European Journal of Political Economy 24 (2008) 661–674

We argue that donor nations' motivation for providing aid also arises from their interest in acquiring a larger share of therecipient nations' imports. The economics of aid, therefore, constitutes a part of donor nations’ commercial strategy to secure alarger trade benefits. Besides pursuing political and strategic objectives, donors also use aid as an instrument for improvinggoodwill while expecting that recipients will reciprocate by buying more of their products. This perspective is similar to amonopolistic firm's marketing strategy of expending resources on promotional activities such as advertising, public relations,coupons, free gifts and charity. On the other hand, aid may also be given as a reward to the recipient nations for promoting importsand removing trade restrictions. This implies that donors can influence recipients to get preferential treatment on the goodsimported from them without entering a formal trade agreement.4

The economic benefits in terms of higher foreign reserves and growing domestic export industries are largest when recipientcountries import goods in which donor countries have a comparative advantage in production. Since all donor nations aredeveloped OECD countries in this study, they tend to have a comparative advantage in the production of capital goods, but not inconsumption goods. Table 1 shows world export share of major products of developed countries under the machinery andtransportation equipment category, UNDP (2005). It reveals that developed countries export a substantially higher share of capitalgoods and, therefore, capture a larger share of the world market in these products.5

We present the following reasons to support the preceding argument. First, capital goods constitute a high share (value) ofrecipient nations’ imports and are mainly produced in the developed donor countries. Second, influencing recipient nations toincrease imports of capital goods seems relatively easier as few countries specialize in their production.6 Lastly, donor nations mayalso influence recipient nations to lower tariff on their imports.

This paper provides a systematic analysis of bilateral aid allocation by first developing a theoretical model to derivesimultaneous optimization decisions of donors. To verify the predictions of the theoretical model, we empirically estimate thedeterminants of aid allocation by 22-Development Assistance Committee (DAC)member countries of OECD to seventy eight net aidrecipient countries over the period 1991–2003.7

The remainder of the paper is organized as follows. The next section lays out a theoretical model and some predictions. Section3 describes the empirical methodology and data. Section 4 presents the estimation results including some sensitivity analysis andSection 5 concludes.

4 Studying trade agreements, Bagwell and Staiger (2001) argue that governments enter into reciprocal and mutually advantageous agreements to obtain areduction of tariff and other trade barriers to expand their production and trade. They further state that such agreements are attractive as a means to achievepolitical objectives.

5 Table 4 shows that this share has witnessed some decline for a few products (office machines, telecom, lorries, ships, etc) which suggests that other countriesalso started specializing in their production.

6 Market for agricultural products (food items, agriculture raw material, etc) is considered to be fairly competitive. However, there may be a sufficient degreefor market power in promoting manufacturing goods (particularly capital goods) because of specialization in their production and, therefore, exports by relativelyfew countries.

7 See Appendix A and B for the list of the 22-DAC countries and the seventy eight net aid recipient countries in our sample, respectively. Since containment ofcommunism rather than development concerns was a major factor for providing aid during the Cold War era (see Boschini and Olofsgård, 2007), we limit ouranalysis to the Post Cold War period. Another constraint was the non-availability of consistent yearly data for majority of developing countries on import bycategory groups prior to the 1990s.

663J. Younas / European Journal of Political Economy 24 (2008) 661–674

2. Theoretical model and predictions

We extend Dudley and Montmarquette's (1976) model of a single donor’s bilateral aid allocation to one that combines aid frommultiple donors. To our knowledge, previously Trumbull andWall (1994) andWall (1995) extended that model, and we also followsimilar approach in the theoretical framework. However, they combine total ODA from all sources (bilateral and multilateral) forformulating a single donors’ objective function, while we do that by combining ODA from 22-DAC countries, which is moreappropriate for the objective of our study.

In this model, we assume that the objective of each donor is to maximize the utility based on the subjectively measured impactof aid on the well-being of the recipient nations’ residents. Therefore, a single objective function of the impact of aid from all jdonors to m recipients is formalized. We assume that all donors pool their aid budget and a representative donor decides howmuch of that is to be allocated to a recipient every year. Supposing that aid is put into good use by the recipients and ignoring timesubscripts for now, we can set up the objective function as follows:

H ¼ ∑m

i¼1wihi ¼ ∑

m

i¼1wihi ai; yi;ni;mi;pið Þ ð1Þ

,……, m (the recipient countries)
i=1where,
H = subjectively measured impact of aid on a recipient countryhi = subjectively measured impact of aid on identical residents of a recipient countryai = aid per capita received by a recipient countryyi = income per capita of a recipient countryni = population size of a recipient countrymi = (vector of) imports, aggregated as well as disaggregated, of a recipient countrypi = political liberties and civil rights in a recipient countrywi = weights measuring importance of a recipient country in the eyes of donors

The impact of aid is an increasing function of the aid per capita that a recipient nation receives. The impact of aid on income percapita depends whether it is a substitute of or complement to income per capita. Theywill be substitutes, if compassion or altruismis the driving force. In this case, more aid is given when per capita income falls. Otherwise, they are complements. Among otherreasons, self-interest motives that tie developed and developing economies together may justify such behavior.

Past studies find a bias in aid allocation against countries with larger populations (Isenman, 1976; Dowling and Hiemenz, 1985;Trumbull andWall, 1994;Wall, 1995; Alesina and Dollar, 2000; Bandyopadhyay andWall, 2007). The literature offers the followingexplanations that consider both supply side and demand side factors: (1) the marginal impact of aid decreases as populationincreases; (2) high population countries lack administrative expertise to absorb large amounts of aid; and (3) it is relatively easierfor donors to wield political influence over a smaller country than a large country.

We assume that donors' strategy of improving goodwill through the regular supply of aid aims to influence recipient nations' toimport goods in which donor countries have a comparative advantage in production, as argued in Section 1. Therefore, if goodwillhas a positive influence on the recipients' demand for imported goods in general and capital goods in particular, then the subjectiveimpact of aid on imports is positive. Moreover, aid may also be given as a reward for promoting imports and following policies toliberalize trade. However, the reward can be significant if donors are major beneficiaries of trade policies of the recipient nations.

The human rights variable captures the donor's perception about the objective function of the recipient government. If arecipient (government) values human rights, it is perceived to put a higher weight on the welfare of its people. In turn, it is likelythat it will utilize the aid to improve their well-being. Furthermore, donors may provide more aid to the recipient nations showinggreater respect for human rights because of their perception that the subjective impact of aid is higher when donors and recipientnations share common human values, Wall (1995).

Following Trumbull and Wall (1994), we also assume that given the total budget of aid, donors maximize the weighted sum ofthe total impact of aid to a recipient each year. The weights reflecting the importance of a recipient in the eyes of donors aredetermined by its colonial history, cultural affinity, political and strategic values, geographic location, etc. The above noted pos-tulation can mathematically be expressed as follows:

AHAai

N 0;AHAyi

b 0 orAHAyi

N 0;AHAni

V 0;AHAmi

N 0;AHApi

N 0 ð2Þ

ing a specific functional form for hi:
Tak
hi ai; yi;ni;mi; pið Þ ¼ aαi mδi p

γi

nθi y

τi

ð3Þ

αb1, 0bδb1, 0bγb1, 0≤θb1, 0b |τ|b1.
0b


Aid and income per capita are considered substitutes if τ is positive, and complements if τ is negative. The total impact of aid asthe sum of the impact on identical residents of a recipient country can be written as:

8 Theestimat

H ¼ ∑m

i¼1wihi ai; yi;ni;mi; pið Þni ¼ ∑

m

i¼1wi

aαi mδi p

γi

nθi y

τi

!ni ð4Þ

noted above, we express the supply behavior of aid from multiple donors as an impact maximization problem of the single
Asdonor where all donors pool their budget for aid. Thus, budget constraint of the jth donor takes the following form:
∑m

i¼1aini ¼ B: ð5Þ

erefore, the maximization problem can be written as follows:
Th
maxai

H ¼ ∑m

i¼1wihi ai; yi;ni;mi;pið Þni s:t: ∑

m

i¼1aini ¼ B ð6Þ

bstituting Eq. (4) in Eq. (6) to set up the Langrangian and deriving first order conditions gives:
Su
L ¼ ∑m

i¼1wi

aαi mδi p

γi

nθi y

τi

!ni þ λ B− ∑

m

i¼1aini

� �ð7Þ

ALAai

¼ αwiaα−1i mδ

i pγi

nθ−1i yτi

−λni ¼ 0 ð8Þ

ALAλ

¼ B− ∑m

i¼1aini ¼ 0: ð9Þ

s the marginal impact of aid. The first order conditions take the following forms:
λ i
λ ¼ αwiaα−1i mδ

i pγi

nθi y

τi

and B ¼ ∑m

i¼1aini ð10Þ

ving Eq. (10) to get the optimal allocation of aid per capita, ai⁎, as:
Sol
a⁎i ¼ αwimδ

i pγi

λnθi y

τi

! 11−α

: ð11Þ

ing the natural log and introducing an error term, we write Eq. (11) as following:
Tak
lna⁎i ¼ β0 þ βi þ ηt þ β1ln yið Þ þ β2ln nið Þ þ β3ln mið Þ þ β4ln pið Þ þ eit : ð12Þ

us, the decision of aid allocation each year is determined by the factors that influence the perceived impact of aid.
ThConsidering that information about the recipients is available to donors with some time lag, we introduce a 1 year lagged timesubscript for all right hand side variables in the Eq. (12).8 Therefore, the basic econometric model takes the following form:
lna⁎it ¼ β0 þ βi þ ηt−1 þ β1ln yi;t−1� �þ β2ln ni;t−1

� �þ β3ln mi;t−1� �þ β4ln pi;t−1

� �þ eit ð13Þ

ere,
wh
β0 ¼ 1=1−αð Þlnα; βi ¼ 1=1−αð Þlnwi; ηt−1 ¼ − 1=1−αð Þlnλ⁎t−1;

β1 ¼ −τ=1−α; β2 ¼ −θ=1−α; β3 ¼ δ=1−α; β4 ¼ γ=1−α:

mentioned above,mi is a vector of a recipient nation's import variables containing aggregated as well as disaggregated goods
Asby their individual commodity groups. Themodel in Eq. (13) predicts that higher total imports positively affect the allocation of aidper capita. A disaggregated analysis of imports shows the impact of imports of goods by their individual category group on aid per
lagging of independent variables also reduces potential problem of simultaneity and contemporaneous correlation in the empirical model. Moreover, alles are calculated using robust standard errors techniques. Section 3 provides detailed discussion on this issue.


capita. It is expected that, relative to others the imports of manufacturing goods, particularly capital goods, will have a greaterimpact on allocation of aid per capita by the donors, as argued in Section 1. The predictions about other variable are noted above.

Eq. (13) also includes a period effect, ηt−1, that is common to all countries within a given year and βi that determines theweights attached to each recipient nation (details to follow in Section 3).

3. The empirical methodology and description of data

3.1. The methodology

We use various model specifications by simultaneously controlling for altruistic as well as self-interest motives of donors, i.e.,their economic, political and strategic considerations. This approach aims to correct the ad hoc econometric treatment and toappropriately assess the determinants of bilateral aid allocation. Regarding the choice of themodel that fits the datawell, we preferusing the pooled ordinary least squares (POLS) for deriving estimation results. The main appeal for using POLS model is that theimpacts of recipient-specific and time-invariant variables which measure the political and strategic considerations for aidallocation can also be estimated. Moreover, POLS also provides a relatively precise measure of variables such as political rights andcivil liberties that witness little variation over time.9

Before proceeding to estimations, we address the possibility of potential simultaneous causation between independent variable(aid per capita) and two independent variables (income per capita and imports). One may argue that income per capita andimports may be endogenous as they not only affect but may also be affected by the flow of aid.10 This may require a simultaneousestimation technique such as two-stage least squares (2SLS). However, the problems with 2SLS are the non-availability of validinstruments and their data for developing countries. Moreover, employing weak instruments can contaminate estimation results.Wooldridge (2003, p.541) states that if we assume that error term μit is uncorrelated (a standard assumption) with all pastendogenous and exogenous variables, then lagged endogenous variables in simultaneous models are treated as predeterminedvariables and they are uncorrelated with μit.11 Therefore, we use a 1 year lagged value for all independent variables in oureconometric model. This technique also makes more economic sense as information to the donors about a recipient is onlyavailable with some time lag.

The sample includes countries that received positive amount of aid each year with the exception of only five countries whoreceived no aid for only 1 year out of the 12 years sample period. Dollar and Levin (2004), using log-log model for aid allocation,substitute a very small value (0.01 million) for a country not receiving aid from a donor in any year. We follow this approach forthose five countries in the sample.

Per capita income may be an inadequate reflection of economic needs for aid, especially in view of high income inequalities inseveral recipient countries. This prompted our use of the infant mortality rate, which relates to the concept of individual well-being.12 Moreover, Per capita income captures economic need while infant mortality signifies physical need (Trumbull and Wall,1994; Wall, 1995; Bandyopadhyay and Wall, 2007). Bandyopadhyay and Wall (2007) state that though economic and physicalneeds are clearly correlated in the long run, they do not necessarily move in the same direction over shorter period of time. Inaddition, we also include multilateral real aid per capita to a recipient country as an additional explanatory variable. Sincemultilateral aid adds to a recipient’s total aid, the bilateral aid may loose its importance. In this case, a recipient feels less con-strained to import goods from bilateral donor countries or concur to their political and strategic concerns. The sign and significanceof the coefficient on multilateral aid will show whether bilateral donors try to maintain their influence on the recipients who alsoreceive more aid from multilateral agencies.13

To analyze whether donor nations provide more aid to recipient nations importing more goods in general or to those who havea tendency to import specific goods by individual category groups, we initially include import share of manufacturing goods andagricultural products in the regressions. Additionally, we subdivide manufacturing goods into basic manufacturers and machineryand transportation equipment.14

9 It is worth noting that including fixed effects for all the countries at the cost of being able to estimate recipient-specific considerations such as colonial history,geographical location, cultural affinity and other politico-strategic concerns does not qualitatively change the basic pattern of our findings. In addition, we alsotried regressions by controlling for regional dummies. However, the results of our variables across all models remained robust with their inclusion.10 Since the literature on aid consistently shows that aid does not cause growth, there is little reason to believe that there would be reverse causation from aid toincome per capita or imports. It is also worth noting that if there is a strong enough endogeneity bias then we might observe positive relationship between aidper capita and income per capita. However, we find negative relationship across all models. Moreover, using imports as share of GDP and individual importscategory as share of total imports further reduces any potential endogeneity bias.11 Maizels and Nissanke (1984), while citing Maddala (1977), state that, “all estimation techniques, including 2SLS, are designed to deal only with the contem-poraneous simultaneity and the lagged endogenous variables are treated in simultaneous models as predetermined variables along with other exogenousvariables in the system.” Therefore, if aid flows can be assumed to affect a country’s economic performance with some time lag, the problem of simultaneous biasis considerably lessened and the necessity of using 2SLS instead of OLS is accordingly greatly diminished.12 The World Bank Report (2006) defines infant mortality rates as the number of infants dying before reaching one year of age per 1000 live births in a givenyear.13 The multilateral aid is given by the World Bank, the IMF and the UN including their regional branches. We are thankful to an anonymous referee for providinginsight for including multilateral aid in the regression.14 Manufacturing goods include basic manufacturer goods, machinery and transportation equipment and chemical products. Agricultural products include allfood items, agricultural raw material and ores and metals.

Fig. 1. Distribution of average real aid (1992–2003).


Since most explanatory variables vary across a wide range (such as population size, per capita income, infant mortality, humanrights and imports by individual category groups), and also exhibit skewed distributions, we prefer to take natural log of allvariables, except dummy variables. Log-log model also helps to reduce the outlier effects on estimations, and with that resultingcoefficients can be interpreted as elasticities. However, the sign and magnitude of the coefficients on the dummy variables canprovide an idea about their impact on aid. Moreover, we also carry out Akaike Information Criterion (AIC) test to ascertain therelative performance of the estimated models. A lower value of AIC is associated with an efficient model.

3.2. Description of data

The data for net ODA from 22-DAC member countries of OECD to seventy eight recipient countries is taken from InternationalDevelopment Statistics (2005). This data contains aid for development purposes and does not include grants, loans, and credits formilitary purposes. Following Neumayer (2003a,b,c), we converted aid data into constant US$2000 using the unit value of theworldimport price index, and then divided by the recipient nations' population to express the dependent variable as real aid per capita.This serves best with the scope of this study because real aid per capita can be expressed in terms of its purchasing power for arepresentative bundle of imports.15 Moreover, such a dependent variable also controls for a recipient's size effect.

Data for income per capita as measured by GDP per capita (purchasing power parity) constant US$2000, population, and infantmortality rates is taken from the World Bank Development Indicators (2006) CD Rom.16 For the human rights measure, we haveused indices for political rights and civil liberties produced by FreedomHouse (2005). Political rights refer to the freedom of peopleto participate in the political process by exercising their voting right, being able to organize political parties to compete for publicoffice, and forming an effective opposition and electing representatives who devise public policies and are accountable for theiractions. Civil liberties entail freedom of expression and religious belief, the prevalence of rule of law, right to form unions, freedomto marry, and freedom to travel. It also signifies the autonomy of people without interference from the state. These two indicatorsare derived from a cross country survey every year. Each of these indices is measured on a 1 (best) to 7 (worst) points scale.Following Trumbull and Wall (1994), Wall (1995) and Neumayer (2003a), we have constructed a combined freedom index byadding indices of political rights and civil liberties and then reverting that index, such that it ranges from 2 (worst) to 14 (best).

Total imports aremeasured as a ratio to GDP.We use the Standard International Trade Classification (SITC) of UNCTAD (2005) tosegregate imported goods by their individual category groups. As noted above, we initially divide total imports into manufacturinggoods and agricultural products, both as a ratio to total imports. Later we further subdivide manufacturing goods into basicmanufacturers and machinery and transportation equipment.17

We also control for the political and strategic values of a recipient nation in the eyes of donors. Existing literature provides someguidance in identifying those variables. Dummies for Israel and Egypt have been included as past studies find that they receivesignificantly greater proportion of aid due to their political and strategic importance in the Middle East (Alesina and Dollar, 2000;Burnside and Dollar, 2000). We also examine whether colonial experience of a recipient has an influence on aid allocation as paststudies find that donors give more aid to their past colonies (Alesina and Dollar, 2000; Neumayer, 2003a). Information about arecipient's colonial history is taken from the CIA World Factbook (2006) and used as a dummy variable taking a value of 1 if arecipient had been a colony of any of the donors in our study, or 0 otherwise.

Fig. 1 illustrates the distribution of average real aid per year over the period 1992–2003. The mean country in our samplereceived $259.5 million, while the median country (Guinea) received only $147 million, reflecting that aid was skewed towards

15 The estimation results using GDP deflator at constant US$2000 for converting aid into real term are similar.16 There are some missing data values for some countries for infant mortality rates. Since infant mortality rates change slowly over time, values for missingobservations are interpolated by calculating averages from available values.17 Basic manufacturer goods mainly include products such as leather, textile, rubber, iron, steel, and non-metallic mineral manufacturers. Machinery andtransportation equipment include capital/investment goods such as power generating machinery and equipment, industrial and telecommunication machinery,and vehicles including aircrafts, railways and ships. Agricultural products include food items, agricultural raw material including live animals and ores and metals.

Fig. 2. Distribution of average real aid per capita (1992–2003).


a top few countries. While a few countries received a significantly large proportion of aid (China, $1630 million; Egypt,$1524 million; Indonesia, $1390 million; Israel, $994 million; Poland, $928 million), ten countries in the sample received less than$10 million on average.

Fig. 2 shows the distribution of average real aid per capita per year over the same period. The mean country received $25 whilethe median country (Togo) received only $14. Israel stands at the top with $175 which is $50 higher than the second highestrecipient country (Dominica $125). On the other hand, among the five highest aid per capita recipients, Israel has the highestaverage real income per capita ($22,194) per year during our sample period, followed by Seychelles ($15,338), Suriname ($6210),Dominica ($5139) and Nicaragua ($3258). At the other extreme, seven countries with average real income per capita below $1450received less than $10 real aid per capita on average.18

Fig. 3 illustrates the correlation between real aid per capita and some of the key explanatory variables. Panel 1 shows that mostof the aid is concentrated towards the countries with lower levels of income per capita and countrieswith higher income per capitareceive very little aid. However, a few countries with significantly higher income per capita in our sample received a larger amountof aid, as noted above. Panel 2 suggests a positive correlation of aid per capita with the infant mortality rates. Panel 3 shows thecorrelation between aid per capita and population, suggesting that aid is mostly concentrated towards less populated countries.Panel 4 illustrates that there is a general positive correlation between aid per capita and human rights, which is pretty evenlydistributed across countries. The correlations between aid per capita and ratio of total imports to GDP, and that withmanufacturingimports to total imports appears to be positive (panels 5 and 6).

4. Estimation results

Although the aid distributions and correlations discussed above provide some insight about donors’ behavior for aid allocation,they are inadequate for drawing a reasonable conclusion.19 Therefore, we need to simultaneously control all explanatory variablesin the regressions to find their individual effects on aid per capita.20

To compare our results with previous findings, we initially include variables that were commonly examined in the previousliterature (Maizels and Nissanke, 1984; Dowling and Hiemenz, 1985; Trumbull and Wall, 1994; Wall, 1995; Alesina and Dollar,2000; Neumayer, 2003a,b,c). Qualitatively, our findings largely support previously established results (Table 2, column 1). Asignificant and negative coefficient on population variable suggests that donors perceive a diminishing marginal impact of aid asthe population of a recipient nation increases, indicating a bias against larger countries. Both income per capita and infantmortality appear to be important indicators of well-being. A significant and negative coefficient on income per capita suggests thataid is given as a substitute of economic well-being. All else equal, a 10% decrease in income per capita leads to a 1.9% increase in aidper capita, while a 10% increase in infant mortality is associated with a 3.5% increase in aid per capita. These initial findings suggestthat donors care about the economic and physical well-beings of the residents in the recipient nations.

A greater respect for human rights by the recipients, as reflected by positive and significant coefficient on political & civil rightsvariable, results in receiving more aid. The positive and significant coefficient on multilateral aid per capita suggests that donorcountries, in order to maintain their influence, provide more aid to the recipients who also receive more aid from multilateralagencies.21 The significant coefficients on dummy variables for Israel and Egypt suggest that they receive a great deal of aid, likely

18 Those countries are Bangladesh, Burundi, Ethiopia, Kenya, Nepal, Togo and Yemen.19 The correlations among majority of the independent variables are not very high with the exception of one between real income per capita and infantmortality rates, and another between real income per capita and real total reserves per capita. To check whether multicollinearity poses a problem for theestimations, the eigenvalues for correlations among explanatory variables were tested and found to be low. Moreover, past studies also routinely include incomeper capita and infant mortality rates in the same regression, suggesting that they provide different information for aid allocation behavior of the donors.20 All regressions are estimated with heteroscedasiticity-robust standard errors using Huber-White sandwich estimator technique. Liug-Box Q-statistics rejectsthe presence of autocorrelations and partial autocorrelations in the residuals.21 Since bilateral donor countries provide funds to multilateral agencies such as World Bank, IMF, the U.N., etc, their likely influence over the aid allocationdecisions of these agencies may also be a factor causing this result.

Fig. 3. Correlations of real bilateral aid per capita and the key explanatory variables (country averages).


due to their political and strategic importance in the Middle East.22 The former colonies are also rewarded with more aid due totheir historical and political link with their respective donors.

Though these results largely support previous findings, we need to include the key variables of interest for this study which arerecipients’ imports by its individual category group to assess their impact on aid. Later, we also include other control variables tocheck the robustness of our results.

First, total imports as a ratio to GDP of a recipient is included in the regressions to see whether donor countries respond tohigher imports in general (column 2). However, its coefficient is statistically insignificant. Next, we include two broader importscategories in the regression:manufacturing goods and agricultural products, both as a ratio to total imports.23 Column 3 shows thatthe estimated coefficient on manufacturing imports is positive and significant at the 5% level, indicating that donors provide moreaid to the recipients importing more of manufacturing goods. All else equal, a 10% increase in manufacturing imports results in a5.8% increase of bilateral aid per capita. On the other hand, agricultural imports have no significant impact on aid. The results for allother variables remain about the same except that sign and significance of the coefficient on income per capita has dropped, whilethe one for infant mortality has increased. This regression explains about 48% of the variance in bilateral aid per capita.

The regression results above show that donors provide substantially larger amount of aid to the recipient nations' importingmanufacturing goods. Since capital goods constitute a significantly larger share (value) of manufacturing imports, donors seem tohave their economic interest in encouraging their imports through aid as high valued added manufacturing goods are typicallyproduced/exported by the donors (see Table 1). Irrespective the self-interest of donors in promoting import of manufacturinggoods, the recipient nations also gain as imports of those goods help increasing their production (and consumption) and they alsoreceive a larger amount of aid.

Further, we divide manufacturing imports into two category groups: (1) basic manufacturing, and (2) machinery andtransportation equipment. The estimated coefficient on machinery and transportation equipment is positive and significant at 1%level, while the coefficient on basic manufacturing goods is insignificant (column 4). This result further supports our hypothesis

22 We also tried regressions by excluding Israel and Egypt from our sample. However, qualitatively the results remain about the same.23 We dropped from the regressions the imports of fuel, chemical products, and goods falling under unallocated category to deal with the problem ofmulticollinearity among import variables. Moreover, we choose to drop only those imported goods that are least relevant to the argument we make in the paperabove.

Table 2Dependent variable: Bilateral real aid per capita

Independent variables (1) (2) (3) (4) (5) (6)

Population −0.222 −0.203 −0.227 −0.237 −0.245 −0.209(7.17)⁎⁎⁎ (6.37)⁎⁎⁎ (6.80)⁎⁎⁎ (7.11)⁎⁎⁎ (6.01)⁎⁎⁎ (4.99)⁎⁎⁎

Income per capita −0.190 −0.148 −0.166 −0.220 −0.176 −0.156(2.58)⁎⁎⁎ (1.99)⁎⁎ (2.15)⁎⁎ (2.71)⁎⁎⁎ (1.94)⁎ (1.71)⁎

Infant mortality 0.350 0.434 0.507 0.423 0.393 0.460(3.38)⁎⁎⁎ (3.65)⁎⁎⁎ (4.08)⁎⁎⁎ (3.30)⁎⁎⁎ (2.92)⁎⁎⁎ (3.25)⁎⁎⁎

Pol. & civ. rights 0.353 0.380 0.356 0.392 0.416 0.274(4.09)⁎⁎⁎ (4.30)⁎⁎⁎ (3.82)⁎⁎⁎ (4.36)⁎⁎⁎ (4.44)⁎⁎⁎ (2.83)⁎⁎⁎

Multilateral aid per capita 0.281 0.279 0.289 0.301 0.288 0.290(7.26)⁎⁎⁎ (7.11)⁎⁎⁎ (7.31)⁎⁎⁎ (7.16)⁎⁎⁎ (6.00)⁎⁎⁎ (5.94)⁎⁎⁎

Imports/GDP – 0.206 0.148 0.098 0.122 0.269(1.38) (1.33) (0.98) (1.02) (2.09)⁎⁎

Mfr. imports/imports – – 0.578 – – –

(2.37)⁎⁎

B. mfr. imports/imports – – - −0.198 −0.201 −0.182(1.24) (1.26) (1.18)

M&TE imports/imports – – - 0.413 0.504 0.431(2.76)⁎⁎⁎ (3.01)⁎⁎⁎ (3.91)⁎⁎⁎

Agr. imports/imports - - −0.152 −0.073 0.025 0.008(1.12) (0.56) (0.17) (0.05)

Reserves per capita – – – – −0.074 −0.063(1.26) (1.05)

Distance – – – – −0.037 −0.031(0.34) (0.29)

Domestic PP (per dollar) – – – – −0.009 −0.007(0.47) (0.36)

Israel 3.843 3.873 3.840 4.023 4.049 4.253(12.38)⁎⁎⁎ (12.52)⁎⁎⁎ (12.02)⁎⁎⁎ (12.14)⁎⁎⁎ (12.36)⁎⁎⁎ (12.85)⁎⁎⁎

Egypt 1.402 1.401 1.583 1.562 1.598 1.578(17.57)⁎⁎⁎ (17.67)⁎⁎⁎ (13.98)⁎⁎⁎ (13.65)⁎⁎⁎ (12.29)⁎⁎⁎ (12.27)⁎⁎⁎

Colony 0.201 0.171 0.167 0.176 0.198 0.259(2.07)⁎⁎ (1.74)⁎ (1.68)⁎ (1.80)⁎ (1.79)⁎ (2.77)⁎⁎⁎

Muslim – – – – – −0.055(0.57)

Roman Catholic – – – – – 0.391(3.64)⁎⁎⁎

R2 0.476 0.477 0.483 0.490 0.491 0.510Observations 936 936 936 936 936 936AIC 2918.2 2918.3 2912.5 2909.2 2912.8 2897.3

Note: Estimated with heteroscedasticity-robust standard errors. Year dummies included but not reported. Columns 1 through 4 test basic model while columns 5and 6 include additional control variables for testing robustness. Absolute t-values are shown in parentheses.Superscripts ⁎⁎⁎, ⁎⁎ and ⁎ indicate significance at 1, 5 and 10% levels, respectively. Pol. & Civ = Political and Civil; Mfr = Manufacturing; B = Basic; M&TE = Machinery& Transportation Equipment; Agr = Agricultural.


that donors seek to increase their trade benefits by encouraging the imports of goods typically produced by them. Aid seems to beused as an instrument by donors to improve their goodwill so that the recipients confer economic benefits by buying more of theirproducts. The course of cultivating friendship and improving goodwill continues even if a recipient imports less from a donor inany year. Therefore, it does notmatter howmuch a recipient currently imports from a donor. Instead, donors provide aid every yearand gradually improve their goodwill with the expectations that the recipients demand more of their products in future.

4.1. Sensitivity analysis

To test the robustness of the results, we include additional control variables expected to have influence on the aid allocationbehavior of donors. Therefore, we include three variables in the regression: log of real total reserves per capita, log of air distance(in kilometers), and log of real domestic purchasing power per dollar.24,25,26 Although the signs with their coefficients are asexpected, but they are insignificant (column 5). However, inclusion of these control variables yields two interesting changes in our

24 Total reserves of a country include monetary gold, special drawing rights, reserves with IMF, and foreign exchange holdings with monetary authorities. Itsdata was taken fromWDI (2006) CD Rom and transformed into constant US$2000 by using the unit value of world import index and then divided by a recipient’spopulation to express as real total reserves per capita.25 Geographical proximity is measured as the minimum air distance, in kilometers, of a recipient country from New York, Rotterdam, and Tokyo. Its data wastaken from Gallup and Sachs (1999). Following Neumayer (2003a,b,c), we used existing data for a geographically close country (neighboring), if distance data for aparticular country is not available.26 Purchasing power conversion factor is a dollar equivalent units of domestic currency required to buy the same amount of goods and services that a dollar willbuy in U.S. Its data was taken from WDI (2006) CD Rom and converted into a real term by using the consumer price index base year 2000 for a recipient.

Table 3Dependent variable: bilateral real aid per capita

Independent variables (1) (2) (3) (4) (5) (6)

Population −0.186 −0.163 −0.187 −0.200 −0.196 −0.157(4.47)⁎⁎⁎ (3.71)⁎⁎⁎ (3.88)⁎⁎⁎ (4.17)⁎⁎⁎ (3.27)⁎⁎⁎ (2.51)⁎⁎

Income per capita −0.141 −0.088 −0.104 −0.164 −0.109 −0.085(1.35) (0.80) (0.95) (1.39) (0.81) (0.62)

Infant mortality 0.341 0.449 0.536 0.426 0.361 0.433(2.28)⁎⁎ (2.58)⁎⁎⁎ (2.98)⁎⁎⁎ (2.24)⁎⁎ (1.92)⁎ (2.20)⁎⁎

Pol. & civ. rights 0.320 0.357 0.327 0.361 0.390 0.238(2.46)⁎⁎ (2.68)⁎⁎⁎ (2.29)⁎⁎ (2.63)⁎⁎⁎ (2.64)⁎⁎⁎ (1.69)⁎

Multilateral aid per capita 0.344 0.340 0.355 0.377 0.370 0.368(6.33)⁎⁎⁎ (6.15)⁎⁎⁎ (6.35)⁎⁎⁎ (6.21)⁎⁎⁎ (5.22)⁎⁎⁎ (5.26)⁎⁎⁎

Imports/GDP – 0.186 0.191 0.119 0.176 0.336(1.13) (1.10) (0.70) (0.92) (1.68)⁎

Mfr. imports/imports – – 0.710 – – –

(2.01)⁎⁎

B. Mfr. imports/imports – – – −0.291 −0.293 −0.275(1.13) (1.10) (1.05)

M&TE imports/imports – – – 0.452 0.543 0.420(1.99)⁎⁎ (2.01)⁎⁎ (1.99)⁎⁎

Agr. imports/imports – – −0.162 −0.095 0.005 0.028(0.76) (0.47) (0.02) (0.12)

Reserves per capita – – – – −0.083 −0.069(0.96) (0.80)

Distance – – – – −0.041 0.050(0.26) (0.33)

Domestic PP (per dollar) – – – – −0.010 −0.007(0.32) (0.23)

Israel 3.914 3.949 3.922 4.153 4.185 4.391(11.75)⁎⁎⁎ (12.00)⁎⁎⁎ (11.51)⁎⁎⁎ (11.25)⁎⁎⁎ (11.41)⁎⁎⁎ (11.92)⁎⁎⁎

Egypt 1.360 1.359 1.563 1.540 1.589 1.561(12.97)⁎⁎⁎ (12.86)⁎⁎⁎ (9.32)⁎⁎⁎ (9.41)⁎⁎⁎ (8.35)⁎⁎⁎ (8.20)⁎⁎⁎

Colony 0.192 0.155 0.146 0.160 0.146 0.214(1.32) (1.05) (0.97) (1.10) (1.01) (1.51)

Muslim – – – – – −0.062(0.45)

Roman Catholic – – – – – 0.392(2.61)⁎⁎⁎

R2 0.563 0.564 0.572 0.577 0.578 0.591Observations 312 312 312 312 312 312AIC 873.9 874.4 873.0 871.8 876.7 870.3

Note: Estimated with heteroscedasticity-robust standard errors. Year dummies included but not reported. Columns 1 through 4 test basic model while columns 5and 6 include additional control variables for testing robustness. Absolute t-values are shown in parentheses.Superscripts ⁎⁎⁎, ⁎⁎ and ⁎ indicate significance at 1, 5 and 10% levels, respectively. Pol. & Civ = Political and Civil; Mfr = Manufacturing; B = Basic; M&TE = Machinery& Transportation Equipment; Agr = Agricultural.


results: First, the significance of coefficient on income per capita drops. Second, the coefficient on machinery and transportationequipment becomes more significant and also increases by 22% (from 0.41 to 0.50). This suggests that, all else equal, donors aremore interested in increasing their trade benefits and less towards responding to adverse income shocks in the recipient countries.This result also confirms that gaining trade benefits are at least a partial motivation behind aid allocation.

Following Alesina and Dollar (2000), we also consider that cultural affinity, as proxied by religious differences, may have animpact on aid allocation. To do this we use a dummy variable value of 1 if the majority of a recipient nations' population is ofCatholic faith or 0 otherwise. We follow the same for Muslims. While the coefficient on the Catholic is positive and significant, it isinsignificant for the Muslim (column 6), indicating that religion may have a role in aid flows, as recipient nations with a Catholicmajority receive more aid. It may be noted that Alesina and Dollar (2000) do not find a significant effect of Catholic or Muslimmajority population of the recipients on aid. The difference of this result may be attributable to the analysis of different time periodas our study completely focuses aid allocation in the Post Cold War era, while their sample period mostly includes observationsduring the ColdWar period. However, this result is no way conclusive and needs to be analyzed under domestic political economyframework in a separate study.

The coefficient on the variable of total import to GDP ratio becomes significant, suggesting that bilateral aid is positively relatedto total imports of a recipient country. Moreover, the size of the coefficient on political & civil rights variable also drops sub-stantially. However, the sign, size, and significance of all other variables remain about the same. This regression explains 51% of thevariance in bilateral aid per capita, indicating a better fit among all other regressions. Moreover, lowest AIC value of this regressionalso suggests that it is a more efficient model.

We also consider finding the estimation results by taking 3-year averages of the data which is aimed to reduce the effect ofunusually high or low levels of aid allocation as donorsmay lack information about a recipient in any 1 year. The estimation results arepresented inTable 3. Interestingly, the coefficient on incomeper capita not only drops substantially but also becomes insignificant in all

27 Note that the regression in column 6 in Table 3 explains 59% of variance in the bilateral aid per capita. Its lowest AIC value also suggests that among all otherthis model is relatively more efficient.

Table 4Dependent variable: Real aid per capita from individual donor countries

Independent variables Canada France Germany Japan U.K. U.S.

Population −0.199 −0.335 0.034 −0.141 −0.007 0.063(4.78)⁎⁎⁎ (5.48)⁎⁎⁎ (0.59) (2.01)⁎⁎ (0.12) (0.60)

Income per capita −0.100 −0.082 −0.838 −0.156 −0.321 −0.284(0.72) (0.40) (5.51)⁎⁎⁎ (0.54) (1.94)⁎ (1.26)

Infant mortality 0.173 0.347 0.284 0.347 0.732 0.566(1.17) (1.29) (1.72)⁎ (1.01) (3.59)⁎⁎⁎ (2.03)⁎⁎

Pol. & civ. rights 0.193 0.022 −0.096 0.259 0.340 0.045(1.49) (0.16) (0.64) (1.33) (2.32)⁎⁎ (0.23)

Multilateral aid PC 0.101 0.134 0.013 0.293 0.100 0.350(2.21)⁎⁎ (2.22)⁎⁎ (0.21) (3.01)⁎⁎⁎ (1.69)⁎ (3.71)⁎⁎⁎

Other bilateral aid PC 0.365 0.222 0.550 0.216 0.278 0.522(6.03)⁎⁎⁎ (3.20)⁎⁎⁎ (7.36)⁎⁎⁎ (2.91)⁎⁎⁎ (4.49)⁎⁎⁎ (6.18)⁎⁎⁎

Imports/GDP 0.512 -0.219 0.292 0.015 0.917 0.711(4.01)⁎⁎⁎ (0.97) (1.68)⁎ (0.06) (4.03)⁎⁎⁎ (3.00)⁎⁎⁎

B. mfr. imports/imports 0.126 0.378 0.193 0.093 −0.079 0.088(3.41)⁎⁎⁎ (4.67)⁎⁎⁎ (1.87)⁎ (1.01) (0.80) (1.01)

M&TE imports/imports 0.109 0.225 0.196 0.368 0.753 −0.027(2.91)⁎⁎⁎ (2.81)⁎⁎⁎ (1.99)⁎⁎ (2.88)⁎⁎⁎ (6.92)⁎⁎⁎ (0.31)

Agr. imports/imports 0.029 0.147 0.086 0.059 −0.086 0.132(0.86) (1.76)⁎ (1.42) (1.37) (1.22) (1.44)

Reserves per capita −0.241 0.083 0.183 0.029 −0.019 0.270(3.88)⁎⁎⁎ (0.93) (2.23)⁎⁎ (0.33) (0.24) (3.34)⁎⁎⁎

Domestic PP (per dollar) −0.087 −0.027 −0.002 −0.015 −0.134 −0.111(3.50)⁎⁎⁎ (0.79) (0.04) (0.37) (3.98)⁎⁎⁎ (2.34)⁎⁎

Colony – 1.144 – – 1.833 –

(4.45)⁎⁎⁎ (11.91)⁎⁎⁎

Muslim 0.264 0.186 −0.278 0.0785 −0.612 0.137(2.10)⁎⁎ (1.09) (1.52) (0.37) (3.52)⁎⁎⁎ (0.75)

Roman Catholic 0.440 0.565 1.198 0.022 0.710 0.942(3.37)⁎⁎⁎ (2.85)⁎⁎⁎ (7.37)⁎⁎⁎ (0.10) (3.55)⁎⁎⁎ (4.31)⁎⁎⁎

Israel – – – – – 8.267(9.83)⁎⁎⁎

Egypt – – – – – 2.022(6.57)⁎⁎⁎

Adjusted R2 0.544 0.531 0.367 0.241 0.556 0.470Observations 684 672 720 648 720 528

Note: Estimated with heteroscedasticity-robust standard errors. Year dummies included but not reported. Absolute t-values are shown in parenthesesSuperscripts ⁎⁎⁎, ⁎⁎ and ⁎ indicate significance at 1, 5 and 10% levels, respectively. Pol. & Civ = Political and Civil; Mfr = Manufacturing; B = Basic; M&TE = Machinery& Transportation Equipment; Agr = Agricultural.


.

regressions. This suggests that over the long-run, donors showminimum interest in reducing economic hardships and poverty in thepoor recipient countries.Moreover, the impact of political rights and civil liberties on aid allocation also decreases (column6). The signand significance of all other variables remain about the same as in the regressions using yearly data.27

These finding, combinedwith the findings of donors’ self-interests, may help to understand why foreign aid remains ineffectivein promoting economic growth and development in the aid-receiving countries, as concluded by most influential aid studies(Alesina and Dollar, 2000; Alesina and Weder 2002; Boone 1996; Burnside and Dollar, 2000). In a comprehensive analysis of aideffectiveness literature, Doucouliagos and Paldam (2008) conclude that development aid has failed to achieve its stated objectivesof improving growth and living standard in developing countries. The ineffectiveness of aid has also been attributed to poorgovernance mechanism, corruption of ruling elites and aid fungibilitiy issues in the recipient countries. Our results indicate thatthe failure of bilateral aid also lies in the fact that donor countries, while allocating aid, attach greater priorities to their economicand political self-interests rather than to increasing development in developing countries.

4.2. Estimations by individual donor countries

Although the results of aggregate bilateral aid from 22-DAC countries suggest that aid flows are largely determined by thecommercial and strategic self-interests of donors. However, those considerations for aid allocation may vary for an individualdonor country. A recipient receiving aid from several sources might feel less constrained to import goods from a donor country.

s


Therefore, at the margin, the effect of donor's goodwill to influence a recipient can be lower than expected. Realizing thediminishing marginal impact of goodwill, a donor country may increase aid allocation to a recipient getting more aid from otherdonor countries as well as multilateral agencies. Fortunately, bilateral import data by category groups on yearly basis exist for amajority of recipient countries in our sample from the top six individual bilateral donors, i.e., Canada, France, Germany, Japan,United Kingdom and United States. Aid from these countries accounts for more than 70% of total bilateral aid. For measuring aidconcentration, we have added two variables which are total bilateral aid from other countries and total aid from multilateralagencies, both as per capita.

Table 4 shows the estimation results. The regression results largely substantiate the findings for aggregate bilateral aid (Tables 2and 3). Except for United States, all individual donors provide more aid to the recipients importing machinery and transportationequipment from them. For United States, political and strategic concerns appear to be stronger than economic gains from aidallocation.28 Moreover, individual donor countries, in order to maintain their influence, appear to provide larger amount of aid to arecipient receiving more aid from other bilateral donor countries and/or multilateral agencies. Canada, France and Germany alsoprovide more aid to recipients who import basic manufactured goods from them. However, imports of agricultural products haveno significant effect on aid allocation by any of these donor countries.

Another interesting finding is that, except Germany and United Kingdom to some extent, no other individual donor countryseems to care about adverse income shocks in poor countries. On the other hand, only United Kingdom and United States appear toconcern about alleviating physical miseries (increasing infant mortality rate). Moreover, all individual donor countries, in order tomaintain their influence, provide larger aid to a recipient also receiving more aid from other bilateral donors and/or multilateralagencies. The larger size and significance on dummy variables for colonial history, Israel and Egypt suggest that usual politicalinterests dominate aid allocation decisions of these donor countries.29 Interestingly, except Japan, all others provide more aid tothe recipient with majority of Roman Catholic population, while United Kingdom appears to provide less aid to the Muslimmajority recipient countries. However, this finding is not conclusive and needs to be investigated in a separate study.

5. Conclusion

This paper argues that bilateral aid from developed OECD countries is disproportionately allocated to recipient nations whohave a greater tendency to import goods in which donor nations have a comparative advantage in production. We first develop atheoretical model to derive simultaneous optimization decisions of donors. To verify the predictions in the theoretical model, weempirically estimate the determinants of aid allocation by simultaneously controlling for altruistic and self-interest motives ofdonors. This approach aims to correct the ad hoc econometric treatment and to appropriately assess the determinants of aidallocation.

The empirical results largely confirm the theoretical predictions in our model. The estimations indicate that a substantiallylarger amount of bilateral aid per capita is provided to the recipients who import capital goods, while imports by other individualcategory groups have no significant effects. Given that developed donor nations are major producers and exporters of capitalgoods, this result at least partially supports their trade benefits motive. On the other hand, aid may also be given as a reward to therecipient nations for promoting imports of capital goods and removing trade restrictions. The recipient nations also gain becausegreater imports of machinery and transportation equipment help increasing their production (and subsequently consumption),and in turn, they receive more aid. On the flip side, poor countries lacking resources, both private and public, to import capitalgoods get penalized in two ways: First, their production capacity remains low without importing those goods and, second, theyreceive a lower amount of aid.

Our findings also suggest that donors are more concerned about alleviating physical miseries (infant mortality) and rewardinggood human rights conditions, but they are less focused on reducing economic hardships (low income per capita). This implies thatresidents of the recipient nations with bad human rights conditions endure sufferings both at the hands of their rulers who denythem basic freedoms, and also by donors who curtail aid. Our study also indicates that economic and political self-interests ofdonor countries dominate their stated objectives for reducing poverty and promoting development through aid in developingcountries. This also provides some insight into the fact that why the majority of the authors seem to agree that aid has nosignificant effect on growth. This study does not delve into the political economy aspects of trade and aid allocation, however, thiscan be an interesting area for future research.

Acknowledgements

I am grateful to Ronald Balvers, Subhayu Bandyopadhyay, Arabinda Basistha, James Irwin, Peter Leeson, Russell Sobel and JasonTaylor for helpful suggestions. I have greatly benefited from the insightful comments of the Editor and two anonymous referees ofthis journal. I also appreciate comments of the conference participants at Southern Economics Association 2006 Meetings inCharleston and seminar participants at Central Michigan University 2007.

28 We also run a separate regression for United States bilateral aid per capita by excluding Israel and Egypt from the data set to see if that changes thesignificance and magnitude of coefficients on other variables. However, the results for other variable remain about the same.29 Since only France and United Kingdom among these individual donors have been colonizers, we introduced colonial dummy for their past colonial links withthe recipient countries.


Appendix A. The 22-DAC member countries of OCED in our sample

Australia
France Luxembourg Sweden Austria Germany Netherlands Switzerland Belgium Greece New Zealand United Kingdom Canada Ireland Norway United States Denmark Italy Portugal Finland Japan Spain
Appendix B. Aid recipient countries in our sample

Algeria
India Senegal Argentina Indonesia Seychelles Bangladesh Israel Singapore Belize Jordan Slovenia Benin Kenya South Africa Bolivia Kuwait Sri Lanka Brazil Latvia St. Kitts and Nevis Burkina Faso Lithuania St. Lucia Burundi Macedonia St. Vincent and the Grenadines Cameroon Madagascar Suriname CAR Malawi Tanzania Chile Malaysia Thailand Chile Maldives Togo Colombia Malta Tunisia Cote d'Ivoire Mauritius Uganda Croatia Mexico Uruguay Cyprus Moldova Venezuela Czech Republic Morocco Yemen Dominica Nepal Zambia Ecuador Nicaragua Zimbabwe Egypt Niger El Salvador Oman Ethiopia Pakistan Gambia Paraguay Grenada Peru Guatemala Philippines Guinea Poland Honduras Romania Hungary Saudi Arabia
Appendix C. Data sources

Variables
Data sources
Net aid from 22-DAC member countries of OECD
International Development Statistics, OECD (2005) Population (millions) World Bank Development Indicators (2006) CD Rom GDP Per Capita PPP ($2000) World Bank Development Indicators (2006) CD Rom Political Rights and Civil Liberties Freedom in the World, Freedom House, New York (2005) GDP (current US $) World Bank Development Indicators (2006) CD Rom Net aid from multilateral agencies International Development Statistics, OECD (2005) Total Imports UNCTAD (2005); World Bank Development Indicators (2006) CD Rom Total Exports UNCTAD (2005); World Bank Development Indicators (2006) CD Rom Imports by Commodity Groups Handbook of Statistics online, United Nation Conference on Trade and Development (2005);
UN Comtrade Database (2005)
Unit Value of World Import Price Index Handbook of Statistics online, United Nation Conference on Trade and Development (2005) Total Reserves (including gold) Handbook of Statistics, UNCTAD (2005): World Bank Development Indicators (2006) CD Rom Domestic PP (Per Dollar) World Bank Development Indicators (2006) CD Rom Infant Mortality Rates World Bank Development Indicators (2006) CD Rom Air Distance (in kilometers) Gallup and Sachs (1999) Colonial Past Central Intelligence Agency (2006). The World Factbook. Roman Catholics Population Central Intelligence Agency (2006). The World Factbook. Muslims Population Central Intelligence Agency (2006). The World Factbook.
Note: DAC = Development Assistance Committee.

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