Is Foreign Aid Motivated by Altruism or Self-Interest? A

Theoretical Model and Empirical Test

Andrea Civelli, Andrew W. Horowitz, Arilton Teixeira�

January 2013

Abstract

We develop a simple theoretical model of bi-lateral foreign aid that generates falsi�able empirical im-

plications and an explicit test for a signi�cant altruistic motivation in bi-lateral foreign aid disbursements.

We then estimate the model with OECD donor-data to search for donor-recipient pairs that satisfy the

theoretical condition for altruistic motivation. We �nd that approximately 8% of donor-recipient pairs

satisfy the theoretical condition for altruism. We argue that since donor motivation may be an important

unobserved characteristic contributing to endogeneity bias in prior estimates of foreign-aid e¤ectiveness,

this project may also contribute to more accurate estimates of aid e¤ectiveness.

JEL Codes: E22, E32, O11, O19

Keywords: Foreign Aid, Altruism, Welfare Analysis, bilateral donors, business cycles

1 Introduction

Imagine an altruistic father who earns $10,000 a month and gives his less successful son $1,000 a month to

supplement the $1,000 the son earns. Utility of both father and son exhibit diminishing marginal utility.

Now an unanticipated income shock reduces both father�s and son�s earned income by 50% �to $5,000 and

$500 per month respectively. Does the father transfer more or less income after the shock? While there is no

unconditional answer to the question we can show that with su¢ cient altruism transfers will increase �that

is, with su¢ cient altruism transfers become counter-cyclical. We employ this theoretical result to develop

an empirical test for altruism in bi-lateral O¢ cial Development Assistance (ODA or foreign aid) �an issue

�A. Civelli: Economics Department, University of Arkansas. E-mail: andrea.civelli@gmail.com. A. Horowitz: EconomicsDepartment, University of Arkansas. E-mail: horowitz@uark.edu. A. Teixeira: FUCAPE Business School, Victoria (Brazil).E-mail: arilton@fucape.br.

1

that has been subject to much debate.1 We �nd evidence that approximately 8% of donor-recipient pairs

satisfy our test for altruistic ODA motivation.

Most prior ODA literature has focused either on the e¤ect of ODA on recipient countries or the motivation

of donors. We shall argue these issues are inextricably intertwined. In particular, important recent works have

asserted that earlier estimates of ODA e¤ects are subject to undermining endogeneity bias (Angus Deaton

(2010), Raghuram Rajan and Arvind Subramanian (2008)). That is, since aid is not randomly assigned

across recipients, the presence of unobserved characteristics which determine both the distribution of aid

and its e¤ectiveness will critically bias estimates of ODA impact. One of the most potentially important of

those unobserved characteristics is the motivation of donors.

Our model will identify counter-cyclical aid �ows as a signal of altruistic ODA motivation. The

cyclicality of aid �ows from both donor and recipient perspectives has been addressed in prior theoretical

and empirical literature. However, the prior focus has primarily been on the e¤ect of aid on recipient business

cycles and its role as stabilizer or destabilizer of recipients. The possibility that cyclical patterns of aid may

provide a signal of ODA motivation has not been considered.

The remainder of the paper is organized as follows: Section 2 provides a review of the literature and

additional background material. Section 3 develops our simple theoretical model of bilateral ODA that

yields a testable empirical condition for signi�cant altruistic motivation. Section 4 provides our preliminary

empirical results. Section 5 summarizes and suggests future extensions.

2 Prior Literature and Additional Background

The motivation for bi-lateral O¢ cial Development Assistance (ODA) has long been debated (see, for instance,

Leonard Dudley and Claude Montmarquette (1976); McKinlay & Little, 1979; Alfred Maizels and Machiko

K. Nissanke (1984); Trumbull & Wall (1994); Javed Younas (2008); Chong & Gradstein (2008)). Many

argue that ODA is ultimately motivated by self-interest (Jean Claue Berthelemy and Ariane Tichit (2004);

Berthelemy (2006); Alberto Alesina and David Dollar (2000); Younas, 2008). This view is prevalent in

the political science literature (Robert A. Packenham (1966); Peter J. Schrader, Steven W. Hook, and

Bruce Taylor (1998); Bruce B. de Mesquite and Alastair Smith (2007); David H. Bearce and Daniel C.

Tirone (2010)). Others argue that the motivations vary signi�cantly across countries and that while ODA

from most countries is motivated by self-interest, other countries appear altruistic (Jakob Svensson (1999)).

Donors� self-reported motivation should also be noted. Over 95% of reported global ODA was provided

by the subset of the OECD countries belonging to the Development Assistance Committee (DAC). DAC1We provide a review of this large literature in Section 2 of this paper. As is standard, our analysis excludes military aid.

O¢ cial de�nitions of all ODA terms used in this paper can be found at http://www.oecd.org/dataoecd/36/32/31723929.htm.

2

members adopt standardized accounting methods and assert altruistic motivation for ODA.2 Though it is

natural to discount donors�self-reported motivation, falsifying altruism is di¢ cult.

A natural starting point for discerning donors�motivation would seem to be measurement of donors�

�return� to ODA. If transfers to impoverished recipients yield no bene�ts to the donor, altruism emerges

as the likely motivation by process of elimination. However, even the most impoverished nations have the

capacity to provide a return to donor�s ODA in the form of supportive votes in multi-lateral institutions

such as the UN and many authors in both economics and political science have taken this as evidence of

self-interest motivation.3 For example, US e¤orts to impose sanctions on (presumed) nuclear proliferators

have depended on a sequence of close UN votes. There is little doubt that in such contexts supportive votes

convey considerable value to the protagonists. Nevertheless, measuring the actual donor return ODA is

extremely di¢ cult. For example, rather than a supportive vote, donor return may be in the form of inaction

by a recipient as when a recipient agrees to not sell uranium ore to a proliferator. This return (in the form

of inaction) will not be captured by counting supportive multi-lateral votes or by any explicit balance sheet

entry. Fortunately, we are able to empirically test our altruistic motivation condition without the need for

direct measurement of donors�return. We will discuss this in detail in the estimation section.

As noted, most economics literature is focused on the e¤ect of ODA on recipient countries rather than

the motivation of donors. An important strand of this literature looks at the relationship between ODA

and the business cycle in both donor and recipient countries (Bulir & Hamaan (2008); Kuhlgatz, Abdulai,

& Barrett, 2010; Stephane Pallage and Michel A. Robe (2001); Pallage, Robe, and Berube (2007); Dabla-

Norris, Minoiu, and Zanna (2010)). Though these models are related to our work our research objective is

distinct. Speci�cally, we seek to identify a theoretical signal of altruistic motivation and then test empirically

for the presence of this signal.

3 Theoretical Model

Following the principle of parsimony, we construct the simplest possible model to generate a distinguishing

empirical signal for altruism among donors. We �rst postulate the following reduced form relationship

between donor and recipient business cycles:

Yr�Yr= k + �dr

Yd�Yd+ 'X + " (1)

2http://www.oecd.org/department/0,2688,en_2649_33721_1_1_1_1_1,00.html3Many of the citations above adopt this rationale.

3

Where the Yi�Yirepresents the output gap of country i = r; d which is de�ned as the ratio of actual GDP Yi

over its trend (or habit) income �Yi. On the right-hand-side of equation 1, k is a constant and X can be

thought of at this stage as embodying other relevant determinants of the recipient�s income. Finally, " is a

residual with mean zero. It is not necessary to impose any restrictions on � so that the income of donor and

recipient may be correlated positively, negatively, or not at all. In general, � will be dictated by the degree

of integration of the recipient country with the global economy as well their trade mix.

Donors and recipients derive utility from their own-consumption: Ci i = r; d. In order to maintain focus

on donors�ODA disbursement decisions in a tractable model we now make �ve simplifying assumptions:

i. Government expenditures and net exports are fully absorbed by consumers;4 ii. Donors may care about

recipient country consumers, but are not altruistic towards recipient country �rms; iii. In recipient countries,

ODA is not directly used for investment; iv. The return to ODA is �consumed� instantaneously by the

government and/or consumers in the donor country; v. There is no strategic interaction between donor

countries. Given these assumptions and letting Adr � 0 represent ODA from donor d to country r, donor

and recipient resources constraints are:5

Cd + Id +RXr=1

Adr = Yd +RXr=1

�drAdr (2)

Cr + Ir �DXr=1

Adr = Yr (3)

where 0 < �dr < 1 is the donor�s return to ODA to recipient r and D and R are the total number of

donors and recipients respectively. Note that these resource constraints capture the fact that donors give

ODA to many recipients and recipients receive ODA from many donors. Clearly, the linearity of ODA return

is an additional simplifying assumption to be interpreted as a local approximation of an interior solution

to the donor�s disbursement problem. However, as will be seen in the empirical section, OECD ODA data

is consistent with interior solutions since, in fact, all donors provide non-zero ODA to virtually all possible

recipients �including such unlikely donor-recipient pairs as the U.S. and Cuba.

Let ud�Cd= �Cd

�and ur

�Cr= �Cr

�be the �own consumption�utility functions with Ci and �Ci the actual

and reference (or habit) consumption level for country i and both utility functions exhibiting diminishing

marginal utility: u0i > 0; u00i < 0. We note that all of the model�s theoretical predications work in levels as

well relative to trend (or habit). Empirically, it is more natural to focus on trend deviations so we adopt that

speci�cation in this paper. Additionally, donors may care about their recipients�utility and internalize it in

4 In our notation Cd and Cr include net ODA, G and NX. Since ODA disbursements are from donors to recipients only,ODA transfers are subtracted from donors�GDP; on the other hand, Cr must include ODA receipts.

5The sub-section 4.1 explains our bridge from theory to empirics.

4

their own total utility function. Speci�cally, let �dr > 0 be donor d�s altruism parameter towards recipient

r. Donor d�s utility maximization problem with respect to ODA is then:

maxAdr

Ud = ud

�Cd�Cd

�+

RXr=1

�drur

�Cr�Cr

�(4)

subject to constraints 2 and 3.

Donor d solves problem 4 by choosing an Adr for each recipient r taking the reference consumption levels

as given. This yields R interior �rst order necessary conditions:6

�(1� �dr)u0d�Cd�Cd

�1�Cd+ �dru

0r

�Cr�Cr

�1�Cr= 0 (5)

for r = 1; ::::; R.

Using the implicit function theorem on the �rst-order-conditions we obtain the partial 6 below which

indicates the change in ODA with respect to donor income for each donor-recipient pair:

@Adr@Yd

= �u00d (1� �dr)

�1�Cd

�2+ �dr�dru

00r

�1�Cr

�2 �Yr�Yd

(1� �dr)2u00d

�1�Cd

�2+ �dru00r

�1�Cr

�2 (6)

Equation 6 provides a surprisingly rich set of empirical implications given the simplicity of the model. To

sign this partial �rst note that the denominator is unambiguously negative by diminishing marginal utility

regardless of the magnitude of � and �. Manipulating the numerator we �nd:

@Adr@Yd

< 0 iff�dr�dr(1� �dr)

>�Yd�Yr

� �Cr�Cd

�2u00du00r

(7)

Countercyclical ODA (@Adr

@Yd< 0) thus requires an altruism parameter in excess of a threshold level for

a given �, �, and risk preferences. The intuition for this signal of altruism is simple, yet compelling: Due

to diminishing marginal utility when the incomes of donor and recipient both fall proportionally the poorer

recipient�s marginal utility of consumption increases faster than that of the richer donor. But for increased

transfers to augment total utility of the donor � must be su¢ ciently large. Hence, given positive co-movement

of donor and recipient income (positive �), countercyclical ODA constitutes a signal of an altruism which we

call strong-altruism to distinguish it from the case where � is positive but not large enough to generate

6We note that an interior solution requires at least an " of altruism and � < 1. Regarding �, we believe � � " to be quitereasonable for our OECD donors. Regarding the � assumption, note that if � � 1 the donor should give its entire GDP inODA, a case we can also con�dently discard. As will become clear, we do not take the presence of an interior solution itself asevidence of signi�cant altruistic motivation. Finally, we note again that, empirically, corner solutions (Adr = 0) are quite rareamong the major OECD donors.

5

@Adr

@Yd< 0. In summary Strong-Altruism occurs when voluntary transfers from a richer to a poorer agent

move inversely with changes in both agents�income, as illustrated in the example of transfers from a father

to son in the opening paragraph.

4 Empirics

4.1 ODA Accounting

In moving from theory to estimation it is important to consider how ODA disbursements and receipts enter

the national income accounts of both donors and recipients. The standard measures of GDP include ODA

as an export for the donor country and as part of consumption and imports for the recipient. Going from

GDP to GNI it is necessary to adjust for those items that generate a trade �ow without the corresponding

income �ow. For a donor, this means that ODA must be subtracted from GDP; for a recipient country,

the total income available has to include the ODA received from the donors since ODA is directly used to

increase consumption. Donor countries choose how to allocate their GDP, Yd (which is determined by the

market activity of a country) between consumption, Cd, investment, Id, and ODA, Adr. Consumption in

equation 2 corresponds to the GNI of a donor country net of investment expenditure and we construct it

empirically subtracting ODA from GDP. Also for the recipient countries, Cr overlaps with the concept of

GNI. Since GDP does not include ODA, we construct Cr by adding total ODA transfers (from all donors)

to a recipient country. From the point of view of a maximizing donor, equation 3 is simply the de�nition

of how recipients�consumption depends on its ODA donations. However, the donor�s side is complicated

by the returns to ODA, which based on our prior discussion, is likely to not be fully included in explicit

national income accounts. This would be the case, as in our earlier example, when the donor �return�is that

the recipient does not sell uranium to a third country. While this return will not appear in national income

accounts our modeling framework assumes the ODA decision maker is aware of these bene�ts (return) and

considers them in allocating ODA. Also note that for donors ODA is very small component of GDP, less

than 0.5% for virtually all OECD donors.

4.2 Data

National account data is drawn from PWT 7.0 while ODA data is from the OECD. The current analysis

utilizes 16 OECD donors and 142 recipients for the period 1991 to 2009.7 The year 1991 was chosen due

to potential structural breaks in the global unobserved ODA model associated with the dissolution of the

7There are many new small DAC donors in recent years. We chose the 15 largest the DAC donors countries over our timeperiod and plus all Scandinavian countries (since they are often noted as altruists in the literature).

6

Soviet Union. The 158 countries also contain no missing data for this time period. Appendix A lists the 158

countries in our sample. All analysis utilizes 2005 International Dollars per person �the reporting basis in

the Penn World Tables (RGDPI). Data taken from OECD was mapped to PWT data. All the variables are

expressed in equivalent PPP per-capita terms. Since the ODA �ows from donor d to recipient r are provided

by the OECD data base in current USD, these are adjusted multiplying the �ows by the ratio between the

PWT GDP (which is already in equivalent PPP per-capita terms) and the current USD GDP from the

OECD. Figure 1 below illustrates Total ODA Disbursements for the 16 donors in our sample (each color

represents a unique donor) as a share of donor GDP and reveals that the majority fall between 0.1-0.5%. It

is interesting to note that none achieve the stated OECD-DAC target of .07% of GDP.

0 2 4 6 8 10 12 14 16 18 200

1

2

3

4

5

6x 10­3

Figure 1: Total ODA Disbursements/GDP - 16 DAC Donors - 1991 - 2009.

Notes: Something Something.

Figure 2 shows ODA relative to GDP for all 142 of the recipient countries �again each line represents

a speci�c country. Note that ODA receipts range from very little to over 20% of GDP for some recipients.

The darker line in Figure 2 represents the average amount of aid received by the 142 countries in our sample,

which is between 2% and 4%. Both Figures show there is considerable variance of ODA as a share of GDP

for some donors and recipients while others are relatively stable.

As noted previously, each donor disburses ODA to a large set of recipients �zero transfers in any year of

our sample among the 2528 donor-recipient pairs are relatively rare. However, most donors appear to have a

stronger systematic ODA relationship in terms of share of GDP with only a much smaller set of recipients.

The remaining recipient countries receive aid and in smaller amounts and some only on an occasional basis.

This characteristic will play an important role in our results.

7

2 4 6 8 10 12 14 16 180

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Figure 2: Total ODA Disbursements/GDP - All Recipients - 1991 - 2009.

Notes: Something Something.

The US is an extreme example of this pattern disbursing ODA to 135 out of 142 countries with two thirds

of the countries receiving less than 0.05% of the total US ODA on average. 58% of US recipients receive,

in total, less than 0.03% of US total disbursements on average while the 10 largest US recipients receive on

average 50% of total US ODA disbursements.

4.3 Estimation

We now move to testing whether the �strong altruism�condition (inequality 7) derived in our theoretical

model holds signi�cantly among our donor-recipient pairs. To test for cases that satisfy this inequality we

must assume a utility function form and we follow the standard procedure of �rst considering Constant

Inter-temporal Elasticity of Substitution (CES). In our case utility becomes: uit =(Cit= �Cit)

1��i

1��i , with t the

time index and the usual result holds that IES = 1�iis the inverse of risk aversion parameter �i.

Equation 1 (the reduced form relationship between recipient and donor business cycles) and equation

5 (the �rst order condition) de�ne the framework for the estimation of the parameters of the model and

the evaluation of the strong-altruism condition. For the baseline case, we further specialize our model by

introducing an auto-regressive term in equation 1. The GDP equation for the recipient countries becomes:

Yrt�Yrt

= k + �drYdt�Ydt

+ 'rYrt�1�Yrt�1

+ "t (8)

This term is just a simple way to capture other idiosyncratic determinants of the economic cycle of a

8

country that re�ect structural characteristics of its speci�c economic environment. An alternative speci�ca-

tion of this equation might include e¤ects such as population changes, trade dynamics, government variables,

and other factors implicit in the auto-regressive term. We compute the trend GDP, �Yt, de-trending the series

by applying a HP �lter with the smoothing parameter set to 100.

The �rst order condition of equation 5 is then rewritten using our constant absolute risk aversion utility

functional form and becomes:

1�Cdt

�Cdt�Cdt

���d� �dr(1� �dr)

1�Crt

�Crt�Crt

���r= 0 (9)

Under the CES assumption, the strong altruism condition expressed in 7 is:8

�dr�dr(1� �dr)

>�d�r

"�Yd�Yr

� �Cr�Cd

�2 �Cd= �Cd

��(�d+1)�Cr= �Cr

��(�r+1)#

(10)

Equation 8 and 9 are then used to jointly estimate the parameters of inequality 10 by GMM. The usual

orhogonality conditions between regressors and the error term in equation 8 provide the necessary conditions

for estimating the coe¢ cients of the �rst equation. We directly use equation 9 in expectations as the last

GMM condition necessary to obtain the estimate of the ratio �dr=(1 � �dr). The full vector of estimated

parameters is � = ['r k �rd �dr= (1� �dr)]. At this stage, we do not have to separately identify � and �.

We rely on the asymptotic properties of the distribution of the GMM estimator to conduct the strong-

altruism test on inequality 10. The vector of estimates �̂ has a normal asymptotic distribution given by:

pT��̂ � �

�! N (0; V )

Where T is the length of the sample and the covariance matrix is de�ned as V =�D��1D0��1; in

which ��1 is the optimal weighting matrix from the GMM procedure and D0 is the gradient matrix of the

GMM conditions with respect to the components of �. The covariance matrix V is estimated evaluating

the gradient at �̂ and using the Newey-West estimator for �. Note also that the test is independent of the

speci�c choice of the risk aversion parameter when they are assumed to be equal.

8Combining 9 and 10, the altruism condition can be further simpli�ed into

�dr >�d

�r

�Cr= �Yr�Cd= �Yd

Cr= �Cr

Cd= �Cd(N1)

Although the condition in 10 and this condition are theoretically equivalent, they can have di¤erent empirical implicationssince condition N1 eliminates the estimation of �dr=(1��dr). We prefer to conduct the strong-altruism test based on condition10 and the GMM procedure outlined in this section for two reasons. First, this allows to explicitly embed the interactionbetween the ODA allocation decision and the business cycle in the estimation, which is the core empirical mechanism of ourmodel. The second reason is that we are also interested in providing an estimate of �dr . This parameter plays a crucial role inthe theoretical justi�cation of the strong-altruism pairs and, in this way, it can be jointly estimated with �.

9

The application of the delta method allows us to derive also a distribution of �rd�dr= (1� �dr) and to

evaluate the condition presented in inequality 10. Using the CES speci�cation for the utility functions and

under the null that �̂rd�̂dr

(1��̂dr)� Z, where Z is the right hand side of 10, the asymptotic distribution of �̂rd�̂dr

(1��̂dr)

is approximated by

pT

0@ �̂rd�̂dr�1� �̂dr

� � Z1A! N

�0; L�̂V L

0�̂

�(11)

Where L0 is the gradient of �rd�dr= (1� �dr) with respect to the components of �, so that L = [0 0 �dr= (1� �dr) �rd].

The gradient is then empirically evaluated at the point de�ned by equation 5 taken with an equality sign

and in expectation.

Finally regarding estimation, note that though the strong altruism condition 10 is independent of the risk

aversion parameter so long as they are equal for donor and recipient, the parameter estimates themselves

are not. We must therefore make an assumption on risk aversion to estimate the model and we adopt a

baseline case of �d = �r = 2. Other risk aversion values were also assumed as part of our robustness checks.

As discussed subsequently, the baseline results are quite robust to reasonable changes in the vector of risk

aversion parameters.

4.4 Summary of Baseline Estimation Results

Since we estimate four parameters ('r, k, �rd, and �dr= (1� �dr)) for all 2528 donor-recipient pairs it

is infeasible to report the entire set of point estimates for all pairs (10,112 point estimates). Therefore,

our principal objective of this section is to summarize results rather than focus on analysis of potential

idiosyncratic altruistic motivation among the 2528 speci�c donor-recipient pairs. Interpretation is provided

in section 4.6 and the conclusion. The �rst general point is that approximately 8% of the sample satisfy the

strong-altruism condition at the 5% con�dence level. Hence, our results suggest that though the altruism

signal is not present in the large majority of ODA transfers, neither is it insigni�cant. Figure 3 below

provides a compact summary of the number of donor-recipients pairs (by donor) that signi�cantly display

the altruism signal for the baseline case. The mean number of signi�cant pairs is xx per donor. The speci�c

recipients represented in Figure 3 are presented in Table XX of Appendix B.

In Table XX we report the � point estimates for those donor recipient pairs with signi�cant parameter

point-estimates and that pass the strong-altruism test at the 5% con�dence level. The full set of point

estimates is available from the authors upon request. We choose to report the signi�cant ��s since this

parameter is the easiest to interpret �capturing a reduced form conditional-correlation between donor and

recipient income relative to trend (equation 8). Note that Table XX does not include all 142 recipients since

10

some recipients do not pass the strong- altruism test with any donor.

0

5

10

15

20

25

30

Aus

tralia

Bel

gium

Can

ada

Den

mar

kFi

nlan

dFr

ance

Ger

man

yIta

lyJa

pan

Spa

inA

ustri

aN

ethe

rland

sN

ew Z

eala

ndN

orw

ayS

wed

enS

witz

erla

ndU

nite

d K

ingd

omU

nite

d S

tate

s

Figure 3: Number of signi�cant pairs by donor that satisfy Strong Altruism.

Notes: Something Something.

Figure 4 below provides an important perspective of the underlying factors driving these signi�cance

results. After readjusting the terms and after multiplying and dividing by Adr the right hand side of 10, the

strong altruism condition can be rewritten as

�dr�dr(1� �dr)

>�d�r

"�Cr= �Yr�Cd= �Yd

�Cr= �Cr

��r�Cd= �Cd

��d Cr=AdrCd=Adr

#(12)

Inequality 12 provides a useful decomposition of the altruism condition into factors that helps to explain

when a pair of countries is more likely to satisfy the condition. The �rst term indicates that the less risk

averse is the donor, relative to the recipient, the more likely is the condition to be satis�ed. Greater recipient

risk aversion implies a more concave utility function and a higher marginal utility payo¤ in transfers from a

rich altruistic donor to a poor recipient.

Moving within the brackets, we see that the lower is trend consumption relative to trend GDP in the

recipient country (the higher trend consumption relative to GDP is in the donor country), the smaller the

right hand side of 12 is and the more likely the condition is satis�ed. Similarly, the higher the deviation of

consumption from the trend is in the recipient country (the lower it is in the donor country), the bigger the

right hand side of 12 is and the less likely the condition is to be satis�ed. Again, both of these terms re�ect

the incentive to transfer from low to high marginal utility agents.

The �nal term of 12 shows that the smaller (larger) is consumption of the recipient (donor) relative to

the ODA disbursement, the more (less) likely is the condition to be satis�ed. The e¤ect of these terms on the

11

likelihood of a positive altruism signal is intuitive. A large disbursement relative to recipient consumption

implies a larger marginal utility impact of ODA, something an altruistic donor will consider in allocation

across recipients. Small ODA relative to donor consumption is consistent with casual empiricism. For

example, the largest recipients of US aid (Egypt and Afghanistan) very likely yield geo-political return.

Cuba, on the other hand, which receives very small amounts of ODA relative to US consumption, routinely

opposes US interests in multi-lateral forums. True altruism is therefore the more natural explanation than

a return, which is consistent with the e¤ect of these terms in our model.

Additional insight on these implications and results of our model can be seen in Figure 4 below. This

scatter plot displays the share of ODA received on average by a recipient country by donor (vertical axis)

against the share of that disbursement in the donor�s total ODA donations (on the horizontal axis). Note

that the mass of those bi-lateral pairs satisfying the altruism condition (the red dots) are those where bi-

lateral ODA is a relatively small share of total ODA from both the recipient�s and donor�s perspective. That

is, the share of recipient�s ODA from a particular donor and the of donor�s ODA to that speci�c recipient are

both small relative to those bi-lateral pairs that do not satisfy the condition. Again, this is quite intuitive

since the competing hypothesis (to altruism) is that donors give ODA to receive a return. If the return is

provided in supportive multi-lateral votes we would expect ODA from donors receiving a return to be large

relative to altruistic donations since the recipient could extract additional ODA (at the margin) in return

for the vote. The fact that the pairs satisfying our altruism condition are indeed massed at small shares for

both recipient and donor is consistent with this line of reasoning.

­0.05 0 0.05 0.1 0.15 0.2 0.25 0.3­0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Figure 4: ODA Vs. share of disbursement.

Notes: Share of a recipient�s ODA from a donor (Y-axis) Vs. share of disbursement in the donor�s total ODApayments (X-axis). Red points correspond to the pairs satisfying the altruism condition.

12

4.5 Robusteness Check

As noted, the results presented thus far are a baseline which assumes the same risk aversion parameter for

both rich and poor countries. However, there is evidence that the poor in LDCs may display high levels of

risk aversion (Mahmud Yesuf and Randall A. Blu¤stone (2009); Mette Wik, Tewodros Aragie Kebede, Olvar

Bergland, and Stein T. Holden (2004)). Under some assumptions this may translate to greater risk aversion

at the country level (see Blackburn and Ukhov (2008) for discussion). As a robustness check we estimated

the model with greater risk aversion parameters for the recipient countries than that of their donors and

found the number of pairs that satisfy the altruism test to generally increase. For example, setting �d = 2

and �r = 3, the percentage of pairs satisfying the �rst strong altruism condition rises to about 18%. In

general, the baseline pairs become a subset of larger set of signi�cant donor-recipient pairs, though there are

some exceptions.

Many additional robustness checks have been performed and the full set is available from the authors

upon request. These include performing the GMM estimation without the auto-regressive component in YY,

variation in the risk aversion parameters, changing the sample dates, and utilizing HP �ltered consumption

levels, rather than the ratio of current and trend observations. The base-line results presented here appear

fairly stable for a large set of small parameter changes.

4.6 Interpretation of Baseline Estimation Results

In this sub-section our objective is to broadly interpret some patterns of altruism-signal signi�cance rather

than providing idiosyncratic explanations among the 183 speci�c donor-recipient pairs (which pass the strong-

altruism test). Donor countries donates money to a large set of recipients. However, they seem to have

a stronger and systematic ODA relationship only with a few of them. The remaining countries receive

occasional aid and in smaller amounts. For instance, the US disburses ODA donations to 135 out of 142

countries. However, the two thirds of the countries receive on average less than 0.05% of the total US ODA

disbursements; 58% gets on average less than 0.03%. The 10 largest recipients receive on average 50% of

total US ODA disbursements. Figure 5 shows the ODA disbursements for the US. This characteristic will

play an important role in our results.

We now consider some �out of model� points of reference for our results. We begin by returning to

the example in the opening paragraph of counter-cyclical transfers between a father and son as a signal

of altruism (given positively correlated income). This motivation describes an unconditional correlation

which, in light of our model, is a proxy for a deeper theoretical relationship embodied in the derivative @Adr

@Yd

on which our altruism tests are based. Therefore, the question of whether the signi�cant pairs identi�ed

13

2 4 6 8 10 12 14 16 180

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Figure 5: ODA shares for the U.S.

Notes: Shares of U.S. ODA disbursements by recipient from 1991 to 2009.

by estimation of our theoretical model also display a negative unconditional-correlation arises naturally. To

explore this we computed the unconditional correlation between donors�output gap and ODA disbursements

to their recipients relative to the donor�s GDP. About 50% of these correlations are negative. In this paper, we

provide a possible explanation for this fact (the large number of countercyclical ODA disbursement) exploring

the possibility that particularly strong counter-cyclical ODA disbursements, properly conditioned, signal

altruistic motivations. Of course, we cannot infer altruistic motivations from simple negative correlations

(whether statistically signi�cant or not) but they provide an interesting point of reference.

This accounts for about 10% of the pairs of countercyclical ODA disbursement. This value increases

up to almost 20% when �d = 2 and �r = 3 is assumed. A second �out of model� comparison to assist in

interpretation is of particular interest in a nascent research area such as this.

5 Conclusions

This povides a new approach to a topic of great importance on both the theoretical and policy levels.

Though most prior research on ODA has attempted to estimate its e¤ects, there are growing concerns that

the endogenous assignment of ODA undermines much of the prior work. We concur with this critique

and believe a potentially important unobserved donor characteristic that may a¤ect both the distribution

of aid and its e¤ectiveness is donor motivation. Hence, we believe the issues of e¤ect and motivation are

inextricably connected. We have developed a theoretical model that generates a testable condition associated

with a special form of altruism that we have dubbed "strong-altruism." Exploration of OECD data provides

14

promising empirically indications that strong-altruism may exist in approximately 5% of donor-recipient

pairs. These results would be consistent with some previous assertions in both economics and political science

that altruism is relatively rare motivation in foreign aid giving. However, these results also contradict that

assertion by many that altruism is exclusively motivated by self-interest.

Identifying altruistic motivation at the country level is an extremely di¢ cult but important measure

problem and we view this paper as a �rst step in a literature that is in its infancy. Indeed, it seems likely

that donor motivations are rarely one-dimensional and typically entail a mixture of both contemporary

motivations and historical relationships. However, it is also certainly the case that altruistic motivation

is relatively more important in some donor-recipient relationships than in others. The identi�cation of

such pairs is a pre-requisite to controlling for the non-random assignment of ODA. Rather than focus on

interpreting the idiosyncratic origins of potential altruism in speci�c donor-recipient pairs we think it more

bene�cial, at this stage, to search for broad patterns consistent with the altruism signal we have identi�ed.

In doing so we have found some tantalizing patters. More rigorous exploration of this possibility and its

implications for improving estimates of aid e¤ectiveness are in progress. Comments, suggestions, and critiques

of this work are warmly welcomed.

Acknowledgements

We thank Aaron Johnson for numerous insightful suggestions, critiques, and exceptional research as-

sistance. We also thank Stephen Smith, James Foster, Jon Rothbaum and other seminar participants at

George Washington University and the 34th Annual Econometric Society Meetings in Brazil (December 2012

�Porto de Galinhas) for insightful comments and suggestions. The usual disclaimers apply.

15

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17

APPENDIX

A Donor and Recipient Countries in Sample

The 18 OECD-DAC countries donor list: Australia, Austria, Belgium, Canada, Denmark, Finland, France,

Germany, Italy, Japan, Netherlands, New Zealand, Norway, Spain, Switzerland, Sweden, UK, USA.

The 142 recipients countries list: Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Ar-

gentina, Bahamas, Bahrain, Bangladesh, Barbados, Belize, Benin, Bermuda, Bhutan, Bolivia, Bosnia and

Herzegovina, Botswana, Brazil, Brunei, Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Central

African Republic, Chad, Chile, China Version 1, China Version 2, Colombia, Comoros, Congo (Dem. Rep.),

Congo (Republic of), Costa Rica, Cote d�Ivoire, Croatia, Cuba, Cyprus, Djibouti, Dominica, Dominican Re-

public, Ecuador, Egypt, El Salvador, Equatorial Guinea, Ethiopia, Fiji, Gabon, Gambia, Ghana, Grenada,

Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hong Kong, India, Indonesia, Iran, Iraq,

Israel, Jamaica, Jordan, Kenya, Kiribati, Korea (Republic of), Kuwait, Laos, Lebanon, Lesotho, Liberia,

Libya, Macao, Macedonia, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mau-

ritania, Mauritius, Mexico, Micronesia, Mongolia, Montenegro, Morocco, Mozambique, Namibia, Nepal,

Nicaragua, Niger, Nigeria, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philip-

pines, Qatar, Rwanda, Samoa, Sao Tome and Principe, Saudi Arabia, Senegal, Seychelles, Sierra Leone,

Singapore, Slovenia, Solomon Islands, Somalia, South Africa, Sri Lanka, St. Kitts and Nevis, St. Lucia,

St.Vincent and Grenadines, Sudan, Suriname, Swaziland, Syria, Tanzania, Thailand, Togo, Tonga, Trinidad

andTobago, Tunisia, Turkey, Uganda, United Arab Emirates, Uruguay, Uzbekistan, Vanuatu, Venezuela,

Vietnam, Yemen, Zambia, Zimbabwe.

B Base-Line Case Point Parameter Estimates

It would be quite infeasible to report the point estimates of the parameters of the model numerically by

donor-recipient pairs. Therefore, we summarize the information about the etimates in the next two �gures.

Figure B1 shows the estimates of �dr and its signi�cance level. The parameter is plotted against its standard

deviation; the level of signi�cance is represented by the straight, blue-dotted lines. If a point lies in the two

most external regions, it is signi�cant at 5% level; if it lies inside the two narrow cones, it is signi�cant at 10%

level; if it is in the most internal region, it is not signi�cant. The red dots correspond to the strong-altruist

pairs and these are compared to the other pairs in yellow. Figure B2 provides the same information for �dr.

This �gure is constructed setting �dr = 0:05 according to the no-arbitrage argument presented in the paper.

18

This allows us to identify the value of the altruistic parameter in the model, which is at the core of our

theory.

The estimates of �dr mainly range between �2 and 2. As one would expect, the majority of altruitic

pairs also have a signi�cant �dr; this is not necessarily the case for �dr. Anyway, condition 7 depends on the

combination of the parameters; a stronger � would actually compensate for a smaller �.

0 0.5 1 1.5 2 2.5 3 3.5 4­4

­3

­2

­1

0

1

2

3

4

5

Figure B1: Point estimates of �dr.

Notes: Red dots identify pairs that satisfy the strong-altruism test. In yellow all the others. The signi�cance of theparameters is shown by the blue, dotted lines. The esternal lines show the 5% signi�cance tresholds. The internallines the 10% level.

19

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1­0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Figure B2: Point estimates of deltadr

Notes: Red dots identify pairs that satisfy the strong-altruism test. In yellow all the others. The signi�cance of theparameters is shown by the blue, dotted lines. The esternal lines show the 5% signi�cance tresholds. The internallines the 10% level.

20