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Is Foreign Aid Motivated by Altruism or Self-Interest? A
Theoretical Model and Empirical Test
Andrea Civelli� Andrew W. Horowitzy Arilton Teixeiraz
University of Arkansas University of Arkansas FUCAPE Business School
June 2013
Abstract
We develop a simple theoretical model of bi-lateral foreign aid that generates falsi�able empirical
implications and an explicit test for a signi�cant altruistic signal 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 20% of donor-recipient pairs
satisfy the theoretical condition for altruism with Scandinavian countries showing, on average, 33% more
altruistic transfers than non-Scandinavian countries. 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
�Economics Department, University of Arkansas. E-mail: [email protected] Department, University of Arkansas. E-mail: [email protected] Business School, Victoria (Brazil). E-mail: [email protected].
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empirical test for altruism in bi-lateral O¢ cial Development Assistance (ODA or foreign aid) �an issue that
has been subject to much debate.1 We �nd evidence that approximately 20% of donor-recipient pairs display
our signal for altruistic ODA motivation and that a large portion of those pairs involve Scandinavian donors.
Scandinavian countries have long been held as examples of altruistic donors, though a theoretical foundation
for these assertions and a rigorous empirical test for altruistic motivation have been largely absent.
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 theoretical model of bi-lateral 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 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 and Little (1979); Alfred Maizels and Machiko
K. Nissanke (1984); Trumbull and Wall (1994); Javed Younas (2008); Chong and 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 ODA1We 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
from most countries is motivated by self-interest, other countries appear altruistic (Jakob Svensson (1999)).
Donors� tself-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). DAC
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 and Hamaan (2008); Kuhlgatz, Abdulai, and
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 The Model
In this section we introduce a theoretical model that generates a distinguishing empirical signal for altruism
among donors. The theoretical model presented in this section is as close as possible to the estimation we will
undertake. More general derivations of the counter-cyclical altruism signal are possible and are indicated
subsequently.2http://www.oecd.org/department/0,2688,en_2649_33721_1_1_1_1_1,00.html3Many of the citations above adopt this rationale.
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3.1 Theoretical Framework
In each period, the donor country planner solves a static utility maximization problem to determine how
much ODA to transfer to each of the R potential recipient countries. The donor derives utility from its
own consumption and from ODA disbursements in a manner to described precisely below. The donor�s own
consumption is de�ned as income, net of investments, and is assumed to be exogenous at the moment of the
planner�s decision. This de�nition implies that government expenditures and net exports are fully absorbed
by consumers. With this framework we can focus fully on the donor�s ODA dispersement problem across
potential recipients. That is, in our model the donor�s decision is whether to forego some consumption in
order to make ODA disbursements to the R potential recipient countries. ODA disbursements need not be
equal across the R potential countries. To keep analysis tractable we also abstract from strategic interaction
among donors.
We �rst introduce some notation and the objective function of a generic donor d; the same analysis
would apply to any of the other D donors. The vector of ODA disbursements by the generic donor d is
de�ned as A = [A1; A2; :::AR] and the donor�s trend income is �Y . These variables are time series but the
time indexes are omitted for ease of notation; we will explicitly re-introduce them only when necessary. The
utility function, which contains a reference level associated with trend income, is
U�A; �Y
�= u
�C; �Y
�+G(A; �Y ) (1)
Note that total utility in (1) includes the standard own consumption component u (�) and a second component,
G (�), that represents the gain from the full vector of ODA disbursements. Global diminishing marginal utility
from consumption is assumed for u (�). All of the model�s theoretical predictions work in levels as well as
relative to a reference level. The resources constraint utilized in the donor�s optimization problem links C
to the ODA donations through the standard accounting relation
C +RXr=1
As = Y � I (2)
where Y � I on the right hand side is the donor�s income net of private investment. For later reference, we
de�ne C0 = Y � I as donor income when no ODA donations are made. Consistent with our discussion above
we will refer to this total absorption term as simply "consumption." Finally, ODA disbursements must be
non-negative Ar > 0 for any r = 1:::R and cannot exceed C0. This generates the second constraint of the
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optimization problemRXr=1
As 6 C0 (3)
We assume the total gain functionG (�) can be expressed as the sum of individual gain functions associated
with the disbursements to each of the R recipients: Ar
G(A; �Y ) =RXr=1
Gr�Ar; �Y
�The gain from each individual transfer, Gr (0), can be decomposed in two distinct components. The �rst
component is a direct (non-altruistic) return from an ODA transfer to recipient r, �r�Ar= �Y
�(for example,
a vote at the UN). The second term is derived purely from altruistic preferences of the donor towards r,
�r�Ar= �Y
�. Note that this speci�cation allow a donor to be solely motivated by the direct-return from
ODA (self-interest), to be altruisic, or any combination of the two motivations.
Gr�A; �Y
�= �r
�A; �Y
�+ �r
�A; �Y
�It is reasonable to assume that there is no gain from either component if no ODA donation is made to a
recipient. Therefore Gr (0) = �r (0) = �r (0) = 0. We also assume �0r; �0r � 0, and �00r ; �
00r � 0 for any r,
but only in a positive neighborhood of Ar = 0. It is not necessary to fully characterize the gain function for
its entire dominion (0; C0) since all observed individual ODA transfers are very small relative to C0 (e.g.,
Section 4 shows that ODA disbursements are typically smaller than :01% of GDP). Hence, we impose only
a minimal set of assumptions on Gr (Ar) close enough to 0 to ensure a solution near Ar = 0. That is, we
approximate the solution around (C;Ar) = (C0; 0).
Empirically, we will also allow the gain functions components to be a¤ected by pair-speci�c shift factors,
X�r and X�r. Hence, ��Ar; �Y ;X�r
�and �
�Ar; �Y ;X�r
�are the complete gain component expressions for
estimation. Examples of shifters for � (the "return" component) in literature are the tightness of the
trade relationship between donor and recipient, geopolitical factors, and colonial relationships. Potentially
important shifters for � (the "altruism" component) are the recipient�s level of consumption without ODA,
cultural and religous factors, the recipient�s population size, political e¢ ciency, and corruption. In our
estimation, we explicitly incorporate the recipient�s initial level of consumption in the altruistic component
by making � (�) proportional to the change in the recipient�s utility due to ODA donation from a donor.
We now consider the utility and budget constraint of the recipient as seen from the perspective of the
donor�s problem (recall that we derive the local solution in the neighborhood around (C;Ar) = (C0; 0)).
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The recipient�s budget constraint is then:
Cr = Cr;0 +Ar (4)
Equation (4) shows that � (Ar;X�r) can be expressed as a function of Cr for given Cr;0 since Ar = Cr �
Cr;0. The implicit assumption here is that altruistic donors care about recipient country consumers, but
do not explicitly consider �rms in their altruistic decisions. The recipient constraint implies that ODA
is �consumed� instantaneously by the government and/or consumers � that is, there is full absorbtion of
recipient government expenditures.
Consistent with clear empirical reality, we assume that constraint (3) is never binding for any donor.
Therefor, the local interior �rst-order-necessary-conditions of the donors problem are statis�ed where the
marginal utility of donor "own-consumption" is equal to the marginal gain (from the total gain function)
for each the recipients. Indirect e¤ects of transfers across recipients that would be conveyed by the shadow
price of constraint (3), were it binding, are absent. Hence, we can obtain the local qualitative theoretical
signal of altruism utilizing the ODA decision to a single representative recipient, r, taking the donor�s ODA
to the other R� 1 potential recipients as already optimally determined. Note that the predetermined ODA
to any (or all) of the other R� 1 recipients may also be zero. Finally, we explicitly account for the reference
level by standardizing the arguments of the utility function by the trend income. To simplify notation, let
~Z = Z = �Y be the ratio to GDP value of variable Z and re-write the utility function (1) after substituting
for constraint (2)
U�~A�= u
~C0 �
Xr
~Ar
!+Xr
�r
�~Ar;X�r
�+Xr
�r
�~Ar;X�r
�(5)
The �rst order condition with respect to the generic donation Ar to recipient r is
�uc�~C�r;0 � ~Ar
�+ �r;A
�~Ar;X�r
�+ �r;A
�~Ar;X�r
�= 0 (6)
where ~C�r;0 = ~C0 �P
j 6=r~Aj . Since we are interested in studying the solution of the problem for a small
positive Ar, we can take a �rst order approximation of (6) around ~Ar = 0. The linear expansions of the
three terms in this equation are
�uc�~C�r;0 � ~Ar
�' �uc (C�r;0) + ucc (C�r;0) ~Ar (7)
�r;A (Ar;X�r) ' �r;A (0;X�r) + �r;AA (0;X�r)Ar
�r;A (Ar;X�r) ' �r;A (0;X�r) + �r;AA (0;X�r)Ar
6
In order to keep the notation in the following explanation more compact, we will replace the derivatives
in (7) with over-bar variables. For instance, let us de�ne �uc � uc (C�r;0) and �ucc � ucc (C�r;0) and adopt
the same convention for � and � too. Using (7) into (6), we obtain
��uc + �ucc ~Ar + ��r;A + ��r;AA ~Ar + ��r;A + ��r;AA ~Ar = 0 (8)
which returns the solution
~A�r =�uc � ��r;A � ��r;A
�ucc + ��r;AA + ��r;AA(9)
The optimal solution of ~A�r has a very clear interpretation. Since the second order derivatives evaluated
at zero ODA are all negative, the denominator of (9) is negative as well. In order to have Ar � 0 as solution,
the numerator of (9) needs to be negative (or equal to zero at most). The necessary condition for positive
ODA is then
�uc < ��r;A + ��r;A (10)
The marginal gain of setting a positive ODA must overcome the marginal loss due to the fall in the donor�s
consumption. If the condition is not satis�ed then Ar = 0 and we have a "corner" solution to the problem.
Figure 1 represents one of the possible cases in which the direct return is quite low and the ODA donation
decision is mostly explained by altruism.
Figure 1: Optimal ODA decisiion in the linearized framework.
Notes: In this example, a positive, small ODA disbursement to recipient r is optimally achieved thanks to a highdegree of altruism in the donor preferences.
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3.2 The Counter-cyclical ODA
We explore now the condition for counter-cyclical ODA in this framework which constitutes a signal of an
altruism which we will call strong-altruism . We evaluate the derivative d ~A�r = ~Y applying the envelope the-
orem to the �rst order condition (9). Before doing this, we postulate the following reduced-form relationship
between donor�s and recipient�s incomes
Yr�Yr= �0 + �r
Yd�Yd+ 'X + " (11)
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 income �Yi. On the right-hand-side of equation 11, 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 �r so that the income of donor and recipient
may be correlated positively, negatively, or not at all. In general, �r will be dictated by the degree of
integration of the recipient country with the global economy as well their trade mix and it would vary across
donor-recipient pairs.
Let us de�ne 1 = �uc� ��r;A���r;A and 2 = �ucc+ ��r;AA+��r;AA, we have that the derivative d ~A�r =d ~Y is
d ~A�rd ~Y
=
�dd ~Y 1
� 2 �
�dd ~Y 2
� 1
( 2)2
The �rst derivative term in this expressions is
d
d ~Y 1 =
d�uc
d ~Y���r;A
d ~Y= �ucc � �r��r;Ac (12)
where the fact that � (�) does not depend on the donor�s income is used; the parameter �r in the last term
is instead derived from (11).4 The other derivative is
d
d ~Y 2 =
d�ucc
d ~Y+��r;AA
d ~Y= �uccc + �r
��r;AAc (13)
Putting the two terms together again, we obtain
d ~A�rd ~Y
=1
2
h�ucc � �r��r;Ac �
��uccc + �r
��r;AAc�~A�r
i4Our de�nition of � (�) makes the return independent from the donor�s income. This seems to be a fair assumption, even
though it would be possible to write a model in which � (�) is, for example, proportional to Y and this would determine anextra term in 12. However, our speci�cation is more general.
8
A counter-cyclical ODA d ~A�r
d ~Y< 0 can be found if
d ~A�rd ~Y
=�ucc � �r��r;Ac �
��uccc + �r
��r;AAc�~A�r
�ucc + ��r;AA + ��r;AA< 0 (14)
since the denominator of this ratio is always negative, the numerator has to be positive in order for (14) to
be satis�ed
�ucc � �r��r;Ac ���uccc + �r
��r;AAc�~A�r > 0 (15)
However, since our approximation of the solution holds only for for small ~Ar, the term multiplied by ~A�r
in (15) would be negligible compared to the terms in (12) in the determination of the sign of (15). The
condition we can focus on is then
�ucc � �r��r;Ac > 0 (16)
The second derivative �ucc is negative by assumption, therefore if also ��r;Ac is negative then condition
(16) becomes
�r >�ucc��r;Ac
(17)
and it can be satis�ed only for �r > 0. In a baseline scenario, we can expect ��r;Ac to be negative. This
condition tells us that counter-cyclical ODAs are more likely to occur when either j�uccj is small relative to����r;Ac�� (high donor�s consumption relative to the recipient�s) or ����r;Ac�� is large relative to j�uccj (which wouldoccur when the donor�s altruism toward recipient r exceeds the threshold implied by (16) for a given �ucc and
�r).
However, this does not have to be always the case since ��r;A also depends on the other shifting variables
in X�r and those might change in response to ~Yr in a way that makes this cross-derivative non-negative.
For example, suppose that an increase in the recipient�s income reduces the degree of corruption of its
government. This would improve the e¤ectiveness of an ODA donation in the donor�s opinion and would
shift the return schedule from the altruistic component of the gain function upward. The total net e¤ect
on ��r;A depends on all these partial e¤ects and we could observed ~A�
r
d ~Y< 0 even when �r is negative. This
possibility seems to be fairly common in the data.
Hence, we characterize this form of altruism as strong-altruism to distinguish it from the case where the
donor has altruistic preferences but not large enough to generate a counter-cyclical ODA. 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.
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3.3 Empirical Strategy
In order to empirically evaluate condition (16), we add some structure to the minimal theoretical assumptions
above considering some speci�c functional form for u (�), � (�) and � (�). The main assumption is that both
donor and recipients have constant absolute risk aversion. This implies negative exponential functional forms
for u (�)
u�~C�
= 1� e�� ~C
u�~Cr
�= 1� e��r ~Cr
where � and �r are the donor�s and recipients�risk aversion parameters. This type of functional form is fairly
common in literature because preferences are easily characterize by the curvature parameter only. In our
context, it also allows us to have very similar �rst and second derivatives which makes the optimal condition
very suitable for the estimation exercise. On the other hand, there is no much guidance about � (�) and � (�).
We choose simple functional forms that satisfy the theoretical properties discussed above. Similarly to the
consumption utility, we parameterize the direct return for the donor from an ODA donation to recipient r
with the same type of negative exponential function as u (�)
�r
�~Ar;X�r
�= �r;0
�1� e�� ~Ar
�
in which the curvature parameter � is the same as the donor�s risk aversion and the new parameter �r;0
represents the direct utility return standardized by donor�s risk aversion of ODA associated to ~Ar = 0.
Finally, as mentioned above, we assume the altruism component to be proportional to the change in the
recipient�s utility due to the receiving of the ODA donation ~Ar
�r
�~Ar;X�r
�= �r;0
��e��r ~Cr + e��r ~Cr;0
�= �r;0
��e��r( ~Cr;0+ ~Ar) + e��r
~Cr;0�
where �r;0 expresses the degree of altruism of the donor toward recipient r. As explained above, � (�) and
� (�) depend also on a set of shifting variables, which can be thought of as a¤ecting �r;0 and �r;0. For sake
of simplicity in the notation, this dependence will not be explicitly reported.5
5A more complete notation for �r;0 and �r;0 would be �r;0 (X�r) and �r;0 (X�r) where ~Cr;0 is not in X�r any more.
10
Adopting these functional forms for the utility, the optimal condition (9) becomes
~A�r =�e��
~C�r;0 � �r;0� � �r;0�re��r~Cr;0
��2e�� ~C�r;0 � �r;0�2 � �r;0�2re��r~Cr;0
(18)
The �rst order condition (8) now yields a regression equation that allows us to estimate the return
parameter �r;0 and the altruism parameter �r;0 conditional on the chosen functional forms and for a given
pair of risk aversion parameters � and �r
�e��~C�r;0
�1 + � ~A�r
�| {z }
y
= �r;0��1� � ~A�r
�| {z }
x1
+ �r;0�re��r ~Cr;0
�1� �r ~A�r
�| {z }
x2
(19)
In general, we may expect �r;0 � 0, a non-negative return rate, and �r;0 � 0, a non-negative altruism
parameter. If this is the case, condition (17) specializes into
�r�r;0 >
��
�r
�2e�r
~Cr;0
e� ~C�r;0(20)
A large enough altruism parameter is required to satisfy the strong-altruism condition. The condition is more
likely to hold the bigger is the risk aversion of the recipient country relative to the donor�s; the smaller is the
recipient�s consumption net of the ODA transfer relative to the donor�s income, ~Cr;0; and the larger is the
initial donor�s consumption, ~C�r;0. It is more rewarding for the donor showing altruism towards the recipient
when ~Cr;0 is small and it is less of an e¤ort for the donor to disburse some ODA when its consumption ~C�r;0
is higher.
There are a few issues that might make the evaluation of this condition less reliable in the framework
outlined so far. The �rst issue is the presence of a possible bias in the estimate of �r;0. The estimates of the
coe¢ cients in regression (19) would be likely a¤ected by a bias similar to an omitted variable or measurement
error bias due to the dependence of �r;0 and �r;0 on the shifting factors. In order to attenuate the bias, we
can control for some of these shifters augmenting (19) with them or using them as instrumental variables in
the regression. The second issue is related to the functional form chosen for the two utility functions and the
calibration of the risk aversion parameters. The right hand side of the condition depends on these choices
and it may obviously be quite sensitive to them. The last issue is related to the sign of �r;0 which does not
necessarily have to be positive.
Fortunately, we are not interested in providing estimates of �r;0 and �r;0 per se since our main goal is
to identify donor-recipient pairs characterized by a strong-altruism signal. In order to avoid these problems,
we propose a di¤erent identi�cation strategy based on the donor�s decision between a zero and a positive
11
~Ar which is made before the decision on how much ODA to donate to recipient r. Using the new functional
forms in condition (10), the donor sets a positive ODA only if
�e��~C�r;0 � �r;0� � �r;0�re��r
~Cr;0 < 0
Let us de�ne the pair of coe¢ cients��r;1; �r;1
�such that the optimal choice of ODA for the donor would be
~A�r = 0. These coe¢ cients satisfy the condition
�e��~C�r;0 = �r;1� + �r;1�re
��r ~Cr;0 (21)
Taking the di¤erential of (21) with respect to ~Y gives
�2e��~C�r;0 = �r�r;1�
2re��r ~Cr;0 (22)
or equivalently
�r�r;1 =
��
�r
�2e�r
~Cr;0
e� ~C�r;0(23)
Combining (20) and (22), we obtain a �nal strong-altruism condition for counter-cyclical ODA
�r (�r;0 � �r;1) > 0 (24)
We can obtain an estimate of �r;1 (along with �r;1) from (21) and compare it to the estimate of �r;0 from
(??) in order to evaluate the condition in (24). The potential bias in the estimates of �r;0 and �r;1 is less
of a problem in evaluating (24), since both �r;0 and �r;1 would be a¤ected by the same bias. Also the
parameterization of the risk aversion of the two countries and the sign of �r;0 are less relevant issues in this
version of the strong-altruism condition. The validity of the test remains obviously conditional on the chosen
functional form of the utility function.
Condition (24) is satis�ed when the di¤erence between the two parameters is larger than zero when
�r > 0
(�r;0 � �r;1) > 0 if �r > 0 (25)
but also in the a second case when
(�r;0 � �r;1) < 0 if �r < 0 (26)
The actual strong-altruism donor-recipients pairs are identi�ed only by the conditions in (25) because it
12
corresponds to the case in which the altruism parameter �r;0 is bigger than the minimum degree of altruism
found in (21). Figure 2 provides a graphical explanation of the full mechanism supporting the strong-altruism
condition and the ODA decision. For given �, �r, ~C�r;0 and ~Cr;0 equation (21) de�nes the set of all (�r; �r)
pairs for which ~A�r = 0. Since �e�� ~C�r;0 and �re��r
~Cr;0 are positive, equation (21) represents a downward
sloping line in the (�r; �r) plane with a positive intercept. On the right hand side of this line we have the
region of (�r; �r) pairs such that ~A�r > 0, while on the left hand side we would have negative ODA. Suppose
�r > 0 and that the estimates of��r;1; �r;1
�from (21) are (�B ; �B). If we move to the left of �B , to point
1 for example, the strong-altruism condition is not satis�ed because (�r;0 � �r;1) < 0 but ~A�r can still be
made positive by a high direct return �r;0. On the other side, moving just a little towards the right would
be enough to get ~A�r > 0; however, if �r;0 is small we would need a larger altruism parameter �r;0 in order
for the strong-altruism condition to hold, as for example in point 2 in Figure 2.
Figure 2: ODA decision and the strong-altruism condition.
Notes: Graphical interpretation of the ODA decision and of the strong-altruism condition. The estimates of��r;1; �r;1
�from (21) are (�B ; �B). The strong-altruism condition can be satis�ed in point 1 but not point 2.
13
4 Empirical Estimation
4.1 ODA Accounting and Data
A donor country allocates its income, Y , to consumption, C, investment, I, and ODA donations to all the
potential recipients, A. We can re-state equation (2) as
Y = C + I +A (27)
In national accounting, ODA disbursements are included in donors�GDP as export items that generate a
trade �ow without the corresponding income �ow. The actual income available to a donor for consumption
and investment must be adjusted for those items. We measure income by GDP and take investment from
national accounting. As explained in section (3), equation (27) implies that our de�nition of consumption
corresponds more generally to the concept of absorption by private and public sector and it includes also
government expenditure and net exports. Symmetrically, for recipient countries, ODA transfers increase
the income available for their consumption. Based on our de�nition of consumption, we can construct total
consumption of a recipient by adding the ODA disbursements from each donor to the recipient�s GDP net
of investment. However, from the point of view of a maximizing donor, equation 3 is simply the de�nition
of how recipient r�s consumption depends on the ODA disbursement from that donor.
National account data is drawn from PWT 7.0 while ODA data is from the OECD. The current analysis
utilizes 19 OECD donors and 137 recipients for the period 1970 to 2010.6 Appendix A lists the 156 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 3 below illustrates total ODA disbursements for the 19 donors in our sample as a share of donor GDP
and reveals that the majority fall between :1 � :5%. It is interesting to note that the stated OECD-DAC
target of :07% of GDP is rarely achieved.
Figure 4 shows ODA relative to GDP for all 137 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 4 represents the average amount of aid received by the 137 countries in our sample,
which is between 2% and 4%. Both Figures illustrate that there is considerable variance of ODA as a share
6There are many new small DAC donors in recent years. We include the 15 largest the DAC donors countries over our timeperiod and, in addition, all Scandinavian countries (since they are often noted as altruist in the literature).
14
1970 1980 1990 2000 20100
1
2
3
4
5
6
7
8
9x 10
3
Figure 3: Total ODA Disbursements as a ratio of GDP - DAC Donors - Sample 1970� 2010.
Notes: Total ODA disbursements for each of the 19 donors in our sample. Each color represents a unique donor.
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. 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 in smaller amounts and some only on an occasional basis. This characteristic will play
an important role in our results.
The US is an extreme example of this pattern disbursing ODA to 130 out of 137 countries with half of
the countries receiving less than :05% of the total US ODA on average over the time period. About 40%
of US recipients receive, in total, less than :03% of US total disbursements on average while the 10 largest
US recipients receive on average 53% of total US ODA disbursements. These characteristics of the ODA
disbursements for the US are presented in Figure 5.
4.2 Estimation
We test now whether the strong-altruism condition, the inequality in (24), derived from the theoretical model
is satis�ed by some of the donor-recipient pairs. The reduced form relationship between recipient and donor
business cycles, equation (11), the �rst order condition of the donor�s optimization problem, equation (19),
and the condition for zero ODA, equation (21), de�ne the framework for the estimation of the parameters
of the model required to evaluate the strong-altruism condition.
15
1970 1975 1980 1985 1990 1995 2000 2005 20100
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Figure 4: Total ODA Disbursements as a ratio of GDP - All Recipients - Sample 1970� 2010.
Notes: Total ODA disbursements for each of the recipient countries in our sample. Each green line represents one ofthe recipients. The dark line is the mean ODA across recipient
1970 1975 1980 1985 1990 1995 2000 2005 20100
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Figure 5: Shares of total ODA for the US.
Notes: Shares of US ODA disbursements by recipient from 1970 to 2010. Each line corresponds to one of therecipients.
16
We further specialize equation (11) introducing an auto-regressive term in the regression
~Yr;t = �0 + 'r ~Yr;t�1 + �r ~Yd;t + "t (28)
This term is just a simple way to capture other idiosyncratic determinants of the economic cycle of a country
that re�ect structural characteristics of its speci�c economic environment. An alternative speci�cation of this
equation might include e¤ects such as population changes, trade dynamics, government variables, and other
factors implicit in the auto-regressive term. The trend GDP, �Yt, necessary to compute the ratio variables in
the equations is constructed applying the HP �lter to the GDP series with the smoothing parameter set to
100.
Equations (28), (19), and (21) are estimated by GMM. The standard orhogonality conditions between
regressors and the error terms of the equations provide the necessary conditions to estimate the coe¢ cients
of the three regressions. The full vector of estimated parameters is � =��0 'r �r �r;0 �r;0 �r;1 �r;1
�0. We
rely on the asymptotic properties of the GMM estimator to conduct the strong-altruism test on inequality
24. The vector of estimates �̂ has a normal asymptotic distribution given by:
pT��̂ � �
�! N (0; V )
where T is the length of the sample and V is the covariance matrix of �̂ obtained from the inverse of the
optimal weighting matrix of the GMM procedure.7 The distribution of �̂r��̂r;0 � �̂r;1
�is found from the
distribution of �̂ applying the delta method; this distribution allows us to construct a statistical test to
evaluate the strong-altruism condition. Under the null hypothesis H0 : �̂r
��̂r;0 � �̂r;1
�� 0, the asymptotic
distribution of �̂r��̂r;0 � �̂r;1
�is approximated by
pT �̂r
��̂r;0 � �̂r;1
�! N
�0; L�̂V L
0�̂
�(29)
where L is the gradient of �r (�r;0 � �r;1) with respect to the components of �, so that L = [0 0 (�r;0 � �r;1) 0 �r 0 � �r].
The gradient is then empirically evaluated at the estimated coe¢ cient vector �̂. A donor recipient pair sat-
is�es the strong-altruism condition if the the null is rejected at 5% level of con�dence.
Finally, note that the parameter estimates and, therefore, the results of the strong-altruism condition test
would depend on the risk aversion of the two countries. We must make an assumption on the risk aversion
parameters 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
7The optimal weighting matrix is computed using a Bartlett kernel and a Newey-West �xed bandwidth.
17
robust to reasonable changes in these parameters.
4.3 Summary of Baseline Estimation Results
Since we estimate the parameters vector �̂ for all donor-recipient pairs (about 2600), it is infeasible to
report the entire set of point estimates for all pairs. Therefore, our principal objective of this section is to
summarize results rather than focus on analysis of potential idiosyncratic altruistic motivation among the
speci�c donor-recipient pairs. An interpretation of the results is provided in the next sub-section. The �rst
general point is that approximately 21% of the pairs satisfy the strong-altruism condition at the �ve percent
con�dence level, corresponding to the case in which �r is positive in condition (25). Hence, our results
suggest that although the altruism signal is not present in the large majority of ODA transfers, neither is
it insigni�cant. There are about 8% of the pairs that pass condition (26) with a negative �r instead; the
total share of predicted counter-cyclical ODA disbursements is then 29%. In order to assess the plausibility
of this percentage, we can compare it to the share of negative unconditional correlations between ODA and
donor�s income found in the data for example, which is about 20%. Even though unconditional correlations
do not express exactly the same theoretical concept measured here by the negative derivative d ~A�r = d ~Y , it
can be considered a �rst acceptable proxy of our counteryclical ODA at this stage. The mass of predicted
counter-cyclical pairs seems to be quite reasonable. Figure 6 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 average number of pairs is 29 per donor. Interestingly, Scandinavian countries show on average an
higher number of strongly-altruistic donor-recipient relationships (36) than non-Scandinavian countries (27).
This result is in line with the stronger Scandinavian altruism often asserted by both economics and political
science academic literature and by the non-academic donor community. The speci�c recipients represented
in Figure 6 are also listed in Table B1 in Appendix B.
It would be infeasible to numerically report the point estimates of the parameters of the model for the
entire set of donor-recipient pairs. Therefore, we summarize the information about the estimates in Figures
7 and 8. Figure 7 illustrates the estimates of the di¤erence (�r;0 � �r;1); the di¤erence is plotted against its
standard deviation and the level of signi�cance is represented by the straight, blue-dotted lines (5% the most
external lines, 10% the internal ones). If a point lies outside the two most external lines, it is signi�cant at
5% level; if it lies inside the two narrow cones, it is signi�cant at 10% level. The red dots correspond to
the strong-altruism pairs which satisfy condition (25) and these are compared to the other pairs in black.
Figures B1-B3 in the Appendix provides the same information for �r, �r;0, and �r;0.
As expected, the strong-altruism condition is often supported by large and signi�cant di¤erences between
18
0
5
10
15
20
25
30
35
40
45
Aus
tralia
Aus
tria
Bel
gium
Can
ada
Den
mar
kFi
nlan
dFr
ance
Ger
man
yIta
lyJa
pan
Luxe
mbo
urg
Net
herla
nds
New
Zea
land
Nor
way
Spa
inS
wed
enS
witz
erla
ndU
nite
d K
ingd
omU
nite
d S
tate
s
Figure 6: Number of signi�cant pairs that satisfy the strong-altruism condition by donor.
Notes: Each bar represents the number of pairs satisfying the strong-altruism condition in (25) by donor.
�r;0 and �r;1. However, we do observe many altruistic pairs too that do not necessarily display strong-altruism.
In interpreting this �gure, it should also be kept in mind that in the theory �r multiplies (�r;0 � �r;1) in
condition (25). Hence, the pairs that satisfy the condition for less signi�cant di¤erences between the two
altruism parameters are compensated by larger �r. Figure 8 provides an empirical replication of the diagram
in Figure 2 and we use it to compare these results with the intuition for the model discussed at the end
of section 3.3. In this �gure, we plot (�r;0 � �r;1) versus��r;0 � �r;1
�for each donor-recipient pair in the
sample; this is basically equivalent to drawing Figure 2 after re-centering the axis on (�b; �b). The intuition
behind the ODA decisional process suggested by Figure B3 is that the majority of the strong-altruism pairs
should be found in the south-east quadrant of Figure 8 when �r is positive and this intuition is con�rmed
by the �gure. In theory, a positive, although not counter-cyclical, ODA decision can occur for an altruistic
donor ((�r;0 � �r;1) > 0) but it should correspond to smaller di¤erences. This is also the case in the �gure
where the black dots are more concentrated near zero.
4.4 Interpretation of Baseline Estimation Results
The strong-altruism condition is more likely to hold the smaller �r;1 is. Equation (23), reported here for
convenience, shows that for given �r �r;1 is directly proportional to �=�r and ~Cr;0 and inversely proportional
to ~C�r;0. We provide a further discussion of these driving factors in this section
�r�r;1 =
��
�r
�2e�r
~Cr;0
e� ~C�r;0
19
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10.1
0.08
0.06
0.04
0.02
0
0.02
0.04
0.06
0.08
0.1
s.d.(δ0δ1)
δ 0 δ1
Figure 7: Point estimates of the di¤erence �r;0 � �r;1.
Notes: Red dots identify pairs that satisfy the strong-altruism condition (25). In black all the others. The signi�canceof the parameters is shown by the blue, dotted lines. The esternal lines show the 5% signi�cance tresholds. Theinternal lines the 10% level.
0.06 0.04 0.02 0 0.02 0.04 0.060.05
0.04
0.03
0.02
0.01
0
0.01
0.02
0.03
0.04
0.05
δ0δ1
ρ 0 ρ1
Figure 8: Bridging empirical results and the model - empirical counterpart of Figure 2
Notes: Plot of (�r;0 � �r;1) versus��r;0 � �r;1
�. Red dots identify pairs that satisfy the strong-altruism condition
(25). In black all the others.
20
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. As explained in the robustness checks below, the
e¤ect of the risk aversion parameters on the number of strong-altruism pairs is fairly small. Similarly, the
lower the recipient�s consumption is relative to the donor�s GDP trend is (the higher the donor�s consumption
is), the more likely the condition is to be satis�ed. Also this e¤ect re�ects the incentive to transfer from low
to high marginal utility agents. Additional insights on these implications and results of our model can be
seen in Figure 9. This scatter plot displays the average recipient�s consumption ~Cr;0 (vertical axis) against
the average donor�s consumption ~C�r;0 (on the horizontal axis). Note that the mass of those bi-lateral
pairs satisfying the altruism condition (the red dots) typically correspond to relatively small recipient�s
consumption levels.
0.68 0.7 0.72 0.74 0.76 0.78 0.8 0.82 0.84 0.860
0.5
1
1.5
2
2.5
3
Donors' consumption
Rec
ipie
nts'
con
sum
ptio
n
Figure 9: Donor-recipient relative consumption levels and the strong-altruism decision.
Notes: Recipient�s consumption ~Cr;0 versus donor�s consumption ~C�r;0. Red points correspond to the pairs satisfyingthe strong-altruism condition.
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 d ~A�r = d ~Y
on which our altruism tests are based. Therefore, the question of whether the signi�cant pairs identi�ed
by estimation of our theoretical model also display a negative unconditional correlation arises naturally.
To explore this question we computed the unconditional correlation between donors�output gap and ODA
disbursements to their recipients relative to the donor�s GDP. As already said, 20% of these correlations are
21
negative. In this paper, we provide a possible explanation for this fact (the large number of counter-cyclical
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 but they provide an interesting point of reference. The pairs satisfying our
strong-altruism test match two �fth of the pairs displaying negative correlation. Jointly considering the
pairs that satisfy conditions (25) and (26) we can explain more the half of the negative correlations.
The next �out of model�comparison to assist in the interpretation of the results is of particular interest
in a growing research area such as this. The Center for Global Development, a non-pro�t think tank focused
on research and policy analysis on the quality of international relations of wealthy nations, publishes an
annual ranking of the e¤ectiveness of international commitment of wealthy governments to poorer countries
known as the Commitment to Development Index (CDI). The overall ranking is based on seven dimensions,
but one of the category they consider is international aid which can be seen as a proxy of the measurement of
altruism adopted in our paper, even though the methodologies followed in measuring aid are quite di¤erent.8
It is interesting to compare this type of ranking, which provide some information on the level of altruism
of donors, with the results from the strong-altruism condition we propose. Even though altruism does not
necessarily implies strong-altruism (as we explained, we can observe donor-recipients pairs with a good degree
of altruism which do not satisfy the strong-altruism condition), one could expect that more altruistic donors
may also have a higher propensity to reveal some sort of strong-altruism preferences. Looking at this ranking
for 2012, we �nd among the best donors Luxembourg, Sweden, Netherlands, Norway, Denmarks which are
also among the most altruistic according to our strong-altruism test. Not surprisingly then, Scandinavian
and some other smaller European nations account for a large share of the strongly altruistic pairs in our
sample. The biggest di¤erence between our results and the CDI is probably represented by France and
Italy, which are in the middle or low part of the CDI ranking but have a very high performance in the
strong-altruism test.
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 low development countries 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
8The index adjusts aid disbursements for quality of aid. It penalizes transfers to worse-governed or rich recipients; itpenalizes donors for tying aid and for parceling out aid in many small projects. Furthermore, ODA disbursements arenot the only type of aid they include in their de�nition. The CDI and further information can be found at the linkhttp://www.cgdev.org/initiative/commitment-development-index/index
22
risk aversion at the country level (see Blackburn and Ukhov (2008) for discussion). Since the relative risk
aversion in the donor-recipient pair may a¤ect the results of the test, 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 � = 2
and �r = 3, the total number of pairs satisfying the strong-altruism condition (25) rises by only a few units
(from 21:3% to 22:1%).
Another important robustness check is about the functional forms and the structure of the model we
adopted. As an alternative, we tried models with linear direct returns from ODA in the donor�s budget
constraint, returns proportional to the loss in utility of the donor�s, constant relative risk aversion utility
functions and we found similar results in the share and combination of donor-recipient pairs that pass the
strong-altruism condition.
Many additional robustness are under implementation and the full set is available from the authors
upon request. These include other variations 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 parameter changes.
5 Conclusions
This paper develops an integrated theoretical and empirical framework for exploring the motivation of
ODA at the donor-recipient pair level. Though much of the foundational research on ODA attempted to
estimate its e¤ect on recipient growth, business-cycles, and well-being there is growing recognition that
the non-random assignment of ODA may taint these results. We concur with this general critique and
believe that donor motivation, in particular, is an important unobserved characteristic that has been largely
ignored. Motivation may a¤ect both the distribution of aid across recipients and its e¤ectiveness. Hence, we
believe the issues of the e¤ect of ODA and its motivation are inextricably connected. Our theoretical model
generates a identi�able altruism signal that we have dubbed "strong-altruism". Estimation of our model using
OECD data indicates that approximately 20% of the total donor-recipient pairs display the strong-altruism
signal. Interestingly, Scandinavian countries show on average 33% more strongly-altruistic donor-recipient
relationships than non-Scandinavian countries. Exceptional Scandinavian altruism has been asserted in both
economics and political science academic literature and by the non-academic donor community. We believe
our results provide the �rst evidence generated by a rigorous theoreticall-empirical framework to support
these assertions.
Looking forward, we intend to utilize the strong-altruism signal as a control for the non-random assign-
23
ment of ODA. This holds the promise of more accurate measurement of the true e¤ects of ODA on growth,
recipient business cycles, and well-being. Our framework is su¢ ciently �exible to allow various disaggre-
gation and consideration of other gain-function shifters. We also intend to analyze the sets donor-recipient
pairs displaying the altruism signal for commonalities. 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.
24
REFERENCES
Abdulai, Awudu, Chrostopher B. Barrett, John Hoddinott. 2005. �Does food aid Really have
disincentive e¤ects? New evidence from sub-Saharan Africa�, World Development, 30(10): 1689-1704.
Alesina, Alberto, and David Dollar. 2000.�Who gives foreign aid to whom and why?�, Journal of
Economic Growth, 5: 33-63.
Bauer, Peter T. 1981. "The Grail of Equality", from Equality, the Third World and Economic Delusion,
Cambridge: Harvard University Press.
Bearce, David H., and Daniel C. Tirone. 2010. �Foreign Aid E¤ectiveness and the Strategic Goals
of Donor Governments�, The Journal of Politics, 72(3): 837-851.
Berthélemy, Jean-Claude, and Ariane Tichit. 2004. �Bilateral donors�aid allocation decisions - a
three-dimensional panel analysis�. International Review of Economics and Finance, 13(3): 253-274.
Berthélemy, Jean-Claude. 2006. �Bilateral Donors�Interest vs. Recipients�Development Motives in
Aid Allocation: Do All Donors Behave the Same?�Review of Development Economics, 10(2): 179�194.
Birdsall, Nancy, and Seven Deadly. 2004. "Sins: Re�ections on Donor Failings�, Center for Global
Development Working Paper 50.
Blackburn, Douglas W., and Andrey Ukhov. 2008. "Individual vs. Aggregate Preferences: The
Case of a Small Fish in a Big Pond", Working paper.
Blouin, Max, and Stéphane Pallage. 2009. �Addressing the Food Aid Curse�, Economics Letters,
104(1): 49-51.
Channing, Arndt, Sam Jones, and Finn Tarp. 2010. "Aid, Growth, and Development: Have We
Come Full Circle?", Journal of Globalization and Development, 1(2): 5.
Dalgaard, Carl-Johan, Henrik Hansen, and Finn Tarp. 2004. �On the Empirics of Foreign Aid
and Growth�, The Economic Journal, 114(June): 191-216
Deaton, Angus. 2010. �Instruments, randomization, and learning about development�, Working Paper,
Research Program in Development Studies, Center for Health and Wellbeing, Princeton University.
De La Croix, David, and Clara Delavallade. 2009. �Why corrupt governments may receive more
foreign aid�, Working Paper, Center for Operations Research and Econometrics.
Dercon, Stefan, and Pramila Krishnan. 2003. �Risk Sharing and Public Transfers�, The Economic
Journal, 113(March): 86-94.
Dreher, Axel, Peter Nunnenkamp, and Rainer Thiele. 2008. "Does US aid buy UN general
assembly votes? A disaggregated analysis", Public Choice, 136(1-2): 139-164.
Dudley, Leonard, and Claude Montmarquette. 1976. �A Model of the Supply of Bilateral Foreign
25
Aid�, The American Economic Review, 66(1): 132-142.
Hansen, Henrik, and Finn Tarp. 2001. "Aid and growth regressions", Journal of Development
Economics, 64(2): 547-570.
Juselius, Katarina, Niels Framroze, and Finn Tarp. 2011. "The Long-Run Impact of Foreign Aid
in 36 African Countries: Insights from Multivariate Time Series Analysis", Wider Working Paper 51.
Maizels, Alfred, and Machiko K. Nissanke. 1984. �Motivations for Aid to Developing Countries�,
World Development, 12(9): 879-900.
de Mesquita, Bruce Bueno, and Alastair Smith. 2007. �Foreign Aid and Policy Concessions�,
Journal of Con�ict Resolution, 51(2): 251-284.
Packenham, Robert A. 1966. �Foreign Aid and the National Interest�, Midwest Journal of Political
Science, 10(2): 214-221.
Pallage, Stephane, and Michel A. Robe. 2001. "Foreign aid and the business cycle", Review of
International Economics, 9(4): 641�672.
Rajan, Raghuram, and Arvind Subramanian. 2007. �Does Aid A¤ect Governance?�, The Ameri-
can Economic Review, 97(2): 322-327.
Rajan, Raghuram, and Arvind Subramanian. 2008. �Aid and Growth: What does the Cross-
Country Evidence Really Show?", Review of Economics and Statistics, 90(4): 643-665.
Schraeder, Peter J., Steven W. Hook, Bruce Taylor. 1998. �Clarifying the foreign aid puzzle: A
comparison of American, Japanese, French, and Swedish aid �ows�, World Politics, 50(2): 294-323.
Svensson, Jakob. 1999. �Aid, Growth and Democracy�, Economics and Politics, 11(3): 275�297.
Wik, Mette, Tewodros Aragie Kebede, Olvar Bergland, and Stein T. Holden. 2004. �On the
measurement of risk aversion from experimental data", Applied Economics, 36(21): 2443-2451.
Yesuf, Mahmud, and Randall A. Blu¤stone. 2009. �Poverty, Risk Aversion, and Path Depen-
dence in Low-Income Countries: Experimental Evidence from Ethiopia.�American Journal of Agricultural
Economics, 91(4): 1022-1037.
Younas, Javed. 2008. �Motivation for bilateral aid allocation: Altruism or trade bene�ts�, European
Journal of Political Economy. 24(3): 661-674.
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APPENDIX
A Donor and Recipient Countries in Sample
The 19 OECD-DAC countries donor list: Australia, Austria, Belgium, Canada, Denmark, Finland, France,
Germany, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Spain, Sweden, Switzerland, UK,
US.
The 137 recipients countries list: Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Ar-
gentina, Bahamas, Bahrain, Bangladesh, Barbados, Belize, Benin, Bermuda, Bhutan, Bolivia, Botswana,
Brazil, Brunei, Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Central African Republic, Chad,
Chile, China, China Taipei, Colombia, Comoros, Congo (Dem. Rep.), Congo (Republic of), Costa Rica, Cote
d�Ivoire, Cuba, Cyprus, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equator-
ial 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, Ko-
rea (Republic of), Kuwait, Laos, Lebanon, Lesotho, Liberia, Libya, Macao, Madagascar, Malawi, Malaysia,
Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Mongolia, Morocco,
Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, Oman, Pakistan, Palau, Panama, Papua New
Guinea, Paraguay, Peru, Philippines, Qatar, Rwanda, Samoa, Sao Tome and Principe, Saudi Arabia, Sene-
gal, Seychelles, Sierra Leone, Singapore, 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 Estimates
Figures B1-B3 shows the estimates of �r, �r;0, and �r;0 corresponding to Figure YY for �r;0. As explained
in the theoretical section of the paper, our estimates of �r;0 and �r;0 provide an ordinal rather than cardinal
measure of the degree of altruism and the direct return parameter of the donor countries. For this reason,
we observe pairs also in the negative quadrant of Figures B2 and B3 . Finally, in Table B1 we list the names
of the recipient countries that satisfy the strong-altruism condition as reported by Figure 6.
27
0 0.5 1 1.52
1.5
1
0.5
0
0.5
1
1.5
2
s.d.(β)
β
Figure B1: Point estimates of �r.
Notes: Red dots identify pairs that satisfy the strong-altruism condition. In black all the others. The signi�cance ofthe parameters is shown by the blue, dotted lines. The esternal lines show the 5% signi�cance tresholds. The internallines the 10% level.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 12
1.5
1
0.5
0
0.5
1
1.5
2
s.d.(δ0)
δ 0
Figure B2: Point estimates of �r;0.
Notes: Red dots identify pairs that satisfy the strong-altruism condition (25). In black all the others. The signi�canceof the parameters is shown by the blue, dotted lines. The esternal lines show the 5% signi�cance tresholds. Theinternal lines the 10% level.
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 12
1.5
1
0.5
0
0.5
1
1.5
2
s.d.(ρ0)
ρ 0
Figure B3: Point estimates of �r;0.
Notes: Red dots identify pairs that satisfy the strong-altruism condition. In black all the others. The signi�cance ofthe parameters is shown by the blue, dotted lines. The esternal lines show the 5% signi�cance tresholds. The internallines the 10% level.
29
Donor Recipients
AustraliaAfghanistan, Bangladesh, Botswana, Egypt, Haiti, Hong Kong, India, Iraq, Maldives, Mozambique,Pakistan, Samoa, SouthAfrica,Tonga.
AustriaAlgeria, Angola, Bolivia, Brazil, Central Africa Rep, Chile, China Taipei, Dem. Rep. Congo, Costa Rica,Cote d�Ivoire, Cyprus, Egypt, Ghana, Guatemala, Haiti, Iraq, Liberia, Madagascar, Malawi, Malaysia,Malta, Mongolia, Morocco, Rwanda, Senegal, South Africa, Tanzania, Togo, Tunisia, Vietnam.
Belgium
Angola, Bolivia, Botswana, Brazil, Burkina Faso, Chile, Costa Rica, Ecuador, Gabon, Ghana, Guatemala,Guinea, Guinea-Bissau, Honduras, India, Indonesia, Iraq, Jamaica, Jordan, Kenya, Liberia,Libya, Madagascar, Malawi, Malaysia, Maldives, Mali, Mongolia, Mozambique, Namibia, Nigeria,Pakistan, Seychelles, South Africa, Tanzania, Thailand, Togo, Tunisia, Uzbekistan, Zambia, Zimbabwe.
CanadaAlgeria ,Angola, Antigua Barbuda, Bolivia, Botswana, Burkina Faso, Cambodia, Chile, Costa Rica, Cuba,El Salvador, Ethiopia, Ghana, Guinea, India, Iraq, Lesotho, Morocco, Pakistan, Sierra Leone,South Africa, St. Lucia, Sudan, Tanzania, Thailand, Turkey.
Denmark
Afghanistan, Algeria, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Central Africa Rep,Chile, China, Colombia, Dem. Rep. Congo, Costa Rica, Cote d�Ivoire, Cuba, El Salvador, Ghana,Guatemala, Haiti, Honduras, Iraq, Jordan, Lesotho, Liberia, Maldives, Mali, Mongolia, Namibia,Nicaragua, Niger, Nigeria, Pakistan, Peru, South Africa, Zambia.
FinlandAfghanistan, Angola, Barbados, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde,Central Africa Rep, Dem. Rep. Congo, Cuba, Ethiopia, Ghana, Guatemala, Honduras, Iraq, Liberia,Mongolia, Namibia, Nicaragua, Pakistan, Philippines, Rwanda, South Africa, Tunisia, Yemen.
France
Algeria, Bolivia, Burkina Faso, Cambodia, Chile, Cote d�Ivoire, Cyprus, Ecuador, Fiji, Guatemala,Guinea ,Guinea-Bissau, Haiti, Honduras, India, Iraq, Jordan, Kenya, Korea, Madagascar, Malaysia,Mauritania, Morocco, Namibia, Nepal, Niger, Nigeria, Pakistan, Philippines, Rwanda, Sao Tome,Senegal, Somalia, South Africa, Sudan, Tanzania, Thailand, Togo, Zambia, Zimbabwe.
GermanyAngola, Botswana, Burundi, Cape Verde, China Taipei, Cyprus, Egypt, Haiti, Indonesia, Kenya,Lebanon, Lesotho, Malawi, Malaysia, Malta, Mauritania, Mauritius, Mozambique, Niger, Pakistan,Philippines, Singapore, Somalia, South Africa, Thailand, Togo, Tunisia, Arab Emirates, Zambia.
Italy
Afghanistan, Algeria, Angola, Bolivia, Burkina Faso, Cape Verde, Chile, Colombia, Dem. Rep. Congo,Costa Rica, Ecuador, Ethiopia, Ghana, Guatemala, Guinea, Haiti, Honduras, Jamaica, Jordan,Kenya, Libya, Madagascar, Malta, Morocco, Mozambique, Niger, Nigeria, Pakistan, Paraguay,Philippines, Rwanda, Senegal, St.Kitts&Nevis, St.Vincent, Tanzania, Tunisia, Uruguay, Yemen, Zambia.
JapanBurkina Faso, Chile, Egypt, India, Indonesia, Madagascar, Malaysia, Mali, Morocco, Mozambique,Pakistan, Singapore, Somalia, South Africa, Tanzania, Thailand, Turkey, Yemen.
Luxembourg
Bolivia, Brazil, Burkina Faso, Cambodia, Chile, Colombia, Ecuador, El Salvador, Ghana, Guatemala,Guinea, Haiti, Honduras, Indonesia, Iran, Kenya, Madagascar, Malawi, Mauritius, Mexico, Morocco,Namibia, Niger, Pakistan, Paraguay, Philippines, Rwanda, Sierra Leone, South Africa, Sudan,Thailand, Togo, Tunisia, Turkey, Venezuela.
Netherlands
Angola, Bolivia, Burundi, Cambodia, Central Africa Rep, Chile, Costa Rica, Cote d�Ivoire, Dominican Rep,Egypt, El Salvador, Ghana, Guatemala, Guinea-Bissau, India, Iraq, Lesotho, Liberia, Malawi, Mali,Morocco, Mozambique, Nigeria, Philippines, Rwanda, Somalia, South Africa, Thailand, Togo, Tunisia,Uruguay, Yemen.
New Zealand Afghanistan, Ethiopia, India, Iraq, Kiribati, Mongolia, Pakistan, South Africa, Sudan, Tanzania, Zambia.
Norway
Afghanistan, Albania, Algeria, Angola, Bolivia, Brazil, Colombia, Dem. Rep. Congo, Costa Rica,Cote d�Ivoire, Cuba, Dominican Rep, Ecuador, El Salvador, Ethiopia, Guatemala, Haiti, Honduras, India,Iraq, Lebanon, Malawi, Maldives, Mali, Mauritania, Mexico, Mongolia, Mozambique, Namibia, Nicaragua,Pakistan, Peru, South Africa, Sudan, Syria, Uzbekistan, Vietnam, Yemen, Zambia.
SpainAngola, Bolivia, Cambodia, Costa Rica, Cote d�Ivoire, Ecuador, Ghana, Guatemala, Honduras, Iran,Kenya, Madagascar, Mauritania, Namibia, Nepal, Niger, Nigeria, Philippines, Rwanda, Senegal, Somalia,Sudan, Togo, Turkey, Venezuela.
Sweden
Afghanistan, Angola, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Central Africa Rep,Dem. Rep. Congo, Costa Rica, Cote d�Ivoire, Cuba, Dominican Rep, El Salvador, Guatemala, Honduras,India, Iran, Iraq, Lesotho, Liberia, Madagascar, Mauritius, Mongolia, Namibia, Nicaragua, Pakistan,Paraguay, Philippines, Rwanda, Sao Tome, Senegal, Sierra Leone, South Africa, Sudan, Swaziland,Togo, Uruguay, Yemen.
SwitzerlandAngola, Bolivia, Burkina Faso, Burundi, Central Africa Rep, Chile, Costa Rica, Cote d�Ivoire, Ghana,Guatemala, Haiti, India, Iraq, Israel, Lesotho, Liberia, Libya, Mauritania, Mongolia, Mozambique,Nepal, Nigeria, Paraguay, South Africa, Tanzania, Togo, Tunisia.
UKAfghanistan, Angola, Belize, Botswana, Brazil, Burundi, Chile, Dem. Rep. Congo, Costa Rica, Dominica,Ghana, Iraq, Kiribati, Lesotho, Madagascar, Mali, Mozambique, Nigeria, Senegal, Seychelles,Sierra Leone, Somalia, South Africa, St. Lucia, Swaziland, Tanzania.
USBolivia, Botswana, Brazil, Colombia, Dem. Rep. Congo, Costa Rica, Cote d�Ivoire, Egypt, El Salvador,Guatemala, Haiti, India, Kenya, Lebanon, Malawi, Mali, Mexico, Morocco, Pakistan, South Africa,Turkey, Yemen.
Table B1: Recipient countries that satisfy the strong-altruism condition.
Notes : This table lists the countries that satisfy the condition for strong-altruism equation (25) in the main text andsummarized in Figure 6.
30