ISS Discussion Paper Series

F-195

Alliances, Security Externalities, and Donor Coordination
Hiroyuki Hoshiro1

Institute of Social Science, The University of Tokyo (hoshiro@iss.utokyo.ac.jp)
11Alliances, Security Externalities, and Donor Coordination

Abstract
Recent research on donor coordination has focused on the need to avoid
duplication to improve aid efficiency. Instead, this study approaches donor
coordination from the viewpoint of security and discusses that overlapping istheoretically preferable because foreign aid has security externalities. In thispaper, I clarify the relationship between bilateral aid from the United States andaid from its allies. While the United States has withheld economic aid to enemy
countries, it has given more aid to recipients that are important for its security.
Similarly, US ally donors have decided to allocate aid in congruence with US
policy goals. As a result, donor coordination (overlapping) occurs between the
United States and its allies. Among allies, big donors can easily respond to thedemands of the United States. Moreover, they have been more likely tocoordinate since the onset of the War on Terror. I statistically demonstratethese theoretical expectations.

Keywords
foreign aid, donor coordination, alliance, security externalities, international
political economy

Introduction
In May 2017, the 45th President of the United States, Donald Trump,accused NATO leaders of being dependent on the United States for their2

security and 'not paying what they should be paying.’ According to him, theytake advantage of US taxpayers (The Washington Post, 25 May, 2017). Jens
Stoltenberg, NATO Secretary General, immediately responded to this andexpressed an increase in military spending by 4.3% (The Washington Post, 28
June, 2017). The President’s dissatisfaction is not directed only toward
European allies. In a CNN interview during the presidential election, Mr. Trump
called for the Japanese government to significantly increase financial support
for US military bases stationed in Japan. He said that Japan’s current fiscal
burden of hosting the US forces in Japan is ‘too little,’ and implied that theywould withdraw if the Japanese government did not provide a sufficient level offinancial support (The Japan Times, 5 May and 16 May, 2016). Donald Trump
has consistently stated that the burden of the United States is huge, and that
other allies are free riding.
However, increasing direct military spending or support for thestationed army is not the only military contribution allies could make. For
example, allies could enforce a policy of not exporting militarily significant
technologies and goods to enemy countries despite the resulting decrease inexports. The COCOM (Coordinating Committee for Multilateral Export Controls),
established by the Western Block during the Cold War, is a typical example ofthis type of contribution to collective security. Member states refrained fromexporting strategically significant technologies or goods to Communist
countries. This was a security contribution from allies that did not require
increased defense expenditures. However, if one country violated the COCOM
accord, the country would benefit in the short run (free rider effect), but if other3allies followed suit, the effect of the accord would disappear. In other words, thecoordination of allied countries is indispensable to preserving the security effectof this economic policy.
In addition to trade, foreign aid policy can contribute to joint security. A
recipient of ODA (Official Development Assistance) from a developed country
can purchase goods using the money. As a result, a surplus fund will be
generated by the recipient, and it is possible to extend the armaments of therecipient country using that fund. In other words, since money is fungible, evenpure economic aid from a donor could contribute to a military buildup. Thus, thechoice of aid recipients and the amount of aid provided is not independent fromthe donor’s national security. Nevertheless, previous research has overlooked
the link between the security implications of such economic aid and donor
coordination. With the exception of the US–Japan relations, few studies analyse
the aid policies of allies. The purpose of this paper is to analyse this connection
to determine whether aid is being coordinated among allies.
In this paper, I clarify the relationship between bilateral ODA from theUnited States, the unitary power, and ODA from its allies. The results presented
in Section IV reveal that bilateral aid allocation of US allies are more likely to beoverlapped with the United States than with other donors in determining both
aid recipients and amounts. Moreover, in the era of the ‘Global War on Terror,’those who provide greater absolute amounts of bilateral ODA are more
overlapped with the United States. Thus, this research has shown thatasymmetric allies like Japan, which suppress military expenditures, cancontribute to the security of the United States through foreign aid. Moreover,4this study also reveals that security externalities of foreign aid could help
explain why the OECD’s call to eliminate duplication through donor coordination
has not succeeded.

I Literature Review on Aid Coordination
Traditional aid researchers have assumed that donors are independentand that each donor country provides aid in accordance with its own interests
and relationships with recipients. In practice, however, donors are notindependent, and some donors’ aid decisions can be of great interest to other
donors and recipients. In recent years, research to assess collective behaviour
among donors has been increasing. Especially after the OECD announced the‘Paris Declaration on Aid Effectiveness’ in 2005 and encouraged efforts toeliminate duplication to donor countries to make aid more effective, research on
aid coordination has been published continuously. 1
Aldasoro et al. (2010) assesses whether donors have improved aid
effectiveness through specialisation and coordination and demonstrated thatfew donors have specialised on a limited set of recipients and aid sectors.
Overlapping aid (similarity of the patterns of aid distribution across different
donors both in recipient countries and aid sectors) increased during the period
1995-2006, suggesting donor coordination has remained elusive. In the same
vain, Frot and Santiso (2011) discover and measure the presence of ‘herding’behaviours among donors. For recipients that suffer massive amounts ofdamage from natural disasters, for example, much humanitarian aid is supplied5

by all the donor countries around the world, similar to investors that flock toinvest in the most promising countries. Fuchs et al. (2015) reveals that export
competition between donors is a major impediment to aid coordination.
Moreover, Steinwand (2015) finds that aid coordination is more likely in thepresence of a lead donor and competition is more likely in the absence of sucha donor. In short, from the viewpoint of aid efficiency—i.e., the interests ofrecipients—coordination among donors is preferable to avoid redundancy andwaste. Moreover, there is a consensus that the coordination that the Paris
Declaration hopes is not occurring at present.
Interestingly, few studies have analysed aid coordination from the pointof view of the donors’ political interests. Moreover, among the studies that do exist,
the subject is limited to US-Japan relations, and does not examine any othercountries. Katada (1997) examines how the US has influenced Japanese foreign
aid behaviour by analysing aid flows from Japan to Latin America, a region where
the United States has been historically dominant. She discovered ‘burden
sharing;’ the Japanese government restrains aid flows when US aid to the region
increases, while it boosts aid when there is a decrease in US aid. In other words,
Katada (1997) insists that aid to the Latin American countries from Japan and theUnited States is coordinated. On the other hand, Tuman et. al. (2009) discovers
that Japan’s aid disbursement pattern was associated with only a limited numberof US security interests, and thus that aid coordination hardly exists. Thus, evenin Japan, where the influence of the United States seems to be large, the extent
to which it coordinates its aid allocation policy with the United States remains
unclear.
6

The above two studies are limited to data on Japan and the US, andKatada (1997) only focuses on Latin America. Moreover, the analysis by Tuman
et. al (2009) does not aid coordination among two countries but explores whether
Japan is helping recipients who are strategically significant to the United States.
In order to provide greater analytic scope, this paper analyses aid coordination
among donors all over the world, using bilateral ODA data for 18 donors and 179
recipients from 1966 to 2015. The purpose of this study is to determine how
security concerns affect coordination among donors.
The primary focus is on the United States, which exerts the greatest
impact on the aid allocation of other donors. The magnitude of its economic,
political, and military power and the extent of its reach do not affect all othercountries. If there is no aid coordination between a group of Western developed
countries and the United States from a security perspective, there may be little
coordination between the other allies. Therefore, this paper focuses on whether
the aid allocations of DAC (Development Assistance Committee) members
countries have been coordinated with their largest and strongest ally—the UnitedStates—and if so, under what conditions this coordination takes place.

II Two Stages of Aid Allocation and Two Types of Donor Coordination
In analysing aid allocation decisions, it is important to note that there are
two stages. The first stage is called the ‘selection’ or ‘eligibility’ stage, where adonor decides whether or not to give economic aid to a potential recipient. The
second stage is called the ‘level’ stage, and a recipient who receives even a small
amount of aid–i.e., not zero at the eligibility stage–is subject to analysis. As
7described in this section and the next section, it is important to consider these
two stages separately from the viewpoints of both theory and method.
Theoretically, it is possible that an independent variable may affect one stage but
not the other. Methodologically, the estimation result may be biased unless wechoose an appropriate model.

Security Externalities, Gaiatsu, and Donor Coordination
Different from the usual assumption that coordination implies reducing
redundancy, overlapping aid is actually desirable from the perspective of thedonors’ security interests because aid has security externalities. ‘Security
externalities’ are uncompensated costs or benefits that the action of an actor
influences security of third parties. 2 When a donor provides aid to a country, therecipient can purchase goods that it would otherwise have had to purchase fromits national budget. As a result, the recipient’s government has surplus funds thatcan be used for other purposes. Since the surplus funds are part of its own money,
no one (not even the donor) can restrict their use. In an extreme case, therecipient can use it to purchase something against the donor’s interests. In otherwords, even if economic assistance is strictly limited to education or infrastructure,etc., it has the potential to lead to the recipient’s military buildup. The funds may
be used to maintain international security, such as strengthening armaments forprotection from foreign enemies as well as maintaining domestic security, suchas suppressing insurgent groups. In other words, aid has both international anddomestic security externalities.
During the Cold War period, the United States avoided providing ODA
8to communist countries such as Cuba and North Korea, while the Soviet Union
provided foreign aid to those countries (Walters 1970). Even after the end of theCold War, rogue nations, such as Iran, Syria, and Libya, received little aid fromthe United States (Lai 2003: 108). Conversely, the United States offered
substantial economic and military assistance to South Vietnam in the 1960s andto Egypt and Israel after the Camp David Accords in 1978. This is because
security externalities were taken into consideration by US policymakers.
As aid has security externalities, major power donors, such as theUnited States and the Soviet Union, should be sensitive to the aid allocations ofother countries. Recently, scholars have focused on the role of foreign aid as abargaining tool, arguing that donors reward recipients change their policies tomaintain donor’s interest and punish recipients that fail to meet their demands(Bueno de Mosquita and Smith 2007, 2009). This reward/punishment policy does
not make sense unless other countries join in. Although the United States has
been one of the top bilateral donors after World War II, the financial resources of
the United States are not inexhaustible, so it also relies on financial support fromits allies. Therefore, as long as aid has security externalities, the United Statesshould be sensitive to the aid allocation behaviour of allies, and its allies shouldbe affected directly/indirectly by the United States. This effect will be observed inboth stages of aid allocation: eligibility and level.
In terms of direct impacts, previous studies on the influence of the UnitedStates on Japan’s aid allocations point out the existence of gaiatsu. Gaiatsu
means pressure from a foreign country (the United States) in Japanese and has
been mainly used in the context of Japanese economic policy. Japan has
9refrained from undertaking major independent foreign economic policy initiatives
even when it has the power and incentives to do so. When it changes its policy,
it is normally responding to outside pressure (Calder 1988). Whether Japan’s aid
policy has been influenced by gaiatsu has become an issue of concern forJapanese aid researchers. For example, Miyashita (2003), who conducts fivecase studies based on an excellent research design, demonstrates that Japanchanged its original aid policy because of pressure from the United States andconfirms the existence of gaiatsu.
Such pressure from the United States may affect not only Japan but also
other allies. Morrow (1991: 914) argues that asymmetric alliances have a tradeoff relationship between national autonomy and security, and states that ally withmajor powers lose some autonomy as a cost of obtaining safety (see also Lake2009 for the fully development of this argument). US allies that cannot makemilitary contributions are likely to be affected by pressure from the United States.
Such countries may have to change their aid policies because of gaiatsu. This
impact includes not only suspension of aid to countries that pose a threat to theUnited States in the eligibility stage but also the increase of aid to securitysignificant countries in the level stage.
Even if apparent gaiatsu does not exist, allies of the United States will
voluntarily refrain from providing aid to threatening countries (indirect impact)because if they provide aid to those countries, the surplus money arising from thefunds could be used for military purposes and there is a possibility that therecipient country will become a threat to themselves and their allies (i.e., security
diseconomy). In other words, donors that have formed an alliance with the United
10States to protect their international safety, adopt aid coordination with the UnitedStates as their norm. To that end, allies may suspend or increase aid for arecipient that raises no direct security concerns.
Thus, if direct or indirect influence works, allied donors will suspend aid
to recipients that are seen as a threat. On the contrary, they will increase aid tocountries that have security significance (i.e., positive externalities). Table 1
shows the types of recipients to be targeted and consequences of the amount ofaid given to those targets in the two stages.

Table 1 appears here

Therefore, from the viewpoint of security, overlap in both the eligibility
and level stages through aid coordination between the United States and its ally
donors is preferable. From the above discussion, the first hypothesis is derived:

H1: In both stages of bilateral aid allocation the donations of US allies are more
likely to be coordinated (overlapped) with the United States than with other
donors. 3

Next, this study considers the conditions under which US influence
becomes stronger, including which allies are most likely to coordinate and when
they are most likely to coordinate. First, US influence should be stronger during
an era when threats to the United States exist worldwide. During the Cold War,Western donors continued to confront the communist block centred around the11

Soviet Union, and the so-called ‘Global War on Terror’ was started by the terrorist
attacks on September 11th, 2001. During these two periods (1966-1989 and 20022015 in the data), the United States was likely intolerant of the free riding of its
allies, and since the allies understood, they probably refrained from aid behaviour
contrary to the will of the United States. In other words, the direct/indirect impactof the United States on its allies’ aid allocations should be stronger in these
periods. Therefore, the second hypothesis is:

H2: During the Cold War or the War on Terror eras, the donors of US allies are
more likely to coordinate (overlap) aid allocation with the United States in bothstages.

Second, donors’ concern for recipient countries’ security is basicallylimited to countries with which they have historical relations (such as a colonial
legacy) or that are neighbouring states (Buzan and Wæver 2003). Therefore,
most donors’ priority areas for aid differ from those of the United States, which isa global power. However, if a donor’s absolute amount of total bilateral aid is large,
it can afford to provide some amount to countries that are not related to its own
security interests. In other words, donors with a large amount of aid do not stayin a specific region and instead allocate aid globally (Figure 1). Since the UnitedStates should be aware of this fact, it may concentrate gaiatsu on donors with alarge absolute amount of aid.

Figure 1 appears here12

On the contrary, it is not realistic to demand that donors with lessavailable bilateral ODA expenditure increase their aid to recipients that are notdirectly related to their security interests. As a consequence, donors with a large
amount of absolute bilateral aid increase the possibility of overlapping with US
aid allocation in the level stage, as shown in Figure 1. Table 2 shows the top fivedonors in each decade.

Table 2 appears here

In contrast, it is relatively easy for small donors to coordinate with theUnited States globally at the eligibility stage because withholding aid does not
impose a cost. Therefore, aid decisions in the eligibility stage are not affected by
the amount of aid. The above discussion leads to the third hypothesis:

H3: Among US allies, donors with a large absolute amount of bilateral aid are
more likely to coordinate (overlap) with the United States only at the level stage.

Table 3 shows the three hypotheses and the predictions associated witheach stage. Hypotheses 1 and 2 are expected to have the same effects in bothstages, but hypothesis 3 does not assert anything about the eligibility stage.

Table 3 appears here
13III Data and Methods
Dependent Variable and the Cragg Hurdle Model
The unit of analysis in this study is dyad-year. The sample uses largescale ‘three-dimensional’ panel data (Berthélemy and Tichit 2004), covering 18
donors, 179 recipients, and 50 years (1966-2015). The membership of the DAC
is 29 as of the end of 2017 (Appendix 1). Since this study aims to grasp the longterm trend by testing the second hypothesis (the Cold War factor), the target
donors are those with data from before 1990. Therefore, 18 countries including
the United States will be analysed in this study.
In this study, I test whether the United States’ allocations of bilateral aid
are coordinated (overlapped) with those of other donors. Therefore, thedependent variable is the amount of aid of each donor allocated to a certain
recipient in each year. This variable requires some caution. First, I use
commitments since they reflect the wishes of the donor more accurately than do
disbursements, which depend in part on the actions of the recipient countries(Berthélemy 2006: 180; White and McGillivray 1995: 166). Second, it isnecessary to carefully handle data with missing values, omitted observations,and zeros. Data on bilateral aid by DAC members are obtained from the OECD
database (Creditor Reporting System). Current OECD bilateral aid commitment
data do not differentiate between a missing value, omitted observations, and avalue of zero. Among the DAC 17 countries, there are only 148 observations ofzero value out of more than 150,000 cases (17 donors × 179 recipients × 50
years). Furthermore, the United States has no value of zero at all. For example,
the commitment value of bilateral aid to Afghanistan by the United States is
14

displayed as ‘..’ in 1997 and 1998. The OECD explains that ‘..’ is ‘nil’ or ‘data notavailable.’ That is, it does not discriminate between whether the mark is zero ora missing value. However, since the United States provided aid to other countries
in those two years and to Afghanistan in other years it is natural to speculate that
the US government decided not to provide aid to Afghanistan during those twoyears rather than that no data could be obtained. 4 Coding these cases as
missing values or omitted observations occludes the possibility that the UnitedStates made a policy decision not to provide aid to Afghanistan in 1997 and 1998,
and therefore, the estimation results could be biased. Hence, whenever an aid
commitment was possible, the contents of those ‘..’ data should be recorded aszero. When there is a possibility that the expected result differs between theeligibility stage and the level stage, careful consideration is required whenhandling zeros and missing values.
To tackle this problem, Kleibl (2013) replaces all missing values andomitted observations with zero values and then randomly replaced 20% of thezero values on the aid variable with missing values. However, this method is stillarbitrary (why not 50% or 80%?). In this study, I code data with a rule that betterreflects reality. If there is a possibility that a recipient received ODA from at least
one DAC donor, I replace the OECD’s ‘..’ data with a zero (see the supplement
document for more detailed criteria). A list of recipients, temporal ranges, anddata that were coded according to the rule is provided in Appendix 2.
The third thing to understand when handling the dependent variables isthe problem that arises as a consequence of logarithmic transformation. In
estimating the allocation of bilateral aid, previous studies have used the logged
15

share or the percentage of the total amount of aid available to each recipient
country (Neumayer 2003; Clist 2011) as a dependent variable. In this research, I
also use the share or percentage of the total amount but not the logged sharebecause, in recent years, the harmful effect of log transformation on dependent
variables has been argued in the international trade gravity model. One problem
is Jensen's inequality, which implies that the expected value of the logarithm of arandom variable and the logarithm of its expected value are not the same. 5
Another problem is that data with a value of zero cannot be logarithmicallytransformed and is treated as a missing value (Burger et al 2009; Silva andTenreyro 2006, 2011). 6 The latter problem is especially serious in this study,
which codes many data to a value of zero. If a logarithmic transformation is
performed, all data with a value of zero become missing values, and estimationof the eligibility stage becomes impossible. However, without the logarithmictransformation, it is not possible to control the disparity between a country with alarge population receiving a large amount of aid and a sparsely populated country
receiving only a small amount. To handle this problem, some prior studies makea logarithmic transformation after changing the zero data to one (then one isconverted to zero). However, scholars have determined and pointed out that thismethod is arbitrary (see Shingal 2015: 399) because it cannot differentiate
between a zero and a value that was originally one.
Therefore, as the dependent variable, I use the share or percentage ofthe total amount of aid in commitments to each recipient country without
logarithmic transformation. That is,
16

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑡𝑡 = 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑡𝑡 / �

𝑛𝑛

𝑖𝑖=1

𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑡𝑡(1)where i is the recipient (n = 179), j is the donor, and t is the year. If a recipient
receives half of the total amount of aid from a donor for example, AIDSHARE
becomes 0.5.
The estimate is made with the hurdle model proposed by Cragg (1971)
with the exponential option. The hurdle model is a two-part estimation of a
selection model (bounded) and an outcome model (unbounded), characterised
as follows: 7

𝑦𝑦𝑖𝑖 = 𝑠𝑠𝑖𝑖 ℎ𝑖𝑖∗(2)where 𝑦𝑦𝑖𝑖 is the observed value of the dependent variable, 𝑠𝑠𝑖𝑖 is the selection

variable, and ℎ𝑖𝑖∗ is the continuous latent variable. 𝑠𝑠𝑖𝑖 is 1 if the dependent
variable is not bounded and 0 otherwise. Formally,𝑠𝑠𝑖𝑖 = �

1 𝑖𝑖𝑖𝑖 𝑧𝑧𝑖𝑖 𝛾𝛾 + 𝜖𝜖𝑖𝑖 > 00otherwise(3)

where 𝑧𝑧𝑖𝑖 is a vector of independent variables, 𝛾𝛾 is a vector of coefficients, and𝜖𝜖𝑖𝑖 is a standard normal error term. Thus, continuous latent variable ℎ𝑖𝑖∗ isobserved only if 𝑠𝑠𝑖𝑖 is 1. The exponential of the outcome model is17ℎ𝑖𝑖∗ = exp (𝑋𝑋𝑖𝑖 𝛽𝛽 + 𝑣𝑣𝑖𝑖 )(4)

where 𝑋𝑋𝑖𝑖 is a vector of independent variables, 𝛽𝛽 is a vector of coefficients, and𝑣𝑣𝑖𝑖 is a standard normal error term.

The following illustrate the reasons why I chose the hurdle model to test

my hypotheses. First, since equation (4) is equal to lnℎ𝑖𝑖∗ = 𝑋𝑋𝑖𝑖 𝛽𝛽 + 𝑣𝑣𝑖𝑖 , it is possible
to control the ODA scale of large and small countries without logarithmicallytransforming the dependent variable in advance. Second, the hurdle model best
fits the objectives and conditions of this study. When estimating determinants ofaid allocations, previous studies have used various models such as Tobit, or
Heckman sample selection model, or have literally separated the eligibility stage
and the level stage. 8 However, these models are not appropriate for estimating
the hypotheses of this study. For example, as is well known, the Tobit model
assumes each independent variable has an equal effect in both stages. However,my theory implies a possibility that the coordination may not be the same for bothstages in the third hypothesis, so the assumption of the Tobit model is too strong
for this study. 9 In the Heckman model, it is known that the bias becomes large
when the independent variables are the same in the eligibility stage and the levelstage (Puhani 2000). Moreover, to make the Heckman model function properly, it
requires independent variables that are good predictors in the selection stage but
not statistically significant in the level stage (Little and Rubin 1987: 230). Finding
such variables is by no means easy. Therefore, there are great advantages tousing the hurdle model in this study 10.
The histogram of the Figure 2 shows the distribution of the dependent18

variable (AIDSHARE). As expected, a number of zero value are observed.
Moreover, it shows few donors give aid more than 20% of their share to single
recipient.

Figure 2 appears here

Independent and Control Variables
The basic independent variable is the share of the total amount of aid
committed to each recipient by the United States (USAIDSHARE) 11 . The
independent variable for the first hypothesis is an interaction term between
USAIDSHARE and a dummy variable that takes on a value of 1 if the country is
a US ally and 0 otherwise. As Appendix 1 shows, 12 out of the 17 donors areallies of the United States. Except for New Zealand, who dissolved its alliancewith the United States in 1986, these states are US allies throughout all periods(1966–2015). If the first hypothesis is correct, the aid of the allies should overlap
with that of the United States, meaning that the variables in both stages are
expected to be positive.
The independent variable for the second hypothesis is an interaction
term between USAIDSHARE and a dummy variable, which takes on a value of 1during the Cold War (1966–1989) or War on Terror (2001–present), and a 0
otherwise. If the hypothesis is correct, the variable in both stages is positive.
Finally, the independent variable for the third hypothesis is the logged
sum of absolute bilateral ODA in commitments of each donor 12 multiplied by
USAIDSHARE. If this hypothesis is correct, then, as the absolute amount of total
19

bilateral aid increases, donors are more likely to be coordinated (i.e., overlapped)with the United States on aid allocation. Thus, the coefficient is expected to bepositive. The latter two hypotheses are tested by restricting the sample to the 12
allies.
It should be noted that the existence of coordination between the UnitedStates and other donors regarding aid allocation does not necessarily mean thatthe former’s aid allocation precedes that of the latter. As mentioned in Section II,
the US influence on allies in aid allocation decisions can be direct or indirect. If
there is strong gaiatsu in an alliance, negotiation and persuasion by United States
policymakers should occur before the policy decision is made and theoverlapping aid allocation behaviour should be done simultaneously by bothcountries. If there is a time lag, free riding is possible and the effect of coordination
will be diminished. In addition, in the case of indirect influence, allies will act incongruence with the expected policy goals of the United States. As aconsequence, the allocation of aid in both countries is likely to be simultaneous.
Therefore, the independent variable takes the same year as the dependent
variable. 13
To avoid omitted variable bias, I include several control variables that
previous research has identified (McGillivray 2003). First of all, two main factors
should be included. One is the strategic interest of donor countries, such as
ensuring security and export promotion. The other is recipient need, such as thedegree of poverty of the recipient country or the degree of damage from a natural
disaster. In this paper, I use logged exports from donors to recipients and voting
similarity in the General Assembly (Kuziemko and Werker 2006; Voeten 2013) to20

represent donor interest and logged GDP per capita and the population ofrecipient countries to represent recipient need. These variables are lagged one
year. In addition to these two factors, donor–recipient relations are also
considered. For example, if the geographic distance between two countries iscloser or they have a colonial legacy, a donor may provide more aid because of
its strong geographical or historic links (Alesina and Dollar 2000). As arepresentation of these relations, I incorporate the logged distance between eachdonor and recipient into the model. Since it is a time invariant variable, it acts as
dyad-fixed effects.
Furthermore, after the research of Burnside and Dollar (2000) who
argued that aid is effective if the recipient’s governance is good, a number ofstudies have analysed the relationship between recipient governance and aid
allocation (Neumayer 2003; Bueno de Mosquita and Smith 2007, 2009; Dietrich2013). Considering this fact, I also add one year lagged recipients’ democracy
score (Polity II).
Finally, I consider the factor of herding. As introduced in Section I, aid
duplication has been observed. To control for this bias, I introduce a variable forthe sum of the other DAC’s share of the total amount of aid commitments to each
recipient. Formally,

𝑚𝑚

𝑛𝑛

𝑘𝑘=1
𝑘𝑘≠𝑗𝑗

𝑖𝑖=1

𝑂𝑂𝑡𝑡ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒_𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑡𝑡 = �( 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑡𝑡 / � 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑡𝑡 )(5)21

where k = other DAC donors and m = 28 (see Appendix.1).

Taking advantage of the three-dimensional data, year-fixed effects,
donor-fixed effects, and recipient-fixed effects are included into the model. The
donor-fixed effects, which absorb all time-invariant donor-specific variables, areparticularly important. By including these effects, it becomes possible to test theeffect of independent variables while avoiding a tautological explanation such as‘Japan follows the United States because it is Japan’ or ‘France is making an
independent aid policy because it is France’ (see also Kleibl 2013).
Table 4 shows each variable and its source.

Table 4 appears here

IV Results
The results of the two-part estimation by the hurdle model are shown in
Table 5, and the marginal effects of each independent variable are shown from
Figure 3 to Figure 5.

Table 5 appears here

The models are divided into two stages: Eligibility (Stage I) and Level(Stage II). Independent variables are the same in the two stages. I briefly examine
the results of the control variables before discussing the effects of the main
independent variables of interest. Most of them have expected sign in the two22

stages. On average, donors provide more aid to recipients that import goods fromthem, are located geographically closer to them, have higher democracy score,
have lower GDP per capita, and receive more aid from other donors. All models
are statistically significant. Interest result is seen in the variable of the populationof recipient countries. Although the sign of this variable is negative in all models
in the Eligibility stage, it turns positive in the Level stage. This indicates that
donors tend to not give aid to recipients whose population is large. But once theydecide to give, more aid is allocated to larger population recipients. 14
The first column of Table 5 (Model 1) is the baseline. USAIDSHARE is
positive and statistically significant in both stages. This indicates that theallocation of bilateral aid by the DAC 17 countries overlaps with that of the UnitedStates when controlling for other factors. The second column (Model 2) examines
Hypothesis 1. The interaction between USAIDSHARE and alliance dummy ispositive and significant in both stages, supporting hypothesis 1. If a donor is a US
ally, the probability of overlapping aid allocation with the United States is higher
than with non-ally donors. The Whisker plots of Figure 3 and 4 show the marginal
effects of USAIDSHARE on AIDSHARE of DAC donors. These show that, on
average, the marginal effect of USAIDSHARE on AIDSHARE of allies is 8% andnon-allies is 3.6% in the eligibility stage, and 7.5% and 3.2% in the level stage.

Figure 3 and 4 appear here

The third to seventh columns (Models 3 to 7) are estimation results inwhich the samples are limited to the 12 donors allied with the US. The third to23

fifth columns (Models 3 to 5) examine the effects of the two global ‘wars.’
Statistically significant results are not observed during the Cold War period(before 1989). The impact of the Cold War on aid allocation has been the subjectof many previous studies, but inconsistent results have been reported (Meernik,
Krueger and Poe 1998; Roundd and Odedokun 2004; Berthelemy and Tichit 2004,
Lundsgaarde, Breuning and Prakash 2007; Clist 2011; Boutton and Carter 2014).
Therefore, the reason that the United States and its donor allies did notcoordinate during the Cold War period as compared to other times is unknown. I
would like to continue to investigate this subject in future research. Conversely,
the interaction term between the War on Terror dummy (after 2001) and
USAIDSHARE is positive and statistically significant in both stages, supporting
hypothesis 2. From the results of Model 5, the probability that each ally donor
overlaps with the aid allocations of the United States after 2001 rises by 19.1%
in the eligibility stage (Figure 3) and 18.7% in the level stage (Figure 4).
Model 6 examines hypothesis 3, which asserts that donors with a large
absolute amount of bilateral aid (if a donor is able to allocate aid more globally)
are more likely to coordinate (overlap) with the United States only at the levelstage. The findings illustrate a positive and statistically significant result. As the
Figure 5 shows, the probability of overlap with US aid allocation will increase as
the absolute amount of bilateral aid increases. This figure also indicates thatwhen the amounts of bilateral aid are small, donors tend to allocate more aid torecipients that get little amount of aid from the United States. But we cannot say‘burden sharing’ is taking place between the Unites States and small donors
because confidence intervals are very large in this case.
24

Figure 5 appears here

The last column (Model 7) includes all variables of hypotheses 2 and 3,
and no difference is found between the results. In short, the results support thethree hypotheses, except in regard to the Cold War factor.

V Robustness Check
In this study, I performed the following supplemental tests as arobustness check. First, to avoid endogeneity in the control variables, I changed
the variables that take t-1 to t-2 and t-3. Second, considering the fact that
relatively less variation in Polity II scores may cause multicollinearity withrecipient fixed effects (Oneal and Russett 2001), Variety of Democracy(Coppedge et al. 2017) data were used instead. Third, instead of binomial(Yes/No) voting similarity at the United Nations General Assembly, data fortrinomial voting similarity (Yes/abstain/No) were used. Fourth, the population and
GDP per capita of donors (both are logged) were also included in the model,
taking into account the possibility that the domestic situation of the donor isaffecting ODA allocations. After all of the checks, the results from the previous
section hardly changed and the robustness of the findings was confirmed. 15
Finally, to test whether the results of this study are sensitive to Japanesedata, I estimated with donors excluding Japan. According to Gibler (2009), Japanis the only allied donor (out of 17) that is an asymmetric ally of the United States.
25

Table 2 indicates that hypothesis 3 seems to apply well to Japan. Moreover,existing literature analysing donor coordination exclusively focuses on US–Japan
relations. In other words, Japan is the most likely case to support this research
hypothesis. Therefore, I tested whether the results would be changed by
excluding Japan. If the results do not change, then support for this hypothesis
should increase. The parsimonious version of the results is shown in Table 6.

Table 6 appears here

These results are almost identical to those shown in Table 5. Therefore,
even if Japan, which is the most likely case, is excluded, the hypotheses of thisstudy are still supported (except for the Cold War element).

VI Conclusion
Recent research on donor coordination has mainly focused on
economic influences. In particular, studies have focused on the need to avoid
duplication to improve aid efficiency. This study instead approaches donor
coordination from the viewpoint of security. That is, I argue that overlapping istheoretically preferable, and empirically confirm that donor coordination has, infact, occurred regularly. Despite its importance, this theme has not been exploredin prior research, except for specifically in the context of US–Japan relations.
Foreign aid has security externalities. While major power donors have
withheld economic aid to enemy countries, they have given more aid to recipients26

that are important for their security. Similarly, US ally donors have decided toallocate aid in congruence with US policy goals, either in response to direct orindirect pressure and/or voluntarily. As a result, donor coordination (overlapping)occurs between the United States and its allies. Among allies, big donors canrelatively easily respond to the demands of the United States. Moreover, they
have been more likely to coordinate since the onset of the War on Terror. The
results of the analysis of the dyadic panel dataset of 18 DAC donors and 179
recipients from 1966 to 2015, and the estimation by the Cragg hurdle model
support these theoretical expectations. In addition, the above hypotheses arelargely supported by the results of several robustness checks, especially in theestimation excluding Japan.
This research makes several important academic contributions. First, it
clearly links foreign aid, security, and donor coordination theoretically as well as
empirically. The connection between security and economic assistance has been
discussed for a long time (Morgenthau 1962), but this research is the first to
attempt to combine it with donor coordination, which is a topic that has attracted
academic scholars and aid practitioners in recent years. While research on donor
coordination has been limited to issues of economic efficiency, this research
introduces a security perspective to shed new light on the topic. Second, thisresearch shows that asymmetric allies like Japan, which suppress military
expenditures, can contribute to the security of the United States through foreign
aid. Of course, direct contributions, such as the dispatching of soldiers to conflictareas, are the most effective forms of military support. However, if an ally(especially a big donor) cannot do this due to domestic constraints, it can support27

the United States by increasing economic assistance in line with US security
interests.
Finally, this study reveals that security externalities of foreign aid could
help explain why the OECD’s call to eliminate duplication through donor
coordination has proved disappointing. In fact, since the War on Terror started in
2001, aid overlapping among allies has become even stronger. Unfortunately, as
long as the War on Terror continues, it is difficult for each Western donor to avoid
aid duplication, which may enhance its security but decreases the effectivenessof economic assistance.

28

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Table 1 Type of Target Recipients and Aid Effect in the Two Stages
Eligibility stage (Stage I)

Level stage (Stage II)

Target Recipient

Enemy

Friend with security significanceConsequence of aid

Security diseconomy

Positive security externalities

Ally donor’s decision

No aidIncrease aid

Table 2 Top Five Donors (Excluding the United States) from 1960 to 2010
1960s

1970s

1980s

1990s

2000s

2010s

1

UK

Japan

Japan

Japan

Japan

Japan

2

FranceUK

FranceFranceFranceFrance3

Japan

France

Italy

UK

UK

UK

4

CanadaCanadaNetherlands

Netherlands

Netherlands

Australia

5Australia

Netherlands

UK

Italy

CanadaCanada(10 year average of total bilateral aid in commitments (except 1966-1969 in the
1960s and 2010-2015 in the 2010s))
Source; OECD CRS Database33

Table 3 Three Hypotheses and their Predictions on aid coordination between the
United States and its ally donors in Both Stages
Eligibility stage (Stage I)

Level stage (Stage II)

H1: Alliance+ (overlap)+ (overlap)

H2: Global Wars

+ (overlap)+ (overlap)

Not predictable+ (overlap)

H3: Total value of ODA

Table 4 List and Sources of Variables
Variable

Definition

SourceAIDSHARE

Share of total amount of ODA to OECD

CRS

each recipient from donors (in databasecommitments) (see equation(1))
USAIDSHARE

Share of total amount of ODA to OECD

CRS

each recipient from the United database
States (in commitments) (seefootnote 11)
lnX (t-1)

Exports

fromdonorsrecipientsto IMF

directionofTrade statistics(Million US dollars)
UNvote (t-1)

Roll-call

votes

34

in

the

UN Voeten(2013)

General Assembly 1946–2015, Version 17.0
Dyadic affinity score using 2
category vote data. From -1(least similar interests) to 1
(most similar interests)
lnDIST

Geographic distance between CEPII database
recipient and donor

lnPOP_R (t-1)

Populationofrecipient World

countries
lnGDPPC_R (t-1)

database

Recipient’s GDP Per Capita World(constant 2010 US dollars)

PTY2 (t-1)

Bank

Bank

database

Recipients democracy score, Marshallandfrom -10 (autocracy) to 10 (full Jaggers (2016)democracy).
OtherDAC_AIDSHARE Sum of the aid share for a OECD

CRS

particular recipient of other database
donors (see equation (5))
Ally

Dummy variable equal to 1 Gibler(2009)

when the donor is an ally of the Version 4.1
United States, and 0 otherwise
Cold_War

Dummy variable equal to 1 if Gibler
year is before 1989, and 0 Version 4.1
otherwise35(2009)

Dummy variable equal to 1 if Gibler

Terror_War(2009)

year is after 2001, and 0 Version 4.1
otherwise
Sum of absolute bilateral ODA OECD

lnAIDTOTAL

from

each

donor

CRS

(in databasecommitments, constant 2015
million US dollars, see footnote
12)

Table 5 Regression Results of the Cragg Hurdle Model(Year, donor, and recipient fixed effects are included but not shown)
Model 1Model 2Model 3Model 4Model 5Model 6

Model 7(base)(Ally)(Cold War)

(War on Terror)(Wars)(Total Aid)

(ALL)

4.223**

2.033**

4.646**

3.773**

3.603**-13.268**-13.377**(0.489)(0.745)(0.657)(0.540)(0.599)(2.732)(2.568)0.381**

0.381**

0.445**

0.445**

0.445**

0.444**

0.445**(0.008)(0.008)(0.010)(0.010)(0.010)(0.010)(0.010)

0.006

0.005

0.112

0.101

0.100

0.095

0.084

(0.055)(0.056)(0.063)(0.063)(0.063)(0.063)(0.063)-0.901**-0.904**-0.731**-0.728**-0.728**-0.728**-0.725**(0.028)(0.028)(0.031)(0.031)(0.031)(0.031)(0.031)0.270**

0.277**

0.337**

0.162

0.163

0.346**

0.173(0.096)(0.097)(0.110)(0.112)(0.112)(0.109)(0.112)-0.649**-0.648**-0.688**-0.704**-0.702**-0.686**-0.702**(0.037)(0.037)(0.043)(0.043)(0.043)(0.042)(0.043)0.016**

0.016**

0.020**

0.020**

0.020**

0.020**

0.020**(0.002)(0.002)(0.003)(0.003)(0.003)(0.003)(0.003)

Stage II: Level
USAIDSHARElnX (t-1)

UNVote (t-1)

lnDISTlnPOP_R (t-1)

lnGDPPC_R (t-1)

PTY2 (t-1)

36OtherDAC_AIDSH

0.888**

0.886**

0.967**

0.942**

0.937**

0.972**

0.946**(0.057)(0.058)(0.067)(0.066)(0.066)(0.067)(0.066)ARE

Ally-0.166(0.119)Ally *

2.916**

USAIDSHARE(0.756)
Cold_War

Cold_War *0.927**

0.228*

0.274**(0.111)(0.092)(0.092)-0.914

0.388

0.811

(0.774)(0.756)(0.779)-0.692**-0.694**-0.633**(0.089)(0.089)(0.089)12.899**13.099**12.754**(1.746)(1.785)(1.791)

USAIDSHARE

Terror_War

Terror_War *USAIDSHARElnAIDTOTAL

lnAIDTOTAL *-0.243**-0.242**(0.024)(0.024)2.357**

2.261**(0.380)(0.351)

USAIDSHARE

1.886

1.806

-0.034

3.562

3.544

1.497

5.001*(1.709)(1.709)(1.939)(1.952)(1.953)(1.936)(1.953)

1.421**

0.137

2.706*

1.230*

1.154

3.029

4.222

(0.473)(0.514)(1.267)(0.602)(0.872)(2.588)(2.590)0.149**

0.150**

0.148**

0.148**

0.148**

0.152**

0.152**(0.005)(0.005)(0.007)(0.007)(0.007)(0.007)(0.007)-0.183**-0.191**-0.046-0.066-0.066-0.017-0.030(0.042)(0.042)(0.049)(0.048)(0.049)(0.049)(0.049)-0.741**-0.744**-0.608**-0.607**-0.607**-0.591**-0.589**(0.024)(0.024)(0.028)(0.028)(0.028)(0.029)(0.029)-0.134*-0.133*-0.212**-0.327**-0.327**-0.236**-0.351**

Stage I: Eligibility
USAIDSHARElnX (t-1)

UNVote (t-1)

lnDISTlnPOP_R (t-1)

37

lnGDPPC_R (t-1)

PTY2 (t-1)

OtherDAC_AIDSH(0.066)(0.066)(0.080)(0.081)(0.081)(0.079)(0.081)-0.250**-0.249**-0.265**-0.263**-0.262**-0.265**-0.265**(0.026)(0.026)(0.031)(0.031)(0.031)(0.031)(0.032)0.013**

0.013**

0.010**

0.009**

0.009**

0.010**

0.009**(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)0.665**

0.663**

0.781**

0.735**

0.735**

0.812**

0.769**(0.061)(0.061)(0.081)(0.080)(0.080)(0.084)(0.083)ARE

Ally-0.054(0.065)Ally *

2.134**

USAIDSHARE(0.604)
Cold_War

Cold_War *-0.039-0.213**-0.222**(0.088)(0.078)(0.080)-1.537

0.125

0.159

(1.126)(0.849)(0.862)-0.224**-0.224**-0.277**(0.077)(0.077)(0.076)27.333**27.410**26.572**(3.404)(3.447)(3.411)

USAIDSHARE

Terror_War

Terror_War *USAIDSHARElnAIDTOTAL

lnAIDTOTAL *USAIDSHARE_cons

0.530**

0.529**(0.023)(0.023)-0.180-0.443(0.359)(0.333)12.145**12.171**

12.464**14.445**14.445**

9.122**

11.185**(1.184)(1.184)(1.408)(1.411)(1.411)(1.413)(1.420)0.552**

0.551**

0.541**

0.540**

0.540**

0.539**

0.538**(0.003)(0.003)(0.004)(0.004)(0.004)(0.004)(0.004)

1.736

1.736

1.717

1.716

1.716

1.714

1.713

(0.006)(0.006)(0.007)(0.007)(0.007)(0.007)(0.007)

Lnsigma
_cons

Sigma

38

Observations
Log
pseudolikelihood
Wald Chi square

67203

67203

46814

46814

46814

46760

46760

222901.23

222917.7

167673.39

167762.2

167762.35

168157.79

168242.97

38147.78**

38194.24**

31852.21**

31953.04**

31974.17**32103.56**

32248.30 **Robust standard errors in parentheses* p < 0.05, ** p < 0.01

Table 6 Regression Results of Cragg Hurdle Model Excluding Japan,
Parsimonious Version
Model 8Model 9Model 10Model 11

Model 12Model 13Model 14(base)(Ally)(Cold War)

(War on Terror)(Wars)(Total Aid)

(ALL)

4.134**

2.104**

4.808**

3.638**

3.602**-16.483**-15.964**(0.511)(0.748)(0.727)(0.575)(0.649)(3.484)(3.209)

Stage II: Level
USAIDSHARE

Ally-0.238*(0.118)Ally *

2.790**

USAIDSHARE(0.764)
Cold_War

Cold_War *0.963**

0.243*

0.320**(0.118)(0.098)(0.098)-1.426

0.082

0.400

(0.848)(0.823)(0.863)-0.713**-0.714**-0.651**(0.095)(0.095)(0.095)14.857**14.899**14.435**(1.865)(1.911)(1.932)

USAIDSHARE

Terror_War

Terror_War *USAIDSHARElnAIDTOTAL

39-0.309**-0.308**(0.025)(0.025)

lnAIDTOTAL *2.853**

2.682**(0.507)(0.468)USAIDSHARE

Stage I: Eligibility
USAIDSHARE

1.492**

0.234

2.974*

1.332*

1.348

3.655

5.325

(0.484)(0.523)(1.336)(0.620)(0.920)(2.797)(2.815)Ally-0.056(0.065)Ally *

2.113**

USAIDSHARE(0.615)
Cold_War

Cold_War *-0.053-0.229**-0.242**(0.091)(0.079)(0.082)-1.768-0.027-0.002(1.180)(0.887)(0.900)-0.226**-0.226**-0.268**(0.079)(0.079)(0.078)28.178**28.162**27.320**(3.449)(3.497)(3.452)

USAIDSHARE

Terror_War

Terror_War *USAIDSHARE

Observations

62758

62758

42369

42369

42369

42315

42315

Log

199679.36

199695.45

144366.23

144462.36

144462.36

144810.23

144903.45

33857.65**

33913.21**

26990.34**

27116.89**

27128.16**

27261.65**

27411.91**

pseudolikelihood
Wald Chi square

Robust standard errors in parentheses* p<0.05, ** p<0.0140United States

Big Donor

Small
donor

41

0

20

Percent
40

60

80

Figure 1 Overlapping security regions of three types of donors

0

.2

.4
AIDSHARE

Figure 2 The distribution of the dependent variable

42

.6

.8

.3
Marginal effects of USAID
.1
.15
.2

.25

0.259

0.191

0.099

0.070
0.053

0.080

0.063

0.061

0.036

0.009

0

.05

0.124

0.086

base

Alliance

No Alliance

Alliance After 2001

Figure 3 Points estimates and 95% confidence intervals for the marginal effectof US bilateral aid on that of other DAC countries (eligibility stage): conditionedon the status of Alliance and after 2001

43

.3
Marginal effects of USAID
.1
.15
.2

.25

0.252

0.187

0.123
0.092
0.079

0.049

0.075
0.056
0.058
0.032

0.008

0

.05

0.064

base

Alliance

No Alliance

Alliance After 2001

Figure 4 Points estimates and 95% confidence intervals marginal effect of US
bilateral aid on that of other DAC (level stage): conditioned on the status ofAlliance and after 2001

44

.2
Marginal effects of USAID
-.1
0
.1
-.2

0

1

2

3

6
4
5
ln(Total bilateral ODA)

7

8

9

10

Figure 5 Marginal effects and 95% confidence interval of US bilateral aid on thatof other DAC countries across the total value of bilateral ODA (logged)*******Appendix. 1***********Country nameODA data existed (Commitment)

Australia

1966, 1968–2015

Yes

Austria

1966–2015

No

Belgium

1968–2015

Yes

Canada1966–2015

Yes

Switzerland

1966, 1968–2015

No

Czech Republic

1998–2001, 2011–2015

Germany

1968–2015

Yes

Denmark

1966–2015

Yes

45

Alliance with the US

Yes (1999–)

Spain1992–2015

Yes (1970–)

Finland1973–2015

No

France1966–2015

Yes

UK

1966–2015

Yes

Greece1996–2015

Yes

Hungary

2007, 2015

Yes (1999–)

Ireland

1974–1993, 1996–2015

No

Iceland2011–2015

Yes

Italy

1966–2015

Yes

Japan

1967–2015

Yes

Korea

1987–1993, 1995–2015

Yes

Luxembourg

1992, 1996–2015

Yes

The Netherlands

1966–2015

Yes

Norway1966–2015

Yes

New Zealand

1972–1988, 1990–1994, 1996, 1998–2015

Yes (–1986)
Yes (1962–1964,

Poland1998–1999, 2003–2007, 2013–2015

Portugal

1991–2015

Yes

Slovak Republic

2002–2003, 2013–2015

No

Slovenia

2012–2015

No

Sweden1966–2015

NoUS

1966, 1968–2015–

1999–)

Appendix.1 List of DAC countries. Donors used in this study are meshed.
Source; OECD CRS Database and Gibler (2009)46

*******Appendix. 2***********
Recipients Name

Data Existed

Afghanistan

1966-2015

Albania

1988-2015

Algeria

1966-2015

Angola1966-2015

Anguilla

1975-2013

Antigua & Barbuda

1974-2015

Argentina

1966-2015

Armenia

1993-2015

Aruba

notes

British Overseas Territories

Former Soviet Union
Constituent country of the

1985-1999

Netherlands

Azerbaijan

1993-2015

Former Soviet Union

Bahamas

1966-1995

Bahrain1967-2004

Bangladesh

1971-2015

Barbados

1966-2010

Belarus2005-2015

Belize1966-2015

Benin

1966-2015

Bermuda1966-1996

Bhutan

1966-2015

Bolivia

1966-2015

Former Soviet Union

British Overseas Territories

47

Bosnia-Herzegovina

1994-2015

Botswana

1966-2015

Brazil1966-2015

Brunei

1966-1995

Burkina Faso

1966-2015

Burundi

1966-2015

Cambodia

1966-2015

Cameroon

1966-2015

Cape Verde

1975-2015

Cayman Islands

1974-1996

Central African Rep.

1966-2015

Chad

1966-2015

Chile

1966-2015

China

1979-2015

Chinese Taipei

1966-1996

Colombia

1966-2015

Comoros

1966-2015

Congo, Dem. Rep.

1966-2015

Congo, Rep.

1966-2015

Cook Islands

1972-2015

Costa Rica

1966-2015

Cote d'Ivoire

1966-2015

Croatia

1994-2010

Former Yugoslavia

British Overseas Territories

Realm of New ZealandFormer Yugoslavia
48

Cuba

1967-2015

Cyprus

1966-1996

Djibouti

1966-2015

Dominica

1974-2015

Dominican Republic

1966-2015

Ecuador

1966-2015

Egypt

1966-2015

El Salvador

1966-2015

Equatorial Guinea

1976-2015

Eritrea

1993-2015

Ethiopia

1966-2015

Fiji

1966-2015

Former Yugoslav
Republic ofMacedonia (FYROM)

1994-2015

Former Yugoslavia

French Polynesia

1966-1999

French overseas collectivities

Gabon

1966-2015

Gambia1966-2015

Georgia

1993-2015

Ghana

1966-2015

Gibraltar

1966-1999

Grenada1974-2015

Guatemala

1966-2015

Former Soviet Union

British Overseas Territories

49

Guinea

1966-2015

Guinea-Bissau

1969-2015

Guyana1966-2015

Haiti

1966-2015

Honduras

1966-2015

Hong Kong, China

1966-1996

India

1966-2015

Indonesia

1966-2015

Iran

1966-2015

Iraq

1966-2015

Israel1966-1996

Jamaica

1966-2015

Jordan

1966-2015

Kazakstan

1993-2015

Kenya

1966-2015

Kiribati

1966-2015

Korea

1966-1999

Korea, Dem. Rep.

1985-2015

Kosovo2009-2015

Kuwait1967-1995

Kyrgyz Republic

1993-2015

Laos

1966-2015

Lebanon

1966-2015

Former Soviet Union

Former Yugoslavia

Former Soviet Union50

Lesotho

1966-2015

Liberia

1966-2015

Libya

1966-1999, 20052015

Macao

1966-1999

Madagascar

1966-2015

Malawi

1966-2015

Malaysia

1966-2015

Maldives

1966-2015

Mali

1966-2015

Malta

1966-2002

Marshall Islands

Former United States Trust
1992-2015

Mauritania

1966-2015

Mauritius

1966-2015

Mayotte1977-2010

Mexico1966-2015

Micronesia

Territory

French overseas department

Former United States Trust
1992-2015

Territory

Moldova

1997-2015

Former Soviet Union

Mongolia

1984-2015

Montenegro

2006-2015

Former Yugoslavia

Montserrat

1974-2015

British Overseas Territories
51

Morocco

1966-2015

Mozambique

1966-2015

Myanmar (Burma)

1966-2015

Namibia

1983-2015

Nauru

1976-2015

Nepal

1966-2015

Netherlands Antilles

1966-1999

New Caledonia

1966-1999

Nicaragua

1966-2015

Niger

1966-2015

Nigeria

1966-2015

Niue

1972-2015

Northern Marianas

Realm of New ZealandFormer United States Trust

1966-1996

Oman

1968-2010

Pakistan

1966-2015

Palau

French special status

Territory

Former United States Trust
1992-2015

Panama1966-2015

Papua New Guinea

1966-2015

Paraguay

1966-2015

Peru

1966-2015

Philippines

1966-2015

Territory

52

Qatar1967-1995

Rwanda1966-2015

Samoa

1966-2015

Sao Tome & Principe

1972-2015

Saudi Arabia

1966-2007

Senegal

1966-2015

Serbia1994-2015

Seychelles

1966-2015

Sierra Leone

1966-2015

Singapore

1966-1995

Slovenia

1994-2002

Solomon Islands

1966-2015

Somalia

1966-2015

South Africa

1993-2015

South Sudan

2011-2015

Sri Lanka

1966-2015

St. Helena

1966-2015

St. Kitts-Nevis

1974-2013

St. Lucia

1974-2015

Former Yugoslavia

Former Yugoslavia

British Overseas Territories

St.Vincent &Grenadines

1974-2015

Sudan

1966-2015

Suriname

1966-2015
53

Swaziland

1966-2015

Syria

1966-2015

Tajikistan

1993-2015

Tanzania

1966-2015

Thailand

1966-2015

Timor-Leste

1974-2015

Togo

1966-2015

Tokelau

1972-2015

Tonga

1966-2015

Trinidad & Tobago

1966-2010

Tunisia

1966-2015

Turkey1966-2015

Turkmenistan

1993-2015

Former Soviet Union

Islands

1974-2007

British Overseas Territories

Tuvalu1976-2015

Uganda1966-2015

Ukraine2005-2015

United Arab Emirates

1967-1995

Uruguay

1966-2015

Uzbekistan

1993-2015

Vanuatu

1966-2015

Venezuela

1966-2015

Former Soviet Union

Realm of New ZealandTurks & Caicos

Former Soviet Union

Former Soviet Union

54

Vietnam

1966-2015

Virgin Islands (UK)

1966-1999

British Overseas Territories

Wallis & Futuna

1970-2015

French overseas collectivities

West Bank & Gaza
Strip

1993-2015

Yemen 16

1966-2015

Zambia1966-2015

Zimbabwe1966-2015

Appendix. 2 List of Recipients55

***Supplement document***(1) Data for recipients are listed based on the OECD’s ‘History of DAC Lists of
Aid Recipient Countries.’ 17(2) Even if a recipient does not have sovereignty, if aid from a DAC member exists,
it is listed as a recipient. For example, in 2005 and 2010 the United States
provided aid to Montserrat, an overseas territory of the UK. This fact indicatesthat it is possible for other countries to also give aid to the British territory.
(3) If a recipient receives aid from at least one donor in a certain year, all the othervalues of ‘..’ are set to zero in that year.
(4) A newly established country (such as the countries of the former Soviet bloc)
is coded with a missing value before its establishment and with a zero once it
receives aid from at least one donor.
(5) If there is an inconsistency between the OECD’s explanation and thecommitment data, priority is given to the latter. For example, Albania is reported
to have been added to the recipient list in 1989, but commitment data exist since1988 (aid from Austria, Italy, and the US). Similarly, South Africa was added to
the list in 1991, but it began to receive assistance from 1993. In the former case,‘..’ for Albania becomes missing before 1988 and a zero value afterward. In thelatter case, all data are coded as missing before 1993.
***End of Supplementary document***56

1

In practice, Djankov et al. (2009) demonstrates that as a result of duplication of aid

programs and the administrative burden accompanied with it, growth performance suffers(see also Kilby 2011; Bigsten and Tengstam 2014).
2

Security externalities of trade and their relations with alliances is developed by Gowa

(1994).
3This theoretical prediction differs from Katada (1997), which finds burden sharing aid

coordination between the U.S. and Japan. To make burden sharing work, extremely close
policy coordination between two nations is necessary. When analysed regions and periods
are limited, burden sharing might be observed. However, the possibility of such
coordination is low when more than 150 recipients and 50 years are analysed. Katada(1997) focuses on only Latin America and, therefore, the result may have sample selection
bias. Moreover, her regression result shows that only the 1975-1982 period is statisticallysignificant.
4

Moreover, other donors, such as the United Kingdom, gave aid to Afghanistan in those

two years.
5
6Namely, E(ln𝐼𝐼𝑖𝑖𝑖𝑖 ) ≠ lnE(𝐼𝐼𝑖𝑖𝑖𝑖 )Recent studies of international trade, therefore, try to estimate level data by the Poisson

Pseudo Maximum Likelihood or (Zero-Inflated) Negative Binominal model.
7

https://www.stata.com/manuals14/rchurdle.pdf (15 January, 2018)8

The last case method is reported to raise a serious estimation problem in the level stage(see Bueno de Mesquita and Smith 2009: appendix).
9

In this respect, the same is true for the Poisson Pseudo Maximum Likelihood or Negative

Binominal models.
10

Moreover, Monte Carlo simulation shows this model performs better than the othertechniques (Manning, Duan, and Rogers 1987; Clist 2011: 1726).
11

𝑛𝑛That is, 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑖𝑖,𝑡𝑡 = 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑖𝑖,𝑡𝑡 / �𝑖𝑖=1 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑖𝑖,𝑡𝑡 where i is the recipient (n = 179)

and t is the year.
12

That is, ln (𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑡𝑡 ) = ln ( �

𝑛𝑛

𝑖𝑖=1

𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑡𝑡 ).

57

13

Another possibility is that the allocation of aid from the United States is the result offollowing allies’ aid policies. Although this is an interesting hypothetical story, it isunrealistic. Therefore, I do not consider it in this study.
14

This result will never be discovered with the Tobit model.

15

The only difference is seen in the version of Model 3 that uses Variety of Democracy

data, where the interaction term between the Cold War dummy and USAIDSHARE isnegative and statistically significant at the 5% level. However, it is not significant in theversion of Model 5, that estimated simultaneously the War on Terror dummy and its
interaction with USAIDSHARE. The Cold War element, including this result, cannot be
explained by my hypotheses. I would like to clarify why in future research.
16

Although Yemen was divided into north and south before 1990, they are treated as one

country in the OECD’s data. Therefore, this study also combines data for the two Yemens.
17‘Chronology of changes in recipient country coverage, 1989-2014’

http://www.oecd.org/dac/stats/historyofdaclistsofaidrecipientcountries.htm (18 April, 2017)58