Information transmission and ownership consolidation in aid programs.
Dreher, Axel ; Langlotz, Sarah ; Marchesi, Silvia 等
Information transmission and ownership consolidation in aid programs.
I. INTRODUCTION
Over the last 20 years geopolitical and global economic
developments have modified the way official foreign aid is given. The
so-called new rhetoric on aid has recognized the importance of
encouraging greater ownership of development programs in recipient
countries (e.g., see the Paris Declaration on Aid Effectiveness, OECD
2005). In particular, ownership has been seen as crucial for the
successful implementation of conditional reform programs and basing
reform designs on context-specific knowledge could be one way to
stimulate recipient countries' ownership. (1)
Donors that aim at maximizing ownership could be expected to grant
substantial leeway to the recipients of their aid. Donor and recipient
preferences on how to use aid can however differ. Donors use parts of
their aid to promote development and improve policies and institutions
(Fleck and Kilby 2010), while recipients might want to use it to grant
political favors to their preferred constituencies or delay the
implementation of reforms (Dreher et al. 2014; von Borzyskowski 2016).
These differences in preferences give donor countries incentives to keep
control of how recipients spend the aid. The differences in preferences
about how to use aid imply a trade-off when deciding about whether and
to what extent control over the aid should be given to the recipient or
kept with the donor. The trade-off is complicated by the role that donor
and recipient country information--and the way such information is
exchanged between them--plays in how to make best use of the aid with
respect to developmental outcomes. The link between the difference in
preferences of donors and recipients in how to use aid, the relative
importance of donor and recipient information, and whether and to what
extent this information is communicated between them, is the focus of
this paper. (2)
Countries' local knowledge often consists of unverifiable
information (or verifiable only at a cost) and so the quality of the
information the recipients provide to the donors crucially depends on
the conflict of interest between the recipient (the sender of the
information) and the donor (the receiver). Communication is complicated
by the fact that donors also own some private information that is
relevant to the implementation of effective polices. In this setting,
mutual communication is important as the donor possesses skills and
information which are useful in processing the country's local
information. Thus, a combination of the private information of the donor
with those of the recipient is required for the design of the
"optimal" policy package. The analytical setting is one of
two-sided incomplete information where agency problems have the indirect
negative effect of preventing full communication between the sender and
the receiver.
As in the study by Marchesi, Sabani, and Dreher (2011), we model
the transmission of information between the donor and the recipient
country as a cheap talk game (Crawford and Sobel 1982). Information is
assumed to be "soft" and the transmission of information to be
costless. We compare two types of incentive schemes (delegation vs.
centralization) relative to the quality of the transmitted information.
We define "centralization" as a framework in which control
rights over policies are assigned to the donor. On the contrary, we
define "delegation"--or "decentralization"--as a
framework in which the recipient country is left with considerable
freedom to devise its own policy actions.
We consider a situation in which the recipient is biased in favor
of the "status quo," whereas the donor is biased in favor of
more (or deeper) policy reforms relative to the recipient. What we have
in mind here is a situation where recipient governments might be corrupt
and incompetent, maintain unsustainable economic policies like high
inflation and budget deficits, or repress minorities. We assume donors
want to use their aid to achieve changes to the status quo, but face
resistance by the recipient government. With both delegation and
centralization, such misalignment of interests prevents full
communication. Therefore, the optimal allocation of control rights over
policies from the donor's perspective will depend on the relative
importance of the two parties' information. It will also depend on
the degree to which donor and recipient preferences differ (which we
refer to as the "agency bias"), simultaneously affecting the
amount of information transmitted and the degree of reforms implemented.
In line with Marchesi, Sabani, and Dreher (2011), the main
theoretical findings are as follows. For a given agency bias, when the
recipient's local knowledge is more important than the donor's
information, their discretion in the choice of policies (delegation)
should be increased. Conversely, there should be less freedom in
designing policies (centralization) when the donor's information is
more relevant. As far as the effect of the agency bias is concerned,
there are two opposing effects. Intuition would suggest that an increase
in the conflict of interest between the donor and the recipient would
make the donor more inclined toward "centralization." The
agency bias, however, also affects the quality of communication
and--since an increase in the bias reduces the amount of information
transferred to the donor by the recipient--the donor's incentive to
delegate may increase, particularly when local knowledge is crucial for
designing the donor's preferred policies.
An immediate empirical implication of the model is to investigate
the way in which aid is committed in relation to information
transmission problems. We focus on two distinct ways of delivering aid,
budget support and project aid. Budget support increases the involvement
of the recipient governments in the decision-making process and is thus
an example of a "delegation-scheme." This is because budget
aid is directly transferred to the recipient government and can be used
by the recipient at some discretion. (3) Conversely, project aid
represents a more "centralized" type of aid. Donors and
recipients negotiate the specific projects the aid is given for. What is
more, donors are usually involved in the details of preparing and
implementing the project, leaving little discretion on how to use the
aid. We therefore consider the relative importance of donor and
recipient private information--and the difference in their preferences
on how to use the aid--as determinants of project and budget aid.
We test our theory focusing on aid given by the 28 bilateral donors
of the OECD's Development Assistance Committee (DAC) to a maximum
of 112 recipient countries over the 1995-2010 period, resulting in more
than 45,000 observations at the donor-recipient-year level. We measure
the bias in donor and recipient preferences with a number of proxies,
among them a measure based on how they vote in the United Nations
General Assembly (UNGA) on a broad range of topics. Our proxies for the
availability of information to the donor relate to how transparent
recipient country policies, data, and local environments are for the
donor. Controlling for the main donor- and recipient-country variables
that determine the dyadic aid relationship, and donor-recipient-pair- as
well as yearfixed effects, we find that misaligned interests and
informational asymmetries differentially influence whether donors grant
their aid as project aid or budget aid, in line with our theory.
The article is organized as follows. Section II briefly describes
the related literature. A sketch of the model is developed in Section
III. Section IV introduces our data, while Section V describes the
empirical model and our results. Section VI concludes.
II. RELATED LITERATURE
This study relates to two strands of literature. The first is the
literature on aid allocation and selectivity. This literature tries to
disentangle the various motives of donors when giving aid, usually
referring to commercial, geo-strategic, developmental, and "good
policy" related motives (see, e.g., Alesina and Dollar 2000;
Dreher, Sturm, and Vreeland 2009a, 2009b; Kuziemko and Werker 2006).
Most directly related to the question we focus on in this paper are
studies that address the choice between project and budget aid (see,
e.g., Chauvet, Collier, and Fuster 2013; Clist, Isopi, and Morrissey
2012; Cordelia and delTAriccia 2007; Hefeker 2006; Koeberle, Stavreski,
and Walliser 2006; Morrissey 2006; Mosley and Abrar 2006; Ouattara and
Strobl 2008).
Cordelia and delTAriccia (2007) relate the choice between project
aid and conditional budget support to the different preferences between
donors and recipients. They show that budget support is preferable to
project aid when the donor's preferences are close to those of the
recipient and the amount of aid is small relative to the
recipients' own resources. Morrissey (2006) also finds that budget
support can safely be granted if recipients allocate spending broadly as
agreed with their donors. Rather than imposing prior actions on the
recipient, donors should then focus on the effectiveness of such
spending when determining eligibility to budget support. (4) In a
similar vein, Mosley and Abrar (2006) show that trustful relations
between donors and recipients are fundamental for the effectiveness of
conditionality, and in particular for those of budget support.
More recently, Chauvet, Collier, and Fuster (2013) have also
related the existence of a conflict of interest between donors and
recipients to the choice of (donors' supervision of) project aid.
Applying principal-agent theory to the performance of aid projects they
show that in a wide range of circumstances the donor should put greater
effort into supervision when the difference between the agent's
preferences and its own is greater. They test this prediction using data
on World Bank project performance and--consistent with their
theory--find that donor supervision of projects is significantly more
effective in improving project performance when interests are widely
divergent. Like we do in this paper, Cordelia and delTAriccia (2007) and
Chauvet, Collier, and Fuster (2013) use a principal-agent framework and
relate the conflict of interest between donors and recipients to the
choice of whether to give aid as budget support or project aid. None of
them, however, has considered the importance of communication between
the donor and the recipient for the design of policies, nor have they
related the choice between these different aid schemes to the importance
of fostering communication between the two.
The second strand of literature to which this paper relates is
primarily concerned with the role of donors (or lenders) in designing
development reforms and thus to the importance of enhancing
recipients' ownership. (5) The principle that ownership is crucial
for the (successful) implementation of reforms is now well established.
As emphasized by various studies including Easterly (2008), Dixit
(2009), Besley and Persson (2011), and Marchesi, Sabani, and Dreher
(2011), institutions and policies are context-specific and donors and
lenders do well to base their policies on a good knowledge of the
recipient country's characteristics, which in turn implies greater
ownership of policies in recipient countries. (6) Nevertheless, the
mechanisms and circumstances under which such knowledge should be
transferred have rarely been investigated. (7)
Finally, we should mention that the use of dyadic data is crucial
in our setting. While much of the aid allocation literature uses monadic
data, (8) our model's implications for donor-recipient
communication requires measures of donor-recipient-specific
relationships (such as the political or ideological distance between
them and bilateral trade). Such data allow us to take into account the
salience of the informational asymmetry (and of the agency bias) in
donor-recipient pairs in addition to more general indicators of
recipient transparency (or bias). For example, even in generally
intrans-parent recipients, for which the use of local information should
in principle be important, specific donors might still decide to opt for
centralization, for example because they have some country-specific
knowledge due to a prolonged relationship with a particular recipient
(which has reduced the relative importance of the recipient's
knowledge over time).
We contribute to the literature both theoretically and empirically.
Regarding theory, we analyze the transmission of information in the
allocation of aid. To our knowledge, it is the first time that
communication is explicitly introduced to the context of foreign aid.
With respect to our empirical models, even though some papers have
considered the importance of distinguishing among different types of aid
flows and some have empirically investigated the determinants of budget
support, we are the first to test whether this choice is responsive to
communication between the donor and the recipient.
III. THEORETICAL FRAMEWORK
The framework relies on the model of Marchesi, Sabani, and Dreher
(2011), which we modify in order to be applicable to the issues central
to this paper. The main change with respect to Marchesi et al. (as well
as to Flarris and Raviv 2005) relates to the different environment in
which we investigate the cheap talk.
To analyze whether the donor has an incentive to delegate the
control of decision-making to recipient governments we focus on the
aspects of the model that are central to derive our hypotheses. For
reasons of clarity, all detailed derivations and proofs are however
shown in Appendices S1 - S3, Supporting Information. (9) The model
features two players--the donor and recipient countries'
governments--that own different types of information both required for
the optimal choice of policies in the recipient country (in the context
of disbursing aid), denoted by p. The recipient country's welfare
is proxied by Y(p) (i.e., the country's per capita national
income), which is a function of policy p. The policy maximizing Y(p) is
denoted by [p.sup.*]. In turn, optimal policy is defined by [p.sup.*] =
g + d, where g and d are stochastic variables that proxy for information
observed only by the recipient government and, respectively, the donor
government; g and d are independently and uniformly distributed on the
intervals [0, G] and [0,D], respectively. This captures that the larger
the interval [0, G] ([0, D]), the larger the informational advantage of
the recipient (donor).
The recipient's superior information over g represents the
local knowledge (for instance information about the country's
economy and sociopolitical characteristics or better knowledge about the
risks and opportunities of local investment projects), which can be seen
as deriving from its closer proximity to the country's culture and
business environment as compared to the donor. The recipient's
informational advantage may depend not only on how relevant its
knowledge is per se, but also on how valuable such information is
relative to the donor. For example, in highly intransparent environments
such informational advantages would be more salient compared to more
transparent ones.
However, the donor's informational advantage d is derived from
its cross-country knowledge. For example, a donor that has implemented
projects in the health sector in a number of different countries has
accumulated project-related knowledge that will be valuable for the
implementation of health projects in the recipient country. Both types
of information are assumed to be (at least partly) "soft,"
that is, they cannot easily be certified.
Events unfold in three stages: allocation of control rights by the
donor, communication, and policy implementation. (10) In the first
stage, the donor either allocates authority over the choice of the
policy vector to the recipient government or retains authority.
Centralization refers to the scheme in which the donor decides on the
policy vector, whereas under decentralization control rights are
allocated to the recipient government. After the first stage of the
game, the real state of the world is revealed to both players. In the
second stage, communication takes place. Under centralization, the
government sends a "message" to the donor regarding its
"local knowledge." Upon receiving the message, the donor
updates its beliefs and chooses the policy vector. Under
decentralization, the donor sends a message to the recipient concerning
its private knowledge of the state of the world. In this case, the
government updates its beliefs and chooses the policy vector. Finally,
in the third stage, the policy is implemented and outcomes are realized.
The donor maximizes the following objective function:
(1) [U.sup.D] = [U.sup.D.sub.0] - [(p - [P.sup.*.sub.D]).sup.2],
where [U.sup.D] decreases with the distance between the actually
implemented policy p and the optimal policy [P.sup.*.sub.D], with
[U.sup.D.sub.0] = [U.sup.D] ([P.sup.*.sub.D]). (11) The optimal policy
choice of the donor deviates from the optimal policy [p.sup.*] by a
factor e > 0 (i.e., [P.sup.*.sub.D] = [p.sup.*] + e). Besides the
recipient country's income, the donor cares as well for the
externality that the choice of a specific policy p could have on the
donor country's economy (e.g., regarding access to markets or
natural resources), in line with the aid allocation literature (e.g.,
Alesina and Dollar 2000; Dreher, Nunnenkamp, and Thiele 2011; Kuziemko
and Werker 2006). e > 0 captures the extent to which the policy
choice of the donor may deviate from its optimal level [p.sup.*] due to
the pressure of such distortions.
The recipient country's government maximizes:
(2) [U.sup.G] = [U.sup.G.sub.0] - [(p - [P.sup.*.sub.G]).sup.2],
which is decreasing in the distance between the implemented policy
p, and the recipient government's preferred policy [P.sup.*.sub.G],
with [U.sup.G.sub.0] = [U.sup.G] ([P.sup.*.sub.G]). (12) The optimal
policy choice of the government deviates from the optimal policy
[p.sup.*] by a factor b > 0 (i.e., [P.sup.*.sub.G] = [p.sup.*] - b).
The recipient government cares about its national per capita income, but
its choice may be constrained by the influence of some interest groups
benefitting from structural distortions (e.g., Drazen 2002). b > 0
captures the extent to which the policy choice of the recipient may
deviate from its optimal level [p.sup.*], for example due to the
pressure of interest groups opposing policy reforms (among others,
Alesina and Drazen 1991; Fernandez and Rodrik 1991; Tabellini and
Alesina 1990). (13)
Therefore, the difference in optimal policies is given by
(3) [P.sup.*.sub.D] - [P.sup.*.sub.G] = [P.sup.*] + e - ([P.sup.*]
- b) = e + b = B,
where B reflects the extent of any conflict of interest among
donors and recipients over the desired policies that might lead to a
deviation from the first best policy [p.sup.*], including, but not
limited to, the presence of some geo-political distortions at the donor
level, and the pressure of local interest groups and re-election
concerns.
A. Communication Game
The donor can choose between centralization or delegation. Opting
for centralization, the donor minimizes the costs of misaligned
incentives and makes full use of its private knowledge. At the same
time, it underutilizes the recipient's information. Under
delegation, the donor allocates policy decision-making to the recipient.
While in this case the recipient's private knowledge is fully
exploited, the results can deviate from the donor's optimal policy
(loss of control).
In the communication equilibrium, the recipient government only
learns the interval to which the realization of d belongs, and hence
obtains only incomplete information about the donor's knowledge.
The smaller the size of the partition interval, the more informative the
donor's message. We denote the maximum number of
intervals--N(D,B)--as a function of the bias B and the length of the
partition of the donors' knowledge D. Following Crawford and Sobel
(1982), the most informative equilibrium--in which the number of
intervals N is maximal--always exists and is a focal equilibrium of the
communication game.
In the focal equilibrium, the donor's ex ante expected welfare
loss increases with the importance of the donor's private
information D, since the donor's private information is not fully
exploited under delegation. Finally, for any given D, the maximum
precision of the information transmitted by the donor decreases with the
extent of the bias B (i.e., the larger the bias B, the less precise and
informative cheap talk will be). However, if the donor chooses
centralization, it fully exploits its own information D and chooses its
preferred policy vector [P.sup.*.sub.D] As centralization results in an
underutilization of the recipient's information G, the donor's
ex ante expected loss is increasing with the recipient's
informational advantage.
The donor determines whether or not to retain its control rights
over policies by comparing its ex ante expected loss under delegation
with its expected loss under centralization. Since both are increasing
in D (under delegation) and G (under centralization), we can identify
cut-off values of D and G at which the scheme choice switches. The
scheme choice, thus, depends on the extent of the conflict of interest
(B) and the relative importance of the two players' respective
informational advantage (D, G).
Figure 1 represents the choice between centralization and
delegation as a function of D and G. The threshold D(G,B) is upward
sloping, and divides the (G,D) plane into two regions (centralization
and delegation) lying below the 45[degrees] line. The donor will opt for
delegation only if the recipient's private information G is
(strictly) greater than its own private information D and greater than
the threshold level D(G,B). The delegation region is smaller than the
centralization region: the agency bias B requires G to be strictly
greater than D in order for delegation to be optimal. This holds because
the loss due to underutilization of the recipient's information is
compensated for by the elimination of the bias and the full exploitation
of the donor's own private information D. Conversely, the donor
always chooses centralization whenever its private information D is more
important than the recipient's private information (i.e., D >
G). Additionally, it opts for centralization if D(G,B) [less than or
equal to] D < G, that is, even when the recipient's
informational advantage G is greater than D, but smaller than the
threshold value D(G, B). (14)
In general, as Figure 1 shows, the threshold D(G,B) is not monotone
in the bias B, as an increase in B has both direct and indirect effects.
Directly, an increase in B increases the agency problem, thus reducing
the donor's incentive to delegate. Indirectly, an increase in B
also reduces the equilibrium amount of information transferred by the
recipient to the donor under centralization, thus making delegation a
better choice. Therefore, an increase in the agency bias, while making
the recipient's choice less attractive to the donor, can also
decrease the incentives of the recipient to communicate its private
information in the centralization game more than in the delegation game.
The net effect can result in switching from centralization to delegation
with an increase in the bias, in order to make full use of the
recipient's private knowledge. (15)
B. Empirical Implications
The model provides some normative indications regarding the
allocation of control rights over policy actions in the donor-recipient
relationship, and testable implications can be derived from the theory.
The main prediction of the model is that delegation should prevail when
the "loss of information" dominates the "loss of
control." This holds true when the importance of the
recipient's knowledge--to be partially lost under
centralization--dominates the importance of the donor's private
information, for a given agency bias. To the contrary, centralization
should prevail when, for a given bias, the importance of the
donors' knowledge dominates the role of the recipient's local
knowledge.
Since budget support increases the involvement of recipient
governments in the decision-making process, it is an example of
"delegation" in the sense of our model. Aid in the form of
budget support is directly given to the recipient, so that control over
the aid money rests with the government of the recipient country rather
than the donor. (16) Alternatively, project aid represents a good
example for a more centralized provision of aid. Projects are usually
selected in close collaboration with the donor, and are closely
supervised, or even directly implemented by the donor, thus leaving less
influence for the recipient government. (17)
We empirically investigate whether or not the share of project aid
and budget aid (to overall aid commitments) are affected by variables
related to the relative importance of donor-recipient informational
asymmetry and by variables capturing the size of the agency bias,
holding recipient country characteristics, their economic performance,
and the dyadic relation between the donors and recipients (as well as
donors' political motivations) constant. Specifically, for any
given bias, budget support (or delegation) should be preferred in
countries whose local knowledge is relatively more important.
Conversely, project aid (or centralization) should prevail when the
recipient's local information is less crucial.
A second important feature of the model is the presence of a
nonmonotonic relationship between delegation and the misalignment of
interests between the donor and recipient. The bias has both direct and
indirect effects working in opposite directions. The direct effect is to
increase the agency problem, thus reducing the donor's incentive to
delegate. The indirect effect both reduces the amount of information
transferred by the donor to the recipient under delegation (leading to
centralization) while at the same time reducing the amount of
information transferred by the recipient to the donor under
centralization (leading to delegation). The overall effect of the agency
bias on delegation cannot be analytically derived. However, we know that
the indirect effect works through the importance of information.
The donor's informational advantage may depend not only on the
relevance of its knowledge per se, but also on how valuable such
information is relative to those of the donor. In less transparent
countries informational advantages are arguably more salient as compared
to more transparent ones. Therefore, we expect that the indirect effect
of the bias on delegation will prevail when the information transferred
by the recipient is of higher value to the donor--that is, in highly
intransparent environments. However, we expect the direct effect to
prevail when the information transferred by the recipient is relatively
less important to the donor, namely in more transparent environments.
Greater transparency increases the share of "hard"
information that can easily be transferred and decreases the importance
of private "soft" knowledge, hence making the informational
asymmetry less salient. (18) As a consequence, given the trade-off
between loss of control and loss of information faced by the donor, in
order to disentangle the direct and the indirect effects of the bias, we
interact the "bias" with "transparency." To the
extent that aid is allocated in line with the model, we expect to find a
negative (or insignificant) interaction between the two, as the
importance of the local information for the donor decreases with
transparency, reducing the (indirect) effect of the bias on
decentralization. The easier donors can access specific local knowledge,
the lower the likelihood that they are willing to delegate
decision-making authority based on the importance of this knowledge.
Budget aid therefore becomes less, and project aid more likely.
IV. DATA
We examine the determinants of budget aid and project aid in a
dyadic donor-recipient setting. Data on general budget support and
project aid are from the DAC's Creditor Reporting System (CRS; OECD
2016). These data are not reported for years prior to 1995. We estimate
separate regressions for the two types of aid rather than using the
ratio of the two, which could reflect their relative importance in one
regression. The reason is that many countries receive no aid from a
particular donor, while others receive aid of only one of the two types.
(19) Zero aid could then not be separated from aid of the category we
would put on the numerator; zero aid in the denominator would make the
share approach infinity. We avoid both problems by investigating the two
types of aid in separate regressions, and comparing the relative
influence of our variables of interest in determining the shares of
these flows in overall aid commitments. (20) In the following, we
propose a number of proxies to measure the extent of the agency bias and
the relative informational advantages of the donor and recipient
governments.
A. Variables of Interest
Our variables of interest are meant to capture the extent of the
agency bias, the donor country's general knowledge, and the
recipient country's local knowledge--and how easily this
information is available. We introduce them in turn.
Agency Bias. Empirically, we are interested in a bias in the
objective function of the recipient country's authorities relative
to the preferences of the donor. According to the political economy
literature, measures of political instability, polarization, and social
division (e.g., Alesina and Drazen 1991; Tabellini and Alesina 1990)
account for a country's "resistance" against reforms (or
status quo bias). (21) For any given policy environment, such countries
will find it more difficult to make changes to their policies to reduce
a given agency bias. With this in mind, we included proxies for
Government Capability and Ethnic Tensions, taken from the International
Country Risk Guide (ICRG). Government Capability ranges from 0 to 12 and
is "a measure of the government's capability in carrying out
its declared programs/policies and its ability to stay in office."
Higher values on this measure of institutional capacity imply that it is
easier for the government to overcome internal resistance and implement
reforms. The status quo bias is thus lower. We include Ethnic Tensions,
which ranges from zero to six, with higher values indicating more
tensions. Ethnic Tensions measure "the degree of tension within a
country which can be attributable to racial, cultural and language
divisions" (PRS Group 1998). (22) It is thus a proxy for
polarization and social division, which the previous literature has
shown to inhibit reforms. At the same time, highly polarized countries
tend to exhibit a certain degree of "favoritism" in their
preferred policies (Franck and Rainer 2012), arguably in opposition to
the average donor's preferences. (23)
In order to capture the dyadic component of the agency bias between
specific donor-recipient-pairs, we include UNGA voting alignment (UNGA
Distance), which captures the political distance between the donor and
recipient. Specifically, we include the dyadic distance between their
ideal points in the UNGA, estimated using a dynamic ordinal spatial
model (Bailey, Strezhnev, and Voeten 2017). This ideal point distance is
constructed to measure the government's preferences on foreign
policy, and to be comparable over time. It is based on dyadic voting
data in the UNGA, but refers to a single dimension in order to avoid
measuring shifts in countries' policy positions when in fact only
the composition of topics in the UNGA has changed, but preferences on
each individual topic have not. While variation in UNGA voting is most
substantial across countries, there is considerable variation within
countries over time as well (Dreher and Jensen 2013; Rommel and Schaudt
2016). UNGA Distance is thus well suited to measure time-varying
differences in preferences over policy. (24)
According to our model, the effect of the agency bias on the extent
of reforms can go either way. As we described above, an increase in the
agency bias per se has both direct and indirect effects which could
either reduce or increase the incentive to delegate, depending on which
of the two effects dominates, on average.
Information. We expect the importance of a recipient's local
knowledge to increase with the salience of the informational asymmetry
between donors and recipients. In particular, local knowledge is crucial
for intransparent countries as less transparency decreases the share of
"hard" knowledge and increases the importance of
"soft" knowledge to be obtained from the recipient. In this
context, facing the trade-off between loss of control and loss of
information, donors might decide to give more importance to information
and, in turn, give greater control to the recipient. If this is the
case, donors will prefer budget aid to project aid. In order to measure
the importance of a recipient's local knowledge, our main index
follows Hollyer, Rosendorff, and Vree-land (2011), who suggest missing
data on standard economic indicators (relating to economic policy and
debt) as indicators of (lack of) transparency. As Hollyer, Rosendorff,
and Vreeland (2011, 1198) point out, this "measure of transparency
[...] directly reflects government decisions to release accurate
economic data." Rather than choosing any arbitrary data series we
evaluate all 1260 series included in the World Bank's (2013) World
Development Indicators. Our resulting indicator for Transparency shows
the share of series for which there are data available in a given
country and year. (25) As we therefore treat missing data as (lack of)
information, the resulting indicator has the advantage that it is
available for all countries and years.
An additional proxy for the availability of information is the
number of Telephone Lines (per 100 people), which is also widely
available. As we explain in Dreher et al. (2016), this variable can be
seen to proxy for all kinds of technological barriers to information
transmission. Which technology is most relevant to capture information
transmission varies over time, so that the easy availability of internet
access or mobile phones will better proxy for information transmission
in more recent years, but not in the earlier years of our sample.
According to the results by Chung, Fleming, and Fleming (2013),
Telephone Lines exerted the strongest effect on trade among a number of
alternative proxies for the quality of information and communication
technology. As Dreher et al. (2016) point out, the number of Telephone
Lines is highly correlated with a combined media access variable ([rho]
= 0.80) and a variable capturing the number of computers per capita
([rho] = 0.87) in those periods where both are available. (26)
An additional way of measuring the salience of the informational
asymmetry is by including information on the dyadic relationship between
specific donors and recipients. We therefore construct a measure for
Bilateral Experience, calculated by the number of years since a donor
has first given aid to a specific recipient country. (27) When countries
have a longer bilateral aid relationship the recipient's local
knowledge seems less important compared to the donor's knowledge.
This is because the donor has gathered experience through previous aid
projects and is thus better informed than without having this
country-specific experience, on average. The need for delegation is
therefore reduced by the number of years since the donor had first
committed aid to the recipient. Similar to Bilateral Experience,
Bilateral Trade proxies for dyadic donor-specific information about the
recipient country. The importance of information costs in determining
trade is well established (e.g., Fink, Mattoo, and Neagu 2005). While
causality between information and trade can be either way, we thus
interpret Bilateral Trade as an additional informational variable at the
dyadic level.
Poor quality of recipient government staff could also be a reason
for a recipient to seek a donor's technical advice and could thus
explain the choice of project aid over budget aid. In order to capture
the quality of recipient government staff, we include the ICRG index of
Bureaucratic Quality. Bureaucratic Quality ranges from zero to four,
with higher values showing "better" environments. High scores
in Bureaucratic Quality indicate that the bureaucracy has the strength
and expertise to govern, without the necessity for drastic changes in
policy or interruptions in government services.
Finally, as a measure that is specific to the donor, rather than
the recipient, we calculate the number of recipients a donor gives aid
to in a particular year to proxy for the donor's most recent
cross-country knowledge. The number of recipients a donor gives aid to
at the same time proxies for the donor's information about
development policies implemented in different countries and contexts and
the global environment in which these policies are embedded at a
particular point in time (Donor Experience).
B. Control Variables
Much of the literature on aid allocation has evaluated whether and
to what extent commercial and political donor interests have shaped the
allocation of aid, but recipient country "need" and
"merit" have also featured prominently (Claessens, Cassimon,
and van Campenhout 2009; Dollar and Levin 2006; Fleck and Kilby 2010;
Hbffler and Outram 2011). Our main specification is parsimonious,
controlling for (log) GDP per capita to take account of development, and
(the log of) Population which also captures "need," but can as
well be taken as proxy for the ease of obtaining a country's
political cooperation (as smaller countries are easier to
"buy"; see, e.g., Boone 1996), and is thus a proxy for the
donors' political interests. (28)
We provide the details of the definitions and sources of the
variables included in the regressions and descriptive statistics in
Appendices S4 and S5. Appendix S6 shows the correlations of the
variables included in the analysis.
V. METHOD AND RESULTS
We use data for the 1995-2010 period and a maximum of 112 recipient
countries, due to data availability. The dependent variables are defined
as shares of total dyadic aid commitments. The regressions are estimated
with ordinary least squares (OLS), at the donor-recipient year level. We
estimate the model with country-pair-fixed effects and year-fixed
effects (clustering standard errors at the country-pair-level). (29) The
regression equations are:
(4) [P.sub.i,j,t] = [[beta].sub.1][X.sub.1i,t-1] +
[[beta].sub.2][X.sub.2i,j,t-1] + [[eta].sub.i,j] + [[tau].sub.t] +
[u.sub.i,j,t],
and
(5) [B.sub.i,j,t] = [[beta].sub.1][X.sub.1i,t-1] +
[[beta].sub.2][X.sub.2i,j,t-1] + [[eta].sub.i,j] + [[tau].sub.t] +
[u.sub.i,j,t],
where [P.sub.i,j,t] and [B.sub.i,j,t] represent project and budget
aid commitments as a share of overall commitments from donor j to
recipient i in year t, and [X.sub.1] and [X.sub.2] are vectors
containing the variables introduced above (lagged by one year). While
[X.sub.1] is the vector of recipient-specific variables, [X.sub.2]
includes variables that vary over donor-recipient-pairs. In one set of
regressions [X.sub.1] and [X.sub.2] include interactions between
Transparency and our dyadic proxy for the agency bias, allowing us to
disentangle the average effect of the bias according to whether
transparency is high or low. Finally, [[eta].sub.i,j] and [[tau].sub.t],
are donor-recipient-pair--and year-fixed effects, respectively, while
[u.sub.i,j,t] is the error term.
Contrary to most of the aid allocation literature, (30) we estimate
rather conservative models, which include country-pair- and year-fixed
effects, and lag the explanatory variables by one year. We therefore
control for unobserved effects that exclusively vary at the
country-pair- and year-level, substantially reducing concerns over
endogeneity. What is more, we investigate aid provided by all 28
bilateral DAC donors rather than aid from a particular donor. We can
therefore account for a variety of observable indicators at the
recipient- and donor-level as well as on the donor-recipient-pair-level,
including information on historical, political and economic ties. While
this does not provide a bullet-proof identification strategy, we are
more conservative than most of the related literature. (31) Still, we
prefer to interpret the coefficients in the models below as conditional
correlations rather than causal effects.
We report the basic results in Table 1. Column 1 shows the results
for project aid excluding the dyadic variables, while column 2 shows
those for budget aid instead. Columns 3 to 6 include characteristics of
the country-pair--the dyadic transparency indicators in columns 3 and 4
and UNGA Distance in columns 5 and 6. Odd column numbers focus on
project aid, while even column numbers refer to budget aid (both
measured as a share of overall dyadic commitments). Across regressions,
the share of budget aid increases with GDP per capita and Population,
while there are no consistent correlations of these control variables
with the share of project aid, at conventional levels of significance.
As can be seen in Table 1, the results are in line with our
hypotheses regarding the effect of what we call "informational
variables," on the provision of aid. In all regressions, budget aid
and project aid increase with greater Transparency. The coefficients for
project aid are however larger compared to those of budget aid in all
regressions, indicating that donors prefer a type of aid that allows
them to keep control when it is comparably easier for them to access
recipient information. (32) Project aid--but not budget aid--increases
with the availability of Telephone Lines and greater Donor Experience as
well, indicating the importance of the informational infrastructure for
donors' preference of project aid over budget aid. Quantitatively,
an increase in Transparency by 0.1 (the mean being 0.65 in column 1)
increases the share of project aid in overall aid commitments by between
0.019 and 0.035 percentage points. An increase in Transparency by one
standard deviation (i.e., 0.12) increases the share of project aid in
overall aid commitments by between 0.02 and 0.04 percentage points. For
the average recipient country in our sample, this represents a six to
12% increase. A one standard deviation increase in Telephone Lines
(representing 13 telephone lines per 100 people) and a one standard
deviation increase in Donor Experience (i.e., 47 recipient-years) imply
an increase in the share of project aid in overall commitments by
between 7 and 10 percentage points, respectively.
The results also show that donors prefer project aid over budget
aid with a longer bilateral aid history in the recipient country,
indicating that donors who are less in need of recipient information
delegate less. The longer a donor has been giving aid to a recipient,
the more experience and knowledge it has accumulated. Consequently, the
informational advantage of the recipient is reduced, which leads to a
positive correlation with project aid but not budget aid. Specifically,
one more year of Bilateral Experience leads to an increase of 0.015
percentage points of overall aid commitments, which represents a yearly
increase of 4%-5% for the average recipient country. In a similar vein,
we introduced Bilateral Trade as an additional proxy of dyadic
information. Again, the results are as expected: Bilateral Trade
significantly reduces the amount of budget aid, in line with the
prediction of the model regarding the importance of information for the
choice of delegating aid policies.
As we are interested in the statistical significances of groups of
variables rather than of individual variables, we rely on tests of their
joint significance in order to evaluate our hypotheses. Specifically, we
evaluate the relative importance of the transparency variables based on
their joint significance in the budget aid--compared to the project
aid--regressions. An F test indicates that all transparency variables
are jointly highly significant for project aid (column 5), but only
marginally significant for budget aid (column 6).
While we consider tests for the joint significance of our variables
of interest to be most appropriate to test our hypotheses, note that
most of the variables are also individually significant. The exception
is Bureaucratic Quality, which is completely insignificant in all
regressions, with no significant differences between project aid and
budget aid. The correlation between budget aid and Transparency is
significant at the 1% level in column 1, but much weaker in significance
when we add the dyadic proxies for transparency, as could be expected.
(33) The correlation between Telephone Lines and project aid, as well as
those between Donor Experience and project aid, is significant at the 1%
level in all regressions though (and insignificant for budget aid).
Bilateral Experience is significant at the 1 % level for project aid,
but weakly significant or insignificant for budget aid, while Bilateral
Trade is (negatively) significant at the 1% level for budget aid only.
In summary, we find strong evidence that donors allocate their aid
in line with the "transparency"-related predictions of our
model. Since transparency indicates the relative importance of the
recipient's knowledge (as compared to the donor's knowledge),
more transparent countries receive more project but not budget aid, as
our theory implies. Donor countries do not need to rely on the
recipient's local knowledge if transparency is high.
Our model is less clear-cut when it comes to making predictions
about the differences in donor and recipient preferences
("bias"). As we have outlined above, the effect of the bias on
delegation could be either direct (reducing delegation) or indirect
(increasing delegation by reducing the amount of communication under
centralization). We thus do not have strong predictions for the effect
of the "bias-related" variables, on average.
According to the results in Table 1, the recipient-specific
measures for the agency bias do not turn out to be significant
determinants of the choice between project and budget aid, on average.
We find no effect of the recipient country's Ethnic Tensions and
Government Capability. We should point out that Ethnic Tensions are
related to the importance of both the bias and the recipient's
private information (when a country is more multifaceted from a social
point of view its local knowledge is more important). As a result, since
these two effects could go into two opposite directions, the
insignificant coefficient of Ethnic Tensions is easy to explain. In the
case of Government Capability, our theory suggests that lower government
stability should lead to higher (lower) amounts of project (budget) aid,
as one would expect centralization to be higher in more unstable
countries. This argument, however, overlooks the fact that an increase
in the bias also has the effect of reducing the degree of communication
under centralization, thus making such a decision costlier. The net
effect could as well then lead to an insignificant coefficient. (34)
We find significant coefficients for our dyadic proxy UNGA
Distance. Specifically, while the share of budget aid decreases with
UNGA Distance, project aid is unaffected. Centralization thus dominates
delegation when the bias of the recipient country relative to the donor
is too large according to this dyadic measure. (35) The direct effect
(reducing delegation) thus dominates the indirect effect (increasing
delegation), on average.
In order to disentangle the direct and indirect effects of the bias
on delegation, we investigate how transparency and agency bias interact.
We focus on the interaction between UNGA Distance and the level of
Transparency. Table 2 presents the results on the differential effect of
the agency bias (UNGA Distance) conditional on the level of Transparency
and Telephone Lines.
Columns 1 and 2 interact Transparency with UNGA Distance. As can be
seen, the effect of UNGA Distance on project aid decreases with
Transparency (column 1), while its effect on budget aid becomes stronger
(column 2), at least at the 10% level of significance. Results are
similar but statistically weaker when we turn to the interaction of
Telephone Lines with UNGA Distance in columns 3 and 4. While the direct
or indirect effect could dominate according to our model on average, the
indirect effect of the bias should prevail in highly intransparent
environments, where the information transferred by the recipient is of
higher value to the donor. As we are interested in how the marginal
effect of the agency bias changes over the range of the transparency
indicators, we calculate average marginal effects and show them in
Figures 2 (project aid) and 3 (budget aid), in tandem with 90%
confidence intervals.
Figures 2 and 3 show that the marginal effect of UNGA Distance on
the amount of project aid decreases with the intensity of Transparency,
while its effect on the amount of budget aid increases with
Transparency. Both effects are significant for low levels of
Transparency, but turn insignificant at conventional levels when
Transparency is high. As transparency increases, the recipient's
local knowledge becomes less relevant, so that donors prefer
centralization (project aid) to delegation (budget support). Only when
transparency is high, the size of the bias loses relevance in predicting
the difference between project aid and budget aid. In highly transparent
countries, donor countries do not depend on recipient government
information and so depend on communication to a lower extent. Overall,
these patterns fit our model's predictions well. (36)
VI. CONCLUSIONS
In this paper we have explored the role of information transmission
between a donor and a recipient country in explaining how donors
allocate budget aid and project aid. By relating the quality of the
information supplied by a recipient country to the donor (and vice
versa) to the misalignment of interests between the two, we analyzed the
properties of different aid schemes relative to the quality of the
transmitted information. More specifically, we have compared an aid
scheme in which control rights over policies are allocated to the donor,
that is, centralization (or project aid), with an aid scheme in which
the recipient is left with more freedom to devise its own policy
actions, that is, delegation (or budget support).
The main theoretical findings are as follows. For a given agency
bias, when recipients' local knowledge is more important than the
donors' information, their discretion in the choice of reforms
(delegation) should be increased. Conversely, there should be less
freedom in designing reforms (centralization) when the donors'
information is more relevant. The impact of the agency bias on
determining the optimal lending scheme remains a priori undetermined as
it can have two countervailing effects at the same time (a direct and an
indirect one).
In the empirical section, we focused on two distinct ways of
delivering aid, budget support and project aid. Budget support increases
the involvement of the recipient government in the decision-making
process and is thus an example of delegation. Conversely, project aid
represents a more centralized type of aid. We investigated the role of
the relative importance of donor and recipient information in
determining which aid scheme is preferred. Controlling for
countries' characteristics, their economic performance and dyadic
relations between donors and recipients, we find that transparency does
influence the relative amount of project versus budget aid. More
specifically, as transparency increases, donors prefer project aid to
budget support. As the agency bias is concerned, the results of our
dyadic measure are in line with our theoretical predictions, according
to which centralization should dominate delegation when the bias is too
large. Finally, the marginal effects of the bias, conditional on
transparency, point to the dominance of the direct over the indirect
effect when transparency is low, leading to a centralization scheme.
Our model suggests that donors who allocate aid taking properly
account of information and preferences will achieve the results they aim
for more effectively. Whether an allocation of aid in line with the
model is likely to increase economic growth or reduce poverty depends on
whether the donor is sufficiently benevolent. According to parts of the
aid effectiveness literature, however, both project aid and budget aid
have not on average been effective with respect to achieving growth
(e.g., Rajan and Subramanian 2008). This could imply that donors in
reality allocate aid in line with other, geopolitical or commercial,
targets. (37) It could also imply that the targeting of aid toward
budget or project aid, while significant, is not yet sufficiently
elaborated. To the extent that donors aim at increases in growth, a more
careful allocation following the recommendations of our model should be
able to improve outcomes with respect to growth. Future research might
then want to investigate whether those parts of budget aid and project
aid that are given in relation to informational advantages are indeed
more effective in improving outcomes than those parts of such aid flows
that are given due to other reasons. A differential analysis for
(groups) of donors could also give additional insights as to which
donors do and do not take account of information and bias, and whether
these differences can explain potentially differential effects of these
donors' aid. Finally, other types of delivering aid might also be
investigated with respect to whether or not they are allocated in light
of information and preferences. (38) We leave these questions for future
research.
ABBREVIATIONS
CPIA: Country Policy and Institutional Assessment
CRS: Creditor Reporting System
DAC: Development Assistance Committee
ICRG: International Country Risk Guide
IMF: International Monetary Fund
OLS: Ordinary Least Squares
PPML: Poisson Pseudo Maximum Likelihood
UNGA: United Nations General Assembly
doi: 10.1111/ecin.12450
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the online
version of this article:
Appendix S1. Definition and properties of the communication game
Appendix S2. Derivation of donor and recipient government's ex
ante expected losses
Appendix S3. Proof of statement in Section III
Appendix S4. Sources and definitions
Appendix S5. Descriptive statistics (estimation sample, column 5
(6) in Table 1)
Appendix S6. Correlations
Appendix S7. Additional figures
AXEL DREHER, SARAH LANGLOTZ and SILVIA MARCHESI *
* We thank Christian Bj0rnskov, Oliver Morrissey, Ola Olsson,
Andrea Presbitero, Bernhard Reinsberg, Jonathan Temple, participants at
the Beyond Basic Questions Workshop (Salzburg 2016), and the Centre for
the Study of African Economics Conference (CSAE, Oxford 2014) for
helpful comments, and Jamie Parsons for proof-reading.
Dreher: Professor, Alfred-Weber-Institute for Economics, Heidelberg
University, Heidelberg 69115, Germany. Phone +49 6221 54 2921, Fax +49
6221 54 3649, E-mail
[email protected]
Langlotz: Dipl.-Vw., Alfred-Weber-Institute for Economics,
Heidelberg University, Heidelberg 69115, Germany. Phone +49 6221 54
3172, Fax +49 6221 54 3649, E-mail
[email protected]
Marchesi: Professor, Dipartimento di Economia, Metodi Quantitativi
e Strategic di Impresa (DEMS), Universita di Milano Bicocca, Milano
20126, Italy. Phone +39 02 6448 3057, Fax +39 02 6448 3085, E-mail
[email protected]
(1.) In the words of Koeberle (2005, 67) ownership is the
"commitment to aid-supported reforms by country authorities and a
majority of domestic stakeholders." According to Khan and Sharma
(2001,13) ownership "refers to a situation in which the policy
content of the program is similar to what the country itself would have
chosen." The International Monetary Fund (IMF 2001,6) defines it as
"a willing assumption of responsibility for an agreed program of
policies, by officials in a borrowing country who have the
responsibility to formulate and carry out those policies, based on an
understanding that the program is achievable and is in the
country's own interest."
(2.) The mechanisms and circumstances under which such information
should be transferred by recipient countries to donors (or lenders) have
rarely been investigated. An exception is Marchesi, Sabani, and Dreher
(2011) who analyzed the specific case of communication between the IMF
and a borrowing country.
(3.) Koeberle, Stavreski, and Walliser (2006) emphasize that budget
support comes with greater country ownership and higher spending on
services that countries prioritize in their own budgets. This does not
imply that the aid transfer is necessarily unconditional. However,
bilateral donors do not usually condition their aid on a large number of
detailed conditions. If conditions are attached to the aid they usually
refer to the general stance of a country's policy, e.g., with
respect to human rights conditions, democracy, or the absence of
corruption.
(4.) In a dynamic framework, Bougheas, Dasgupta, and Morrissey
(2007) also address the choice between conditional and unconditional
transfers. They show that conditionality is self-perpetuating even when
it is not optimal. The results in Bougheas et al. thus question the
wisdom of conditionality at large. Also see Dreher (2009).
(5.) More generally, the relationship between decentralization and
development has been analyzed, among others, by Bardhan (2002) and
Lessmann and Markwardt (2010).
(6.) More recently, Basurto, Dupas, and Robinson (2015) have shown
that a decentralized allocation of subsidies in rural Malawi may offer
informational advantages, despite of being prone to elite capture.
(7.) An exception is Marchesi, Sabani, and Dreher (2011),
who--building on the cheap talk literature (Crawford and Sobel 1982;
Dessein 2002; Harris and Raviv 2005, 2008)--have identified and tested
the conditions under which it is optimal for the IMF to delegate control
to a recipient country in order to maximize the quality of a reform
program. More recently, and in a different context, Dreher et al. (2016)
explore the role of information transmission in explaining the optimal
degree of decentralization across countries.
(8.) For exceptions see Berthelemy (2006), Claessens, Cassimon, and
van Campenhout (2009), Dreher, Nunnenkamp, and Thiele (2011), Dietrich
(2013), Barthel et al. (2014), and Winters and Martinez (2015).
(9.) Specifically, Appendix S1 defines and shows the properties of
the communication game, Appendix S2 derives the ex ante expected losses
of the federal and local governments, while Appendix S3 contains proofs
of the statements made in Section III.A.
(10.) The analytics feature the case in which the donor cannot
commit to an incentive-compatible decision rule in which the revelation
principle applies. This assumption fits in well with the specific
relationship between a donor and a recipient government in which the
principal cannot use a standard mechanism to elicit private information
from the agent.
(11.) The utility function (Equation (1)) can be derived from a
more general objective function [[??].sup.D] = Y(p) + [gamma]E(p), where
Y is the recipient country's output and E(p) measures the
externalities that the choice of a specific policy p could have on the
donor country's economy. The parameter [gamma] (0 [less than or
equal to] [gamma] [less than or equal to] 1) denotes the importance of
such spillover effects. Specifically, if the recipient country is big,
[gamma] will tend to 1, while for very small countries [gamma] will be
close to 0. Taking a Taylor expansion of [[??].sup.D] (p) up to the
second term, one obtains the form in Equation (1).
(12.) The more general function is [[??].sup.G] = Y(p) +
[theta]C(p), where C are contributions from special interest groups. We
assume that C decreases with p and that the parameter [theta] (O [less
than or equal to]theta] [less than or equal to] 1) denotes the
importance of lobbies. Using a Taylor expansion of [[??].sup.G](p) up to
the second term, one obtains Equation (2).
(13.) We should emphasize that our scenario of conflicting
preferences does not characterize all donor-recipient relationships, as
there could be cases where preferences over core policies are relatively
closely aligned. Even in such cases, however, contentious issues are
likely and each party owns some private information so that the essence
of the model keeps to be applicable.
(14.) This is to some extent similar to how Bougheas, Dasgupta, and
Morrissey (2007) relate the choice of conditionality to donors'
priors on recipient types.
(15.) Since the derivative of D(G,B) with respect to B cannot be
analytically derived, this result is obtained by numerical simulations
(see Harris and Raviv 2005).
(16.) Some donors make their aid conditional on the implementation
of certain policies, or on the absence of corruption, human rights
violations, or restrictions on democracy. This holds in particular for
multilateral donors like the IMF or the World Bank that we do not focus
on in this paper. Some bilateral aid agencies also attach conditions.
For example, the United States' Millennium Challenge Corporation
conditions its aid on the absence of corruption, government
effectiveness, and low inflation, among others. This does not restrict
the recipient governments on deciding about what to use the aid for,
however. The same holds for those parts of aid that are restricted to be
spent in the donor country (so-called tied aid). While tying aid reduces
its value for the recipient, the recipient government is free to decide
about which projects or purposes to use the aid for.
(17.) This assumes that aid is not fully fungible, which is
supported by the recent literature. For example, Van de Sijpe (2013, 26)
shows "little evidence that aid is fully or even largely fungible;
rather, most point estimates suggest limited fungibility." See also
Milner, Nielson, and Findley (2016). As we focus on donor choices rather
than recipient country policies it is sufficient for our analysis that
donors expect aid not to be fully fungible, independent of whether or
not it is in fact fungible after the donors delivered the aid.
(18.) For example, the relative share of "soft" to
"hard" information is likely to depend on the quality of the
communication infrastructure.
(19.) This is particularly severe for budget aid, with less than
900 non-zero dyad-years in our sample (see Appendix S5 for details and
Winters and Martinez 2015 for a similar approach). The inclusion of
dyad-specific fixed effects ensures that donor-recipient pairs with no
positive aid over the entire sample period do not contribute to the
estimates.
(20.) Note that the share of project and budget aid to overall
commitments is substantially larger than one for some observations. The
reason is that overall commitment data provided by the OECD include
repayments, while project and budget aid commitments do not include
repayments. When we replicate our main regressions excluding those
observations our results are broadly unchanged. As a second test for
robustness, we compute overall gross commitments based on data for the
subcategories. Results again remain robust. Also note that while the
mean values for project aid and budget aid are rather low in our sample
due to a large number of zero donor-recipient-year observations, project
aid is on average 70% of total commitments when we focus on nonzero
amounts, while the share of budget aid is close to 30% (see Appendix S5
for summary statistics including and excluding zero-observations). The
two do not sum up to total commitments, which also include donors'
administrative costs, unallocated or unspecified aid, aid for refugees
in donor countries, developmental food aid, other commodity assistance,
debt relief, and humanitarian aid.
(21.) In Tabellini and Alesina (1990), given a situation of
political instability and polarization, a balanced budget does not
represent a political equilibrium. This is because the current majority
does not internalize the costs of budget deficits and the more this is
the case, the greater the difference between its preferences and the
expected preferences of future majorities. Alesina and Drazen (1991)
find that when stabilization has significant distributional implications
a "war of attrition" among different socioeconomic groups may
delay stabilization.
(22.) We alternatively include a binary indicator for Autonomous
Regions and the share of Subnational Expen ditures/Total Expenditures as
proxies for stronger regional vetoes and thus a larger status quo bias.
(23.) We should emphasize that Ethnic Tensions could at the same
time be related to the importance of the recipient's private
information (when a country is more multifaceted from a social point of
view its local knowledge is more important).
(24.) As an alternative measure for the dyadic bias we use a binary
variable on Democratic Distance relying on the Polity IV index of
democracy. We calculate Democratic Distance between the donor and
recipient as one if either the donor or the recipient is a democracy
(i.e., a Polity IV index larger than five), while the respective other
country is not a democracy (i.e., has a value below six). We expect the
agency bias to be smaller among democracies, as democratic countries
tend to agree on a broad set of principles regarding political and
economic liberalism (Voeten 2000). Furthermore, we use an indicator of
Ideological Distance, measured as the absolute difference between the
donor and recipient government on a left-to-right spectrum.
(25.) Missing data entries can result from a number of reasons. For
example, (1) the recipient government might have the data but does not
report them, (2) the recipient government does not have the information,
or (3) the recipient reports the data to the World Bank but Bank staff
choses to not report them, for example, because they consider them
insufficiently reliable. In all these cases, missing data proxy for
intrans-parent environments that make the recipient's private
information more important relative to the donor's. This would not
be the case if data that have been missing at the time the decisions
about how to give aid have been made had later been included to the
database. In this case we would report Transparency to be too high.
However, the congelation between our missing indicator variable and an
indicator constructed in analogy based on an earlier--2005--version of
the World Development Indicators is very high. Correlation between our
indicator and those of Hollyer, Rosendorff, and Vreeland (2011) is 0.80
and our results are robust to using their index instead of ours. Our
indicator is also significantly correlated with the HRV Index of
transparency (Hollyer, Rosendorff, and Vreeland 2014), which uses
patterns in the missing data to model transparency as a latent variable,
and a Combined Transparency indicator based on 29 sources taken from
Williams (2015).
(26.) Media Access is a composite indicator including access to TV,
radio, papers, and internet (using data from Banks 2011). Internet Users
and Telephone Lines are also highly correlated (rho=0.64), but sample
size is reduced substantially when we include Internet Users. We test
robustness to using Newspapers in circulation (per 1,000 inhabitants)
and the number of Internet Users per 100 people. Interestingly, the
correlation between Telephone Lines and Transparency is weak, indicating
that these measures account for different aspects of transparency (see
Hollyer, Rosendorff, and Vreeland 2013 for a detailed discussion of
these differences). We therefore include these two measures at the same
time rather than separately.
(27.) Due to data availability, we compute the number from 1970,
leading to a maximum experience of 40 years.
(28.) We tested robustness by including other control variables. We
included the World Bank's Country Policy and Institutional
Assessment (CPIA) in order to control for "recipient merit."
We included the recipient country's KOF Index of Globalization
(Dreher 2006) to capture its general openness. We controlled for those
types of aid that are neither project aid nor budget aid (e.g.,
humanitarian aid or aid related to refugees). What is more, we also
tested robustness to excluding GDP and population. None of our results
is affected by these changes.
(29.) In a previous version of this article, we estimated our
regressions using Poisson pseudo maximum likelihood (PPML) at the
recipient-country-level, with project aid and budget aid in levels
rather than shares (Dreher and Marchesi 2013). Results were in line with
the model's predictions. When we run PPML in our dyadic fixed
effects model, the incidental parameter problem becomes paramount, and
models do not converge. One might also think of using models such as
Tobit or Heckman--two commonly used methods in the aid allocation
literature--but their use would be problematic with our data (see
Sigelman and Zeng 1999). Tobit may lead to biased estimates when zero
observations are not the result of censoring mechanisms, while Heckman
is inefficient when the dependent variable is exclusively nonnegative.
What is more, in our short sample the dyadic-fixed effects Tobit
estimates are biased due to the incidental parameter problem. Crucially,
the Tobit and Heckman models do not converge with our dyadic-fixed
effects. When we instead run seperate models for the selection of aid
recipients and the allocation of aid amounts (including dyadic fixed
effects), we find strong results in line with the model at the selection
stage, but weaker results when it comes to allocation. When we estimate
a Heckman Selection model with donor-, year-, and recipient-fixed
effects instead of dyadic fixed effects results are similar. Information
and preference divergence thus seem to be more important when deciding
about whether to give aid at all, rather than deciding about the amount
of aid. When we run Tobit with donor-, year-, and recipientfixed effects
results are overall in line with the model's predictions. In order
to better capture long-term relationships between donors and recipients,
we have also run specifications (1) excluding country-pair-fixed
effects, (2) including fixed-effects for recipients rather than
country-pairs, and (3) including donor- and recipient-fixed effects in
tandem. Our overall results are robust to these changes. We also
included recipient-year- and donor-year-fixed effects in addition to the
country-pair-fixed effects. Unsurprisingly, most coefficients are no
longer significant at conventional levels in this specification, with
the exception of those variables that vary at the
recipient-donor-year-level. Results are robust when we include
recipient-year- and donor-year-fixed effects instead of the
country-pair-fixed effects.
(30.) See, for example, Alesina and Dollar (2000), Dollar and Levin
(2006), and Nordtveit (2012).
(31.) Again, Alesina and Dollar (2000), or Dollar and Levin (2006)
are useful examples. Also see Dreher, Nun nenkamp, and Thiele (2011).
(32.) In column 5, while the coefficient of transparency becomes
marginally insignificant, its magnitude remains much larger for project
than budget aid, consistent with the results of columns 1 to 4.
Similarly, in column 3 of Table 2, the coefficient of transparency
becomes insignificant but is still much larger for project aid than
budget aid.
(33.) In a robustness test, we exclude repayments from total
commitments, such that the shares do not exceed 100%. Transparency then
remains statistically significant at the 10% level in columns 3 and 5.
The effect also remains statistically significant in columns 3 and 5 (at
the 1% level) when we replace the dependent variable with the log of
project and budget aid in constant USD (rather than using shares).
(34.) We considered the alternative monadic proxies Autonomous
Regions and Subnational Expenditures/Total Expenditures and the dyadic
proxies Democratic Distance and Ideological Distance and also obtained
no robust results for these bias-related variables in either direction,
on average.
(35.) We should stress here that the influence of the agency bias
on the amount of budget aid and project aid is also consistent with the
(theroretical) results of Cordelia and dell'Ariccia (2007) who find
that budget support should be preferred to project aid when the
donor's preferences are close to those of the recipient.
(36.) Appendix S7 shows similar figures focusing on the interaction
between UNGA Distance and Telephone Lines. Overall, results are similar
to those shown in Figures 2 and 3.
(37.) As shown by Dreher, Eichenauer, and Gehring (Forthcoming),
donors' geopolitical motives for grantig aid reduce the
effectiveness of aid in increasing economic growth.
(38.) As one example, our model could be used to explain the
increasing amount of aid that is channeled via multilateral institutions
as non-core aid ("multi-bi aid"), see Eichenauer and Reinsberg
(2017) and Reinsberg (2017).
Caption: FIGURE 1 Choice among Centralization and Delegation as a
Function of D and G
Caption: FIGURE 2 Marginal Effect of UNGA Distance as Transparency
Changes
Caption: FIGURE 3 Marginal Effect of UNGA Distance as Transparency
Changes
TABLE 1
Main Results
(1) (2) (3)
Project Aid Budget Aid Project Aid
(log) GDP per -0.0407 0.0062 *** -0.0412
capita (t - 1) (0.2900) (0.0097) (0.3037)
(log) Population 0.0108 0.0075 ** -0.0798 *
(t - 1) (0.8090) (0.0464) (0.0806)
Transparency 0.3538 *** 0.0178 ** 0.2498 *
(t - 1) (0.0069) (0.0211) (0.0211)
Telephone 0.0073 *** -0.0001 0.0058 ***
lines (t- 1) (0.0000) (0.2497) (0.0000)
Donor experience 0.0019 *** -0.0000 0.0019 ***
(t - 1) (0.0000) (0.8831) (0.0000)
Bureaucratic -0.0013 -0.0001 -0.0057
quality (t - 1) (0.8997) (0.9005) (0.5831)
Bilateral 0.0150 ***
history (t - 1) (0.0000)
(log) Bilateral -0.0007
trade (f - 1) (0.5567)
Government 0.0032 0.0003 0.0031
capability (0.5359) (0.3357) (0.5549)
(t - 1)
Ethnic tensions 0.0032 -0.0002 0.0022
(t- 1) (0.7401) (0.6848) (0.8224)
UNGA distance
(t - 1)
Adj. R-squared 6.004 0.001 0.004
F Statistic 32.658 2.005 32.783
Number of 46378 46378 46014
observations
Number of 3126 3126 3126
country pairs
Number of 112 112 112
recipients
(4) (5) (6)
Budget Aid Project Aid Budget Aid
(log) GDP per 0.0069 *** -0.0280 0.0088 ***
capita (t - 1) (0.0066) (0.5111) (0.0032)
(log) Population 0.0070 * -0.0885 * 0.0078**
(t - 1) (0.0594) (0.0576) (0.0388)
Transparency 0.0167 ** 0.1913 0.0136 *
(t - 1) (0.0274) (0.1237) (0.0880)
Telephone -0.0001 0.0052 *** -0.0001
lines (t- 1) (0.1486) (0.0001) (0.1025)
Donor experience -0.0000 0.0020 *** -0.0000
(t - 1) (0.8440) (0.0000) (0.9207)
Bureaucratic -0.0001 -0.0035 -0.0003
quality (t - 1) (0.8448) (0.7346) (0.6522)
Bilateral 0.0002 * 0.0153 *** 0.0001
history (t - 1) (0.0933) (0.0000) (0.1901)
(log) Bilateral -0.0002 *** -0.0007 -0.0003 ***
trade (f - 1) (0.0078) (0.5197) (0.0060)
Government 0.0003 0.0024 0.0003
capability (0.3504) (0.6593) (0.2516)
(t - 1)
Ethnic tensions -0.0002 0.0061 -0.0002
(t- 1) (0.7595) (0.5182)) (0.7223)
UNGA distance -0.0012 -0.0024 *
(t - 1) (0.9748) (0.0588)
Adj. R-squared 0.001 0.005 0.001
F Statistic 2.074 32.261 2.249
Number of 46014 43944 43944
observations
Number of 3126 3039 3039
country pairs
Number of 112 109 109
recipients
Notes: OLS at the donor-recipient-year level. Donor-recipient
-fixed-and year-fixed effects are included. Standard errors
are in parentheses (clustered at the donor-recipient level;
significance levels; * 0.10, ** 0.05, *** 0.01).
TABLE 2
Interaction Effects
(1) (2)
Project Aid Budget Aid
(log) GDP per capita (t - 1) -0.0300 0.0090 ***
(0.4769) (0.0026)
(log) Population (t - 1) -0.0755 * 0.0061
(0.0914) (0.1003)
Transparency (t - 1) 0.5143 ** -0.0300.0299 *
(0.0405) (0.0711)
Telephone lines (t - 1) 0.0050 *** -0.0001
(0.0004) (0.1990)
Donor experience (t - 1) 0.0020 *** 0.0000
(0.0000) (0.9326)
Bureaucratic quality (t - 1) -0.0032 -0.0003
(0.7560) (0.6112)
Bilateral history (t - 1) 0.0157 *** 0.0001
(0.0000) (0.4343)
(log) Bilateral trade (t - 1) -0.0007 -0.0003 ***
(0.5486) (0.0052)
Government capability (t - 1) 0.0029 0.0002
(0.5947) (0.3738)
Ethnic tensions (t - 1) 0.0062 -0.0002
(0.5096) (0.6937)
UNGA distance (t - 1) 0.1244 ** -0.0193 ***
(0.0141) (0.0028)
UNGA*Transparency (t - 1) -0.1971 * 0.0265 ***
(0.0725) (0.0082)
UNGA*Telephone lines (t - 1)
Adj. R-squared 0.005 0.001
F Statistic 31.523 2.170
Number of observations 43944 43944
Number of country pairs 3039 3039
Number of recipients 109 109
(3) (4)
Project Aid Budget Aid
(log) GDP per capita (t - 1) -0.0269 0.0083 ***
(0.5137) (0.0050)
(log) Population (t - 1) -0.0875 * 0.0074 **
(0.0582) (0.0493)
Transparency (t - 1) 0.1869 0.0154 *
(0.1399) (0.0546)
Telephone lines (t - 1) 0.0057 ** -0.0003 ***
(0.0127) (0.0022)
Donor experience (t - 1) 0.0020 *** -0.0000
(0.0000) (0.9822)
Bureaucratic quality (t - 1) -0.0036 -0.0003
(0.7266) (0.6991)
Bilateral history (t - 1) 0.0153 *** 0.0001
(0.0000) 0.0007
(log) Bilateral trade (t - 1) -0.0007 -0.0003 ***
(0.5169) (0.0068)
Government capability (t - 1) 0.0024 0.0003
(0.6545) (0.2751)
Ethnic tensions (t - 1) 0.0062 -0.0002
(0.5035) (0.6622)
UNGA distance (t - 1) 0.0018 -0.0036 **
(0.9657) (0.0153)
UNGA*Transparency (t - 1)
UNGA*Telephone lines (t - 1) -0.0003 0.0001 ***
(0.7552) (0.0065)
Adj. R-squared 0.005 0.001
F Statistic 31.231 2.171
Number of observations 43944 43944
Number of country pairs 3039 3039
Number of recipients 109 109
Notes: OLS at the donor-recipient-year level. Donor-recipient
-fixed-and year-fixed effects are included. Standard errors are
in parentheses (clustered at the donor-recipient-level;
significance levels: * 0.10, ** 0.05, *** 0.01).
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