metadata: authors: 0: author: Raschky,Paul A.
1: author: Schwindt,Manijeh
count: World
docna: 0: docna: On the channel and type of international
            disaster aid
docty: Policy Research Working Paper
subtopic: Conditional Cash Transfers,Development Economics & Aid Effectiveness,National Governance,Government Policies,Disability,Services & Transfers to Poor,International Trade and Trade Rules,Youth and Governance,Economic Assistance,Foreign Aid,Access of Poor to Social Services,Governance Indicators
teratopic: Environment,Gender,Macroeconomics and Economic Growth,Urban Development,Governance
abstracts: cdata!: Research suggests that a donor countrys
            decision to provide post-disaster assistance is not only
            driven by the severity of a disaster and the resulting
            humanitarian needs in the recipient country, but also by
            strategic considerations. The authors argue that the
            identification of the determinants of the size of disaster
            assistance is a first step in the analysis of the donors
            behavior. Since all aid is not motivated by the same
            reasons, the evaluation of the donor countrys behavior
            requires a second step accounting for the type and the
            channel of aid provided. Using data on international
            disaster assistance between 2000 and 2007, the analysis
            examines both the donor countries' decision on the
            channel (bilateral versus multilateral) and the type of
            disaster relief (cash versus in-kind). The empirical results
            suggest that international disaster relief is not as much
            driven by the needs of the recipient country, but also by
            strategic interests (for example, oil or trade
            relationships) of the donor country. Bilateral and cash
            transfers are used as a vehicle to signal strategic
            interests, while multilateral and in-kind transfers are
            chosen to control for misuse in badly governed recipient countries.
repnme: repnme: On the channel and type of international
            disaster aid
display_title: On the channel and type of
            international disaster aid
listing_relative_url: /research/2009/06/10628832/channel-type-international-disaster-aid
topicv3: Trade m1326924 698,Cash Transfers m1327437 1608,Governance Indicators m1327488 834,Humanitarian Aid m1503719 3039,Conditional Cash Transfers m1586414 3552
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Environment,Gender,Macroeconomics and Economic Growth,Urban Development,Governance
no_of_pages: 31
author_keys: 0: author_key: 1006515
1: author_key: 1006514
model_extractions: WPS4953 Policy Research Working Paper 4953 On the Channel and Type of International Disaster Aid _Paul A. Raschky Manijeh Schwindt_ The World Bank Sustainable Development Network Vice Presidency Global Facility for Disaster Reduction and Recovery Unit June 2009
Policy Research Working Paper 4953

# **Abstract**

Research suggests that a donor country’s decision to provide post-disaster assistance is not only driven by the severity of a disaster and the resulting humanitarian needs in the recipient country, but also by strategic considerations. The authors argue that the identification of the determinants of the _size_ of disaster assistance is a first step in the analysis of the donor’s behavior. Since all aid is not motivated by the same reasons, the evaluation of the donor country’s behavior requires a second step accounting for the _type_ and the _channel_ of aid provided. Using data on international disaster assistance between 2000 and 2007, the analysis examines both the donor countries' decision on the channel (bilateral versus multilateral) and the type of disaster relief (cash versus in-kind). The empirical results suggest that international disaster relief is not as much driven by the needs of the recipient country, but also by strategic interests (for example, oil or trade relationships) of the donor country. Bilateral and cash transfers are used as a vehicle to signal strategic interests, while multilateral and in-kind transfers are chosen to control for misuse in badly governed recipient countries.

This paper—a product of the Global Facility for Disaster Reduction and Recovery Unit, Sustainable Development Network Vice Presidency—is part of a larger effort in the department to disseminate the emerging findings of the forth coming joint World Bank-UN Assessment of the Economics of Disaster Risk Reduction. The team leader for the Assessment— iApurva Sanghi—ican be contacted at “asanghi@worldbank.org.” We would like to thank Jesus Crespo-Cuaresma, Howard Kunreuther, Harald Oberhofer, Michael Pfaffermayer, S. Ramachandran, Apurva Sanghi, Mark Schelker, Reimund Schwarze, Martina Tonizzo and Hannelore Weck-Hannemann as well as participants of a workshop in Zurich 2008 and a seminar at the World Bank in 2009 for their useful comments.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
On the Channel and Type of International Disaster Aid Paul A. Raschky* Manijeh Schwindt* _Keywords_ : Foreign aid, natural disasters, bilateral vs. multilateral, type of aid _JEL classification_ : O17, O19, Q54

***** Institute of Public Finance, University of Innsbruck, Universitaetsstrasse 15, A-6020 Innsbruck, Email: <u>paul.raschky@uibk.ac.at</u> and <u>manijeh.schwindt@uibk.ac.at.</u> This paper –a product of the Global Facility for Disaster Reduction and Recovery Unit, Sustainable Development Network Vice Presidency- is part of a larger effort in the department to disseminate the emerging findings of the forth coming joint World Bank-UN Assessment of the Economics of Disaster Risk Reduction. The team leader for the Assessment – Apurva Sanghi – can be contacted at “asanghi@worldbank.org”. We would like to thank Jesus Crespo-Cuaresma, Howard Kunreuther, Harald Oberhofer, Michael Pfaffermayer, S. Ramachandran, Apurva Sanghi, Mark Schelker, Reimund Schwarze, Martina Tonizzo and Hannelore Weck-Hannemann as well as participants of a workshop in Zurich 2008 and a seminar at the World Bank in 2009 for their useful comments.
# **1  Introduction**

Research suggests that by 2015 the number of people affected by natural disasters could rise by more than 50 percent to an average of more than 375 million each year (Bailey 2009). Such an enormous increase in the number of people facing risks of natural disasters will inevitably increase the need for humanitarian assistance. Budget constraints call for an improvement of disaster assistance strategies and a more efficient application of scarce resources. Therefore, a better understanding of the allocation of disaster relief, including the quantity and quality, is necessary. Why did the United States assist in 43% of flood relief cases in Kenya with cash transfers and only in 20% of the flood relief cases in Bolivia (despite both recipient countries having comparable numbers of fatalities from floods between 2000 and 2007)? Why did Mozambique receive 59% of United States' flood relief inflows bilaterally and the remaining via a multilateral agency, while India received only 14% of its United States' flood assistance bilaterally?

In order to shed more light on these questions, we combine aspects of two broad strands in the literature on foreign assistance. First, we refer to the literature on the type of aid, with discussions on the effectiveness of cash and in-kind transfers, respectively (e.g. Currie & Gahvari 2008). The argument of paternalism brought forward in this literature, can be seen as an explanation for the tendency to provide in-kind or restricted aid transfers, in particular to recipient nations with weak institutions. Recent contributions analyzing moral hazard behavior of the recipient country include Amegashie, Ouattara & Strobl (2007) and Svensson (2000).

Second, our paper relates to the vast amount of literature investigating the allocation of foreign aid. The seminal paper by Dudley & Montmarquette (1976), which argues that the supply of foreign aid will be explained by the donor countrys' demand for foreign aid impact, paved the way for a discussion of donors' motives behind contributing aid. The majority of these papers showed that foreign aid allocation is determined by the donor countries' strategic and political interests rather than by the recipient countries' need. For example, Kuziemko & Werker (2006) find that more foreign aid is paid to countries which rotate on a seat in the U.N. security council, Dudley & Montmarquette (1976) show that foreign aid is induced by the level of exports from donor to recipient countries and the results of Alesina & Dollar (2000) suggest that colonial ties result in greater aid allocation. More specifically and of great relevance for this paper, Fink & Redaelli (2009) find that not only foreign aid but also international emergency assistance is determined by political and strategic interests. They show that more emergency aid is paid to countries which are located closer to the donor country, export oil or were former colonies of the donor country. In addition, the results of Eisensee & Strömberg (2007) suggest that U.S. disaster relief is driven by the level of media coverage of disasters. By contrast Olsen, Carstensen & Hoyen (2003) find a rather limited role of news coverage. They use case-studies in order to show that the size of humanitarian assistance is determined by the donor country's interest in stability and security in the affected country as well as by the local presence of international organizations with lobbying power. The former argument is consistent with what Bermeo (2007) calls strategic development or strategic stabilization. According to her argument and in contrast to the above mentioned literature, all foreign aid is used for strategic interests. However, development or stabilization of the recipient country can be part of the strategic goal as well. In this case, donor interests and recipient needs coincide.

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The analysis so far had a focus on the determinants of the size of aid and emergency assistance, respectively, including the implicit assumption that all donors give the same type of aid and use the same channels or that all aid is motivated by the same reasons. We argue that the analysis of the amount of aid is a necessary first step but not sufficient to derive implications about donor countries’ behavior. In order to get a more comprehensive picture on the motivation of donor countries’ incentives to provide humanitarian aid and to design more efficient mechanisms of international (disaster) assistance, the decision on both the type and the channel of aid need to be considered. What criteria does a donor country use in order to decide whether to assist by cash transfers or in-kind transfers? Why do countries pay bilateral aid to one country and multilateral aid to another? Maintaining the terminology of Bermeo (2007) and distinguishing between strategic stabilization goals and non-stabilization goals, we would expect that donor countries try to maximize aid effectiveness by their choice of the type and channel of aid, if strategic stabilization is the main objective. Whereas we would expect them to choose the type of aid, which the recipient country values the highest, when their primary interests are non-stabilization goals.

Strategic stabilization requires that transfers reach their desired recipients. However, in comparison to cash transfers, restricted transfers, e.g. in-kind transfers, might be better suited for the reduction of moral hazard behavior and efficient targeting (e.g. Amegashie et al. 2007<sup>1</sup> , Besley & Coate 1991, Gahvari & Mattos 2007, Svensson 2000). Note, that the term cash transfer does not include conditional cash transfers. Literature on conditional cash transfers suggests that depending on the design this form of assistance can be very effective. For example, Doocy, Gabriel, Collins, Robinson & Stevenson (2006) describe the implementation of cash for work programs after the Tsunami in Aceh.<sup>2</sup> Not only the type of aid, but also the channel of aid might be relevant for the ability of transfers to achieve stabilization. Opposed to bilateral assistance, multilateral agencies might have better information about the risks in aid receiving countries and hence account for them in their allocation decisions (Weck-Hannemann & Schneider 1991). Moreover, since donor countries lack commitment power, Svensson (2000) argues that the delegation of aid to agencies which are less risk averse and have plausible commitment techniques could provide incentives in the receipient country to generate own effort. On the other hand, non-stabilization goals are more probable to achieve, if the aid receiving government values the assistance highly. It is reasonable to assume that governments value cash payments higher than in-kind payments because they can use it in accordance with their own preferences. Moreover, donor countries transferring money directly to recipient governments might be more successful in building up political ties, since bilateral transfers are more visible for recipients than countries which act anonymously via a multilateral agency. For that reason, strategic stabilization goals should increase the probability of multilateral and in-kind transfers whereas nonstabilization goals should induce bilateral and cash transfers.<sup>3</sup> The remainder of the paper is organized as follows: In section 2 we present the theory and derive

> 1 In fact, Amegashie et al. (2007) investigate how donor countries' choice of the composition of cash and in-kind transfers adjusts to changes of the moral hazard behavior of recipient countries. While multilateral donors reward (penalize) decreases (increases) in moral hazard behavior by reducing (rising) the proportion of in-kind relative to cash-transfers, bilateral donors do not react to changes in moral hazard behavior.

> 2 For a more detailed analysis of aid programs using conditional cash transfers see Fiszbein, Schady, Ferreira, Kelleher, Olinto & Skofias (2009).

> 3 Needless to say that the decision on the type and channel of post-disaster assistance is determined by other characteristics (e.g. disaster type) as well. Of course we control for these variables in the empirical part of this paper.

3
the signs for the partial derivatives of the probability of the different types of aid. Research design, data and econometric strategy are presented in section 3. Finally, section 4 concludes.

# **2  Theory**

In order to derive the factors which explain the choice of the type and channel of disaster aid made by donor countries, we use a technical framework applied by Huber & Nowotny (2008). They analyze an individuals decision to commute and to migrate in another region opposed to the possibility to stay in the home region.

First, we allow each donor country to choose between bilateral disaster assistance, multilateral disaster assistance and no disaster assistance. The donor country's decision to assist or not and its choice between bilateral and multilateral disaster aid are based on two instruments, which according to Bermeo (2007), both contribute to foreign policy purposes. First, because of strategic reasons, the donor country might be interested in the stabilization of the recipient country in the aftermath of a disaster. Strategic stabilization could, for example, be motivated by the intention to mitigate negative spill-over effects on the donor country's economy or to prevent large flows of disaster-refugees. Hence, under these circumstances donor interests and recipient needs coincide. Second, the donor country might have other strategic interests not related to the strategic stabilization goal, which the donor country wants to promote, e.g. political ties to oil exporting countries.

> To be more precise, consider a donor country with GDP _YD_ which decides a) whether to contribute an amount of _T_ to a country which was hit by a natural disaster and b) whether to transfer this amount directly to the recipient government (bilateral (B)) or via a multilateral agency (M). Empirical results suggest that it is reasonable to assume that disaster assistance _h_ contributes to a higher level of stabilization in the recipient country _S R_ , ∀ _h_ = _B_ , _M_ and hence a higher utility level of the donating country if e.g. good governance indicators in the recipient country are high (Burnside & Dollar 2000). Therefore, the stabilization effect is determined by the ability of multilateral and bilateral disaster aid to circumvent the adverse effects of bad governance, respectively. Due to a more profound knowledge of the specifics of the recipient countries resulting from the presence in affected countries, we assume that the control mechanism using multilateral disaster assistance is better compared to the control in the case of bilateral disaster assistance. Moreover, Svensson (2000) suggests that the delegation of aid to international agencies with fully developed commitment technologies contributes to a higher effecieny level. For this reason, stabilization is easier to enforce with multilateral assistance for a given level of good governance. Moreover, for higher levels of good governance, strict control mechanisms are less of a constraint. In order to gain utility from better access to strategic _h_

> interests<sup>_I_</sup> _R_<sup>,</sup> ∀ _h_ = _B_ , _M_ in the recipient country, it is important for the donor country to ensure that the source of disaster assistance is visible for the recipient country. We assume that bilateral disaster assistance is more visible than multilateral disaster assistance and thus secures strategic

> interest in a better way. Assuming an additive utility function, the utility _U B_ of a donor country choosing bilateral disaster assistance is determined by its own governmental purchases _YD_ − _T_ , by the stabilization effect in the recipient country _S RB_ and by the strategic advantages<sup>_I_</sup> _RB_<sup>induced</sup>

4
by bilateral disaster aid. We formulate the donor country's utility function for bilateral emergency assistance as Equivalent to equation (1) the donor country's utility in the case of multilateral disaster assistance is

> The terms ε _B_ and ε _M_ are random utility components for donating bilateral and multilateral, respectively. Apart from bilateral and multilateral disaster assistance, the potential donor country can decide not to assist. In this case the donor country's utility is

_U N_ = _YD_ + ε _N_ . (3) We use equations (1), (2) and (3) in order to calculate the utility differentials between bilateral disaster assistance ( _U B_ ) , multilateral disaster assistance ( _U M_ ) and no disaster assistance ( _U N_ ) .

Equations (4) and (5) state that higher levels of stabilization _S Rh_ achieved with bilateral and multilateral disaster aid as well as higher strategic gains, increase the utility of disaster assistance in comparison to not assisting. However, assistance bears costs in the form of a reduction in a donor country's government purchases.

> Equation (6) shows that the stabilization differential ( _S RM_ − _S RB_ > 0) increases the utility gain from multilateral disaster assistance, whereas the strategy differential ( _I RM_ − _I RB_ < 0) increases the utility gain from bilateral disaster assistance.

In order to simplify (4), (5) and (6) we define the direct utility gains by 5
and rewrite (4), (5) and (6) as

> with ξ _B_ = ε _B_ − ε _N_ .

with ξ _M_ = ε _M_ − ε _N_ .

Equations (10) - (12) state that countries will decide not to assist, if ξ _B_ < −Ω _BN_ and ξ _M_ < −Ω _MN_ . However, for ξ _B_ > −Ω _BN_ and ξ _M_ > −Ω _MN_ there will be bilateral or multilateral disaster assistance, depending on the relation of Ω _BN_ and Ω _MN_ . Countries will choose bilateral disaster assistance for ξ _B_ > −Ω _BN_ and ( ξ _M_ −ξ _B_ ) < −Ω _MB_ , whereas they will assist via a multilateral agency if ξ _M_ > −Ω _MN_ and ( ξ _M_ −ξ _B_ ) > −Ω _MB_ .

> We can now determine the probability that a donor country chooses bilateral assistance ( _PB_ ) ,

> multilateral assistance ( _PM_ ) and no assistance ( _PN_ ) .

Using comparative statics we can derive the derivatives of probabilities of assisting by bilateral or multilateral transfers and not assisting (see table 1).

Table 1: The choice between bilateral, multilateral and no disaster assistance subject to selected variables

||_T_|_B_<br>_R_<br>_S_|_M_<br>_R_<br>_S_|_B_<br>_R_<sup>_I_</sup>|_M_<br>_R_<sup>_I_</sup>|
|---|---|---|---|---|---|
|_B_<br>_P_|-|+|-|+|-|
|_M_<br>_P_|-|-|+|-|+|
|_N_<br>_P_|+|-|-|-|-|

6
Now that we have determined the partial derivatives of the probabilities to pay bilateral disaster assistance, multilateral disaster assistance and no assistance, we vary the options of the donor country, by distinguishing between cash (C) and in-kind (IK) transfers in the case of bilateral assistance. In accordance to (1) - (3), we formulate the donor country's utility depending on the type of aid, as: Note that in-kind transfers are assumed to cause some additional transportation costs ( _C_ ) , which will be higher for large distances between donor and recipient country. Moreover, we would expect that in-kind transfers can better control for the adverse effects of bad governance compared to cash transfers, whereas cash transfers might be valued higher than in-kind transfers from the aid receiving country and hence be more suited for achieving strategic goals. Using the same approach as above, we determine the following signs of the partial derivatives of the probabilities to choose cash, in-kind, multilateral or not any assistance (see table 2).

Table 2: The choice between cash, in-kind, multilateral and no disaster assistance subject to selected variables

||_T_|_IK_<br>_R_<br>_S_|_C_<br>_R_<br>_S_|_M_<br>_R_<br>_S_|_IK_<br>_R_<sup>_I_</sup>|_C_<br>_R_<sup>_I_</sup>|_M_<br>_R_<sup>_I_</sup>|_C_|
|---|---|---|---|---|---|---|---|---|
|_IK_<br>_P_|-|+|-|-|+|-|-|-|
|_C_<br>_P_|-|-|+|-|-|+|-|+|
|_M_<br>_P_|-|-|-|+|-|-|+|+|
|_N_<br>_P_|+|-|-|-|-|-|-|+|

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# **3  Empirical analysis**

## **3.1  Research design and data**

We are interested in the decision of potential donor countries (i.e. every country that has not been directly affected by a disaster) to provide post-disaster assistance and the channel and type the actual donors choose. In order to examine the effect of humanitarian needs of a recipient country and strategic interests of a donor country, we construct a basic dyadic data set for each major natural disaster (that is included in the EM-DAT data set) in a given country between 2000 and 2007. For any given disaster in a country, all remaining countries are considered as potential donor nations. Including only those cases where one potential donor actually provided aid in our regression would truncate the data. All potential donors that did not provide post-disaster assistance are coded zero and this information is used in the first stage selection estimates. The combination of 228 disasters, where information on both the channel and type of disaster aid is available, and 187 potential donor nations, results in a basic dataset of 42,636 observations. However, this number is reduced to between 20,077 and 25,836 (depending on the specification) due to missing data. Of the aforementioned observations, between 2,603 and 3,123 (depending on the specification) observations are actual aid contribution (dependent variable=1).

The dependent variables are dummies that switch to 1 if a donor has contributed post-disaster assistance (in the selection equation), switch to 1 if the if the contribution was bilateral (in the channel equation) or switch to 1 if the type of contribution was cash (in the type equation). In figures 1 and 2 we show the breakdown of total aid in bilateral vs. multilateral and cash vs. inkind by major recipients. Pakistan received 602 contributions. Among these 602 contributions, 463 (77%) were made via a multilateral agency and 139 (23%) were made bilaterally. Peru on the other end of the list, received 99 contributions where 54 (55%) where made multilaterally and 45 (45%) bilaterally. The differences between recipient countries in the type of disaster aid received is even bigger. Out of 100 contributions to Haiti only 1 (1%) was bilateral cash, while Mozambique received 53 (16%) bilateral aid contributions out of 335 disaster assistance flows. The main explanatory variables include indicators for humanitarian needs by the recipient and variables accounting for strategic interests of the donor. The former group includes the number of fatalities in a disaster (in thousand) and the level of gross domestic product per capita (GDP p.c.). The latter group contains information on the donor's trade volume with the recipient and the percentage of fuel exports of total merchandise exports by the recipient. These two variables are widely used as empirical proxies for strategic interests in the existing aid allocation literature (e.g. Fink & Redaelli 2009, Berthelemy 2004). In addition we include dummies for the type of natural disaster.

In choosing other relevant covariates, we follow the existing empirical literature (e.g. Fink & Redaelli 2009, Alesina & Dollar 2000): Size of the recipient country (population), distance between donor and recipient, oneness for trade, colonial history between the donor and the recipient as well as an updated version of Gartzke's affinity index that is constructed using voting patterns in the United Nations General Assembly. The index ranges between 1 (recipient and donor always voted the same way) and -1 (recipient and donor never voted the same way). In addition we include two dummy variables that account for common religious beliefs and common language in the donor and recipient countries. The donor nation’s level of development measured via the GDP p.c. is also included. Table 15 gives an overview of the data sources used.

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# **3.2  Econometric strategy**

Based on the theoretical concept in section 2, our goal is to identify the driving factors of the likelihood of choosing 1) a certain channel for disaster aid (bilateral vs. multilateral) and 2) a certain type of disaster aid (cash vs. in-kind). The decision on both the channel and the type however, is conditional on the decision to provide post-disaster assistance at all. The resulting selection problem can be formulated as follows: where<sup>_y_</sup> 1<sup>denotes the selection variable and equals 1 if aid is given, zero otherwise,</sup><sup>_y_</sup> 2<sup>denotes</sup> the second stage channel (1 if aid is bilateral, zero if multilateral) or type (1 if type of aid was cash, zero if in-kind) and _x_ is a vector of covariates. In our case, the sample selection model consists of two stages: The first stage defines the cases where actual post-disaster aid is given. The selection variable is a latent variable<sup>_y_</sup> 1*<sup>and equals 1 if aid is given. The second stage is the</sup> outcome stage and is estimated in two separate specifications. In the first specification it describes the cases where bilateral aid was given, while in the second specification it describes the cases when cash was contributed rather than in-kind. In either of the two specifications we denote this second stage latent variable as<sup>_y_</sup> 2*<sup>. We derive the following system:</sup>

> <sup>_x_</sup> _i_<sup>and</sup><sup>_u_</sup> _i_<sup>are the explanatory variables and the error terms for the first and the second stage. The</sup> correlation between the two equations, ρ , indicates if there is actual sample selection. The traditional Heckman model (Heckman 1979) requires that the second stage outcome equation is estimated using OLS. Dubin & Rivers (1989) developed an extended selection model where second stage is estimated using a probit model, which is applied in this paper. The latent

> variables _y_ * are related to the observed variables _y_ in the following way:

The application of a sample selection model requires unique information in the explanatory variables<sup>_x_</sup> 1<sup>and</sup><sup>_x_</sup> 2<sup>to separately identify the parameters in the selection and the outcome stage.</sup> To deal with this issue we use the donor nation's population as additional selection variable in the first stage selection.

9
# **3.3  Results**

The results are structured by the strategic variables of interest, oil and trade. We start presenting estimates of the second stage estimates on the decision on the channel of disaster aid including the fraction of fuel exports of total merchandise export if a contribution has been made (Table 5). The dependent variable is a dummy that equals to 1 if the contribution was bilateral and 0 if it was multilateral. Fatalities do not appear to have an impact on the choice of the aid channel, but the number of people affected has a significant positive impact. More distant countries are also more likely to receive aid via a multilateral agency. Countries with a higher fraction of fuel exports and better governance indicators are more likely to receive bilateral aid. These results allow for interpretations: First, the bilateral channel is preferred over the multilateral channel because it is more attributable to the donor and thus supports strategic interests in a better way. Second, donors are more likely to give aid via a multilateral agency because these agencies might have a better competence in controlling the sound use of the aid in recipient nation's with low good governance indicators.

The interaction term of oil and good governance indicators have a negative sign and are only significant at the 5%-level for corruption control (column (5)). The signalling effect of bilateral aid might by decreasing with better regulatory quality or corruption control.

Table 6 presents the results of the first stage selection equation. As expected, countries that have suffered more fatalities and where more people where affected from a disaster and that are poorer are more likely to receive international disaster assistance. In line with the standard ODA literature, donors appear to favor more open recipient countries. Interestingly, smaller nations are less likely to receive disaster aid. This result stays in contrast to the small-country bias found in ODA decisions. The likelihood of receiving aid increases in the number of fatalities and if donor and recipient country have a common language. In accordance with the findings of Fink & Redaelli (2009) and opposed to the existing literature on ODA decision, affinity does not increase the likelihood of receiving aid from a donor. Interestingly, if the donor and the recipient share the same religious beliefs, the likelihood of receiving aid significantly decreases. One possible explanation for this result is that the data on religion only controls for potential frictions between large religious groups and not within the large religious groups (e.g. Sunnite and Shiite).

In the specification presented in column (2) we have included oil (percentage of fuel exports from total merchandise exports) as an additional regressor. A higher fraction of fuel exports increases the likelihood of receiving disaster aid. These results remain robust after the inclusion of regulatory quality (column (3)) and corruption control (column (5)). Including the oil variable with the good governance indicators (columns (4) and (6)) reveals that the likelihood of receiving aid decreases for a recipient nation that has large fuel exports and good regulatory quality and increases if the recipient has large fuel exports and good corruption control.

We now turn to the estimates of the specifications using trade volume as the second indicator for strategic interests. The results of the aid selection equations in table 8 are pretty similar to the first stage results for the oil estimates in table 6. The effects of strategic interests and good governance as well as the interaction of these two variables reveal a similar trend for the trade estimates. Donors are more likely to donate via a multilateral agency if there has been a major disaster causing a large number of fatalities and if the affected country is poor. In contrast to the oil estimates, there are differences in the second stage estimates on the channel of aid. The 10
interaction between trade volume and governance indicators is positive and highly significant.

The next step consisted of changing the specification of the second stage estimates and examining the decision on the type of aid. The dependent variable is the probability of giving bilateral cash. We only present the results of the second stage for both the oil and trade estimates. Interestingly, fuel exports do not appear to have a significant effect on the likelihood of receiving cash (table 9), while trade volume has a significantly positive effect throughout all specifications (table 10). Humanitarian aspects appear to have adverse effects on the likelihood of receiving cash. Donors prefer to donate cash to richer nations and after smaller disasters with fewer people killed. Regarding the strategic variables we find similar patterns as in the choice of the aid channel.

The analysis so far has assumed that the variables that explain the choice on the composition of aid do not differ between countries. The empirical literature, however, suggests that donors' decision on ODA (e.g. Alesina & Dollar 2000, Kuziemko & Werker 2006) and disaster aid (e.g. Fink & Redaelli 2009) are not the same across donor nations. For expositional convenience, we limit our analysis to a comparison between OECD and non-OECD countries. This robustness test basically splits the sample in OECD and non-OECD donor subsamples and repeats the estimates in tables 5 - 10 for each subsample, respectively.<sup>4</sup> Unfortunately, the the estimates did not converge using a heckman probit estimator. We therefore applied a simple conditional probit model, estimating the likelihoods of receiving bilateral aid or bilateral cash conditional that aid has been given. Table 11 presents the coefficients of the second conditional probit models for both OECD and non-OECD donors decisions on the channel of disaster aid. A first glance reveals that the pooled estimates in tables 5 - 10 are mainly driven by the OECD sample. OECD donors are more likely to provide bilateral disaster aid if the recipient export share of fuel is large and if the recipient nation has good governance indicators. There is a smaller probability of giving bilateral aid if the disaster caused a lot of fatalities and if the recipient has a larger per capita income. In contrast, fuel exports appear to have a negative and not significant impact on the decision on the channel of disaster aid for non-OECD countries. One possible explanation is that the majority of donors in this subsample are countries from the Middle East and North Africa (MENA) which are already endowed with large oil resources. Good governance indicators also influence the decision in both subsamples, where the coefficients for the non-OECD contries is actually larger. Table 12 reports the result for the second strategic variable, trade. Again the results of the OECD donor group are comparable to the results of the main sample. The estimates for non-OECD again reveal that strategic interests do not play a significant role in their decision. We find some indication that the trade relationship between the donor and the recipient has a positive and significant influence on the decision to provide bilateral aid. Tables 13 and 14 show the results for the probit estimates on the type of aid for each subsample and each strategic variable, respectively. Again, the decision on the type of aid is driven by strategic interests in the case of OECD countries, while strategic interests have no significant influence on non-OECD countries decision. However, the only explanatory variables that have a significant influence in the non-OECD subsample are the disaster type dummies and the common language dummy.

> 4 We only present the coefficients of the second stage estimates for the key variables. The results of the first stage estimates as well as the full estimation table including all other covariates are available from the authors.

11
# **4  Conclusion**

Research of the past 30 years suggests that foreign aid is allocated due to strategic and political interests rather than due to recipient needs. The aim of this paper was to show that strategic concerns not only dominate the choice of the size of aid, but also the decision on the type and the channel of aid. Our theoretical results suggest that donor countries assist by multilateral or inkind transfers rather than by bilateral or cash transfers, if their main interest is the stabilization of the recipient country, whereas they choose bilateral or cash transfers if they account for nonstabilization goals. The empirical application shows that a) recipient countries with good governance are rewarded by bilateral or cash transfers, b) countries with lower levels of GDP p.c. or higher death tolls are more probable to receive multilateral or in-kind transfers and c) the probability of getting bilateral or cash assistance increases if the donor country has strategic interests (e.g. oil or trade) in the receiving country. Moreover, we find that good governance is less of a constraint for bilateral or cash transfers if the donor country has strategic interests in the same country. We show that donor countries deliberately choose the type and channel of aid in conformity with their goals, however, strategic interests seem to dominate stabilization goals. Interestingly, the reasons to provide bilateral aid as well as cash aid differ between OECD countries and non-OECD countries. OECD countries are more likely to give bilateral aid and cash if the recipient has oil resources, is a trading partner and has sound institutions. In contrast, non-OECD countries’ decisions on the channel and type are not influenced by strategic interests. These countries are more likely to provide bilateral aid and cash if the recipient has suffered a large number of casualties in a disaster.

12
# **References**

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13
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14
Table 3: List of recipient countries' total fatalities and number of disasters

|**Recipient**|**Fatalities**|**Disasters**|**Recipient**|**Fatalities**|**Disasters**|
|---|---|---|---|---|---|
|Afghanistan|669|6|Lao, PDR|15|1|
|Albania|1|1|Madagascar|602|5|
|Algeria|971|4|Malawi|567|3|
|Argentina|23|1|Malaysia|80|1|
|Armenia|n.a.|1|Maldives|102|2|
|Azerbaijan|31|1|Mali|2|2|
|Bahamas, The|1|1|Mauritania|1|1|
|Bangladesh<br>|2,309<br>|4<br>|Mexico<br>|84<br>|3<br>|
|Belize|44|3|Micronesia, Fed. Sts.|48|3|
|Bolivia|271|6|<br>Moldova|-|1|
|Botswana|3|1|Mongolia|23|2|
|Brazil|50|1|Morocco|708|2|
|Bulgaria|17|1|Mozambique|908|3|
|Cambodia|403|2|Myanmar|307|2|
|Central African Rep.|1|1|Namibia|2|1|
|<br>Chile|40|3|Nepal|657|2|
|China|1,185|4|Nicaragua|33|4|
|Colombia|109|2|Niger|4|2|
|Comoros|1|3|<br>Oman|76|1|
|Costa Rica|24|4|Pakistan|74,137|7|
|Cuba|22|4|Panama|11|1|
|Czech Republic|18|1|Papua New Guinea|n.a.|1|
|<br>Djibouti|51|2|<br>Peru|815|5|
|Dominica|2|1|Philippines|3,070|5|
|Dominican Republic|830|3|<br>Poland|27|1|
|<br>Ecuador|21|4|Portugal|14|1|
|El Salvador|863|3|Romania|33|2|
|Ethiopia|498|1|Russian Federation|101|2|
|<br>Fiji|17|1|Senegal|28|1|
|Georgia|6|3|Seychelles|3|1|
|<br>Ghana|72|3|<br>Solomon Islands|52|2|
|Grenada|39|1|Somalia|350|2|
|Guatemala|n.a.|1|Sri Lanka|35,634|3|
|Guinea|n.a.|1|St. Lucia|1|1|
|Guyana|34|1|Sudan|85|3|
|<br>Haiti|2,857|4|Suriname|3|1|
|Honduras|21|2|Tajikistan|27|4|
|Hungary|1|2|Thailand|8,449|2|
|<br>India|38,730|7|Togo|41|1|
|Indonesia|172,214|10|<br>Tonga|n.a.|1|
|Iran|28,110|5|Turkey|219|2|
|Jamaica|<br>29|4|<br>Uganda|18|1|
|Japan|40|1|<br>Ukraine|9|1|
|Kenya|173|3|Uruguay|9|2|
|<br>Korea, DPR|934|3|<br>Vanuatu|3|3|
|Korea, Republic of|210|2|Venezuela|80|2|
|Kyrgyzstan|38|2|Vietnam|844|3|
||||Zimbabwe|70|1|

15
Table 4: List of donor countries' total contributions and number of donations

|**Donor**|**Total contribution**|**Events**|**Donor**|**Total contribution**|**Events**|
|---|---|---|---|---|---|
||**(inUSD)**|||**(inUSD)**||
|Afghanistan|500,000|2|Iceland|473,627|13|
|Algeria|2,489,199|5|India|23,630,944|10|
|<br>Andorra|58,386|2|Indonesia|n.a.|1|
|Angola|n.a.|1|Iran|347,380|3|
|Argentina|n.a.|8|Ireland|40,573,378|131|
|Armenia|n.a.|2|Israel|2,357,000|17|
|Australia|54,936,086|115|Italy|76,690,358|121|
|Austria|11,436,846|40|<br>Japan|445,981,017|195|
|Azerbaijan|622,000|4|Jordan|n.a.|3|
|Bahrain|n.a.|1|Kazakhstan|n.a.|2|
|Bangladesh|100,000|2|Kenya|75,000|1|
|<br>Belarus|113,018|1|<br>Korea, DPR|130,000|5|
|Belgium|44,886,419|79|Korea, Republic of|1,576,709|22|
|<br>Bolivia|n.a.|1|<br>Kuwait|3,366,013|12|
|Botswana|482,000|3|Kyrgyzstan|27,093,596|2|
|Brazil|<br>200,000|13|<br>Lao, PDR|<br>75,000|3|
|Bulgaria|103,717|2|Latvia|446,726|6|
|Burundi|20,000|1|Lebanon|n.a.|1|
|Canada|108,799,910|204|Lesotho|110,000|2|
|Chile|30,000|7|Libya|1,500,000|6|
|China|14,009,631|56|Liechtenstein|305,278|7|
|Colombia|100,000|5|Lithuania|252,631|5|
|Costa Rica|<br>n.a.|1|Luxembourg|<br>12,165,218|46|
|Croatia|n.a.|2|<br>Malawi|<br>100,000|2|
|Cuba|129,965|7|Malaysia|5,138,948|18|
|Cyprus|756,462|17|<br>Malta|10,854,817|1|
|Czech Republic|5,498,495|20|Mauritania|200,336|3|
|<br>Denmark|60,283,135|146|Mauritius|80,000|3|
|Dominican Republic|196,370|3|Mexico|4,127,922|8|
|Ecuador|13,237|4|Moldova|455,307|5|
|Egypt|300,000|3|Monaco|640,081|16|
|El Salvador|n.a.|1|Morocco|496,980|9|
|Eritrea|n.a.|1|Namibia|800,000|1|
|Estonia|577,084|9|Nepal|235,391|4|
|Fiji|<br>9,700|1|<br>Netherlands|<br>101,964,604|139|
|<br>Finland|25,055,726|56|New Zealand|15,536,259|50|
|France|48,601,080|118|Nicaragua|n.a.|1|
|Gabon|<br>200,000|1|<br>Nigeria|1,150,000|3|
|Germany|<br>174,339,341|371|<br>Norway|<br>117,858,752|223|
|<br>Ghana|<br>100,000|1|<br>Oman|<br>100,000|3|
|Greece|27,047,570|51|Pakistan|157,560|3|
|Guatemala|n.a.|2|Palau|51,772|2|
|Guyana|20,000|1|Panama|<br>n.a.|2|
|<br>Honduras|n.a.|1|Peru|111,130|8|
|Hungary|1,005,267|19|Poland|6,966,713|25|

16
Table 4: List of donor countries’ total contributions and Number of donations (cont.)

|**Donor**|**Total contribution**|**Events**|
|---|---|---|
||**(inUSD)**||
|Portugal|10,127,312|30|
|Qatar|22,350,468|13|
|Romania|2,639,255|8|
|Russian Federation|6,615,748|27|
|Rwanda|10,000|2|
|San Marino|19,807|1|
|Saudi Arabia (Kingdom of)|83,804,806|63|
|Seychelles|n.a.|1|
|Singapore|4,850,500|23|
|Slovakia|2,705,516|21|
|Slovenia|709,334|17|
|South Africa|3,852,500|10|
|Spain|73,199,347|78|
|Sri Lanka|n.a.|1|
|Sudan|10,000|1|
|Swaziland|15,000|1|
|Sweden|107,626,853|210|
|Switzerland|19,264,147|113|
|Syrian Arab Republic|n.a.|5|
|Tajikistan|n.a.|1|
|Thailand|1,085,202|13|
|Trinidad and Tobago|2,625,000|5|
|Tunisia|n.a.|3|
|Turkey|40,724,138|58|
|Ukraine|n.a.|2|
|United Arab Emirates|34,668,256|35|
|United Kingdom|306,310,134|343|
|United States of America|460,435,164|495|
|Venezuela|1,800,000|11|
|Vietnam|n.a.|1|
|Zambia|20,000|3|

17
<!-- Start of picture text --> Pakistan<br>Indonesia<br>Sri Lanka<br>Mozambique<br>India<br>El Salvador<br>Iran<br>Niger<br>Haiti<br>Peru<br>0 100 200 300 400 500<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan<br>Indonesia<br>Sri Lanka<br>Mozambique<br>India<br>El Salvador<br>Iran<br>Niger<br>Haiti<br>Peru<br>0 20 40 60 80 100<br><!-- End of picture text -->
||Table 5: A|id & bilateral|OIL – 2<sup>nd</sup>sta|ge||
|---|---|---|---|---|---|
|)<br>|1<br>=<br>(<br>_X_<br>_bilateral_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|_Ln(Fatalities)_|0.008|0.026|0.024|0.019|0.019|
||(0.016)|(0.017)|(0.018)|(0.017)|(0.016)|
|_Ln(Affected)_|0.032***|0.024***|0.023***|0.010|0.014**|
||(0.005)|(0.006)|(0.007)|(0.007)|(0.007)|
|_Ln(GDP p.c.)_|-0.053|-0.119**|-0.125**|-0.138***|-0.122**|
||(0.042)|(0.048)|(0.060)|(0.054)|(0.055)|
|_Ln(Population)_|-0.103***|-0.100***|-0.100***|-0.096***|-0.099***|
||(0.026)|(0.027)|(0.027)|(0.028)|(0.028)|
|_Trade (% of GDP)_|-0.001|-0.002|-0.002|-0.001|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|_Distance_|-0.070***|-0.082***|-0.082***|-0.082***|-0.079***|
||(0.009)|(0.010)|(0.010)|(0.010)|(0.010)|
|_Affinity index_|0.679**|0.470|0.467|0.546*|0.563*|
||(0.296)|(0.317)|(0.318)|(0.319)|(0.319)|
|_Common religion_|-0.028|0.003|0.002|-0.045|-0.048|
||(0.073)|(0.084)|(0.085)|(0.084)|(0.084)|
|_Common language_|0.137|-0.067|-0.067|-0.019|-0.023|
||(0.088)|(0.124)|(0.124)|(0.122)|(0.129)|
|_Former colony_|-0.091|-0.166*|-0.166*|-0.151|-0.177*|
||(0.090)|(0.099)|(0.099)|(0.100)|(0.100)|
|_Ln(GDP p.c.) donor_|-0.188***|-0.149***|-0.152***|-0.162***|-0.161***|
||(0.052)|(0.051)|(0.051)|(0.051)|(0.050)|
|_Fuel exports (% of_|0.002**|0.009***|0.008***|0.006***|-0.003|
|_merchandise exports)_|(0.001)|(0.001)|(0.003)|(0.001)|(0.004)|
|_Regulatory quality_||0.504***|0.522***|||
|||(0.091)|(0.122)|||
|_Fuel exports_×|||-0.001|||
|_Regulatory quality_|||(0.004)|||
|_Corruption control_||||0.387***|0.477***|
|||||(0.104)|(0.110)|
|_Fuel exports_×|||||-0.014**|
|_Corruption control_|||||(0.006)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|ρ|0.717***|0.793***|0.788***|0.773***|0.776***|
||(0.120)|(0.123)|(0.123)|(0.121)|(0.119)|
|Constant|1.308|1.731**|1.813*|2.197**|2.077**|
||(0.800)|(0.843)|(0.943)|(0.909)|(0.896)|
|Loglikelihood|-7258.304|-6054.057|-6053.534|-6056.025|6032.289|
|N|3158|2632|2632|2632|2632|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is bilateral, a dummy that switches to 1 if the aid flow was bilateral. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

19
Table 6: Aid & Bilateral OIL- 1<sup>st</sup> stage

|)<br>|<br>1<br>=<br>(<br>_Z_<br>_aid_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Ln(Fatalities)_|0.158***<br>(0.008)|0.154***<br>(0.009)|0.155***<br>(0.009)|0.153***<br>(0.009)|0.149***<br>(0.009)|
|_Ln(Affected)_|0.064***|0.056***|0.058***|0.059***|0.055***|
||(0.003)|(0.004)|(0.004)|(0.004)|(0.004)|
|_Ln(GDP p.c.)_|-0.094***|-0.141***|-0.130***|-0.084***|-0.094***|
||(0.017)|(0.023)|(0.025)|(0.024)|(0.024)|
|_Ln(Population)_|-0.304***|-0.283***|-0.284***|-0.287***|-0.289***|
||(0.013)|(0.015)|(0.015)|(0.015)|(0.015)|
|_Trade (% of GDP)_|-0.004***|-0.003***|-0.003***|-0.003***|-0.004***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|_Distance_|-0.070***|-0.072***|-0.073***|-0.072***|-0.072***|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|_Affinity index_|-0.627***|-0.601***|-0.601***|-0.599***|-0.588***|
||(0.093)|(0.102)|(0.102)|(0.102)|(0.102)|
|_Common religion_|0.115***|0.081**|0.081**|0.079*|0.074*|
||(0.036)|(0.041)|(0.041)|(0.041)|(0.041)|
|_Common language_|0.278***|0.252***|0.249***|0.264***|0.264***|
||(0.049)|(0.059)|(0.060)|(0.059)|(0.060)|
|_Former colony_|-0.009|-0.023|-0.023|-0.020|-0.012|
||(0.051)|(0.053)|(0.053)|(0.053)|(0.054)|
|_Ln(GDP p.c.) donor_|0.607***|0.602***|0.602***|0.601***|0.602***|
||(0.016)|(0.017)|(0.017)|(0.017)|(0.017)|
|_Pop. (in mio.) donor_|0.001***|0.001***|0.001***|0.001***|0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|_Fuel exports (% of_|0.002***|0.004***|0.006***|0.003***|0.012***|
|_merchandise exports)_|(0.001)|(0.001)|(0.002)|(0.001)|(0.002)|
|_Regulatory quality_||0.072|0.044|||
|||(0.049)|(0.051)|||
|_Fuel exports_×|||0.002|||
|_Regulatory quality_|||(0.002)|||
|_Corruption control_||||-0.134***|-0.213***|
|||||(0.041)|(0.045)|
|_Fuel exports_×|||||0.012***|
|_Corruption control_|||||(0.002)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|Constant|-1.694***|-1.589***|-1.688***|-2.042***|-1.910***|
||(0.350)|(0.390)|(0.398)|(0.400)|(0.407)|
|N|26811|22246|22246|22246|22246|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is aid, a dummy that switches to 1 if the donor has made a contribution. ***, **, * indicate significance at the 1, 5 and 10%level, respectively.

20
Table 7: Aid & bilateral TRADE – 2<sup>nd</sup> stage

|)<br>|1<br>=<br>(<br>_X_<br>_bilateral_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Ln(Fatalities)_|0.017|0.014|0.014|0.013|0.018|
||(0.014)|(0.015)|(0.015)|(0.015)|(0.015)|
|_Ln(Affected)_|0.035***<br>(0.004)|0.028***<br>(0.005)|0.028***<br>(0.005)|0.025***<br>(0.006)|0.022***<br>(0.006)|
|_Ln(GDP p.c.)_|-0.147***|-0.133***|-0.148***|-0.149***|-0.154***|
||(0.040)|(0.044)|(0.045)|(0.048)|(0.049)|
|_Ln(Population)_|-0.254***|-0.244***|-0.257***|-0.240***|-0.253***|
||(0.029)|(0.031)|(0.031)|(0.031)|(0.032)|
|_Trade (% of GDP)_|-0.002**|-0.002*|-0.002**|-0.002**|-0.002*|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|_Distance_|-0.055***|-0.064***|-0.062***|-0.063***|-0.062***|
||(0.008)|(0.009)|(0.009)|(0.010)|(0.010)|
|_Affinity index_|0.469*|0.387|0.356|0.388|0.360|
||(0.271)|(0.296)|(0.298)|(0.298)|(0.298)|
|_Common religion_|-0.007|0.003|-0.001|0.001|0.008|
||(0.064)|(0.075)|(0.076)|(0.075)|(0.076)|
|_Common language_|0.165**|0.049|0.036|0.066|0.015|
||(0.079)|(0.105)|(0.104)|(0.104)|(0.105)|
|_Former colony_|-0.222***|-0.257***|-0.271***|-0.258***|-0.236**|
||(0.084)|(0.093)|(0.092)|(0.093)|(0.094)|
|_Ln(GDP p.c.) donor_|-0.159***|-0.150***|-0.160***|-0.156***|-0.173***|
||(0.048)|(0.048)|(0.048)|(0.048)|(0.051)|
|_Trade with donor_|0.124***|0.119***|0.142***|0.120***|0.188***|
||(0.014)|(0.015)|(0.017)|(0.015)|(0.023)|
|_Regulatory quality_||0.027|-0.184*|||
|||(0.069)|(0.106)|||
|_Trade with donor_×|||0.055***|||
|_Regulatory quality_|||(0.021)|||
|_Corruption control_||||0.102|-0.398**|
|||||(0.088)|(0.158)|
|_Trade with donor_×|||||0.117***|
|_Corruption control_|||||(0.028)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|ρ|0.973***|0.970***|0.950***|0.964***|0.918***|
||(0.130)|(0.132)|(0.132)|(0.135)|(0.140)|
|Constant|3.840***|3.735***|4.071***|3.957***|4.052***|
||(0.750)|(0.803)|(0.811)|(0.843)|(0.852)|
|Log likelihood|-7546.738|-6398.397|-6394.050|-6386.357|-6356.830|
|N|3284|2757|2757|2757|2757|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is bilateral, a dummy that switches to 1 if the aid flow was bilateral. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

21
||Table 8: Aid &|Bilateral TRA|DE – 1<sup>st</sup>stage|||
|---|---|---|---|---|---|
|)<br>|1<br>=<br>(<br>_Z_<br>_aid_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|_Ln(Fatalities)_|0.135***|0.128***|0.128***<br>|0.128***|0.136***|
||(0.008)|(0.008)|(0.008)<br>|(0.008)|(0.008)|
|_Ln(Affected)_|0.062***|0.055***|0.055***<br>|0.060***|0.057***|
||(0.003)|(0.004)|(0.004)<br>|(0.004)|(0.004)|
|_Ln(GDP p.c.)_|-0.197***|-0.205***|-0.205***|-0.149***|-0.161***|
||(0.017)|(0.021)|(0.021)<br>|(0.022)|(0.022)|
|_Ln(Population)_|-0.399***|-0.372***|-0.372***|-0.374***|-0.384***|
||(0.015)|(0.016)|(0.016)<br>|(0.016)|(0.016)|
|_Trade (% of GDP)_|-0.005***|-0.004***|-0.004***|-0.004***|-0.004***|
||(0.001)|(0.001)|(0.001)<br>|(0.001)|(0.001)|
|_Distance_|-0.050***|-0.053***|-0.052***|-0.052***|-0.053***|
||(0.004)|(0.004)|(0.004)<br>|(0.004)|(0.004)|
|_Affinity index_|-0.661***|-0.647***|-0.649***|-0.645***|-0.698***|
||(0.092)|(0.101)|(0.101)<br>|(0.101)|(0.102)|
|_Common religion_|0.130***|0.100***|0.100***<br>|0.103***|0.112***|
||(0.034)|(0.038)|(0.039)<br>|(0.039)|(0.039)|
|_Common language_|0.216***|0.212***|0.211***<br>|0.216***|0.190***|
||(0.048)|(0.057)|(0.057)<br>|(0.056)|(0.057)|
|_Former colony_|-0.227***|-0.226***|-0.225***|-0.226***|-0.208***|
||(0.051)|(0.054)|(0.054)<br>|(0.054)|(0.054)|
|_Ln(GDP p.c.) donor_|0.514***|0.516***|0.516***<br>|0.516***|0.515***|
||(0.017)|(0.018)|(0.018)<br>|(0.018)|(0.018)|
|_Pop. (in mio.) donor_|0.001***|0.001***|0.001***<br>|0.001***|0.001***|
||(0.000)|(0.000)|(0.000)<br>|(0.000)|(0.000)|
|_Trade with donor_|0.136***|0.126***|0.127***<br>|0.127***|0.176***|
||(0.008)|(0.009)|(0.010)<br>|(0.009)|(0.011)|
|_Regulatory quality_||0.018|0.001|||
|||(0.035)|(0.049)|||
|_Trade with donor_×|||0.005|||
|_Regulatory quality_|||(0.010)|||
|_Corruption control_||||-0.191***|-0.515***|
|||||(0.036)|(0.052)|
|_Trade with donor_×|||||0.085***|
|_Corruption control_|||||(0.010)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|Constant|1.209***|0.937**|0.935**<br>|0.397|0.445|
||(0.376)|(0.422)|(0.422)<br>|(0.418)|(0.421)|
|N|26585|22259|22259<br>|22259|22259|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is aid, a dummy that switches to 1 if the donor has made a contribution. ***, **, * indicate significance at the 1, 5 and 10%level, respectively.

22
||Table9: Ai|d & bilateral O|IL -CASH - 2 <sup>_n_</sup>|<sup>_d_</sup>stage||
|---|---|---|---|---|---|
|)<br>|1<br>=<br>(<br>_V_<br>_cash_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|_Ln(Fatalities)_|-0.060**|-0.042|-0.055*|-0.044|-0.043*|
||(0.023)|(0.027)|(0.028)|(0.027)|(0.026)|
|_Ln(Affected)_|0.014|0.008|0.001|-0.001|0.001|
||(0.009)|(0.010)|(0.011)|(0.010)|(0.010)|
|_Ln(GDP p.c.)_|-0.094*|-0.152**|-0.220***|-0.189***|-0.184**|
||(0.053)|(0.064)|(0.079)|(0.072)|(0.072)|
|_Ln(Population)_|0.045|0.028|0.027|0.030|0.031|
||(0.041)|(0.046)|(0.046)|(0.045)|(0.046)|
|_Trade (% of GDP)_|0.003**|0.002|0.002|0.003|0.003*|
||(0.001)|(0.002)|(0.002)|(0.002)|(0.002)|
|_Distance_|0.011|-0.017|-0.014|-0.017|-0.016|
||(0.013)|(0.015)|(0.015)|(0.015)|(0.015)|
|_Affinity index_|-0.129|-0.215|-0.243|-0.191|-0.183|
||(0.302)|(0.337)|(0.337)|(0.334)|(0.333)|
|_Common religion_|0.199**|0.175|0.170|0.149|0.147|
||(0.092)|(0.109)|(0.109)|(0.108)|(0.108)|
|_Common language_|-0.153|-0.289*|-0.277|-0.272|-0.276|
||(0.120)|(0.168)|(0.169)|(0.169)|(0.170)|
|_Former colony_|-0.369***|-0.553***|-0.557***|-0.551***|-0.567***|
||(0.129)|(0.166)|(0.166)|(0.168)|(0.164)|
|_Ln(GDP p.c.) donor_|-0.187***|-0.154**|-0.164**|-0.159**|-0.159**|
||(0.060)|(0.068)|(0.066)|(0.066)|(0.066)|
|_Fuel exports (% of_|-0.001|0.002|-0.004|0.001|-0.003|
|_merchandise exports)_|(0.002)|(0.002)|(0.004)|(0.002)|(0.005)|
|_Regulatory quality_||0.228*|0.390**|||
|||(0.119)|(0.157)|||
|_Fuel exports_×|||-0.008|||
|_Regulatory quality_|||(0.005)|||
|_Corruption control_||||0.302**|0.341**|
|||||(0.140)|(0.146)|
|_Fuel exports_×|||||-0.006|
|_Corruption control_|||||(0.007)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|ρ|-0.102|-0.044|-0.057|-0.052|-0.052|
||(0.141)|(0.158)|(0.154)|(0.153)|(0.153)|
|Constant|-0.170|0.448|1.257|0.994|0.901|
||(0.955)|(1.028)|(1.171)|(1.106)|(1.098)|
|Log likelihood|-6631.837|-5529.674|-5527.802|-5526.460|-5511.599|
|N|3158|2632|2632|2632|2632|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is cash, a dummy that switches to 1 if the bilateral aid contribution was cash. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

23
Table 10: Aid & bilateral TRADE -CASH – 2<sup>nd</sup> stage

|)<br>|1<br>=<br>(<br>_V_<br>_cash_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Ln(Fatalities)_|-0.035|-0.032|-0.033|-0.033|-0.039|
||(0.026)|(0.027)|(0.028)|(0.027)|(0.029)|
|_Ln(Affected)_|0.019**|0.008|0.007|0.003|-0.002|
||(0.010)|(0.010)|(0.010)|(0.011)|(0.011)|
|_Ln(GDP p.c.)_|-0.171***|-0.149**|-0.152**|-0.189***|-0.170**|
||(0.060)|(0.068)|(0.069)|(0.073)|(0.074)|
|_Ln(Population)_|-0.082|-0.051|-0.051|-0.048|-0.030|
||(0.066)|(0.069)|(0.071)|(0.070)|(0.075)|
|_Trade (% of GDP)_|0.002|0.002|0.002|0.002|0.002|
||(0.001)|(0.002)|(0.002)|(0.002)|(0.002)|
|_Distance_|0.018|-0.008|-0.005|-0.007|-0.001|
||(0.012)|(0.014)|(0.014)|(0.014)|(0.015)|
|_Affinity index_|-0.304|-0.307|-0.324|-0.325|-0.321|
||(0.294)|(0.322)|(0.323)|(0.322)|(0.320)|
|_Common religion_|0.249***|0.206**|0.203**|0.201**|0.201**|
||(0.086)|(0.101)|(0.101)|(0.101)|(0.101)|
|_Common language_|-0.134|-0.239|-0.253*|-0.224|-0.282*|
||(0.116)|(0.154)|(0.152)|(0.154)|(0.150)|
|_Former colony_|-0.431***|-0.543***|-0.549***|-0.544***|-0.516***|
||(0.125)|(0.153)|(0.153)|(0.154)|(0.156)|
|_Ln(GDP p.c.) donor_|-0.173**|-0.173**|-0.182**|-0.177**|-0.216***|
||(0.070)|(0.071)|(0.073)|(0.071)|(0.071)|
|_Trade with donor_|0.078***|0.049|0.057*|0.049|0.091**|
||(0.029)|(0.031)|(0.032)|(0.031)|(0.041)|
|_Regulatory quality_||0.009|-0.100|||
|||(0.097)|(0.147)|||
|_Trade with donor_×|||0.027|||
|_Regulatory quality_|||(0.029)|||
|_Corruption control_||||0.170|-0.266|
|||||(0.132)|(0.227)|
|_Trade with donor_×|||||0.094**|
|_Corruption control_|||||(0.038)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|ρ|0.052|-0.004|-0.026|-0.009|-0.134|
||(0.197)|(0.198)|(0.206)|(0.199)|(0.210)|
|Constant|1.650*|1.522|1.623|1.978*|1.920*|
||(0.980)|(1.053)|(1.058)|(1.106)|(1.100)|
|Log likelihood|-6908.274|-5846.983|-5846.317|-5784.745|-5784.063|
|N|3284|2757|2757|2757|2757|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is cash, a dummy that switches to 1 if the bilateral aid contribution was cash. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

24
Table 11: Aid & bilateral OIL – 2<sup>nd</sup> stage - Robustness Test OECD vs. NON-OECD

|)<br>|1<br>=<br>(<br>_X_<br>_bilateral_<br>_Pr_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||OE|CD||
|_Ln(Fatalities)_|-0.060***|-0.057***|-0.066***|-0.062***|
||(0.006)|(0.019)|(0.017)|(0.017)|
|_Ln(Affected)_|0.006|0.008|-0.012|-0.008|
||(0.007)|(0.008)|(0.008)|(0.008)|
|_Ln(GDP p.c.)_|-0.067|-0.053|-0.128*|-0.113|
||(0.064)|(0.078)|(0.069)|(0.070)|
|_Fuel exports (% of_|0.009***|0.011***|0.007***|-0.004|
|_merchandise exports)_|(0.002)|(0.004)|(0.002)|(0.005)|
|_Regulatory quality_|0.540***|0.504***|||
||(0.119)|(0.157)|||
|_Fuel exports_×||0.002|||
|_Regulatory quality_||(0.005)|||
|_Corruption control_|||0.550***<br>(0.134)|0.652***<br>(0.140)|
|_Fuel exports_×||||-0.017**|
|_Corruption control_||||(0.004)|
|||NON-|OECD||
|_Ln(Fatalities)_|-0.088**|-0.100**|-0.107**|-0.102**|
||(0.046)|(0.051)|(0.046)|(0.044)|
|_Ln(Affected)_|0.033*|0.022|-0.002|0.001|
||(0.017)|(0.024)|(0.018)|(0.019)|
|_Ln(GDP p.c.)_|-0.064|-0.157|-0.337|-0.290|
||(0.161)|(0.089)|(0.208)|(0.197)|
|_Fuel exports (%_|-0.000|-0.007|-0.003|-0.026**|
|_of merchandise exports)_|(0.004)|(0.010)|(0.003)|(0.011)|
|_Regulatory quality_|0.631**|0.886*|||
||(0.305)|(0.512)|||
|_Fuel exports_×||-0.009|||
|_Regulatory quality_||(0.014)|||
|_Corruption control_|||0.901**|1.424***|
||||(0.372)|(0.396)|
|_Fuel exports_×||||-0.035**|
|_Corruption control_||||(0.017)|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is bilateral, a dummy that switches to 1 if the aid flow was bilateral. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

25
Table 12: Aid & bilateral TRADE – 2<sup>nd</sup> stage - Robustness Test OECD vs. NON-OECD

|)<br>|1<br>=<br>(<br>_X_<br>_bilateral_<br>_Pr_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||OE|CD||
|_Ln(Fatalities)_|-0.072***|-0.070***|-0.074***|-0.070***|
||(0.016)|(0.016)|(0.016)|(0.017)|
|_Ln(Affected)_|0.006|0.007|0.001|-0.000|
||(0.006)|(0.006)|(0.007)|(0.007)|
|_Ln(GDP p.c.)_|-0.058|-0.070|-0.111*|-0.108*|
||(0.061)|(0.061)|(0.065)|(0.065)|
|_Trade with donor_|0.106***|0.135***|0.107***|0.153***|
||(0.021)|(0.024)|(0.021)|(0.030)|
|_Regulatory quality_|-0.057|-0.332**|||
||(0.086)|(0.134)|||
|_Trade with donor_×||0.069**|||
|_Regulatory quality_||(0.028)|||
|_Corruption control_|||0.234**|-0.148|
||||(0.117)|(0.216)|
|_Trade with donor_×||||0.081**|
|_Corruption control_||||(0.038)|
|||NON-|OECD||
|_Ln(Fatalities)_|-0.077**|-0.074*|-0.088**|-0.080*|
||(0.041)|(0.041)|(0.046)|(0.044)|
|_Ln(Affected)_|0.022|0.025|-0.013|-0.022|
||(0.015)|(0.016)|(0.016)|(0.016)|
|_Ln(GDP p.c.)_|-0.057|-0.062|-0.354*|-0.335*|
||(0.140)|(0.145)|(0.190)|(0.192)|
|_Trade with donor_|-0.022|0.027|-0.030|-0.113|
||(0.036)|(0.051)|(0.035)|(0.075)|
|_Regulatory quality_|0.432**|0.205|||
||(0.194)|(0.278)|||
|_Trade with donor_×||0.082|||
|_Regulatory quality_||(0.066)|||
|_Corruption control_|||1.053***<br>(0.355)|0.294<br>(0.464)|
|_Trade with donor_×||||0.196**|
|_Corruption control_||||(0.088)|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is bilateral, a dummy that switches to 1 if the aid flow was bilateral. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

26
Table 13: Aid & bilateral OIL - CASH- Robustness Test OECD vs. NON-OECD

|)<br>|1<br>=<br>(<br>_V_<br>_cash_<br>_Pr_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||OE|CD||
|_Ln(Fatalities)_|-0.079***|-0.091***|-0.079***|-0.079***|
||(0.019)|(0.020)|(0.019)|(0.019)|
|_Ln(Affected)_|0.017**|0.009|0.009|0.012|
||(0.008)|(0.009)|(0.009)|(0.009)|
|_Ln(GDP p.c.)_|-0.170***|-0.244***|-0.181***|-0.172**|
||(0.063)|(0.077)|(0.067)|(0.068)|
|_Fuel exports (% of_|0.003|-0.004|0.001|-0.008*|
|_merchandise exports)_|(0.002)|(0.004)|(0.002)|(0.005)|
|_Regulatory quality_|0.299***|0.475***|||
||(0.112)|(0.145)|||
|_Fuel exports_×||-0.010*|||
|_Regulatory quality_||(0.005)|||
|_Corruption control_|||0.264**<br>(0.124)|0.347***<br>(0.128)|
|_Fuel exports_×||||-0.014**|
|_Corruption control_||||(0.007)|
|||NON-|OECD||
|_Ln(Fatalities)_|-0.006|-0.023|-0.008|-0.005|
||(0.048)|(0.051)|(0.049)|(0.051)|
|_Ln(Affected)_|-0.000|-0.017|-0.013|-0.019|
||(0.017)|(0.026)|(0.023)|(0.024)|
|_Ln(GDP p.c.)_|0.161|0.012|-0.020|-0.008|
||(0.152)|(0.208)|(0.199)|(0.196)|
|_Fuel exports (%_|-0.010*|-0.019*|-0.007|0.005|
|_of merchandise exports)_|(0.005)|(0.011)|(0.005)|(0.025)|
|_Regulatory quality_|-0.125|0.173|||
||(0.305)|(0.408)|||
|_Fuel exports_×||-0.013|||
|_Regulatory quality_||(0.013)|||
|_Corruption control_|||0.480|0.360|
||||(0.426)|(0.437)|
|_Fuel exports_×||||0.021|
|_Corruption control_||||(0.017)|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is cash, a dummy that switches to 1 if the bilateral aid contribution was cash. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

27
Table 14: Aid & bilateral TRADE-CASH - Robustness Test OECD vs. NON-OECD

|)<br>|1<br>=<br>(<br>_V_<br>_cash_<br>_Pr_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||OE|CD||
|_Ln(Fatalities)_|-0.065***|-0.064***|-0.064***|-0.060***|
||(0.018)|(0.018)|(0.018)|(0.018)|
|_Ln(Affected)_|0.016**|0.017**|0.013|0.013*|
||(0.008)|(0.008)|(0.008)|(0.008)|
|_Ln(GDP p.c.)_|-0.167***|-0.170***|-0.187***|-0.180***|
||(0.062)|(0.062)|(0.066)|(0.066)|
|_Trade with donor_|0.063***|0.068***|0.064***|0.105***|
||(0.023)|(0.024)|(0.023)|(0.027)|
|_Regulatory quality_|0.082|0.009|||
||(0.098)|(0.172)|||
|_Trade with donor_×||0.015|||
|_Regulatory quality_||(0.030)|||
|_Corruption control_|||0.143|-0.285|
||||(0.115)|(0.215)|
|_Trade with donor_×||||0.077**|
|_Corruption control_||||(0.032)|
|||NON-|OECD||
|_Ln(Fatalities)_|0.010|0.019|0.000|0.022|
||(0.042)|(0.043)|(0.044)|(0.048)|
|_Ln(Affected)_|-0.021|-0.019|-0.033|-0.047**|
||(0.019)|(0.020)|(0.022)|(0.024)|
|_Ln(GDP p.c.)_|0.129|0.081|-0.052|-0.065|
||(0.145)|(0.150)|(0.181)|(0.180)|
|_Trade with donor_|-0.050|0.010|-0.090|-0.080|
||(0.043)|(0.056)|(0.065)|(0.064)|
|_Regulatory quality_|-0.210|-0.554**|||
||(0.231)|(0.270)|||
|_Trade with donor_×||0.130*|||
|_Regulatory quality_||(0.073)|||
|_Corruption control_|||0.457|-0.161|
||||(0.372)|(0.504)|
|_Trade with donor_×||||0.196**|
|_Corruption control_||||(0.092)|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is cash, a dummy that switches to 1 if the bilateral aid contribution was cash. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

28
Table 15. Variable Definition and Source

|**Variable**|**Description**|**Source**|
|---|---|---|
|Fatalities|Total number killed by a natural disaster|EM-DAT, CRED (2008)|
|Affected|Total number affected by a natural disaster|EM-DAT, CRED (2008)|
|Disaster dummies|Describe which type of natural disaster occurred.|EM-DAT, CRED (2008)|
|Emergency aid|Dummy variables describing the channel<br>(bilateral vs. multilateral) and type (cash vs.<br>in-kind) of emergency relief|FTS, OCHA (2009)|
|GDP|Real GDP per capita (US Dollars in 2000 prices)|Penn World Table Version 6.2|
|POP|Total Population expressed in thousands|World Bank,<br>World Development Indicators|
|Distance|Distance between donor's and recipient's<br>capitals|World Bank,<br>World Development Indicators|
|Trade|Exports plus Imports of goods and<br>services divided by GDP|World Bank,<br>World Development Indicators|
|Corruption<br>control|Perception of the extent to which public power is<br>exercised for private gain, including<br>both petty and grand forms of corruption, as well<br>as influence of elites.|Kaufmann et al. (2008)|
|Regulatory<br>quality|Perception of the ability of the government to<br>formulate and implement sound policies and<br>regulations that permit and promote private<br>sector development.|Kaufmann et al. (2008)|
|Governance|Replaces either Corruption control<br>or Regulatory quality in Table 16|Kaufmann et al. (2008)|
|Democracy|Proxy for the political system in a country.<br>Includes information on competitiveness and<br>openness of executive recruitment, constraints<br>on chief executive, regulation and competitiveness<br>of participation.|Marshall & Jaggers<br>(2005)|
|Affinity index|Extended Gartzke index on voting patterns<br>in the UN General Assembly. Takes a value<br>between -1 (donor and recipient never voted the<br>same and 1 donor and recipient always voted the<br>same.|Voeten and Merdzanovic (2009)|
|Former colony|Dummy variable<br>1 = recipient was once donor's colony|Correlates of War<br>2 Project (2008)|

29