metadata: authors: 0: author: Raschky,Paul A.
1: author: Schwindt,Manijeh
count: World
docna: 0: docna: Aid, natural disasters and the
            samaritan's dilemma
docty: Policy Research Working Paper
subtopic: Hazard Risk Management,Poverty Reduction Strategies,Social Risk Management,Law and Justice Institutions,Natural Disasters,Disaster Management
teratopic: Conflict and Development,Environment,Urban Development,Health, Nutrition and Population
abstracts: cdata!: This paper discusses the impact of
            foreign aid on the recipient country's preparedness
            against natural disasters. The theoretical model shows that
            foreign aid can have two opposing effects on a
            country's level of mitigating activities. In order to
            test the theoretical propositions, the authors analyze the
            effect of foreign aid dependence on ex-ante risk-management
            activity proxied by the death toll from major storms, floods
            and earthquakes occurring worldwide between 1980 and 2002.
            They find evidence that the crowding-out effect of foreign
            aid outweighs the preventive effect in the case of storms,
            while there is mixed evidence in the case of floods and earthquakes.
repnme: repnme: Aid, natural disasters and the
            samaritan's dilemma
display_title: Aid, natural disasters and the
            samaritan's dilemma
listing_relative_url: /research/2009/06/10628776/aid-natural-disasters-samaritans-dilemma
topicv3: Disaster Preparedness m1327179 1705,Earthquake m1503308 3154,Flood m1503315 3216,Income Inequality m1503891 2668,Mortality m1504365 3661
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Conflict and Development,Environment,Urban Development,Health, Nutrition and Population
no_of_pages: 43
author_keys: 0: author_key: 1006515
1: author_key: 1006514
model_extractions: WPS4952 Policy Research Working Paper 4952 Aid, Natural Disasters and the Samaritan’s Dilemma _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 4952

# **Abstract**

This paper discusses the impact of foreign aid on the recipient country's preparedness against natural disasters. The theoretical model shows that foreign aid can have two opposing effects on a country's level of mitigating activities. In order to test the theoretical propositions, the authors analyze the effect of foreign aid dependence on ex-ante risk-management activity proxied by the death toll from major storms, floods and earthquakes occurring worldwide between 1980 and 2002. They find evidence that the crowding-out effect of foreign aid outweighs the preventive effect in the case of storms, while there is mixed evidence in the case of floods and earthquakes.

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 thank Jesus Crespo-Cuaresma, Carol Heller, Roland Hodler, Phil Keefer, Howard Kunreuther, Susanne Neckermann, Harald Oberhofer, Apurva Sanghi, Mark Schelker, Reimund Schwarze and Hannelore Weck-Hannemann as well as participants of a workshop on political economy in Zurich 2007 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
Aid, Natural Disasters and the Samaritan’s Dilemma Paul A. Raschky* Manijeh Schwindt* _Keywords_ : Foreign Aid, Samaritan's Dilemma, Natural Hazards _JEL classification_ : O17, O19, Q54

***** Institute of Public Finance, University of Innsbruck, Universitaetsstrasse 15, A-6020 Innsbruck and alpS - Center for Natural Hazard Management, Innsbruck, Austria. Emails: <u>paul.raschky@uibk.ac.at; manijeh.schwindt@uibk.ac.at. This paper –a product of the Global Facility for</u> 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 thank Jesus Crespo-Cuaresma, Carol Heller, Roland Hodler, Phil Keefer, Howard Kunreuther, Susanne Neckermann, Harald Oberhofer, Apurva Sanghi, Mark Schelker, Reimund Schwarze and Hannelore Weck-Hannemann as well as participants of a

workshop on political economy in Zurich 2007 for their useful comments.
# **1  Introduction**

The debate among economic scholars whether foreign aid is a boon or a bane for developing countries has been going on for decades. This paper further augments the existing discussion by analyzing the effects of foreign aid on the recipient country's capacity to deal with natural hazards such as floods, earthquakes and windstorms. Interestingly, less developed nations do not necessarily experience more natural disaster events, but the death toll from disasters is on average higher in poorer nations (Kahn 2005). Economic scholars explain these differences in vulnerability through the level of development (Kahn 2005, Strömberg 2007), institutional quality (Kahn 2005), the level of democracy (e.g. Tavares 2004) or income inequality (Anbarci, Escaleras & Register 2005). The role of foreign aid, however, has only received minor attention so far (e.g. Strömberg 2007, Cohen & Werker 2008).

In general, foreign aid can have two opposing effects on the recipient nation's disaster preparedness: First, aid flows can have a _preventive effect_ by directly or indirectly improving a nation's preparedness against natural hazards. In recent years, a number of international initiatives emerged that try to implement disaster preparedness (e.g. awareness-building, installation of monitoring systems, structural measures) in overall aid policies (OCHA 2008). Additionally, general aid flows in infrastructure or social sector projects can create positive externalities for the recipient country's disaster preparedness. An improved telecommunication and transport system for example, facilitates early-warning and evacuation. Better housing structures make communities more resilient against the forces of nature.

Second, while the natural hazard itself is an exogenous shock, the human reaction to such an event is driven by incentives as a result of millions of dollars yearly spent on disaster relief. Foreign aid received in the past might increase the predictability of ex-post relief and induce decision-makers to shirk responsibilities by reducing ex-ante protection activities. Such a reaction could result in higher financial losses and higher death tolls. For this reason, the disincentives induced by the large amount of relief are likely to further exacerbate the sustainable development of regions that are especially vulnerable to large scale disasters. The research focus in our paper is on this so called _crowding-out effect_ of foreign aid.

Ex-ante risk management strategies against large-scale disasters are generally not implemented by individuals voluntarily, since individuals' incentives for protection against disasters are distorted by a lack of interest and information as well as an underestimation of risk occurrence (Kunreuther & Pauly 2006). This public-good problem explains that the vast majority of protective measures (e.g. structural measures, building codes, zoning, early warning) are in the realm of governments and bureaucratic agencies. The ability to provide these public goods is influenced by the level of GDP (Kahn 2005), income inequality (e.g. Anbarci et al. 2005) or the level of heterogeneity among groups in society (e.g. Alesina & Drazen 1991).

These factors can help to explain the large number of fatalities following natural disasters, especially in developing countries. To limit the destabilization following a disaster, ad-hoc disaster relief from international organizations, national governments, non-governmental organizations as well as private donors is usually paid to the affected countries. Anecdotal evidence also suggests an increase in official development assistance (ODA) in order to ensure long term recovery from the disaster. In 1998 Hurricane "Mitch" devastated large parts of Central 2
America. In Honduras alone the death toll reached 5,600 and the estimated financial losses were $ 3.8 billion. The international community responded to this disaster by increasing the official development assistance from $ 361 million (1997) to $ 1.7 billion (1999) and the annual ODAinflows still remain at a relatively higher level than prior to 1998 ($ 640 million) (Bermeo 2007). There is no doubt that international assistance after such a devastating event was strongly needed. However, these ex-post payments could set ex-ante incentives that crowd out the willingness of political agents to put effort in sufficient preventive action. Based on the conclusion from the Samaritan's Dilemma, the anticipation of charity in the case of a large-scale disaster, might induce governments to diminish protection (Buchanan 1975, Coate 1995), since "(...) current decisions of economic agents depend in part upon their expectations of future policy actions." ( p.474, Kydland & Prescott 1977). Note, that the argumentation in this paper is limited to collective action, not including a crowding-out effect induced by individuals shirking responsibilities. A prominent example is the failure on the insurance market: Moral hazard and adverse selection present only a partial explanation for imperfections in the market for natural hazard insurance. Kunreuther (2000) defined the situation of distorted demand and insufficient supply on the market for natural hazard insurance as _disaster syndrome_ . Individuals tend to underinsure because a) they underestimate the risk of low-probability-high-loss events and b) they expect financial relief by the government or private charity. Private charity and governmental relief is a premium-free insurance against natural hazards. If a disaster occurs, individuals without market insurance are better off because they receive financial support from governmental relief programs. The theoretical model by Raschky & Weck-Hannemann (2007) shows that a higher degree of institutionalization of governmental relief or charity further decreases individual demand for insurance and increases the reliance on aid in a disaster situation. The phenomenon of _charity hazard_ (Browne & Hoyt 2000) could also apply to international disaster assistance.

This problem has already been identified in the foreign aid literature in another context as a consequence of producing disincentives through the provision of foreign aid (Gibson, Andersson, Ostrom & Shivakumar 2005). Foreign aid is often paid in order to close the financial gap between the required investment which is necessary to achieve a targeted growth rate and the available resources. The results of Easterly (1999) suggest that the provision of foreign aid based on the financial gap induces recipient countries to reduce savings and hence to increase the financial gap even further. In addition, the work by Cashel-Cordo & Craig (1990) and Khan & Hoshino (1992) provide evidence that international aid might have a negative effect on public-sector fiscal behavior in the recipient countries. In the natural hazard context, the theoretical model developed by Cohen & Werker (2008) suggests that the presence of ex-post relief can distort the relation of ex-ante protection to ex-post relief. However, the model we construct goes one step further by showing that the anticipation of foreign aid can result in a higher death toll from natural disasters. Moreover, to our knowledge, the connection between foreign aid and its influence on the effectiveness of preventive capacity has, so far, not been empirically analyzed.

The remainder of the paper is structured as follows: The next section recapitulates the basic idea of Buchanan’s Samaritan’s Dilemma. Section 3 establishes a theoretical model to analyze the interaction between donor and recipient in the context of natural hazard prevention. Section 4 presents our dataset. In section 5, we test the hypothesis on three different types of natural disasters. Sections 6 and 7 present the results and conclude with suggestions on the redeployment of foreign aid in order to minimize the adverse crowding-out effect.

3
# **2 The Samaritan’s Dilemma**

Buchanan (1975) introduced the term Samaritan’s Dilemma in economics in order to show that altruism can induce adverse behavior of potential recipients based on the failure of the donor to act strategically.

In order to get a better understanding of the pervading effect, we adapt the two-by-two game as used by Buchanan (1975) of a donor and a recipient.<sup>1</sup>

**Figure 1: The Active Samaritan's Dilemma**

|||**Reci**|**pient**|
|---|---|---|---|
|||**High effort**|**Low effort**|
|**D**|**No aid**|2,2|1,1|
|**onor**|**Aid**|4,3|3,4|

The comparison of the pay-offs shows that the donor will always help, independent of the level of the recipient’s effort. This situation is refered to as the active Samaritan’s Dilemma. However, the donor would prefer the recipient putting in high effort (donor’s pay-off is 4) as compared to the recipient putting in low effort (donor’s pay off is 3). Since the recipient knows that the donor’s dominant strategy is to help, he will act strategically and respond by choosing a low level of effort. Hence, the Nash equilibrium in pure strategies is the pay-off combination (3,4).

Apart from the active Samaritan’s Dilemma, Buchanan constructed the situation of the passive Samaritan’s Dilemma, where helping is not the donor’s dominant strategy. In fact, the donor reacts to high effort by not helping and to low effort by helping.

**Figure 2: The Passive Samaritan's Dilemma**

|||**Reci**|**pient**|
|---|---|---|---|
|||**High effort**|**Low effort**|
|**D**|**No aid**|4,2|1,1|
|**onor**|**Aid**|2,3|3,4|

Now, the donor prefers the situation where the recipient puts in high effort and the donor does not assist (donor’s pay off is 4), whereas the recipient prefers to put in low effort and to get assistance of the donor. The game has two equilibria, the pay off combinations (4,2) and (3,4). However, even in this situation, a strategic acting recipient chooses a low level of effort and forces the donor into pay-off combination (3,4).

In both cases, the dilemma of the Samaritan is his exploitation due to his inability to commit not to help. Since the donor cannot reach the prefered pay-off, the burden is fully on his side. Also the Samaritan’s Dilemma is interlinked with moral hazard behavior it is important to distinguish between the two terms. Knowing or expecting to receive assistance from the donor induces the

> 1 See also Gibson et al. (2005) and Schmidtchen (1999)

4
receipient to shirk responsibilities and to put in low effort, this is known as moral hazard. As we will see in the following the consequences of this behavior can be devastating for the recipient.

In the following we will adapt the Samaritan’s Dilemma to an international context, with a donor and a recipient country. Note, that our analysis varies in two major points from Buchanan’s example. First, decision makers in our context are governments, not individuals. Hence, moral hazard behavior takes place on a collective level rather than on an individual level. Second, the games presented above are simultaneous games, whereas our case can be interpreted as a Stackleberg game (Coate 1995).

# **3  The Model**

This section aims to construct a theoretical model, which allows the derivation of the hypothesis, necessary for the empirical analysis of a potential effect of foreign aid on the degree of protection. We first give an intuitive description of the theoretical story behind the model and then present the formal model itself.

## **3.1  Intuitive description**

We imagine a world with a rich donor and a poor recipient country. The donor country receives utility from the recipient country's utility, which has resulted in foreign aid flows in the past. The recipient country is in danger of natural hazards. Past foreign aid flows can, in part, have a preventative effect on the vulnerability of the recipient country and increase the probability that wealth and human beings survive a natural hazard, if a) disaster preparedness measures are implemented in the foreign aid initiative of the past or if b) aid flows are used for measures which create positive externalities, hence contribute indirectly to improved disaster preparedness e.g. infrastructure. However, the recipient country itself can install protection as well, by imposing a proportional tax rate. This tax has two opposing effects on the expected utility of a representative person in the recipient country. On the one hand it contributes to a higher probability of surviving a disaster on the other hand it reduces consumption possibilities. Apart from the above described preventative effect, foreign aid flows of the past can have two negative effects on the probability of surviving, both of which are due to a reduction of the tax rate. First, the fraction of past foreign aid which contributes to a higher survival probability is interchangeable with the tax rate. Hence, aid receiving governments might decide to reduce the fungible tax rate and rely on the fraction of past foreign aid which contributes to prevention instead. In order to get a better idea of this problem of fungibility, imagine a government that strives to reach a certain level of protection. Since past foreign aid partly provides protection, the aspired level of protection can be obtained with a lower level of the tax rate. Second, foreign aid experiences in the past might increase the predictability of ex-post charity and induce the government of the recipient country to shirk responsibilities. Since a higher tax rate increases the survival probability, the need for disaster relief in case of a natural hazard is reduced. Hence, in the bad state the recipient country does not have an incentive to increase the tax rate because a higher level of protection reduces ex-post charity. In other words, the anticipation of foreign aid crowds out the tax rate. Moreover, in the good state, protection measures reduce the utility of a representative person in the recipient country by lessening consumption possibilities. Thus, the anticipation of disaster relief reduces the incentive to implement protection measures (crowding-out). The net outcome of the positive preventative effect and the two negative effects of past foreign aid can result in a lower survival 5
probability if the negative effects outweigh the preventative effect.

# **3.2  Formal model**

We assume a potential "donor country", _D_ and a "recipient country" _R_ , each with identical individuals earning an income of _YD_ and _YR_ respectively ( _YD_ > _YR_ ) .

The recipient country is in danger of a natural hazard, which occurs with probability _p_ , 0 ≤ _p_ ≤ 1. There are two factors that can reduce the impact of natural hazards on society: First, the recipient country is assumed to have experienced foreign aid in the past. This transfer increases the utility of a representative individual in country _R_ by a) increasing the consumption possibilities and b) contributing - directly or indirectly - to a better risk prevention. In the following, we use the term preventive effect for the latter. Second, the government of country _R_ can choose a level of protection, which is financed by a proportional tax rate τ , with τ ∈ (1,0 _YR_ ) by maximizing the utility of a representative person. Hence, the probability that wealth (e.g. capital stock) as well as human beings survive a natural hazard is determined by the tax revenue τ _YR_ as well as the fraction, α , of foreign aid p.c., _T_ , inducing the preventive effect, with The idea to analyze survival probability stems from the model developed by Anbarci et al. (2005) and is, as we will demonstrate in section 4, important for our empirical applications.<sup>2</sup> We assume that the fraction α is exogenous. The government's choice of τ is subject to a trade-off: On the one hand a rise in the tax rate reduces the disposable income and therefore the consumption possibilities, on the other hand it increases the probability of surviving a natural hazard. To be more precise, there exists a threshold level, τ<sup>~</sup> , below which the positive effect of a higher probability of surviving outweighs the utility reduction due to a lower level of consumption, whereas for τ > τ ~ the negative effect dominates. In the following we will focus on tax levels ' smaller than τ<sup>~</sup> . In this relevant spectrum, we can observe a utility function with, _U R_ ( τ ) > 0 and '' _U R_ < 0 . Apart from the preventive effect, past foreign aid can have a negative effect on the survival probability due to the fungibility of the tax rate. This negative effect on the tax rate  ∂τ  stems directly from the interchangability of past foreign aid  < 0  . In the following, we call  ∂ _T_  this the fungibility effect.

Country _D_ receives utility from consumption _C_ and from country _R_ 's utility _U R_ , _U D_ = _C_ + δ _U R_ , with δ > 0 . A positive value of δ can be the result of altruism as well as selfinterest of the donating country due to strategic considerations. The incorporation of the recipient country's utility level in the utility of the donating country could have a second effect on the

> 2 Note that our model differs in three major points from the application of Anbarci et al. (2005). First, survival probability is not only determined by the tax revenue but also by foreign aid received in the past. Second, we do not account for income inequality within a country since this is not our main focus. Needless to say that we control for income inequality in the empirical application. Third, as mentioned above, we believe that the majority of protection measures in fact been used, are described by the characteristics of public goods and financed by taxes.

6
recipient country's choice of the tax rate τ , if we account for ex-post relief in case of a disaster. That is, due to foreign aid experiences in the past, the government of the recipient country might anticipate the possibility of ex-post disaster relief ω and interpret foreign aid experiences of the past as implicit insurance for the future. Hence, past foreign aid experiences do not only have a positive preventive and a negative fungibility effect on the survival probability, but also an adverse crowding-out effect. In the following, we will first derive the crowding-out of the tax rate τ and second determine the net effect on survival probability _q_ .

Our model is mainly based on Coate (1995). However, there are two fundamental differences. First, while the analysis of Coate (1995) mainly focuses on a Samaritan's Dilemma problem between individuals on a national level, the aim of this work is to investigate the existence of the Samaritan's Dilemma on an international level, where the aid-receiving government shirks responsibilities. Second, Coate (1995) uses three groups of actors, namely two rich individuals, a poor individual and the government, with the latter trying to maximize the welfare of the rich by providing an ex-ante payment to the poor person. Hence, he assumes an ex-ante payment which is primary of strategic nature and aims to reduce the ex-post payment, which is the Samaritan's Dilemma problem of the rich. However, we assume that the ex-ante payment does not have any influence on the ex-post decision, because our focus is not to analyze how the ex-ante payment affects ex-post relief, but rather how it affects the level of protection chosen by the recipient country's government through the preventive as well as the two negative effects.

The intuitive explanations illustrate that both the donor and the recipient country's behavior are relevant to derive the forces which impact the value of the tax rate τ necessary for the implementation of protection measures. Using backward induction, we first focus on the behavior of the donor country. Once, the bad state for the recipient country arises and given the level of the tax rate τ , the reaction function of the rich country is

> with _q_ = _YR_ + α _T_ . The optimal ex-post emergency aid is implicitly defined by the following first-order-condition, which states that the marginal benefit of the donor country, which results from the higher utility of the recipient country, must equal the marginal costs of the donor country due to a lower level of consumption.

In order to derive how the donating country's choice of ex-post aid adjusts to changes of the poor country, we have to discuss the following expression: The denominator of the expression is negative if we assume that the impact of the ex-post transfer on the utility is comparable to the impact of income. A given level of transfer induces increases in the utility level, but the effect is smaller for higher levels of initial utility.

7
The sign of the nominator is now essential for the determination of the adjustment of the donating country's ex-post transfer to variations of the tax rate undertaken by the recipient country's government. A positive nominator would imply that a rise of the tax rate τ would increase the marginal utility of the ex-post transfer (prevention and relief are complements), whereas a negative nominator would suggest that an increase in the tax rate would lead to a decrease of the marginal utility of the ex-post transfer (prevention and relief are substitutes). If the increase of the tax rate has no influence on the marginal utility of the ex-post transfer the nominator reaches the value of zero. Cohen & Werker (2008) point out the importance of this distinction: Prevention and ex-post relief being complements would imply that the donating country would increase the level of aid for higher levels of protection. In contrast, a substitutability of prevention and aid, suggests that the donating country reacts on higher levels of protection by cutting the level of expost relief. We believe that both the tax rate and the ex-post payment increase the ability to cope with the potential consequences of natural hazards and that these contributions are largely substitutable, since, in the majority of cases, relief is even productive when protection measures are not implemented. Hence, given the substitutability of prevention and aid, the expression above is negative and states that the donor country will reduce the level of ex-post relief for increasing values of τ .

The government of the recipient country is assumed to maximize the expected utility of a representative consumer by taking into account the donor country's reaction function (4).

We first analyze how changes in the tax rate τ affect the recipient country's utility in the bad state. For ω > 0 , increases in the value of the tax rate which contribute to a higher level of protection have no influence on the recipient country's utility in the bad state, since higher tax rates will, ceteris paribus, increase the survival probability and hence reduce the need for ex-post charity. The anticipation of foreign aid is perfectly crowding-out the tax rate τ , necessary for protection measures. In the good state however, protection measures reduce the utility of the recipient country, since the tax rate reduces the disposable income without contributing to a higher probability of surviving. Thus, allowing for the possibility of ex-post charity, provides an incentive for the aid receiving country to reduce protective measures (crowding-out effect).

The survival probability _q_ in the bad state is determined by the positive preventive effect and the two negative effects.

From equation (6) we can see that foreign aid received in the past will result in a lower survival probability, if the sum of crowding-out and fungibility effect outweighs the preventive effect.

# **4  The Data**

8
We test the propositions of our theoretical model on three different types of natural disasters: storms, floodings and earthquakes. Table 1 provides an overview of the countries in each disaster sub-sample. Our sample of nations is very similar to the sample applied in Kahn (2005)<sup>3</sup> . In their empirical analysis of the impact of disasters on long-term growth, Skidmore & Toya (2002) point out the importance to distinguish between different types of disasters. They argue that climatic disasters (e.g. storms, floods) are more easily forecast and therefore evacuation or taking cover is easier. In comparison, geologic disasters (e.g. earthquakes) are harder to predict. The former are more of a threat to property while the latter type of disasters are a threat to both property and life. They find that climatic disasters have a positive impact on long-run growth, while geologic disasters decrease growth in the long-run.

# [Table 1 about here]

Apart from the differences in the effect on economic development, different types of natural hazards require different forms of protective measures. Although activities in natural hazard management show common features (e.g. public good character of enforcing zoning and building codes) they might differ in terms of affordability and duration. For example, the construction of earthquake resistant buildings is very cost-intensive and cannot be afforded by the majority of individuals living in developing countries. Forecasts for storms, cyclones or other climatological events are often provided by international agencies and communicated to national governments. If these governments forward a warning to the people at risk, lifes can be saved at relatively low costs. Therefore, we build three subsamples for each type of natural hazard instead of simply pooling observations from all natural hazards.

We compiled our dataset from a number of sources4: The data on disaster victims, major disaster, magnitude and the number of people effected are taken from the most comprehensive data set on disasters, the EM-DAT by the Center for Research on the Epidemiology of Disasters (CRED) in Brussels. EM-DAT has collected around 12,000 reports of different disasters, such as floods, storms, earthquakes, volcanic eruptions, landslides as well as man-made disasters. A natural disaster has to fulfill at least one of the following criteria in order to be included in the database: 10 or more people reported killed, 100 people reported affected, declaration of a state of emergency, call for international assistance. Therefore, disasters that occurred in thinly populated areas are not included in the database and in the analysis. Information on the magnitude of storms (wind speed in kph) and earthquakes (Richter scale) also stems from this database. Data on the magnitude of major flood events (i.e. flooded area in km ) are only available for a small number of flood disasters in the dataset. We therefore obtained historical data on precipitation from the Global Historical Climatology Network by the National Oceanic and Atmospheric Administration (NOAA). The data is collected from thousands of land stations. The earliest entry of a country's monthly mean precipitation ranges back to 1697 (United Kingdom). We used the number of months in year and country when precipitation was larger than one standard deviation above the long-term mean of precipitation in a given month as an additional explanatory variable in our estimations.

> 3 We excluded Israel and Egypt from our sample due to these countries' special position in U.S. foreign aid policy (see Alesina & Dollar 2000).

> 4 See Table 17 for a description of the data and the sources ́.

9
Data for our main variable of interest, foreign aid p. c., stem from the World Development Indicators (WDI). Although EM-DAT provides figures on international disaster assistance for some major earthquake events, there is no comprehensive collection on financial disaster aid that compiled such data in a concise manner. The study by stroemberg07a used data on U.S. disaster assistance and its determinants. In contrast, we assume that expectations about international disaster relief are formed by general aid dependency. Therefore we use figures on overall development aid. National data on real GDP per capita and openness for trade are taken from the Penn World Table Version 6.2. Regarding data on income inequality, one could consider the largest collection of data on Gini coefficients is the World Income Inequality Database (WIID, 2000). This data has been already used in the empirical analysis on disaster victims by Strömberg (2007). The problem with measuring income inequality over time and between countries is that the concepts and units of income change thus raising questions on the consistency and quality of the data used Strömberg (2007). To circumvent potential problems and biases, Grün & Klasen (2003) perform a regression-based adjustment to make the Gini-coefficients more comparable. We use the data from that study for our analysis.

Our measure for institutional quality was compiled by Kaufmann, Kraay & Mastruzzi, (2008) for the Worldbank. Institutional quality is defined via six dimensions of governance: Control of Corruption, i.e. the extent to which political power is misused for private benefits. Governmental effectiveness, i. e. the quality and credibility of public and civil service and the independence from political pressure. Political stability, i.e. the probability that the current government will be overthrown by a coup d' tat. Regulatory quality, i.e. the government's ability to design and enforce sound regulations and promote private sector development. Rule of law, i.e. the protection of property rights and the quality of contract enforcement, the police and the courts. Voice and accountability, i.e. freedom of expression and association as well as the possibility to actively participate in the selection of the government. Our second proxy measure for institutional quality is a country's democracy level. We use the Polity 2 variable, which ranges between -10 (autocratic) and 10 (democratic). Information on a nation's colonial background is provided by Correlates of War 2 Project (2008).

As additional controls, we introduce the general mortality risk from each natural disaster. A recent study by Columbia University and the World Bank (Dilley, Chen, Deichmann, LernerLam, Arnold, Agwe, Buys, Kjekstad, Lyon & Yetman 2005) uses spatial data on historical disaster occurrence and population density and constructs a measure of the geographical distribution of mortality risk from natural disasters worldwide. In the appendix Figures 3, 5 and 7 show the regions with storm, flood and earthquake mortality risk in the world, respectively and Figures 4, 6 and 8 are maps focused on each risk in Asia in order to get a better idea of the resolution of the data. The darker the grid cells, the larger the mortality risk on a 10-point scale. We use this GIS-data and combine it with a shape file of national boundaries to calculate a country's mean exposure to disaster mortality. Data for additional geographical controls (latitude and elevation) stem from the dataset provided by (Kahn 2005). Table 2 reports the mean, standard deviation, minimum and maximum of the variables in the estimated cross-section models for each sub-sample.

[Table 2 about here] 10
# **5  Empirical Strategy**

Based on the propositions put forward by our theoretical model and the specification applied by Kahn (2005), we derive the following reduced-form specification for our baseline estimates: Our dependent variable is the number of deaths, _DEATH ijt_ , at event _i_ in country _j_ at year _t_ . Our main variable of interest is foreign aid in period _t_ − 1 per capita, _Ln_ ( _AID j_ , _t_ − 1) . This variable a) accounts for the fungibility effect of past foreign aid and b) serves as our empirical proxy for the crowding-out effect i.e. the amount of anticipated ex-post aid that might crowd out national collective hazard management activity. To account for a country's level of development and its general ability to install protective measures we include _GDP_ per capita. In order to reduce potential reversed causality between disaster fatalities and income we use the first lag of _GDP_ p.c., _Ln_ ( _GDPj_ , _t_ − 1 ) . We expect that richer countries suffer c. p. less deaths from natural disasters.

In contrast to general belief, the correlation between _GDP_ p. c. and _AID_ p. c. in each of our three sub-samples is moderate (between -0.104 and -0.152). This is in line with the results of empirical studies (e.g. Frey & Schneider, 1986, Alesina & Dollar 2000) that identify political and strategic considerations as the driving forces behind foreign aid.

To account for a nation's size and density of population we introduce the natural log of population, _Ln_ ( _POPjt_ ) , and the natural log of population density, _Ln_ ( _POPDENS jt_ ) , respectively. Gassebner, Keck & Teh (2009) show a negative relationship between the occurrence of disasters and a nation's trade volume. Alesina & Dollar (2000) find that bilateral aid flows are positively related to a nation's openness for trade. To control for potential spurious correlation that might stem from this interrelationship, we include a nation's openness for trade, _OPEN jt_ , in our specification.

Apart from these socio-economic variables, the main explanatory factors for disaster fatalities are a disaster's magnitude, and additional climatic, geographical and topographical factors. All else equal, more powerful disasters should kill more people. We include wind speed measured in kilometers per hour as a magnitude variable for our storm sub-sample, the number of months per year _t_ in the country _j_ , with a monthly precipitation that is one standard deviation above the long time precipitation mean in country _j_ for our flood sub-sample and the Richter scale for the earthquake sub-sample.

Nations that are more exposed to natural disasters could also be the subject of more international aid contributions. Thus, we control for a nation's exposure to each disaster type via a proxy variable. Using GIS-data on the mortality risk and a shape file of a country's national boundaries we are able to construct the mean mortality risk from each disaster type for each nation, 11
_NATHAZRISK j_ . The expectations on the country's exposure to disasters are ambiguous. On the one hand, a higher ex-ante risk could be an indicator for more knowledge and better precaution. On the other hand, these proxies could indicate a higher magnitude of events and thus increase the number of fatalities. As a second indicator we use latitude, which accounts for possibility that the occurrence and intensity of climatic disasters might be correlated with their distance to the equator. As a third geographical indicator we use the mean elevation of a country. We also include continent-specific fixed effects, υ _c_ , and a time trend, η , (both in accordance to the specification by Kahn (2005). ε _ijct_ is the error term.

The dependent variable, _DEATH ijt_ , is a discrete, strictly positive count variable. Using simple ordinary least squares (OLS) with the underlying distributional assumption of a normally distributed continuous variable could result in biased estimates. Kahn (2005) log-transformed the dependent variable to overcome this problem. An alternative way is to assume that the data are Poisson distributed and use Poisson regression models. However, if the conditional mean and the variance function of the dependent are not equal, these estimators can overestimate the significance. A more flexible approach is the negative binomial model, which will be applied in this paper.

The empirical estimation is complicated by the fact that the relationship between disaster fatalities and past foreign aid are influenced simultaneously by omitted factors. We try to circumvent this problem by applying an instrumental variable (IV) estimator. Our instruments for past foreign aid are motivated by two seminal contributions in the field of development economics: The first instrument is a country's bilateral political alignement with the USA, measured by an updated version of Gartzke's affinity index (Voeten and Merdzanovic 2009). This index is constructed using voting patterns in the United Nations General Assembly. The index ranges between 1 (recipient and USA always voted the same way) and -1 (recipient and USAnever voted the same way). The choice of our second set of instruments refers to work by Fink & Redaelli (2009). They find that oil exporting countries are also more likely to receive international emergency aid. We therefore include the level of country _j_ ′ _s_ oil reserves and natural gas production as additional instruments.

In addition, we perform a number of robustness tests: First we control for the influence of institutional quality. Kahn (2005) already suggests that better institutional quality serves as an insurance against mortality risk from natural disasters. We therefore expect the sign to be negative. The second set of estimates looks at the effect of aid from different donor nations. After that, we examine the additional influence of factors that might increase the anticipation of aid. As already mentioned, Alesina & Dollar (2000) showed that former colonies are c. p. more likely to receive foreign aid. We therefore estimate alternative specifications where we control for these factors and the interaction with lagged aid.

The studies by Anbarci et al. (2005) and Kahn (2005) suggest that a country's income inequality _GINI jt_ is an impediment for collective actions and might increase the death toll. In contrast, Eisensee & Strömberg (2007) who uses the UN-WIDER GINI data, does not find any significant relationship between inequality and disaster fatalities. Therefore, the effect of the _GINI_ -variable is in principle ambiguous. We run additional specifications that include income inequality of 12
country _j_ in year _t_ as additional robustness checks.

In line with the empirical strategy by Kahn (2005), our second set of estimates analyzes the determinants of the annual national death toll from each of the three different natural hazards. The EM-DAT disaster dataset only includes major disasters that have exceeded a certain threshold in magnitude (e.g. number of fatalities, state of emergency). A nation could experience a natural disaster but a sufficient amount of protective measures can limit the overall losses and fatalities and thus prohibit the event being included in the dataset. Constructing a panel dataset allows us to analyze this first-stage process as well and use this information as an additional control in a second stage regression of the disaster fatalities.

Using the negative binomial model would assume that the observations with zero death-counts are generated by the same underlying process. This might not apply to our analysis. A zero outcome in the death count can arise from two regimes: Either no disaster took place (regime 1) or a large scale disaster took place and protective measures prevented any fatalities (regime 2). We therefore apply a zero-inflated negative binomial model (ZINB) which allows for overdispersion in the dependent variable and treats the zero outcomes of the dependent variable different from the positive counts. The model combines a binary variable<sup>_c_</sup> _i_<sup>with a standard</sup> count variable _y_<sup>*</sup> _i_ such that the observed variable is given by The probability that _c_ = 1 is denoted by ω _i_ . We apply the following probit model to estimate the influence of covariates<sup>_z_</sup> _i_<sup>on</sup> ω _i_ .

The corresponding log-likelihood function is given by The first stage probit model captures the process governing that a potential deadly disaster takes place in country _j_ at time _t_ . The second stage estimates the death count once a disaster has taken place by applying a negative binomial estimator.

# **6  Results**

13
The first set of results for fatalities from storms, floods and earthquakes is presented in Table 3. Our hypothesis, that lagged foreign aid, _Ln_ ( _AID j_ , _t_ − 1 ) , has an increasing effect on the number of disaster fatalities is only confirmed in the storm sub-sample. The results are robust if the mean of annual aid of the preceding 3 years, _Ln_ ( 3 _yr_ . _AID j_ − _t_ − 1 ) , is taken instead of the first lag of aid (column 3.2). The results for floods and earthquakes neither confirm nor reject our hypothesis. The coefficients of the lagged aid as well as the 3-year-mean aid variable do have a positive sign for flood fatalities and a negative sign for earthquake fatalities, but they are not significantly different from zero. These results indicate that the negative effects of aid might be relevant for wind-storm disasters and to a certain extent for floods, but not for earthquakes. The differences between the disaster sub-samples seems to be connected to the choice of our empirical proxy. As already mentioned, foreign aid can have a preventive as well as a fungibility and crowding-out effect. The results from the storm and flood sub-sample suggest that the negative effects outweigh the prevention effect while we find some indication that the relationship is vice versa in the case of earthquakes.

Table 4 summarizes the results of the IV estimates, where we instrumented lagged aid with lagged values of country _j’s_ bilateral political affinity with the USA, its oil reserves and natural gas production. The coefficient for lagged aid is again positive and significant for the storm subsample. While the sign of the coefficient changes in the flood sample and stays negative in the earthquake sample. Again, the estimates with the floods and earthquakes subsamples do not yield significant results for the lagged aid variable. However, the signs of the aid coefficients are positive in all three subsamples.

Kahn (2005) showed that nations with better institutions suffer less disaster fatalities and there is a wide discussion in the aid-literature on the effect of institutions on development aid. We control for this interrelationship in Tables 5, 6 and 7 for each disaster-type separately. The results for the storm sample in Table 6 show that the lagged aid variable is still robust after including various measures of institutional quality. Apart from the measures for democracy, _DEMOCRACY jt_ , none of the institutional variables appear to have an significant effect on the number of storm fatalities. The coefficients of lagged aid in the flood sample, change sign, while the coefficients in the earthquakes sub-sample do not change. Again the coefficients of lagged aid in the floods and earthquakes sub-samples are not significantly different from zero. Interestingly, the majority of institutional variables appear to have a large and significant mitigative effect against floods and earthquakes.

In Table 8 we analyze the effects of aid from different, major donor countries (France, Germany, Japan, United Kingdom and USA). We only report the coefficients for aid. The sign of the coefficients of lagged aid do not change, however, in the storm sub-sample the effect of the aidvariable from individual donor countries is not significant, while the effect for floods and earthqukes becomes significant.

These results once again point out the importance of distinguishing between different types of disasters and indicate that future research should have a closer look at the variations in forms of development by different donor nations. The remaining coefficients of lagged aid in the subsamples are not significantly different from zero. Table 9 adds a dummy for the colonial background and an interaction term with foreign aid to the baseline specification. Lagged foreign 14
aid is no longer significant for the storm sub-sample, neither is the colony dummy nor the interaction term. In contrast to our expectations, we find some evidence that aid-flows in former colonies (interaction term between lagged aid and U.K. colony for the storm subsample and interaction term between French colony and lagged aid for the flood sub-sample) actually reduces the number of flood victims. These results could indicate that increased aid-flows due to colonial history had direct or indirect effects on disaster preparedness that outweigh the (even higher) crowding-out effect of aid-flows to former colonies.

Table 10 introduces an interaction term between the lagged aid and the institutional quality variable. As before, there is mixed evidence of the effect of aid on disaster preparedness. However, there is a general trend observable that the interaction term of lagged aid and institutional quality has a decreasing effect on disaster fatalities. These results contribute to the discussion on the relationship between institutional quality and aid effectiveness (Burnside & Dollar 2000, Easterly, Levine & Roodman 2004) In accordance with the discussion in this literature, our results appear to be rather fragile and only have explorative character. Our results are robust to the inclusion of the GINI-variable. The GINI-coefficient itself does not appear to have a significant effect on the number of disaster fatalities.

In the next step we reproduce our results using ZINB (Tables 11-17 in the appendix). Again the results differ between the sub-samples. Lagged aid seems to have an increasing effect on disaster fatalities (at least at the 10 % significance level) in the case of storms. Regarding the estimates for the flood sub-sample we find an interesting pattern: in the first stage probit, lagged aid appears to increase the probability that the number of deaths from floods is positive. However, the second stage suggests that aid has a mitigating effect on the total number of flood deaths. The estimates using the earthquake sub-sample show a similar pattern but the coefficient of aid in the second stage is not significant. We interpret these results as follows: Foreign aid flows reduce the overall extent of flood and earthquake disasters. Nevertheless, aid can also crowd-out the incentives of governments to install preventive measures or enact zoning or building codes that increase the probability of survival or, in other words, reduce the probability that a deadly disaster occurs.

This general pattern persists throughout the ZINB estimates. Reproducing the estimates with institutional quality, lagged aid and the interaction between aid and institutions shows that the overall effect of aid in either sub-sample is reduced. While in the flood subsample the lagged aid coefficient is positive the interaction term between institutional quality and aid is negative. For floods and earthquakes the relationship is mainly vice versa. The results need to be interpreted with caution as some of the estimates did not converge.

# **7  Concluding Remarks**

Based on the results of the existing literature on the socio-economic determinants of a country's vulnerability against catastrophic events (Anbarci et al. 2005, Kahn 2005), we analyzed the effect of foreign aid dependence on the recipient country's protection against large-scale disasters. Our theoretical model suggests that foreign aid can have both negative and postive effects. If the sum of fungibility and crowding-out effect outweighs the preventive effect, foreign aid in previous periods will decrease incentives to provide protective measures and ultimately lead to a higher death toll from natural disasters. The empirical section of our study provides some evidence for 15
charity hazard on international level. Estimates using data on major storm events show that increases in the level of past foreign aid imply higher death tolls resulting from natural disasters. For flood disasters and major earthquakes we find some evidence that past foreign aid can increase the probability that the death toll from disasters is non-zero, however, it also reduces the total number of deaths from disasters. As we are using lagged foreign aid as an empirical proxy for anticipated disaster aid, it is not possible to disentangle the two negative effects and the positive (side-)effects of foreign aid on infrastructure and disaster management. The major implication for future research is that an analysis of the effects of aid on natural hazard management largely differs across types of disasters and that a better empirical proxy for the anticipation of international disaster aid is needed.

The major policy implication of our results is not a call for a reduction of foreign aid but rather a call for rethinking strategies for international assistance and redesigning existing aid programs. The design of transfers, foreign aid in particular, has been drawing attention in economics for a long time (e.g. Nichols 1982, Besley & Coate 1991, Coate 1995, Gahvari & Mattos 2007). The major findings of these articles are that unrestricted transfers induce people to diminish ex-ante protection activities (charity hazard) and to shirk responsibilities. In order to avoid these problems "the tying of aid" (Jepma 1991) through in-kind transfers and conditional transfers, is of interest. In fact, the theoretical model of Coate (1995) shows that in-kind transfers (e.g. provision of insurance) and conditional transfers can overcome the adverse effects of altruism. Applied to the natural hazard context, this result would suggest that the provision of ex-ante in-kind transfers (e.g. building of a dike, early warning system for earthquakes) to countries at risk could reduce the dilemma situation. Since this form of transfer is less fungible, the possibility of misuse is limited allowing for a higher probability of surviving natural disasters.

Although in-kind and restricted transfers reduce the above mentioned weaknesses of unrestricted aid, they are criticized for causing dead-weight-losses due to a lack of information about recipients' preferences. Moreover, due to asymmetric information, even the provision of in-kind transfers does not ensure the protection of the most vulnerable people. However, the valuation of in-kind and cash transfers in the case of natural hazards might be somehow different, since mitigation activities do not only require financial capital but also expertise. Since developing countries in particular lack this expertise, unrestricted aid will not necessarily lead to efficient targeting. Although alternative adaptation strategies require future research, the impression at first glance is that ex-ante in-kind transfers could be a possible solution in case of natural hazards, since mitigation activities are likely to reduce the extent of disasters and the need for ex-post relief. A lower vulnerability to catastrophic events could reduce (at least to a certain extent) developing countries' dependency on foreign aid and contribute to a more independent development of these countries. As economic research on the efficiency of in-kind transfers is far from satisfying, it requires a better understanding of the incentives created by in-kind transfers before developing alternative instruments of foreign assistance.

16
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Table 1: List of countries and number of natural disasters

|**Country**|**Storms**|**Floods**|**Earthquakes**|**Country**|**Storms**|**Floods**|**Earthquakes**|
|---|---|---|---|---|---|---|---|
|Argentina|3|28|1|Mozambique|2|8|0|
|<br>Australia|18|27|3|<br>Nepal|0|19|3|
|Austria|0|7|0|New Zealand|3|23|1|
|Bangladesh|33|35|3|Nicaragua|3|4|3|
|Bolivia|0|15|2|Nigeria|0|14|0|
|Brazil|0|50|1|<br>Pakistan|3|24|14|
|Canada|4|11|0|Panama|1|0|2|
|Chile|0|14|4|Papua New Guinea|2|4|6|
|China|21|103|57|<br>Peru|0|25|16|
|Colombia|1|27|9|Philippines|47|44|10|
|Costa Rica|2|8|8|<br>Portugal|1|5|0|
|Denmark|1|0|0|<br>South Africa|2|16|3|
|Ecuador|0|8|8|Spain|4|11|1|
|El Salvador|2|2|4|<br>Sri Lanka|1|28|0|
|France|5|28|0|Switzerland|2|5|0|
|Greece|0|6|15|Tanzania|0|14|1|
|Guatemala|1|4|7|Thailand|1|34|0|
|Honduras|3|11|2|Turkey|1|17|19|
|India|15|95|13|<br>United Kingdom|10|13|1|
|Indonesia|0|62|37|<br>United States|34|90|18|
|Iran|2|45|37|Venezuela|1|15|6|
|Ireland|1|2|0|Vietnam|8|7|0|
|Italy|0|0|10|**TOTAL**|289|1109|365|
|<br>Japan|14|14|20|||||
|<br>Kenya|0|5|1|||||
|<br>Korea, South|6|17|0|||||
|<br>Madagascar|11|2|0|||||
|<br>Malawi|0|11|1|||||
|Malaysia|1|13|0|||||
|<br>Mexico|19|26|17|||||

20
|||Table 2: Desc|riptive statistics|||
|---|---|---|---|---|---|
|**Obs.**<br>|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|
|**Storms**||||||
|_FATALITIES_|289|226.723|1232.926|0.000|14600.000|
|_AID p. c._|289|0.792|3.770|-1.022|45.062|
|_GDP p. c._|289|10082.490|10136.190|648.365|34364.500|
|_GINI_|289|43.056|9.049|25.216|64.796|
|_POP (in tsd.)_|289|217809.900|344382.700|2745.429|1271085.000|
|<br>_POPDENS_|289|230.988|279.104|1.976|1024.392|
|_OPEN_|289|49.772|30.740|12.672|218.796|
|_MAGNITUDE_|289|155.886|57.410|45.000|300.000|
|_NATHAZ - RISK_|289|3.566|3.187|0.000|7.855|
|_LATITUDE_|289|28.085|13.582|2.165|61.063|
|_ELEVATION_|289|601.594|449.689|34.259|1839.950|
|**Floods**||||||
|_FATALITIES_|1109|125.649|956.011|0.000|30000.000|
|_AID p. c._|1109|0.758|3.079|-1.023|33.328|
|_GDP p. c._|1109|7496.065|8185.186|389.963|35107.520|
|<br>_GINI_|1109|44.203|8.990|25.216|64.796|
|_POP (in tsd.)_|1109|249678.700|372010.200|2299.124|1271085.000|
|<br>_POPDENS_|1109|131.481|162.153|1.943|972.416|
|_OPEN_|1109|45.678|29.358|9.275|228.874|
|_MAGNITUDE_|1109|1.654|1.691|0.000|8.000|
|_NATHAZ - RISK_|1109|4.282|2.528|0.026|9.854|
|_LATITUDE_|1109|25.347|14.976|0.422|61.063|
|_ELEVATION_|1109|778.552|552.312|85.476|2565.382|
|**Earthquakes**||||||
|_FATALITIES_|365|381.715|2637.994|0.000|40000.000|
|_AID p. c._|365|1.552|5.562|-0.005|40.578|
|_GDP p. c._|365|5907.357|5978.683|482.528|35107.520|
|<br>_GINI_|365|44.055|8.756|25.216|64.196|
|_POP (in tsd.)_|365|45.020|25.089|10.418|214.423|
|<br>_POPDENS_|365|280543.200|420309.300|2434.262|1271085.000|
|_OPEN_|365|109.141|114.505|1.976|989.455|
|_MAGNITUDE_|365|6.159|0.881|4.000|8.000|
|_NATHAZ - RISK_|365|2.279|2.001|0.000|7.369|
|_LATITUDE_|365|25.274|14.519|0.422|53.887|
|_ELEVATION_|365|980.628|545.394|85.476|2565.382|

21
Table 3: The determinants of disaster fatalities

||Stor|ms|Flo|ods|Earthq|uakes|
|---|---|---|---|---|---|---|
||3.1|3.2|3.3|3.4|3.5|3.6|
|Ln(_AIDj,t-1_)|0.794**||0.098||-0.960||
||(0.347)||(0.250)||(0.753)||
|Ln(_3yr._ _AIDj,t-1)_||0.761**||0.051||-0.991|
|||(0.308)||(0.164)||(0.744)|
|Ln(_GDPj,t-1_)|-0.552***|-0.548***|-0.135|-0.192|-0.951**|-0.969**|
||(0.137)|(0.136)|(0.152)|(0.120)|(0.479)|(0.460)|
|Ln(_POP jt)_|0.010|0.032|0.307***|0.317***|-0.997|-0.998|
||(0.307)|(0.313)|(0.116)|(0.116)|(1.120)|(1.052)|
|Ln_(POPDENSjt)_|-0.002|-0.006|-0.009|0.088|0.007|-0.006|
||(0.285)|(0.282)|(0.232)|(0.192)|(0.985)|(0.920)|
|_OPENjt_|0.003|0.003|-0.012***|-0.013***|-0.032***|-0.035***|
||(0.007)|(0.006)|(0.002)|(0.002)|(0.012)|(0.011)|
|_MAGNITUDEijt _|0.012***|0.012***|0.420***|0.430***|2.283***|2.266***|
||(0.003)|(0.003)|(0.126)|(0.126)|(0.281)|(0.282)|
|_NATHAZ – RISKj _|-0.179**|-0.179**|0.168|0.129|0.060|0.092|
||(0.075)|(0.074)|(0.168)|(0.091)|(0.327)|(0.292)|
|_LATITUDEj_|-0.014|-0.014|-0.034***|-0.028**|0.019|0.017|
||(0.017)|(0.017)|(0.012)|(0.009)|(0.091)|(0.080)|
|_ELEVATIONj_|-0.001***|-0.001***|0.001***|0.001***|-0.001**|-0.001**|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|
|Obs.|292|292|1125|1125|367|367|
|Log pseudolikelihood|-1388.273|-1388.273|-5013.813|-5077.955|-1445.147|-1444.850|
|Prob>χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

22
Table 4: The determinants of disaster fatalities - IV-estimates

||Storms|Floods|Earthquakes|
|---|---|---|---|
|Ln(_AIDj,t-1_)|1.270**|0.416|1.280|
||(0.623)|(0.617)|(1.822)|
|Ln(_GDPj,t-1_)|-0.190*|0.101|-0.330|
||(0.111)|(0.141)|(0.232)|
|Ln(_POP jt)_|0.177|0.371**|0.752|
||(0.311)|(0.157)|(0.760)|
|Ln_(POPDENSjt)_|-0.118|-0.010|-1.334*|
||(0.237)|(0.131)|(0.779)|
|_OPENjt_|-0.000|-0.015***|-0.020**|
||(0.006)|(0.003)|(0.008)|
|_MAGNITUDEijt _|0.009***|-0.019|1.186***|
||(0.002)|(0.023)|(0.168)|
|_NATHAZ – RISKj _|-0.100|0.145**|0.345*|
||(0.085)|(0.059)|(0.201)|
|_LATITUDEj_|-0.042***|-0.031***|0.009|
||(0.015)|(0.012)|(0.029)|
|_ELEVATIONj_|-0.001*|0.001***|-0.001|
||(0.000)|(0.000)|(0.001)|
|Obs.|212|818|292|
|Centered R2|0.418|0.340|0.213|
|Prob> F|0.000|0.000|0.000|
|Sargan Test|0.897|0.066|0.144|
|Underid. Test|0.000|0.000|0.001|
|Shea’s R2|0.419|0.114|0.061|

_Notes:_ IV-estimates. Dependent variable is Ln(1+ _DEATHijt_ ). The first lags of Gartzke's affinity index with the USA, _AFFINITY USAj,t-1,_ the country’s oil resources _, OIL RESOURCESj,t-1_ and natural gas production, _GAS PRODj,t-1_ , were used as additional instruments for Ln( _AIDj,t-1_ ). Continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

23
Table 5: Storm fatalities and institutional quality

||5.1|5.2|5.3|5.4|5.5|5.6|5.7|
|---|---|---|---|---|---|---|---|
|Ln(_AIDj,t-1_)<br>Ln(_GDPj,t-1_)<br>_CORR CONTjt_|0.784**<br>(0.342)<br>-0.673***<br>(0.169)<br>0.474<br>(0.485)|0.817**<br>(0.353)<br>-0.667***<br>(0.152)|0.726**<br>(0.307)<br>-0.627***<br>(0.138)|0.833*<br>(0.442)<br>-0.577***<br>(0.129)|0.767**<br>(0.332)<br>-0.658***<br>(0.175)|0.772**<br>(0.329)<br>-0.372**<br>(0.160)|1.075***<br>(0.345)<br>-0.208*<br>(0.115)|
|_GOV EFFjt_||0.389<br>(0.304)||||||
|_POL STABjt_|||0.532*<br>(0.202)|||||
|_REG QALjt_||||0.123<br>(0.572)||||
|_RULE LAWjt_|||||0.269<br>(0.419)|||
|_VOICE ACCjt_||||||-0.454<br>(0.373)||
|_DEMOCRACYjt_|||||||-0.116***<br>|
||||||||(0.025)|
|Ln(_POP jt)_|0.048|0.054|0.016|-0.001|-0.003|-0.081|<br>-0.032|
||(0.315)|(0.307)|(0.322)|(0.296)|(0.308)|(0.295)|(0.209)|
|Ln_(POPDENSjt)_|0.102|0.047|0.227|0.015|0.048|0.032|0.279|
|_OPENjt_|(0.236)<br>0.001|(0.250)<br>0.000|(0.262)<br>0.001|(0.282)<br>0.002|(0.260)<br>0.002|(0.246)<br>0.001|(0.170)<br>0.005|
|_MAGNITUDEijt_<br> _NATHAZ – RISKj_|(0.006)<br>0.012***<br>(0.003)<br>-0.200**|(0.005)<br>0.012***<br>(0.003)<br>-0.178**|(0.006)<br>0.013***<br>(0.003)<br>-0.208**|(0.006)<br>0.012***<br>(0.002)<br>-0.189**|(0.006)<br>0.012***<br>(0.003)<br>-0.168**|(0.006)<br>0.013***<br>(0.002)<br>-0.188***|(0.005)<br>0.012***<br>(0.002)<br>-0.225***|
|_LATITUDEj_|(0.081)<br>-0.031|(0.074)<br>-0.027|(0.066)<br>-0.034**|(0.075)<br>-0.015|(0.079)<br>-0.020|(0.071)<br>-0.018|(0.079)<br>-0.037***|
|_ELEVATIONj_|(0.028)<br>-0.001***<br>(0.000)|(0.020)<br>-0.001***<br>(0.000)|(0.016)<br>-0.001***<br>(0.000)|(0.020)<br>-0.001***<br>(0.000)|(0.020)<br>-0.001***<br>(0.000)|(0.014)<br>-0.002***<br>(0.000)|(0.011)<br>-0.002***<br>(0.000)|
|Obs.|<br>292|<br>292|<br>292|<br>292|<br>292|<br>292|<br>292|
|Log pseudolikelihood|-1387.450|-1387.637|-1386.003|-1388.219|-1388.010|-1387.101|-1377.600|
|Prob>χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|0.000|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

24
Table 6: Flood fatalities and institutional quality

||6.1|6.2|6.3|6.4|6.5|6.6|6.7|
|---|---|---|---|---|---|---|---|
|Ln(_AIDj,t-1_)|-0.438|-0.334|-0.454|-0.371|-0.262|-0.334|-0.417|
||(0.287)|(0.273)|(0.366)|(0.266)|(0.279)|(0.307)|(0.352)|
|Ln(_GDPj,t-1_)|-0.032|-0.004|-0.332|-0.144|-0.141|-0.131|-0.267|
|_CORR CONTjt_|(0.130)<br>-1.183***<br>(0.321)|(0.154)|(0.175)|(0.147)|(0.134)|(0.146)|(0.154)|
|_GOV EFFjt_||-1.014***<br>(0.295)||||||
|_POL STABjt_|||0.216<br>(0.235)|||||
|_REG QALjt_||||-0.946**<br>(0.445)||||
|_RULE LAWjt_|||||-0.770***<br>(0.153)|||
|_VOICE ACCjt_||||||-0.481*<br>(0.157)||
|_DEMOCRACYjt_|||||||-0.010<br>(0.024)|
|Ln(_POP jt)_|0.268|0.378**|0.151|0.341*|0.367*|0.218|0.193|
|<br>Ln_(POPDENSjt)_|(0.163)<br>0.306|(0.189)<br>0.201|(0.209)<br>0.112|(0.174)<br>0.251|(0.170)<br>0.077|(0.171)<br>0.078|(0.197)<br>0.086|
|_OPENjt_|(0.304)<br>-0.007|(0.261)<br>0.006|(0.299)<br>-0.014***|(0.290)<br>-0.006|(0.277)<br>-0.010**|(0.273)<br>-0.013***|(0.292)<br>-0.012***|
||(0.004)|(0.004)|(0.005)|(0.004)|(0.004)|(0.004)|(0.004)|
|_MAGNITUDEijt_|0.090**|0.107**|0.120**|0.091*|0.119**|0.135**|0.125**|
||(0.043)|(0.049)|(0.054)|(0.046)|(0.051)|(0.056)|(0.056)|
|_NATHAZ – RISKj_|<br>0.077|<br>0.109|<br>0.169|<br>0.151|<br>0.192|<br>0.229|<br>0.184|
||(0.110)|(0.101)|(0.143)|(0.109)|(0.120)|(0.132)|(0.0141)|
|_LATITUDEj_|<br>-0.001|<br>-0.014|<br>-0.048***|<br>-0.033**|<br>-0.023|<br>-0.035**|<br>-0.043***|
|_ELEVATIONj_|(0.017)<br>0.001***|(0.015)<br>0.001***|(0.018)<br>0.001***|(0.013)<br>0.001***|(0.015)<br>0.001***|(0.015)<br>0.001***|(0.016)<br>0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Obs.|<br>1125|<br>1125|<br>1125|<br>1125|<br>1125|<br>1125|<br>1125|
|Log pseudolikelihood<br>Prob>χ<sup>2</sup>|-5093.929<br>0.000|-5103.844<br>0.000|-5123.730<br>0.000|-5101.884<br>0.000|-5108.906<br>0.000|-5119.118<br>0.000|-5124.959<br>0.000|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

25
Table 7: Earthquake fatalities and institutional quality

||7.1|7.2|7.3|7.4|7.5|7.6|7.7|
|---|---|---|---|---|---|---|---|
|Ln(_AIDj,t-1_)|-0.220|-0.524|-0.686|-0.275|-0.635|-1.028|-1.204**|
||(0.531)|(0.418)|(0.979)|(0.453)|(0.823)|(0.991)|(0.501)|
|Ln(_GDPj,t-1_)|-0.372|-0.093|-0.413|-0.564*|-0.739|-0.970*|-0.981***|
||(0.394)|(0.286)|(0.442)|(0.317)|(0.608)|(0.516)|(0.259)|
|_CORR CONTjt_|-1.371<br>(0.738)|||||||
|_GOV EFFjt_||-1.999***<br>(0.503)||||||
|_POL STABjt_|||-1.226<br>(0.309)|||||
|_REG QALjt_||||-1.376***<br>(0.411)||||
|_RULE LAWjt_|||||-0.550<br>(0.727)|||
|_VOICE ACCjt_||||||0.099<br>(0.490)||
|_DEMOCRACYjt_|||||||0.128***<br>(0.027)|
|Ln(_POP jt)_|-0.258|0.112|-0.849|0.462|-0.718|-0.998|<br>-0.947*|
||(0.686)|(0.462)|(0.656)|(0.703)|(1.260)|(1.084)|(0.550)|
|Ln_(POPDENSjt)_|-0.210|-0.041|0.286|-0.222|-0.021|0.004|-0.003|
||(0.565)|(0.426)|(0.681)|(0.577)|(1.031)|(0.941)|(0.489)|
|_OPENjt_|-0.036***|-0.029***|-0.028***|-0.023**|-0.034**|-0.031**|-0.020*|
||(0.011)|(0.009)|(0.010)|(0.009)|(0.011)|(0.014)|(0.011)|
|_MAGNITUDEijt_|2.174***|2.164***|2.210***|2.269***|2.272***|2.282***|2.332***|
||(0.302)|(0.307)|(0.295)|(0.300)|(0.291)|(0.281)|(0.247)|
|_NATHAZ – RISKj_|<br>0.198|<br>0.159|<br>-0.065|<br>0.633**|<br>0.095|<br>0.064|<br>-0.042|
||(0.223)|(0.169)|(0.330)|(0.248)|(0.340)|(0.314)|(0.226)|
|_LATITUDEj_|<br>0.026|<br>0.021|<br>0.046|<br>-0.020|<br>0.023|<br>0.018|<br>0.014|
||(0.064)|(0.040)|(0.058)|(0.052)|(0.094)|(0.086)|(0.040)|
|_ELEVATIONj_|<br>-0.002**|<br>-0.002***|<br>-0.001**|<br>-0.001*|<br>-0.002**|<br>-0.001|<br>-0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Obs.|367|367|367|367|367|367|367|
|Log pseudolikelihood|-1441.739|-1435.797|-1440.266|-1436.581|-1444.365|-1445.120|-1439.325|
|Prob>χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|0.000|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

26
Table 8: Disaster fatalities and aid from different donor countries

||Storms|Floods|Earthquakes|
|---|---|---|---|
|Ln_(FRENCH AIDj,t-1)_|0.789|0.136|-2.723***|
||(0.616)|(0.496)|(0.923)|
|Ln_(GERMAN AIDj,t-1)_|0.513|-0.566***|-1.038*|
||(0.437)|(0.215)|(0.562)|
|Ln_(JAPANESE AIDj,t-1)_|0.102|-0.429**|-1.236***|
||(0.148)|(0.170)|(0.231)|
|Ln_(UK AIDj,t-1)_|-0.538|-0.662**|0.375|
||(0.418)|(0.302)|(1.568)|
|Ln_(USA AIDj,t-1)_|-0.053|-0.690**|-0.957**|
||(0.272)|(0.271)|(0.441)|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Covariates from the baseline specification (equation (5)), continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

27
Table 9: Disaster fatalities, aid and colonial background

||Storms|Floods|Earthquakes|
|---|---|---|---|
|Ln(_AIDj,t-1_)|0.644|-0.262|0.029|
||(0.412)|(0.605)|(0.634)|
|_COLONYj _|0.275|-0.115|2.897**|
||(0.641)|(0.509)|(1.246)|
|_COLONYj*AIDj,t-1_|0.380|-0.299|-1.027|
||(0.501)|(0.487)|(0.657)|
|Ln_(FRENCH AIDj,t-1)_|-1.379|0.152|-2.650|
||(0.501)|(0.312)|(0.866)|
|_FRENCH COLONYj _|1.207***|-0.421|n.a.|
||(0.291)|(0.243)||
|_FRENCH COLONYj*AIDj,t-1_|-1.303|-4.400***|n.a.|
||(0.927)|(0.390)||
|Ln_(UK AIDj,t-1)_|0.266|-0.712*|-1.019|
||(0.469)|(0.387)|(2.019)|
|_UK COLONYj _|0.005|-0.633*|-0.148|
||(0.445)|(0.373)|(1.490)|
|_UK COLONYj*AIDj,t-1_|-2.430**|0.077|4.078|
||(0.800)|(0.402)|(3.303)|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Covariates from the baseline specification (equation (5)), continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

28
Table 10: Disaster fatalities, aid and institutional quality

||Storms|Floods|Earthquakes|
|---|---|---|---|
|Ln(_AIDj,t-1_)|0.687|-0.144|-0.567|
||(0.546)|(0.122)|(0.716)|
|_CORR CONTjt_|0.517|-1.163***|-1.683*|
||(0.524)|(0.382)|(0.867)|
|_CORR CONTjt* AIDj,t-1_|-0.186|-0.054|0.915|
||(0.573)|(0.274)|(0.753)|
|Ln(_AIDj,t-1_)|0.288|-0.297|-0.296|
||(0.500)|(0.329)|(0.353)|
|_GOV EFFjt_|0.537|-1.033***|-2.199***|
||(0.430)|(0.320)|(0.510)|
|_GOV EFFjt* AIDj,t-1_|-1.015|0.072|-0.403|
||(1.103)|(0.335)|(0.304)|
|Ln(_AIDj,t-1_)|1.133**|-0.523|0.263|
||(0.440)|(0.522)|(0.493)|
|_POL STABjt_|0.354|0.264|-2.939***|
||(0.322)|(0.363)|(0.790)|
|_POL STABjt* AIDj,t-1_|0.496|-0.097|1.968***|
||(0.320)|(0.443)|(0.599)|
|Ln(_AIDj,t-1_)|0.842*|-0.362|-0.608|
||(0.481)|(0.279)|(0.586)|
|_REG QALjt_|<br>0.120|<br>-1.084*|<br>-1.713***|
||(0.618)|(0..568)|(0.436)|
|_REG QALjt* AIDj,t-1_|0.011|0.340|1.299*|
||(0.732)|(0.400)|(0.786)|
|Ln(_AIDj,t-1_)|-0.092|-0.613**|-0.805|
||(0..687)|(0.247)|(0.927)|
|_RULE LAWjt_|0.552|-0.608**|-0.903|
||(0.433)|(0.257)|(0.957)|
|_RULE LAWjt* AIDj,t-1_|-1.143|-0.681**|1.060|
||(0.791)|(0.293)|(0.952)|
|Ln(_AIDj,t-1_)|0.831***|-0.359|-1.958|
||(0.304)|(0.328)|(1.341)|
|_VOICE ACCjt_|-0.301|-0.501*|0.051|
||(0.408)|(0.300)|(0.642)|
|_VOICE ACCjt* AIDj,t-1_|<br>-0.532|<br>-0.196|<br>0.943|
||(0.572)|(0.765)|(0.817)|
|Ln(_AIDjt-1_)|0.814*|-0.483|-2.959***|
|_,_|(0.465)|(0.447)|(0.478)|
|_DEMOCRACY_|-0125***|-0012|0099***|
|_jt_|.|.|.|
||(0.054)|(0.026)|(0.030)|
|_DEMOCRACYjt* AIDj,t-1_|0.039|0.010|0.181|
||(0.054)|(0.033)|(0.053)|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ .. Standard errors (in parentheses) are adjusted for clustering on country-level. Covariates from the baseline specification (equation (5)), continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

29
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Table 11: The determinants of disaster fatalities - ZINB

||Sto|rms|Flo|ods|Earth|quakes|
|---|---|---|---|---|---|---|
||11.1|11.2|11.3|11.4|11.5|11.6|
|Ln(_AIDj,t-1_)|0.252*<br>(0.133)||-0.263**<br>(0.116)||-0.344<br>(0.263)||
|Ln(_3yr._ _AIDj,t-1)_||0.229*||-0.319**||-0.338|
|||(0.125)||(0.136)||(0.312)|
|Ln(_GDPj,t-1_)|-0.690***|-0.729***|-0.542***|-0.605***|-0.406|-0.528|
||(0.260)|(0.887)|(0.215)|(0.203)|(0.745)|(0.863)|
|_GINIjt_|-0.010|-0.015|-0.033*|-0.032*|-0.002|-0.001|
||(0.022)|(0.023)|(0.017)|(0.018)|(0.033)|(0.036)|
|Ln(_POP jt)_|<br>0.703***|<br>0.746***|<br>0.196|<br>0.215|<br>0.341|<br>0.420|
||(0.134)|(0.138)|(0.138)|(0.145)|(0.333)|(0.335)|
|Ln_(POPDENSjt)_|-0.049|-0.075|-0.076|-0.104|-0.855**|-0.883**|
||(0.146)|(0.147)|(0.131)|(0.149)|(0.373)|(0.374)|
|_OPENjt_|<br>0.004|<br>0.006|<br>-0.003|<br>-0.002|<br>-0.002|<br>-0.001|
||(0.005)|(0.005)|(0.004)|(0.004)|(0.012)|(0.014)|
|_DISASTERSjt _|<br>0.097**|<br>0.090**|<br>0.384***|<br>0.336***|<br>0.028|<br>0.023|
||(0.0437)|(0.0407)|(0.128)|(0.127)|(0.219)|(0.239)|
|_NATHAZ – RISKj _|<br>0.934***|<br>0.982***|<br>0.352*|<br>0.345*|<br>2.093**|<br>2.126**|
||(0.207)|(0.215)|(0.186)|(0.202)|(0.875)|(0.943)|
|**1**<sup>**st**</sup>**stage Probit model**|||||||
|<br>Ln(_AIDj,t-1_)|-0.165||-0.376**||-0.483**||
||(0.133)||(0.174)||(0.219)||
|Ln(_3yr._ _AIDj,t-1)_||-0.070||-0.293||-0.925**|
|||(0.165)||(0.184)||(0.417)|
|Ln(_GDPj,t-1_)|-0.107|<br>-0.066|-0.473**|<br>-0.308|0.249|<br>-0.528|
||(0.321)|(0.887)|(0.215)|(0.233)|(0.353)|(1.051)|
|Ln(_POP jt)_|-0.446***|-0.385*|-0.598*|-0.557**|-0.063|-0.872**|
||(0.138)|(0.232)|(0.178)|(0.210)|(0.337)|(0.425)|
|_DISASTERSjt _|<br>-8.916***|<br>-10.150|<br>-8.363***|<br>-8.163***|<br>-12.330***|<br>-17.410***|
||(1.115)|(8.761)|(0.702)|(0.661)|(2.826)|(5.295)|
|_NATHAZ – RISKj _|<br>0.0530|<br>0.288|<br>-0.163|<br>-0.159|<br>1.470|<br>1.446**|
||(0.207)|(0.215)|(0.186)|(0.202)|(0.875)|(0.943)|
|Obs.|2186|1978|2174|1966|2186|1978|
|Nonzero Obs.|430|403|605|560|197|177|
|Log pseudolikelihood|-2468.351|-2282.336|-3614.626|-3307.053|-1200.485|-1080.443|
|<br>Prob>χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|
|VuongTest(z-value) <sup>a</sup>|14.21***|14.05***|16.55***|16.36***|7.22***|6.97***|

_Notes:_ ZINB-estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. The 1<sup>st</sup> stage probit regression estimates the probability that nobody in nation and year died from the specific natural disaster. The 2<sup>nd</sup> stage negative-binomial estimates the number of disaster deaths. Continent dummies, time trend and constant term included in all 2<sup>nd</sup> stage negative-binomial specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.<sup>a</sup> The test compares the ZINB with a negative binomial model and selects the one that is closest to the true conditional distribution (Vuong 1989).

33
Table 12: The determinants of Storm fatalities and Institutions - ZINB

||12.1|12.2|12.3|12.4|12.5|12.6|12.7<sup>b</sup>|
|---|---|---|---|---|---|---|---|
|Ln(_AIDjt-1_)|0.239*|0.230*|0.205|0.249*|0.243*|0.258*|0.162|
|_,_<br>Ln(_AIDj,t-1_)|(0.143)<br>-0.656**|(0.135)<br>-0.576**|(0.147)<br>-0.572**|(0.135)<br>-0.668**|(0.140)<br>-0.650**|(0.137)<br>-0.717***|(0.132)<br>-0.203|
|Ln(_GDPj,t-1_)|(0.260)<br>-0.087<br>(0210)|(0.281)|(0.236)|(0.271)|(0.260)|(0.261)|(0.687)|
|_CORR CONTjt_|.|-0.215<br>(0.224)||||||
|_GOV EFFjt_|||-0.425<br>(0.263)|||||
|_POL STABjt_||||-0.062<br>(0.259)||||
|_REG QALjt_|||||-0.073<br>(0.200)|||
|_RULE LAWjt_||||||0.059<br>(0.193)||
|_VOICE ACCjt_|||||||-0.003<br>|
||||||||(0.021)|
|_GINIjt_|-0.011|-0.013|-0.014|-0.010|-0.011|-0.010|<br>-0.037|
|Ln(_POP jt)_|(0.022)<br>0.706***|(0.022)<br>0.712***|(0.021)<br>0.671***|(0.022)<br>0.704***|(0.022)<br>0.702***|(0.022)<br>0.707***|(0.028)<br>0.094|
||(0.134)|(0.137)|(0.134)|(0.133)|(0.134)|(0.135)|(0.214)|
|_DISASTERSjt _|0.101**|0.107**|0.125**|0.0981**|0.100**|0.0957**|0.162***|
|_NATHAZ – RISKj _|(0.046)<br>0.947***|(0.043)<br>0.919***|(0.052)<br>0.997***|(0.044)<br>0.934***|(0.043)<br>0.930***|(0.044)<br>0.923***|(0.052)<br>0.764|
||(0.208)|(0.201)|(0.236)|(0.204)|(0.205)|(0.207)|(0.469)|
|**1**<sup>**st**</sup>**stage Probit**||||||||
|<br>Ln(_AIDj,t-1_)|-0.161|-0.156|-0.164|-0.165|-0.164|-0.167|-0.135|
||(0.132)|(0.129)|(0.132)|(0.132)|(0.132)|(0.133)|(0.438)|
|Ln(_GDPj,t-1_)|-0.111|-0.104|-0.132|-0.108|-0.110|-0.104|0.205|
||(0.316)|(0.311)|(0.319)|(0.316)|(0.315)|(0.319)|(6.521)|
|Ln(_POP jt)_|<br>-0.446***|<br>-0.449***|<br>-0.413***|<br>-0.445***|<br>-0.446***|<br>-0.445***|<br>-0.570|
||(0.140)|(0.138)|(0.153)|(0.137)|(0.139)|(0.135)|(1.446)|
|_DISASTERSjt _|<br>-8.764***|<br>-9.271***|<br>-8.951***|<br>-8.985***|<br>-9.018***|<br>-8.820***|<br>-11.920***|
||(1.149)|(1.174)|(1.097)|(1.106)|(1.127)|(1.076)|(1.251)|
|_NATHAZ – RISKj _|<br>0.061|<br>0.060|<br>0.037|<br>0.051|<br>0.054|<br>0.047|<br>0.036|
||(0.304)|(0.295)|(0.286)|(0.291)|(0.294)|(0.288)|(2.202)|
|Obs.|2186|2186|2186|2186|2186|2186|2118|
|Nonzero Obs.|430|430|430|430|430|430|425|
|Log pseudolikelihood<br> <sup>2</sup>|-2468.210<br>|-2467.615<br>|-2465.740<br>|-2468.301<br>|-2468.258<br>|-2468.28<br>|-2466.052<br>|
|Prob>χ|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|VuongTest(z-value) <sup>a</sup>|14.28***|14.21***|14.56***|14.24***|14.23***|14.20***|13.99***|

_Notes:_ ZINB-estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. The 1<sup>st</sup> stage probit regression estimates the probability that nobody in nation and year died from the specific natural disaster. The 2<sup>nd</sup> stage negative-binomial estimates the number of disaster deaths. Continent dummies, time trend and constant term included in all 2<sup>nd</sup> stage negative-binomial specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.<sup>a</sup> The test compares the ZINB with a negative binomial model and selects the one that is closest to the true conditional distribution (Vuong 1989).<sup>b</sup> indicates that ZINB model did not converge.

34
Table 13: The determinants of Flood fatalities and Institutions - ZINB

||13.1|13.2|13.3|13.4|13.5|13.6|13.7|
|---|---|---|---|---|---|---|---|
|Ln(_AIDj,t-1_)<br>Ln(_AIDj,t-1_)<br>Ln(_GDPj,t-1_)<br>_CORR CONTjt_|-0.344***<br>(0.119)<br>-0.273<br>(0.166)<br>-0.807***<br>(0.219)|-0.321***<br>(0.110)<br>-0.192<br>(0.179)<br>-0.846***<br>(0.213)|-0.292**<br>(0.121)<br>-0.499***<br>(0.164)|-0.187*<br>(0.102)<br>-0.296*<br>(0.154)|-0.298**<br>(0.117)<br>-0.262<br>(0.166)|-0.260**<br>(0.115)<br>-0.381**<br>(0.164)|-0.216*<br>(0.130)<br>-0.452***<br>(0.159)|
|_GOV EFFjt_|||-0.318*<br>(0.174)|||||
|_POL STABjt_||||-0.696***<br>(0.221)||||
|_REG QALjt_|||||-0.631***<br>(0.194)|||
|_RULE LAWjt_||||||-0.376**<br>(0.191)||
|_VOICE ACCjt_|||||||-0.021<br>(0.0204)|
|_GINIjt_|-0.024|-0.025|-0.036**|-0.031*|-0.027*|-0.028|<br>-0.031*|
||(0.016)|(0.017)|(0.017)|(0.017)|(0.016)|(0.017)|(0.017)|
|Ln(_POP jt)_|0.164|0.264*|0.150|0.288*|0.191|0.172|0.207|
||(0.149)|(0.150)|(0.150)|(0.142)|(0.147)|(0.142)|(0.138)|
|_DISASTERSjt _|0.314***|0.314***|0.403***|0.339***|0.343***|0.358***|0.372***|
|_NATHAZ – RISKj _|(0.109)<br>0.239|(0.114)<br>0.321**|(0.124)<br>0.372**|(0.111)<br>0.340**|(0.114)<br>0.344**|(0.124)<br>0.451**|(0.128)<br>0.391**|
||(0.166)|(0.157)|(0.169)|(0.168)|(0.171)|(0.183)|(0.177)|
|**1**<sup>**st**</sup>**stage Probit**||||||||
|<br>Ln(_AIDj,t-1_)|-0.306*|-0.306*|-0.371**|-0.327*|-0.332*|-0.370**|-0.417**|
||(0.171)|(0.175)|(0.182)|(0.170)|(0.173)|(0.171)|(0.200)|
|Ln(_GDPj,t-1_)|<br>-0.402*|<br>-0.398*|<br>-0.457**|<br>-0.454**|<br>-0.436**|<br>-0.470**|<br>-0.488**|
|<br>Ln(_POP t)_|(0.223)<br>-0.513***|(0.227)<br>-0.515***|(0.222)<br>-0.590***|(0.219)<br>-0.546***|(0.217)<br>-0.554***|(0.212)<br>-0.591***|(0.218)<br>-0.613***|
|_j_<br>_DISASTERSjt _|(0.194)<br>-8.038***|(0.196)<br>-8.006***|(0.186)<br>-8.330***|(0.180)<br>-8.153***|(0.188)<br>-8.182***|(0.178)<br>-8.305***|(0.199)<br>-8.472***|
||(0.660)|(0.703)|(0.739)|(0.684)|(0.700)|(0.700)|(0.837)|
|_NATHAZ – RISKj _|-0.175|-0.151|-0.149|-0.167|-0.161|-0.148|-0.124|
||(0.156)|(0.158)|(0.169)|(0.164)|(0.161)|(0.166)|(0.185)|
|Obs.|<br>2174|<br>2174|<br>2174|<br>2174|<br>2174|<br>2174|<br>2106|
|Nonzero Obs.|605|605|605|605|605|605|601|
|Log pseudolikelihood<br> <sup>2</sup>|-3595.895<br>|-3596.888<br>|-3609.905<br>|-3601.927<br>|-3602.016<br>|-3609.569<br>|-3588.584<br>|
|Prob>χ|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|VuongTest (z-value)<sup>a</sup>|16.34***|16.27***|16.68***|16.43***|16.38***|16.37***|16.57***|

_Notes:_ ZINB-estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. The 1<sup>st</sup> stage probit regression estimates the probability that nobody in nation and year died from the specific natural disaster. The 2<sup>nd</sup> stage negative-binomial estimates the number of disaster deaths. Continent dummies, time trend and constant term included in all 2<sup>nd</sup> stage negative-binomial specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.<sup>a</sup> The test compares the ZINB with a negative binomial model and selects the one that is closest to the true conditional distribution (Vuong 1989).

35
36
Table 14: The determinants of Earthquake fatalities and Institutions - ZINB

||14.1|14.2|14.3|14.4|14.5|14.6|14.7|
|---|---|---|---|---|---|---|---|
|Ln(_AIDjt-1_)|-0.448***|-0.425***|-0.545**|-0.158|-0.449***|-0.294|-0.288|
|_,_|(0.174)|(0.157)|(0.269)|(0.246)|(0.173)|(0.275)|(0.308)|
|Ln(_AIDj,t-1_)|0.479|0.795***|0.100|0.003|0.344|-0.125|-0.273|
||(0.321)|(0.230)|(0.485)|(0.675)|(0.367)|(0.832)|(0.852)|
|Ln(_GDPj,t-1_)|-1.423***<br>(0.369)|||||||
|_CORR CONTjt_||-1.996***<br>(0.297)||||||
|_GOV EFFjt_|||-1.062<br>(0.665)|||||
|_POL STABjt_||||-1.044***<br>(0.293)||||
|_REG QALjt_|||||-1.305***<br>(0.310)|||
|_RULE LAWjt_||||||-0.284<br>(0.540)||
|_VOICE ACCjt_|||||||-0.014<br>(0.064)|
|_GINIjt_|0.012|-0.003|0.001|-0.012|-0.019|-0.010|-0.010|
||(0.026)|(0.020)|(0.028)|(0.043)|(0.027)|(0.034)|(0.037)|
|Ln(_POP jt)_|<br>0.101|<br>0.384*|<br>-0.193|<br>0.609*|<br>0.230|<br>0.390|<br>0.432|
||(0.297)|(0.207)|(0.513)|(0.348)|(0.271)|(0.341)|(0.345)|
|_DISASTERSjt _|0.127|0.110|0.0966|0.218|0.105|-0.0118|0.0210|
||(0.232)|(0.200)|(0.212)|(0.303)|(0.256)|(0.232)|(0.223)|
|_NATHAZ – RISKj _|<br>0.999|<br>1.075**|<br>0.925|<br>1.921*|<br>1.324**|<br>2.090**|<br>2.263**|
||(0.722)|(0.496)|(1.138)|(0.984)|(0.668)|(0.929)|(0.919)|
|**1**<sup>**st**</sup>**stage Probit**||||||||
|<br>Ln(_AIDj,t-1_)|-0.447|-0.443**|-0.554|-0.459|-0.461**|-0.490|-0.425|
||(0.979)|(0.214)|(0.398)|(0.451)|(0.206)|(0.510)|(0.576)|
|Ln(_GDPj,t-1_)|0.584|0.423|0.925|0.766|0.314|0.851|0.746*|
||(1.811)|(0.397)|(0)|(0)|(0)|(0)|(0.365)|
|Ln(_POP jt)_|<br>-0.150|<br>-0.115|<br>-0.316|<br>-0.199|<br>-0.0789|<br>-0.247|<br>-0.195|
||(1.413)|(0.226)|(0.302)|(0.353)|(0.276)|(0.387)|(0.440)|
|_DISASTERSt_|<br>-10.94|<br>-10.36***|<br>-12.26***|<br>-11.01***|<br>-10.83***|<br>-12.00***|<br>-12.07***|
|_j _|(0)|(0.823)|(1.832)|(1.541)|(1.561)|(1.953)|(1.708)|
|_NATHAZ – RISKj _|1.143|1.146***|1.104**|1.116***|1.319*|1.096***|1.165***|
||(0.955)|(0.407)|(0.437)|(0.398)|(0.674)|(0.364)|(0.392)|
|Obs.|<br>2186|<br>2186|<br>2186|<br>2186|<br>2186|<br>2186|<br>2118|
|Nonzero Obs.|197|197|197|197|197|197|197|
|Log pseudolikelihood|-1194.289|-1186.551|-1198.308|-1195.723|-1194.314|-1200.931|-1201.144|
|Prob>χ<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|VuongTest (z-value)<sup>a</sup>|7.30***|7.33***|7.09***|7.52***|7.28***|7.13***|7.13***|

_Notes:_ ZINB-estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. The 1<sup>st</sup> stage probit regression estimates the probability that nobody in nation and year died from the specific natural disaster. The 2<sup>nd</sup> stage negative-binomial estimates the number of disaster deaths. Continent dummies, time trend and constant term included in all 2<sup>nd</sup> stage negative-binomial specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.<sup>a</sup> The test compares the ZINB with a negative binomial model and selects the one that is closest to the true conditional distribution (Vuong 1989).

37
Table 15: Disaster fatalities and aid from different donor countries - ZINB

||Stor|ms|Flo|ods|Earthq|uakes|
|---|---|---|---|---|---|---|
||Coeff.|ZI Probit|Coeff.|ZI Probit|Coeff.|ZI Probit|
|Ln_(FRENCH AIDj,t-1)_|-0.100|-0.153|0.008|-0.123|1.011<sup>~~a~~</sup>|-0.090|
||(0.423)|(0.535)|(0.248)|(0.219)|(0.814)|(1.787)|
|Ln_(GERMAN AIDj,t-1)_|0.371|-0.342|-0.337***|-0.428***|-1.475***|-1.117|
||(0.229)|(0.787)|(0.117)|(0.164)|(0.354)|(1.140)|
|Ln_(JAPANESE AIDj,t-1)_|0.158<sup>a</sup>|-0.450|-0.284***|-0.150|-0.804***|-0.294|
||(0.185)|(0.403)|(0.080)|(0.108)|(0.245)|(0.438)|
|Ln_(UK AIDj,t-1)_|-0.725|-0.188|-0.682***|-0.593|-0.437|-3.064|
||(0.441)|(0.692)|(0.252)|(0.925)|(0.571)|(1.700)|
|Ln_(USA AIDj,t-1)_|0.500**<sup>a</sup>|0.071|-0.440***|-0.195|-0.042<sup>a</sup>|-1.278|
||(0.228)|(0.433)|(0.107)|(0.158)|(0.294)|(0.698)|

_Notes:_ ZINB-estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. The<sup>1st</sup> stage probit regression estimates the probability that nobody in nation and year died from the specific natural disaster. The 2<sup>nd</sup> stage negative-binomial estimates the number of disaster deaths. Covariates from the ZINB baseline specification (Table 11), continent dummies, time trend and constant term included in all 2<sup>nd</sup> stage negative-binomial specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively. aindicates that ZINB model did not converge.

38
Table 16: Disaster fatalities, aid and institutional quality

||Sto|rms|Flo|ods|Earth|quakes|
|---|---|---|---|---|---|---|
||Coeff.|ZI Probit|Coeff.|ZI Probit|Coeff.|ZI Probit|
|Ln(_AIDjt-1_)|0.377***|-0.150|-0.394***|-0.200|-0.603***<sup>~~a~~</sup>|-0.942|
|_,_|(0.129)|(0.131)|(0.139)|(0.229)|(0.193)|(1.726)|
|_CORR CONTjt_|0.623**|0.478|-1.199***|-0.562|-1.923***|-7.954*|
||(0.253)|(0.355)|(0.232)|(0.820)|(0.430)|(4.195)|
|_CORR CONTjt* AIDjt-1_|-0.317***|-0.264|0.323***|0.172|0.219|3.462|
|_,_|(0.117)|(0.218)|(0.101)|(0.216)|(0.321)|(2.481)|
|Ln(_AIDjt-1_)|0.379***|-0.493|-0.269**|-0.377**|-0.462**|-0.352|
|_,_|(0.145)|(0.476)|(0.124)|(0.177)|(0.230)|(0.255)|
|_GOV EFFjt_|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
||n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|_GOV EFFjt* AIDj,t-1_|<br>-0.442***|<br>-0.720*|<br>0.030|<br>0.005|<br>-0.870***|<br>-0.625**|
||(0.106)|(0.391)|(0.086)|(0.096)|(0.208)|(0.303)|
|Ln(_AIDj,t-1_)|<br>0.149^a|<br>-0.329|<br>-0.277***|<br>-0.345**|<br>-0.163|<br>65.008***|
||(0.162)|(0.642)|(0.075)|(0.131)|(0.446)|(0.518)|
|_POL STABjt_|<br>0.153|<br>0.644|<br>-1.076***|<br>0.183|<br>-2.256***|<br>-93.576|
||(0.305)|n.a.|(0.181)|(0.242)|(0.773)|(0.835)|
|_POL STABjt* AIDj,t-1_|-0.413***|-0.431***|0.360***|-0.087|0.633|-17.019***|
||(0.103)|(0.155)|(0.070)|(0.089)|(0.464)|(0.835)|
|Ln(_AIDj,t-1_)|<br>0.373**|<br>-0.176|<br>-0.427***|<br>-0.308*|<br>-0.243|<br>-0.184|
||(0.162)|(0.123)|(0.146)|(0.180)|(0.187)|(0.736)|
|_REG QALjt_|0.243|-0.180|-1.536***|-0.058|-1.802***|-0.194|
||(0.329)|(0.565)|(0.467)|(0.343)|(0.669)|(1.464)|
|_REG QALjt* AIDjt-1_|-0.313*|-0.093|0.461***|0.006|0.532|-0.690|
|_,_|(0.182)|(0.187)|(0.139)|(0.104)|(0.474)<br>|(0.772)|
|Ln(_AIDj,t-1_)|0.370**|-0.086|-0.366***|-0.357***|-0.449***<sup>a</sup>|0.467|
||(0.161)|(0.141)|(0.128)|(0.179)|(0.179)|(13.840)|
|_RULE LAWjt_|<br>0.293|<br>0.435|<br>-1.048***|<br>0.053|<br>-1.371***|<br>-16.037***|
||(0.263)|(0.607)|(0.228)|(0.317)|(0.479)|(3.634)|
|_RULE LAWjt* AIDj,t-1_|-0.312**|-0.188|0.330***|-0.102|0.016|5.938***|
||(0.149)|(0.148)|(0.101)|(0.116)|(0.327)|(5.626)|
|Ln(_AIDjt-1_)|<br>0.429**|<br>0.023|<br>-0.363***|<br>-0.352**|<br>-0.265|<br>696.449***|
|_,_|(0.184)|(0.245)|(0.138)|(0.173)|(0.224)|(17.202)|
|_VOICE ACCjt_|<br>0.644*|<br>1.214|<br>-0.868***|<br>-0.009|<br>0.546|<br>613.177***|
||(0.337)|(1.522)|(0.282)|(0.383)|(0.437)|(14.891)|
|_*_|<br>-0393*|<br>-0448|<br>0287**|<br>0045|<br>0546|<br>-674046***|
|_VOICE ACCjt AIDjt-1_|.|.|.|.|.|.|
|_,_|(0.215)|(0.851)|(0.129)|(0.120)|(0.437)|(16.594)|
|Ln(_AIDj,t-1_)|<br>.207|<br>11.926***|<br>-0.272*|<br>-0.501**|<br>-0.492|<br>10.489|
||(0.139)|(0.308)|(0.159)|(0.208)|(0.397)|(30.378)|
|_DEMOCRACYjt_|0.013|14.300***|0.052|-0.082*|-0.103^a|1.224|
||(0.033)|(0.270)|(0.039)|(0.043)|(0.119)|(17.077)|
|_DEMOCRACYjt* AIDj,t-1_|<br>0.001|<br>-5.037***|<br>0.012|<br>0.021|<br>0.049|<br>-0.909|
||(0.017)|(0.123)|(0.014)|(0.013)|(0.054)|(4.648)|

_Notes:_ ZINB-estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. The<sup>1st</sup> stage probit regression estimates the probability that nobody in nation and year died from the specific natural disaster. The 2<sup>nd</sup> stage negative-binomial estimates the number of disaster deaths. Covariates from the ZINB baseline specification (Table 11), continent dummies, time trend and constant term included in all 2<sup>nd</sup> stage negative-binomial specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.<sup>a</sup> indicates that ZINB model did not converge.

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Table 17: Variable Definition and Source

|**Variable**|**Description**|**Source**|
|---|---|---|
|_DEATH_|Total number killed by a natural disaster|EM-DAT, CRED (2008)|
|_AID_|Aid per capita (current US Dollars)<br>Total real ODA commitments in recipient country per<br>capita|World Bank,<br>WDI|
|_GDP p.c._|Real GDP per capita (US Dollars in 2000 prices)|Penn World Table<br>Version 6.2|
|_GINI_|Measure for income inequality|gruen03|
|_POP_|Total Population expressed in thousands|World Bank,<br>WDI|
|_POPDENS_|People per square kilometer|World Bank,<br>WDI|
|_OPEN_|Exports plus Imports of goods and services divided by<br>GDP|Penn World Table<br>Version 6.2|
|_MAGNITUDE_|Disaster magnitude expressed in:<br>_Storms:_Wind speed measured in Kilometers per hour<br>_Floods:_Number of months per year in the country,<br>with a precipitation sum that is 1 standard deviation<br>above the long time precipitation mean in the country<br>_Earthquakes:_Richter scale|EM-DAT, CRED (2008)<br>NOAA (2008)<br>EM-DAT, CRED (2008)|
|_NATHAZ-RISK_|GIS-DATA on spatial mortality risk<br>Country mean (0-10)|Dilley et al. (2005)|
|_LATITUDE_|Absolute Latitude in degrees|Kahn (2005)|
|_ELEVATION_|Elevation in meters|Kahn (2005)|
|_CORR CONT_|Perception of the extent to which public<br>power is exercised for private gain, including<br>both petty and grand forms of corruption,<br>as well as influence of elites.|Kaufmann et al. (2008)|
|_GOV EFF_|Perception of the quality of public<br>services, the quality of the civil service<br>and the degree of its independence from<br>political pressures, the quality of policy<br>formulation and implementation, and the|Kaufmann et al. (2008)|

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||credibility of the government's commitment<br>to such policies.||
|---|---|---|
|_POL STAB_|Perception of the likelihood that the government<br>will be destabilized or overthrown by un-<br>constitutional or violent means, including<br>politically-motivated violence and terrorism.|Kaufmann et al. (2008)|
|_REG QAL_|Perception of the ability of the government<br>to formulate and implement sound policies and<br>regulations that permit and promote private<br>sector development.|Kaufmann et al. (2008)|
|_RULE LAW_|perceptions of the extent to which agents have<br>confidence in and abide by the rules of society,<br>and in particular the quality of contract<br>enforcement, property rights, the police, and the<br>courts, as well as the likelihood of crime and violence.|Kaufmann et al. (2008)|
|_VOICE ACC_|Perception of the extent to which a<br>country's citizens are able to participate<br>in selecting their government, as well as<br>freedom of expression, freedom of association,<br>and a free media.|Kaufmann et al. (2008)|
|_DEMOCRACY_|Proxy for the political system in a country.<br>Include information on competitiveness and openness<br>of executive recruitment, constraints on chief executive, <br>regulation and competitiveness of participation.|Marshall & Jaggers<br>(2005)<br> <br>|
|_COLONY_|Colonial Background<br>Dummy variable that switches to one if the<br>country has ever been a colony|Correlates of War<br>2 Project (2008)<br>|
|_AFFINITY USA_|Extended Gartzke index on voting patterns<br>in the UN General Assembly. Takes a value between -1<br>(USA and recipient never voted the<br>Same) and 1 (USA and recipient always voted the<br>same).|Voeten and<br>Merdzandovic<br>(2009)|
|_OIL RESOURCES_|Proved oil reserves in 1000 tons|US Energy<br>Information<br>Administration|
|_GAS PROD_|Annual production of natural gas in terajoules|US Energy<br>Information<br>Administration|

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