_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