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 ́.

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