and meteorological sources, that comprise droughts, earthquakes, floods, storms, and temperature extremes in more than 100 countries for over 30 years.<sup>1</sup> The disaster index exploits physical measures of disaster intensity: precipitation, Richter scale, wind speed, and temperature. As such, it is more suitable for estimating causal relations between natural disasters and economic variables. Other databases (EM-DAT, NatCatSERVICE, and similar) contain disaster indicators that are functions of economic development and, therefore, may lead to biased estimates (Felbermayr and Groeschl, 2014).

More particularly, the weather variation represents the difference in monthly rainfall in mm, defined as the proportional deviation of total monthly rainfall from average monthly rainfall of the entire available period. Flooding events are measured by the positive difference in total monthly precipitation, while droughts are indicated with the dummy variable. It is equal to one if at least three subsequent months have rainfall below 50 percent of the long-run average monthly mean, or if at least five months within a year have rainfall below 50 percent of the long-run monthly mean, and zero otherwise. The database uses the maximum realization within a single earthquake event as the measure of the physical disaster intensity of that earthquake. Similarly, the maximum total wind speed in knots on a country basis is their disaster intensity measure for storms. Temperature extremes are measured as the percentage difference between the maximum temperature in one month from the corresponding long-run monthly mean. Strong positive deviations are interpreted as heat waves, while strong negative ones are defined as cold waves.

The overall disaster index uses the inverse of the standard deviation of a disaster type within a country over all years as precision weights to assure that no single disaster component dominates the movement of the whole index. Furthermore, as the impact of a given disaster on the economy of a country clearly depends on the disaster intensity relative to the overall size of the country, the authors scale all respective disaster variables by land area, as suggested in Skidmore and Toya (2002).

The rest of the data comes from the World Bank WDI. The exceptions are the Polity index which is taken from the Polity IV Project and gross government debt-to-GDP ratios from the IMF Historical Public Debt Database. The final dataset covers an unbalanced panel of 108 economies over the period between 1979 and 2010.

> 1The unbalanced panel covers 108 countries over the period 1979-2010: Albania, Algeria, Angola, Argentina, Armenia, Australia, Azerbaijan, Bangladesh, Belarus, Belgium, Bolivia, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cameroon, Canada, Chad, China, Colombia, Congo, Rep., Costa Rica, Cˆote d’Ivoire, Croatia, Cyprus, Czech Republic, Denmark, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Ethiopia, Finland, France, Gabon, Germany, Greece, Guatemala, Haiti, Honduras, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kenya, Korea, Rep., Kuwait, Kyrgyz Republic, Lao PDR, Liberia, Madagascar, Malaysia, Mali, Mauritania, Mexico, Mongolia, Morocco, Mozambique, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, North Macedonia, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Romania, Russian Federation, Rwanda, Senegal, Serbia, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, Sweden, Switzerland, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Togo, Trinidad and Tobago, Tunisia, Uganda, Ukraine, United Kingdom, United States, Uruguay, Vietnam, Yemen, Rep. and Zambia.

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