# 2. Data

We use weather data taken from the _Terrestrial Air Temperature and Precipitation Version 4.01_ compiled by the University of Delaware (Willmott and Matsuura 2001) that has widely been used in the economics literature (Dell Jones and Olken 2012, Burke Hsiang and Miguel 2015, among others). This data set provides monthly total precipitation and temperature at a 0.5-degree spatial resolution. Data are available for each month between 1901 and 2014. Annual precipitation at the cell level between 1990 and 2014 ranged between 3mL in Sudan to 10,187mL in India.

To define when a grid cell experienced a shock, the z-score of rainfall is calculated in each grid cell, and in rainit−raini each time period as per zit = , where zit is the z-score of rainfall in grid cell _i_ at time _t_ , rainit is σi rainfall in grid cell _i_ at time _t_ , raini is mean rainfall in grid cell _i_ over the full time period of the climate dataset, and σi is the standard deviation of rainfall in grid cell _i_ over the same time period. The z-score for rainfall is interpreted as the number of SDs away from the long-run mean in time _t_ . Once z-scores are calculated, binary variables are generated to indicate whether the z-score passes certain thresholds. A grid cell is considered to have a _dry shock_ if rainfall in a given year is lower than the long-run annual mean for the grid cell by at least X SD ( z≤X), where X can be any range of integer values. Similarly, a grid cell is considered to have a _wet shock_ if rainfall in a given year is higher than the long-run annual mean for the grid cell by at least X SD ( z≥X). Doing this allows for a semiparametric test of the impacts of deviations from the long-run mean. It also allows for impacts to be both nonlinear and nonsymmetric around zero. These properties are important for distinguishing the possible heterogeneous impacts of deluges versus droughts.

Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka and Guillaume (2018). The data are primarily based on sub-national GDP per capita data constructed by Gennaioli, _et al._ (2013) and covers 82 countries, representing 85% of the global population and 92% of global total GDP (PPP) in 2015. Population data is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).

To give an indication of how wealth and economic composition impact the relationship between droughts and economic growth, we use World Bank income group classifications to divide the world into developing countries (that includes low-income, lower-middle and upper-middle income countries), and high-income countries. Classifications are based on mean per-capita GNI in 2015 where low-income countries have GNI per capita below $1,025, middle-income countries are between $4,036 and $12,475, and high-income countries are above $12,475. We also use the Global Aridity Index and Potential Evapotranspiration Climate Database (Trabucco and Zomer 2019) to differentiate grid cells based on their aridity. Additionally, local and upstream shares of forest cover are measured using satellite data from the European Space Agency.

# 3. Empirical Strategy

Our econometric specification uses a panel fixed-effects model to link data on droughts to data on economic growth at the level of 0.5-degree grid cells (approximately 56 kilometers x 56 kilometers at the equator) between 1991 and 2014, the period for which economic data is available at a granular scale.

In order to estimate the impact of droughts on economic growth, we follow much of the empirical climate change literature and estimate a reduced-form production function-style equation (Dell Jones and Olken 2012, Burke Hsiang and Miguel 2015; Deryugina and Hsiang, 2017; Newell et al., 2018; Kahn et al., 2019;