With 3 bins, coefficients α2 and α3 measure the impact of these dry and wet conditions relative to the near normal condition (the excluded category is −1 < z< 1 ).

git = α1 + α2Zit−1 + α3Zit+1 + βXit + γi + φt + μc×t + εi,t (3) We estimate several such 3-bin models, using different thresholds for k= 1.6, 1.3, 0.8 and 0.5 to check for sensitivity of the results to different thresholds. Next, we estimate models with different number of bins and corresponding thresholds together such that the estimated coefficients demonstrate how drought responses may change across the entire distribution of drought severity.

Following standard practices in the literature that use statistical analysis, we employ a fixed effects panel regression because of the wide variety of latent and non-latent factors that can impact economic growth. A very large and long-standing economics literature exists on the determinants of economic growth and development (see, for instance, Ciccone and Jarocinsky 2010; Acemoglu, Johnson, and Robinson 2005; Barro 2003). In this paper, we are not attempting to explore these factors. Rather, we wish to isolate the impact of one such potential impediment to growth—drought. In order to do so, we control for factors that may simultaneously impact both economic growth and droughts.

This is done through controlling for a strong set of fixed effects and time trends, including: grid cell fixed effects ( γi ), which control for time invariant, local characteristics that may impact economic growth; year fixed effects ( φt ), which controls for changes in global patterns of economic growth; country-specific time trends ( μc×t ) which accounts for country specific trends in both droughts and economic growth. Since weather variables tend to be correlated over time in the same location, we also include a vector Xit that accounts for a quadratic in temperature to account for the well-established response of the overall economy to temperature increases. The rich set of fixed effects and controls in this specification isolates localized and unexpected fluctuations in shocks, facilitating causal inference, and is necessary to isolate the impact of exogenous changes in droughts on economic growth. Similar approaches have been used in previous climate and weather impact studies (Burke and Tanutama, 2019; Callahan and Mankin, 2022; Damania, Desbureaux, and Zaveri, 2020).

Standard errors are clustered at the Administrative 1 level (the largest subnational level in a country, e.g., a state in the US) in order to account for spatial and serial correlation. We test the robustness of the results through additional tests for spatial autocorrelation to account for spatial and temporal dependence. Following other literature, we weight observations based on grid cell population. Because observations corresponding to grids with higher population have smaller variance, population weighting in the grid cell regressions accounts for heteroscedasticity and yields more efficient estimates (Damania, Desbureaux, and Zaveri 2020; Solon et al. 2015).

Unlike studies which estimate economy-wide costs for specific drought events ex-post, our econometric approach yields estimates of economic impact for marginal increases in the occurrence and intensity of droughts. This information can be particularly useful for policy makers who need to allocate scarce financial resources for drought assistance and emergency programs.

Droughts covary with other variables that may drive heterogeneity in the effect of drought such as recent and past climate history, as well as income. Therefore, we also consider models that allow heterogeneity in the response to droughts, by soil moisture conditions, aridity and income characteristics of the grid cell. Equation (1) is modified to allow interaction between the drought variables and different measures of