heterogeneity ( Hit ). This will allow us to ascertain the sensitivity of drought impacts across recent wet and dry periods, arid versus humid climatic zones and income classes.

gi,t = f(Dit) × Hit + βXit + γi + φt + μc×t + εi,t (4) To estimate how antecedent wet or dry conditions affect the relationship, the analysis calculates how much rainfall has deviated from long-run averages in the three years prior to the shock by summing z-scores over the three-year period. If there were significant rainfall deficits, these deficits would stack, and the cumulative z-score calculation would record a very deep three-year water deficit. This cumulative z-score is interacted with the main dry shock variables in the regression to assess whether wetter or drier conditions prior to a dry shock determines its impact on growth. For example, a higher and positive cumulative z-score captures rainfall increases in previous years that can raise green water or soil moisture in the root zone of crops.

To investigate the role of forests and landscapes in influencing conditions through which green water is maintained, past local and upstream share of forest cover is measured using satellite data from the European Space Agency. High forested areas denote places where the forest area is more than the 50th percentile of the forest distribution among all grid cells in each country. The interaction of high forested areas with the main dry shock variables in the regression allows us to assess whether higher local and upstream forest cover can help buffer the growth impacts of dry shocks.

Geography can also matter and different regions with different climatological baseline climates may respond differently to droughts. This also allows us to test for heterogenous responses to droughts. On the one hand, if regions where dry shocks are common are able to adapt to drier conditions, droughts should be less harmful to economic growth. In other words, economic growth would be less sensitive to dry shocks in arid areas. Conversely, recurring dry shocks may have cumulative impacts that weaken adaptive capacity. We test for such effects using a binary indicator for arid and semi- arid areas as the interaction term. Likewise, poorer countries may also face higher marginal damages due to the limited ability of exposed populations to manage the consequences of droughts. We test this hypothesis by splitting the sample by country income level to give an indication of how wealth and economic composition impact this relationship. Our analytical strategy therefore incorporates both an absolute metric of baseline climate, as well as the relative ability of different populations in different regions to manage the risks of droughts.

# 4. Results

This section is split into 4 subsections. Section 4.1 presents the main results of 1 SD dry shocks from estimating equation (3) and the various heterogeneities from estimating equation (4). Section 4.2 presents the impacts of dry shocks along the distribution of drought severity. To provide a sense of magnitudes of loss over the entire time-period, Section 4.3 uses the estimates to calculate the cumulative mean annual losses in GDP per capita growth in each grid cell. Section 4.4. presents robustness checks.

## 4.1 Impact of 1 SD rainfall shocks

Table 1 shows the main results. Columns (1) and (2) provide estimates of equation (1) for different specifications for rainfall. Column 1 includes a quadratic in precipitation and column 2 models rainfall as a z- score. In both models the coefficient is positive and significant showing that generally higher precipitation is associated with increases in growth.