Column (3) estimates equation (3) and uses three categories of bins comparing 1 SD dry and wet shocks relative to the excluded category ( −1 < z< 1) . Results in column (3) show that a 1 SD dry shock reduces GDP growth by 0.47 percentage points, with effects concentrated in the developing world where the impacts are slightly larger than in the full sample at 0.54 percentage points. In the high-income country sample, the impact of a 1 SD dry shock is small and statistically insignificant, indicating a precisely estimated null impact.

Positive 1 SD shocks show a less consistent pattern across income classes. They improve growth in the developing country sample but can be harmful in high-income countries. This may be because aboveaverage rainfall could both be a blessing, in the form of increased water resources, or a curse, in the form of flooding. Because most developing countries are in drier parts of the world and are more dependent on agriculture, above-average rainfall is typically associated with increases in crop yields and may act as a buffer for future dry shocks by storing soil moisture in the root zone. These results are consistent with the hydrological literature which emphasizes the importance of green water for supporting biomass growth (Li et al, 2019). In high-income countries, the negative effects of wet shocks reflect the effects of floods on infrastructure and urban assets. Recent research has found that in high-income countries extreme wet events, such as extreme daily rainfall or the number of wet days, are particularly adverse for economic growth via the manufacturing and services sectors (Kotz, Levermann, and Wenz, 2022). Improvements in disaster risk management and relief also mean that recovery from floods is rapid in developed economies, with limited observable effect on economic activity (Kocornik-Mina et al. 2020).

**Table 1 Impact of rainfall on GDP per capita growth**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. Var.<br>Δlog(GDPp.c.)|Global|High Income|Developing|Global|High Income|Developing|
|Precip|1.5734***|-0.2635|1.9399***||||
||(0.334)|(0.494)|(0.381)||||
|Precip sq|-0.2506***|-0.0869|-0.3000***||||
||(0.069)|(0.118)|(0.081)||||
|Dry shock (z<-1)||||-0.4761***|-0.1103|-0.5429***|
|||||(0.107)|(0.100)|(0.125)|
|Wet shock (z>1)||||0.0981|-0.4115+|0.2721*|
|||||(0.121)|(0.226)|(0.137)|
|Avg Temp(C)|0.6928**|0.2420*|0.7683*|0.6992**|0.2458*|0.7802*|
||(0.225)|(0.107)|(0.338)|(0.225)|(0.108)|(0.340)|
|Avg Temp Sq.|-0.0211**|-0.0129***|-0.0238**|-0.0220***|-0.0130***|-0.0250**|
||(0.006)|(0.004)|(0.008)|(0.006)|(0.004)|(0.008)|
|N|869447|201466|667981|869447|201466|667981|
|Cell FE|y|y|y|y|Y|Y|
|Year FE|y|y|y|y|Y|Y|
|Country Trends|y|y|y|y|Y|Y|
|R-sq|0.319|0.392|0.305|0.319|0.393|0.305|

_Note: Dependent variable is change in gridcell log(GDP p.c.). Observations are weighted by population. Standard errors in parentheses are clustered at the Administrative 1 level. Statistical significance is given by + p<0.1, * p<0.05, ** p<0.01, *** p<0.001._ Is the relationship between dry shocks and GDP in the developing world explained by agriculture, the sector that is more directly affected by rainfall variability? We use data from the ESA CCI project to determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split the sample based on different shares of cropland ranging from less than 20 percent to more than 75 percent. Having