output, while specifications that lack household fixed effects tend to show a negative relationship. This is regardless of whether the specification is linear or quadratic. By contrast, in Uganda, quadratic specifications tend to result in negative coefficients on variance while linear specifications tend to result in positive coefficients. This is regardless of whether household fixed effects are included or not.

While with rainfall we could conclude that MERRA-2 and ERA5 were poor choices, with temperature all three products produce roughly similar results. But this appears to have as much to do with the lack of a strong relationship between the temperature metrics and outcomes, as with the products themselves. Where we do find differences, it is likely to be due to cross-country variation and with the specific functional form of the regression. To the extent that these differences may reflect the on-the-ground reality in each country, it suggests that the use of variance, or any temperature metric, requires specific knowledge about the country and application where it will be used.

# **4.3.4 Selecting a Remote Sensing Product**

Summarizing our results, mean and total rainfall are clearly substitutes for each other. While these two metrics perform substantially better than most other rainfall metrics, the choice between them seems to be immaterial. The same is true for the number of and share of rainy days. These two metrics outperform other rainfall metrics but produce results extremely similar to one other. In terms of remote sensing products, MERRA-2 is seemingly inferior to the other five precipitation products. Regardless of which of the four rainfall metrics are used, coefficients on these metrics derived from MERRA-2 data tend to be non-significant while coefficients derived from the other remote sensing products are significant. This suggests that MERRA-2 suffers from classical measurement error, with sufficient noise introduced into the data to result in rainfall metrics being uncorrelated with outcomes. ERA5 is also inferior to CHIRPS, CPC, ARC2, and TAMSAT. As is evinced by the descriptive statistics, ERA5 tends to report much higher volumes of rainfall, which suggests the existence of non-classical measurement error. This conclusion is supported by our specification charts for mean and total rainfall, where coefficients derived from ERA5 data tend to be more positive than coefficients from other remote sensing products. This bias in results for ERA5 does not carry over to the number or share of rainy days, since these metrics are not based on the volume of rainfall, which is where ERA5 deviates from the other remote sensing products. Based on this evidence, we conclude that MERRA-2 and ERA5 are poor choices when seeking a remote sensing source for precipitation to match with household data.

Dissimilar from rainfall, for temperature we are unable to draw strong conclusions. In general, results for all three temperature products are broadly alike, suggesting one could use any of the three. But this general similarity is disturbed by differences in highly specific cases. Unlike rainfall, we cannot identify patterns of differences, such as with MERRA-2 and ERA5. Therefore, we caution 42