on the objective facts of the weather frequently do not exist, as it would require rain gauges and thermometers at every location, as well as someone to record the relevant data on a daily (or hourly) basis. This lack of data on the objective facts of the weather is exactly why economists use remote sensing data. Because of this lack of data on the objective facts, we can only compare extraction methods, weather metrics, and remote sensing products relative to each other. For the most part, this does constrain our ability to reject or fail to reject a hypothesis. Extraction methods clearly do not impact results, there are clearly good and bad weather metrics, and some precipitation products clearly report different volumes of precipitation than others. Where we are limited by the lack of objective facts is in drawing conclusions about temperature products. Without knowing the actual temperature experienced by LSMS-ISA households, we cannot determine if the lack of correlation between temperature metrics and outcomes is either a reflection of the true relationship or evidence that all three products mismeasure temperature.

In terms of establishing a set of best practices, we recommend the following:

- Researchers need not be concerned about weather data matched to obfuscated household locations. Our results do not change substantially based on which obfuscation method we conduct the analysis on. The current spatial resolution of publicly available remote sensing weather is not fine enough for common extraction methods to result in mismeasurement of the weather that is experienced by a household. Researchers should feel comfortable matching obfuscated coordinates with weather data and need not worry that extraction methods will materially alter their results.

- Researchers must take much more care in justifying their choice of weather metric. For the LSMS-ISA data, mean daily rainfall, total seasonal rainfall, the number of rainy days, and percent of rainy days are consistent and positive predictors of outcome in a large number of models and countries. Performance of the other weather metrics are more inconsistent, frequently producing coefficients with opposite signs in similar setting. Since several weather metrics perform equally well, and many weather metrics perform equally poor, one needs to provide a reasonable defense for why one metric (e.g., mean daily) was selected and other metrics (e.g., total seasonal) were not. This is especially true when identification of causal impacts relies on the choice of a weather metric. While specific cases might require a specific weather metric, the reasoning should be made explicit and a justification of why other metrics are inappropriate should be provided.

- Researchers should provide evidence that results are robust to the choice of weather metric. There exists substantial cross-country heterogeneity in our results related to rainfall and temperature metrics. This suggests that results are sensitive to specific locations and applications. The current standard in applied economics research is to provide a variety of robustness checks to differences in assumptions about model specification or to changes in the underlying

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