rainfall patterns. While our two outcomes are both measures of agricultural productivity, many papers that use weather data to investigate non-agricultural outcomes justify the use of weather in identification because of its relationship to agricultural productivity and productivity’s relationship with the non-agricultural outcome of interest (Jayachandran, 2006; Deschˆene and Greenstone, 2007; Corno et al., 2020). Therefore, we believe our conclusions, and the best practices built on those conclusions, have broad applicability to the use of remote sensing weather data in economics.

# **5.2 Future Work**

There are a number of extensions to this work which are currently ongoing. Some of these relate to data and analysis defined in our pre-analysis plan while others are exploratory in that they are not pre-specified.

In terms of pre-specified elements, we are presently working to incorporate data from Mali as well as additional weather metrics. LSMS-ISA data from Mali comes from two rounds but those rounds are not panel data, thus we could not use it as part of the current analysis which includes household fixed effects in four of six model specification. We are also working to incorporate indices of weather variables (e.g. evapotranspiration, water requirement satisfaction index, Palmer drought severity index, standardized precipitation evapotranspiration index). These additional weather metrics are part of the pre-analysis plan but cannot be generated for all remote sensing products. Because of this, and to maintain blinding of the products, we excluded these indices from the current analysis. Additional details on the complete scope of our pre-specified analyses can be found at OSF and in Michler et al. (2021).

With respect to exploratory analyses, at present we have four research questions we are seeking to answer. The first is the degree to which results vary by agro-ecological zone. The analysis in this paper focused on differences across country, which is the level at which data are collected and organized in the LSMS-ISA. Economics researchers is frequently conducted using nationally representative data or regional data from within a single country. However, the relevant organizing structure of weather is not the political boundaries of a country but the geographies that create different agro-ecological zones. We plan to replicate much of the current analysis, ignoring crosscountry differences, and instead focusing on differences in agro-ecological zone. Our objective with this question is to determine if some weather metrics or some remote sensing products perform better in one agro-ecological zone versus another. If this is the case, it suggests that researchers using national data may need to use different metrics to measure weather for households in different regions of the country or may need to use data from different remote sensing sources depending on which agro-ecological zone a household lives within.

A second research question which we plan to explore is the degree to which the inclusion of household fixed effects impact results. Here, the integration of cross-sectional data from Mali is critical. Additionally, we will incorporate two waves of cross-sectional data from Malawi, and a 47