single cross section from Tanzania. Our goal is to unpack the result in this paper regarding how little weather matters once one controls for time-invariant household unobservables. While panel data is becoming more common in developing countries, many researchers still rely on cross-sectional data. We are interested in understanding the degree to which results using cross-sectional data may be biased because of the inability to control for household unobservables. If weather matters in cross-sectional contexts but no longer matters once time-invariant household unobservables are controlled for, it indicates that weather may fail the exclusion restrict necessary for its use as an instrumental variable.

Our third research question concerns the extent to which correctly specifying the growing season matters for the use of weather data. The growing seasons differ in northern and southern Nigeria, as do the growing seasons in different regions of Uganda. To account for these differences, we specify different seasons for households in these countries. But, even in a country like Malawi, with a single unimodal growing season, the start and end date of that season can vary depending on where a household is located in the country. Differences in Tanzania can be even more pronounced and our failure to account for this variation may explain why weather appears to matter so little in that country compared to the other five countries. The issue of defining the growing season is another example of potential measurement error in the use of remote sensing weather data. We plan to explore methods for defining the growing season at the household-level based on self-reported planting dates in the LSMS-ISA data along with using remote sensing data to determine the onset of rainfall. If the definition of growing season does matter, it suggest that researchers need to take much more care in defining what days or months to take weather data from.

Our final research question is whether machine learning methods can be effectively used to select weather metrics and remote sensing sources. Our approach has relied on traditional regression techniques to estimate a large contingent of regressions and then determine which metrics matter over a large set of results. However, these research questions also seem well suited to a number of machine learning techniques, in particular feature selection. We are interested to see how the findings of a machine learning algorithm comport with our findings using traditional regression analysis.

# **6 Conclusion**

In this paper, we investigate the significance and magnitude of the effect of measurement error from various sources on modeling agricultural productivity. Using geospatial weather data from a set of weather products with geo-referenced household survey data from six Sub-Saharan African countries, we are able to provide systematic evidence of measurement error, as related to spatial measurement of weather metrics over a large set of crops and countries. We find that while the extraction method does not introduce substantial measurement error into the analysis, results vary depending on the choice of weather metric and the choice of remote sensing data source. Based 48