adheres to T7 in that several products overlap and all have downward trajectories in the number of GDD per year. By contrast, Malawi, Niger, and Tanzania all deviate from T7, though each deviates in its own way. In Malawi, there is effectively no overlapping in the confidence intervals of the three products and while CPC and ERA5 have flat slopes, MERRA-2 produces a strongly divergent downward trajectory. Niger is similar to Malawi in that there is almost no overlap in confidence intervals and MERRA-2 has a strong downward trajectory. However, in Niger, CPC and ERA5 have strong upward trajectories. Further, in Malawi MERRA-2 produces the fewest GDDs while in Niger it produces the most. Finally, in Tanzania, there is almost perfect agreement between ERA5 and MERRA-2, both of which show no change in GDD over time. By contrast, CPC is in almost complete disagreement, producing fewer and fewer GDDs over time.

Summarizing the descriptive evidence: there can be substantial differences in the weather reported by each EO product. Again, this is not surprising since each product uses different data and different methods to produce gridded weather data. If all that these descriptive figures showed was one EO product consistently reporting higher levels of rainfall and temperature than other products, researchers could easily adjust their methods to account for this over reporting. However, Figures 4 through 7 document not just differences in cardinality (one product reports a few more mm of rain) but in ordinality. In most countries ERA5 reports the most rain but in Niger it reports the least. In most countries TAMSAT falls in the middle in terms of how many days are without rain, but in Uganda it reports the driest conditions. And while compared to rainfall, there is much more agreement among the EO temperature products, there are still significant deviations, particularly in the number of GDD produced by the products. In Malawi, MERRA-2 produces the fewest GDD but in Niger it produces the most. In most countries, CPC is in agreement with ERA5 but in Tanzania CPC disagrees with both ERA5 and MERRA-2. Based on these changes in the ordering of EO products across countries, we conclude that researchers’ selection of EO product cannot be country-agnostic.

# **4 Analysis Plan**

A common feature of research on measurement error is that the researchers have a measure of objective truth. They can trace the boundaries of a plot of land (Abay et al., 2019), they can weigh the quantity of harvest (Lobell et al., 2020), they can determine the exact location of a household (Michler et al., 2022). In our setting, we lack an objective measure of the truth regarding what the weather was like for a given household in a given year. And, given the paucity of gauge station data on the Continent, the typical gold standard for weather data does not exist. This lack of an objective measure for quantifying mismeasure in EO weather data informs how we implemented our research design. First, we developed a pre-analysis plan and registered it at Open Science Framework (Michler et al., 2019). While pre-analysis plans have become common in experimental economics, they are still relatively uncommon for binding researchers’ hands when using observational data 14