almost unlimited degrees of freedom to _p_ -hack their way to his or her desired results.

# **5.2 Coefficient Ordering**

While the specification charts summarize a large amount of information in a compact form, it can be difficult to detect the extent to which the diamonds indicating EO product form a pattern or are patternless. To dig further into coefficient ordering, we present a series of figures which represent the order (ranking) of coefficients within a country, across models, by EO source using bumpline functions (Naqvi, 2024). These are presented for total seasonal rainfall in Figure 12 and mean daily temperature in Figure 13. Results for days without rain and GDD are functionally similar and so we present figures for those variables in the Appendix. Within each country panel, the x-axis (each column) presents a different outcome variable (value or quantity) and a different econometric specification (weather only, weather with FE, and weather with fixed effects and inputs). The y-axis (each row) represents the ranking of the coefficient size for each EO source within each regression. They are ordered from one to six, with one representing the largest coefficient and six representing the smallest coefficient. These results give greater insight to the changes in ordinality across specifications and across countries.

We first consider the findings from total seasonal rainfall (see Figure 12). Going country by country: in Ethiopia, we see that in the weather only specification, CPC and TAMSAT have large coefficients while ERA5 has the smallest. This ordering changes once FE are included, with ERA5 producing the largest coefficients and TAMSAT and CPC producing the smallest. In Malawi, the same trend does not hold. Rather, there appear to be trends based on the dependent variable, with ARC2 and TAMSAT producing large coefficients when the value of total farm production is the dependent variable and small coefficients when the dependent variable is quantity of maize yield. Thus, what drives heterogeneity in results in Ethiopia differs in Malawi. Results in Niger appear almost chaotic, with order of EO products jumping around with any change in specification of dependent variable. It is difficult to identify any trends other than a lack of trends. Next, we consider Nigeria, which does not demonstrate a specification- or dependent variable-based pattern, but generally has ARC2, CPC, and ERA5 ordered in the last three places with MERRA-2, CHIRPS, and TAMSAT in the top three places. Compared to the previous countries, the ordering of EO products in Nigeria appears robust, suggesting one would get similar results regardless of which EO product one chose. This can be confirmed by referencing the specification charts in Figure 8. A similar result holds in Tanzania, where we observe that ARC2 generally falls into the fourth position and TAMSAT into the sixth. Finally, we consider Uganda which somewhat resembles Ethiopia. In the weather only specifications MERRA-2 and ERA5 produce the largest coefficients while ARC2 and TAMSAT produce the smallest. This order reverses when FEs are included. Overall, based on the findings for total seasonal rainfall, we can draw some general trends about ordering or reordering of coefficients based on EO products within countries, but we cannot draw trends across 20