# **6 Conclusions and Recommendations for Researchers**

The use of remotely sensed EO weather data in economic contexts has become commonplace. Researchers use a variety of EO products to produce a host of different weather metrics to predict numerous different economic outcomes. If each EO product reported the same values for rainfall and temperature for a given location for a given time on a given day, then the choice of EO product would be immaterial. Unfortunately, this is not the case. Each product, while attempting to measure the objective truth of the weather, produces its own “truth.” The findings produced are not immutable truths.

Using LSMS-ISA data from six African countries in combination with nine EO weather data products, we demonstrate the lack of consistency in how EO weather products report on the weather. Importantly, the differences across products do not simply affect the cardinality, or relative size, of coefficients from different EO products. The shifts we see cannot be ascribed to an affine transformation of one EO product by another. Rather, what we document are changes in ordinality, the ranking of coefficients by size. In some countries one EO product may produce large, positive and significant coefficients while another EO product produces negative and significant coefficients. This order can swap if one moves to another country or if, within a give country, one changes the econometric specification or how the agricultural productivity is quantified. The unwary researcher may end up with results and draw conclusions that are a function of their choice of EO product and could have ended up with completely different results and conclusions if they had chosen a different EO product. Knowing this, the unscrupulous researcher can select the EO product that measures the “truth” in the way that produces the results and conclusion they want.

Given the lack of gold standard gauge station data in many countries, where does this disparity in how EO products measure the weather leave researchers who want to incorporate this type of data into economic contexts? As we cannot know the truth of what weather occurred where, we are left in a second-best world where more work may be required to demonstrate the accuracy of our scientific findings. Based on the results in this paper, we recommend that researchers carefully choose which remotely sensed EO source to use in their analysis. More justification needs to be given in papers for the choice of one EO data set over another. In EO product documentation there is often a list of known issues, such as over-estimation of rainfall events or difficulty in capturing precipitation in rugged terrain. In the same way aseconomists already document and justify choices in terms of variable selection, we also need to document and justify our selection of EO product. Additionally, researchers may need to demonstrate that key results are not a function of their choice of EO product. Economists already verify the robustness of results to changes in data and specification. Checks for robustness to alternative EO weather products may also be called for, particularly if weather is a key component of the story or plays a role in causal identification.

Potentially the most important recommendation resulting from our findings is the need to recognize, as Stephen Jay Gould wrote, that science is a social phenomenon, not the collection and 22