may be using the “wrong” data, or a poor proxy, to measure the truth of what weather occurred where and when. This can introduce either classical or non-classical measurement error into our estimates and predictions. Second, if economists are aware that different EO products report different numbers, then we can engage in _p_ -hacking, HARKing, and other unethical behavior by choosing the EO product that gives us the results we want.

To determine the magnitude of this disparity and its impact on the use of EO data in economic contexts, we integrate nine EO geospatial weather datasets with georeferenced longitudinal household survey data. The survey data comes from six Sub-Saharan African countries and was collected under the World Bank’s Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) initiative. We assess the degree to which mismeasurement matters by modeling the relationship between weather and smallholder agricultural productivity, as captured in the LSMS-ISA survey data. We use a simple heuristic to compare coefficients from regressions of yield on weather as measured by one EO product to coefficients when weather is measured by a different EO product. Our goal is to provide guidance to researchers looking to integrate EO weather data with socioeconomic survey data regarding the degree to which their results may be dependent on their choice of remote sensing EO product.

We find that the different measurement methods by EO products affect not only the cardinality of coefficients across the products but also coefficient ordinality. The first result is unsurprising. Obviously, continuous variables measured differently by different sources will result in different effect sizes. In applied economics, we are typically not too concerned about differences in relative size (cardinality) across specifications, as we understand that different choices in data cleaning and modelling will affect the exact size of a coefficient. What we care about is getting the order correct (ordinality). That using different EO products affects the ordinality of coefficients means that EO products are not simply affine transformations of one another. One can completely reverse the ordering of coefficient size across EO products by simply changing the specification, country, or dependent variable. A researcher can essentially get whatever result she or he wants through the judicious choice of which EO weather product to use.

That different EO sources produce a different orderings of outcomes suggests that many results in economics that rely on EO weather data, or any model-derived EO product, may not be robust to the choice of EO product. As Jain (2020) has pointed out, this is a concern for the large body of economic research that has relied on EO weather data for identification of causal effects. This literature includes important contributions to understanding human capital formation (Maccini and Yang, 2009; Shah and Steinberg, 2017; Garg et al., 2020), labor markets (Jayachandran, 2006; Chen et al., 2017; Kaur, 2019; Morten, 2019), conflict and institutions (Br ̈uckner and Ciccone, 2011; Sarsons, 2015; K ̈onig et al., 2017), agricultural production and economic growth (Miguel et al., 2004; Deschˆene and Greenstone, 2007; Barrios et al., 2010; Dell et al., 2012; Yeh et al., 2020), intra-household bargaining power (Corno et al., 2020), technology adoption (Taraz, 2018; Jagnani 3