as the number of days without rain or the variance in temperature, may have a negative impact on yields but we maintain the assumption that this impact will be statistically significant. Our empirical results then allow us to update our prior based on where the weight of the evidence lays. If the vast majority of a set of obfuscation/source combinations show that the number of rainy days significantly increases yield, then we retain our prior belief. We can then claim that an obfuscation/source combination that does not result in a significant coefficient, or results in a significant negative coefficient, on the number of rainy days suffers from mismeasurement. Conversely, if the vast majority of a set of obfuscation/source combinations show that average maximum daily temperature in the growing season has no significant impact on yield, then we reject our prior belief. We can then claim that an obfuscation/source combination that shows a significant impact on yields suffers from mismeasurement.

Our lack of data on the objective facts of precipitation and temperature informed 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 a researcher’s hands when using observational data (Janzen and Michler, 2021). The use of a pre-analysis plan allowed us to pre-define the sources of data for inclusion in the study, what metrics would be tested using what functional forms, and how we would compare results across models in the absence of formal statistical tests. Second, we adopted a blinding strategy to help ensure objectivity in the analysis. In this blinding strategy the authors were divided into two groups: the Data Generating Group and the Data Analysis Group. Kilic and Murray were in the Data Generating Group and had full responsibility for extracting the remote sensing data and matching it to the household records in the LSMS-ISA data to create a number of different paired weather-LSMS-ISA data sets.<sup>3</sup> In these data sets, the source of the weather data and the obfuscation methods was anonymized prior to sharing with the Data Analysis Group. Josephson and Michler made up the Data Analysis Group and had full responsibility for cleaning the LSMS-ISA production data, running the regressions, and conducting and writing the analysis. The pre-specified analysis was carried out on the anonymized data sets and these results were posted to arXiv.org prior to unblinding (Michler et al., 2021).<sup>4</sup> The generation of data sets in this manner preserves the objectivity of any findings regarding differences in outcomes between different remote sensing products and types of obfuscation.

Our results have implications for two distinct streams of literature. The first is for the literature

> 3For example, in one data set the remote sensing weather data product may be matched with the exact household GPS coordinates in the LSMS-ISA, while in another data set the remote sensing weather data may be matched with low-level administrative centroid.

> 4An initial incomplete draft of the paper was posted to arXiv.org on 22 December 2020 (arXiv:2012.11768v1). This draft was a placeholder and posted to satisfy project reporting requirements at the World Bank. On 19 August 2021 the complete anonymized draft was posted (arXiv:2012.11768v2). On 23 August 2021 the Data Generating Group shared the key to de-anonymize the data with the Data Analysis Group. The version of the paper in hand is the updated paper (v3) containing the same analysis as the anonymized v2 of the paper, just replacing the anonymized placeholders with the actual de-anonymized names.

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