arise from the use of different extraction methods does not substantially affect estimates. When researchers use publicly available data with obfuscated GPS information, they should feel confident that matching those coordinates with remote sensing data will not introduce measurement error into the analysis.

Our second hypothesis concerned whether the choice of metrics to measure precipitation and temperature are critical to the analysis. Here the concern is that the process economists use to choose one metric, say mean temperature, over another, say growing degree days, is _ad hoc_ and lacks the specificity necessary to accurately measure the weather that matters. Certain metrics may mismeasure the relevant meteorological event. If, for example, what matters to human capital development is the number of days with temperatures above a certain threshold, as in Garg et al. (2020), then mean daily temperature mismeasures the relevant event. The results presented in section 4.2 lead us to reject Hypothesis 2. Based on our heuristics, some weather metrics clearly perform better than others. For precipitation, mean daily rainfall, total seasonal rainfall, the number of rainy days in a growing season, and the share of days in the growing season that saw rain are all strongly correlated with outcomes. For temperature, mean, median, and variance of daily temperature are all frequently correlated with outcomes. Among these seven metrics, mean and total rainfall are highly correlated and could be substituted for each other. Similarly, rainy days and percentage of rainy days are highly correlated, as is mean and median daily temperature. One could substitute these metrics for each other without appreciable differences in results.

Hypothesis 3 considered whether remote sensing products mismeasured the objective facts regarding the volume of precipitation and the temperature in a given location on a given day. The overwhelming evidence presented in section 4.3 is that some products do, in fact, mismeasure the weather, though there is general agreement among several products. Specifically, MERRA-2 appears to suffer from classical measurement error, producing noisy data that results in rainfall metrics frequently being uncorrelated with outcomes. ERA5 appears to suffer from non-classical measurement error, producing overestimates of the volume of precipitation and leading to biased coefficients on rainfall metrics. The other four precipitation products all generally report similar data for precipitation. The three temperature products also generally agree with one other. Although there is some evidence that ERA5 reports different temperature data than the other two sources, these differences do not appear to be substantial enough to impact results. However, this lack of impact may be due to the overall poor quality of even the best temperature metrics in predicting outcomes. Whether this is due to temperature actually being uncorrelated with outcomes, or all three products suffering from measurement error is unclear.

An important limitation in our analysis is that we lack data on the objective fact of how much precipitation there actually was or what the temperature actually was in a given location on a given day. While one can easily measure the true size of a field (Carletto et al., 2017), the true weight of a harvest (Abay et al., 2019), or the true variety of a seed (Kosmowski et al., 2019), data 44