Complicating these matters is the degrees of freedom a researcher has regarding which of many possible weather metrics to use in their analysis. The choice of how to quantify precipitation or temperature can result in the increased likelihood of Type I errors. The opportunity for _p_ -hacking is especially pernicious in economic research, where there is no clear theory on just how rainfall or temperature may increase migration or spur conflict or discourage adoption of a new technology. Should the percentage change in rainfall from one year to the next be used to predict conflict, as in Miguel et al. (2004), or should total rainfall in a year be used, as in Br ̈uckner and Ciccone (2011)? Should deviations in rainfall in the year of one’s birth be used to predict human capital formation, as in Maccini and Yang (2009), or should days when the temperature exceeded 29<sup>_◦_</sup> Celsius be used, as in Garg et al. (2020)? Even in estimating agricultural production, where one might assume a clear and well defined agronomic response, there is little consensus on what matters. Anything from seasonal rainfall to growing degree days (GDD) to deviations in these from the long-run average to a combination of these and their higher moments can and have been used to predict yields (Stallings, 1961; Shaw, 1964; Oury, 1965; Deschˆene and Greenstone, 2007; Ortiz-Bobea and Just, 2012; Tack et al., 2012; Burke et al., 2015; Michler et al., 2019; McCarthy et al., 2021). The choice of how rainfall, temperature, or their combination enters the production function appears _ad hoc_ and is almost never justified by the researcher. This raises the concern that, absent pre-analysis plans, researchers may engage in data dredging or _p_ -hacking in order to find the weather metric that generates the results they want. This issue is particularly problematic in instrumental variable (IV) estimation (Brodeur et al., 2016, 2020), which is a common use of weather data in economics research.

A final potential source of concern in terms of mismeasurement is that the process of spatially linking remote sensing data with public use data on plots, households, or communities creates another source of measurement error. This source is introduced by the obfuscation of GPS coordinates of sampled households in order to protect the privacy of survey respondents. Figure 3 visualizes the mismeasurement introduced by a number of different obfuscation methods, which may shift a unit record’s true GPS coordinates from a location experiencing high rainfall to one experiencing drought conditions. While this is not an issue when the unit record is the country, or when the researcher conducted the data collection herself, the economics literature increasingly relies on census, administrative, or third-party data in which the true location of the unit records are obscured. Obfuscating coordinates for the sake of privacy sacrifices accuracy and precision, with respect to estimates, and may result in Type II errors.

In relation to the above, we investigate three specific hypotheses:

1. _H_ 0<sup>1- different obfuscation procedures implemented to preserve privacy of farms or households</sup> have no impact on estimates of agricultural productivity.

2. _H_ 0<sup>2- different weather metrics have the same impact on estimates of agricultural productivity.</sup>

> and thus a valid instrument. This latter question has been addressed by Deaton (2010) and Sarsons (2015).

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