on measurement error in economic data. Most research in this stream has focused on mismeasurement in the context of agricultural production (Carletto et al., 2017; Abay et al., 2019; Kosmowski et al., 2019; Lobell et al., 2020; Abay, 2020; Abay et al., 2021; Gollin and Udry, 2021; Kilic et al., 2021) but a related literature focuses on mismeasurement in everything from time preferences (Anderson et al., 2008) to contingent valuation (Flachaire and Hollard, 2006). Unlike most of the work on mismeasurement in agricultural production, we lack data on the objective fact that may be measured. As such, we develop new methods to assess the degree of measurement error relying on the richness of the LSMS-ISA data and a combination of a number of different remote sensing data sets. By quantifying the magnitude and significance of measurement error, we can better understand the potential effects of mismeasurement in the remote sensing products commonly relied upon by economists.

Our results also have implications for the large and varied stream of literature that relies on remote sensing weather data for causal identification. This includes seminal and recent work on human capital formation (Maccini and Yang, 2009; Shah and Steinberg, 2017), 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), intrahousehold bargaining (Corno et al., 2020), technology adoption (Suri, 2011; Jagnani et al., 2021; Arag ́on et al., 2021). Our finding suggests that economists need to be much more careful about the remote sensing data source and the metric they use to measure weather. While our analysis provides practical guidance about what sources and metrics are generally reliable, researchers may need to demonstrate the robustness of their results to different sources and metrics when these are key to their identification strategy.

The paper is organized as follows: in Section 2 we discuss the sources and characteristics of the weather data and the household data. We also provide details on how data was integrated, including specifics on how the blinded data was combined. The section concludes by presenting some descriptive evidence of mismeasurement in the weather data. Section 3 provides details of the pre-analysis plan, specifically our estimation strategy and approach to inference. Section 4 discusses results, first covering differences by obfuscation method, then by weather metric, and finally by remote sensing product. Section 5 provides a summary of the results and recommends six best practices for researchers looking to use remote sensing data in combination with socioeconomic data. We also outline future work and then, in Section 6, conclude.

# **2 Data**

We use existing, publicly available satellite-based weather data products combined with publicly available unit-record survey data that have been generated as part of the World Bank LSMS-ISA initiative and that are made available through the World Bank Microdata Library. In this section, 7