we first briefly describe the weather data and household data. We then discuss the blinding of the research team and the data integration process. We conclude with a discussion of some descriptive statistics for the combined weather-household data sets.

# **2.1 Weather Data**

We use a variety of public domain sources of weather data sets representing different modeling types, input sources and spatial resolutions. Table 1 briefly describes each data sources, including the length of record, spatial and temporal resolution, and the type of data recorded. Although there are many possible weather products to consider, we sought to include the remote sensing data products most commonly used by economists. However, to ensure consistency and enable the production of common metrics across the analysis, we imposed two inclusion criteria. The source had to have (1) high temporal resolution (daily) and (2) a minimum 30-year length of record (19872017). Daily resolution was necessary for us to calculate some of the most common weather metrics used in the literature today, such as growing degree days (GDD). A consistent length of record was necessary to cover all years of the LSMS-ISA, provide sufficient historical data to measure seasonal deviations in long-term trends, and provide weather data sets all of the same length. This last point was necessary in order to maintain the blinding of the Data Analysis Group from the data sources. Unfortunately, this criteria meant that some data sources used by economists, such as the various versions of the monthly _Terrestrial Air Temperature and Precipitation_ from the Center for Climatic Research at the University of Delaware was excluded.<sup>5</sup> While all the weather product data used in this analysis are publicly available, accessing the data for analysis remains challenging due to differences in formats and platforms. See online Appendix A for more details on each remote sensing product and guidance for economists on merging these data with survey data.

## **2.1.1 Merged Gauge and Satellite Data**

In the past two decades, improvements in the accuracy of rainfall estimation in data scarce environments have been achieved by merging rain gauge data, which provide site-level observations, with data from meteorological satellites, which provide valuable indirect information at full coverage. We make use of three data products in this category.

First, the African Rainfall Climatology version 2 (ARC2) was developed to provide long-term daily rainfall monitoring for improved famine early warning systems over Africa (Novella and Thiaw, 2013). Input sources are daily Global Telecommunications System (GTS) rain gauge data and the Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI) calculated from cloud-top infrared (IR) temperatures. Daily data are produced at 0 _._ 1<sup>_◦_</sup> resolution. A number

> 5Despite its lack of daily data, the product from the Center for Climatic Research at the University of Delaware remains popular among economists. Papers which rely on these data include Corno et al. (2020), Dell et al. (2012), Ito and Kurosaki (2009), Jayachandran (2006), Kaur (2019), Sarsons (2015), and Shah and Steinberg (2017).

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