data sets were created using all information sources available at CPC, GTS daily reports and CPC special collections (Chen et al., 2008). Extensive pre-processing and cleaning of gauge data includes comparison with contemporaneous data from satellite and other sources. Precipitation data are gridded at 0 _._ 5<sup>_◦_</sup> resolution using an Optimal Interpolation (OI) technique, which accounts for orographic effects. The global temperature data set is gridded at the same resolution using the Shepard Algorithm. A series of papers use CPC data, including Tennant and Gilmore (2020) and Williams and Travis (2019).

# **2.2 Household Survey Data**

The World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMSISA) is a household survey program that provides financial and technical assistance to national statistical offices in Sub-Saharan Africa for the design and implementation of national, multitopic longitudinal household surveys with a focus on agriculture. As detailed below, the analysis leverages data from several rounds of panel household surveys conducted over the last decade by the respective national statistical office in Ethiopia, Malawi, Niger, Nigeria, Uganda and Tanzania, with support from the LSMS-ISA initiative.<sup>6</sup> Table 2 provides a summary of the countries, years, and observations used in the analysis. Appendix B provides greater details on each country’s sampling frame and data collection process.

In Ethiopia, we use the data from the 2011/12, 2013/14 and 2015/16 rounds of the Ethiopia Socioeconomic Survey (ESS), which has been conducted by the Central Statistical Agency of Ethiopia (CSA, 2014; CSA, 2015; CSA, 2017). The Wave 1 data is representative at the regional level for the most populous regions in the country while Wave 2 and 3 expanded to include 1,500 households in urban areas. After data cleaning to remove urban and non-agricultural rural households, we are left with 7,272 household observations across three survey waves.

In Malawi, the LSMS-ISA data includes two separate surveys: the cross-sectional Integrated Household Survey (IHS), and the longitudinal Integrated Household Panel Survey (IHPS) (NSO, 2012; NSO, 2015; NSO, 2017). This analysis relies on the data from the IHPS, which is representative at the national-, urban/rural-, and regional-level. Data comes from 2010/11, 2013, and 2016/17. A key IHPS design feature is that starting in 2013, the survey attempted to track all individuals that changed locations between the survey waves, and brought into the sample the new households that the movers formed/joined. Our analysis relies on households that did not move vis`a-vis the baseline interview location, since it is not obvious what the appropriate reference location historical weather data should be drawn from for calculating seasonal deviations from long-term trends. Should the long-term trend be the weather in the location the households lived in the past

> 6LSMS-ISA has supported nationally-representative cross-sectional surveys in Mali in 2014 and 2017. The data from Mali will be incorporated as part of future work, per the analysis plan (Michler et al., 2019). While Burkina Faso has also been supported by the LSMS-ISA, the resulting survey data cannot be used in our analysis since the sampled households were not georeferenced.

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