builds on a high-quality panel household survey infrastructure that was in place prior to the 2014/15 floods, we have two pre-flood data points, coupled with one data point in the post-flood period.

The identification of the target sample of 590 rural FIAS households was driven by several factors. The financial arrangements for the FIAS fieldwork implementation did not permit the preparations to take place prior to August 2015. Once there was clarity around the survey implementation, the budget constraints, together with the research team’s desire to maximize inter-annual comparability (i.e. IHPS 2013 versus FIAS 2015) of the timing of the household interviews, led the research team to focus on the IHPS sub-sample that had been interviewed in the time frame of August-December 2013, and that were residing either in a Southern region district or Ntcheu; a Central region district that borders the Southern region and that was the most adversely-affected Central region district during the 2014/15 floods. The target households that moved in their entirety to other districts between the IHPS 2013 and the FIAS 2015 interview were also tracked – a crucial design decision to fully understand flood impacts. The final FIAS sample size was 558 households, representing an impressive attrition rate of 5.3 percent with respect to the target sample.

FIAS survey instruments were modeled after the multi-topic Household Questionnaire and the Agriculture Questionnaire that had been used for the IHPS 2013. The Household Questionnaire collected individualdisaggregated information on demographics, education, health, wage employment, nonfarm enterprises, as well as data on housing, food consumption, food and non-food expenditures, food security, and durable and agricultural asset ownership, among other topics. The sample households that were involved in agricultural activities (through ownership and/or cultivation of land, and/or ownership of livestock) were administered the Agriculture Questionnaire, which solicited information on land areas, labor and nonlabor input use, crop cultivation and production at the plot<sup>10</sup> level for the 2014/15 growing season. Similar to the IHPS and the IHS3 practice, all FIAS household locations were geo-referenced in order to link the household survey data with publicly available geospatial biophysical and agro-climatic data.

The resulting data set has extensive information on agricultural production and productivity; household consumption and expenditures; household caloric intake; as well as risk-coping mechanisms. Bringing in household location-specific geospatial variables, we are also able to compute objective measures of household exposure to flooding. First, we use the National Oceanic and Atmospheric Administration (NOAA) ARC2 rainfall estimate data covering the period 1983-2015, and generate the percent difference in flowering season rainfall in the relevant cropping period and long-term mean flowering season rainfall.<sup>11</sup> Figure 1 depicts the kernel densities of the percent deviation of the 2012/13 flowering season rainfall, and the 2014/15 flowering season rainfall from historical average flowering season rainfall. All households in the sample experienced rainfall over the historical average in 2015, and the mean rainfall difference was over 55 percent, well above the 14.4 percent mean difference observed in 2013.

> 10 A plot was defined as a continuous piece of land on which a unique crop or a mixture of crops is grown, under a uniform, consistent crop management system, not split by a path of more than one meter in width. Plot boundaries were defined in accordance with the crops grown and the operator.

> 11 We calculate the flowering season rainfall as the cumulative rainfall over the last dekad in December through the third dekad in January.

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