# **3.1.Descriptive Statistics**

# **3.1.1. Flood Events, Welfare Outcomes**

The percent difference measure can capture both above and below rainfall yields, though we note here that in 2015, all areas received greater than average rainfall. The measure captures deviations from expected rainfall and is expected to have a negative impact on yields. Alone, the deviation measure is not likely to adequately capture the extent of flooding, since flood damage is also related to geological and topographical features in addition to rainfall (Merz et al., 2007). To better capture the extent of flooding, we match the data to the mean flood intensity generated from University of Maryland’s flood simulation model, which is at a rather coarse resolution, of .125 arc degrees, or approximately 14 km<sup>2</sup> in Malawi. In order to obtain a more precise flooding estimate, we consider two household-specific variables that are expected to be highly correlated with flood events, namely elevation at household GPS location at 90m resolution<sup>12</sup> , and distance from household GPS location to the nearest river (Merz et al., 2007; National Research Council, 2015). We subsequently perform a principal component analysis (PCA) of the mean flood intensity, elevation and distance to nearest river, and compute an index of flood affectedness. As shown in Table 1, the PCA scores are lower for those areas with higher mean flood intensity, further from a river, and higher in elevation. We multiply the resulting index by -1 for more intuitive interpretation as a measure of flood affectedness; thus, a higher score is associated with higher mean flood intensity, closer to a river, and lower in elevation.

To present descriptive statistics in a meaningful way, we define three categories of flood-affectedness using index thresholds. Since maize is the dominant crop in Malawi, with 87 percent of the households in our sample cultivating maize, we define the thresholds by maximizing the difference between mean yields across three groups, while keeping a minimum of 20 percent of the sample in each group. This leads to 154 households categorized as being low-affected, 256 households as medium-affected, and 148 households as high-affected.

As shown in Table 2, maize yields were indeed much lower in 2015 versus 2013, for all three categories of flood affectedness, and for both local and hybrid maize. The differences in mean and distribution of overall, local and hybrid yields are significantly different for households located in the medium- and highaffected areas. In low-affected areas, the mean local maize yield is not statistically different across years, though the distribution is; on the other hand, while the mean hybrid yield is significantly lower in 2015, the distribution of hybrid yields is not statistically different in 2015 versus 2013.

While analyzing the impacts of floods on crop production is not the focus of this paper, we expect that the primary impact of floods on household welfare measures of interest is through the impact on crop production. McCarthy et al. (2017) use the flood-affectedness index to analyze the impact of floods on

> 12 The description can be found at: <u><mark>https://lta.cr.usgs.gov/srtmgl3.html.</mark></u>

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