(Carter et al., 2007; Little et al., 2006). Although this approach facilitates causal identification, the choice of an extraordinary phenomenon undermines the external validity of the findings. Such studies are informative only of the impact of the dramatic weather event and not of recurring droughts or floods. Our analysis focuses on droughts and excess rainfall during the main growing season in Malawi (November to April) and evaluates the implications of weather shocks on households’ vulnerability to poverty. This vulnerability analysis first identifies the impact of extreme weather events during the growing season on the consumption per capita in the months following the harvest time. The study uses the spatial and temporal variation in droughts and excess rainfall experienced by the 35,627 households surveyed in the three rounds of the Integrated Households Survey (IHS) (2004– 2016).<sup>1</sup> The Self-calibrating Palmer Drought Severity Index (SC-PDSI) is used as a measure of drought at the second lowest administrative division in Malawi (traditional authority [TA]). SCPDSI is calculated from temperature, precipitation, and locally available soil water content (Barichivich, Osborn, and Jones, 2018; van der Schrier et al., 2013). To measure floods, we use raw precipitation data to calculate the number of days with rainfall above the 98th percentile in each TA (NASA GES DISC, 2019). This is a rough approximation of flooding. In reality, flooding is a function of topography, rainfall amount and intensity, soil moisture, and vegetation. The use of SC-PDSI and extremely high rainfall enables us to address the endogeneity in the measurement of weather shocks. Self-reported shocks, quick postdisaster assessment of damages, and the Normalized Difference Vegetation Index (NDVI) are all potentially endogenous. For instance, the amount of losses could be higher in relatively wealthier areas that have more physical assets that could be damaged, but the households may as well be more resilient. Farmers may also delay planting due to a late onset of rainy season, which in turn lowers the NDVI. The latter index is indeed used to measure the onset and end of growing seasons (Yu, Luedeling, and Xu, 2010). In addition to these communitylevel shocks, we include idiosyncratic shocks, which are self-reported and likely to be endogenous, in our model.

Another major empirical challenge in identifying the welfare effects of natural disasters is the difficulty of disentangling private and public assistance to the affected households from the change in their non-transfer consumption. Previous studies (Chaudhuri, Jalan, and Suryahadi, 2002; Gunther and Harttgen, 2009; Hill and Porter, 2017; Suryahadi and Sumarto, 2003) compare households’ total consumption, which includes transfers received from the government, nongovernmental organizations, relatives, and migrant family members.

1 Not only is the assessment of vulnerability critical in the context of climate change, but it is even more relevant as 51.5 percent of Malawians lived in poverty in 2016. This rate is even higher (59.5 percent) among rural households who tend to be the most affected by weather shocks (Malawi NSSO and World Bank 2018).

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