The analysis is based on the consumption aggregates used for official poverty estimation. However, to allow estimation of vulnerability in the absence of assistance, we exclude selfreported food aid and cash transfer in the first consumption estimate (‘without aid’) and exclude food aid only in the second estimate (‘without food aid’).<sup>2</sup> The food aid and cash transfer information in these estimations are taken from the assistance module of the survey questionnaires. In addition, households report that the part of food consumed comes from in-kind transfers and food aid. This item-level in-kind transfer is aggregated to quantify the total amount of food transfers received by the household. By excluding the transfers, as reported in the food consumption module of the questionnaires, from the total consumption, we built an alternative index of food consumption (‘without transfers’).<sup>3</sup> Part 2 of Table 1 presents the measures of weather shocks. To select the appropriate drought and rainfall indexes, we mapped several indicators over multiple years. Existing empirical evidence and expert knowledge of which districts were hit most by dramatic weather events and the years extreme weather events guided the selection of indicators. The measure of drought used in this study is based on CRU’s Global SC-PDSI during the growing season. It is calculated from temperature, precipitation, and locally available soil water content (Barichivich and Jones, 2018; van der Schrier et al., 2013). We averaged this index (for each month) at the TA level to merge it with the IHS. The Palmer index varies from −10 to 10 with values lower than −2 indicating droughts. For TAs with Palmer index above −2, that is, those that did not experience drought, a value of zero is assigned. For those with index below −2, the absolute value of the index is used such that higher positive values correspond with severe droughts. This definition makes the interpretation of the drought coefficient easier. To generate a rough indicator of floods, we aggregated raw precipitation data from NASA’s MERRA-2 model and we calculated the number of days with rainfall above the 98th percentile in each TA (NASA GES DISC, 2019).<sup>4</sup> All the weather indexes are defined for the growing season preceding the household surveys. Since maize growing season is from November to April in Malawi, households interviewed in March or April 2004 are matched to shocks occurring in the 2002/03 growing season. Those households that were interviewed in May 2004 to March 2005 (84% of IHS2) are linked to the preceding 2003/04 growing season. Similarly, 85% of IHS3 observations are associated with weather indicators in the 2010/11 growing season, and 97% of IHS4 data corresponds to shocks that occurred

> 2 Some extremely poor households have zero consumption per capita when food aid and cash transfers are excluded.

> 3 This could only be created for IHS 3 and IHS 4 due to unavailability of documentation and do-file for IHS 2 consumption aggregate. Even if it is likely to be more accurate than the self-reported food aid, we found it difficult to reconstruct this in a consistent manner for IHS 2.

> 4 Constructing a precise indicator of flooding requires extensive analysis of rainfall, topography, and plant coverage. This is beyond the scope of this study.

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