In order to the compare drought and flood impacts using LSMS-ISA data with those using EM-DAT data, we use two metrics: the share of individuals “affected” by the shock and the estimated total value of damages. Since the LSMS-ISA surveys run every two to three years, we only retain events in the EM-DAT database for which the start or end date is within a year containing LSMS-ISA data. The comparison for flood shocks is possible in fewer countries and years due to limitations in the microdata questionnaire’s scope in some cases (Table 18).

In order to compute the total number of individuals impacted by a shock within a specified period in the EM-DAT database, we aggregate the total number of people “affected” by the shock in the macro data. Affected persons are those that are reportedly injured, homeless, or otherwise in need of “immediate assistance.”<sup>29</sup> To estimate the total number of individuals affected by a shock in the LSMS-ISA microdata, we construct population weights by multiplying household weights by household size. These weights are then used as expansion factors, which we multiply by a dummy variable equal to one in the household report a shock on any of its cultivated plots. We then add up this product to calculate an expansion estimator.<sup>57,58</sup> To obtain shares of the total population, the numbers of individuals affected in both EM-DAT and the LSMS-ISA data are divided by total yearly population estimates drawn from a library of the World Bank World Development Indicators.<sup>53</sup> We then compute the estimated damages from both droughts and floods in the periods and years covered by LSMS-ISA data. We first aggregate the “total damages” estimated in the EM-DAT database, defined as the values of total losses “directly or indirectly related to the disaster”, in 2022 USD values. Using LSMSISA microdata, we aggregate the estimated value of crop losses for each household. In this case, the value of losses is calculated by multiplying the potential value of total output using _current_ prices by the estimated percentage of output lost at the plot level. Contrary to the rest of our analysis, we do not apply a common set of prices to value output and losses, but rather median crop prices faced by farmers at the time of their crop production. The loss values are then converted to 2022 USD values, in order to allow comparison with EM-DAT data. As above, we use population weights as expansion factors, that we multiply with our loss value estimates.<sup>57,58</sup>

# _Sampling weights_

In the survey data, household sampling weights are used to compute estimates that are representative of the national or subnational population. These reflect the inverse probability of selection into the sample, are adjusted to account for non-response and survey design choices, and are post-stratified to ensure that they sum to known household population totals.<sup>59</sup> Moreover, a further adjustment was made to ensure that the weights in the study sample sum to the total population of households, because only a subset of LSMS-ISA households report cultivating seasonal crops. More formally, <mark>we can define a set of households indexed by h (where h = 1, ... , N</mark> 1 <mark>) within a country-wave, where each household is associated with a sample weight W</mark> h<sup><mark>. After restricting the dataset</mark></sup> <mark>to households that cultivate crops, the total number of households in the country-wave drops from N</mark> 1 <mark>to N</mark> 2 <mark>, and weights are adjusted such that:</mark> 15