Also, we add community variables that indicate the frequency of events based on historical data. For this purpose, we use the database from EM-DAT (Guha-Sapir et al., 2022), which offers a comprehensive database of natural disasters that took place between 2000 and 2023 at the state and district levels. The database is generated from various sources, including UN agencies, nongovernmental organizations, reinsurance companies, research institutes, and press agencies. The variables obtained from EM-DAT include the number of occurrences of each natural disaster (including floods, landslides, cyclones/hurricanes, drought, wildfires, or extreme temperature events) that took place in a specific year of survey. Additionally, we considered a set of variables that indicate the frequency of the same natural disaster occurring during the lag year of the survey period.

We include drought variables using SPEI data at community level (ADM1, ADM2, or ADM 3). SPEI is a drought index proposed by Vicente-Serrano et al. (2010). The SPEI has a spatial resolution of 0.5°; thus, to aggregate to ADM 3, ADM 2 or ADM 1, we impute each administrative unit with the value of SPEI following the equation i max{a m ); a )} where a is for absolute value and is max or min depending on the result of the conditional ii mm mm mmiimm function. SSIISSII = SSIISSII aa(SSIISSII aa(SSIISSII aa mm Different scientific disciplines use this multi-scalar index to measure drought severity according to its intensity and duration. The advantage of the SPEI regarding other drought indexes, such as the Palmer Drought Severity Index (PDSI) or the Standardized Precipitation Index (SPI), is that it considers the effect of the potential evapotranspiration (PET). It is measured using different time scales to identify different drought types. Short-time scales are mainly related to soil water content and headwater river discharge; medium-time scales involve reservoir storage and river discharge, while long-time scales are associated with variations in groundwater storage. Thus, Acosta et al. (2021) express that SPEI-1 (1 month of scale) is unsuitable for an economic analysis in which researchers are interested in determining how persistent climatic events affect productive systems and farmers’ living conditions. They decide to use SPEIs 12, 24, and 48 months. Acosta et al. (2021) compute a severe drought dummy derived from the SPEI index, which takes one if the value of the SPEI is in the lowest five percentiles of the SPEI distribution and zero otherwise. Birhan et al. (2021) use the SPEI-6 for a shortterm drought variable and SPEI-24 for a long-term drought variable. They construct a dummy to capture exposure to a short-term drought that takes the value one if the score of SPEI-6 is -0.5 or lower and zero otherwise and another dummy to capture exposure to a long-term drought that takes the value one if the score of SPEI-24 is -0.5 or lower and zero otherwise.<sup>11</sup> For our analysis, we use the SPEI-6 and SPEI-24 index and their squared.<sup>12</sup> Negative values of SPEI indicate dryness and positive values wetness. The SPEI database enables imputation at the lowest available household survey unit.

> 11 Vollenweider (2014) uses the SPEI-3 as a standardized measure of weather. Other authors such as Edossa et al. (2014), Bischiniotis et al. (2018), and Drumond et al. (2021) provide a classification of SPEI in categories of extremely, severely, moderately, or normal drought or wet period. Edossa et al. (2014) and Bischiniotis et al. (2018) classified the SPEI value for Africa as Extremely dry if SPEI ≤ -2; Severely dry if SPEI > -2 and SPEI ≤ -1.5; Moderately dry if SPEI > -1.5 and SPEI ≤ -1; Mild drought if SPEI > -1 and SPEI ≤ -0.5; Near normal dry if SPEI > -0.5 and SPEI ≤ 0; Near normal wet if SPEI > 0 and SPEI ≤ 0.5; Mild wet if SPEI > 0.5 and SPEI ≤ 1; Moderately wet if SPEI > 1 and SPEI ≤ 1.5; Severely wet if SPEI > 1.5 and SPEI ≤ 2; and Extremely wet if SPEI > 2.

For South America, Drumond et al. (2021) classify the SPEI as Extremely dry if SPEI ≤ -2; Severely dry if SPEI > -2 and SPEI ≤ -1.5; Moderately dry if SPEI > -1.5 and SPEI ≤ -1; Mild drought if SPEI > -1 and SPEI ≤ 0; Mild wet if SPEI > 0 and SPEI ≤ 1; Moderately wet if SPEI > 1 and SPEI ≤ 1.5; Severely wet if SPEI > 1.5 and SPEI ≤ 2; and Extremely wet if SPEI > 2.

> 12 In another model, we employ four dummies that attempt to capture the effects of a short-term drought (1 if score of SPEI-6 <=-1), a short-term wet (1 if score of SPEI-6 >=1), long-term drought (1 if score of SPEI-24 <=-1), and a long-term wet (1 if score of SPEI-24 >=1) period. However, these binary measures do not always

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