population with at least secondary education in the state, the population density (urban dummy). We select these variables based on the community variables employed in similar studies.<sup>9</sup> The climate change data ( is based on CCKP information at province/state level. Our study exploits monthly average temperature data from 1940 to 2022 by province from the CCKP (2023) database to II) approximate climate change signs over the natural temperature variability. We calculate a climate change signals index considering changes in temperature trends per time interval. By province, we estimate the monthly temperature average trend in 1940-2020 and 2000-2020. The index is the sum of the absolute differences between both trends over 2000-2020. A higher index indicates a more evident climate change signal. We also use precipitation data from CCKP by province. We compute the number of times in the period 1901-2021 that the z-score of the aggregated accumulated precipitation in mm is above or below two standard deviations.<sup>10</sup> And the rest of natural hazards variables at level 2 ( _N_ ) correspond to data from ThinkHazard! Platform, EM-DAT, and SPEI database which provide data at lower administrative geographic units than province level. However, it is essential to note that the sample representativeness of the survey does not apply to lower unit levels than province/state level. As a result, the outcomes are presented at either the province or country level.

From ThinkHazard! Platform, we use the index prepared by the platform that classifies hazards as high with 4 (potentially severe dangerous events), medium with 3 (potentially dangerous events), low with 2 (potentially dangerous events are less likely to occur but still possible), and very low with 1 (potentially damaging effects are unlikely to occur). ThinkHazard (TH) levels are derived from hazard maps applying the spatial distribution of hazard intensity (e.g., flood depth) at a given frequency or 'return period' based on a probabilistic analysis. We consider the most common climate hazards in LAC, i.e., river floods, urban floods, coastal floods, landslides, droughts, cyclones/hurricanes, wildfires, and extreme temperatures. However, for some countries, there are hazards that do not show variability in the index (for example, small countries present a very high or very low damage event in the total area of the country from a specific hazard). These cases are left aside from the analysis. This index is available at level 2 of GAUL (ADM 2), which means it is calculated based on data for a specific sub-geographic unit within each country state. When the imputation of TH data is at the state/province level (i.e., level 1) due to the survey design, we calculate an average TH index using the indexes of level 2. This is done to ensure that the imputation accurately reflects the variability of the index at level 2 rather than using the maximum principle of TH. However, for all other situations where imputation is not at level 1 and is at level 2, we rely solely on the TH index at level 2.

> 9 In the model proposed by Skoufias et al. (2023), community-level variables include the average distance to an all-weather road, the proportion of households engaged in agriculture, the proportion of household heads employed, the proportion of household heads with at least secondary education, and the average dietary diversity. The community level variables are derived by taking the average of those households sampled from each unit. Gunther and Harttgen (2009) include as community-level variables an infrastructure index using fourteen characteristics estimated via principal component analysis (bus stop, community road, provincial road, national road, secondary education facility, tertiary education facility, hospital, safe water, electricity, veterinary, agricultural services, fertilizer, market, bank), population density through a city dummy, the mean educational level, the percentage of households working in the formal sector, and the percentage of households possessing an enterprise within the community. The data on household characteristics are taken from the national household survey and the others from the census. Meanwhile, Rude and Robayo-Abril (2023) control for municipality characteristics including the employment rate, the unemployment rate, the share of self-employed and entrepreneurs, the share of public sector employees, the share of children attending school, the share of the population with secondary education, the crime index, the security index, and the environmental (natural hazards) index. The source of data for municipality level variables is the household survey data.

> 10 The mean and standard deviation correspond to the total period of 1901-2021.

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