**Table 2.** Aggregate data of hazards and climate change databases.

|**Source**|**Hazards**|**Index**|**Type**|**Level**|
|---|---|---|---|---|
|ThinkHazard!|Floods (river, urban, and coastal),<br>landslides, cyclones, wildfires,<br>extreme temperatures.|TH index<br>Average of TH index|Modeled|ADM 2<br>ADM 1|
|EM-DAT|Floods, landslides, droughts,<br>cyclones, wildfires.|Number of occurrences in<br>the survey year and lag<br>year|Historical|ADM 2<br>ADM 1|
|SPEI|Drought|SPEI-6<br>SPEI-24|Modeled|ADM 3,<br>ADM 2,<br>ADM 1|
|CCKP|Climate change|Temperature trend<br>difference (trend 2000-<br>2020 vs trend 1940-2020)|Historical|ADM 1|
||Precipitation|Number of extreme<br>aggregated accumulated<br>precipitation in mm events<br>in 1901-2021|Historical|ADM 1|

Note: we aggregate the three floods TH indexes into one. We construct a weighted indicator considering that some areas can experience the three types of floods. In contrast, other sites can experience only two or one of them (e.g., districts far away from the sea without shore or sea exit are less likely to experience coastal floods. Moreover, the coastal flood TH index has no data in these cases). Table B1 in the Appendix shows how this weighted index is constructed. The SPEI-6 and SPEI-24 index time scales correspond at the beginning of the household survey period. The SPEI has a spatial resolution of 0.5°; thus, to aggregate to ADM 3, ADM 2 or ADM 1, we use the aggregate equation i m ); a )} where a is for absolute value and is max or min depending on the result of the conditional function. ii mm mm mmiimm SSIISSII = SSIISSII mmaamm{a aa(SSIISSII aa(SSIISSII aa mm The income equation (equation 4) of the model includes the community variables Z and C from the SEDLAC and the CCKP data. On the other hand, the remaining community variables N are included in the expected variance equations (equations 5, 6 and 7). We assume that the community variables from household survey (Z) and climate change (C) can offer valuable insights into the expected average income. These variables are not prone to quick variations in variance and some of them change slowly, however they can still have a significant impact on income distribution shifting the distribution and changing the expected mean. The remaining community variables are considered more volatile and unpredictable and are better suited to explain the expected variance of income.

# **_Variables used in the assessment_**

We use the per capita household income adjusted by countries’ CPI and PPP 2017 as the main wellbeing outcome of interest. We also adopt an international poverty line adjusted by PPP 2017 of 6.85 USD daily. We select as household-specific variables for LASSO the following: salaried household head, labor informal household head, the household dependency ratio (number of members of the household divided by the number of income earners in the household); if household head’s main principal income is from agricultural activities, dwelling's materials of low quality, dwelling with toilet facilities linked to sewer, access to electricity, access to safe water, overcrowding (more than three members per bedroom), the household has a car, fridge, computer, washing machine, a appear consistently among the different countries; mainly due that in some countries, the index range is lower than in others.

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