Defining household welfare is challenging and complex, and there is no perfect welfare indicator to measure it. Nevertheless, absolute poverty is usually a reasonable approximation of household welfare, correlating with vulnerability to natural hazards. Thus, we approximate household welfare using a monetary poverty measure, and we measure households' vulnerability to absolute poverty (poverty from now on) in a setting where it is possible that households suffer damage from natural hazards. In LAC, monetary poverty is usually computed over household incomes and not consumption since all countries in the region collect income information more widely and periodically, and only a few collect frequently consumption information. In addition, it is essential to recognize that the vulnerability concept encompasses the future well-being of the household, which is dynamic. One approach to achieving this is by utilizing long panel data. However, periodic panel datasets are not available in the region, and to overcome this, we propose a model based on a crosssectional study.

Since the concept of vulnerability includes the susceptibility to be harmed and the capacity to cope and adapt, we measure household vulnerability as the propensity of being poor conditioned to observed natural hazards' potential damage, community exposure, and current coping and adaptation strategies. We extend Chaudhuri's (2003) and Skoufias et al. (2021) approach, where shocks are unobserved, and explicitly introduce aggregate shocks through observed natural hazard variables in the model. We also incorporate some elements of Hill and Porter's (2017) study working at the household level. And we exploit the spatial variability of the cross-section data as much as the survey design allows (i.e., for some countries, we go down at the district level, and for others, at the state/province level). Thus, we propose to estimate the probability of being vulnerable to poverty using the following Probit model: P (P P H ), h,dd,rr dd,rr dd,rr = 1 | HH, EE, CC, DD) = ΦΦ(αα+ ββ H PPHH + θθEEθ PPθ PPee h,dd,rr h,dd,rr rr h,dd,rr + φφCCPPθθC CC + γγDDeeDDPPCCPPHHθθhCCiiθθ + λλ + εε where h represents households, indicates districts whenever the data permits (and states when the data does not allow a more disaggregate unit), and regions. The variable P P is HH a dummy that takes value 1 if the household is poor (i.e., their per capita household income does not exceed the international poverty line of USD 6.85 per day adjusted by PPP 2017). PP H H is a vector of aggregate variables, including indexes of potential damage from natural hazards and the number of natural events occurred in the past. is a PPHH (HH) kk vector of aggregate variables, including the presence of people (natural logarithm of density), infrastructure (percentage of urbanization area), and resources (percentage of cropland area). EEθ PPθ PPee (EE) is a vector of household variables related to coping mechanisms and adapting strategies at the household level, including income diversification and occupation, social assistance, housing conditions, access to the Internet, and household head's education level. CCPPθθC CC (CC) ii includes the size of the household, household head's age, squared age, and gender. And is the error term. DDeeDDPPCCPPHHθθhCCiiθθ (DD) h,dd,rr εε

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