considers the household’s prospects due to the adverse effects from shocks such as natural disasters, their exposure, and the capacity to mitigate or recover from them. One of the best ways to understand the capacity to recover, bearing in mind the household's current characteristics and future shocks, is to involve the well-being dynamics perspective in the vulnerability to poverty assessment through long-term panel data (Chaudhuri, 2003). However, even though the quality and the number of panel data have increased in Latin America and the Caribbean, long-term panel databases still need to be available in the region. Nonetheless, the literature has built measures of vulnerability to poverty in the context of cross-sectional data (Chaudhuri et al., 2002; Chaudhuri, 2003; Dercon, 2006; Bérgolo et al., 2010; Hill & Porter, 2017; Skoufias, Vinha, and Beyene, 2021, among others).

In this study, we develop vulnerability to poverty measures under a climate risk framework using cross-sectional data. To do this, we model vulnerability to poverty as a function of variables that can be related to hazards, exposure, sensitivity, adaptative capacity, and household demographic characteristics. Based on a micro-framework, we model the probability of being poor, including current households' coping mechanisms and observable aggregate measures that are proxies of exposure and natural shocks. Our analysis aimed to cover as many countries as possible, focusing on those where administrative-level data on hazards and exposure could be aggregated due to the survey design at the district level. Ultimately, we included seven countries in our analysis: Brazil, the Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, and Peru.

Our approach presents differences from others. Typically, natural disaster literature measures disasters from risk metrics and maps, implicitly including household adaptation capacity (Ward et al., 2020; Cremen et al., 2022, among others). In contrast, Chaudhuri's (2003) and earlier works measure vulnerability to poverty by estimating the consumption process and drawing inferences about future consumption prospects, such as mean and variance, based on household conditions and unobserved idiosyncratic shocks without taking into account aggregate shocks. To do this, the authors model the idiosyncratic shocks (household-level shocks) through the variance of the disturbance term of the linear consumption equation. Skoufias, Vinha, and Beyene (2021) account for unobserved idiosyncratic and community covariate shocks modeling consumption through multilevel models and the variance of the disturbance term of the model. Hill and Porter (2017), for their vulnerability computation, work with objective measures of aggregate and idiosyncratic shocks. They seek exogenous sources of shocks to estimate causal effects. They use crop losses as a measure of rainfall shocks and control for the probability of drought in each community using the historical distribution of droughts, considering the timing of the shock as exogenous. As idiosyncratic shocks, they include the household member's death or job loss. Other studies have introduced climate change and natural disaster impacts taking a macro-level perspective (Hallegatte et al., 2016, 2017; Jafino et al., 2020, among others). Our focus is to analyze the household's vulnerability to poverty in a context of climate risk at a micro-level perspective by analyzing the household's socioeconomic conditions and adaptation capacity, as well as the potential natural disasters that could impact them and their exposure based on their location of residence.

To estimate vulnerability to poverty, we make an effort to include aggregate shocks explicitly in our model. In the region, the data reveals differences in natural hazard occurrences among subregions; for instance, in the Caribbean and Central America, storms, including tropical 3