of risk (risk-induced -RI), and particularly to classify those that are climate risk-induced (CRI) or idiosyncratic risk-induced (IRI). Our model is a mixed/hybrid approach from the two main ways of operationalizing vulnerability to poverty described by Skoufias and Baez (2023), which are the “multilevel or hierarchical” approach and the “simulation” approach. The multilevel or hierarchical approach does not use any explicit information on community-level climate or weather shocks. Instead, it decomposes the error term into a covariate (community level) and an idiosyncratic component. Unlike the multilevel approach, the simulation approach uses concrete information on the incidence and intensity of the aggregate and idiosyncratic shocks experienced by households. The approach in this paper essentially introduces explicit information on climate and weather within a multilevel model. The climate is characterized by the historical means of rainfall and temperature data from the geographical area where the household is located. Additionally, recent weather-related events, such as natural disasters, impact the variability of unobserved shocks at the community level. We use household-level data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), along with various natural disaster and extreme weather data sources, such as EM-DAT, ThinkHazard! Platform, and Climate Change Knowledge Portal (CCKP). Additionally, we incorporate drought indexes, such as the Standardized Precipitation-Evapotranspiration Index (SPEI), into our analysis.

We find that Guatemala and Honduras have the highest incidence of vulnerability to poverty, followed by Colombia and Peru. Conversely, Uruguay and Chile exhibit the lowest incidence. Our findings suggest that out of the approximately 184 million people vulnerable to poverty in the region, 109.5 million are PI, and around 74.5 million are RI, where almost 14.5 million are CRI. By examining the spatial distribution of the vulnerable households, we can identify their location within the countries and gain a better understanding of their exposure to natural hazards to design more efficient policies targeting for the most vulnerable populations.

The rest of the paper is organized into four sections. Section 2 presents the methodology, where we discuss our approach to modeling per capita household income within the vulnerability to poverty framework with climate disaster risk. We also describe the decomposition by PI, CRI, and IRI. Section 3 provides our data sources, including household surveys, natural disasters, climate, and extreme weather data. Section 4 presents the main results, counting the incidence of vulnerability to poverty in fifteen Latin American countries and the spatial distribution of vulnerable households. Finally, Section 5 concludes the paper with a summary of our findings and their potential implications for policy makers.

# **2 Methodology**

Following Chaudhuri (2003), the main difference between poverty and vulnerability to poverty is risk. The potential sources of risk are diverse. Households can experience risks from adverse aggregate shocks and adverse idiosyncratic shocks specific to individual families (i.e., ranging from general ones like macroeconomic shocks or changes in international prices due to harvests that suffer from droughts or floods to more specific ones like the principal income household earner losing her job). The nature and magnitude of household risks lead to wide variability in household's income or consumption. A household's vulnerability to poverty depends on income volatility and the ability to smooth consumption. While consumption is a smoother measure, income is typically more readily available in household surveys conducted in Latin America and the Caribbean (LAC). Our objective is to incorporate as many countries as possible into our analysis. To achieve this, we proposed a model considering the per capita total family income, acknowledging that the main predicted variable of the model to measure vulnerability is more volatile than other well-being proxies, such as consumption.

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