predicting poverty. The correlation between the predicted probability of being poor and the actual poverty status are significant compared to other studies. In addition, when we examined the headcount of households with a predicted probability of being poor larger than 0.50 and the headcount of poverty, we found that the ratio is close to 1 for most countries (for more details, see Appendix A).

We also test for confounding of coping variables dropping hazard variables. We find that for example some coping variables are related to hazards but as we expect hazards are not related to coping. Even though we are not trying to estimate the causal effects of harm or coping variables, the results align with the fact that harm variables can affect the present household's characteristics/copings but not the other way around. The coping and characteristics of the household seem not to affect the variables of harm as is expected. We also estimate the model excluding ThinkHazard! indexes and including the number of occurrences from EM-DAT which is a proxy of frequency of hazards based on historical data. The estimations of EMDAT coefficients do not show significant differences between both models (Table A.2 in the Appendix).

Following Chaudhuri (2003), we model the household income per capita considering idiosyncratic shocks. These shocks enter directly into the vulnerability estimation through the standard deviation of the income model's error term making distributional assumptions in the error variance to draw the poverty rate. However, identifying which type of shocks and risks are operating in the disturbance term is puzzling, making this a handicap. The incidence under this model works as an upper bound as it considers other shocks and risks that fall in the disturbance term, which are not captured through the observables. The results highly correlate with the original vulnerability measures from the Probit model. The details and results of these robustness checks are in Appendix B.

We also performed one more robust check constraining the hazard coefficients to positive values. Vulnerability to poverty using our approach might be higher or less than the actual poverty. The empirical strategy of estimating vulnerability to poverty conditioned to hazards, exposure, and coping factors may predict a high probability of being poor even if households do not experience considerable hazards. Further, it may not identify households with a high susceptibility to hazards as vulnerable when the potential damage of hazards correlates negatively with districts' poverty. The latter is a critical caveat of the model, and we explore the effects of this miss-allocation using a restricted non-linear model where we force hazard coefficients to be positive. We estimate a non-linear probability model, which we named the restricted model, where we set the hazard coefficients as positive. At first, it is not an appealing option to impose structure on the model forcing the sign of the coefficients, but it can be an appropriate robustness check exercise. The results of this exercise show a high correlation between the vulnerability rates computed with the restricted and Probit model (see Figure B.1 in Appendix B).

# **5. Conclusions**

We merge nationally representative household datasets of seven countries in the LAC region with aggregate natural disaster and area data to estimate the household vulnerability to poverty in the context of climate risk. We find considerable heterogeneities in terms of the vulnerability to poverty among events. For example, cyclones in Mexico and earthquakes in 25