# **4. Results**

Table 4 presents the estimated coefficients from the hazard term of the full Probit model that includes all the natural hazards, controls for regional fixed effects, and standard errors clustered within administrative units. It also displays the estimated average marginal effects revealing heterogeneous effects among natural hazards across countries. In the case of Brazil, both floods and droughts present a direct correlation with household poverty. Conversely, earthquakes, cyclones, and landslides positively correlate with poor households in the Dominican Republic. Poverty in Ecuador and Peru correlates positively with the number of landslides and in El Salvador with the number of droughts, earthquakes, and cyclones. In Honduras, the hazards that positively relate to poverty are earthquakes and droughts. Finally, cyclones and floods are positively associated with household poverty in Mexico.

Moreover, the correlations presented in Table 4 lead to some interesting conjectures. For example, when both ThinkHazard! and EM-DAT coefficients are positive, such as cyclones in Mexico, the most common hazards coincide with the most disruptive hazards regarding poverty status suggesting a double high association. But, in some cases, significant estimations show opposite signs in hazard variables, e.g., floods in Brazil show a positive correlation with the ThinkHazard! index and a negative correlation with the EM-DAT variable. ThinkHazard! is based on the likelihood of each region experiencing a hazard with enough intensity to cause damage. Meanwhile, the EM-DAT measures provide the frequency of past events. When the coefficient of ThinkHazard! is positive and the coefficient of EMDAT is negative, it suggests, on the one hand, that hazards with more intensity of causing damage are more likely to have a greater impact on communities than expected. On the other hand, more frequency (controlling already for intensity) could imply that communities that have experienced these events in the past could develop some forms to prevent and adapt between occurrences, preventing a continuous cycle of poverty. Conversely, when the coefficient of ThinkHazard! is negative and the coefficient of EM-DAT is positive, it is not necessarily the severity of the natural hazard that plunges households into current poverty but rather the frequency of such events. The constant recurrence of the past of these hazards could create a cycle of economic setbacks for families, leading to the positive relationship of the present. Something interesting to note in this case is that the higher potential damage from hazards is associated with areas where more affluent households reside. Although these households may have more resources to cope and adapt, they face a higher risk of losing valuable assets and goods.

Also, the estimations indicate the relevance of coping mechanisms (although these estimates are not shown in Table 4, they are similar to those estimated with the initial poverty model presented in Appendix A and are available upon request). For instance, having an informal household head, a high dependency ratio, and a primary income from agriculture increases a household's vulnerability to falling into poverty, and larger households are also more likely to be vulnerable. Receiving benefits from social assistance programs is positively correlated with the household's vulnerability. On the other hand, households with salaried household heads, those with a larger share of labor income, better housing conditions, and assets are less likely to be vulnerable.

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