metadata: authors: 0: author: Canavire Bacarreza,Gustavo Javier
1: author: Conconi,Adriana
2: author: Olivieri,Sergio Daniel
3: author: Serio,Monserrat
count: Latin America & Caribbean,Latin America
docna: 0: docna: Understanding Vulnerability to Poverty and
            Natural Disasters in Latin America and the Caribbean
docty: Policy Research Working Paper
theme: Inclusive Growth,Mitigation,Data Development and Capacity Building,Economic Policy,Economic Growth and Planning,Environment and Natural Resource Management,Public Sector Management,Climate change,Adaptation,Data production, accessibility and use
abstracts: cdata!: This paper provides the first measures
            of vulnerability to poverty in Latin America and the
            Caribbean at the household level looking at natural hazards
            such as floods, landslides, cyclones, earthquakes, and
            droughts. It considers seven countries in the region:
            Brazil, the Dominican Republic, Ecuador, El Salvador,
            Honduras, Mexico, and Peru. The paper constructs a unique
            data base that links household surveys from the
            Socio-Economic Database for Latin America and the Caribbean
            database to data on natural hazards from the ThinkHazard
            database and Emergency Events Database, which permits the
            first estimates of how household exposure to natural events
            raises the probability of being poor. The results suggest
            that vulnerability to poverty is related to households'
            coping and adaptation strategies, and there is great
            heterogeneity depending on the natural hazard assumed. The
            evidence generated by this approach helps in understanding
            the lack of coping mechanisms and adaptation strategies
            among vulnerable households. This understanding can be used
            to mitigate the effects of natural disasters. The
            evidence-based approach is useful for identifying specific
            vulnerable communities in disaster-prone areas. Furthermore,
            the information can be presented in the most disaggregated
            representative unit that the data allow so that policy
            makers can build more efficient management policies in their location.
display_title: Understanding Vulnerability to
            Poverty and Natural Disasters in Latin America and the Caribbean
listing_relative_url: /research/2024/06/34350877/understanding-vulnerability-poverty-natural-disasters-latin-america-caribbean
topicv3: Shocks and Vulnerability to Poverty
            m1326691 1160,Poverty and Climate Change m1327423 587
publishtoextweb_dt: 2024-06-26T00:00:00Z
dois: 10.1596/1813-9450-10825
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 49
author_keys: 0: author_key: 715971
1: author_key: 1206391
2: author_key: 926510
3: author_key: 2141671
model_extractions: Policy Research Working Paper 10825

# Understanding Vulnerability to Poverty and Natural Disasters in Latin America and the Caribbean

_Gustavo J. Canavire Bacarreza Adriana Conconi Sergio Olivieri Monserrat Serio_ Poverty and Equity Global Practice June 2024
Policy Research Working Paper 10825

# **Abstract**

This paper provides the first measures of vulnerability to poverty in Latin America and the Caribbean at the household level looking at natural hazards such as floods, landslides, cyclones, earthquakes, and droughts. It considers seven countries in the region: Brazil, the Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, and Peru. The paper constructs a unique data base that links household surveys from the Socio-Economic Database for Latin America and the Caribbean database to data on natural hazards from the ThinkHazard database and Emergency Events Database, which permits the first estimates of how household exposure to natural events raises the probability of being poor. The results suggest that vulnerability to poverty is related to households’ coping and adaptation strategies, and there is great heterogeneity depending on the natural hazard assumed. The evidence generated by this approach helps in understanding the lack of coping mechanisms and adaptation strategies among vulnerable households. This understanding can be used to mitigate the effects of natural disasters. The evidence-based approach is useful for identifying specific vulnerable communities in disaster-prone areas. Furthermore, the information can be presented in the most disaggregated representative unit that the data allow so that policy makers can build more efficient management policies in their location.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gcanavire@worldbank.org, solivieri@worldbank.org, mserio@worldbank.org, and aconconi@worldbank.org _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **<mark>Understanding Vulnerability to Poverty and Natural Disasters in Latin America and the Caribbean</mark>**<sup>*****</sup>

<mark>Gustavo J. Canavire Bacarreza Adriana Conconi Sergio Olivieri Monserrat Serio</mark>

## **JEL Classification:** Q54, I32, D1.

**<mark>Keywords:</mark>** <mark>Vulnerability, Natural Disasters, Poverty, LAC.</mark>

> * We are very grateful to Carlos Rodriguez-Castelan, Ruth Hill, Ghazala Mansuri, Alejandro de La Fuente, Hugo Ñopo, Lander Bosch, and Mattia Amadio for their valuable discussions and comments. The authors would also like to thank Miki Khanh Doan, Juan Jose Miranda Montero, and William Maloney for their insightful suggestions and revisions. The findings, interpretations, and conclusions in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank Group or the countries in the study.
# **1. Introduction**

Climate change is a threat to the objective of sustainably eradicating poverty. Poor people are more often and harshly affected by climate change as they have fewer mechanisms to cope, more losses, and less support to recover. But even those not in poverty can suffer negative impacts that condition their future welfare prospects. Climate change can affect people's wellbeing through various channels, and it can also have an impact on natural disasters, leading to further negative consequences for people. The identification of the specific impact of climate change on natural disaster events such as floods, landslides, droughts, and storms is challenging and complex to estimate. However, as Hallegatte (2014) mentions, climate change has played an increasing role over time, dramatically escalating natural hazards, and displaying heterogeneous effects on people's well-being depending on the type of hazard. In Latin America and the Caribbean (LAC), the acceleration of climate change has shown a sharp increase in the frequency and intensity of extreme weather events (World Bank, 2022). In recent years, some of the deadliest natural catastrophes have occurred, including the earthquakes in Haiti (230,000 deaths in 2010 and 2,248 deaths in 2021), the Brumadinho mudslide in Brazil (300 deaths in 2019), and hurricanes Maria (3,059 deaths in 2017) and Eta (183 deaths in 2020) (UNDP, 2022). The number of people in the region affected by natural disasters has also increased substantially over the last decades. Between 2000 and 2019, the Emergency Events Database (EM-DAT) indicates that 41 million people have been affected by floods, while droughts have impacted the lives of approximately 53 million people (GuhaSapir, et al., 2022). In this study, we look at natural disasters and the mechanisms of households to cope with them.

This study is framed within a climate risk framework, with a specific focus on the household perspective. As per the terminology of the Intergovernmental Panel on Climate Change (IPCC), disaster risk is the result of the interplay between hazard, exposure, and vulnerability dimensions. Hazard refers to the possible future occurrence of natural or human-induced physical events that may have adverse effects on vulnerable and exposed elements (White, 1973; UNDRO, 1980; Cardona, 1990; UNDHA, 1992; Birkmann, 2006b). Exposure refers to the inventory of elements in an area in which hazard events may occur (Cardona, 1990; UNISDR, 2004, 2009b). And vulnerability refers to the propensity of exposed elements such as human beings, their livelihoods, and assets to suffer adverse effects when hazard events impact. Moreover, for international organizations such as the World Bank, the Intergovernmental Panel on Climate Change (IPCC), the European Commission, and the ISO standards, among others, the term vulnerability encompasses a variety of concepts and elements, including the sensitivity or susceptibility, the weaknesses, and the lack of capacity to cope and adapt that favor the potential adverse effects.

As previously mentioned, there is a connection between climate change and the increased intensity and frequency of natural hazards. These hazards can impact the well-being of households, potentially leading to poverty. Vulnerability to poverty of a household is understood as the probability that households will fall into poverty. It is an ex-ante condition, a latent variable that is not directly observable but depends on the future household welfare. The primary distinction between poverty and vulnerability to poverty is that the latter 2
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
cyclones such as hurricanes, are more prevalent. In South America, floods occur more frequently, and droughts have an important impact affecting people. Following EM-DAT, we found that the five most common and impactful natural disasters in the region are floods, tropical storms (including tropical cyclones such as hurricanes), droughts, earthquakes, and landslides.<sup>2</sup> Thus, we use objective measures of these five hazards in the model, but unlike Hill and Porter (2017), we do not model or draw the distribution of these future shocks. Our cross-sectional measures indicate the potential damage from natural hazards based on a probabilistic analysis which is interpreted as a proxy of future aggregate shocks. We also do not include idiosyncratic shocks in the model. Although, we explore them when we perform the Chaudhuri procedure as a robustness exercise.

Finally, this study aims to provide disaggregated data to policy makers to build policies to reduce vulnerability to poverty due to natural hazards in LAC. In this paper, we address the effects of climate risk in the region, which is a relatively unexplored aspect in the region. While there have been some macro-level papers relying on simulations such as Jafino et al. (2020), Solano-Rodríguez et al. (2021), Banerjee et al. (2021) or on agricultural and livestock productivity such as Reyer et al. (2017) and Lachaud et al. (2022) simulations; our main contribution lies in a micro-level perspective focusing on households. Caruso (2017) explores natural disasters’ long-run effects in the region in the 20th century at the micro level exploiting census data. And other studies focus on individual countries within the region or specific types of natural hazards (e.g., Albert, Bustos, and Ponticelli, 2023; Acevedo et al., 2023; Rude and Robayo-Abril, 2023; Aguilar-Gomez et al., 2022). However, there is a lack of comprehensive understanding regarding climate risk in the overall region in the present context. We contribute to addressing this gap by choosing various natural hazards across different countries in the region. The paper's main contribution is the analysis of vulnerable households' coping and adaptation mechanisms at the micro-level.

The paper is structured as follows. The next section presents the conceptual framework and the methodology. Section 3 describes the data, and section 4 presents the results. Section 5 summarizes the main findings and presents a final discussion.

# **2. Measuring vulnerability to poverty including natural hazards in LAC**

# **Methodology**

Building on the definition of vulnerability, we propose a model to measure vulnerability to poverty, including natural disasters and exposure in the region. Although a household's vulnerability is an ex-ante condition and not directly observable until the adverse shock occurs (Ravallion, 1988; Chaudhuri, 2003), it is possible to summarize it as the probability of the household's welfare falling below a minimum standard in the future.

> 2 Hurricanes are a type of tropical cyclones in the Atlantic Ocean. From now on, we will refer to cyclones instead of hurricanes to follow the terminology of the aggregate natural hazard datasets used in the analysis. Following, ThinkHazard! methodology, cyclones, tropical storms, hurricanes, and typhoons, despite their distinct names, all describe the same hazard: a non-frontal storm system characterized by a low-pressure center, spiral rain bands, and strong winds. Thus, we decide to use the term cyclones to encompass a broader scope of this hazard.

4
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

5
We estimate the equation for each country individually and use it to predict the probability of households falling into poverty. We begin by considering one natural hazard at a time and then estimate the full model by taking into account all five natural hazards.

# **Measuring vulnerability index**

Once we predict each household's probability of falling into poverty, we classify each household as vulnerable if it has a predicted probability of being poor greater than 0.50. The choice of the threshold of 0.50 is arbitrary; nevertheless, it has been used in several studies for its simplicity and intuitive interpretation (Chaudhuri, 2003, among others). Likewise, we perform the analysis considering other thresholds too.

After classifying households as vulnerable or not, we compute the incidence of vulnerability using the headcount ratio. In the cases where household data allows state representation, we compute vulnerable ratios at state levels in maps. It is important to note that this measure of household vulnerability does not account for the depth and severity of the vulnerability.

The possibility of overcoming the damage caused by natural disasters depends predominantly on households' coping and adaptation mechanisms. Hence, we provide conditional and unconditional profiles to explore the coping and adaptation mechanisms of vulnerable households. For example, evidence shows that the same natural hazards affect people far more than others when their livelihoods depend on specific physical assets such as livestock, their consumption is closer to subsistence levels, and they cannot rely on savings to smooth out the impacts or even if they are going through a critical moment in life for the development of human capital, the impacts on education and health can extend the risks (Hallegatte et al., 2017).

The conditional profiles show the percentage of vulnerable households lacking the coping mechanisms included as controls in the regressions. The unconditional profiles explore the lack of coping mechanisms of the households outside of the model. In particular, we assess the percentage of vulnerable households reporting only one sector of activity and only one income earner in the household. We look at the percentage of vulnerable households without legal title ownership of the dwelling, whereas it is crucial to have dwelling and land titles to enhance investments in housing infrastructure to protect it from disasters. We also compute the percentage of vulnerable households where at least one member does not have medical insurance because health costs can potentially affect the family's future well-being. Finally, we explore the percentage of vulnerable households without remittances; households with remittances are more likely to smooth their consumption due to this external source of income.

Further, we compute the percentage of vulnerable households with children younger than 5 years old, vulnerable households with children less than 12 years old attending school, and vulnerable households with all their members being elders (65 years old or older). These measures aim to identify groups facing greater risk from natural disasters. The first two groups correspond to children in different stages of life who may suffer more from reductions in their investments in human capital. The third group faces a significant risk due to their low potential capacity to generate an income flow that allows them to restock their assets and wealth better.

6
Next, we explore whether there are differences in coping and adaptation mechanisms among poor and non-poor vulnerable households. To do this, we calculate the above household profiles, differentiating them by poor status.

In addition, we carried out a counterfactual exercise. In this exercise, we estimated the households' vulnerability in a scenario where all households face the lowest possible hazards and compare the estimated vulnerability rates with the actual vulnerability incidence. Thus, we set to all households the minimum hazard they can face, that is, the minimum number of events that occurred among districts and a value of very low damage (more details are presented in Appendix B).

# **3. Data and descriptive statistics**

The datasets for the analysis are the national Household Surveys of each country of the study from the SEDLAC database. This database provides harmonized information of coping and adaptation mechanisms at the household level and poverty status for different countries of the region. The analysis focuses on seven countries: Brazil, the Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, and Peru. We use household survey data, pre-dating Covid19, from 2018 for Mexico and from 2019 for the rest of the countries. Except for Brazil survey, the surveys allow for the identification of the district where households reside. This enables the imputation of hazard and exposure aggregate data at the district level for each household. In the case of Brazil, the survey design led to impute at the state level. However, despite the imputation being carried out at the district level in most countries, the results need to be analyzed at the level of survey representation, which is at the state level.

To identify poor households, we utilize a monetary poverty measure assuming a poverty line of 6.85 dollars a day for the per capita total household income adjusted by 2017 PPP. The household poverty rate is 20% in Brazil, 15% in the Dominican Republic, 21% in Ecuador, 23% in El Salvador, 45% in Honduras, 26% in Mexico, and 26% in Peru (Table 1).

**Table 1.** Household poverty headcount ratio by country.

|Country|Brazil|Dominican<br>Rep.|Ecuador|El Salvador|Honduras|Mexico|Peru|
|---|---|---|---|---|---|---|---|
|_Poverty adjusted by P_|_PP 2017_|||||||
|Poor HH (6.85 U$S)|20%|15%|21%|23%|45%|26%|26%|
|Poor HH (3.65 U$S)|8%|2%|6%|5%|23%|7%|8%|
|Poor HH(2.15 U$S)|4%|1%|1%|1%|11%|2%|2%|

Note: Households are poor if their total income per capita falls below the a-day International Poverty Line (adjusted by 2017 PPP and CPI prices). Source: authors' calculations based on the SEDLAC database.

The selection and construction of variables of households' coping mechanisms, adaptation strategies, and socio-demographic characteristics are detailed in Appendix A. Here Table 2 provides basic information on all seven Latin American countries of coping and demographic variables ( and ) included in the model. Statistics suggest a low occupation protection among households and a lack of income diversification to deal with natural hazards. The level of informality is high; on average, in Ecuador and Peru, half of the household heads have an CC DD informal job. Moreover, in Ecuador, Honduras, and Peru, nearly one household in four reports 7
that the household head's principal occupation is agriculture. And, in most countries, except El Salvador and Honduras, households benefit from social protection that can be adapted as a safety net to respond to natural disasters. Moreover, the results show, on average, a significant proportion of fragile infrastructure and buildings susceptible to natural disasters and limited access to toilet facilities. In El Salvador and Mexico, the percentage of households living in fragile dwellings is to around one-quarter, and the percentage of households with access to toilet facilities linked to the sewer ranges, on average, from 23 percent to 68 percent, depending on the country.

**Table 2.** Average of households with coping and adaptation mechanisms.

|**Category**|**Variable**|**Brazil**|**Domini-**<br>**can**<br>**Rep.**|**Ecuador**|**El**<br>**Salvador**|**Hondu-**<br>**ras**|**Mexico**|**Peru**|
|---|---|---|---|---|---|---|---|---|
||HH head is salaried in<br>the main occupation|38.9%|38.4%|43.3%|40.6%|35.8%|56.8%|33.9%|
||HH head is informal in<br>the main occupation|27.0%|36.6%|50.6%|39.6%|42.8%|32.5%|51.5%|
|Income<br>|The non-labor income<br>per capita PPP 2017|194.98|116.47|115.15|88.19|39.55|89.32|100.81|
|diversification<br>and<br>|<br>Ln. non-labor income<br>per capita PPP 2017|2.75|2.92|2.94|2.14|1.67|2.23|3.55|
|occupation<br>protection|Labor income as a<br>share of total income|67.1%|74.7%|73.3%|75.6%|78.6%|77.9%|71.8%|
||Dependencyrate<br>HH head's principal<br>income is from<br>agriculture|1.8<br>6.4%|1.7<br>8.4%|1.7<br>23.8%|2.1<br>14.3%|2.2<br>23.9%|1.8<br>12.8%|1.9<br>26.6%|
|Social<br>protection|HH receives benefits<br>from social assistance|19.3%|26.3%|18.6%|1.9%|12.2%|28.9%|47.2%|
||Dwelling's materials of<br>low quality|3.2%|5.9%|19.7%|25.6%|11.4%|25.8%|12.6%|
|Housing|Toilet facilities linked<br>to sewer|67.7%|23.2%|64.0%|35.6%|38.3%|62.1%|73.0%|
||Access to electricity|99.8%|99.1%|99.0%|97.6%|94.1%|99.5%|95.6%|
||Overcrowding|3.5%|3.9%|28.9%|8.7%|17.0%|10.6%|8.9%|
||Car|49.4%|20.9%|23.3%|20.2%|23.6%|42.5%|11.8%|
|Assets|Fridge|98.1%|87.9%|80.8%|75.7%|72.4%|86.3%|54.5%|
|ownership|Computer|41.7%|16.7%|41.7%|16.7%|17.2%|27.4%|32.7%|
||Washingmachine|65.9%|78.7%|41.0%|21.1%|.|67.9%|30.5%|
|TICs|Access to Internet|37.7%|25.9%|36.4%|23.3%|27.4%|40.6%|35.9%|
|Education|Years of education of<br>HH head|8.5|8.7|8.6|6.8|6.2|8.8|8.9|
||Number of hh members|3.0|3.1|6.0|3.5|4.1|3.6|3.6|
|Demographics|Age household head|48|49|51|50|50|50|53|
||Male household head|52.8%|63.1%|71.3%|62.9%|65.8%|71.3%|68.8%|

Source: authors' calculations based on the SEDLAC database.

For hazard variables ( ), we exploit two types of data, the number of occurrences of natural disasters in the region in the past and the potential damage expected from them. For the first one, we use the number of natural disasters that occurred in 2000-2020 at the state level from HH the EM-DAT of the Université Catholique de Louvain, Belgium. This database offers a 8
<!-- Start of picture text --> Caribbean Central America South America<br>Drought Drought Drought witerre<br>Flood 4% 6% 5% 7<br>Flood Volcanic<br>m Landslide 25% activity Flood Volcanic<br>TM Tropical cyclone 4% 32% activity3%<br>mTM ConvectiveExtreme temperature storm Earthquake Earthquake “loos<br>m Other storms 13% 12% .<br>Earthquake<br>mTM VolcanicMass movement activity (dry) ee. temperatureExtreme ropicalLandslide3%9 Extreme<br>m Epidemic a 3% cyclone27% temperature Landslide11%<br><!-- End of picture text -->
shaking) at a given frequency or 'return period' based on a probabilistic analysis (more details are presented in Appendix A). Although, the potential damage captured through the TH indexes is not a projection, it allows us to observe which types of natural events could affect more in the areas where households reside. But it is worth noting that TH scores do not represent hazard risks per se, they are only physical hazards from probabilistic models, and their use for risk-related assessment is discouraged. ThinkHazard! methodology provides a general view of the hazards for a given location to promote disaster and climate resilience. It is a simple flagging system highlighting the hazards in an area. Moreover, the scores emphasize the likelihood of different natural hazards affecting areas. Thus, the data can show some uncertainties in specific areas that fail to cover the full range of hazard categories and intensities (see more detail in ThinkHazard! Platform, 2022).

The TH indexes are available from level 2 of administrative units (ADM 2) of FAO's classification. In the case of Brazil, the index is used at level 1 because the geographic variable available in the Household Survey is only at the state level. To capture the differences among administrative units of level 2, we use the average of the indexes of level 2 (see Appendix A). So, this country's index cannot be compared with the other countries' TH indexes. We use data from local areas to control for exposure variables ( ). By definition, exposure is the presence of people, livelihoods, species or ecosystems, environmental functions, services, resources, infrastructure, or economic, social, or cultural assets in places and settings that EE could be adversely affected by events (IPCC, 2014). Following this definition, we include population density (people per sq. km of the district or state area), percentage of cropland, and urbanization of the district or state area. The data source of type of land is from the MODIS 500m Landcover Dataset NASA LP DAAC at the USGS EROS Center, retrieved from Google Earth Engine Data Catalog based on the 2018 Images LC type 1 with 17 classes of land use. We employ class 12 for the cropland area (where at least 60% of the area is cultivated), class 13 for the urban area (at least 30% is impervious surface area, including building materials, asphalt, and vehicles), and the sum of all the classes for the total area of the administrative units at level 2.

Table 3 summarizes the average and standard deviation of the main variables of hazards and exposure at administrative level used in the analysis. The results indicate that Central America and Mexico face an elevated potential damage from cyclones and a higher frequency of occurrences of this hazard. In South America, floods are more prevalent, but countries like Peru and Ecuador evidence on average a higher potential damage from landslides than floods.

10
**Table 3.** Aggregate statistics of natural hazards and exposure by country.

|||Brazil||Do|minican|Rep.||Ecuado|r|E|l Salvad|or||Hondura|s||Mexico|||Perú||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Obs.|Mean|SD|Obs.|Mean|SD|Obs.|Mean|SD|Obs.|Mean|SD|Obs.|Mean|SD|Obs.|Mean|SD|Obs.|<br>Mean|SD|
|_Hazards_||||||||||||||||||||||
|TH index at AD|M 2|||||||||||||||||||||
|Floods|27|2.66|0.7|134|3.10|1.1|219|2.07|1.2|211|1.74|1.1|225|1.97|1.2|879|1.98|1.2|181|<br>1.64|1.1|
|Landslides|27|1.53|0.4|135|2.90|0.8|222|3.27|1.0|225|3.23|0.8|227|3.48|0.7|883|2.45|1.2|182|<br>3.36|0.8|
|Earthquake|27|1.19|0.3|136|3.00|0.0|223|3.28|0.5|225|4.00|0.0|230|3.29|0.5|881|2.09|1.0|182|<br>3.23|0.5|
|Cyclones|5|1.00|0.0|22|4.00|0.0|82|1.00|0.0|2|4.00|0.0|28|4.00|0.0|903|3.94|0.3|4|1.00|0.0|
|Droughts|27|1.59|0.7|136|3.00|0.0|223|1.00|0.0|225|3.00|0.0|230|2.11|0.3|903|2.31|0.9|182|<br>1.41|0.7|
|EM-DAT: Num|ber of eve|nts (2000-2|021) at AD|M 1||||||||||||||||||
|Floods|23|6.87|6.36|32|6.75|2.51|24|5.08|3.49|14|5.50|1.70|16|6.69|2.63|<br>32|3.19|3.16|25|9.20|3.74|
|Landslides|23|0.39|0.94|32|0.00|0.00|24|0.29|0.55|14|0.07|0.27|16|0.00|0.00|<br>32|0.25|0.80|25|0.56|0.87|
|Earthquake|23|0.04|0.21|32|0.13|0.34|24|0.67|0.82|14|1.86|0.66|16|0.50|0.73|<br>32|0.78|1.39|25|1.60|1.53|
|Cyclones|23|0.09|0.29|32|11.66|1.77|24|0.00|0.00|14|5.64|1.28|16|4.00|2.22|<br>32|6.63|5.97|25|0.00|0.00|
|Droughts|23|1.57|1.53|32|0.00|0.00|24|0.29|0.46|14|2.07|1.07|16|3.38|1.78|<br>32|0.50|0.67|25|1.20|0.41|
|_Exposure at AD_|_M 2_|||||||||||||||||||||
|_Density_|27|75.7|119.9|135|350|1173|25|84|80|225|599|1290|230|124|135|902|746|2094|182|<br>68|296|
|Ln. density|27|3.4|1.5|135|4.8|1.2|25|3.9|1.3|225|5.6|1.1|230|4.4|0.9|902|4.9|1.9|182|<br>3.0|1.4|
|Area|27|314,318|376,072|135|325|302|223|1,131|1,884|<br>225|86|91|230|387|743|902|1,462|3,201|<br>182|<br>6,829|13,195|
|% cropland|27|5%|7%|135|6%|11%|223|3%|10%|225|5%|11%|230|2%|6%|902|18%|25%|182|<br>2%|4%|
|% urbanization|<br>27|1%|2%|135|3%|9%|223|2%|6%|225|2%|8%|230|0%|1%|902|4%|15%|182|<br>1%|4%|

Note: density in Brazil and Ecuador is at the state level. The statistics are computed before imputations. Source: authors' calculations based on ThinkHazard! EM-DAT, and SEDLAC database.

11
# **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.

12
**Table 4.** Probit model estimations including all hazards.

||Br|azil|Dominica|n Republic|Ec|uador|El Sa|lvador|Hon|duras|M|exico||Peru|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|Probit|Mg.<br>Effects|
|TH index Flood|0.105|0.012|0.000|0.000|-0.032|-0.006|-0.025|-0.005|-0.036|-0.008|0.025|0.005|-0.018|-0.003|
||[0.056]*|[0.006]*|[0.042]|[0.006]|[0.042]|[0.008]|[0.017]|[0.003]|[0.032]|[0.007]|[0.013]*|[0.003]*|[0.023]|[0.004]|
|Number of Floods|-0.014|-0.002|-0.086|-0.012|0.018|0.003|-0.089|-0.017|-0.039|-0.008|0.021|0.004|-0.009|-0.001|
||[0.005]***|[0.001]***|[0.084]|[0.012]|[0.022]|[0.004]|[0.031]***|[0.006]***|[0.019]**|[0.004]**|[0.008]**|[0.002]**|[0.005]*|[0.001]*|
|TH index Landslides|-0.333|-0.038|0.063|0.009|-0.026|-0.005|-0.010|-0.002|0.027|0.006|-0.012|-0.002|-0.074|-0.012|
||[0.158]**|[0.018]**|[0.067]|[0.009]|[0.049]|[0.009]|[0.023]|[0.004]|[0.044]|[0.010]|[0.017]|[0.003]|[0.024]***|<br>[0.004]***|
|Number of Landslides|0.231|0.026|||0.171|0.032|-0.130|-0.025|||-0.099|-0.019|0.047|0.008|
||[0.064]***|[0.007]***|||[0.085]**|[0.016]**|[0.124]|[0.024]|||[0.019]***|[0.004]***|[0.020]**|[0.003]**|
|TH index Earthquake|0.079|0.009|||0.068|0.013|||0.105|0.023|-0.010|-0.002|-0.022|-0.004|
||[0.092]|[0.011]|||[0.074]|[0.014]|||[0.123]|[0.027]|[0.023]|[0.005]|[0.040]|[0.006]|
|Number of Earthquakes|-0.546<br>[0.201]***|-0.062<br>[0.023]***|0.878<br>[0.294]***|0.125<br>[0.042]***|-0.109<br>[0.057]*|-0.021<br>[0.011]*|0.765<br>[0.168]***|0.146<br>[0.032]***|0.019<br>[0.056]|0.004<br>[0.012]|-0.036<br>[0.021]*|-0.007<br>[0.004]*|-0.022<br>[0.017]|-0.003<br>[0.003]|
|TH index Cyclones|||||||||||0.158<br>[0.029]***|0.031<br>[0.006]***|||
|Number of Cyclones|||0.059<br>[0.050]|0.008<br>[0.007]|||0.300<br>[0.074]***|0.057<br>[0.014]***|0.039<br>[0.023]*|0.009<br>[0.005]*|0.003<br>[0.003]|0.001<br>[0.001]|||
|TH index Drought|0.065|0.007|||||||0.012|0.003|-0.049|-0.010|-0.002|-0.000|
||[0.083]|[0.009]|||||||[0.145]|[0.032]|[0.022]**|[0.004]**|[0.024]|[0.004]|
|Number of Droughts|0.062|0.007|||-0.028|-0.005|0.962|0.183|0.032|0.007|-0.023|-0.004|-0.030|-0.005|
||[0.020]***|[0.002]***|||[0.094]|[0.018]|[0.212]***|[0.040]***|[0.030]|[0.007]|[0.034]|[0.007]|[0.045]|[0.007]|
|Controlling by exposure<br>Clli b i|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|ontrong y copng|||||||||||||||
|mechanisms|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Controlling byregion|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by districts in brackets (by states in Brazil and Ecuador). Source: authors' calculations based on ThinkHazard! EM-DAT, and SEDLAC database.

13
We also estimate Probit models for every natural hazard separately. Figure 3 presents the average marginal effects of each natural hazard of these estimations. Positive marginal effects indicate a positive relationship between household poverty and the hazard of the event. In Brazil, earthquakes and droughts show positive effects, _ceteris paribus_ . In the Dominican Republic, the number of earthquakes and landslides' potential damage positively correlate to poverty. The number of landslides and the earthquake's potential damage in Ecuador positively correlate to poverty. The droughts and the number of landslides matter in El Salvador. In Honduras, mainly earthquakes are associated with household poverty. In Mexico, cyclones are the natural hazards associated with household poverty. In Peru, the number of landslides positively associates with poverty. Owing to the differences in natural hazard effects, below, when we present the results of vulnerability that come from the disasters separately models, we are going to present the results from the models that only include earthquakes in Honduras, cyclones in Mexico, floods in Peru, droughts in Brazil and El Salvador, and landslides in the Dominican Republic and Ecuador. Additionally, it is important to highlight that we do not count on the potential compounding effects of these hazards by incorporating their interactions into the models (for example, including the interaction between floods and landslides). This is an aspect that remains to be studied.

**Figure 3.** Average marginal effects from the models with natural hazards separately.

<!-- Start of picture text --> TH index Floods Number of Floods TH index Landslides Number of Landslides<br>TH index Earthquakes Number of Earthquakes TH index Cyclones Number of Cyclones<br>TH index Droughts Number of Droughts<br>B R AZIL M E XIC O PE R U DOM INIC AN E L HONDUR AS E C UADOR<br>R E P. SALVADOR<br>0.027<br>0.026***<br>0.024*<br>0.019<br>0.015** 0.015<br>0.014 0.014<br>0.011<br>0.009<br>0.008*** 0.008<br>0.005** 0.007** 0.007<br>0.004<br>0.003 0.003 0.003 0.003<br>0.002** 0.002<br>0.001** 0.001 0.001<br>-0.001<br>-0.002 -0.002<br>-0.003 -0.003<br>-0.004<br>-0.005**<br>-0.008 -0.006** -0.008<br>-0.009**<br>-0.010***<br>-0.011***<br>-0.013*** -0.013** -0.015<br><!-- End of picture text --> Note: *** p<0.01, ** p<0.05, * p<0.1. Each family color bar corresponds to a specification that includes one type of natural hazard controlled by exposure, coping mechanisms, and region. Source: authors' calculations based on ThinkHazard!, EM-DAT, and SEDLAC database.

14
The results show that Honduras has the highest vulnerability to poverty rate (41%), and the Dominican Republic has the lowest (7%). The vulnerability incidence in Peru is 24%, indicating that almost a quarter of the households have a predicted probability over 0.50 of falling into poverty. In Mexico, the vulnerability rate is 20%, El Salvador 17%, Brazil 16%, and Ecuador 14%.

**Figure 4.** Vulnerability incidence (% of vulnerable households with a predicted probability greater than 0.50 including all hazards).

<!-- Start of picture text --> Honduras 41%<br>Peru 24%<br>Mexico 20%<br>El Salvador 17%<br>Brazil 16%<br>Ecuador 14%<br>Dom. Rep. 7%<br>0% 10% 20% 30% 40% 50%<br><!-- End of picture text --> Source: authors' calculations.

In Figure 5, the maps show the percentage of vulnerable households in each state in the case of the regressions with only one natural hazard at a time. In the case of Brazil, droughts are the hazards included in the model. The results show higher vulnerability in the north in line with the fact that poverty, exposure, and natural hazards concentrate more in that region and that households in this area have fewer coping mechanisms than those in the south. The Dominican Republic shows that some administrative units present a vulnerability rate for landslides notably higher than the national average. The vulnerability rate of the provinces Independencia, Baoruco, and Pedernales is 36%, 25%, and 24%, respectively. Also, the map shows that Barahona and Puerto del Plata have districts with a higher vulnerability rate than the rest. In Ecuador, the vulnerable households are concentrated in the east, the region with more effective poverty rates. But where the TH index of landslides suggests more potential damage is in the central region, where some cantons also show a high vulnerability. El Salvador's vulnerability rate for droughts, except for San Salvador, is very similar among provinces. But some differences are found between districts. In particular, some districts of Morazan and Ahuachapan show a high rate in comparison to districts of other provinces. Honduras shows that the administrative unit at level 1 with the highest percentage of vulnerable households when earthquakes are considered is Lempira (78%). The map shows that many other districts present a sizeable rate. But, in Lempira's case, all their districts present a significant rate. Lempira is located in the west, corresponding to a region with significant potential earthquake damage.

15
With cyclones, Mexican households show a high vulnerability rate in Chiapas (59%), Oaxaca (56%), and Guerrero (46%), followed by Veracruz (39%) and Tabasco (37%). Furthermore, these provinces broadly suffer from a high amount of cyclone activity. From the results obtained for floods in Peru, the percentage of vulnerable households increases in the mountain regions in the center and tropical regions in the east. In the eastern is where floods are more potentially damaging. Even though Loreto province experiences more potential damage from floods, administrative units in the mountain regions with high poverty rates show the most significant vulnerability rates, such as Huancavelica, followed by Ayacucho and Cajamarca. It is worth noting that the coastal region is comparatively less vulnerable to floods mainly because is relatively less poor than other regions. However, it is important to consider the potential impact of coastal floods on this area, such as the El Niño Costero, which have not been addressed here.

16
Su of vulnenaisie: ioe pny BA % a-Ailey state: state: asete rom oer : icc~ 2 ‘ae' ‘. =aa » ee, = : : iN] e ete ei ——a Ee | a PC ey a — | el — a _— — — — | a C —_T = as — a —= — Dt — — — — a PY — a es a — _ as —_ — az be — as as = ; = a — a — __ — — i — 7 — = = = a —_ — — = = — |= **=** = —_ — = — TM = | | | = _= Le = — = =e== || a —= ] = - 7=:7 F === —_ = ls= =- . t= = = = 7= Le :L:== ; | =: a.; 1 ||

<!-- Start of picture text --> =aa<br>=<br>ete ei<br><!-- End of picture text --> <!-- Start of picture text --> a-- Ailey state: state:<br>: :<br>iN] e<br><!-- End of picture text -->
Following the literature, we are using a threshold of 0.50, but we also perform the analysis for other thresholds obtaining vulnerability curves (Figure C.1 in Appendix C shows results with other thresholds). Although the levels are different, the pattern and conclusions are replicated in a similar manner.

In the counterfactual exercise, the vulnerability in Mexico decreases from 20% to 12%, suggesting the cyclones contribute to vulnerability by 8 p.p. This difference represents around 11 million individuals who become vulnerable when the hazard from cyclones is considered. In Honduras, the simulation of earthquake hazard suggest that this hazard raises vulnerability from 33% to 41%; this difference of 8 p.p. represents around 600,000 (0.6 M) new vulnerable people. In the rest of the countries, the vulnerability increases 1 p.p. In Brazil, droughts increase vulnerability from 15% to 16% (nearly 2 million individuals becoming vulnerable). For landslides, the vulnerability rate in the Dominican Republic ranges from 6% to 7% and in Ecuador from 13% to 14% (0.1 M and 0.28 M individuals become vulnerable, respectively). In El Salvador, the vulnerability rate from droughts specification changes from 16% to 17% (almost 100,000 individuals becoming vulnerable).

According to the World Bank (2022) report based on Jafino et al. (2020), climatic events could push between 2.4 million and 5.8 million people in the region into extreme poverty by 2030. In this study, from the simulated scenario, we estimate that natural hazards push around 14.2 million people to vulnerability in moderate poverty. Although this is a lower bound because we are including only one hazard per country, the number is in line with other simulations in macro frameworks.

**Figure 6.** Vulnerability rates comparison between counterfactual scenario and actual scenario.

<!-- Start of picture text --> Low harm Actual harm<br>45% 41% 41% 41%<br>39%<br>40% 37%<br>33%<br>35%<br>30%<br>25%<br>20%<br>17%<br>20% 14% 16% 16%<br>15% 12%14% 13% 11%14% 12% 15%<br>10% 6%7%<br>5%<br>0%<br>Floods Landslides Earthquakes Cyclones Droughts<br>Ecuador Mexico Dom. Rep. Ecuador Brazil Honduras Dom. Rep. Ecuador Brazil Honduras Mexico Honduras Brazil El Salvador Honduras<br><!-- End of picture text --> Note: The results correspond to the specifications, including one type of natural hazard controlled by exposure, coping mechanisms, and region. Source: authors' calculations based on ThinkHazard!, EMDAT, and SEDLAC database.

**Vulnerability profiles**

18
We perform a profile assessment to understand the strategies of vulnerable households for coping or adapting in the face of natural disasters. For each country, we compute the percentage of vulnerable and non-vulnerable households looking whether the head of the household is a non-salaried worker, whether they work in only one sector, whether there is only one income earner in the household, whether the primary source of income is from agriculture, whether the dwelling has low-quality materials, whether the household receives benefits from social assistance, and whether the household does not receive are any remittances from abroad. We also compute the percentage of households with children under 5 years old, children under 12 years old, and households with all their members elders 65 years old or older. Table C.1 in Appendix C shows these percentages at the country level for the seven countries according to each specific natural hazard.

In Figure 7, we show Mexico's profile from the model conditioned to cyclones hazard. The percentage of households with primary income from agriculture, operating in the informal sector and not having salaried household heads is higher among the vulnerable than the nonvulnerable. Risk assessments show that agricultural production and income from agriculture can be depressed for years as an additional impact of natural hazards (Hallegatte et al., 2017). Households highly dependent on agriculture and ecosystems are more exposed to cyclones. Thus, diversification of incomes, safety nets to smooth consumption, and climate-smart agriculture practices need to be on the country's agenda. In addition, physical infrastructure and housing with more climate and disaster strength to reduce damage to dwellings are also needed. The percentage of households living in fragile dwellings is significantly higher among the vulnerable households, showing that these dwellings can be particularly affected by cyclones. The rate of households with kids is higher among the vulnerable than the nonvulnerable, but this is not the case for elders. Therefore, vulnerability reduction measures need to include responses to avoid or diminish the permanent impacts of cyclones, especially on children's education and health.

**Figure 7.** Mexican households' coping mechanisms and adaptation strategies.

<!-- Start of picture text --> 80%<br>Non Vulnerable Vulnerable<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>HH head informal<br>HH head not a salaried worker Only one sector of activity reported Only one income earner in HH HH head's main income from agriculture Presence of children <5 Only elders (65 or more) in HH Presence of children <12 attending school Dwelling's materials of low quality Roof's materials of low quality Walls' materials of low quality Benefits from social assistance<br><!-- End of picture text --> Source: authors' calculations.

19
To check for differences between poor and non-poor households, we divided the sample by household's poor status. Figure C.1 in Appendix C shows that the vulnerability rate includes poor and non-poor households. This is due to the vulnerability model used in the analysis, which may include, among the vulnerable, households with a high probability of being poor (a probability over 0.50) even when they are not actually poor. Nonetheless, the percentage of non-poor households vulnerable to poverty is lower than that of poor households. Figure 8 shows that vulnerable households present a similar lack of coping mechanisms and adaptation strategies, whether poor or non-poor.

**Figure 8.** Vulnerable Mexican households' coping mechanisms and adaptation strategies by poor status.

<!-- Start of picture text --> Non poor Poor<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>not a informal no more one income children only children dwelling's roof's walls' not<br>salaried than one income from younger elders (65 attending materials materials materials benefits<br>worker sector of earner agriculture than 5 or more) school from<br>activity social<br>program<br><!-- End of picture text --> Source: authors' calculations.

The profiles at the national level of the other countries are in Table C.1 in Appendix C. The results suggest that in Brazil, strengthening vulnerable households to engage in multiple economic sectors is necessary. Vulnerable households report more working only in one sector of activity and having only one income earner. Also, the incidence of households with children is higher among vulnerable households. Children's incidence and lack of health insurance in the Dominican Republic are mainly distinguished among vulnerable from nonvulnerable households. In Ecuador, the prevalence of diversification from agricultural income and dwelling materials shows that fragile buildings and lack of income diversification mainly encompass vulnerable households. In El Salvador and Honduras, vulnerable households lack income diversification and occupation protection, and they also show a significant percentage of households with children whose human capital may be affected by natural hazards. In Peru, the lack of income and occupation diversification is noticeable, leaving households less able to cope and recover.

Moreover, these profiles can be extrapolated or go as low as the survey allows it to analyze at the state or district level and see the degree of lack of adaptation and coping mechanisms of different regions. For example, the maps in Figure 9 present at the state level the potential hazard from cyclones, the percentage of households without coping mechanisms, and specific groups of households at significant risk in Mexico. The maps in green correspond to all households (vulnerable and non-vulnerable), and the maps in red correspond to only 20
<!-- Start of picture text --> TH index « Cyclones Nurioer ofCycoras<br>Pi “: 4 I _| q =<br>4 . ~~ jam *1 asR<br>3<br>a * so"aly£ hy Gea4... bs<br>Yas: tq d —A<br>Low ee“at i moh .<br>HGweD ee ainan<br>hhc chat as: | Bid<br>9% of vulnerable hh withby main income trem agnoultune 9 of vulnerable‘of low bh quality with dwelling’sby state materiaks<br>Hird ath prgeicctndCanaslate = 050 HHf with peedicied‘Cyohnesprobability > 1.50<br>be<br>i<br>Fae ae ,<br>miataSe - “ be-‘ miBiasecet)66,38<br>Sp 00g. 17]<br>% of vulnerableHHower the dwellinghh denot by state have legal title *4 offran Wiraraéia5 if reHh Fouad wih onnidren ea” sounger<br>with predicted probabdity > 0.50 Pile peace eta ee<br>n 7<br>Weis [ “, TM =<br>pe oa 9<br>oh<br>mySia82 $718 Byod42.5. ne=<br><!-- End of picture text -->
<!-- Start of picture text --> TH index - Drought Number of Droughts<br>nehpi f A eyif al4<br>Ce ee 2<br>rae<br>y Oe ee | ranele : J<br>=<br>if<br>; : mi2.0.5. a+ a<br>m2.1.3.0a jet a Ter,B0.0,01 i<br>oot. ae ONe data i<br>%4 of vulnerable hh with main income from agriculture % of vulnerable hh without access water supply<br>by state system by state<br>HHwith predictedDraughtprabability> 0.50 HHwith predicted probability> 0.50<br>‘att Droughts7<br>Fs :<br>La<br>ls<br>t ,<br>Mii? 28 : a<br>oloia: fF See cd<br><!-- End of picture text -->

<!-- Start of picture text --> TH index - Landslides<br>c, s<br>le .<br>Os<br>im rtME! oan<br>rae”<br><!-- End of picture text --> <!-- Start of picture text --> ‘> Of bhw ith remittances<br>by district<br>HH wth predioted probably » O80<br>i. <. é ee % at rae<br>=e ve . ae ae Se ne .<br>7 rf % af noe wey tay’s :<br>Aae Piq r—<br>He .”<br><!-- End of picture text -->
<!-- Start of picture text --> TH index - Landslides:<br>’<br>.<br>SHEDtow<br>BHiG<br>Na data<br><!-- End of picture text --> <!-- Start of picture text --> % of vulnerable hh notown the dwelling<br>by state<br>HM Wh predicted promaciity = 0.50<br>(Learrchelchies,<br>k<br>= -<br>mmB21.52.65 y<br>Gy 15.21 ia,<br>Oj.054.4<br><!-- End of picture text -->
<!-- Start of picture text --> TH index - Floods<br>i ‘\<br>ee,<br>Lo y Net<br>ey ft er<br>BHIG ‘ pe ;<br>No data<br><!-- End of picture text --> <!-- Start of picture text --> a<br>=<br>oO<br><!-- End of picture text --> <!-- Start of picture text --> =<br>B<br>Oo<br><!-- End of picture text --> <!-- Start of picture text --> TH index - Earthquake<br>+ ve | 1<br>5 i ‘s<br>f<br>4<br>MEO wd<br>mG 7<br><!-- End of picture text -->

<!-- Start of picture text --> % of vulnerable hh with dwelling's materials<br>of low quality by district<br>HH with predicted probability > 0.50<br>Earthquakes<br>Ps otha > | .<br>aoe t<br>= a<br>sn) eee9<br>ore “ws * i<br>ieAdana yte<br><!-- End of picture text -->
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
Honduras imply a substantial increase in vulnerability to poverty. Brazilian and Salvadoran households present a higher vulnerability when drought-prone areas are considered. In Ecuador, landslides and earthquakes relate more to poverty.

Regarding policy, many households in the region are unable to cope and adapt to these events, primarily through the lack of income diversification and dwelling material. The results draw attention to specific regions with higher vulnerability to poverty incidence. The methodology proposed in this paper can be an initial tool to explore households' coping strategies and the weakened capacity to respond to natural hazards, to identify priorities for action to reduce households' vulnerability. This paper examines a few mechanisms in the profiles and maps. But the analysis can be extended depending on the action and the country in evaluation. Safety nets generally increase the household's capacity to cope with events and risks. However, other risk management instruments such as insurance, land use, and building regulations can help households manage flood and landslide risks more. On the other hand, early warning systems can also reduce cyclones' impacts, and the awareness of being prepared can help for earthquakes.

In our study, we acknowledge the role that several other characteristics can have in helping to cope with the adverse effects of natural hazards, such as the access to and utilization of financial services. However, it is important to note that not all countries incorporate some specific information in their surveys, leading to a lack of standardized variables within the databases in the region. For instance, while the survey in Peru addresses financial access, including account ownership and credit card usage, this detailed information is only uniformly available across some surveyed countries. Consequently, our ability to comprehensively analyze how financial services contribute to hazard coping in the region is constrained. Nonetheless, this type of specific coping mechanism could be incorporated in studies focused specifically on individual countries, which is beyond the scope of our current research.

In our study, we employed a composite measure (e.g., TH index) for a systematic analysis of natural hazards, rather than relying on specific indexes for each hazard such as FATHOM for floods or SPEI for droughts. Although the latter approach offers more precision, our decision to use the composite measure was to facilitate comparison among many countries and hazards. We are aware that the precision of the indexes used is lower, and it can lead to certain limitations. Although, our approach allows including specific indexes for each hazard. We let this as a next step for future research towards improving the overall measures of vulnerability to poverty in the countries in the region.

Finally, the methodology avoids several limitations of cross-section data using explicitly natural hazard potential damage data. However, we do not explore the compounding effects of these hazards, such as the interaction between floods and landslides, as well as interactions between hazards and specific household characteristics. This is something to address in upcoming steps. Also, some caveats to the analysis concerning unobserved household characteristics, actual natural hazard impact on vulnerability (causality), and the interrelationship between hazards and other climate change impacts remain. Including climate change measures, such as extreme temperature and rains, as well as other types of disasters (e.g., pluvial and coastal floods), is the following apparent step. Also, in this study, the scarce availability of long-panel data limits the consistent estimates of vulnerability to poverty. Performing our approach using short synthetic panels to include dynamics in the model is a 26
forward step that could enhance the analysis. Synthetic panels based on matching individuals with the same time-invariant characteristics in consecutive cross-sections have been recently proposed as a substitute for such data (Moreno, Bourguignon, and Dang, 2021). Here we collapse the number of occurrences in a period but using short synthetic panels, we could include the time variability and explore observable households' strategies to cope with hazards across periods.

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