metadata: authors: 0: author: De La Fuente,Alejandro
1: author: Serio,Monserrat
count: Latin America & Caribbean,Peru,Colombia,Honduras,Uruguay,Chile
docna: 0: docna: Vulnerability to Climate Risk–Induced
            Poverty in Latin America and the Caribbean
docty: Policy Research Working Paper
abstracts: cdata!: This study offers the first
            comprehensive estimates of populations in Latin America and
            the Caribbean at heightened risk of poverty or deeper
            impoverishment due to structural factors and risks,
            particularly climate-related events. The populations of
            Peru, Colombia, and Honduras exhibit the highest levels of
            vulnerability to climate-induced poverty in relative terms,
            while those of Uruguay and Chile rank as the least
            vulnerable. Although structural factors and idiosyncratic
            risks drive most households into poverty, climate-related
            events exert a disproportionately severe impact on welfare
            when they occur. The study also maps the geographic
            distribution of vulnerable populations and examines the
            coverage of social assistance programs within these groups.
display_title: Vulnerability to Climate
            Risk–Induced Poverty in Latin America and the Caribbean
listing_relative_url: /research/2024/12/34436869/vulnerability-climate-risk–induced-poverty-latin-america-caribbean
topicv3: Climate Risk Management m1326584 573,Poverty m1326687 695
publishtoextweb_dt: 2024-12-16T00:00:00Z
dois: 10.1596/1813-9450-11002
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 59
author_keys: 0: author_key: 1007700
1: author_key: 2141671
model_extractions: Policy Research Working Paper 11002 Vulnerability to Climate Risk–Induced Poverty in Latin America and the Caribbean _Alejandro De la Fuente Monserrat Serio_ Poverty Global Department December 2024
Policy Research Working Paper 11002

# **Abstract**

This study offers the first comprehensive estimates of populations in Latin America and the Caribbean at heightened risk of poverty or deeper impoverishment due to structural factors and risks, particularly climate-related events. The populations of Peru, Colombia, and Honduras exhibit the highest levels of vulnerability to climate-induced poverty in relative terms, while those of Uruguay and Chile rank as the least vulnerable. Although structural factors and idiosyncratic risks drive most households into poverty, climate-related events exert a disproportionately severe impact on welfare when they occur. The study also maps the geographic distribution of vulnerable populations and examines the coverage of social assistance programs within these groups.

This paper is a product of the Poverty Global Department. 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 adelafuente@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
an WORLD BANKGROUP CLIMATECSPSUPPORT FACILITY
# **Abbreviations & Acronyms**

|ADM|Administrative Unit|
|---|---|
|AR6|Sixth Assessment Report of the Intergovernmental Panel for Climate Change|
|CCKP|Climate Change Knowledge Portal|
|CR|Climate Risk|
|CRI|Climate Risk Induced|
|CPI|Consumer Price Index|
|EM-DAT|Emergency Events Database|
|FAO|Food and Agriculture Organization|
|GAUL|Global Administrative Unit Layers|
|GDP|Gross Domestic Product|
|HH|Household|
|IPCC|Intergovernmental Panel for Climate Change|
|IPCF|Per Capita Household Income|
|IRI|Idiosyncratic Risk Induced|
|LAC|Latin America and the Caribbean|
|LASSO|Least Absolute Shrinkage and Selection Operator|
|n.a.|Not available|
|PDSI|Palmer Drought Severity Index|
|PET|Potential Evapotranspiration|
|PI|Poverty Induced|
|PL|Poverty Line|
|PPP|Purchasing Power Parity|
|RI|Risk Induced|
|SEDLAC|Socio-Economic Database for Latin America and the Caribbean|
|SPEI|Standardized Precipitation-Evapotranspiration Index|
|SPI|Standardized Precipitation Index|
|TH|ThinkHazard|
|UNDHA|United Nations Department of Humanitarian Affairs|
|UNDRO|United Nations Disaster Relief Coordinator|
|UNDRR|United Nations Office for Disaster Risk Reduction|
|UNFCCC|United Nations Framework Convention on Climate Change|
|UNHCR|United Nations High Commissioner for Refugees|
|UNISDR|United Nations International Strategy for Disaster Reduction|
|USD|United States Dollar|

2
# **1 Introduction**

The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) highlights that climate risk arises from the convergence of climate hazards, vulnerability, and exposure. Following Cardona et al. (2012), 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, 2006). On the other hand, exposure refers to the inventory of elements in an area where hazard events may occur (Cardona, 1990; UNISDR, 2004, 2009). Lastly, vulnerability refers to the susceptibility, sensitivity, and lack of resilience or capacities that favor adverse effects on exposed elements such as human beings, their livelihoods, and assets when impacted by hazard events (UNDRO, 1980; Cardona, 1990). According to IPCC (2007), vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability describes the likelihood of suffering adverse effects. It is associated with a set of conditions of people that derive from the historical and prevailing cultural, social, environmental, political, and economic contexts. Moreover, climate risk has the potential to drive people into poverty. For instance, Skoufias et al. (2023) find that in Ethiopia, households living in drought-prone lowlands have a significantly higher vulnerability to poverty than households in other ecological zones in the country. Similarly, Malawian households' exposure to droughts and excess rainfall shocks decreases their consumption and elevates their vulnerability to poverty (Baquie & Fuje, 2020). Climate risk can also have long-term impacts on wealth, spanning generations (Damania et al., 2017; Carrillo, 2020; Aguilar & Vicarelli, 2022; among others).

Over the last 50 years, the Latin America and the Caribbean region has had a rise in natural disasters and extreme weather events, as reported in the Emergency Events Database (EM-DAT) of the Université Catholique de Louvain, Belgium (Guha-Sapir et al., 2022). These events have resulted in fewer deaths over the years but higher damage costs, especially in the Caribbean islands when scaled by GDP. This study aims to provide a comprehensive analysis of the vulnerability to poverty in a climate risk framework. It seeks to estimate the number of people in the region who are at risk of being pushed into poverty due to climate disaster risk such as floods, droughts, or extreme temperature events.

In our analysis, we simplify the climate disaster risk by reducing dimensions and treating it as a type of covariate risk. We do this by using natural disasters that have occurred, extreme weather conditions that households have been exposed to, and natural hazard geographical indexes. We model the per capita household income and identify poor households within the vulnerability to poverty framework. The framework considers climate disaster risk, idiosyncratic risk, community factors, and specific household characteristics. The climate disaster risk is interpreted as an aggregate shock, while idiosyncratic risks are specific shocks to each household. Both risks are modeled through the shape of the per capita income distribution, estimating the expected variance. On the other hand, the expected mean income is estimated based on the household and community characteristics. It offers a more structural idea of the proportion of households already poverty-induced independent of the type of risk they face. This framework has been proposed by Chaudhuri (2002, 2003) and modeled by Gunther & Hartgen (2009) following Hox (2010) and used by Skoufias et al. (2023) for Ethiopia and Rud and Robayo-Abril (2023) for El Salvador.

However, our analysis differs and expands past studies in various ways. It explicitly considers climate disaster risk as an aggregate shock by exploiting natural disaster and extreme weather data. Doing so allows us to establish the proportion of households in different countries within the region that are likely to become poor due to structural characteristics (poverty-induced -PI) or due to the occurrence 3
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.

4
We follow the approach proposed by Skoufias et al. (2023) to estimate a measure of “vulnerability to poverty” over different countries of LAC using cross-sectional household data and aggregate data on the propensity of climate hazards in the region. The method is based on Gunther & Hartgen (2009) following Hox (2010) and Chaudhuri’s (2002, 2003) model. The approach includes covariate (aggregate) and idiosyncratic (specific) shocks, modeling them as a function of observable households and community (district or state) characteristics. Moreover, the model distinguishes between povertyinduced (PI) and risk-induced (RI) household vulnerability.

Specifically, the following two-level hierarchical model of income is estimated: ln I I i i 2 i i (1) where denotes the households ( ) at level one, and j denotes the communities where households resided at level two (such as districts or states, depending on the country’s data). We exploit five rounds of surveys per country, so we control for a vector of dummies ii ii= 1, ... , NN i where each dummy takes value 1 if data correspond to each period survey.<sup>2</sup> We further assumed that the tt coefficients and vary across communities. In hierarchical models, this is a varying-intercept and varying-slope model. The varying coefficients are modeled using community characteristics 0ii 1ii ββ ββ related to socioeconomic conditions and climate change as ii ii ZZ II 00 01 02 , (2) 0ii ii ii ooii ββ = γγ10 + γγ11 ZZ + γγ12 II + uu . (3) 1ii ii ii 1ii The error terms and ββ are residuals of level two, representing the unexplained differences in = γγ + γγ ZZ + γγ II + uu income across communities. Replacing equations (2) and (3) into equation (1), we obtain the full ooii 1ii uu uu model re-written as ln I I i 00 01 02 10 11 12 i 2 i i i , (4) ii ii ii ii ooii 1ii = γγ00 + γγ01ZZ + γγ02II + �γγ10 + γγi 11ZZ + γγi II �XX12 + ββi tt 2+ uui + uu XX + eei i . ii ii ii ii ooii 1ii The (natural logarithm) per capita household income (= γγ + γγ ZZ + γγ II + γγ XX + γγ ZZ ln IXX + γγI i ) is assumed to be affected by observed II XX + ββ tt + uu + uu XX + ee characteristics, including a set of specific household characteristics i , community characteristics and , and an unexplained part i i named error term. The error term includes a ii XX ZZ community component capturing the covariate shocks on the household’s income. This term ii ooii 1ii II uu + uu XX + ee includes an error term representing the unexplained variance across communities of the intercept ooii uu and the error term i reflecting the unexplained variance across communities of the slope . 0ii ββ This last term includes an idiosyncratic part through i , which can capture an indirect effect of 1ii 1ii uu XX ββ covariate shocks through household characteristics.<sup>3</sup> At the same time, i captures the remaining XX idiosyncratic household shocks. ee

> 2 Here and in the rest of the paper, we drop subscript t for simplicity. For some countries, data for five rounds is unavailable; in these cases, we use at least two rounds of surveys.

> 3 It is important to note that in analyzing the impact of covariate shocks on households’ income at the community level, the error terms and i play a crucial role. However, following Skoufias et al. (2023) and Gunter and Harttgen (2010), we chose not to analyze and together. Instead, these studies focused solely on the 0ii 1ii uu uu XX 0ii 1ii uu uu

5
cov(u ) #0.

(InIPCF —_InPL) (9 47 “ “ 7 )x < In PL) (u ~~(————_—_ —_—___~~ =~p +7 +7 +79 +7 +47 )x P +7 +7 ++ +7 +7 YX
<!-- Start of picture text --> 6 fi +t +f +H +f<br>P= pr(InIPCF >InPL)<br>(Vv ) é<br>6<br>6<br><!-- End of picture text -->
<!-- Start of picture text --> G6 G 6<br><!-- End of picture text -->
across communities. In this paper, community is all households within the province level, which is a large area, so it is implicitly assumed that covariate shocks are not correlated across provinces. Finally, it is important to consider the benefits and costs of merging climate and natural hazards data with household survey data. The main costs involved are time, effort, limitations, and the need to make adhoc decisions to match the climate and natural hazards variables with households, as detailed in Skoufias and Baez (2023). Moreover, if the vulnerability related to idiosyncratic shocks is significantly higher than the vulnerability related to covariate climate shocks, the benefit of including climate and natural shocks in the multilevel approach might be attenuated. This paper aims to capture the part of the expected variance related to N so that the proposed model could provide insights from a conceptual perspective. However, from a cost perspective, there might be better options. If the costs of including climate and natural hazards data are very high, running a simple model with only Z as covariate variables in the equations may offer a more suitable alternative. Nonetheless, the interpretation of the volatility in each case would be different.

The analysis is conducted individually for each country, using country-specific variables to estimate the household’s expected mean income, and the expected variances. In practice, **t** he following steps are carried out to measure vulnerability to poverty due to climate risk (CR) in each country:<sup>8</sup>

- 1- Estimate the natural logarithm per capita income equation (1) assuming constant coefficients at the household level, including only household-specific variables ( i ): XX

- ln I I i 0 1 i 2 i i = ββ + ββ XX + ββ tt + ee

To perform this estimation, we first estimate the equation considering 19 pre-determined household variables with LASSO and choose the final sub-set of variables following LASSO results. 2- Once the household-specific variables ( i ) are chosen from LASSO, we estimate a multilevel model with random intercept, and the variables , , i and interactions ( i and i ): XX ln I I i 1 i 2 3 i 3ZZii IIii XX4 i 5 i i ZZiiXX IIiiXX where = ββ0ii00+ ββ XX. + ββ ZZii + ββ ZZiiXX + ββ IIii + ββ IIiiXX + ββ tt + ee Then, we identify the interactions that are significant for the estimation of the final full model (step ββ0ii = γγ + uuooii 4). 3- Estimate a multilevel model with i , random intercept and slopes. ln I I i i 2 i XXi where = ββ0ii00+ ββ1iiXX, + ββ tt + ee and ββ0ii = γγ10 + uu.ooii In order to reduce computation time, we only consider 4 variables from ββ1ii = γγ + uu1ii i having random slope each time. Then, we identify the significant random slope effect parameters to keep them in the estimation XX of the final full model (step 4). 4- Run the full multilevel model (equation 4) with random intercept and random slope including , , i , the significant interactions ( i and i ) identified in step 2 and the variables with ii ZZ statistically significance random slope identified in step 3. ii ii ii II XX ZZ XX II XX

> 8 We use the Skoufias et al. (2023) toolkit and manual accessed by the Equity Policy Lab to implement these steps.

9
5- Estimate the expected mean of the natural log. per capita income (ln I�I ı ), the residuals i and using estimation of step 4. eê uu�6- Sum ooii i and and estimate the expected total variance following equation 7: 2 ooii i � 0 1 2 i 3 i 4 i i . eê uu� ooii ii ii �uu� + eê = ππ + ππ NN + ππ XX NN + ππ XX + ππ tt + θθ Identify the significant interactions parameters to keep them in the estimation of the final equation 7. 7- Predict the expected variance ( 2 i ) using estimation of step 6 including only significant interactions. σσ�uuooii+ee � ln P ı 8- Compute the probability of being vulnerable to poverty as i ~~(~~ ). 2 −lnIIPPI ~~�~~<sup>σσ�</sup> VV<sup>�</sup> = ΦΦ uuooii+eei 9- Classify the households as vulnerable and non-vulnerable using the threshold of 0.29.

10.4- Classify the households as vulnerable and non-vulnerable using the threshold of 0.29. 11- Classify the households as PI those vulnerable households from step 9 with ln I�I i 12- Classify the households as RI those vulnerable households from step 9 with ln I�I i < ln IIPP. ln I <mark>14- <13?></mark> �I i Classify the households that under steps 9 and 10.4 are vulnerable as CRI if their We interpret these households as vulnerable to poverty due to climate or natural > ln IIPP. disaster risk. These are the households that are not PI but can be pushed into poverty due to CR. > ln IIPP.

# **3 Data**

As we mentioned, we exploit five years of survey data per country to expand the variability of data, specifically in small countries such as the Dominican Republic, El Salvador, Honduras, and Panama. The surveys provide information at the individual and household levels. The data source is the SEDLAC database, which provides harmonized and standardized household survey data for LAC countries. Although most household surveys are representative at the state (province) level, some allow to identify lower levels, such as districts or localities where households reside. Thus, we also exploit information of climate hazards at the administrative unit level where each household is located. In our model, to construct the first group of variables (the dependent variable, IPCF, and household characteristics regressors, ), we need information at level 1. Hence, we use survey data at household level. The second group of variables is at level 2 and corresponds to community characteristics XX (regressors of aggregate household characteristics climate change and community natural hazards ). The aggregate data is prepared at FAO Global Administrative Unit Layers (GAUL) using ZZ, II, ADM 1, ADM 2, or ADM 3, depending on the imputation level. NN 10
Our first aim was extending the analysis to as many countries as possible, subject to the feasibility of imputation. Hence, we ended up analyzing 15 countries of the region. In cases where imputation was not feasible for certain periods, we used data from at least two survey rounds in those countries. Table 1 presents the corresponding countries, the rounds used in each country, and levels of aggregate data imputation.

**Table 1.** National household surveys used and level of imputation.

|**Country**|**Survey**|**Years (rounds)**|**Community-level**<br>**Income Equation**<br>**(Z, C)**|**Community-**<br>**level Risk**<br>**Equation (N)**|
|---|---|---|---|---|
|Argentina|EPHC-S2|2003, 2008, 2011,<br>2017,2021|ADM 1|ADM 1|
|Bolivia (Plurinational<br>State of)|EH|2002, 2008, 2011,<br>2017,2021|ADM 1|ADM 1|
|Brazil|PNAD, PNADC-E5|2008, 2011, 2017,<br>2021|ADM 1|ADM 1|
|Brazil (cities)|PNAD, PNADC-E5|2017, 2021|ADM 1|ADM 2|
|Chile|CASEN|2009, 2011, 2017,<br>2022|ADM 1|ADM 2|
|Colombia|GEIH|2011, 2021|ADM 1|ADM 1|
|Dominican Republic|ENFT, ECNFT-Q03|2002, 2008, 2011,<br>2017, 2021|ADM 1|ADM 2|
|Ecuador|ENEMDU|2008, 2011, 2017,<br>2022|ADM 1|ADM 2|
|El Salvador|EHPM|2002, 2008, 2011,<br>2017,2022|ADM 1|ADM 2|
|Guatemala|ENCOVI|2011, 2014|ADM 1|ADM 1|
|Honduras|EPHPM|2008, 2011, 2017,<br>2019|ADM 1|ADM 2|
|Mexico|ENIGH, ENIGHNS|2002, 2008, 2010,<br>2016, 2022|ADM 1|ADM 2|
|Panama|EH|2002, 2008, 2011,<br>2017, 2021|ADM 1|ADM 1|
|Paraguay|EPH|2002, 2008, 2011,<br>2017, 2022|ADM 1|ADM 1|
|Peru|ENAHO|2002, 2008, 2011,<br>2017,2021|ADM 1|ADM 3|
|Uruguay|ECH, ECH-S2|2002, 2008, 2011,<br>2017,2021|ADM 1|ADM 1|

Note: Province or state level imputation is indicated with ADM 1, district imputation is indicated with ADM 2, and lower-unit levels are indicated with ADM 3.

To construct the aggregate household characteristics, we use the SEDLAC household survey data at province/state level. We generate the following household aggregate characteristics: the proportion (ZZ), of households engaged in agriculture in the state, the state's employment rate, the state's unemployment rate, the share of self-employed and entrepreneurs in the state, the share of the 11
population with at least secondary education in the state, the population density (urban dummy). We select these variables based on the community variables employed in similar studies.<sup>9</sup> The climate change data ( is based on CCKP information at province/state level. Our study exploits monthly average temperature data from 1940 to 2022 by province from the CCKP (2023) database to II) approximate climate change signs over the natural temperature variability. We calculate a climate change signals index considering changes in temperature trends per time interval. By province, we estimate the monthly temperature average trend in 1940-2020 and 2000-2020. The index is the sum of the absolute differences between both trends over 2000-2020. A higher index indicates a more evident climate change signal. We also use precipitation data from CCKP by province. We compute the number of times in the period 1901-2021 that the z-score of the aggregated accumulated precipitation in mm is above or below two standard deviations.<sup>10</sup> And the rest of natural hazards variables at level 2 ( _N_ ) correspond to data from ThinkHazard! Platform, EM-DAT, and SPEI database which provide data at lower administrative geographic units than province level. However, it is essential to note that the sample representativeness of the survey does not apply to lower unit levels than province/state level. As a result, the outcomes are presented at either the province or country level.

From ThinkHazard! Platform, we use the index prepared by the platform that classifies hazards as high with 4 (potentially severe dangerous events), medium with 3 (potentially dangerous events), low with 2 (potentially dangerous events are less likely to occur but still possible), and very low with 1 (potentially damaging effects are unlikely to occur). ThinkHazard (TH) levels are derived from hazard maps applying the spatial distribution of hazard intensity (e.g., flood depth) at a given frequency or 'return period' based on a probabilistic analysis. We consider the most common climate hazards in LAC, i.e., river floods, urban floods, coastal floods, landslides, droughts, cyclones/hurricanes, wildfires, and extreme temperatures. However, for some countries, there are hazards that do not show variability in the index (for example, small countries present a very high or very low damage event in the total area of the country from a specific hazard). These cases are left aside from the analysis. This index is available at level 2 of GAUL (ADM 2), which means it is calculated based on data for a specific sub-geographic unit within each country state. When the imputation of TH data is at the state/province level (i.e., level 1) due to the survey design, we calculate an average TH index using the indexes of level 2. This is done to ensure that the imputation accurately reflects the variability of the index at level 2 rather than using the maximum principle of TH. However, for all other situations where imputation is not at level 1 and is at level 2, we rely solely on the TH index at level 2.

> 9 In the model proposed by Skoufias et al. (2023), community-level variables include the average distance to an all-weather road, the proportion of households engaged in agriculture, the proportion of household heads employed, the proportion of household heads with at least secondary education, and the average dietary diversity. The community level variables are derived by taking the average of those households sampled from each unit. Gunther and Harttgen (2009) include as community-level variables an infrastructure index using fourteen characteristics estimated via principal component analysis (bus stop, community road, provincial road, national road, secondary education facility, tertiary education facility, hospital, safe water, electricity, veterinary, agricultural services, fertilizer, market, bank), population density through a city dummy, the mean educational level, the percentage of households working in the formal sector, and the percentage of households possessing an enterprise within the community. The data on household characteristics are taken from the national household survey and the others from the census. Meanwhile, Rude and Robayo-Abril (2023) control for municipality characteristics including the employment rate, the unemployment rate, the share of self-employed and entrepreneurs, the share of public sector employees, the share of children attending school, the share of the population with secondary education, the crime index, the security index, and the environmental (natural hazards) index. The source of data for municipality level variables is the household survey data.

> 10 The mean and standard deviation correspond to the total period of 1901-2021.

12
Also, we add community variables that indicate the frequency of events based on historical data. For this purpose, we use the database from EM-DAT (Guha-Sapir et al., 2022), which offers a comprehensive database of natural disasters that took place between 2000 and 2023 at the state and district levels. The database is generated from various sources, including UN agencies, nongovernmental organizations, reinsurance companies, research institutes, and press agencies. The variables obtained from EM-DAT include the number of occurrences of each natural disaster (including floods, landslides, cyclones/hurricanes, drought, wildfires, or extreme temperature events) that took place in a specific year of survey. Additionally, we considered a set of variables that indicate the frequency of the same natural disaster occurring during the lag year of the survey period.

We include drought variables using SPEI data at community level (ADM1, ADM2, or ADM 3). SPEI is a drought index proposed by Vicente-Serrano et al. (2010). The SPEI has a spatial resolution of 0.5°; thus, to aggregate to ADM 3, ADM 2 or ADM 1, we impute each administrative unit with the value of SPEI following the equation i max{a m ); a )} where a is for absolute value and is max or min depending on the result of the conditional ii mm mm mmiimm function. SSIISSII = SSIISSII aa(SSIISSII aa(SSIISSII aa mm Different scientific disciplines use this multi-scalar index to measure drought severity according to its intensity and duration. The advantage of the SPEI regarding other drought indexes, such as the Palmer Drought Severity Index (PDSI) or the Standardized Precipitation Index (SPI), is that it considers the effect of the potential evapotranspiration (PET). It is measured using different time scales to identify different drought types. Short-time scales are mainly related to soil water content and headwater river discharge; medium-time scales involve reservoir storage and river discharge, while long-time scales are associated with variations in groundwater storage. Thus, Acosta et al. (2021) express that SPEI-1 (1 month of scale) is unsuitable for an economic analysis in which researchers are interested in determining how persistent climatic events affect productive systems and farmers’ living conditions. They decide to use SPEIs 12, 24, and 48 months. Acosta et al. (2021) compute a severe drought dummy derived from the SPEI index, which takes one if the value of the SPEI is in the lowest five percentiles of the SPEI distribution and zero otherwise. Birhan et al. (2021) use the SPEI-6 for a shortterm drought variable and SPEI-24 for a long-term drought variable. They construct a dummy to capture exposure to a short-term drought that takes the value one if the score of SPEI-6 is -0.5 or lower and zero otherwise and another dummy to capture exposure to a long-term drought that takes the value one if the score of SPEI-24 is -0.5 or lower and zero otherwise.<sup>11</sup> For our analysis, we use the SPEI-6 and SPEI-24 index and their squared.<sup>12</sup> Negative values of SPEI indicate dryness and positive values wetness. The SPEI database enables imputation at the lowest available household survey unit.

> 11 Vollenweider (2014) uses the SPEI-3 as a standardized measure of weather. Other authors such as Edossa et al. (2014), Bischiniotis et al. (2018), and Drumond et al. (2021) provide a classification of SPEI in categories of extremely, severely, moderately, or normal drought or wet period. Edossa et al. (2014) and Bischiniotis et al. (2018) classified the SPEI value for Africa as Extremely dry if SPEI ≤ -2; Severely dry if SPEI > -2 and SPEI ≤ -1.5; Moderately dry if SPEI > -1.5 and SPEI ≤ -1; Mild drought if SPEI > -1 and SPEI ≤ -0.5; Near normal dry if SPEI > -0.5 and SPEI ≤ 0; Near normal wet if SPEI > 0 and SPEI ≤ 0.5; Mild wet if SPEI > 0.5 and SPEI ≤ 1; Moderately wet if SPEI > 1 and SPEI ≤ 1.5; Severely wet if SPEI > 1.5 and SPEI ≤ 2; and Extremely wet if SPEI > 2.

For South America, Drumond et al. (2021) classify the SPEI as Extremely dry if SPEI ≤ -2; Severely dry if SPEI > -2 and SPEI ≤ -1.5; Moderately dry if SPEI > -1.5 and SPEI ≤ -1; Mild drought if SPEI > -1 and SPEI ≤ 0; Mild wet if SPEI > 0 and SPEI ≤ 1; Moderately wet if SPEI > 1 and SPEI ≤ 1.5; Severely wet if SPEI > 1.5 and SPEI ≤ 2; and Extremely wet if SPEI > 2.

> 12 In another model, we employ four dummies that attempt to capture the effects of a short-term drought (1 if score of SPEI-6 <=-1), a short-term wet (1 if score of SPEI-6 >=1), long-term drought (1 if score of SPEI-24 <=-1), and a long-term wet (1 if score of SPEI-24 >=1) period. However, these binary measures do not always

13
**Table 2.** Aggregate data of hazards and climate change databases.

|**Source**|**Hazards**|**Index**|**Type**|**Level**|
|---|---|---|---|---|
|ThinkHazard!|Floods (river, urban, and coastal),<br>landslides, cyclones, wildfires,<br>extreme temperatures.|TH index<br>Average of TH index|Modeled|ADM 2<br>ADM 1|
|EM-DAT|Floods, landslides, droughts,<br>cyclones, wildfires.|Number of occurrences in<br>the survey year and lag<br>year|Historical|ADM 2<br>ADM 1|
|SPEI|Drought|SPEI-6<br>SPEI-24|Modeled|ADM 3,<br>ADM 2,<br>ADM 1|
|CCKP|Climate change|Temperature trend<br>difference (trend 2000-<br>2020 vs trend 1940-2020)|Historical|ADM 1|
||Precipitation|Number of extreme<br>aggregated accumulated<br>precipitation in mm events<br>in 1901-2021|Historical|ADM 1|

Note: we aggregate the three floods TH indexes into one. We construct a weighted indicator considering that some areas can experience the three types of floods. In contrast, other sites can experience only two or one of them (e.g., districts far away from the sea without shore or sea exit are less likely to experience coastal floods. Moreover, the coastal flood TH index has no data in these cases). Table B1 in the Appendix shows how this weighted index is constructed. The SPEI-6 and SPEI-24 index time scales correspond at the beginning of the household survey period. The SPEI has a spatial resolution of 0.5°; thus, to aggregate to ADM 3, ADM 2 or ADM 1, we use the aggregate equation i m ); a )} where a is for absolute value and is max or min depending on the result of the conditional function. ii mm mm mmiimm SSIISSII = SSIISSII mmaamm{a aa(SSIISSII aa(SSIISSII aa mm The income equation (equation 4) of the model includes the community variables Z and C from the SEDLAC and the CCKP data. On the other hand, the remaining community variables N are included in the expected variance equations (equations 5, 6 and 7). We assume that the community variables from household survey (Z) and climate change (C) can offer valuable insights into the expected average income. These variables are not prone to quick variations in variance and some of them change slowly, however they can still have a significant impact on income distribution shifting the distribution and changing the expected mean. The remaining community variables are considered more volatile and unpredictable and are better suited to explain the expected variance of income.

# **_Variables used in the assessment_**

We use the per capita household income adjusted by countries’ CPI and PPP 2017 as the main wellbeing outcome of interest. We also adopt an international poverty line adjusted by PPP 2017 of 6.85 USD daily. We select as household-specific variables for LASSO the following: salaried household head, labor informal household head, the household dependency ratio (number of members of the household divided by the number of income earners in the household); if household head’s main principal income is from agricultural activities, dwelling's materials of low quality, dwelling with toilet facilities linked to sewer, access to electricity, access to safe water, overcrowding (more than three members per bedroom), the household has a car, fridge, computer, washing machine, a appear consistently among the different countries; mainly due that in some countries, the index range is lower than in others.

14
household with access to the Internet, number of household members, age of the household head, sex of the household head and years of education of the household head.<sup>13</sup> Initially, we assume all countries have the same four random slope variables. These variables are: 1) the household’s principal income from agriculture, 2) the presence of toilet facilities in the dwelling that are linked to the sewer, 3) access to electricity, and 4) access to water. We select these variables based on the assumption that natural hazards can particularly affect these dimensions. However, after performing LASSO and multilevel estimations, not all four variables are always included in each country's case. Depending on LASSO results and the significance of the varying-slope coefficients, all of them or a subset of them ended up being included in each country's model. Finally, all the community-level variables from household survey ( ) and climate change ( ) are included in each country’s income equation. On the other hand, the natural hazard and disaster variables ( ) selected ii ii ZZ II for the expected variance equations vary depending on the variability of the country's index. ii NN Table B2 in the Appendix details the variables used for each country estimation. Column (2), named Income Community-Level Covariates, refers to the eight community covariates variables and of the income equation of the model. Column (3), named Individual and HH characteristics at the ii ii ZZ II household level, refers to the household-specific variables characteristics i of the model chosen after LASSO and column (4) named Slope vars includes the variables that we assume have varying XX coefficients. Column (5), named Risk Community-Level Covariates, shows the community covariates variables of the expected variance equations of the model. Finally, the assessment is conducted at the household level using the most recent household survey NNii year available in each country, even though the estimations are based on the five rounds. The poverty rate and the vulnerability to poverty incidence are computed over households, not individuals. Henceforth, the main results will be presented as household incidence. To get the number of people involved in the analysis, we make some extra computations based on the number of members of each household. In the cases where the results pertain to individuals, we will explicitly specify it.

# **4 Results**

The vulnerability to poverty varies widely across countries, ranging from 2 percent in Chile to 64 percent in Guatemala when analyzing household incidence. Additionally, approximately one-third of the countries have a household incidence of vulnerability to poverty between 25 percent and 45 percent, and about half of the countries have an individual incidence in that range. The percentage of the population vulnerable to poverty ranges from 4 percent in Chile to 73 percent in Guatemala. In addition, the rates of vulnerability are fairly heterogenous within most countries. Table B3 in the Appendix presents all the vulnerability to poverty measures at the national level, further stratified by rural/urban areas and the country’s regions. Figure B2 shows the vulnerability to poverty incidence by state.

> 13 While it is possible to perform LASSO using many variables, we have limited the number of variables to the ones mentioned above to ensure the comparison between the countries. However, more household characteristics can be added if needed for a specific country study.

15
<!-- Start of picture text --> Gatenaa i 73%<br>Honduras 67%<br>Colombia NE 52%<br>Per 45%<br>El Salvador i 42%<br>Brazil 37%<br>Ecuador 35%<br>Mexico 27%<br>Paraguay ME 24%<br>Brazil- cities MN 26%<br>DominicanRep, MN 27%<br>Bolivia (Plurinational State of) HN 24%<br>Panama MM 18%<br>Argentina ME 17%<br>Uruguay MM 10%<br>Chile MI 4%<br>0% 20% 40% 60% 80%<br><!-- End of picture text --> <!-- Start of picture text --> Guatenala Es 64%<br>Honduras i 62%<br>Colonbia TT 43%<br>Peru 40%<br>El Salvador 3.4%<br>Brazi] DE 28%<br>Ecuador 25%<br>Mexico ME 19%<br>Paraguay ME 19%<br>Brazil-cities MEM 19%<br>DominicanRep. ME 18%<br>Bolivia (Plurinational State of) HEN 18%<br>Panana ME 9%<br>Argentina Hl 7%<br>Uruguay Ml 5%<br>Chile H 2%<br>0% 10% 20% 30% 40% 50% 60% 70%<br><!-- End of picture text --> <!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>10%<br>is°° alt | ii| _. Hl| | ol| al a| | all<br>2 & z & § 8 & 38 - & S<br>=a eS ‘3 a iG)<br>&° a I<br>s3<br>2 a<br>ee)<br>fea}<br>TM Poverty - Estimated rate TM Vulnerability rate<br><!-- End of picture text -->
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<!-- Start of picture text --> uP] aR<br>90.00 30.00<br>sooo SSS 8.1<br>70.00 25.00<br>60.00 20.00<br>50.0040.00 13.24 15.00 5.86 5.99 2.40<br>30.00 10.00 1.32<br>= 2.68 2.13<br>“0.00 1 0.00. 1 a | :eeewom ot OS 038Sw0.17<br>ol Ss s n S&S nan~ = sg Sgroa 2eggS 2<br>a 8 8 7 8 € § § Bee g & B 8<br>2 9 gs 5 « A 2 COB vo Gg & s)<br>~ a 0 Aen &§<br>a&5<br>Q<br><!-- End of picture text -->

<!-- Start of picture text --> 60.20<br>= PI<br>= CRI<br>IRI<br>109.48<br>14.49<br><!-- End of picture text -->
<!-- Start of picture text --> 100.0%sobs eee<br>60.0%<br>40.0%<br>“ee0.0% ee<br>s Pe Ss ~ as = eS) r » S oS s oe Re<br>a ©) 7 e) S 3 We g s A eo) RS cS s ES<br>yy FF OT KT K Ss es “es<br>- S SS<br>s eS<br>~~s<br>s<br>i<br>><br>mPI gIRI aCRI<br>90.00 30.00<br>80.00 25.00<br>70.00<br>60.00 20.00<br>50.005 15.00<br>40.00<br>30.00 10.00<br>20.005.00 f 0.005.00 Zeoe — — _<br>roens)& => = &FP =So se2 s& Soe sS) &$ oF : sSs oyne => osSs<br>» S SP PX FT SM SS OS PS<br>v Q“wy GS Y RSASYY Sses><br>oe a)<br><<br>~~<br>g<br>Na<br>><br>ao}<br><!-- End of picture text -->
<!-- Start of picture text --> 800<br>700<br>600 = Total vulnerability<br>®CRI<br>500<br>400<br>300<br>200<br>100Q : ¥ ‘ .<br><¢Ss ¢£ £2 6 5S€ 88 S$8 62 Bg8 &E, 2 >F EE ecGesp> o<br>Be F* S$ gs §€ 6 8 eS &F RF 8 FO<br>5 G 8 a a = 2 5 Ba Ss @ a@ §<br>wy um 2 sy<br>a g @Ss<br>a 3?<br>2<br>Ko)<br>isa<br><!-- End of picture text -->
**Table 3.** Sudden and slow onset type distribution.

|||**Sudden**|**-onset**<br>|||**Slow-o**|**nset**<br>||
|---|---|---|---|---|---|---|---|---|
|**Individuals**|**Total**<br>|**PI**|**Risk in**|**duced**|**Total**<br>|**PI**|**Risk i**|**nduced**|
||**Vulnerabilit**||||**Vulnerabilit**||||
||**y**||**CRI**|**IRI**|**y**||**CRI**|**IRI**|
||**(1)**|**(2)**|**(4)**|**(5)**|**(1)**|**(2)**|**(4)**|**(5)**|
|Argentina|100%|50.2%|13.3%|36.5%|100%|49.9%|12.3%|37.8%|
|Bolivia(Plurinational State of)|100%|55.8%|7.0%|37.2%|100%|54.4%|7.5%|38.1%|
|Brazil|100%|57.1%|9.2%|33.8%|100%|57.0%|9.1%|33.9%|
|Chile|100%|36.8%|8.6%|54.7%|100%|36.1%|6.8%|57.1%|
|Colombia|100%|67.6%|8.2%|24.2%|100%|67.6%|8.6%|23.8%|
|Dominican Republic|100%|49.2%|5.2%|45.6%|100%|49.4%|5.6%|45.0%|
|Ecuador|100%|51.5%|5.7%|42.8%|100%|51.1%|7.7%|41.2%|
|El Salvador|100%|50.1%|1.4%|48.5%|100%|50.3%|1.2%|48.5%|
|Guatemala|100%|79.2%|2.9%|17.9%|100%|79.3%|2.9%|17.8%|
|Honduras|100%|75.9%|5.0%|19.1%|100%|75.6%|5.3%|19.1%|
|Mexico|100%|53.8%|6.4%|39.9%|100%|54.0%|6.7%|39.2%|
|Panama|100%|50.6%|12.6%|36.8%|100%|49.8%|13.2%|37.0%|
|Paraguay|100%|43.4%|6.8%|49.8%|100%|43.7%|7.5%|48.8%|
|Peru|100%|59.9%|9.7%|30.4%|100%|59.9%|9.5%|30.6%|
|Uruguay|100%|53.5%|3.4%|43.1%|100%|53.8%|3.5%|42.7%|
|Total region|100%|59.4%|7.9%|32.7%|100%|59.4%|8.0%|32.6%|

Note: Percentages of individuals. Source: Own elaboration based on household survey data, CCKP historical data, ThinkHazard! data, EM-DAT occurrences, and SPEI.

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<!-- Start of picture text --> . Pi 10<br>% ;<br>=<br>; 3 06<br>> 4 me y Z : ae 3 a<br>or - ay ‘ 0.2<br>og ce 14 31 48 65<br>g<br>. Se 3 TH: river, urban, and costal floods<br>i rw 4<br><!-- End of picture text -->
<!-- Start of picture text --> i ae<br>ed xs : go<br>w. ¢<br>‘ wate C 08<br>=<br>ORT a B06<br>, a x a : esv< oDae . 3 040.2<br>a Pana’<br>ia 10 20 30 40<br>7 so TH: landslides<br>* : * bs<br><!-- End of picture text -->
<!-- Start of picture text --> “><br>le Pa 1.0<br>: 08<br>=<br>B 06<br>(Ul ge ©<br>> a 2 04<br>. y at<br>J ” ie => 0.2<br>00<br>eeit ae -3.2-22-120208 18 28<br>fF ag ltl s<br>. , » SD<br><!-- End of picture text --> <!-- Start of picture text --> ‘i<br>‘<br>06<br>7 Fo<br>303<br>:<br>Jaan<br>10 20 30 40<br>: TH:Cyclones<br><!-- End of picture text -->
<!-- Start of picture text --> Pern<br>Diy<br>Bolivia (Plurinational State of) T7777.<br>Gutemla IT.<br>Arentin ir<br>Brazil<br>a: i<br>Mexico a. = CRI with social assistance<br>Ecuador [Lees = Total vulnerable with social<br>Biazil-cites EL assistance<br>Panama fl<br>Dominican Rep. SF<br>Pray<br>Honduras Se<br>El Salvador |<br>Colombia<br>0% 20% 40% 60% 80% 100% 120%<br><!-- End of picture text -->
poverty line but are still vulnerable due to climate hazards and disaster risk. In the Dominican Republic, only 29% of vulnerable households receive social assistance, and around 33% of the vulnerable households’ CRI receive benefits. In Peru, 95% of those that are CRI receive social assistance, while in Chile and Guatemala, around 51% and 56%, respectively, are receiving assistance. However, in Brazil 45% of such households receive benefits; in Mexico 39%, and in Ecuador, only 29%.

**Table 4.** Vulnerable population with social assistance.

|**Country**|**Total vul**<br>**social**|**nerables with**<br>**assistance**|**CRI w**<br>**assi**|**ith social**<br>**stance**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
|Argentina|48%|2,171,105|52%|300,582|
|Bolivia (Plurinational State of)|84%|2,551,026|81%|165,486|
|Brazil|46%|37,345,797|45%|3,222,014|
|Brazil - cities|38%|5,078,187|36%|157,934|
|Chile|43%|375,818|51%|29,350|
|Colombia|0%|6,134|0%|648|
|Dominican Republic|29%|755,186|33%|42,408|
|Ecuador|41%|2,455,263|29%|118,453|
|El Salvador|1%|23,885|1%|350|
|Guatemala|62%|7,939,265|56%|197,752|
|Honduras|17%|1,181,143|12%|44,253|
|Mexico|42%|12,679,045|39%|748,155|
|Panama|30%|240,230|35%|32,272|
|Paraguay|25%|326,955|36%|35,001|
|Peru|95%|13,858,743|95%|1,331,120|
|Uruguay|86%|316,021|93%|12,640|

Note: Columns (1) and (3) the percentage of households with social assistance and columns (2) and (4) present the number of individuals with social assistance. Total vulnerables and CRI with social assistance are those vulnerable/vulnerable climate risk induced that report having at least one family member beneficiary of a social program or assistance. The indicators correspond to the latest available household survey year of each country. Source: Own elaboration.

Moreover, Figure 16 illustrates the two sides of social assistance for vulnerable households in two large countries in the region. In Peru, the percentage of CRI households who report having received benefits from social assistance is very high in all regions but decreases as we move towards the coastal region (although still large). On the other hand, in Mexico, this percentage is lower throughout the country, particularly in the north. In the southern part of Mexico, despite the high potential natural disasters and vulnerability, only half of the vulnerable households' CRI receive social assistance.

26
**Figure 16.** Percentage of CRI households that receive benefits from social assistance.

<!-- Start of picture text --> (.95,1]<br>(.9,.95]<br>(.85,.9]<br>(.8,.85]<br>(.75,.8]<br>(.7,.75]<br>(.95,1] (.65,.7]<br>(.9,.95] (.6,.65]<br>(.85,.9] (.55,.6]<br>(.8,.85] (.5,.55]<br>(.75,.8](.7,.75](.65,.7](.6,.65](.55,.6](.5,.55] (.45,.5](.4,.45](.35,.4](.3,.35](.25,.3]<br>(.45,.5] (.2,.25]<br>(.4,.45] (.15,.2]<br>(.35,.4] (.1,.15]<br>(.3,.35] (.05,.1]<br>(.25,.3] [0,.05]<br>(.2,.25]<br>(.15,.2]<br>(.1,.15]<br>(.05,.1]<br>[0,.05]<br><!-- End of picture text --> It is helpful to simulate the minimum resources required to overcome vulnerability to poverty. We can do this by analyzing how much income each person needs to reduce the probability of vulnerability to less than 0.29, based on the estimated mean and variance of income from the model. This difference represents the income needed to be no longer considered vulnerable. Despite our measures being from a model that assumes all else remains constant except for the specific change that is analyzed (i.e., income change). The results help show the potential present cost of climate change and idiosyncratic risks. Table 5 shows the principal estimations for CRI individuals. The average income difference needed to overcome CRI vulnerability goes from 47 constant $ PPP 2017 per person per month in Uruguay to 90 in Brazil cities. If we consider the total annual income change as a percentage of GDP, we find that it represents a smaller proportion than the Conditional Cash Transfer (CCT) programs in 2009.

27
<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>FFX< S S) FSF> ~ EF> FSF> $ EF> SEES> a) . SFE‘ Ag SFs<br>S>xs 3sre.)os SS“= 8cS° crsS> os>re©>> <SoS Zs.c?> ge<oe ueS\<br>Ss =<br>s<br>~<—<br>~<br>s<br>2°<br>@ Average income per person CRI @ Average income per person IRI<br><!-- End of picture text -->
<!-- Start of picture text --> 160 20%<br>140 18%<br>0 16%<br>2 - 14%<br>100 e 12%<br>80 e 299g 10%,<br>60 © e 8%<br>* 6%<br>40<br>200 | | j = _ | = = _ 4%2%0%<br>S > = NS ry > 2 nS > $ & - : : re<br>FF LK MEF EF PES SSF SISSY<br>FSSS & CSSY eSee QPSSS NSEE OS wo SSSo ©<br>wer > SUF eS<br>& o s<br>Ss o><br>ss ‘<br>3<br>S<br>ae<br>RS<br>23s<br>@ Percentage of GDP @ Average income difference per person<br><!-- End of picture text -->
Note: GDP adjusted by PPP constant 2017 international currency of most recent year survey. Monthly average per capita income adjusted by PPP constant 2017 international currency.

Source: Own elaboration and World Bank’s World Development Indicators.

# **5 Conclusions**

<mark>This study provides a comprehensive analysis of vulnerability to poverty in the Latin America and the Caribbean region. The assessment is based on household-level data from the</mark> SEDLA <mark>C database, along with various natural hazards data sources. The study simplifies the climate disaster risk concept by reducing dimensions and treating it as a type of covariate risk that can push households into poverty. The results show that t</mark> he incidence of <mark>vulnerability to poverty is notably higher in Guatemala and Honduras, where most of the households are poverty-induced, followed by Colombia and Peru. Moreover, when population adjustments are considered, we find some heterogeneities. Meanwhile, in Guatemala and Honduras, more than 750 and 690 people per 1,000 population are vulnerable, respectively (either by the expected mean – poverty-induced- or the expected variance – risk-induced); when the results focus on</mark> climate <mark>hazards risk (CRI), Argentina and Panama have a higher proportion of vulnerable household covariate risk-induced.</mark> <mark>Overall, the study estimates that approximately 184 million people are vulnerable to poverty. Of those, around 109.5 million are PI, 14.5 million are induced by CRI and 60 million are IRI. Among the sources of risk, idiosyncratic risk is consistently more prominent than climate risk across all countries. However, when the total climate</mark> ris <mark>k effects through variance are considered, 8 percent of people in the region that are not PI or IRI are vulnerable to poverty. In addition, bi-maps enable the identification of provinces with a higher incidence of vulnerability to poverty, along with the districts with high natural hazards. They provide evidence for policy makers to start designing appropriate policies and strategies to mitigate the impact of</mark> climate <mark>hazards on the most vulnerable populations. Finally, the study highlights the need to address the indirect effects of climate risk in the region, which might be more closely associated with household resilience.</mark>

# **References**

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Baquie S. & Fuje H. (2020). Vulnerability to Poverty following Extreme Weather Events in Malawi. _Policy Research Working Papers_ N° 35. The World Bank. https://doi.org/10.1596/1813-9450-9435.

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