metadata: authors: 0: author: Winsemius,Hessel C.
1: author: Jongman,Brenden
2: author: Veldkamp,Ted I.E.
3: author: Hallegatte,Stephane
4: author: Bangalore,Mook
5: author: Ward,Philip J.
count: World
docna: 0: docna: Disaster risk, climate change, and poverty :
            assessing the global exposure of poor people to floods and droughts
docty: Policy Research Working Paper
subtopic: Science of Climate Change,Climate Change and Health,Climate Change and Environment,Hydrology,Inequality,Natural Disasters
teratopic: Macroeconomics and Economic Growth,Environment,Communities and Human Settlements,Poverty Reduction,Health, Nutrition and Population,Science and Technology Development,Industry,Urban Development,Social Protections and Labor
abstracts: cdata!: People living in poverty are
            particularly vulnerable to shocks, including those caused by
            natural disasters such as floods and droughts. Previous
            studies in local contexts have shown that poor people are
            also often overrepresented in hazard-prone areas. However,
            systematic evidence across countries demonstrating this
            finding is lacking. This paper analyzes at the country level
            whether poor people are disproportionally exposed to floods
            and droughts, and how this exposure may change in a future
            climate. To this end, household survey data with spatial
            identifiers from 52 countries are combined with present-day
            and future flood and drought hazard maps. The paper defines
            and calculates a “poverty exposure bias” and finds support
            that poor people are often overexposed to droughts and urban
            floods. For floods, no such signal is found for rural
            households, suggesting that different mechanisms—such as
            land scarcity—are more important drivers in urban areas. The
            poverty exposure bias does not change significantly under
            future climate scenarios, although the absolute number of
            people potentially exposed to floods or droughts can
            increase or decrease significantly, depending on the
            scenario and the region. The study finds some evidence of
            regional patterns: in particular, many countries in Africa
            exhibit a positive poverty exposure bias for floods and
            droughts. For these hot spots, implementing risk-sensitive
            land-use and development policies that protect poor people
            should be a priority.
repnme: repnme: Disaster risk, climate change, and poverty
            : assessing the global exposure of poor people to floods and droughts
display_title: Disaster risk, climate change, and
            poverty : assessing the global exposure of poor people to
            floods and droughts
listing_relative_url: /research/2015/11/25250857/disaster-risk-climate-change-poverty-assessing-global-exposure-poor-people-floods-droughts
topicv3: Water m1326522 137,Poverty m1326687 695,Climate Change m1326829 567,Drought m1503313 3162,Flood m1503315 3216
publishtoextweb_dt: 2016-02-08T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Environment,Communities and Human Settlements,Poverty Reduction,Health, Nutrition and Population,Science and Technology Development,Industry,Urban Development,Social Protections and Labor
no_of_pages: 35
author_keys: 0: author_key: 1352818
1: author_key: 1103816
model_extractions: WPS7480 Policy Research Working Paper 7480 Shock Waves: Managing the Impacts of Climate Change on Poverty _Background Paper_ Disaster Risk, Climate Change, and Poverty Assessing the Global Exposure of Poor People to Floods and Droughts _Hessel C. Winsemius Brenden Jongman Ted I.E. Veldkamp Stephane Hallegatte Mook Bangalore Philip J. Ward_ Development Economics Climate Change Cross-Cutting Solutions Area November 2015
Policy Research Working Paper 7480

# **Abstract**

People living in poverty are particularly vulnerable to shocks, including those caused by natural disasters such as floods and droughts. Previous studies in local contexts have shown that poor people are also often overrepresented in hazardprone areas. However, systematic evidence across countries demonstrating this finding is lacking. This paper analyzes at the country level whether poor people are disproportionally exposed to floods and droughts, and how this exposure may change in a future climate. To this end, household survey data with spatial identifiers from 52 countries are combined with present-day and future flood and drought hazard maps. The paper defines and calculates a “poverty exposure bias” and finds support that poor people are often overexposed to droughts and urban floods. For floods, no such signal is found for rural households, suggesting that different mechanisms—such as land scarcity—are more important drivers in urban areas. The poverty exposure bias does not change significantly under future climate scenarios, although the absolute number of people potentially exposed to floods or droughts can increase or decrease significantly, depending on the scenario and the region. The study finds some evidence of regional patterns: in particular, many countries in Africa exhibit a positive poverty exposure bias for floods and droughts. For these hot spots, implementing risk-sensitive land-use and development policies that protect poor people should be a priority.

This paper was commissioned by the World Bank Group’s Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank Group’s flagship report: “Shock Waves: Managing the Impacts of Climate Change on Poverty.” 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://econ.worldbank. org. The authors may be contacted at Hessel.Winsemius@deltares.nl.

_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
# **Disaster Risk, Climate Change, and Poverty: Assessing the Global Exposure of Poor People to Floods and Droughts**

Hessel C. Winsemius<sup>1</sup> , Brenden Jongman<sup>2, 3</sup> , Ted I.E. Veldkamp<sup>3</sup> , Stephane Hallegatte<sup>4</sup> , Mook Bangalore<sup>4</sup> , and Philip J. Ward<sup>3</sup> 1 Deltares, Delft 2 Global Facility for Disaster Reduction and Recovery, World Bank Group, Washington, DC 3 Institute for Environmental Studies, Vrije Universiteit, Amsterdam 4 Climate Change Group, World Bank Group, Washington, DC

## **Keywords**

Poverty, floods, droughts, global scale, exposure, climate change

## **JEL**

Q54, I32, Q50, I30
# **1. Introduction**

Globally, an estimated 700 million people live below the USD $1.90/day poverty line, with many more balancing just above it<sup>1</sup> (World Bank, 2015a). This substantial part of the world population is particularly vulnerable to external shocks, including those caused by natural disasters such as floods and droughts. Such disasters can reduce household income and destroy houses and productive capital. For example, after the 2004 floods in Bangladesh, Brouwer et al. (2007) found that poor households who were affected by the flood lost more than twice as much of their total income as affected non‐ poor households. This illustrates the consistent finding that poor people are more vulnerable to disaster events (Carter et al., 2007; Rabbani et al., 2013; Patankar and Patwardhan, Forthcoming). In addition to losing more, poor households have a relatively lower capacity to deal with shocks compared to non‐poor households due to lower access to savings, borrowing, or social protection (Kundzewicz and Kaczmarek, 2000; Masozera et al., 2007; Highfield et al., 2014).

Natural disasters have consistently been shown to be a key factor responsible for pushing vulnerable households into poverty, and also for keeping households poor (Sen, 2003; Krishna, 2006). Just as importantly, the exposure to natural hazards may reduce incentives to invest and save, since the possibility of losing a home due to a flood or livestock due to a drought makes these investments less attractive (Cole et al., 2013; Elbers et al., 2007). This vulnerability of poor people to natural disaster risk is particularly worrying in the context of climate change, which may increase the frequency, intensity, and spatial distribution of heavy precipitation, floods, and droughts (IPCC, 2012b). Beyond its aggregated impacts, future climate change may represent a significant obstacle to the sustained eradication of poverty (Hallegatte et al., 2016).

Several previous studies have attempted to draw statistical relationships between national‐level economic indicators and reported disaster losses on a global scale to find out if poor countries are more affected by natural hazards (Ferreira et al., 2011; Jongman et al., 2015; Kahn, 2005; Peduzzi et al., 2009; Shepherd et al., 2013; Toya and Skidmore, 2007). Although these studies reveal a statistical relationship between vulnerability and average income, they do not investigate the spatial or social distribution of the losses within a country. Recent advances in the global spatial modelling of flood risk (Arnell and Lloyd‐Hughes, 2013; Ceola et al, 2014; Hirabayashi et al., 2013; Pappenberger et al., 2012; Ward et al., 2013, 2014, 2015; Winsemius et al., 2013, 2015) and drought risk (Prudhomme et al., 2014; Schewe et al., 2014) have led to improved estimates of the global population exposed to natural hazards under the current and future climate, but these have not been translated into results specific for different incomes within a country.

To our knowledge, the relationship between poverty and exposure to floods and droughts has only been studied on a case‐study basis for a few countries. A literature review of 13 of such studies, conducted as part of this paper, shows that poor people are often disproportionately overrepresented in hazard‐prone areas. As shown in Figure 1, only one of the 13 studies reviewed finds that non‐poor people are more exposed than poor people. Although these cases highlight that a relationship may

> 1 <u>http://www.worldbank.org/en/topic/poverty/overview</u>

2
<!-- Start of picture text --> Mg Poor<br>2ar [+9 Nonpoor<br>o= 80<br>a<br>=<br>=<br>@<br>= 60<br>al<br>o<br>as]<br><!-- End of picture text --> <!-- Start of picture text --> 22o3 40<br>fis]<br>a<br>a<br>a<br>A,<br>w 20<br>o<br>&<br>° sae ae a ee oe oF FS oF Ss<br>x ye § & os ¢ € # $ & Ss £<br> £=FfF~ GG FSF ¢S geSor ¥~ SFS FF so-SF&--SF<br>id id<br><!-- End of picture text -->
floods and droughts have a higher risk of falling into poverty or being trapped in poverty. Both aspects are discussed in more detail below.

Localization choices across regions and cities are in the first place driven by socioeconomic considerations (housing prices, proximity to jobs, amenities), much more than by natural hazards (Hallegatte, 2012). In particular, households may be willing to accept high levels of risk to get access to opportunities. In a case study in Mumbai for instance, households in flood areas report that they are well aware of the flood risks, but accept them due to the opportunities offered by the area (access to jobs, schools, and health care facilities in particular (Patankar, 2015)). Compounding this incentive for people to reside in flood zones and close to opportunities is the reality that transport is often unreliable, unsafe, or expensive (Dudwick et al., 2011; Gentilini, 2015). In some rural areas, proximity to water offers cheaper transport opportunities and regular floods may increase agricultural productivity (Loayza et al., 2012). People may also settle in risky areas to benefit from opportunities with industries driven by exports in coastal areas (Fleisher and Chen, 1997). These opportunities attract all people – rich and poor – to places that are exposed to natural hazards.

However, at the city or neighborhood level, where the opportunity factors are broadly similar, but risk of floods may be different from neighborhood to neighborhood, poor people might be more exposed due to the effect of lower housing prices in flood zones (Bin and Landry, 2013; Husby et al., 2014). A review of empirical studies for mostly developed countries finds that the range of prices between flood‐exposed and non‐flood‐exposed houses varies widely; a meta‐analysis of 37 studies mostly in rich countries finds a spread of −7 percent to +1 percent (Beltran et al., 2015). Poorer people having less financial resources to spend on housing and in general a lower willingness and ability to pay for safety, are more likely to live in at‐risk areas. This factor is more likely to exist for floods than for droughts, due to the small‐scale variability in flood hazard (for example with floods, impacts can be very different in areas 100 meters apart). Also, it is likely that frequent floods are better included in land and housing prices than flood risks linked to exceptional events (Hallstrom and Smith, 2005).

Alternatively, causality may also go from flood and drought exposure to poverty. Evidence shows that floods affect household livelihood and prospects and increase local poverty levels through the loss of income and assets (see for instance Rodriguez‐Oreggia et al., 2013 for an analysis in Mexico). The risk associated with exposure to droughts has been found to increase poverty ex‐post (Dercon, 2004; Carter et al., 2007). Further, the impact of disaster risk on poverty occurs through both the visible ex‐ post channel (the losses when a disaster occurs), as well as the less obvious but as important ex‐ante channel: households exposed to weather risk have been shown to reduce investment in productive assets and to select low‐risk, low‐return activities (Cole et al., 2013; Elbers et al., 2007). This link from natural hazard exposure to poverty may create a feedback loop, in which poor households have no choice but to settle in at‐risk zones and as a result face increased challenges to escaping poverty.

This review illustrates the complexity of interactions between natural hazards and poverty. Diversity in this relationship amongst countries is to be expected and therefore evidence from a limited amount of case studies or from cross‐country analyses (e.g., Kim, 2012) is not enough to conclude an unequivocal relationship between the existence of poverty and flood and drought hazard. This study therefore aims for a geographically wider analysis of the relationship between natural hazards and poverty than performed before.

4
<!-- Start of picture text --> GLOFRIS Flooding p GLOFRIS Flooding & Wealth map Hazard & Wealth statistics R o ding<br><!-- End of picture text --> <!-- Start of picture text --> § Rea= ied USAID'sDHS Wealth map - Colombia sa J + A}il<br><!-- End of picture text --> <!-- Start of picture text --> a Ma te bw eS ee Pps peat Pn ot My<br><!-- End of picture text --> <!-- Start of picture text --> 9 SRG. ‘ we he i Bia re ; —<br>9 {eee at: a? f ih \ —— 0 .*<br><!-- End of picture text --> <!-- Start of picture text --> —— ~~ - gf . . AV ALAM,<br>Hazard300map legend Wealthmap legend<br>= Inundation depth (cm) =2: Relative drought intensity (LTM Q months) ©©© 1st2nd3rd Wealth Wealth quintilequintilequintile (lowest 20%) — ©@ Sth4th WealthWealth quintilequintile (highest20%)<br><!-- End of picture text -->
GLOFRIS model cascade inundation downscaling technique, which is described in detail in Winsemius et al. (2013) and applied at global scale in Ward et al. (2013) and Jongman et al. (2015). This method uses the global hydrological model PCR‐GLOBWB‐DynRout (Van Beek and Bierkens, 2009; Van Beek et al., 2011) to simulate a set of maps showing inundation extent and depth for each grid cell and for all return periods. Three limitations are important to acknowledge at this point. First, we assume that there are no flood protection standards in place. Even though data on protection standards are available for a number of high‐income countries (Jongman et al., 2014), a global database of flood protection standards does not exist. However, as protection standards are related to a country’s wealth, it is likely that protection standards across the developing countries investigated are relatively low. Second, coastal floods and storm surges are not considered in the analysis (as the model is for river floods only), even though they can have large consequences (Hallegatte et al., 2013). And third, smaller rivers, and pluvial and flash flooding are not represented in the global inundation modelling at its current scale.

# **3.1.2. Drought hazard**

While defining flood conditions may be straightforward (e.g. inundation depth per grid cell per return period), there are many possible definitions of a drought. According to IPCC (2012a) meteorological droughts refer to deficits of precipitation; hydrological droughts refer to negative anomalies in water availability from surface water or groundwater, regardless of human demand; and socio‐economic droughts include demand to calculate a water deficit. Each of these drought phenomena can also be defined over different timescales, which are relevant for different issues (e.g. damages to buildings vs. agricultural losses). Here, we applied a variable monthly threshold method (namely the 80% exceedance probability of discharge, _Q80_ ) to estimate the yearly maximum cumulative discharge deficit (with respect to the _Q80_ threshold) per grid cell at 0.5° resolution as a measure of hydrological drought (Lehner and Döll, 2001; Wada et al., 2013; Wanders and Wada, 2014), using outputs from the PCR‐GLOBWB model Van Beek et al., 2011). A cumulative discharge deficit is the accumulated amount of discharge under the Q80 threshold over a continuous period of time.

The resulting maps express the relative intensity of drought conditions to long term mean stream flow conditions and can be interpreted as the amount of time a long‐term mean discharge would be needed to overcome the maximum accumulated deficit volume occurring with a certain return period. We assumed that hazardous conditions will occur when this value exceeds 3 months, moreover, we tested the robustness of our results using a 1‐month and 6‐month threshold. The indicator does not include information on groundwater availability or upstream water use. Furthermore, this indicator does not account for the spatial variability in water needs. The resulting drought values should therefore be interpreted as conservative (underestimating drought hazard), although for an assessment of the actual water scarcity conditions one should include local water needs, upstream water uses, water transfers, and natural or man‐made water storages (Wada et al., 2013).

# **3.1.3. Future flood and drought hazard**

Further, the models are used to estimate future climate change impacts on flood and drought hazard, for different time periods (1960‐1999, 2010‐2049, 2030‐2069, and 2060‐2099), using meteorological outputs from GCMs, forced by two representative concentration pathways (RCPs, Van Vuuren et al., 2011), which represent scenarios of future concentrations of greenhouse gases (RCP 2.6 and 8.5), and five global climate models (GCMs). Since the GCMs used contain bias due to unrepresented intra‐ 6
annual and inter‐annual variability (Johnson et al., 2011), we use the difference in annual exposed people between GCM forced model runs in the future and the past to establish changes in the exposure.

# **3.2. Poverty data sets**

A comprehensive spatial database to examine the distribution of poverty within and across countries is not yet available at the required spatial resolution.<sup>2</sup> However, household surveys do contain some spatial information to approximate the location of a household, which we employ in this analysis. Our main analysis is undertaken using spatial wealth data sets from USAID’s DHS surveys, and is supplemented by small‐area estimates of poverty developed by the World Bank and partner countries.<sup>3</sup> Fifty‐two country‐wide DHS surveys contain household‐level data on wealth, as well as geo‐referenced data providing the coordinate location of the survey cluster (see Appendix B). While the sampling frame differs in each survey, there are typically 500‐1,000 survey clusters for each survey, with each cluster containing approximately 25 households. The specific indicator that we use in this study is the DHS “wealth index”, an indicator that was used previously to represent poverty (e.g. Barros et al., 2012; Fox, 2012; Ward and Kaczan, 2014). The wealth index is country‐specific and assigns an overall score to a household based on weighted scores of the assets they own.<sup>4</sup> All households in each country are classified in five quintiles (with quintile 1 having the lowest wealth, and quintile 5 the highest). We furthermore classified urban and rural households into quintiles, which enabled us to investigate the exposure across urban and rural populations separately. Importantly, as the wealth index used by DHS is generated relative to other households in the same country, it is comparable only within a country, and not between countries. Wealth index scores can therefore not be directly compared internationally.

To guarantee anonymity of the interviewed households, the geographical locations of the clusters have been randomly allocated by DHS within a radius of maximum 2 km from the real location for urban areas, and 5 km for rural areas. Since floods (opposed to droughts) have a high spatial variability, this uncertainty may affect the flood exposure estimates in particular. To investigate the propagation of the uncertainty of the geographical locations into our flood exposure estimates, we perform 100 random displacements of all geographical locations with a radius of 2 km for urban and 5 km for rural areas, and compute the exposure for each of the 100 displacements. This uncertainty analysis provides an idea of the robustness of results with respect to the random displacement of household locations, but also to errors in the flood maps, for instance linked to topography.

One issue with DHS surveys – and almost all other household surveys – is that they have not been designed to be representative at small spatial scales. Our randomization technique does not explore this error. As a result, they cannot be used to analyze exposure at the very small scale, e.g. in one

> 2 Although recent initiatives try and estimate global poverty at high‐resolution gridded scales, see for example WorldPop (2015).

> 3 <u>https://openknowledge.worldbank.org/handle/10986/6800</u>

> 4 ‐ For more information, see the DHS wealth index page: http://dhsprogram.com/topics/wealth <u>index/</u>

7
particular city or village. Therefore, in this paper we only use aggregate results at the country, country‐ urban and country‐rural level.

We complement our global analysis with two country‐level case studies (Morocco and Malawi) for which small‐scale poverty maps are available (poverty data representative at the local level). In these case studies we only focus on floods. We focus on the within‐country geographical distribution of poverty. Country‐level poverty maps, which represent the percentage of the population living below the USD $1.25/day line (this is in consumption terms, using 2005 purchasing power parity (PPP) exchange rates) are developed using the methodology presented in Elbers et al. (2003), combining household surveys with census data.

# **3.3. Analyzing the relationships between poverty and floods/droughts**

To investigate the global exposure of poor people to floods and droughts, we define a ‘poverty exposure bias’ (PEB) that measures the fraction of poor people exposed, compared to the fraction of all people exposed per country. When estimating the number of people exposed (and not exposed), we multiply the exposed households with their household size and we use household weights to ensure the representativeness of our results. We compute the PEB using: Where _Ip_ is the PEB, _fp_ and _f_ are the fraction of people exposed to floods/droughts in the poorest household quintile within a country and in the entire population, respectively. If _Ip_ is lower than zero, this means that poor people are less exposed to floods/droughts than average. If _Ip_ is above zero, poor people are more exposed than average. The mean values (denoted by the over bar within Eq. 1) of all estimated values for _fp_ and _f_ , based on the geographical randomization process (see Section 3.2), are used in Eq. 1 in the case of floods. In the case of droughts, only a single value is computed because the drought indicator used has a much larger scale (0.5°) than the uncertainty in household location, and therefore this single value is used instead. Since the wealth index is comparable only within and not between countries, the PEB we calculate in this paper is an estimate of whether poor people are more or less exposed compared to the entire population _within_ a specific country. Aggregation of all wealth index data for all countries and computation of a single global PEB is not possible with the data currently available.

# **3.4. Geographical uncertainty and sample size robustness**

From the 100 results with random replacements, we assess the uncertainty of the results due to uncertainty in geographical location as described in Section 3.2. From the 100 varied results, we also establish the standard deviation of _fp_ . If, for a given country, the standard deviation of _fp_ is relatively large with respect to the mean, we treat the country’s results as uncertain.

A further uncertainty assessment is performed for both floods and droughts to test the significance of the results, which ensures that the results for countries having a very low number of exposed households can be labelled insignificant. We test how robust a positive (or negative) PEB is, given the size of exposed people by bootstrapping with 5,000 samples. Bootstrapping is a significance test to 8
examine whether the samples in the distribution are dense enough to make a robust estimation of the PEB. For a given country, 5,000 bootstrap populations of the size of the original population are generated, by randomly drawing samples with replacement. In this way, 5,000 values for the PEB for each country are drawn. This process is performed for the nation‐wide populations, as well as the urban and rural subdivisions. In case the expected value is positive ( _Ip_ > 0), we use the 5,000 samples to test for a 95% confidence for H( _Ip_ > 0). When the expected value is negative, we test for a 95% confidence for H( _Ip_ < 0).

# **4. Results and discussion**

# **4.1. Aggregated results**

Figure 3 shows the poverty‐exposure bias in the 52 countries, in the form of histograms, for the 10‐ year (in grey) and 100‐year (in red) return period event. Results for all countries are provided in Table 1. It shows results for floods nation‐wide (column A); flood exposed urban households (column B); droughts nation‐wide (column C); and drought exposed rural households (column D). Figure 3 shows that on the national scale, poor are not consistently over or underexposed: for floods and droughts, the median poverty‐exposure bias is close to zero, and there are countries with large positive and negative biases. This result supports the review in Section 2 **Error! Reference source not found.** : the idea that the relationship between poverty and disaster exposure is impacted by multiple channels and is therefore complex.

For floods, Figure 3 shows that about half of the countries in the analysis have an _Ip_ >0, indicating that in those countries the share of poor people (quintile 1) exposed to flooding, is higher than the share of all households exposed to flooding (note that at this point, no significance test is displayed in the histograms). The other half of the countries exhibits negative exposure biases, consistent with the expectation of a large diversity across countries: results vary from a minimum of –0.78 in Rwanda to a maximum of 1.82 (i.e. the poorest quintile is 182% more exposed than average) in Angola. For both numbers the sign of the estimate is significant (p < 0.05).

For droughts, the country‐scale results also have a median PEB around 0. The number of countries included in the histogram (15) is much smaller because for many countries, the threshold for being exposed to a drought (set at 3 months) is not reached for any of the households. For floods, 50 out of 52 countries show households that are flooded and are therefore included in the histogram.

9
<!-- Start of picture text --> 14 T 1<br>(A) Flood - nation wide (B) Flood - urban only<br>12 rf on<br>5 6 | \ il<br>co, | | M<br>0 N MM AV YWA//<br>4 3 “2 Ll 0 1 Z 3 44 3 “2 «1 0 1 2 3 4<br>Poverty Exposure Bias<br>14 —, —<br><!-- End of picture text -->

<!-- Start of picture text --> ‘wo 8 it i<br>o tot HH<br>a) ot He<br>4 ae +<br>0i__AN4 #4 2 -1aewy0 1 2 \ 3 4 4 3 -2 alMWINV|0 a A2 2 4<br>Poverty Exposure Bias<br>——- Return Period 10 yr _—-------- Median value Return Period 10 yr<br>——Return Period 100 yr ........ Median value Return Period 100 yr<br><!-- End of picture text -->
than zero (in case of negative exposure bias) by means of bootstrapping, as explained in Section 3.4.<sup>5</sup> For floods at the national‐level, under present‐day climate conditions, 35 out of the 52 countries show a significant result. Of these 35, about half (17) exhibit an over‐exposure of poor people to (riverine) floods. Another relevant question is whether there are more people (in absolute terms) living in countries with a positive exposure bias. Using country‐level population data from the World Bank’s World Development Indicators (World Bank, 2015b), we find that 60% of the analyzed population live in countries with a positive and significant exposure bias.

Moreover, regional patterns become visible. In particular, the countries in Southern Africa and the Horn of Africa (except for Ethiopia, Rwanda, Zimbabwe and Mozambique) and the Arab Republic of Egypt have a strong over‐exposure of poor people to floods, although not all countries show significant results (Tanzania and Democratic Republic of Congo). In Western Africa, the results show a mixed pattern, although in countries with larger rivers and delta areas (notably Benin, Nigeria and Cameroon), there appears to be a tendency towards poor people being disproportionally exposed to floods. Turning to Asia, Indonesia also gives a moderate but significant signal that poor people are more exposed to floods than average; the same can be seen for South America in Colombia and Guyana.

As discussed earlier, there are also a number of countries where poor people are found to be less exposed to floods than average. These include some of the Asian countries of our sample (Cambodia, Nepal and Philippines, although the PEB for the last is insignificant), some West African countries and most of the countries investigated in Central and South America.

These regional patterns suggest that the variability of the PEB across countries is not a random mechanism nor an artefact of the analysis, but may be a consequence of regional development patterns. It supports the idea that there are different mechanisms at play for regions at different development stages or with different characteristics (e.g., urbanization rates). The balance between these mechanisms may vary, resulting in different PEB values between regions.

It must be noted that the results may be uncertain because the hydrological and inundation model performance may regionally be poorer. In particular in semi‐arid regions with large inland wetland areas, such as the Niger River in West Africa, much of the discharge may evaporate within these wetlands. The hydrological model used in this study (as with most global hydrological models) does not accurately represent this river discharge loss, which may cause downstream overestimation of flood depth (see e.g. Neal et al., 2012; Ward et al., 2013).

> 5 The additional uncertainty due to uncertain geographic position of the households was found to be insignificant and is therefore not displayed here.

11
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<!-- Start of picture text --> Poverty exposure bias RP: 100<br>SiN Central/South America a0 — Africa on corn, Asia<br>ee. —ye ST | ~<br>FR tee tp ihe a = i<br>- a 5 = a Noo : z= ‘Sa 30°N| ; er<br>o ‘ of:<br>\ ba J y:<br>Shen >. 4 eee<br>6o"w 30°W o 30°E 60°E a5°E 11S*E 145°E<br>BS <-10 B 02-01 OO005-01 803-05<br>B-10-05 0-01-0005 801-02 Bos-10<br>805-03 0 -0:05-005 8 02-03 Blo<br>B O3-02<br>Poverty exposure bias RP: 100<br>30°N ayey,: Central/South America inn : TF“ PoaAfricahast cea Z —— cow,| ie aAsia FN TH<br>. A ey: el a 4 | ~ ee ees = 5<br>.A 4rf an ; (3 = eA x a.:<br>ee LY : i f Ay<br>r . eg<br>f 20°S .> ( fe o TE. ‘See E<br>eo"w 30°W oos30°E 60°E DiaS°E pif1iS*E 145°E<br>gS <-10 B O2-01 OO05-01 803-05<br>@-10--05 0 -01--0.05 GO1-02 @o5-10<br>805-03 0 -0:05-005 8 02-03 B10<br>B 03- 02<br><!-- End of picture text -->
Table 1 Poverty exposure bias and increase in exposure for floods and droughts. For countries where none of the households within the DHS survey were exposed, not available (NA) is stated. Significant results are in bold.

|||Povertyex<br>|posure bias<br>||Increase i<br>of all ho<br>|n exposure<br>useholds<br>|
|---|---|---|---|---|---|---|
||Floo|ds|Droug|hts|Floods|Droughts|
|Country|Nation‐wide|<br>urban|Nation‐wide|<br>urban|Natio|n‐wide|
|ALBANIA|‐0.10|**0.56**|NA|NA|**9.11**|0.00|
|ANGOLA|**1.82**|**2.37**|**0.67**|**1.74**|**0.35**|0.00|
|BANGLADESH|0.02|0.00|**1.61**|NA|**39.55**|0.00|
|BENIN|**0.84**|‐0.24|**1.57**|**2.61**|**3.42**|‐17.08|
|BOLIVIA|‐0.08|**0.39**|**‐0.32**|**‐0.40**|**‐10.67**|938.85|
|BURKINA FASO|‐0.30|0.32|‐0.01|**‐0.30**|56.00|‐1.18|
|BURUNDI|NA|NA|NA|NA|**19.07**|0.00|
|CAMBODIA|**‐0.25**|0.02|0.15|NA|**18.83**|0.00|
|CAMEROON|**0.38**|**0.45**|**2.21**|**2.51**|**2.17**|‐9.45|
|CENTRAL AFRICAN REPUBLIC|**0.18**|**0.81**|NA|NA|**‐8.02**|**‐38.67**|
|COLOMBIA|**1.19**|**1.90**|**2.46**|**2.80**|**9.65**|0.00|
|COMOROS|NA|NA|NA|NA|0.00|0.00|
|COTE D'IVOIRE|‐0.02|0.36|NA|NA|**‐0.96**|0.00|
|CONGO, DEM. REP.|‐0.09|**1.83**|**0.42**|**1.76**|**3.00**|0.00|
|DOMINICAN REPUBLIC|**‐0.40**|‐0.09|NA|NA|‐27.87|0.00|
|EGYPT, ARAB REP.|**0.58**|**0.55**|0.03|**0.22**|**42.92**|**‐4.30**|
|ETHIOPIA|**‐0.33**|**‐0.85**|**0.67**|NA|**12.41**|‐47.23|
|GABON|**0.72**|**1.25**|NA|NA|**‐3.05**|0.00|
|GHANA|0.23|**‐0.39**|**1.15**|**1.80**|**‐10.28**|51.57|
|GUINEA|**1.12**|**2.05**|NA|NA|**10.11**|0.00|
|GUYANA|**0.42**|‐0.05|**2.60**|NA|**‐23.23**|0.00|
|HAITI|**‐0.48**|**3.52**|NA|NA|**‐28.02**|0.00|
|HONDURAS|**‐0.66**|**‐0.31**|**0.51**|**0.76**|**‐11.80**|**7.34**|
|INDONESIA|**0.33**|**1.03**|**0.49**|**0.33**|**9.89**|‐38.39|
|JORDAN|**1.55**|**2.08**|**0.15**|**‐0.25**|‐51.70|**278.59**|
|KENYA|**0.64**|**1.56**|**2.92**|NA|**12.88**|‐21.93|
|KYRGYZSTAN|0.17|**1.15**|**1.45**|NA|**13.21**|0.00|
|LESOTHO|‐0.11|**1.55**|**0.70**|**0.82**|**0.94**|0.00|
|LIBERIA|**‐0.43**|**‐0.69**|NA|NA|**7.71**|0.00|
|MADAGASCAR|**‐0.16**|**‐0.60**|**2.28**|NA|**6.51**|0.00|
|MALAWI|0.10|**‐0.68**|**‐0.40**|NA|**‐1.47**|0.00|
|MALI|**‐0.39**|‐0.36|‐0.03|0.22|**37.42**|55.27|
|MOLDOVA, REPUBLIC OF|**‐0.52**|‐0.03|NA|NA|**‐31.39**|0.00|
|MOROCCO|**‐0.24**|0.25|‐0.08|**0.44**|‐70.26|1122.62|
|MOZAMBIQUE|**‐0.27**|‐0.08|‐0.28|**0.86**|**3.57**|0.00|
|NAMIBIA|**0.35**|0.19|**‐0.99**|**‐0.60**|**‐12.30**|**41.21**|
|NEPAL|**‐0.61**|**0.59**|NA|NA|**14.84**|0.00|
|NIGER|**‐0.39**|0.29|**‐0.22**|‐0.07|90.28|271.40|
|NIGERIA|**0.52**|**1.06**|**1.28**|**0.50**|**17.37**|355.44|
|PERU|**‐0.49**|**0.17**|**‐0.72**|**‐0.62**|**20.97**|**2.39**|
|PHILIPPINES|‐012|018|**084**|**120**|**1045**|000|
|<br>RWANDA|.<br>**‐0.78**|.<br>**‐1.00**|**.**<br>NA|**.**<br>NA|**.**<br>**13.04**|.<br>0.00|
|SENEGAL|**‐0.25**|**1.78**|**1.99**|**1.81**|‐5.42|0.00|
|SIERRA LEONE|**0.69**|**2.63**|NA|NA|**13.38**|0.00|
|SWAZILAND|0.13|**‐0.66**|NA|NA|**‐7.92**|0.00|
|TAJIKISTAN|‐0.16|0.11|**1.05**|NA|**‐8.91**|0.00|
|TANZANIA, UNITED REP.|‐0.10|0.01|**‐0.58**|‐0.01|**1.03**|0.00|
|TIMOR‐LESTE|NA|NA|NA|NA|**‐12.34**|0.00|
|TOGO|0.21|0.13|**0.72**|**1.47**|**2.01**|356.18|
|UGANDA<br>|**0.65**<br>|NA<br>|**3.09**<br>|**1.52**<br>|**31.70**<br>|‐11.22<br>|
|ZAMBIA|**0.68**|**3.40**|**1.25**|‐0.13|**3.31**|0.00|
|ZIMBABWE|**‐1.00**|‐0.31|**0.49**|NA|**‐1.92**|0.00|

15
# **4.3. The impact of climate change**

Climate change is likely to increase the number of people exposed to floods and droughts. To estimate the range of increase in population exposure, we overlay the drought and flood hazard maps used for this paper with present‐day and projected population density data sets,<sup>7</sup> for a high‐emissions pathway consistent with a 4°C increase in global temperatures. This pathway is known as Representative Concentration Pathway (RCP) 8.5, where global annual GHG emissions (measured in CO2‐equivalents) continue to rise throughout the 21st century. We run the analysis for five Global Circulation Models (GCMs).<sup>8</sup> Across the five models, under a high‐emissions scenario (RCP 8.5), for droughts, we find the number of people exposed could increase by 9–17 percent in 2030 and 50–90 percent in 2080. For floods, the number of people exposed to river floods could increase by 4–15 percent in 2030 and 12– 29 percent in 2080.

To assess how poverty exposure may change in the future as a result of climate change, we calculate the same PEB for a low‐emissions pathway consistent with a 2°C increase (RCP 2.6, where global annual GHG emissions peak between 2010‐2020, and decline substantially thereafter) and RCP 8.5 and for five GCMs. Besides this, we have used the two scenarios to estimate from the household surveys, how much exposure change is to be expected in the future due to climate change. To ensure that we only see the impact of climate on exposure, we do not include compounding effects such as migration and population growth.

The PEB does not change significantly under the two different future climate scenarios and is therefore not displayed. Of course, hazard does not drive exposure and exposure bias alone. The PEB will change in the future due to other driving mechanisms not assessed in this paper, such as migration, changes in the spatial distribution of poverty, or the general increase in income within countries. Countries with rapid urbanization – such as many in Africa – may exhibit major changes in their flood exposure patterns in the coming decades, independently of climate change and other changes in hazards.

Regions where climate change causes an increase in the annual expected number of people exposed to floods and droughts, and where poor people are generally already more exposed than average (i.e. _Ip_ > 0 ) should be treated as highly climate‐sensitive regions for poor people. To locate these, Figure 7 shows the percentage change per country in the annual expected number of people exposed to floods between 1980 and 2050, based on the household data and RCP 8.5, and Figure 8 shows the same for droughts (Table 1 also reproduces results for all countries). RCP 2.6 shows similar changes in exposure although it takes longer before these changes are reached. In some countries, the number of flood‐ exposed people under climate change rises rapidly; this is the case in the Horn of Africa, parts of West Africa, Egypt, Bangladesh, Colombia, and Bolivia. For droughts, the different GCMs employed show more disagreement in drought extremes, causing less significant results. However, if we use the GCM ensemble average we can again see that in particular West African countries show an increase in the number of exposed people.

> 7 For present day, Landscan is used. For 2030 and 2080, data from the Shared Socio‐economic Pathways (SSP4 and SSP5) is used. For more information on the SSPs, see O’Niell et al. 2014.

> 8 A factor delta approach was used to bias correct for the GCM uncertainty. That is, we examined the factor increase between historical GCM runs and future ones (2030,2080) and superimposed this factor increase on top of the EUWATCH results.

16
<!-- Start of picture text --> rcp8p5 exposure increase [%] 2050<br>_—<br>: Central/South America 40N — on 6 corn Asia<br><!-- End of picture text --> <!-- Start of picture text --> mi rt Nee<br>- 60"w 30°w o 30°E 60°E : 85°E 115°E 145°E<br>S <-50.0 O -15.0=-5.0 8 5.0=15.0 @ 250-500<br>@ -50.0--25.0 © -§.0~5.0 © 150-250 8 >50.0<br>@ -25.0 --15.0<br><!-- End of picture text --> rcp8p5 exposure increase [%] 2050 <!-- Start of picture text --> sow 30°~w o° 30°E 60°E a5°E 115°E 145°E<br>g <-50.0 O -1$.0--5.0 @ 5.0-15.0 @ 25.0-50.0<br>@ -50.0- -25.0 0 50-50 © 150-250 8 > 50.0<br>@ -25.0 = -15.0<br><!-- End of picture text -->
<!-- Start of picture text --> Increasing poor exposure rcp8p5 2050<br>30°N = America 40°N — ; ee— mR] (OON Asia<br><!-- End of picture text --> <!-- Start of picture text --> Increasing poor exposure rcp8p5 2050<br>son = Sermatsou America won 5 SS a wN Asia<br>° 30"N oF Pa<br><!-- End of picture text -->
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or drought maps and local household surveys designed to be representative at the local scale. For example, in Mumbai, local‐scale flood data and household surveys revealed that an over‐exposure of poor people to floods can be observed (Hallegatte et al., 2016), but varies at very small scale, from one street to the next (Patankar and Patwardhan, 2014).

# **5. Limitations and recommendations for further research**

Consistent with expectations, there is high variability across countries and poor people are not over‐ exposed to natural hazards everywhere. But the analysis is limited by data availability as the DHS samples are too small to look at regions and within‐country variability. The limited number of households per country has implications for the results for droughts in particular: in many countries, there is no overlap between zones with extreme drought conditions (e.g. a minimum of 3 months drought, at 100 year return period yielded only 15 countries with significant results) and households, meaning that no estimate of the PEB for droughts could be made in these cases. A larger number of observations per country would therefore make the results of our analysis more robust. Furthermore, it would allow a more local scale analysis, as performed for Morocco and Malawi, for all countries.

A related limitation is the spatial scale of the analysis. DHS samples are rarely representative within sub‐national regions, which limits our ability to examine the poverty exposure bias within specific regions of a country. Furthermore, even the sub‐national poverty maps are at the commune or traditional authority level, while higher‐resolution data (e.g. poverty maps within a city) would be able to better capture dynamics at the local level, where lower land prices may push poorer people into more risky areas.

Furthermore, ideally, we would compare our results across countries and not just within them. However, the wealth index calculated by DHS is country‐specific, meaning that the same value for the wealth index across two different countries may imply a different level of wealth. While some authors have recently suggested the DHS wealth index may be compared across countries (Rutstein and Staveteig, 2013), country‐to‐country comparability remains difficult. This is one reason why we use relative thresholds (e.g. quintiles) rather than absolute thresholds. Another reason for which we use relative numbers is that if a standard poverty line is used, in some countries an overwhelming majority of the population would be classified as poor (e.g., 70‐80% in Malawi with a 1.25USD/day poverty line).

The flood hazard maps used in this study only show whether each grid‐cell is flooded (yes or no) for each return period. In reality, the impacts of flooding on people and their assets and productivity are dependent on several other factors. Exposure is only one component of risk: protection (e.g., with dikes or water reservoirs), vulnerability of assets and livelihoods (e.g., quality of housing, savings at financial institutions, and diversification of income), and ability to cope and recover (e.g., access to social protection and credit) are also critical to determine the impact of natural hazards on well‐being.

Households living in high‐quality elevated houses may not suffer much from a shallow flood (e.g., 10 cm depth), while informal housing may already be damaged. The inundation depth has been shown to be the main determinant of damages to assets (e.g. Penning‐Rowsell and Chatterton, 1977; Penning‐Rowsell et al., 1994; Wind et al., 1999; Merz et al., 2010; Meyer et al., 2013). In future studies the relationship between the actual depth occurring and wealth could also be examined. Numerous 20
studies have also shown flood duration to play an important role in flood impacts (Parker et al., 1987; FEMA, 2005; Dang et al., 2010), especially for indirect losses (Lekuthai and Vongvisessomjai, 2001) flood‐related health issues (Dang et al., 2010); and flood level rise rate is especially important in terms of mortality (Jonkman et al., 2009). More research is required to examine how these could impact on poverty. Future research may also expand to other hazards, as has been done for extreme temperatures (Park et al., 2015).

Similarly, households that are highly vulnerable to droughts (e.g. with assets strongly relying on available water) may already experience problems during a one‐month drought condition, although others may only be exposed if the drought lasts three months or more. To assess the robustness of the drought indicator applied, we test our results using a one‐month and six‐month threshold. Obviously, more people are exposed with a one‐month threshold than with a six‐month threshold. For the aggregated PEB results (Supp. Figure 6), we can only find a significant number of exposed households in six countries using a six‐month drought threshold with a return period of 10 years; this increases to up to 50 countries, when considering one‐month droughts as threshold with a 100 year return period. Notably, median PEB values are above zero for the 100 year return‐period drought, and decreases toward and below zero for lower return periods (10 years) droughts and higher drought thresholds.

This suggests that the small sample sizes make it very hard to find a robust exposure bias pattern in many countries. Nonetheless, we found consistent results on the sign of the PEB for sub‐Saharan Africa (Nigeria, Cameroon, Democratic Republic of Congo, Togo, and Benin (not significant for a one‐ month threshold)), Southeast Asia (Philippines, Indonesia (not significant for a six‐month threshold)) and Colombia (when comparing the one‐, three‐ and six‐month threshold results under the 100‐year return period). Other countries showed mixed results over the different threshold values and therefore results over these countries should be treated with a lower confidence.

# **6. Conclusions**

In this paper, we investigated at the country level whether poor and non‐poor people are disproportionally exposed to current floods and droughts and how this exposure changes in a changing climate. To this end, we combine global drought and flood hazard and household survey data in 52 countries. The general conclusion is that across countries, poor people are over‐exposed to droughts and urban floods. But the situation differs strongly across countries, within countries and based on the type of hazard – for instance, for floods at the national level, we find little evidence of an over‐ exposure or under‐exposure of poor people.

Geographically, the countries where the strongest positive poverty exposure bias (PEB) is found, are concentrated in Africa for both perils. For floods, the countries in Sub‐Saharan Africa as well as the horn of Africa and Egypt experience the highest positive PEB. For droughts, poor people are more exposed in almost all countries in Africa, and also South‐East Asia shows this signal.

We find that in urban areas, poor people are disproportionally exposed to floods compared to the average, while such a signal is not found for rural households. This is particularly noticeable in Africa, with the exception of several western African countries. In some countries, the absence of a positive PEB at national level may be due to the fact that the gap in wealth between cities and rural areas is 21
large, combined with the fact that flood hazard is often high in cities. The urban‐rural gaps in income and flood risk may thus hide the fact that poor people are more exposed. This hypothesis is further supported by subnational analyses in two countries (Morocco and Malawi), where several regions are identified where poor people are clearly over‐exposed, when compared with the rest of the population in the country.

A particular concern is the fact that some of the countries where poor people are overexposed will also experience more frequent flooding or droughts in the future due to climate change. Poverty‐ vulnerability hotspots appear when combining countries with an overexposure of the poor and an increase in hazards expected from climate change. This signal is found in Burkina Faso, Burundi, Egypt, Ethiopia, Guinea, Kenya, Nigeria, Sierra Leone and Uganda for floods. For drought, Nigeria and Ghana were found to be in this situation although results for Ghana were found to be less robust.

Exposure, the topic of this paper, is only one component of risk. Almost everywhere, the other components of risk – from protection to vulnerability to the ability to cope and recover – are also biased against poor people (Hallegatte et al., 2016), which means that even in places without a poverty bias, poor people may still be at risk. Protection levels and quality are lower in poor countries, and lower in poor neighborhoods and regions. Poor people live in low quality houses that suffer more damage in case of floods, and they have most if not all of their assets in material form, making them more vulnerable to floods. Finally, poor people have limited access to recovery support, such as social protection and credit.

All of these factors combined with the results in this paper advocate for disaster risk management as a pro‐poor policy. In countries where we find that poor people are disproportionally exposed to floods and droughts, it is particularly important to integrate risk management policies within poverty reduction strategies, to understand the underlying drivers of the exposure bias, and to correct it through better land‐use regulation and other supporting policies. Critically, such policies should support the access of poor people to opportunities, and not stifle them. Where hazards will become more frequent or more intense, implementing risk‐sensitive land‐use policies that protect poor people, such as flood zoning, should be a priority.

# **Acknowledgements**

We thank Adrien Vogt‐Schilb and Anne Zimmer for their careful review of this paper, and Tom Pullum, Ruilin Ren, and Clara Burgert from ICF International for their very helpful guidance on using the DHS data. We are grateful for financial support from the Global Facility for Disaster Reduction and Recovery (and thank Alanna Simpson as being the main counter‐part from GFDRR) and the World Bank, under the work program on “Poverty and Climate Change,” led by the Office of the Chief Economist of the Climate Change Group. We also acknowledge support from Earth2Observe, EU FP7 project grant agreement no. 603608. Furthermore, P.J. Ward received additional financial support from a VENI grant for the Netherlands Organisation for Scientific Research (NWO).

# **Author contributions**

H.C.W. and P.J.W. computed the flood hazard maps for all projections. T.I.E.V. and P.J.W. established the drought hazard layers for all projections. M.B. prepared shapefiles from the DHS survey data, for use in this study. H.C.W., B.J., P.J.W. and T.I.E.V. established the computational framework to compute the poverty exposure bias. M.B. and S.H. performed background study on poverty and 22