metadata: authors: 0: author: Hallegatte,Stephane
1: author: Bangalore,Mook
2: author: Vogt-Schilb,Adrien Camille
count: World
docna: 0: docna: Assessing socioeconomic resilience to floods
            in 90 countries
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Social Protections & Assistance,Hazard Risk Management,Social Risk Management,Inequality,Natural Disasters,Disaster Management
teratopic: Public Sector Development,Social Protections and Labor,Governance,Environment,Science and Technology Development,Poverty Reduction,Macroeconomics and Economic Growth,Health, Nutrition and Population,Industry
abstracts: cdata!: This paper presents a model to assess
            the socioeconomic resilience to natural disasters of an
            economy, defined as its capacity to mitigate the impact of
            disaster-related asset losses on welfare, and a tool to help
            decision makers identify the most promising policy options
            to reduce welfare losses due to floods. Calibrated with
            household surveys, the model suggests that welfare losses
            from the July 2005 floods in Mumbai were almost double the
            asset losses, because losses were concentrated on poor and
            vulnerable populations. Applied to river floods in 90
            countries, the model provides estimates of country-level
            socioeconomic resilience. Because floods disproportionally
            affect poor people, each $1 of global flood asset loss is
            equivalent to a $1.6 reduction in the affected
            country's national income, on average. The model also
            assesses and ranks policy levers to reduce flood losses in
            each country. It shows that considering asset losses is
            insufficient to assess disaster risk management policies.
            The same reduction in asset losses results in different
            welfare gains depending on who benefits. And some policies,
            such as adaptive social protection, do not reduce asset
            losses, but still reduce welfare losses. Asset and welfare
            losses can even move in opposite directions: increasing by
            one percentage point the share of income of the bottom 20
            percent in the 90 countries would increase asset losses by
            0.6 percent, since more wealth would be at risk. But it
            would also reduce the impact of income losses on wellbeing,
            and ultimately reduce welfare losses by 3.4 percent.
repnme: repnme: Assessing socioeconomic resilience to
            floods in 90 countries
display_title: Assessing socioeconomic resilience
            to floods in 90 countries
listing_relative_url: /research/2016/05/26361020/assessing-socioeconomic-resilience-floods-90-countries
topicv3: Disaster Risk Management m1326682 820,Poverty m1326687 695,Social Protection and Labor m1327194 697,Flood m1503315 3216,Health m1504661 3480
publishtoextweb_dt: 2016-05-09T00:00:00Z
historic_topic: Public Sector Development,Social Protections and Labor,Governance,Environment,Science and Technology Development,Poverty Reduction,Macroeconomics and Economic Growth,Health, Nutrition and Population,Industry
no_of_pages: 29
author_keys: 0: author_key: 1103816
1: author_key: 1272850
model_extractions: WPS7663 Policy Research Working Paper 7663 Assessing Socioeconomic Resilience to Floods in 90 Countries _Stephane Hallegatte Mook Bangalore Adrien Vogt-Schilb_ Climate Change Cross-Cutting Solutions Area May 2016
Policy Research Working Paper 7663

# **Abstract**

This paper presents a model to assess the socioeconomic resilience to natural disasters of an economy, defined as its capacity to mitigate the impact of disaster-related asset losses on welfare, and a tool to help decision makers identify the most promising policy options to reduce welfare losses due to floods. Calibrated with household surveys, the model suggests that welfare losses from the July 2005 floods in Mumbai were almost double the asset losses, because losses were concentrated on poor and vulnerable populations. Applied to river floods in 90 countries, the model provides estimates of country-level socioeconomic resilience. Because floods disproportionally affect poor people, each $1 of global flood asset loss is equivalent to a $1.6 reduction in the affected country’s national income, on average.

The model also assesses and ranks policy levers to reduce flood losses in each country. It shows that considering asset losses is insufficient to assess disaster risk management policies. The same reduction in asset losses results in different welfare gains depending on who benefits. And some policies, such as adaptive social protection, do not reduce asset losses, but still reduce welfare losses. Asset and welfare losses can even move in opposite directions: increasing by one percentage point the share of income of the bottom 20 percent in the 90 countries would increase asset losses by 0.6 percent, since more wealth would be at risk. But it would also reduce the impact of income losses on wellbeing, and ultimately reduce welfare losses by 3.4 percent.

This paper is a product of the Climate Change Cross-Cutting Solutions Area. 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 shallegatte@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
# **Assessing Socioeconomic Resilience to Floods in 90 Countries**

Stephane Hallegatte, Mook Bangalore, Adrien Vogt‐Schilb Climate Policy Team, Climate Change Group, The World Bank 1818 H St NW Washington DC 20009 USA contact: <u>shallegatte@worldbank.org</u>

**JEL:** Q54, I31, I32, Q50 **Keywords** : Natural disasters, vulnerability, poverty, wellbeing, climate change
# Introduction

The most immediate consequences of floods are the fatalities and casualties and the first priority of disaster risk management is to save lives. But natural disasters also have economic consequences, which affect wellbeing and need to be accounted for and managed (Cavallo and Noy, 2011; Rose, 2009; Skoufias, 2003). These economic consequences and wellbeing losses depend on the value of what is lost or damaged, and on many other factors, including how long it takes to rebuild, how asset losses translate into income losses, and how coping mechanisms and ex‐post support (from insurance to social protection) protect the victims and help smooth consumption losses (Carter et al., 2007; Le De et al., 2013). In addition, wellbeing losses are larger when losses are concentrated, especially when concentrated on poor people – as is often the case (Hallegatte et al., 2016a). Disasters can also have irreversible and long‐term health consequences, particularly on children (Dercon, 2004; Maccini and Yang, 2009).

Many policies can minimize wellbeing losses and protect the population: from building dikes and restoring mangroves to better land‐use planning and early warning, to evacuation, insurance and social safety nets. Risk management policies are best designed as holistic strategies that combine many of these levers (World Bank, 2013).

Designing such a consistent policy package is challenging; here we propose an approach and a model to support this process. In this work‐in‐progress, we combine data on flood hazard, population and asset location, asset vulnerability, and socioeconomic characteristics and combine insights from natural and social sciences to assess how floods affect wellbeing, measured using a _social welfare function_ ( _welfare_ is the metric used by economists to measure wellbeing). We present the model using a case study – the 2005 floods in Mumbai, India, for which macro‐ and micro‐economic data are available. We then use the model to define and assess the socioeconomic resilience of 90 countries to river floods, identify policy priorities to reduce the impact of floods on wellbeing, and help design holistic risk management strategies tailored to each country.

An Online Technical Note<sup>1</sup> provides all the equations and technical details of the model, sensitivity analysis to show the robustness of the approach, and a comparison with other indicators for resilience or vulnerability. The model and the data are also available online.<sup>2</sup> We propose a new quantifiable definition of _socioeconomic resilience_ , the ratio of _asset_ losses to _welfare_ losses<sup>3</sup> : Socioeconomic resilience �<sup>Asset losses</sup> Welfare losses

> 1 The online technical note is available at

<u>http://pubdocs.worldbank.org/en/362361462825935820/pdf/Online‐Technical‐Note‐submit.pdf</u> 2 The code and data set are available at github.com/adrivsh/resilience_indicator_public/

> 3 This definition combines the previously proposed notions of macroeconomic and microeconomic resilience (Hallegatte, 2014).

2
With this definition, socioeconomic resilience can be considered as a driver of the risk to welfare – measured through the expected welfare losses due to floods – along with the three usual drivers, hazard (the probability an event occurs), exposure (the population and assets located in the affected area) and asset vulnerability (the fraction of asset value lost when affected by a flood): Socioeconomic resilience ( _resilience_ for short in this paper) measures the ability of an economy to minimize the impact of asset losses on wellbeing and is one part of the ability to _resist_ , _absorb_ , _accommodate_ and _recover in a timely and efficient manner_ to asset losses (the qualitative definition of resilience from the United Nations) _._ In an idealized case of perfect risk‐sharing across the population, no irreversible impacts on human capital, and no pre‐existing inequality, welfare losses are equal to asset losses. Socioeconomic resilience at 50% means that welfare losses are twice as large as asset losses, and could be reduced by half if inequality disappeared, losses were perfectly shared, and irreversible impacts were avoided.

We develop and use a model to estimate expected asset losses and expected welfare losses, and quantify socioeconomic resilience in 90 countries. Like all models, ours is incomplete and our assessment provides a partial view of resilience. For instance, we do not include many non‐economic components such as the link between disasters, conflicts, and state fragility. Nevertheless, our quantification informs on the ability of economies to deal with natural disasters and on the prioritization of policy options to improve resilience.

We find that some policy options can reduce welfare losses by increasing socioeconomic resilience, from an unchanged (or even increased) amount of asset losses. For instance, increasing by one percentage point the share of income of the bottom 20 percent in the 90 countries would increase asset losses by 0.6%, since more wealth would be at risk. But it would also reduce the impact of income losses on wellbeing, and ultimately reduce welfare losses by 3.4%. This finding suggests that the common practice of tracking only asset losses (IPCC, 2012) may give an overly pessimistic view of progress made by countries in terms of disaster risk management; and that taking into account distributional impacts and ex‐post support mechanisms can better inform policy recommendations.

We use the model to rank a set of policy levers in 90 countries, according to their country‐specific efficacy to reduce risk to welfare through its four drivers. We display this information using country‐level _scorecards_ , which describe each country with a set of sub‐indicators (such as protection level, access to early warning, and social protection for poor and non‐poor people) and report how improving each sub‐ indicator would impact risk (and resilience) for that country. (In contrast, existing indicators for resilience, risk, or vulnerability attribute the same weights to each sub‐indicator in every country; see the Online Technical Note.) We find for instance that social safety nets are more important in countries with weak physical protection against floods, or high asset vulnerability. Our scorecards provide an innovative framework to assess in a consistent manner the benefits from hard measures (e.g., dikes, building norms) and softer options (e.g., post‐disaster support, financial inclusion). They provide an input to the cost‐ 3
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to nonpoor people. The _elasticity of the marginal utility of consumption_ is the parameter that describes how $1 in consumption loss affects differently poor and non‐poor people. Implicitly, it sets _distributional weights_ , i.e. the weight attributed to poor people vs. the rest of the population in the aggregation of costs and benefits in an economic analysis (Fleurbaey and Hammond, 2004). We use a standard value of 1.5 but, as this parameter is a normative choice, we explore in the Online Technical Note how results change for different values. Higher values give more importance to poor people, lead to higher estimates of welfare losses, and make it relatively more important to use policy instruments targeted towards poor people to reduce welfare risks at the country‐level.

Average losses for poor and non‐poor people may not capture the full impact of the disaster: in each category, losses are heterogeneous and some households may lose everything, and experience long‐term effects or fall into _poverty traps_ . In Mumbai, household surveys show that the median asset and income loss per capita was approximately Rs. 9,300, while the average loss was substantially higher at around Rs. 13,700. Losses across the population follow a lognormal distribution with a long tail: median losses are moderate, but some households lost almost all their income. For the people experiencing large losses, the welfare impact of the shock is not only related to the net present value of the flow of consumption losses, but also to possible long‐term effects, such as reduction in food intake, health effects and disability, and exclusion from job markets, which can lead households to fall into poverty traps (Barnett et al., 2008; Carter et al., 2007; Kraay and McKenzie, 2014; Maccini and Yang, 2009). The risk of poverty traps is particularly acute for children, as severe health impacts or interruptions in education can have lifelong impacts on earnings.<sup>5</sup> To account for these effects, we calculate the number of people with very low post‐disaster, post‐ response consumption and assume that a fraction of them are unable to fulfill their basic needs and suffer from losses that go beyond the impact of a change in aggregate consumption. We count individuals with income levels below a threshold equal to 10% of average GDP per capita in the country. Of those with very low income levels, we exclude households who save at a financial institution, assuming that these savings allow smoothing the shock (through dissaving or borrowing to smooth the shock). Finally, we also exclude those who are assumed protected by the socioeconomic environment. We assume that in a country where healthcare is available and affordable, where children attend school at least until the end of primary school, and where unemployment is low, people are less likely to fall into poverty traps. In the absence of a full model for these effects, we assume that the fraction of people falling into poverty traps is inversely proportional to a simple indicator that averages measures of access to health, access to education, and employment (see the Online Technical Note).

People who have very low income and are not protected by their own savings or the socioeconomic environment are assumed to fall into a poverty trap (or to transmit poverty to their children), leading to an additional welfare loss. This welfare loss is estimated to be equal to the discounted value of the loss of the average individual’s income in the country, returning to normal over 40 years (one generation). This is of course a crude simplification of reality – which consists of a continuum of intertwined impacts on

> 5 (Barrett and Constas, 2014) propose a definition of _development resilience_ that focuses on the capacity of people to avoid such poverty traps.

7
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<!-- Start of picture text --> x<br>a<br>-8.8<br>[5 Asset losses “9.8<br>(8) Welfare losses<br>Reduce Reduce Double Cut Double Halve poverty Double Increase<br>exposure asset post-disaster reconstruction income vulnerability bias access income share<br>by 5% vulnerability support duration by diversification to savings of bottom<br>by 5% a third quintile by 10%<br><!-- End of picture text -->
provide here an indication of the benefits from these tools, making it possible to compare them with implementation costs and alternative approaches such as land‐use planning or building retrofitting.

Regarding poverty, these results indicate that there is no trade‐off between poverty reduction and risk management. Even if poverty is reduced without improving the exposure or asset vulnerability to floods, thus increasing asset losses when disasters hit (by 1.1% in our model), the net effect on welfare is positive (losses are reduced by 3.2%) due to the increased ability of poor people to cope with asset losses. This is an important finding, for example for city managers who may worry that better living standards and higher‐value buildings increase asset risks: while they do, wellbeing risks are ultimately reduced.

To account for the uncertainty in the exposure to the flood and its consequences and in socioeconomic characteristics (e.g., diversification), we perform a systematic sensitivity analysis by varying all uncertain parameters 33% above and below their central value, and measure the robustness of our findings (see the Online Technical Note for full details). Error bars in Figure 4 report the resulting interquartile uncertainty on the impacts of the various policies on asset and welfare losses. It shows our results to be robust. We find also that the relative ranking of the policies is stable and not sensitive to the uncertainties. Additionally, we explore changes in the elasticity of the marginal utility of consumption, which is a normative choice, not an uncertainty. We find that it affects only the magnitude of the benefits from reducing poverty or the exposure bias, but it does not affect our qualitative results and the ranking of policy options.

# Improving socioeconomic resilience to floods in 90 countries

We use this model to quantify socioeconomic resilience in 90 countries and assess how different policy options can improve resilience and reduce the welfare impacts of river floods. Since we cannot rely on ex‐ post observation and household surveys to calibrate the model, we use proxies and national‐level data. Also, we do not calculate the resilience for one event – like for Mumbai in 2005 – but the resilience to many possible flood events. We calculate asset and welfare losses for several return periods (between 5 and 1,000 years) and estimate the socioeconomic resilience as the ratio of expected asset losses to expected welfare losses. A complete description of the model is available in the Online Technical Note.

Hazard is represented by a flood’s probability of occurrence, above the level of protection provided by embankments and artificial dikes. In each country, we set to zero the exposure to all events with a return period equal or lower than the protection level. We calibrate the protection level by country using the global open and collaborative database FLOPROS (Scussolini et al., 2015). FLORPOS provides estimates at the sub‐national level based on expert estimates of _de facto_ protection if available, _de jure_ legislation and performance standards otherwise, or simple economic modeling if both de facto and de jure data are lacking.

Above the protection level, population and asset exposure to floods is estimated using GLOFRIS, a global river flood model (Jongman et al., 2015, Jongman et al., 2012). This model provides gridded flood inundation estimates at the 1km<sup>2</sup> resolution for 9 return periods (from 5 to 1000 years), all countries, and in all large river basins. The model does not include all floods: coastal floods are not taken into account, 9
and flash floods and small river basins have not been represented in a global model at this stage. These flood maps are overlaid with a global population density data set, Landscan (Geographic Information Science and Technology, 2015) to estimate the percentage of the population exposed to floods in each country, river basin, and for each return period.

An important complication is the difference between the exposure to a flood in a basin for a given return period (say, the 100‐year flood), and the number of people that are affected in the country _at once_ : while 2.1 percent of Brazil’s population is exposed to a 100‐year event in our simulations, this population is distributed in many river basins that will not flood at the same time. Here, we make the simplifying assumption that river basins in each country are independent, neglecting cross‐basin correlation (Jongman et al., 2014), and we perform a Monte Carlo analysis to derive exposure in a country at a given return period by aggregating simulations at the basin level.

We use estimations of the exposure bias published by the World Bank (Hallegatte et al., 2016a). The World Bank study overlays the same GLOFRIS flood maps with geo‐localized household surveys (from the Demographic and Health Survey) to assess the exposure of poor people relative to the exposure of non‐ poor people within 52 countries. Countries where data are not available are attributed the average exposure bias.

We proxy asset vulnerability and the asset‐vulnerability bias using a global data set of building types (USGS PAGER). We classify buildings in three categories (low, medium, and high quality), which we match to simple damage‐depth functions (Hallegatte et al., 2013), and we assume richer households live in and use higher quality assets. What fraction of assets is lost to a disaster also depends on softer measures, such as the existence of early warning systems. In addition to being an effective way of reducing casualties and fatalities, early warning allows households to plan for a disaster, and move some of their assets outside (or above) the affected zone, thereby reducing asset losses (Hallegatte, 2012; Kreibich et al., 2005) **.** Based on previous case studies, we assume that asset losses are reduced by 20 percent when people have access to early warnings, and we use data from the Hyogo Framework for Action (HFA) monitoring system on early warning to estimate the fraction of population with such access.

For income diversification, social protection and financial inclusion, we build on global databases such as the Atlas for Social Protection – Indicators of Resilience and Equity (ASPIRE) and Global Financial Inclusion (FINDEX). Parameters related to the socioeconomic characteristics that modify risks of irreversible human capital losses – employment, healthcare, and education – are from the World Development Indicators (WDI).

Adaptive social protection, where social protection benefits and/or beneficiaries is expanded automatically in the aftermath of a disaster, is an efficient tool to reduce the welfare impact of disasters (Davies et al., 2013; Hallegatte et al., 2016c). It is impossible to predict the support that will be provided after a disaster, so we assume a willingness to share the losses, and proxy for the ability to provide such support, depending on institutional capacity and public financial management. To measure countries’ ability to manage public finance and reallocate resources in times of crisis, we use sovereign credit ratings. We use data from the HFA on contingent finance, the existence of plans for emergency response and 10
social protection scale‐up. More in‐depth analyses of the post‐disaster _financing gap_ have been performed using specific models, investigating the various mechanisms available to finance post‐disaster actions, including budget reallocation, domestic and international borrowing, and specific instruments such as catastrophe‐bonds, insurance contracts, or reserve funds (Cardenas et al., 2007; Hochrainer‐ Stigler et al., 2014). In a next phase, results from these more sophisticated models can be used to refine our estimates of the ability of each country to fund crisis management and provide post‐disaster support.

Our methodology to assess asset risk is a simplification of state‐of‐the‐art catastrophe modeling (Aerts et al., 2014; Michel‐Kerjan et al., 2013; UN‐ISDR, 2015), applicable with global data. As a result, our estimates for flood probability, exposure and asset vulnerability have to be considered with caution. Our simplifications are acceptable as we focus on how asset losses translate into welfare losses (socioeconomic resilience), not specifically on asset losses. The sensitivity analysis presented in the Online Technical Note concludes that our estimates of socioeconomic resilience and our ranking of policy options are largely independent of aggregate asset exposure and vulnerability. Our main results are thus robust to the simplicity of the hydrological modeling and to errors in exposure and asset vulnerability data.

We assess the resilience of 90 countries to floods, by calculating the ratio of expected asset losses to expected welfare losses. Resilience averages 61% across our sample, ranging from 25% to 86%. Resilience in Malawi is 53%, which means that $1 of asset losses in Malawi has the same impact on welfare as a reduction of Malawi’s national income by almost $2.<sup>6</sup> All 90 countries are represented in Map 1, and the list of countries with their risk and resilience information is in the Appendix below. Risk to wellbeing (expected welfare losses in percent of GDP) decreases rapidly with income per capita (Figure 5a), mostly due to better protection and lower asset vulnerability.

High‐income countries also tend to have higher resilience (Figure 5b), but resilience has a large variance even when GDP per capita is controlled for, especially at low income levels. Resilience varies across countries of similar wealth because welfare consequences depend on a multitude of factors, including pre‐existing inequality and safety nets to reduce the instantaneous impacts of a disaster. This finding suggests that all countries – regardless of their geography or income level – can act to reduce risk by increasing resilience.

> 6 Our measure of socioeconomic resilience does not include the fact that a reduction of national income by $2 has a larger impact in a low‐income country than in a high‐income country. While including this fact would be straightforward, it would imply to make inter‐country welfare comparisons, which is not required in our analysis.

11
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less frequent floods than nonpoor people), low inequality (the income share of the bottom 20 percent is 8.5 percent, among the highest in our sample), large transfers from social protection, and the strong socioeconomic environment. It does not mean that the risk to welfare is particularly low, however. Due to a low protection level, annual welfare losses reach almost 1% of GDP in our analysis.

Interestingly, resilience is only weakly correlated to exposure (0.23) and risk (‐0.18). This is because many drivers of resilience are socioeconomic conditions that are outside the domain of traditional disaster risk management, focused on asset losses. It seems obvious that no country ever decided to reduce income inequality because of a high exposure to natural hazards, even though inequality is a major driver of our resilience indicator.

Reducing poverty reduces welfare losses from floods in all 90 countries. Increasing the wealth of poor people increases total asset losses, but increases even more the ability of poor people to cope with those asset losses. This result may remind the widely accepted fact that richer countries tend be more resilient than poorer ones; while richer countries experience larger economic losses, they suffer fewer casualties and fatalities than poorer countries (Guha‐Sapir et al., 2013; Kahn, 2005). But this result is different: our analysis investigates welfare losses – not casualties or asset losses – and accounts for the distribution of losses and resources _within_ – not across – countries.

# Main drivers of risk and resilience

To identify the parameters which best explain differences in resilience, we conduct a simple sensitivity analysis. For each input parameter (exposure, income share of bottom quintile, etc.), we estimate the risk to assets, risk to welfare, and socioeconomic resilience if we did not know that parameter: that is, we change that parameter value in all countries to its average across countries. We then compare these estimates to the original ones computed with the original parameter values, and we call _error_ the average of the relative difference (Figures 6‐8). A large error means that knowing the value of the parameter is important, either because the model is sensitive to it, or because the actual values vary widely across countries. A small error means that the parameter is not that important when estimating resilience, and disregarding it in the analysis would lead to similar results.

For the risk to assets, figure 6, the most important parameters are population exposure, protection, and asset vulnerability. If these parameters are wrong, the estimate of the risk to asset is also wrong. We find here the three usual components (hazard, exposure, and vulnerability). For instance, replacing exposure in all countries with the average exposure across countries (5.1% of the population) would result in an average 180% error in the estimate of risk to assets on average. The other parameters also enter the calculation: for instance, the productivity of capital and the exposure bias is used to translate population exposure into an exposure expressed in asset value. Access to early warning has an impact as it decreases effective capital vulnerability.

13
Exposure ii | 80 Hazard (return period) i 1 70 Vulnerability without early warning i 66 Productivity of capital HJ 37 Access to early warning § 8.6 Exposure bias] 3.3 Income share of bottom quintile] 0.61 Avoided losses with early warning|0 Poverty headcount] 0 GDP per capita (PPP USD)|0
Productivity of capital iy | 3 Exposure biasa &

Asset-vulnerability bias ig © Income share of bottom quintile i 7.7 Social protection for poor people Si 5. Ready to scale-up social protection HJ 2.6 Vulnerability without early warning 9 2.4 Contingent finance J 1.3 Basic education § | .2 Access to health care §j 1

Exposure EEE | 80 Hazard (return period) iia | 70 Vulnerability without early warning Gj 70 Productivity of capital Bj 28 Access to early warning § 9. | Asset-vulnerability bias § 7.9 Income share of bottom quintile § 6.8 Social protection for poor people§ 6.4 Exposure bias] 5 Ready to scale-up social protection| 2.7
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<!-- Start of picture text --> Resilience aw TM® 5.3 .<br>* 53% 8° To improve<br>resilience<br><!-- End of picture text --> <!-- Start of picture text --> 3 yr Income share of bottom quintile 2 +an 10 1.7<br>5%<br><!-- End of picture text --> <!-- Start of picture text --> across countries 10%<br>Driver rin.‘: ——~ countriesmax across Social protection for poor people 9ne$8 30<br>Malawi 3%<br><!-- End of picture text --> <!-- Start of picture text --> Asset-vulnerability bias 9 San ee 321 —42<br>94%<br>Ready to scale-up social protection 9a 100 78<br><!-- End of picture text --> <!-- Start of picture text --> This(expected scorecardwelfare showslosses the riskin percent to welfareof GDP),in Malawiand its Basic education 25__ +ee 100 100<br>drivers: hazard (captured with protection level), 60%<br>exposure,Drivers areasset rankedvulnerability, according toand theirresilience. efficacy to Access to health care 3) ceo 97 100<br>reduce risk, from the most to the least promising. 80%<br>theLimitedlist canimprovements substantiallyin reducethe driversrisk, whileat the largetop of U I t ‘he —100<br>improvements would be required in drivers at the NEP oO 8% aa<br>bottominformationto obtainis quantifieda similarinreduction. the right-mostThis column, i rm ~a_. 4<br>which indicates how much each driver needs to change Social protection for non-poor people 1 4% 54 oo<br>to reduce risk by 10%. For each driver, the<br>scorecard provides the distribution of values across ‘. Se i “<br>allindicatecountrieswhere withMalawi the minimumstands with andrespect the maximum,to the 89to Financial management ny 100 160<br>other countries. The number is green if the ‘ \ \<br>corresponding change is achievable without exceeding Contingent finance | »_}_/_s 100 160<br>the best performance among all countries in the 60%<br>sample. It is orange if one tenth of the change is 7<br>achievable without exceeding the best performance. Time to reconstruct 9 10 —2<br>Otherwise, the number is red. On the right, asset 3yr<br>vulnerability is broken down into 2 sub-indicators, XS, :<br>and socio-economic resilience into 14 sub- Access to finance for poor people 9 . 59 210<br>indicators. Similarly to drivers, sub-indicators are 5%<br>ranked according to their efficacy to improve i<br>resilience by 5.3% or reduce asset vulnerability by Access to finance for non-poor people; . 69 410<br>3%. 10%<br><!-- End of picture text -->
are effective and areas in which the country is lagging, they are obvious candidates to increase resilience. Further, such policies may be development priorities, irrespective of flood risk.

The second lever is exposure bias. Poor people in Malawi are 10% more likely to be exposed to floods than non‐poor people. Bringing this value to ‐11%, so that poor people are _less_ likely than the average to be affected, would increase resilience by 5.3%. Asset quality also matters: reducing the excess vulnerability of poor people’s assets (vs. the rest of population) from 94 to 52% increases resilience by 5.3 points. This could be done by better enforcing building norms, or by supporting access to higher‐quality buildings for poor people.

In Figure 11, we show a similar scorecard for Sweden, where risk is much lower (at 0.03% of GDP), mostly because protection is much better. Resilience is also higher, at 71%. In Sweden, decreasing risk by 10% would require increasing resilience by 7.1 points, which would require increasing the income share of the bottom 20 percent by 6.5 points, from 10 to 16.5%, which would be unprecedented. Or it would take an increase in social protection towards poor people by 1800 points, which is impossible since it would then exceed 100%. Improving resilience in Sweden seems much more difficult than in Malawi, mostly because low‐hanging fruits have already been captured. Only planning for faster reconstruction, protection investments, and policies to reduce exposure – especially for poor people – and reducing asset vulnerability appear promising.

Results are thus country‐specific, which support the choice of using a model instead of a weighted average of sub‐indicators, in which the weights are global and cannot be adjusted to local circumstances. The model allows for instance to identify specific situations:

- In countries where poor people have more vulnerable assets, it is particularly important to protect them with social protection instruments. This explains for instance why transfers to poor people is found a powerful policy lever in France (an increase by 21 point is enough to reduce risk by 10%, because the asset‐vulnerability bias is 205%) while it is not efficient in Sweden (a 1800‐point increase in social protection would be required, because the asset‐vulnerability bias is only 1%). Germany is an intermediate case with an asset‐vulnerability bias around 50% and where increasing social protection toward the poor by 64 points reduces risk by 10%.

- In countries where social safety nets are largely absent (e.g., Democratic Republic of Congo) many individuals lose everything from disasters and become dependent on education and health care services provided by the government and on good alternative employment opportunities – such policies appear high in the priority list.

In two countries, Hungary and Georgia, increasing the asset‐vulnerability bias slightly increases resilience. This is because of relatively low inequalities and the very high level of protection provided by the social protection system to the poor (91% and 98%, respectively), compared with the support provided to the non‐poor (53% and 54%, respectively). With such protection levels, it improves resilience to have more vulnerability concentrated on the poor, instead of the non‐poor.

18
<!-- Start of picture text --> Sweden<br><!-- End of picture text --> <!-- Start of picture text --> Change needed To reduce<br>i to reduce vulnerability<br>Risk to welfare 0.03%gee —5 tisk by 10% by 2.9%<br><!-- End of picture text --> <!-- Start of picture text --> 3.5%<br>Asset vulnerability 49Ss 60 =2.9 Access to early warning 9 80% 400. =<br>11%<br><!-- End of picture text --> <!-- Start of picture text --> Resilience 25eat 86 71 To improve<br>ite resilience<br><!-- End of picture text --> <!-- Start of picture text --> 54 yr Income share of bottom quintile 2porR10 6.5<br>10%<br><!-- End of picture text --> <!-- Start of picture text --> Driver wine ~— — Counies’ Time to reconstruct 9 . 10 -14<br>Sweden 3yr<br><!-- End of picture text --> <!-- Start of picture text --> Asset-vulnerability bias 9fat ae 321 140<br>1%<br>Social protection for non-poor people =fa 54 1800<br><!-- End of picture text --> <!-- Start of picture text --> This(expected scorecardwelfare showslosses the riskin percent to welfareof GDP),in Swedenand its Social protection for poor people 9NPR° 98 1900<br>drivers: hazard (captured with protection level), 46%<br>exposure,Orivers areasset rankedvulnerability,according toand theirresilience. efficacy to Ready to scale-up social protection 9 | Beer {itis 100 3700<br>reduce risk, from the most to the least promising. 73%<br>Limitedthe list canimprovementssubstantiallyin reducethe driversrisk, atwhilethe largetop of ul 1 nt ft Nee = _~7000<br>improvements would be required in drivers at the cilia 9 B% 28<br>bottominformationto obtainis quantifieda similarinreduction. the right-mostThis column, ee<br>which indicates how much each driver needs to change Basic education :25 oem” 7000<br>toscorecardreduce riskprovidesby 10%. the distributionFor each driver, of values acrossthe age N<br>all countries with the minimum and the maximum, to Access to health care 31 oe 7000<br>indicate where Sweden stands with respect to the 89<br>other countries. The number is green if the . aS Fe<br>corresponding change is achievable without exceeding Financial management 9 +100 7400<br>the best performance among all countries in the 100%<br>sample.achievableIt iswithout orange exceedingif one tenththeof thebest changeperformance.is Contingent. finance 9 \ \___»_h_4+| 100 7400<br>Otherwise, the number is red. On the right, asset 80%<br>vulnerabilityand socio-economicis brokenresilience down intointo 2 sub-indicators,14 sub- Access to finance for non-poor people 1WOR369 15000<br>indicators. Similarly to drivers, sub-indicators are 67%<br>rankedresilienceaccording by 7.1%to ortheir reduceefficacy assetto vulnerabilityimprove by Access to finance for poor people 9i 53000<br>2.9%. 59%<br><!-- End of picture text -->
then the scorecard provides the resulting increase in resilience and decrease in risk. The socioeconomic resilience can also be used to translate in welfare terms the risk reduction benefits which are typically expressed in asset losses: if a new dike can reduce expected asset losses by $1 million per year in Malawi – with its 53 percent resilience – then welfare benefits can be estimated as equivalent to $2 million of additional consumption, assuming the dike protects the “average” population and is not biased toward non‐poor people, for instance.

These estimates can serve as an input to a cost‐benefit analysis that would also need to account for the cost of these options and their benefits unrelated to resilience. For instance, developing social protection brings benefits that go beyond increased resilience and include economic benefits even in the absence of shocks: an analysis of resilience cannot alone determine the desirability of such a policy. However, the scorecards can contribute to a discussion on a broad set of options to reduce natural risks and increase resilience, and ensure all options are discussed, from preventive actions like flood zoning to ex‐post options like insurance, contingent finance and social protection. The scorecard provides an integrated framework to discuss and compare these options, and could even help break the silos in governments and local authorities, where ministries or departments in charge of social protection, building norms and urban planning may not work well together or not even consider flood risks in their decisions.

Our analysis of Malawi and Sweden – and the 88 other countries – is a first‐round estimate using globally open data. It is a starting point for policy design and should be complemented by local studies (Aerts et al., 2014; Keating et al., 2014; Michel‐Kerjan et al., 2013). At the local or national level, for instance, the flood maps from the global model can be replaced by results from local analyses at higher resolution, including flash floods and small basins. Local data on flood protection and better exposure data can often be mobilized (Aerts et al., 2014). And socioeconomic characteristics can be refined, accounting for instance for the institutional capacity to scale‐up social protection beyond what a global database can reasonably aim at providing (Pelham et al., 2011). But in spite of all these limits, our global approach may contribute to the monitoring of country and global progress in terms of resilience, and our findings already provide insights into promising policy options, such as adaptive and well‐targeted social protection, and show that “good development” increases resilience, especially if it reduces poverty and improves social safety nets.

# Discussion

Our socioeconomic resilience remains an imperfect metric, in the sense that it does not include all the dimensions discussed in the resilience field (Barrett and Constas, 2014; Engle et al., 2013; Keating et al., 2014). Our framework looks at the socioeconomic resilience, but disregards direct human and welfare effects (death, injuries, psychological impacts, etc.), cultural and heritage losses (e.g., the destruction of historical assets), social and political destabilization, and environmental degradation (for instance when disasters affect industrial facilities and create local pollution). The framework proposed here is for socioeconomic resilience, not for a broader concept of resilience.

Issues related to conflicts and government stability are not explicitly recognized, even though they indirectly influence our results since fragile governments usually provide little social protection and have 20
limited ability to respond to shocks. We also do not account for the possibility that a disaster (or the response to it) magnifies pre‐existing conflicts.

We have disregarded the impact on natural capital, in spite of its importance in the income of poor population across the world (Angelsen et al., 2014) and the impact of natural disasters on soils (through salinization or erosion), fish stocks, or trees. Including natural capital in the assessment would meet many data related issues, on the local importance of natural capital in income and on the vulnerability of natural capital to floods and other disasters.

Also, the ability of individual firms to cope with the shock and continue to produce in the disaster aftermath – the _static resilience_ of (Rose, 2009) – depends on many factors that would need to be included in the analysis. Various methodologies have been proposed to assess these parameters, using input‐ output or general equilibrium models (Santos and Haimes, 2004; Rose and Wei, 2013; Hallegatte, 2014a) or explicit modelling of supply‐chains (Battiston et al., 2007; Henriet et al., 2012). But more work is needed to assess this resilience based on the data and indicators that are available in all countries.

Further, our framework does not address the ability to “build back better” after a disaster and the possibility for reconstruction to lead to an improved situation. It also takes the current exposure and vulnerability as a given, and investigates policy options without accounting for feedback in terms of risk‐ taking decisions. Better ability to manage risks – e.g., through access to insurance and social protection – could indeed have further positive economic impacts through more risk‐taking, innovation, and specialization (Elbers et al., 2007; World Bank, 2013). It can also have negative impacts through moral hazard and excessive risk‐taking (Michel‐Kerjan, 2010). These feedbacks and relationships have to be explored before any risk management policy is implemented, but they often depend on implementation details and cannot be assessed through a global analysis.

The response to a shock is not fully native to a country, but is also driven by foreign development assistance (Hochrainer, 2009), which is not explicitly taken into account in the indicator. We do capture some aspects of development assistance. For instance, countries may be able to provide social protection thanks to budget support from abroad (for instance, Ethiopia receives significant support for its Productive Safety Nets Program). Also, the ability to scale‐up support after disasters – as included in the HFA reporting – depends on concessional resources and international support (e.g., through CAT‐DDOs). Humanitarian and emergency response is not included in our analysis, however. This may create a “resilience bias” towards middle‐income countries that need less to rely on overseas assistance. However, one positive aspect of not including humanitarian assistance is that countries with low resilience can be highlighted as potential targets for development assistance.

The assessment of physical risk used in this analysis focuses on river floods, but the analysis can be expanded to other hazards such as high winds, earthquakes, and droughts. Also, climate change is affecting the frequency and intensity of weather hazards, and there is a growing interest in defining metrics related to the ability to adapt to these changes. Combining new hazard scenarios with our socioeconomic resilience can be one of the building blocks of an indicator of climate change resilience (Engle et al., 2013). Finally, many of the countries that are likely to be the most vulnerable to climate 21
change are also those where data is lacking. Producing an exhaustive map of socioeconomic resilience would require data collection in these countries or developing a reduced, less data intensive, version of the model presented here (for instance based only on the parameters identified in Figures 6 to 8).

The Online Technical Note reviews other indicators that can usefully complement our approach with different methodologies or focuses (e.g., some methodologies give more weight to institutional factors; others accounts for community‐level characteristics); see also a review in (Noy and Yonson, 2016). We also provide a comparison of our measure of socioeconomic resilience and our estimates of welfare risks with two other vulnerability or resilience indicators, namely ND‐Gain and InfoRM. Our indicator adds to the literature and existing indicators because (1) it is based on a formal theoretical framework and on a formal and quantified definition of resilience (the ratio of asset and welfare losses); (2) it adds a focus on the poorest and most vulnerable by distinguishing between the characteristics of the poorest 20 percent and the rest of the population; (3) it provides an associated tool to assess the benefits from various risk management policies such as adaptive social protection or early warning systems.

# Acknowledgements

This article benefited from comments and suggestions from anonymous referees and from Paolo Bazzurro, Charlotte Benson, David Bevan, Laura Bonzanigo, Sebastian Boreux, Daniel Clarke, Carlo del Ninno, Patrice Dumas, Nate Engle, Marianne Fay, Chico Ferreira, Chris Field, Francis Ghesquiere, Ruth Hill, Maddalena Honorati, Kazuko Ishigaki, Brenden Jongman, Tamaro Kane, Kouassi Kouadio, Norman Loayza, Hugh MacLeman, Reinhard Mechler, Martin Melecky, Rick Murnane, Ulf Narloch, James Newman, Ilan Noy, James Orr, Richard Poulter, Valentin Przyluski, Rob Reid, Adam Rose, Rae Rosen, Julie Rozenberg, Alanna Simpson, Emmanuel Skoufias, Vladimir Stenek, Tse‐Ling The, Maarten van Aalst, Sebastian von Dahlen, Claudio Volonte, and Ruslan Yemtsov. This paper benefited from support from DFID and the GFDRR.

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# Appendix: Resilience and risk for all 90 countries

||**GDP**<br>**per**<br>**capita**<br> USD<br>PPP|**Population**<br>|**Protection**<br>**level**<br>return<br>period<br>in years|**Asset**<br>**exposure**<br>% of<br>GDP|**Asset**<br>**vulnerability**<br> % asset<br>value|**Resilience**<br>%|**Total**<br>**risk**<br>% of<br>GDP|
|---|---|---|---|---|---|---|---|
|Albania|9,961|2,897,366|15.5|38.2|33.8|71.0|1.19|
|Argentina|8,087|41,446,246|64.4|27.3|32.3|52.9|0.21|
|Armenia|7,527|2,976,566|11.5|9.5|12.4|69.1|0.13|
|Australia|42,834|23,129,300|100.0|15.6|44.7|71.3|0.08|
|Austria|4,056|8,479,823|600.6|37.6|16.0|69.2|0.01|
|Azerbaijan|16,593|9,416,801|14.0|8.2|12.4|58.7|0.12|
|Bangladesh|2,853|56,594,962|50.0|76.7|38.0|64.2|0.76|
|Belarus|17,055|9,466,000|14.5|8.8|26.2|68.3|0.19|
|Belgium|0,609|11,182,817|213.0|51.0|14.7|76.5|0.04|
|Benin|1,733|10,323,474|6.4|10.5|60.3|41.7|2.04|
|Bolivia|5,934|10,671,200|6.7|10.3|25.4|48.2|0.73|
|Bosnia and|9,387|3,829,307|18.8|40.9|38.4|54.3|1.50|
|Herzegovina||||||||
|Botswana|15,247|2,021,144|16.7|10.8|55.8|55.4|0.50|
|Brazil|14,555|00,361,925|22.2|18.4|57.0|58.1|0.76|
|Bulgaria|5,695|7,265,115|28.3|12.4|39.3|67.2|0.21|
|Burkina Faso|1,630|16,934,839|1.9|2.0|60.0|70.8|0.82|
|Cambodia|2,944|15,135,169|5.3|24.5|24.5|48.1|2.44|

25
|Cameroon|2,739|22,253,959|6.7|7.8|17.0|30.5|0.61|
|---|---|---|---|---|---|---|---|
|Canada|<br>1,899|<br>35,154,279|84.1|11.3|13.9|69.6|0.02|
|Colombia|<br>12,025|<br>48,321,405|19.7|11.5|27.7|25.4|0.40|
|Congo, Dem. Rep.|<br>783|<br>67,513,677|2.0|11.4|51.1|60.1|4.85|
|<br>Congo, Rep.|5,680|<br>4,447,632|3.4|8.6|15.9|34.9|1.11|
|<br>Costa Rica|3,431|4,872,166|17.0|3.8|17.9|45.4|0.07|
|Croatia|<br>0,049|<br>4,255,700|179.8|50.2|38.6|74.6|0.12|
|Czech Republic|8,124|10,514,272|86.8|29.3|39.0|76.0|0.14|
|Denmark|2,483|5,614,932|25.7|2.4|12.6|77.9|0.01|
|Dominican Republic|1,795|10,403,761|77.0|9.4|13.1|74.0|0.02|
|Ecuador|10,541|15,737,878|18.4|25.7|60.0|62.5|1.14|
|Egypt, Arab Rep.|10,733|82,056,378|31.4|38.5|47.2|49.7|0.94|
|El Salvador|7,515|6,340,454|6.8|6.2|32.7|63.6|0.40|
|Estonia|<br>25,254|<br>1,317,997|46.5|6.9|12.9|64.8|0.03|
|Finland|<br>38,821|<br>5,438,972|48.2|13.6|14.2|81.2|0.04|
|France|<br>7,217|<br>65,939,866|122.6|32.7|15.7|75.0|0.04|
|Gabon|18,646|1,671,711|16.9|25.5|21.5|29.3|0.97|
|Georgia|6,930|4,487,200|23.4|23.0|15.6|79.4|0.17|
|Germany|42,884|80,651,873|106.9|27.4|13.3|75.6|0.04|
|Greece|24,305|11,027,549|25.3|7.3|17.5|58.9|0.07|
|Guatemala|7,063|15,468,203|8.0|4.8|13.2|25.2|0.25|
|Honduras|4,445|8,097,688|7.2|11.0|24.6|65.6|0.60|
|Hungary|22,707|9,893,899|100.0|46.9|38.3|81.6|0.17|
|Ireland|44,647|4,597,558|200.0|12.5|12.8|71.1|0.01|
|Italy|<br>33,924|<br>60,233,948|62.6|23.4|15.0|64.8|0.07|
|<br>Jamaica|<br>8,607|<br>2,714,734|0.5|0.1|26.7|49.5|0.06|
|Jordan|<br>11,405|<br>6,460,000|16.6|2.0|49.3|49.6|0.08|
|Kenya|2,705|44,353,691|2.1|3.3|51.6|38.9|1.56|
|Kyrgyz Republic|3,110|5,719,600|3.1|6.2|21.4|63.1|0.58|
|Lao PDR|4,667|6,769,727|6.8|65.9|14.1|56.8|2.34|
|Latvia|21,833|2,012,647|59.7|25.1|12.7|66.0|0.08|
|Liberia|850|4,294,077|6.1|11.7|60.0|59.7|2.08|
|Lithuania|24,470|2,957,689|49.8|25.8|15.3|72.8|0.11|
|Madagascar|<br>1,369|<br>22,924,851|2.2|11.6|32.2|58.8|2.51|
|Malawi|755|16,362,567|2.8|6.9|32.5|53.2|1.23|
|Malaysia|22,589|29,716,965|34.9|42.6|13.0|57.6|0.21|
|Mali|1,589|15,301,650|7.0|11.0|15.2|41.6|0.54|
|Mexico|16,291|122,332,399|39.4|16.5|39.4|59.3|0.23|
|Moldova|4,521|3,558,566|10.5|23.0|38.7|86.0|0.94|
|Mongolia|9,132|2,839,073|6.3|16.4|12.9|66.2|0.45|
|Nepal|2,173|27,797,457|2.3|17.0|34.2|81.5|2.94|
|Netherlands|45,021|16,804,432|4,489.6|133.2|12.8|75.3|0.00|
|Niger|887|17,831,270|2.5|9.3|15.5|64.5|0.85|
|<br>Nigeria|5,423|<br>173,615,345|7.0|3.7|60.2|33.6|0.72|
|<br>Pakistan|<br>4,454|<br>182,142,594|7.2|17.4|49.7|59.5|1.74|
|Panama|<br>18,793|<br>3,864,170|14.3|3.0|60.0|47.5|0.23|
|Paraguay|7,833|6,802,295|20.1|7.0|32.1|46.9|0.19|
|Peru|11,396|30,375,603|93.1|26.1|30.1|73.7|0.11|
|Philippines|6,326|98,393,574|8.3|15.7|12.3|76.1|0.27|
|Poland|22,835|38,514,479|200.0|15.4|13.1|66.3|0.01|
|Romania|18,184|19,981,358|50.0|28.0|45.1|74.5|0.28|
|Rwanda|1,426|11,776,522|2.2|4.6|60.6|58.3|2.08|

26
|Senegal|2,170|14,133,280|5.2|10.2|61.8|77.0|1.39|
|---|---|---|---|---|---|---|---|
|Serbia|12,892|7,164,132|20.5|48.5|38.7|71.2|1.16|
|Sierra Leone|1,495|6,092,075|1.8|9.0|60.3|47.2|4.76|
|Slovak Republic|25,759|5,413,393|100.0|50.1|38.8|66.7|0.28|
|Slovenia|27,368|2,059,953|56.6|49.6|36.8|78.8|0.32|
|South Africa|12,454|53,157,490|100.0|4.3|55.7|46.0|0.05|
|Spain|31,683|46,617,825|98.2|25.1|11.0|73.8|0.04|
|Sri Lanka|9,426|20,483,000|6.4|15.6|60.7|65.3|1.84|
|Sweden|43,540|9,600,379|53.6|9.9|12.8|71.2|0.03|
|Syrian Arab|4,959|22,845,550|9.0|17.0|40.3|61.4|1.06|
|Republic||||||||
|Tanzania|2,365|49,253,126|2.0|4.0|32.6|55.0|1.09|
|Thailand|13,932|67,010,502|22.5|66.7|25.1|63.7|1.00|
|Turkey|18,567|74,932,641|18.2|6.7|32.4|55.2|0.19|
|Uganda|1,621|37,578,876|1.5|0.5|27.3|32.5|0.23|
|Ukraine|8,508|45,489,600|9.3|15.0|40.8|72.1|0.89|
|United Kingdom|36,931|64,106,779|225.7|14.1|12.8|68.3|0.01|
|United States|51,340|316,128,839|460.2|12.0|13.5|62.8|0.00|
|Uruguay|18,966|3,407,062|46.9|6.3|32.5|56.9|0.07|
|Vietnam|5,125|89,708,900|17.6|74.7|12.5|71.6|0.61|
|Yemen, Rep.|3,832|24,407,381|6.4|6.9|13.0|72.5|0.18|
|Zambia|3,800|14,538,640|6.5|3.8|32.8|30.8|0.45|

27