metadata: authors: 0: author: Walsh,Brian James
1: author: Hallegatte,Stephane
count: Sri Lanka
docna: 0: docna: Socioeconomic Resilience in Sri Lanka:
            Natural Disaster Poverty and Wellbeing Impact Assessment
docty: Policy Research Working Paper
subtopic: Inequality,Social Protections & Assistance,Disaster Management,Hazard Risk Management,Social Risk Management,Disability,Services & Transfers to Poor,Access of Poor to Social Services,Economic Assistance,Natural Disasters
teratopic: Social Protections and Labor,Urban Development,Social Development,Macroeconomics and Economic Growth,Poverty Reduction,Environment,Conflict and Development
abstracts: cdata!: Traditional risk assessments use asset
            losses as the main metric to measure the severity of a
            disaster. This paper proposes an expanded risk assessment
            based on a framework that adds socioeconomic resilience and
            uses wellbeing losses as the main measure of disaster
            severity. Using an agent-based model that represents
            explicitly the recovery and reconstruction process at the
            household level, this risk assessment provides new insights
            into disaster risks in Sri Lanka. The analysis indicates
            that regular flooding events can move tens of thousands of
            Sri Lankans into transient poverty at once, hindering the
            country's recent progress on poverty eradication and
            shared prosperity. As metrics of disaster impacts, poverty
            incidence and well-being losses facilitate quantification of
            the benefits of interventions like rapid post-disaster
            support and adaptive social protection systems. Such
            investments efficiently reduce wellbeing losses by making
            exposed and vulnerable populations more resilient.
            Nationally and on average, the bottom income quintile
            suffers only 7 percent of the total asset losses but 32
            percent of the total wellbeing losses. Average annual
            wellbeing losses due to fluvial flooding in Sri Lanka are
            estimated at US$119 million per year, more than double the
            asset losses of US$78 million. Asset losses are reported to
            be highly concentrated in Colombo district, and wellbeing
            losses are more widely distributed throughout the country.
            Finally, the paper applies the socioeconomic resilience
            framework to a cost-benefit analysis of prospective adaptive
            social protection systems, based on enrollment in Samurdhi,
            the main social support system in Sri Lanka.
geo_reg_key: 517198,517191,517212
repnme: repnme: Socioeconomic Resilience in Sri Lanka:
            Natural Disaster Poverty and Wellbeing Impact Assessment
display_title: Socioeconomic Resilience in Sri
            Lanka: Natural Disaster Poverty and Wellbeing Impact Assessment
listing_relative_url: /research/2019/09/31409196/socioeconomic-resilience-sri-lanka-natural-disaster-poverty-wellbeing-impact-assessment
topicv3: Disaster Risk Management m1326682 820,Poverty m1326687 695,Social Protection and Labor m1327194 697,Cash Transfers m1327437 1608,Natural Disaster m1503325 3138
publishtoextweb_dt: 2019-09-16T00:00:00Z
historic_topic: Social Protections and Labor,Urban Development,Social Development,Macroeconomics and Economic Growth,Poverty Reduction,Environment,Conflict and Development
no_of_pages: 63
author_keys: 0: author_key: 1529077
1: author_key: 1103816
model_extractions: Policy Research Working Paper 9015 Socioeconomic Resilience in Sri Lanka Natural Disaster Poverty and Wellbeing Impact Assessment _Brian Walsh Stephane Hallegatte_ Climate Change Group September 2019
Policy Research Working Paper 9015

# **Abstract**

Traditional risk assessments use asset losses as the main metric to measure the severity of a disaster. This paper proposes an expanded risk assessment based on a framework that adds socioeconomic resilience and uses wellbeing losses as the main measure of disaster severity. Using an agent-based model that represents explicitly the recovery and reconstruction process at the household level, this risk assessment provides new insights into disaster risks in Sri Lanka. The analysis indicates that regular flooding events can move tens of thousands of Sri Lankans into transient poverty at once, hindering the country’s recent progress on poverty eradication and shared prosperity. As metrics of disaster impacts, poverty incidence and well-being losses facilitate quantification of the benefits of interventions like rapid post-disaster support and adaptive social protection systems. Such investments efficiently reduce wellbeing losses by making exposed and vulnerable populations more resilient. Nationally and on average, the bottom income quintile suffers only 7 percent of the total asset losses but 32 percent of the total wellbeing losses. Average annual wellbeing losses due to fluvial flooding in Sri Lanka are estimated at US$119 million per year, more than double the asset losses of US$78 million. Asset losses are reported to be highly concentrated in Colombo district, and wellbeing losses are more widely distributed throughout the country. Finally, the paper applies the socioeconomic resilience framework to a cost-benefit analysis of prospective adaptive social protection systems, based on enrollment in Samurdhi, the main social support system in Sri Lanka.

This paper is a product of the Climate Change Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bwalsh1@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
# S O C I O E C O N O M I C

# R E S I L I E N C E I N S R I L A N K A

**Natural disaster poverty & wellbeing impact assessment**

brian walsh & stéphane hallegatte Keywords: natural risks, resilience, risk assessment, welfare, Sri Lanka, social protection, cost benefit analysis JEL: D15, D30, D63, D78, Q54, R11
Contents 2

# contents

|1|Intr|oduct|ion|5|
|---|---|---|---|---|
|2|Sri|Lanka|country risk profle|9|
||2.1|Risk|to assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|9|
||2.2|Inco|me and consumption poverty<br>. . . . . . . . . . . . . . . . . . . . . .|12|
||2.3|Risk|to wellbeing at the national level . . . . . . . . . . . . . . . . . . . .|17|
|3|Soc|ioecon|omic resilience to disasters|18|
||3.1|Dist|rict-level risk summary<br>. . . . . . . . . . . . . . . . . . . . . . . . . .|21|
||3.2|Poor|est quintile risk summary<br>. . . . . . . . . . . . . . . . . . . . . . . .|23|
|4|Ad|aptive|social protection|25|
||4.1|Prox|y means test (PMT) . . . . . . . . . . . . . . . . . . . . . . . . . . . .|26|
||4.2|Case|study: 50-year food in Rathnapura<br>. . . . . . . . . . . . . . . . . .|27|
||4.3|Post|-disaster support to Samurdhi enrollees<br>. . . . . . . . . . . . . . . .|29|
||4.4|Post|-disaster support to PMT-qualifed households<br>. . . . . . . . . . . .|32|
||4.5|Nati|onwide cost-beneft analysis of Adaptive Social Protection<br>. . . . .|32|
|5|Con|clusio|ns|33|
|6|Tec|hnical|Appendix — Methodology and model description|37|
|li<br>Fig|st <br>ure|of <br>1|figures<br>Traditional DRM heuristic . . . . . . . . . . . . . . . . . . . . . .|8|
|Fig|ure|2|District-level asset risk in Sri Lanka.<br>. . . . . . . . . . . . . . . .|11|
|Fig|ure|3|Impact of a disaster on per capita income and consumption<br>. .|13|
|Fig|ure|4|Time to recover 90% of assets destroyed in 100-year precipita-<br>tion food. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|16|
|Fig|ure|5|Socioeconomic resilience to precipitation fooding in Sri Lanka .|19|
|Fig|ure|6|District-level wellbeing risk in Sri Lanka . . . . . . . . . . . . . .|22|
|Fig|ure|7|Asset and wellbeing losses in Rathnapura food<br>. . . . . . . . .|27|
|Fig|ure|8|Asset and wellbeing losses, before and after Samurdhi scaleup .|28|
List of Tables 3

|Figure9|Cost-beneft analysis of Samurdhi scale-up<br>. . . . . . . . . . . .|29|
|---|---|---|
|Figure10|Cost-beneft analysis of scale-up versus scale-out . . . . . . . . .|30|
|Figure11|Cost-beneft analysis of post-disaster support scale-out using||
||PMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|31|
|Figure12|Avoided wellbeing losses for various ASP programs . . . . . . .|33|
|Figure13|Household asset vulnerability (vh)<br>. . . . . . . . . . . . . . . . .|47|
|Figure14|Household asset losses and reconstruction . . . . . . . . . . . . .|48|
|Figure15|Household consumption losses during reconstruction . . . . . .|52|
|Figure16<br>list of t|Household reconstruction time (τh) . . . . . . . . . . . . . . . . .<br>ables|56|
|Table1|Asset risk from precipitation fooding, by district . . . . . . . . .|10|
|Table2|Risk to assets, socioeconomic resilience, and risk to wellbeing .|21|
|Table3|Risk to assets, socioeconomic resilience, and risk to wellbeing<br>for the poorest quintile . . . . . . . . . . . . . . . . . . . . . . . .|23|
|Table4|Per capita risk to assets and wellbeing for the poorest quintile .|24|
|Table5|Total household expenditures, by district, as reported by the<br>2016HIES.<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|42|
|Table6|Asset vulnerability categories . . . . . . . . . . . . . . . . . . . .|46|

# abstract

Traditional risk assessments use asset losses as the main metric to measure the severity of a disaster. This paper proposes an expanded risk assessment based on a framework that adds socioeconomic resilience and uses losses as the main mea- wellbeing sure of disaster severity. Using an agent-based model that represents explicitly the recovery and reconstruction process at the household level, this risk assessment provides new insights into disaster risks in Sri Lanka. The analysis indicates that regular
List of Tables 4 flooding events can move tens of thousands of Sri Lankans into transient poverty at once, hindering the country’s recent progress on poverty eradication and shared prosperity. As metrics of disaster impacts, poverty incidence and well-being losses facilitate quantification of the benefits of interventions like rapid post-disaster support and adaptive social protection systems. Such investments efficiently reduce wellbeing losses by making exposed and vulnerable populations more resilient. Nationally and on average, the bottom income quintile suffers only 7 percent of the total asset losses but 32 percent of the total wellbeing losses. Average annual wellbeing losses due to fluvial flooding in Sri Lanka are estimated at US$119 million per year, more than double the asset losses of US$78 million. Asset losses are reported to be highly concentrated in Colombo district, and wellbeing losses are more widely distributed throughout the country. Finally, the paper applies the socioeconomic resilience framework to a cost-benefit analysis of prospective adaptive social protection systems, based on enrollment in Samurdhi, the main social support system in Sri Lanka.
introduction 5

# 1 introduction

Sri Lanka has enjoyed rapid progress on poverty reduction and shared prosperity since the cessation of the decades-long conflict in 2009. In the last 10 years, GDP per capita has doubled from US$2,000 to US$4,000 (current US), and the national poverty incidence has been halved, from 9% to 4%, as measured by the national household income & expenditures survey. Despite this progress, the country scored modestly on several indicators related to disaster risk management in the 2015 Country Policy and Institutional Assessment, including quality of public administration; fiscal policy and macroeconomic management; social protection; and transparency, accountability, and corruption in the public sector.

Given this context, effective disaster risk management (DRM) strategies are essential to the continuation of and in Sri Lanka. Parts of the peace prosperity country are affected destructive monsoon regularly by floods, particularly during regional seasons. In 2017, for example, high winds, severe flooding, and landslides struck the southwestern districts of Sri Lanka–most directly, Galle, Kalutara, Matara, and Rathnapura–causing approximately 50-60 billion LKR (333-400 million USD) in damages, as estimated in the Ministry of Finance (2017) Annual Report. According to the World Bank post disaster needs assessment, the government disbursed US$6.6 million in emergency relief, and total recovery needs were later estimated at nearly US$800 million. Although the 2017 floods were exceptional, 2016 also saw unusually destructive and such events could become more in the future as a flooding, frequent result of climate change.

Asset losses are used to connote disasters’ but routinely scope, they provide only a partial measure of the total cost of any event. Other loss dimensions include lost wages and other income, insurance liabilities, days of school and doctor visits skipped, and meals foregone. For example: imagine that two identical, neighboring households experience the same property damage from a flood. If only one of the households is insured, that one will be compensated for its losses, while the other one might relocate, pull its children out of school, or reduce its consumption below the poverty line in order to recover. There are many different types of disaster costs,
introduction 6 and their magnitude, distribution, and urgency depends on the socioeconomic status of affected households.

Asset losses measure a dimension of disaster costs that accrues mostly to the wealthy. By definition, wealthy individuals have the most assets to lose. Therefore, asset losses often define–and accurately summarize–their experience of a shock. Conversely, the poor definitionally have few assets to lose, so damage to their collective property is a small fraction of aggregate asset losses. However, the poor also lack the resources and instruments to smooth income shocks while maintaining their consumption, or to recover their asset stock. Therefore, their experience of shocks is better described by other loss metrics. After most shocks, the poor are more likely than the wealthy to forego consumption of food, health, or education, and to take longer to recover.

Disaster risk management strategies that seek to maximize impact in terms of avoided asset losses will almost inevitably favor central business districts and other areas with high concentrations of valuable assets, leaving the poor to resort to humanitarian assistance when disasters inevitably occur. By highlighting these areas, asset losses disincentivize investments in slums and informal settlements, even when small investments could significantly reduce disease transmission, absenteeism from work and school, transportation costs, lost wages, or other types of disaster costs.

Finally, asset losses obscure the relationship between disasters and development in Sri Lanka. The long-term impacts of disasters on communities’ wellbeing and prospects depends not only on direct or immediate impacts (asset losses), but also on the duration of the recovery and reconstruction period and on the tools affected populations have to cope with and recover from the shock, such as social transfers, formal and informal post-disaster support, savings, insurance, and access to credit. Households that lack access to these tools will struggle to cope with shocks, and some could fall into chronic poverty as a result.

To provide a more comprehensive measure of disaster impacts, the _U_ nbreakable report introduced the concept of wellbeing losses. Wellbeing losses incorporate people’s socio-economic resilience, including (1) their ability to maintain their consumption for the duration of their recovery, (2) their ability to save or borrow to rebuild their asset
introduction 7 stock, and (3) the decreasing returns in consumption–that is, the fact that people who live on $2 per day are more affected by a $1 loss than are richer individuals.

The analysis presented in this paper applies this approach to floods in Sri Lanka. It combines flood maps from a global model developed by SSBN (now Fathom) with the Sri Lanka Household Income & Expenditures Survey (HIES) to model flood impacts at the household level, using an agent-based model to represent the recovery dynamics [1]. The full methodology and detailed model description is provided in the technical appendix to this document, and in [2].

The analysis provides information to better target DRM interventions both spatially (where should we act?) and sectorally (which type of intervention is the most efficient?). Compared with previous work in Sri Lanka, the analysis innovates by measuring disaster risk with several metrics that complement asset losses (or the cost to repair or replace damaged buildings, infrastructure, productive equipment, etc.). These metrics include (1) traditional asset losses, which highlight the experience of the wealthy; (2) poverty-related measures such as poverty headcount, which highlight the experience of the poor and near-poor; (3) wellbeing losses, which provide a balanced estimate of the impact on poor and rich households; and (4) socioeconomic resilience, an indicator that measures the ability of the population to cope with and recover from asset losses. This broad perspective is intended to complement traditional, more spatially-detailed risk assessments in Sri Lanka.

One major conclusion of this work is that alternative and new metrics of disaster impacts–including poverty headcount, poverty gap, and wellbeing losses–can be used to quantify the value of interventions currently outside the traditional riskmanagement toolbox. Asset-focused risk-management strategies focus primarily on protection infrastructure, such as dikes, and the position and condition of assets, for instance with land-use and codes. can plans building Wellbeing-focused strategies utilize a wider set of available measures, such as financial inclusion, private and public insurance, disaster-responsive social safety nets, macro-fiscal policies, and disaster preparedness and contingent planning. Even if they do not reduce asset losses, these types of measures can bolster communities’ socioeconomic resilience, or their to with and recover and reduce the capacity cope from asset losses when they occur, wellbeing impact of natural disasters.
|

<!-- Start of picture text --> ASSET LOSSES<br>WELL-BEING LOSSES<br>1. Hazard 2. Exposure 3. Vulnerability 4. Socioeconomic<br>resilience<br><!-- End of picture text -->
sri lanka country risk profile 9

# 2 sri lanka country risk profile

## 2.1 Risk to assets

Typically, risk assessments incorporate information on the _hazards_ (the natural occurrence of destructive events such as severe winds, surges, floods, and earthquakes); _exposure_ (the value of natural and built assets that might face a destructive event); and _vulnerability_ (the expected consequences to exposed assets when a destructive event occurs) of the targeted area. Together, these three dimensions describe average annual asset losses in the area of interest.

Table 1 on the following page displays expected annual asset losses to precipitation flooding, by district. These values, based on the SSBN model, are long-term averages of the or _total replacement cost of household income-generating assets_ damaged destroyed by precipitation flooding each year [1].<sup>1</sup> Figure 2 maps total asset risk, expressed at left in US$ and at right as a percentage of district-level aggregated household expenditures (AHE). According to this model, asset risk in Sri Lanka is highly concentrated in greater Colombo (over 50% of the total; left panel), but other districts throughout the country also face significant losses, when expressed in percentage of local AHE.<sup>2</sup> Significant differences across districts are explained by variations in the hazard, and of each of the Most asset losses exposure, vulnerability part country. notably, are heavily concentrated in greater Colombo and surrounding districts. This is to be expected, as it reflects the high concentration of valuable assets in and around the capital (elevated exposure). Differences in hazards are secondary but still important:

> 1 The total repair and replacement value of assets damaged or destroyed by floods in each district is a direct output of the SSBN/Fathom model [1], but this analysis is particularly interested in disaster impacts on the assets, consumption, and welfare of _households_ . One issue faced here is the difference between the Gross Domestic Income (GDI) derived from national accounts and the aggregate household expenditures (AHE) calculated from household surveys (in this case, HIES). As is well known, the latter tend to report lower expenditures than national accounts [3]. In this analysis, we work based on the AHE. Therefore, the expected losses in Table 1 have been reduced by a scale factor, equal to total household consumption over nominal regional productivity (cf. Tab. 5), and express average annual damage or destruction to assets used by Sri Lankan households to generate income, by district. Asset losses in Jaffna, Kilinochchi, and Matara are omitted due to their exclusion from available SSBN/Fathom flood maps.

> 2 SSBN is a global model subject to limited availability and variable quality data. Therefore, its precision and accuracy can be low in some places, and we would seek ideally to replace these inputs with highresolution local models.
sri lanka country risk profile 10

|**Ris**<br>**precip**<br>|**k to ass**<br>**itation**<br>|**ets from**<br>**fooding**<br>|
|---|---|---|
|**District**|mLKR|mUS$|
|Colombo<br>|6,040|40.2|
|Rathnapura|1,230|8.19|
|Kalutara<br>|1,120|7.43|
|Gampaha|1,040|6.93|
|Puttalam|498|3.32|
|Kurunegala|363|2.42|
|Polonnaruwa|315|2.10|
|Hambantota|233|1.55|
|Trincomalee|189|1.26|
|Kegalle|151|1.00|
|Galle|101|0.67|
|Batticaloa|89|0.59|
|Mannar<br>|79|0.53|
|Anuradhapura|71|0.47|
|Kandy|64|0.43|
|Ampara|46|0.30|
|Badulla|44|0.29|
|Matale|26|0.17|
|Vavuniya|7|0.04|
|Moneragala|6|0.04|
|Mullaitivu|4|0.03|
|Nuwara Eliya|0|<0.01|
|<br>Jaffna|–|–|
|Kilinochchi|–|–|
|Matara|–|–|
|Total|11,700|78.02|

**Table 1:** Expected annual asset losses (tabulated in millions of LKR and thousands of US$) from exposure to precipitation floods in Sri Lanka. (US$1 = LKR 150). Asset loss exceedance curves were not available from the Fathom model for Jaffna, Kilinochchi, or Matara.
|

<!-- Start of picture text --> 3 6 9 12 15<br>Asset losses [per person, per year]<br>National avg. = $3.60 per cap.<br><!-- End of picture text --> <!-- Start of picture text --> 16 24 32 40<br>Asset losses by district [mil. US$ per year]<br>National total = US$78.0 million<br><!-- End of picture text -->
sri lanka country risk profile 12 Clearly, a more disaggregated approach will be required. To do so, the rest of this analysis goes deeper in the distributional analysis, moving from the regional scale to the household level. In the next section, we merge regional asset loss data with household-level socioeconomic characteristics to examine disaster impacts on household income and consumption. This novel approach will allow us to develop estimates of the number of individuals pushed into transient poverty each year by disasters. Subsequently, we study how exposure to flooding affects households’ consumption and wellbeing and use these insights to assess new policy tools for managing the risk of the poor and vulnerable.

# 2.2 Income and consumption poverty

The disaggregation of asset losses to the household level allows us to move from expected asset losses to the secondary effects, including income and consumption losses, that must be expected when households are affected by shocks. From this perspective, we begin to see disaster losses not just in terms of economic costs, but as an obstacle to poverty reduction and other development imperatives.

The technical details of asset loss disaggregation to the household level are discussed in the technical appendix to this report. In this section, we present the main insights generated by the union of the SSBN model results with the household income & expenditures survey (HIES), focusing on how individual households’ socioeconomic characteristics can mitigate or magnify the impact of disasters.

# _Income poverty_

When disasters damage or destroy the assets on which individuals rely for their livelihood — including not only their own shop or field, but also somebody else’s factory — affected households face income losses. Some may receive extraordinary public assistance or additional remittances, which supplement their regular income while they rebuild, while others will be left to their own devices. Net income losses incorporate these socioeconomic characteristics, which influence households’ recovery pathways.
sri lanka country risk profile 13

# 50-year precipitation flood in Rathnapura

<!-- Start of picture text --> 120 Subsistence line<br>Increase of 7,500 (0.6% of regional pop.)<br> in income subsistence<br>100 Poverty line<br>Increase of 11,300 (1.0% of regional pop.)<br> in income poverty<br>80<br>60 Pre-disaster income<br>(FIES data)<br>Post-disaster income<br>(modeled)<br>40<br>20<br>0<br>0 250 500 750 1000 1250 1500 1750 2000<br>Income (USD per person, per year)<br>120 Subsistence line<br>Increase of 0 (0.0% of regional pop.)<br> in consumption subsistence<br>100 Poverty line<br>Increase of 11,000 (0.9% of regional pop.)<br> in consumption poverty<br>80<br>60 Pre-disaster consumption<br>(FIES data)<br>Post-disaster consumption<br>(modeled)<br>40<br>20<br>0<br>0 250 500 750 1000 1250 1500 1750 2000<br>Consumption (USD per person, per year)<br>Population (,000)<br>Population (,000)<br><!-- End of picture text -->

**Figure 3:** Per capita income (top) and consumption (bottom) in Rathnapura district, before and immediately after 50-year precipitation flooding, in US$. Income losses take into account the lost productivity of disaster-affected assets, while consumption losses additionally include reconstruction costs, post-disaster support, and savings.

Therefore, income losses describe better than asset losses the real impacts of disasters on Sri Lankans’ wellbeing and prospects.

To return to a historical example: damages from the 2017 floods totaled roughly US$400 million in the 5 most directly-affected districts (Galle, Hambantota, Kalutara, Matara, and Rathnapura). In Rathnapura, total damages were estimated at US$51.6 million, more than 6 times greater than long-term average annual losses in the district (US$8.2 million, cf. Table 1). These losses correspond roughly with the 50-year
sri lanka country risk profile 14 precipitation flood, meaning that there is a 2% chance of flooding damage in excess of this value in any given year.

The top panel of Figure 3 on the previous page illustrates the expected impact of a 2017-like flood event on individual incomes in Rathnapura district. The black outline indicates the regional income distribution as reported in HIES, while the red histogram illustrates the expected income distribution immediately following a 50year flood in the region. This distribution shows a mode around US$450 per person, with 10% of the district below the line. The bar on the per year, living poverty large right shows the number of people with incomes higher than US$2,000 per year; the poverty and subsistence lines are indicated by the dotted lines around US$300 and US$350 per year, respectively. Income losses resulting from this 2017-like event are expected to push some 11,300 individuals into income poverty in Rathnapura, and 5,700 below the subsistence income level, meaning they cannot meet basic needs.

At the national level and for the average year, we estimate that over 5,600 Sri Lankans face income poverty or subsistence for some amount of time every year due to precipitation flooding. However, large events can impoverish broad segments of the population when they occur. For example, the expected destruction from nationwide 50-year flooding could move nearly 34,000 individuals nationwide into transient income poverty or subsistence. Of these, the model indicates that just over a thousand will still be in poverty 10 years later. This suggests that disasters may have a long-term impact on the income and consumption of some Sri Lankan households.

# _Consumption poverty_

Income losses represent a complementary perspective to asset losses, and generate new insights into the costs of disasters in Sri Lanka. However, as a metric of disaster impacts, income losses may yet underestimate the link between poverty and disasters. First, they do not account for the reconstruction costs that households incur in rebuilding their assets after a disaster. Second, while wealthy households may have access to considerable savings, credit, remittances, and insurance to finance this recovery, these resources are often available only informally, if at all, to poor households [4, 5]. This is important, because access to these tools can mean the difference
sri lanka country risk profile 15 between a speedy recovery and a long-term poverty trap [6, 7, 8]. These costs and resources all impact households’ ability to maintain consumption after a shock, or their _consumption losses_ .

Over the long term average, and inclusive of all districts for which flooding data are available, nearly 6, 000 Sri Lankans face transient consumption poverty due to precipitation flooding every year. This is equivalent to 0.5% of national poverty incidence, with significant variation across districts. In some districts, the number of individuals pushed into subsistence represents a significant fraction of the HIES incidence of subsistence. This result is most notable in greater Colombo, reaching 3.5% of the chronic poverty incidence there. These results suggest that precipitation flooding events may be major drivers of extreme poverty in certain districts of Sri Lanka. In these areas, disaster risk management strategies can be a highly efficient tool to decrease poverty and subsistence incidence, either by reducing asset losses, or by enhancing households’ abilities to recover from disasters.<sup>3</sup>

# Socioeconomic characteristics and disaster recovery dynamics

Asset losses (and the foregoing income poverty analysis) describe individual households’ status in the instant after a hazard occurred, and provide useful insights into how governments and other first responders should target humanitarian relief. However, another important question is whether disaster-affected households become mired in poverty or achieve a speedy recovery. In contrast to asset losses, income and consumption losses can inform policy makers on how to manage the duration of disaster impacts.

Expanding our analysis of the 2017-like floods, Figure 4 on the following page maps the expected time to recover 90% of the assets destroyed when a 50-year precipitation strikes each district. The indicates that coastal districts in the northern flood map half of the country take the longest to recover. When disasters strike communities in these districts, many of which are national minorities, recovery is expected to proceed

> 3 Consumption losses are net of household savings, which can be used to smooth consumption after a shock, or to finance each household’s recovery from the initial event. In the absence of reliable data on household savings, we assume that each household has "precautionary" savings–i.e., liquid savings available for consumption– equivalent to one month’s worth of wages.
|

<!-- Start of picture text --> 4 5 6 7<br>Time to reconstruct 90% of assets destroyed<br>by 50-year precipitation flood [years]<br>National avg. = 3.9 years<br><!-- End of picture text -->
sri lanka country risk profile 17

# 2.3 Risk to wellbeing at the national level

The foregoing has shown that, even if disaster-affected households suffer identical asset losses, their consumption losses and recovery time will vary according to their socioeconomic status and the resources available to them (for instance because insurance, savings, remittances, and public support help some households to smooth the consumption shocks). Going further, $1 in consumption losses can have very different consequences for individual households, depending on their income. In particular, while rich households will be able to down their or cut on spend savings luxury consumption, poorer households will often have to cut on basic needs and essential consumption like food, health, or education, threatening their health, human capital, and long-term prospects.

Disaster risk strategies and budgets should account for these differences, and _wellbeing losses_ do precisely this. While $1 in asset or consumption losses affects a poor individual more than a rich one, wellbeing losses are defined such that a $1 wellbeing loss affects a rich and a poor individual equally. Wellbeing losses are calculated from consumption losses using a classical “welfare function.” This operation translates into wellbeing the value of a household’s consumption at each point in its unique recovery, with decreasing returns to represent the fact that increasing consumption by $1 increases more the wellbeing of a poor individual (compared with a rich person).<sup>4</sup> The difference in the wellbeing generated by $1 of consumption is a simple proxy for the continuum from survival consumption (the very first units of consumption that have the largest impact on wellbeing) to luxury consumption (which enhances wellbeing less and less).

Wellbeing losses integrate each household’s consumption losses over the duration of its recovery, and give more weight to the consumption losses experienced by poor people than to the losses experienced by richer people. In this way, wellbeing losses account for socioeconomic differences among households, and correct for the prowealthy bias inherent in asset losses without relying on binary thresholds like the

> 4 Here, the unit of analysis is the household. It would be useful to do it at the individual level, to uncover intra-household distributional effects, linked to gender or age. In the absence of withinhousehold data, we make the strong assumption that pre-disaster consumption and disaster losses are distributed equally per capita within each household.
socioeconomic resilience to disasters 18 poverty line. As a metric, they capture more fully the costs of disasters and the benefits of prospective DRM investments than do asset losses. Therefore, wellbeinginformed strategies are not merely more equitable, but more cost-effective than assetinformed strategies.

As discussed in Section 2.1 on page 9, we estimate the replacement value of household income-generating assets damaged or destroyed by precipitation flooding in Sri Lanka at US$78 million (cf. Table 2 on page 21), equivalent to 0.35% of AHE. Wellbeing losses are much higher, at US$122 million (0.53% of AHE) per year. In other words, the real impact of flooding in Sri Lanka is equivalent to a decrease in national consumption by US$122 million.<sup>5</sup>

# 3 socioeconomic resilience to disasters

The analysis has moved from asset to income, consumption, and wellbeing losses, incorporating additional relevant household-level socioeconomic characteristics at each stage. To summarize these developments, we return to the traditional risk-assessment framework (cf. 1 on page 8). The three traditional components (i.e., hazard, exposure, and vulnerability) predict asset losses, but not the full impacts on people’s wellbeing. In response to this theoretical and practical shortcoming, we have effectively added a fourth component, called _socioeconomic resilience_ , which we now define as the capacity of affected to with and recover from these losses and as populations cope quantify the ratio of expected asset losses to wellbeing losses.

At the national level, precipitation flooding causes Sri Lankan households to have US$78 million per year in asset losses and US$119 million per year in wellbeing losses. In other words, the average annual impact of flooding on wellbeing in Sri Lanka is 53% greater than is represented by asset losses. Socioeconomic resilience is the ratio of these two metrics, and, therefore, the socioeconomic resilience of Sri Lanka to disasters is 66% ( 119<sup><u>78</u>=0.66).</sup> As a matter of macro-fiscal policy, the government

> 5 More precisely, the impact on wellbeing is equivalent to a US$122 million decrease in consumption that would be optimally shared across households in the country and across time. Or it is equivalent to a US$122 million decrease in consumption that would be equally shared across households in the country, if all households were identical (i.e. if all inequality had disappeared).
|

<!-- Start of picture text --> &<br>w<br>°<br>20% 40% 60% 80% 100%<br>Socioeconomic resilience<br>National avg. = 65%<br><!-- End of picture text -->
socioeconomic resilience to disasters 20 Although this result already deepens our understanding of the distributional impacts of flooding in Sri Lanka, the national average of socioeconomic resilience belies significant regional, sub-regional, and socioeconomic variation in the capacity of households to with and recover from disasters. resolution cope Increasing spatial just one step, Figure 5 on the preceding page maps socioeconomic resilience to disasters at the regional level. Among all regions, greater Colombo enjoys the highest resilience (105%). This is due not just to its overall wealth and poverty rate of just 1%, but also to its high degree of financial inclusion and social protection coverage (30% higher than the national average). As a result of these resilience-building characteristics, wellbeing losses to disasters in Colombo district are slightly lower than asset losses. Of course, among the 2.3 million residents of the capital, there are sigin disaster and this should not be construed as nificant disparities impacts, analysis neglecting or minimizing the challenges facing the urban poor. However, the "average” resident of Colombo is better prepared to cope with and rebuild from disasters than the "average" Sri Lankan outside the capital.

At the other end of development in Sri Lanka, socioeconomic resilience to disasters is less than or equal to 30% in Trincomalee, Batticaloa, Batticaloa, and Mullaitivu. This indicates that the average resident of these districts struggles to cope with and recover from shocks when they occur. This struggle results in a lower likelihood of recovery in the long term, and is due to a complex of factors (e.g., poverty incidence, diversity of income sources, financial inclusion, and social protection enrollment), the net effects of which, wellbeing losses are designed to measure. On average, residents of these districts suffer wellbeing losses three times greater than their asset losses when floods occur. Because of these dynamics, wellbeing losses are less concentrated in greater Colombo than are asset losses: while flooding in greater Colombo causes on average 52% of nationwide asset losses, it causes 32% of nationwide wellbeing losses. This suggests that well-designed DRM interventions have a large potential to help vulnerable communities (which are almost always fragile in other dimensions, as well) cope when shocks inevitably occur, and particularly outside the capital.
socioeconomic resilience to disasters 21

||**So**<br>|**cioeconomic**<br>||
|---|---|---|---|
|**District**|**Asset risk**<br>[,000US$/year]|**resilience**<br>[%]|**Well-being risk**<br>[,000US$/year]|
|Colombo|40,235|105|38,260|
|Rathnapura|8,194|32|25,487|
|Kalutara|7,434|62|12,050|
|Gampaha|6,931|73|9,449|
|Puttalam|3,321|46|7,252|
|Trincomalee<br>|1,257|29|4,315|
|Kurunegala|2,418|56|4,315|
|Polonnaruwa|2,099|49|4,312|
|Hambantota|1,552|61|2,540|
|Kegalle|1,006|41|2,442|
|Batticaloa|592|26|2,240|
|Mannar|528|38|1,396|
|Galle|673|53|1,280|
|Badulla|293|31|948|
|Kandy|430|49|880|
|Anuradhapura|473|55|860|
|Ampara|304|42|722|
|Matale|170|44|384|
|Moneragala|40|30|136|
|Mullaitivu|27|26|104|
|Vavuniya|44|54|82|
|Nuwara Eliya|1|34|4|
|Matara|–|40|–|
|Jaffna|–|31|–|
|Kilinochchi|–|18|–|
|Total|78,021|65|119,462|

**Table 2:** Risk to assets, risk to wellbeing, and socioeconomic resilience for the entire population of each district of Sri Lanka. Note: though our hazard dataset does not report asset loss exceedance curves for Matara, Jaffna, or Kilinochchi, still we are able to assess their respective levels of socioeconomic resilience using household survey data, and the fact that resilience is the ratio of asset to wellbeing losses.

# 3.1 District-level risk summary

The annual risk to assets, risk to wellbeing, and socioeconomic resilience for each district of Sri Lanka are summarized in Table 2. According to the SSBN model, annual asset losses to precipitation flooding are highly concentrated in Colombo district, totaling over US$40 million per year (1.0% of AHE). Rathnapura district suffers the second highest losses, valued at US$8.2 million per year (0.9% of AHE).
|

<!-- Start of picture text --> 4 8 12 16 20<br>Wellbeing losses [US$ per person, per year]<br>National avg. = $5.40 per cap.<br><!-- End of picture text --> <!-- Start of picture text --> 16 24 32<br>Wellbeing losses by district [mil. US$ per year]<br>National total = US$119.5 million<br><!-- End of picture text -->
socioeconomic resilience to disasters 23

||**Annual**<br>**So**<br>**asset risk**|**cioeconomic**<br>**resilience **|**Ann**<br> **wellbei**|**ual**<br>**ng risk**|
|---|---|---|---|---|
|**District**<br>|[,000US$] [% of total]|[%]|[,000US$]|[% total]|
|Colombo|2,891<br>7|25|11,574|31|
|Rathnapura|701<br>9|8|8,587|34|
|Kalutara|451<br>6|12|3,627|31|
|Gampaha|519<br>7|19|2,751|30|
|Puttalam|279<br>8|13|2,211|31|
|Trincomalee|97<br>8|6|1,568|37|
|Kurunegala|176<br>7|12|1,430|34|
|Polonnaruwa|162<br>8|13|1,231|29|
|Kegalle|81<br>8|10|846|35|
|Batticaloa|50<br>8|7|767|35|
|Hambantota|118<br>8|16|722|29|
|Galle|50<br>7|13|396|31|
|Mannar|48<br>9|16|304|22|
|Kandy|29<br>7|10|303|35|
|Badulla|22<br>8|7|303|32|
|Anuradhapura|36<br>8|14|258|31|
|Ampara|28<br>9|14|200|28|
|Matale|13<br>7|10|128|34|
|Moneragala|4<br>10|8|49|36|
|Mullaitivu|2<br>8|7|32|31|
|Vavuniya|3<br>8|15|23|29|
|Nuwara Eliya|0<br>9|9|1|34|
|Matara|–<br>–|9|–|–|
|Jaffna|–<br>–|8|–|–|
|Kilinochchi|–<br>–|5|–|–|
|Total|5,761<br>7|15|37,313|32|

**Table 3:** Risk to assets, socioeconomic resilience, and risk to wellbeing for the poorest 20% (by expenditures) of the population of each district.

# 3.2 Poorest quintile risk summary

Because our results are based on information at the household level, we are able to assess asset and wellbeing losses not just at the district level, but also for different income groups.<sup>7</sup> Table 3 lists annual asset and wellbeing losses for the poorest quintile in each district. Across all districts, the asset losses of the poorest 20% are US$5.7 million, or just 7% of total asset losses. On the other hand, the wellbeing losses of the

> 7 It is also possible to look at different subgroups in the country or at the district scale (e.g., per occupation, head of household gender, household size, ethnic background or religion, social transfer enrollees), within the limits of the representativeness of the household survey.
socioeconomic resilience to disasters 24

|**District**|**Q1 population**<br>[thousands]|**Per ca**<br>Asset<br> [US$ per|**pita losses**<br>Well-being<br>cap., per year]|
|---|---|---|---|
|national result|4,367|1.42|9.47|
|Rathnapura|236|2.96|37.07|
|Colombo|473|6.08|24.80|
|Trincomalee|82|1.17|19.18|
|Mannar|20|2.32|14.89|
|Kalutara|259|1.74|14.44|
|Polonnaruwa|90|1.83|14.19|
|Puttalam|165|1.68|14.14|
|Batticaloa|114|0.45|7.25|
|Gampaha|479|1.10|6.02|
|Hambantota|133|0.89|5.61|
|Kegalle|185|0.44|4.73|
|Kurunegala|350|0.51|4.26|
|Galle|231|0.22|1.81|
|Badulla|181|0.12|1.68|
|Mullaitivu|18|0.11|1.67|
|Anuradhapura|178|0.20|1.47|
|Ampara|141|0.19|1.39|
|Matale|111|0.11|1.19|
|Kandy|303|0.10|1.03|
|Vavuniya|37|0.09|0.65|
|Moneragala|100|0.04|0.51|
|Nuwara Eliya|154|0.00|0.01|
|Kilinochchi|24|–|–|
|Jaffna|124|–|–|
|Matara|179|–|–|

**Table 4:** Annual _per capita_ asset and wellbeing losses for the poorest 20% (by expenditures) of the population of each district.

poorest quintile give a better sense of their experience of disasters: valued at US$37.3 million per year, the wellbeing losses of the poorest quintile are 32% of total wellbeing losses. It means that on average, and in wellbeing terms, the poorest quintile suffers from losses that are twice as large as the average individual loss in the country.

To this point in the discussion of results, it is likely that the district-level averages underrepresent the urban poor, who face higher costs of living in search of economic opportunity, and who may not only _not_ receive support from their home communities, but are expected to send remittances home. Indeed, when we limit our analysis to just the poorest quintile in each district, the range of socioeconomic resilience
adaptive social protection 25 values narrows significantly. For example, recall that the entire population of greater Colombo enjoys on average a resilience of 105% (cf. Table 2). However, the poorest 20% of residents of the capital have a resilience of only 25% (cf. Table 3). On average, the poorest residents of Colombo district lose just over US$6 (LKR 900) per person, per year to precipitation flooding (7% of total asset losses), but this equates to US$25 (LKR 3,800) per person, per year in wellbeing losses, or 30% of total wellbeing losses (cf. Table 4 on the previous page).

In other words, the aggregate wealth of the capital district does not imply that its poorest residents are well-protected from or resilient to asset losses. To the contrary: the low resilience of the poor to asset losses, combined with the large contribution of disasters to poverty incidence in Colombo, suggests that interventions to reduce asset losses and build resilience of the could be effective for disaster poor very reducing exposure and poverty incidence in Colombo and other urban areas.

# 4 adaptive social protection

In addition to these diagnostics, the extension of the traditional hazard-exposureframework to include socioeconomic resilience has the of ex- vulnerability benefit panding the disaster risk management "toolbox." Traditional risk-management strategies seek to mitigate hazards as well as the exposure and vulnerability of assets. Other tools (e.g., post-disaster support, financial inclusion, private and public insurance, and sovereign contingent credit) are not easily included in risk assessments because they do not affect asset losses. In a wellbeing-informed framework, however, the benefits of these interventions become obvious and quantifiable: to the degree that they allow households to maintain a healthy degree of consumption while they rebuild, these interventions increase socioeconomic resilience and reduce wellbeing losses to disasters.

Here, we use avoided wellbeing losses to assess the benefits of post-disaster support in general, and in terms of various reforms to _Samurdhi_ , the most significant social protection system in Sri Lanka. The Samurdhi program was established by
adaptive social protection 26 the Government of Sri Lanka in 1995, with the main goal of reducing poverty in the country.

# 4.1 Proxy means test (PMT)

A proxy means test (PMT) is an index of observable and verifiable household characteristics, such as the quality of the household’s dwelling, ownership of durable goods, demographic structure, and the education and employment status of adult household members [9]. PMT scores provide a proxy for household incomes, and are commonly used to determine or optimize eligibility for social safety net programs in situations Since the PMT essentially ranks households by welfare level, the same formula can be used for targeting a range of different welfare programs, adopting different eligibility cutoff scores for each program and possibly adding other criteria as well. a common for various based on a PMT Having targeting approach programs, single score, ensures consistency of targeting across programs, minimizes overlaps, and provides a basis for future harmonization of programs.

Samurdhi program benefits are currently distributed on the basis of self-reported income. However, the Sri Lankan government is in the process of updating this system, and one component of this reform is to identify beneficiaries using a standard vector of household attributes in place of self-reported incomes. This shift is expected to significantly improve the targeting of Samurdhi benefits to the poor in Sri Lanka.

In this analysis, we construct four illustrative disaster-responsive adaptive social protection (ASP) systems. Like Samurdhi, these ASP systems distribute cash transfers to designated households on a one-time or monthly basis. Enrollment and disbursements for each ASP alternative are based disaster-affected status, existing Samurdhi enrollment, and PMT scores, as described in the following sections. In some cases, we use a PMT score of 887 as a cutoff for benefits. This value corresponds to the 25th percentile of the “true" per capita consumption distribution in Sri Lanka, as measured by the detailed HIES survey [10].
adaptive social protection 27

# 4.2 Case study: 50-year flood in Rathnapura

When the 50-year precipitation flood event strikes Rathnapura, it is expected to cause US$43 million in asset losses and US$131 million in wellbeing losses. In Figure 7, cumulative asset and wellbeing losses are plotted against PMT scores (i.e. the y-axis shows total asset and wellbeing losses for all households with PMT less than the value on the x-axis). The annotations inside the figure describe the asset and welllosses for each income losses are being quintile, independently. Notably, wellbeing 8.4 times greater than asset losses for the poorest quintile in Rathnapura (socioeconomic resilience =<sup><u>4.7</u></sup> 38.0<sup>= 12%), while wellbeing losses are less than asset losses for the</sup> wealthiest quintile (socioeconomic resilience =<sup><u>16</u></sup> 15<sup><u>.</u></sup> .<sup><u>1</u></sup> 0<sup>=107%).Thisindicatesthatthe</sup> wealthiest residents of Rathnapura are as resilient to shocks as the average resident of Colombo (cf. Table 2).

<!-- Start of picture text --> 50-year precipitation flood in Rathnapura<br>Total wellbeing losses = $130.8 million Wealthiest<br>quintile<br>Total asset losses = $42.9 million $15.0 mil.<br>$16.1 mil.<br>Fourth<br>$20.1 mil.<br>120 $8.9 mil.<br>Third<br>$25.3 mil.<br>100 $7.2 mil.<br>Second<br>80 $32.3 mil.<br>$6.1 mil.<br>60<br>Poorest<br>quintile<br>$38.0 mil.<br>40 $4.7 mil.<br>20<br>0<br>850 900 950 1000 1050 1100<br>Household income [PMT]<br>Cumulative losses [mil. US$]<br><!-- End of picture text -->

**Figure 7:** Expected household asset and wellbeing losses in a 50-year precipitation flooding event in Rathnapura. The y-axis shows cumulative expected losses for all affected households with PMT less than the value shown on the x-axis.
adaptive social protection 28 In Figure 8, the two leftmost sets of bars present again expected asset and wellbeing losses, by quintile, for this event. The figure shows that while the richest households are expected to lose more assets, the poorest households are expected to suffer the greatest wellbeing losses. The figure also shows how small, uniform, lump sum postdisaster support delivered with perfect targeting to all affected households (“uniform payout") could theoretically reduce wellbeing losses, especially for the poorest households. The first quintile sees its wellbeing losses reduced by around a third, while impact is small on the richest quintile.<sup>8</sup> In the next section, we will look in much greater detail at optimizing ASP response to a particular event–here, the 50-year precipitation flood in Rathnapura.

<!-- Start of picture text --> 1400 Poorest quintile<br>Second<br>Third<br>1200<br>Fourth<br>Wealthiest quintile<br>1000<br>800<br>600<br>400<br>200<br>0<br>Asset loss Wellbeing loss Net cash benefit of Wellbeing loss<br>uniform payout with uniform payout<br>Disaster losses (US$ per affected person)<br><!-- End of picture text -->

**Figure 8:** Cost-benefit analysis of uniform payout to all affected individuals in response to a 50-year precipitation flooding event in Rathnapura.

> 8 It is important to note that even if the amount of post-disaster support is equal to asset losses, it does not fully cancel wellbeing losses: indeed, post-disaster support maintains consumption, but consumption losses are larger than asset losses. This result is consistent with intuition: even if people are immediately given in cash the cost of rebuilding their houses and replacing their assets, they would still experience wellbeing losses during the reconstruction period, since assets and houses cannot be replaced instantaneously.
adaptive social protection 29 <!-- Start of picture text --> benefit 1 month-equivalent top-up to existing<br>cost = 2.57<br>Samurdhi enrollees in Rathnapura<br>3.0 Benefit after 50-year flood<br>3.0<br>2.5<br>2.6<br>2.18<br>2.0<br>1.5<br>2.69<br>Cost<br>1.2 1.33<br>1.0<br>0.84<br>0.5 0.49 0.48 3.12<br>0.15<br>0.0<br>Scaleup to Scaleup to Scaleup to Perfect<br>all Samurdhi all affected & all affected & targeting within<br>beneficiaries 66% of unaffected 33% of unaffected Samurdhi<br>Inclusion error<br>Cost of Samurdhi scaleup [mil. USD]<br><!-- End of picture text -->

**Figure 9:** Cost-benefit analysis of Samurdhi scaleup in response to a 50-year precipitation flooding event in Rathnapura, as a function of the inclusion error on post-disaster support.

# 4.3 Post-disaster support to Samurdhi enrollees

In practice, it is much easier to leverage existing social protection systems to deliver post disaster support, taking advantage of established beneficiary rolls and delivery mechanisms, than to implement new systems. In order to model a more realistic post-disaster response than the “uniform payout" presented above, we simulate a scaleup of Samurdhi benefits to households already enrolled in the program. In this simulation, households enrolled in Samurdhi _before the disaster_ receive a one-time top up, valued at one month of their normal receipts. This system takes advantage of the established assets and procedures of Samurdhi, on the assumption that the program is already well-targeted for delivering aid to the poor. The cost of the program is
adaptive social protection 30 distributed to all Sri Lankan households via a flat tax on income, and we assume that there is no transaction cost to run the program.

<!-- Start of picture text --> 1 month-equivalent Samurdhi benefit<br>payment to Rathnapura Benefit: 0.82M cost = 4.17<br>0.8 after 50-year flood<br>Perfect targeting<br>0.6<br>benefit<br>cost = 3.12 Benefit: 0.48M<br>0.4<br>0.2 Cost: 0.2M<br>Cost: 0.15M<br>0.0<br>Scaleup to affected Scaleout to affected<br>Samurdhi enrollees using PMT<br>(3.6% of district (4.6% of district<br>are beneficiaries) are beneficiaries)<br>Cost, benefit of post-disaster support [mil. USD]<br><!-- End of picture text -->

**Figure 10:** Cost-benefit analysis of scale-up using Samurdhi enrollment, compared to scaleout using PMT (upper threshold = 887) in response to a 50-year precipitation flooding event in Rathnapura. Perfect targeting is assumed in both cases.

In response to the 50-year flood in Rathnapura, the Samurdhi scaleup disburses US$1.2 million to US$150,000 in post-disaster support, depending on how accurately disaster-affected households can be identified among all Samurdhi enrollees in the district (cf. Figure 9). At the high end of this cost range, a payout to every household in the district that is enrolled in Samurdhi (i.e., without any targeting of affected households) reduces wellbeing losses by US$3.2 million, delivering a benefit-to-cost ratio (BCR) of 2.67 on the investment in post-disaster support.<sup>9</sup> At the other end of the estimated range, assuming it would be possible to deliver aid strictly to af-

> 9 Note that the benefits calculated here include the wellbeing benefits of non-affected people who receive post-disaster support; the assessment is not limited to affected populations.
adaptive social protection 31 fected households without any inclusion or exclusion error, we calculate US$500,000 in avoided wellbeing losses. This idealized scenario achieves a BCR ratio of 3.22.<sup>10</sup> In Figure 10, we compare the costs and benefits of scaling Samurdhi benefits up (top up to existing beneficiaries), versus out (disbursements based on PMT scores). For the “out" scenario, poor households are ranked according to their PMT score, and all households with PMT less than 887 (including those already enrolled in Samurdhi) receive the average per capita, monthly Samurdhi payout for their district[10]. We assume perfect targeting for both “up" and “out." Our analysis indicates that, while the scaleout is more expensive at US$1.5 million, due to the high PMT threshold for support, it achieves a better BCR than the more straightforward scaleup of benefits to households already enrolled in Samurdhi. This result suggests that targeting of Samurdhi benefits can be improved in Rathnapura district.

<!-- Start of picture text --> 50-year precipitation flood in Rathnapura<br>Samurdhi to affected for 1 month (perfect targeting)<br>Poorest Second Third Fourth Wealthiest<br>20.0 quintile quintile<br>17.5<br>15.0<br>12.5<br>10.0<br>7.5<br>5.0<br>PDS cost<br>2.5<br>0.0<br>850 900 950 1000 1050 1100 1150<br>Upper PMT threshold for post-disaster support<br>Marginal impact at threshold [US$ per next enrollee]<br><!-- End of picture text -->

**Figure 11:** This plot shows the marginal cost and benefit of increasing the PMT threshold for PDS, across the range of PMT scores in Rathnapura.

> 10 Note that the flat tax payment mechanism used here effects a net transfer from unaffected districts into Rathnapura; district-level risk pooling, progressive taxation, social insurance-like systems, and more complex alternatives can also be modeled.
adaptive social protection 32

# 4.4 Post-disaster support to PMT-qualified households

Finally, Figure 11 presents the marginal cost and benefit of post-disaster support to flood-affected households in Rathnapura. For each PMT value on the x-axis, the plot shows the expected value (blue) and cost (green) of raising the PMT threshold to add an additional household. Because the payout is uniform, the cost is constant across all PMT values. However, the expected benefit falls from US$20 per enrollee with the lowest PMT scores, to negative BCR for enrollees with the highest PMT scores. The lines cross around 950, indicating a net benefit to the provision of post disaster support to all households with PMT scores below this threshold. In this way, the PMT can be used to optimize the coverage and value of post-disaster support to affected communities using Samurdhi as a delivery mechanism.

# 4.5 Nationwide cost-benefit analysis of Adaptive Social Protection

In Figure 12, we plot the expected benefits of four different ASP programs, by flooding magnitude (return period), for all districts in Sri Lanka. In bold at right, the criteria for beneficiary selection are detailed. In order of increasing cost, we measure the benefits of delivering a one-time payout, equal to a month of Samurdhi, to the following groups: all existing Samurdhi enrollees who are affected by flooding; all affected individuals with PMT ⩽ 887 (irrespective of Samurdhi enrollment); all affected individuals; and all Samurdhi enrollees, without targeting. Assuming any flood with at least a 10-year return period triggers a payout from these programs, their annual cost ranges from US$48 thousand to US$1.22 million, depending on the number of beneficiaries in each program.

Figure 12 also details the average, expected benefit-cost ratio (BCR) for each of these programs. Even assuming perfect targeting, the program that delivers lump sum payouts to all affected individuals has a relatively high cost and low return on investment (BCR = 1.2) because this program does not impose a means test on recipients. The top-up to all existing Samurdhi enrollees delivers an BCR of 2.0, followed by perfect targeting of affected individuals who are already enrolled in Samurdhi
conclusions 33 Expected benefit of ASP (1 month Samurdhi top-up) by return period & beneficiary group <!-- Start of picture text --> Cost of ASP<br>after 50-year flood All Samurdhi enrollees<br>(no targeting)<br>25 $14.3M Annual cost = 1.43MProgram BCR = 2.0<br>10<br>All affected<br>5 (perfect targeting)<br>Annual cost = 306K<br>Program BCR = 1.2<br>$3.5M<br>Affected and PMT 887<br>(perfect targeting)<br>Annual cost = 60K<br>Program BCR = 3.5<br>$676K<br>Samurdhi enrollee and affected<br>(perfect targeting)<br>Annual cost = 48K<br>Program BCR = 2.4<br>$532K<br>1<br>10 25 50 100 500 1000<br>Return period [years]<br>Avoided wellbeing losses [mil. US$]<br><!-- End of picture text -->

**Figure 12:** Expected benefit of four ASP responses to precipitation flooding, by disaster magnitude (return period) and inclusive of all districts in Sri Lanka. Benefit is the expected reduction to wellbeing losses with ASP, compared to a scenario with no post-disaster support. Note: both axes are logarithmic.

(BCR = 2.4). Finally, a new social protection system targeted at poor (PMT ⩽ 887), flood-affected individuals delivers the greatest return (BCR = 3.5) at a cost of US$60K per year.

These cost and benefit estimates are based on simple coverage criteria, applied uniformly to all districts. In practice, criteria for post-disaster support may vary by province and disaster magnitude. This framework can model all such complexities, to the and of the household and hazard up granularity representativeness survey maps used as inputs.

# 5 conclusions

This paper has presented the results of a risk assessment based on an expanded framework, which includes in the analysis the ability of affected households to cope with and recover from disaster asset losses and uses “wellbeing losses” as its main measure of disaster severity. This framework adds to the three usual components
conclusions 34 of a risk assessment — hazard, exposure, and vulnerability — a fourth component, socioeconomic resilience. Like the traditional components of risk management, socioeconomic resilience can be measured at any degree of spatial resolution, from the household to national averages.

Using a new agent-based model that represents explicitly the recovery and reconstruction process at the household level, this risk assessment provides more insight into flood impacts in Sri Lanka than a traditional risk assessment. In particular, it shows how the regions and communities identified as priorities for risk-management interventions differ depending on which risk metric is used. While a simple costbenefit analysis based on asset losses would drive risk reduction investments toward the richest regions and areas, a focus on poverty or wellbeing rebalances the analysis and leads to a set of priorities to integrate DRM into the larger development agenda.

In parallel, measuring disaster impacts through poverty and wellbeing implications helps quantify the benefits of interventions that may not reduce asset losses, but do reduce their wellbeing consequences by making the population more resilient. These interventions include financial inclusion, social protection, and more generally the provision of post-disaster support to affected households.

The model and data used in this analysis have many limitations. For instance, the 2016 HIES is only statistically representative at the district level and does not offer the geolocalization of households. Further, the SSBN model is itself a complex and imperfect exercise. Finally, the model does not represent all coping mechanisms available to households, such as international remittances or temporary migrations. However, even with these limits and simplifications, the introduction of socioeconomic resilience and household characteristics into risk assessments provides actionable insights and appears as a promising research agenda.