Input: eligible expenditures, and other details will be defined in an updated DCRM Manual for Phase II, approval of which by the World Bank is a disbursement condition. The sub-component will: (a) fund data collection to measure strains on schools, health facilities, and water points in the RHDs; and (b) finance approximately 41 infrastructure subprojects. Through ongoing analytical work, the World Bank has developed an Artificial Intelligence (AI)-based model that predicts refugee inflows before they occur. [21] If the World Bank is able to raise funds to run the AI model live, the GoU will undertake an assessment to consider using it for Phase II to facilitate the disbursement of DCRM funding to districts before large refugee inflows occur, supporting preparedness and minimizing stress on host populations and refugees in the RHDs. **Component 2: Integrated Natural Resource Management (US$30 million equivalent)** 30. **Component 2 will support investments in environmental restoration and protection to respond to ecosystem** **degradation in the RHDs and additional impacts caused by climate change and refugee presence.** The expansion of farming by refugees and host communities is driving rapid deforestation in the RHDs, reducing the extent of natural forests and woodlands. Refugees and hosts also harvest wood for

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Input: **6. Summary and Conclusions** In this paper, we have constructed a spatial panel database of cyclone tracks and coastal landfalls in Bangladesh, West Bengal and Odisha. For 1960-2016, our tracking data come from the Bangladesh Meteorological Department (BMD) for cyclones with Bangladesh landfalls and the India Meteorological Department (IMD) for cyclones with landfalls in West Bengal and Odisha. For storms before 1960, our data come from country reports in the global IBTrACS database maintained by the Global Data Center for Meteorology of the US National Oceanic and Atmospheric Administration. We determine each storm’s landfall as its first intersection with an ordered line of coastline points, spaced at .001 decimal degrees (approximately 100 meters). We identify each cyclone’s primary coastal impact zone as the set of coastline points lying within the cyclone’s radial distance from its landfall point to its zone of maximum wind speed. Although we confine our exercise to Bangladesh and the two Indian coastal states, our methods are general and could be expanded to an arbitrarily-large set of coastal locations. We use the database for an analysis of cyclone strike locations and impact zones in seven historical periods: 1877-1900, 1901-1920, 1921-1940, 1941-1960, 1961-1980, 1981-2000 and 2001-2016. Although we

spatial panel database of cyclone tracks and coastal landfalls

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Input: flood risks. Indeed, large coastal cities are likely to be risk hotspots. Hallegatte et al. (2013) estimate the flood risks in the world’s 136 largest coastal cities. They show that population and asset growth, climate change and subsidence are all likely to contribute to a drastic increase in global average flood losses – from $6 billion per year in 2005 to over $60 billion in 2050, assuming proactive adaptation actions in all cities. Kulp and Strauss (2019) conduct a global assessment of the potential impact of sea level rise on the world’s coastal population. The global scale of their study and the use of high-resolution flood and population data makes it the closest comparable analysis of flood risk. Their assessment suggests that today between 190 million and 630 million people (depending on the emissions scenario considered) live in areas that could be inundated by sea level rise by the year 2100. They also estimate that one billion people live on land that is less than 10 meters above current high tide lines. This present study complements the one by Kulp and Strauss (2019) in several ways: First, it considers not only the one billion people living near current high tide

high-resolution flood and population data

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Input: conduct due diligence on the creditworthiness of the proposed borrowers. To minimize borrowers’ moral hazard, there will be no direct contact between the BDF and end borrowers; all information required by the BDF on borrowers and projects will be collected through the PFIs. Full details will be included in a PCG Manual, acceptance of which by the World Bank will be a condition of disbursement for this sub-component. 36. **Eligibility criteria.** The PCG will be open to creditworthy refugees. Detailed terms and conditions of the guarantees will be spelled out in the PCG manual and in a PCG Agreement to be signed between the BDF and PFIs. The BDF will be solely responsible for the selection of PFIs and monitoring of compliance with the eligibility criteria. 37. _Sub-component 2(d): Graduation for vulnerable groups (US$6.5 million)._ To support highly vulnerable refugees and host community members who are not bankable, graduation-style programming will be introduced in Phase II. Sub-component activities will target individuals living in extreme poverty who face barriers such as lack of collateral, limited financial literacy and social exclusion. MINEMA will contract an implementing partner to support at least 2,500 households (70 percent refugee/30 percent host community members/60 percent femaleheaded)

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Input: 90m x 90m pixel. The map shows a high concentration of flood-exposed populations in Hanoi, as well as urbanized areas along the Red River. The granularity offered by these pre-processed population exposure maps could be valuable for various applications; e.g. for the development of disaster mitigation plans prior to a flood event, or to estimate the number of affected people during flood events in order to prepare the scale-up of shock-responsive social protection systems. However, several limitations of these maps should be noted: First, poverty estimates are not available with the same resolution and are not incorporated in these maps. Second, while the maps can help to identify the scale of overall vulnerability and the location of pockets of vulnerability, they cannot replace detailed needs assessments for the targeting of post-disaster support or the evaluation of damages after specific events. 2 These maps will be made available publicly, and will be available upon request in the meanwhile. 23

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Input: 0.183 0.076 0.107 0.104 Mean of dep var. 0.328 0.111 0.137 0.214 0.209 0.928 0.841 _Note:_ Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and district fixed effects, urban dummy, age, and years of education. Active is active status of mine in the survey year. *** p<0.01, **p<0.05, *p<0.1. Results for women’s partners also available upon request. OLS = ordinary least squares. The women and men samples have different sampling frames and therefore we are not analyzing these results jointly. Two further caveats should be noted: (i) with many variables tested, such evidence should not be given too much weight, (ii) number of sampled men in the treatment category is quite small. Out of 7,157 sampled med, only 484 men live close to active mines. A bigger treated sample would provide more reliable results. [10] **5.2 Child health** We explore effects on child health, such as size at birth, infant mortality, anthropometrics, and incidence of cough, diarrhea, and fever. Panel A and Panel B use different variable definitions 10 Results for sampled women’s partners are similar. 18

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Input: to establish or expand their operations. This will include improving the scale and quality of community tree nurseries, and the establishment of tree plantations with market orientation, based on locally appropriate and climate-resilient species with commercial demand and also adapted to future climate conditions. The use of indigenous tree species will be promoted where technically viable. Inter-cropping within plantations during the early years of growth will provide co-benefits to group members from food and cash crops. Groups will also be supported to establish wood fuel plantations in public institutions with kitchens (e.g., schools and health facilities) to provide a sustainable supply of fuel for mass catering. LIPW under this sub-component will be limited to these institutional woodlots, as the nursery and plantations are productive assets from which participants will draw Page 10

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Input: the marketing restricted sample, which is restricted only to villages where at least one person purchased insurance in the following year. The average ratio of payout to premium (for those who received payouts) for the full sample is 3.6; for those in the marketing restricted sample it is 4.5. All regressions contain state fixed effects. Errors are clustered at the weather station level. ***p _<_ 0.01, **p _<_ 0.05, *p _<_ 0.1. _Source_ : Authors’ analysis based on data from BASIX. a payout flips to negative and significant. However, the payout ratio is positive and significant while the payout ratio squared is negative and significant. Together, this suggests that people who receive very small payouts are less likely to purchase insurance, while those who have large payouts are more likely to purchase. The overall effect of receiving a payout switches from negative to positive when the payout ratio reaches around .77. One point of concern with these results is that there are many cases where there are multiple purchasers of insurance in a certain village in one year and then zero in the next year. Although this could be the result of people simply being unsatisfied with insurance, the large

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Input: wetlands are associated with an island or mainland coast), as described in Hoozemans and Hulsbergen (1995). [15] Five possible responses to SLR corresponding to categories of wetland migratory potential were defined for the DIVA database: 1. No, or hardly any change; 2. A retreat of the coastline, combined with inland migration of coastal ecosystems; 3. A retreat of the coastline without the possibility of inland migration due to topography (e.g., coastlines with relatively high relief); 4. A possible retreat of the coastline but increase of flooding area behind the coastline (“ponding”); and 5. Total loss of the coastal ecosystem (Hoozmans et al. 1993). ###### **II.2 Methodology** The procedure used in this analysis followed several steps. First, the SRTM database was used to identify inundation zones. [16] Second, a country surface for wetlands was constructed from the polygons extracted from the Global Lakes and Wetlands database. Third, migratory potential of wetlands were assigned from the WMP classification of the coastline from the DIVA database. Fourth, the country surface of wetlands was overlaid with the inundation zone layer. The analysis then determined the area of wetlands that would be exposed to increased SLR and the area of wetlands that may be lost

DIVA database
SRTM database
Global Lakes and Wetlands database

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Input: Development to Foster Private Sector and Growth_ _in Rwanda._ 14 A transit district hosts asylum-seekers in the immediate period after they have come across the border. They stay in Transit Centers while their refugee claims are assessed and while the government identifies a long-term site for their resettlement. Rubavu has experienced major inflows from the DRC in recent times, with many asylum seekers staying for months. The district is being included in the project for an Integrated Craft & Production Center (ICPC) so that asylum seekers can work and seek training opportunities immediately upon arrival. This is expected to expedite the process of socio-economic inclusion. The ICPC will also be used by host community members in the district. 15 The two roads are an exception. They have been pre-identified by the government. For other subprojects, the GoR has prepared an indicative list, which will be validated through additional consultations with refugees, host communities and other stakeholders. The list includes: (a) classroom construction for a school and TVET center in Gisagara; (b) health facilities in Gatsibo and Nyamagabe; (c) water systems in Kirehe and Nyamagabe; (d) markets in Kirehe and Karongi; and (e) ICPCs in Kirehe and Rubavu. 16 The only

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Input: women as of 29 September 2024 [1] and children [2] ## 98% 34% of Protection Monitoring respondents are Temporary Protection holders ## 34% of Protection Monitoring respondents are of respondents consider accommodation as Temporary Protection holders their highest priority need ## 67% 29% ## 29% of working age respondents are employed in Czechia 1 Ministry of the Interior, Czechia 2 Ministry of the Interior, Czechia of respondents need more information about job opportunities

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Input: language skills leads to the inability to upscale professional skills and to seek meaningful employment opportunities matching qualifications. When seeking employment through agencies, women reported cases of labour exploitation with jobs that often involve long working hours, no breaks, no social protection coverage, and no annual leave. From protection monitoring, 24% of respondents reported working excessively long hours. 12% indicated they did not have an employment contract, while 3% reported the confiscation of their documentation (e.g., passport) by their employer. ### Protection Risk IV **Access to adequate healthcare:** In focus group discussions conducted by UNHCR with elderly Ukrainian refugees [3], access to adequate healthcare emerged as the main concern. Participants highlighted significant barriers, including difficulties finding a general practitioner and understanding a medical insurance system that is new to them. Refugees highlighted a particular challenge in doctors neglecting to consider medical histories from Ukraine, resulting in misdiagnoses and incorrect treatment. Some instances, such as when prescribed medications conflicted with known conditions, illustrated a disconnect between patient backgrounds and treatment approaches in the Czech healthcare system. This forced some refugees to rely on bringing medications from Ukraine, exacerbated by difficulties obtaining longterm prescriptions. Language barriers also hinder effective communication of health

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Input: **1.** **Introduction** Natural disasters are estimated to cause an average of over $300 billion in direct asset losses every year; this estimate increases to $520 billion when considering the well-being (or consumption) losses experienced by people (Hallegatte et al. 2017). While each country faces its individual set of natural hazards – including cyclones, earthquakes, or wildfires – floods are one of the most common and severe hazards to disrupt people’s livelihoods around the world. Especially in lower income countries where infrastructure systems – including drainage and flood protection – tend to be less developed, floods often cause unmitigated damage and suffering. Recent events, ranging from Bangladesh and Nigeria to the United States and Vietnam, illustrate that the threat is a global reality. Not only rare and major floods, but also smaller and frequent events can revert years of progress in poverty reduction and development. In the coming years, land subsidence, rapid coastal urbanization, and climate change are bound to result in increasing exposure of people and their livelihoods. In this study we estimate the number of people who are directly exposed to the risk of intense flooding in 189 countries. We do so by using high-resolution flood and population data

high-resolution flood and population data

high-resolution flood and population data

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Input: and gold production was booming. The first gold rush occurred between 1892 and 1901, and the second after World War I. Gold production decreased at the dawn of independence in 1957, and remained low until the 1980s. Over the last 20 years, Ghana has been experiencing its third gold rush. During this period, annual gold production has increased by 700 percent, as shown in Figure 1. It is the expansion that has happened during this recent gold rush that is used in this analysis to understand the socioeconomic effects of mining. The high international gold price was a driving factor in the expansion of small-scale mining, such as the 2,700% increase in gold mining territory around the Offin River between 2008 and 2012 (Hausermann et al., 2018). Between 2006 and 2012, two large-scale mines opened in Ghana, but no mine closed down (Table 1) possibly due to the high gold price increasing profitability and extending life length. The expansion across artisanal small-, medium-, and large-scale mining contributed to an increase in total production that rose from 541,147 oz in 1990 to 3,119,823 oz in 2009 according official Ghana statistics (Bloch and Owusu, 2012). This production increase led to an increased

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Input: occupants and measures taken by the recently passed Land Act to restore such security, based on recognition of the importance of land-related investment and higher agricultural productivity for pro-poor growth that will help narrow the gaps between rural and urban well-being in Uganda. We use this to outline the estimation strategy to be utilized and discuss some of the associated econometric issues. **2.1 Origins and incidence of overlapping tenure in Uganda** The main reason for the prevalence of overlapping property in Uganda’s land tenure system dates back to colonial occupation. Under the 1900 Buganda agreement, [1] the British awarded large tracts of “ _mailo_ ” land, together with any smallholders occupying them, to the Buganda king and his notables (Brett 1973). Lands not covered under this agreement were declared Crown Land, allowing the government to alienate 1 In this agreement, the total area of Uganda, estimated at 19,600 square miles, was divided into three broad classes. The first class, comprising 958 square miles, was given to the Buganda king (kabaka), the second one, amounting to a total of 8,000 square miles, was distributed equally among 1,000 chiefs and private land owners. The remainder was declared Crown Land and vested in

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Input: relevant expertise. Accessed at [http://www.compassonline.org/pdf_files/EBM_Consensus_Statement_v12.pdf](http://www.compassonline.org/pdf_files/EBM_Consensus_Statement_v12.pdf) Millennium Ecosystem Assessment. 2005. _Ecosystems and Human Well-Being: Wetlands and_ _Waters Synthesis_ . World Resources Institute, Washington, DC. Nicholls, R.J., Hoozemans, F.J.M. and M. Marchand. 1999. Increasing flood risk and wetland losses due to global sea-level rise: Regional and global analyses”. _Global Environmental_ _Change_, 9. S69-S87. Nicholls, R.J. 2004. Coastal flooding and wetland Loss in the 21 [st] century: Changes under the SRES climate and socio-economic scenarios. _Global Environmental Change_, 14, 69-86. Nicholls, R.J., Hanson, S., Herweijer, C., Patmore, N., Hallegatte, S., Corfee-Morlot, J., Chateau, J., and R. Muir-Wood. 2007. _Ranking Port Cities with High Exposure and Vulnerability to_ _Climate Extremes_ . OECD Environment Directorate, Environment Working Papers No. 1. Nicholls, R. J., and A. Cazenave. 2010. Sea-level rise and its impact on coastal zones. _Science,_ 328, 1517-20. Overpeck J., Otto-Bliesner B., Miller G., Muhs D., Alley R. and J. Kiehl. 2006. Paleoclimatic evidence for future ice-sheet instability and rapid sea-level rise. _Science_, 311,1747-1750. Pfeffer, W. T., Harper, J.T., and S. O’Neel. 2008: Kinematic constraints on glacier contributions to 21st-century sea-level rise. _Science_, 321, 1340-1343. Rahmsdorf, S. 2007. A semi-empirical approach to projecting future sea-level rise. _Science_, 308, 368-370. Semeniuk, V. 1994. Predicting the effect

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Input: **3.1.** **Flooding hazard data** Floods can be caused by a variety of factors. The most common types are considered in this analysis: **Fluvial flooding** occurs when intense precipitation or snow melt causes rivers to overflow. **Pluvial flooding** occurs when rainwater builds up beyond the absorptive capacity of soil. Country-level pluvial and fluvial flood maps, developed by Fathom Ltd, are used in this analysis (Smith et al. 2015). The data sets provide information on flood extents and flood depth at an approximate spatial resolution of 90 meters and are available for all countries. The maps are based on a global hydrological and topographic model. The model simulates flood events with return periods of 5, 20, 50, 100, 250, and 500 years. This study considers flooding with a 100-year return period. A 1-in-100-year event, for example, is expected to occur once every 100 years on average (i.e. it has a probability of 1% of occurring in any given year). As with all global flood maps, the effects of artificial flood protection structures like dikes are not incorporated. Two editions of the Fathom global flood maps are available; 2016 and 2019. Wherever available, the newer version has been used. The 2019 version differs

Country-level pluvial and fluvial flood maps

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Input: financial and social sustainability by means of internal mobility, selfreliance, and inclusion in national services. The design of DRDIP II fully aligns with these lessons through its focus on selfreliance and integration of refugees into national service delivery systems. 54. **Multi-sectoral development investments directed to forcibly displaced populations and host communities can** **improve welfare, mitigate the negative effects of displacement, generate positive externalities, and promote social** **cohesion.** The 2022 World Bank report, _Social Cohesion and Forced Displacement,_ concludes that to pre-empt social tensions that can emerge in situations of forced displacement, governments and humanitarian and development agencies need to direct short- and long-term assistance to both the displaced and host communities. This can help reduce real and perceived inequalities and prevent host communities from forming negative attitudes about newly arriving displaced persons. DRDIP II will continue the area-based approach of Phase I, providing multi-sectoral support to respond to the impacts of forced displacement, covering refugees and host communities. **III.** **PROJECT IMPLEMENTATION** **A.** **Institutional and Implementation Arrangements** 55. **The project will retain the same arrangements as Phase I**, but with enhanced coordination and collaboration mechanisms under OPM’s DoR. OPM will implement DRDIP II at the national level through a PIST, which

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Input: models. By modeling changes in river discharge and inundation areas, they show that a high-concentration climate change scenario could lead to a large increase in flood frequencies in Southeast Asia, India, East and Central Africa, and large parts of Latin America. Yet, existing models are ill-suited to provide reliable long-term projections of climate change at the local level. The IPCC’s Fifth Assessment report shows that forecasts for average temperature and rainfall changes by mid-century vary significantly depending on which model and which assumptions are considered. However, it is not only the change in the mean that matters – already regions around the world are experiencing changes in the variability of climatic events; in the form of increased frequency of formally rare and extreme events, such as droughts and intense floods. In the meanwhile, the continued urbanization of high-risk areas will mean that more and more people will be exposed to flood risks. Indeed, large coastal cities are likely to be risk hotspots. Hallegatte et al. (2013) estimate the flood risks in the world’s 136 largest coastal cities. They show that population and asset growth, climate change and subsidence are all likely to contribute to a drastic increase in global average

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Input: and effectiveness of resource utilization, minimize duplicative efforts, support address unmet needs effectively, and foster programmatic coherence. Operating as an intersectoral platform, the CBI TWG, accountable to the 3RP coordination in Türkiye, focuses on managing both sector-specific and multipurpose cash assistance. This coordinated approach is designed to meet the basic and specific needs of vulnerable populations, ensuring that the assistance provided through CVA is both targeted and impactful. This approach has become increasingly relevant and critical, especially considering the challenging circumstances following the 2023 earthquake, which necessitated a broader and more targeted coordination of resources and assistance. 1 Republic of Türkiye, Ministry of Interior, Presidency of Migration Management, Statistics (24/04/2025), _Temporary Protection_, [https://en.goc.gov.tr/temporary-protection27](https://en.goc.gov.tr/temporary-protection27) 2 Republic of Türkiye, Ministry of Interior, Presidency of Migration Management, Press Release (20/08/2024) https://www.goc.gov.tr/ulkemizdeki-yabanci-sayilarina-iliskin-ortaya-atilan-gercek-disi-iddialar-hakkinda-basin-aciklamasi 1

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Input: were assigned from the WMP classification of the coastline from the DIVA database. Fourth, the country surface of wetlands was overlaid with the inundation zone layer. The analysis then determined the area of wetlands that would be exposed to increased SLR and the area of wetlands that may be lost due to SLR. More specifically: 15 We acknowledge that the migratory potential of wetlands also depends on a wide range of additional factors that are site-specific and highly variable such as the continued flow of sediment and nutrients from inland stream as well as human activities. Such detailed information was not available on a global scale. 16 It should be noted that the SRTM database suffers from known limitation in urban as well as forested areas where the SRTM elevation data may capture the height of building or trees instead of ground level elevation. A similar limitation is noted by Nicholls et al. (2007). 8

DIVA database
SRTM database
SRTM elevation data

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Input: been conducted according to IPF policy and directives to verify that** **the current systems provide reasonable assurance of resource allocation for their designated purpose.** The assessment focuses on the capacities of the implementing agencies, leveraging insights from previous projects. Detailed arrangements for budgeting, accounting, staffing, internal controls, fund flow, reporting, and auditing have been outlined during project preparation, with measures to mitigate fiduciary risks. **B. Results Monitoring, Evaluation, and Verification Arrangements** 61. **The project’s monitoring and evaluation (M&E) arrangements reflect the capacity constraints and operational** **challenges faced by a project with two implementing agencies, which lack an integrated management information** **system, including the M&E.** The project will be implemented with necessary oversight at the local level, where M&E capacities will be mobilized for the collection, organization and analysis of project-related data. The main instrument for M&E will be the Results Framework (Annex 1). The indicators at PDO and intermediate levels, as well as their respective baselines, midterm, and end-of-project targets, will serve as the basis for reporting progress. The indicators, data sources, and data collection methods take into account the limited institutional capacities in the project target locations. All relevant indicators are disaggregated by gender, and where applicable by refugees

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Input: **The World Bank** Socio-economic Inclusion of Refugees & Host Communities in Rwanda Project Phase II (P509677) **D. Project Components** 16. **Jya Mbere Phase II investments will support priority domains from the self-reliance index – education,** **health, labor and income – and implementation of the government’s graduation strategies for Rwandan** **nationals and refugees.** The strategic evolution to focus on self-reliance will drive design changes for Phase II compared to Phase I, including: (a) more funding will go to climate-resilient economic infrastructure and livelihood, including to address job skills gaps, which is a priority for the country as a whole and for refugees; [13] and (b) new economic opportunity activities will be added for vulnerable groups and people who cannot access finance from formal financial institutions. Project activities will be organized in five components, as described below. **Component 1: Access to Services and Socio-economic Investments (US$41.1 million equivalent, of which US$13.3** **million equivalent from the WHR)** 17. **Phase II will continue to support much-needed infrastructure in the five refugee-hosting districts, plus one** **transit district, Rubavu.** [14] As per Phase I, priority investments will be identified through the government’s District Development Strategy (DDS) process, which draws on consultations from the village, cell, sector

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Input: climate change. Nevertheless, the findings presented in this paper should be interpreted considering a number of caveats. While we use current and future flood data, we only use current population and poverty data, as reliably projecting these socioeconomic trends spatially into the future is almost impossible. Changes in these trends – among many other factors – can lower socioeconomic 7 Disaggregated results per district, and results using depth as an indicator can be found in the online supplementary materials.

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Input: **The World Bank** Integrated Community Resilience Project (P506969) PROJECT APPRAISAL DOCUMENT and seasonal rivers (wadi) are vital for supporting oases and recharging shallow aquifers. Projections indicate climate will intensify extreme heat, with the number of high heat index days nearly doubling from 66 to 123. This will severely affect livelihoods, particularly in rural areas where livestock rearing—a key source of income for 60 percent of the rural population—is projected to reduce revenues by up to 24 percent. Livestock also represents a form of savings and insurance for nearly one-third of poor households (30 percent), compared to just 11 percent among non-poor households [17] . Moreover, climate change will impact human productivity, with labor output expected to decrease by as much as 3.5 percent by mid-century due to heat stress. The poor, who rely heavily on natural resources, are the most vulnerable to these impacts. Additionally, climate-induced diseases such as malaria have increased significantly, with a 37-fold rise over the past two decades. Combined with heat-related health issues, this is expected to further reduce the labor supply by up to 1.5 percent. 17. **The project is fully aligned with the World Bank’s Country Partnership Framework (CPF) for Djibouti (2022-** **2026), which

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Input: with effective stakeholder engagement, procurement, and contract management capabilities. **C. Project Beneficiaries** 38. There are two cohorts of primary beneficiaries under the project: (a) refugees and former refugees who will benefit from a more inclusive national policy regime and (b) refugee and host community members in and around the Meheba settlement and Kalumbila District, who will benefit from better infrastructure, services, and support to agribusiness. The total number of direct beneficiaries under the project is estimated to be 172,837. [38] 39. **Refugees and former refugees nationally.** There are currently 101,837 [39] refugees and former refugees in Zambia who will be direct beneficiaries under Component 1. Legislative and policy changes stemming from Zambia’s National Refugee Policy which will be implemented nationally will have a positively impact on their lives. This cohort will also benefit from the issuance of national ID cards which for the first time will place them on the Zambia national registry. 40. **Refugees and host communities in Kalumbila District.** They will benefit from infrastructure, health, and education services and support to farming activities and agribusiness. The estimated number of specific beneficiaries per project component together with total direct beneficiaries is detailed in Table 1. **Table 1. Project Beneficiaries

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Input: these interventions. **Sub-component 1.2.: Financial Inclusion Services (total IDA US$1 Million, including WHR US$0.3 Million)** 35. **This sub- component will support FIS activities for 1,500 beneficiaries who are members of households receiving C4N** **(including an estimated 500 refugees)** . This initiative aims to enhance financial inclusion and promote economic resilience by providing a one-off US$500 financial inclusion grant and technical support services for the poor and vulnerable households so they can try out household self-help activities. Eligible beneficiaries must meet key criteria, including (i) being part of a household benefiting from cash transfers, (ii) willingness to engage in small household self-help activities focused on improving their wellbeing, (iii) completing training on household self-help activities to be provided by a service provider that will be selected by the MASS, and (iv) presenting a concise action plan outlining their proposed self-help activity. Among refugee beneficiaries, it is expected that some households may opt for in-kind support from other development partners – and where this will be the case, their election into the FIS will not be constrained by not being participants into the C4N. 36. This subcomponent will also seek to achieve greater financial inclusion for women by promoting more women representation

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Input: of coverage turned out to be a multiple of the fair price (around three times in either case). [5] Why is insurance so costly? Several reasons are invoked, including supply-side constraints induced by either agency costs or adverse selection, problems of information opacity of tail events, coordination failures, [6] and oligopolistic practices. While the securitization [7] of catastrophic risk through the issuance of catastrophe bonds may in the future induce greater market discipline, until now it has fallen short of reducing the costs of insurance to actuarially fair levels. [8] If insurance is so expensive, why do countries still buy it? One reason could be risk aversion; another could be the presence of concavities in the production function and/or convexities in the borrowing cost function that create hedging opportunities as in Froot et al. (1993). While both these assumptions may play an important role for the demand of insurance, nonetheless we think that they only partially justify why a 1 By exposure we denote the probability of being hit by a natural disaster and by vulnerabilty the expected loss associated with any of such disasters. 2 See Elrich and Becker (1972). 3 See Hofman and Brukoff (2006) for a survey

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Input: affects civilians more than the military, especially in the 2nd quarter of 2023 **(Ref graph3).** In fact, the reported ED incidents resulted in a total of 132 victims [4] and survivors, most of whom (52%) were civilians (killed or injured). **The percentage** **of civilians killed is almost double that of military** **killed,** which puts the threat on civilians more than militaries and reflects the need to strengthen mine action within affected communities. The affected regions are those of Tillabery (areas of the 3 borders in the Sahel) and Diffa (region of the Lake Chad basin). **The Tillabery region has 7 affected** departments [5] **, and the Diffa region has 4** . A minimal number of ED incidents have also been reported in 3 other regions: Agadez, Dosso and Niamey ( **Ref graph2** ). This situation puts more than 200,000 people at risk of ED incidents in the affected areas. The prevalence of ED incidents related also leads to restrictions on the mobility of civilian populations on the roads to weekly markets, and the main cities of communities/departments. About 36% of those interviewed [6] say they encounter mobility constraints due to the activities of the NSAGs and military operations. This indicator

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Input: 698 Stein the results could be biased. For instance, assume that there were a number of villages that experienced a rainfall shock but received very low payouts, making them unhappy with insurance. If the marketing teams knew this, they may have decided to not market to as many of these villages, therefore censoring villages that received a payout but were likely to have few repeat buyers. Regressions that use previous years’ payout characteristics to try to predict whether insurance is sold in a village the following year do not reveal any patterns that would suggest selection bias (shown in table S.5 in the supplemental [appendix, available at https://academic.oup.com/wber), but they may miss more subtle selection patterns.](https://academic.oup.com/wber) It is possible that the coefficient for the marketing restricted sample is upward biased and it therefore would be reasonable to regard the coefficients in columns 1 and 3 as lower and upper bounds, respectively. The payout ratio has a positive and strongly significant effect while the squared term is smaller and negative. This suggests that higher insurance payouts result in greater propensity to purchase the following year, but that the marginal effects flatten out for larger payouts. Also, the simple dummy of receiving

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Input: the scenarios presented in Table 2, and aggregate our results at the district level to estimate the number of people affected. To include the poverty dimension, we use the poverty headcount rate in each district to estimate the percentage of poor people exposed. For instance, if 20,000 people are exposed to floods in District X, and District X has a poverty headcount rate of 20%, 1000 poor people are exposed to floods in that district. [5] Slum Exposure in Ho Chi Minh City For the HCMC analysis, we estimate the general exposure to flooding, for the whole province of HCMC and in each of its 24 districts. The flood maps used here are based on a model of HCMC, and are not the same map as used in the figurative example in Section 4.1. 5 As evidenced in the HCMC analysis with slum data, poor people are often not evenly distributed but clustered in particular areas. However, due to data limitations across the country, we assume poverty is evenly distributed within a district when conducting the national-level analysis.

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Input: order to look at the results of weather separately from the effects of insurance, I analyze how previous weather events affected insurance purchase decisions in the first year that insurance was offered to BASIX customers, which was 2005. To accomplish this, I first aggregate the purchasing data to the village level and then test to see whether villages that experienced a rainfall shock in 2004 had more insurance purchasers in 2005 than

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Input: Policy Research Working Paper 4310 #### **Abstract** The need for land-related investment to ensure sustainable land management and increase productivity of land use is widely recognized. However, there is little rigorous evidence on the effects of property rights for increasing agricultural productivity and contributing toward poverty reduction in Africa. Whether and by how much overlapping property rights reduce investment incentives, and the scope for policies to counter such disincentives, are thus important policy issues. Using information on parcels under ownership and usufruct by the same household from a nationally representative survey in Uganda, the authors find significant disincentives associated with overlapping property rights on short and long-term investments. The paper combines this result with information on crop productivity to obtain a rough estimate of the magnitudes involved. The authors make suggestions on ways to eliminate such inefficiencies. This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the group to assess the impact of land policies. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kdeininger@worldbank.org. _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage

nationally representative survey in Uganda

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Input: **The World Bank** Costa Rica Results in Education (CORE) (P181174) Procurement Prior Review Thresholds detailed in Annex I of the Bank’s Procurement Procedure are to be observed. The Project will use the Systematic Tracking of Exchanges in Procurement (STEP) to plan, record, clear and track procurement transactions for the Project. An initial Procurement Plan has been prepared by the MEP in accordance with the results provided by the PPSD, including a timeline for implementation. **C. Legal Operational Policies** **D. Environmental and Social** 53. The Project will finance consulting services to design, develop and implement educational tools, information systems, platforms and other software, as well as to develop assessments, policies, plans and strategies for the educational sector. The Project will also finance the equipping of selected schools with internet connections and hardware (e.g., notebooks, tablets, smart boards, projectors, headphones, robotics, computational kits). The Project will not finance any type of civil works. The environmental and social risk rating is classified as Moderate. The Project is expected to have mostly positive social impacts through improved education quality, inclusion, and resilience. Impacts on vulnerable and systematically excluded groups are expected to be positive with limited negative social effects. **V.** **GRIEVANCE REDRESS SERVICES** 54.

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Input: Economics of Disasters and Climate Change Fig. 4 Location of slum areas and locations with urban expansion in the city of HCMC. Source: (PUMA 2013) We then overlay this flood layer with the population density data set, to estimate the number of people per population grid cell that are exposed to floods. As the population density data set is at a lower resolution (1 km) than the flood data (90 m), we estimate the percentage of the population grid cell which is flooded, and multiply this percentage by the population in that grid cell. For instance, if a population grid cell has 500 people, and 10% of that cell is flooded (based on the flood data), then we estimate 50 people to be exposed to floods in that cell. In doing so, we assume that the population is evenly distributed within a grid cell. We run this analysis for all the scenarios presented in Table 2, and aggregate our results at the district level to estimate the number of people affected. To include the poverty dimension, we use the poverty headcount rate in each district to estimate the percentage of poor people exposed. For instance, if 20,000 people are exposed

poverty headcount rate
flood data
population density data set

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Input: large-scale gold mines in Ghana. We find that a new large-scale gold mine changes economic outcomes, such as access to employment and cash earnings. In addition, it raises local wages and expenditure on housing and energy. An important welfare indicator in developing countries is infant mortality, and we note a large and significant decrease in mortality rates among young children, at both the local and district levels. [1] We hypothesize that increased access to prenatal care is one of the mechanisms behind the increased survival rate. 1 In the 2010 Ghana population census average district size is 112,000 3

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Input: Economics of Disasters and Climate Change Fig. 2 Flood maps showing inundation depth (cm) in case of a: (a) 10-year return period flood under current conditions, (b) 25-year return period flood under current conditions; (c) 50-year return period flood under current conditions; (d) 10-year return period flood given a 30 cm sea level rise; (e) 25-year return period flood given a 30 cm sea level rise; and (f) 50-year return period flood given a 30 cm sea level rise Local-Level Data on Urban Areas and Potential Slums in Ho Chi Minh City The spatial socioeconomic data set used for HCMC is a data set of potential slum areas from 2000 to 2010, from the Platform for Urban Management and Analysis (PUMA), a city-level data set developed by the World Bank (World Bank 2015).This data was collected via satellite in the year 2012, through a combination of visual interpretation of various sources and vintages of imagery. To guide the identification of slums, previous work has provided information on the appearance and geographical extent of slums in HCMC. Surveys of poverty in the city find the appearance of slums in HCMC to be characterized as densely built small households and shelters that

Platform for Urban Management and Analysis

spatial socioeconomic data set used for HCMC

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Input: _Note:_ In panel A, active is status of mine in birth year; in panel B, active is active status of mine in survey year. Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and district fixed effects, urban dummy, age, and years of education. *** p<0.01, **p<0.05, *p<0.1. OLS = ordinary least squares. An active mine is associated with a decrease in cough among children under age five (panel B of table 5), and children are also (insignificantly) more likely to have a health card. An active mine is associated with insignificant decreases in the anthropometrics measures (World Health Organization measures in standard deviations), such as height-for-age and weight-for-age. However, the standard errors for these coefficients are very large relative to the estimated coefficients, which is why the effects are imprecisely estimated. **5.3 Spatial heterogeneity of results** Thus far we have used a cutoff distance of 20 km. Panel A of figure 4 shows that the largest treatment effect for services for women is found within 10 km of a mine, with an 8 percentage point increase in the probability that a woman works in the service sector. This is equivalent to a

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Input: reduce the time lag it takes to deliver support given the evolution over the last five years. The duration of C4N will be 24 months (2 years) whereas SBCC will be phased throughout the implementation of the project. 32. A total of 2,860 refugees - including student refugees in Djibouti-Ville will benefit from the C4N. This support, which proved critical for children from rural vulnerable households during covid-19, will help refugee children remain in school longer and improve their educational outcomes, addressing barriers such as school fees and additional basic costs. 33. **The C4N will cover all six regions with beneficiary coverage in each region determined based on a** **predetermined set of targeting criteria.** In accordance with the principle of convergence which the project promotes, efforts will be made to achieve overlap of C4N and SBCC interventions with component 2 interventions in Dikhil by maximizing beneficiary coverage in the region. A combination of geographical targeting, categorical targeting, ProxyMeans Targeting (PMT) and community-based targeting (CBT) mechanisms will be used in a complementary way to identify beneficiaries and minimize exclusion and inclusion errors that may arise from inadequate coverage of the social registry and biases that result from community beneficiary identification. The

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Input: high impact intensity and others have none. The first and second moments of the spatial impact distribution also change over time: The median landfall location shifts eastward from northern Odisha to the Bangladesh/West Bengal border area, while locational variance increases markedly after 1960. We also consider the overall distribution of impacts during the 140-year period covered by our database. Despite pronounced period-to-period shifts in impact locations, the overall distribution is far from uniform. We find the highest impact zones in northern Odisha and the Sundarbans region of West Bengal, with somewhat lower impacts in the zone between those two areas and a pattern of decline from the high-impact zones to the southern coastline boundaries of Odisha and Bangladesh. We should note that these boundary results are not artifacts of our methodology: We have also incorporated cyclone strikes in proximate areas south of the end points. Finally, despite the pronounced bimodality of the spatial distribution, we also find significant variation within zones. For example (Figure 8c), western coastal Bangladesh, generally a mid-impact zone, also has significant stretches of coast that have very low historical impacts. To summarize, our focal coastline area has witnessed highly-varied long- and short-term distributions of cyclonic storm

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Input: Several important questions can be addressed with these data. The first relates to locational stability: What are the permanent and transient components of household and community responses to long- and short-run cyclone clustering, and to the power of recent cyclones? Outright relocation will be part of the response, but so will relocation of prime-age household members to less-impacted inland areas where their earnings and residence will offer financial insurance and a potential safe harbor from future cyclones. The size and speed of the response will depend critically on expectations about future cyclone strikes, which can be inferred from the highly varied data in our cyclone panel. Other questions of interest can also be explored once cyclone-related controls are introduced. For example, how much coastal outmigration reflects the “pull” of urban opportunities, and how much reflects the “push” of cyclone-related hazards? How do disaster mitigation policies affect responses to these hazards? Some coastal zones in our study area invest significant resources in damage prevention via polder construction and other measures, while others offer compensation once cyclone-related damage has occurred. Incorporation of controls from our cyclone panel database may permit more accurate estimation of the timing and magnitude of responses to these

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Input: since FY 1998, and all AAA since FY 2000. Using the appropriate thematic codes, we calculate total Bank lending and AAA by country and environmental theme. Our five environmental indicators and the institutional problem indicator are constructed to match the corresponding thematic categories in the project database. The seventh thematic 9

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Input: works. The environmental and social risk rating is classified as Moderate. The Project is expected to have mostly positive social impacts through improved education quality, inclusion, and resilience. Impacts on vulnerable and systematically excluded groups are expected to be positive with limited negative social effects. **V.** **GRIEVANCE REDRESS SERVICES** 54. _**Grievance Redress**_ . Communities and individuals who believe that they are adversely affected by a project supported by the World Bank may submit complaints to existing project-level grievance mechanisms or the Bank’s Grievance Redress Service (GRS). The GRS ensures that complaints received are promptly reviewed in order to address project-related concerns. Project affected communities and individuals may submit their complaint to the Bank’s independent Accountability Mechanism (AM). The AM houses the Inspection Panel, which determines whether harm occurred, or could occur, as a result of Bank non-compliance with its policies and procedures, and the Dispute Resolution Service, which provides communities and borrowers with the opportunity to address complaints through dispute resolution. Complaints may be submitted to the AM at any time after concerns have been brought directly to the attention of Bank Management and after Management has been given an opportunity to respond. For information on how to submit [complaints

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Input: a reliable, accurate, and timely manner; and (c) project assets will be appropriately safeguarded. 75. **Based on the assessment conducted, the FM risk is rated moderate.** Project arrangements will rely on the existing public FM system at the IAs and the districts, which is adequate. The IAs have experience managing World Bank-financed operations and have mostly complied with key FM deliverables. However, the following potential risks are identified that relate to all the IAs, unless otherwise specified: (a) the Rwanda Feeder Roads Development Project (P126498), implemented by RTDA and Phase I of Jya Mbere under MINEMA have experienced challenges, including low budget execution, and qualified audit opinions on the achievement of value for money; (b) inadequate preparation of the AWPB, which could lead to errors in financial reporting and potential misallocation of expenditure; (c) delayed and/or poor-quality financial reporting; (d) staff capacity constraints; (e) poor planning, budgeting and budget monitoring systems; (f) possible ineligible expenditure due to potential BRD non-compliance with the MG manual guidelines; (g) inadequate and delayed implementation of internal and external audit recommendations; and (h) lack of automated accounting system at BRD. 76. **FM risk mitigating measures include** : (a) MINEMA will update the FM guidelines in

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Input: we use a cutoff distance of 20 km, we assume there is little economic footprint beyond that distance. Of course, any such distance is arbitrarily chosen, which is why we try different specifications to explore the spatial heterogeneity by varying this distance (using 10 km, 20 km, through 50 km) as well as a spatial lag structure (using 0 to 10 km, 10 to 20 km, through 40 to 50 km distance bins). [4] Second, we collapse the DHS mining data at the district level. [5] The number of districts has changed over time in Ghana, because districts with high population growth have been split into smaller districts. To avoid endogeneity concerns, we use the baseline number of districts that existed at the start of our analysis period, which are 137. Eleven of these districts have industrial mining. Because some mines are close to district boundaries, we additionally test whether there is an effect in neighboring districts. **3.1 Resource data** The Raw Materials Data are from InterraRMG (2013). The data set contains information on past or current industrial mines. All mines have information on annual production volumes, ownership structure, and GPS coordinates on location. We complete this data with exact

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Input: project as a whole. MINEMA conducted public and stakeholder consultations across the project target areas to gather feedback on the proposed activities, address concerns, and explore ideas to mitigate potential adverse impacts on socio-economic activities and livelihoods. The project’s stakeholder engagement plan, built on inclusive consultations, incorporates a comprehensive grievance redress mechanism (GRM) that provides stakeholders with accessible channels to resolve project-related grievances and complaints promptly. **B. Fiduciary** **Financial Management (FM)** 74. **An FM assessment was carried out for the project in accordance with the World Bank policy and directives** **on Investment Project Financing (IPF).** The assessment was carried out on the IAs – MINEMA, RTDA, BRD, BDF and the six districts – during project preparation to determine whether they have acceptable FM arrangements, which will ensure that: (a) funds are used for the intended purposes in an effective, efficient and economical way; (b) financial reports will be prepared in a reliable, accurate, and timely manner; and (c) project assets will be appropriately safeguarded. 75. **Based on the assessment conducted, the FM risk is rated moderate.** Project arrangements will rely on the existing public FM system at the IAs and the districts, which is adequate. The IAs have experience managing

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Input: at national-level, but notes that examining how these dynamics is distributed spatially within the country is still not possible (Rozenberg and Hallegatte 2016). Nonetheless, as a stress test, a number of papers in the field have employed the same strategy we follow in this paper, of using current socio-economic characteristics to examine potential future trends, which is considered standard practice (Hirabayashi et al. 2013; Koks et al. 2015; Winsemius et al. 2015). 3 While we considered other metrics of poverty (e.g. the Multi-dimensional Poverty Index), the only available data that was spatially explicit was the headcount and headcount rate, which we use for the analysis. This headcount rate uses an income definition of poverty, with those earning less than $1.25 USD per day classified as poor.

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Input: IGAs, small businesses and** **cooperatives all contribute to economic empowerment and development in target areas.** By aligning Sub-component 2.2 activities with the socio-economic infrastructure and productive facilities developed under Sub-component 2.1, the project aims to maximize productivity, improve market access, and bolster local economies. Together, these efforts will foster inclusive growth and resilience by ensuring that IGAs benefit from essential infrastructure, such as grain mills, seed centers, other productive facilities and local markets, creating a supportive environment that amplifies the economic impact across communities. The project will strengthen private sector linkages, drawing from good practices identified in the Yemen Emergency Crisis Response Program (ECRP) (2016-2022) (P159053), which used a community-driven approach to sector and business selection, and provided support to SMEs and MSMEs to finance productive investments and support financial inclusion for vulnerable communities. The World Bank’s Private Sector for Refugees (PS4R) initiative also offers good practice on how to create mutually beneficial relationships between the private sector and refugee communities on entrepreneurship, employment, investment, and products and services [23] . 52. **Technical expertise will be mobilized to identify key opportunities and effective strategies for catalyzing local** **economic initiatives** . This expertise will provide specialized guidance and advice, ensuring the

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Input: provinces combined contribute the rest. By contrast, agriculture is high in labor intensity (24 percent of total employment) but with low productivity (the contribution to GDP was 3.4 percent in 2021). [7] 4. **Zambia’s economy and population are highly vulnerable to climate change.** The country’s dependence on rainfed agriculture, which employs two-thirds of the workforce, and on hydropower makes it particularly vulnerable to climate shocks, threatening food production, electricity supply, and economic growth. While the country experiences low exposure to natural disaster risks in general, resilience is hindered by social vulnerability. Adverse impacts of climate change include increased frequency and severity of seasonal droughts, higher temperatures, flash floods, occasional dry spells, and changes in the growing season. In agriculture, the key risk stemming from climate change is the projected lower maize yields, as this is the country’s staple crop. Zambia is currently experiencing drought conditions in 84 of its 116 1 Zamstats.gov.zm. 2 Zambia Statistics Agency. 2023. _Highlights of the 2022 Poverty Assessment in Zambia_ . World Bank. 2023. _Zambia Gender Assessment_ . 3 Zambia: Selected Issues, IMF, 2023. 4 2022 Census of Population and Housing, GRZ 2023. 5 According to the 2021 Labour Force Survey Report by Zamstat, the

2022 Census of Population and Housing
2021 Labour Force Survey

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Input: activities to be implemented under DRDIP II in the 13 refugee settlements, 12 RHDs, refugee transit districts, and selected secondary cities. 82. **The potential E&S risks that may arise from component 1 civil works may include:** noise, dust, soil, water pollution, construction debris, sedimentation and deposition of fine materials in downstream watercourses during construction; soil and water pollution due to construction and occupational hazards. Some of the project activities under component 2 on INRM will also have positive environmental impacts. 83. **There are risks to sensitive ecosystems and biodiversity hotspots in the western and south-western RHDs.** The western region of the project falls within the Albertine Rift valley, considered one of the global biodiversity hotspots, with many tropical high forests remaining but under high pressure. The western region has a dense network of national parks, wildlife reserves, and natural forests. Kyangwali, Rwamwanja, and Kiryandongo refugee settlements are situated near Bugoma Central Forest Reserve, Katonga Wildlife Reserve, and Kibeka Central Forest Reserve, which have already been encroached upon. Likewise, the south-western region has Lake Nakivale and Mburo-Nakivale wetland system, which serves as a habitat for unique species of birds, animals, plants, and fish. Page 23

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

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Input: To construct a water-resource indicator, we draw on two sources of information. The first is an estimated geographic distribution of excess demand for water resources (surface and sub-surface) in Vörösmarty, et al. (2000). We use GIS to compute the total population residing in excess-demand areas identified by this research. The second information source is a database of deaths and injuries from floods maintained by the Centre for Research on the Epidemiology of Disasters (CRED, Université Catholique de Louvain). For each of the Bank's partner countries, we calculate the sum of deaths and injuries for all recorded floods since 1960. In constructing an indicator for flood damage, we weight deaths to injuries in the ratio 50:1. Using equal weights, we combine our indicators for demand pressure and floods into a composite indicator of water-related problems. [4] We derive our indicator for environmental policy and institutional problems from two sources. The first is the World Bank's Country Policy and Institutional Assessment (CPIA) database, which rates environmental policies and institutions on a numerical scale of 1 (the lowest) to 6. For this exercise, we reverse the scaling (1 becomes the highest) and normalize the ratings so that countries with the greatest problems score

Country Policy and Institutional Assessment (CPIA) database

database of deaths and injuries from floods

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Input: will be served by health centers, resulting in 50 maternal lives saved per year and a 15 percent reduction in other causes of morbidity (baseline morbidity of 19 percent). [28] The NPV of these investments is estimated at US$1.3 million at a 12 percent discount rate, with an ERR of 29 percent. 61. _Communal WASH_ investments reduce morbidity from diarrhea and other waterborne diseases, along with time and cost savings to access water. The analysis assumes the mortality rate due to unsafe water will reduce by 10 percent (from 25 deaths per 100,000 to 22.5), along with time savings of two hours per week per beneficiary household in accessing clean water and US$20 in annual cost savings per household (CEIC data). The NPV of these WASH investments is estimated at US$1.1 million at a 12 percent discount rate, with an ERR of 28 percent. 62. Additional assumptions used in the analysis for this component include: (a) value of a statistical life: US$2,394; [29] and (b) annual O&M costs of five percent of total investment value. [30] 63. The results of the analysis for this component and the sensitivity analysis are summarized in the tables below: **Table 6: NPV and ERR

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Input: we would have liked a sample to include data from the same households before and after the reform. The fact that NBS changed its panel of households in 2011 makes this impossible. The ability to use two panel data sets, each with multiple observations per household before and after the reform, still allows us to control for time-invariant household characteristics. [10] This implies that we can use a triple-difference approach that focuses on changes between the first and second panel period for the same households between treated and non-treated households in pre- and post-reform periods. Given the involvement of supervisors resident in the sample villages and the use of detailed logbooks to record consumption on a daily basis (Chen and Ravallion 1996), NBS data on consumption are considered of exceptionally high quality (Jalan and Ravallion 1999). Beyond information on consumption, the survey includes (less precise) data on income and individual members’ labor supply to farm or off-farm activities, migration, income from different sources, and an account of agricultural output and inputs. We also have data on key village characteristics including total working age population, agricultural land, and distance to public health and education facilities to control for village level time-varying

two panel data sets
data on key village characteristics
data on income

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Input: direct and indirect human actions. Fifth, we have not estimated the net present value of the coastal wetland loss over a period of time. The remainder of the paper is organized as follows. Section 2 summarizes the data sources and describes the methodology. Section 3 presents area estimates of wetlands at risk from sea-level rise (SLR) as well as the economic value of these projected losses. Section 4 briefly concludes. ##### **Data and Methodology** ###### **II.1 Data** In order to assess the exposure of wetlands at risk from SLR, we employed Geographic Information System (GIS) software to overlay the area of the wetlands with the inundation zones projected for 1m SLR. We have used the best available spatially-disaggregated global data sets from various sources, including the National Aeronautics and Space Administration (NASA), the US Geological survey (USGS), the World Wildlife Fund (WWF), and the Dynamic and Interactive Assessment of National, Regional and Global Vulnerability of Coastal Zones to Climate Change and Sea-Level Rise (DINAS-Coast) project. In particular: _Country boundaries and regions and coastlines_ . Country coastlines were extracted from the World Vector Shoreline, a standard National Geospatial Intelligence Agency (formerly Defense Mapping Agency) product at a nominal scale of 1:250,000.

the best available spatially-disaggregated global data sets

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Input: vulnerability to flooding, drought, and landslides and their adaptation and resilience priorities; (b) climate-sensitive planning; and (c) climate-resilient infrastructure standards for local government authorities and community institutions, including energy efficiency and renewable energy in schools and health facilities to respond to climate risks. Targeted capacity building will be provided for women in the community committees to strengthen their capacity to engage. 29. _Sub-component 1.3: Displacement Crisis Response Mechanism (DCRM) (US$10 million)._ The DCRM is a contingency financing mechanism for RHDs that experience large refugee inflows. Funds from this sub-component will be rapidly disbursed to districts that reach pre-agreed population triggers in terms of children per school classroom, patients per health facility, and people per water point. The DCRM financing will be used for infrastructure construction, upgrading or rehabilitation to expand education, health, and water service capacity in districts experiencing public service pressure caused by large refugee inflows. The DCRM triggers, eligible expenditures, and other details will be defined in an updated DCRM Manual for Phase II, approval of which by the World Bank is a disbursement condition. The sub-component will: (a) fund data collection to measure strains on schools, health facilities, and water points in the RHDs; and (b) finance

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Input: the needs of communities to intensify their coping strategies including livelihood-seeking activities that may expose them to ED risks. The application of the [sanctions in progress following the coup in Niger could increase the cost of living](https://ecowas.int/wp-content/uploads/2023/07/COMMUNIQUE-FINAL-CINQUANTE-ET-UNIEME-SOMMET-EXTRAORDINAIRE-DE-LA-CONFERENCE-DES-CHEFS-DETAT-ET-DE-GOUVERNEMENT-DE-LA-CEDEAO-SUR-LA-SITUATION-POLITIQUE-AU-NIGER.pdf) and increase the vital needs of already vulnerable populations. This new situation would expose them to the risk of enrolment in NSAG for a salary and therefore increase the capacity of NSAG in the installation of ED. In addition, the population could intensify their survival strategies by engaging in several types of activities to adapt to the high cost of living, including the search for subsistence in areas potentially affected by ED. Consequently, an upsurge in ED incidents is very likely. ## **III. Major challenges in mine action interventions (MA)** Graph4. Mapping of MA activities, 30 juin 2023 In January 2023, the Protection Cluster launched a joint initiative of mapping protection interventions including mine action. The analysis of the data resulting from this mapping combined with the analysis of the protection response monitoring data brought out the observation below: - Only two protection actors have interventions in MA which cover only a few localities of the 7 out of 11 departments affected by

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Input: The World Bank Economic Review 695 BASIX conducts marketing visits through village meetings and door-to-door visits. The first step is to hold a group meeting in the village, where potential customers are shown a marketing video that includes details about rainfall insurance (and other BASIX products). It then speaks with visitors and answers questions. The BASIX team then makes a follow-up visit where it goes door to door, trying to sell BASIX products, including rainfall insurance. Data The data set consists of the entire set of BASIX’s purchasers of rainfall index insurance from 2005 through 2007, which covers six states. [3] Though it ran small pilots in 2003 and 2004, BASIX began to mass-market rainfall insurance starting in 2005. The data contain limited personal information about each customer, including their location, how many policies they purchased, and what payouts they received during that season. The BASIX data covers 42 weather stations and includes a total of 19,882 customers during the period 2005–2007. [4] After numerous rainfall shocks in 2006, BASIX realized that many customers who had purchased only a small amount of insurance were disappointed that they received small payouts. In response, BASIX instituted a rule in 2007 that required

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Input: highly-developed. The resulting composite indicator for South Africa (8.49) is about 2.5 times Chad’s indicator value (3.44). Kenya has about the same composite indicator value as South Africa (8.35), but the indicator components are quite different. Kenya’s environmental indicator (2.39) is somewhat more than half of South Africa’s (4.24), but Kenya’s institutional problem indicator (3.5) is about 1.8 times South Africa’s. As a result, the products of the two indicators are nearly the same for the two countries. Of the four countries, Nigeria has by far the largest composite indicator value (25.29) because of the size of its overall environmental indicator (8.43). the equal-weighted index. Our results show that correlations among the indicators remain at .95 or higher, over a broad range of plausible definitions. 7

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Input: that net receipts from these schemes were, if anything, lower to for households inside the prefecture boundary than outside. Treatment and control areas may already have followed different growth trajectories before reforms. The standard way to check whether may have been the case is to test for parallel trends. [13] As discussed in detail below, we cannot reject the hypothesis of no significant differences in pre-reform trends between households inside and outside the border for overall household welfare and the share of income derived from agriculture non-farm employment, migration, and local wages and, with one exception, individuals’ time use, and agricultural yields and profits. Some significant pre-reform trends exist, however, with respect to use of agricultural inputs and crop choice. They point towards marked declines in agricultural assets ( 18% in Chengdu vs. +35% in villages outside the prefecture boundary) and use of inputs (-38% vs. -16%). Output shares of wheat and other grains increased and those of oil crops decreased in Chengdu while the opposite was true for households in neighboring counties. Also, key interventions in place during the period of concern are a new rural pension scheme (Lei _et al._ 2013), cooperative medical scheme (Wagstaff _et al._ 2009),

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A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source