ai4data/datause-dataset-rechunked huggingface dataset:
**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
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
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
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
**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
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
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
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
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
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
**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
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.
**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
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
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
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
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
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
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.
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