ai4data/datause-dataset-rechunked huggingface dataset:
before_ active*mines -0.013 0.025 -0.018* 0.002 0.003 0.075*** -0.028 (0.040) (0.030) (0.009) (0.021) (0.024) (0.028) (0.037) _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. *** p<0.01, **p<0.05, *p<0.1. Panel A is limited to bottom 40% in the income distribution, panel B uses urban interaction, and panel C has a count variable for active mines. Panel D1 drops sample between 20 and 40 km away, and D2 drops individual samples two years before mine opening. 151 women are sampled within 20 km from an active mine and in an urban area, and 246 women are sampled within 20 km from a mine regardless of its activity status and in an urban area. **Table A4 Spillovers on employment across districts** Panel A: Using production in the previous period (1) (2) (3) (4) (5) (6) (7) not agri- service profess- manual earns works VARIABLES working culture or sales ional work cash all year gold period 0.004 -0.009** 0.003* 0.004*** -0.002 0.001 0.006 District (0.004) (0.004) (0.002) (0.002) (0.004) (0.003) (0.004) neighbor -0.004 0.005 -0.001 -0.002*** 0.001 0.008* -0.002 gold production (0.004) (0.
(0.003) (0.004) neighbor -0.004 0.005 -0.001 -0.002*** 0.001 0.008* -0.002 gold production (0.004) (0.004) (0.004)
_Preparing country boundaries and coastlines_ . Countries and regions were identified with data from the World Bank and Exclusive Economic Zones from VLIZ (2011). The coastlines are derived from the SRTM 90 meter digital elevation model (DEM) data files used as a mask for calculating country totals for wetlands. Information on WMP categories for the Coastline was downloaded from the DIVA GIS database. _Building coastal terrain models (DTM)_ . Coastal terrain models derived from the SRTM 90 meter DEM data files were converted into an ESRI ArcGIS data format, and merged to conform to country boundaries in the ArcGIS environment. The analysis includes SRTM tiles, which are 5 x 5 decimal degrees, with a coastline. _Identifying inundation zones:_ Inundation zones were derived from the DTM by setting the value to 1 for SLR equal to 1 m. _Calculating exposure indicators_ . Delineated inundation zones were overlaid with wetlands to calculate exposure of wetlands to a 1 m SLR. Low elevation wetlands are within the Low Elevation Coastal Zone [17] . For the area calculation, grids representing cell areas in square kilometers at different resolutions were created, using the length of a degree of latitude and longitude at the cell center.
caregiving responsibilities over business activities. This norm restricts their time and engagement in income-generating pursuits and weakens their perceived legitimacy as entrepreneurs, reducing their chances of qualifying for credit. Additionally, women are often expected to rely on family support, especially from spouses, instead of seeking independent financial solutions, distancing them from formal financial institutions and financial products. 31 Baseline failure rates by segment are extremely difficult to assess. As such, the analysis uses conservative assumptions based on extensive discussions with key stakeholders and potential beneficiaries. These assumptions have been tested for sensitivity. 32 CEIC data 33 In the absence of available survey data, the analysis uses proxies for these losses and repair costs from survey data from neighboring Burundi. Based on this data, annual flooding losses amount to ~US$89 per household and repair costs amount to US$25.60 per household. 34 [Gendered Social Norms Diagnostic and their Impact on Women’s Financial Inclusion in Rwanda, 2022, Access to Finance Rwanda](https://afr.rw/downloads/gendered-social-norms-diagnostic-and-their-impact-on-womens-financial-inclusion-in-rwanda/) Page 18
with the notion that reduction of expropriation threats led to more effective use of agricultural land that created jobs for the less-skilled. **4.2 Impacts on time use** If, for example by promoting land-related investment or productivity-enhancing transfers of land to more efficient uses or users via rental markets, reforms increased productivity of land use and wages or changed certain activities’ relative productivity, we would expect corresponding shifts in overall labor supply or time allocation across sectors. [15] As we have individual level data on labor supply, we can use regressions for all individuals of working age (16-60 or 16-55 for males and females) in the sample to disaggregate estimated reform effects by gender. [16] Doing so suggests that reforms led to an expansion of labor market opportunities and a significant increase in total labor supply by males -with the young (16-40 years old) focusing on agriculture and the old (41-60 years old) on off-farm work and similar shifts, though no change in aggregate labor supply, by females. 14 With a mean cultivated area of 3.45 mu, the estimated benefit per mu in terms of consumption and income is 3,150*0.077/3.45 and 4,928*0.067/3.45, respectively. . 15 As we have information on time
million people in Sub-Saharan Africa are estimated to live in both extreme poverty (using a $1.9 a day definition) and significant flood risk – thus making them particularly vulnerable to prolonged adverse impacts on livelihoods and well-being. The remainder of this study is structured as follows. Section 2 offers an overview of the existing evidence base, especially on flood risk and poverty. Section 3 summarizes the flood risk, population, and poverty data used in the analysis. Section 4 details the analytical methodology and computational process. Section 5 presents and discusses the results. [1] ##### **2. Existing evidence** Prior to the availability of high-resolution global flood hazard maps, studies relied on historical disaster catalogues to produce rough global exposure estimates. Peduzzi et al. (2009) use a global inventory of recorded flood events from EM-DAT to estimate an exposure indicator at the country level. Yet, the lacking data on the spatial distribution and coincidence of flood risk and population, this approach does not allow a robust estimation of exposure headcounts. Jongman et al. (2012) provide the first global flood exposure analysis that utilizes spatial hazard and population data, albeit at relatively coarse resolution by current standards (10 x 10 kilometers for population
**Calculate sums** - For each flood risk array, calculate the total number of people exposed and add the results to the World Bank global administrative map shapefile ##### **5. Results** For each of the countries analyzed, the results are available as raster files with a 90m spatial resolution and as shapefiles with data aggregated to the admin 1 (sub-national), admin 0 (national), regional, and global levels. In this section, we present visualizations of key findings as maps, using a variety of spatial scales, as well as graphs to highlight pertinent insights. **5.1.** **Global and regional flood exposure** Our estimates show that globally 2.2 billion people are exposed to some level of flood risk; 1.47 billion people, or 19 percent of the world population, are exposed to a significant level of flood risk (i.e. facing inundation depths of over 0.15 meter in the event of a 1-in-100 year flood, or _moderate_ risk or higher in Figure 2). In other words, considering a global population of 7.7 billion (World Bank, 2019), approximately one in five people in the world are exposed to substantial flood risk. 10
maps developed for this study allow for this potential in future work. For the HCMC analysis, the location of the slum areas in the PUMA data set are mainly restricted to the old town. Furthermore, slum areas are often difficult to define (with PUMA only identifying potential slums) and the data we have likely does not capture all slum areas within HCMC. In terms of the hazard, the flood maps for HCMC show flood depth and extent from the river and from sea (when looking at the sea level rise scenario). Pluvial flooding and possible ‘sink’-areas in the city are not taken into account. Moreover, the lowest return period we have flood maps for is not low, compared to what is experienced in the city. Some areas of HCMC are flooded every year. Since this analysis used a flood with a 10-year return period as the flooding scenario with the highest recurrence interval we were not able to capture the relative differences in exposure to these yearly/bi-annual flooding events (and we hypothesize that poor people are relatively more exposed to these types of flooding than the general population). Despite these limitations, the analysis presented at the country and city-level can
GRZ’s decentralization policy. This includes procurement and FM responsibilities. Given that the bulk of technical activities are at the district level, adherence with the ESF will rest with the SPMU. The staffing profile of the SPMU will include (a) project manager, (b) environmental specialist, (c) social specialist, (d) civil engineer, (e) agribusiness specialist, (f) project accountant, (g) procurement specialist, (h) admin assistant, and (i) driver. The TOR and eligibility criteria for the above staff will be detailed in the PIM. Detailed institutional and implementation arrangements are provided at annex 1. **B. Results Monitoring and Evaluation Arrangements** 52. **The primary responsibility for overall project M&E will lie with the national PMU at the MoHAIS.** The PMU will have M&E capacity to support the establishment of a data platform to track project implementation across different Page 23
**The World Bank** Costa Rica Results in Education (CORE) (P181174) 9. MEP/PIU should retain all records contracts, invoices, bills, receipts, and other documents) of expenditures related to the Project until at least either one year after the Bank has accepted the audited financial statements covering the period during which the last withdrawal from the loan account was made or two years after the closing date, whichever is later. MEP will give the Bank access to these records to examine them. 10. **FM Supervision** . Project supervision will review the implementation of FM arrangements and performance, identify corrective actions as necessary, and monitor fiduciary risks. Supervision missions will take place on an annual basis and will include desk review of project IFRs and audit reports, following-up on any issues raised by auditors, as appropriate; participation in project supervisions at least once a year, to look into the operation of the control systems and arrangements described in this assessment; and updating the FM rating in the Financial Management Implementation Support and Supervision Report, as needed. In addition, during the first year of execution, the Project will be closely monitored (biannually) to verify that the FM arrangements are working as intended and to
to build resilience in districts experiencing large refugee inflows. A pilot to reduce gender-based violence (GBV) and violence against children (VAC) also contributed to social cohesion. A 2022 World Bank study on forced displacement reinforced the value of projects such as DRDIP, concluding that ‘multi-sectoral development investments directed to refugees and host communities can improve welfare, mitigate the negative effects of displacement, generate positive externalities for host communities, and promote social cohesion.’ [19] 21. **DRDIP II will be informed by lessons learned from Phase I and from other development responses in Uganda** **and the region over the last seven years.** Based on implementation experience and stakeholder consultations, key lessons include: (a) the CDD approach – particularly community-led procurement – is effective in the Uganda context for planning, community engagement, targeting, monitoring, and implementation; (b) the Labor-Intensive Public Works (LIPW) approach used for integrated natural resource management (INRM) subprojects based on the watershed approach has demonstrated encouraging results for environmental improvement, though with sustainability challenges for commercial assets such as tree plantations; (c) infrastructure investments have been of high quality and have improved basic services, including reported reductions in pupil-classroom, pupil-desk, and pupil-sanitation facility ratios. However, operations and maintenance (O&M) needs
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 impacts during the past 140 years. Our database therefore incorporates three major elements that are important for research on expectations formation and adaptation: pronounced long-term impact clustering; highly-varied clustering over 20-year periods; and a pronounced west/east trend. As previously noted, such variation is necessary for using cyclone impacts as effective statistical controls in comparative adaptation studies. We conclude that our panel database can make a significant contribution to household- and community-level studies that also incorporate social, economic, demographic and political variables from the relevant regions of India and Bangladesh. 21
with partner countries. We accept these overall preferences, and assume that future thematic lending shares will be identical to the lending shares for the period 1998 - 2003. **Table 7.1: Tests of the Cobb-Douglas Allocation Rule:** **Environmental Lending and AAA** (Standard errors in parentheses) Log Log Lending AAA Log Environmental Problem 1.136 1.265 Indicator (EPI) (0.18)** (0.21)** Log OED Success Probability (OSP) 2.602 1.143 (0.73)** (0.87) Constant -9.960 -5.099 (3.09)** (3.67) Observations 139 139 R-squared 0.28 0.21 Adj. R-Squared 0.27 0.20 **F [EPI = OSP = 1]** **2.65** **0.78** **Prob. > F** **0.07** **0.46** significant at 5%; ** significant at 1% _____________________________________________________________ We also use the lending shares as guidelines for AAA, since the Bank’s analytical and advisory activities are supposed to serve its lending program. Table 7.2 presents percent changes associated with movement from actual to optimal lending by sector and region. In Sub-Saharan Africa, for example, the overall gap between actual and optimal environmental lending is small (+5%), but thematic gaps vary from around -40% for climate change and biodiversity to +64% for land. South Asia has a relatively large overall gap (+33%), and thematic gaps ranging from -60% or less for biodiversity and climate change to over
GoR has progressive laws and policies on forced displacement.** The World Bank, in consultation with the United Nations High Commissioner for Refugees (UNHCR), has confirmed that Rwanda’s refugee protection framework is adequate for the purposes of access to the Window for Host Communities and Refugees (WHR). [5] Rwanda is a signatory to the 1951 Refugee Convention and the Global Compact on Refugees (GCR). The 2014 _Law_ _Relating to Refugees_ complies with international law and entrenches the right to work and freedom of movement. At the policy level, Rwanda’s _Strategic Plan for Refugee Inclusion 2019-2024_ made commitments to: (a) provide all [1 Data is from April 30, 2025. There are also 14,936 asylum-seekers in Rwanda: see https://data.unhcr.org/en/country/rwa](https://data.unhcr.org/en/country/rwa) [2 Loschmann, C., Bilgili, O. & Siegel, M. (2019) “Considering the benefits of hosting refugees: evidence of refugee camps influencing local labour](https://link.springer.com/article/10.1186/s40176-018-0138-2) [market activity and economic welfare in Rwanda,” IZA Journal of Development & Migration, pp. 1-23.](https://link.springer.com/article/10.1186/s40176-018-0138-2) 3 The self-reliance survey is part of the “Enhancing Self-Reliance and Preparedness for Forced Displacement in the Great Lakes Region” activity [(P500793) and draws on the global Refugee Self-Reliance Initiative index (www.refugeeselfreliance.org).](http://www.refugeeselfreliance.org/) 4 Phase I of Jya Mbere was approved on April 30, 2019, and will close
ship-to-shore radio after 1900, aircraft observation after 1940, and satellite observation after 1960. Measurement error has undoubtedly fallen over the years, but we cannot judge whether earlier technologies imparted any systematic bias to the recording of cyclone occurrences, track locations, wind speeds and storm radii. If historical observations have been unbiased estimates of storm characteristics, then the aggregative estimates in this paper should be also be regarded as unbiased, but with significantly-decreasing variance over the 140-year period. The remainder of the paper is organized as follows. Section 2 provides a technical discussion of database construction. Section 3 employs the database to investigate trends in the incidence and power of Indian Ocean cyclonic storms from 1877 to 2016. Section 4 focuses on trends in the spatial distribution of cyclone landfalls in Bangladesh, West Bengal and Odisha, while Section 5 broadens the analysis to impact zones around cyclone landfalls. Section 6 summarizes and concludes the paper. **2. Georeferenced Database Construction** We combine data from BMD, IMD and IBTrACS to document all recorded cyclonic storms in the Indian Ocean region from 1877 to 2016. We construct a panel database; the component for each storm includes a unique identification number; year and month of
have any positive expenditure on these, and we saw earlier that electricity access changes with the mine. This confirms that, among those who spend anything on electricity, they spend more on it in mining communities. **Table 12 Using GLSS: Household income and expenditure** (1) (2) (3) (4) (5) (6) (7) (8) (9) ln ln ln ln household level ln expenditure wages wages wages pc total health hh all women men exp. exp. food housing education energy active*mine 0.520** 0.694*** 0.391 -0.178* -0.126 -0.069 0.316** -0.168 0.297** (0.226) (0.241) (0.238) (0.093) (0.089) (0.095) (0.139) (0.199) (0.119) Observations 6,226 2,914 3,312 7,522 7,522 7,396 7,420 6,541 4,752 R-squared 0.121 0.128 0.118 0.959 0.964 0.963 0.933 0.837 0.950 _controls_ individual Y Y Y hh head Y Y Y Y Y Y hh size Y Y Y Y Y district fe Y Y Y Y Y Y Y Y Y year fe Y Y Y Y Y Y Y Y Y deflated N N N Y Y N N N N mean (ln) 15.30 15.29 15.31 13.04 14.19 13.42 10.88 10.74 9.52 _Note:_ (1) Annual wages and salaries for individuals in all ages (nondeflated). (2) Annual wages and salaries for women in all ages
**Annexe 1 :** Some examples of ED incidents _(Sources : Protection Monitoring, CNCCAI, INSO)_ - _**20/03/23:**_ _around 8 a.m. the vehicle of a local authority jumped on an improvised_ _explosive device at Bougoum, Torodi-Niamey axis (Tillabery region)_ - _**25/05/23 :**_ _ED in Ngouba at the passage of shepherds. 3 dead and cows killed (Diffa region)_ - _**16/06/23**_ _: EE against a DFS vehicle in Chetima Wango. 7 dead / 4 injured (Diffa region)_ - _**6/06/23 :**_ _an explosive remnant of war (ERW) exploded against 03 children in the village of_ _Agali (department/municipality of Dosso). The victims who were looking for scrap metal_ _probably confused the device with a piece of iron that they hit with a hammer. The explosion_ _caused the death of a child and 02 injured. It should be noted that the CNCCAI Regional Branch,_ _which is supposed to coordinate EDRE activities at the regional level, does not exist in Dosso_ _due to a lack of resources._ - _**4/07/2023 :**_ _around 9 a.m., an Improvised Explosive Device (IED) allegedly exploded at the_ _passage of vehicle of the DFS Niyya operation, who were patrolling on the Torodi-Makalondi_ _axis, not far from the village of Niaktiré (Tillabéry region)._ **Annex 2:**
**(3)** **(4)** Would have been payout in 2004 −3.843*** −4.592*** −5.045** −3.788* (0.987) (1.039) (2.173) (1.898) Ratio of hypothetical 2004 payout to 2005 Premium 4.365 −0.755 (4.610) (5.543) Payout ratio squared −1.991 −0.279 (1.814) (2.064) Constant 8.001*** 0.651 7.985*** 1.015 (0.714) (6.341) (0.713) (6.494) Weather station constants NO YES NO YES Observations 733 733 733 733 R-squared 0.073 0.094 0.075 0.097 _Notes_ : The dependent variable is the number of buyers in 2005, which was the first year rainfall insurance was offered to our sample. The unit of observation is the village. Would have been Payout in 2004 is a dummy that takes a value of 1 if there would have been a payout in 2004 had the 2005 policy been offered in that year. This is calculated based on daily rainfall data from APHRODITE and each policy’s payout structure. The weather station constants are the premium in 2005, the average historical payout, the total number of historical payouts, and the standard deviation of rainfall. Historical variables are calculated for the period 1962-2004. Observations are weighted by the quality of rainfall data. If there are no rainfall stations contributing to the APHRODITE data in 2004 within a .75°x.75° grid around
spirit of a Bstress-test^ to examine the seriousness of the issue, this paper employs flood hazard maps and spatial socioeconomic data to examine the following questions in context of Vietnam: 1. How many people are exposed currently? How might this change under climate change? 2. Where is exposure highest currently? How might this change under climate change? 3. How many poor people are exposed currently? How might this change under climate change? Furthermore, given that the dynamics of poverty and natural disasters (and particularly, floods) occur at the local level, analyses at the national scale (or even at the province or district level) may miss important mechanisms and small-scale differences, from one city block to the next. To complement the country-level analysis, we also focus at the local level within Ho Chi Minh City (HCMC), a city with high flood exposure. Here, we combine high-resolution flood hazard data with spatial data on slum location to examine the distribution of exposure across poor and non-poor locations. While prior studies have examined flood risk in Vietnam, this paper provides two main contributions. First, we develop new high-resolution flood hazard datasets, which incorporate both riverine and coastal flooding and consider climate change. [1]
skewed. [10] As the scatter plot in Figure 5.1 suggests, the association between overall environmental problems and lending is very strong for those countries that have received environmental loans. **Figure 5.1: World Bank Environmental Lending vs.** **Overall Environmental Problems (Log Scale)** In a log-log regression of environmental lending on the overall environmental indicator (Table 5.1, column 1), the estimated response elasticity is .70, with an associated t-statistic of 10.3 and regression R [2] (adjusted for degrees of freedom) of .53. 10 Conventional regression and correlation analysis assume that variable distributions do not contain extreme “outlier” observations, because such outliers can sharply skew the results. In this case, both nominal and per-capita distributions are extremely skewed. Log measures, on the other hand, have regular, approximately-normal distributions with no outliers. 12