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[{"key": "rafael-000", "text": "br>exceed the end targets because the RF is based on data from 2017, which was the last time MoES had a comprehensive Education Management Information System (EMIS). Enrollments in target areas have increased in the interim for reasons outside the influence of the project. The new, recently developed EMIS is being populated with values to be available by the end of the year.|Current values are based on a survey conducted by the PCU in August/September 2023. Values exceed the end targets because the RF is based on data from 2017, which was the last time MoES had a comprehensive Education Management Information System (EMIS). Enrollments in target areas have increased in the interim for reasons outside the influence of the project. The new, recently developed EMIS is being populated with values to be available by the end of the year.|Current values are based on a survey conducted by the PCU in August/September 2023. Values exceed the end targets because the RF is based on data from 2017, which was the last time MoES had a comprehensive Education Management Information System (EMIS). Enrollments in target areas have increased in the interim for reasons outside the influence of the project. The new, recently developed EMIS is being populated with values to be available by the end of the year.|Current values are based on a survey conducted by the PCU in August/September 2023. Values exceed the end targets because the RF is based on data from 2017, which was the last time MoES had a comprehensive Education Management Information System (EMIS). Enrollments in target areas have increased in the interim for reasons outside the influence of", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:004008:3:1:0", "start": 420, "end": 447, "surface": "survey conducted by the PCU", "probe_tag": "keep", "probe_score": 0.9298, "luna_label": 1, "luna_reason": "Current values are based on this completed survey."}]}, {"key": "rafael-001", "text": "#### **CHAPTER 3: SOCIO- ECONOMIC ENVIRONMENT**\n\n**2.1 Total economic value (Forest Use/Economic values)**\nThe population of Nakasongola District, particularly in sub-countries surrounding\nthe two CFR’s of Katugo and Kasagala has steadily been increasing; which has\nboth direct impacts on the forest resources. Table 8 shows the population trend\nof these sub-counties.\n\n**_Table 8: Population Growth and Distribution in Sub-counties_** **.**\n\n\n\n|Sub-county|Land Area (km) 2|No. of people (1991)|No. Of people (2002)|Population Density persons/km2|\n|---|---|---|---|---|\n|Kakooge|554.99|17,475|20,615|37|\n|Wabinyonyi|437.73|16,977|13,621|31|\n|**Total**|||||\n\n\n_Source: Nakasongola Local Government 2002 (Population census)_\n\nThe two (2) CFRs are major grazing areas for cattle. Although this is illegal, cattle\nowners overlook legal implications. It should be noted that Kasagala, has no\ngrazing land vegetation limitation compared to Katugo Plantation (the planted\narea of Kasagala is still quite small). Table 9 indicates the cattle stocking in subcountries.\n\n**_Table 9: Cattle stocking in sub-countries Adjacent to CFRs._**\n\n|Forest Reserve|Sub- county Adjacent|No. Of cattle|\n|---|---|---|\n|Katugo|Kakooge|19,342|\n|Kasa", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:015910:20:0:0", "start": 683, "end": 716, "surface": "Nakasongola Local Government 2002", "probe_tag": "keep", "probe_score": 0.9299, "luna_label": 1, "luna_reason": "Source citation for population census data presented in the table."}]}, {"key": "rafael-002", "text": "**Kenya Informal Settlements Improvement Project: Machakos Region**\nResettlement Action Plan Report for Kariobangi Informal Settlement\n\n\n**DEFINITION OF TERMS USED IN THIS REPORT**\n\nThe following terms shall have the following meanings, unless the context dictates\notherwise 1 [^1: Water Supply and Sanitation Service Improvement Project (WaSSIP). 2007. Resettlement Policy Framework, RP\n583. Government of the Republic of Kenya\nRepcon Associates. 2011. The Kenya Informal Settlements Improvement Programme: Resettlement Policy] :\n\n**Census** : A field survey carried out to identify and determine the number of Project Affected\nPersons (PAP) or Displaced Persons (DPs) within the project area boundaries. The meaning\nof the word also embraces the criteria for eligibility for compensation, resettlement and other\nmeasures emanating from consultations with affected communities.\n\n**Project Affected Person:** This is a person affected by land use or acquisition needs of the\nKenya Informal Settlements Improvement Project (KISIP). The person is affected because\ns/he may lose “title to land or right to its use”, and/or “title rights or other rights to\nstructures constructed on the land” (thus s/he may lose, be denied, or be restricted access to\neconomic assets, shelter, income sources, or means of livelihood). The person is affected\nwhether or not s/he must move to another location.\n\n**Compensation:** The payment in kind, cash or other assets given in exchange for the\nacquisition of land including fixed assets thereon.\n\n**Cut-off date:** The date of commencement of the census of PAPs or DPs within the project\narea boundaries. This is the date on and beyond which any other person who occupies the\nland delineated for project use, will not be eligible for compensation.\n\n**Effective cut-off date:** The date of the meeting held at the conclusion of the census survey\nbut before disclosure of", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:018022:5:0:0", "start": 1597, "end": 1618, "surface": "census of PAPs or DPs", "probe_tag": "keep", "probe_score": 0.9141, "luna_label": 0, "luna_reason": "Defined census concept, not cited as existing data used for analysis or finding."}]}, {"key": "rafael-003", "text": "**The World Bank**\nUganda Secondary Education Expansion Project (P166570)\n\n\n**Figure 3: Survival rates in primary education.**\n\n\n\n100\n\n\n90\n\n\n80\n\n\n70\n\n\n60\n\n\n50\n\n\n40\n\n\n30\n\n\n20\n\n\n10\n\n\n0\n\n\n\n100\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGrade1 Grade2 Grade3 Grade4 Grade5 Grade6 Grade7 Grade8 Grade9\n\n\nSource: Facing Forward, 2017\n\n6. **In spite of increased access to schooling, the average level of education of the work force remains low and does**\n**not meet labor market requirements.** Uganda will need to absorb an additional 600,000 new entrants to the labor\nmarket each year between 2014-2020. In order to sustainably increase welfare, these entrants must find productive\nemployment. 9 [^9: Uganda job diagnostics/strategy, World Bank, 2018, draft.] Estimates from the National Household Survey (UNHS) (2016) show that only entrants with postsecondary education can escape informal sector work. In order to increase the employability and productivity of the\nexpanding workforce, supply of quality education, especially for low-income, rural households and girls, is critical.\nAccording to the UNHS, only one in five people aged 15 and above completed secondary education. Thus, a large\nnumber of youth enter the job market without foundational skills of basic literacy and numeracy, as well as generic\nskills essential for life and work.\n\n**B.** **Sectoral and Institutional Context**\n\n7. **Uganda is the pioneer in terms of introducing universal access to secondary education in Sub-Saharan Africa.** The\nsecondary education sub-sector in Uganda is centrally managed and comprises six grades, Senior 1 (S1) to Senior 6\n(S6). S1-S4 is categorized as ordinary (‘O’) level, or lower secondary, while S5-S6 is Advanced (‘A’) level, or upper\nsecondary. In 2007, Uganda introduced the Uganda", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:014224:4:0:0", "start": 764, "end": 789, "surface": "National Household Survey", "probe_tag": "keep", "probe_score": 0.9113, "luna_label": 1, "luna_reason": "2016 household survey provides evidence on escaping informal sector work."}, {"key": "fcv_pads_east_africa:014224:4:0:1", "start": 1088, "end": 1092, "surface": "UNHS", "probe_tag": "keep", "probe_score": 0.955, "luna_label": 1, "luna_reason": "UNHS supports the finding that one in five completed secondary education."}]}, {"key": "rafael-004", "text": "### 1.8 11\n\n\n\nCurrent account balance . **.2**\n\n\n\nFinancing items (net)\nChanges in net reserves .. .\n_Memo:_\nReserves including gold _(US$ millions)_\nConversion rate _(DEC, locaW/US$)_ 177.7 177.7 177.7\n\n\n**EXTERNAL DEBT and RESOURCE FLOWS**\n\n**1979** **1989** **1998** **1999**\n_(US$ millions)_ **Compositlon of total debt, 1998 (USS milIlona)**\nTotal debt outstanding and disbursed 26 **179** 288\nIBRD 0 0 0\nIDA 0 26 50 49 G 15 B\n\nTotal debt service 2 15 6\nIBRD 0 0 0 C: 9\nIDA 0 0 1 1\n\nComposition of net resource flows E: 119\nOfficial grants 5 32 48\nOfficial creditors -1 3 2\nPrivate creditors 0 -1 0\nForeign direct investment 0 0 6 D: **95**\nPortfolio equity 0 0 0\n\nWorld Bank program\n\nCommitments 0 9 3 A - IBRD E - Bilateral\nDisbursements 0 2 2 1 B - IDA D - Other rrultilateral F **-** Private\nPrincipal repayments 0 0 0 0 C- IMF G - Short-term\nNet flows 0 2 2 1\nInterest payments 0 0 0 0\nNet transfers 0 2 1 1\n\n\nNote: This table was produced from the Development Economics central database. 9/13/00", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:017306:63:1:0", "start": 960, "end": 998, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.946, "luna_label": 1, "luna_reason": "Named database cited as the source of the presented data table."}]}, {"key": "rafael-005", "text": "3\n\n\n**2. Project development objective** (see Annex 1)\n\nThe objective of the project is to enhance the quality of education and to increase enrollment in\nprimary schools.\n\n\n**3. Key performance indicators:** (see Annex 1)\n\nThe key performance indicator is an increased number of students enrolled in grades 1-9, especially\namong girls.\n\n\nB. **STRATEGIC CONTEXT**\n\n\n**1. Sector-related Country Assistance Strategy (CAS) goal supported by the project** (see Annex 1)\n\n\n**Document number:** P 7403 DJI **Date of latest CAS discussion:** (scheduled for) 12/19/00\n\nThe CAS has been prepared in the context of the country's economic difficulties and deepening\npoverty. Despite Djibouti's relatively high nominal per capita income (US$790 versus an average of\nUS$510 for Sub-Saharan Africa, and US$100 for Ethiopia), Djibouti has one of the poorest social\nindicators in the world (poverty, illiteracy, maternal and infant mortality, and morbidity), according to\nthe UNDP Human Development Index, ranking 157th among 174 countries.\n\n\nThe Republic of Djibouti has very few natural resources and the economy is mainly dependent on the\nport, external financial assistance, the French military and associated services. However, with the\n\ndecreased amount of external assistance, and deepening structural problems, the country has suffered\neconomic stagnation over the past decade and a half. As a result, per capita Gross Domestic Product\n(GDP) declined by 50% in real terms since 1985. The switch of Ethiopia's transit traffic from Assab in\nEritrea to Djibouti in mid-1998 and the consequent four-fold increase in port traffic has opened new,\nas yet not fully exploited, opportunities for investment and growth. Djibouti's open economic policies\nand relative stability, characterized by a liberal trade policy and exchange system, which operates free\n\nof capital or", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:014952:6:0:0", "start": 959, "end": 987, "surface": "UNDP Human Development Index", "probe_tag": "keep", "probe_score": 0.9623, "luna_label": 1, "luna_reason": "Named index provides the cited ranking supporting Djibouti's poor social indicators."}]}, {"key": "rafael-006", "text": "**ALTERNATIVES TO THE APPROACH TAKEN IN REGARD TO THE**\n**SHREDDING TENDER**\n\n\nThe water hyacinth is a reflection of the wider problem of eutrophication of Lake Victoria.\nWater hyacinth can be found in most African Great Lakes. For example, one can see individual\nhyacinth plants in Lake Malawi, and it is likely that the hyacinth has been present in Lake\nMalawi at least as long as it has in Lake Victoria. Yet there are no mats of hyacinth in Lake\nMalawi, and it is not a major problem there. The difference is in the quality of lake water. Lake\nVictoria is eutrophic and Lake Malawi is oligotrophic. If the water hyacinth were not present in\nLake Victoria, then the nutrient bound in the hyacinth mats would, in all likelihood be tied up in\nphytoplankton or other plants living in the Lake. **_The hyacinth do not ADD nutrients as implied_**\n**_in the complaints, they only make use of the nutrients already in the water. The sources of the_**\n**_nutrients in the Lake are surface runoff from the catchment and atmospheric deposition._**\n**_Control of the eutrophication process has little or nothing to do with water hyacinths, which_**\n**_are really only a symptom of the “illness” (i.e. nutrient enrichment of the Lake) rather than_**\n**_the “illness” itself._** .\n\n\nGiven that it would be impossible to do a thorough EA in anything less than 3-5 years or\nmore (the time it would take to collect the minimum amount of baseline data, assuming that the\nLVEMP suffers from no", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "sample:fcv_pads_east_africa:020447:33:0:0", "start": 1466, "end": 1479, "surface": "baseline data", "probe_tag": "confusion", "probe_score": 0.2009, "luna_label": 0, "luna_reason": "Refers to baseline data collection required in future."}]}, {"key": "rafael-007", "text": "Annex 11\nPage 2 of 4\n\n\nThe country's General Environmental Law, expected to be promulgated very shortly, is divided into four\nmajor titles:\n\n\nTitle 1: Concepts, Objectives and General Principles, and Institutional\nOrganization\nTitle 2: Protection of Environments\nTitle 3: Protection of Animal and Plant Species\nTitle 4: Regulation of Pollution\n\n\nImplementing decrees should quickly specify the conditions for putting the main chapters of this\nframework legislation into effect.\n\n\n_Other Agencies And Bodies Involved_\n\n\nAs regards environmental education, the Directorate of the Environment maintains close collaboration\nwith the National Education Research and Pedagogic Information Center _[Centre de Recherche, et de_\n_Production dInformation de l 'Education Nationale-_ CRIPEN], in particular through the formulation of an\n\nawareness campaign strategy on environmental problems.\n\n\nInfrastructure facilities in the education sector are provided by the Directorate of Housing, Urban\nDevelopment, Environment, and Regional Development (DHU). However, as the current reform process\nis not yet completed, a number of serious malfunctions are preventing the Directorate from perforning\nthe role of executing agency assigned to it in the past. The Planning Unit of the Ministry of Education\nwill be the contracting authority's representative for implementation of this project.\n\n\nBecause the system for gathering and analyzing data on the public health system is no longer operational,\nas was confirmed during the visit made to Djibouti's Pelletier Hospital and to the Djibouti-City health\ndistrict, reliable country-wide epidemiological data are unfortunately unavailable. This state of affairs\napplies to the school population in particular. Under these conditions, it will be difficult to define and use\nindicators capable of measuring the success of actions to mitigate the environmental impacts of the\nproject.\n\n\n**Potential Impacts of the Project**\n\n\nThe mission by an IDA environment specialist to the Republic of Djibouti in June 2000 confirmed the\ndegraded situation of the sanitary facilities in all of the schools visited. Discussions with school staff and\nparents of students revealed the concern felt by the latter regarding the", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:019683:65:0:0", "start": 1605, "end": 1638, "surface": "country-wide epidemiological data", "probe_tag": "confusion", "probe_score": 0.3364, "luna_label": 0, "luna_reason": "States epidemiological data are unavailable without analyzing or substituting estimates."}]}, {"key": "rafael-008", "text": "Resettlement Policy Framework Page **31** of **73**\n\n\n - Being near a service centre where the resettlement area imposes transport costs\nto get the service;\n\n\n \n##### **Method of property valuation**\n\n\nThere are at least two main methods for property valuation: cost replacement method and\nmarket value method. In the first method all assets are listed, materials of constructions are\nidentified, current costs to replace those assets is then calculated based on a unit rate for the\ndifferent components. Depreciation of the materials or assets may be considered in the\ncalculation. The critical issue in replacement method is land value. Land of a similar size\nmaybe replaced but the value of land is not only its size; rather it is the location value. In\nprivate ownership of land location factor has to be considered in determining the location and\nsize of the new plot; whereas in cases of public ownership of land the principle is to replace\nland of a similar size or standard size for the category of use in an appropriate location.\n\n\nIn the second method, the market value method, there are two approaches:\n\n\nCase 1: Assets are inventoried, conditions are determined, current unit rate for the\ncomponents is defined and applied, depreciation rate is applied to the cost of the cost of the\nconstruction, land value is determined based on value index as determined by land market\ninventory, value of good name for the trade is valuated based on market inventory. Therefore,\nthe value of the property is the sum of the current value of the construction, the good name of\nthe trade, and land value.\n\n\nCase2: Value of property may be determined based on a research using broker’s data for the\nvalue of property in an area where the property is located; or the property may be auctioned to\ndetermine its real market value.\n\n\nThe principle used for property valuation in cases where the property is to be demolished is\nthe cost replacement method. This method is used in all regional", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:015996:30:0:0", "start": 1426, "end": 1447, "surface": "land market\ninventory", "probe_tag": "confusion", "probe_score": 0.4345, "luna_label": 1, "luna_reason": "Existing market inventory determines the value index used in property valuation."}, {"key": "fcv_pads_east_africa:015996:30:0:2", "start": 1726, "end": 1739, "surface": "broker’s data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Broker data are used to determine property value through market research."}]}, {"key": "rafael-009", "text": "Annex 2\nPage 4 of 4\n\n**Project Component 3 -** US$ **4.10 million**\n\n**Improve the Government's Capacity to Manage Sector Reforms** will be addressed through:\n(i) supporting the activities of the CNOSEGE in its effort to coordinate the reforms and raise\nresources to support the reform; (ii) capacity building support to the Ministry of Education key\nunits such as the Planning Unit _(Service de la Planification),_ and the Education Projects Bureau\n(BEPE); and (iii) other technical assistance necessary to improve private public partnership in\neducation, development of an effective gender strategy, and possible reforms in textbook policy.\n\n\n_Detailed Description of Sub-components:_\n\n\n_(i) CNOSEGE:_ The Executive Secretariat of CNOSEGE is responsible for implementing the\nreform program. The project will finance the secretariat including its fund-raising activities.\n\n\n_(ii) Support to MOE Planning Unit:_ The project will finance capacity building of the Ministry of\nEducation's Planning Unit, through expert assistance in dealing with the collection of educational\nstatistics, the production of a _carte scolaire_ and analysis of census or household survey data, and\nkey staff in the BEPE including the purchase of project management tools (finance and\nprocurement). The planning unit will also carry out the monitoring and evaluation studies\nrequired under the project. In addition, the unit will implement and monitor the Environmental\nManagement Plan (EMP).\n\n\n_(iii) Additional Technical Assistance_ will also be provided to enable the ministry to develop more\ncost effective strategies including exploring the possibility of cost effective public/private\npartnerships. Technical Assistance will also be provided to develop strategies to reduce the\ngender gap in enrollments as well as the gap by income group. Pilot studies will also be financed\nunder the credit for optimal designs in school construction, demand financing, sanitation upkeep\nand maintenance, and", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:010914:35:0:0", "start": 1138, "end": 1169, "surface": "census or household survey data", "probe_tag": "confusion", "probe_score": 0.104, "luna_label": 0, "luna_reason": "Planned capacity-building activity to analyze data, not an existing analysis used here."}]}, {"key": "rafael-010", "text": "required for local government involvement, and arrangements for maintenance, monitoring and\nevaluation (M&E). Social capital enhancing activities would be a mandatory part of all sub-projects, and\nwould be tailored to support activities chosen by the communities.\n\nSupport to Decentralized Government Structures. Most local administrations are beginning\nto operate again with a limited number of staff and other inputs. District and chiefdom authorities are\nvery weak, however, and lack the financial and human resources needed to address their concerns and\npnorities effectively. NGOs have demonstrated their ability to implement successful community-based\nsocial and economic projects and have played a key role in shelter reconstruction activities. With the\ngradual strengthening of local government capacity, partnerships between community groups and local\nauthorities are expected to increase. Upon completion of initial training, district and chiefdom authorities\nwould be required to demonstrate that they have used the training by showing that there have been some\nimprovements in their community. For instance, at the end of each training session, district authorities\nwould be required to develop a simple action plan that specifies some activities that NSAP or other\npartners could support. District and chiefdom authorities would also gain experience in implementing,\nsupporting or overseeing community development activities.\n\nHealth. The unfavorable health indicators in Sierra Leone can be attributed to several factors.\nHigh fertility, female genital mutilation and the presence of HIV/AIDS increase morbidity and mortality\nrisks for women and children. Many risk factors that have contributed to HIV/AIDS epidemics in other\nAfrican countries have long been present in Sierra Leone, and the protracted conflict has created the\nconditions for explosive growth in HIV/AIDS infection rates. The Centers for Disease Control carried\nout a survey in 2002 which found the HIV prevalence among adults (aged 1549) to be 6.1 %; in\nFreetown, 4% in rural areas and 4.9% nationwide. In response to the crisis, Government has developed\na multi-sector HIV/AIDS Program, which is being supported by various", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:021039:10:0:0", "start": 1950, "end": 1964, "surface": "survey in 2002", "probe_tag": "confusion", "probe_score": 0.7887, "luna_label": 1, "luna_reason": "Past survey attributed to CDC and tied to HIV prevalence findings."}]}, {"key": "rafael-011", "text": "###### Resettlement Action Plan (RAP) For Dire Dam Rehabilitation Project\n\n**7.3 Method of Valuation**\n\nThere are three commonly known methods of valuing assets and properties, namely,\nincome based approach, replacement cost a market data or comparable sales\napproach. However, in this RAP, the methodology for valuing assets is referred at Full\nReplacement Cost. Full Replacement Cost is one method of valuation of property and\nthat determines the amount of replacement through compensation.\n\nThe concept of Full Replacement Cost is based on the premise that the costs of\nreplacing productive assets that would be expropriated for purpose of project activity.\nThe replacement cost approach involves; direct replacement of expropriated assets and\ncovers an amount that is sufficient for asset and property replacement.\n\n**7.4 Valuation for permanent loss of Farm Land / Crop Loss**\n\nThe project impact is perceived in terms of permanent land loss/crop loss will be\ncreated because of the need to expand the existing sanitary zone by limited size.\nAccordingly it is apparent some part of farm land that is owned by individuals will be\nexpropriated.\n\nAccording to the information obtained from PAPs and physical observation the farm\nland is used in most case to cultivate wheat and been. With this consideration the\nproductivity of the land is considered based on above mentioned dominantly produced\ncrop using data that is obtained from Woreda Agriculture office.\n\nHence, the land that will be expropriated from the farmers is initially valuated in\nconsidering the average crop yield obtained per hectare against the land size that would\nbe lost as the result of project activity.\n\n**7.5 Valuation for Grazing land**\n\nGrazing land in the area has a considerable economic benefit for the local communities\nin the aspect of raising livestock for different purpose including livestock products used\nfor domestic consumption and market consumption and livestock labour for different\npurpose.\n\nWith this consideration the benefit of grazing land for ones household is valuated\nagainst grass obtained from one", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:020132:48:0:0", "start": 1192, "end": 1196, "surface": "PAPs", "probe_tag": "confusion", "probe_score": 0.6107, "luna_label": 0, "luna_reason": "Names information providers, not an eligible data resource"}, {"key": "fcv_pads_east_africa:020132:48:0:1", "start": 1409, "end": 1461, "surface": "data that is obtained from Woreda Agriculture office", "probe_tag": "confusion", "probe_score": 0.87, "luna_label": 1, "luna_reason": "Agriculture office data directly informs crop-yield valuation calculations."}]}, {"key": "rafael-012", "text": "27. **Adequacy of monitoring and evaluation arrangements:** Regular monitoring and\nevaluation allowed the project to produce good quality data for management and evaluation.\nTracer studies provided data on key performance indicators. Three Beneficiary\nAssessments were carried out with samples of youth, employers, and trainers to provide\nfeedback on project implementation, constraints, and interns’ and employers’ satisfaction\nwith the program. In addition, an employer tracer survey was conducted to collect feedback\nfrom employers on program implementation, quality of training, and interns’ retention rates.\nThe evaluation of the project merits recognition for being based on a rigorous random\nexperimental design.\n\n28. **Relationships and coordination with partners/stakeholders:** The project\nmaintained good working relationships with all its key stakeholders and partners.\n\n29. **Adequacy of transition arrangements for regular operation of project-**\n**supported activities after loan/credit closing:** Lessons learned have been shared with\nrelevant government institutions ready for KYEP scale up. Plans have been put in place to\nutilize private sector employers and the staff who were involved in implementation of the\nKYEP in the scale up of the program.\n\n\n**CAPACITY BUILDING AND POLICY DEVELOPMENT COMPONENT**\n\n30. The following table summarizes the activities planned, tasks carried out, and results\nachieved.\n\n**Table of Activities**\n\n\n\n\n\n|Activities|Tasks carried out|Achievements/Resu lts|\n|---|---|---|\n|1. Facilitating release of funds to KEPSA|Requisitioning for funds from the World Bank through the National Treasury to KEPSA and back-flow reporting.|The flow of funds to KEPSA was consistent throughout the project life,", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:017049:65:0:0", "start": 463, "end": 485, "surface": "employer tracer survey", "probe_tag": "confusion", "probe_score": 0.273, "luna_label": 0, "luna_reason": "Survey was conducted to collect feedback, so it is project-produced data."}]}, {"key": "rafael-013", "text": " from the last JRIS mission (99,344,644 ETB).\n\n - **_LH grant performance_** : 2,608,253,185 ETB disbursed for 78,005 HHs and 2,561,639,214 ETB grant\ntransferred to 77,155 (55,526F) HHs.\n\n - **_Employment:_** from the first wave beneficiaries, 72,430 (52,315F) and 1,956(913F) employed in\nself-employment and wage employment pathways, respectively.\n\n - **_Impact Evaluation:_** The baseline data (collected in January 2022), follow-up I (Sept/Oct 2023), and\nFollow-up II (Sept/Oct 2024) fieldwork data have been collected and the final datasets have been\nsubmitted to the WB.\n\n\n19", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:005761:18:2:0", "start": 490, "end": 504, "surface": "fieldwork data", "probe_tag": "confusion", "probe_score": 0.371, "luna_label": 0, "luna_reason": "The sentence states that project fieldwork data were collected, indicating production rather than use."}]}, {"key": "rafael-014", "text": ".g. bitumen binders\nfor asphalt concrete). Although climate change is not in the manual, it can be included using the\nmethod in Section 3.2.4 and applying the new temperatures in\nthe calculations.\n\n\n#### **3.3 Structural resilience**\n\nStructural design is critical to the resilience of buildings and\nother structures, to withstand environmental shocks and\nstresses during the project’s lifetime. In terms of physical\nresilience, in addition to changes in temperature resulting\nfrom climate change, structural design should also take into\naccount actions of wind, and seismic design. The frequency\nand magnitude of high wind events, or earthquakes, in Kenya\nare not anticipated to be impacted by climate change and\nthus ‘normal’ application of the relevant design codes will\nprovide structural resilience.\n\n\nThe following codes should be applied for structural resilience:\n\n\n - The draft National Building Code 2022\n\n\n### **Summary**\n\n- Follow the draft National\nBuilding Code 2022\n\n\n- Any gaps? – the National\nBuilding Code should be\nupdated to reflect the\nEurocodes\n\n\n\n\n- Where there are gaps in the National Building Code, the Code itself should be updated\nto reflect Eurocodes to make it easier for municipal officials and local builders to\nunderstand standards and the requirements. The following Eurocodes in particular may\nbe relevant:\n\n\n`o` Eurocode 1: Actions on structures - Part 1-4: General actions - Wind actions, the\n\nexisting wind map for Kenya is valid, climate change is anticipated to have limited\nimpact on maximum gust speed. Wind data, as necessary, can be provided by\nKMS.\n\n\n21/61", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:007268:21:1:0", "start": 1443, "end": 1461, "surface": "wind map for Kenya", "probe_tag": "confusion", "probe_score": 0.7015, "luna_label": 1, "luna_reason": "Existing wind map informs assessment of climate impacts on maximum gust speed."}, {"key": "fcv_pads_east_africa:007268:21:1:1", "start": 1548, "end": 1557, "surface": "Wind data", "probe_tag": "confusion", "probe_score": 0.5102, "luna_label": 0, "luna_reason": "Data availability is stated, without an analyzed finding or actual use."}]}, {"key": "rafael-015", "text": "21\n\n\nthey have to purchase water from trucks which costs four times as high than a household connection;\nand qat consumption poses a major social, income and productivity issue.\n\n\nIn order to capture as much of the school-age population presently out of school due to the lack of\nexisting places, the project will construct additional/rehabilitate classrooms. In addition, sanitation\n\nservices will be rehabilitated, and a study will be undertaken on which sanitation services best serve\nthe area, especially in a drought-prone area, and the most cost-effective methods of implementation\nand maintenance. The project will finance a study to analyze the factors that hinder girls' attendance\nand achievement, and of the feasibility of measures to overcome them, including the issues\n\nsurrounding access to education by the poor. The problems are cross-sectoral which the project, in\nPhase I, will not address (unemployed youth, health issues, non-Djiboutian school-age population,\netc.). See Section C above for program details.\n\n\nThe IDA's regional team will discuss with Government on updating the initial 1997 poverty\n\nassessment in order to produce a better picture of the issues as they exist now. In addition, it is\nenvisaged that IDA will discuss the rising health issues with the Government and the best ways for\naddressing these problems.\n\n\n_6.2 Participatory Approach: How are key stakeholders participating in the project?_\n\n\nKey stakeholders participated in the National Educational Forum _(Etats-Generaux de l'Education)_\nwhich was held in December 1999. This included officials, teachers, parents, students, members of\nparliament and the general public. The project is based on the outcome of the conference. The new\neducation law (approved in August 2000) sets in place the conditions for broadening participation in\nDjibouti's education system. It provides for setting up school management committees with parent\n\nand community involvement. The law also provides for the creation of conditions to increase private", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:009595:24:0:0", "start": 1107, "end": 1131, "surface": "1997 poverty\n\nassessment", "probe_tag": "confusion", "probe_score": 0.6264, "luna_label": 0, "luna_reason": "The sentence describes a planned update, not actual use of existing assessment findings."}]}, {"key": "rafael-016", "text": "**8**\n\n\n**by** electronic means. In full recognition that the Association shall rely upon such representations\nand warranties, including without limitation, the representations and warranties contained in the\n_Terms and Conditions of Use ofSecure Identification Credentials in connection with_ _Use_ **_of_**\n_Electronic Means to Process Applications and Supporting Documentation_ (\"Terms and\nConditions of Use of **SIDC\"),** the Recipient represents and warrants to the Association that it will\ncause such persons to abide **by** those terms and conditions.]\n\n\nThis Authorization replaces and supersedes any Authorization currently in the Association\nrecords with respect to this Agreement.\n\n\n[Name], [position] Specimen Signature:\n\n\n[Name], [position] Specimen Signature:\n\n\n[Name], [position] Specimen Signature:\n\n\nYours truly,\n\n\n/ signed /\n\n\n[Position]", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:013075:7:0:0", "start": 640, "end": 659, "surface": "Association\nrecords", "probe_tag": "confusion", "probe_score": 0.1328, "luna_label": 0, "luna_reason": "Routine association records referenced administratively, not used as data."}]}, {"key": "rafael-017", "text": "aged 1549) to be 6.1 %; in\nFreetown, 4% in rural areas and 4.9% nationwide. In response to the crisis, Government has developed\na multi-sector HIV/AIDS Program, which is being supported by various partners, including the Bank.\n\nEducation. Although gross primary school enrollment rose by about 7% when the government\nrecently introduced universal free primary education, low enrollment, education of children who were\nforcibly recruited during the war, and gender imbalance are still an issue (male/female pupil ration of\n165:100) and only 52% of teachers are qualified. A large number of schools were rehabilitated through\nthe IDA financed CRRP Project.\n\nRisk and Vulnerability. The biggest risk that Sierra Leone's poor face is a return to civil\nconflict, political instability and chaos in public administration that would prevent the government from\nresponding to the population's needs for food, shelter and economically productive activity. The project\nis expected to respond to this risk through investments in rehabilitation, employment, and the\nreinforcement of basic services. As conditions improve, endogenous resistance to a resurgence of\nconflict is expected to increase. However there is still a need to understand the profile of risks, identify\nhigh risk groups, define the interface between vulnerability mapping and poverty mapping, coordinate\npublic programs to reduce nsks and reinforce the coping capacity of the poor. Initially, a participatory\nassessment of risks and vulnerability will be commissioned using available and forthcoming data from\nthe living standards measurement survey (LSMS) of 2003. Risk and vulnerability concepts have already\nbeen introduced into the PRSP preparation process by including appropriate questions in the 2003\nLSMS. This should enhance the poverty diagnostic dimensions of the PRSP, and inform the\ndevelopment of strategies to ensure that poverty levels do not increase.\n\nRisk and vulnerability concepts would be introduced in the design of individual sub-projects\nselected by communities. Sub-projects would address the most common risks faced by communities,\nsuch as inadequate", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:011693:10:1:0", "start": 1571, "end": 1606, "surface": "living standards measurement survey", "probe_tag": "confusion", "probe_score": 0.4707, "luna_label": 1, "luna_reason": "Existing 2003 survey data are used for risk and vulnerability assessment."}, {"key": "fcv_pads_east_africa:011693:10:1:1", "start": 1760, "end": 1769, "surface": "2003\nLSMS", "probe_tag": "keep", "probe_score": 0.9626, "luna_label": 0, "luna_reason": "Questions are being added to a forthcoming survey, so its data do not yet exist."}]}, {"key": "rafael-018", "text": " foundation for democratic and sustainable local\n\n\n\ndevelopment. The Community Development Program will finance social and economic\ninfrastructure and support social capital building activities to facilitate the restoration of basic\nsocial services such as health and education and provide an incentive for teachers, health workers\nand displaced persons to return to their communities. The Rural Public Works and Shelter\nprograms will provide employment for demobilized soldiers and unemployed youth, housing for\ndisplaced persons and feeder roads to stimulate local economic activities. The innovative\nactivities including training and technical support will strengthen local government capacity to\nplan, contract, manage and sustain investments in local development and engage a wide array of\n\n\n\nstakeholders in participatory processes that contribute to sustainable local development.\n\n\n\nTargeting will be consistent with the Government's 2002-2003 National Recovery\nStrategy and the March 3, 2002 Transitional Support Strategy. Resources will be directed to (a)\nnewly accessible areas that have not received any support in more than a decade; and (b) remote\nareas that have received little, if any support from the ongoing IDA-financed CRRP or other\nsimilar projects. The results of the living standards measurement survey currently underway will\nbe available at the end of 2003 and will be used to review the validity of existing targeting\nmodalities.\n\n\nTarget Populations: Target groups include demobilized soldiers and unemployed youth,\nrefugees, IDPs, female-headed households, child laborers, orphans, primary school dropouts,\n\n\n_- 8 -_", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:012096:12:1:0", "start": 1291, "end": 1326, "surface": "living standards measurement survey", "probe_tag": "confusion", "probe_score": 0.253, "luna_label": 0, "luna_reason": "Survey is currently underway; its results will become available and be used later."}]}, {"key": "rafael-019", "text": "** |\n| Sub-component 2.1: Improving availability of essential HPTs and delivery of key RMNCAH and NCDs interventions at the primary care level (US$90 million):| **HPTs for Climate Sensitive diseases**: Pharmaceuticals for climate sensitive conditions such as malaria, diarrheal diseases, and other climate sensitive conditions will be included in the pharmaceutical list. This will improve the capacity of primary care level facilities to provide better health services in the face of the increasing burden of disease due to climate change. Seasonal case and pharmaceutical consumption data will be used to inform procurement of the pharmaceuticals to ensure adequate supplies based on seasonal patterns to address climate sensitive conditions.**(adaptation)** |\n|Sub-component 2.3: Improving access to and utilization of quality health services in refugee and host communities (US$40 million)| **Climate sensitive community health service planning:** Climate sensitive planning including the use of climate vulnerability and meteorologic data will be used to guide the distribution of essential HTPs, diagnostic and medical equipment avoiding stormy, heavy rains and heavy flooding days. Specifically, data on climate vulnerable locations and data on previous use of HPTs for climate sensitive diseases as well as patterns of pharmaceutical use following climate shocks will be used to ensure adequate quantities of pharmaceuticals are being used. To ensure pharmaceuticals are available ahead of shocks and that these do not impact distribution, funding will be made available, and planning", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:000910:46:1:0", "start": 589, "end": 629, "surface": "case and pharmaceutical consumption data", "probe_tag": "confusion", "probe_score": 0.1196, "luna_label": 0, "luna_reason": "Future planned use of data to inform pharmaceutical procurement"}]}, {"key": "rafael-020", "text": " scenario 2 (60 percent in yields increase over the\n20 years) would be economically desirable (no returns and negative NPV). The results given in the table\nabove are based on the assumptions of the third scenario (doubling of yields over the 20 years).\nFurthermore, because former agricultural projects showed that doubling yields is a challenging target to\nachieve, those returns are not very robust. The sensitivity analysis also suggests that a contributing factor\nfor the moderate efficiency of KAPP phase 1 have been low yields in maize and potato in 2008: a 25\npercent increase in maize yields in 2008 would have improved the FRR and ERR up to 41 percent and 59\npercent, respectively. The project is also sensitive to input and output prices. If the inputs’ price increases\nsignificantly by 10 percent or the output price decreases by 10 percent, the ERR would fall below the 12\npercent threshold, suggesting risks to economic sustainability.\n\n**(e)** **Price data**\n\n13. Like in many other low-income countries, collecting rural price data is a significant challenge, as\nno official rural price data exists. The calculations use average price data collected on internet trade\ninformation platforms 18 [^18: www.ratin.com] and other documentation from KARI. Price data is not disaggregated by\ngeographical region or around the agricultural cropping cycle. It is based on average values, therefore\nignoring the sometimes hefty price fluctuations during the cropping cycle. All costs and benefits were\ncalculated in constant 2009 prices.\n\n14. A specific effort was made to derive economic prices from financial prices. It is assumed the\nproject will not generate any exportable output. FAO’s “food balance sheets” 19 [^19: http://faostat.fao.org/site/368/default.aspx#ancor. The import/ domestic supply ratio is around 18%.] show that Kenya depends\non food imports to assure its", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:010100:48:1:2", "start": 1030, "end": 1046, "surface": "rural price data", "probe_tag": "confusion", "probe_score": 0.5616, "luna_label": 1, "luna_reason": "Existing-data gap is declared and motivates use of substitute price data."}, {"key": "fcv_pads_east_africa:010100:48:1:3", "start": 1136, "end": 1204, "surface": "average price data collected on internet trade\ninformation platforms", "probe_tag": "confusion", "probe_score": 0.7189, "luna_label": 1, "luna_reason": "Existing price data from internet platforms directly support economic calculations."}, {"key": "fcv_pads_east_africa:010100:48:1:4", "start": 1708, "end": 1727, "surface": "food balance sheets", "probe_tag": "confusion", "probe_score": 0.7282, "luna_label": 1, "luna_reason": "FAO food balance sheets support the stated import-domestic supply ratio."}]}, {"key": "rafael-021", "text": "**The World Bank**\nInnovative Systems to Promote Integrated, Resilient and Enhanced Responses to Women and Girls’ Health\n(P504281)\n\n\n3. **Ethiopia reports the third highest electricity access deficit in Sub-Saharan Africa.** The greatest access deficits are\nfound in rural and deep-rural areas. About 96 percent of urban households are connected to the grid (99.9 percent in\nAddis Ababa), while only 27 percent of rural households have access to electricity services.\n\n\n4. **Ethiopia is highly vulnerable to climate change.** Ethiopia is one of the most drought-prone countries in the world,\nand the frequency and intensity of droughts are being exacerbated by climate change. Over the past 100 years, Ethiopia\nhad experienced 19 periods of widespread and severe food shortages due to droughts, affecting an average of 1.5 million\npeople annually.\n\n5. Inadvertently, the health sector contributes to air pollution. Pollutants from traditional incinerators in big cities\nlike Addis Ababa have negative impacts on the health of women and girls living in remote rural areas where particles\nfrom these pollutants have been traced in untreated waters fetched by these mothers and girls for household usage.\n\n6. **Challenges remain in Ethiopia’s digital development landscape.** Data from 2023 shows that Ethiopia had 66.80\nmillion active cellular mobile connections; 75% of Ethiopian men and 55% of women own a cell phone. More broadly,\n30.7 percent of all internet users in Ethiopia engaged with at least one social media platform in January 2023. Of the\nsocial media user base at that time, 34.1 percent were female, and 65.9 percent were male.\n\n7. These contexts and disparities affect service delivery challenges. Reliable electricity and water are necessary to\noperate health facilities; women in climate-affected and rural areas are less likely to use facilities that are understaffed,\nlack water and electricity and functioning equipment. They are more likely to not", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:003054:3:0:0", "start": 1273, "end": 1287, "surface": "Data from 2023", "probe_tag": "confusion", "probe_score": 0.3267, "luna_label": 0, "luna_reason": "Bare date-only data qualifier lacks an identified source or producer."}]}, {"key": "rafael-022", "text": "* Popular Benchmarks\n**children.**\n\n\n**Project** **Development** **Outcome** **/** **Impact** **Project reports:** **(from** **Objective** **to Goal)**\n**Objective:** **Indicators:**\n**Assist** **war affected** - Improved social capital and - Initial Social Assessment - Communities in the NSAP\n**communities** **to restore** organizational development; (to establish indicators and target areas are assisted to\n**infrastructure,** **services** **and** - Increased access to and use methodologies for social ensure a reduced risk of\n**build** **local** **capacity for** of social and economic capital and organizational conflict\n**collective** **action.** Priority infrastructure and services development)\nwill be given to areas not - Proportion of NSAP - Annual social assessments; - NACSA complements and\npreviously serviced by investments targeted to newly - NaCSA M&E data; extends the work of other\ngovernment, newly accessible accessible areas, & areas - M&E data of relevant line agencies and rninistries\nand the most vulnerable previously not served, and mninistries; in support of the PRSP's\npopulation groups within those vulnerable people within these - Beneficiary Assessment poverty reduction and\nareas. areas; (BAs) biannually; decentralization objectives\n\n - Proportion of sub-projects - Participatory evaluation\nthat reflect priorities of reports for a random sample of - NACSA is fully integrated\ntargeted communities and assisted conmmunities; into national planning and\nbeneficiaries; and - Technical audits resource allocation\n\n - Proportion of sub-projects frameworks (such as the\noperative 24emonths after National Recovery Strategy,\ncorpletion. the PRSP, and", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:019922:29:1:1", "start": 961, "end": 969, "surface": "M&E data", "probe_tag": "confusion", "probe_score": 0.1144, "luna_label": 1, "luna_reason": "Existing agency monitoring data is declared as a project-report source."}]}, {"key": "rafael-023", "text": " approaches that support structural strengthening, like roofing material that can sustain strong winds and rains, incorporating defense mechanisms against floods, landslides, and soil erosion (US$3M); iii) introduce or improve rainwater harvesting systems (US$4M) and drainage and sanitation systems (US$4M) where appropriate; iv) all beneficiary schools will need to plant trees, enabling instruction to continue in the shade during high heat days (US$2M); v) implement an awareness campaign on climate change (US$1M), including information on school-specific evacuation protocols at the onset of climate-related emergencies; vi) the child-friendly schools interventions will include content on environmental safety and protocols at the onset of climate change- induced emergencies like flash floods, increasing the capacity of teachers, pupils, school management, and community’s capacity to address climate change-related issues (US$1.2M); vii) the updated BRMS will include design considerations for schools to be used as shelters during climate related and other emergencies (such as use of wind-resistant materials, emergency lighting, and backup power supply) (US$2M). The proposed climate interventions will be financed by IDA. All infrastructure activities under DLI #5 (US$189M) are to address existing vulnerabilities due to climate change and extreme climate events expected in the future.|\n|RA 3: Supporting Use of Data for Improved System Management| DLI #6. Percentage of schools with student-level data in EMIS|**_M", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:002824:29:6:0", "start": 1568, "end": 1586, "surface": "student-level data", "probe_tag": "drop", "probe_score": 0.041, "luna_label": 0, "luna_reason": "Standalone indicator fragment within a DLI table"}, {"key": "fcv_pads_east_africa:002824:29:6:1", "start": 1593, "end": 1597, "surface": "EMIS", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "EMIS data underpin the stated percentage indicator for schools with student-level records."}]}, {"key": "rafael-024", "text": "|No|Questions|Responses|\n|---|---|---|\n|1 |What is your preferred mode of communication during project preparation, implementation and Closure? |Phone calls, letters, emails, TV’s, radio talk shows among others. |\n|2 | How do you normally get information about community, project activities? (e.g for NEMA, explain about disclosure and feedback and the multi-sectoral approach towards disseminating information) |Letters, emails, phone calls, Community feedback, reconnaissance surveys, RAP reports among others. |\n|3 | Are there any limitations about time of day or location for public consultations? Day/Time/location preferences |Day time is most appropriate for consultations and most especially morning hours as long as there is efficient and effective mobilization. |\n|4 |What need-specific resources might be needed to enable vulnerable, marginalized people participate in meaningful, free prior informed and fair consultation process? | The vulnerable group need people whom they are familiar with in the communities like L. Cs to be speak to them during community engagements. |\n|5 |Describe briefly what kind of information should be disclosed, type of method that should be used to communicate to each stakeholder group? What kind of<", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:003663:60:0:0", "start": 539, "end": 561, "surface": "reconnaissance surveys", "probe_tag": "drop", "probe_score": 0.0095, "luna_label": 0, "luna_reason": "Merely names surveys as an information source without citing findings or actual data use."}]}, {"key": "rafael-025", "text": "24\n\n\n**During negotiations the following assurances were received:**\n\n\n1. Agreement on triggers for subsequent phases\n2. Agreement on monitoring and impact assessment studies\n3. Agreement on project monitoring indicators\n4. Agreement on finalized bidding documents for the first batch of schools\n\n\n**Actions to be included in Development Credit Agreement:**\n\n\n_Financial_\n\n\n1. Audits and Project Management Reports.\n2. Dated covenant on the selection of the auditor before March 31, 2001.\n\n\n_Management_\n\n\n1. Daied covenant on a baseline survey to establish gender and social class distribution of students\nbefore December 31, 2001.\n2. Provide regular reports on monitoring indicators and prepare a draft midterm report for review\nwith IDA before September 15, 2002.\n\n\n**In addition the following Management conditions are included in supplemental letters attached**\n**to the Developinent Credit Agreement:**\n\n\n1. Triggers from Phase I to Phase II in APL\n2. Triggers from Phase II to Phase III in APL\n\n\nH. READINESS **FOR IMPLEMENTATION**\n\n\nL. a) The engineering design documents for the first year's activities are complete and ready for the\n\nstart of project implementation.\nD b) Not applicable.\n\n3 2. The procurement documents for the first year's activities are complete and ready for the start of\nproject implementation.\n\n\nK/ 3. The Project Implementation Plan has been appraised and found to be realistic and of satisfactory\n\nquality.\n##### D 4. \"he following 7tems are lacking and are discussed under loan conditions (Section G):", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:014680:27:0:0", "start": 529, "end": 544, "surface": "baseline survey", "probe_tag": "drop", "probe_score": 0.0491, "luna_label": 0, "luna_reason": "Baseline survey is a planned future activity under a dated covenant."}]}, {"key": "rafael-026", "text": " and labor force indicators and better real sector economic used to inform the third Medium-Term Plan (MTP-3)| Poverty monitoring statistics used in third revenue sharing formula by the Kenya Commission on Revenue Allocation (KCRA)|Annual| Program Progress Report| KNBS|\n| **Produce regular** **poverty** **monitoring data** **and statistics**| ☐| ☒| Yes/No| Poverty statistics are outdated (2005/06) and insufficient capacity to monitor poverty at least once every 3 years | Kenya Integrated Household Budget Survey KIHBS 2015/16 partly conducted and progress report produced and available online| Updated benchmark (2015/16) poverty measures produced and disseminated| Poverty monitoring data collected by Kenya Continuous Household Survey (KCHS)| KCHS poverty estimates produced and disseminated|<br", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:011647:43:2:0", "start": 382, "end": 397, "surface": "monitoring data", "probe_tag": "drop", "probe_score": 0.0255, "luna_label": 0, "luna_reason": "The row requires producing regular monitoring data, so it describes data production."}, {"key": "fcv_pads_east_africa:011647:43:2:2", "start": 939, "end": 943, "surface": "KCHS", "probe_tag": "confusion", "probe_score": 0.8054, "luna_label": 0, "luna_reason": "KCHS estimates are presented as produced and disseminated project outputs."}]}, {"key": "rafael-027", "text": "\n\n\n\nversions of the Global Procurement Plan and the following year's annual procurement plan. Participating\n\n\n\nService Providers will be required to include procurement plans in their subproject proposals. The\n\n\n\ntechnical team reviewing community subproject proposals must ensure adequacy of a sub-project\n\n\n\nprocurement plan before the proposal is approved. Each quarter, NaCSA will submit to IDA a\n\n\n\nprocurement monitoring report as part of the Financial Management Report (FMR), to show how each\n\n\n\ncontract on the procurement plan has progressed. The POM will include sample formats <sup", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:010305:45:6:0", "start": 641, "end": 670, "surface": "procurement monitoring report", "probe_tag": "drop", "probe_score": 0.0054, "luna_label": 0, "luna_reason": "Routine procurement progress reporting for project administration, not substantive data reuse."}]}, {"key": "rafael-028", "text": "|Col1|PSNP IV|Col3|IMPLEMENTATION STATUS REPORT (ISR) RF INDICATOR TRACKING SHEET|Col5|Col6|Col7|\n|---|---|---|---|---|---|---|\n||**PSNP IV**|**PSNP IV**|**Reported as of August 28, 2018**|**Reported as of August 28, 2018**|**Reported as of August 28, 2018**|**Reported as of August 28, 2018**|\n| | | |30-Jun-18|30-Jun-18|30-Jun-18|30-Jun-18|\n| | | |Numerator|Denominator|Percentage|Absolute Value (Y/N, absolute number, days, etc)|\n|13|**% OF CLIENTS RECEIVING REGULAR PAYMENTS WITHIN THE** **AGREED TIME FRAME (20 DAYS FOR CASH AND 30 DAYS FOR** **FOOD) ** |**% OF CLIENTS RECEIVING REGULAR PAYMENTS WITHIN THE** **AGREED TIME FRAME (20 DAYS FOR CASH AND 30 DAYS FOR** **FOOD) ** |||28.5%||\n| |**CASH **| |||40%||\n||RAW DATA SOURCES:||||FIC Report||\n||COMMENTS ||This", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "sample:fcv_pads_east_africa:017191:52:0:0", "start": 769, "end": 777, "surface": "RAW DATA", "probe_tag": "drop", "probe_score": 0.0318, "luna_label": 0, "luna_reason": "Standalone table label, not an independently used data resource."}]}, {"key": "rafael-029", "text": "83. _Procurement Delays:_ Cost of the civil works component at the time of bidding\nvaried substantially from that at the feasibility and detailed design stage due to delays in\nthe procurement process of about four years. The eventual bid prices could therefore not\nenable the project to be implemented as appraised and consequently some components\nhad to be dropped. In future, design reviews and updates of the cost estimates will be\nne **c** essary just before calling for bids. It would also be advisable to avoid a long\nprequalification period. It is also worthwhile to explore the possibility of commencing\nprocurement prior to credit effectiveness in order to provide for procurement lead-time.\n\n\n84. _Monitoring and Evaluation Indicators of the Projects:_ At appraisal, the baseline\ndata for the outcome indicators were not provided. These were provided in the ISR4 in\nAugust 2005. As a proxy for increased industrial and agricultural activity, the traffic\nincrease on the project roads was used to reflect the agricultural productivity benefits to\nthe rural population. The lesson learned is that baseline data should be provided at the\nappraisal stage for appropriate reading of the PDO indicators.\n\n\n**7.** **Comments on Issues Raised by Borrower/Implementing Agencies/Partners**\n\n**(a) Borrower/implementing agencies: None**\n\n**(b) Co financiers: No co-financier**\n\n**(c) Other partners and stakeholders** : **None**\n\n\n23", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:018941:34:0:0", "start": 781, "end": 794, "surface": "baseline\ndata", "probe_tag": "drop", "probe_score": 0.0445, "luna_label": 1, "luna_reason": "Baseline data absence is documented and motivates use of a proxy indicator."}, {"key": "fcv_pads_east_africa:018941:34:0:1", "start": 1105, "end": 1118, "surface": "baseline data", "probe_tag": "confusion", "probe_score": 0.1153, "luna_label": 0, "luna_reason": "The sentence recommends future provision of baseline data rather than using it as existing evidence."}]}, {"key": "rafael-030", "text": "Project** **Manaeem2nt** **and** million) administrative data appropriate, and clear in\n\n\n\ndefining the and\n**Innovative Activities**\n\n\n\nresponsibilities of all parties;\nNaCSA retains competent\n**(a) Capacity Building**\n\n\n\nstaff;\n\n - Other governnent and donor\n**(b)** **Information and**\n\nsupport mobilized for\n\n\n\nsupport mobilized for\n**Sensitization**\ndecentralization to\ncomplement NaCSA efforts;\n(c) **Monitoring and**\n\n\n\n**Evaluation**\n\n\n\n**(d)** **Technical Assistance**\n\n\n(e) **Operating** Expenses\n\n\n\n-28", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:010305:32:1:0", "start": 64, "end": 83, "surface": "administrative data", "probe_tag": "drop", "probe_score": 0.0277, "luna_label": 0, "luna_reason": "Generic administrative data is mentioned without a concrete use or attributed finding."}]}, {"key": "rafael-031", "text": "**The World Bank**\nEnhancing Shared Prosperity through Equitable Services (P151432)\n\n\n\n|DLI IN01065485 ACTION DLI 38|9.11: Dire Dawa City Administration and Sidama Region commenced procurement data entry for five basic sectors based on agreed KPIs and furnished data captured in the first three quarters of EFY2014|Col3|Col4|Col5|\n|---|---|---|---|---|\n|**Type of DLI**|**Scalability**|**Unit of Measure**|**Total Allocated Amount (USD)**|**As % of Total Financing Amount**|\n|Output|Yes|Number|10,000,000.00|0.00|\n|**Period** Baseline|**Value** 0.00|**Value** 0.00|**Allocated Amount (USD)**|**Formula**|\n|2020|||0.00||\n|2022 |2.00|2.00|10,000,000.00||\n|**DLI IN01065486 ACTION** **DLI 39**|11.9: PDC, Ministry of Health and Ministry of Water Irrigation and Electricity implement the Digital MRS system|11.9: PDC, Ministry of Health and Ministry of Water Irrigation and Electricity implement the Digital MRS system|11.9: PDC, Ministry of Health and Ministry of Water Irrigation and Electricity implement the Digital MRS system|11.9: PDC, Ministry of Health and Ministry of Water Irrigation and Electricity implement the Digital MRS system|\n|**Type of DLI**|**Scal", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:006929:36:0:0", "start": 268, "end": 320, "surface": "data captured in the first three quarters of EFY2014", "probe_tag": "drop", "probe_score": 0.0121, "luna_label": 0, "luna_reason": "Data capture is part of the project’s procurement data-entry activity."}]}, {"key": "rafael-032", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population - Beneficiary assessments\ngroups (female headed - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:017166:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0, "luna_reason": "Project M&E data listed as future verification machinery, not existing analyzed evidence."}, {"key": "fcv_pads_east_africa:017166:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "NaCSA administrative data is cited as a verification source for program-awareness indicators."}]}, {"key": "rafael-033", "text": "ANR/DPs|\n|8|Awareness creation for oversight bodies|At project start-up and midterm|MoFEC and MoANR|\n|9|Commodity management  Strengthening the Commodity Management Unit within the MoANR  Implementation of the commodity tracking system (from procurement to distribution of the food) piloted by the Food Management Improvement Program of the WFP across PSNP_woredas_  Assessing the staffing gap at all levels and filling those positions  Producing quarterly consolidated commodity flow status report by the MoANR to the DPs similar to the IFRs for financial resources  Annual commodity audit reports|  Three months after effectiveness  Ongoing  Ongoing  Quarterly  Annually|MoANR (FFSCD)|\n\n\n\n**_Financial management Covenants and Other Agreements_**\n\n\n48. FM-related covenants include the following:\n\n\n(a) Maintenance of a satisfactory FM system for the program.\n\n\n(b) Submission of IFRs for the program for each fiscal quarter within 60 days of the end\n\nof the quarter by the MoFEC and submission of consolidated commodity flow status\nreport for each fiscal quarter within 60 days of the end of the quarter by the\nMoANR.\n\n\n32", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:021160:39:1:0", "start": 636, "end": 666, "surface": "Annual commodity audit reports", "probe_tag": "drop", "probe_score": 0.0352, "luna_label": 0, "luna_reason": "Annual project audit reporting is planned routine monitoring, not existing data use."}]}, {"key": "rafael-034", "text": "\ntargeted communities and assisted conmmunities; into national planning and\nbeneficiaries; and - Technical audits resource allocation\n\n - Proportion of sub-projects frameworks (such as the\noperative 24emonths after National Recovery Strategy,\ncorpletion. the PRSP, and the MTEF)\n\n\n**Output** **from** **each** **Output Indicators:** **Project** **reports:** **(from** **Outputs to Objective)**\n**Component:**\n**1.** Community-Driven\n**Program** (CDP)\nl(a) Rural social and Ia. 1 At least 1,000 - M&E data; - Targeting mnechanisms are\neconomic infrastructure and 'community based\" - NaCSA Progress reports efficient and implemented with\nservices are established, sub-projects implemented minimal political interference;\nupgraded and used. (breakdown by type and\nlocation).\n\n\nla.2 At least 90% of - Annual technical audit -Line agencies and/or other\n\n\n-25", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:018740:29:2:0", "start": 530, "end": 538, "surface": "M&E data", "probe_tag": "drop", "probe_score": 0.023, "luna_label": 0, "luna_reason": "Embedded in an output-indicators table as project reporting data."}]}, {"key": "rafael-035", "text": "arbitrary control group. The ENEU sample is representative of\n\n\nmetropolitan areas while the CONALEP graduate tracer survey is representative nationally. The\n\n\ndifference in geographical coverage of the two groups makes comparison difficult. Second, the\n\n\ncontrol groups were constructed ad hoc. The control groups included individuals between the\n\n\nages of 17 and 30: (A) those who have completed lower-secondary education; (B) those who\n\n\nhave completed non-professional, elementary vocational training (CECATI), and (C) those who\n\n\nhave completed one to three years of general academic (non-vocational) high school. Some\n\n\ndoubts remain with respect to the second group, since the ENEU survey does not distinguish\n\n\nbetween formal and informal training/technical courses.\n\n\nLee (1998) compares the individuals from the Encuesta de Egresados 1994 (the treatment\n\n\ngroup) with two other groups. One group comprises all 1991 graduates from upper-secondary\n\n\n6", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002610:5:1:1", "start": 93, "end": 123, "surface": "CONALEP graduate tracer survey", "probe_tag": "keep", "probe_score": 0.9329, "luna_label": 1, "luna_reason": "Named tracer survey used to establish national representativeness and enable comparison."}]}, {"key": "rafael-036", "text": ", the present findings could only identify and carefully assess the main effects while\n\n\nhighlighting a few notable impact mechanisms without being exhaustive. Among others, future research\n\n\ncould look at the impact channels in detail in order to deliver a more nuanced understanding of why and\n\n\nunder which conditions e-procurement delivers the hoped for impacts. On the one hand, a more detailed\n\n\nunderstanding of administrative preconditions for successful e-procurement reform can be explored by\n\n\ncombining data from the procuring entity survey with procurement records. On the other hand, a better\n\n\nunderstanding of the constraints imposed and opportunities presented by different bidding markets could\n\n\nbe investigated by additionally drawing on bidder registration data and a tailored bidder survey.\n\n\n98. While more research is needed in this area, our findings already lend themselves to a range of policy\n\n\nrecommendations in and beyond Bangladesh: i) Introduce and expand the scope of e-procurement systems\n\n\nto encompass all public procurement tenders; and ii) Capitalize on the available data in the e-procurement\n\n\nsystem by developing a contract performance dashboard which enables continuous monitoring and\n\n\noptimization of processes in real-time.\n\n\n37", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000946:38:1:0", "start": 529, "end": 552, "surface": "procuring entity survey", "probe_tag": "confusion", "probe_score": 0.4352, "luna_label": 0, "luna_reason": "Future research proposes combining the survey with procurement records."}]}, {"key": "rafael-037", "text": "||||\n|||||||\n|||||||\n|||~~Taiwan, Ch~~| ~~ ina~~|~~Japan~~ ||\n||| Korea|Hong Ko |ng, China||\n||||~~Singapore~~|||\n|||||||\n\n\n0 10,000 20,000 30,000 40,000 50,000\n\nGDP per capita ($US)\n\n\n\n\n\n\n\n\n\n\n- Data refer to 2001 the latest year for which complete data are available. The predicted figures are from a regression of the\nnatural logarithm of total health expenditure (expressed as a share of GDP) on the natural logarithm of GDP per capita. Data are\nfrom the 2005 World Health Report annexes in the cases of Japan, Korea and Singapore. The per capita income data for Hong\nKong (China) and Taiwan (China) are from the World Bank’s World Development Indicators. Data on health spending are from\nhttp://www.hwfb.gov.hk/statistics/download/dha/en/table1.pdf in the case of Hong Kong (China) and from\nhttp://www.doh.gov.tw/ufile/doc/200411_Statistic%20of%20Expenditure%20for%20Health,%201991-2003.pdf in the case of\nTaiwan (China).", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:003004:3:1:1", "start": 555, "end": 577, "surface": "per capita income data", "probe_tag": "confusion", "probe_score": 0.8892, "luna_label": 1, "luna_reason": "World Bank World Development Indicators provide the income data used in the chart."}, {"key": "prwp:003004:3:1:3", "start": 675, "end": 698, "surface": "Data on health spending", "probe_tag": "confusion", "probe_score": 0.65, "luna_label": 1, "luna_reason": "Health-spending data are cited as sources for the presented comparison."}]}, {"key": "rafael-038", "text": " higher beliefs about ration portability in the control group four months\n\nafter our experiment. 3 To measure the intensity of government awareness campaigns, we\n\n\nexploit additional, out-of-sample data on beliefs about ration portability which we collected\n\n\nboth before and after our information experiment. We find that the reduction in perceived\n\n\nportability caused by our experiment was driven by states with more intensive government\n\n\n2Specifically, the script told households that not all ration shop owners are aware of the ONORC program,\nthat they should bring their unique ID card called _Aadhaar_ along with a copy of their household’s ration\ncard—which must be linked to their _Aadhaar_ —that the shopkeeper may ask to see additional ID cards, that\nolder versions of ration cards may not be accepted or may require manual adjustment to the ID, that the\nshop must be equipped with an electronic point-of-sale system.\n\n3Under the PDS, state governments are responsible for grain distribution, portability implementation,\nand publicity and awareness campaigns. See, for example, publications by the Ministry of Consumer Affairs,\nFood & Public Distribution [here](https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1749917) and the Ministry of Information and Broadcasting [here.](https://transformingindia.mygov.in/wp-content/uploads/2021/10/ONORC-eng.pdf)\n\n\n3", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000880:4:1:0", "start": 195, "end": 249, "surface": "out-of-sample data on beliefs about ration portability", "probe_tag": "confusion", "probe_score": 0.2475, "luna_label": 1, "luna_reason": "Existing collected data are analyzed to explain changes in perceived ration portability."}]}, {"key": "rafael-039", "text": "Wives in households with other co-wives are slightly more likely to be involved in decisions directly than\nwives who live with their in-laws. But when they are not directly involved in the decision, these wives are\nless likely to have effective power, particularly in terms of water decisions. In these households, when one\nof the wives is not directly involved in these decisions, the male head of household is making the decision.\nIn either case, wives who are not directly involved in decision-making have little influence in the decision\nmade.\n\nYoung unmarried adults living with their parents are the least likely to be involved in decisions directly\n(Table 5). Few individuals in the younger age bracket make decisions in all seven activities. On average,\nyoung men and women make between two and three decisions directly, with young women making fewer\ndecisions directly than young men across the decision-making activities. When not directly involved in the\ndecisions, young men and women have similar levels of effective power with the exception of expenditures.\nYoung men have more effective power in household expenditure decisions than young women.\nCorrespondingly, the qualitative data suggests younger men often have some involvement in expenditure\ndecisions if they are earning income.\n\nIn terms of water activities, our qualitative data shows that while both young men and women are involved\nin collecting water, they have little direct involvement in the water decisions. The quantitative data confirms\nthat young men and women are directly engaged in less than one decision on average of the three water\nactivities, and that they have similar levels of effective power when not involved in the decision (Table 5).\n\n\n\n**Table 5.** Average share of activities young women and men who are not yet married and living with\nparents are directly engaged in the decision and share of those not directly involved but with effective\n\n\n\npower\n\n\n\nAverage share of decision making\nactivities in which women and men\n\n\n\nAverage share of decision", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001329:17:0:2", "start": 1493, "end": 1510, "surface": "quantitative data", "probe_tag": "confusion", "probe_score": 0.8745, "luna_label": 1, "luna_reason": "Quantitative data supports concrete findings about young adults’ decision-making involvement."}]}, {"key": "rafael-040", "text": " 0.00 0.00 0.00\nP-val t1=t2 1.00 0.16 0.65 0.34 0.28 0.66\n\n\nNotes: Outcomes used in Columns 1-5 are constructed from administrative data, whereas the outcome used in Column 6 is constructed from self-reported data. Outcome 1 takes the value of 1 if the respondent\ncompleted at least 1 module on the app or attended 1 session of the in-person training course. Outcome 2 takes the value of 1 if respondent completed at least 8 modules on the app or attended at least 8\nsessions of the in-person training course. Outcome 3 indicates the number of modules started on the app, or the number of classes attended, as there is no similar measure for in-person training. Outcome\n4 indicates the number of modules partially completed, where the respondent completes at least half the lessons in a module, or the number of classes attended, as there is no partial measure for in-person\nattendance. Outcome 5 indicates the number of modules completed on the app, or the number of in-person sessions attended. Outcome 6 indicates the number of courses completed on the app, or the number\nof in-person sessions attended according to self-reported data from the follow-up survey. Results are obtained from OLS regressions of the outcome on three treatment dummies, controlling for LASSO selected\ncontrols, and randomization cohort fixed effects. Robust standard errors are in parentheses. - _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001281:30:1:0", "start": 195, "end": 213, "surface": "self-reported data", "probe_tag": "confusion", "probe_score": 0.5557, "luna_label": 1, "luna_reason": "Self-reported follow-up data construct an outcome used in OLS analysis."}, {"key": "prwp:001281:30:1:1", "start": 1119, "end": 1163, "surface": "self-reported data from the follow-up survey", "probe_tag": "keep", "probe_score": 0.9079, "luna_label": 1, "luna_reason": "Follow-up survey data construct an analyzed outcome in the regression."}]}, {"key": "rafael-041", "text": "> 38.1\nNumber of obs. 991 377\n\n\nNotes: Own calculations based on Malawi Integrated Household Survey (2004/05).\nCash Crops: hybrid maize, groundnuts, cotton and sugar cane.\nFood Crops: maize, cassava, potato, ground beans, rice, finger millet, sorghum, pearl millet, beans, soybeans and\npigeon peas.\n\n\n18", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:003768:20:1:0", "start": 107, "end": 141, "surface": "Malawi Integrated Household Survey", "probe_tag": "confusion", "probe_score": 0.8361, "luna_label": 1, "luna_reason": "Own calculations are based on this named household survey."}]}, {"key": "rafael-042", "text": " when the firms self-report. In the following analysis, the paper differenti\n\nates the data used in the estimation by inspections and self-reported data. Given the results\n\n\nin table 3, inspection data are more reliable, but carrying out the same type of analysis for\n\n\nthe self-reported data will help to have a better idea of the kind of reforms that are needed\n\n\nin India’s monitoring system. Even if firms over-report compliance in self-reporting, it still\n\n\nis important information about the behavior of firms regarding environmental regulations.\n\n\n**5.2** **Water** **and** **Air** **Pollution**\n\n\nNext, the paper explores whether there is a higher rate of compliance with air or water\n\n\nenvironmental regulations. This is captured by including a variable equal to one when the\n\n\nmeasures are on air emissions data and omitting the water information variable. Table 4\n\n\nshows that firms seem to comply more with air emissions than with water effluents when the\n\n\ninformation is self-reported, but inspections find that firms tend to be more in compliance\n\n\nwith water regulations.\n\n\n14", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002181:15:2:1", "start": 186, "end": 201, "surface": "inspection data", "probe_tag": "confusion", "probe_score": 0.6752, "luna_label": 1, "luna_reason": "Inspection data are analyzed and judged more reliable than self-reported data."}]}, {"key": "rafael-043", "text": "As a result, most studies on housing affordability are country specific and rely on policy\n\ndriven thresholds and criteria. By far, the largest literature documenting housing affordability is\n\n\nfrom the United States and Australia. In the United States, there is a large literature in documenting\n\n\nhousing affordability outcomes across cities, income, and race (Quigley and Raphael 2004,\n\n\nMillennial Housing Commission 2002). In Australia, the topic has received substantial empirical\n\n\nas well as methodological attention (Gabriel, et al. 2005, Gan and Hill 2009, Wood, Ong and\n\n\nCigdem 2014, Yates and Gabriel 2006). The United Kingdom, the Netherlands, and Canada are\n\n\nanother set developed economies that have systemically documented affordability in the housing\n\n\nliterature (Milligan 2003, Haffner and Boumeester 2015, Haffner and Boumeester 2010, Moore\n\n\nand Skaburskis 2004, Stone 2006).\n\n\nIn the developing world, China and Malaysia have been the most studied (Chen, Hao and\n\n\nStephens 2010, Yang and Chen 2014, Hashim 2010, Suhaida, et al. 2010). Some cross-country\n\n\nanalysis from Africa has been undertaken using standardized housing expenditure surveys, but it\n\n\nis not clear what drives these cross-country differences (Lozano and Young 2014). The range of\n\n\nthese studies demonstrate the lack of international comparability on the subject and the ongoing\n\n\nsearch for an easily replicable approach across countries (World Bank 2020).\n\n\nDriven by prudential banking regulation, by far the most common approach is to measure\n\n\naffordability as housing expenditure over income, or the expenditure to income ratio. This debt\n\n\nservice concept has grown in popularity globally, although the prevailing thresholds between 20\n\n\nand 40 percent lack simple interpretations and have been critiqued (Mayo, Malpezzi and Gross\n\n\n1986). In many developing countries, definitions of minimum housing or of inadequate housing\n\n\nhave been developed based on housing structures and these definitions often include the materials\n\n\nof the dwelling as well as the type of water and sanitation options for", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001007:43:0:0", "start": 1128, "end": 1168, "surface": "standardized housing expenditure surveys", "probe_tag": "confusion", "probe_score": 0.1661, "luna_label": 1, "luna_reason": "Existing surveys are used for cross-country housing affordability analysis."}]}, {"key": "rafael-044", "text": "\n|Pakistan |1990-91, 2006-07, 2012-13, 2017-18 (4) |2010, 2011, 2014, 2016-2017, 2017- 2018 (5) |1994-1998, 1999- 2004, 2010-2014, 2017-2019 |(0) |Every year between 2005- 2018 (13) |\n|Sri Lanka |1987**, 2006, 2016 (3) |(0) |(0) |(0) |Every year between 2005- 2015 and 2017- 2019 (13) |\n|Bhutan |(0)|2010 (1)|(0)|(0)|2013, 2014, 2015 (3)|\n\n\n\nAmong these data sources, DHS provided the most comprehensive combination of gender-relevant\n\noutcome variables across all coutnries and time periods. WVS, though not available for all countries in the\n\nregion, was selected due to availability of gender attitide variables that could serve as proxies for relevant\n\nnorms. A brief description of each is included below.\n\n\n+ Special DHS data type and not used.\n\n- Special DHS data type.\n** Not used in the analysis.\n\n\n50", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000571:51:2:0", "start": 463, "end": 466, "surface": "DHS", "probe_tag": "confusion", "probe_score": 0.5767, "luna_label": 1, "luna_reason": "Named DHS data provided gender-relevant outcome variables for analysis."}]}, {"key": "rafael-045", "text": "### **Does Better Access to Finance Help Firms Deal with the COVID-19** **Pandemic? Evidence from Firm-Level Survey Data**\n\nMohammad Amin a and Domenico Viganola b\n\nKeywords: COVID-19, Access to Finance, Sales, Firm performance, Pandemic\nJEL Codes: D22, G00, G10, L23, L25\n\na Corresponding author. Senior Economist, Enterprise Analysis Unit, DECEA, World Bank,\nWashington, DC. Email: [mamin@worldbank.org](mailto:mamin@worldbank.org)\nb Consultant, Enterprise Analysis Unit, DECEA, World Bank, Washington, DC. Email:\n[dviganola@worldbank.org](mailto:dviganola@worldbank.org)\nThe findings, interpretations, and conclusions expressed in this paper are entirely those of the\nauthors. They do not necessarily represent the views of the International Bank for Reconstruction\nand Development/World Bank and its affiliated organizations, or those of the Executive Directors\nof the World Bank or the governments they represent.\nWe thank the Enterprise Analysis Unit of the Development Economics Global Indicators\nDepartment of the World Bank Group for making the data available.\nWe would like to thank Jorge Luis Rodriguez Meza and Norman Loayza for providing very helpful\ncomments. All remaining errors are our own.", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000299:2:0:0", "start": 98, "end": 120, "surface": "Firm-Level Survey Data", "probe_tag": "confusion", "probe_score": 0.172, "luna_label": 1, "luna_reason": "Firm-level survey data is identified as the evidence underlying the paper."}, {"key": "prwp:000299:2:0:1", "start": 1027, "end": 1066, "surface": "Development Economics Global Indicators", "probe_tag": "confusion", "probe_score": 0.8866, "luna_label": 0, "luna_reason": "Acknowledges data availability without showing the named source's data being used."}]}, {"key": "rafael-046", "text": "Policy Research Working Paper\n8381\nTransnational Terrorist Recruitment\nEvidence from Daesh Personnel Records\u0003\nAnne Brockmeyer\nQuy-Toan Do\nClément Joubert\nMohamed Abdel Jelil\nKartika Bhatia\nDevelopment Research Group\n &\nMiddle East and North Africa Region\nOffice of the Chief Economist\nMarch 2018\nPublic Disclosure Authorized\nPublic Disclosure Authorized\nPublic Disclosure Authorized\nPublic Disclosure Authorized", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002348:0:0:0", "start": 85, "end": 108, "surface": "Daesh Personnel Records", "probe_tag": "drop", "probe_score": 0.0242, "luna_label": 1, "luna_reason": "Daesh personnel records are cited as evidence for the working paper's analysis."}]}, {"key": "rafael-047", "text": " 4 5 − 9 10 − 19 20 − 99 100+ Keep formal account|\n|0 10 20 30 40 50 Burkina Faso (2015) 1 − 4 5 − 9 10 − 19 20 − 99 100+ Keep formal account (SYSCOA)|0 10 20 30 40 50 Cameroon (2008) 1 − 4 5 − 9 10 − 19 20 − 99 100+ Tax registeration|0 10 20 30 40 50 Ghana (2013) 1 − 4 5 − 9 10 − 19 20 − 99 100+ Tax registeration|0 10 20 30 40 50 Rwanda (2013) 1 − 4 5 − 9 10 − 19 20 − 99 100+ Tax registeration|\n\n\n\n19", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000985:20:1:1", "start": 304, "end": 321, "surface": "Tax registeration", "probe_tag": "drop", "probe_score": 0.0117, "luna_label": 0, "luna_reason": "Standalone table label, not a cited or used data resource."}]}, {"key": "rafael-048", "text": "Appendix Α: Information on data sources\n\n\nTable A1: List of candidate geospatial variables.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Variable|Source|Approximate Resolution|Year|\n|---|---|---|---|\n|Population structure |WorldPop (https://www.worldpop.org) |100 m |2018 |\n|Population density|WorldPop |100 m |2018 |\n|Temperature |TerraClimate (https://www.climatologylab.org/terraclimat e.html)|4 km |2018 |\n|Palmer Draught Severity Index (PSDI) |TerraClimate|4 km |2018 |\n|Distance to OSM major roads |WorldPop |100 m |2016 |\n|Radiance of night- time lights|VIIRS (https://eogdata.mines.edu/products/vnl/) |500 m |2018 |\n|Net primary production|FAO Remote Sensing for Water Productivity (WaPOR) 2.1 (https://data.apps.fao.org/wapor/?lang=en) |240 m |2018 |\n|Rainfall|Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (https://www.chc.ucsb.edu/data/chirps) |5", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001372:28:0:0", "start": 220, "end": 228, "surface": "WorldPop", "probe_tag": "confusion", "probe_score": 0.0615, "luna_label": 0, "luna_reason": "Source entry embedded in a candidate-variable table, not an independent data-use mention."}, {"key": "prwp:001372:28:0:2", "start": 350, "end": 362, "surface": "TerraClimate", "probe_tag": "drop", "probe_score": 0.0138, "luna_label": 0, "luna_reason": "Standalone source entry within a table of candidate geospatial variables."}, {"key": "prwp:001372:28:0:4", "start": 727, "end": 755, "surface": "FAO Remote Sensing for Water", "probe_tag": "confusion", "probe_score": 0.2623, "luna_label": 1, "luna_reason": "Named geospatial data source listed for net primary production."}]}, {"key": "rafael-049", "text": "cases, including journey planning, the mapping of topography, as well as demographic indicators. | Following data must be online to qualify for assessment: • Markings of national traffic routes • Markings of relief/heights • Markings of water stretches • National borders Coordinates - Note: To qualify, data must contain geographic projections that enable to interpret coordinates |\n|Administrative Boundaries | Data on administrative units or areas defined for the purpose of administration by a (local) government.The development of this category draws on work ofFAO Global Administrative Unit Layers (GAUL)project, as well as theUNGIWG. |Open data about administrative zones has many use cases: Who are the candidates in my region? Which government bodies administer my region? How is wealth distributed across regions? The Index assesses two administrative boundary levels (e.g. federal states = level 1, and municipalities = level 2). |Following data must be online to qualify for assessment: • Boundary level 1 • Boundary level 2 (not required, if country has only one level) • Coordinates of administrative zone (latitude,", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001873:48:2:3", "start": 308, "end": 336, "surface": "National borders Coordinates", "probe_tag": "drop", "probe_score": 0.0213, "luna_label": 0, "luna_reason": "Fragment names mapping attributes, not an existing data resource or data use."}, {"key": "prwp:001873:48:2:4", "start": 493, "end": 530, "surface": "Data on administrative units or areas", "probe_tag": "confusion", "probe_score": 0.0991, "luna_label": 0, "luna_reason": "Defines an administrative data category without citing its data for analysis or a finding."}]}, {"key": "rafael-050", "text": "**To Impute or Not to Impute? A Review of Alternative Poverty**\n**Estimation Methods in the Context of Unavailable Consumption Data**\n\n\nHai-Anh H. Dang *\n\n\nKey words: poverty, imputation, consumption, wealth index, synthetic panels, household survey\n\nJEL: C15, I32, O15\n\n\n[* Dang (hdang@worldbank.org) is an economist in the Survey Unit, Development Data Group, World Bank, a non-](mailto:hdang@worldbank.org)\nresident research scholar with School of Public and Environmental Affairs, Indiana University, and a non-resident\nsenior research fellow with Vietnam’s Academy of Social Sciences. We would like to thank Gero Carletto, Dean\nJolliffe, and Peter Lanjouw for helpful discussions on related work. We are grateful to the UK Department of\nInternational Development for funding assistance through its Strategic Research Program (SRP) and Knowledge for\nChange (KCP) program.", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002315:2:0:0", "start": 115, "end": 131, "surface": "Consumption Data", "probe_tag": "drop", "probe_score": 0.0143, "luna_label": 0, "luna_reason": "Title states consumption data are unavailable, without using an existing dataset."}]}, {"key": "rafael-051", "text": "> $116.47 $20.16 0.000\nDiscrepancy between expert’s Round 1 prediction and mean for $61.43 $44.38 0.000\n\n\n\n_Group 1_\n\n\n\n_mean_\n\n\n\n_mean_\n\n\n\nhis/her country/region\n\n\n\n$61.43 $44.38 0.000\n\n\n\nRatio of expert’s Round 1 prediction to mean for his/her\n\n\n\n2.17 0.27 0.000\n\n\n\ncountry/region\n\n\n\nProximity of Delphi estimate to result of an actual SP survey 6.48 5.78 0.043\nDifficulty of successfully implementing a SP survey on this topic 5.47 5.85 0.230\nNumber of CV surveys carried out 7.22 7.18 0.490\nNumber of CE surveys carried out 3.77 3.63 0.448\nNumber of surveys (CV+CE) carried out about biodiversity and 3.22 3.56 0.363\n\n\n\necosystem services\n\n\n\n3.22 3.56 0.363\n\n\n\nNumber of benefit transfer exercises 2.58 2.20 0.329\nJournals: 1 st principal component -0.079 -0.272 0.261\nJournals: 2 nd principal component -0.118 0.112 0.166\nJournals: 3 rd principal component -0.104 -0.215 0.291\nJournals: 4 th principal component 0.072 0.148 0.352\nNumber of CV and CE papers published in national and 4.65 2.72 0.012\n\n\n\ninternational journals in past 5 years\n\n\n\n4.65 2.72 0.012\n\n\n\nNumber of", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000008:27:1:2", "start": 547, "end": 557, "surface": "CE surveys", "probe_tag": "drop", "probe_score": 0.0175, "luna_label": 0, "luna_reason": "Fragment appears as a row label within a statistical table."}]}, {"key": "rafael-052", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\n**Most Ukrainian refugee households**\n**are led by women alone.** According\nto the 2024 Socio-Economic Inclusion\nSurvey (SEIS), 67% of Ukrainian refugee\nhouseholds are led by women without\nadult partners. When changes are\ncompared to the 2023 edition, the MultiSector Needs Assessment (MSNA) survey,\n5% of households include a person with\ndisability, down from 10% previous year,\n\n\n\nand 48% include a chronically ill person,\ncompared to 49% in 2023. Furthermore,\naverage household size is 2.4 persons\n(compared to 2.7 previous year), 57%\ninclude children (compared to 52%), and\n6% include a pregnant or breastfeeding\nmother (same as previously; UNHCR,\n2025a). Only 15% of households are\nnuclear families with a working age man\nand woman with children.\n\n\n\n**Chart 5. Ukrainian refugee households’ demographic composition**\n\n\n**with no**\n**children (43%)**\n\n\n\n**with one child**\n\n**or more (57%)**\n\n\n\nwith one child\n\nwith two or more children\n\n\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Table 1. 10 poviats with most Ukrainian refugees** **Chart 6. Local population shares of Ukrainian refugees**\n\n\n**1. Warsaw** 109,705\n\n\n**2. Wroclaw** 53,901\n\n\n**3. Cracow** 33,057\n\n\n**4. Poznan** 23,991\n\n\n**5. Gdansk** 17,829\n\n\n**6. Lodz** 15,902\n\n\n**7. Szczecin** 14,318\n\n\n**8. Poznanski** 14,028\n\n\n**9. Pruszkowski** 12,206\n\n\n\n\n\n\n\n18-59 female and male\n\n\n\n\n\n60+ female and male\n\ntwo", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "jad_paddy_docs:000001:5:0:0", "start": 159, "end": 195, "surface": "2024 Socio-Economic Inclusion\nSurvey", "probe_tag": "keep", "probe_score": 0.9662, "luna_label": 1, "luna_reason": "Named survey supports the reported 67% household composition finding."}, {"key": "jad_paddy_docs:000001:5:0:1", "start": 332, "end": 360, "surface": "MultiSector Needs Assessment", "probe_tag": "keep", "probe_score": 0.962, "luna_label": 1, "luna_reason": "Named survey referenced for household disability and demographic findings."}]}, {"key": "rafael-053", "text": "zyciowa-i-ekonomiczna-migrantow-z-Ukrainy-w-Polsce_raport-z-badania-2022-r.pdf)\nService of Ukraine (2021).\n\n\n\nPoland 2023 assess that around 56% 23\nof refugee arrivals had completed higher\neducation. This means that Ukrainians\nwith higher education were more likely\nto relocate to Poland or leave Ukraine in\ngeneral, having the necessary means to\nachieve this. On average refugees with a\nbachelor’s degree or higher had an over\n30% higher employment rate than peers\nwithout a degree, according to MSNA\nPoland 2023 survey.\n\n\n\n\n\n22 After excluding Germany and Switzerland as outliers. 23 Due to possible differences in methodologies data from this surveys should not be directly compared\n\n\n20\n\n\n\n21", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "jad_paddy_docs:000007:10:3:0", "start": 512, "end": 535, "surface": "MSNA\nPoland 2023 survey", "probe_tag": "keep", "probe_score": 0.9622, "luna_label": 1, "luna_reason": "Named survey supports the reported employment-rate finding."}]}, {"key": "rafael-054", "text": "**Analysis of the impact of refugees from Ukraine on the economy of Poland**\n\n\n**Economists have advanced an array of factors that could be causing these macro-level effects. While macro-level**\n**regressions show a positive impact of immigration on productivity, they cannot show the exact channels through which**\n**these effects come about. It is thus uncertain which are dominant. Among them are:**\n\n\n# Conclusions\n\n\n\n**Analysis of the impact of refugees from Ukraine on the economy of Poland**\n\n\n\nDeloitte’s D.Climate general equilibrium\nmodel finds that refugees from Ukraine\ncontributed 0.7-1.1% to GDP cumulatively in\n2023. In the long-term this effect will grow\nto 0.9-1.35% as the economy fully adjusts.\nThe increases in government revenue from\ndirect and indirect taxes due to wages\nand private consumption of refugees from\nUkraine was likewise modelled.\nThese increases amounted to 0.8-1.0%\nhigher general government revenues in\n2022, 1.3-1.6% in 2023, and 0.95-1.13%\nin the long term. This implies that, while\nthere are no precise public data on the\ngovernment support to refugees from\nUkraine, while a government official quoted\ncost figures of 15-20 billion PLN in 2022\nand around 5 billion in 2023 50 [^50: E.g. vice-president of Polish Development Found Bartosz Marczuk estimated it at around 16 billion PLN, but this estimation also included spending of\nNGOs which was combined with spendings of local governments [Polska pomoc dla Ukrainy 2022 - ile kosztowała? - Infor.pl.](https://www.infor.pl/prawo/nowosci-prawne/5635962,Polska-pomoc-dla-Ukrainy-2022-ile-kosztowala.html)] have been\nalready offset by refugees via taxes of\n12.3-15.2 billion PLN in 2022 and 18.2-22.5\nbillion PLN in 2023.\n\n\nAll the modelling results are conservative\nlower bound estimates, as econometric\nstudies from other", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:20:0:0", "start": 1044, "end": 1055, "surface": "public data", "probe_tag": "keep", "probe_score": 0.9561, "luna_label": 1, "luna_reason": "Absence of precise public data motivates substitute modeled and official estimates."}]}, {"key": "rafael-055", "text": " elaboration based on Eurostat\n(Labour Force Survey) data.\n\n\n**Occupational downgrading is**\n**widespread among Ukrainian refugees**\n**in Poland.** SEIS data shows that 40% of\nUkrainian refugees aged 25–64 hold a\ntertiary degree – exceeding the share of\nPolish citizens in the same age bracket,\naccording to Eurostat’s 2023 annual\naverage. While ZUS data available as of\nJune 30, 2024, shows occupational groups\nfor less than half of the socially insured\nin Poland, it reflects a visible mismatch\nbetween the refugees’ education and the\njobs they perform. Only 12% of Ukrainian\nrefugees (less than one-third of the share\nin terms of tertiary education) worked in\noccupational groups which require tertiary\neducation, that is managers, specialists,\nand technicians (ISCO 1-3), compared to\n37% of Polish citizens (almost the same\nas the tertiary education share in the\n25-64 age group).\n\n\n\n2022 2023", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:13:2:0", "start": 148, "end": 157, "surface": "SEIS data", "probe_tag": "keep", "probe_score": 0.9975, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:13:2:1", "start": 346, "end": 354, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9902, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-056", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n#### **Model calibration**\n\n\n\nWe introduce a positive productivity\nshock into the Deloitte D.Climate\nmodel, reflecting empirical findings that\nrefugee inflows coincided with stronger\nlabour-market outcomes, to offset any\npotential adverse effects. Although higher\nemployment rates among Ukrainian\nrefugees correlate with improved job and\nwage outcomes for Polish citizens, we take\na deliberately conservative approach in\nour general equilibrium simulations. While\ndescriptive statistics and econometric\nestimates point in a positive direction, the\navailable data remain too sparse for firm\n\n\n**Chart 31. Labour market activity rates**\n\nWomen in 15-64 age group\n\n\n\nconclusions. 31 [^31: Deloitte has not received data that would be detailed as to citizenship, poviat, sex, age group, occupational group, and ZUS insurance code that would be suitable\nfor econometric approach.] On the other hand, a default\nDeloitte D.Climate model estimation is\nin line with the previously mentioned\ncanonical model. This yields a 1.35%\ndecrease in wages and a 0.4 percentage\npoint increase in the unemployment rate,\nwhich is implausible. Therefore, a positive\nmarginal productivity of labour shock has\nbeen added to the model and calibrated to\neliminate the impact on the unemployment\nrate throughout the simulation years of\n2022, 2023, and 2024. While it is possible\nthat the unemployment rate could fall\ndespite the influx of Ukrainian refugees due\n\n\n\nto factors other than productivity growth,\nthis is unlikely. Any negative impact could\nonly arise if other workers left the labour\nforce or reduced their working hours. No\nevidence of this is seen in recent Eurostat’s\nLabour Force Survey data for the Polish\neconomy, where activity rates continued\nto grow, while the average number of usual\nweekly hours worked in full- and parttime employment remained fairly stable,\nparticularly for women (who should be\ncloser substitutes for Ukrainian refugees,\nmost of whom are also women; see charts\nbelow).\n\n\n\nAnalysis of the impact of refugees from", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:21:0:0", "start": 1729, "end": 1764, "surface": "Eurostat’s\nLabour Force Survey data", "probe_tag": "keep", "probe_score": 0.9924, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-057", "text": ">self-employment intermediate Language|advanced fuent self-employment intermediate Language|advanced fuent self-employment intermediate Language|advanced fuent self-employment intermediate Language|advanced fuent self-employment intermediate Language|advanced fuent self-employment intermediate Language|advanced fuent self-employment intermediate Language|advanced fuent self-employment intermediate Language|advanced fuent self-employment intermediate Language|advanced fuent self-employment intermediate Language|advanced fuent self-employment intermediate Language|advanced fuent self-employment intermediate Language|\n\n\n\nNote: Statistically significant results are given in green. Confidence bars reflect standard errors. Sample has been 833 individuals aged 18-64.\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 23. Average number of months since arrival of a Ukrainian refugee by Polish language fluency**\n\nN=702, age 18-64\n\n\n29\n\n\n\nAge Sector\n\n\nNote: Statistically significant results are given in green. n=681, age individuals aged 18-64.\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey.\n\n\n\nFluent\n\n\n\nAdvanced\n\n\n\nIntermediate\n\n\n\nNone\n\n\n\nBeginner\n\n\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey.\n\n\n**Chart 24. What Ukrainian", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:16:2:0", "start": 952, "end": 969, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9952, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-058", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\nJust like last year, employment rates demonstrated disparity by age group, gender, education level, the\npresence of a disability, and being in need of MHPSS. Disability was found to be associated with the largest\ndrop in employment, with this factor halving the probability of working in 2024 when compared to the sample\noverall. Having MHPSS needs and being female were also associated with a lower employment likelihood,\nalthough to significantly smaller degrees.\n\n\nCompared to the host population, the refugee sample demonstrated higher employment rates at lower age\nbrackets (15-19 and 20-24), which, considering the findings on poverty, can be interpreted as a coping strategy\nassociated with low income. Higher age brackets (55-59 and 60-64) saw the highest gap in the employment\nrate compared to hosts, supporting the hypothesis that older age individuals have more difficulty integrating\ninto the local labor market.\n\n\n**REGIONAL EMPLOYMENT RATE BY AGE AND POPULATION**\n\n\nRefugees (2023) Refugees (2024) Hosts (2023)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n52%\n\n\n\n\n\n\n\n15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64\n\n\nSource: Survey data, ILO\n\n\n\n**REGIONAL REFUGEE EMPLOYMENT RATE BY POPULATION**\n\n**GROUP (2024)**\n\n\n\n**EMPLOYMENT RATE BY EDUCATION LEVEL**\n\n\nRefugees (2023) Refugees (2024) Hosts (2023)\n\n\n96%\n92%\n86%\n\n\n\nOverall\n\n\nMale\n\n\nFemale\n\n\nWith severe psychological\n\ndistress\n\n\nWith a disability\n\n\nSource: Survey data, SAG estimates\n\n\n\n64%\n\n\n67%\n\n\n63%\n\n\n57%\n\n\n\n72%\n\n\n\n\n\n49%\n\n\n\n\n\n\n\nTechnical or\n\nVocational\n\n\n\nBachelor's Master'", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000010:10:0:0", "start": 1238, "end": 1254, "surface": "Survey data, ILO", "probe_tag": "keep", "probe_score": 0.9987, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:10:0:1", "start": 1552, "end": 1565, "surface": "SAG estimates", "probe_tag": "keep", "probe_score": 0.9922, "luna_label": 1, "luna_reason": "Source-line attribution identifies estimates underlying the presented employment-rate chart."}]}, {"key": "rafael-059", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**Republic of Moldova: the case for housing costs corrections in income-based poverty metrics**\n\n\nData from the Republic of Moldova highlights the limitations of poverty metrics that rely solely on disposable\nincome, as they fail to account for vulnerability related to asset ownership. Based on income alone, the 2024\npoverty rate suggests that Ukrainian refugees are able to attain a higher standard of living than their hosts, with\nonly 10% living in poverty compared to 32% of Moldovans. This apparent disparity is largely driven by the\nsizeable financial support that Ukrainians received from humanitarian organizations in 2024, which amounted\nto 44% of their total household income. While the above comparison could suggest that the provided aid is\nexcessive, such an interpretation overlooks an important factor – namely that only 5% of Moldovans incur rental\nexpenses compare to 41% of refugees. In fact, including utilities, housing costs were estimated to consume 46%\nof refugees’ disposable income (inclusive of humanitarian aid), whereas they account for just 14% for Moldovan\nhouseholds. In essence, these findings indicate that refugees are using the entirety of their aid to cover\naccommodation needs. Adjusting disposable income at the household level to reflect these additional housing\nexpenses raises the poverty rate for refugees to 43%, surpassing that of Moldovans.\n\n\n**Poverty is associated with tangibly inferior living conditions, worse healthcare coverage,**\n**more frequently children being out of school, and having to skip meals**\n\n\nRefugee households with members living in poverty were found to more often not report feeling safe when\nwalking alone in their neighborhood after dark (18% vs 10% for those not at risk), to significantly more frequently\nlive in collective housing (27% vs 8%), and to feel under pressure to leave their accommodation (29% vs 11%", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000010:6:0:0", "start": 206, "end": 239, "surface": "Data from the Republic of Moldova", "probe_tag": "keep", "probe_score": 0.943, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-060", "text": " previous\nemployment background, compared to 50% of men. One possible explanation for this discrepancy is the higher\nproportion of men employed in sectors like construction and IT prior to displacement. These fields often\ndemand fewer country-specific qualifications, such as proficiency in the local language, thereby facilitating\neasier integration into similar roles in the host country.\n\n\n\n**DISTRIBUTION OF WORKING AGE POPULATION BY**\n**HIGHEST EDUCATION LEVEL ATTAINED**\n\n\nTechnical or Vocational Bachelor Master's Doctoral\n\n\n\n**WAGE PREMIUMS FOR EDUCATION LEVEL GAINS: HOSTS**\n**VERSUS REFUGEES, %**\n\n\n\nRefugee eduction\nwage premium\n(mean, 2024)\n\n\nBachelor's or\n\nabove\n\n\n\nHosts (2023)\n\n\nRefugees (2024)\n\n\nSource: Survey data, ILO\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1%\n\n\n\nHost education wage\npremium (median,\n2022)\n\n\nTechnical or\n\nvocational\n\n\n\nNote: Wage premiums computed with lower secondary education as\nthe baseline\n\n\nSource: Survey data, Eurostat, SAG estimates\n\n\n18. The difference in median wages by highest education level attained. Weighted equivalently to refugee weights for comparability\n19. The wage gap for refugees was computed based on the difference in weighted means (instead of the difference in medians)\n\n\n**13**", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000010:12:1:0", "start": 734, "end": 745, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9987, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-061", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\n© UNHCR / Anna Liminowicz\n\n\nDispersing refugees to weaker labour\nmarkets can also have negative social\noutcomes. Albarosa and Elsner (2022) report\nmore anti-immigrant incidents during the\nEuropean refugee crisis (2015/2016) in areas\nof Germany with high unemployment rates\nand shares of right-wing voters. Damm and\nDustmann (2014) show that immigrant men\ndispersed to areas of Denmark with more\nconvicted criminals, experience higher future\ncrime conviction probabilities, but not overall\nneighbourhood crime rate.\n\n\nThe estimated employment rate of refugees\nfrom Ukraine in Poland is above 60% and\nwas among the highest in the OECD 27 [^27: Deloitte elaboration based on the aggregation in OECD International Migration Outlook 2023] .\nAlthough there is uncertainty around\nemployment rate in different countries due\nto uncertainty regarding refugee numbers\n\nwhile MSNA Poland 2023 gives a slightly\nlower figure of 61%. In comparison, the\nemployment rate equalled 61% in the United\n\n\n\nBy refraining from any refugee dispersal\npolicies, Poland and other EU Member\nStates may have improved labour market\ninclusion. In many European countries\nrefugees are geographically dispersed after\narrival to spread the cost of hosting them,\nease the stress on the housing market\nand public services, and to avoid creating\nethnic enclaves. The effects, however, can\nbe detrimental to labour market inclusion\nand may be contrary to other aims of the\npolicy. Dispersal pushes at least some of\nthe refugees into regions with weak labour\nmarkets and few co-nationals already\nsettled, who otherwise could transmit\nimportant information about employment\nopportunities. Fasani, Frattini, and Minale\n(2022) conducted the first cross-country\nstudy of refugee dispersal policies.\nTheir LFS sample covers refugees who\nexperienced dispersal policies in Finland,\nGermany, Ireland, Netherlands, Norway,\nSweden, Switzerland and the UK (no data\nfor Denmark). They find that non-EU15\nrefugees aged 25-64 who arrived when a\ndispersal policy was in place experience\n4.5", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:12:0:0", "start": 778, "end": 819, "surface": "OECD International Migration Outlook 2023", "probe_tag": "keep", "probe_score": 0.9998, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:12:0:1", "start": 951, "end": 967, "surface": "MSNA Poland 2023", "probe_tag": "keep", "probe_score": 0.9984, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-062", "text": "||Szczecin ||Gdańsk||||\n||Poznański|Bydgoszcz||~~Katowice~~||||\n|||||Łęczyń|ski||Lubiński|\n\n\n|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|\n|---|---|---|---|---|---|---|---|\n|||||||||\n|||||||||\n|||||||||\n|||||Warszawa||||\n|||||||||\n|||||||||\n|||Łodź|Wrocław|Kraków|||R² = 0,16|\n||Poz|nański|Szczecin|||||\n|||Gdańsk ~~Bydgoszcz~~||Katowice Łęczy|ński||Lubiński|\n\n\n\n5000 6000 7000 8000 9000 10000 11000 12000\n\n\n**Average monthly gross earnings in enterprise sector in poviat of employment in 2022**\n\n\n\n\n\n8%\n\n\n7%\n\n\n6%\n\n\n5%\n\n\n4%\n\n\n3%\n\n\n2%\n\n\n1%\n\n\n0%\n\n4000\n\n\n10%\n\n\n9%\n\n\n8%\n\n\n7%\n\n\n6%\n\n\n5%\n\n\n4%\n\n\n3%\n\n\n2%\n\n\n1%\n\n\n0%\n\n4000\n\n\n\nUkrainian refugee educational attainment\nimplies from 18% (assuming refugees’\neducational attainment from November 2022\nsurvey) to 19% (July-August 2023) higher\n\nearnings than the general population. 36\n\n\nPoviat-level geographical distribution of\nUkrainian refugees is more concentrated in\nhigh productivity agglomeration", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:16:1:0", "start": 726, "end": 746, "surface": "November 2022\nsurvey", "probe_tag": "keep", "probe_score": 0.967, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-063", "text": "THE ROLE OF HOUSING SUPPORT AND EMPLOYMENT FACILITATION IN ECONOMIC VULNERABILITY OF REFUGEES FROM UKRAINE\n\n\n**SHARE OF YOUNG UKRAINIANS (AGED 15 TO 24) WHO ARE NOT ENGAGED IN EMPLOYMENT, EDUCATION, OR TRAINING**\n**(NEET) BY COUNTRY, %** **1,2,3**\n\n\nHost country Refugees, excluding online education Refugees\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Hungary Moldova Poland Romania Slovakia Region\n\n\n\n1. Data reported by the national statistics service was used as a\nreference for Moldova\n\n2. With the exception of Poland and the Czech Republic, the reliability\nof data by country is hindered by a relatively low number of\nobservations\n\n[3. Host country data is based on indicators reported by the OECD for](https://data.oecd.org/youthinac/youth-not-in-employment-education-or-training-neet.htm)\n[2022](https://data.oecd.org/youthinac/youth-not-in-employment-education-or-training-neet.htm)\n\n\n**UKRAINE REFUGEE YOUTH ACTIVITY BY AGE**\n\n\n\n**The gender divide: female – led households are**\n**more economically vulnerable than their male –**\n**led counterparts**\nThe share of households that are composed of\nfemale only adults with or without children\nconstitute 65% of the total sample across the\nregion. Compared to households with male only\nadults, the vast majority of which are without\nchildren, they are 51% more likely to find themselves\nbelow the poverty line.\n\n\n**DISTRIBUTION OF HOUSEHOLDS BY GENDER OF**\n**ADULTS, %**\n\n\n\nNEET\n\n\n15", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000000:8:0:0", "start": 432, "end": 480, "surface": "Data reported by the national statistics service", "probe_tag": "keep", "probe_score": 0.9647, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-064", "text": " banks) further strengthens\nthat effect. In total, general government\nrevenue increased by 2.0% in 2022, 2.75%\nin 2023, and 2.94% in 2024. In monetary\nterms this amounts to PLN 25.0 billion\nin 2022, PLN 39.1 billion in 2023, and\nPLN 47.0 billion in 2024 22 . In the long term,\nrefugees should increase annual general\ngovernment revenue by around 2.7%.\n\n\n**Ultimately, Ukrainian refugees**\n**generate additional output and**\n**demand.** This results in an increase in real\nGDP, and is especially beneficial for public\nfinance. Although the influx of refugees\nwas costly at the start, the additional\ngeneral government revenue they provided\nwas more than enough to compensate\nfor the expense 23 . Tight labour market\nhelped absorb the increase in labour force,\nmitigating negative impacts of increased\ncompetition on the native workforce. Over\ntime, increased productivity should benefit\nnative workers, as it is the primary driver of\nlong-term wage growth. 24 [^24: For a discussion on the stable long-term relationship between wages and labour productivity, see for example Meager & Speckesser (2011).]\n\n\n\n22 Deloitte own calculations based on Informacja kwartalna o stanie finansów publicznych - Ministerstwo Finansów - Portal Gov.pl for respective years. The general\ngovernment income share in GDP in 2024 was calculated based on the data for Q1–Q3 and based on that total general government income was calculated using\nforecast for GDP from Wytyczne dotyczące wskaźników makroekonomicznych - Ministerstwo Finansów - Portal Gov.pl.\n\n23 Model treats general government sector as a whole, as such cost and income internal structure may differ creating institutions with financial loses while other may\nhave disproportionate increase of income.\n\n24 For", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:12:1:0", "start": 1180, "end": 1230, "surface": "Informacja kwartalna o stanie finansów publicznych", "probe_tag": "keep", "probe_score": 0.955, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:12:1:1", "start": 1376, "end": 1390, "surface": "data for Q1–Q3", "probe_tag": "keep", "probe_score": 0.9504, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-065", "text": " or low employment level, similar (baseline) or lower\n(conservative) productivity. In the conservative productivity scenario,\nwe assumed approximately 10% lower refugee productivity estimated based\non incomes reported in MSNA Poland 2023 and data from Statistics Poland.\n\n\n**Table 2.** Cumulative changes in main indicators in 2023\n\n\n**SCENARIO 1** **SCENARIO 2** **SCENARIO 3** **SCENARIO 4**\n\n\n\n**Low employment**\n**conservative**\n**productivity**\n\n\n\n**High employment**\n**conservative**\n**productivity**\n\n\n\n**Low employment**\n**baseline**\n**productivity**\n\n\n\n**High employment**\n**baseline**\n**productivity**\n\n\n\nThe main impact of refugees is in growth\nof the economy. Refugees increase both\nsupply as workers and entrepreneurs as\nwell as demand as consumers. Increase\nin GDP is not directly proportional to the\nincrease in population or employment.\nNet benefits are lowered both due to\na decrease in capital to labour ratio as\nwell as an increase in competition on\nthe labour market. Moreover, increase in\ndemand in tight labour market conditions\nresults in higher inflation and lower\nprice competitiveness of Polish products\nwhich lower its overall positive impact.\nNevertheless, the positive impact of\nrefugees on the economy is significant in\nevery scenario considered.\n\n\nIn 2022 it amounts to real GDP\nbeing higher by 0.5-0.8% and in\n2023 cumulatively by 0.7-1.1%.\nThis corresponds to GDP being\nhigher by 24-36.9 billion PLN in\n2023 43 .\n\n\nIn the long term, total potential GDP should\nbe higher by around 0.9-1.35% due to\nrefugees contributions. 44\n\n\nOur results are consistent with the\nprevious, similar studies. In estimating\nGDP impacts we take an approach that", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:17:2:0", "start": 242, "end": 269, "surface": "data from Statistics Poland", "probe_tag": "keep", "probe_score": 0.9998, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-066", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n\n\nnegative perceptions of Ukrainian refugees in some\nhost communities including perceived overuse of\nhealth services and preferential treatment 6 [^6: Kerusauskaite, I., Nimkar, R., Mulloy, L., Slota, A. (2023). Risks to Community Cohesion between Ukrainian Refugees and Host\nCommunities in Central Europe. Community Cohesion in Central Europe project.] . As the\nrefugees stay longer, sustainable solutions to\nmeeting health needs of refugees and host\ncommunities, including sustainable financing,\nrefugee data integration, integration of the refugee\nhealth workforce, and enhanced service delivery\nincluding mental health services, are critical.\n\n\nThrough regional multi-agency collaboration,\nmultisector needs assessments have been\nconducted in refugee-receiving countries since\n2022 to collect information on the needs of\nrefugees, including those related to health, nutrition\nand mental health and psychosocial support. These\nassessments support partners’ understanding of the\nlevel of access to essential services among\nrefugees and outcomes enable governments and\npartners to identify priorities for the response. In\n2024, a social-economic lens was added in\nassessing the needs of refugees in the SocioEconomic Insights Study (SEIS) conducted in ten\ncountries (Bulgaria, Czechia, Estonia, Hungary,\nLatvia, Lithuania, Poland, Republic of Moldova,\nRomania, and Slovakia).\n\n\n# Methodology\n\nThe regional analysis is grounded in consolidated\ndata from the Socio-Economic Insights Survey\n(SEIS), conducted across ten countries: Bulgaria,\nCzechia, Estonia, Hungary, Latvia, Lithuania, Poland,\nRepublic of Moldova, Romania, and Slovakia. Data\nfor the country-specific SEISs were collected\nthrough in-person interviews from May to July 2024.\n\n\nThe total sample size comprises **8,720 households**\nand **19,803 household members**, with respondents\nproviding information on behalf of all individuals\nwithin their households.\n\n\n**COUNTRY** **SAMPLE SIZE 2023** **SAMPLE SIZE 2024**\n\n\nBulgaria 1,054</", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000004:5:0:0", "start": 1286, "end": 1314, "surface": "SocioEconomic Insights Study", "probe_tag": "keep", "probe_score": 0.9778, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000004:5:0:1", "start": 1540, "end": 1570, "surface": "Socio-Economic Insights Survey", "probe_tag": "keep", "probe_score": 0.9908, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-067", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**REGIONAL REFUGEE EMPLOYMENT RATE BY LEVEL OF**\n\n**LOCAL LANGUAGE KNOWLEDGE (2024)**\n\n\nEmployment rate Share of the refugee population (rhs)\n\n\n\n80%\n\n\n60%\n\n\n40%\n\n\n20%\n\n\n0%\n\n\n\n35%\n\n\n\nDoes not\nunderstand\n\n\n\nBeginner Intermediate Advanced Fluent\n\n\n\nSource: Survey data\n\n\nThe 2024 survey introduced a new question on local language proficiency, reinforcing previous findings of a\nstrong correlation between language skills and employment. Respondents with at least an intermediate level of\nlocal language proficiency reported nearly twice the employment rate compared to those with no knowledge\n(9% of respondents). Even Ukrainians with only a basic understanding—limited to a few words or phrases (28%\nof the sample)—experienced a notable increase in employment compared to those with no local language\nskills 16 [^16: The data also demonstrates that the employment rate of refugees fluent in the local language is lower than for those with\nan intermediate or advanced knowledge. The reason for this is that the former group is heavily concentrated in lower age\nbrackets, with almost 30% being 15-19 years old] .\n\n\nFinally, unlike for the host population, refugee employment rates were found to be practically the same for all\neducation levels above technical or vocational 17, implying that local employment markets may not be valuing\nadvanced degrees. Possible explanations include impediments to foreign qualifications recognition and other\nbarriers that are preventing placement into high-skilled jobs (such as language, a mismatch between\nqualifications and local demand, etc.).\n\n\n**Wages also improved but remain significantly below host levels**\n\n\nDespite a 28% increase in the regional weighted average wage of refugees in 2024, this figure still stands 30%\nlower than that of the local population. This means that, on average, Ukrainians earned roughly two-thirds of\nwhat their hosts did", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000010:11:0:0", "start": 376, "end": 387, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.999, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:11:0:1", "start": 394, "end": 405, "surface": "2024 survey", "probe_tag": "keep", "probe_score": 0.9953, "luna_label": 1, "luna_reason": "Survey findings support reported employment and language-proficiency relationships."}]}, {"key": "rafael-068", "text": " less about\n‘desire’ and more about 'capacity', what in\ndevelopment terms includes social capital,\nhuman capital (like education and skills),\nand even psychological readiness. That\ncapacity is shaped by a combination of\npublic policies and personal circumstance. 6\n\n© UNHCR / Anna Liminowicz\n\n\n6 Crucial will also be the ‘pull’ factors in Ukraine, which will be determined by the conditions of future peace. The longer the full-scale war continues, the smaller will\nbe the percentage of refugees who are willing to return to Ukraine (Tokariuk, 2025). Some 1.3 million refugees have already returned to Ukraine. However, report that\neconomic opportunities in areas of return are inferior than what they had expected before returning, and while half of respondents are currently working, only around\na quarter reported being able to cover all or most of their basic needs, and only around a third report feeling safe in their current locations (UNHCR, 2025c).\n\n\n13\n\n\n\n**Chart 8. Ukrainian refugee household incomes from Poland and Ukraine in 2023 and 2024**\n\n\nMSNA 2023 SEIS 2024 MSNA 2023 SEIS 2024 MSNA 2023 SEIS 2024\n\nIncome from Poland Income from Ukraine Unclassified income\n\n\nWork Benefits Remittances Other\n\n\nNote: There are minor differences in the MSNA and SEIS income categories, as in MSNA remittances refer to all remittances from friends and family, while in SEIS\nto remittances from Ukraine.\n\nSource: Deloitte own elaboration based on MSNA (July-August 2023) and SEIS (May-June 2024) UNHCR (2024, 2023) surveys.\n\n\n12", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "jad_paddy_docs:000001:6:2:0", "start": 1458, "end": 1462, "surface": "MSNA", "probe_tag": "keep", "probe_score": 0.9618, "luna_label": 1, "luna_reason": "Named survey cited as the source for charted refugee household income data."}, {"key": "jad_paddy_docs:000001:6:2:1", "start": 1507, "end": 1533, "surface": "UNHCR (2024, 2023) surveys", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Surveys are cited as sources for the chart's income analysis."}]}, {"key": "rafael-069", "text": " https://data2.unhcr.org/en/situations/ukraine\n\n2 PESEL UKR is a version of Polish national ID number for Ukrainian citizens in connection with the armed conflict in the territory of that country.\n\n\n06\n\n\n\n**Chart 2. Ukrainians registered for social insurance**\n\n\nNote: Refugees are identified by PESEL UKR status.\n\nSource: Deloitte own elaboration based on ZUS data.\n\n\n\nUkrainian refugees Pre-war Ukrainians\n\n\n\n07", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "jad_paddy_docs:000001:3:2:0", "start": 364, "end": 372, "surface": "ZUS data", "probe_tag": "confusion", "probe_score": 0.6506, "luna_label": 1, "luna_reason": "ZUS data underlies the chart of Ukrainians registered for social insurance."}]}, {"key": "rafael-070", "text": " the Deloitte D.Climate model, economic\nimpact of Ukrainian refugees amounted\nto a higher real GDP by 1.5% in 2022, as\nthey initially entered the labour market.\nWith more refugees finding employment,\ntheir impact grew to 2.3% GDP in 2023,\nand further to 2.7% GDP in 2024. This\ncorresponds to GDP being higher by\nPLN 98.7 billion in 2024. In the long term, as\nthe refugees acquire more country-specific\nskills and firms invest to restore their\ncapital-to-labour ratio, the impact will grow\nto 3.2% GDP by 2030. Refugees contribute\nto the economy by increasing the labour\nsupply as both workers and entrepreneurs,\nand by boosting demand as consumers.\nThe increase in GDP is not directly\n\n\n\nproportional to the increase in population\nor employment. 20 [^20: As outlined in the Appendix on modelling strategy, the total number of refugees was set at 2.6% of the total population, while their share in total employment as\ngrowing from 1.5% in 2022 to 2.4% in 2024.] On the one hand,\nincrease in productivity further boosts\nthe economy, on the other net benefits\nare lowered both due to a decrease in\nthe capital-to-labour ratio, as well as an\nincrease in competition in the labour\nmarket. Moreover, the increase in demand\nin tight labour market conditions work in\nthe direction of higher inflation and lower\nprice competitiveness of Polish products\nwhich decrease its overall positive impact.\n\n\n**The results are in line with the**\n**optimistic scenario from the**\n**previous Deloitte (2024) report.** The\ncurrent report is different from the one\nfrom 2024 in that we account for the\npositive productivity shock reflected in\nthe labour market data, which further\n\n\n\n**The impact of Ukrainian refugees**\n**on the Polish economy is estimated**\n**with the Deloitte D.Climate general**\n**equilibrium model** ", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:11:3:0", "start": 1635, "end": 1653, "surface": "labour market data", "probe_tag": "confusion", "probe_score": 0.0971, "luna_label": 1, "luna_reason": "Existing labour market data are used to reflect a productivity shock in the model."}]}, {"key": "rafael-071", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 19. Tertiary education and corresponding**\n**occupational groups shares**\n\n\n\n**Chart 20. Ukrainian refugees median net wages by**\n**educational attainment**\n\n\n4,300 4,275\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\ncountry-specific knowledge and business\nnetworks) are more difficult to address\nthan others (such as language skills and\noccupational licensing). However, if the gap\nwas narrowed by half, the average wage of\nrefugees would increase by approximately\n10%. Assuming that the productivity\nincrease is equivalent to the wage increase,\nthis would result in PLN 3.5 billion of added\n\n\n\n**Addressing occupational downgrading**\n**could bring macroeconomic benefits.**\nTo demonstrate the potential impact,\na simulation was conducted in which\nthe underrepresentation of refugees in\nhigher-paying occupations was reduced by\nhalf. It was not assumed that there would\nbe no differences between refugees and\nPolish citizens, as some barriers (such as\n\n\n\n40%\n\n\nTertiary education share\n\n25-64 age group\nsurveys\n\n\n\nManagers, professionals,\nand technicians ZUS-insured\n\nemployment share\n\n\n\n\n#### **4.2 Access to regulated professions**\n\n\n\nnatives and this gap was largest for men,\nworkers in the highest education level, and\nnonnaturalized immigrants. This has an\nimportant impact on wages, because, as\nshowed in a seminal work by Kleiner and\nKrueger (2013) and confirmed in several\nanalyses, working in a regulated profession\ncomes with a significant wage premium.\nBrücker et al. (2021), who analysed\nGerman data, found that occupational\nrecognition led to full convergence of\nimmigrants’ earnings to those of their\nnative counterparts. Tani (2020) found\nthat in Australia, licensing raised hourly\n\n\n\nvalue to the economy. This estimate may\nunderestimate the potential benefits,\nas the boost in productivity would most\nlikely rise not just employee wages, but\nemployer profits as well", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:14:0:0", "start": 1104, "end": 1127, "surface": "25-64 age group\nsurveys", "probe_tag": "confusion", "probe_score": 0.5821, "luna_label": 0, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:14:0:1", "start": 1617, "end": 1628, "surface": "German data", "probe_tag": "keep", "probe_score": 0.9229, "luna_label": 1, "luna_reason": "German data supports the finding that occupational recognition equalized immigrant earnings."}]}, {"key": "rafael-072", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n# Recommendations\n\n\n\nHost countries and communities have made\ncommendable and continuous efforts to support\nrefugees in accessing essential health and MHPSS\nservices. While most respondents reported\nadequate access, barriers to services changed and\nincreased for some refugees. These changes do not\ntake the same form for all refugees, and\nintersectional factors related to gender, age,\ndisability, and chronic illness continue to impact the\nexperiences and needs of refugees from Ukraine in\nthis regard. The recommendations below highlight\nkey priorities and actions for governments and RRP\npartners to address these challenges and enhance\nrefugees’ access to care in host countries.\n\n\nThese actions aim to support governments and\nlocal communities in strengthening health and\nMHPSS systems in areas impacted by refugee\narrivals and advancing the integration of refugees\ninto national systems. Achieving these goals will\nrequire long-term planning and sustained funding\ncommitments.\n\n\n\n\n\n\n\n9. [Integration of migrant and refugee data in health information systems in Europe: advancing evidence, policy and practice](https://doi.org/10.1016/j.lanepe.2023.100744)\n\n\n**28**", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000004:27:0:0", "start": 1084, "end": 1108, "surface": "migrant and refugee data", "probe_tag": "confusion", "probe_score": 0.74, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael-073", "text": " policies in Finland,\nGermany, Ireland, Netherlands, Norway,\nSweden, Switzerland and the UK (no data\nfor Denmark). They find that non-EU15\nrefugees aged 25-64 who arrived when a\ndispersal policy was in place experience\n4.5 percentage points lower employment\nrates than for those not exposed to such\na policy. This may understate the effect,\nas for refugees who arrived 10 or less\nyears before the survey, the effect is\n17.5 percentage points, while it becomes\nstatistically insignificant afterwards.\nFor the six countries for which data on\nresidence is available, non-dispersed\nrefugees show clearly stronger clustering in\neconomically stronger regions (measured\nby GDP per capita).\n\n\n24\n\n\n\n25\n\n\n\nEconomic research on dispersal policies\nand labour market inclusion focuses on\nthe strength of local labour markets and\nsize of local ethnic networks. While some\nscholars find only the strength of local\nlabour markets to be significant and not\nco-national networks (Foged, Hasager, and\nPeri, 2022), or inconsistent results for conational networks (Müller, Pannatier, and\nViarengo, 2022), others find effects only for\nco-national networks (Damm, 2014).\n\n\nMigrants dispersed to weaker labour\nmarkets experience poorer inclusion\noutcomes in subsequent years, and vice\nversa. These outcomes can pertain to\nemployment, earnings, or human capital\naccumulation, and have been shown in\nDenmark (Foged, Hasager, and Peri, 2022;\nAzlor, Damm, and Schiltz-Nielsen, 2020),\nGermany (Aksoy, Giray, Poutvaara, and\nSchikora, 2020), Norway (Godøy, 2017),\nSweden (Åslund, Östh, and Zenou, 2010;\nÅslund and Rooth, 2007), and Switzerland\n(Müller, Pannatier, and Viarengo, 2022),\nas well as the previously described crosscountry study. This could in principle stem", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:12:1:0", "start": 532, "end": 549, "surface": "data on\nresidence", "probe_tag": "confusion", "probe_score": 0.3768, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-074", "text": ", 2024 shows that\n38% of Ukrainian refugees worked in\nelementary occupations, much more than\npre-war Ukrainians (25%), non-Ukrainian\nforeigners (18%), and Polish citizens (10%).\n\n\n\n\n\n\n\n\n\n\n\nMSNA Jul-Aug 2023 SEIS May-Jun 2024\n\n\n\n\n\n\n\n\n\nMSNA Jul-Aug 2023 SEIS May-Jun 2024\n\n\n\nSource: Deloitte own elaboration based on MSNA and SEIS UNHCR surveys conducted in July-August 2023 and May-June 2024.\n\n\n\nWhile the share seems least favourable\namong Ukrainian refugees, their situation\nimproved the most in the two years since\nJune 30, 2022 (by 10 pp. compared with\n9 pp. for the pre-war Ukrainians, 2 pp. for\nnon-Ukrainian foreigners and 1 pp. for\nPolish citizens). On the other hand, in\nQ2 2024, managers and specialists, the\ntwo highest paid occupational groups,\ncomprised 8% of both Ukrainian refugees\nand pre-war Ukrainians, 22% of nonUkrainian foreigners (who include many\n\n\n\nIT specialists and executives), and 28% of\nPolish citizens. Their shares increased\nin the past two years by 3 pp. among\nUkrainian refugees and pre-war Ukrainians,\n2 pp. among other foreigners, and 1 pp.\namong Polish citizens – this data shows\nthat though Ukrainian refugees may be\nlargely employed in the less attractive\noccupational groups, they are also the\nfastest to progress toward more attractive\nprofessions. 11 [^11: Since 2021, ZUS has been requesting information about the occupation of non-agricultural workers who first join the social insurance system (most farmers have a\nseparate social insurance system). Unfortunately, this data is not yet comprehensive, as on June 30, 2022, it included 4.8 million people, and June 30, 2024, 7.2 million]\n\n\n\n**Ukrainian refugees in Poland have**\n**clearly improved their economic**\n**situation over the past year.** As\nindicated in chapter 1, the share of\nUkrainian refugee household incomes\nderived from work in Poland has increased\nfrom 74% in the July-August 2023 MSNA\nsurvey to 76% in the May-June 2024", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:7:1:0", "start": 324, "end": 342, "surface": "SEIS UNHCR surveys", "probe_tag": "keep", "probe_score": 0.9957, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:7:1:1", "start": 1902, "end": 1913, "surface": "MSNA\nsurvey", "probe_tag": "confusion", "probe_score": 0.645, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-075", "text": " and highest incomes.\nDetails are available in the Online Technical Appendix.\n\n27 Note that the GUS (2024) data for the host population is not exactly comparable, as it does not include microenterprises, it covers an earlier period and focuses on\ngross and average wages.\n\n\n28\n\n\n\n28 Lessem and Sanders (2020) modelled immigrant wage growth in the United States, finding that in a counterfactual model eliminating barriers to occupational entry\nwould lead to only small earnings increase for the average immigrant, but a substantial increase for the most highly skilled.\n\n\n29", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:14:4:0", "start": 96, "end": 111, "surface": "GUS (2024) data", "probe_tag": "confusion", "probe_score": 0.2908, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-076", "text": " rate of native or other\nimmigrants, except an actual slight positive\nimpact on the wages of native women.\nEven in a conservative scenario that\nassumes negative effects of labour\nmarket competition in the form of higher\nunemployment and slower real wage\ngrowth, an increase in the labour force\ntranslates into larger personal incomes and\nhigher private consumption, which results\nin a larger tax revenue. These effects were\nstrengthened by an influx of capital from\nabroad.\n\n###### In total, the general government revenue increased by 0.8-1.1% in 2022 and 1.05-1.45% in 2023. In monetary terms, this amounts to 10.1-13.7 billion PLN in 2022 and 14.7-19.9 billion PLN in 2023.\n\n\n\nIf estimates quoted by a government offical\nof public expenses on refugees of around\n15 billion PLN in 2022 and 5 billion in 2023 13 [^13: E.g. vice-president of Polish Development Found Bartosz Marczuk estimated it at around 16 billion PLN, but this estimation also included spending of\nNGOs which was combined with spendings of local governments [Polska pomoc dla Ukrainy 2022 - ile kosztowała? - Infor.pl.](https://www.infor.pl/prawo/nowosci-prawne/5635962,Polska-pomoc-dla-Ukrainy-2022-ile-kosztowala.html)]\nare accurate, we can conclude that they\nwere more than offset by the additional tax\nrevenue. In the long-term, refugees should\nincrease yearly government revenue by\naround 0.85-1.3%.\n\n\n**Unaccounted positive externalities**\nAll our theoretical economic modelling\nresults are conservative lower bound\nestimates, as econometric studies from\nother countries have found immigration\nto have additionally a positive impact\non labour productivity that cannot be\naccounted for using the available data.\nAccording to these econometric studies,\nimmigration can not only raise economic\noutput (i.e., more workers equal more\nproduction), but more importantly labour\nproductivity (i.e., more value added per\nworker)", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:4:2:0", "start": 1689, "end": 1703, "surface": "available data", "probe_tag": "confusion", "probe_score": 0.6607, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael-077", "text": " for host country poverty](https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions)\nassessments\n\n3. Results for the Czech Republic not individually presented due to\nsampling limitations\n\n4. The host country poverty rate was based on [OECD indicators for](https://stats.oecd.org/)\n[2021 that were indexed towards 2023 using consumer price index](https://stats.oecd.org/)\n(CPI) data\n\n\nRefugee households also report having to engage\nin harmful coping strategies to meet basic needs.\nSome 8% and 15% had to resort to emergency\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n5 Belarus, Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, the Republic of Moldova, Poland, Romania, and Slovakia\n\n\n\n6 Defined as the total after-tax income of the household (including wages, transfers, social protection benefits, etc.) divided by the\nsquare root of the household size\n\n\n\n7 Based on [OECD data from 2021, which was indexed by the CPI for 2022 and 2023 for each respective country](https://stats.oecd.org/)\n\n\n\n**4**", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000000:3:2:0", "start": 287, "end": 302, "surface": "OECD indicators", "probe_tag": "keep", "probe_score": 0.9908, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000000:3:2:1", "start": 924, "end": 943, "surface": "OECD data from 2021", "probe_tag": "confusion", "probe_score": 0.8555, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-078", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n\n\nmeasles vaccination coverage for children\nstood at 83%, similar to 84% in 2023, falling\nshort of the 95% target.\n\n\n\n\n\n\n\nInformal support also played a vital role, with\n33% receiving help from family or friends and\n12% accessing spiritual support. Overall, 88%\nof those who received support reported\nimproved wellbeing, though there are notable\ndifferences depending on gender and age.\n\n\nThe recommendations drawn from this analysis\nfocus on addressing the health and mental health\nand psychosocial needs and barriers identified in\nthe SEIS, tailoring them to the specific data and\ncontext of each country. To enhance policy\ndevelopment, it will be crucial to improve monitoring\nof refugees’ health, including sexual and\nreproductive health and mental health, through\ninclusion of disaggregated refugee data into\nnational data systems. This will require effective\ncollaboration among health organizations, statistical\noffices, and partners. Addressing capacity issues in\nnational health systems, such as workforce\nshortages and long wait times, can be supported\nthrough telemedicine and temporarily integrating\nUkrainian healthcare workers. Refugees with\nchronic illnesses and disabilities require targeted\ninterventions to meet their health and MHPSS\nneeds, including through health financing\nmechanisms. Continued efforts are also required to\naddress persistent access barriers through contextspecific strategies, including providing refugees\nwith information on navigating health systems and\npreventive health services such as vaccination.\n\n\nExpanding community-based MHPSS services that\nintegrate formal services and promote the use of\ninformal supports will enhance service delivery.\nPublic awareness campaigns, tailored to both\nrefugees and host communities, should aim to\nreduce stigma and improve knowledge of available\nsupport. Gender-responsive and age-sensitive\napproaches are necessary, particularly for adult men\nand adolescent boys, to encourage help-seeking\nbehaviours and ensure that services are tailored to\nthe specific needs of children and adolescents.\nLastly, further research is required to gain a deeper\nunderstanding of unmet health needs—including\nSRH and MHPSS needs—and the barriers to access", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000004:3:0:0", "start": 608, "end": 612, "surface": "SEIS", "probe_tag": "confusion", "probe_score": 0.6005, "luna_label": 1, "luna_reason": "SEIS identifies health needs and barriers underlying the recommendations."}, {"key": "sample:jad_paddy_docs:000004:3:0:1", "start": 853, "end": 879, "surface": "disaggregated refugee data", "probe_tag": "confusion", "probe_score": 0.4115, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael-079", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\nVocational or lower Secondary Higher\n\n\n\nData is limited, but it appears refugees\nfound work in sectors where the labour\nmarket was particularly tight. Publicly\navailable data does not give the numbers\nof social security registrations of native\nworkers by NACE sector. For this reason,\nin the absence of administrative data,\nto approximate the share of Ukrainian\nworkers in particular sectors it was\nnecessary to use Statistics Poland survey\ndata. The share of workers with Ukrainian\ncitizenship grew more in sectors that were\nexperiencing the highest wage and salaries\ngrowth before refugee displacement. This\neased their entry into the labour market.\nThe refugees were also quick to set-up their\nown businesses or become self-employed.\nAccommodation and food, as a sector,\nhas been experiencing a particularly large\ngrowth in wages and salaries in 2021, as it\nre-opened after the Covid-19 pandemic,\nand subsequently it recorded a particularly\nlarge growth in the share of Ukrainian\nworkers. Transportation has been the only\nsector in which the share and number of\nUkrainian workers declined.\n\n\nA significant number of refugees are\nconcentrated in the urban areas of\nPoland. For one, cities generally record\nlower unemployment rates and higher\nwork productivity, though the costs of\nliving remain higher than in rural areas.\nAccording to the active population in the\nPESEL database, over 30% of all PESEL UKR\nholders had them issued in the country’s\n12 biggest cities. At the same time, the\nresults for the MSNA Poland 2023 survey\nsuggest that these 12 biggest cities are\ninhabited by over 35% of refugees.\n\n\n\nRefugees record higher levels of\nemployment inclusion in European\ncountries that have relatively better labour\nmarket situations. In countries with lower\nunemployment, refugees fare better in\nthe labour market. Since women make\nup the majority of refugees of working\nage, the situation of women on the labour\nmarket is especially important. As such,\nfemale unemployment", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:10:0:0", "start": 567, "end": 596, "surface": "Statistics Poland survey\ndata", "probe_tag": "keep", "probe_score": 0.9774, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:10:0:1", "start": 1518, "end": 1532, "surface": "PESEL database", "probe_tag": "confusion", "probe_score": 0.8853, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:10:0:2", "start": 1658, "end": 1681, "surface": "MSNA Poland 2023 survey", "probe_tag": "keep", "probe_score": 0.9768, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-080", "text": " circular migration, with Ukrainians\ncoming to Poland for half of the year,\nthen returning to Ukraine for another six\nmonths, and coming back to Poland. The\ndata on employers’ declarations do not\nreveal the actual number of Ukrainian\ncitizens who followed this system – a single\nperson could hold several declarations,\nbecause with every change of employer\nor promotion at the same employer they\nhad to ask for a new declaration. What is\nmore, with stays shorter than one year,\n\n\n\nthose people fell outside the definitions\nof population used by Statistics Poland\n(Główny Urząd Statystyczny, GUS,\nPoland’s statistical office). The National\nBank of Poland estimated that between\n2014 and 2018, there were approximately\none to two million Ukrainian workers\nin Poland at a time (Strzelecki, Growiec\nand Wyszyński, 2022). According to the\n2021 Polish National Census, one year\nbefore the outbreak of the full-scale war\nin Ukraine there were about one million\nUkrainian citizens residing in Poland,\nalmost all of them on a temporary basis.\n\n\n\nAfter the full-scale Russian invasion of\nUkraine, Poland experienced a massive\ninflux of refugees – with more than\n27 million border crossings from Ukraine\nand more than 1.9 million applications for\nprotection submitted by April 7, 2025 1 . Not\nall of those people stayed in Poland. Many\n\n\n\nof them later returned to Ukraine or moved\nto other countries. By February 14, 2025,\nPESEL UKR 2 [^2: PESEL UKR is a version of Polish national ID number for Ukrainian citizens in connection with the armed conflict in the territory of that country.] holders who remained\nin Poland stood at less than 1 million,\nwhile the border movement balance\nbetween Poland and Ukraine was slightly\nbelow 2 million.\n\n\n\n1 UNHCR data, https://data2.unhcr.org/en/situations/ukraine\n\n2 PESEL UKR is a version of Polish national ID number for Ukrainian citizens in connection with the armed conflict in", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:3:1:0", "start": 157, "end": 188, "surface": "data on employers’ declarations", "probe_tag": "confusion", "probe_score": 0.2361, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:3:1:1", "start": 834, "end": 861, "surface": "2021 Polish National Census", "probe_tag": "confusion", "probe_score": 0.6833, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:3:1:2", "start": 1757, "end": 1767, "surface": "UNHCR data", "probe_tag": "keep", "probe_score": 0.9905, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-081", "text": " 15,902\n\n\n**7. Szczecin** 14,318\n\n\n**8. Poznanski** 14,028\n\n\n**9. Pruszkowski** 12,206\n\n\n\n\n\n\n\n18-59 female and male\n\n\n\n\n\n60+ female and male\n\ntwo 18-59 or 60+ females\n\nother households\n\n\n\n\n\n\n\n18-59 female with one or\nmore 60+ individuals\n\n\n\ntwo 18-59\n\nfemales\n\n\n\n0.3% 7.4%\n\n\n\n\n\n**10. Katowice** 11,591\n\n\n\nNote that poviat-level population does not include Ukrainian refugees, who were added to calculate their appropriate shares.\n\nSource: Deloitte own elaboration based on the PESEL database as of September 2024 and GUS population data as of mid-2024.\n\n#### **1.3 Households income sources**\n\n\n\n18-59 female\nand male\n\n\nother\nhouseholds\n\n\n\n\n\nNote: Some groups that constituted less than 2% of households have been omitted.\n\nSource: Deloitte own elaboration based on SEIS (May-June 2024) UNHCR (2024) survey.\n\n\n\n**The majority of refugees settled in**\n**major cities, especially Warsaw and**\n**Wroclaw, and their vicinities.** Eight most\npopulous poviats in Poland 5 comprising\n16% of the host population, are also the\ntop eight poviats in terms of the number\nof Ukrainian refugees, 29% of whom\nreside there. This shows that Ukrainian\nrefugees are more concentrated in the\nlargest cities than the host population,\nas they migrated to the most attractive\n\n\n5 Seven cities and Poznański poviat.\n\n\n10\n\n\n\n11\n\n\n\nlabour markets, where they have better\nchances of supporting themselves.\nUkrainian refugees make up the largest\nshares of the population in the city of\nWroclaw (7.4%), Przemysl, a city on the\nUkrainian border (6.5%), and in Pruszkowski\npoviat", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:5:1:0", "start": 477, "end": 491, "surface": "PESEL database", "probe_tag": "keep", "probe_score": 0.9914, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:5:1:1", "start": 517, "end": 536, "surface": "GUS population data", "probe_tag": "keep", "probe_score": 0.9517, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:5:1:2", "start": 766, "end": 770, "surface": "SEIS", "probe_tag": "confusion", "probe_score": 0.4326, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:5:1:3", "start": 787, "end": 806, "surface": "UNHCR (2024) survey", "probe_tag": "keep", "probe_score": 0.9423, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-082", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 7. Income of Ukrainian refugee households by source**\n\n\n**2%**\n**0.1%**\n\n\n\nWork (regular, part-time, self-employment, remote, other, remote in Ukraine)\n\n\nRemittances from Ukraine\n\n\nPolish social benefits\n\n\nUkrainian social benefits\n\n\nOther (capital, loans, other)\n\n\n\nSource: Deloitte own elaboration based on SEIS (May-June 2024) UNHCR (2024) survey.\n\nNote: Deloitte worked with disaggregated household-level data, ensuring comparability\n(converting all Ukrainian hryvnia incomes into Polish zloty based on daily exchange rates\nin the time of the interview, and 3-month into 1-month remittance incomes).\n\n\n\n**In 2024, Ukrainian refugee households**\n**increasingly sourced their income from**\n**Poland, rather than from Ukraine.** This\ncan be seen when comparing the MSNA\nsurvey conducted in July-August 2023 and\nSEIS in May-June 2024 (UNHCR, 2024, 2023).\n\n\n\nWhile some methodological differences\napply, we can see that incomes earned\nin Poland grew from 81% in 2023 to 90%\nin 2024. Conversely, incomes from Ukraine\ndeclined from 18% to just 9%. This change\ndemonstrates that Ukrainian refugees\n\n\n\ncontinue to integrate economically.\nIt comes as no surprise, given that\nemployment rates and wages of Ukrainian\nrefugees in Poland increased during that\ntime, which is the focus of the next chapter.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**According to academic literature,**\n**better access to host country’s public**\n**services increases the likelihood of**\n**refugees returning to their country of**\n**origin, while labour market integration**\n**decreases it.** Some evidence shows that\nwhen refugees from Ukraine have access to\neducation, healthcare", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:6:0:0", "start": 392, "end": 396, "surface": "SEIS", "probe_tag": "keep", "probe_score": 0.9986, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:6:0:1", "start": 413, "end": 432, "surface": "UNHCR (2024) survey", "probe_tag": "keep", "probe_score": 0.9967, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:6:0:2", "start": 462, "end": 496, "surface": "disaggregated household-level data", "probe_tag": "keep", "probe_score": 0.9675, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:6:0:3", "start": 849, "end": 860, "surface": "MSNA\nsurvey", "probe_tag": "keep", "probe_score": 0.9869, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:6:0:4", "start": 895, "end": 899, "surface": "SEIS", "probe_tag": "drop", "probe_score": 0.0007, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-083", "text": " working refugees there is a\nsignificant number of entrepreneurs.\nThe high employment rate of refugees in\nPoland covers not only employees, but\nalso the self-employed. The available data\nalso shows that refugees from Ukraine\nin Poland are likely to start their own\nbusinesses. ZUS (social security) statistics\non insured refugees indicate that around\n5% of them have set up a business or are\nfreelancers. Similar results can be gleaned\nfrom the MSNA Poland 2023 survey results,\nwhich show that slightly more than 5% of\nrespondent households receive income\nfrom self-employment or similar activities.\nThis percentage appears to be slightly\nhigher for men, at over 6%, than women.\n\n\n\n\n\n30 Eurostat data, https://ec.europa.eu/eurostat/databrowser/view/migr_asytpsm/default/table?lang=en\n\n\n\n26 27", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:13:1:0", "start": 277, "end": 329, "surface": "ZUS (social security) statistics\non insured refugees", "probe_tag": "confusion", "probe_score": 0.4385, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:13:1:1", "start": 445, "end": 476, "surface": "MSNA Poland 2023 survey results", "probe_tag": "confusion", "probe_score": 0.7324, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:13:1:2", "start": 688, "end": 701, "surface": "Eurostat data", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael-084", "text": "**The World Bank**\nSouth Sudan Enhancing Community Resilience and Local Governance Project (P169949)\n\n\n10. **While the situation remains fluid, the recent reduction in armed conflict may facilitate**\n**substantial population movement.** About 1.3 million people have returned from displacement within or\noutside South Sudan since 2016. Of these, 644,174 returned in the short period between the signing of\nthe revitalized peace agreement in September 2018 until June 2019. 26 [^26: IOM DTM, Round 6 (op cit.).] The largest number of returns were\nto Jonglei (191,052), Upper Nile (164,068), and Western Bahr Ghazal (162,770). 27 [^27: Ibid.] These figures indicate\nan accelerating pace of return since the agreement was signed. According to recent intention surveys by\nboth the United Nations High Commission for Refugees and IOM, the main pull factors for return are\nimproved security, family reunification, access to basic services, and livelihood opportunities. About 30\npercent of refugees in neighboring countries consider returning, with IDPs being slightly more willing to\nreturn over the next 12 months. Yet, illegal land occupations remain an issue for the displaced, who\nsometimes will illegally occupy land in place of their own inaccessible properties during return processes.\nIntercommunal conflict continues to create new displacement in locations such as Unity, Warrap, Lakes,\nWestern Bahr-el-Ghazal, Central Equatoria, and Jonglei. 28 [^28: United Nations Mission in South Sudan (UNMISS) (August 2019) ( _Mimeo_ ).] Such factors created at least 167,979 new\ndisplacements in 2019, for instance. 29 [^29: UNOCHA Humanitarian Needs Overview, p.13 and IOM DTM, p.4] Up until 2019, 67 percent of displacements were estimated to have\nbeen driven by", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000058:6:0:0", "start": 493, "end": 509, "surface": "IOM DTM, Round 6", "probe_tag": "keep", "probe_score": 0.901, "luna_label": 1, "luna_reason": "Named IOM displacement dataset cited for return figures."}, {"key": "jdc_operational:000058:6:0:1", "start": 769, "end": 786, "surface": "intention surveys", "probe_tag": "keep", "probe_score": 0.9156, "luna_label": 1, "luna_reason": "Named organizations’ intention surveys support reported return preferences and pull factors."}]}, {"key": "rafael-085", "text": "**The World Bank**\nLebanon Electricity Transmission Project (P170769)\n\n\nDeposit inflows have decelerated sharply since 2011, and annual foreign direct investment (FDI)\ndecreased by an average of US$1 billion, increasing uncertainty on the economy's ability to meet its\nfinancing needs. The limited financing did not go into productive areas, resulting in sub-par\ninfrastructure which is not conducive to job creation and productivity, further perpetuating the cycle of\nlow growth. Lebanon’s quality in overall infrastructure ranks 130 out of 137 countries. 3 [^3: World Economic Forum, Global Competitiveness Index 2017-2018.]\n\n4. **Lebanon is a fragile country, with challenges further exacerbated by a large influx of**\n**refugees.** In addition to being a proxy for many international influences, the country also suffers from\nfrequent internal sectarian tensions which in the past have led to extended episodes of violence and\nconflicts along confessional lines. The security concern in the region has affected the tourism industry\non which the economy depends. Government institutions are captured by political parties, with informal\npower sharing that often results in frequent coalitions, parliamentary blocs, and national unity\ngovernment. The social contract is undermined by poor infrastructure and service delivery, particularly\nelectricity supply. On the other hand, Lebanon enjoys a high level of human capital as the quality of\n(private) education and health facilities is high. Most of Lebanon’s resilience has come from a strong\nprivate sector despite a challenging political environment. Since the Syrian crisis in 2011, Lebanon\nhas been hosting displaced Syrians with the number estimated at 1-1.5 million by 2015, which\nrepresents the largest concentration of refugees per capita in the world, in addition to the existing\n300,000 Palestinian refugees. The country, with large international aid, has managed to provide the\nrefugees with basic services, but this no doubt adds a tremendous pressure on the already weak public\nservice system, especially as the refugees tend to concentrate in already", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000010:3:0:0", "start": 597, "end": 625, "surface": "Global Competitiveness Index", "probe_tag": "keep", "probe_score": 0.9376, "luna_label": 1, "luna_reason": "Named index cited as the source for Lebanon’s infrastructure ranking."}]}, {"key": "rafael-086", "text": "> With a strong reliance on subsistence farming and pastoralism, rural\ncommunities are particularly affected by extreme weather events and natural disasters, including the\nrecent desert locust invasion. Historical records show a large year-to-year variability in precipitation, but\ndroughts have become more frequent and widespread since the 1960s. 3 The seasonality and intensity of\nthe rainy season are also changing, resulting in more frequent and extreme flooding in many parts of the\ncountry. More intense and variable weather events are predicted for the future. 4 The consequences of\nclimate volatility are intensifying intercommunal conflict over natural resources, ongoing population\ndisplacement, and worsening food insecurity. The 2019 exceptionally intense seasonal flood, with 900,000\n\n\n1 UNOCHA (United Nations Office for Coordination of Humanitarian Affairs). 2020. _Humanitarian Needs Overview 2020_, p. 3\n2 Germanwatch. 2019. _Global Climate Risk Index 2020_, p. 42.\n3 United States Agency for International Development (USAID) (2019 _), South Sudan Climate Vulnerability Profile_, p. 2.\n4 ThinkHazard (2019). South Sudan.\n\n\nJune 22, 2020 Page 3 of 23", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000058:2:1:0", "start": 203, "end": 221, "surface": "Historical records", "probe_tag": "keep", "probe_score": 0.9172, "luna_label": 1, "luna_reason": "Existing historical records support the stated precipitation variability finding."}]}, {"key": "rafael-087", "text": " **The SEZs, which will enjoy trade preference to the EU, are already well established and**\n**serviced.** They have idle capacity (vacant land and capacity potential in existing factories). For example,\nin Al Mafraq, a 23 km 2 zone adjacent to Al Zaatari camp, only 10 percent of the land is occupied.\n\n\n12. **Jordan has a weak business environment.** In parallel with investment promotion efforts to target\nthe new opportunities described earlier, substantial reform of the business environment is required. With\nregard to Doing Business Indicators, Jordan ranks 113th globally, out of 189 countries, in 2016 (down from\n107 last year). Moreover, the business environment is reputed to be unpredictable as policy changes can\noccur with neither consultation nor notice. Implementation of regulations is also an issue—it is often\narbitrary and unpredictable. Syrian investors and other restricted nationalities (such as Iraqi and Yemeni)\nface a different treatment with regard to business entry, such as solvency requirements in the form of large\nbank deposits to obtain an investor status (JOD 250,000). In addition to addressing the business\nenvironment, other important areas that will require significant improvement as part of the implementation\nof the Compact include: (a) access to finance, which has been identified as one of the most important\nobstacles to firms operations by the recent Enterprise Survey (2013–2014); (b) incubation type support for\nsome industries—provision of rentable factory space; (c) transportation and child care (core issues for\nwomen’s employment); (d) trade facilitation; and (e) skills development. Not all of these are covered by\nthe PforR. Skills development and vocational training, as well as access to finance, are supported by other\ndonors and other programs.\n\n\n13. **Another core goal of this Bank-supported Program is", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000045:11:1:0", "start": 536, "end": 561, "surface": "Doing Business Indicators", "probe_tag": "keep", "probe_score": 0.9489, "luna_label": 1, "luna_reason": "Indicators support Jordan’s stated global business-environment ranking."}, {"key": "jdc_operational:000045:11:1:1", "start": 1407, "end": 1424, "surface": "Enterprise Survey", "probe_tag": "keep", "probe_score": 0.9001, "luna_label": 1, "luna_reason": "Enterprise Survey data identify access to finance as a major obstacle."}]}, {"key": "rafael-088", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\nprofessional artists.\n\n\n**47.** The model results show that the project benefit-cost ratio (BCR) is expected to exceed the poverty rate, i.e., the\nmost natural alternative to compare with a uniform (random) cash distribution. 49 According to this model, the BCR is\n0.92. This outperforms by a factor of 3.7 the threshold of 25%, i.e., the poverty rate in Lebanon 50 . The proposed\napproach is therefore expected to reach vulnerable households more effectively, compared to a random distribution\nof cash to the poor.\n\n\n**48.** **Cultural and creative industries are key drivers of the creative economy and represent important sources of**\n**employment, economic growth, and innovation, thus contributing to city competitiveness and sustainability.** 51\nAccording to the Cities, Culture and Creativity report, in 2013 the revenues of cultural and creative industries were\nestimated to be around $2.25 trillion, i.e., three percent of global GDP. 52 In 2015, it was estimated that CCI in Lebanon\ncontributed almost five percent to Lebanon’s GDP and 4.5 percent to national employment, with an average annual\ngrowth rate of over eight percent. 53 Approximately 20 percent of the economically active population (362,000 people)\nare estimated to be working in the sector, including both formal and informal professionals. 54 Many CCIs also generate\na large number of non-creative jobs. A preliminary calculation using UNESCO data suggests that overall for every\ncreative job in a CCI, 1.", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000012:26:0:0", "start": 1585, "end": 1596, "surface": "UNESCO data", "probe_tag": "keep", "probe_score": 0.9511, "luna_label": 1, "luna_reason": "UNESCO data supports a preliminary calculation about creative-sector employment."}]}, {"key": "rafael-089", "text": " secondary schooling from households in underserved areas? When parents are asked in\nsurveys about the reasons why their children do not pursue their education beyond a certain level, the distance\nto school does not come up strongly, but this is deceptive because the effect of distance may be felt through\nhigher costs, and cost is often cited as a constraint for schooling. To measure the impact of the distance to schools\non enrollment and completion, regression analysis was conducted with national household surveys. Results are\nshown in Table 5.1. While effects are not always statistically significant, there is clear evidence that longer\ndistances to schools in terms of the time needed to reach schools leads to lower enrollment at the primary level,\nand lower completion rates at the junior secondary levels were effects are large (a coefficient of -0.16 suggests a\nnegative impact on the likelihood of completing of up to 16 percentage points versus the reference category of a\nschool located within 15 minutes of the community).\n\n**Table 5.1: Marginal Impact of Time Needed to Reach Schools on Enrollment and Completion by Cycle**\n\n\n\nStarting\nprimary\n\n\n\nComplete primary\n\nconditional\non starting primary\n\n\n\nStarting junior\nsecondary conditional\non completing primary\n\n\n\nCompleting junior\nsecondary conditional\n\n\n\nschool on starting primary on completing primary on starting junior sec.\n\nAged 9-12 Aged 12-15 Aged 15-18 Aged 18-21\nLess than 15 minutes Reference Reference<", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000018:92:1:0", "start": 494, "end": 520, "surface": "national household surveys", "probe_tag": "keep", "probe_score": 0.9388, "luna_label": 1, "luna_reason": "Regression analysis used existing national household surveys to estimate enrollment impacts."}]}, {"key": "rafael-090", "text": "_Procedures for preparing and approving RAP_\n\n54. _Project Screening_ . Once the subprojects are identified by unions or municipalities, the PMU\nwill obtain all permits/approvals related to the Project. Thereafter, they will cooperate with unions or\nmunicipalities to carry out social screening to determine whether or not the subprojects will result in\nany resettlement impact. The PMU will then decide on the need for the preparation of a Resettlement\nAction Plan (RAP) or an abbreviated RAP.\n\n55. _Socioeconomic and Inventory Survey_ . Following the identification of the subprojects that\nmay involve involuntary resettlement, the PMU in cooperation with unions and municipalities will\ncarry out a socio-economic study and census survey, in which baseline data within the subproject’s\ntarget areas is collected. This information shall include the PAPs and related household members or\ndependents, total land holdings, and affected assets. This information will be put in writing and shall\nbe used in determining the appropriate compensation and assistance for each affected\nindividual/household.\n\n56. _RAP preparation, review and approval._ Once the census survey is completed, the PMU will\nwork with relevant unions and municipalities to prepare the RAP. The RAP, including the proposed\nmitigation measures within the plan, will be reviewed and approved by CDR’s Board and\nsubsequently sent to the World Bank for final review and approval.\n\n57. _RAP disclosure and implementation_ . Once the RAP is approved by the Bank, it will be\ntranslated into Arabic and disclosed locally as well as in InfoShop at the Bank. The PMU and unions\nand municipalities are responsible for the implementation of the RAP.\n\n_Grievance Redress Mechanisms_\n\n58. A multi-level grievance redress mechanism has been established for the proposed Project.\nThe procedures for handling grievances are as follows:\n\n- The affected person should file his/her grievance in writing to the relevant municipality. The\nmunicipality should respond within 14 days.", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000003:54:0:0", "start": 701, "end": 739, "surface": "socio-economic study and census survey", "probe_tag": "confusion", "probe_score": 0.5883, "luna_label": 0, "luna_reason": "Planned survey and study will be carried out to collect baseline data."}, {"key": "jdc_operational:000003:54:0:1", "start": 1153, "end": 1166, "surface": "census survey", "probe_tag": "confusion", "probe_score": 0.716, "luna_label": 0, "luna_reason": "The census survey will be carried out for future project resettlement planning."}]}, {"key": "rafael-091", "text": "**The World Bank**\nBeirut Critical Environment Recovery, Restoration and Waste Management Program (P176635)\n\n\nurgent action as identified in the RDNA. Lack of containment is also likely cause for site contamination and an\nadditional challenge for port restoration planning.\n\n9. **The institutional responsibility to address these impacts and overall environment management in Lebanon rests**\n**with the Ministry of Environment (MoE).** However, significant weaknesses exist both in terms of technical\ncapacity and with the overall regulatory framework in the country. While there are regulations for handling,\nstorage, and management of hazardous waste (Decree No. 5606 of 2019) and health care waste management\n(Decree No. 13389 of 2004), the capacity of MoE to enforce and monitor implementation of these regulations is\nweak. In addition, there are no specific regulations for the management of chemical substances and stockpiles in\nLebanon, which is regarded as an important factor for the disaster at PoB.\n\n10. **Infrastructure for the treatment and disposal of hazardous and chemical waste is absent in Lebanon, which is**\n**critical for the management of waste generated due to the explosion.** While the country has limited capacity for\nthe collection and temporary storage of hazardous waste, there are no facilities for their treatment or final\ndisposal. As a consequence, hazardous materials including electronic waste is mixed with municipal solid waste\nand disposed in the existing municipal solid waste facilities. Development of adequate facilities is essential for the\nmanagement of hazardous waste generated due to PoB explosion, as well as for the overall needs of Lebanon. In\ncase of healthcare waste, infectious waste is adequately treated at dedicated treatment facilities and segregated\ncytotoxic waste is being shipped abroad under the Basel Convention requirements on the Control of\nTransboundary Movements of Hazardous Wastes and their Disposal (1992) 5", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000042:4:0:0", "start": 145, "end": 149, "surface": "RDNA", "probe_tag": "confusion", "probe_score": 0.0839, "luna_label": 1, "luna_reason": "Named assessment cited as identifying urgent environmental action"}]}, {"key": "rafael-092", "text": "**The World Bank**\nRoads and Employment Project (P160223)\n\n\nby snow resulting in large economic losses.\n\n\n39. This component is therefore aimed at improving the capacity of the MPWT to deal with road\nemergency works, especially those induced by snow and climate extremes. This component will finance\nthe purchase of road vehicles and equipment, particularly those needed for snow removal and landslides\nrepairs. This component will finance the purchase of 15 wheel loaders, 10 snow blowers, 5 salt spreaders,\nand 10 four wheel drive vehicles. This component will also assist in revising the existing emergency\nprocedures of MPWT, and its capacity to plan for extreme weather event, including the timely and proper\nmobilization and dispatching of its equipment. Given its strong linkages to the climate change agenda, this\ncomponent could also benefit at later stages from support from disaster risk management and climate\nadaptation funds.\n\n\n**Component 3: Capacity Building and Implementation Support (US$7.5 million)**\n\n\n40. This component is aimed at building the capacity of the Lebanese agencies in the planning and\nmanagement of the road sector. It will also contribute to the training and capacity building of contractors\nand workers on new and improved road construction and maintenance techniques. This component will\nfinance consultancy services and related software and IT equipment, to support the following\nsubcomponents:\n\n\n41. **Subcomponent 1.** Strengthen national road asset management (US$2 million). This\nsubcomponent will finance the creation of a road asset database for the trunk network in Lebanon, the\ncollection of the basic information for the database (such as road condition visual surveys, IRAP\nassessment of road safety, and traffic counts on select road sections), and the revision of design and\nmaintenance standards to reflect changing climate conditions, particularly related to drainage and slope\nprotection/stabilization. This subcomponent will also finance the preparation of bidding documents and\ntraining on performance‐based contracts for road maintenance.\n\n\n42. **Sub", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000008:27:0:0", "start": 1568, "end": 1587, "surface": "road asset database", "probe_tag": "confusion", "probe_score": 0.0632, "luna_label": 0, "luna_reason": "Database creation and associated data collection are planned project activities."}, {"key": "jdc_operational:000008:27:0:1", "start": 1688, "end": 1717, "surface": "road condition visual surveys", "probe_tag": "confusion", "probe_score": 0.1105, "luna_label": 0, "luna_reason": "Surveys are planned for collection as part of creating the road asset database."}, {"key": "jdc_operational:000008:27:0:2", "start": 1719, "end": 1749, "surface": "IRAP\nassessment of road safety", "probe_tag": "confusion", "probe_score": 0.7303, "luna_label": 0, "luna_reason": "Assessment data will be collected for the road asset database."}, {"key": "jdc_operational:000008:27:0:3", "start": 1755, "end": 1769, "surface": "traffic counts", "probe_tag": "confusion", "probe_score": 0.3006, "luna_label": 0, "luna_reason": "Traffic counts are planned for collection to create a road asset database."}]}, {"key": "rafael-093", "text": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\nlearning agenda. For further details see Annex 4. The Results Framework also includes an indicator relating to the\nadoption of energy efficient and climate resilience infrastructure under Component 3.\n\n64. **Citizen engagement.** The project will work on strengthening citizen engagement, and all beneficiaries, including\nrefugees are key partners in its implementation. Specifically, the project will utilize the following citizen engagement\nmechanisms: (a) participatory decision-making and mobilization of women entrepreneurs through support to existing\nand new women platforms at the village, district, and refugee settlement levels; (b) participatory planning in the design\nof infrastructure for women; and (c) implementation of a grievance redress mechanism (GRM). The GRM will ensure that\nqueries or clarifications about the project are responded to in a timely manner, and that grievances are addressed\nefficiently and effectively. The proposed project will further solicit periodic feedback from beneficiaries through\nbeneficiary satisfaction surveys as well as spot checks and includes a results indicator on the percentage of the project’s\ngrievance redress systems that are addressed.\n\n**C. Project Costs and Financing**\n\n65. **The total project costs are US$217 million, which is to be financed through an IDA grant including US$36 million**\n**from the IDA19 WHR** **for host communities and refugees matched with $4 million from Uganda’s PBA.** See Table 4 for\ndetails.\n\n\n**Table 4: Project Costs by Component**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Project Component|Cost (US$ millions)|Of which WHR (US$ millions)|\n|---|---|---|\n|1. Support for Women Empowerment and Enterprise Development Services, including in host and refugee communities | 42.0<", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000025:31:0:0", "start": 1144, "end": 1176, "surface": "beneficiary satisfaction surveys", "probe_tag": "confusion", "probe_score": 0.5477, "luna_label": 0, "luna_reason": "Future project surveys are planned to solicit beneficiary feedback."}]}, {"key": "rafael-094", "text": "**The World Bank**\nSouth Sudan Enhancing Community Resilience and Local Governance Project (P169949)\n\n\n\n\n\n\n\nOther Decision (as needed)\n\n\n**B. Introduction and Context**\n\n\nCountry Context\n\n\n1. **South Sudan was beset by decades of armed conflicts even prior to its independence in 2011,**\n**and these have only become increasingly complex in the years since.** Southern Sudan, as the region was\ncalled before independence, has been marred by conflict since 1955, just a year before Sudan attained its\nindependence from British colonial rule. The region experienced systematic marginalization and\nunderdevelopment under both British and Sudanese rule, inhibiting it from developing its physical and\nhuman capital. Consequently, at its independence in July 2011, South Sudan ranked almost at the bottom\nof the global development indicators with little infrastructure, basic services provided almost entirely\nthrough humanitarian aid, and an economy completely dependent on oil. Renewed civil conflict broke out\nin December 2013 and has only recently subsided with the formation of a new government in February\n2020, pursuant to the terms of the September 2018 Revitalized Peace Agreement. As a result of decades\nof violence, nearly 7.5 million people of the estimated 14 million total population rely on some type of\nhumanitarian assistance or protection. 1 [^1: UNOCHA (United Nations Office for Coordination of Humanitarian Affairs). 2020. _Humanitarian Needs Overview 2020_, p. 3]\n\n\n2. **The country is highly vulnerable to climate change and natural disasters, and increased stress**\n**on natural resources is fueling local conflicts** . The Global Climate Risk Index ranked the country 125 out\nof 171 between 1998 and 2018. 2 [^2: Germanwatch. 2019. _Global Climate Risk Index 2020_, p. 42.] With a strong reliance on subsistence farming and pastoralism, rural", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000058:2:0:0", "start": 807, "end": 836, "surface": "global development indicators", "probe_tag": "confusion", "probe_score": 0.8837, "luna_label": 1, "luna_reason": "Indicators support South Sudan’s near-bottom global development ranking."}]}, {"key": "rafael-095", "text": "**Annex 7. Economic Analysis**\n\n\n1. The net effect of the Program at the individual’s level is calculated as the additional benefit that\na representative child obtains as a result of the Program. This effect is estimated from a present\ndiscounted value (PDV) calculation. This approach estimates the stream of benefits and costs of schooling\nover a lifetime in the labor market with and without the Program.\n\n2. Data for this analysis are obtained mainly from the 2010 _Income and Expenditure Survey_, a\nhousehold survey of the labor force, and from the 2015 Population and Housing Census conducted by the\nDepartment of Statistics. This representative information accounts for the entire population of Jordan of\nall ages. It is worth noting that the estimates below are considered under estimates as they do not account\nfor the social benefits of more and better education.\n\n_Estimation of expected economic benefits_\n\n3. The private benefits (returns to schooling) are measured following the standard literature on cost‐\nbenefit analysis for investments in education and by calculating the earnings over the course of the\nworking life. Age‐earnings profiles are built based on the education levels of the Jordan, namely: (i)\nincomplete primary (0‐2 grades completed); (ii) incomplete lower secondary (3‐9 grades completed); (iii)\nincomplete upper secondary school (10 grades completed); (iv) completed upper secondary but not post‐\nsecondary (11‐12 grades completed); and (v) post‐secondary (13 grades completed and above).\n\n4. These age‐earnings profiles are constructed separately for two population groups which differ\nsignificantly in their education and labor market experiences, namely (i) men; and; (ii) women. These are\nenriched by incorporating: (1) the probability of employment in three types of employment (wage\nemployment, self‐employment, and unpaid employment); (2) the probability of employment in the public\nsector; (", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000041:61:0:0", "start": 464, "end": 500, "surface": "2010 _Income and Expenditure Survey_", "probe_tag": "confusion", "probe_score": 0.8975, "luna_label": 1, "luna_reason": "Survey data are used as the main basis for economic analysis."}, {"key": "jdc_operational:000041:61:0:1", "start": 504, "end": 539, "surface": "household survey of the labor force", "probe_tag": "confusion", "probe_score": 0.3426, "luna_label": 1, "luna_reason": "Existing labor-force survey is identified as a primary data source for economic analysis."}, {"key": "jdc_operational:000041:61:0:2", "start": 554, "end": 588, "surface": "2015 Population and Housing Census", "probe_tag": "keep", "probe_score": 0.9283, "luna_label": 1, "luna_reason": "Census data are used for the program's economic analysis."}]}, {"key": "rafael-096", "text": "**The World Bank**\nEmergency Food Security Project (P178936)\n\n\nand distribution of basic commodities (e.g. rice, cooking oil, milk powder, sugar), including with private sector\nparticipation 30 ; (ii) assessing options for the regional food security coordination role that Jordan can play, focusing\non regional risk management instruments, including for addressing climate related risks, and Jordan’s potential to\nhost a regional commodities market; (iii) evaluation of food policy options to smooth future supply shocks in a\nfiscally sustainable way; and (iv) support ongoing efforts to realign public support to the livestock sub-sector\ntowards more sustainable resource use and effective targeting of the most vulnerable. Activities (i, ii and iv) would\nsupport potential reductions in food loss and waste, with corresponding greenhouse gas emission reductions.\nGender differentiated assessments will be considered to help identify more inclusive policies related to food\nsecurity, including the participation of women-owned/led enterprises as key informants.\n\n**38.** **While the monitoring of wheat distribution along the value chain is salient, access to bread can be better**\n**assessed** . There are well established control systems in place to ensure that grains are efficiently transformed into\nwheat and distributed to end users. Better understanding the characteristics of end users may enable better\ntargeting of policies focusing on the consumption of basic commodities in the country 31 . With that in mind, the\nproject will support the development of a “bread map” 32 where location of bakeries and other points of sale of\nbread will be visualized and correlated with population density to inform on the distributional aspects of food and\nguide the more equitable access of basic food by the population of Jordan. The map will be made available online,\nto the public 33", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000024:23:0:0", "start": 1592, "end": 1601, "surface": "bread map", "probe_tag": "confusion", "probe_score": 0.0657, "luna_label": 0, "luna_reason": "Project will develop a future map visualizing bakery and bread-sale locations."}]}, {"key": "rafael-097", "text": "--|---|\n|**Indicator Name **|**Definition/Description **|**Frequency **|**Datasource **|**Methodology for Data** **Collection ** |**Responsibility for Data** **Collection **|\n|Students benefiting from direct interventions to enhance learning||Midterm and project end. |EMIS, project reports | The indicator includes: Students benefiting from teachers trained and supported under the project* - 2,358,000 Students receiving scholarships - 86,000 AEP - 6,600 * 2017 EMIS data. Annual enrollment of students includes all public school students| MoES |\n\n\nPage 50 of 96", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000018:55:1:0", "start": 515, "end": 524, "surface": "EMIS data", "probe_tag": "confusion", "probe_score": 0.8281, "luna_label": 1, "luna_reason": "2017 EMIS data supports concrete student enrollment figures."}]}, {"key": "rafael-098", "text": "**The World Bank**\nLebanon: Wheat supply emergency response project (P178866)\n\n\nCrescent Societies, which will confirm that the bakeries service areas that include poor and vulnerable groups are\nreceiving the flour as per Framework Agreement. In addition, the project will finance high frequency ‘Listening to\nPoor and Vulnerable Household Surveys’, entailing data collection on bread prices and consumption for the poor\nand vulnerable households, by collecting random sampling and surveying (biweekly) using UNHCR and WFP\nbeneficiary lists. This information will be triangulated at MOET level with information consolidated from the\nconsumer protection agency, GM, and price monitoring system, and used to adopt appropriate remedies, such as\nincluding in the Framework Agreement a preferential distribution clause for bakeries located in areas where most\nof the poor and vulnerable groups are located. Additional social risks are associated with the consultancy services\nand technical assistance under Component 2 that will help MOET’s planned transition from the current wheat\nsubsidy system to a more market-oriented system. Such risks will be mitigated through recommendations in the\nstudy to ensure linkages with the social safety net programs like ESSN, a clear communication campaign, and an\neffective and widespread dissemination of the grievance mechanism. All the mitigation measures will be covered\nin the Environmental and Social Management Plan (ESMP), which will be prepared before signing Framework\nAgreements with local importers and as a disbursement condition.\n\n80. The Environmental risk rating is Moderate. The project is limited to procurement of wheat to maintain\nthe supply during the market disruptions caused by the war in Ukraine. The project finance will not involve any\ncivil works nor any other activities after the vessels deliver the shipment at the ports of Beirut and/or Tripoli.\nThere are some associated activities, which are not directly financed by the project but are directly and\nsignificantly related to the project; will be carried out contemporaneously with the project; and necessary for the\nproject to be viable and would not have been conducted if the project did", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000019:33:0:0", "start": 297, "end": 347, "surface": "Listening to\nPoor and Vulnerable Household Surveys", "probe_tag": "confusion", "probe_score": 0.3834, "luna_label": 0, "luna_reason": "Project-financed surveys entail future data collection."}, {"key": "jdc_operational:000019:33:0:1", "start": 509, "end": 540, "surface": "UNHCR and WFP\nbeneficiary lists", "probe_tag": "confusion", "probe_score": 0.7232, "luna_label": 1, "luna_reason": "Existing UNHCR and WFP lists are used to draw the survey sample."}]}, {"key": "rafael-099", "text": " and\ndeceleration in private consumption. The pandemic has also stalled telecommunications infrastructure\ninvestments and dampened consumer demand because of the falling purchasing power. For poor and vulnerable\nhouseholds in Uganda, the impact of COVID-19 is especially severe. Since the COVID-19 outbreak, 91 percent of\n\n\n1 2020. _Uganda Economic Update_, _Strengthening Social Protection to Reduce Vulnerability and Promote Inclusive Growth_, 2020. World\nBank.\n2 Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat. 2015. _The_, _World Population_\n_Prospects: The 2015 Revision_ .\n3 2020. Uganda Economic Update, Strengthening Social Protection to Reduce Vulnerability and Promote Inclusive Growth, 2020. World\nBank.\n4 World Bank. 2016. _The Uganda Poverty Assessment Report 2016_ . Washington, DC: World Bank.\nhttp://pubdocs.worldbank.org/en/381951474255092375/pdf/Uganda-Poverty-Assessment-Report-2016.pdf\n5 World Bank Data.\n6 World Bank Data.\n7 World Bank. 2020. _Uganda Economic Update_, _Strengthening Social Protection to Reduce Vulnerability and Promote Inclusive Growth_,\n2020. World Bank.\n8 World Bank. 2020. _Uganda Economic Update, 1_ 6 _th Edition, December 2020:_ Investing in Uganda’s Youth. December 2020.\n\n\nPage 1 of 76", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000023:13:2:0", "start": 964, "end": 979, "surface": "World Bank Data", "probe_tag": "confusion", "probe_score": 0.1107, "luna_label": 0, "luna_reason": "Standalone numbered reference entry without shown data use."}]}, {"key": "rafael-100", "text": "**The World Bank**\nLebanon Health Resilience Project (P163476)\n\n\n**Box 2. Lebanon Emergency Primary Healthcare Restoration Project (EPHRP)**\n**Objective**\nThe objective of the EPHRP is to assist the GoL in reducing the social, economic, and health impacts of the Syrian\ncrisis on poor Lebanese by subsidizing a package of essential health care services.\n**Beneficiaries**\nThis project targets 150,000 of the 340,000 poor Lebanese identified by the NPTP as living below the poverty\nline, using a proxy means testing targeting mechanism.\n**Essential Health Care Package**\nThe project provides beneficiaries with a package of essential health care services comprising the following:\n(i) three age- and gender-specific wellness packages (age 0-18, females 19 years and above, males 19 years and\nabove); (ii) two care packages for the most common non-communicable diseases in Lebanon, diabetes and\nhypertension; and (iii) an antenatal package.\n**Providers**\nServices are provided to beneficiaries through 75 of the 204 MoPH network centers. Network facilities are\nmanaged by NGOs (67 percent), local municipalities (20 percent), MoPH (11 percent), and MoSA (2 percent).\nProvider participation is voluntary and is governed by the legal agreement between the MoPH and the managing\nentity.\n**Quality of Care**\nQuality of care is monitored through the PHCC accreditation program implemented by the MoPH in collaboration\nwith Accreditation Canada International. Currently, all 75 PHCCs are within the accreditation program. The\nquality of clinical care is also monitored by the MoPH through clinical indicators captured in the Health\nInformation System.\n**Contracting and Provider Payment Mechanism**\nThe MoPH purchases the package of services for the beneficiary population from PHCCs. Provider payment is\nbased on capitation and is output-based. The average per", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000032:16:0:0", "start": 1617, "end": 1642, "surface": "Health\nInformation System", "probe_tag": "confusion", "probe_score": 0.5564, "luna_label": 1, "luna_reason": "System indicators are used to monitor clinical care quality."}]}, {"key": "rafael-101", "text": "Network coverage is another serious constraint to higher adoption of mobile broadband, with sharp**\n**regional disparities.** While more than 95 percent of the population is covered by mobile telephony networks (2G),\n\n\n23 World Bank Group. 2017. “Global Findex Database”, World Bank Group, 2017\nhttps://globalfindex.worldbank.org/sites/globalfindex/files/2018-04/2017%20Findex%20full%20report_0.pdf\n24 NITA-U (National Information Technology Authority of Uganda). 2018. National Information Technology Survey 2017/18 Report. NITA\nUganda, March 2018.\n25 NITA-U (National Information Technology Authority of Uganda). 2018. National Information Technology Survey 2017/18 Report. NITA\nUganda, March 2018.\n26 Economic Policy Research Centre. 2019. “Women’s Economic Empowerment in Uganda: Inequalities and Implications” Policy Brief No.\n110, Economic Policy Research Centre, November 2019. Kampala: Economic Policy Research Centre. Available at: https://eprcug.org/allpublications/614-women-s-economic-empowerment-in-uganda-inequalities-and-implications\n27 The State of ICT in Uganda. Research ICT Africa, 2019. “The State of ICT in Uganda.” https://researchictafrica.net/publication/the-stateof-ict-in-uganda\n28 ITU.\n29 The State of ICT in Uganda. Research ICT Africa, 2019. “The State of ICT in Uganda.” https://researchictafrica.net/publication/the-stateof-ict-in-uganda\n30 The State of ICT in Uganda. Research ICT Africa,2019. “The State of ICT in Uganda.” https://researchictafrica.net/publication/the", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000023:16:2:0", "start": 247, "end": 269, "surface": "Global Findex Database", "probe_tag": "confusion", "probe_score": 0.6843, "luna_label": 0, "luna_reason": "Bibliographic reference entry without shown data use in the passage"}, {"key": "jdc_operational:000023:16:2:1", "start": 470, "end": 523, "surface": "National Information Technology Survey 2017/18 Report", "probe_tag": "confusion", "probe_score": 0.8559, "luna_label": 0, "luna_reason": "Bibliographic reference entry without shown data use."}]}, {"key": "rafael-102", "text": " 640 heritage buildings which were damaged by the blast are of residential use.\nAmong these buildings, UNESCO interventions have focused on securing the most severely damaged buildings\nusing international and DGA standards 95, with a lot of 14 heritage buildings in Rmeil, Saifi and Medawer. 96\nHowever, most of the affected historic residential units did not receive any attention, and those damaged\nbuildings that have been addressed require further complex technical interventions to ensure their habitability.\n\n\n89 World Bank Group; European Union; United Nations. (2020). _Beirut Rapid Damage and Needs Assessment_ . Washington, DC.: World Bank Group.\n\n90 The 87,552 damaged residential building represented about 51 percent of the 171,887 housing units assessed\n91 Port surrounding areas include Marfaa, Medawar, Saifeh, and Remeil\n\n92 Combined low-income apartment buildings and low-income, single-family housing asset typologies.\n\n93 RDNA\n94 Buildings with less than 10 percent of physical damage.\n95 Prior the PoB explosion, many heritage buildings were object of inappropriate interventions using concrete and other non-compatible materials,\nwhich have weakened the historical structural fabrics.\n96 Other damaged buildings were stabilized by other organizations, including Aliph (12), Association for Protecting Natural Sites and Old Buildings in\nLebanon (APSAD) (5), Beirut Built Heritage Rescue (BBHR) (12), private contractors ‘on pro-bono’ basis and others.\n\n\nPage 52 of 66", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000012:57:2:0", "start": 964, "end": 968, "surface": "RDNA", "probe_tag": "confusion", "probe_score": 0.2307, "luna_label": 0, "luna_reason": "Standalone acronym fragment does not itself identify a usable data resource."}]}, {"key": "rafael-103", "text": " by i)\na series of conducive Government policies 18, and ii) the significant uptake of mobile phone subscribers, which grew\nfrom 14.7 million in 2010 to 21.7 million in 2018, representing an increasing penetration rate that has reached 56%\nof the total population. 19 Developing digital infrastructure fosters growth. Based on recent analysis 20 by the World\nBank Africa Chief Economist’s Office, closing the digital infrastructure gap 21 in the East and Southern Africa region\ncould result in 1.5 percentage point growth increase in economic growth per capita. If complemented by expansion\nin human capital development, the growth effect could increase to 3.87 percentage points. Indirectly, the digital\nsector contributes to enhancing productivity, facilitating information exchange, and improving service delivery across\nthe economy.\n\n5. **The substantial increase in mobile phone ownership (71% of the population) is laying the foundation for Uganda’s**\n**digital transformation and enabling the rapid take-up of various e-services.** According to the 2017/2018 survey by\n\n\n10 The Horn of Africa sub-region is defined as Djibouti, Eritrea, Somalia, Sudan, Kenya, Uganda and South Sudan.\n[11 United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) 2018. https://www.unocha.org/southern-and-](https://www.unocha.org/southern-and-eastern-africa-rosea/uganda)\n[eastern-africa-rosea/uganda](https://www.unocha.org/southern-and-eastern-africa-rosea/uganda)\n[12 UNHCR, 2018. https://www.un.", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000063:3:1:0", "start": 1100, "end": 1116, "surface": "2017/2018 survey", "probe_tag": "confusion", "probe_score": 0.5173, "luna_label": 1, "luna_reason": "Survey supports the stated 71% mobile-phone ownership finding."}]}, {"key": "rafael-104", "text": "22. **Moreover, investments in strengthening data systems (both technical and financial) need to**\n**continue to deepen the use of data for evidence‐based decision making in the sector and further**\n**improve resource allocation.** The MOE has successfully deployed an education management\ninformation system (EMIS), which is now hosting data on all schools and students in the system.\nAdditional investments in a geographical information system (GIS) are ongoing and will allow MOE to\nbetter plan for expansion of access across all regions in the country. Leveraging the data available\nthrough the EMIS for decision making in the sector is a key opportunity for the MOE which will require\nadditional technical assistance and capacity building to materialize. In addition, the collection,\nanalysis, and use of student learning data and disaggregated and gender‐sensitive data are essential\nfor monitoring, targeting pedagogical interventions, and improving teacher practices in the classroom.\n\n\n**C.** **Relationship to the Country Partnership Framework and Rationale for Use of**\n**Instrument**\n\n23. **_Relationship to the CPF._** The proposed operation is fully aligned with the Jordan Country\nPartnership Framework (CPF) discussed by the World Bank Group Board on July 14, 2016. The CPF\ncovers the period FY17–22 and highlights the economic, geopolitical, and social challenges that Jordan\nhas been facing, particularly with the Syrian refugee crisis. The CPF also acknowledges Jordan’s\ncommitment to reforms for a more sustainable growth path with stronger job creation, better service\ndelivery, a more conducive investment climate, and a larger involvement of citizens in the decision‐\nmaking process. In this respect, it is fully aligned with the government’s Vision Jordan 2025. The\nproposed operation directly supports the second pillar of the CPF, which aims to improve the quality\nand equity of service delivery, particularly its objective 2.2. “Improved delivery of education services.”\nThe proposed", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000041:14:0:0", "start": 810, "end": 831, "surface": "student learning data", "probe_tag": "confusion", "probe_score": 0.2375, "luna_label": 0, "luna_reason": "Generic data is mentioned for intended monitoring, without an attributed finding or figure."}, {"key": "jdc_operational:000041:14:0:1", "start": 836, "end": 875, "surface": "disaggregated and gender‐sensitive data", "probe_tag": "confusion", "probe_score": 0.125, "luna_label": 0, "luna_reason": "Data collection is presented as an essential activity, not existing data use."}]}, {"key": "rafael-105", "text": "**The World Bank**\nRoads and Employment Project (P160223)\n\n\n19. **Subcomponent 4.** Support training activities (US$0.5 million). This subcomponent will support\ntraining activities to build the technical skills of MPWT and CDR staff, as well as workers and small\ncontractors. It will support training on soft skills as well as technical skills related to the work to be\ncarried out at selected project sites. In particular, this subcomponent will also support the training of\nsmall local contractors and their workers on proper routine maintenance requirements and\ntechniques, environmental and social aspects, and health and safety aspects. The implementation of\nthis subcomponent could be in collaboration with other interested donors such as the ILO. This\nsubcomponent could benefit from grants from interested donors. A technical supervision manual for\nCDR and MPWT regional offices will be prepared to improve their monitoring and supervision efforts\nof road condition.\n\n\n20. **Subcomponent 5.** Support for Project Implementation (US$1 million). This subcomponent will finance\nthe hiring of required experts by the implementing agency (CDR) to properly undertake the\nimplementation and monitoring of the project.\n\n\n**Table 1.5. Road Safety Fatalities and Injuries in Lebanon** **16** [^16: Lebanese ISF.]\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Year|Total Number of Fatalities|Number of Syrian fatalities|Percentage of Syrian Fatalities|Total Number of Injuries|Number of Syrian Injuries|Percentage of Syrian Injuries|\n|---|---|---|---|---|---|---|\n|2011|508|52|10.2|6050|418|", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000008:58:0:0", "start": 1234, "end": 1280, "surface": "Road Safety Fatalities and Injuries in Lebanon", "probe_tag": "confusion", "probe_score": 0.1287, "luna_label": 0, "luna_reason": "Standalone table caption, not an independent data-use mention."}]}, {"key": "rafael-106", "text": "/PR/PM/MEPD/SE/SG/DGEP/2017 dated September 23, 2017 and it is the first step\ntoward building a Unified Social Registry (USR). Currently the Government, through the _Cellule Filets Sociaux_, is moving towards\nfinalizing the USR manual and procuring all necessary hardware (servers, mainframes) and software to establish the registry. It is\nenvisaged that a USR unit will eventually be created within the Government.\n\n\nPage 13", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000035:17:2:0", "start": 96, "end": 119, "surface": "Unified Social Registry", "probe_tag": "confusion", "probe_score": 0.3782, "luna_label": 0, "luna_reason": "Registry is being built, with procurement and establishment still planned."}]}, {"key": "rafael-107", "text": " covering, _inter alia_, the following: i) Information systems used to\nidentify best available offers of products and terms of trade (affecting prices, quality, availability and transportation of products); ii) Process\nmaps of procurement processes (identifying number of transaction steps, and potential time and cost savings derived from undertaking\nprocedural streamlining and automation); iii) Level of contestability and transparency of procurement systems as well as perceptions by\nstakeholders.\n31 While the scope of this project is not on targeting (access) consumption of bread, but rather on ensuring availability of grains, project\nmonitoring will be carried out with focus on grain purchases and also the availability of bread at points of sale.\n32 A consultative approach will be used for the development of the “bread maps” and “animal feed maps”. Including beneficiaries will help\ngain a better understanding of the characteristics of end users and enable better, tailored policies. Beneficiaries can provide valuable\nfeedback on the accessibility of bakeries and bread sale points, as well as grain distribution centres. Visualization of the maps would be\ndisability-inclusive and consider the needs of people with limited literacy.\n33 Digitally, the map could be delivered through a Food Citizen Platform that serves as an interactive tool, is accessible through mobile\nphones, allows real-time feedback and provides information on food and nutrition beyond the scope of wheat and barley.\n\n\nPage 19 of 54", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000024:23:2:0", "start": 826, "end": 836, "surface": "bread maps", "probe_tag": "drop", "probe_score": 0.0241, "luna_label": 0, "luna_reason": "Maps are planned for future development, not existing data used in analysis."}, {"key": "jdc_operational:000024:23:2:1", "start": 843, "end": 859, "surface": "animal feed maps", "probe_tag": "confusion", "probe_score": 0.1407, "luna_label": 0, "luna_reason": "Maps are planned outputs being developed, not existing data used for analysis."}]}, {"key": "rafael-108", "text": "**The World Bank**\nLebanon Health Resilience Project (P163476)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Indicator Name|Core|Unit of Measure|Baseline|End Target|Frequency|Data Source/Methodology|Responsibility for Data Collection|\n|---|---|---|---|---|---|---|---|\n|**Name:**Health personnel receiving training||Number|0.00|1000.00|Bi-annual |PMU |PMU |\n|Description:Number of health personnel receiving training through the project|Description:Number of health personnel receiving training through the project|Description:Number of health personnel receiving training through the project|Description:Number of health personnel receiving training through the project|Description:Number of health personnel receiving training through the project|Description:Number of health personnel receiving training through the project|Description:Number of health personnel receiving training through the project|Description:Number of health personnel receiving training through the project|\n|||||||||\n|**Name:**Maintain Client Satisfaction (PHCCs & Hospitals)||Percentage|75.00|75.00|Annual |Client satisfaction survey |PMU |\n|Description:Share of users satisfied by the received health care services|Description:Share of users satisfied by the received health care services|Description:Share of users satisfied by the received health care services|Description:Share of users satisfied by the received health care services|Description:Share of users satisfied by the received health care services|Description:Share of users satisfied by the received health care services|Description:Share of users satisfied by the received", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000032:45:0:0", "start": 1099, "end": 1125, "surface": "Client satisfaction survey", "probe_tag": "drop", "probe_score": 0.0334, "luna_label": 0, "luna_reason": "Planned annual project monitoring survey, not an existing data source used for analysis."}]}, {"key": "rafael-109", "text": "*Program Financing Data**|**Program Financing Data**|**Program Financing Data**|**Program Financing Data**|**Program Financing Data**|**Program Financing Data**|\n|[ X ]|Loan|[ ]|Grant|Grant|Grant|[x ]|[x ]|Other|Other|Other|Other|Other|Other|\n|[ X ]|Credit|||||||||||||\n|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|\n|", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000045:5:3:0", "start": 1, "end": 23, "surface": "Program Financing Data", "probe_tag": "drop", "probe_score": 0.0181, "luna_label": 0, "luna_reason": "Standalone table header, not a substantive data resource or data use."}]}, {"key": "rafael-110", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\n**23.** **Disbursements and Flow of Funds.** Funds will be channeled directly from the World Bank to the UN-Habitat\nbank account in US dollars outside Lebanon. UN-Habitat will submit withdrawal applications to the World Bank to\nrequest funds using the report-based disbursement method (i.e., based on IFRs for a six month expenditure\nprojection). The format and content of the IFRs is provided in the Disbursement and Financial Information Letter\n(DFIL). The amount of the advance will be based on a projection of six months expenditures. The disbursement\ncategories, allocations, and percentage of expenditures to be financed are shown in the table below.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Category|Amount of the Grant Allocated (expressed in USD)|Percentage of Expenditures to be Financed (inclusive of Taxes)|\n|---|---|---|\n|(1) Goods, works, non-consulting services, and consulting services under Part 1 of the Project|8,275,520|100%|\n|(2) Consulting services, non- consulting services and CCI Grants under Part 2 of the Project|1,951,857|100%|\n|(3) (a) Indirect Cost under Part 3 of the project (5%) (3) (b) Direct Cost under Part 3 of the Project|607,143 1,915,480|100%|\n|**TOTAL AMOUNT**|", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000012:52:0:0", "start": 408, "end": 412, "surface": "IFRs", "probe_tag": "confusion", "probe_score": 0.0923, "luna_label": 0, "luna_reason": "Financial reports used for disbursement administration, not substantive data analysis."}]}, {"key": "rafael-111", "text": "**The World Bank**\nLebanon Health Resilience Project (P163476)\n\n\n\n\n\n\n\nPage 6 of 54", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000032:8:0:0", "start": 19, "end": 52, "surface": "Lebanon Health Resilience Project", "probe_tag": "drop", "probe_score": 0.0403, "luna_label": 0, "luna_reason": "Names a project, not an existing data resource or data use."}]}, {"key": "rafael-112", "text": " emergency crew as part of the maintenance activities are key Project features. Enhancement of skills in handling environmental risks, road safety awareness, and safety data management are part of contingency planning. Component 2: Institutional Strengthening will entail the enhancement of institutional skills in handling natural hazards and environmental risks in the project corridor areas and will lead to better contingency planning to help improve climate resilience. Component 3: Road Safety – Road safety awareness campaigns in the Project area, and road safety data collection and management system are part of contingency planning and help improve climate resilience and adaptation through the information gathering which will inform decision making and gear key strategies. Component 4: Contingent Emergency Response – The ability to support emergencies in the wake of natural disasters will help climate resilience of the road network and communities.|\n|---|---|\n|**Link to** **project** **activities**|This project will enhance resilience of the road network and of the communities served by the Koboko, Yumbe and Moyo (KYM) road corridor. This will be done through four project components, as follows: Component 1: Road Upgrading Works - This project component will support civil works for widening and upgrading of the approximately 105 km-long KYM road corridor from the gravel weather prone surface to bituminous paved road standards. The unpaved laterite road which is the current state is vulnerable to extreme weather conditions and climate change risks, especially the projected increase in precipitation levels that can lead to more frequent and intense flooding and erosion. Paving the surface of the road is therefore a good climate change adaptation measure.", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000050:77:1:0", "start": 585, "end": 629, "surface": "safety data collection and management system", "probe_tag": "drop", "probe_score": 0.0362, "luna_label": 0, "luna_reason": "Project data collection system is planned to gather information for future decisions."}]}, {"key": "rafael-113", "text": "br>trained in Child Friendly School Program Component 2: all staff trained in AEP Component 3: all teachers, headteachers and cluster staff trained |MoES |\n|Grievances registered and addressed in line with Grievance Redress Mechanism |Data source: Grievance Log Books, Grievance Redress System Data Base, Quarterly Grievance Redress Status Reports| Semi- annually |PCU/MoES | Administrative Data |PCU/MoES |\n\n\nPage 56 of 96", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000018:61:1:0", "start": 281, "end": 294, "surface": "Grievance Log", "probe_tag": "drop", "probe_score": 0.0416, "luna_label": 0, "luna_reason": "Routine grievance-administration log used for project monitoring"}, {"key": "jdc_operational:000018:61:1:1", "start": 326, "end": 342, "surface": "System Data Base", "probe_tag": "drop", "probe_score": 0.0271, "luna_label": 1, "luna_reason": "System database is cited as a data source for registered and addressed grievances."}, {"key": "jdc_operational:000018:61:1:2", "start": 436, "end": 455, "surface": "Administrative Data", "probe_tag": "drop", "probe_score": 0.0092, "luna_label": 0, "luna_reason": "Standalone table cell naming an administrative data category, not a cited data use."}]}, {"key": "rafael-114", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n\n\n|New electricity connections (predominantly energized by solar PV and battery storage) in 12 secondary cities|Col2|Quarterly|Reports of SNE, progres s reports of PIU|Data provided by SNE operational units and Owner's Engineer|PIU of SNE|\n|---|---|---|---|---|---|\n|Electricity connections (predominantly energized by solar PV and battery storage) in new cities and towns, out of which| |Quarterly | Progress reports of the PIU of the Ministry of Petroleum and Energy |Data will be collected by M&E specialists of the PIU of Ministry of Petroleum and Energy from progress reports made by private operators and checked through site visits. |PIU of the Ministry of Petroleum and Energy |\n|In cities housing host communities||Quarterly |Reports of SNE, progres s reports of PIU | Data provided by SNE operational units and Owner's Engineer |PIU of SNE |\n|Female-headed households electrified under subcomponents 1.1 - 1.3||Quarterly |<", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000051:59:0:1", "start": 249, "end": 269, "surface": "Data provided by SNE", "probe_tag": "drop", "probe_score": 0.0481, "luna_label": 0, "luna_reason": "Planned monitoring data source in a project results table, not existing data use."}]}, {"key": "rafael-115", "text": ">sanitation service delivery and support long-term investments in infrastructure development, in RHDs in the West Nile and Northern region. Locations targeted for solar based water pumping have already been identified in Arua, Yumbe, Moyo, Adjumani, Lamwo, and Kiryandongo |Provide digital enabling environment for remove water monitoring and strengthen efficiencies and effectiveness of water management systems. |\n|**Gender Based Violence** **and Violence Against** **Children Prevention and** **Response Services in** **Uganda’s Refugee-** **Hosting Districts Report** _Status: Analysis_ _completed,_|Total 0.5 RSW/ WHR N/A|To mitigate GBV and prevent violence against children through engagement in productive activities in 4 RHDs.|Increased access to more affordable connectivity will also increase likelihood of GB online risks. Project will support the project objective indirectly by including awareness and mitigation measures in digital skills training. Digital connectivity will strengthen case management for GBV and violence against|\n\n\nPage 64 of 76", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000023:76:3:0", "start": 610, "end": 634, "surface": "Hosting Districts Report", "probe_tag": "drop", "probe_score": 0.0276, "luna_label": 0, "luna_reason": "Fragment of a table title, not an independently used data resource."}]}, {"key": "rafael-116", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\n\n\n\n|Col1|installment of livelihood grant.|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|Number of beneficiaries receiving Economic Opportunities who are female youth|Number of beneficiaries receiving economic opportunities under Component 2, in accordance with the Project Operations Manual, of which are youth and female, defined as women between the ages of 18 and 35 years, and have received at least 1 installment of livelihood grant. | This indicator will be measured, at a minimum, on a quarterly basis |SNSOP MIS |Beneficiary data will be collected during registration and updated during project implementation. Payment data will be regularly updated in the SNSOP MIS |The Implementing Partner in charge of Component 2 will be responsible for data collection. |\n|Percentage of grievances resolved through the GRM|Number of all grievances which are lodged, processed and resolved through the SNSOP GRM divided by all complaints which are lodged, processed and/or resolved, expressed as a<br", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000057:63:0:0", "start": 685, "end": 694, "surface": "SNSOP MIS", "probe_tag": "drop", "probe_score": 0.0283, "luna_label": 0, "luna_reason": "Project MIS data will be collected and updated for future indicator monitoring."}]}, {"key": "rafael-117", "text": "**The World Bank**\nEmergency Food Security Project (P178936)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPage 3 of 54", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000024:7:0:0", "start": 19, "end": 50, "surface": "Emergency Food Security Project", "probe_tag": "drop", "probe_score": 0.0223, "luna_label": 0, "luna_reason": "Project title, not a cited or used data resource"}]}, {"key": "rafael-118", "text": "**The World Bank**\nChad - Refugees and Host Communities Support Project (P164748)\n\n\n\n\n\n\n\n\n\n\n|for Performance Based Financing|Col2|Col3|Health|by region on level of utilization of the standard forms Performance Based Financing introduced by the P148052 Mother and Child Health Services Strengthening Project. Information is based on representative surveys.|Col6|\n|---|---|---|---|---|---|\n|Cash transfer beneficiaries (households)||Quarterly |Baseline data collected from UNHCR and WFP on number of refugees receiving cash transfers in target areas. The CFS is launching a baseline study which will help to confirm baseline numbers, to be reviewed at MTR.|CFS local offices produce simple reports by region on number of households receiving the transfer. This number is than multiplied by 5 since the average size of families is of five member. The reports are then consolidated by the CFS. Data are non-cumulative by cohort. |CFS |\n\n\n\nPage 52", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000035:56:0:0", "start": 362, "end": 384, "surface": "representative surveys", "probe_tag": "drop", "probe_score": 0.0259, "luna_label": 0, "luna_reason": "Generic surveys are named without an attributed finding or concrete claim."}]}, {"key": "rafael-119", "text": " from one stage to another. It is also driven in part by the curriculum which is geared to\npreparing students for the French baccalaureate examnination and may be contextually difficult for\nDjiboutians from less educated families.\n\n\n**3. Income and Gender Gaps in Enrollment Rates**\n\nEven though the main constraint at present appears to be school places, there is already evidence of\ngender and income gaps which cannot be explained by lack of school places alone. These are\nexpected to become more prominent over time as enrollment rates rise.\n\n\nAccording to the household expenditure survey data, in urban areas, the Net Enrollment Rate in\nPrimary Enrollment is 50% higher for the highest expenditure quintile compared to the lowest\nexpenditure quintile. The inequity is even worse in secondary education (lower secondary education\nis part of basic education but the survey data did not separate the two), where the NER of the highest\nquintile is 420% higher than the NER of the lowest quintile. The problem in urban areas is access demand exists among all groups but the rationing of sets ends up benefiting the better off who live in\nareas where schools have historically been located. Any further expansion of places will help the\npoorer segments of the population more particularly if care is taken to site the schools in areas where\nthe poor live.\n\n\nThere are also significant gender gaps and research indicates that educated mothers play a key role in\nthe country's overall development. There is a shortage of school places and any rationing works to\nthe detriment of girls enrollment. Parents are less willing for their girls to attend school because in\npar., they may view the curriculum as foreign. In addition, despite the fact the education is officially\nfree, poor families still have difficulty paying the cost of books and materials. They prefer to use\ntheir constrained resources for their boys who they feel have a better labor market potential. Finally,\nthe data from the Household Survey, showed that even if girls go to school, their parents pull them\nout at an", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000022:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1, "luna_reason": "Survey data supports concrete enrollment-rate comparisons across expenditure quintiles."}, {"key": "refugee_pads:000022:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Survey data supports reported enrollment-rate disparities."}]}, {"key": "rafael-120", "text": " . Prepared by Banyan Global.\n17 56 percent of children under 5 years of age in Burundi are stunted, with 61 percent in Ngozi, 66 percent in Muyinga, 59 percent in\nCankuzo, and 52 percent in Ruyigi: DHS 2016-17\n18 Data as of 2017. See _[https://data.worldbank.org/indicator/sp.dyn.tfrt.in](https://data.worldbank.org/indicator/sp.dyn.tfrt.in)_\n\n\nPage 10 of 86", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000110:15:2:0", "start": 199, "end": 210, "surface": "DHS 2016-17", "probe_tag": "keep", "probe_score": 0.9204, "luna_label": 1, "luna_reason": "DHS data support reported stunting percentages across Burundi provinces."}]}, {"key": "rafael-121", "text": "**The World Bank**\nEnhancing Connectivity and Resilience in the Far North of Cameroon for Inclusiveness Project (P178207)\n\n\n16. **In the Far North region of Cameroon, refugee-hosting areas need special attention due to the increased**\n**demands of hosting displaced populations, and the resulting pressures on public service delivery systems and**\n**infrastructure.** This poor region of Cameroon is greatly impacted by large flows of incoming refugees. According\nto UNHCR statistics, 78,722 refugees, 16.4 percent of Cameroon’s refugee population, are in the Minawao camp\nin Mayo Tsanaga, 27 and the highest number of refugees outside of the Minawao camp are located around Mora\non the axis of the MDK road.\n\n\n17. **Among refugee and host populations in the Far North region of Cameroon, women and young people are the**\n**most vulnerable, and poor connectivity is exacerbating the challenges they are facing.** The proportion of young\npeople in refugee camps without access to education remains high, and women’s literacy is a particularly thorny\nissue. Twenty-one percent of the refugees registered in the Far North them are women between the ages of 18\nand 59, and more than 48 percent of refugees are school-aged children. 28 Climate-resilient infrastructures are\nimportant for supplying various camps with core relief resources and improving refugee and host communities’\naccess to the basic socioeconomic infrastructure. The absence of reliable means of transport and financial\nsupport for families—maternal and child support—are the two main challenges for the refugees and host\ncommunities in the Far North of Cameroon. These challenges prevent them from attending and completing\neducation or accessing health centers, even when school and health infrastructure is available. In addition,\nwater, sanitation, and hygiene infrastructure is needed in schools, and", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000003:18:0:0", "start": 467, "end": 483, "surface": "UNHCR statistics", "probe_tag": "keep", "probe_score": 0.9508, "luna_label": 1, "luna_reason": "UNHCR statistics support the reported refugee population figures."}]}, {"key": "rafael-122", "text": " access to credit, learns about new market opportunities,\nand acquires the skills needed to successfully operate their businesses. 22 Studies of women entrepreneurs in Uganda find\nthat women who work closely with a mentor—often male, and usually a family member—are more likely to transition\ninto higher-profit sectors. 23 24\n\n15. **Additional factors that block women from developing growth-oriented enterprises in profitable sectors are**\n**related to the failure of existing business development services to address the needs of women-owned firms** .\nAccording to an enterprise survey conducted in 2014, MSMEs lacked key skills needed for business growth. Only 28\npercent of firms surveyed said they do book-keeping to track revenues and expenses; a mere 10 percent had invested in\ntraining for employees; and just 36 percent had access to the internet. Female-owned firms appear to be particularly\nlacking when it comes to the use of standard business practices. A recent microenterprise survey showed a gender gap\nof 24 percentage points on an index of adoption of good business practices. Few training courses address the specific\nchallenges of formalizing a business, including meeting tax obligations, preparing proper records, fulfilling reporting\nrequirements, and obtaining licenses. Training tends to focus on limited topics, such as financial or computer literacy,\nbut leaves out training in life skills and support for network. Yet, global evidence demonstrates that developing socioemotional skills, through psychology-based trainings, are as important to enterprise success as strengthening business\nskills. 25 Finally, many business development services continue to train women for sectors where women-owned firms are\nover-represented, such as small trade or food service, rather than where they could diversify their business and earn\nhigher profits.\n\n\n19 Delecourt, S. and Fitzpatrick, A. 2021.", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000088:14:1:0", "start": 603, "end": 620, "surface": "enterprise survey", "probe_tag": "keep", "probe_score": 0.9434, "luna_label": 1, "luna_reason": "Existing 2014 survey supports the finding that MSMEs lacked key business skills."}, {"key": "refugee_pads:000088:14:1:1", "start": 1009, "end": 1031, "surface": "microenterprise survey", "probe_tag": "keep", "probe_score": 0.9147, "luna_label": 1, "luna_reason": "Survey provides the attributed 24-point gender gap finding."}]}, {"key": "rafael-123", "text": " ToRs for the recruitment of an independent external\nauditor, acceptable to IDA/IBRD, based on acceptable ToRs – who will audit both agencies based on a single contract ; and\n(f) completing the recruitment of an experienced FM specialist officer and an accountant (for the MASS component).\n\n\n**Table 1.2. Risks and Mitigating Measure**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Risks|Risk Rating|Risk Mitigating Measures|Residual Risk Rating|Conditions for Effectiveness (Y/N)|\n|---|---|---|---|---|\n|**Inherent Risk**|**Inherent Risk**|**Inherent Risk**|**Inherent Risk**||\n|**Country level**: Poor governance and slow pace of implementation of public financial management (PFM) reforms that might hamper the overall PFM environment.|H|In the wake of the 2023 PEFA assessment, the PFM bottlenecks identified by the 2023 PEFA informed the design of a PFM reform strategy. A specific TA, with the support of the World Bank and other donors, was implemented to assist the government in developing a coherent PFM strategy based on the PEFA recommendations. The government, with the support of the World Bank and other donors, has since developed a public finance reform roadmap for the period 2024-2027. A PFM reform strategy and associated action plan ", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000180:45:1:0", "start": 770, "end": 790, "surface": "2023 PEFA assessment", "probe_tag": "keep", "probe_score": 0.9149, "luna_label": 1, "luna_reason": "Existing PEFA assessment identified bottlenecks informing PFM reform strategy."}]}, {"key": "rafael-124", "text": "W) losses affect the financial performance of WSS service providers as energy costs\naccount for around 30–40 percent of their minimal budgets for operational expenses. While reduction of\nenergy consumption by water utilities may not lead to significant reduction in GHG emissions, as the\ncountry is reliant on hydropower for almost 95 percent of electricity generation, optimizing energy use\nfor water service provision will simultaneously improve financial performance of utilities and overcome\nseasonal constraints in availability of electricity which affects operation of the systems.\n\n\n11. **The burden of deficient water supply is especially affecting the poor.** In rural areas, house\nconnections are available to 34 percent of the poorest households compared to 80 percent in urban areas,\nwhich demonstrates that the gap in services is largely correlated with location rather than income of\nhouseholds. However, the gap between rich and poor in drinking water service provision is much less\npronounced than is commonly seen in other low-income countries. Most of the gap is the result of\nlocation, as most poor people reside in rural areas. In urban areas, about 80 percent of the poorest\npopulation use house connections (compared to 99 percent of the richest households). Data from the\n\n\n20 TajStat. 2020. Population Census Data.\n\n\nPage 10 of 89", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000162:13:1:0", "start": 1315, "end": 1337, "surface": "Population Census Data", "probe_tag": "keep", "probe_score": 0.9221, "luna_label": 1, "luna_reason": "TajStat 2020 census data source the preceding household-access findings."}]}, {"key": "rafael-125", "text": " or more pilot agencies; and (d) introducing, where appropriate, mechanisms to enhance oversight\nand monitoring of procurement, including the participation of communities and nongovernmental\nbodies, to enhance performance.\n\n\n**Component 4. Enhancing the use of statistics for policy making**\n\n\n38. The end goal of any statistical system is to produce high-quality data to inform policies and\nmake them publicly available. Achieving this goal requires an investment not only in the production of\nmicro-data (censuses and surveys) and routine statistics (prices, national account, external trade, and so\non) but also in data processing, analyzing, archiving, and dissemination. Given the large demand of the\nnational statistical system, the choice is made to focus either on the areas where the World Bank clearly\nhas a comparative advantage among the donors or areas not supported by the other donors, namely,\nhousehold survey, population census, national account, and archiving and dissemination. At the core of\nall this are two aspects: data collection and capacity building. The philosophy underlying data collection\nis to improve the design of the surveys to take into account the most recent methodological approaches.\nAs for capacity building, the preference is given either to train staff locally or to use the learning-bydoing approach. Three subcomponents are distinguished as follows:\n\n\n**Subcomponent 4.1. Improving poverty-related data**\n\n\n39. **Objective** . The objective of this subcomponent is to improve the poverty related data\nproduction and analysis.\n\n\n40. **Current status.** INS has implemented living conditions surveys in 1996, 2001, 2007, and 2014.\nThe last three surveys have used very close methodologies and poverty indicators are comparable over\n\n\nPage 54 of 93", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000044:57:1:0", "start": 909, "end": 925, "surface": "household survey", "probe_tag": "confusion", "probe_score": 0.4212, "luna_label": 0, "luna_reason": "Listed as a planned statistical production focus, not existing survey data used."}, {"key": "refugee_pads:000044:57:1:2", "start": 946, "end": 962, "surface": "national account", "probe_tag": "confusion", "probe_score": 0.2907, "luna_label": 0, "luna_reason": "Listed as an area for statistical production, not existing data use."}, {"key": "refugee_pads:000044:57:1:3", "start": 1629, "end": 1654, "surface": "living conditions surveys", "probe_tag": "confusion", "probe_score": 0.7585, "luna_label": 1, "luna_reason": "Existing survey rounds support comparison of methodologies and poverty indicators."}]}, {"key": "rafael-126", "text": "\nframework advance the integration of refugees and foster an enabling environment for them to live in safety and\nwith dignity. Uganda is also implementing the Comprehensive Refugee Response Framework (CRRF) in accordance\nwith the New York Declaration for Refugees and Migrants that is guiding and framing all refugee-related activities.\nThese, combine with the aim to ensure that the refugee response provides support to both refugees and RHDs,\nputting them on a path to self-reliance and by bridging humanitarian and development ways of working. Uganda\nhas reiterated its ongoing commitments to refugee protection in the context of COVID-19 in Uganda’s Strategy\nNote on Support to Refugees and RHDs. Since initial eligibility for WHR resources, Uganda has been implementing\nRefugee and Host Community Sector Response Plans for: education; health; water and environment; and jobs and\n\n\n36 Based on the Uganda Refugee Protection Assessment Update August 2-18, 2021.\n\n\nPage 18 of 92", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000073:23:1:0", "start": 902, "end": 945, "surface": "Uganda Refugee Protection Assessment Update", "probe_tag": "confusion", "probe_score": 0.8664, "luna_label": 1, "luna_reason": "Named assessment cited as the basis for the surrounding refugee-protection analysis."}]}, {"key": "rafael-127", "text": "|No system|System functional|\n|Number of workers registered in the employment database (Number)||0.00|30,000.00|\n|Number of employers formalized (Number)||0.00|500.00|\n|Number of beneficiaries enrolled in training database (Number)||0.00|45,000.00|\n|Number of refugee beneficiaries enroled in training database (Number)||0.00|22,500.00|\n|Number of employers receiving technical assistance (Number)||0.00|10,000.00|\n|Proportion of trainees satisfied with the training received (Percentage)||0.00|75.00|\n|Proportion of farmers satisfied with the program (Percentage)||0.00|75.00|\n|Beneficiaries of job-focused interventions (CRI, Number)||0.00|38,000.00|\n\n\nPage 43 of 85", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000114:46:1:0", "start": 67, "end": 86, "surface": "employment database", "probe_tag": "confusion", "probe_score": 0.5756, "luna_label": 0, "luna_reason": "Database is merely named as an indicator source, with no shown data use."}]}, {"key": "rafael-128", "text": " a reduction in the poverty rate (headcount poverty)\nand reduction of the poverty gap – as well as to reinforce systems that should allow for such reduction to continue after\nthe project. Currently, a substantial share of the population in Djibouti remains poor (21.1 percent poverty rate in 2017).\nAs for human capital, Djibouti has one of the lowest rankings in MENA and ranks 172 among 188 countries in the HDI.\nNotable challenges include some of the highest rates of stunting and wasting for children under five. Although poverty\nreduction (though hard to attribute without an expensive impact evaluation) is expected to be the main effect of the\nprogram’s expansion, as a second order effect, the project may have an impact on overall human development outcomes\n(although limited given the scope of the proposed conditionalities).\n\n63. **Simulating the impact of PNSF expansion on headcount poverty in Djibouti** . The proposed project aims to expand\nPNSF to cover 5,000 of the poorest and most vulnerable households in Djibouti, providing critical consumption smoothing\nto selected beneficiaries. Based on the latest plans for the expansion of the program and using the latest available\nhousehold survey in Djibouti (Enquête Djiboutienne Auprès de Ménages, EDAM 4), this economic analysis aims to\ncalculate the impact of the proposed cash transfer program on head count poverty. The simulation approximates a US$56\nper month transfer to 5,000 households. The total allocation of the program is simulated through a three-step process.\nFirst, 5,000 households are selected randomly in regions of the interior based on the percentage of poor population\n(excluding Djibouti Ville). Second, the benefits are distributed only to households in these areas. Finally, households are\nrandomly assigned to receive benefits so that their consumption levels are increased, until the target of 5,000 households\nis reached.\n\n\nPage 21 of 44", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000154:25:1:0", "start": 1193, "end": 1221, "surface": "household survey in Djibouti", "probe_tag": "confusion", "probe_score": 0.5144, "luna_label": 1, "luna_reason": "Existing survey data are used to calculate simulated poverty impacts."}, {"key": "refugee_pads:000154:25:1:2", "start": 1263, "end": 1269, "surface": "EDAM 4", "probe_tag": "keep", "probe_score": 0.9406, "luna_label": 1, "luna_reason": "Named household survey used to simulate cash-transfer impacts on poverty."}]}, {"key": "rafael-129", "text": " audits and financial verification). The\ntotal loan amount subject to the modified provisions is estimated at between US$38-50 million,\nof which up to US$21 million represent contracts subject to retroactive financing. To mitigate\nthe risk of some international firms not learning of the opportunities on time, packages above\nUS$500,000, which are still to be procured, will additionally have timely notification in UNDB\nonline and the JPD website would include the English version of the advertisement (see Annex 3\nfor further details). All project procurement above the US$5 million threshold will remain\nsubject to standard Bank terms, conditions and policies, including the application of the ACGs.\n\n\n58. Regarding application of Anti-Corruption Guidelines, it should be noted that the JPD\ncontracts do not explicitly contain clauses related to fraud and corruption, but are governed by\nthe national anti-corruption rules providing for the exclusive jurisdiction of the ACC, an\nindependent agency, in cases of fraud and corruption. Therefore, a waiver of some provisions of\nthe World Bank Anti-Corruption Guidelines is sought to rely on the Borrower’s national ACC to\ninvestigate cases of fraud and corruption in the use of project funds for the aforementioned\ncontracts (under US$5 million). The waiver specifically proposes the modification of paragraph\n9 and the deletion of paragraph 10, which currently provide, among others, for the right of the\nBank to inspect accounts, records and all other documents of the Borrower or other recipients of\nloan proceeds relating to the Project. These paragraphs will be substituted with modified\nprovisions stating the obligation of the Borrower to undertake investigations in case of allegation\nof fraud and corruption and provide related reports to the Bank (Modified Anti-Corruption\nProvisions; see Annex 3). This approach is similar and along the lines adopted in Program-forResults Financing (PforR) projects. All other procurement for contracts for goods and services\n\n\n28", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000064:27:1:0", "start": 416, "end": 427, "surface": "UNDB\nonline", "probe_tag": "confusion", "probe_score": 0.6278, "luna_label": 0, "luna_reason": "Procurement notification platform is mentioned without data being used."}]}, {"key": "rafael-130", "text": " all of its food requirements 4 [^4: The country recently leased tracts of land in Ethiopia to grow food in an attempt to lower its import bill. Facts in paragraph 2 are also from the same\nsource.] . Therefore, the economy is quite dependent on the service\nsector, specifically the commercial activities that supports the trade sector given the country’s strategic location\nas a Red Sea transit point with about two-thirds of the port’s activities supporting imports and exports to and from\nEthiopia. The country is home to about a million individuals and is multiethnic in nature with Somalis, Afar and\nArabs. Djibouti city accounts for about 70 percent of the country’s population, with the remaining population\nspread across the five regions. In these administrative regions, the rural share varies quite a bit ranging from 40\npercent of the population in Ali Sabieh to 77 percent in Tadjourah. On aggregate about 15 percent of the\npopulation live in rural areas, with the rest residing in the country’s cities and towns.\n\n3. **Djibouti's stability in an otherwise unstable region has made the country a destination for refugees**\n**from neighboring countries** . The fragile, conflict and violence (FCV) context in countries surrounding Djibouti,\nheightens the need for the country to respond to the needs of refugees and host communities and prepare for\nlikely future refugee flows, asylum seekers and migrants. Unrest in neighboring countries has forced refugees to\nseek shelter in Djibouti since the mid-seventies. United Nations High Commissioner for Refugees (UNHCR) update\non refugees and asylum seekers in Djibouti as of January 31, 2022, states that there are 34,810 such individuals in\nthe country. These populations are concentrated in three regions - Djibouti City (7111), Ali Sabieh (24886) and\nObock (2813). There are two locations within Ali Sabieh - Ali-Addeh (17018) and Holl-Holl (7868). These numbers\nrepresent the total number of refugees and asylum seekers", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000195:11:1:0", "start": 1588, "end": 1637, "surface": "update\non refugees and asylum seekers in Djibouti", "probe_tag": "confusion", "probe_score": 0.8956, "luna_label": 1, "luna_reason": "UNHCR update supports the reported refugee and asylum-seeker count."}]}, {"key": "rafael-131", "text": "CURRENCY EQUIVALENTS\n\n\nExchange Rate Effective Date: January 26, 2004\n\n\nCurrency Unit = I L S 1 = 0.224\n\nUS$1 = 4.461 I L S\n\n\nFISCAL YEAR\nJanuary 1 - December 31\n\n\n\nABBREVIATIONS _AND_ ACRONYMS\n\n\n\n~ _ - **\"_-I-** - - _ _ - _ _ I\n\n\n\n~ _ - **\"_-I-** - - _ _ I\n\nBP BankProce&re -- **I** - - - - MOH _ x I _2_ Ministry \" of Health - __ \" .\nMOPT {Ministry of Post and Telecommunication\n\n\n\nCFAA -1Country Financial Accountability Assessment :MOSA 1Ministry of Social Affars\n\nCPAR Country Procurement Assessment Review NGO INon-Govemmental Organization\nDepartment for Intemational Development\nDFID- _ _ -_ (UnlteEl*%&?d I I ~ I \" I _ _ OM \" 9peratlonal Map! **!** \" __\nDGAA Directorate General for Admnistration of Aid OP,Operational Policy\n\nEC European Comssion PA Palestine Authority\nEmergency Municipal Services Rehabilitation\n\nE PEJ?'?, - - - **IPA?** - p r - p APpFus_al pocpmen! - - \n\n\nSecondary Emergency Services Support Project lPCBS\n\n\n\nPalestine Central Bureau of Statistics\n\n\n\nESSP Secondary Emergency Services Support Project lPCBS Palestine Central Bureau of Statistics\nESSR Emergency Services Support Project ;PCT Project Coordination Team\nFIS Financial Information System,PHECS 'Palestine Expenditure and Consumption Survey\nFMIS $Financial and Management Information System **bLCS** !Palestine Living Conditions Survey\nFMIS Manj+gemcnt I;fo-Fatiqn System ~ ~ P F \" _ iProject Prpration Facilgy\n\n\n\n$Financial and Management Information System **bLCS** !", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000047:1:0:0", "start": 1682, "end": 1726, "surface": "Palestine Expenditure and Consumption Survey", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Survey is merely defined in an abbreviation list, with no data use or finding."}, {"key": "refugee_pads:000047:1:0:1", "start": 1787, "end": 1821, "surface": "Palestine Living Conditions Survey", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Survey is only defined in an acronym list; no data use or finding is shown."}]}, {"key": "rafael-132", "text": " respective city\ncontract. The TLUs in Yaoundé and Douala will be composed of dedicated staff drawn from the\nCTD and include: a project manager, a civil engineer, a social specialist, an accountant and a junior\nassistant (recent graduate). The TLU personnel will benefit from an additional premium, indexed\non performance and results. In the other cities, the TLUs will mobilize municipal staff in parallel\nto their usual activities due to the scarcity of management staff. In the CUs, the TLUs will bring\ntogether staff from both levels of local government (CU and CA). There will be annual audits of\nthe TLUs.\n\n\n50. The project’s intervention in the northern cities (Maroua and Kousséri) will require\nconflict-sensitive approaches, specific implementation arrangements and possibly adapted project\nactivities. These will be further defined in coordination with other donors intervening in these areas\n(particularly AFD) and based on the findings of the RPBA (expected to be available in Fall 2017).\n\n\n**B.** **Results Monitoring and Evaluation**\n\n\n51. Continuous M&E will be an integral part of project implementation under the overall\nresponsibility of the PCU. Each TLU will be responsible for day-to-day monitoring of project\nactivities at the city level and providing regular updates to the PCU on the implementation of the\nCity Contract. Project monitoring will be based on biannually progress reports, including updates\non the results framework included in the Project Appraisal Document. At mid-term review (MTR)\nand before project closing, a beneficiary assessment will be undertaken. In addition to the Results\nFramework indicators, the project M&E system will also capture sector data defined by the GoC\n(MINEPAT, MINHDU, and so on) to inform policy and progress in implementation of national\nstrategies. The M&E manual will provide further details on the results framework, social/gender", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000144:27:1:1", "start": 1717, "end": 1724, "surface": "MINEPAT", "probe_tag": "confusion", "probe_score": 0.2955, "luna_label": 0, "luna_reason": "Institution name alone is not a cited or used data resource."}]}, {"key": "rafael-133", "text": " secondhand clothes, or have established restaurants and kiosks and engage in petty trading.\nThese are mostly conducted on an informal basis, as the costs of obtaining official work and business permits are high.\n\n\n13. **Zambia is taking a more progressive approach to refugee inclusion.** There is a general recognition in Government\nthat a shift to self-reliance and a move away from humanitarian support is required. Zambia’s Eighth National\nDevelopment Plan (8NDP) has a vision for a more decentralized approach to economic and social development, which will\nsupport the greater inclusion of approximately 101,837 15 [^15: Zambia National Statistical Report, Ministry of Home Affairs and Internal Security, May 2024.] refugees, asylum seekers, and former refugees that currently\nreside in the country.\n\n\n14. **In August 2023, the GRZ endorsed a new National Refugee Policy, approved by the Cabinet in November 2023.**\nThe Office of the Commissioner for Refugees, in close cooperation with the MoHAIS and the United Nations High\nCommissioner for Refugees (UNHCR), developed the new policy to address existing legal gaps and enhance coordination\nwithin government. Its implementation is expected to ease reservations to the 1951 Convention and harmonize the 2017\nRefugee Act with other national legislation (for example, the Immigration and Deportation Act of 2010) that will enhance\nopportunities for protection and solutions for self-reliance. 16 [^16: They include, among others _,_ the Immigration and Deportation Act, Refugees Act No.1 of 2017 and amendments to other legislation, such as the\nLands Act CAP 184, Higher Education Bursaries and Scholarships Act no.31 of 2016, Birth and Death Registration Act CAP 51, Citizenship Act of No] To assist policy delivery, the Government has established an\ninteragency National Steering Committee chaired by the Office of the Vice President. In March 2023, the MoHAIS launched\na plan for the Modernization of Refugee and Host Community Settlement Areas (MORHCSA)", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000187:14:1:0", "start": 638, "end": 672, "surface": "Zambia National Statistical Report", "probe_tag": "confusion", "probe_score": 0.6527, "luna_label": 1, "luna_reason": "Named statistical report cited as source for refugee population figure."}]}, {"key": "rafael-134", "text": "ficiaries satisfied with the community infrastructures financed by the project Percentage of direct beneficiaries of community infrastructures that are globally satisfied with the infrastructures Once Survey Survey at end of project SEAS |Beneficiaries satisfied with the community infrastructures financed by the project Percentage of direct beneficiaries of community infrastructures that are globally satisfied with the infrastructures Once Survey Survey at end of project SEAS |Beneficiaries satisfied with the community infrastructures financed by the project Percentage of direct beneficiaries of community infrastructures that are globally satisfied with the infrastructures Once Survey Survey at end of project SEAS |Beneficiaries satisfied with the community infrastructures financed by the project Percentage of direct beneficiaries of community infrastructures that are globally satisfied with the infrastructures Once Survey Survey at end of project SEAS |Beneficiaries satisfied with the community infrastructures financed by the project Percentage of direct beneficiaries of community", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000154:37:7:0", "start": 248, "end": 272, "surface": "Survey at end of project", "probe_tag": "confusion", "probe_score": 0.119, "luna_label": 0, "luna_reason": "Planned end-of-project survey, not existing data used for analysis."}]}, {"key": "rafael-135", "text": " of the post-harvest\nlosses resulting from investments into climate-smart market infrastructure. In the model for\nmaize/groundnut/sesame storage, the WOP considers that beneficiaries will be able to store their production in\ntraditional storage infrastructures, while the investment in the WP will materialize into modern storage (two\nwarehouses of a capacity of 1,000 tons each) and drying equipment, leading to a sensible reduction of post-harvest\nlosses from 16 to 8 percent. In the case of fish-related infrastructure, a group of 560 fish vendors 58 will benefit from\naccess to cold-storage, resulting in a reduction of losses from 25 to 15 percent.\n\n\n20. **Access to inputs** : To estimate the benefits of access to inputs, two crop models have been developed reacted\nto the cultivation of maize and groundnut. It has been assumed that maize production will benefit from\n“intensification kit”, defined as one-year support with improved seeds and fertilizer, while groundnut production\nonly benefits from improved seeds. In these, the WOP situation is characterized by the cultivation of maize and\ngroundnuts, each on 1 ha and 1 cycle per year, with low access to inputs and low yields (yields estimated at 1200\nkg/hectare in the case of maize and 500 kg/hectare in the case of groundnut). Access to inputs in the WP situation\nand capacity building will result in yield improvements, reaching 2,500 kg/ hectare in the case of maize and 650\nkg/hectare in the case of groundnuts.\n\n\n58 The number of vendors has been estimated based on data collected during the field mission, January 2023.\n\n\n**Page 65 of 88**", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000183:69:2:0", "start": 1548, "end": 1587, "surface": "data collected during the field mission", "probe_tag": "confusion", "probe_score": 0.6726, "luna_label": 1, "luna_reason": "Existing field-mission data supports the vendor-number estimate."}]}, {"key": "rafael-136", "text": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n\ni) The PMU uses a register “spreadsheet” to record the details of purchased vaccines under the project,\nincluding among others, description, reference to contract, quantity, location, and the Governorate in\nwhich the vaccines were deployed;\nii) The PMU will prepare a detailed distribution plan, which will include among others the description of\nall vaccines and beneficiary governorate;\niii) All items will be traceable;\niv) Special conditioned warehouse register will be used for the received vaccines;\nv) Special committees will be established to receive the purchased vaccines. The committee will be\nresponsible for vaccine inception upon delivery at the location to confirm quantity and quality as per\nthe signed contract;\nvi) Items will be stored in a designated area that would be easy to differentiate from all other inventory\n(vaccines) items;\nvii) Warehouses will be maintained to provide the necessary conditions to protect the vaccines from\nweather, heat, theft, damaged, etc.); and\nviii) Annual stocktaking will be performed by Directorates of Health, and the PMU will compare to its own\nregister of assets.\n\n\n14. **Financial audit** : The project’s financial statements will be audited annually by an independent auditor\nacceptable to the World Bank, in accordance with internationally accepted auditing standards and terms of\nreference cleared by the World Bank. The PMU will be responsible for preparing the TORs for the auditor and will\nsubmit them to the World Bank for clearance. The audit scope will cover the activities of the project implemented\nby the PMU. The audit report will be sent to the Bank no later than 6 months following the end of the project’s\nfiscal year. The report will include an opinion on the project’s financial statement. The auditor will also be\nrequested to provide an opinion on the project’s effectiveness of internal control system including the vaccines\nsafeguard", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000058:57:0:0", "start": 1167, "end": 1185, "surface": "register of assets", "probe_tag": "confusion", "probe_score": 0.4457, "luna_label": 0, "luna_reason": "Routine asset-register reconciliation and stocktaking, not substantive data reuse."}]}, {"key": "rafael-137", "text": " sensitive to the choice of the discount rate. The use\nof a higher discount rate of 10.2 percent would produce economic returns just equivalent to the\ntotal amount of the project (US$5.6 million). There are many benefits that are difficult to\nquantify, particularly associated with the recreation areas. This is thus a conservative rate that\nshows that the project is economically justified.\n\n53. **The assessment is based on the characteristics of the households and property of Q7.**\nThe determinants of consumption levels and real estate values in Q7 are based on the 2012\nhousehold survey for the city of Djibouti (EDAM 3) and the Population and Housing Census of\n2009 (RGPH) and take into account the impact of neighborhood characteristics such as the\noverall unemployment rate. The improvement of the residents’ health is measured through the\nnumber of Disability-Adjusted Life Years (DALYs) saved due to lower rates of diarrhea,\nrespiratory infections and paratyphoid fever. The estimate of the opportunity cost of flood is\nbased on an international benchmark.\n\n54. Activities surrounding street rehabilitation will use labor-intensive techniques and locally\nproduced materials. Overall, the implementation of the project should therefore generate demand\nin Djibouti for labor, goods, and services amounting to US$3.57 million. In addition to\nstrengthening households’ resilience to flooding, the project’s success will depend on its capacity\nto trigger economic growth in the neighborhood, expected to generate an additional impact of\nabout US$2.43 million in Quartier 7. Easier and quicker access to enter or exit the neighborhood\nwill increase the population’s overall economic opportunities, efficiency, and security. If the\nnumber of jobs in the retail and catering sectors expands—like in other neighborhoods where\n\n\n15 This includes DRM partners: Djibouti Center for Research Studies (CERD), Civil Defense, and Executive Secretariat for Disaster\nManagement, Meteorology Agency.\n\n\n15", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000067:24:1:0", "start": 571, "end": 617, "surface": "2012\nhousehold survey for the city of Djibouti", "probe_tag": "confusion", "probe_score": 0.6404, "luna_label": 1, "luna_reason": "Survey data underpin consumption and real estate value calculations."}, {"key": "refugee_pads:000067:24:1:1", "start": 619, "end": 625, "surface": "EDAM 3", "probe_tag": "confusion", "probe_score": 0.8595, "luna_label": 1, "luna_reason": "Named household survey used to estimate consumption and real estate values."}, {"key": "refugee_pads:000067:24:1:2", "start": 635, "end": 672, "surface": "Population and Housing Census of\n2009", "probe_tag": "confusion", "probe_score": 0.7162, "luna_label": 1, "luna_reason": "Census data inform property values and household consumption analysis."}]}, {"key": "rafael-138", "text": "**Box A.2.1: National ID card in Burundi**\n\n\nThe information on the present ID card includes: last and first names, names of\nparents, birth date, civil status, occupation, a black and white picture of the bearer\nand his/her signature. It also includes an ID number and the places of residence.\nTo obtain an ID card, one must present a birth certificate and proof of residence\nsigned by the _chef de colline_ and the commune administrator. Cost in 2016 is BIF\n2,500.00 (US$ 1.61).\n\nBurundi has piloted a biometric ID card. This card would include: first and last\nname, sex, birth date, province, commune, current residence, parents’ names and\nchildren’s names, CAM card number, Social Security National Institute number,\n_Mutuelle de la Fonction Publique_ number, profession, place of birth, picture,\nfingerprint, profession. It would be machine-readable per the requirements of the\nEAC.\n\n\n32. T **he sub-component will support the background analysis for the implementation of**\n**the database and its implementation.** These include:\n\n(a) Design and implementation of key tools: updated poverty and malnutrition maps,\n\ndevelopment of community-based targeting criteria and processes, development of\nregistration questionnaire, construction of proxy-means test score, the organization of the\ndifferent committees involved in the registration, the implementation of the PMT survey,\nstoring and analysis of the data as well as the preparation of beneficiary lists for the cash\ntransfer program and its complementary activities. It will also support the acquisition of\nkey equipment (hardware, software, back-up equipment).\n\n(b) Implementation of the targeting and registration in selected areas including the organization\n\nand support of the targeting committees, the implementation and processing of\nquestionnaires and the preparation of the list of registered households as well as eligible\nhouseholds for the cash", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000157:57:0:0", "start": 1369, "end": 1379, "surface": "PMT survey", "probe_tag": "confusion", "probe_score": 0.3145, "luna_label": 0, "luna_reason": "The project will implement the PMT survey as part of targeting activities."}]}, {"key": "rafael-139", "text": "also be provided to properly levy, collect and account for local duties and taxes. NaCSA staff would be\ngiven an opportunity to visit Community Driven Development Projects in comparable countries to\ncapitalize on their experiences. Regional and district line ministry staff would be trained in community\nmobilization, conflict resolution, social capital building, and technical appraisal skills.\n\n\n(b) Substantial IEEC activities linked to the various sub-projects are envisaged. These activities\nwould be undertaken using existing IEC materials endorsed by the various line ministries. For example,\nin the case of the rehabilitation of a health post, IEC messages could be envisaged to inform the\npopulation on the proper use of insecticide treated bed nets as a means of preventing malaria.\n\n\n(c) Monitoring and evaluation at the community, district, regional and central levels would be\ngiven high priority, and linked regularly and directly with NaCSA decision making on NSAP policy,\nstrategy and operational matters. These activities would be directly undertaken, or commissioned by,\nstaff of NaCSA's Planning, Monitoring and Evaluation Directorate. The Project Design Matrix (logical\nframework, Annex 1) would form the basis for monitoring NSAP outputs, outcomes and impact. An\nassessment of project status would accompany each NaCSA work program and budget submitted\nsemi-annually to the NaCSA Board. Other M&E activities would include a pre-project Social\nAssessment; establishment of NSAP baseline data (in conjunction with the collection of data for the\nCRRP Implementation Completion Report); social assessments during implementation; annual technical\naudits; beneficiary assessments; incorporation of NaCSA into GOSL's semi-annual public expenditure\ntracking surveys (PETS); and independent impact assessments.\n\n\nIn addition to conventional sub-project monitoring and evaluation (incorporated in the\nsub-project cycle as outlined in the Operations Manual), a pilot participatory monitoring and evaluation\nsystem would be introduced in a representative sample of the predominant types of CDP sub-projects.\nBeneficiary communities would identify quantitative and qualitative indicators", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000052:36:0:1", "start": 1743, "end": 1778, "surface": "public expenditure\ntracking surveys", "probe_tag": "confusion", "probe_score": 0.3091, "luna_label": 0, "luna_reason": "Planned incorporation into future monitoring surveys, not citation of existing used data."}]}, {"key": "rafael-140", "text": " gender norms and household dynamics play a critical role in causing these gaps.\nAccording to a 2022 study by Access to Finance Rwanda, deeply ingrained societal expectations shape how women\nparticipate in economic life and influence their capacity to leverage assets—particularly land and property—as\ncollateral. _34_ One pervasive norm is that women should prioritize family and caregiving responsibilities over business\nactivities. This norm restricts their time and engagement in income-generating pursuits and weakens their\nperceived legitimacy as entrepreneurs, reducing their chances of qualifying for credit. Additionally, women are\noften expected to rely on family support, especially from spouses, instead of seeking independent financial\nsolutions, distancing them from formal financial institutions and financial products.\n\n\n31 Baseline failure rates by segment are extremely difficult to assess. As such, the analysis uses conservative assumptions based on extensive\ndiscussions with key stakeholders and potential beneficiaries. These assumptions have been tested for sensitivity.\n32 CEIC data\n33 In the absence of available survey data, the analysis uses proxies for these losses and repair costs from survey data from neighboring\nBurundi. Based on this data, annual flooding losses amount to ~US$89 per household and repair costs amount to US$25.60 per household.\n34 [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/)\n\n\nPage 18", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000188:33:2:0", "start": 1150, "end": 1161, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.7369, "luna_label": 1, "luna_reason": "Unavailable survey data motivates substitute proxy estimates."}, {"key": "refugee_pads:000188:33:2:1", "start": 1228, "end": 1264, "surface": "survey data from neighboring\nBurundi", "probe_tag": "keep", "probe_score": 0.9535, "luna_label": 1, "luna_reason": "Existing Burundi survey data are used as proxies for losses and repair costs."}]}, {"key": "rafael-141", "text": " attirer d'importants investissements étrangers directs\n(IED). En 2006, les entrées d'IED ont connu une forte accélération suite à la construction du\nterminal à conteneurs de Doraleh et la zone franche du port de Djibouti. Entre 2006 et 2010,\nles entrées d'IED se chiffraient en moyenne à plus de 14 pourcent du produit intérieur brut\n(PIB), et la formation brute de capital a atteint 31 pourcent du PIB (Graphique 1). Par\nconséquent, le taux de croissance s'est accéléré en 2002-2012 pour atteindre une moyenne\nd'environ quatre pourcent par an. L'inflation est restée assez bien maîtrisée au cours de cette\npériode. Elle s'est stabilisée à environ 3,8 pourcent au cours des quatre dernières années,\naprès avoir culminé au plus fort du choc de la hausse des prix de la nourriture et du carburant\nde 2007 à 2008, en raison de la dépendance quasi totale du pays sur les denrées alimentaires\nimportées.\n\n3.\nDjibouti est un pays à revenu intermédiaire-faible, avec un PIB par\nhabitant en 2011 de 1430 et 2500 dollars EU en parité de pouvoir d'achat (PPA). Entre 2002\net 2012, la croissance du PIB a atteint en moyenne environ 4 pourcent par an (Graphique 1).\nToutefois, le niveau de pauvreté reste élevé même s’il a légèrement diminué au cours de la\ndernière décennie. Selon les statistiques du Gouvernement, la", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000134:5:1:0", "start": 1275, "end": 1303, "surface": "statistiques du Gouvernement", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Government statistics are cited as evidence for the poverty assessment."}]}, {"key": "rafael-142", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population - Beneficiary assessments\ngroups (female headed - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000176:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Existing NaCSA administrative data is listed as a verification source for program awareness results."}]}, {"key": "rafael-143", "text": "**The World Bank**\nAdvancing Sustainability in Performance, Infrastructure, and Reliability of the Energy Sector in the West Bank and\nGaza (P170928)\n\n\n\n\n\n\n|Indicator Name|DLI|Baseline|Intermediate Targets|End Target|\n|---|---|---|---|---|\n||||**1 **||\n|PENRA publishes on its website results of citizen engagement survey (Number)||0.00|1.00|2.00|\n|Grievances registered related to delivery of project benefits that are actually addressed (Percentage) ||0.00|90.00|90.00|\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Monitoring & Evaluation Plan: PDO Indicators|Col2|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Indicator Name **|**Definition/Description **|**Frequency **|**Datasource **|**Methodology for Data** **Collection **|**Responsibility for Data** **Collection **|\n|Non-technical losses West Bank|Average based on non- technical losses recorded for each distribution company in West Bank|Annual |Non-technical losses reported by each distribution company individually |Primary data |PENRA PMU |\n|Non-technical losses Gaza|Non-technical losses as per Gaza Distribution Company (GEDCO)", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000133:47:0:0", "start": 306, "end": 323, "surface": "engagement survey", "probe_tag": "confusion", "probe_score": 0.1281, "luna_label": 0, "luna_reason": "Survey phrase appears within a project indicator table."}, {"key": "refugee_pads:000133:47:0:1", "start": 1020, "end": 1029, "surface": "PENRA PMU", "probe_tag": "drop", "probe_score": 0.0406, "luna_label": 0, "luna_reason": "Identifies the project unit responsible for producing primary monitoring data."}]}, {"key": "rafael-144", "text": " Collection|MoH / TPM|\n|**Percentage of complaints to Grievance Redress Mechanisms satisfactorily addressed in a timely manner**|**Percentage of complaints to Grievance Redress Mechanisms satisfactorily addressed in a timely manner**|\n|Description|Percentage of complaints submitted to the GRM addressed according to the protocol and within agreed time period.|\n|Frequency|Quarterly|\n|Data source|UNICEF|\n|Methodology for Data Collection|UNICEF to provide data / TPM to verify|\n|Responsibility for Data Collection|UNICEF; PMU|\n|**Percentage of completeness of reporting by facilities**|**Percentage of completeness of reporting by facilities**|\n|Description|Percentage of facilities that submit complete reports within the required deadline.|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data Collection|DHIS2|\n|Responsibility for Data Collection|MoH/ PMU|\n|**Percentage of states that conducted quarterly coordination meetings with a review of data and documented with minutes including** **action items and follow-up**|**Percentage of states that conducted quarterly coordination meetings with a review of data and documented with minutes including** **action items and follow-up**|\n|Description|Percentage of State’s quarterly health service delivery coordination meetings for the health sector held with a review of data included in the meeting and documented with minutes which include action items and follow-up on action items. Meetings are to be held quarterly in each state. Four meetings are expected each year per state. CHDs and implementing partners will be participated in the review|\n|Frequency", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000006:54:1:1", "start": 790, "end": 795, "surface": "DHIS2", "probe_tag": "drop", "probe_score": 0.0497, "luna_label": 0, "luna_reason": "Names DHIS2 as a reporting source without showing its data being analyzed or used."}]}, {"key": "rafael-145", "text": "Jobs-focused interventions contribute to the jobs agenda and have an explicitly stated and substantive link to creating more, better, and/or inclusive jobs. Beneficiaries include individuals, workers, microenterprises, SMEs, and so on. The indicator captures the number of beneficiaries supported through the following job-focused interventions of the project: productive infrastructure (1.1), matching grants (1.2), supplier development/linkage programs (1.3), and weather-index insurance program and PPCG fund (2.2).|\n|Responsibility for Data Collection PPCG Fund Manager, implementing partners, and productive infrastructure providers. Data will be aggregated by the PIU.|Responsibility for Data Collection PPCG Fund Manager, implementing partners, and productive infrastructure providers. Data will be aggregated by the PIU.|\n|**Beneficiaries of job-focused interventions - women (Number)CRI**|**Beneficiaries of job-focused interventions - women (Number)CRI**|\n|Description Number of female beneficiaries reached by interventions that contribute to the jobs agenda in operations supported by the World Bank.|Description Number of female beneficiaries reached by interventions that contribute to the jobs agenda in operations supported by the World Bank.|\n|Frequency Semi-annually|Frequency Semi-annually|\n|Data Source|Implementing partners and project records: Consolidation of project records from PPCG Fund Manager, implementing|\n\n\n\n~~Page 35 of 55~~", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000004:38:4:0", "start": 1369, "end": 1410, "surface": "Implementing partners and project records", "probe_tag": "drop", "probe_score": 0.0232, "luna_label": 0, "luna_reason": "Project records are routine administrative monitoring paperwork, not external data reuse."}]}, {"key": "rafael-146", "text": "**The World Bank**\nUganda Development Response to Displacement Impacts Project Phase II\n(P510476)\n\n\n\nPROJECT APPRAISAL\n\nDOCUMENT\n\n\n\n\n\n\n\n\n\n|Frequency|Quarterly|\n|---|---|\n|Data source|Project MIS and Project Progress Reports|\n|Methodology for Data Collection|Monitoring project implementation, including through regular phone surveys to project-supported groups and monitoring by project-trained Community Resource Persons|\n|Responsibility for Data Collection|IA |\n|**Project-supported institutions that access formal finance (Number)**|**Project-supported institutions that access formal finance (Number)**|\n|Description|Quantitative indicator on project supported institutions under component 3 that successfully access formal finance. This would be an indication of advancement and sustainability.|\n|Frequency|Quarterly|\n|Data source|Project MIS and Project Progress Reports|\n|Methodology for Data Collection|Monitoring project implementation.|\n|Responsibility for Data Collection|IA|\n|**Project Management, Accountability Systems and Coordination**|**Project Management, Accountability Systems and Coordination**|\n|**Complaints received through the grievance redress mechanism that are resolved (Percentage)**|**Complaints received through the grievance redress mechanism that are resolved (Percentage)**|\n|Description|Quantitative indicator counting number of grievances registered, addressed and resolved.|\n|Frequency|Quarterly|\n|Data source|Project GRM|\n|Methodology for Data Collection|Monitoring of grievances addressed project GRM|\n|Responsibility for Data Collection|AI, including Inspectorate of Government|\n|**Actions identified in the Community Score Card that have been addressed by the project (Percentage)**", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000186:48:0:0", "start": 183, "end": 223, "surface": "Project MIS and Project Progress Reports", "probe_tag": "confusion", "probe_score": 0.2259, "luna_label": 0, "luna_reason": "Project monitoring source cell inside an M&E table"}, {"key": "refugee_pads:000186:48:0:1", "start": 1468, "end": 1479, "surface": "Project GRM", "probe_tag": "drop", "probe_score": 0.0329, "luna_label": 0, "luna_reason": "Project grievance mechanism is named without showing actual data use."}]}, {"key": "rafael-147", "text": "OPRC Operational Procurement Review Committee\nPAD Project Appraisal Document\nPDO Project Development Objective\nPFS Project Financial Statement(s)\nPIU Project Implementation Unit\nPLM Person with Limited Mobility\nPIM Project Implementation Manual\nPPP Public-Private Partnership\nPPSD Project Procurement Strategy Development\nQCBS Quality- and Cost-Based Selection\nRAP Resettlement Action Plan\nROW Right-of-Way\nRPA Regional Procurement Adviser\nRPTA Railways and Public Transport Authority\nSOE Statement of Expenditure\nSPS Stated Preference Survey(s)\nSTEP Systematic Tracking of Exchanges in Procurement\nUIFR Unaudited Interim Financial Report\nUN United Nations\nUTDP Urban Transport Development Project\nVAT Value Added Tax\nWA Withdrawal Application", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000074:3:0:0", "start": 514, "end": 542, "surface": "SPS Stated Preference Survey", "probe_tag": "drop", "probe_score": 0.0472, "luna_label": 0, "luna_reason": "Glossary definition only; no survey data are cited, analyzed, or used."}]}, {"key": "rafael-148", "text": "**The World Bank**\nEmergency Food Security Project (P178936)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPage 5 of 54", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000078:9:0:0", "start": 19, "end": 50, "surface": "Emergency Food Security Project", "probe_tag": "drop", "probe_score": 0.0215, "luna_label": 0, "luna_reason": "Project title only; it does not cite or use an existing data resource."}]}, {"key": "rafael-149", "text": "br>beneficiaries affected by climate change to existing social programs|**Sub-component 3.2:** **Development of a** **National** **Social** **Registry** **(US$2** **million)** Registration of beneficiaries and referral to social programs - The Social Registry will collect socio-economic data from beneficiaries that can help determine households’ vulnerability to climate change based on their own indicators but also on the geographical location where they live - The Social Registry will have the capacity to refer beneficiaries affected by climate change to existing social programs|**Sub-component 3.2:** **Development of a** **National** **Social** **Registry** **(US$2** **million)** Registration of beneficiaries and referral to social programs - The Social Registry will collect socio-economic data from beneficiaries that can help determine households’ vulnerability to climate change based on their own indicators but also on the geographical location where they live - The Social Registry will have the capacity to refer beneficiaries affected by climate change to existing social<br", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000130:88:3:0", "start": 324, "end": 343, "surface": "socio-economic data", "probe_tag": "confusion", "probe_score": 0.0767, "luna_label": 0, "luna_reason": "The registry will collect this data, making it future project-produced information."}]}, {"key": "rafael-150", "text": "**The World Bank**\nSouth Sudan Health Sector Transformation Project (HSTP) (P181385)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Col1|age-specific mortality rates of that period, expressed per 1000 live births|\n|---|---|\n|Frequency|Annually|\n|Data source|Survey|\n|Methodology for Data Collection|Survey|\n|Responsibility for Data Collection|Third Party Monitor / PMU|\n|**Under ive years’ mortality rate (per 1000 live births) for refugees**|**Under ive years’ mortality rate (per 1000 live births) for refugees**|\n|Description|The probability of a child born in a specific year or period dying before reaching the age of 5 years, if subject to age-specific mortality rates of that period, expressed per 1000 live births|\n|Frequency|Annually|\n|Data source|Survey|\n|Methodology for Data Collection|Survey|\n|Responsibility for Data Collection|Third Party Monitor / PMU|\n|** ntermittent prevention o malaria during pregnancy ( p≥3)**|** ntermittent prevention o malaria during pregnancy ( p≥3)**|\n|Description|Percentage of women who received three or more doses of intermittent preventive treatment during antenatal care visits during their last pregnancy|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data Collection|DHIS2|\n|Responsibility for Data Collection|MoH and UNICEF; Measures subcomponent 1.1 Under UNICEF|\n|**Maternal mortality ratio**|**Maternal mortality ratio**|\n|Description|Number of maternal deaths from any cause related to or aggravated by pregnancy or its management (excluding", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000006:51:0:1", "start": 1196, "end": 1201, "surface": "DHIS2", "probe_tag": "drop", "probe_score": 0.0293, "luna_label": 0, "luna_reason": "Names DHIS2 as a data source without showing its data being used."}]}, {"key": "rafael-151", "text": " capita -O 2 -0 5 Lower-middle-income _group_\nESports of goods and servic\"s\n\n\n**STRUCTURE ofthe ECONOMY**\n\n**1979** **1989** **1998** **1999** **Growth rates of output and Investment ()**\n_{%I ol GOP)_\nAgriculture 3.4 _.._ **_2._**\nIndustry 21.0 O.Manufacturing 5.6 **_-2_** **_94_** _as_ _se_ _s_\nServices **75.6** 6\n\nPrivate consumption **-r.**\nGeneral government consumplion **G**\nImports of goods and services\n\n\n\n**1979-89** **1989-99** **1998** **1999**\n_(average annual orowth)_\nAgriculture\n\n\n\nIndustry\n\n\n\nManufacturing\nServices\n\n\n\nPrivate consumption\nGenerai government consumption\nGross domestic investment\nImports of goods and services\nGross national product 1 7 **1.4**\n\n\nNote. 1999 data are preliminary estimates.\nThis table was produced from the Development Economics central database.\n\nThe diamonds show four kev midicators in the country (in bold) compared with its income-group average. 11 data are missing, Ihe diamond will", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000170:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "drop", "probe_score": 0.0227, "luna_label": 0, "luna_reason": "Bare date-qualified data phrase without an identified source or substantive finding."}]}, {"key": "rafael-152", "text": "PF); (vii) monitoring and evaluation arrangements; (viii) communications\narrangements; and (ix) the composition and terms of reference (ToR) of the project steering committee, a role which is\nexpected to be assumed by the existing PNSF Steering Committee. This committee is chaired by the Secretary of State\nfor Social Affairs and composed of representatives from various relevant stakeholders. The project steering committee\nis expected to approve the project’s annual work plans and budgets and ensure coordination with technical ministries\nand other donors.\n\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n58. The results monitoring framework assesses progress towards the PDO through key indicators, focusing on\nproviding targeted cash transfers to poor households and supporting access to community-level interventions that\nimprove human capital. In addition, intermediate indicators will be used to monitor the progress of each component over\nthe life of the project. The SEAS will collect data for the activities they will implement. SEAS will be responsible for\naggregating results data and preparing periodic reports on results as specified in the Financing Agreement (FA).\nMonitoring will occur at each stage of project implementation to identify arising problems and issues and to promptly\nconsider and adopt corrective measures.\n\n59. The project will conduct a mid-term review and several evaluations to gauge progress towards the PDO, to assess\nthe impact of the project on the beneficiaries, the quality of the works carried out, as well as overall project efficiency.\nFor component 1, these evaluations will include a process evaluation and a targeting assessment to evaluate the accuracy\nof safety net targeting procedures. For component 3, evaluations will include technical audits of infrastructures built (at\nmid-term and end of project) and audits of adherence to environmental and social safeguards (at mid-term and end of\nproject). Beneficiary satisfaction surveys will also be conducted.\n\n\nPage 20 of 44", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000063:24:1:0", "start": 1091, "end": 1103, "surface": "results data", "probe_tag": "drop", "probe_score": 0.0422, "luna_label": 0, "luna_reason": "Planned project monitoring data aggregation and periodic reporting, not existing external data use."}, {"key": "refugee_pads:000063:24:1:1", "start": 1963, "end": 1995, "surface": "Beneficiary satisfaction surveys", "probe_tag": "confusion", "probe_score": 0.1567, "luna_label": 0, "luna_reason": "Surveys are planned to be conducted by the project."}]}, {"key": "rafael-153", "text": "|Col1|ABBREVIATIONS AND ACRONYMS|\n|---|---|\n|AIP|Annual Investment Plan|\n|ACLED|Armed Conflict Location and Event Data|\n|AERMW|Agency for the Execution of Road Maintenance Works|\n|CBO|Community-based Organizations|\n|CEC|Citizen Engagement Committee|\n|CEDP|Casamance Economic Development Project|\n|CERC|Contingent Emergency Response Component|\n|COVID-19|Coronavirus Disease|\n|CPF|Country Partnership Framework|\n|CSO|Civil Society Organization|\n|DDP|Department Development Plan|\n|DFIL|Disbursement Financial Information Letter|\n|ESCP|Environmental and Social Commitment Plan|\n|ESRS|Environmental and Social Review Summary|\n|ESP|_Emerging Senegal Plan (Plan Senegal Emergent)_|\n|ESMF|Environmental and Social Management Framework|\n|ESMP|Environmental and Social Management Plan|\n|FCV|Fragility Conflict and Violence|\n|FI|Financial Intermediaries|\n|FM|Financial Management|\n|FP|Facilitating Partners|\n|FY|Fiscal Year|\n|GBV|Gender-based Violence|\n|GDP|Gross Domestic Product|\n|GII|Gender Inequality Index|\n|GoS|Government of Senegal|\n|GRM|Grievance Redress Mechanism|\n|IFC|International Finance Corporation|\n|IFR|Interim Financial Report|\n|IPF|Investment Project Financing|\n|IRR|Internal Rate of Return|\n|LDP|Local Development Plan|\n|LIW|Labor Intensive Work|\n|LMIC|Lower Middle-income Country|\n|LMP|Labor Management Procedure|\n|M&E|Monitoring and Evaluation|\n|MIS|Management Information System|\n|MPI|Multi-dimensional Poverty Index|\n|MTR|Mid-term Review|\n|NGO|Non-governmental", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000116:2:0:0", "start": 80, "end": 118, "surface": "Armed Conflict Location and Event Data", "probe_tag": "drop", "probe_score": 0.0332, "luna_label": 0, "luna_reason": "Glossary definition names ACLED without showing its data being used."}]}, {"key": "rafael-154", "text": "13\n\n\n**_Monitoring and Evaluation_**\n\n\nMonitoring will be done according to the development indicators given in the attachment to Annex 1.\nThe project will strengthen the capacity of CNOSEGE, and the Planning Unit of the Ministry so that\nmonitoring reports on the implementation of the reform can include key progress and impact\nindicators. Currently the Planning unit generates statistical data on all aspects of the education sector,\nhowever this can be further strengthened to monitor progress on key reform objectives such as access,\nequity and quality. In addition, during the donors round-table UNESCO offered support to develop an\nEducation Management Information System (EMIS). If this is not in place by the end of Phase I of the\nAPL, this would be a priority item for Phase II.\n\n\nEvaluation of the impact of the reforms will be done by CNOSEGE by recruiting experts in this field\n\nand an initial evaluation will be done at the end of Phase I. Particular areas of impact assessment will\nbe student performance and success in reaching out to disadvantaged groups. Normally, student\nperformance would be measured by overall test results but as the pool of students widens to include\nstudents from less advantaged socioeconomic groups, there will be a downward pressure on test\nscores. The Planning Unit of the Ministry will be strengthened to monitor progress in reaching out to\ndisadvantaged groups and test scores of students by socioeconomic background. Staff will carry out a\nrandom survey (5 to 10% sample) of students by socioeconomic background in 2001 to establish a\nbaseline. To keep the survey simple, the socioeconomic background questions will be limited to easily\nidentified categories such as day-laborers, civil servants, shopkeepers etc. The survey will be repeated\nin 2005 and 2110.\n\n\n**D.** PROJECT RATIONALE\n\n\n**1. Project alternatives considered and reasons for rejection**\n\nOriginally, the project was designed as a Sector Investment Loan, however, given the Government's\ncommitment to the education sector, and the", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000029:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "drop", "probe_score": 0.0402, "luna_label": 0, "luna_reason": "The Planning unit currently generates this data, so it is project-produced rather than reused."}]}, {"key": "rafael-155", "text": "**PROTECTION BRIEF III SLOVAKIA**\n\n**July 2023 – March 2024**\n\n\nsubsidies, and only newly arrived refugees and those with certain vulnerabilities are targeted by the\nsupport. 32\n\n#### Inclusion in the Social Protection System\n\n\nThe Government of Slovakia has enabled Temporary Protection holders to access significant portions of\nthe public social protection system on par with Slovak nationals. Notably, refugees can avail themselves\nof numerous State-provided social protection benefits, primarily channeled through the Offices of\nLabour, Social Affairs and Family. A key instrument is the Material Need Assistance, 33 designed for\nindividuals whose household income falls below the national subsistence minimum threshold. 34 This\nassistance is particularly relevant for older persons, single mothers with children, persons with disabilities\nor serious medical condition, or newly arrived refugees. According to official data, more than 12,000\nrefugees received the Material Need Assistance in January 2024. 35 Another vital social protection\ninstrument is the Disability Allowance for Refugees, 36 available to refugees with two different degrees\nof severe disability. The Offices of Labour, Social Affairs and Family conduct individual assessments to\ndetermine eligibility and the amount of the assistance. According to official data, more than 1,400\nrefugees received the Disability Allowance for Refugees in September 2023. 37 Other important\ninstruments available to Temporary Protection holders include the Childcare Allowance, 38 Subsidy to\nSupport Child’s Nutrition Habits, 39 Subsidy to Support Child’s Education, 40 and the Substitute Care\nAllowance. 41 Moreover, the State-supported accommodation system for refugees", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000476:4:0:0", "start": 947, "end": 960, "surface": "official data", "probe_tag": "keep", "probe_score": 0.9232, "luna_label": 1, "luna_reason": "Official data supports the reported number of refugees receiving assistance."}]}, {"key": "rafael-156", "text": "**2007 Global Trends**\n\n\nUNHCR assistance departed from Thailand (14,600), Kenya (6,500), the United Republic of\nTanzania (6,200), Malaysia (5,600), and Turkey (2,700). These five UNHCR offices together\naccounted for 7 out of every 10 UNHCR-assisted resettlement departures in 2007.\n\nA total of 75,300 refugees were admitted by 14\nresettlement countries, including the United\nStates of America (48,300) 20 [^20: Resettlement statistics for the United States of America may also include persons resettled for the purpose of family\nreunification.], Canada (11,200),\nAustralia (9,600), Sweden (1,800), Norway\n(1,100), and New Zealand (740). Overall, this\nwas 5 per cent above the total for 2006 (71,700).\nOver the last few years, States in Latin America\nhave emerged as new resettlement countries,\nalbeit at a lower scale, offering a durable\nsolution for refugees primarily from Colombia.\n\n\n_A US-bound refugee from Bhutan bids her friends and relatives_\n\nLocal integration _goodbye in eastern Nepal’s Sanischare camp. UNHCR/ V. Tan_\n\nWhile the degree and nature of local integration is difficult to measure in quantitative terms,\nsome countries document the acquisition of nationality, the final and crucial step towards\nobtaining the full protection of the asylum country. Even in those cases where refugees acquire\nthe citizenship through naturalization, statistical data is usually very limited as the countries\nconcerned generally do not distinguish between refugees and others who have been naturalized.\nMoreover, national laws in many countries do not permit refugees to become naturalized. The\nnaturalization of refugees is both restricted and under-reported.\n\nThe limited data on naturalization of refugees available to UNHCR show that during the past\ndecade, more than 1 million refugees were granted citizenship by their asylum country. The", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000651:10:0:0", "start": 430, "end": 486, "surface": "Resettlement statistics for the United States of America", "probe_tag": "keep", "probe_score": 0.9073, "luna_label": 1, "luna_reason": "Statistics qualify the reported U.S. refugee resettlement figure."}]}, {"key": "rafael-157", "text": "*\n\n\n\n**Housing (dwelling, sharing, evictions, rent**\n**costs)**\n\n\n\n**Livelihoods strategies (income sources,**\n**expenditure, debts, coping strategies, assets)**\n\n\n\n**Mobility (migration history, future**\n**intentions, return)**\n\n\n\nFor the area-based analysis, these areas are\ngrouped into three different geographical strata\nthat are analysed comparatively (a description\nof each area’s characteristics is provided in the\nfollowing section):\n\n\n\n**The Sulaymaniyah District Centre, being the**\n**district hosting the largest number of IDPs and**\n**refugees;**\n\n\n\n**Periphery district centres, which encompass**\n**all the district centres surrounding the**\n**Sulaymaniyah centre with a relevant population**\n**of IDPs or refugees;**\n\n\n\nThe sample drawn from each of the targeted\nsubdistricts was proportionate to the size of\neach population group in that subdistrict (Table\n1). Population figures for the host community\nand IDPs were facilitated by the Sulaymaniyah\nStatistics Office based on an internal census\ncarried out in 2015, which included IDPs pre- and\npost-2014; figures for refugees were facilitated by\nUNHCR. Population weights were subsequently\napplied during the analysis in order to obtain\nresults applicable to all urban areas at the\ngovernorate level.\n\n\nThe sample size used allows for an extrapolation\nof statistically significant results with a 5% margin\nof error for each geographical stratum, except\nfor the Kalar and Kifri segment (results are\nsignificant with a 10% margin of error due to a\nsmaller sample size available). The results are also\nrepresentative for each population group with a\n5% margin of error.\n\n\n\n**Kalar and Kifri district centres, which**\n**are areas that hold special relevance for the**\n**humanitarian partners", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000440:8:1:0", "start": 995, "end": 1010, "surface": "internal census", "probe_tag": "keep", "probe_score": 0.9192, "luna_label": 1, "luna_reason": "Existing 2015 census supplied population figures used for sampling and analysis."}]}, {"key": "rafael-158", "text": "a\n\ninstancia”. Las 189.000 solicitudes restantes se cursaron en segunda instancia,\n\n\n\nincluidas las presentadas ante tribunales y otros órganos de apelación.\n\n\n\n**(36)**\n\nincluidas las presentadas ante tribunales y otros órganos de apelación.\n\n\nl deterioro de la situación humanitaria en manera justa o eficiente, ACNUR puede realizar la\nvarios países a lo largo del año se refleja determinación de la condición de refugiado en virtud\nclaramente en los datos estadísticos sobre de su mandato. En los últimos años, ACNUR ha\nlas personas que presentan solicitudes de registrado un aumento del número de solicitudes\nasilo durante el periodo abarcado en este de asilo individuales, pero el máximo histórico se\ninforme. En 2014 se presentaron ante los Estados o alcanzó en 2014, año en el que la organización registró\n# **E**\n\n\n\nl deterioro de la situación humanitaria en\nvarios países a lo largo del año se refleja\nclaramente en los datos estadísticos sobre\nlas personas que presentan solicitudes de\nasilo durante el periodo abarcado en este\ninforme. En 2014 se presentaron ante los Estados o\n# **E**\nante ACNUR más de 1,66 millones de solicitudes\nindividuales de asilo o de la condición de refugiado\nen 157 países o territorios, el nivel más alto del que\nse tiene constancia. Aunque la cifra provisional\nde 2014 representó un aumento del 54% de las\nsolicitudes de asilo en el mundo con respecto a\n2013 (1,08 millones)", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000206:26:1:0", "start": 453, "end": 471, "surface": "datos estadísticos", "probe_tag": "keep", "probe_score": 0.9089, "luna_label": 1, "luna_reason": "Statistical data support concrete asylum-application figures and year-on-year comparison."}]}, {"key": "rafael-159", "text": "% es rural\n(Ortega y Ospina, 2012: 39). Aquí es importante tener en cuenta que si bien el conflicto armado\nha golpeado fuertemente a las zonas rurales del país, actualmente ciudades de la periferia como\nTumaco se encuentran gravemente amenazadas. En este mismo sentido es importante tener en cuenta\nque a pesar de que se reconozca a ciudades como Tumaco y Puerto Asís como zonas urbanas estas\ntienen características muy diferentes a las ciudades del interior, los procesos de descentralización\nde las últimas décadas si bien han dado mayor presencia a las municipalidades, los procesos de\nurbanización y desarrollo económico de estas son todavía insipientes.\n#### **1.2 Crecimiento progresivo del refugio en los últimos años**\n\nDe manera coincidente con estudios como el de Quito y de Guayaquil vemos que el ingreso de la\npoblación colombiana refugiada hacia Ecuador ha aumentado progresivamente.\n\n\nDe 420 casos encuestados, el 17,6% están en Ecuador desde el 2011; entre el 2008-2010\ningresaron el 43,6%; entre el 2000 y el 2007 ingresaron el 28,6%; las personas que ingresaron\nhasta 1999 representan el 3,9%, y para marzo del 2012 fecha en que fue levantada la encuesta se\nencontraron 25 casos de personas que recientemente entraron al Ecuador (ver gráfico 2).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n(54,3%) seguido de Nariño con el 12,1%\ny Caquetá 9,3%.\n\nEstos resultados indican que la pro", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000895:6:2:0", "start": 1163, "end": 1171, "surface": "encuesta", "probe_tag": "keep", "probe_score": 0.909, "luna_label": 1, "luna_reason": "Encuesta de 2012 sustenta los porcentajes y casos reportados."}]}, {"key": "rafael-160", "text": "To estimate the causal effect of refugee arrivals on host communities’ attitudes\n\ntowards immigrants and income, the authors: (a) compare attitudes and income at the\n\nregional level four years before and four years after large, sudden arrivals of refugees;\n\n(b) compare regions that experienced large increases in refugees with those that did\n\nnot within the same country; and (c) compare effects across different hosting situations\n\n(i.e., by employment and encampment policies).\n\n\nThe analysis is based on data covering the period from 2005 to 2018, including: (i)\n\ndata on attitudes and income from Gallup World Poll (GWP) and 12 additional public\n\nopinion surveys; (ii) refugee populations at the sub-national level from UNHCR; and\n\n(iii) data on policies on camps from UNHCR and on _de jure_ access to the labor market\n\n[from Blair et al. (2021).](https://doi.org/10.1017/S0020818321000369)\n\n\nPreliminary findings:\n\n - **Across all regions, large and sudden arrivals of refugees do not have a**\n\n**negative effect on average attitudes towards immigrants or income.** On\n\naverage, there is little effect in the periods immediately following a large wave of\n\nrefugees across affected regions in lower- and middle-income countries.\n\n - **There is little evidence that attitudes towards immigrants or income vary**\n\n**across camp and non-camp settings or across environments with progressive**\n\n**and restrictive labor market policies.**\n\n\nThe authors conclude that, “while restrictive policies are often justified to benefit host\n\ncommunities, there is little evidence to support the argument.”\n\n\n**The Economic Effects of Immigration Pardons: Evidence from**\n**Venezuelan Entrepreneurs**\n\n\n_Dany Bahar, Bo Cowgill, and Jorge Guzman (2022)_\n_Working Paper_\n_[http://dx", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001300:20:0:2", "start": 743, "end": 779, "surface": "data on policies on camps from UNHCR", "probe_tag": "keep", "probe_score": 0.9471, "luna_label": 1, "luna_reason": "UNHCR data on camp policies informs analysis of refugee-hosting situations."}]}, {"key": "rafael-161", "text": "MONITOREO DE PROTECCIÓN\n\n\n##### **6. Acceso a Documentación**\n\n\n\n76%\n\n\n\n\n\n\n\nSolicitante de la\n\ncondición de\n\nrefugiado\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRefugiado\nreconocido\n\n\n\nSolicitud ha sido\n\nrechazada\n\n\n\nRetiró solicitud Intenciones de\nsolicitar refugio\n\nen Panamá\n\n\n\nIntenciones de\nsolicitar refugio\n\nen otro país\n\n\n\nNo tiene\nintenciones de\nsolicitar refugio /\n\n\n\nNo sabe / No ha\n\n\n\n_Fuente: PMT, HFS1, HFS2, HFS3_ decidido*\n_*Sólo se preguntó \"No sabe /no ha decidido solicitar refugio\" en PMT 2%. Se agrega a 2% \"No tiene intenciones de solicitar refugio.\"_\n\n\n\ndecidido*\n\n\n\nUNHCR / Febrero 2022 23", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001181:21:0:0", "start": 383, "end": 386, "surface": "PMT", "probe_tag": "confusion", "probe_score": 0.3196, "luna_label": 1, "luna_reason": "Source-line cites PMT as the resource underlying presented protection-monitoring figures."}, {"key": "reliefweb:001181:21:0:2", "start": 394, "end": 398, "surface": "HFS2", "probe_tag": "confusion", "probe_score": 0.4818, "luna_label": 1, "luna_reason": "Named source cited for the table’s documented protection-access finding."}]}, {"key": "rafael-162", "text": " Pas de contre-vérification ni de triangulation parmi les différentes sources concernant le même thème. Exécution d’une analyse quantitative des données qualitatives. eux de disposer de plus de données. Pas de prise en compte de la limite de saturation alors que les données supplémentaires ne fournissent pas de nouvelles informations ni de différents types d’informations.|\n|Les notes prises sur le terrain sont faciles à analyser. Le guide d’interview doit être respecté à la lettre. Il est (toujours) mieux de disposer de plus d’informateurs. |Les notes prises sur le terrain sont faciles à analyser. Le guide d’interview doit être respecté à la lettre. Il est (toujours) mieux de disposer de plus d’informateurs. |Les notes prises sur le terrain sont faciles à analyser. Le guide d’interview doit être respecté à la lettre. Il est (toujours) mieux de disposer de plus d’informateurs. |Pas d’organisation chronologique ni d’organisation par mots clés le jour même de leur rédaction. Pas d’adaptation des questions en fonction des situations et des individus. Pas de sélection des informateurs les plus instruits, des informateurs les plus au courant des probl", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001477:23:1:0", "start": 154, "end": 174, "surface": "données qualitatives", "probe_tag": "confusion", "probe_score": 0.0908, "luna_label": 1, "luna_reason": "Qualitative data are explicitly analyzed, indicating existing data use."}]}, {"key": "rafael-163", "text": "### RECOMMENDATIONS\n\n**The following recommendations are based on the findings from this study, including suggestions made by the**\n**UNHCR offices themselves in the online survey.**\n\n\n\nInstitutional Level\n\n\nĐ CwC activities need to be scaled up and\n\n**mainstreamed** at the corporate level to go beyond\nthe current random approach that is based on the\nknow-how and commitment of individual staff\nmembers. UNHCR needs a CwC policy that prescribes\nobligatory measures and secures adequate resources.\nA feedback mechanism to measure the impact of\nCwC activities should also be created.\n\n\nĐ Communicating with audiences spread thousands\n\nof kilometres apart, from countries of origin to\ndiaspora communities in Europe, is a highly complex\nform of communication. UNHCR as an organisation\nhas considerable experience in CwC, but it is\ndistributed erratically. The organisation therefore\nneeds to **build staff capacities** as widely as possible,\nin all operations and at all levels. The best way to\ndo so is to develop an **advanced programme for**\n**CwC trainers** who would instruct and guide less\nexperienced staff members, thus evening out the\ninconsistencies between offices.\n\n\nCentral Mediterranean Route\n\n\nĐ Current UNHCR and TRS **awareness activities** are\n\n**no match for the flood of disinformation** PoC\nare exposed to. In order to enable PoC to make\ninformed decisions, UNHCR needs to **expand the**\n**TRS approach** . The characteristics of the TRS\napproach include engagement with the communities\nin the countries of origin and the diaspora, the\nuse of testimonials from community members,\nand consistent messaging through a multitude of\nchannels. TRS-styled campaigns should be developed\nfor all major groups using the Central Mediterranean\nRoute.\n\n\nĐ As digital platforms are accessible across borders,\n\nUNHCR offices can create synergies by **using**\n**the same platforms along the entire Central**\n**Mediterranean Route** .", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001467:7:0:0", "start": 166, "end": 179, "surface": "online survey", "probe_tag": "confusion", "probe_score": 0.567, "luna_label": 1, "luna_reason": "Existing online survey findings inform the study's recommendations."}]}, {"key": "rafael-164", "text": "1\nOVERVIEW\nThis report is the eleventh in the series of Protection \nTrends reports prepared by the South Sudan Protection \nCluster, with inputs from Child Protection, Sexual and \nGender Based Violence (SGBV), and Mine Action \nsub-clusters.1 This paper is a departure from previous \nTrends reports, instead providing an overview of the \nprotection trends for the entire year. The paper provides \nan overview of the protection situation followed by a \ndiscussion of trends based on data collected during \nreporting period for general protection trends, child \nprotection, GBV, and mine action. This includes an \noverview of the context, access to basic services, forced \ndisplacement and population movement patterns, \nfamily tracing and reunification, grave violations of child \nrights, sexual and gender based violence (SGBV), and \nexplosive hazards. \nThis report is not an exhaustive overview of the context \nin South Sudan in 2017, rather it highlights trends \nand observations of the serious protection trends \nimpacting the civilian population in South Sudan to \ninform the response of both humanitarian and political \nactors. For more detail on protection trends relating \nto specific periods, please refer to previous quarterly \ntrends reports. To contextualize the reporting period, \nthe paper uses data going back to 2013, to understand \nthe progressive impact of conflict and insecurity on \nprotection concerns. The information presented in \nthe report is based on a broad range of sources. The \noperational environment currently limits the availability \nof data on some key protection concerns. Furthermore, \nlack of humanitarian presence and reliance on remote \ndata collection for some locations present major \nchallenges in providing an accurate and comprehensive \ndepiction of the protection situation in some areas.\nCONTEXT OVERVIEW\nThe continued armed conflict in South Sudan in 2017 \nhas dire consequences for civilians resulting in civilian \ndeaths, separation, and conflict related sexual \nviolence. These issues are compounded by growing \nfood insecurity, limited basic services, widespread \nhealth issues, and exacerbated by heavy rains and \nflooding. There was little improvement in the overall \nprotection environment and civilians continued to \nflee from active conflict and reports of fighting across", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001475:1:0:0", "start": 480, "end": 519, "surface": "data collected during \nreporting period", "probe_tag": "confusion", "probe_score": 0.2496, "luna_label": 1, "luna_reason": "Existing reporting-period data supports discussion of protection trends."}, {"key": "reliefweb:001475:1:0:1", "start": 1306, "end": 1329, "surface": "data going back to 2013", "probe_tag": "confusion", "probe_score": 0.2132, "luna_label": 1, "luna_reason": "Existing historical data is used to contextualize conflict impacts."}]}, {"key": "rafael-165", "text": "social and cultural importance of such locations, contrasted with primary and capital cities. Secondary cities can\nbe intermediary cities and vice versa. This study uses all of these terms but seeks to do so in their appropriate\ncontexts, linking them to their usage by stakeholders. When presenting data from secondary research or\ninterviews, it adopts these terms as used by their original source.\n\n\nThe term “ **urban initiative** ” is used to refer to the full range of urban programmes and activities, implemented\nby protection stakeholders in collaboration with cities and municipalities. Urban initiatives can be international\nand involve multiple partners, as is the case with International Centre for Migration Policy Development’s\n(ICMPD) MC2CM project, implemented in partnership with UCLG and UN-Habitat. At the same time, initiatives\ncan also describe single programme activities, for instance a counselling service run by a municipality with\ntechnical inputs from an external stakeholder such as UNHCR.\n\n\n[UNHCR defines the](https://www.unhcr.org/glossary) **protection of vulnerable people on the move** as “all activities aimed at achieving\nfull respect for the rights of the individual in accordance with the letter and spirit of international human\nrights, refugee and humanitarian law. Protection involves creating an environment conducive to respect for\nhuman beings, preventing and/or alleviating the immediate effects of a specific pattern of abuse, and restoring\ndignified conditions of life through reparation, restitution and rehabilitation.” Targeting **vulnerable migrant**\n**populations** [, the International Organization for Migration’s (IOM’s)](https://www.iom.int/protection) **protection interventions** include, amongst\nothers, “child protection, risk mitigation, response to and prevention of gender-based violence, countertrafficking, alternatives to detention, mental health and psychosocial support, land property and reparations and\n[inclusion of", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001416:3:0:0", "start": 300, "end": 342, "surface": "data from secondary research or\ninterviews", "probe_tag": "confusion", "probe_score": 0.0554, "luna_label": 0, "luna_reason": "Generic data source is named without an attributed finding or concrete claim."}]}, {"key": "rafael-166", "text": "**22** UNHCR Mid-Year Trends 2014", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000565:21:0:0", "start": 7, "end": 33, "surface": "UNHCR Mid-Year Trends 2014", "probe_tag": "confusion", "probe_score": 0.327, "luna_label": 0, "luna_reason": "Standalone report title with no shown data use or cited finding."}]}, {"key": "rafael-167", "text": "%**~~ ~~**1%**~~|\n\n\n\nDISCLAIMER: figures do not add up to 100 per cent due to rounding.\n\n\n**24** [The international migrant dataset includes refugees and asylum-seekers in specific countries. Source: https://www.un.org/en/development/](https://www.un.org/en/development/desa/population/migration/data/estimates2/data/UN_MigrantStockByAgeAndSex_2019.xlsx)\n[desa/population/migration/data/estimates2/data/UN_MigrantStockByAgeAndSex_2019.xlsx](https://www.un.org/en/development/desa/population/migration/data/estimates2/data/UN_MigrantStockByAgeAndSex_2019.xlsx)\n**25** [Source: https://population.un.org/wpp2019/Download/Standard/Population/](https://population.un.org/wpp2019/Download/Standard/Population/)\n**26** Demographic data is available for 80 per cent of refugees globally. For the remainder, UNHCR has estimated the demographics based on the\nrefugee data available in host countries and other countries in the same region. This figure excludes Venezuelans displaced abroad.\n**27** To enable the comparison, data for the 5-19 and 20-24 age ranges in the world population and international migrant data was apportioned to\nUNHCR’s 5-11, 12-17 and 18-24 ranges.\n**28** [https://www.unhcr.org/steppingup/](https://www.unhcr.org/steppingup/)\n\n\nUNHCR > **GLOBAL", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001657:18:5:0", "start": 716, "end": 732, "surface": "Demographic data", "probe_tag": "confusion", "probe_score": 0.2601, "luna_label": 1, "luna_reason": "Generic demographic data supports the concrete 80-percent availability finding."}, {"key": "reliefweb:001657:18:5:1", "start": 853, "end": 865, "surface": "refugee data", "probe_tag": "confusion", "probe_score": 0.4284, "luna_label": 1, "luna_reason": "Existing refugee data underpins UNHCR demographic estimates for countries lacking complete data."}]}, {"key": "rafael-168", "text": " the Shona population report being able to\nread and write in at least one language, 93 percent of urban nationals can do so. Male Shonas report\na slightly higher literacy rate at 93 percent, compared to 85 percent of women. In contrast, urban\nnational rates are 95 percent and 91 percent for men and women, respectively. Considering the two\nofficial languages in Kenya, English and Swahili, more than 75 percent of the Shona community population can read and write in at least one of them: English (74 percent) and Swahili (87 percent).\n\n\n**The employment rate of the Shona community is higher than for urban nationals, although this does**\n**not translate into lower poverty rates; most of the employed Shona are self-employed.** Most of the\nworking-age Shona population is employed, which is slightly higher than the urban Kenyan average\n(73 percent vs. 69 percent). However, the Shona poverty rate remains higher than for nationals. Moreover, nearly 8 in 10 Shona employed are self-employed compared to only 3 in 10 among nationals.\nSuch a differing trend may be explained by the lack of identity documents needed to access the formal\nemployment market, which may force most of the Shona community to engage in self-employment to\nearn a living. Only 2 percent of the Shona report being unemployed, which is in line with the 4 percent\nof urban Kenyans, whereas 24 percent are outside the labor force (OLF) compared to the national\nurban average of 27 percent.\n\n\n**The findings from the Shona SES also reflect differences in intra-household bargaining power among**\n**men and women.** Overall, Shona community women are less likely to be involved in decision making,\nbe less educated, and have lower labor force participation. Women and girls in the Shona community\nhave lower overall educational attainment, attendance, and literacy rates than Shona men and boys.\nIn addition, when the two official languages in Kenya", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001424:12:1:0", "start": 1488, "end": 1497, "surface": "Shona SES", "probe_tag": "confusion", "probe_score": 0.7977, "luna_label": 1, "luna_reason": "Findings from the named Shona SES support household bargaining-power analysis."}]}, {"key": "rafael-169", "text": " Durante la mayor parte del\npasado decenio, las cifras de desplazamiento oscilaron\nentre 38 millones y 43 millones de personas al año.\nSin embargo, a partir de 2011, cuando era de 42,5\nmillones, la cifra ha aumentado hasta llegar a la actual\nde 59,5 millones, lo que representa un incremento\ndel 40% en tan solo tres años. Este crecimiento\nplantea dificultades para encontrar respuestas\nadecuadas a estas crisis, que de forma creciente son\ncausa del desplazamiento múltiple de personas o de\nmovimientos secundarios en busca de seguridad.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**(3)** Esta cifra incluye a 19,5 millones de refugiados: 14,4 millones bajo el mandato de ACNUR y 5,1 millones de refugiados palestinos inscritos en el Organismo de Obras\n\nPúblicas y Socorro de las Naciones Unidas para los Refugiados de Palestina en el Cercano Oriente (UNRWA). La cifra global también incluye a 38,2 millones de\ndesplazados internos (fuente: IDMC) y a casi 1,8 millones de personas cuyas solicitudes de asilo no se habían resuelto al final del periodo del informe.\n\n\n\n**(4)** Fuente para las poblaciones nacionales: Naciones Unidas, División de Población, Perspectivas de la población mundial: La revisión de 2012, Nueva York, 2013. A los fines\n\nde este análisis, se ha tenido en cuenta la población de variante media de fertilidad de 2014.\n\n\n\nACNUR Tendencias Globales 2014 **5**", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000206:4:2:0", "start": 915, "end": 919, "surface": "IDMC", "probe_tag": "confusion", "probe_score": 0.6815, "luna_label": 1, "luna_reason": "IDMC is cited as the source for the displaced-person population figure."}]}, {"key": "rafael-170", "text": "**Endnotes**\n\n1. These figures represent the total number of migrants and refugees who arrived in Italy and Malta through the Central Mediterranean Sea after departing\nfrom Algeria, Libya, and Tunisia in 2022. The numbers may include migrants and refugees who made more than one attempt to cross the sea from these\ncountries. These figures also include those who were intercepted or rescued by the authorities of Algeria, Libya and Tunisia and disembarked in these\ncountries in 2022.\n\n\nData on disembarkation in the three North African countries is sourced from a variety of channels, including official reports shared by national authorities,\nand media monitoring, and data gathered at the disembarkation points where UNHCR and IOM partners were present.\n\n\nData on arrivals in Italy and Malta is received from the Ministry of Interior of both countries.\n\n\n2. Central Mediterranean Route refers to sea departures from Algeria, Libya, and Tunisia, and to sea arrivals to Italy and Malta through the Mediterranean\nSea.\n\n\n3. Sources: UNHCR, IOM, UNDSS, Italian MoI, Official website of the Tunisian Ministry of Interior and the social media pages of the Tunisian Ministry of\nInterior and Tunisian National Guard, media and social media monitoring.\n\n\n4. Arrivals from Algeria were also recorded in Spain, but these fall outside the scope of this factsheet.\n\n\n5. See IOM [Missing Migrants Project and UNHCR](https://missingmigrants.iom.int/) [Dead and Missing at Sea dashboard to explore data. The numbers of dead and missing in this factsheet](https://data2.unhcr.org/en/dataviz/95?sv=0&geo=0&_gl=1*1ju2cm2*_rup_ga*MTQ0ODMwODQ4Ny4xNjgzNjQxNzc2*_rup_ga_EVDQT", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000791:5:0:1", "start": 758, "end": 793, "surface": "Data on arrivals in Italy and Malta", "probe_tag": "confusion", "probe_score": 0.8586, "luna_label": 1, "luna_reason": "Existing arrival data is sourced from both countries’ Ministries of Interior."}, {"key": "reliefweb:000791:5:0:2", "start": 1448, "end": 1481, "surface": "Dead and Missing at Sea dashboard", "probe_tag": "confusion", "probe_score": 0.8065, "luna_label": 1, "luna_reason": "Dashboard data underlies the factsheet’s reported dead and missing figures."}]}, {"key": "rafael-171", "text": "**PILLAR II. MULTISECTORAL**\n**RESPONSE**\n\n\nSince the development of the five-year strategy for the\neffective fight against SGBV, UNHCR and partners have\nexerted much effort in the camps despite the financial\nand material challenges as well as the lack of qualified\nmanpower. Still, there has been a clear improvement in\nthe quality and access to available services.\n\n\nin the treatment and handling of victims of sexual and\ngender-based violence UNHCR and partners in Chad\nimplemented a holistic and multisectoral approach. This\napproach consists of five concurrent responses: clinical\nmanagement of rape and other gender-based violence,\npsychosocial support, legal support, material support,\nsecurity and safety.\n\n\nIn 2016, some 78.9% of the victims received\npsychosocial support and treatment. In addition, 39.6%\nof victims had access to medical services, 30.1% were\nsupported materially, 6.4% legally, 26.6% with security\narrangements, 16.19% supported with incomegenerating activities and 1.2% had access to a safe\nenvironment.\n\n\nOverall, the statistical data analysis on access to and use\nof services available in 2016 indicates that psychosocial\ncare remains the most solicited service once again,\nfollowed by medical care.\n\n\n1. Access to medical services\n\n\nThe clinical management of SGBV cases in general and\nof rape in particular (as per the WHO standard\nprotocol) remains a major problem in Chad and by\nextension in the refugee population (lack of qualified\nmanpower, scarcity of inputs (pep kit), ignorance of the\nneed to arrive within 72 hours of an incident to a\nmedical structure on the part of the refugees, etc.)\n\n\nUNHCR, CNARR and humanitarian partner\norganizations elaborated SOPs for SGBV that also\naddress the challenges of medical management of\nSGBV in the camps. A free treatment service is\navailable in the health centers in refugee camps (trauma\ninjury management, STI treatment, pregnancy\nprevention, voluntary testing and pep", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001383:18:0:0", "start": 1047, "end": 1063, "surface": "statistical data", "probe_tag": "confusion", "probe_score": 0.1818, "luna_label": 1, "luna_reason": "Statistical data supports the 2016 analysis of service access and use."}]}, {"key": "rafael-172", "text": "|1969|\n|Republic of**KAZAKHSTAN**|6,121|Unknown|15,844|9|1999|1999|1995|\n|State of**KUWAIT**|398|-|1,519|157|-|-|1969|\n|**KYRGHYZ** Republic|3,292|Unknown|3,753|453|1996|1996|1992|\n\n\n222 United Nations Office of the High Commissioner for Refugees, 17 April 2006, (as of 1 January 2005) at www.unhcr.org.\n223 Internal Displacement Monitoring Centre (as of 24 July 2006) at www.globalidpproject.org.\n224 United Nations Office of the High Commissioner for Refugees, 17 April 2006, (as of 1 January 2005) at www.unhcr.org.\n225 United Nations Office of the High Commissioner for Refugees, 17 April 2006, (as of 1 January 2005) at www.unhcr.org.\n226 United Nations Office of the High Commissioner for Refugees, September 2005.\n227 European Union, 2002.\n228 Norwegian Refugee Council, December 2005.\n229 Norwegian Refugee Council, 2002.\n230 United Nations, March 2006.\n231 United Nations Humanitarian Coordinator, 27 April 2006.\n232 Norwegian Refugee Council, April 2002.\n233 Internal Displacement Monitoring Centre, July 2006.\n234 United Nations Assistance Mission for Iraq, June 2006 based on a 2001 UNHABITAT survey.", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000351:37:3:0", "start": 1090, "end": 1111, "surface": "2001 UNHABITAT survey", "probe_tag": "confusion", "probe_score": 0.5213, "luna_label": 1, "luna_reason": "Existing named survey provides the basis for the cited information."}]}, {"key": "rafael-173", "text": "Blueprint Initiative: Social protection systems for children in Libya Report – 2022\n\n\nFurthermore, some KIs from the MoSA, as well as social workers (15/53), reported that children of Libyan\nmothers and non-Libyan fathers could not be identified by this database. Indeed, these families must\nfirst be registered with the ‘Database for Foreigners’ within the CRA, which is meant to provide each\nfamily with a unique identification number. However, according to a senior KI at the Wife’s and Children’s\nGrant project, the database did not include any households at the time of data collection, reportedly\ndue to a lack of awareness among the population about the requirement to do so, as well as how to\nregister.\n\n\nThe SSolF’s system\n\n\nThe SSolF’s information management system appears to be widely paper-based. The majority of\nemployee KIs reported that the Information and Documentation Department within the SSolF is in\ncharge of storing beneficiaries' files at the municipal level (15/22). It was also reported that, although\ndigital databases exist (on Excel), most of the departments and offices work on hard copies. According\nto these KIs, this is mostly due to computer illiteracy of staff and a lack of funding to buy the necessary\ndevices, including computers. Moreover, KIs from offices in Sebha also reported frequent power cuts as\na reason for working with paper-based systems. As for the Emergency Assistance;", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001686:31:0:0", "start": 354, "end": 377, "surface": "Database for Foreigners", "probe_tag": "confusion", "probe_score": 0.2444, "luna_label": 1, "luna_reason": "Named database cited to explain identification failures and absence of registered households."}, {"key": "reliefweb:001686:31:0:1", "start": 1164, "end": 1181, "surface": "digital databases", "probe_tag": "confusion", "probe_score": 0.6863, "luna_label": 0, "luna_reason": "Names databases without showing their data informing analysis or decisions."}]}, {"key": "rafael-174", "text": "|Asylum Levels and Trends in Industrialized Countries 2007|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|**Table 6. Annual asylum applications lodged in industrialized countries by origin, 2007** Covering 29 major asylum countries which provided monthly data to UNHCR. Values between 1 and 4 have been replaced with an asterisk. See Annex I for country codes used. |**Table 6. Annual asylum applications lodged in industrialized countries by origin, 2007** Covering 29 major asylum countries which provided monthly data to UNHCR. Values between 1 and 4 have been replaced with an asterisk. See Annex I for country codes used. |**Table 6. Annual asylum applications lodged in industrialized countries by origin, 2007** Covering 29 major asylum countries which provided monthly data to UNHCR. Values between 1 and 4 have been replaced with an asterisk.<br", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001058:17:0:1", "start": 353, "end": 365, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.0846, "luna_label": 0, "luna_reason": "Fragment appears within a table caption and is not independently usable."}]}, {"key": "rafael-175", "text": " experience during the trainings. In some cases, this was a result of lack of access to\ncomputers or the internet during training sessions. In other cases, participants criticized the curriculum,\nwhich felt overly focused on theoretical principles, rather than real-life examples of how they may\napply their learnings.\n\n\n**Financial barriers and time restrictions prevent some from taking advantage of training**\n**opportunities.** Although many workshop participants shared their appreciation for comprehensive,\nlong-term training programs, they likewise noted that it can be difficult to balance these programs\nwith the ongoing need to earn money and support their families. This was especially pronounced in\nurban areas where the pressures of rent, utilities, food and transportation were more top of mind than\nthey are for those living in refugee camps. The reality of having to forgo work in favor of training was\nrestrictive.\n\n\n\n\n\n**Despite the growing prevalence of smartphones, many refugees perceive access to computers**\n**as essential to conducting digital work.** Without a doubt, smartphone penetration is increasing\nin nearly all areas of the seven countries in this study. Smartphones, and associated mobile data,\nprovide access to the internet and especially to social media services, of which many were\npopular with participants including Facebook, Instagram, WhatsApp, and Telegram. Nevertheless,\nmany workshop participants indicated that their devices are not sufficient for digital work and\nperceive that digital work can only be completed on computers.\n\n\n\nprovide access to the internet and especially to social media services, of which many were\n\n**Soft skills and English language training were consistently among the most desired components**\n\npopular with participants including Facebook, Instagram, WhatsApp, and Telegram. Nevertheless,\n\n**of training programs.** Participants across multiple countries emphasized the importance of soft skills\n\nmany workshop participants indicated that their devices are not sufficient for digital work and\n\ndevelopment, including effective communication, negotiation, critical thinking and problem-solving.\n\nperceive that digital work can only be completed on computers", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001358:9:2:0", "start": 1216, "end": 1227, "surface": "mobile data", "probe_tag": "confusion", "probe_score": 0.5966, "luna_label": 0, "luna_reason": "Names an infrastructure resource without showing data used for analysis or a finding."}]}, {"key": "rafael-176", "text": "SUMMARY OF KEY SECTORS / THEMATIC AREAS ASSOCIATED KEY DATA SETS IN RWANDA\n\n\n\n\n\n\n\n\n\nRecommendations to improve data on refugees in Rwanda include a concerted effort to invest\n\nin standalone data production activities, and to leverage on existing avenues. Some of the\n\nexamples includes:\n\n\n - Inclusion of refugees in the next Integrated Household Living Conditions Survey (EICV)\n\nnational household survey.\n\n - A standalone skills’ survey of refugees.\n\n - Population and Housing Census: While ensuring that the POCs are included in the\n\nAugust 2022 Census, UNHCR Rwanda to take steps by reinforcing contacts with NISR,\n\nand collaborating with MINEMA and NISR to obtain the POCs data and/or study the\n\nresults of the Census data on POCs.\n\n - An ongoing self-reliance study, with a planned two rounds of panel survey, will produce\n\ndimensions and indicators on self-reliance of refugees.\n\n - UNHCR Flagship Survey: UNHCR Rwanda has been offered the opportunity to explore\n\na UNHCR financed comprehensive modules of surveys on a representative sample of\n\nrefugees. The UNHCR Rwanda Operation to proactively take steps to bring this activity\n\nonboard.\n\n - Statelessness: The ongoing activity of stateless population verification exercise in\n\nRwanda could be leveraged to undertake a study on the stateless population.\n\n - Potential linkages between proGres and other data systems could yield fruit.\n\n\nUNHCR / June 2022 / Version 1\n\n\n\n19", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001182:18:0:4", "start": 691, "end": 700, "surface": "POCs data", "probe_tag": "confusion", "probe_score": 0.5456, "luna_label": 0, "luna_reason": "Planned acquisition and study of census data, not already-used evidence."}, {"key": "reliefweb:001182:18:0:6", "start": 826, "end": 838, "surface": "panel survey", "probe_tag": "confusion", "probe_score": 0.5868, "luna_label": 0, "luna_reason": "Planned panel survey will produce new indicators, so it is future data production."}]}, {"key": "rafael-177", "text": " de**\n**empezar una nueva vida.**\n\n**El reasentamiento beneficia a un**\n**número relativamente pequeño de re-**\n**fugiados: en 2012, menos del 1% de los**\n**refugiados del mundo se beneficiaron**\n**de esta solución duradera. En los últi-**\n**mos diez años, se reasentaron unos**\n**836.500 refugiados, frente a 7,2 mi-**\n**llones de refugiados repatriados. En**\n**los últimos años, ACNUR y los Esta-**\n**dos trabajan para aumentar el uso del**\n\n\n\n**reasentamiento como solución dura-**\n**dera estratégica.**\n\n**La integración local es un proceso**\n**complejo y gradual que incluye dimen-**\n**siones económicas, sociales y culturales,**\n**distintas, aunque relacionadas. Para**\n**muchos, adquirir la nacionalidad del**\n**país de asilo es la culminación de este**\n**proceso. El análisis de los datos sobre**\n**integración local que aparecen en este**\n**informe está limitado a las estadísticas**\n**disponibles sobre naturalización de**\n**refugiados en países de acogida.**\n\n\n\n**Estrategias para soluciones integrales**\n\n\n\n**cuelas, y el conflicto armado que volvió a**\n**desenc", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001451:16:1:0", "start": 878, "end": 890, "surface": "estadísticas", "probe_tag": "confusion", "probe_score": 0.2032, "luna_label": 0, "luna_reason": "Only states analysis is limited to available statistics, without an attributed finding."}]}, {"key": "rafael-178", "text": "jados\n\n- abandonados por motivos de violencia, que busca\nestablecer un mecanismo de protección jurídica de\nbienes de las personas desplazadas por violencia\natendidas por la SEDH. La ruta inicia desde el momento\nde la solicitud de protección ante de la Dirección de\nProtección a Personas Desplazadas Internamente por\nla Violencia (DIPPDIV) de la SEDH, en donde se aplica\nuna ficha de identificación de bienes, posteriormente se\n\n\n\nrealiza un cruce de información con el Registro Unificado\nde Registros de la Propiedad del IP, se dictamina el caso\ny finalmente se procede a la inscripción del bien en\nun módulo que se creará en el sistema de información\ndel IP. En 2021, se continuará el proceso de revisión,\nsocialización y validación del instrumento, con el fin de\ndesarrollar ejercicios de pilotaje en 2022.\n\nLa Dirección de la Infancia, Niñez, Adolescencia y Familia\n(DINAF) como garante de derechos de la niñez y la\nadolescencia, con el apoyo del ACNUR y World Vision\nHonduras (WVH) han realizado diferentes actividades de\nfortalecimiento en el Programa de Antenas de Protección\nen las oficinas de Tegucigalpa, San Pedro Sula, Olancho\n\n\n**EL ARTE COMUNITARIO COMO ESTRATEGIA**\n**PARA FORTALECER LA PARTICIPACIÓN**\n**E INCLUSIÓN DE LAS COMUNIDADES EN**\n**RIESGO EN SAN PEDRO SULA**\n\nArte Comunitario es una estrategia de intervención\ncomunitaria liderada", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000936:39:2:0", "start": 629, "end": 658, "surface": "sistema de información\ndel IP", "probe_tag": "confusion", "probe_score": 0.6512, "luna_label": 0, "luna_reason": "Future system module will be created; data resource does not yet exist."}]}, {"key": "rafael-179", "text": "CHAPTER **8**\n\n\nFigure 21 **\u0003|** **Demographic characteristics available on UNHCR’s population of concern** **|** 2006-2016\n\n\n70\n\n\n60\n\n\n50\n\n\n40\n\n\n30\n\n\n20\n\n\n10\n\n\n0\n\n\n2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016\n\n\nTotal pop. of concern Sex-disaggregated data available Age-disaggregated data available\n\n\n\nIn 2014 and 2015, 164 countries provided at least\nsome sex-disaggregated data on populations of\nconcern. But in 2016, this number declined to 147\ncountries, representing 59 per cent of the entire\npopulation of concern. Some of this decline can be\naccounted for by changes in how the data are\nreported (Figure 21). 70 From the available data, men\naccounted for 20.4 million and women for 19.3 million\npeople out of the total population of concern.\n\n\nIn 2016, 140 countries reported any data\ndisaggregated by age, compared with 141 in 2015.\nEven though the total population covered increased,\nthe proportion covered was still only 35 per cent. Of\nthe 23.9 million people covered in 2016, 12.7 million\nwere children below the age of 18, or 53 per cent, a\nsmall increase in the proportion of the population of\nconcern from 2015.\n\n\nThe coverage of disaggregated data by population\ngroup varied. Refugees and asylum-seekers tended to\nhave the best coverage, and IDPs and stateless people\nthe least. In 2016, sex-disaggregated data were\navailable for 10.7 million refugees, and agedisaggregated data were available for 9.7 million (out of\nthe 17.2 million total population), representing 62 per\ncent and 56 per cent of the refugee population,\nrespectively. Among IDPs, 56 per cent of the\npopulation was covered by sex-disaggregated data,\nwhile only 26 per cent was covered by agedisaggregated data. For asylum-seekers,", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000712:53:0:0", "start": 244, "end": 266, "surface": "Sex-disaggregated data", "probe_tag": "drop", "probe_score": 0.045, "luna_label": 1, "luna_reason": "Existing sex-disaggregated data support reported population coverage findings."}, {"key": "reliefweb:000712:53:0:1", "start": 277, "end": 299, "surface": "Age-disaggregated data", "probe_tag": "drop", "probe_score": 0.0216, "luna_label": 1, "luna_reason": "Age-disaggregated data support the reported coverage and population findings."}, {"key": "reliefweb:000712:53:0:3", "start": 368, "end": 390, "surface": "sex-disaggregated data", "probe_tag": "confusion", "probe_score": 0.6836, "luna_label": 1, "luna_reason": "Disaggregated data support reported country coverage and population figures."}]}, {"key": "rafael-180", "text": " City Heran City Lana City Dashti Bahasht Shahan City Shary Andazyaran Pirash Village Zilan City Heran City_2 Lam Lite Ayinda City_1 Zheyan City Chawder City Marina_3 Dubai City Marina_1 _3 Kamarany Shary Andazyaran © OpenStreetMap (and) contributors, CC-BY-SA Ü|Iraq Iran Syria Turkey|\n\n\n###### 21\n\n**Information Management**\n\n**as Coordination Support**\n\n\n###### 42\n\n**3RP Financial Requirements**\n\n\n\n**Sulaymaniyah: Arbat Camp**\n\n\n\n**as Coordination Support**", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001143:2:18:0", "start": 276, "end": 289, "surface": "OpenStreetMap", "probe_tag": "drop", "probe_score": 0.037, "luna_label": 0, "luna_reason": "Map attribution names OpenStreetMap without showing its data being used."}]}, {"key": "rafael-181", "text": "**UNHCR • UNICEF • IOM** September 2019\n\n\n\n**ACCESS TO EDUCATION IN**\n**PRACTICE**\n\n\n_Data and definitions used across Europe_\n_do not allow for a full comparative analysis._\n_This section therefore provides a snapshot_\n_of the situation in selected European_\n_countries, illustrating_ **_the diversity of_**\n**_situations and disparities with regards_**\n**_to the availability, relevance and_**\n**_timeliness of data_** _on refugee and migrant_\n_children’s access to education. This is largely_\n_due to diverging national legislation, varying_\n_responsible authorities (national vs. federal/_\n_regional), and tools and methodologies to_\n_collect and analyse education data and_\n_statistics. Moreover, while in some countries_\n_data is recorded based on the migration_\n_status of children, in others this is done with_\n_a focus on citizenship or language skills._\n\n**Bulgaria**\n\n\nÔ **Ô** _Refugee and migrant children are_\n_recorded_ _in_ _national_ _education_\n_statistics only if they are asylum-_\n_seekers or beneficiaries of international_\n_protection._\n\n\nÔ **Ô** As of the end of December 2018,\nschool enrolment for refugee and\nmigrant children was five times\nhigher compared to the 20162017 school year due to increased\noutreach and support provided by\nthe government and humanitarian\nagencies.\n\n\n\nÔ **Ô** **50%** or **81** out of **161** schoolage refugee and migrant children\naccommodated in government\nreception centres in December\n2018 were enrolled in primary and\nsecondary public schools, while a\ntotal of **121** asylum-seeking and\nrefugee children were registered\noverall in the formal education\nsystem in the beginning of", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000457:4:0:0", "start": 659, "end": 673, "surface": "education data", "probe_tag": "drop", "probe_score": 0.0451, "luna_label": 0, "luna_reason": "Generic data resource is discussed for availability and collection, not used for a finding."}]}, {"key": "rafael-182", "text": "/gifmm-colombia-resultados-evaluacion-conjunta-de-necesidades-para-poblacion-con-22)\nxix Colombian Observatory of Feminicides.\nxx National Observatory of Gender-based Violence, The national rate for 2024 is 262.3 per 100,000 inhabitants.\nxxi UARIV, Resolution 0171 of February 24, 2016, by which confinement is defined as a victimizing event in the framework of Law 1448 of 2011, issued by the\nVictims Unit.\nxxii Ibid.\nxxiii Office of the Attorney General of Colombia, Directive 002 of January 14, 2025.\nxxiv Integral Action Against Antipersonnel Mines Viewer (2025). National Report on APM/UXO/IEDs Victims, available at: AICMA Viewer.\n\n\n**Methodology**\n\n\nIn September 2024, the Protection Thematic Group (GTP) of Norte de Santander and the Protection Cluster, in\ncollaboration with the Areas of Responsibility (AoR) of Gender-Based Violence, Child Protection, and Mine Action,\norganized a mission to the Catatumbo sub-region. The objective was to update the regional context and identify the main\nprotection risks in the area. This mission identified the imminent risk of forced internal displacement and the confinement\nof rural communities. However, at the beginning of 2025, a humanitarian emergency occurred that required a coordinated\nresponse from the Local Coordination Team of Norte de Santander (ELC) for immediate humanitarian aid and institutional\ncoordination. This protection analysis document is based on quantitative and qualitative data from inter-sectoral\nassessments, rapid protection assessments, and reports prepared by partners from the subnational and national teams of\nthe Protection Cluster/Sector and the corresponding Areas of Responsibility.\n\n\nPage 13", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000324:12:4:1", "start": 623, "end": 635, "surface": "AICMA Viewer", "probe_tag": "confusion", "probe_score": 0.5565, "luna_label": 1, "luna_reason": "Named source platform cited with the National Report on APM/UXO/IEDs Victims."}, {"key": "reliefweb:000324:12:4:3", "start": 1488, "end": 1516, "surface": "rapid protection assessments", "probe_tag": "drop", "probe_score": 0.0316, "luna_label": 1, "luna_reason": "Existing assessments provide quantitative and qualitative data for the protection analysis."}]}, {"key": "rafael-183", "text": ">2022, subject to Covid situation. Lack of willingness and capacity of partners Staff Turn-over Lack of UNHCR capacity/ M&E resource person support due to lack of funding 40,000 (Annual Surveys) Annual Indicator Survey (Livelihoods Assessments) Report Participatory Assessments Partners Progress Reports PDM? Socioeconomic Assessment (with JDC) |M&E stock take exercise to consolidate achievement made so far + developing a framework for efficient and real-time results tracking using in-house capacity; Review, Workshop, Field Visits to validate the results so far collected and draft M&E framework developed as part of the strategy Validation Exercise, piloting annual indicator survey UNHCR M&E Consultant with Livelihoods Team (UNHCR and MINEMA) By end 2021/early 2022, subject to Covid situation. Lack of willingness and capacity of partners <br", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001259:57:9:2", "start": 817, "end": 833, "surface": "indicator survey", "probe_tag": "drop", "probe_score": 0.0429, "luna_label": 0, "luna_reason": "Annual indicator survey is being piloted as a planned data-production activity."}]}, {"key": "rafael-184", "text": "refugee settlements.|Advocate for telecommunication companies to improve telecommunication coverage in refugee settlements.|Advocate for telecommunication companies to improve telecommunication coverage in refugee settlements.|Advocate for telecommunication companies to improve telecommunication coverage in refugee settlements.|Advocate for telecommunication companies to improve telecommunication coverage in refugee settlements.|Advocate for telecommunication companies to improve telecommunication coverage in refugee settlements.|Advocate for telecommunication companies to improve telecommunication coverage in refugee settlements.|\n||Cross-Cutting Approaches|Cross-Cutting Approaches|Strengthen data- driven approaches to refugee livelihoods.|Conduct socio-economic profiling of refugee households, including baseline, midline, and end-line surveys, market assessments, and economic database development.|Conduct 3 assessments of the strategy, 1 per year|1|100000|3|300000||\n||Cross-Cutting Approaches|Cross-Cutting Approaches|Strengthen data- driven approaches to refugee livelihoods.|Collaborate with host government agencies to integrate refugee data into national systems, ensuring interoperability.|||||0|0|\n||Cross-Cutting Approaches|Cross-Cutting Approaches|Strengthen data- driven approaches to refugee livelihoods.|Partner with the World Bank and<", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000822:21:1:0", "start": 1245, "end": 1257, "surface": "refugee data", "probe_tag": "drop", "probe_score": 0.0347, "luna_label": 0, "luna_reason": "Planned integration of refugee data into national systems, not existing data use."}]}, {"key": "rafael-185", "text": "://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_eiord&lang=en)\n[have left the territory as a result of an order to leave (voluntary or forced) 2015-2017.](http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_eirtn&lang=en)\n\n\n48 THE WAY FORWARD", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001185:47:2:0", "start": 52, "end": 62, "surface": "migr_eiord", "probe_tag": "confusion", "probe_score": 0.1202, "luna_label": 0, "luna_reason": "Standalone dataset code in URL lacks shown data use or attributed finding."}, {"key": "reliefweb:001185:47:2:1", "start": 220, "end": 230, "surface": "migr_eirtn", "probe_tag": "drop", "probe_score": 0.0384, "luna_label": 0, "luna_reason": "Bare dataset URL parameter fragment, not a standalone data-use mention."}]}, {"key": "rafael-186", "text": "**Time: 15:00**\n\n# **Next Steps**\n\n\n- **Summary write up of workshop discussions**\n\n- **Preliminary output shared with ISCG to support RRP planning**\n\n- **Follow up agreements to improve or consolidate data sets**\n\n- **Bilateral meetings on specific identified issues**\n\n- **Work together, share information and jointly find solutions** ☺\n\n\n**Winterization Workshop, October 2022**", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001548:50:0:0", "start": 202, "end": 211, "surface": "data sets", "probe_tag": "drop", "probe_score": 0.0271, "luna_label": 0, "luna_reason": "Next-step agreement concerns future data-set improvement, not existing data use."}]}, {"key": "rafael-187", "text": "**Table of contents**\n**Paving pathways for inclusion: A global overview of refugee education data**\n\n#### Table of f i gures\n\n\nFigure 1: Overview of the number of questionnaires covering different education areas by target\n7\npopulation and presence of refugee identification questions\n\nFigure 2: Framework for the inclusion of refugees in education data systems 23\n\nFigure 3: Percentage of refugees hosted by selected countries of the total global refugee population,\n28\nas of September 2021\n\nFigure 4: Disaggregation of 621 reviewed DCEs by target population and type 31\n\nFigure 5: Disaggregation by SES and personal characteristics 32\n\nFigure 6: Number of questionnaires containing refugee identification questions by type of question 33\n\nFigure 7: Percentage of questionnaires that ask proxy and criteria questions by question 34\n\nFigure 8: Number of questionnaires with access questions that target refugees or include refugee\n36\nidentification questions\n\nFigure 9: Number of questionnaires with access questions by sub-indicator and target population 37\n\nFigure 10: Percentage of questionnaires asking refugee identification and access questions by\n38\nrefugee identification type and indicator\n\nFigure 11: Number of questionnaires with quality questions that target refugees or include refugee\n41\nidentification questions\n\nFigure 12: Number of questionnaires with quality questions by target population 42\n\nFigure 13: Percentage of questionnaires asking refugee identification and quality questions by\n43\n<", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000382:14:0:0", "start": 76, "end": 98, "surface": "refugee education data", "probe_tag": "drop", "probe_score": 0.0256, "luna_label": 0, "luna_reason": "Generic topic phrase names data without attributing a concrete finding or analysis."}]}, {"key": "rafael-188", "text": " and challenges in how data is translated from insight and into action.\nFor the purposes of this research, the following data lifecycle was used:\n### ‣ PLANNING : Defining the specific purposes/objectives of the data activity, the\n\nend-users and uses of the insights, identifying relevant partnerships, and\ndesigning a strategy to implement the data activity ( _who_ does _what when_, and\n_how_ ).\n### ‣ COLLECTION : Gathering data directly from those in the field or collating it from\n\nsurveys, censuses, voting or health records, business operations, web-based\ncollections, and other relevant, accessible sources.\n\n\n5", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000499:5:1:2", "start": 506, "end": 530, "surface": "voting or health records", "probe_tag": "confusion", "probe_score": 0.1128, "luna_label": 0, "luna_reason": "Listed as sources being gathered during a data-collection activity, not analyzed data."}, {"key": "reliefweb:000499:5:1:3", "start": 553, "end": 574, "surface": "web-based\ncollections", "probe_tag": "drop", "probe_score": 0.0061, "luna_label": 0, "luna_reason": "Names generic web collections without showing analysis, findings, or substantive data use."}]}, {"key": "rafael-189", "text": "ضية الخاصة بالعنف الجنسي والعنف الجندري. وقد أصدرت ال� ت\nيركّز **مرص** و **لبنان** و **ردنال** **أ** ي رس � ف ي هن ربات ال أ المفوضية تقريراً بعنوان «نساء بمفردهن»، حول الالجئات السوريات اللوا� ت\n\nدوار، والعزلة، والعنف الجنسي والعنف الجندري.مان المالي ، وتغ�ي ال أ عى مسائل الإسكان، والغذاء، والصحة، والعمل وال أ\n\n\n**نظـام إدارة المعلومات حول العنف الجندري**\n\n)، لضمان جمع وإدارة وتشارك بيانات آمنة GBVIMS( دعمت المفوضية ن� نظام إدارة المعلومات حول العنف الجندري ش\nي العمليات المختلفة. فنظام إدارة المعلومات حول العنف الجندري وأخالقية ورسية حول العنف الجنسي والعنف الج", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001417:37:2:0", "start": 476, "end": 482, "surface": "GBVIMS", "probe_tag": "drop", "probe_score": 0.0376, "luna_label": 0, "luna_reason": "Names an information system without showing its data being used for analysis or decisions."}]}, {"key": "rafael-190", "text": "Conversations examined the CRRF capacity to deliver timely humanitarian response with the depth of\n\ndevelopment programmes as well as doing so without undermining humanitarian principles and accountability.\n\nEnsuring that the views of diverse groups (ex. women youth, children, persons with disabilities, LGBTI) are\n\nreflected in the CRRF was also stressed, as was the need for an effective continuum of protection across borders\n\nand throughout the displacement cycle.\n\n\nCRRF implementation examples underscored that people, not structures implement the CRRF. Confusion in the\n\ndialogue accentuated the need to translate the CRRF into plain language and make it accessible to the whole\nof-society. This should include the application of consistent definitions and appropriately disaggregated data so\nas to be able to tell stories of change. 20 The need for more awareness raising, conducting outreach, localising\n\nthe CRRF and following the Principles of Partnership were also discussed.\n\n\nFor the sake of clarity, inputs from the NGO community are organised here according to clauses 6-8 from\nAppendix 1 of the New York declaration. 21 [^21: Clauses 6a, 6e, 8a, and 8c were not significant topics of discussion at the Consultations.]\n\n\n**Walk the talk- A multi-stakeholder approach in resourcing the comprehensive refugee response** **22** [^22: This session included discussion of New York Declaration, Appendix 1, 6.a to 6.d.]\n\n\nThis session discussed how cooperation with multilateral donors and private-sector partners can provide\n\nresources to Comprehensive Refugee Responses in a prompt, predictable, consistent and flexible manner. It\nalso addressed challenges in the transition from the ‘business as usual’ to a new way of working.\n\n\nThe Deputy High Commissioner for Refugees opened the discussion, acknowledging that a core challenge to\n\nCRRF implementation is the need to be operational very quickly while operating with little money. Impacting the\n\nquality and continuity of response, insufficient funding – especially for national NGOs, host communities and\n\nlocal government – was", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000146:17:0:0", "start": 779, "end": 797, "surface": "disaggregated data", "probe_tag": "drop", "probe_score": 0.0309, "luna_label": 0, "luna_reason": "Generic data is recommended, but no existing finding or concrete use is shown."}]}, {"key": "rafael-191", "text": "(training duration of 2.5 weeks) or to an experienced, professional enumerator, but with less than\n\n\na day of IPV training. 27 While 21% of the women reported having experienced physical or sexual\n\n\nIPV to the untrained enumerators, 26% reported IPV to the trained ones (Jansen et al. 2004).\n\n\nAnother relevant data quality issue that we want to note from a companion study in the same\n\n\nsetting (Jeong et al. 2021) is that time into the survey at the point at which a question is asked\n\n\nappears to adversely impact response quality, a phenomenon known as survey fatigue. While fatigue\n\n\nwould be a consideration for any survey, it may be particularly germane for IPV measurement as\n\n\nmost surveys place the IPV module at the end - for example, the standard DHS surveys ask about\n\n\ndomestic violence at the end; we also chose to always place the IPV module at the end, even as\n\n\nwe randomized the location of other survey modules within the survey. While this is usually done\n\n\nto minimize shame or embarrassment stemming from continued interaction with the enumerator\n\n\nafter having answered the IPV module, we reiterate our read that concerns about stigmatization\n\n\nfrom the enumerator are very likely overblown, 28 and purported remedial actions, such as SI or late\n\n\nplacement within the survey may be opening up non-obvious channels of bias.\n\n\n27This was done in an effort to speed up the fieldwork midway through surveying after the assassination of then\nPrime Minister Zoran ~~D~~ in ~~d~~ i´c in March 2003.\n28In our study, we asked our enumerators a few debriefing questions after the IPV module and they reported that\namong respondents who reported any IPV incidence in FTFI, 44% in Liberia and 28% in Malawi shared more about\ntheir IPV experience with them than what was", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001571:18:0:0", "start": 770, "end": 781, "surface": "DHS surveys", "probe_tag": "confusion", "probe_score": 0.8643, "luna_label": 0, "luna_reason": "Names surveys as an example of questionnaire placement, without using their data."}]}, {"key": "rafael-192", "text": "multiple locations are mentioned. Our algorithm performs 25% better than LNEx even when comparing\n\n\nwhether any location extracted from the tweet is near the true location.\n\n\nAnalyzing the crash data produced using our algorithm and focusing on the truth dataset within the city\n\n\nlimits of Nairobi, we find that all crashes from July 2017 to July 2018 can be found in 435 clusters, each with\n\n\na maximum diameter of 300 m. Of these clusters, 67% have two or more crashes and there are 56 clusters\n\n\nwith 10 or more crashes. Additionally, 66 crash clusters represent over 50% of all the crashes. When looking\n\n\nat the 7.5 years of crowdsourced data for the city of Nairobi, the number of crash clusters does not grow\n\n\nlinearly, implying that the locations where crashes occur and are reported in Twitter are consistent across\n\n\nyears. Only 14% of crash locations have only a single crash, and there are 443 crash clusters with 10 or more\n\n\ncrashes. We see the concentration of crashes even more when we note that only 9% of crash clusters (133 of\n\n\n1,375) represent 50% of the crashes reported (Figure 3 shows crash heatmaps for the truth dataset from July\n\n\n2017 to July 2018 and for 2012-2020).\n\n\n**Figure** **3.** **Heatmap** **of** **crashes** Data in panel a is from July 2017 - July 2018, where we use the manually\ncoded Twitter dataset. Data in panel b is for August 2012 - July 2020. Road data comes from\nOpenStreetMap.\n\n##### **Discussion**\n\n\nCities are constantly evolving and understanding urban mobility is critical to creating urban designs that\n\n\nhelp to manage risks for pedestrians and vehicles. Severe data limitations hinder the development of policy\n\n\ninterventions needed to manage risks, especially in low- and middle-income resource-constrained countries.\n\n\nClosing the data deprivation gap can help avert divergence in socioeconomic conditions between data-poor\n\n\nand -", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000063:10:0:0", "start": 189, "end": 199, "surface": "crash data", "probe_tag": "confusion", "probe_score": 0.4104, "luna_label": 0, "luna_reason": "Crash data are produced using the project’s algorithm."}]}, {"key": "rafael-193", "text": "it is clear that the magnitudes are quite small. There are several reasons that help explain this\n\n\nfact.\n\n\nAs discussed in Section 1, the reforms in cotton markets have been relatively successful\n\n\nbut have not been smooth. Initially, the public monopoly was transformed into a private\n\n\nmonopoly. Entry was useful in early stages, but the failure of the outgrowing scheme limited the\n\n\nexpansion of the sector. In fact, the outgrower scheme was still failing in 1998, when the\n\n\nhousehold survey data was collected. This means that the evolution in income shares that we can\n\n\ncapture with the available data does not reveal the whole benefits on the reforms.\n\n\nUsing additional farm data, Brambilla and Porto (2005) report that the cotton reforms had\n\n\ntwo distinctive effects on cotton farming. First, there is a decline of the land area devoted to\n\n\ncotton in 1998-1999 (when the outgrowing scheme was failing) followed by a significant\n\n\nincrease in area planted in 2000-2001, when the outgrower scheme was perfected with the\n\n\nentrance of Dunavant. Second, the authors find that farm productivity in cotton showed a similar\n\n\npattern, declining in 1998-1999 and increasing in 2000-2001. In additional, their findings\n\n\nindicate that income shares increased, on average, by roughly 10-20 percent. These additional\n\n\nfactors could help increase the average gains from a 12 percent price increase to roughly 2\n\n\npercent of household income, on average. Although these figures are higher, the effects seem\n\n\nstill fairly low. The study by Brambilla and Porto (2005) reveals another reason why the increase\n\n\nin cotton prices may not have large impacts. Productivity of smallholder cotton production has\n\n\nbeen traditionally very low in Zambia. Although there is some evidence that the reforms have\n\n\ncaused productivity to increase in recent years, there is still room for improvements in this area.\n\n\nOne additional reason for the small impacts is that cotton activities are not really\n\n\nwidespread in Zambia. More importantly, we have only considered a first order", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002911:16:0:0", "start": 481, "end": 502, "surface": "household survey data", "probe_tag": "confusion", "probe_score": 0.7382, "luna_label": 1, "luna_reason": "Existing household survey data supports analysis of income-share changes."}]}, {"key": "rafael-194", "text": " the maintenance of a bank account.\nAnd credit can be used to acquire such non-financial assets as a home or an automobile, to\nsmooth consumer purchases over time, or to finance small business activities. The cost of credit\noutside the formal banking system is high, as the data in Box 1, below. Finally, all Mexicans\nmay pay a price as a result of such a large percentage of the population being outside of the\nbanking system, as informal savings are often not channeled to productive activities, lowering\nthe potential rate of growth of the economy. Box 2, on the following page, estimates the costs of\nbeing unbanked to a median income couple in Mexico City at 15% of total income. The individuals who are unbanked bear most of these costs.\n\n\n\n\n\n\n\n\n\n\n\n\n\n46", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:003049:45:1:0", "start": 274, "end": 287, "surface": "data in Box 1", "probe_tag": "confusion", "probe_score": 0.5122, "luna_label": 1, "luna_reason": "Box 1 data supports the claim that formal banking credit costs are high."}]}, {"key": "rafael-195", "text": "Oxford_\n_Bulletin of Economics and Statistics_, Vol. 43, No. 4 November.\n50. Simar, L. and P. Wilson (2000) “Statistical inference in non-parametric frontier\nmodels: The state of the art” _Journal of Productivity Analysis_, 13 pp 49-78.\n51. The State Failure Task Force, 2004. Political Instability (aka State Failure) Task\nForce Data, http://www.cidcm.umd.edu/inscr/stfail/sfdata.htm\n52. Tanzi V. 2004. Measuring efficiency in public expenditure. Paper presented in\nConference on Public Expenditure Evaluation and Growth. The World Bank.\nOctober.\n53. Varian, H. 1990 “Goodness-of-fit in optimizing models”, _Journal of Econometrics,_\n46, pp. 125-140.\n54. Wheelock D. and Wilson, P. 2003. “Robust Non-parametric estimation of efficiency\nand technical change in U.S. Commercial Banking” Working Paper. Federal Reserve\nBank of St. Louis. November.\n55. Wilson, P. (2004) “A preliminary non-parametric analysis of public education and\nhealth expenditures in developing countries”. Mimeo. The World Bank.\n56. Wobst, P.and C. Arndt. “HIV/AIDS and primary school performance in Tanzania”.\nPresented at the 25 th International Conference of Agricultural Economists. Durban,\nSouth Africa.\n57. World Bank, 2004. _Making Services Work for the Poor. World Development Report_\n_2004._\n58. Worthington, A. 2001. “An empirical survey of frontier efficiency measurement\ntechniques in education” _Education Economics_, Vol. 9, No. 3\n\n\n39", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002859:40:1:0", "start": 277, "end": 334, "surface": "Political Instability (aka State Failure) Task\nForce Data", "probe_tag": "confusion", "probe_score": 0.4208, "luna_label": 0, "luna_reason": "Bibliography entry citing a named dataset, without demonstrated data use."}]}, {"key": "rafael-196", "text": "Fourth, practitioners emphasize the value of community-owned “score-cards” in mobilizing communities to take\n\naction. In interventions 2 and 3 the community created its own report card by testing children in math and reading. Both\n\nthe results and the tools were transferred to community members.\n\n\nPratham is the largest educational NGO in India. It has demonstrated success in several randomized evaluations\n\nof its programs and reaches millions of children throughout India. Pratham designed and runs the the Annual State of\n\nEducation Report (ASER), which tests children in all of India’s nearly 600 districts every year, and is extremely prominent\n\nin the discourse on education in India. The organization takes community participation in education very seriously (it is\n\nthe backbone of its flagship “Read India” program) and devotes considerable resources to make sure that the program is\n\n\nimplemented as well as possible. Pratham’s motivation and expertise thus made it an obvious candidate for implementing\n\nthese interventions.\n\n\nTaken together this suggests that we can be reasonably sure that what we are evaluating were well-designed and\n\neffectively implemented programs. It is also clear that they reached their intermediate goals: encouraging participation,\n\nholding meetings focused on education, and generating discussion, interest, and willingness on the part of at least some\n\npeople to act (as evidenced by the fact that Pratham was effective at recruiting volunteers for the reading camps).\n\n\n**3.3** **Why might we have expected these interventions to work?**\n\n\nThese interventions involved a combination of more information (about learning levels and levers for change such\n\nas the VEC) and more coordination on doing something about education. The predicted impact of more coordination\n\nseems unambiguous: More action and better results is what we would have expected.\n\nGiving people more information, on the other hand, need not always promote greater collective action. For\n\nexample, if the only people who were acting initially were those who believed that the returns to collective action were very\n\nlarge, information", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:003791:12:0:0", "start": 512, "end": 545, "surface": "Annual State of\n\nEducation Report", "probe_tag": "keep", "probe_score": 0.914, "luna_label": 1, "luna_reason": "Named existing education report referenced as a prominent data resource."}]}, {"key": "rafael-197", "text": " board committees (20.45%), an independent board\n\n\n(26.85%), and a BEBCHUK index equal to 1 (18.60%). However, when INV_PROT1\n\n\nindex is equal or greater than 1.7, companies tend to have only one board committee\n\n\n(25.7%), a not independent board (46.33%), and a BEBCHUK index equal to 1\n\n\n(40.06%). There is, therefore, not a clear and monotonically relation between investor\n\n\nprotection at the country level and the existence of board committees. The largest\n\n\nmajority of companies have a low BEBCHUCK indicator, but there is an equal split\n\n\nin terms of board independence across the level of investor protection INV_PROT1.\n\n\n**Financial data**\n\n\nFor US companies, financial data are obtained from COMPUSTAT, while for\n\n\nnon-US companies we use Worldscope data. As mentioned before, our companies are\n\n\n16", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002461:16:1:2", "start": 750, "end": 765, "surface": "Worldscope data", "probe_tag": "keep", "probe_score": 0.9754, "luna_label": 1, "luna_reason": "Named financial dataset used for non-US companies' financial data."}]}, {"key": "rafael-198", "text": ".11\n(0.23) (0.33) (0.29) (0.37) (1.88) (0.33) (0.31) (0.24)\n\n\nObservations 6,510,590 5,378,875 3,827,927 2,531,263 648,764 4,359,788 5,447,012 7,328,290\n\n\nFixed Efectsf\nFirm ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓\nOrigin-product-year ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓\nDestination-product-year ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓\nOrigin-destination ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓\n\n\nNotes: The dependent variable is the log unit value at the firm-product-origin-destination-year level in all columns. Significance: *** p _<_ 0.01, **\np _<_ 0.05, and - p _<_ 0.1 are based on robust standard errors, which are reported in parentheses. Data sources: Thirteen datasets of firms’ exports from\nthe World Bank Exporter Dynamics Database, China’s Customs Authority, and Egypt’s Customs Authority.\n\n\n49", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002308:52:4:1", "start": 628, "end": 665, "surface": "World Bank Exporter Dynamics Database", "probe_tag": "keep", "probe_score": 0.9179, "luna_label": 1, "luna_reason": "Named database cited as a source for export regression analysis."}]}, {"key": "rafael-199", "text": "*The OECD and RICYT basically provide the same definitions for sectors that finance and\n\n\nperform R&D. The OECD refers to the productive sector as “Business Enterprise Sector”, which\n\n\ncovers private and public enterprises.\n\n\n**R&D Personnel**\n\n\nThe data on R&D personnel corresponds to scientists and engineers, comprising persons working\n\n\nin those capacities, i.e. as persons with scientific or technological training (usually completion of\n\n\nthird level education) in any field of science, who are engaged in professional work on R&D\n\n\nactivities, administrators and other high-level personnel who direct the execution of R&D\n\n\nactivities.\n\n\n*RICYT divides R&D personnel in the following categories: Researchers (professionals who\n\n\ncreate new knowledge, methods and systems, and administer them), Graduate Students, and\n\n\nSupport Personnel (technicians and other support personnel).\n\n\n\n**2. Database on Innovation: Variables and Sources**\n\n\n\n\n\n\n\n\n\n\n\n\n|Variable|Definition|Units|Source|\n|---|---|---|---|\n|R&D Variables |R&D Variables |R&D Variables |R&D Variables |\n|Pat |Total patents granted by the USPTO by year for each country |Unit |U.S. Patent & Trademark Office |\n|Patepo |Total patents granted by the EPO by year for each country", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002988:29:0:0", "start": 271, "end": 292, "surface": "data on R&D personnel", "probe_tag": "confusion", "probe_score": 0.4353, "luna_label": 0, "luna_reason": "Generic data phrase is defined conceptually without an attributed finding or analytical use."}]}, {"key": "rafael-200", "text": "that occur within different distance radii of the DHS household-cluster centroid. We\n\n\ncount all events that occur within different distance belts—for example, 0–50 km or 51–\n\n\n100 km. We take a threshold of 50 km to define distant events, but we conduct sensitivity\n\n\nanalyses increasing and decreasing this threshold. We also build a continuous indicator\n\n\nthat counts all events that occur beyond 50 km from the household cluster and weighs\n\n\nthese events using the inverse of the distance. For example, all attacks within a radius\n\n\nof 51 km of the household cluster are assigned a weight of 1, and attacks farther away\n\n\nreceive weights smaller than 1. We also consider the number of deaths as an alternative\n\n\nmeasure of violence.\n\n\nWe also use information on attacks conducted by the Fulani. Between 2008 and 2018,\n\n\nthe ACLED data register 1,038 violent events in which at least one of the agents involved\n\n\nwas Fulani. About 70 percent of these events are classified as violence against civilians\n\n\nand 25 percent as battles, while the remainder are explosions or remote violence. These\n\n\nevents led to 7,236 registered fatalities. Most of the violent events are concentrated in the\n\n\nnorth central region. About 25 percent of the events occurred in Benue State, followed\n\n\nby 22 percent in Plateau State, and 9 percent in Taraba State (these statistics are not\n\n\ndisplayed in any table).\n\n\n_Displacement_ _data._ We use data on the number of displaced people from the IOM’s DTM\n\n\nproject, particularly the site- and location-assessment databases. We use these data to\n\n\ncalculate the number of IDPs who live near the household cluster. To do so, we count\n\n\nthe number of IDPs residing within a 10 km radius of the DHS household cluster. In\n\n\naddition, we build binary indicators at the LGA level indicating the presence of IDPs.\n\n\nSince July 2014, the IOM has been collecting displacement data from key informants\n\n\n(such as village chiefs and religious leaders) at IDP-hosting locations.", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000984:17:0:1", "start": 1430, "end": 1468, "surface": "data on the number of displaced people", "probe_tag": "confusion", "probe_score": 0.8117, "luna_label": 1, "luna_reason": "IOM DTM data are used to calculate nearby IDP counts and indicators."}]}, {"key": "rafael-201", "text": "consumption may do a better job in this respect, but will still be an imperfect welfare indicator\n\n\ngiven that inter-temporal markets do not work perfectly. There are also uncertainties about how\n\n\nbest to normalize for heterogeneity in consumption needs, such as stemming from demographic\n\n\ndifferences between households (Pollak, 1991). 4 And even if we had an ideal measure of\n\n\naverage economic welfare for a household, health status is an individual attribute, which will\n\n\ndepend directly on personal economic welfare. Individual income is a flawed metric of\n\n\nindividual command over commodities given some degree of income pooling within households.\n\n\nAnd household expenditure may well be equally flawed as an indicator of individual economic\n\n\nwelfare given that pooling is almost certainly incomplete. 5 [^5: It is instructive that, in the same setting, Ravallion and Loskhin (2001) find evidence that many\n“non-income” factors at the individual and household levels impinge on perceived economic welfare in\nRussia at given current incomes or expenditures on consumption deflated by standard poverty lines.]\n\n\nIn short, heterogeneity in relevant individual and household circumstances, inter\n\ntemporal consumption smoothing and inter-personal income sharing may entail that neither\n\n\nmeasured current income nor consumption are particularly good proxies for economic welfare,\n\n\nas relevant to health. This too will lead one to underestimate the true economic gradient in\n\n\nhealth using the methods commonly found in the literature.\n\n\nThis paper proposes a method of estimating the economic gradient in health status that is\n\n\nlikely to be more robust to non-ignorable errors in _SAH_ data and the measurement errors in data\n\n\non incomes or expenditures. The essential idea is to use largely independent covariates of both\n\n\n_SAH_ and perceived economic welfare to attempt to purge the raw data of the non-ignorable\n\n\nerrors, prior to testing for the economic gradient in health status. To calibrate a broader measure\n\n\nof economic welfare we draw on subjective data, though recognizing that this too contains\n\n\nmeasurement errors when used as an indicator of objective economic welfare. Our estimation\n\n\n4 For example, the poverty lines used", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002912:3:0:0", "start": 2083, "end": 2098, "surface": "subjective data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Subjective data are used to calibrate a broader economic welfare measure."}]}, {"key": "rafael-202", "text": "no. 224. https://www.imf.org/en/Publications/WP/Issues/2020/11/05/COVID-19-and-the-CPI-Is\nInflation-Underestimated-49856.\n\n\nRodriguez-Takeuchi, Laura, and Katsushi S Imai. 2013. “Food Price Surges and Poverty in Urban\n\nColombia: New Evidence from Household Survey Data.” _Food Policy_ 43 (C): 227–36.\n\nhttps://doi.org/10.1016/j.foodpol.2013.09.017.\n\n\nSanchez-Paramo, Carolina, and Ambar Narayan. 2020. “Impact of COVID-19 on Households: What Do\n\nPhone Surveys Tell Us?” Voices [Blog]. 2020. https://blogs.worldbank.org/voices/impact-covid-19\nhouseholds-what-do-phone-surveys-tell\nus?cid=ECR_E_NewsletterWeekly_EN_EXT&deliveryName=DM85808 .\n\n\nSkoufias, Emmanuel, Sailesh Tiwari, and Hassan Zaman. 2012. “Crises, Food Prices, and the Income\n\nElasticity of Micronutrients: Estimates from Indonesia.” _The World Bank Economic Review_ 26 (3):\n\n415–42. https://doi.org/10.1093/wber/lhr054.\n\n\nVu, Linh, and Paul Glewwe. 2011. “Impacts of Rising Food Prices on Poverty and Welfare in Vietnam.”\n\n_Journal of Agricultural and Resource Economics_ 36 (1): 14–27.\n\n\nWorld Bank. 2020a. “Iran Economic Monitor, Fall 2020 : Weathering the Triple-Shock.” Washington, DC.\n\nhttps://openknowledge.worldbank.org/handle/10986/34973.\n\n\n———.", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002397:22:0:0", "start": 247, "end": 268, "surface": "Household Survey Data", "probe_tag": "confusion", "probe_score": 0.3301, "luna_label": 0, "luna_reason": "Fragment appears within a bibliography title, not as evidence used in the passage."}]}, {"key": "rafael-203", "text": " 0 _,_ (6)\n\n\n\nsince _dσ_ _dϵ_\n\n\n\n_dσ_ _dϵ_ _>_ 0 and _ϵ >_ 1. Therefore, both the value of the investment option _X_ and the critical threshold\n\n\n\n_C_ ∗ are increasing functions of the variance _σ_ 2 :\n\n\n\n_X_ ∝ _σ_ 2 _ϵ_ _,_ _C_ ∗ ∝ _σ_ 2 _._\n\n\nThis proportionality demonstrates that as uncertainty (volatility) increases, the value of the option\n\n\nto delay investment rises, similar to financial options where greater volatility expands the range of\n\npossible outcomes. This flexibility allows firms to respond more effectively to future changes, such as\n\n\nshifts in prices or market trends. Firms can capitalize on upside opportunities by exercising the option\n\n\nto invest or expand, while avoiding losses by deferring in unfavorable conditions. This asymmetry,\n\n\nwhich favors the investor, means that higher uncertainty or a broader range of potential outcomes\n\n\nbenefits the firm by increasing the value of waiting.\n\n\nThe following sections outline the data sources and empirical strategies used to", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001211:7:1:0", "start": 1158, "end": 1170, "surface": "data sources", "probe_tag": "confusion", "probe_score": 0.8435, "luna_label": 0, "luna_reason": "Merely introduces forthcoming data-source sections without identifying or using existing data."}]}, {"key": "rafael-204", "text": "$575 billion, including projects already executed, in implementation phase and planned. Based\non this data, US$525 billion or 91 percent of total identified investments is expected to benefit IBRD or\nIDA-eligible countries. 3 Only US$66 billion in completed projects has been identified as of end-2016; most\nBRI investment is still in the construction or planning phase. The analysis largely focuses on 50 developing\ncountries, which have been identified as lying on the original BRI transport and connectivity corridors (De\nSoyres et al., 2018) and for which debt and macroeconomic data are available. This approach implies that,\nfor example, only two Sub-Saharan African countries, namely Kenya and Tanzania, are included in the\ndatabase, despite significant Chinese investment in Sub-Saharan Africa, some of which has been recently\nrebranded as BRI related. 4 [^4: In October 2018, the Forum on China-Africa Cooperation announced US$60 billion financing to African Countries\nand this financing has been re-branded as part of the BRI (see https://thediplomat.com/2018/09/focac-2018rebranding-china-in-africa/).]\n\n\nSeveral countries have already scaled back on BRI investments or requested debt relief from China. In\nOctober 2018, Malaysia decided to suspend and renegotiate US$20 billion worth of projects fearing not\nto be able to repay such debt and Pakistan cut down the cost of a large railway project. China had to\nprovide debt relief to several borrowing countries in recent years 5 [^5: Hurley _et al._ (2018) provide a comprehensive list.] ; in some rare cases, difficulties in\nrepaying debt has resulted in China taking possession of key infrastructure in lieu of repayment in Sri\n\n\n2 See, for example, “How Big Is China’s Belt and Road?”, April 3, 2018, Center of Strategic and International Studies.\n3 US$ 10 billion of financing directed to Latin American Countries was not", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000014:3:1:0", "start": 571, "end": 598, "surface": "debt and macroeconomic data", "probe_tag": "confusion", "probe_score": 0.6758, "luna_label": 0, "luna_reason": "States data availability as a selection criterion without showing analyzed findings."}]}, {"key": "rafael-205", "text": "##### **7 Conclusions**\n\nPrevious studies of the elasticity of input tariffs on firm level productivity have docu\n\nmented sizeable effects that are difficult to reconcile with firms’ cost minimization prior\n\n\nto the trade reform. We revisit this result incorporating the fact that when firms expand\n\n\ntheir foreign sourcing, there are important changes in their input costs. When input\n\n\ntariffs drop, firms substitute towards lower variable, but higher ordering and inventory\n\n\nholding cost inputs. The use of aggregate price deflators in the estimation of productiv\n\nity is ill suited to capture the heterogeneity in firms’ input costs. We propose to control\n\n\nfor firm level input costs that capture price differentials, ordering costs and inventory\n\n\nholding costs by including import intensity and the inventory usage in the estimation of\n\n\nproductivity. When we apply the control function, the elasticity of productivity to input\n\n\ntariffs drops by 37% in the case of India’s trade liberalization of the 1990s.\n\n\nThese findings illustrate that, without quantity data, it is important to account for\n\n\nthe different margins of input cost in the evaluation of the firm performance. While\n\n\nthis paper focused on the setting of trade liberalizations, similar mismeasurement might\n\n\nbe consequential in the response to other trade shocks. For example, during large de\n\nvaluations, productivity drops have been accompanied by large responses in inventories\n\n\n(Gopinath and Neiman (2014)). On the other hand, in this paper, we do not take a stand\n\n\non whether the effects are driven by physical efficiency or markups, but the additional\n\n\ncosts of engaging in international trade we refer to might as well be viewed as requiring\n\n\nhigher per unit markups. An interesting avenue of future work is to incorporate the or\n\ndering and inventory holding costs described here and reassess the documented increase\n\n\nof markups after trade liberalizations (De Loecker et al. (2016), Brandt et al. (2017)).\n\n\nFinally, this paper emphasizes that trade liberalizations affect multiple", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001747:34:0:0", "start": 1059, "end": 1072, "surface": "quantity data", "probe_tag": "confusion", "probe_score": 0.7117, "luna_label": 0, "luna_reason": "Notes missing quantity data without citing analysis or substitute estimates."}]}, {"key": "rafael-206", "text": " of countries (albeit only 11 African countries are included)\nand comes to a similar premature deindustrialization for Sub-Saharan Africa and\nLatin America.\n\n\nThus, there is a dearth of literature focused on sub-Saharan Africa’s manufacturing experience, and those that do have utilized a limited number of SSA\ncountries due to data inadmissibility. This paper attempts to fill this void by assembling data on 41 sub-Saharan African countries from multiple sources including\nthe Groningen Growth and Development Center 10-sector Database, the Maddison Project Database, the World Development Indicators, and the International\nLabor Organization’s ILOSTAT database. Our data spans 1960-2016, which is\nthe longest panel data on output and employment shares on Sub-Saharan African\ncountries to our knowledge. If sub-Saharan African countries are experiencing\nprematurely deindustrialization, this has potentially negative ramifications for the\nregion’s economic development.\n\n\n1See also Dasgupta & Singh (2005).\n\n\n2", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001839:4:1:1", "start": 479, "end": 537, "surface": "Groningen Growth and Development Center 10-sector Database", "probe_tag": "keep", "probe_score": 0.9203, "luna_label": 1, "luna_reason": "Named existing database used to assemble panel data for economic analysis."}]}, {"key": "rafael-207", "text": "Figure 5. Work stoppages in Lao PDR, PNG, and Vietnam\n\nLao PDR\n\n\nNote: 95% confidence intervals for difference from Q1 are shown (rather than from zero). Including controls for respondent\ncharacteristics do not qualitatively change results. See Appendix Table 2 for regression tables with controls.\nSource: HFPS; Round 3 is used for Vietnam Jul-Sep 2020\n\nIndonesia and the Philippines - Like other countries discussed above, Indonesia and the Philippines\nexperienced a steep decline in mobility early in 2020, but unlike the others, they faced a pandemic-induced\nhealth crisis relatively early in 2020 and a prolonged period of depressed mobility (lower than -20 percent).\nFar-reaching mobility restrictions translated into employment shocks that were similarly widespread across\nthe welfare distribution (Figure 6). A wide range of sectors experienced work stoppages during early stages\nof the pandemic, including sectors such as retail, transportation, and finance in which significant shares of\nwealthier workers were employed. For example, in Indonesia, HFPS data indicate that more than half of\nworkers in the top 20 percent of the welfare distribution were employed in retail, transportation, or\n\n\n14", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001702:15:0:0", "start": 1058, "end": 1067, "surface": "HFPS data", "probe_tag": "keep", "probe_score": 0.9801, "luna_label": 1, "luna_reason": "HFPS data support a concrete employment-distribution finding."}]}, {"key": "rafael-208", "text": "**Data collection**\n\nQuantitative surveys were administered on tablets and paper questionnaires with all enrolled\nparticipants at two time points (wave 1, August 2018 - February 2019 and wave 3, June 2021 January 2022). Surveys included questions on employment and income, client history, sexual\nbehavior, HIV/STI risks, mental health, and gender-based violence. Surveys at the end of the study\nperiod included additional questions about changes in work and sexual activity related to COVID19. An additional survey was administered via phone to a subset of participants in the control arm\nonly (wave 2, July-October 2020), and included a reduced set of survey questions focused on\nwealth and income, income shocks related to COVID-19, food security, and sexual history.\n\nQualitative interviews were conducted with a subset of 20 wave 3 participants using a semistructured interview guide to elicit views on the intervention and to understand the effects of\nCOVID-19 on employment, income, client type, and work conditions. The interview guide was\ninformed by preliminary findings from the wave 3 quantitative surveys which indicated that\nparticipants had been experiencing changes in income and work activity related to COVID-19,\nthus the interviews served to gather additional perspectives and insights into the experiences of\nFSW during COVID-19. Interviews were approximately 60 minutes in duration and audio\nrecorded.\n\nAll data were collected in Kiswahili by trained research assistants at Innovations for Poverty\nAction Tanzania.\n\n**Data analysis**\n\nQuantitative data were analyzed in STATA version 15.1. Descriptive statistics were generated to\n1) assess the demographic characteristics of participants at enrollment (wave 1, 2018) and to\ncompare the overall study population with those who were reached for phone surveys (wave 2,\n2020) and at the end of the study period (wave 3, 2021); 2) examine self-reported data on\nemployment and income before and during COVID-19. Linear first difference regression with\nrobust standard", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001040:5:0:0", "start": 21, "end": 41, "surface": "Quantitative surveys", "probe_tag": "confusion", "probe_score": 0.1699, "luna_label": 0, "luna_reason": "Surveys were administered as part of the study's data collection."}]}, {"key": "rafael-209", "text": " and especially so in non-project\nareas. Local officials’ payment of their own property taxes increases substantially in non-project\nareas, and they also are more likely than project-area officials to report efforts to improve local\ntax collection.\n\nTo offer ideas and recommendations from research for further innovation in the design of\nsuch projects in Tanzania and beyond, we closely reviewed project documents to understand the\ngeneral design and theory of change on which these capacity building projects are founded. We\nfind that these projects share a common, global template that is being applied across different\ncountries and contexts, centered on the role of an Annual Performance Assessment which is expected to verify whether local governments have certain institutional features that are found in\nhigh state capacity countries: such as, existence of planning documents, council meeting minutes, audit reports, procurement tenders and the like. 26 Qualitative research has critiqued this\napproach to building state capacity as “isomorphic mimicry” (Andrews et al., 2017), whereby\ndeveloping countries are made to produce documents and establish protocols that resemble institutions in donor countries, but fail to effectively perform the functions of a state. The lack of\ndifference in measured outcomes of state functioning in the data from Tanzania offers quantitative evidence that is consistent with such critiques.\n\nAlternatively, the amounts committed or the scope of the capacity building component might\nbe too small to make a dent. 27 Further, the rationale for using a financial incentive approach is\n\n\n26See examples from Egypt (World Bank, 2018a), Ethiopia (World Bank, 2014b), India (West Bengal) (World Bank,\n2016a), Indonesia (World Bank, 2005), Uganda (World Bank, 2016b), and Vietnam (World Bank, 2014a).\n27For the project in Tanzania, the total donor contribution was US$255 million, but only US$54 million was\n\n\n27", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000123:29:1:0", "start": 1357, "end": 1375, "surface": "data from Tanzania", "probe_tag": "confusion", "probe_score": 0.38, "luna_label": 1, "luna_reason": "Tanzanian data support quantitative evidence about unchanged state-functioning outcomes."}]}, {"key": "rafael-210", "text": " and social risk management documents as well as\ndetailed engineering designs of civil works and carrying out land acquisition, and resettlement\nand rehabilitation associated with upgrading works under Part 1(a) and maintenance of the road\ncorridor for five years post-construction.\n\n45. This component will support civil works for widening and upgrading of the approximately 105 km-long\nKoboko-Yumbe-Moyo road corridor to bituminous paved road standards adopting the World Bank’s\nguidelines and requirements with respect to the procurement, social and environmental safeguards. The\nworks will be maintained for a period of five years post completion of construction of works. This component\nwill also support the associated (i) construction supervision consultants, (ii) third-party audit consultants to\nperform semi-annual integrated performance audits covering, among others, engineering designs,\nmanagement of social and environmental issues including implementation of the SEA/SH action plan, and\nquality assurance, (iii) safeguards management consultants for implementation of the Resettlement Action\nPlans, (iv) Consultants/Non-Governmental Organizations for implementation of Gender Based Violence,\nViolence Against Children, and HIV/AIDS action plans, (v) Road User Satisfaction Survey consultants to carry\nout baseline, midterm, and end-stage user satisfaction surveys - disaggregated by gender, refugees and\nhosts, (vi) consultants for monitoring and evaluation of Project’s outcome and intermediate indicators; and\n(vi) preparation of environmental and social risk management documents as well as detailed engineering\ndesigns of civil works and carrying out land acquisition, and resettlement and rehabilitation.\n\n\nPage 21 of 80", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000050:25:1:0", "start": 1265, "end": 1294, "surface": "Road User Satisfaction Survey", "probe_tag": "confusion", "probe_score": 0.3883, "luna_label": 0, "luna_reason": "Consultants will carry out future baseline, midterm, and end-stage satisfaction surveys."}]}, {"key": "rafael-211", "text": "**The World Bank**\nNational Youth Opportunities Towards Advancement Project (P179414)\n\n\n\n|Col1|component 1 or under recognition of prior learning and received a certificate from the National Industrial Training Authority.|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|Youth beneficiaries receiving business development support and grant|This indicator measures the youth who received business development support under component 2 such as access to networks and open market. The targets are cumulative, unique individuals.|Bi-annual |MIS |MIS data reported by MSEA |MYAAS through MSEA |\n|Host community youth beneficiaries receiving business development support and grant|This indicator measures host community youth beneficiaries in Garissa, Turkana and Wajir Counties who received business development support under component 2 such as access to networks and open market. The targets are cumulative, unique individuals.| Semi- Annual |MIS |MIS data reported by MSEA |MSEA |\n|Refugee youth beneficiaries receiving business development support and grant|This indicator measures refugee youth beneficiaries in Garissa, Turkana and Wajir Counties who received business development support under component 2 such", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:002639:59:0:0", "start": 583, "end": 591, "surface": "MIS data", "probe_tag": "confusion", "probe_score": 0.2639, "luna_label": 0, "luna_reason": "Names MIS data without showing an analyzed finding or substantive data use."}]}, {"key": "rafael-212", "text": "Figure 1: A district map of India\n\n\nNotes: Light gray lines denote district borders, while black lines\n\ndenote state borders. The gray shaded area is the Ganga Basin\n\n(240 districts), while the black shaded area is our group of four\n\n“treatment” districts (Kanpur, Unnao, Fatehpur, and Rae Bareli).\n\n\n29", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:006810:30:0:0", "start": 12, "end": 33, "surface": "district map of India", "probe_tag": "confusion", "probe_score": 0.4457, "luna_label": 0, "luna_reason": "Figure caption describing a map, not a standalone cited data resource."}]}, {"key": "rafael-213", "text": "19-Impact-on-Refugees-in-Ethiopia-Results-from-a-High-Frequency-Phone-Survey-of-Refugees.pdf)\nIraq: _Report Forthcoming_\n[Kenya: See https://www.kenyacovidtracker.org/rrps and Socioeconomic impacts of COVID-19 in Kenya](https://www.kenyacovidtracker.org/rrps)\n[Uganda: For information on refugees see Monitoring Social and Economic Impacts of COVID-19 on Refugees in Uganda :](https://documents1.worldbank.org/curated/en/682171613766616044/pdf/Monitoring-Social-and-Economic-Impacts-of-COVID-19-on-Refugees-in-Uganda-Results-from-the-High-Frequency-Phone-Survey-First-Round.pdf)\n[Results from the High-Frequency Phone Survey - First Round, and Third Round Results for Ugandans can be found here. A](https://documents1.worldbank.org/curated/en/682171613766616044/pdf/Monitoring-Social-and-Economic-Impacts-of-COVID-19-on-Refugees-in-Uganda-Results-from-the-High-Frequency-Phone-Survey-First-Round.pdf)\n[policy paper comparing refugee and Ugandan welfare can be found here.](https://documents.worldbank.org/en/publication/documents-reports/documentdetail/794251624902794307/one-year-in-the-pandemic-results-from-the-high-frequency-phone-survey", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000604:17:3:0", "start": 604, "end": 631, "surface": "High-Frequency Phone Survey", "probe_tag": "confusion", "probe_score": 0.8228, "luna_label": 1, "luna_reason": "Named existing phone survey cited as the source of linked results."}]}, {"key": "rafael-214", "text": " and mostly kept in paper form.** Procurement-related records\n(from advertisements to final invoices) are also maintained in hard copy. The execution of Program procurement\ntransactions through the JONEPS will ensure the systematic tracking, recording, and processing of procurement. It will also\nfacilitate the enhancement of procurement performance and the retrieval of procurement records when needed. The\nJONEPS developed a contract management module. However, it is not fully operational, and it is being enhanced to\naccommodate works contracts as well. In parallel, the JONEPS is having discussions with the GFMIS to develop an\nApplication Programming Interface (API) to connect both systems and consolidate data. Once the contract management\nfunction is tested, consolidation will be meaningful.\n\n\n**68.** **According to Article 84 of the Procurement Bylaw No. 8/2022, Jordanian courts will be referred to for the settlement**\n**of disputes during the execution of contracts.** However, the contract may provide other dispute resolution methods,\nstarting with amicable settlement and escalating to arbitration. The contracting parties may have recourse to a third party\nfor the settlement of disputes using conciliation and mediation. This could be done by appointing dispute experts or dispute\nreview boards, along with the related appointment procedures for such conciliators.\n\n\n_Internal Controls and Internal Audit_\n\n\n**69.** **The overall control environment continues to be acceptable for Program implementation** . Internal controls and the\ninternal audit function are governed by the applicable Financial By-law (1994) and its Amendment (2015) and the Financial\nControl By-law (2011) and its Amendment (2015). Each ministry has its own internal audit unit in the Finance Department,\nand an Internal Control Unit (ICU) is responsible for the effective operation of the internal financial control system. In the\nministries participating in the Program, there is also a financial control unit comprised of employees from the MOF, which\nperforms a", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000181:75:1:0", "start": 34, "end": 61, "surface": "Procurement-related records", "probe_tag": "confusion", "probe_score": 0.0901, "luna_label": 0, "luna_reason": "Routine procurement records maintained for administrative tracking, not substantive data use."}]}, {"key": "rafael-215", "text": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda (P176747)\n\n\n\n|Col1|training, disaggregated by age and disability status.|Col3|Col4|completion to the MGLSD or the PSFU.|each month.|\n|---|---|---|---|---|---|\n|Women in RHDs|||| ||\n|Refugee women|| ||||\n|Beneficiaries supported by the project stating that their involvement in decision- making over household expenditures has increased|Number of women that are empowered in the household sphere. Beneficiaries are the women entrepreneurs that benefit from at least two services supported by the project.| Baseline data when enterprises start, annual surveys from year 2. |Surveys of a panel of women entrepreneur s benefiting from the project. |This will be calculated through annual surveys of enterprises participating in the project. |MGLSD to administer the surveys, and compile and report the data. |\n|Women in RHD|| ||||\n|Refugee women|||| ||\n|Women microenterprises accessing credit from a formal financial institution (Number", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000025:52:0:0", "start": 864, "end": 878, "surface": "annual surveys", "probe_tag": "confusion", "probe_score": 0.0743, "luna_label": 0, "luna_reason": "Future project surveys will generate the indicator data."}]}, {"key": "rafael-216", "text": ". The with-project scenario in\nthe ex-ante analysis assumed pavement overlay as the intervention type; during the additional financing stage, this\nwas changed to full structural rehabilitation due to the steep increase in heavy goods vehicle composition on the\nproject roads. The ex-post analysis reflects this intervention change. Both scenarios include annual routine\nmaintenance and future periodic maintenance delivered through an OPRC covering rehabilitation and maintenance\nover a 10-year contract period. After that period, responsive condition-based maintenance is assumed.\n\n\n4. The ex-post economic analysis evaluated the improved road sections, replicating the appraisal evaluation based on\nthe revised intervention type, actual costs, and actual traffic volumes at completion.\n\n\n5. Based on the feasibility study and technical design reports, the pre-project road surface conditions measured in\n2019, expressed as the International Roughness Index (IRI), were as follows: Lot 1 (Koloni–Soroti of 27.3 km) = 4.08\nm/km; Lot 2A (Soroti–Lira of 123.8km) = 4.90 m/km; and Lot 2B (Lira–Kamdini of 66.5 km) = 4.50 m/km.\n\n\n6. The intervention type on the project roads changed fundamentally between appraisal and implementation — from\na pavement overlay assumed at appraisal to full structural rehabilitation, driven by the exponential increase in heavy\ngoods vehicle traffic on the corridor, as shown in Table 1. Pavement thickness was increased on both Lot 1 and Lot\n2 to accommodate the substantially higher traffic loading, while the 20-year design life target remains unchanged\nin the ex-post analysis. Two additional scope changes are noted: the weighbridge on Lot 1 at km 46+700 was\ntransferred from IDA to Government of Uganda financing due to insufficient time for completion under IDA, and\nthe OPRC contract, originally planned for signing in late 2014/early 2015, was not signed until 2018.", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:003408:40:1:0", "start": 929, "end": 958, "surface": "International Roughness Index", "probe_tag": "confusion", "probe_score": 0.3722, "luna_label": 1, "luna_reason": "IRI measurements from feasibility and technical design reports support reported road conditions."}]}, {"key": "rafael-217", "text": "**Table 1.** List of Variables and Data Sources\n\n\n**Indicator** **Description** **Source**\n\n\n\nSocial trust Percentage of respondents stating that, “Generally speaking,\nmost people can be trusted”; survey waves mapped to 4-year\nperiods as follows: survey 2005-2009 (period 1), survey 20102014 (period 2), survey 2017-2019 (period 3)\n\nWBG engagement WBG own commitment (% of GDP): Total amount committed\n\nfor projects related to domestic resource mobilization by\napproval fiscal year as a share of the recipient country’s GDP\n(scaled by factor 1,000 for better readability)\n\n\n_Source:_ Authors’ compilation.\n\n\n\nWorld Values Survey\n\n\nWorld Bank (public and internal\ndata) as reported in Table A3 in\nthe appendix\n\n\n\n100\n\n\n80\n\n\n60\n\n\n40\n\n\n20\n\n\n0\n\n\n100\n\n\n80\n\n\n60\n\n\n40\n\n\n20\n\n\n0\n\n\n\n6 8 10 12\n\nLog GDP per capita\n\n\n0 100 200 300 400\n\nTrade Openness\n\n\n\n**Figure 2.** Economic Fundamentals and Revenues, 2016-2019\n\n\n\nShare of Agriculture in GDP\n\n\n0 20 40 60 80 100\n\nAge Dependency Ratio\n\n\n\n100\n\n\n80\n\n\n60\n\n\n40\n\n\n20\n\n\n0\n\n\n\n_Notes:_ The sample consists of 182 countries. The dotted line represents the result of a bivariate linear regression of revenue\non the indicator shown on the x-axis (see Table A2 in the appendix).\n_Source:_ Authors’ analysis based on the variables and data sources described in Table 1.\n\n\nThe indicator of technological readiness (see Table 1, Panel D) is countries’ score on the\n“Technological Readiness Index” from the World Economic Forum’s Global Competitiveness Report.\n\n\n10", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001867:11:0:2", "start": 1419, "end": 1448, "surface": "Technological Readiness Index", "probe_tag": "confusion", "probe_score": 0.5856, "luna_label": 1, "luna_reason": "Named index from the Global Competitiveness Report used as an analytical indicator."}]}, {"key": "rafael-218", "text": " (Net Enrolment Ratio), and the percentage of overage refugees included in each education cycle.\n\n\nIf available, age:\n\n - % primary/secondary school-aged refugee students\nenrolled in national education system’s primary or\nsecondary schools;\n\n\n - [% overage (see: http://uis.unesco.org/en/glos-](http://uis.unesco.org/en/glossary-term/over-age-students)\n[sary-term/over-age-students) refugee students](http://uis.unesco.org/en/glossary-term/over-age-students)\nenrolled in national education system’s primary\nor secondary schools.\n\n\n**Methods and Guidance:** The UNESCO Institute\n[for Statistics (http://uis.unesco.org) has guidelines on](http://uis.unesco.org)\nthe compilation of data to calculate Gross Enrolment\nRatio. However, enrolment and population data referring only to refugees should be considered.\n\n\nIf disaggregation by age is possible, and therefore it is\npossible to calculate the Net Enrolment Ratio and the\npercentage of overage students enrolled in each education cycle, guidance on Net Enrolment Ratio and\ndefinition of “overage students” can also be found on\n[http://uis.unesco.org.](http://uis.unesco.org)\n\n###### Data Sources\n\n\nEducation data may be derived from administrative\nsources typically coordinated and disseminated through\nthe Ministry of Education. Additional sources may include school registers, school surveys or census for\ndata on enrolment by level of education; population\ncensus or estimates for school-age population. UNHCR’s\nRefugee Education Information Management System\n(REMIS) may also serve as a complementary data source", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000789:21:1:4", "start": 1413, "end": 1430, "surface": "population\ncensus", "probe_tag": "confusion", "probe_score": 0.204, "luna_label": 0, "luna_reason": "Potential source is merely listed; no census data finding or analysis is attributed."}]}, {"key": "rafael-219", "text": "**The World Bank**\nAgricultural Employment Support for Refugees and Turkish Citizens through Enhanced Market Linkages (P171543)\n\n\nincorporated inputs from local employers had double the impact on employment and earnings relative to\na traditional program with no inputs from employers. Building on international evidence, the proposed\nproject aims to provide skill training to workers in line with the demands of employers, which will be\ndetermined through a needs assessment and updated regularly.\n\n\n68. **While wage subsidies can be effective tools to improve the employability of vulnerable**\n**populations, consideration should be given to how to avoid deadweight losses.** Lessons learned in other\ncountries identify four potential effects that would lead to a program being ineffective or detrimental to\nemployment: 55 [^55: Hirshleifer, Sarojini, McKenzie, David, Almeida, Rita, and Cristobal Ridao-Cano. 2014. _The Impact of Vocational Training for_\n_the Unemployed: Experimental Evidence from Turkey (English)_ . EnGender Impact: the World Bank's Gender Impact Evaluation\nDatabase. Washington, DC: World Bank Group. _[http://documents.worldbank.org/curated/en/209871468310469519/The-](http://documents.worldbank.org/curated/en/209871468310469519/The-impact-of-vocational-training-for-the-unemployed-experimental-evidence-from-Turkey)_\n_[impact-of-vocational-training-for-the-unemployed-experimental-evidence-from-Turkey](http://documents.worldbank.org/curated/en/209871468310469519/The-impact-of-vocational-training-for-the-unemployed-experimental-evidence-from-Turkey)_ . Bördős, K., Csillag, M., and A. Scharl.\n2015. _What Works in Wage Subsidies for Young People: A Review of Issues, Theory, Policies and Evidence_ (No.\n994898973402676). International Labour Organization.] (a) employer may substitute a worker eligible for the subsidy with another who is\nineligible; (b) employers may use the wage subsidy to hire a worker that they would have hired even\nwithout the subsidy; (c) by improving the employability of the workers with their initial human capital, the\nsubsidy", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000114:31:0:0", "start": 1066, "end": 1099, "surface": "Gender Impact Evaluation\nDatabase", "probe_tag": "confusion", "probe_score": 0.4461, "luna_label": 1, "luna_reason": "Named database cited as the source for an evaluation informing program lessons."}]}, {"key": "rafael-220", "text": "**The World Bank**\nStrengthening Lebanon’s Covid-19 Response (P178587)\n\n\n**C.** **Learning Agenda**\n\n\n14. **The project will support adaptive learning throughout implementation, as well as from international**\n**organizations including the WHO, International Monetary Fund (IMF), Centers for Disease Control (CDC), United**\n**Nations Children's Fund (UNICEF), and others.** It will adjust to emerging technical, social, and economic evidence,\nas applicable, and incorporate lessons learned from ongoing global vaccine rollout and COVID-19-related service\ndelivery. In Lebanon, this learning agenda involves a continuation of on-going World Bank technical assistance\n(TA), including:\n\n\n\n\n- **Technical:** Monitoring of vaccine deployment readiness assessments and technical support to regular\nupdates of the NDVP\n\n- **Social behaviors** : The following data collection initiatives will be undertaken to improve knowledge and\nunderstanding of perceptions and attitudes towards vaccination as well as performance of the vaccination\ninitiative **:**\n\n\n\na. Facebook Surveys to assess beliefs and attitudes towards COVID-19 vaccination and identify\n\ncauses of hesitancy\nb. Testing of communication messages to improve uptake of vaccines\nc. Iterative Beneficiary Monitoring (IBM): an iterative feedback loop that collects information\n\ndirectly from beneficiaries and identifies challenges at the local level\nd. Third-party monitoring of vaccine deployment: Performance score cards to monitor\n\n\n\nperformance by vaccination sites and identify challenges in vaccination deployment\n\n- **Digital vaccination registry:** An innovative approach for data transparency based on the digitalization of\nvaccination data has significantly enhanced public trust in the COVID-19 vaccination program. This approach\nutilizes the digital platform pioneered by the Inter-ministerial and Municipal Platform for Assessment\nCoordination and Tracking (IMPACT), hosted by the oversight body Central Inspection Bureau (CIB).\n\n\n\n**II.", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000000:15:0:0", "start": 1052, "end": 1068, "surface": "Facebook Surveys", "probe_tag": "confusion", "probe_score": 0.6936, "luna_label": 0, "luna_reason": "Planned surveys will collect data for future vaccination learning."}, {"key": "jdc_operational:000000:15:0:1", "start": 1576, "end": 1604, "surface": "Digital vaccination registry", "probe_tag": "confusion", "probe_score": 0.1188, "luna_label": 1, "luna_reason": "Registry data supports transparency and a concrete public-trust finding."}]}, {"key": "rafael-221", "text": "Annex 11\nPage 2 of 4\n\n\nThe country's General Environmental Law, expected to be promulgated very shortly, is divided into four\nmajor titles:\n\n\nTitle 1: Concepts, Objectives and General Principles, and Institutional\nOrganization\nTitle 2: Protection of Environments\nTitle 3: Protection of Animal and Plant Species\nTitle 4: Regulation of Pollution\n\n\nImplementing decrees should quickly specify the conditions for putting the main chapters of this\nframework legislation into effect.\n\n\n_Other Agencies And Bodies Involved_\n\n\nAs regards environmental education, the Directorate of the Environment maintains close collaboration\nwith the National Education Research and Pedagogic Information Center _[Centre de Recherche, et de_\n_Production dInformation de l 'Education Nationale-_ CRIPEN], in particular through the formulation of an\n\nawareness campaign strategy on environmental problems.\n\n\nInfrastructure facilities in the education sector are provided by the Directorate of Housing, Urban\nDevelopment, Environment, and Regional Development (DHU). However, as the current reform process\nis not yet completed, a number of serious malfunctions are preventing the Directorate from perforning\nthe role of executing agency assigned to it in the past. The Planning Unit of the Ministry of Education\nwill be the contracting authority's representative for implementation of this project.\n\n\nBecause the system for gathering and analyzing data on the public health system is no longer operational,\nas was confirmed during the visit made to Djibouti's Pelletier Hospital and to the Djibouti-City health\ndistrict, reliable country-wide epidemiological data are unfortunately unavailable. This state of affairs\napplies to the school population in particular. Under these conditions, it will be difficult to define and use\nindicators capable of measuring the success of actions to mitigate the environmental impacts of the\nproject.\n\n\n**Potential Impacts of the Project**\n\n\nThe mission by an IDA environment specialist to the Republic of Djibouti in June 2000 confirmed the\ndegraded situation of the sanitary facilities in all of the schools visited. Discussions with school staff and\nparents of students revealed the concern felt by the latter regarding the", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:014881:65:0:0", "start": 1605, "end": 1638, "surface": "country-wide epidemiological data", "probe_tag": "confusion", "probe_score": 0.3364, "luna_label": 0, "luna_reason": "States epidemiological data are unavailable without substitute estimates or analysis."}]}, {"key": "rafael-222", "text": " this paper, as they measure\n\nthe management and delivery of aid while the remaining 5 measure quality of country systems etc.\n\n(Table 1).\n\n\n**Table 1: Ten PD Survey Indicators**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Principle|Indicators|How Measured|\n|---|---|---|\n|Alignment|**Ind 3:** Aid flows are aligned with national priorities (aid on budget)|Aid flows reported in the annual budget by government/aid flows actually disbursed by development partners (%)|\n|Alignment|**Ind 4:**Strengthen capacity by coordinated support (technical assistance)|Amount of coordinated technical cooperation / total technical cooperation (%)|\n|Alignment|**Ind 5a:**Use of country PFM systems|Use of PFM/total assistance (%)|\n|Alignment|**Ind 5b:**Use of country procurement systems |Use of procurement / total assistance (%)|\n|Alignment|**Ind 6:**Strengthen capacity by avoiding parallel project implementation units (PIUs)|Number of parallel PIUs|\n|Alignment|**Ind 7:**Aid is more predictable|Actual aid flows recorded by the government/scheduled aid flows by development partners (%)|\n|Alignment|**Ind 8:** Aid is untied|OECD-DAC data|\n|Harmonization|**", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:005210:6:1:0", "start": 159, "end": 179, "surface": "PD Survey Indicators", "probe_tag": "confusion", "probe_score": 0.3121, "luna_label": 0, "luna_reason": "Standalone table caption naming survey indicators, not an independently used data resource."}]}, {"key": "rafael-223", "text": " recommendations\nare presented for each of the five countries. Overall\nrecommendations include a call to take advantage of\nexisting regional interagency initiatives as platforms for\nregional advocacy around child labour law and policy,\nsharing of expertise across borders, and tracking regional\nchild labour trends and program outcomes.\n\n\nCapacity development and resources are needed among\nactors in all relevant sectors. Child labour data may need\nsupport to harmonize their systems and increase data\nevidence. Investments are needed to develop the social\nservice workforce in all countries, as well as to build\nteachers’ protection capacity.\n\n\n1 Matz, Peter, (2016). Child labour within the Syrian refugee response:\nStocktaking report\n2 UNHCR/WFP/UNICEF, (2021). Vulnerability Assessment of Syrian Refugees\nin Lebanon\n\n\n\n6", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000489:4:2:0", "start": 423, "end": 440, "surface": "Child labour data", "probe_tag": "confusion", "probe_score": 0.4352, "luna_label": 0, "luna_reason": "Generic data resource mentioned for system harmonization, without an attributed finding."}]}, {"key": "rafael-224", "text": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n\n**ANNEX 3: Gender Analysis and Action Plan**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Problem statement. In Ethiopia, there is a notable gender gap in the existing ID system (Kebele ID) coverage. According to the ID4D-Findex Survey (2017), 36 percent of the population ages 18 and older lack a Kebele ID, with significant gender gap of 46 percent of women lacking one, compared to 25 percent of men.|Col2|Col3|\n|---|---|---|\n|**ANALYSIS:** **Gender gaps identified**|**ACTIONS:** **Proposed actions Taken to address gaps**|**INDICATORS**: **How bridging the gap** **will be measured**|\n|**Women have less knowledge about benefits** **of having an ID.**Country-specific research, including a Social Risk Analysis and a Gender Gap in ID Study, outline low literacy, a general lack of awareness on the day-to-day use of ID, perceived irrelevance of formal identification, and limited knowledge of individual rights as key factors contributing to lower Kebele ID enrolment by women. Based on the most recent data from 2017, the adult (age 15 and above) literacy rate for men is 59 percent, compared to 44 percent for women (World Bank 2022), which can make it harder for women to navigate the ID registration process.|", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000005:48:0:0", "start": 283, "end": 301, "surface": "ID4D-Findex Survey", "probe_tag": "confusion", "probe_score": 0.6765, "luna_label": 1, "luna_reason": "Named 2017 survey supports reported gender gaps in Kebele ID coverage."}, {"key": "refugee_pads:000005:48:0:1", "start": 1118, "end": 1132, "surface": "data from 2017", "probe_tag": "confusion", "probe_score": 0.1756, "luna_label": 0, "luna_reason": "Bare date-only qualifier cannot inherit the World Bank source or literacy finding."}]}, {"key": "rafael-225", "text": " due to irregularities and lack of transparency in local tax administration, obstruction of\ninvestments by some unscrupulous politicians, and a general perception that LGs do not offer useful support,\nwithin their mandates, for local private sector development 11 . In addition, little support is provided to local\nfirms by municipal LGs, even though they have the mandate to provide support to micro-enterprises and\nother firms through the Commercial Office. There is also an absence of meaningful public private dialogue,\nparticularly in terms of consulting the private sector in the development of local development plans. The\nstudy made three main recommendations to LGs in Uganda: (i) to make infrastructure investments that are\nbetter prioritized according to local economic potentials – building on the major recent investments in roads\nand connectivity to transition to other strategic investments in tourism site development, market\n\n4 Arch Design Ltd, 2012 – Municipal Assets Inventory and Conditions Assessment Final Report\n5 From UGX 37 million in 1993/4 to UGX1.6 trillion in 2011/12.\n6 Local Government Finance Commission (2012) **.** Review of Local Government Financing: Financing Management and\nAccountability for Decentralized Service Delivery.\n7 FDA section 7.2\n8 DDEG has replaced the Local Development Grant (LDG) as part of the broader GoU IGFT reform\n9 For example, unit costs of paving 1 km of urban road ranges between US$800,000 to US$1 million and for a primary drainage\n(with box culverts at road crossings and armoflex linings) about US$500,000 to US$1.1 million per km.\n10 USAID (2015) Ugandan Decentralization Policy and Issues Arising in the Health and Education Sectors: A Political Economy\nStudy. October 2015.\n11 World Bank 2016. _Uganda - Repositioning local governments for economic growth_ . Washington, DC: World Bank.\n\n\n2", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000006:9:2:0", "start": 980, "end": 1032, "surface": "Municipal Assets Inventory and Conditions Assessment", "probe_tag": "confusion", "probe_score": 0.6535, "luna_label": 1, "luna_reason": "Named assessment report cited as an existing source."}]}, {"key": "rafael-226", "text": "Iganga, Kayunga, Mukono, Wakiso, Luwero, Mityana and Buikwe. The details of the\nprojects, their scope (size and area) are presented as annexures herein. The ESMP\nfurther covers the environmental and social aspects of the project during construction\nand operational phases.\n\n\nMethodology\nThe methods used in the development of this ESMP included but were not limited to:\n\nSite inspection and observation\nVisits were made to the proposed project areas during the course of developing this\nESMP. Site visits were conducted with the aim of identifying potential causes of\nenvironmental and social risks. This was aimed at ensuring that appropriate\nenhancement and/or mitigation measures are prescribed to manage any potential\nrisks.\n\nConsultation with stakeholders\nConsultations were carried out through interviews and discussions with relevant\nstakeholders including communities to ensure public participation in the ESMP\ndevelopment process.\n\n\nREA Team Engaging Works Supervisor BDL Community Engagement in Makonge & Kyambogo\nVillage,\nDocument Review\nThe Literature reviewed included; Background data concerning the local communities,\nincluding from the UBOS National Census (2014); Demographic and Health Survey\n(2006), and District Development plans, legal and policy frameworks, ESMF for ERT\nIII and the World Bank safeguard requirements among others.\n\n\n2", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:008341:7:0:1", "start": 1181, "end": 1210, "surface": "Demographic and Health Survey", "probe_tag": "confusion", "probe_score": 0.8644, "luna_label": 1, "luna_reason": "Existing 2006 survey cited as reviewed background data."}]}, {"key": "rafael-227", "text": "The general procedure to calculate the fuel shock's indirect effects is as follows. Firstly, we map the\n\n\nhousehold consumption expenses to the IO matrix sectors. Secondly, we match the Gasoline and Diesel\n\n\nitem to a fuel sector in the IO matrix. We then determine the price shock according to the forecasted price\n\n\nchange. 5 [^5: We calculate a weighted price shock based on gasoline and diesel annual sales based on Paraguay’s Ministry of Industry and\nCommerce data.] Finally, the model is solved, and the percentage change in final prices is matched with the items\n\n\nin the household survey according to the mapping of the first step. The purchasing power variation due to\n\n\nthe percentage change in final prices is calculated similarly to the Paasche Variation for the direct effect\n\n\nshock.\n\n\nb. Data manipulation\n\n\nData comes from two main sources, the “Encuesta de Ingresos y Gastos 2011-2012 (EIG)” and the\n\n\n“Encuesta Permanente de Hogares 2019 (EPH)”, both household surveys available in Paraguay. The EIG is\n\n\na household survey that contains expenditure records for almost 6,000 households and is representative\n\n\nat the national level, urban and rural areas, the country’s capital, Asunción, and the following departments:\n\n\nSan Pedro, Caaguazú, Itapúa, Alto Paraná, and Central. The EPH surveys 5,099 households and is\n\n\nrepresentative at the national level, urban and rural areas, and Asunción, and the following departments:\n\n\nSan Pedro, Caaguazú, Itapúa, Alto Paraná, Central, and a group assigned as “others” that agglomerates the\n\n\nrest of the deparments in the country. The EPH survey contains information on unemployment, income,\n\n\nand the main demographic characteristics of the household that allow measurement of household welfare\n\n\nin Paraguay.\n\n\nIn order to construct market income and disposable income (see\n\n\n) following the CEQ approach, we use income and demographic information from\n\n\nthe EPH. However, as", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000898:14:0:1", "start": 590, "end": 606, "surface": "household survey", "probe_tag": "confusion", "probe_score": 0.7077, "luna_label": 1, "luna_reason": "Household survey data are used to map price effects and calculate purchasing-power variation."}]}, {"key": "rafael-228", "text": " made 11 key findings._\n\n\n1. Refugees have similar access to mobile networks as the global population. However, when we look\nat the urban and rural split, rural refugees have less access to connectivity and are often overlooked\nin connectivity initiatives.\n\n\nGiven that developing countries today host more than 80 per cent of the world’s refugees, the\nperception has arisen that a large part of the world’s refugee population must live in areas not well\ncovered by mobile networks. UNHCR’s research has determined that this is not the case. Using the\nmost recent data, UNHCR has found that 93 2 [^2: Affordability constraints – the most significant hurdle to overcome in connecting refugees – cause\naverage phone ownership and internet access for refugee households to be much lower than for\nglobal households.] per cent of all refugees live in places that are covered\nby at least a 2G network, and that 62 per cent live in locations covered by 3G networks.\n\n\nHowever, there are large differences in the availability of mobile networks when the urban and rural\ndata are examined separately. Unsurprisingly, urban areas tend to have better coverage. Ninety per\ncent of refugees living in urban areas are covered by 3G networks, similar to the proportion of global\nurban population living in 3G areas (89 per cent).\n\n**Refugees vs. Global Population:**\n**Mobile Network Coverage**\n\n100%\n\n\n\n80%\n\n\n60%\n\n\n40%\n\n\n20%\n\n\n0%\n\n\n\nRefugees* Global\n\nPop**\n\n\n\n\n\nownership\n\n\n\n\n\n\n\n**Global Household**\n**Phone Ownership**\n\n\n\n\n\nFigure 10: Refugee\n\n\n\nversus global\nhousehold phone\n\n\n\n**Refugee Household**\n\n\n\n\n\n\n\n\n\n\n\n\n\nRefugees* Global\n\nPop**\n\n\n\nRefugees* Global\n\nPop**\n\n\n\n\n### **VS.**\n\n\n\nRural Urban Total\n\n3G 2G No Coverage\n\n\nIn rural areas, while coverage and quality is progressively improving thanks to increasing mobile\nnetwork penetration, refugees risk being overlooked in these expansion plans. For instance, only\n17 per cent of rural refugees live", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000123:6:1:0", "start": 1057, "end": 1077, "surface": "urban and rural\ndata", "probe_tag": "confusion", "probe_score": 0.5276, "luna_label": 1, "luna_reason": "Urban and rural data are analyzed to establish coverage differences."}]}, {"key": "rafael-229", "text": "communities and schools. The ECEEP, therefore, i s designed to lay the ‘groundwork’ for\nextensive reforms at the ECE level (with significant impacts expected also at the primary\nlevel) and build on the innovative and successful models o f education provision implemented\nin Egypt and in the region and on the strengths and comparative advantages of the specific\ndevelopment partners (World Bank, CIDA, and WFP).\n\n\n16. The proposed project supports the Bank’s strategy in Egypt. Key objectives o f the Egypt\nCountry Assistance Strategy (CAS, June 2001) are the reduction - f poverty and\nunemployment through: (i) upgrading human capital; (ii) removing obstacles to private sector\ndevelopment; and (iii) addressing critical policy issues o f agriculture development and water\nresource management. The proposed project directly supports the attainment - f the first\nobjective (upgrading human capital) through the provision o f ECE programs o f adequate\nquality. The proposed project is also consistent with the MENA Regional Strategy (2002),\nwhich includes ‘education’ as one - f the focus areas. The project builds on the\nrecommendations - f the ECE Strategic Options paper (2002) prepared by the World Bank\n\nand endorsed by the GOE.\n\n\n17. The ECEEP has been prepared in a participatory manner with the MOE as well as CIDA and\nWFP and is also jointly financed. In this project the World Bank is leveraging about US$90\nmillion o f Government and donor funds with US$20 million o f Bank loan.\n\n\n**B.** **PROJECT DESCRIPTION**\n**1.** **Lending instrument**\nSpecific Investment Loan (SIL)\n\n\n**Project Financing Data**\n\n**[XI Loan** [ **]Credit** [ **] Grant** [ **] Guarantee** [ **]Other:**\n\n\n\n**Commitment fee:** 0.85", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000040:9:0:0", "start": 1592, "end": 1614, "surface": "Project Financing Data", "probe_tag": "confusion", "probe_score": 0.1326, "luna_label": 0, "luna_reason": "Standalone table header, not a substantive data-use mention."}]}, {"key": "rafael-230", "text": " Grade Six, as determined by the primary\nschool, allows one to enter upon lower secondary education ( _enseignement moyen_ ), which consists of four years.\nCompletion of basic education is certified with the _Brevet d’Etudes Fondamentales_ (BEF), which is granted upon successful\ncompletion of examinations. In 2019/2020, about 39 percent of primary schools were community schools, established\nand managed by the community 5 [^5: 46 percent were public schools and 15 percent private (Statistics Education Yearbook 2019-2010)] . While the primary school system is officially bilingual, only 7 percent of students were\nenrolled in bilingual schools. The main language of instruction is French for 87 percent of students and Arab for 6 percent.\nMulti grade teaching is common, with 40 percent of teachers in a classroom teaching two or more grades.\n\n\n7. **The school population is mostly composed of primary-school children, of which about 63 percent are at the**\n**appropriate age** . According to the Fourth Household Living Standards Measurement Survey ( _Enquête sur la_\n_Consommation des ménages et le le Secteur Informel au Tchad_ - ECOSIT IV), roughly three-quarters (75 percent) of inschool children were enrolled in primary school in 2018, followed respectively by those enrolled in lower secondary (15\npercent), upper secondary (6 percent), University (2 percent) and pre-primary ( 1 percent). The distribution of students\nby age group within school levels shows that a meaningful number of them were not enrolled at the appropriate age. As\nshown in Graph 1, the proportion of students enrolled at the right level of education decreases as one moves to higher\nlevels, up to upper secondary: about 70 percent of children attending pre-primary were at the appropriate age; 63\npercent for primary; 37 percent for lower secondary; and 22 percent for upper secondary.\n\n\n3 United Nations High Commissioner for Refugees", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000009:3:1:0", "start": 1019, "end": 1064, "surface": "Household Living Standards Measurement Survey", "probe_tag": "confusion", "probe_score": 0.8337, "luna_label": 1, "luna_reason": "ECOSIT IV survey supplies enrollment figures for 2018."}]}, {"key": "rafael-231", "text": "A counterpart shortfall meant the project could not complete implementation/roll out of the\nconsultancies/studies financed under component 1, so $6.3 million remained uncommitted. SDR 2.08\n($2.94 million) of this was reallocated to KCC. The rest was cancelled. Disbursements from GOU were\nlower than expected due to this cancellation.\n\n\n**6. Sustainability**\n\n\n_6.1_ _Rationale for sustainability rating:_\nLikely\n\n\nIn the design phase, the teams took care to maximize sustainability of the system being tested, by building\nin the following: (i) efforts to enhance the local revenue base; (ii) affordability of investments; (iii)\nstakeholder participation; (iv) per capita investment level within government’s fiscal potential; (v) LG and\ncommunity responsibility for development and O&M costs; and (vi) charge of user fees. These contributed\nto the likely sustainability of the main project results, namely the decentralized 'system' that included the\ngrants and the capacity improvements in LGs. However, one element of the system, namely the ability to\nraise local revenues necessary for the O&M of local infrastructure investments proved a consistent\nchallenge and remains an obstacle to sustainability of the local investment rather the system itself.\n\nThe fiscal impact of LGDP was 3% of the national budget so did not pose much risk to macro\nsustainability. At its peak LGDP would only finance 7% of the development budget. On the other hand,\nthe LGDP funds were spent in line with national sectoral priorities, reinforcing the intended expenditure\npattern under the PEAP. There were sustainable improvements in capacity and understanding by LGs of\ntheir roles and responsibilities, particularly in planning, budgeting and financial management, as evidenced\nby the Annual Assessments and Audits. This also demonstrates improved capacity in the MoLG. The\nmajor instruments for monitoring LG performance were initiated under LGDP I and integrated more\nstrongly into mainstream", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:017863:17:0:0", "start": 1771, "end": 1800, "surface": "Annual Assessments and Audits", "probe_tag": "confusion", "probe_score": 0.4497, "luna_label": 1, "luna_reason": "Assessments and audits provide evidence of improved local-government capacity."}]}, {"key": "rafael-232", "text": "stayed.\n\n\nOur objective is to identify the factors that facilitated or hampered the return of\n\n\nthese refugees. However, an inherent challenge in the literature on conflict and forced\n\n\nmigration is the absence of a complete longitudinal data set for conditions in countries\n\n\nof asylum and origin that can be mapped onto refugee characteristics. Establishing\n\n\ncausality is even more challenging. We make progress on the first part by combin\n\ning different sources and types of data. For demographic characteristics of refugees\n\n\nand their arrival and return information, we use administrative data from the Profile\n\n\nGlobal Registration System (ProGres) database of UNHCR. For the conditions faced\n\n\nby refugees in exile, we use vulnerability surveys conducted by UN agencies in Jordan\n\n\nand Lebanon, and complement these with a new household survey comprising similar\n\n\ndemographic and socioeconomic modules but also including vignettes about the drivers\n\n\nof return. Finally, for conditions in Syria, we have compiled a novel monthly conflict\n\n\nevents data set to use along with nighttime light emissions data that proxies access to\n\n\nutilities.\n\n\nThese sources are utilized in two different but complementary ways. First, we exploit\n\n\nthe temporal and spatial variation of the nightlights and conflict events series to build a\n\n\nsub-district-month panel for conditions inside Syria. This is used to analyze the impact\n\n\nof changes in conflict and luminosity patterns on return in an aggregate manner using\n\n\nordinary least squares (OLS) and Poisson quasi maximum likelihood (PQML) count\n\n\nmodels. Second, we use the detailed information on refugee characteristics provided by\n\n\nProGres together with conditions in countries of asylum, 4 to analyze individual return\n\n\ndecisions. Given that we have arrival and - where applicable - return dates for each\n\n\nrefugee, we can study their likelihood of return for a given month using both discrete\n\n\n4Since the conditions in countries of asylum are only captured for a small sample of registered\nrefugees in Lebanon and Jordan, we approximate host country conditions with district averages for\nthe full sample.\n\n\n3", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002426:4:0:5", "start": 1083, "end": 1113, "surface": "nighttime light emissions data", "probe_tag": "confusion", "probe_score": 0.8232, "luna_label": 1, "luna_reason": "Existing nightlight data proxy utility access and supports panel analysis."}]}, {"key": "rafael-233", "text": " encuestados en el HFS2 que indicaron haber tenido\n\nque trabajar a cambio de alojamiento; y la cantidad de personas encuestadas que indicó haber tenido\n\nque mudarse a un apartamento o vivienda más económica aumento 7% con respecto a los resultados\n\nobtenidos en los ejercicios HFS1 y HFS2.\n\n\nDentro de las nacionalidades que recurrieron con mayor proporción a los mecanismos de\n\nafrontamiento para la situación de vivienda se puede mencionar que 48% de los hogares de\n\nnacionalidad nicaragüense y salvadoreña indicaron no haber podido pagar la renta por varios meses,\n\n44% los de nacionalidad venezolana y colombiana. 34% de los hogares de nacionalidad nicaragüense\n\ny salvadoreña señalaron haber tenido que trabajar a cambio de alojamiento, seguido por 30% de\n\nhogares de nacionalidad colombiana y 26% de nacionalidad venezolana.\n\n\nDentro de las principales medidas consideradas para hacer frente a la situación de vivienda por los\n\nhogares encuestados se puede señalar que algunas familias tomaron varias opciones. Así, un 67%\n\nindicó haber establecido acuerdos, ya sean formales o informales, con el arrendador; 53% indicó\n\n\nUNHCR / Febrero 2022 30", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001181:28:1:0", "start": 19, "end": 23, "surface": "HFS2", "probe_tag": "confusion", "probe_score": 0.2623, "luna_label": 1, "luna_reason": "HFS2 respondents' reported housing coping outcomes are used as findings."}]}, {"key": "rafael-234", "text": " in Jamjang and Maban do not plan to\n\n\n23 For example, the International Organization for Migration (IOM) staff note reports that the Marial Bai Agreement helps cattle-keeper and\nfarming communities in Western Bahr el Ghazal and Warrap States prevent, manage, and resolve conflicts that had repeatedly occurred during\nthe January–April dry season through IOM-supported dialogues and sensitization exercises that provide a safe space for these communities to\ndiscuss challenges and identify local solutions.\n24 South Sudan: Refugee and Asylum Seeker Population, UNHCR, January 31, 2022. https://data2.unhcr.org/en/country/ssd.\n25 Refugees in South Sudan – by months, UNHCR, January 31, 2022. https://data2.unhcr.org/en/country/ssd.\n\n\nPage 11 of 73", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000028:16:2:0", "start": 629, "end": 664, "surface": "Refugees in South Sudan – by months", "probe_tag": "confusion", "probe_score": 0.6965, "luna_label": 1, "luna_reason": "Named UNHCR refugee population resource cited as an existing data source."}]}, {"key": "rafael-235", "text": "26.|A draft National report on the findings of the National Survey was done. The report is awaiting presentation to senior management for validation in this financial year 2025/2026.|A draft National report on the findings of the National Survey was done. The report is awaiting presentation to senior management for validation in this financial year 2025/2026.|A draft National report on the findings of the National Survey was done. The report is awaiting presentation to senior management for validation in this financial year 2025/2026.|A draft National report on the findings of the National Survey was done. The report is awaiting presentation to senior management for validation in this financial year 2025/2026.|\n|New classrooms constructed in existing schools as per the needs-based school infrastructure investment plan (Number)|0|Jan/2022|26,726|05-Dec-2025|||13,400|Dec/2029|\n|New classrooms constructed in existing schools as per the needs-based school infrastructure investment plan (Number)|Comments on achieving targets|Comments on achieving targets|A total of 26,726 classrooms (cumulative) have been constructed. 12,085 classrooms were constructed during the reporting period of Nov-Dec 2024.|A total of 26,726 classrooms (cumulative) have been constructed. 12,085 classrooms were constructed during the reporting period of Nov-Dec 2024.|A total of 26,726 classrooms (cumulative) have been constructed. 12,085 classrooms were constructed during the reporting period of Nov-Dec 2024.|A total of 26,726 classrooms (cumulative) have been constructed. 12,085 classrooms", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:005625:24:1:0", "start": 51, "end": 66, "surface": "National Survey", "probe_tag": "confusion", "probe_score": 0.5471, "luna_label": 1, "luna_reason": "Survey findings are used in a draft national report."}]}, {"key": "rafael-236", "text": " propose the use of survey data to measure preharvest losses in crop production. However, there is yet scant evidence applying the method for\nmicro-level loss measurement and a need for refinement to make full use of a variety of data\nsources including agricultural household survey data and geospatial data (FAO, 2018). This paper\nconducts a thorough descriptive assessment of the suitability of existing survey data to measure\npre-harvest production losses of annual crops in LMICs at the micro-level. For this, it draws on a\ndiverse set of data sources, rich plot-level data on harvest, inputs and different losses proxies along\nwith household- and community-level data from the Living Standards Measurement Study –\nIntegrated Surveys on Agriculture (LSMS-ISA), geospatial data, as well as experimental data from\ncrop cut and survey experiments, in three SSA countries.\n\nThe paper’s approach is guided by a set of six distinct questions that we attempt to empirically test\nand mold into concrete recommendations for survey design and measurement. These questions fit\ninto two broad issues for the measurement of disaster crop losses, the **quantification** of losses (i.e.\n\n\n2 Authors’ calculations based on (FAO et al., 2020; Our World In Data, no date; World Bank, no date)\n3 Throughout this paper, we follow the definitions employed in (FAO, 2021, pp. 198–200):\n“Disaster: A serious disruption of the functioning of a community or a society at any scale due to hazardous events\ninteracting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human,\nmaterial, economic and environmental loss and impacts (UNDRR Terminology).”\n“Damage: The monetary value of total or partial destruction of physical assets and infrastructure in disaster-affected\nareas, expressed as replacement and/or repair costs.” In agriculture, this comprises, for example, the destruction of\nstored agricultural inputs", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001848:3:1:0", "start": 253, "end": 287, "surface": "agricultural household survey data", "probe_tag": "confusion", "probe_score": 0.2674, "luna_label": 0, "luna_reason": "Generic survey-data mention lacks an attributed finding or concrete analytical use."}]}, {"key": "rafael-237", "text": "Annex 11\nPage 2 of 4\n\n\nThe country's General Environmental Law, expected to be promulgated very shortly, is divided into four\nmajor titles:\n\n\nTitle 1: Concepts, Objectives and General Principles, and Institutional\nOrganization\nTitle 2: Protection of Environments\nTitle 3: Protection of Animal and Plant Species\nTitle 4: Regulation of Pollution\n\n\nImplementing decrees should quickly specify the conditions for putting the main chapters of this\nframework legislation into effect.\n\n\n_Other Agencies And Bodies Involved_\n\n\nAs regards environmental education, the Directorate of the Environment maintains close collaboration\nwith the National Education Research and Pedagogic Information Center _[Centre de Recherche, et de_\n_Production dInformation de l 'Education Nationale-_ CRIPEN], in particular through the formulation of an\n\nawareness campaign strategy on environmental problems.\n\n\nInfrastructure facilities in the education sector are provided by the Directorate of Housing, Urban\nDevelopment, Environment, and Regional Development (DHU). However, as the current reform process\nis not yet completed, a number of serious malfunctions are preventing the Directorate from perforning\nthe role of executing agency assigned to it in the past. The Planning Unit of the Ministry of Education\nwill be the contracting authority's representative for implementation of this project.\n\n\nBecause the system for gathering and analyzing data on the public health system is no longer operational,\nas was confirmed during the visit made to Djibouti's Pelletier Hospital and to the Djibouti-City health\ndistrict, reliable country-wide epidemiological data are unfortunately unavailable. This state of affairs\napplies to the school population in particular. Under these conditions, it will be difficult to define and use\nindicators capable of measuring the success of actions to mitigate the environmental impacts of the\nproject.\n\n\n**Potential Impacts of the Project**\n\n\nThe mission by an IDA environment specialist to the Republic of Djibouti in June 2000 confirmed the\ndegraded situation of the sanitary facilities in all of the schools visited. Discussions with school staff and\nparents of students revealed the concern felt by the latter regarding the", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:010810:65:0:0", "start": 1605, "end": 1638, "surface": "country-wide epidemiological data", "probe_tag": "confusion", "probe_score": 0.3364, "luna_label": 0, "luna_reason": "States epidemiological data are unavailable without citing or using existing data."}]}, {"key": "rafael-238", "text": "**The World Bank**\nEmergency Food Security Project (P178936)\n\n\nWithdrawal Application.\n\n\n - Withdrawal Applications submitted to the Bank will be prepared separately by MoITS, final\nauthorization and signatures sits with MOPIC before submission to the Bank. Project expenditures\nwill be documented through IFRs;\n\n - MoITS will have the sole responsibility to disburse on behalf of the project to suppliers, contractors,\nand consultants. Additionally, MoITS will maintain a monthly reconciliation statement between their\nrecords and the Bank’s records per the World Bank’s Client Connection. Such reconciliation will set\nout the disbursements by category as well as the DA balance. Disbursement and payment requests\nwill be based on approved contracts and services predefined in the project documents;\n\n - DAs bank account records will be reconciled with bank statements on a monthly basis by the MoITS\nA copy of each bank reconciliation statement together with a copy of the relevant bank statement\nwill be reviewed monthly by the project’s Financial Officer at MoITS, who will investigate and resolve\nany identified differences. Detailed banking arrangements, including control procedures over all\nbank transactions (for example, check signatories, transfers, and so on), are documented in the\nFinancial Section of the POM **.**\n\n**Flow of Funds for each component**\n\n\nPage 50 of 54", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000024:54:0:0", "start": 811, "end": 835, "surface": "DAs bank account records", "probe_tag": "confusion", "probe_score": 0.2835, "luna_label": 0, "luna_reason": "Routine financial reconciliation records used for project administration."}]}, {"key": "rafael-239", "text": "Disbursement forecast\n\n\n\nContract number\n*Contract subject\nAwardee\n*Launching date\n-Expected delivery date\n-Non objection date\n*Expected date of final delivery\n*Bidder nationality\n*Contract allocation (general account, budget, loan\ncategory, geographic area)\n*List of contracts\nManagement of financial -Standard financial statements (balance sheet;\naccounts statement of sources and uses of funds/income\nstatement, ...)\n*LACI reports for the project duration\nFixed Assets management -Inventory of Fixed Assets (type, quantity, valuation,\ndate of service, etc.)\n\n\n\nSupplier\nAccounting category ; budgetary and accounting\nallocation of fixed assets\n\n\n\nLocation\nDepreciation\n-Disposal of Fixed assets\n\n\n\n**Module** Functions\nSorting parameters Project ID and currency used\n\n - Fiscal years\nCurrency\nDecentralized data entry locations\n\n\n\nChart of accounts, managerial reports, geographic\nareas of intervention, etc.\n\n\n\n\n - Books of accounts\nDonors\n\n - Contracts\nCategories of disbursement\nUser Management Data storage ; restitution ; correction; cleaning; etc.\n\n - Import/export of data to other Tempro modules\n\n\n\nIt is expected that the application would be modified to differentiate the operations from the\nprojects, as well as funding sources to allow for reporting in financial and accounting terms of the\nproject objectives and activities. The concept should allow for proper monitoring of the project\nduring the life of the credit, namely: (i) chart of accounts; (ii) by category, component, and subcomponent; (iii) by geography (type of establishment, site and district); (iv) by category of\nexpenses; and (v) in local and foreign currency. Reporting of multi-level data is planned, which\n**wiU** bring about a more dynamic approach to the management of the project, and which should", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000097:51:0:0", "start": 1720, "end": 1736, "surface": "multi-level data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Planned project reporting of data is future activity, not existing data use."}]}]