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[{"key": "paddy-000", "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": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:018928:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Survey data supports reported enrollment disparities and separation limitation."}, {"key": "fcv_pads_east_africa:018928:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1, "luna_reason": "Household Survey data support the finding that parents withdraw girls from school."}]}, {"key": "paddy-001", "text": "NB: Total Forest cover estimated in this table is based on 92820 sample points distributed to each zone with\n\n[cumulative area coverage of 37,127,169 ha as spatial data obtained from Oromia Planning and Development](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjXqf7w6_T9AhUx_rsIHeydBs0QFnoECAwQAQ&url=https%3A%2F%2Foropedc.org%2Fen%2F&usg=AOvVaw0yraohwOuggoyCfWZE8aS4)\n\nCommission. Gross Deforestation per year is **[34,553](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjXqf7w6_T9AhUx_rsIHeydBs0QFnoECAwQAQ&url=https%3A%2F%2Foropedc.org%2Fen%2F&usg=AOvVaw0yraohwOuggoyCfWZE8aS4)** ha/year. Negative signs under a and c respectively\n\n[showed forest net forest gain than forest loss and rate of forest development in hectares per year, while](https://www.google.com/url?sa=t&rct", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:007333:50:0:0", "start": 156, "end": 168, "surface": "spatial data", "probe_tag": "keep", "probe_score": 0.9079, "luna_label": 1, "luna_reason": "Spatial data from Oromia Planning and Development supports the reported cumulative area coverage."}]}, {"key": "paddy-002", "text": " 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 age when they think they can help around the household.", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:013436:38:2:0", "start": 166, "end": 182, "surface": "Household Survey", "probe_tag": "keep", "probe_score": 0.966, "luna_label": 1, "luna_reason": "Survey data are explicitly used to support a concrete finding about girls’ schooling."}]}, {"key": "paddy-003", "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": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:010150:38:1:1", "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 differences across expenditure quintiles."}, {"key": "fcv_pads_east_africa:010150:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1, "luna_reason": "Household Survey data supports the finding that parents withdraw girls from school."}]}, {"key": "paddy-004", "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": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:018972:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1, "luna_reason": "Survey data supports reported enrollment-rate disparities across expenditure quintiles."}]}, {"key": "paddy-005", "text": "16\n\n\naddressed the Donors Roundtable and committed to increase Government resources to education to\nover 25% of the budget and noted that the government viewed education as the main source of future\ngrowth in Djibouti.\n\n\n**5. Value added of Bank support in this project**\n\nIDA has been supporting the national consensus building process through the National Education\nForum. The proposed project will help demonstrate that a consensus building approach that involves\n\nall elements of civil society is effective and produces results. In addition the use of an APL\ndemonstrates the long-term commitment by IDA to assist the Government in its strategic goal of\nreaching full enrollment in basic education. It is also hoped that the use of the IDA credit will further\ndecrease the construction unit cost (as IDA is supporting the use of local construction materials which\nshould be cheaper), help develop more cost-effective classroom designs, and provide the environment\nwith a more efficient procurement process.\n\n\n**E. SUMMARY PROJECT ANALYSIS** (Detailed assessments are in the project file, see Annex 8)\n\n\n**1. Economic (see Annex 4)**\n\n\nOther (specify) NPV=US$ million; ERR = ** % (see Annex 4)\n\n\n_** ERR = Over 11% based on system efficiency gains alone without allowing for development_\n_benefits, public goods nature of education and poverty reduction benefits._\n\n\nDjibouti's main resource base is its population and in order to achieve sustained development, the\ncountry needs to improve the quality of its human resource base. Quality starts with improved basic\neducation and school enrollments. In addition, the issue of equity arises. According to the household\nexpenditure survey data, in urban areas, the net enrollment rate (NER) at the primary level in the\nsurvey year (1996) was 50% greater for the highest expenditure quintile compared to the lowest\nexpenditure quintile. The inequity is even more pronounced in secondary education (lower secondary\neducation is part of basic education but the survey", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:013805:19:0:0", "start": 1661, "end": 1694, "surface": "household\nexpenditure survey data", "probe_tag": "keep", "probe_score": 0.944, "luna_label": 1, "luna_reason": "Survey data supports a concrete enrollment-rate comparison from 1996."}]}, {"key": "paddy-006", "text": " from 2017, since which time the MoES has been updating the comprehensive Education Management Information System (EMIS). Enrollments in target areas have increased in the interim for reasons outside the influence of the project.|Total enrolment is drawn from 1,416 government schools (S1-S4) as of September 2024. Values exceed the end targets because the Results Framework (RF) is based on data from 2017, since which time the MoES has been updating the comprehensive Education Management Information System (EMIS). Enrollments in target areas have increased in the interim for reasons outside the influence of the project.|Total enrolment is drawn from 1,416 government schools (S1-S4) as of September 2024. Values exceed the end targets because the Results Framework (RF) is based on data from 2017, since which time the MoES has been updating the comprehensive Education Management Information System (EMIS). Enrollments in target areas have increased in the interim for reasons outside the influence of the project.|Total enrolment is drawn from 1,416 government schools (S1-S4) as of September 2024. Values exceed the end targets because the Results Framework (RF) is based on data from 2017, since which time the MoES has been updating the comprehensive Education Management Information System (EMIS). Enrollments in target areas have increased in the interim for reasons outside the influence of the project.|\n||16,000.00|Sep/2017|26,285|31-Jan-2025|26,285|15-May-2025|21,400.00|Jun/2025|\n\n\nJun 30, 2025", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:003401:2:5:1", "start": 404, "end": 421, "surface": "data from 2017", "probe_tag": "confusion", "probe_score": 0.1813, "luna_label": 1, "luna_reason": "2017 data underpin the Results Framework and explain values exceeding targets."}]}, {"key": "paddy-007", "text": "_the Bank’s Anti-Corruption Guidelines, including without limitation_\n_the Bank’s right to sanction and the Bank’s inspection and audit_\n_rights;_\n\n\n - _The rights for the Bank to review procurement documentation and_\n_activities;_\n\n\nWhen other national procurement arrangements other than national open\ncompetitive procurement arrangements are applied by the Borrower,\nsuch arrangements shall be subject to paragraph 5.5 of the Procurement\nRegulations.\n\n\n**_Leased Assets_** _as specified under paragraph 5.10_ of the Procurement\nRegulations: Leasing may be used for those contracts identified in the\nProcurement Plan tables. _“Not Applicable”_\n\n\n**_Procurement of Second Hand Goods_** _as specified under paragraph 5.11_ of\nthe Procurement Regulations – is allowed for those contracts identified in the\nProcurement Plan tables _“Not Applicable”_\n\n\n**_Domestic preference_** _as specified under paragraph 5.51_ of the Procurement\nRegulations **_(Goods and Works)_** . _Not Applicable_\n\n\nGoods: [is not applicable/is applicable for those contracts identified in the\nProcurement Plan tables];\n\n\nWorks: [is not applicable/is applicable for those contracts identified in the\nProcurement Plan tables]\n\n\n**Proposed Procedures for CDD Components** (as per paragraph. 6.52 and\nAnnex III, paragraphs 6.9 and 6.10 of the World Bank Procurement\nRegulations for IPF Borrowers, dated July 2016)\n\n\n**Other Relevant Procurement Information** _. None_", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:000316:1:0:0", "start": 604, "end": 627, "surface": "Procurement Plan tables", "probe_tag": "confusion", "probe_score": 0.227, "luna_label": 0, "luna_reason": "Routine procurement planning tables identify contracts, not substantive data evidence."}]}, {"key": "paddy-008", "text": "sup>recently come under Government control.\n\n\n\n**2.** **Main sector** issues **and** **Government strategy:**\n\n\n\n_Sector Issues._\n_Poverty in Sierra Leone._ Sierra Leone has the lowest Human Development Index **in** the world\n\n\n\nand has a GNP per capita of only US$130 compared to the average for Sub-Saharan Africa of $470.\n\n\n\nOver 82% of the population currently lives below the poverty line and life expectancy is only 38 years.\n\n\n\nFertility, infant and child mortality are high and over a third of children and a fourth of adults are\n\n\n\nmalnourished. The pnmary school enrollment</s", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:011976:7:1:0", "start": 251, "end": 274, "surface": "Human Development Index", "probe_tag": "confusion", "probe_score": 0.8186, "luna_label": 1, "luna_reason": "Named index supports the concrete ranking that Sierra Leone is lowest worldwide."}]}, {"key": "paddy-009", "text": "The World Bank\n\n|Number of people with access to a basic package of nutrition services (CBN), % female|Col2|Number|Value|0.00|55800000.00|44125000.00|\n|---|---|---|---|---|---|---|\n|Number of people with access to a basic package of nutrition services (CBN), % female||Number|Date|29-Apr-2008|30-Apr-2014|31-May-2014|\n|Number of people with access to a basic package of nutrition services (CBN), % female||Number|Comments|Source: Routine CBN data, FMOH (revised indicator definition, baseline value and data source)|Source: Routine CBN data, FMOH|Routine CBN data, FMOH|\n|Number and percentage of children 6-59 months receiving a dose of Vitamin A every 6 months||Number Sub Type Breakdown|Value|10200000.00|12159933.00|11300000.00|\n|Number and percentage of children 6-59 months receiving a dose of Vitamin A every 6 months||Number Sub Type Breakdown|Date|29-Apr-2008|30-Apr-2014|31-May-2014|\n|Number and percentage of children 6-59 months receiving a dose of Vitamin A every 6 months||Number Sub Type Breakdown|Comments|UNICEF EOS 2008 data (revised indicator definition and baseline value)|Source: FMOH 2010-", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:018912:2:0:0", "start": 439, "end": 455, "surface": "Routine CBN data", "probe_tag": "confusion", "probe_score": 0.5665, "luna_label": 1, "luna_reason": "Source data cited for reported nutrition-service indicator values."}, {"key": "fcv_pads_east_africa:018912:2:0:1", "start": 1085, "end": 1105, "surface": "UNICEF EOS 2008 data", "probe_tag": "confusion", "probe_score": 0.6706, "luna_label": 1, "luna_reason": "Named UNICEF 2008 data source cited for the indicator baseline and values."}]}, {"key": "paddy-010", "text": "**KN BS**\nKenya National Bureau of Statistics\nAnnual Reports and Financial Statements for the Year ended June **30,** 2020\n\n\n**Interest** **rate** **risk**\n\nInterest rate risk is the risk that the entity's financial condition may be adversely\naffected as a result of changes in interest rate levels. The Bureau's interest rate\n\nrisk arises from bank deposits. This exposes it to cash flow interest rate risk. The\ninterest rate risk exposure arises mainly from interest rate movements on the\nentity's deposits.\n\nSensitivity analysis:\n\nThe Bureau analyses its interest rate exposure on a dynamic basis **by** conducting\n\na sensitivity analysis. This involves determining the impact on profit or loss of\ndefined rate shifts. The sensitivity analysis for interest rate risk assumes that all\nother variables, in particular foreign exchange rates, remain constant. The\nanalysis has been performed on the same basis as the prior year.\n\n\nUsing the end of the year figures, the sensitivity analysis indicates the impact on\nthe statement of comprehensive income if current floating interest rates\nincrease/decrease **by** **5** **%.**\n\n\nAt 30th June 2020, if the interest rates on the bank balances had\ndecreased/increased **by** **5** percentage points with all other variables held constant,\nthe impact on surplus for the year would have been higher/lower as hereunder:\n\n\n**2019 / 2020** **2018/2019**\n**KShs** **KShs**\n**Interest** **Income** **63,828,579.00** **273,483,716**\n**Change** **In** **Interest Rates**\n\n**5%** **3,191,429** **13,674,186**\n**-5%", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:011741:55:0:0", "start": 940, "end": 963, "surface": "end of the year figures", "probe_tag": "confusion", "probe_score": 0.551, "luna_label": 1, "luna_reason": "Year-end figures are used in the sensitivity analysis calculation."}]}, {"key": "paddy-011", "text": "br>ertake Violence Against Child ren (VAC) survey and various activities to deliver safe scho ols under Lot 4 in 93 schools located in 11 Districts in Wes t Nile sub region|IDA / D7010|Expansion of Lower Seconda ry Education|Post|Quality And Cost- Based Selection|Open - Internationa l||0.00|Under Implement ation|2023-05-11||2023-06-01||2023-07-15||||2023-08-12||2023-09-11||2023-10-16||2023-11-20||2024-11-19||\n|UG-MOES-359658-CS-CQS / Procurement of NGOs to und ertake Violence Against Child ren (VAC) survey and various activities to deliver safe scho ols in 7 schools in 5 Districts i n Karamoja sub region|IDA / D7010|Expansion of Lower Seconda ry Education|Post|Consultant Qualifi cation Selection|Limited||0.00|Under Implement ation|2023-05-11||||2023-05-25||||||||2023-06-24||2023-07-29||2024-01-25||\n|UG-MOES-360180-CS-CQS / Procurement of a firm to pre pare a background paper on the Private Education and Tr aining Policy|IDA / D7010|Improving Teachers Support and", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:003128:7:8:0", "start": 40, "end": 52, "surface": "(VAC) survey", "probe_tag": "confusion", "probe_score": 0.1692, "luna_label": 0, "luna_reason": "Survey is being procured to undertake future project activities."}]}, {"key": "paddy-012", "text": " Medical Bureau (UMMB) hospitals and health facilities, National\nHealth Tutors database at MoES; In-Service Training database at MoH-HRD; **UNMEB**\nExamination Management System; **UAHEB** Examination Management System; **18**\nHealth facilities under Uganda Community Based Health Care Association **(UHCBHA);**\n\nall the **111** districts; Kampala City Council Authority; and **33** Municipalities.\n\n\n**UHSSP** specifically supported the roll out of the iHRIS in **31** districts out of the 111\ndistricts. The Project procured computers, scanners, web cameras, and printers as\nwell as training users including the District Local Government leaders in the **31**\ndistricts. The Project further procured equipment to help set up an iHRIS help desk.\nThese were installed and the help desk is now functional. However in addition to being\na help desk for iHRIS, the MoH considered the same equipment adequate to run a call\ncentre and its set up is now in advanced stages.\n\n\n**3.3.7** **Challenges** **to** **the implementation** **of the Human Resource** **Component**\n\n\ni) Processing institutional invoices: processing of applications, reviewing\n\ninvoices for payment and monitoring progress of students at the various\nschools was a laborious exercise. **A** recommendation has been made to\nmainstream management of scholarships to take advantage of the skills\nand human resource in the Ministry's Human Resource Department.\nUnder URMCHIP, the Project will nonetheless continue to take keen\ninterest in the implementation of the scholarship scheme and will work\nclosely with the HRD department.\n\n\nii) Wage **bill** shortfalls: Whereas the Project has trained several health\n\nworkers, several of these have not been recruited, redeployed or\npromoted due", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:020091:21:1:0", "start": 56, "end": 87, "surface": "National\nHealth Tutors database", "probe_tag": "confusion", "probe_score": 0.5085, "luna_label": 0, "luna_reason": "Database is merely listed; no data use, analysis, targeting, or finding is shown."}, {"key": "fcv_pads_east_africa:020091:21:1:1", "start": 97, "end": 136, "surface": "In-Service Training database at MoH-HRD", "probe_tag": "confusion", "probe_score": 0.4059, "luna_label": 0, "luna_reason": "Database is listed without showing its data being used for analysis or decisions."}]}, {"key": "paddy-013", "text": "**The World Bank** Implementation Status & Results Report\nKenya Devolution Support Project (P149129)\n\n\n\n\n\n\n\n\n\n**Disbursement Linked Indicators**\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\n12/11/2018 Page 12 of 14", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:011584:11:0:0", "start": 112, "end": 142, "surface": "Disbursement Linked Indicators", "probe_tag": "confusion", "probe_score": 0.0893, "luna_label": 0, "luna_reason": "Standalone section heading, not a cited or used data resource."}]}, {"key": "paddy-014", "text": "**Kenya Informal Settlements Improvement Project: Machakos Region**\nResettlement Action Plan Report for Swahili Informal Settlement\n\n\n**g)** **Loss of Access to Natural Resources and Health Facilities, Water and Sanitation, and**\n**Energy, and Proposed Mitigation Measures**\n\nHighly recommended under this Report, as discussed above, to the extent possible, is reinstallation of PAPs within the Informal Settlement. Discouraged is re-installation to new\nareas outside the Project beneficiary settlement. In the Project beneficiary area, established\nfrom the field survey carried out in late October and early November 2013:\n\n1) Loss of access to natural resources, and health facilities are not anticipated.\n\nThe land on which the proposed infrastructure improvements will be carried out is largely\npublic land reserved for roads and way leaves. On this land: no health facility was observed\nto be constructed on it; no part of the land was observed to be a protected area that takes the\ncomplete ban on the exercise of private rights; and, no part of the land was observed to have\na predominant land use comparable to a park. The predominant land use of the informal\nsettlement is residential.\n\n2) Temporary disruption to electricity supply to the Settlement is anticipated.\n\n\n\nOn the roads targeted for improvement\nunder the Project, as Tables 9 and 21\nillustrates, are electricity poles with electricity\nlines supplying electricity to households in\nthe Informal Settlement. Towards effective\nimplementation of the Project, the electricity\npoles have to be shifted (relocated). This will\n\nnecessitate disconnection of power supply to\nthe Settlement, thus interrupting electricity\nsupply to the Settlement. The situation is\nexpected to be temporary. In connection, this\nRAP report finds it necessary to propose\nmitigation measures. Vis-à-vis, the Project\nshall:\n\na) Work in close collaboration with the\n\nnetwork service provider responsible for\nelectrical energy transmission,\ndistribution and", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:020520:45:0:0", "start": 565, "end": 577, "surface": "field survey", "probe_tag": "confusion", "probe_score": 0.3038, "luna_label": 1, "luna_reason": "Past field survey supports observations about project-area impacts."}]}, {"key": "paddy-015", "text": "**Independent Evaluation Group (IEG)** Implementation Completion Report (ICR) Review\nEthiopia UIIDP (P163452)\n\n\nThis indicator was monitorable and related to the revenue mobilization (through own-source revenue) efforts\nof the eligible ULGs. This indicator is directly related to the sustainability of the urban infrastructure and is\nrated as substantial.\n\n\n**Rating**\nSubstantial\n\n# **DLI 9**\n\n**DLI**\nDLI # 9. Regional Public Procurement and Property Administration Agencies (RPPAA) had conducted timely\nand quality procurement audit of eligible ULG's accounts and performance.\n\n\n**Rationale**\nThis indicator was intended to ensure that the regional public procurement agencies conduct timely and\nquality procurement audits of eligible ULGs accounts. The indicator was monitorable. The indicator is rated as\nsubstantial.\n\n\n**Rating**\nSubstantial\n\n# **DLI 10**\n\n**DLI**\nDLI# 10. Strengthening institutional performance, infrastructure and service delivery, maintenance and job\ncreation for 44 ULGs.\n\n\n**Rationale**\nThe team clarified that DLI was a legacy DLI, grounded in historical performance data from the 2017/18\nAnnual Performance Assessment (APA). This DLI guaranteed the sustainability of previously attained results,\nThe legacy DLI was vital for the 44 ULGs to ensure performance consistency, while new ULGs, due to their\nearly-stage capacity, were not considered under this DLI. This DLI was monitorable. Since this DLI would\nhelp the 44ULGs to show sustained improvements in urban governance and infrastructure development\nachieved over time, this DLI is rated as substantial.\n\n\n**Rating**\nSubstantial\n\n\nOVERALL RELEVANCE DLI TBL\n\n# **OVERALL RELEVANCE RATING**\n\n**Rationale**\n\n\nPage 8 of 16", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:003596:7:0:0", "start": 1074, "end": 1101, "surface": "historical performance data", "probe_tag": "confusion", "probe_score": 0.6512, "luna_label": 1, "luna_reason": "Existing performance data from the 2017/18 assessment grounded the DLI."}, {"key": "fcv_pads_east_africa:003596:7:0:1", "start": 1111, "end": 1148, "surface": "2017/18\nAnnual Performance Assessment", "probe_tag": "keep", "probe_score": 0.9182, "luna_label": 1, "luna_reason": "Historical assessment data grounded the DLI indicator."}]}, {"key": "paddy-016", "text": "**Independent Evaluation Group (IEG)** Implementation Completion Report (ICR) Review\nRural Productive Safety Net Project (P163438)\n\n\n - 2,770,188 PSNP core beneficiaries received their cash payments through e-payments, against a\n\nbaseline of 420,000, **considerably surpassing the PDI target** of 2,000,000.\n\n\nPublic works sub-projects support the creation of sustainable community assets and an improved enabling\nenvironment for livelihoods.\n\n\n - 85 percent of rural safety net public works sub-projects met a common standard, **substantially**\n\n**achieving** the PDI target of 90 percent (this PDI is associated with Objective 2 in the results\nframework but is nonetheless relevant also for Objective 1).\n\n - 84.5 percent of public works subprojects were selected and implemented according to the\n\ngovernment’s management guidelines, against a baseline of 75 percent, **substantially achieving** the\n85 percent target. The ICRR notes that the same indicator appeared in the PSNP results framework,\nbut with a higher target of 90%, so that the target was effectively, if not formally, downwardly revised.\n(This IRI is associated in the results framework with Objective 2, and thus also reported below.).\n\n - The 2019 PWIA found that across ten watersheds and rangeland areas sampled, 183,000 tons\n\nCO2/Anum were sequestered, as well as substantial improvements in watershed resource\nmanagement. Across 8 sampled watersheds, soil erosion was reduced by 36 percent, woody biomass\nin non-pastoral watersheds increased by 169 percent, and carbon sequestration resulting from\nincreased vegetation cover increased from 3.25 to 4 MT Co2 per hectare between 2015 and 2019.\nThis translated into livelihoods and health benefits for agricultural households: crop yields in sample\nwatersheds grew by 24 percent, irrigated crop production rose from 12", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:004012:7:0:0", "start": 1235, "end": 1244, "surface": "2019 PWIA", "probe_tag": "confusion", "probe_score": 0.6934, "luna_label": 1, "luna_reason": "PWIA findings provide measured watershed, sequestration, and livelihood results."}]}, {"key": "paddy-017", "text": "_Kenya_ _Off-Grid_ _Solar Access Project (KOSAP)_\n_Project Information_ _and Overall Performance_\n_For the financial year ended June_ **_30,_** _2021_\n\n\n\nNim ____ Position **title** **Contact** **information** **Responsibilities**\n\n\n\nensuring KFM implemeton as\nper KOSAP safeguards\nframework documents\nHenry Karanja Environmental Mobile: +254 **723** 416820 - Mainstream environmental\nSafeguard Email: hkaranja@snv.org safeguards in the implementation\nSpecialist arrangements of the RBF **&** Debt\nFacilities\n\n - Monitor and report on\nenvironmental safeguards\npolicies and actions **by** the\nrecipients of RBF and Debt\nFacilities\nDevelop and implement a\ncomprehensive Environmental\nand Social Management Plan for\nRBF **&** Debt Facilities\nDennis Kibira Data Analyst **SSP** Mobile: +254 **712** 274466 - Evaluation of proposals and\nRBF Email: dkibira@snv.org recommend awardees\n\n - Prepare **SSP** RBF agreements\nfor signing between MoE and\n\n\n\nawardees\n\n- Processing payment requests\n(including preparation of\nverification packages for IVA)\n\n- Maintain **SSP** RBF facility CRM\ndatabase\n\n\n\n\n- Support bidding process\n\nMaintain RBF facility CRM\ndatabase\n\n- General implementation support\n\n\n\nBaraka Megiroo Senior Investment Mobile: **+255 767** 450433 - Preparation of the Debt Facility\nOfficer Email: baraka@sunfunder.com Implementation Manual and\nupdating when required.\nResponsible for managing the\nDebt Facility and achieving the\nfacilities objectives.\n\nFacility promotion and proposal\nsolicitation with solar company\nborrowers in emerging markets,\nincluding origination, project and\ncompany due diligence, loan\nstructuring, negotiation,\nexecution.\n\n - Preparation of Requests for\nProposals, Evaluation of\nproposals and", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:002172:9:0:1", "start": 1178, "end": 1203, "surface": "RBF facility CRM\ndatabase", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "CRM database is merely maintained; no substantive use of its data is shown."}]}, {"key": "paddy-018", "text": "**Jinja Municipal Council**\n\n\nFig 1: Jinja Refuse Dump Sketch Map showing points of geophysical\n\nmeasurement and lithology\n\n\n**Work done and Analysis of Measurements**\nFour points in a straight line through the middle of the proposed dump were\nsounded and can provide an overall picture of the underneath of the refuse pit.\n\n**_VES 101_**\nThe original field data showed a slow and gradual decrease in the apparent\nResistivities of the soil layers with depth. The computer modelling of the field data\nusing the computer programs gives a 3-layered model. This progressive decrease\nin resistivities with depth is a sign that compaction levels were decreasing\ndownward and therefore the soil layers were becoming increasingly pervious with\ndepth. This point marks the start of the transition to the soils found in valley\nsettings. The generated stratigraphy at that point is; Topsoils to 2m deep, Laterite\n(Murram) from 3m-10m weathered rocks from 10 to 30m and a fresh granite to a\ndepth beyond 50m.\n\n\n57", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:015894:69:0:0", "start": 352, "end": 362, "surface": "field data", "probe_tag": "confusion", "probe_score": 0.2958, "luna_label": 1, "luna_reason": "Existing field data supports resistivity analysis and soil-layer findings."}]}, {"key": "paddy-019", "text": "**Annex 1:** **Project** **Design** **Summary**\n\n**SIERRA LEONE:** **NATIONAL** **SOCIAL ACTION** **PROJECT**\n\n\n. **Hierarchy o.Qbijctives -'** - . **diator** **r**, t **,P** **jCitidaI** **r!** **As's-umptIons,Y-**\n**Sector-related** **CAS** **Goal:** **Sector** **Indicators:** **Sector/ country reports:** **(from** **Goal** **to Bank** **Mission)**\nMitigate the risk of renewed 1. National conflict/security- - UNHCR/OCHA reports - Continued peace and\n**conflict** **and lay foundation** related indicators - Household Income and regional security\n**for** **poverty reduction and** 2. Inter-regional disparities in Expenditure Surveys - Economic and political\n**improvements** **in nutrition,** I-PRSP & PRSP core - PETS surveys stability\n**health, education** **and** indicators - Strategic Planning and\n**targeting** **the rural** 3. Inter-regional disparities in Action Process (SPP) reports\n**population,** women **and** Popular Benchmarks\n**children.**\n\n\n**Project** **Development** **Outcome** **/** **Impact** **Project reports:** **(from** *", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:014575:29:0:1", "start": 721, "end": 733, "surface": "PETS surveys", "probe_tag": "confusion", "probe_score": 0.8347, "luna_label": 1, "luna_reason": "Named survey source listed for sector indicators and country reporting."}]}, {"key": "paddy-020", "text": "completed sub-projects local authorities provide\nconform to ministry standards, resources and staff to operate\ndesigns and norms. and maintain facilities (e.g.\nprovision of teachers and\n\ntextbooks in the case of\nprimary schools);\n\n\nl(b) Targeted communities are lb. 1 At least 90% of - Beneficiary/ impact - Sub-projects reflect\nempowered to carry out projects are assessed as assessments and other beneficiary needs and\npriority investments. successful by communities evaluation reports improved access to social and\n(achieve rmnimum expected economic services;\noutputs and\noutcomes/imnpacts).\n\n - Supervision missions - PPA methodology is\nlb.2 All supported - Beneficiary Assessments internalized by NaCSA and\ncommunities have conducted - NSAP quarterly progress partners;\nparticipatory needs reports\nassessments and project - Participatory M&E results\nidentification using PPA\napproach. - Continuous social - Capacity building efforts\nI assessment process provided and/or coordinated\nlb.3 100% of communities - Participatory M&E results by NaCSA are appropriate and\nhave project management effective;\nstructures in place and trained\ncommunity members.\n\n**2.** Pilot and Special 2a.1 100 km. of feeder roads - Supervision missions; - NaCSA's commitment to\n\n**Programs in** Newly rehabilitated. - NSAP quarterly reports; pilot both programs remains\n**Accessible** **Areas** - Annual technical audits; strong\n2a.2 800,000 person days of - NaCSA M&E data\n**2(a)** **Rural Public Works** temporary employment\n**Program:** created.\n\nInfrastructure constructed 2a.3 250,000 \"woman days\" of\nand/or upgraded using labor temporary employment\nintensive techniques. created.\n\n\n2a.4 At mid-term review the\ncost per day", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:019805:30:0:0", "start": 1658, "end": 1672, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Listed as planned project monitoring verification data, not existing evidence used."}]}, {"key": "paddy-021", "text": "br>for Child Mortality Estimation ** UN MDG Report 2011 *** World Health Statistics 2011**** UN Data 2003-2009 ***** United Nations Children’s’ Emergency Fund (UNICEF) Progress for children 2007|* Levels and Trends in Child Mortality Repost 2011 Estimates developed by the UN inter-agency Group for Child Mortality Estimation ** UN MDG Report 2011 *** World Health Statistics 2011**** UN Data 2003-2009 ***** United Nations Children’s’ Emergency Fund (UNICEF) Progress for children 2007|\n\n\n\n**B.** **Program Scope**\n\n6. The HSDP IV provides the overarching strategic framework for the health sector and reflects\nGovernment of Ethiopia’s commitment to achieve the Health MDGs. It supports human capital\ndevelopment and remains the critical vehicle for achieving Ethiopia’s Growth and Transformation Plan\n(GTP) goals related to health. HSDP IV envisions a strong “client centric” approach to improve access to\n\n\n1 Ethiopia Demographic and Health Survey 2011\n\n5", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:008239:4:2:0", "start": 66, "end": 94, "surface": "World Health Statistics 2011", "probe_tag": "confusion", "probe_score": 0.4578, "luna_label": 1, "luna_reason": "Named statistics report cited as source for child mortality estimates."}, {"key": "fcv_pads_east_africa:008239:4:2:1", "start": 99, "end": 116, "surface": "UN Data 2003-2009", "probe_tag": "confusion", "probe_score": 0.2899, "luna_label": 1, "luna_reason": "Named UN Data source cited for child mortality estimates."}]}, {"key": "paddy-022", "text": " Created Guidance and Counseling Departments in all institutions .\n\n - Provided in-service training for 22,000 teachers - the ICR does not state the subjects.\n\n - Conducted University education and financing and capacity development study .\n\n - Conducted an Open University Study.\n\n\nOutcomes\n\n - Secondary enrollment increased from 0.8 million in 2008 to 1.7 million in January 2011. Attribution of this\nincrease to the project's outputs is unlikely, however .\n\n\n**(4) Strengthening sector management** **: Negligible**\n\n\nOutputs\n\n - Created spatial maps of all public and private primary and secondary schools .\n\n - 11 percent of the total education budget was allocated to recurrent expenditures for primary education and\nthis share has declined from 14.7% in 2006; the target was 15%.\n\n - The Education Management Information System was linked with HIV data .\n\n - Information and computer technology equipment for district level staff was provided .\n\n - Unspecified training provided to Ministry, provincial and district staff .\n\n - Evaluations completed by third-parties on topics of school health,Technical, Industrial, Vocational and\nEntrepreneurship Training, teaching, and student learning, but the ICR did not report any of the findings or\nresults of these reports.\n\n - The ICR reported that awareness of HIV/AIDS has increased among school children and that the teaching", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:016373:5:2:0", "start": 866, "end": 874, "surface": "HIV data", "probe_tag": "confusion", "probe_score": 0.5297, "luna_label": 0, "luna_reason": "Generic HIV data is merely linked to an information system, with no demonstrated use."}]}, {"key": "paddy-023", "text": "Annex 1\nPage 3 **of** 3\n\n\n**Key Performance**\n**Hierarchy of Objectives** **Indicators** **Monitoring &** **Critical Assumptions**\n**Evaluation**\n**Project Components / Sub-** **Inputs: (budget for each** **Project reports:** **(from Components to**\n**components:** **component)** **Outputs)**\n\n\nImprove Access: provision of US$5.8 million MOE monitoring Capacity within the\nclassrooms. Number of schools reports construction sector to handle\nconstructed per year; the volume of school\nimproved design and construction.\nefficiency.\nCreate Conditions for Quality US$1.1 million School surveys; student Good textbook distribution;\nImprovement: access to Number of textbooks per learning achievement management training\neducational materials; student; autonomous school reports (MOE effectiveness; Government\nimproved school management; salaries paid on monitoring reports). commitment to paying\nmanagement; teacher a timely basis teacher salaries.\nmotivation.\n\n\nImprove Government's US$4.1 million Project monitoring Purpose and integrity\nCapacity to Manage Sector: Project effectively reports; study reports. maintained within project\ncapacity building within the implemented and management; stakeholder\nMOE and its related services; management improved; participation in pilot studies.\npilot studies. reports with implementable\nresults.\n\n\n**Annexe 1 Attachment: Program and Project Monitorin** **Tar** **ets**\n**_Year_** _2001-02 2002-03_ **_2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10_**\nPrimary Enrollment Boys 19,125 21,506 24,300 26,627 29,867 31,696 34,457 37,217 40,129\nPrimary Enrollment Girls 14,875 17,994 20,", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:018523:31:0:0", "start": 577, "end": 591, "surface": "School surveys", "probe_tag": "confusion", "probe_score": 0.1078, "luna_label": 0, "luna_reason": "Planned project monitoring survey, with no existing finding or demonstrated data use."}]}, {"key": "paddy-024", "text": "**Consultancy Services for Design Review, Preparation of Concept Design and**\n**Tender Document For Nekempt – Bure Road DESIGN-BUILD- MAINTENANCE Project Ethiopian Roads Authority**\n\n\n\n(i) A socio-economic survey will be completed to determine scope and nature of\n\nresettlement impacts.\n(ii) The socio-economic study will be carried out to collect data in the selected sub\nproject sites.\n(iii) The socio-economic assessment will focus on the potential affected communities,\n\nincluding some demographic data, description of the area, livelihoods, the local\nparticipation process, and establishing baseline information on livelihoods and\nincome, landholding, etc.\n\nAnnex 1 describes the requirements for the RAP in detail. In general, the RAP contains the\nfollowing information:\n\n\n(i) Baseline Census;\n(ii) Socio-Economic Survey;\n(iii) Specific Compensation Rates and Standards;\n(iv) Entitlements related to any additional impacts;\n(v) Site Description;\n(vi) Programs to Improve or Restore Livelihoods and Standards of Living;\n(vii)Detailed cost estimates and Implementation Schedule.\n\n\nWhere relocation or loss of shelter occurs, the RPF requires that measures to assist the\ndisplaced persons be implemented in accordance with the Resettlement Action Plans.\n\n\n32\n**Resettlement Policy Framework (RPF), October 2013** **DESIGN REVIEW CONSULTANT**\n**ETHIO Infra Engineering Plc**", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:011792:39:0:0", "start": 207, "end": 228, "surface": "socio-economic survey", "probe_tag": "confusion", "probe_score": 0.6135, "luna_label": 0, "luna_reason": "Survey will be completed as a planned data-collection activity."}, {"key": "fcv_pads_east_africa:011792:39:0:3", "start": 821, "end": 842, "surface": "Socio-Economic Survey", "probe_tag": "drop", "probe_score": 0.0304, "luna_label": 0, "luna_reason": "Listed as RAP content without an attributed finding or demonstrated data use."}]}, {"key": "paddy-025", "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": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:015182:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "drop", "probe_score": 0.0227, "luna_label": 0, "luna_reason": "States data status as preliminary estimates without showing substantive data use."}]}, {"key": "paddy-026", "text": " or adoption or market-ready stage) across\nprograms and platforms of the Project, the PDO indicator focused mainly on counting the number of firms\nregardless of their stages of innovation. As a result, aggregation of data on firms that developed one or more\ninnovation lacked uniformity.\n\n\nOverall, the PDO indicator exceeded both its original and revised targets. Also, the results related to increase\nin innovation can be fully attributed to Project contributions for 104 firms, which is significantly above the\nrevised target of 60 firms. However, there were some issues: (a) PDO indicator target was significantly\nreduced by almost half at Project restructuring; (b) aggregation of data on innovation lacked uniformity; and\n(c) attribution of result to Project contributions on ecosystem intermediaries and startups is limited. As a\nresult, the Project **substantially** achieved its revised objective (to increase innovation in select private sector\nfirms), but with moderate shortcomings as noted.\n\n\n**Revised Rating**\nSubstantial\n\n# **OBJECTIVE 2**\n\n**Objective**\nTo increase productivity in select private sector firms\n\n\n**Rationale**\n**Theory of change.** Based on the information in the ICR and in the PAD, the following is the logical sequence\nfor achieving this Project objective: The activities (implementing the SME linkages and upgrading program\ninvolving services to support SMEs in improving their managerial and technical skills and their use and\naccess to technology) would lead to outputs (SMEs competitively selected and funded using milestone-linkedgrants). These activities and outputs would contribute to outcomes: SMEs (that benefitted from SME linkages\nand upgrading program) incorporated specific upgrades in their companies and demonstrated above average\n\n\nPage 10 of 22", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:003501:9:1:0", "start": 686, "end": 704, "surface": "data on innovation", "probe_tag": "drop", "probe_score": 0.0244, "luna_label": 1, "luna_reason": "Data are analyzed through the finding that aggregation lacked uniformity."}]}, {"key": "paddy-027", "text": ".6\n4.0\n6.3\n\n3.0\n6.1\n\n**14.0**\n5.1\n\n\n\n**2005** **2005-08**\n\n\n5.6 5.9\n19 3.6\n4.4 5.6\n\n\n\n**2005**\n\n\n33.5\n20.9\n9.0\n45.6\n\n76.7\n142\n27.7\n\n\n**2005**\n\n\n5.1\n9.1\n6.7\n7.2\n\n5.0\n7.5\n115\n202\n\n\n\nIevelopment diamond.\n\n\nLife expectancy\n\n \n\nGN i Gross\n\nper\ncapita enrollment\n##### i I\n\n\nAccess to improvedwatersource\n\n\n. . Uganda\n\nLow-incomegmup\n\n\n**Economlc** ratios'\n\n\nTrade\n\n\ni\nDomestic, /$' Capital\nsavings **id,!'** . formation\n\nI\n\n\nIndebtedness\n\n-\"- -Uganda\n\nLow-income group\n\n\nGrowth of capital and GDP (%)\n\n**20** \n\n**02** **03** **_04_** **05**\n\n\n**---GCF**\n#### -GDP \nGrowth of exports and imports **_(Oh)_**\n\n30 T\n\n\n\n(avenge annualgrowfh)\nAgriculture\nIndustry\n\n\n\n**1985.85** **1985-05**\n\n\n4.0 4.0\n9.3 8.4\n9.8 8.5\n6.8 7.2\n\n\n\nManufacturing\n\n\n\nServices\n\n\n\nHousehold final consumption expenditure 5.4 5.2\n\nGeneral gov't final consumption expenditure 4.8 5.5\nGross capital formation 7.6 5.3\n\nImports of goods and services 3.9 5.5\n\n\n\nNote: 2005 data are preliminaryestimates.\nThis table was produced from the Development Economics LDB database.\n'Thediamonds showfourkeyindicators in thecountry(in bo1d)comparedwith its income-groupaverage.", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:020086:29:2:0", "start": 933, "end": 942, "surface": "2005 data", "probe_tag": "keep", "probe_score": 0.9132, "luna_label": 0, "luna_reason": "Bare year-qualified data phrase does not identify or use a distinct source."}, {"key": "fcv_pads_east_africa:020086:29:2:1", "start": 1002, "end": 1036, "surface": "Development Economics LDB database", "probe_tag": "drop", "probe_score": 0.018, "luna_label": 1, "luna_reason": "Named database cited as the source producing the table’s presented data."}]}, {"key": "paddy-028", "text": ">||52,493.37|0.00|Pending Implementation|2019-01-14||2019-01-24||2019-02-14||2019-03-21||2019-09-17||\n|KCCA/KIIDP2-CONS/18- 19/00241 / Contract renewal of 10 CAM Data collectors for a period of six Months (Okello Daniel, Kobwemi Tanazias, LLaborot Majorie, Katusiime Doreen, Emanuel Adiba, Nalwoga Winfred, Guma Gilbert, Natukunda Doreen, Ishebo Jackson, Ntungwa Paul |IDA / 53840|Component 2 - Institutional and Systems Development Support|Post|Individual Consultant Selection|Direct ||5,405.41|0.00|Pending Implementation|2019-01-11||2019-01-21||2019-02-11||2019-03-18||2019-09-14||\n|KCCA/CONS-KIIDP2/18- 19/00371 / Renewal of Contract for four GIS Officers for a period of One year (Bamweyana Ivan, Kazana Christopher, Magumba Martin, Okello Daniel) |IDA / 53840|Component 2 - Institutional and Systems Development Support|Post|Individual Consultant Selection|Direct ||65,902.70|0", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:018304:9:3:0", "start": 167, "end": 175, "surface": "CAM Data", "probe_tag": "drop", "probe_score": 0.0138, "luna_label": 0, "luna_reason": "Contract renewal concerns data collectors, indicating project data production rather than existing data use."}]}, {"key": "paddy-029", "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_paddy", "spans": [{"key": "fcv_pads_east_africa:020969:29:2:0", "start": 530, "end": 538, "surface": "M&E data", "probe_tag": "drop", "probe_score": 0.023, "luna_label": 0, "luna_reason": "Project monitoring/reporting data are listed as planned output evidence, not existing analytical use."}]}, {"key": "paddy-030", "text": "excavation and installation of mark stones.| The Contractor shall implement the provisions of the project OSH measures that have been developed for the project. Among others: All project workers will be provided with adequate PPE like helmets, gumboots, gloves, overalls etc. and it will be mandatory for workers to wear protective clothing while on duty. The PPE shall be inspected regularly and maintained or replaced as necessary.| NFA Contractor Supervising Consultant|Throughout project implementation| Presence and use of PPE at the sites Health and safety incidents Records of trainings Records of toolbox meetings Presence and number of first aid kits on site Records of orientation meetings |Continuous observation during field visits Quarterly review of training records Weekly review of records of toolbox meetings Quarterly review of orientation records | NFA/PCU|20000000|\n\n\n\n46", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:002287:45:1:0", "start": 713, "end": 731, "surface": "Records of toolbox", "probe_tag": "drop", "probe_score": 0.0199, "luna_label": 0, "luna_reason": "Standalone table fragment naming routine toolbox-meeting records."}, {"key": "fcv_pads_east_africa:002287:45:1:3", "start": 950, "end": 968, "surface": "records of toolbox", "probe_tag": "drop", "probe_score": 0.0297, "luna_label": 0, "luna_reason": "Fragment within a monitoring table, not an independently used data source."}]}, {"key": "paddy-031", "text": "anzi Ag. Permanent Secretary- Ministry of Water and Environment, Chairperson|57. Eng. Gilbert Kimanzi Ag. Permanent Secretary- Ministry of Water and Environment, Chairperson|57. Eng. Gilbert Kimanzi Ag. Permanent Secretary- Ministry of Water and Environment, Chairperson|\n|58. Mr. George Owoyesigire Ag. Commissioner, Wildlife and Antiquities, Representing Co-Chairperson|58. Mr. George Owoyesigire Ag. Commissioner, Wildlife and Antiquities, Representing Co-Chairperson|58. Mr. George Owoyesigire Ag. Commissioner, Wildlife and Antiquities, Representing Co-Chairperson|\n|59. Ms. Margaret A. Mwebesa Commissioner, Climate Change Department & NPC IFPA-CD Project|59. Ms. Margaret A. Mwebesa Commissioner, Climate Change Department & NPC IFPA-CD Project|59. Ms. Margaret A. Mwebesa Commissioner, Climate Change Department & NPC IFPA-CD Project|\n|60. Mr. James Ssemwanga Chairperson, Uganda Timbers Growers Association|60. Mr. James Ssemwanga Chairperson, Uganda Timbers Growers Association|60. Mr. James Ssemwanga Chairperson, Uganda Timbers Growers Association|\n|61. Mr. Bafaki Charles Assistant Commissioner, Office of the Prime Minister|", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:004555:14:5:1", "start": 706, "end": 721, "surface": "IFPA-CD Project", "probe_tag": "drop", "probe_score": 0.0135, "luna_label": 0, "luna_reason": "Names a project only; no existing data resource or data use is shown."}]}, {"key": "paddy-032", "text": "**FINANCIAL** SECTOR **SUPPORT** PROJECT\n**ANNUAL** REPORT **AND** **FINANCIAL STATEMENTS**\n\nFOR **THE** YEAR **ENDED** **30 JUNE,** 2022\n\n\n**ANNEX** **11 (A)** **ASSET** **REGISTER**\n\n\n\n**THE NATIONAL** **TREASURY** **AND** **PLANNING**\n\n\n\n**FINANCIAL** **SECTOR** **SUPPORT** **PROJECT**\n\n\n\n**ASSETS REGISTER** **AS OF 30TH JUNE** **2022**\n\n\n\n**ASSETS REGISTER** **AS OF 30TH JUNE** **2022**\n\n**No.** **Item** **Make** **Model** **Serial** **Qty** **@** **Cost** **Total Cost** **Year of**", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:007308:44:0:1", "start": 438, "end": 453, "surface": "ASSETS REGISTER", "probe_tag": "drop", "probe_score": 0.0339, "luna_label": 0, "luna_reason": "Standalone asset-register table header, not a substantive data-use mention."}]}, {"key": "paddy-033", "text": "|RAP/ESIA phase|Stakeholder activity|Monitoring Indicators|Verification source|\n|---|---|---|---|\n|Implementation|Engagements over disclosure of approved RAP/ESIA|• No and level of planned meetings accomplished • No of planned presentations done • No and type of disclosure materials developed and distributed/used (including radio ads) • No of daily activity reports submitted • No and category of vulnerable groups engaged • No of stakeholder concerns recorded and responded to|• Meeting and attendance registers • Meeting minutes • Daily debrief reports • Daily and monthly activity reports and • Sample materials and tools used • Grievance registers|\n\n\n\n**6.5** **REPORTING ON STAKEHOLDER ENGAGEMENT**\nReporting during stakeholder engagements for planning phase in respect to this assignment is\nanticipated at two levels:\n\n\n1) Internal reporting within the stakeholder team.\n2) External reporting between JBN (consultant) and UNRA (client) or any other external\nstakeholder. External reporting is quite clear and elaborated in the ToRs and contract as\nshared between the client and JBN management. This is also re-echoed in the\ncommunications plan for this SEP.\n\n\nThe Consultant specialist officers and CLOs in charge of planned engagements will prepare daily\ndebriefs and reports about stakeholder engagements carried out. Minutes will be prepared of all\nengagements and will be filed for", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:016069:63:0:0", "start": 402, "end": 424, "surface": "daily activity reports", "probe_tag": "drop", "probe_score": 0.0306, "luna_label": 0, "luna_reason": "Routine project activity reporting and submission, not substantive data reuse."}, {"key": "fcv_pads_east_africa:016069:63:0:2", "start": 620, "end": 641, "surface": "Daily debrief reports", "probe_tag": "confusion", "probe_score": 0.1484, "luna_label": 0, "luna_reason": "Planned project engagement reporting, not an existing analytical data source."}]}, {"key": "paddy-034", "text": " the situation that was agreed upon with\nthe Borrower.\n\nSigned by:\nFinancial Management v I _N_\n##### Specialist K {dO'.i1, O0\n\n(FMS-OPR) Rafika Chaouali, MNSHD Date\n\n\n**Part II:** **Procurement/Contract Management** System\n\nI have reviewed the procurement/contract management system relating to this project, including\nthe format and content of the section on Project Management Reports (PMRs) on procurement\n\nmonitoring. The objective of the review was to determine whether the procurement/contract\nmanagement system adopted by the project conforms to IDA's guidelines for procurement in\ninvestment projects. My review was based on the \"Assessment of Agency's Capacity to\nImplement Project Procurement, Setting of Prior Review Thresholds and Procurement\nSupervision Plan\" guidelines issued by IDA.\n\nI confirm that the project satisfies IDA's minimum procurement management requirements.\nHowever, in my opinion, the project does not have in place an adequate procurement/contract\nmanagement system that can provide the appropriate data on major procurement and contract\nmanagement (PMR - Section 3) as required by IDA.", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:016067:54:1:0", "start": 1032, "end": 1081, "surface": "data on major procurement and contract\nmanagement", "probe_tag": "drop", "probe_score": 0.0296, "luna_label": 0, "luna_reason": "Procurement management data is cited as compliance system paperwork, not substantive data reuse."}]}, {"key": "paddy-035", "text": "|Item|Description of the risk/impact as identified in ESS2|Proposed risk mitigation measures|Project Activities|Responsible entities|\n|---|---|---|---|---|\n|||any material changes to the terms or conditions of employment occur. • The project shall also have GRMs for project workers (direct workers and contracted workers) to promptly address their workplace grievances and concerns • All workers will be provided with written contracts.|implement facility quality of care plans Subcomponent 2.3- strengthen community health service, rehabilitate health facilities Component 3 coordination and management, monitoring, evaluation and reporting||\n|**2 **|Risks associated with a labor influx, like transmission of diseases to workers and the neighborhood, including HIV/AIDS through project activities|• Training and sensitization HIV/AIDS– targeting workers, learners, and communities under supervision of the PMT. • Contractors to employ unskilled and semi-skilled workers at the local level and through the local administrators to reduce influx. • Contractors to ensure workers sign and comply with Code of Conduct (CoC). |• Rehabilitation of health facilities; and support towards management of the", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:005485:13:0:0", "start": 60, "end": 64, "surface": "ESS2", "probe_tag": "drop", "probe_score": 0.0091, "luna_label": 0, "luna_reason": "Standalone table header reference, not an existing data resource or data use."}]}, {"key": "paddy-036", "text": "expenditure survey from 2010 with the unit record population census data from 2008\n\nusing the ELL approach. This poverty map serves as our benchmark. We then ignore\n\nthe population census data and obtain a second poverty map for Malawi by combining\n\nthe household expenditure survey data with predictors of poverty derived from publicly\n\navailable remote sensing data.\n\n\nAn important motivation for using data from Malawi is that we have access to the\n\nexact coordinates of the survey PSUs. This enables us to produce the remote-sensing\nbased poverty map both using the publicly available geographic coordinates (that have\n\nbeen subjected to random offset to protect the anonymity of households) and exact\n\ncoordinates (that are not publicly available) to investigate whether the random offsets\n\nmight undermine the approach - and inspect the correspondence.\n\n\nOur findings are two-fold. First, assessments of the correspondence between the\n\nremote-sensing-based poverty estimates and the census-based poverty estimates de\npend on how the correspondence is evaluated. The two approaches reveal the same\n\npatterns in the geography of poverty in Malawi; it is hard to tell the difference in a\n\nside-by-side comparison of the two poverty maps. The statistical correlation between\n\nthe two different small area estimates of poverty is above 0.9, again confirming their\n\nclose correspondence. However, there are districts for which the two approaches obtain\n\nmarkedly different estimates of poverty. Accordingly, poverty maps obtained using re\nmote sensing data can be expected to do well when the decision maker is interested\n\nin estimates or comparisons of poverty between assemblies of areas. Yet, the decision\n\nmaker may not be served as well by remote-sensing-based estimates when the focus\n\nis on estimates for specific small areas. Second, the random offsets embedded in the\n\ngeographic coordinates of the public use data do not meaningfully alter the estimated\n\ngeography of poverty. But they may, however, lead to an under-estimation of the stan\ndard errors of the small area poverty estimates (i.e. an over-estimation of precision)", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001165:6:0:2", "start": 170, "end": 192, "surface": "population census data", "probe_tag": "keep", "probe_score": 0.9696, "luna_label": 1, "luna_reason": "Malawi’s 2008 census data underpin the benchmark poverty map and comparison."}, {"key": "prwp:001165:6:0:6", "start": 1908, "end": 1923, "surface": "public use data", "probe_tag": "keep", "probe_score": 0.9544, "luna_label": 1, "luna_reason": "Public-use data underpin findings about geographic offsets and poverty estimates."}]}, {"key": "paddy-037", "text": "seek solutions and interact with Q&A platforms in the era of generative AI. Subsequently, Stack\n\n\nOverflow announced a layoff of 28% of its workforce in October 2023, further highlighting the\n\n\nbroader implications of these shifting dynamics. In contrast, Hugging Face, known for its AI model\n\n\ndevelopment tools, saw a dramatic increase in visits, nearly doubling from November 2022 to May\n\n\n2023. This surge demonstrates the complementary role of generative AI like ChatGPT in fostering\n\n\nnew demands within the IT industry and labor market. This transformation not only affects\n\n\nuser behavior but also has major implications for the workforce and skill requirements in the tech\n\n\nindustry.\n\n\n(a) Text-related websites (b) IT professional websites\n\n\nFigure 18: ChatGPT and other websites Monthly traffic, relative to 2023m3\n\n\n_Note:_ Data are based on Semrush’s estimates. In panel (b), HIC, UMIC, LMIC, and LIC stand for high-income\ncountries, upper-middle-income countries, lower-middle-income countries, and low-income-countries, respectively.\n\n\nIn addition to website traffic analysis, monitoring Google Trends for specific keywords offers\n\n\nvaluable insights into the popularity and demand of internet activities. Google Trends measures\n\n\nthe relative frequency of specific search queries over designated periods and geographic locations,\n\n\nproviding an index that reflects public interest in various topics. This data is valuable as it offers\n\n\nnearly real-time insights into public preferences and behaviors, thereby complementing the website\n\n\ntraffic data analyzed earlier. Numerous studies have utilized Google Trends data across different\n\n\neconomic contexts, such as predicting GDP trajectories (Choi and Varian 2012) and evaluating the\n\n\nimpact of COVID-19 on population well-being (Brodeur, Clark, Fleche, and Powdthavee 2021).\n\n\n40", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001331:41:0:1", "start": 1545, "end": 1567, "surface": "website\n\n\ntraffic data", "probe_tag": "keep", "probe_score": 0.9297, "luna_label": 1, "luna_reason": "Existing website traffic data were analyzed and used to complement Google Trends insights."}]}, {"key": "paddy-038", "text": " and its enforcement affect\n\n\nlabor market outcomes immediately. We refer to some work below, but not that there is a need for\n\n\nfuture research in this important field. Some research has investigated the implications of employer\n\n\nconcentration on wages and employment. Such concentration can possibly occur in settings in which\n\n\nno effective merger control is given. Posner (2021) argues that monopsonsized labor markets favor\n\n\nwage suppression by firms that know of the frictions that workers face in switching to a different\n\n\nlabor market. Similarly, monopsonies are also associated with higher consumer prices and slower\n\n\ngrowth, which is again harmful to workers. The author thus calls on antitrust institutions to take\n\n\nthe labor market implications of antitrust more into account when designing and enforcing the\n\n\nassociated laws.\n\n\nSchubert et al. (2022) rely on detailed geographical information from workers’ curricula and develop\n\n\na new instrument to identify the causal effects of employer concentration. They find such\n\n\nconcentration to reduce wages substantially, especially if occupational outward mobility of workers is\n\n\nlow: moving from the median to the 95th percentile of employer concentration reduces wages by 2.6\n\n\npercent on average and by 7.3 percent for workers in the lowest quartile of outward occupational\n\n\nmobility. However, the authors conclude that the aggregate importance of this effect on wages in the\n\n\nUnited States is minor, as only 10 percent of the population lives in areas with substantial levels of\n\n\nemployer concentration. The extent to which the concerns related to employer concentration matter\n\n\nin LMIC settings remains an open question. While employer concentration is likely lower in these\n\n\n25", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001327:26:1:0", "start": 887, "end": 935, "surface": "geographical information from workers’ curricula", "probe_tag": "keep", "probe_score": 0.9074, "luna_label": 1, "luna_reason": "Existing worker curriculum information supports a study identifying employer concentration effects."}]}, {"key": "paddy-039", "text": "the surveyed public schools may systematically differ from those using other schools (such as,\n\n\nthe users of private schools 10 ).\n\n\nSelection bias would be more of a concern in analyzing satisfaction with (and usage of)\n\n\nbasic services, as opposed to utilities. Basic utilities like water or electricity tend to be\n\n\nuniversally used and often have single providers servicing entire areas, with limited scope for\n\n\nusers to exercise choice. In the case of basic services such as health and education these\n\n\nconditions are less likely to hold. This increases the likelihood of selection bias and makes the\n\n\ntypical approach of matching users’ satisfaction with services with objective data from public\n\n\nservice providers problematic.\n\n\nTo address these issues, a modified version of the disconfirmation model is presented in\n\n\nthe next section to identify the determinants of satisfaction, using the restricted sample of\n\n\nhouseholds who can be matched with the objective data for each type of facility. A reduced\n\nform version of this model is estimated using the matched sample of household and facility\n\n\nlevel data. In addition, to examine whether selection bias is a significant concern, a Heckman 2\n\nstage selection model is estimated where satisfaction is estimated on the matched dataset only\n\n\n_after_ the factors that determine a household’s choice of a facility are taken into account.\n\n\n**IV. Results**\n\n\nAn accurate analysis of the determinants of reported satisfaction will require modeling\n\n\nthe determinants of satisfaction levels, _after_ taking into account the household specific indicators\n\n\n10 For example, Lankford et al (1995) shows that socio-economic characteristics, including income and\nparental education and family composition, along with the location of a household and school\ncharacteristics, influence strongly the choice between public and private schools in the United States.\n\n12", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:004226:13:0:2", "start": 1296, "end": 1311, "surface": "matched dataset", "probe_tag": "keep", "probe_score": 0.9365, "luna_label": 1, "luna_reason": "Matched dataset is used to estimate the selection model."}]}, {"key": "paddy-040", "text": "\n**3** **Hicksian Separability: Are Relative Prices of Elementary Goods Constant across Space and Time?**\n\nThe measurement of household living standards and poverty in many developing countries, including\nMalawi, relies on unit values to proxy for prices faced by households in different localities and time\nperiods. Unit values are ratios of “item-level” expenditures to associated quantities of consumption commonly specific to food consumption at home and based on most knowledgeable respondents’\nself-reports. The length and specificity of HCES food consumption modules exhibit significant crosscountry heterogeneity. As such, food consumption items, referred to as “commodity groups”, can\ninclude a mix of (i) variety/quality-differentiated versions of specific food types (e.g., broken rice,\nmedium grain rice and long grain rice, being listed as separate items), (ii) specific food types broken\ndown within aggregate food groups, however without variety/quality differentiation (e.g., Cassava,\nSweet Potato, Yellow Potato, being listed as separate items under the Roots and Tubers food group),\nand/or (iii) aggregate food groups that may appear as single line items in a module but that may\ncombine different types of food items (e.g. Vegetables and Fruits).\n\nUnder the assumption of Hicksian separability, if the price vector for the elementary goods within a\ngroup is decomposed into (i) a scalar term that raises or lowers the price level of all goods in the group\nacross locations, and (ii) a reference price vector of the relative price of each good within the group,\nit is the inter-area scalar variation that dominates intra-group variation in relative prices (Deaton,\n1988). Were this not true, when the unit value from one household is compared to the unit value of a\nhousehold elsewhere (or to the unit values of the same household in a different season)", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001552:7:1:0", "start": 544, "end": 573, "surface": "HCES food consumption modules", "probe_tag": "confusion", "probe_score": 0.8534, "luna_label": 1, "luna_reason": "Existing food consumption modules support a concrete heterogeneity finding."}]}, {"key": "paddy-041", "text": "###### Appendix 5 – Weighted Distribution of Imputation in Duplicated HBS\n\n\n\n_Figure A 18: Imputed energy spending share of duplicated_\n_HBS-weighted PMM_\n\n\n\n_Figure A 19: Imputed energy spending share of duplicated_\n_HBS - unweighted PMM._\n\n\n\n_Figure A 20: Imputed energy spending share of duplicated HBS - unweighted PMM with survey weights as control._\n\n\n_Notes: The graphs show the distribution of energy spending shares in Green and imputed values in Orange. Distributions are weighted by_\n\n_survey weights in this case. Source: Matched dataset, consisting of EU-SILC (2020, income reference year 2019) and HBS (2019)._\n\n\n53", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001602:54:0:0", "start": 565, "end": 572, "surface": "EU-SILC", "probe_tag": "confusion", "probe_score": 0.8389, "luna_label": 1, "luna_reason": "Named EU-SILC dataset is cited as the source of presented distributions."}, {"key": "prwp:001602:54:0:1", "start": 612, "end": 615, "surface": "HBS", "probe_tag": "confusion", "probe_score": 0.4755, "luna_label": 1, "luna_reason": "HBS is a cited dataset used in the figures’ weighted spending-share analysis."}]}, {"key": "paddy-042", "text": " 2019–20|Col2|Col3|Col4|\n|---|---|---|---|\n| |Census 2017 |HIES 2018–19 |PSLM 2019–20 |\n|Total weighted population, weighted |207,684,626 |207,881,239 |187,539,838 |\n|Total numbers of households, weighted |32,205,111 |33,326,273 |35,475,036 |\n|Average household size, weighted |6.39 |6.21 |5.29 |\n|_Note: According to PBS, the average population growth rate based on the comparison between Census 1998 and 2017 was_ _2.4 percent annually. The annual growth rate in the number of households was 1.9 percent annually._ |_Note: According to PBS, the average population growth rate based on the comparison between Census 1998 and 2017 was_ _2.4 percent annually. The annual growth rate in the number of households was 1.9 percent annually._ |_Note: According to PBS, the average population growth rate based on the comparison between Census 1998 and 2017 was_ _2.4 percent annually. The annual growth rate in the number of households was 1.9 percent annually._ |_Note: According to PBS, the average population growth rate based on the comparison between Census 1998 and 2017 was_ _2.4 percent annually. The annual growth rate in the number of households was 1.9 percent annually._ |", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001181:9:1:0", "start": 49, "end": 60, "surface": "Census 2017", "probe_tag": "confusion", "probe_score": 0.8811, "luna_label": 0, "luna_reason": "Standalone table column header, not an independent data-use mention."}, {"key": "prwp:001181:9:1:2", "start": 82, "end": 94, "surface": "PSLM 2019–20", "probe_tag": "confusion", "probe_score": 0.7969, "luna_label": 0, "luna_reason": "Standalone survey column header within a comparative table."}]}, {"key": "paddy-043", "text": "16 6258 100\nHenan 8763 54646 42 289 4875 6531 96\nHubei 5512 10577 32 231 4300 6063 181\nHunan 6209 33699 37 304 4768 6477 304\nGuangdong 6439 5301 30 231 4134 5974 87\nGuanqi 4324 22742 33 300 5177 7466 158\nHanan 674 1912 5 25 460 702 5\nSichun 10897 71311 54 414 5882 6912 251\nGuizhou 3315 21508 31 252 4620 6631 368\nYunan 3782 21603 38 263 5006 6968 385\n\nTotal 106999 482164 697 6374 97730 126329 4701\n\nNote : columns (1)-(2) are from China Statistical Year Book, 1992. Columns (3)-(7) are calculated from the 1992 CHS.\n\n\n23", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:002548:22:1:1", "start": 564, "end": 572, "surface": "1992 CHS", "probe_tag": "confusion", "probe_score": 0.789, "luna_label": 1, "luna_reason": "Existing CHS data are used to calculate table columns."}]}, {"key": "paddy-044", "text": "not have state participation or is below 10%, those firms and their subsidiaries are excluded from\nthe. This extra step helped to update/remove linkages that were no longer valid as of 2019.\n\n\n4.2.2 Cleaning Rules: Ensuring alignment with conceptual framework and SOE\nharmonized definition\n\n\n - _Drop sectors not included in the database:_ As per the global SOE definition described earlier, we\nexclude public administration, defense, social security (including pension funds), education,\nhuman health, social work activities, and activities of extraterritorial organizations sectors, among\nother activities. 43\n\n\n - _Drop entities that are not aligned with the SOE definition:_ ORBIS includes ministries, public\nauthorities, regulators, and Central Banks as entities. We carefully reviewed that those are not\ncounted as SOEs. 44\n\n\n - _Keep domestic firms only:_ Although the database allows to trace both domestic and foreign-based\nfirms linked to a specific country-government, for the purpose of the subsequent data validation\nand for the creation of the country-level dashboards (e.g., revenues as % GDP), only domestic\nfirms are kept in the database. Domestic firms are defined as those with the same country ISO\ncode as the government. 45\n\n\n - _Drop entities that refer to branches as opposed to companies_ : Using the ORBIS interface branch\nvariable, we delete all branches that are not SOEs because they are not separate legal entities.\n\n\n4.2.3 Country Team Validation: Data Validation and Integration\n\n\nEven after combining the information from ORBIS and the SOE Supplementary databases, important data\ngaps remain. To address this issue, we engaged with country teams and country experts to help review\nthe information collected and to retrieve as much of the missing information as possible. The steps carried\nout through the Country Team Validation are the following:\n\n\n1)", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000709:27:0:0", "start": 1609, "end": 1636, "surface": "SOE Supplementary databases", "probe_tag": "confusion", "probe_score": 0.2656, "luna_label": 1, "luna_reason": "Existing supplementary databases are combined to identify remaining data gaps."}]}, {"key": "paddy-045", "text": " exporting at\n\nhigher levels of income. Panel B further details production activities in four broad industry groups.\n\nIt shows that income from production activities within agriculture, mining, and “light”\n\nmanufacturing industries (including food and textiles industries) shifts towards production\n\n\n7 For the purpose of Figure 1, data for eight countries has been added to the dataset, in particular data for\nsome low-income countries, see Appendix G. However, the data for these eight countries have a lower\nlevel of detail and hence are not used in the rest of the paper.\n\n\n9", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001152:10:2:1", "start": 402, "end": 436, "surface": "data for\nsome low-income countries", "probe_tag": "confusion", "probe_score": 0.7226, "luna_label": 1, "luna_reason": "Existing country data were added for Figure 1, though excluded from later analysis."}, {"key": "prwp:001152:10:2:2", "start": 467, "end": 497, "surface": "data for these eight countries", "probe_tag": "confusion", "probe_score": 0.5243, "luna_label": 0, "luna_reason": "Existing data are explicitly excluded from subsequent analysis, showing no actual use."}]}, {"key": "paddy-046", "text": " education. Thus, lack of parent’s\neducation is a serious risk factor of youth leaving school early and ending up in a low\npaid job.\n\n\nTable 3.6: Intergenerational Education Linkages in Argentina (percent)\nEducation Level of Youth\nNone Primary Secondary Tertiary Total\nNone 0.0 35.7 55.6 8.7 100.0\nEducation level Primary 0.0 18.3 62.5 19.2 100.0\nof parents Secondary 0.2 9.3 58.6 31.9 100.0\n\nTertiary 0.0 7.3 45.2 47.5 100.0\n_Note_ : Parents' education level defined as highest educated parent.\n_Source_ : Own calculations on ESCVJA (four urban areas)\n\n\nFinancial means in the household may also increase youth development, since in\naddition to schooling financial means allow access to computers, internet, mobile phones,\netc. Some 55 percent of young Argentines in the four cities in the sample have a mobile\nphone and 42 percent have a computer. The internet is used by 70 percent and practically\nall of these have an email account. Thus, a large proportion of youth are able to\ncommunicate and interact using technical equipment, which is needed to work\nproductively, especially in the growing service sector.\n\n\nMost youth attend school, but a third are either idle, unemployed, or working.\nFigures 3.3 and 3.4 show that two-", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:003692:11:1:0", "start": 621, "end": 627, "surface": "ESCVJA", "probe_tag": "confusion", "probe_score": 0.856, "luna_label": 1, "luna_reason": "Named source dataset underlying calculations and reported education-linkage findings."}]}, {"key": "paddy-047", "text": "###### **3.2 Weather Data**\n\nWe match our daily arrest counts with daily weather data from the PRISM Climate Group’s\n\n\ngridded re-analysis product. The PRISM product provides daily information on minimum\n\n\nand maximum temperature, minimum and maximum vapor pressure deficit, dew point, and\n\n\nprecipitation on a 4km by 4km grid for the continental United States. We aggregate these\n\n\nmeasures to the county level by taking the average across the grid points within the county.\n\n\nWe assign daily maximum temperature to one of 12 5 _◦_ F temperature bins from 40 _◦_ F up to\n\n\n100 _◦_ F. Days below 40 _◦_ F and above 100 _◦_ F are included in separate bins. We also bin daily\n\n\nprecipitation to control for the impacts of particularly rainy days. We assign days to four\n\n\nexclusive precipitation bins: no precipitation, less than half an inch, one half to one inch,\n\n\nand more than one inch.\n\n###### **3.3 Socio-economic and Neighborhood Data**\n\n\nWe collect socio-economic data at the census block group level from the 5-year American\n\n\nCommunity Survey (ACS) for every year in our sample. We use the geocoded addresses from\n\n\nthe arrest data to assign individuals to census block groups and categorize arrests based on\n\n\nthe characteristics of the block group in which the defendant lives at the time of the arrest.\n\n\nWe focus in particular on median income, the poverty rate, and the median housing age.\n\n\nThese allow us to classify arrests as occurring in block groups based on income or poverty\n\n\nrates and by the age of the housing stock, which we take as a proxy for the presence of\n\n\nair conditioning. We", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000506:13:0:0", "start": 13, "end": 25, "surface": "Weather Data", "probe_tag": "drop", "probe_score": 0.0314, "luna_label": 1, "luna_reason": "Weather data from PRISM are matched to arrest counts for analysis."}]}, {"key": "paddy-048", "text": "recent phenomenon.\n\n\nTo establish that our mobile coverage measure significantly predicts actual mobile phone\n\n\nusage, we regress mobile phone ownership dummy and indicators for usage of specific mobile\n\n\nphone services on our mobile coverage variables. In Table 1, we present results of this exercise\n\n\n- the correlation between mobile coverage rates and uptake of mobile phone services – using\n\n\ndata from the Demographic Health Surveys (DHS). Across all panels, we observe a strong and\n\n\npositive correlation between the mobile coverage rates and ownership of a mobile phone, use of\n\n\nmobile money for financial transactions and mobile broadband internet usage. These findings\n\n\nprovide support to the use of our coverage data to evaluate the effect of mobile phones on health\n\n\noutcomes.\n\n\n**2.2** **Health Outcomes Data**\n\n\nThe health outcomes data come from the DHS and span 25 countries in Africa, of which 23 are\n\nin Sub-Saharan Africa. 13 The DHS datasets are collected using standardized questionnaires,\n\n\nwith some adaptations based on country specific needs. To allow comparability across coun\n\ntries and over time, the DHS program standardizes variable names and definitions, and cleaner\n\nand consistent datasets are released to users as “recodes\". 14 The Integrated Public Use Mi\n\ncrodata Series (IPUMS) compiles these country level datasets into consolidated multi-country\n\n\ndata. We extract key variables on infant mortality, health seeking behavior and health care uti\n\nlization at the child and the household level from the IPUMS repository.\n\n\nThe DHS surveys consist of three core questionnaires: the household, women’s and men’s\n\n\nquestionnaires. We rely on information from the first two in this paper. The household ques\n\ntionnaire covers household roster, age and gender of household members, relationship status\n\n\nwith household head, education and place of residence. The women’s questionnaire collects in\n\nformation on mother’s characteristics including age, marital status and education; reproductive", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:002063:10:0:2", "start": 804, "end": 824, "surface": "Health Outcomes Data", "probe_tag": "drop", "probe_score": 0.0404, "luna_label": 0, "luna_reason": "Generic section heading without an attributed finding or concrete data use."}]}, {"key": "paddy-049", "text": "- access to credit bureau loan-level data – to analyze borrower characteristics\nand assess risk, particularly for consumer loans 64 .\nLonger-term measures would include:\n\n - data collection on the financial condition of corporates and households (e.g.\nbalance sheets, leverage ratios, income indicators etc.) – to better understand\nthe connection between developments in the real sector and financing patterns\n(i.e. demand- versus supply-side factors)\n\n - identification and analysis of alternative financing sources for companies and\nhouseholds outside the formal financial system – to quantify their importance\nand assess their impact on formal financing sources\n\n - development of a more detailed chart of accounts on bank revenues by loan\nproduct (including both interest income and fees) – to assess bank\nperformance when analyzing competition issues in different credit segments.\n\n\n**Transparency and disclosure** : The development and public disclosure of\nstandardized credit affordability indicators (i.e. interest rate time series by type of loan\nproduct and by provider) as well as of accessibility indicators (i.e. loan volumes by\nproduct, firm size, economic sector and state) would greatly contribute to a more\ntransparent credit market. As has been the experience in other countries, publication of\nthese indicators could provide a further impetus to competition across credit providers 65\nand help borrowers become aware of credit pricing differences and hence more\nselective 66 ; these indicators could also allow the authorities to better track credit market\ndevelopments and formulate policy in areas such as financial access and competition.\n\n\n**Promotion of SME financing** : As mentioned previously, the prospects for SME\nfinancing growth are less positive relative to other market segments, at least in the short\nterm. Given the importance of SMEs for Mexico’s economy, the authorities will need to\ncontinue to promote SME financing and strengthen the credit infrastructure for this\nmarket segment. In", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:003471:40:0:0", "start": 12, "end": 41, "surface": "credit bureau loan-level data", "probe_tag": "confusion", "probe_score": 0.4173, "luna_label": 1, "luna_reason": "Existing credit bureau data are used to analyze borrower characteristics and risk."}, {"key": "prwp:003471:40:0:1", "start": 983, "end": 1027, "surface": "standardized credit affordability indicators", "probe_tag": "drop", "probe_score": 0.0434, "luna_label": 0, "luna_reason": "Indicators are proposed for future development and disclosure, not cited as existing data."}]}, {"key": "paddy-050", "text": "Jalan, J., and Ravallion, M. (1999). Are the poor less well insured? Evidence on vulnerability to\nincome risk in rural China. _Journal of Development Economics_, 58(1), 61-81.\n\nJanzen, S. A., and Carter, M. R. (2018). After the Drought: The Impact of Microinsurance on\nConsumption Smoothing and Asset Protection. _American Journal of Agricultural Economics_,\naay061.\n\nJensen, N., Stoeffler, Q., Fava, F., Vrieling, A., Atzberger, C., Meroni, M., Mude, A. and Carter,\nM. (2019). Does the design matter? Comparing satellite-based indices for insuring pastoralists\nagainst drought. _Ecological economics_, 162, 59-73.\n\nKarlan D., Ratan A., Zinman J. (2014). Savings by and for the Poor: A Research Review and\nAgenda, _Review of Income and Wealth_, 60 (1): 36–78.\n\nKazianga, H., and Udry, C. (2006). Consumption smoothing? Livestock, insurance and drought in\nrural Burkina Faso. _Journal of Development Economics_, 79(2), 413-446.\n\nKnippenberg, E., Jensen, N., and Constas, M. (2019). Quantifying household resilience with high\nfrequency data: Temporal dynamics and methodological options. _World Development_, 121, 1-15.\n\nKsoll C., Lilleør B., Lønborg J., Rasmussen O. (2016). ‘Impact of Village Savings and Loan\nAssociations: Evidence from a Cluster Randomized Trial’, _Journal of Development Economics_,\n120: 70–85.\n\nLeuven, E., and Sianesi", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000282:34:0:0", "start": 1019, "end": 1038, "surface": "high\nfrequency data", "probe_tag": "drop", "probe_score": 0.0289, "luna_label": 0, "luna_reason": "Phrase is part of a bibliography entry, not a data-use statement."}]}, {"key": "paddy-051", "text": "simistic scenario). Overall average years of schooling is based on population projections across the scenarios. See Section 4.1.3. _(4) _Based on the Kim and Loayza (2019) model, assuming the TFP overall determinants index follows a similar trajectory as that of Korea (over 1985‐2014) under the optimistic scenario, and the UAE under the pessimistic scenario. TFP growth under the moderate scenario is a simple average of the values under the other two scenarios. See Section 4.1.4. _(5) _The optimistic scenario under the voluntary mobility case is based directly on projections from United Nations (2017). For other scenarios, the UN projections are adjusted to reflect higher or lower refugee returns. See Section 4.1.2. _(6) _United Nations (2017). _(7) _Based on ILO projections up to 2030 of the labor force participation rates, held constant thereafter. |*Multiply by 100 to obtain parameter/variable values in percent share or growth ", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000498:30:19:1", "start": 790, "end": 805, "surface": "ILO projections", "probe_tag": "drop", "probe_score": 0.0423, "luna_label": 1, "luna_reason": "ILO projections provide labor-force participation inputs through 2030."}]}, {"key": "paddy-052", "text": " _, D_ _i_ 2 _, D_ _i_ 3 for whether reference plot _i_\n\ncontributes, respectively, only flow data, only failure data, or both flow and failure data, and assuming\nthat _µ_ is normally distributed with variance _σµ_ 2, the full log-likelihood is\n\n\n\n_µ_ 2, the full log-likelihood is\n\n\n\n\n \n) _,_ (D.9)\n\n\n\n\n\n\n_i_\n\n\n\n\n_f_ _i_ ( _µ_ )\n\n\n\nlog\n\n\n\n��\n\n\n_µ_\n\n\n\n_ℓi_ ( _µ, κµ_ ) _d_ Φ( _µ_\n\n_σµ_\n\n\n\nwhere\n\n\n\n_L_ =\n\n\n_ℓi_ ( _µ, ξ_ ) =\n\n\n\n\n_F_</", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001317:45:1:0", "start": 194, "end": 203, "surface": "flow data", "probe_tag": "drop", "probe_score": 0.0185, "luna_label": 1, "luna_reason": "Flow data are explicitly used as inputs to the likelihood analysis."}, {"key": "prwp:001317:45:1:2", "start": 210, "end": 222, "surface": "failure data", "probe_tag": "confusion", "probe_score": 0.0676, "luna_label": 0, "luna_reason": "Generic data type is merely defined, with no cited source or analytical finding."}]}, {"key": "paddy-053", "text": " a certain point, indicating that combining multiple indicators may lead to over-�itting.\n\nTable 4 presents the regression outcomes for the GLM employing RFE. The initial three models are based\n\n\n\ndrop beyond a certain point, indicating that combining multiple indicators may lead to over-�itting.\n\nTable 4 presents the regression outcomes for the GLM employing RFE. The initial three models are based\non: (1) normalized continuous indicator data, (2) binary alert data, and (3) binary alarm data. A Brier\nscore nearing zero and a pseudo R-squared value, computed as 1− log loss/ uninformative log loss,\napproximating 0.6 indicate that these simple models possess a commendable predictive capability\n\n**Table 4: Foundational GLM results** regarding actual escalations in food security.\n\n\n\n**Table 4: Foundational GLM results**\n\n\n\n**Table 4: Foundational GLM results**\n\nRegression results for three models: 1) normalized continuous indicator data, 2) binary alert data, and 3) binary alarm data. To\nassess model performance, the B rier score, pseudo R-squared an d a weighted average of error ty pes, optimizing the probabilit y\ncut-off used of classi�ication, wer e calculated.\n\n**(1) GLM Continuous** **<mark", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001638:15:1:3", "start": 452, "end": 469, "surface": "binary alert data", "probe_tag": "drop", "probe_score": 0.0495, "luna_label": 1, "luna_reason": "Binary alert data are used in GLM models assessing predictive performance."}]}, {"key": "paddy-054", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n# **Summary of findings and** **recommendations**\n\nThe 2024 round of the SEIS survey indicates that while the financial vulnerability of refugees from Ukraine\nresiding in neighboring countries - specifically those included in the [Regional Refugee Response Plan - has](https://www.unhcr.org/europe/publications/regional-refugee-response-plan-2025-2026)\ndeclined over the past year 1 [^1: Compared to the [2023 MSNA data](https://data.unhcr.org/en/documents/details/108068)], one in five still live with an income below the poverty line 2 . When factoring in the\ndisproportionately high accommodation costs refugees face, largely due to the region’s high homeownership\nrates among locals, the poverty rate 3 rises to 40%, more than three times that of host communities 4 .\n\n\nThe data also shows that poverty 5 has a very tangible effect on living conditions and protection risks. This\ngroup feel less safe, less secure in term of accommodation tenure, more frequently misses out on needed\nhealthcare, more often has children out of school, and is much more frequently forced to resort to skipping\nmeals because of lack of funds. In addition, vulnerable populations, such as older adults and individuals with\ndisabilities or MHPSS 6 needs exhibit significantly higher poverty rates when compared to the refugees overall.\nThese connections are important to bear in mind when designing government, development, and humanitarian\nsupport programs.\n\n\nEmployment continues to be closely associated with lower poverty rates, though ultimately, it’s the size of\nincome that is generated by working household members that", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "jad_paddy_docs:000010:2:0:0", "start": 180, "end": 191, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.9164, "luna_label": 1, "luna_reason": "2024 SEIS survey provides poverty and vulnerability findings."}, {"key": "jad_paddy_docs:000010:2:0:1", "start": 533, "end": 547, "surface": "2023 MSNA data", "probe_tag": "keep", "probe_score": 0.9544, "luna_label": 1, "luna_reason": "Existing 2023 MSNA survey data support the reported year-over-year comparison."}]}, {"key": "paddy-055", "text": " Poland of\n38%. Even before becoming refugees, they\nwould have required support to enter the\nworkforce and now, in the host country,\nthey would be likely to benefit from such\nhelp even more. Some of them may be\ndiscouraged by not having been able to find\na job, others may be marginally attached\nworkers who fell outside the labour\nforce, but have some desire and ability to\nreturn to work.\n\n\n\n**Chart 25. Ukrainian refugees’ employment rate in the 18-64 age group by previous**\n**status in Ukraine**\n\n\n91%\n\n\n\nHousehold\nresponsibilities\n\n\n\nOthers Studying Employed Self-employed\n\n\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey conducted in May and June 2024.\n\n\n\n29 A dummy variable that takes the value of 1 in case of none, beginner, or intermediate language knowledge, and 0 for other levels has been regressed against a\nnumber of explanatory variables.\n\n\n34 35", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "jad_paddy_docs:000001:17:3:0", "start": 624, "end": 641, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9715, "luna_label": 1, "luna_reason": "Named survey cited as the basis for the employment-rate chart."}]}, {"key": "paddy-056", "text": "\nThe only hard data on these flows is the\nnumber of Ukrainian workers with social\ninsurance, which nevertheless understates\nthe numbers, as certain kinds of legal\nwork often undertaken by temporary\nemployees did not require it, and some\nUkrainians worked in the shadow economy.\nThe number of workers with Ukrainian\ncitizenship and social insurance increased\nfrom just 33 thousand at the end of 2013,\nto 500 thousand on 30th September\n2019, and 627 thousand at the end of\n2021. Some of this increase reflects the\ntransitioning of Ukrainians to more regular\nwork arrangements and securing of work\npermits. The yearly rate of the number of\nregistrations peaked at 106% at the end\nof 2015, and even with the recent refugee\ninflow never reached such a high pace\nagain. The most recent data from 30th\nSeptember 2023 counts 753 thousand\nworkers with Ukrainian citizenship\nregistered for social security, including 225\nthousand refugees.\n\n\n\n**Source:** Deloitte own elaboration based on ZUS data. Quarter 3 2023 shows the most recent\ndata available.\n\n13\n\n\n\nThe structure of the Ukrainian population\nin Poland changed radically after 24th\nFebruary 2022. Up until 2021, Ukrainians\nin Poland were mostly men (close to\ntwo-thirds) looking for work, often leaving\ntheir families back in Ukraine. After the\nbeginning of the full-scale war in Ukraine,\nrefugees fleeing the war started to arrive.\nThey were primarily women and children.\nThis is consistent with a change in the\nnature of migration flows, from primarily\neconomic migrants to forcefully displaced\nrefugees.\n\n\n\n**Chart 2.** Number of Ukrainian men and women registered in Poland for social security.\n\n\n800\n\n\n700\n\n\n600\n\n\n500\n\n\n400\n\n\n300\n\n\n200\n\n\n100\n\n\n0\n\n2021 Q42 022 Q4 2023 Q3\n\n\n\nNumber of insured men with\nUkrainian citizenship\n\n\n\nNumber of insured women with\nUkrainian citizenship", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "jad_paddy_docs:000007:6:1:0", "start": 979, "end": 987, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9503, "luna_label": 1, "luna_reason": "ZUS data underlies reported Ukrainian worker registration figures"}]}, {"key": "paddy-057", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**UKRAINE REFUGEE VS HOST GROSS MONTHLY WAGES, EUR/MONTH**\n\n\nMinimum wage (2024) Refugee mean wage (2023) Refugee mean wage (2024) Host mean wage (2024)\n\n\n2,500\n\n\n2,000\n\n\n1,500\n\n\n1,000\n\n\n500\n\n\n\n0\n\n\n\nBulgaria Czechia Estonia Hungary Latvia Lithuania Moldova Poland Romania Slovakia Region\n\n\n\nNote: Mean wages have been estimated by dividing household employment income by the total number of working hours and then computing a\nweighted average across households with weights proportional to total working hours. As the survey asked for net income, weighted means were then\nconverted to gross amounts for comparability based on host country tax rates.\n\n\nSource: Survey data, Eurostat, SAG estimates\n\n\nSimilar to its impact on employment status, education seems to have a much less pronounced effect on wage\npremiums for refugees compared to hosts, also suggesting the presence of underemployment. While,\naccording to Eurostat data and SAG estimates, a local with an advanced degree can expect to earn nearly 80%\nmore than someone with only lower secondary education 18 [^18: The difference in median wages by highest education level attained. Weighted equivalently to refugee weights for comparability], the same wage gap 19 for Ukrainians stands as just\n16% based on survey data.\n\n\nSkills-job mismatching also becomes evident when analyzing the current employment of refugees compared to\ntheir pre-war employment in Ukraine, as nearly 60% have transitioned to entirely different economic sectors.\nThis phenomenon is more pronounced among women, with 63% having shifted to roles outside their 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", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000010:12:0:0", "start": 775, "end": 786, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9977, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:12:0:1", "start": 1030, "end": 1043, "surface": "Eurostat data", "probe_tag": "keep", "probe_score": 0.9991, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:12:0:2", "start": 1403, "end": 1414, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9928, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-058", "text": ",\npharmacists (0.01%), and psychologists\n(0.09%), compared to the following shares\nfor Polish citizens: 0.26%, 0.19%, and 0.18%,\nrespectively.\n\n\n\n**One of the most effective forms**\n**of support offered to refugees**\n**and other immigrants in terms of**\n**employment and earnings to be found**\n**in the literature is language training.**\nFoged et al. (2024) analysed labour market\noutcomes of language training, placement\nin strong labour markets, active labour\nmarket policies, cutting welfare benefits,\nand placement in co-ethnic networks that\nwere directed at refugees in Denmark.\nThanks to unusually detailed Danish data,\nthey could follow individual refugees who\narrived in Denmark between 1987 and\n2008, for at least 10 years, and in most\ncases for 15 years. They found intensive\nlanguage training introduced in 1999 to be\nthe most effective of all policies, accounting\nfor a 5-6 pp. increase in the probability of\nemployment and a USD 3,000 increase\nin annual earnings (2015 figures). While\nlocating refugees in strong labour markets\nalso had considerable positive effects,\nthe report found only some evidence that\nActive Labour Market Policies (ALMPs)\nfocused on matching refugees with deficit\noccupations improved their employment\nprospects and no evidence of positive\neffects of cutting benefits or placing\nrefugees in co-ethnic networks. Heller\nand Mumma (2023) exploited randomized\nenrolment lotteries for a publicly-funded", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:15:4:0", "start": 613, "end": 624, "surface": "Danish data", "probe_tag": "keep", "probe_score": 0.9788, "luna_label": 1, "luna_reason": "Detailed Danish data supports employment and earnings findings."}]}, {"key": "paddy-059", "text": " 12.** Structure by occupational group of all employed persons and refugees from Ukraine\n\n\nAll employed persons Ukrainian refugees\n\n\n\n\n\n\n\n40% 30% 20% 10% 0% 10% 20% 30% 40%\n\n\n\n\n\n**Source:** Deloitte own elaboration based on Statistics Poland and ZUS data.\n\n\n24 Due to possible differences in methodologies data from this surveys should not be directly compared\n25 Elementary occupations include: Cleaners and helpers; Agricultural, forestry and fishery labourers; Labourers in mining, construction, manufacturing\nand transport; Food preparation assistants; Street and related sales and services workers; Refuse workers and other elementary workers.\n\n22\n\n\n\n23\n\n\n\n26 Act of March 12, 2022 on assistance to citizens of Ukraine in connection with the armed conflict on the territory of the country", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:11:2:0", "start": 246, "end": 254, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9297, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-060", "text": "\nincrease in Polish citizens' employment\nrates by 0.5% and a decrease in the\nunemployment rate by 0.3%, in the\npreferred specification with dummy\nvariables for all quarters. All results are\nstatistically significant at a 0.01 level.\n\n\n\n**Chart 26. Polish citizens employment rate** **Chart 27. Polish citizens unemployment rate**\n\nEurostat survey data, 20-64 age group Eurostat survey data, 20-64 age group\n\n\n\n3.6%\n\n\n\n\n\n83.8%\n\n\n\n83.6%\n\n\n\n\n\n83.2%\n\n\n\n**Chart 28. Effect of a 1 pp. change in employment share of Ukrainian refugees on**\n\nPanel model of all 380 poviats quarterly data from Q1 2022 to Q2 2024. Results are statistically significant at a 0.01 level.\n\n\nPolish citizens employment rate Unemployment rate\n\n\n1.0 1.0\n\n\n0 0\n\n\n-1.0 -1.0\n\n\n-2.0 -2.0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n2021-Q2 2022-Q2 2023-Q2 2024-Q2 2021-Q2 2022-Q2 2023-Q2 2024-Q2\n\n\n\nSeasonal Quarterly No time Seasonal\ndummies dummies dummies dummies\n\n\n\nQuarterly\ndummies\n\n\n\nNo time\ndummies\n\n\n\nSource: Deloitte own elaboration based\nof Eurostat data (Labour Force Survey).\n\n\n38\n\n\n\nMales Source: Deloitte own elaboration based Males\n\n\n\nSource: Deloitte own elaboration based\nof Eurostat data (Labour Force Survey).\n\n\n\nFemales of Eurostat data (Labour Force Survey). Females\n\n\n\nSource: Deloitte own elaboration based on GUS and ZUS data. All continuous variables have been regressed in first differences to account for non-stationarity Polish\ncitizens employment rates and registered unemployment rates. For details see the Online Technical Appendix.\n\n\n39", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:19:2:0", "start": 331, "end": 351, "surface": "Eurostat survey data", "probe_tag": "keep", "probe_score": 0.9506, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:19:2:1", "start": 989, "end": 1002, "surface": "Eurostat data", "probe_tag": "keep", "probe_score": 0.9995, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:19:2:2", "start": 1004, "end": 1023, "surface": "Labour Force Survey", "probe_tag": "keep", "probe_score": 0.9886, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:19:2:3", "start": 1271, "end": 1274, "surface": "GUS", "probe_tag": "keep", "probe_score": 0.9985, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:19:2:4", "start": 1279, "end": 1287, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9836, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-061", "text": "Increased competition on the labour\nmarket could partially offset benefits\nfrom refugees. Although (due to a tight\nlabour market) increase in labour force\nis almost entirely absorbed into working\nforce, there should be slight increase\nin unemployment rate. Ultimately, it is\nestimated that it was higher by 0.14-0.25\npp. in 2022 and by 0.18-0.3 pp in 2023\nwhich corresponds to respectively 24-42\nthousand and 33-54 thousand additional\npeople being unemployed 45 [^45: Based on number of economically active people in III quarter of 2023 according to labour market survey.] . In the long\nrun, the unemployment rate should remain\nhigher by 0.15-0.3 pp. Because of that, it is\nestimated that growth of real wages was\nslower in 2022 and 2023. It is estimated\nthat due to the influx of refugees, real\nwages were lower in 2022 by 0.45-0.85%\nand in 2023 by 0.65-1.15%. Although in\neffect this is negative, it also means lower\ninflationary pressure from the labour\nmarket in the short term. Long-term real\nwages should be around 0.55-1.0% lower\nthan in a scenario without refugees.\nThat said, the actual labour market\neffect is likely to be null as evidenced by\neconometric studies (Gromadzki and\nLewandowski, 2023; Peri, 2014), which are\nelaborated on in Chapter 4. Gromadzki\nand Lewandowski (2023) in the early\nmonths of 2022 find no effect of Ukrainian\nrefugees on earnings, employment, and\nunemployment rate of natives and other\nimmigrants, except an actual slight positive\nimpact on the wages of native women.\n\n\nEven with the increase in unemployment\nand lower real wages, an increase in labour\nforce means a higher wage pool, which\nmeans higher tax income.\n\n\n\nMoreover, boosts in private consumption\nboth due to increase in population as\nwell as higher average spending rates\nmeans that refugees increased state\nincome from taxation on consumption.\nThese effects will be strengthened by\ninflux of capital from", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:18:0:0", "start": 561, "end": 581, "surface": "labour market survey", "probe_tag": "keep", "probe_score": 0.9911, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-062", "text": "s/average-\ngross-wage-in-the-second-quarter-2024,281,43.html\n\n\n14\n\n\n\n9 These employment rates are very close to the ones from the Polish central bank surveys of Ukrainian refugees, which showed 62% in July 2023 and 68% in July\n2024 (NBP, 2024). NBP (2024) age group was slightly different, describing adults as 18 years or older.\n\n10 See the note on median wage estimation method in the Online Technical Appendix.\n\n11 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- out of about 16 million socially insured workers. Nevertheless, it is still a very large sample and thus a useful proxy, especially in the case of Ukrainian refugees who\npresumably all should have their occupations listed as they did not arrive before 2022.\n\n\n15", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:7:3:0", "start": 141, "end": 190, "surface": "Polish central bank surveys of Ukrainian refugees", "probe_tag": "keep", "probe_score": 0.9856, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-063", "text": " (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's Doctoral\n\n\n\n30%\n\n\n\nLower\nsecondary or\n\nbelow\n\n\n\nSource: Survey data, SAG estimates\n\n\n\n**11**", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000010:10:1:0", "start": 130, "end": 141, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9985, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:10:1:1", "start": 309, "end": 320, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9994, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-064", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\n**Recently arrived Ukrainian refugees,**\n**those with below tertiary education**\n**and in older age group are most**\n**likely to communicate in Polish at**\n**intermediate and lower levels.** Based\non the SEIS UNHCR survey, a logistic\nregression has been performed to\nfind out which categories of Ukrainian\nrefugees may most require Polish language\nimprovement. 29 [^29: A dummy variable that takes the value of 1 in case of none, beginner, or intermediate language knowledge, and 0 for other levels has been regressed against a\nnumber of explanatory variables.] Results show that the\nodds of only zero to intermediate Polish\nknowledge decrease with every month\nsince arrival. Ukrainian refugees in the\n18 to 29 age group have the lowest odds\nof having an intermediate or lower level of\nPolish. It translates into a 38% probability,\neven lower than the 41% for refugees with\ntertiary education. The group with the\nhighest odds (70% probability) of zero to\n\n\n\nintermediate Polish are Ukrainian refugees\naged 50 to 64. The results by employment\nsectors are not statistically significant,\nother than for manufacturing. The results\nare intuitive, with the best language skills\namong refugees working in health and\neducation, and the lowest among those\nworking in construction, other services,\nand trade.\n\n\n**Addressing the gap in language**\n**fluency would yield significant**\n**macroeconomic benefits.** To illustrate\nthe macroeconomic impact of public\nintervention, we have assumed that half\nof the current language gap is addressed,\nso that the share of working refugees not\nspeaking Polish fluently falls from 82%\nto 41%. Assuming that the productivity\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n#### **4.5 Recommendations**\n\n##### Language courses\n\n\n**Ukrainian refugees in Poland earn higher wages when they", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:17:0:0", "start": 280, "end": 297, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9807, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-065", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n\n### **Child health,** **vaccination and** **nutrition**\n\n\n\n\n\n\n\n\n\nTimely initiation\nof breastfeeding\n(within one hour\nof delivery)\n\nExclusive\nbreastfeeding\nunder 6 months\n\n\n\n0-23\n153/274 57%\nmonths\n\n\n0-5\n15/38 37%\nmonths\n\n\n\n\n- Excluding Estonia, Hungary\n\n\nBreastfeeding practices for infants and young\nchildren directly influence their nutritional health\nduring the first two years of life and play a crucial\nrole in child survival. From the survey results, the\nproportion of children 0-23 months who had timely\ninitiation of breastfeeding was 57% and the rate of\nexclusive breastfeeding for the first six months of\nlife was 37% in the region. Data need to be\ninterpreted with caution given the very low number\nof respondents.\n\n\nTwo doses of measles vaccine are recommended\nfor optimal protection against measles; the survey\nassessed therefore first and second dose measles\nvaccination coverage in children aged 9 months to\n5 years. In average, 83% of children received at\nleast one measles vaccine, similar to results from\n2023 when 84% of children had received at least\none dose. Coverage was lowest in Romania with\n71% where respondents reported also greater\n\n\n\nconstraints in accessing health services. Vaccine\ncoverage increased notably in Moldova, Czechia\nand Bulgaria compared to 2023. In comparison,\nmeasles vaccination coverage within Ukraine\nreached 92% for the 1st dose of measles vaccine\nand 87% for second dose 8 (WHO, 2023).\n\n\nRegionally, only 54% of all children received the\nrecommended second measles vaccine.\nVaccination coverage is below the 95% target\nrequired to interrupt community transmission of\nmeasles.\n\n\n**% OF CHILDREN RECEIVED AT LEAST ONE MEASLES**\n**VACCINE**\n\n\n2023 2024\n\n\n\nData need to be interpreted", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000004:17:0:0", "start": 527, "end": 541, "surface": "survey results", "probe_tag": "keep", "probe_score": 0.993, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-066", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**Living in a vulnerable household is associated with higher poverty risks**\n\n\nJust like last year, members of households with vulnerabilities were found to more likely be living in poverty\nthan the general refugee population. Almost half of individuals living with an older adult (age 65+) reported an\nequivalized disposable income below the poverty threshold. For individuals living with household members\nwith a disability or members with MHPSS needs 11 [^11: Defined as someone feeling so upset, anxious, worried, agitated, or depressed that it affects daily functioning] these rates stood at 41% and 27%, respectively. Gender of the\nhead of household, however, was not found to have a significant impact on the poverty rate. Mixed gender (at\nleast one male and one female head) was associated with higher income, though likely due to increased\nchances of multiple breadwinners being present in the household.\n\n\n**REFUGEE POVERTY RATES BY VULNERABILITY CHARACTERISTIC AND GENDER OF HEAD OF HOUSEHOLD**\n\n\n\nNo Yes\n\n\n\n\n\nFemale Male Mixed\n\n\nHead of\nhousehold\n\ngender\n\n\n\nOlder adult\n(65+) present\n\n\n\nHousehold\nmember with\n\n\n\na disability\n\n\n\nHousehold\nmember with\nMHPSS needs\n\npresent\n\n\n\npresent\n\n\n\nSource: Survey data, SAG estimates\n\n\n**Refugee housing expenses are on average much higher than for nationals, which implies an**\n**even greater disparity in financial wellbeing**\n\n\nAt the regional level, the weighted average share of the host population living in rented housing was calculated\nat 13% based on Eurostat data. This figure is dwarfed by 60% of refugee households fully paying rent for their\naccommodation and 21% partially paying, as per the SEIS survey. Likewise, accommodation expenses as a\nshare of disposable income were estimated at 17% for hosts, including mortgages, compared to 32% for\nrefugees. This essentially implies that refugees, on average", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000010:4:0:0", "start": 1331, "end": 1342, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9992, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:4:0:1", "start": 1634, "end": 1647, "surface": "Eurostat data", "probe_tag": "keep", "probe_score": 0.9853, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:4:0:2", "start": 1780, "end": 1791, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.9409, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-067", "text": ", 2024, Ukrainian refugees\ngained an estimated 7% in earnings having\nshifted towards better paid occupations,\npre-war Ukrainians 5%, non-Ukrainian\nforeigners 4%, and Polish citizens 1%.\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\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**Ukrainian refugees have been slowly**\n**closing their wage gap to Polish**\n**citizens across all wage levels.** Once\nZUS administrative data on Ukrainian\nrefugees with employment contracts is\ndivided into employee cells based on\n380 poviats, 2 sexes, 7 age groups, and\n10 main occupational groups (including\n\n\n\nunallocated), the gap in social contributions\nbases towards Polish citizens narrows.\nThe largest group (16%) is positioned\nbetween 90% and 100% of Polish citizens\n(median 93%). This is an improvement over\ntwo years, when the majority (13%) situated\nbetween only 80% and 90% (median 82%). 12\n\n\n\n12 Unfortunately, the data is available in a format that is not suitable for econometric modelling and thus these are only comparisons between thousands of employee\ncells and average social contributions bases.", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:8:1:0", "start": 368, "end": 391, "surface": "ZUS administrative data", "probe_tag": "keep", "probe_score": 0.9468, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-068", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n# **Background**\n\nOver three years have elapsed since the start of the full-scale invasion of Ukraine, an event which has led to\nthe largest displacement crisis in Europe since World War II. As of the end of 2024, 6.3 million refugees from\n[Ukraine were recorded across Europe, close to 2 million of whom are located in ten Regional Refugee](https://www.unhcr.org/europe/publications/regional-refugee-response-plan-2025-2026)\n[Response Plan](https://www.unhcr.org/europe/publications/regional-refugee-response-plan-2025-2026) countries: Bulgaria, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Republic of Moldova,\nRomania, and Slovakia. This report aims to assess the livelihood situation of this population based on the 2024\nround of data collected by the Socio-Economic Insights Survey (SEIS), which received responses from 8,723\nhouseholds containing 19,803 individuals. Figures for 2023 are derived from a similar exercise 7 [^7: The MSNA, which ran in 7 countries: Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and Slovakia] conducted in\n2023.\n\n# **Key findings**\n\n**Refugee poverty rates remain high, albeit improved from 2023**\n\n\nThe 2024 SEIS equivalized 8 disposable income data indicates that just over one in five refugees (23%) residing\nin the region are living in poverty 9 . This figure is almost double that of host country nationals (12%), implying a\nlarge gap in economic vulnerability. Compared to 2023, poverty rates have decreased substantially (from\n36% ", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000010:3:0:0", "start": 879, "end": 909, "surface": "Socio-Economic Insights Survey", "probe_tag": "keep", "probe_score": 0.9956, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:3:0:1", "start": 1071, "end": 1075, "surface": "MSNA", "probe_tag": "keep", "probe_score": 0.9978, "luna_label": 1, "luna_reason": "2023 figures are derived from this named survey exercise."}, {"key": "sample:jad_paddy_docs:000010:3:0:2", "start": 1301, "end": 1310, "surface": "2024 SEIS", "probe_tag": "keep", "probe_score": 0.9978, "luna_label": 1, "luna_reason": "Named 2024 survey data supports the reported refugee poverty finding."}, {"key": "sample:jad_paddy_docs:000010:3:0:3", "start": 1336, "end": 1358, "surface": "disposable income data", "probe_tag": "keep", "probe_score": 0.9316, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-069", "text": "term nature of\nmost of these stays (6 months in a span 12\nmonths), fact that one person could hold\nseveral permits (as a result of changing jobs\nor positions within the same company),\nlegal changes, and decentralized nature of\npermits all hinder the reliability of these\nfigures. Two-thirds of such migrants have\nbeen men, some of whom left families\nin Ukraine temporarily to bring or send\nmoney back. Migration has been primarily\nbased on firm sponsorship, where\ncompanies provided job offers and handled\nformalities, which resulted in very high\nemployment rates – 94% as of November\n2022 according to NBP (2023) estimates.\n\n\n\nThe refugee inflow had a different\ndemographic composition than the pre2022 economic migration. It primarily\nincluded working age women (41%) and\nchildren (40%). 5 [^5: According to the active PESEL UKR database in October 2023.] Refugees from Ukraine did\nnot plan to move, and many had special\nneeds. In October 2023, nearly half of all\nrefugee households included a person\nwith a chronic illness, and some 10%\nincluded one with a Washington Group\nlevel 3 disability. Over a third included a\nsingle parent and a fifth an elderly person. 6 [^6: Deloitte calculations based on Multi-Sector Needs Assessment Poland 2023 survey data provided by UNHCR.]\nDespite these difficulties, refugees began\nentering the labour market surprisingly\nquickly – attaining an employment rate of\n28% in May 2022 and 65% in November\n2022 among working age persons (NBP,\n2023) –\n##### **753 thousand** Ukrainian workers, including 225 thousand refugees, had registered for social security by September 30, 2023.\n\n\n\n**Situation of Ukrainian refugees on the**\n**labour market in Poland**\nUkrainian refugees, despite war trauma\nand other difficulties, have quickly\nbecome a part of society as **consumers,**\n**employees, entrepreneurs, and**\n*", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:3:2:0", "start": 832, "end": 850, "surface": "PESEL UKR database", "probe_tag": "keep", "probe_score": 0.9824, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:3:2:1", "start": 1226, "end": 1279, "surface": "Multi-Sector Needs Assessment Poland 2023 survey data", "probe_tag": "keep", "probe_score": 0.9992, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-070", "text": " elaboration based on Polish Border Guard Headquarter and PESEL data.\n\n\n\n\n\n\n\nMost of the refugees from Ukraine\ncurrently living in Poland are women and\nchildren, though over half of the total\npopulation is of working age.\nThe best population data available is the\nactive PESEL database as of 10th October\n2023. According to the registry, 63.7%\nof them are women and 36.3% are men.\nThe database also includes the age of the\nUkrainian PESEL holders, which indicates\nthat over half (56%) are of working age\n(18-64), which amounts to more than 536\nthousand people. More precisely, women\nyounger than 18 make up 19% of the\npopulation, women aged 18-64 account\nfor around 41% of the population, while\nthose aged 65 and above are around 3%\nof total population. The incoming men are\nmostly young, those younger than 18 stand\nfor around 20% of all refugees, men aged\n18-64 make up 15% of the population and\nmen older than 64 account for only 1%.\n\n\nMany of the refugees that settled in Poland\nremain in special needs or otherwise\nprecarious households. According to the\nMSNA Poland 2023 survey, nearly half of all\nrefugee households have a person with a\nchronic illness, while in nearly 10% there is\na disabled person (Washington Group level\n3 disability). In over a third of all households\nis a single parent and over a fifth houses\nan elderly person (10% of households are\ncomprised of exclusively elderly people).\n\n\n\n\n\n\n\n\n\n\n\n\n\nhouseholds with\n\npersons with\n\nchronic ilness\n\n\n\n\n\n\n\nhouseholds\n\nwith a single\n\nparent\n\n\n\nhouseholds with\n\none or more\n\nolder persons\n\n\n\nhouseholds\n\nwith disabled\n\nindividuals\n\n\n\nhouseholds\n\nexclusively\n\nwith elderly\n\n\n\n18 UNHCR data, [https://data2.unhcr.org/en/situations/ukraine](https://data2.unhcr.org/en/situations/ukraine)\n\n\n14\n\n\n\n**Source:*", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "jad_paddy_docs:000007:7:2:0", "start": 58, "end": 68, "surface": "PESEL data", "probe_tag": "keep", "probe_score": 0.9593, "luna_label": 1, "luna_reason": "PESEL registry data supports refugee demographic estimates and age calculations."}, {"key": "jad_paddy_docs:000007:7:2:1", "start": 271, "end": 285, "surface": "PESEL database", "probe_tag": "keep", "probe_score": 0.9804, "luna_label": 1, "luna_reason": "PESEL registry data provides refugee gender and age figures."}, {"key": "jad_paddy_docs:000007:7:2:2", "start": 1060, "end": 1083, "surface": "MSNA Poland 2023 survey", "probe_tag": "keep", "probe_score": 0.9526, "luna_label": 1, "luna_reason": "Named survey cited for concrete household vulnerability findings."}, {"key": "jad_paddy_docs:000007:7:2:3", "start": 1644, "end": 1654, "surface": "UNHCR data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Platform source named without shown UNHCR data use or attributed finding."}]}, {"key": "paddy-071", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nThe above results do not distinguish\nbetween Ukrainian refugees improving\nthe labour market outcomes in the\npoviats they arrived in and Ukrainian\nrefugees disproportionately moving to\nthe poviats with better performance,\nwhich continued to perform better in\nsubsequent quarters. This distinction is\nmostly academic, as causality is always\nuncertain in social sciences, as effects can\nrun both ways. However, instrumental\nvariable regressions have been performed\nto ascertain causal effects. This approach\nis widely used in studies focused on the\nimpact of migration on socio-economic\noutcomes. This econometric technique\nadditionally uses variables that correlate\nwell with Ukrainian refugees’ employment\nshares, but do not directly cause changes\nto the Polish citizens' employment rate\nor to the unemployment rate. Two\nvariables have been used: the first was\nthe share of Ukrainian children in Polish\nschools, and the second was the pre-war\ndistribution of Ukrainian citizens based\non notifications of entrusting work to\na foreigner. Unfortunately, panel fixed\neffects regressions with quarterly dummies\nshowed statistically insignificant results\nfor either instruments or both. In addition,\nregressions using only the instrumental\nvariable for the distribution of pre-war\nUkrainian citizens reveal only a modest\nlink to the employment share of Ukrainian\nrefugees.\n\n\nSimilarly to the above results, an early\nanalysis by Gromadzki and Lewandowski\n(2023) found no impact on the employment\nrate or unemployment rate. In a peerreviewed scientific article, they examined\nthe impact of Ukrainian refugees on the\nlabour market outcomes of Polish women\nfrom January to April 2022 (as most\nUkrainian refugees are female). They found\nthat the proportion of Ukrainian refugees\nin a given poviat had no statistically\nsignificant impact on the employment rate\nor unemployment rate of Polish women.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 29. Effect of a 1 pp. change in employment share of Ukrainian refugees on gross wage change**\nCross-section model of all 380 poviat", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:20:0:0", "start": 1062, "end": 1109, "surface": "notifications of entrusting work to\na foreigner", "probe_tag": "confusion", "probe_score": 0.6331, "luna_label": 1, "luna_reason": "Notifications supplied pre-war distribution data used as an instrumental variable."}]}, {"key": "paddy-072", "text": " disease prevention, detection\nand treatment programmes, increasing the health\nrisks.\n\n\n\nMental health is a significant concern, with\nparticularly high needs in populations fleeing\nconflict. WHO estimates that one in five people\naffected by conflict (22.1%) have a mental health\ncondition, including 13.0% with mild, 4.0% with\nmoderate, and 5.1% with severe conditions 4 .\nRefugees may have experienced conflict-related\nviolence and other adversities prior to displacement\nand likely face current stressors associated with\nadapting to new environments, languages, and\ncultures, economic and housing instability, limited\nsocial support networks and uncertainties about\ntheir future and those left behind in Ukraine. These\nfactors may contribute to mental health and\npsychosocial problems that can persist for many\nyears if left unaddressed.\n\n\nRefugee receiving and hosting countries have\nshown generosity and kept borders open while\nadapting to a protracted stay of the refugees as the\nsituation in Ukraine remains volatile and prospects\nfor refugees returning home remain grim. Under\nthe Temporary Protection Directive or other\nprotection mechanisms implemented in most\ncountries, refugees have access to health care\nbenefits, but certain groups may be left out\naccording to country-specific practices – for\nexample, those who do not apply for temporary\nprotection and those who temporarily return to\nUkraine or move from one location to another and\nare deregistered 5 .\n\n\nHealth systems in refugee-receiving countries have\nmade substantial efforts to meet the needs of\nUkrainian refugees. However, challenges in host\ncountry health systems, such as health workforce\nshortages, long waiting lists, and language barriers\naffect access to care and may discourage healthseeking among refugees. Refugees’ movement\nwithin and out of countries present challenges for\nlocal health providers to register and provide\nrequired services. At the same time, there are\n\n\n\n1. [Ukraine Refugee Situation ( accessed 12 Dec 2024)](https://data.unhcr.org/en/situations", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "jad_paddy_docs:000004:4:1:0", "start": 1987, "end": 2012, "surface": "Ukraine Refugee Situation", "probe_tag": "confusion", "probe_score": 0.8274, "luna_label": 0, "luna_reason": "Named data portal resource with no shown figures, claim, or analytical use."}]}, {"key": "paddy-073", "text": " whom are also women; see charts\nbelow).\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\nIn the model, shocks were calibrated using\ndata for 2022-2024. In the case of 2022,\nwe adjusted the data to reflect refugee\narrivals after February, fixing their share\nat roughly 2.6 percent of the population\nper PESEL registry figures. We then\ncalibrated refugee employment to match\nthe NBP’s 2022 survey (NBP, 2024), the\nUNHCR’s 2023 MSNA, and the 2024 SEIS\nsurvey - implying their employment share\nrose from 1.5 percent to 2.4 percent of\ntotal employment in Poland. To keep the\nregional labour supply balance, equivalent\noffsets were applied in the broader\nEastern Europe aggregate. 35 [^35: Aggregate region in the D.Climate model, that consists of Ukraine, Russia, Belarus, Moldova, Czechia, Slovakia, Hungary, Romania, and Bulgaria.] Furthermore,\nit was assumed that refugees have higher\nspending needs and thus a lower saving\nrate than other earners in Poland for\n2022 and 2023. Moreover, according\nto National Bank of Ukraine data, the\nconsumption of Ukrainian refugees has\nbeen partially financed by savings in\nUkrainian banks in 2022 and 2023, which\nwas modelled as them having a negative\nsaving rate, while being offset by lowering\ninvestment levels in Eastern Europe. 36 [^36: In other words, it was assumed that money that would be spent e.g. through credit action for investments in Eastern Europe were spent for consumption in Poland.] By\n2024, it was assumed that their situation on\nthe labour market had stabilised and that\nthere had been no further changes in their\nsavings.\n\n\n\nDeloitte D.Climate, 32 33 is a general\nequilibrium model that uses consumer\nand producer optimisation to calculate\nchanges in the economy in response\nto shocks. This enables the impact of\nshocks to be assessed by considering\nsupply and", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "jad_paddy_docs:000001:21:1:0", "start": 332, "end": 354, "surface": "PESEL registry figures", "probe_tag": "keep", "probe_score": 0.9666, "luna_label": 1, "luna_reason": "Registry figures calibrate the modeled refugee population share."}, {"key": "jad_paddy_docs:000001:21:1:1", "start": 407, "end": 424, "surface": "NBP’s 2022 survey", "probe_tag": "keep", "probe_score": 0.9576, "luna_label": 1, "luna_reason": "Survey data calibrate refugee employment share in the economic model."}, {"key": "jad_paddy_docs:000001:21:1:2", "start": 450, "end": 459, "surface": "2023 MSNA", "probe_tag": "confusion", "probe_score": 0.8044, "luna_label": 1, "luna_reason": "UNHCR survey used to calibrate refugee employment in the model."}, {"key": "jad_paddy_docs:000001:21:1:3", "start": 469, "end": 485, "surface": "2024 SEIS\nsurvey", "probe_tag": "confusion", "probe_score": 0.8934, "luna_label": 1, "luna_reason": "Survey data calibrated refugee employment and supported the employment-share estimate."}, {"key": "jad_paddy_docs:000001:21:1:4", "start": 1039, "end": 1068, "surface": "National Bank of Ukraine data", "probe_tag": "keep", "probe_score": 0.9409, "luna_label": 1, "luna_reason": "Named bank data supports modeling refugee consumption financed by Ukrainian savings."}]}, {"key": "paddy-074", "text": ".\nTo maximise the positive impact\nof refugees on economy, policies\nhelping their integration into the\nlabour market that both allow\ntheir maximal employment as\nwell minimise market mismatch\nbetween demand for specific\nskills and their abilities are\ncrucial. The second part of this\nrecommendation, achieved\neither through improvements\nin utilisation of skills of refugees\nor trainings giving them abilities\ndemanded by the labour market,\nis integral as it should lower\ncosts for the local labour force.\n\n\n\n45 Based on number of economically active people in III quarter of 2023 according to labour market survey.\n46 E.g. vice-president of Polish Development Found Bartosz Marczuk estimated it at around 16 billion PLN, but this estimation also included spending of NGOs\nwhich was combined with spendings of local governments Polska pomoc dla Ukrainy 2022 - ile kosztowała? - Infor.pl.\n47 Excluding spending of private households estimated as further 10 billion PLN.\n48 Model treats general government sector as a whole, as such cost and income internal structure may differ creating institutions with financial loses while\nother may have disproportionate increase of income.", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:18:2:0", "start": 592, "end": 612, "surface": "labour market survey", "probe_tag": "confusion", "probe_score": 0.7997, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-075", "text": " stronger increase in\nlabour productivity than was assumed. As\na result, the positive impact of Ukrainian\nrefugees on the economy is greater than\npreviously expected.\n\n\n\n**As Ukrainian refugees entered the**\n**labour market, the economy adapted,**\n**resulting in more specialization and**\n**higher productivity.** In a simplistic\nsupply-demand framework, the influx\nof Ukrainian refugees should have\ncaused some Polish workers to become\nunemployed or leave the labour force, or\nreal wages to fall. This has not happened.\nFirst, among Polish citizens employment\nrates have grown, and unemployment\nrates have fallen. Second, poviats in which\nemployment share of Ukrainian refugees\nhas grown by 1 pp., saw 0.5 pp. higher\nemployment rates among Polish citizens,\nand 0.3 pp. lower unemployment rates.\n\n\n\nThird, there is no evidence of lowered\nwages, in fact the limited available data\nsuggests that Ukrainian refugees may have\ncaused higher wage growth in poviats\nwhich they have moved to. These are\ncommon findings well documented in\nacademic literature, that as immigrants\nenter the labour market, native workers\nspecialize in complementary, higher\nvalue tasks, which we see empirically in\nPolish workers moving to more attractive\noccupational groups. This can be seen in\nthe data, as Polish citizens are moving to\nbetter paid occupations. It constitutes a\npositive shock to productivity which is what\ncounterbalances labour market pressures. 17 [^17: For the literature review, underlying empirical evidence, and model calibration refer to the appendix on modelling strategy.]\n\n\n\nSource: Deloitte D.Climate estimates. For details see\nthe Online Technical Appendix.\n\n\n**The main impact of Ukrainian refugees**\n**is expanding the economy and putting**\n**it on a higher growth path.** According\nto 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", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:11:2:0", "start": 857, "end": 879, "surface": "limited available data", "probe_tag": "confusion", "probe_score": 0.0747, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-076", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\nthe survey data side, only households that did not have any missing information on income (respondents were\nasked to provide both sources and amounts) were included in the calculation. For households claiming no or\nvery little income an imputation was made based on expenses and the duration of stay in the host country.\n\n\nIt is important to note that while the poverty definition adopted in this assessment allows for comparisons\nacross the countries covered (limitations apply to the Republic of Moldova), it may not be directly comparable\nto other studies, as approaches tend to vary quite significantly. The setup of the questionnaire, the sampling\nmethodology, the processing of income data (including any imputations), the approach to equivalizing income,\nand finally the location of the poverty line itself in the income range all have a substantial impact on poverty\nindicators.\n\n\nIn order to compute refugee wages, household net employment income was divided by the total number of\nworking hours reported by all employed members. This figure was then weighted by the total number of\nworking hours and averaged overall all households within a given country (while also respecting poststratification weights). The result was then converted to a monthly wage assuming employment at 40 hours per\nweek and 4.33 weeks in a month. For comparability with host population data, net wages were converted to\ntheir gross equivalent utilizing local tax regulations.\n\n# **Limitations**\n\nThe statistical significance of the SEIS results is limited by the non-probabilistic selection of respondents.\nMoreover, the use of convenience sampling likely led to a larger share of data being collected from more\nvulnerable households.\n\n\nThere was also a notably high non-response rate regarding questions related to income and expenditure,\nwhich likely resulted in non-response bias. The income module of the SEIS was also materially different from", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000010:14:0:0", "start": 112, "end": 123, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.7089, "luna_label": 1, "luna_reason": "Survey data were used to select observations for the poverty calculation."}, {"key": "sample:jad_paddy_docs:000010:14:0:1", "start": 792, "end": 803, "surface": "income data", "probe_tag": "confusion", "probe_score": 0.6388, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:14:0:2", "start": 1464, "end": 1484, "surface": "host population data", "probe_tag": "keep", "probe_score": 0.9628, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-077", "text": "HELPING HANDS\nTHE ROLE OF \nHOUSING \nSUPPORT AND \nEMPLOYMENT \nFACILITATION IN \nECONOMIC \nVULNERABILITY \nOF REFUGEES \nFROM UKRAINE\nAn inter-agency \nexploration of socio-\neconomic data\nApril 2024", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000000:0:0:0", "start": 161, "end": 181, "surface": "socio-\neconomic data", "probe_tag": "confusion", "probe_score": 0.4913, "luna_label": 0, "luna_reason": null}]}, {"key": "paddy-078", "text": "s fight for its people. Strategies for refugee and\ndiaspora engagement, Ukraine Forum, Chatham House, February.\n\n\nUNHCR (2023). Poland: Multi-Sector Needs Assessment — Results Overview\n(MSNA 2023), October, https://data.unhcr.org/fr/documents/details/104427\n\n\nUNHCR (2025a). Poland: Socio-Economic Insights Survey in Poland - Results\n\n\n\nAnalysis (SEIS 2024). UNHCR, October, https://data.unhcr.org/en/documents/\ndetails/115045\n\n\nUNHCR (2025b). High employment rates, but low wages: a poverty assessment\nof Ukrainian refugees in neighboring countries, Regional Refugee Response for\nthe Ukraine Situation, Regional Bureau for Europe, UNHCR.\n\n\nUNHCR (2025c). Ukraine Multi-year Strategy 2025 – 2027, UNHCR, November.\n\n\nUrban M. (2022). Refugees will lift economy's potential, but challenges remain,\nResearch Briefing | Poland. Oxford Economics, https://www.oxfordeconomics.\ncom/resource/refugees-in-poland-will-lift-economys-potential-but-challenges-\nremain/\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n47", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:23:5:0", "start": 290, "end": 330, "surface": "Socio-Economic Insights Survey in Poland", "probe_tag": "confusion", "probe_score": 0.0851, "luna_label": 0, "luna_reason": null}]}, {"key": "paddy-079", "text": ", have quickly\nbecome a part of society as **consumers,**\n**employees, entrepreneurs, and**\n**taxpayers** . Currently between 225 and\n350 thousand refugees from Ukraine are\nestimated to be working in Poland. The\nlower bound is the number from social\nsecurity data, while the higher bound is\nthe product of employment rate from the\nsurveys and working age population with\nactive PESEL UKR numbers (Chapter 2).\n\n\nStructural worker shortages, one of\nthe lowest unemployment rates in the\nEuropean Union, record high vacancies,\nand high education attainment of refugees\neased their labour market integration. The\nnumber of Polish citizens aged 20-64 has\ndeclined by 2.6 million from its peak in early\n2010. 7 [^7: According to the Labour Force Survey data from Eurostat.] Despite COVID-19 and geopolitical\nshocks, the unemployment rate oscillated\nin recent years around 3% in Poland, and\nin February 2022 only Czechia exhibited a\nlower rate in the EU. 8 [^8: According to the harmonized unemployment rates from Eurostat.] In Q4‘2021 the share\nof companies reporting vacancies stood at\n49%, the highest level on record, and has\nbeen slowly declining since then. 9 [^9: According to the quarterly NBP survey.]\nIn July-August 2023, 56% of refugees\ndeclared possessing tertiary education and\ntheir employment rate has been almost\none-third higher than for others. 10 [^10: According to the UNHCR (2023) survey.]\n\n\n\n3 As of December 2023, according to UNHCR, based on governmental sources [Situation Ukraine Refugee Situation (unhcr.org)](https://data.unhcr.org/en/situations/ukraine)\n4 According to the active PESEL UKR database.\n5", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:3:3:0", "start": 269, "end": 289, "surface": "social\nsecurity data", "probe_tag": "keep", "probe_score": 0.9971, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:3:3:1", "start": 763, "end": 801, "surface": "Labour Force Survey data from Eurostat", "probe_tag": "keep", "probe_score": 0.999, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:3:3:2", "start": 1239, "end": 1259, "surface": "quarterly NBP survey", "probe_tag": "keep", "probe_score": 0.9762, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:3:3:3", "start": 1451, "end": 1470, "surface": "UNHCR (2023) survey", "probe_tag": "keep", "probe_score": 0.9926, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:3:3:4", "start": 1553, "end": 1588, "surface": "Situation Ukraine Refugee Situation", "probe_tag": "confusion", "probe_score": 0.2281, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:3:3:5", "start": 1671, "end": 1696, "surface": "active PESEL UKR database", "probe_tag": "confusion", "probe_score": 0.735, "luna_label": 1, "luna_reason": "Named database cited as the source for active PESEL UKR population figures."}]}, {"key": "paddy-080", "text": " of refugees.\nIn Swedish data, Edin, Fredriksson,\nand Aslund (2003) found that refugees\ndispersed to areas with more co-nationals\nexperience higher earnings.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\nKingdom, 56% in Sweden, 53% in Lithuania\nand 51% in the Czech Republic. The estimate\nfor Poland was based on November 2022\nNational Bank of Poland survey data.\nThis relatively high value is supported by a\nUNHCR Assessment from 2nd November,\nin which survey results show that 72% of\nrefugees are in the labour force, with 61%\nemployed and 11% unemployed.\nThe most refugees are employed in\nmanufacturing – 14%, accommodation and\nfood service – 12%, and trade and repair –\n6%. 89% of the survey respondents were\nwomen 29 .\n\n\n\n27 Deloitte elaboration based on the aggregation in OECD International Migration Outlook 2023\n28 After the closing date for our report, NBP (2024) published new data, showing a slight drop in Ukrainian refugees employment rate to 62% that does not\nchange our general conclusions.\n29 UNHCR Multi Sectorial Needs Assessment October 2023", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:12:3:0", "start": 17, "end": 29, "surface": "Swedish data", "probe_tag": "keep", "probe_score": 0.9998, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:12:3:1", "start": 345, "end": 394, "surface": "November 2022\nNational Bank of Poland survey data", "probe_tag": "keep", "probe_score": 0.9998, "luna_label": 1, "luna_reason": "Survey data supports the reported estimate for Poland."}, {"key": "sample:jad_paddy_docs:000007:12:3:2", "start": 823, "end": 864, "surface": "OECD International Migration Outlook 2023", "probe_tag": "confusion", "probe_score": 0.8972, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-081", "text": ".**\nUkrainian refugees earn the highest\nwages in manufacturing, health, and\naccommodation and food service activities,\nwhile the lowest in education, other\nservices, and construction. A comparison\nto median earnings in these sectors in the\neconomy as a whole (after recalculation\nfrom gross to net earnings) changes that\norder, with Ukrainian refugees employed in\naccommodation and food service activities,\nand construction earning more than\n100% of all workers median, and education,\nhealth services, and transportation and\nstorage on the lowest ranks. Earnings\n\n\n\nrelative to the total economy would be\nlower, if gross wages were compared,\nbecause Ukrainian refugees are less\nlikely to have employment contracts and\npay social contributions on their entire\nearnings. 16 Ukrainian refugees employed\nin education have the lowest median\nnet wages when compared to other\nsectors. This is likely due to occupational\nregulations that prevent persons with nonEU citizenship from working as teachers in\npublic schools, which is further discussed\nin chapter 4.\n\n\n\n4,198 4,216\n\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey and GUS data.\n\n\n© UNHCR / Anna Liminowicz\n\n\n\n16 As refugees more often than average in the Polish economy work based on civil law contracts or self-employment, they may not be covered by employee\nprotections denoted in the labour code. Furthermore, if they pay lower pension contributions, they will receive lower pensions in the future, as Poland has a defined\ncontribution system. It also lowers their sick leave benefits.\n\n\n20\n\n\n\n21", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:10:1:0", "start": 1124, "end": 1141, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9874, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:10:1:1", "start": 1146, "end": 1154, "surface": "GUS data", "probe_tag": "confusion", "probe_score": 0.8815, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-082", "text": "D.Climate** general equilibrium modelling\nstrategy compared to the previous Deloitte\n(2024) report, accounting for the impact\nof Ukrainian refugees on productivity.\nOver the past two decades, research by\nacademic economists has shown that\nviewing immigration solely through the lens\nof supply and demand severely constrains\nour understanding of the process\n(Peri, 2016). Such a model suggests that\nimmigration increases economic output\n\n\n36\n\n\n\nbut has negative labour market effects due\nto increased competition for jobs among\nworkers. However, little of these theoretical\nnegative labour market effects can be seen\nin empirical data. This is because immigrant\nworkers encourage further specialisation\namong native workers and firms, which\nincreases productivity and offsets the\nnegative effects. The same effects can\nbe expected in the case of refugees\nentering the Polish labour market – besides\nincreasing labour supply and competing\nwith Polish workers, they provide new skills,\nideas, and allow Polish workers to specialize\nin higher value-added tasks. These effects\nwere included in the estimates of the\nimpact of Ukrainian refugees on Polish\nGDP by Monitor Deloitte (2022) and Oxford\nEconomics (2022), but these estimates\nwere based on literature rather than Polish\nempirical data. In Deloitte’s 2024 study,\nthese effects were omitted due to a lack\nof empirical data with which to calibrate\nthem specifically. This study uses available\ndata, albeit limited, to create a conservative\nscenario that includes positive impacts on\nproductivity.", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:18:3:0", "start": 619, "end": 633, "surface": "empirical data", "probe_tag": "keep", "probe_score": 0.9246, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:18:3:1", "start": 1266, "end": 1287, "surface": "Polish\nempirical data", "probe_tag": "confusion", "probe_score": 0.7406, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-083", "text": "## Introduction\n\n##### Nearly two years after the beginning of the full-scale war in Ukraine, the positive impact of the refugees on the Polish economy becomes clearly visible.\n\n\n\nOn their arrival, the impact of refugees\non the economy primarily manifested\nthrough higher consumption, that was\nfinanced mainly by increased governmental\nspending, civil society, international\norganisations, and savings brought\nfrom Ukraine. While these added to a\nsignificant initial increase in consumption,\nmultiple sources of financing and a lack of\naggregated data (particularly on general\ngovernment expenses) make its magnitude\nuncertain. Although in the short term it\nstimulated the economy, drawing just on\nsavings was not sustainable. Over time,\nhowever, refugees started to work as\nemployees and entrepreneurs, adding\nnot only to the demand, but also to the\nsupply of the economy, contributing to its\nlong-term growth. The focus of this report\nis this structural impact of refugees on\nthe economy as consumers, employers,\nentrepreneurs, and taxpayers.\n\n\nThe beginning of the full-scale war in\nUkraine resulted in a large inflow of\nrefugees into Poland outlined in Chapter 1.\nThis cohort differs from the pre-2022\nUkrainian economic migrants, most notably\nin its demographic makeup which primarily\ncomprises children and working age women.\n\n\nThe government quickly granted refugees\nfrom Ukraine access to the labour market,\nhealthcare, and schooling, facilitating the\nprocess of inclusion described in Chapter\n2. Considering their psychological stress\nand needs in terms of child and elderly\ncare, refugees began entering the labour\n\n\n\nmarket surprisingly quickly – attaining\nan employment rate of 28% in May 2022\nand 65% in November 2022 (NBP, 2023).\nBy 30th September 2023 more than\n10 thousand ran their own businesses\naccording to the administrative social\nsecurity ZUS data. Based on the MultiSector Needs Assessment Poland 2023\nsurvey conducted in July-August 2023, we\ncalculate that 80% of the income of refugee\nhouseholds is derived from employment,\nwith an additional 5% coming from\nremittances and 2% from Ukrainian pension", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:5:0:0", "start": 536, "end": 551, "surface": "aggregated data", "probe_tag": "confusion", "probe_score": 0.612, "luna_label": 0, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:5:0:1", "start": 1831, "end": 1870, "surface": "administrative social\nsecurity ZUS data", "probe_tag": "keep", "probe_score": 0.9935, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-084", "text": "11,530** **27,302** **Jun-Sep**\n\n\n\nSample distribution by geographical strata,\nfollowed by random selection of districts, and\nconvenience sampling for household selection.\n\nSample distribution by geographical strata, and\nsimple random selection of households from cash\nenrolment lists.\n\nTwo strata (collective sites vs. private\naccommodation); random selection of districts and\nconvenience sampling for household selection.\n\n\n### **Limitations**\n\nThe statistical significance of the MSNA results is\nlimited by the non-probabilistic selection of\nrespondents. Moreover, the use of convenience\nsampling likely led to a larger share of data being\ncollected from more vulnerable households.\n\n\nThere was also a notably high non-response rate\nregarding questions related to income and\nexpenditure, which likely resulted in non-response\nbias. The income module of the MSNA was also\nmaterially different from the one employed by EU\nSILC, which may limit comparability of this data to\nthat of host populations.\n\n\n\nIt is also important to highlight that there were slight\ndifferences in the questionnaire across countries.\nNot all questions were consistently included in all\ncountry-level surveys, and some answer options\nwere individually adjusted. To mitigate the impact of\nthese differences, the regional analysis focused on\ndata that could be matched. Certain indicators that\nmay have been available by country have thus been\nexcluded from this assessment.\n\n\nLastly, the survey was conducted during the\nsummer months, coinciding with both host country\nand Ukraine school holidays. This period often sees\nmany households temporarily visiting Ukraine,\nwhich impacted the accessibility of households and\nposed challenges in meeting targets, particularly in\ncertain countries and geographic locations.\n\n\n**11**", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000000:10:1:0", "start": 300, "end": 315, "surface": "enrolment lists", "probe_tag": "confusion", "probe_score": 0.5108, "luna_label": 1, "luna_reason": "Existing enrolment lists are used as a sampling frame for household selection."}, {"key": "sample:jad_paddy_docs:000000:10:1:1", "start": 1206, "end": 1227, "surface": "country-level surveys", "probe_tag": "drop", "probe_score": 0.0037, "luna_label": 1, "luna_reason": "Existing country surveys explain questionnaire differences affecting the regional analysis."}]}, {"key": "paddy-085", "text": " SRH\nservices and one in five did not trust local\nhealthcare providers, in addition to facing language\nbarriers.\n\n\nWomen with a disability reported more barriers with\n11% across the region (N=29) compared to those\nwithout disability (5%).\n\n\nAn in-depth assessment is needed to better\nunderstand sexual and reproductive health (SRH)\nneeds, the role of SRH access barriers in decisions\nto visit Ukraine, and how these barriers vary among\nwomen of different age groups, pregnant and\nbreastfeeding women, and women with disabilities.\n\n\n**Support services for survivors of gender-based**\n**violence**\nServices for survivors of gender-based violence\nencompass a range of functions, including safety\nand security, legal assistance, healthcare, mental\nhealth and psychosocial support. A critical\ncomponent is access to clinical management of\nrape to ensure timely medical treatment and care.\nAs this service is provided by the health care sector,\nas part of SRH, it is included in this analysis.\n\n\nThe SEIS identified gaps in awareness about on\navailable GBV services. In 2024, 38% of\nrespondents were unaware of health services\nproviding support to GBV survivors in their area,\nwhile 58% were unaware of available psychosocial\nsupport services. Respondents were less aware of\nhealth services in rural areas (45%) compared to\nurban areas (37%). Key barriers to accessing\nGBV-related services in general included lack of\nawareness (58%), language and cultural barriers\n(53%) and stigma/ shame (46%). This indicates that\nadditional coordinated efforts between the health,\nprotection and GBV working groups and partners\nare required to enable access to all lifesaving\nGBV-related services including clinical management\nof rape.\n\n\n**17**", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000004:16:1:0", "start": 994, "end": 998, "surface": "SEIS", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-086", "text": " below average in the Center, Eastern and Western regions. For example, primary\neducation GER is around 95% in the South while it varies between 20%-40% in the North and the\nprobability of never have attended school is especially high in the North. However, drop-out rates in\nprimary school are especially high in the Southern regions, indicating challenges of their own.\n\nAnalysis of EMIS and household survey (see Table 6 in Annex 7) data indicate that, to increase\naccess, one should aim to create new schools and tackle the cost constraints faced by certain\nhouseholds, especially in the Northern regions. On the other hand, to increase survival rates,\nespecially in Southern Regions, one should aim to increase textbooks-to-student ratios, percentage\nof permanent school building, the number of latrines, and demand-side factors related to return to\neducation (not useful, no interest), more diffuse categories such as “family not wanting” and\n“others” and, to a lesser extent, to the opportunity cost (work).\n\nLearning: Learning levels are low and point to the urgency of tackling quality issues. On the two\nProgramme d’Analyse des Systèmes Educatifs (PASEC) assessments carried out, 2004 and 2010, no\naverage student scores reached the 50% threshold and 1 in 4 students score less than 10%. Similarly,\non tests used, in the context of the Public Expenditure Tracking Survey (PETS) to assess students’\nlearning outcomes from a small sample of 88 schools located in 7 regions, average scores were 23%\nand 32% in French and 53% and 59% in math, for grade 2 and 4 respectively. Apart from PASEC,\nthere exist no standardized tools used to assess of student learning at the national level. At the\nsecondary and higher education levels, except for passing rates on the end-of-cycle exam, evidence\non quality outcomes is lacking.\n\nEvidence from", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000020:4:1:0", "start": 385, "end": 389, "surface": "EMIS", "probe_tag": "keep", "probe_score": 0.9413, "luna_label": 1, "luna_reason": "EMIS data are analyzed to inform education-access recommendations."}, {"key": "jdc_operational:000020:4:1:1", "start": 394, "end": 410, "surface": "household survey", "probe_tag": "keep", "probe_score": 0.9294, "luna_label": 1, "luna_reason": "Household survey data inform analysis and recommendations on education access."}]}, {"key": "paddy-087", "text": "000 non-Arabs in Jordan. It is not clear how many of these are working informally.\n31 Economic migrants are subject to a minimum wage, which is lower than the minimum wage for Jordanian\nworkers. The separate minimum wage makes non-Jordanian workers more attractive to employers and therefore\nruns counter to the overall Government policy of promoting Jordanians workers over others.\n32 The remaining 26 percent are in trade (7 percent), construction (6 percent), and hotels (5 percent). See The\nNational Employment Strategy 2011-2020: An Update and Future Directions (ILO, 2015) based on data for\n2009-2014.\n33 See Tamkeen (undated) Breaking the Silence!! Irregular migrant workers in Jordan: between marginalization\nand integration. And Tamkeen (2014) Forgotten Rights: The Working and Living Conditions of Migrant\nWorkers in the Agricultural Sector in Jordan. Both available at www.tamkeen-jo.org.\n34 Better Work Jordan is part of the global Better Work partnership between ILO and the IFC in collaboration\nwith local and international stakeholders. See more at:\nhttp://betterwork.org/jordan/?page_id=7#sthash.Y2cqymct.dpuf\n35 The _kafala_ system requires all unskilled laborers to have an in-country sponsor, usually their employer, who\nis responsible for their visa and legal status. This practice has been criticized by human rights organizations for\ncreating easy opportunities for the exploitation of workers, as many employers take away passports and abuse\ntheir workers with little chance of legal repercussions. Unlike in the Gulf countries, the _kafala_ system is not\nspecified by Jordanian law (as it is in", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000045:68:2:0", "start": 588, "end": 606, "surface": "data for\n2009-2014", "probe_tag": "keep", "probe_score": 0.9573, "luna_label": 0, "luna_reason": "Bare date-only data qualifier cannot inherit the cited report's source or finding."}]}, {"key": "paddy-088", "text": ", by supporting transactions and marketplaces that are on-demand, paperless, and cashless,\nand available through the internet from anywhere in the world.\n\n32. **The component will have a significant focus on online trust, cyber-security and digital ID systems.** The\nDigital Vision sets out an ambitious framework for accelerating Uganda’s digital transformation but requires\nnew capabilities and mechanisms to be realized. For example, the new Data Protection Act of 2019 will\nrequire attention as a key legislative measure for building trust among the government, businesses and\ncitizens in the digital economy. New initiatives around leveraging the “ _fourth industrial revolution_ ”, which\nconsists of new emerging technologies such as artificial intelligence, big data and internet of things, while\nmanaging its associated risks in Uganda also require support. This component will also fully integrate\npolicies, regulations and mechanisms related to cybersecurity and digital privacy. 63 [^63: According to the 2017/2018 NITA-U Survey, only 19% of Internet users consider themselves to be at any risk and only 18.5% of\nInternet users are aware of any Ugandan laws governing electronic communications and transactions while many individuals have\nbeen victims of cybercrimes over the previous 12 months. Among Internet users, only 20.1% are aware that they can report\ncybercrimes to law enforcement and other agencies under the Computer Misuse Act 2011 while only 3% have ever reported such\ncybercrimes committed against them to anyone, making their recurrence more likely.\nhttps://www.nita.go.ug/sites/default/files/publications/National%20IT%20Survey%20April%2010th.pdf] Finally, with the\nemergence of the digital economy, traditional paper-based civil registration and national ID systems are\nincreasingly giving way to interoperable digital identity management systems with electronic signature and\nother trust service capabilities. Given the fundamental need for secure and accurate online identification\nand authentication, digital ID and other trust services—such as e-signatures—form part of the core\nfoundation or a “", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000063:19:1:0", "start": 1037, "end": 1050, "surface": "NITA-U Survey", "probe_tag": "keep", "probe_score": 0.9579, "luna_label": 1, "luna_reason": "2017/2018 survey provides concrete findings on internet users' cyber-risk awareness."}]}, {"key": "paddy-089", "text": "**The World Bank**\nCHAD Improving Learning Outcomes Project (P175803)\n\n\n_Graph 1: Distribution of the number of school population_\n\n\nSource: ECOSIT IV\n\n\n8. **The inefficiency of the primary education is structurally impeding the performance of the education system**\n**despite some improvements in the completion rate** . Over the last decade, the Gross Enrollment Rate (GER) in the lower\nand upper secondary education remained quite low at 28 percent and 18 percent, on average, respectively. However,\nthat of the primary education increased from 2011 (91 percent) to 2014 (107 percent) before declining progressively to\n82 percent in 2017, following the oil price shock of 2015-2016 and associated budgetary decisions at the expense of the\neducation sector. Since 2018, the GER started increasing again. Administrative data shows that the primary completion\nrate had an increasing trend over the same period (rising from 37 percent in 2011 to 45 percent in 2020), and the gender\ngap significantly decreased since 2018. Nevertheless, this progress has not been sufficient to lead to a significant increase\nin GER in the secondary education. Repetition and dropout rates are much higher in primary education, the former being\nthe same for both girls and boys (17 percent) while the latter is 33 percent for girls and 29 percent for boys. The number\nof dropout children in primary school is almost twice as high than that of both lower and upper secondary school (725,795\nvs. 383,093 for secondary school) 6 . This is in addition to about 48 percent of children (47 percent for boys and 51 percent\nfor girls) of primary-school age who had never attended school, yielding an out of school children rate of 52%.\n\n\nGraph 2: Gross Enrollment Rate (GER) and Primary Completion Rate, 2011-2020\n\n\n6 ECOSIT IV, 2019\n\n\nAug 03, 2021 Page 5 of 27", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000009:4:0:0", "start": 141, "end": 150, "surface": "ECOSIT IV", "probe_tag": "keep", "probe_score": 0.9536, "luna_label": 1, "luna_reason": "Named survey source for the graph and reported education statistics."}, {"key": "jdc_operational:000009:4:0:1", "start": 806, "end": 825, "surface": "Administrative data", "probe_tag": "keep", "probe_score": 0.9421, "luna_label": 1, "luna_reason": "Administrative data supports reported primary completion-rate trends."}]}, {"key": "paddy-090", "text": "59 For the economic\nanalysis, these costs are adjusted to net off the taxes, duties, and transfer payments in a similar way as\nthe adjustment described for households above.\n\n7. **WTP.** While the avoided cost methodology is used to proxy benefits of switching to SHSs for\nhouseholds, estimates of WTP are relied upon to proxy benefits derived by households when switching to\nelectricity delivered though a mini-grid or grid solution. While data on WTP for electricity in Chad are not\navailable, according to the results of the most recent expenditure survey data, households currently use\non average 2.5 light points across rural areas in Chad for around five hours a day for which they spend\nabout US$4.7 per month per household. 60 [^60: Results of the household expenditure survey are provided in annex 6.] Even if an inefficient 40 W light point is assumed, a household\nin Chad would consume only about 15 kWh per month for which it is currently spending about US¢30 per\nkWh, which can be considered as a lower bound on WTP for electricity. As WTP per kWh would decrease\nwith greater consumption, and to be on the conservative side, for the analysis, a WTP of US¢25 per kWh\nis used.\n\n8. **While the analysis does not consider other indirect benefits, it is expected that the project will**\n**contribute toward other economic benefits that are more difficult to quantify and monetize.** These\nindirect benefits include improved air quality from reduced consumption of kerosene; reduced poisoning\nand accidental fires; and wider benefits that can be linked to access to modern electricity solutions such\nas improved health, improved connectivity, and improved security. Access to modern energy solutions is\nalso expected to increase income-generating opportunities and improve the socioeconomic situation of\nhouseholds and MSMEs, with an expected positive impact on education and overall lifestyle. This means\nthat the results from the economic analysis can be considered as", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000051:83:1:0", "start": 546, "end": 569, "surface": "expenditure survey data", "probe_tag": "keep", "probe_score": 0.95, "luna_label": 1, "luna_reason": "Existing household expenditure survey data supports concrete electricity-use and spending estimates."}]}, {"key": "paddy-091", "text": " BDC/PDC mobilization will\n\n\n52 Population figures for urban areas would be calculated based on a headcount or by complementing 2008 census with other data sources (for\nexample, DTM.)\n53 In Wau town, the only urban area expected to be targeted under the project, the communities will be mobilized into Quarter Development\nCommittees (QDCs) and Block Development Committees (BkDCS) in line with the urban local institutional structure.\n54 The Participatory Planning and Budgeting Guide for Local Governments in Southern Sudan (2010), which helps operationalize the LGA, calls for\nbroad and inclusive membership with strong gender representation.\n55 Types of capacity-building activities include familiarity with conflict/disaster risks and needs identification, resource mapping, local\ndevelopment planning, project identification, budgeting, project implementation, oversight/monitoring, and social audit methods.\n\n\nPage 21 of 94", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000049:26:2:0", "start": 128, "end": 139, "surface": "2008 census", "probe_tag": "keep", "probe_score": 0.9124, "luna_label": 1, "luna_reason": "Existing census data used to calculate urban population figures."}]}, {"key": "paddy-092", "text": "**The World Bank**\nEnhancing Community Resilience and Local Governance Project Phase II (P177093)\n\n\nKey Results (From PCN)\n\n23. The achievement of the Project Development Objective (PDO) will be measured against the following proposed\nkey results:\n\n(a) Number of people with access to improved infrastructure due to the project (disaggregated by infrastructure\ntype, gender, and displacement status)\n(b) Percentage of subprojects that are functional 50 [^50: ‘Functional’ is defined as ‘subproject being utilized by the beneficiaries as intended or as designed’. ‘Partly Functional’ is defined as ‘subprojects not fully utilized as\nintended or as designed’. ‘Non-functional’ is defined as ‘subprojects that are not operational, uncompleted, abandoned, or used outside its intended purpose or by\nunintended beneficiaries’.] at project completion (disaggregated by infrastructure type)\n(c) Percentage of community institutions 51 [^51: As under the ongoing ECRP, ECRP-II will continue to mobilize communities into Boma Development Committees (BDCs) and Payam Development Committees (PDCs)\nin line with the Local Government Act 2009.] that are functional and accountable as measured by an institutional\nmaturity index\n(d) Percentage of women-led operational and maintenance committees that are functional and engaged in\nincome-generating activities\n(e) Improvements in levels of satisfaction with government support to local service delivery (as measured from\nbaseline with community scorecards)\n\n\n**D. Concept Description**\n\n**Guiding Principles**\n\n24. **The proposed project builds from the ongoing ECRP.** ECRP became effective on September 3, 2020 with a budget\nof US$45 million and a closing date of July 31, 2023. The ECRP is supporting a participatory decision-making process to\nidentify key basic service needs in 21 vulnerable counties as identified through the Bank’s vulnerability index. The\nvulnerability index includes indicators such as exposure to violence, exposure to climate-sensitive natural hazards,\nconcentration of", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000027:10:0:0", "start": 1493, "end": 1513, "surface": "community scorecards", "probe_tag": "confusion", "probe_score": 0.118, "luna_label": 0, "luna_reason": "Proposed baseline scorecards are planned project measurement, not existing data use."}, {"key": "jdc_operational:000027:10:0:1", "start": 1897, "end": 1916, "surface": "vulnerability index", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Bank vulnerability index used to identify basic service needs in vulnerable counties."}]}, {"key": "paddy-093", "text": "**The World Bank**\nChad Rural Mobility and Connectivity Project (P164747)\n\n\naction and harmonization between development partners. The key reasons for the choice of Mandoul and\nMoyen-Chari are as follows:\n\n\n(a) **High level of poverty and food insecurity.** In Mandoul and Moyen-Chari, 26 percent and 19\n\npercent, respectively, of the population (209,000 and 143,000 persons) suffer from severe\nfood insecurity.\n\n\n(b) **Agricultural production potential** **_._** In the 2016–2017 season, Mandoul and Moyen-Chari\n\nproduced 127,102 and 91,067 tons of grains, respectively, together representing 7 percent\nof total national production, plus 189,792 tons and 111,688 tons, respectively of vegetables,\noilseeds, roots and tubers, amounting to 16 percent of total national production of these\ncrops. 3 [^3: _Source:_ 2016–2017 agricultural season statistics from the Directorate of Agricultural Production and Statistics, Ministry of\nAgriculture.]\n\n\n(c) **Complementarity with agriculture investments.** The World Bank is currently implementing\nin those two provinces the Agriculture Climate Resilience and Productivity Enhancement\nProject (P162956) (PROPAD) with the Ministry in charge of Agriculture which aims to\nenhancing rural household food security and nutrition, boost household income and help\nmarket a larger share of the production. In all agriculture projects, there is a rural roads\ncomponent to better connect the project area. By concentrating the investments of this\nproject in the two targeted provinces, rural transport investments will be combined, and\nresources will be maximized through the shared provision of inputs and equipment from\nboth World Bank-financed projects.\n\n\n(d) **Synergy with other development partners’ operations.** In addition to the EU-funded SAN in\n\nadjacent regions with which this project may share some technical assistance, the African\nDevelopment Bank has financed a project to make the fishing sector more productive in", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000048:15:0:0", "start": 823, "end": 863, "surface": "2016–2017 agricultural season statistics", "probe_tag": "confusion", "probe_score": 0.8724, "luna_label": 1, "luna_reason": "Source statistics support reported provincial agricultural production figures."}]}, {"key": "paddy-094", "text": "2007-2010, but fell to two percent in 2012. Economic volatility poses a serious obstacle to the\ncountry’s ambitions for sustainable growth. With a recent financial crisis in Europe and conflict in\nneighboring countries, the economy is expected to weather the turbulence but growth projections\nhave been lowered to around two percent.\n\n3. Lebanon’s unemployment rate—particularly youth and female unemployment—is high (34\npercent youth unemployment, 18 percent female unemployment, and 11 percent total\nunemployment). Around 14 percent of university graduates and 15 percent of those with secondary\neducation are unemployed, relative to 10 percent among workers with no education, and only 7\npercent among those with primary education. Lebanon’s service-based industries are particularly\nimportant for the economy and represent its most dynamic sectors. Financial services accounted for\n10.4 percent of the country’s GDP in 2010, communication and transportation 8.2 percent, and\neducation 7.4 percent. However, Lebanon still has to deal with tough competition from neighboring\neconomies that have attracted large portions of foreign direct investment, as well as a share of\nLebanon’s most educated and talented people. In 2012 the Economist Intelligence Unit ranked\nBeirut 117 out of 120 cities in a global competitiveness survey, while Abu Dhabi, Dubai, and Doha\nranked (respectively) 40, 41, and 47.\n\n4. Compounding Lebanon’s lackluster economic performance and high unemployment rate\nhas been the massive influx of Syrian refugees fleeing the neighboring war. In April 2014, the\nUnited Nations High Commissioner for Refugees (UNHCR) announced that the number of Syrian\nrefugees in Lebanon had surpassed one million . The presence of these refugees represents an\nenormous burden on Lebanon’s economy, particularly taxing social services such as education and\nhealthcare. Despite the humanitarian aid that has flowed into Lebanon from various international aid\nagencies and donors, the country is still not well-equipped to deal with the refugee crisis.\n\n5. Considering the constraints that have drained the country’s", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000004:1:0:0", "start": 1300, "end": 1329, "surface": "global competitiveness survey", "probe_tag": "confusion", "probe_score": 0.6978, "luna_label": 1, "luna_reason": "Survey supports the cited Economist Intelligence Unit city competitiveness ranking."}]}, {"key": "paddy-095", "text": "**The World Bank**\nUganda: Investment for Industrial Transformation and Employment (P171607)\n\n\nGDP grew at 3.1 percent in FY20, less than half the 6.8 percent recorded in FY19, due to the effects of the COVID-19\ncrisis and is expected to grow at a similar level in FY21. Economic activity stalled during the latter part of the fiscal year\ndue to a domestic lockdown that lasted over four months, border closures for everything but essential cargo, and the\nspillover effects of disruption in global demand and supply chains due to the COVID-19 pandemic. This resulted in a sharp\ncontraction in public investment and deceleration in private consumption, which hit the industrial and certain service sectors\nparticularly hard. On a calendar year basis, real GDP growth is unlikely to exceed 1 percent during 2020, compared to 6.7\npercent in 2019, and, as a result, real per capita GDP growth is expected to contract by about 2.5 percent. Even if the GDP\ngrowth rebounds strongly by 2022, the level of per capita GDP is likely to remain well below its pre-COVID trajectory. 1 [^1: See Uganda Economic Update 16th Edition, September 2020.]\n\n\nSectoral and Institutional Context\n\n**COVID-19 is a significant threat to emerging economic transformation in Uganda and puts prospects of new jobs**\n**in danger.** Data from the June 2020 2 [^2: Uganda Bureau of Statistics June 2020; conducted with the support of the World Bank.] Uganda Bureau of Statistics high frequency phone survey on the impact of the\nCOVID-19 pandemic, shows that the following sectors lost the highest number of workers: services 43 percent, commerce\n43 percent and transport 39 percent. It is expected that the hardest-hit firms will be exporters to international markets,\nmanufacturing companies and start-ups. The floriculture industry, for example, which employs over 10,000\npeople, is facing severe disruptions in its supply chains as air cargo companies", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000043:3:0:0", "start": 1313, "end": 1336, "surface": "Data from the June 2020", "probe_tag": "confusion", "probe_score": 0.5643, "luna_label": 0, "luna_reason": "Bare date qualifier cannot inherit the named survey from surrounding context."}, {"key": "jdc_operational:000043:3:0:1", "start": 1469, "end": 1496, "surface": "high frequency phone survey", "probe_tag": "confusion", "probe_score": 0.7856, "luna_label": 1, "luna_reason": "Existing survey data are attributed to COVID-19 worker-loss findings."}]}, {"key": "paddy-096", "text": "**The World Bank**\nChad - Refugees and Host Communities Support Project (P164748)\n\n\nside, this sub-component will support progress in legal protections for refugees; compliance with\ninternational treaties, protocols and agreements; and platforms for public debate on refugees.\n\n\n35. **Sub-component 3.2 (US$2 million equivalent) will strengthen social protection**\n**systems, including ongoing activities on targeting and registration, such as those carried by**\n**the SCOPE program of WFP.** This sub-component will support adaptation of the PFS targeting\nsystem to the needs of this project, including by expanding coverage of the poverty targeting\nsystem to project areas, and expanding the list of poor households to also encompass refugee\nhouseholds. Moreover, this sub-component will support adaptation of the Management\nInformation System (MIS) currently being developed by CFS, with new modules to support the\nmanagement, monitoring and evaluation (M&E) of project implementation. The new modules will\nbe fully interoperable with the Unified Social Registry. Data protection will follow principles\ndeveloped for the establishment of the Unified Social Registry under the PFS (Box 3). This subcomponent will also finance the expansion of systems currently being used by CFS, including\nbeneficiary registration, secure payment system (by mobile phone technologies, if possible),\ngrievance redress mechanisms and accompanying measures, monitoring and evaluation, and geotechnologies for enhanced supervision. Further, the sub-component will also support capacity\nbuilding of the Ministry of Economy and Development Planning; Ministry of Public Security,\nTerritorial Administration and Local Governance; Ministry of Women, Protection of Childhood\nand National Solidarity; and other ministries involved in social protection.\n\n\nPage 20", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000035:24:0:0", "start": 1042, "end": 1065, "surface": "Unified Social Registry", "probe_tag": "confusion", "probe_score": 0.2646, "luna_label": 0, "luna_reason": "Registry is mentioned as an interoperable system, without use of its data."}]}, {"key": "paddy-097", "text": "**The World Bank**\nEnhancing Community Resilience and Local Governance Project Phase II (P177093)\n\n\nreturn to Sudan in the near or medium term. 26 As humanitarian assistance in refugee situations tends to\ndecline over time, there is a need for development efforts that promote local integration as a durable\nsolution, consolidate humanitarian gains, and capacitate government to assume responsibility for service\ndelivery, with a focus on addressing the particular challenges that women and girls face.\n\n\n**Figure 1. Locations of Refugees in South Sudan**\n\n\n_Source:_ Adapted from UNHCR (2021) South Sudan Refugees and Asylum-Seekers by State, April 30, 2021. 27 [^27: South Sudan: Refugee and Asylum Seeker Population, UNHCR, April 30, 2021.\nfile:///C:/Users/wb374705/Downloads/Refugee%20and%20Asylum%20Seekers%20Population_30April.pdf]\n\n\n12. **The World Bank Group (WBG), following consultation with UNHCR, confirms that the protection**\n**framework for refugees in South Sudan is adequate.** UNHCR has provided the WBG with an overall\npositive assessment of South Sudan’s protection framework while highlighting a set of protection-related\nchallenges. In addition to the legal framework in place, the Government has maintained its policy of\ngranting refugees access to its territory and installing practical arrangements for their initial reception and\nregistration. Refugees are granted freedom of movement and in principle are free to settle anywhere in\nthe country. The Commission for Refugee Affairs (CRA) has played an important role in coordinating\ngovernment policy and establishing a presence in key refugee-affected areas despite capacity challenges\nrelated to a lack of technical, human, and financial resources. The World Bank will closely engage with\nUNHCR to ensure that South Sudan’s refugee protection framework remains adequate, including through\n\n\n26 World Bank staff conducted these focus group discussions with multiple refugee groups in Pariang and Maban during a joint WHR eligibility\nmission with UNHCR in", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000028:17:0:0", "start": 605, "end": 653, "surface": "South Sudan Refugees and Asylum-Seekers by State", "probe_tag": "confusion", "probe_score": 0.3655, "luna_label": 1, "luna_reason": "UNHCR source dataset cited for the refugee-location figure."}]}, {"key": "paddy-098", "text": " evaluation\nof NDP-1 in Uganda, March 2019. State of Uganda Population Report 2018.\n4 World Bank World Development Indicators (2017).\n5 Uganda National Household Survey, Uganda Bureau of Statistics, 2017.\n6 World Bank World Development Indicators (2013).\n7 Uganda’s economy slowed from an average of 7% annual GDP growth in the early 2000s to 4.5% in the 5 years leading up to 2017.\n8 Uganda: Driving inclusive socio-economic progress through mobile-enabled digital transformation, GSMA, 2019.\n9 Ibid.\n\n\nJul 22, 2019 Page 3 of 28", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000063:2:2:0", "start": 44, "end": 82, "surface": "State of Uganda Population Report 2018", "probe_tag": "keep", "probe_score": 0.9325, "luna_label": 1, "luna_reason": "Named existing population report cited as a source."}, {"key": "jdc_operational:000063:2:2:1", "start": 86, "end": 125, "surface": "World Bank World Development Indicators", "probe_tag": "confusion", "probe_score": 0.889, "luna_label": 1, "luna_reason": "Named World Bank indicator dataset cited as an existing statistical source."}]}, {"key": "paddy-099", "text": " Under the PBCs, the World Bank\nwill finance particular expenditures which are a part of the project’s budget of eligible activities. These\nexpenditures are clearly identifiable in GoU integrated financial management information system and are referred\nto as Eligible Expenditure Programs and include expenditures under Component 1. Following a sector-support\nprogram principle, the World Bank funds earned through PBCs may not be separately tracked and the World Bank\nwill accommodate withdrawal applications from the financing as long as the overall expenditures eligible under\nthe EEPs are more than or equal to the amount to be withdrawn from the World Bank, and fiduciary control and\noversight of the funding is acceptable to the World Bank. Total EEPs will be annually tracked through external\naudits and aggregated for the life of project. The expenditure mechanism satisfies Bank policy and in particular\nthe three pillars in OP 6.00, namely, (a) the expenditures are productive and necessary for the success of the\nsector program; (b) they contribute to solutions within a fiscally sustainable framework; and (c) acceptable\noversight arrangements are in place.\n\n\n99. **Eligible Expenditure Program** for the PBC component will include the following:\n(i) Vote (500-800) LGs School Capitation Grants;\n(ii) Vote (500-800) LG School Inspection; and\n(iii) Vote (500-800) Staff Salaries for Secondary Education.\n\n100. **Audits.** The Ministry has an active Internal Audit department with practical experience on the previous\nand existing IDA projects. The internal audit unit is guided by an internal audit manual issued by the GoU that\nemphasizes a risk-based approach and value for money audits, policy and procedures, compliance reviews, and\nspecial investigations. The department needs to improve on submission of internal audit reports. External auditing\nis primarily a responsibility of the Auditor General for all government programs and projects. The audit may be\nsubcontracted to private auditors, with the final", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000018:38:1:0", "start": 181, "end": 235, "surface": "GoU integrated financial management information system", "probe_tag": "confusion", "probe_score": 0.716, "luna_label": 0, "luna_reason": "Financial management system supports expenditure tracking, routine project bookkeeping rather than substantive data analysis."}]}, {"key": "paddy-100", "text": "**The World Bank**\nSouth Sudan Enhancing Community Resilience and Local Governance Project (P169949)\n\n\nconflict-affected areas and more stable areas; and ensure that more conflict-affected areas benefit from\nthe project as they have historically been deprived of assistance due to insecurity. The targeting principles\nare summarized in figure 1.\n\n\n**Figure 1. Geographic Targeting Principles**\n\n\n\n**Vulnerability**\n\n\n•Concentration of\nreturnees\n•Access to basic\nservices\n•Food insecurity\n•Violence\n•Exposure to\nclimate-sensitive\nnatural hazards\n•Remoteness\n\n\n\n**Feasibility**\n\n\n•Accessibility\n•Security\n•Local dynamics\n•County government\nsupport\n\n\n\n**Equity**\n\n\n•Both conflictaffected areas and\nmore stable areas\n\n\n\n**Quick Wins**\n\n\n•LGSDP unfunded\nsubproject\n\n\n\n31. Combined with quick wins, the ECRP will target both conflict-affected and more stable counties\nas shown figure 2 (see annex 2 for details).\n\n\n**Figure 2. Vulnerable Counties with Quick Wins**\n\n\n32. **Subproject budget allocation.** The subproject budget allocation for the new counties will be\ncalculated on a per capita basis using humanitarian agencies’ latest population data. There will be two\nrounds of allocations per county to maximize communities’ learning by doing. Counties will need to meet\na set of basic performance indicators to be eligible for the second allocation. These include (a)\nparticipation rate of women, youths, IDPs, and returnees in the subproject planning and implementation;\n(b) satisfactory collaboration during community mobilization; (c) timely implementation of the\n\n\nPage 20 of 94", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000049:25:0:0", "start": 1130, "end": 1145, "surface": "population data", "probe_tag": "confusion", "probe_score": 0.7052, "luna_label": 1, "luna_reason": "Humanitarian agencies’ population data calculate per-capita subproject allocations."}]}, {"key": "paddy-101", "text": "**The World Bank**\nLebanon: Wheat supply emergency response project (P178866)\n\n\n**I.** **STRATEGIC CONTEXT**\n\n\n**A. Country Context**\n\n\n1. **The war in Ukraine has delivered a major shock to global commodity markets** . In 2021, Russia was the\nlargest natural gas-exporting country in the world, and the second-largest crude oil and condensates-exporting\ncountry. Russia and Ukraine together represent between 25 and 30 percent of the global wheat market, while\nRussia (and Belarus) are also major exporters of fertilizers. The disruptions from the conflict (sanctions,\nbreakdowns in supply, etc.) had an immediate impact on global fuel and food prices. Oil prices have increased by\nabout 90 percent compared to March 2021. Wheat prices have increased by 50 percent since early February 2022\nand 80 percent since March 2021, and they are now at an all-time high. The cutoff of exports from Russia and\nUkraine poses an immediate threat to major wheat-consuming countries with high shares of wheat imports from\nRussia and/or Ukraine.\n\n2. **Lebanon is heavily dependent on wheat imports, most of which came from Ukraine and Russia before**\n**the crisis** . A country with limited agronomic potential for wheat production, Lebanon imports about 80 percent\nof all the wheat it consumes. In the years preceding the conflict, Lebanon has been importing about 580 thousand\ntons of wheat annually (2018-2020 average, COMTRADE) for a yearly trade value of US$134 million (2018-2020\naverage, COMTRADE). In 2020, 96 percent of Lebanon’s wheat imports were sourced from Ukraine (80 percent of\ntotal wheat imports), and Russia (16 percent of total wheat imports), respectively.\n\n3. **The conflict comes at a time when Lebanon has been grappling with the direst of shocks**, starting", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000019:11:0:0", "start": 1408, "end": 1416, "surface": "COMTRADE", "probe_tag": "keep", "probe_score": 0.913, "luna_label": 1, "luna_reason": "COMTRADE data supports Lebanon's reported average wheat import volume."}]}, {"key": "paddy-102", "text": " the potential to expand markets for commodities produced by small‐scale\nproducers, enhance the local economy, and contribute to strengthen communities’ resilience.\n\n\n3. **Methodology.** The analysis characterizes potential benefit streams from subcomponent 1.1 and 1.2,\nwhich cannot be quantified in this analysis; and provides a cost‐benefit analysis (CBA) for investments to\nrestart crop and livestock production under subcomponent 2.1 (with a budget of US$7.5 million). For the\nCBA, assumptions and results are reported in constant US$ terms. Where applicable the analysis uses the\nshadow exchange rate, of US$1 equal to 110 SSP (March 2017, source: personal communication). For the\neconomic analysis, financial prices and costs are converted to economic values using conversion factors\nranging from 0.7 to 1.1, to reflect the differences between local and imported crop prices including\ntransport and non‐tariff barriers, a 15 percent value added tax (agricultural inputs are exempt), and the\nshadow cost of labor. 19 [^19: Based on data in Table 32 of World Bank 2012 (Agricultural Potential, Rural Roads, and Farm Competitiveness in South Sudan),\nthe following factors were used: sorghum price factor = 0.7, maize price factor = 1.1, a simple average was used for the investment\ncosts factor = 0.9, a lower factor was used for the shadow cost of labor = 0.8, and the operating costs factor was approximated\nas 0.85 assuming no VAT and 75 percent labor.] Most assumptions are taken from the economic analysis of a related project ‐\nSouthern Sudan EFCRP (P147900; 2014).\n\n\n4. **Beneficiaries.** The project targets approximately about: (i) 250,000 people benefiting from direct\nfood assistance; (ii) 200,000 children and pregnant/lactating mothers benefiting from nutrition support;\n(iii) 200,", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000038:64:1:0", "start": 1049, "end": 1084, "surface": "data in Table 32 of World Bank 2012", "probe_tag": "confusion", "probe_score": 0.8334, "luna_label": 1, "luna_reason": "Existing World Bank table data informs conversion-factor calculations."}]}, {"key": "paddy-103", "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_paddy", "spans": [{"key": "jdc_operational:000062:9:2:0", "start": 980, "end": 1032, "surface": "Municipal Assets Inventory and Conditions Assessment", "probe_tag": "confusion", "probe_score": 0.6535, "luna_label": 1, "luna_reason": "Named existing assessment report cited as a source."}]}, {"key": "paddy-104", "text": " pressures. The regionalization of the terrorist\nthreat posed by Boko Haram has further disrupted Chad’s economy, trade with its neighbors and\nthe overall fiscal situation, given the significant investment by the Government of Chad in regional\nsecurity initiatives. This investment is primarily in the form of increased participation of the\nChadian armed forces in internationally supported military efforts against Boko Haram, including\nthe Joint Multinational Force and the G5 Sahel Cross-Border Joint Force. 2 [^2: The G5 Sahel countries (Burkina Faso, Chad, Mali, Mauritania and Niger) are facing an increase in terrorist threats and organized\ncrime, which are destabilizing the region. To address these common challenges, two initiatives have been launched: The G5 Sahel\nCross-Border Joint Force, which illustrates the willingness of African nations to take charge of their own security; and the Sahel\nAlliance, which is based on reciprocal accountability between the major development partners and the G5 States and on significant\ninvestments in economic and social development in the G5 Sahel countries. The Sahel Alliance was launched in July 2017 by\nFrance, Germany and the EU, with the WB, UNDP and AfDB as founding partners, now joined by Italy, Spain, the United Kingdom\nand Luxembourg. Source: G5 Sahel Joint Force and the Sahel Alliance, _France Diplomatie_, January 2018.]\n\n3. **The 2015 Chad Systematic Country Diagnostic (SCD) found that Chad’s high rate of**\n**monetary poverty is accompanied by very low human development indicators.** **3** [^3: Republic of Chad: Priorities for Ending Poverty and Boosting Shared Prosperity; Systematic Country Diagnostic, The World\nBank Group, Report No. 96537-TD, Washington, 2015.] Chad ranks\n184 th out of 187 countries in the 2014 Human Development Indicators Index. Average schooling\nwas just 1.5 years in 2009. The adult literacy rate was 22 percent and the literacy rate", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000035:10:1:0", "start": 1409, "end": 1448, "surface": "2015 Chad Systematic Country Diagnostic", "probe_tag": "confusion", "probe_score": 0.8042, "luna_label": 1, "luna_reason": "Named diagnostic cited as evidence for Chad’s poverty and human development findings."}, {"key": "jdc_operational:000035:10:1:1", "start": 1818, "end": 1857, "surface": "2014 Human Development Indicators Index", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Index ranking provides a concrete development finding."}]}, {"key": "paddy-105", "text": "|Procurement Risks|Mitigation Measures|\n|---|---|\n|Managing fraud and corruption and noncompliance. |_Ex ante_due diligence of firms being selected will be attempted using databases available in country and externally. Post review of contracts will be scheduled immediately on award of contracts for all contracts that would have been usually prior reviewed.|\n\n\n\n**.**\n\n\n\n**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n\n83. **The procurement risk is Substantial and will be mitigated by the following measures:** (i) the Recipient will\nutilize rapid disbursement procedures and simplified procurement processes in accordance with emergency\noperations norms; (ii) the World Bank will provide BFP leveraging its comparative advantage as convener\nwith the objective of facilitating borrowers’ access to available supplies at competitive prices, as described\nin the procurement section of this document. BFP in identifying suppliers and facilitating contracting\nbetween them and borrowers may bring a perception that the World Bank is acting beyond its role as a\nfinancier with greater reputational and potentially litigation risks – these would relate to questions of\ntransparency, equity in terms of which borrowers get access to what and when, issues with quality,\ntimeliness of delivery, value for money, and any other issues of contractual non-performance by the\nsuppliers identified by the World Bank. To partially mitigate these risks, the World Bank and the Recipient\nwill clearly delineate the roles and responsibilities of the World Bank and the Recipients for whom the World\nBank facilitates access to available supplies. Moreover, BFP is provided to mitigate the greater risk that the\nWorld Bank could be providing financing for medical supplies that may not be readily available to developing\ncountries. This more proactive approach in assisting borrowers is justified as an effective way to\ncomplement other procurement options and help clients", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000039:30:0:0", "start": 181, "end": 226, "surface": "databases available in country and externally", "probe_tag": "confusion", "probe_score": 0.481, "luna_label": 0, "luna_reason": "Databases support routine procurement due diligence, not substantive data analysis."}]}, {"key": "paddy-106", "text": "br>Review of the online publications. • Approved Bid Evaluation Reports recommending contract award. • Signed contracts submitted to IDA. |\n|Result 1.3. Construction progress: A. 30% completion: foundation level complete, floor slab and walls have reached window level; B. 70% completion: all facilities roofed; C. 90% completion (i.e. substantial completion): all facilities plastered, painted, windows and doors fitted, water &| • No. of schools where the buildings are 30%, 70%, 90% and 100% completed respectively. • Supervision reports are published online by district, by school, and by contractor. • Construction progress reports by the MoES. • Publish the List of completed schools with completed civil works online, and publish the certificate online. • Verify that District and headteachers have certified school completion for all completed schools submitted for funding and publish the certified information online.|• Reports of site visits by construction supervisory staff. • A third-party verification of construction progress of all schools. • Certification of school completion from District", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000018:48:2:0", "start": 790, "end": 815, "surface": "List of completed schools", "probe_tag": "confusion", "probe_score": 0.1412, "luna_label": 0, "luna_reason": "Imperative requires future publication of a project completion list."}]}, {"key": "paddy-107", "text": " COVID-19 pandemic, and Uganda will likely be adversely affected by this. 22 [^22: Uganda remittances were US$1.3 billion in 2019, US$1.425 billion in 2018, and US$1.2 billion in 2017—World Bank 2017–2019 data.]\n\n\n**Issues that Need to Be Addressed in the Medium-Term to Continue and Restart Economic Transformation and**\n**Job Creation**\n\n\n13. **Overall, the financial sector in Uganda is not positioned to mitigate liquidity shocks.** Despite being well\ncapitalized and profitable, the sector exhibits a lack of innovation and its services fail to reach large segments of\nthe market. As of June 2020, the sector remains adequately capitalized, with the aggregate industry total capital\nand core capital adequacy ratios at 22.7 percent and 21.1 percent, which are well above the minimum capital\nadequacy requirements of 12 percent and 10 percent respectively. However, NPLs in commercial banks rose to\n6.01 percent as of June 2020, up from 3.79 percent a year earlier. Between March and July 2020, new loan\ndisbursements in Tier 1-3 institutions declined sharply. Movement restrictions, changes in branch opening hours\nand declines in loan demand led to a strong decline in loan applications. From March to April 2020, loan\napplications declined by 91 percent in number and 52 percent in terms of value. In the subsequent quarter loan\ngrowth very gradually began to pick up again growing 6.35 percent in value terms. 23 [^23: Economic Policy Research Center (EPRC), 2020.] Even prior to the crisis, formal\nfinancial sector lending to MSMEs remained moderate.\n\n\n14. **The low capacity of Small and Medium Enterprises (SMEs) is a barrier to further growth.** SMEs face\nchallenges with producing at the scale and efficiency required of export markets. According to a survey conducted\nin 2014, ", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000021:17:1:0", "start": 195, "end": 220, "surface": "World Bank 2017–2019 data", "probe_tag": "confusion", "probe_score": 0.7985, "luna_label": 1, "luna_reason": "World Bank data supports cited remittance figures for 2017–2019."}, {"key": "jdc_operational:000021:17:1:1", "start": 1787, "end": 1811, "surface": "survey conducted\nin 2014", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Generic survey mention lacks an attached finding or concrete attributed result."}]}, {"key": "paddy-108", "text": "**The World Bank**\nRoads and Employment Project (P160223)\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|municipalities consulted on road priorities||||||meetings from/to union of municipalities. ||\n| Description:This indicators measure consultations with local authorities, and indirectly with the public, on their road rehabilitation priorities.| Description:This indicators measure consultations with local authorities, and indirectly with the public, on their road rehabilitation priorities.| Description:This indicators measure consultations with local authorities, and indirectly with the public, on their road rehabilitation priorities.| Description:This indicators measure consultations with local authorities, and indirectly with the public, on their road rehabilitation priorities.| Description:This indicators measure consultations with local authorities, and indirectly with the public, on their road rehabilitation priorities.| Description:This indicators measure consultations with local authorities, and indirectly with ", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000008:51:0:0", "start": 19, "end": 47, "surface": "Roads and Employment Project", "probe_tag": "confusion", "probe_score": 0.0896, "luna_label": 0, "luna_reason": "Project title, not a data resource or data-use mention."}]}, {"key": "paddy-109", "text": "|PAD DATA SHEET|Col2|Col3|Col4|Col5|Col6|Col7|\n|---|---|---|---|---|---|---|\n|_Lebanon_|_Lebanon_|_Lebanon_|_Lebanon_|_Lebanon_|_Lebanon_|_Lebanon_|\n|_Emergency Education System Stabilization (P152898)_|_Emergency Education System Stabilization (P152898)_|_Emergency Education System Stabilization (P152898)_|_Emergency Education System Stabilization (P152898)_|_Emergency Education System Stabilization (P152898)_|_Emergency Education System Stabilization (P152898)_|_Emergency Education System Stabilization (P152898)_|\n|**PROJECT APPRAISAL DOCUMENT**|**PROJECT APPRAISAL DOCUMENT**|**PROJECT APPRAISAL DOCUMENT**|**PROJECT APPRAISAL DOCUMENT**|**PROJECT APPRAISAL DOCUMENT**|**PROJECT APPRAISAL DOCUMENT**|**PROJECT APPRAISAL DOCUMENT**|\n|_MIDDLE EAST AND NORTH AFRICA_|_MIDDLE EAST AND NORTH AFRICA_|_MIDDLE EAST AND NORTH AFRICA_|_MIDDLE EAST AND NORTH AFRICA_|_MIDDLE EAST AND NORTH AFRICA_|_MIDDLE EAST AND NORTH AFRICA_|_MIDDLE EAST AND NORTH AFRICA_|\n|Report No.: PAD1190|Report No.: PAD1190|Report No.: PAD1190|Report No.: PAD1190|Report No.: PAD1190|Report No.: PAD1190|Report No.: PAD1190|\n|**Basic Information", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000030:4:0:0", "start": 1, "end": 15, "surface": "PAD DATA SHEET", "probe_tag": "confusion", "probe_score": 0.1531, "luna_label": 0, "luna_reason": "Standalone table header, not a data resource or data use"}]}, {"key": "paddy-110", "text": "br>Partner |\n|Number of beneficiary households receiving cash transfer for participating in the behavioral change communication training|The number of beneficiary households that participate in behavioral change communication training activities to receive their cash transfer.|This indicator will be measured, at a minimum, on a quarterly basis |SNSOP Management Information System |Attendance data collected during each training session |Implementing Partner |\n|Number of beneficiary households receiving Direct Income Support|The number of total beneficiary HHs that are selected to participate in DIS under sub-component 1.2, in accordance with the Project Operations Manual,| This indicator will be measured, at a minimum,|Registration and payment data from the SNSOP MIS |Beneficiary data will be collected during registration and updated over the course of project implementation.|Selected Implementing Partner |\n\n\nPage 56 of 74", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000057:60:1:0", "start": 450, "end": 465, "surface": "Attendance data", "probe_tag": "drop", "probe_score": 0.0093, "luna_label": 0, "luna_reason": "Attendance data will be collected during project training sessions."}, {"key": "jdc_operational:000057:60:1:1", "start": 907, "end": 923, "surface": "Beneficiary data", "probe_tag": "confusion", "probe_score": 0.0994, "luna_label": 0, "luna_reason": "Data will be collected and updated during project implementation."}]}, {"key": "paddy-111", "text": "**The World Bank**\nUganda: Roads and Bridges in the Refugee Hosting Districts Project (P171339)\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Sr. No.|Description|Cost|Bank financing|GoU financing|\n|---|---|---|---|---|\n|14|Consultancy to study mobility patterns and barriers for women in the refugee hosting districts and the design of potential pilots for refugees to benefit from road upgrading|0.5|0.5|0|\n|15|Strengthen the skills in handling climate change and natural disasters risks in refugee hosting areas, improve contingency planning and the climate resilience of local communities, and update of the Road Design Manuals and Specifications for civil works to account for climate change parameters|0.5|0.5|0|\n|**Component 3: Road Safety**|**Component 3: Road Safety**|**2 **|**2 **|**0 **|\n|16|Completion of development and operationalization of a Road Accident Database Management System|1|1|0|\n|17|Training and awareness campaigns in the Project area (including a communication specialist)|1|1|0|\n|**Component 4: Contingent Emergency Response**|**Component 4: Contingent Emergency Response**|**0 **|**0 **|**0 **|\n\n\n61. **Financing Instrument.** The lending instrument for the Project is Investment Project Financing. The Project\nwill be funded by a grant from the IDA Window for Host Communities and Refugees and the National IDA.\n\n\n62. **Readiness for Implementation.** Steps have been", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000050:31:0:0", "start": 855, "end": 895, "surface": "Road Accident Database Management System", "probe_tag": "drop", "probe_score": 0.0242, "luna_label": 0, "luna_reason": "Table activity entry, not evidence of data use."}]}, {"key": "paddy-112", "text": "SNSOP Management Information System (MIS) |Data on participation in Component 2 will be collected at registration where based on the targeting and registration process outlined in the Project Operations Manual, eligible beneficiaries will be allocated to Component 2. Data on payment of the livelihood grant will be collected through the SNSOP MIS that will be linked with SNSOP payment data |The Implementing Partner responsible for Component 2 will be responsible for data collection |\n|Eligible beneficiary households with functional income-generating investments| The total number of households with functional|This indicator will be|SNSOP Management|Data will be collected through routine M&E|Implementing Partner |\n\n\nPage 53 of 74", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000057:57:1:0", "start": 397, "end": 406, "surface": "SNSOP MIS", "probe_tag": "drop", "probe_score": 0.0361, "luna_label": 0, "luna_reason": "Future data collection through the MIS is planned project monitoring production."}]}, {"key": "paddy-113", "text": " platform will be granted to firms that meet a set of eligibility criteria (will not required\nformalization) and will specifically target female-owned enterprises and MSMEs that employ refugees. The\ndigital platform will represent a dynamic marketplace for accelerators, incubators and consultancies – eligible\nservice providers will be vetted and selected based on a competitive tender – and will lay the foundation for\nan improved business development environment for MSMEs, increasing their ability to take advantage of\ngrowing markets in manufacturing and export supply chains.\n\n\n**6.** **Implementation support and monitoring & evaluation.** The objective of this component is to assist in the\ndevelopment and implementation of the different facilities, and to provide guidance and support by collecting\nand measuring key output and impact data. The monitoring component of the M&E approach will require\ndata collection across different dimensions of the Project: (1) Performance Tracking data (e.g. sales,\nemployment, wages, transactions, etc); (2) Activity Tracking data reflecting the Theory of Change (e.g. as\nreflected by the number of loans serviced on the project’s web platform, the number of receivables purchased\non the factoring platform, the number of refugees receiving business training, etc.); (3) Key Results data (e.g.\nvalue of private investment in manufacturing firms, formal employment in manufacturing firms, etc); and (4)\nKey Risks tracking (e.g. project implementation performance, NPL ratio of banks and PAR of MFIs, etc). The\nevaluation component will build on the data collected under the monitoring component, but additionally\nfocus on implementing a structured impact evaluation to measure the impact and attribution of the different\npolicies under the project i.e. incubators, industrial parks, etc.,\n\n\nPage 84 of 92", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000021:89:1:0", "start": 973, "end": 998, "surface": "Performance Tracking data", "probe_tag": "drop", "probe_score": 0.0417, "luna_label": 0, "luna_reason": "Project monitoring data are to be collected as part of the M&E component."}, {"key": "jdc_operational:000021:89:1:1", "start": 1055, "end": 1077, "surface": "Activity Tracking data", "probe_tag": "confusion", "probe_score": 0.0538, "luna_label": 0, "luna_reason": "Project monitoring data collection is planned, not existing data use."}, {"key": "jdc_operational:000021:89:1:2", "start": 1318, "end": 1334, "surface": "Key Results data", "probe_tag": "drop", "probe_score": 0.0382, "luna_label": 0, "luna_reason": "Key results data are planned for collection under project monitoring."}, {"key": "jdc_operational:000021:89:1:3", "start": 1449, "end": 1467, "surface": "Key Risks tracking", "probe_tag": "confusion", "probe_score": 0.1806, "luna_label": 0, "luna_reason": "Planned project monitoring of key risks, not an existing data resource."}]}, {"key": "paddy-114", "text": " tools being used. MoITS will utilize\nthe functionalities available in their Oracle database and accounting module when recording transactions\nrelated to the Project operations, by creating a separate cost center for the Project which is used for\nrecording the day-to-day transactions and large contract purchases under both components, the FO might\nalso use excel sheet in support of the Oracle system to prepare Bank required reports such as the WA-IFRs\nand the Semiannual IFRs. Furthermore, the assigned FO will work on improving the automated linkage\nbetween the MoITS’ Oracle based accounting system and the IFRs to be produced for the Bank purposes on\nexcel spread sheets.\n\n\n**9.** **Financial Section of the POM** : MoITS will develop the FM section of the POM used in the project\nwhich will cover all administrative, financial, and accounting, budgetary, and human resources procedures\nrelevant to the additional activities to be financed under the project. The POM should describe the payment\nprocedures, including controls and oversight arrangements. A POM acceptable to the Bank should be\nsubmitted within two months after project effectiveness date.\n\n**10.** **Training and Implementation Support.** The World Bank team will intensively supervise the project,\nparticularly early in implementation, and will provide adequate training to the FO to ensure full\nunderstanding and application of the World Bank financial management and disbursement Policies and\nProcedures.\n\n**11.** **Financial Reporting and Monitoring** : MoITS will work very closely with MOPIC and MOF, although\nfunds disbursements and WA will be vested with MOF and MOPIC, MoITS will be solely responsible for: (i)\nconsolidating the loan financial data; (ii) preparing activity budgets (disbursement plan) quarterly as well as\nannually, monthly DA reconciliation statements, and periodic Statements of Expenditures (SOEs) (if needed),\nwithdrawal schedule for approval, semi-annual IFRs (", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000024:51:1:0", "start": 1711, "end": 1730, "surface": "loan financial data", "probe_tag": "drop", "probe_score": 0.0326, "luna_label": 0, "luna_reason": "Routine project financial data used for consolidation and reporting."}]}, {"key": "paddy-115", "text": " (b) the capacity of Uganda National Roads Authority to manage environmental, social and road safety\nrisks.\n\n\nKey Results\n\n\n25. The PDO level indicators are:\n\n(a) enhance road transport connectivity in select refugee hosting districts of Uganda\n\n(i) Travel time on the Project road corridor dis-aggregated by buses, motorbikes, and trucks\n\n(modes used by refugees/hosts and for trade)\n(ii) Vehicle Operating Costs on the Project road corridor dis-aggregated by buses, motorbikes,\n\ncars, and trucks (modes used by refugees/hosts and for trade)\n(iii) Time of closure of Project road corridor in a year for movement of trucks\n(iv) Percentage increase in trade volumes using the Project road (dis-aggregated by refugees,\n\nhosts)\n\n\n(b) enhance the capacity of Uganda National Roads Authority to manage environmental, social and\nroad safety risks\n\n\n(v) Fully operational Environmental and Social Management System in place\n(vi) Crash data entered into system, publicly reported, and used in decision making\n(vii) Annual number of fatalities or serious injuries involving construction vehicles or at\n\n\n39 Includes 13 permanent staff, 20 contract staff, and 6 technical advisors\n\n\nApr 07, 2020 Page 12 of 16", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000001:11:1:0", "start": 922, "end": 932, "surface": "Crash data", "probe_tag": "drop", "probe_score": 0.0248, "luna_label": 0, "luna_reason": "PDO indicator describes planned crash-data reporting and decision use, not existing evidence."}]}, {"key": "paddy-116", "text": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n\n|AFD|ABBREVIATIONS AND ACRONYMS Agence Française de Développement (French Agency for Development)|\n|---|---|\n|AFD |_Agence Française de Développement_ (French Agency for Development) |\n| AMR | Anti-microbial Resistance |\n| BFP | World Bank Facilitated Procurement |\n| CDC | Center for Disease Control |\n| CERC | Contingency Emergency Response Component |\n| COVID-19 | Coronavirus Disease |\n| CPIA | Country Policy and Institutional Assessment |\n| DA | Designated Account |\n| DFIL | Disbursement and Financial Information Letter |\n| DHIS | District Health Information System |\n| DLI | Disbursement-linked Indicators |\n| ECHO | European Civil Protection and Humanitarian Aid Operations |\n| EID | Emerging Infectious Disease |\n| ESCP | Environmental and Social Commitment Plan |\n| ESF | Environmental and", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000039:2:0:0", "start": 739, "end": 773, "surface": "District Health Information System", "probe_tag": "drop", "probe_score": 0.0056, "luna_label": 0, "luna_reason": "System name is defined without showing any use of its data."}, {"key": "jdc_operational:000039:2:0:1", "start": 796, "end": 826, "surface": "Disbursement-linked Indicators", "probe_tag": "drop", "probe_score": 0.004, "luna_label": 0, "luna_reason": "Defines a disbursement mechanism without citing or using indicator data."}]}, {"key": "paddy-117", "text": "**The World Bank**\nUganda Secondary Education Expansion Project (P166570)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Col1|- Five + teachers trained in elimination of VAC. - Records of VAC cases reported and actions taken. - Five + key policy documents and reference materials on elimination of VAC.|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|Number of students enrolled in AEP| Number of AEP students, cumulative |Annually |Project M&E reports |Headcount. Target includes the baseline figure, i.e. students enroled before the project. About 6,600 students will be enroled under the project. |MoES |\n|Number of refugee students covered by the capitation grants program|Number of students who have benefited from the capitation grants for three school terms during one school year. |Annually |Project M&E reports. LG audit reports | LG audit reports |MoES |\n|Number of school administrators trained| Number of school administrators (Headteachers and Deputy headteachers) from existing lower secondary schools who completed training |Annually |Project M&E reports |", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000018:60:0:0", "start": 151, "end": 171, "surface": "Records of VAC cases", "probe_tag": "drop", "probe_score": 0.0379, "luna_label": 0, "luna_reason": "Fragment appears inside a project results table and is not an independent data citation."}]}, {"key": "paddy-118", "text": "**The World Bank**\nRoads and Employment Project (P160223)\n\n\n**ANNEX 2: IMPLEMENTATION ARRANGEMENTS**\n\n\n**COUNTRY : Lebanon**\n**Roads and Employment Project**\n\n**Project Institutional and Implementation Arrangements**\n\n\n1. The project implementation entity is CDR. All technical, fiduciary, safeguards, and monitoring\naspects will be executed directly by CDR therefore avoiding the complication of multiple agencies’\nimplementation. CDR has a long and well established cooperation with the World Bank and other\ndonors, and its performance at project implementation has been generally satisfactory. CDR will,\nhowever, ensure coordination with the relevant government agencies, particularly the MPWT,\nregarding the priorities, technical aspects, and project requirements. The selection of priority\nroads for the project will be undertaken in consultation with MPWT based on the results of the\nvisual survey and the agreed criteria. MPWT will also identify and submit its needs for emergency\nequipment and desired technical specifications to CDR who will undertake the procurement of\nsuch equipment. The SNRSC will inform CDR about its capacity building needs and will draft and\nreview terms of Reference for the required services, with Bank support, before CDR proceeds with\nthe procurement of such services. The same road asset management system will also be installed\nwithin both CDR and MPWT and will involve the joint participation and training of MPWT and CDR\nstaff on the utilization of the new system, therefore reinforcing sustainability given both MPWT\nand CDR active responsibilities in the road sector in Lebanon.\n\n\n2. To ensure further coordination and capacity building, two road engineers from MPWT (one from\nplanning and one from maintenance) will be primarily dedicated to support CDR in the\nimplementation of the project, providing day‐to‐day on‐the‐job learning. The project will also\nfund one road engineer, through the PIU, who will be housed within MPWT working with the\nGeneral Director for Roads and Buildings. World Bank experts", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000008:59:0:0", "start": 890, "end": 903, "surface": "visual survey", "probe_tag": "drop", "probe_score": 0.0211, "luna_label": 1, "luna_reason": "Survey results inform selection of priority roads."}]}, {"key": "paddy-119", "text": "**Collection **|**Responsibility for Data** **Collection **|\n|Students benefiting from direct interventions to enhance learning||Anuual |EMIS data |Census at school level |Uganda Beareau of Statistics, MoES |\n|Students benefiting from direct interventions to enhance learning - Female||||||\n|PDO Indicator 2. Number of children supported with distance/home-based learning interventions|No. of students in primary and lower secondary evidently issued with self-|Bi-monthly |SMS surveys |SMS surveys. Signed goods received registers|NCDC, DES |\n\n\nPage 30 of 43\n\n\nOfficial Use", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000034:34:1:0", "start": 146, "end": 155, "surface": "EMIS data", "probe_tag": "confusion", "probe_score": 0.5021, "luna_label": 0, "luna_reason": "Table cell naming a data source, without demonstrated analytical use."}, {"key": "jdc_operational:000034:34:1:1", "start": 160, "end": 182, "surface": "Census at school level", "probe_tag": "confusion", "probe_score": 0.6147, "luna_label": 1, "luna_reason": "Names a school-level census serving as the indicator’s data source."}, {"key": "jdc_operational:000034:34:1:2", "start": 515, "end": 526, "surface": "SMS surveys", "probe_tag": "drop", "probe_score": 0.0398, "luna_label": 0, "luna_reason": "Planned bi-monthly indicator data collection, not cited existing survey evidence."}]}, {"key": "paddy-120", "text": " during missions and ISRs |SNSOP MIS which hosts beneficiary registration and payment data |The implementing partner will collect beneficiary data during targeting and registration. The payment service provider and implementing agency will document payment data |Implementing Partner |\n|Beneficiary households of social safety net programs- Refugees|The number of total beneficiaries HHs that are|This indicator will be|SNSOP MIS which hosts|The implementing partner will collect|Implementing Partner |\n\n\nPage 51 of 74", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000057:55:1:0", "start": 163, "end": 179, "surface": "beneficiary data", "probe_tag": "drop", "probe_score": 0.0147, "luna_label": 0, "luna_reason": "The implementing partner will collect it during future targeting and registration."}]}, {"key": "paddy-121", "text": "41 \nresults including \nAB report. \nDLI 2 \nAnnual public \ndisclosure by \nBetter Work \nJordan of \nreport on \nfactory-level \ncompliance \nwith a list of at \nleast 29 social \nand \nenvironmental-\nrelated items \nThe GoJ will support the implementation by \nBetter Work Jordan of public disclosure of \nfactory level compliance with 29 or more \nselected issues assessed by Better Work. Better \nWork will implement this public disclosure of \nfactory level compliance information via its \nwebsite http://betterwork.org/jordan. \nNo \nBetter Work \nJordan Website \nAB \n \nUpon notification \nfrom the PMU, AB \nwill check the \nBetter Work Jordan \nwebsite each year to \nverify public \ndisclosure and will \nissue the \nverification report \nwithin 2 months \nfollowing PMU \nnotification. \nThe AB report will \naccompany the \ndocumentation \nsubmitted by \nMOPIC to the \nWorld Bank \nconfirming \nachievement of \nresults. \nDLI 3 \nEstablishment \nand \nimplementation \nof selected \nsimplified and \npredictable \nregulations for \nthe private \nsector \nincluding \nhousehold \nbusinesses \nDLR 3.1: A reform establishing a predictability \nprocess for issuance of business regulations has \nbeen identified and adopted following an \ninclusive public-private dialogue and a \nmeasurement system (including baseline \nidentification) has been prepared. \n \nDLR 3.2: \nOne key business regulatory reform has been \nidentified following an inclusive public-private \ndialogue; and a measurement system covering \nthe time, cost, and complexity of the compliance \nprocess has been prepared (including baseline \nidentification). \nYes for DLR 3.3 \nNo for the rest \nDLR 3.1 and \nDLR 3.2: Prime \nMinister’s Office \n \nDLR 3.3: \nMunicipalities \nrecords \n \nDLR 3.4 and 3.5: \nSurveys \n \n \n \nIndependent \nVerification \nAgent (IVA) \n \n \n \n \nDLR 3.1 and 3.2: \nUpon achievement \nof results, Office of \nPrime Minister will \nsubmit to the PMU \ndocumentation of \nadoption of reform. \nThe PMU will \nsubmit \ndocumentation to \nthe World Bank \nconfirming \nachievement of \nresults.", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000045:49:0:0", "start": 1679, "end": 1702, "surface": "Municipalities \nrecords", "probe_tag": "drop", "probe_score": 0.0364, "luna_label": 0, "luna_reason": "Listed as planned DLI verification documentation, not existing data used for analysis."}]}, {"key": "paddy-122", "text": "The World Bank\nMauritania Water and Sanitation Sectoral Project (P167328)\n\n\n\n8. **Access to water and sanitation**\n**is relatively high, especially in urban**\n\npopulation had access to basic\n\nshould be noted that these figures are\nfor nationals only, and do not include\nrefugees. According to a 2016\ninventory of piped systems, solar pumping is used by 80 percent of the water posts and 50 percent of the smallscale water systems ( _alimentation en eau potable_, AEP) and mini-AEPs.\n\n\n9. **The development of sanitation lags water supply** . In 2015, 63 percent of the urban population, and 20\npercent of the rural population had access to an improved sanitation facility (either individually or shared with\nother households) 9 [^9: UNICEF/WHO, Joint Monitoring Program – 2015 Estimates Updated in 2017.] . Open defecation is prevalent in rural areas, where it is practiced by 61 percent of the\npopulation. While there has been some improvement in terms of access to sanitation in institutions and public\nlocations, only 35 percent of schools and 61 percent of health centers have onsite sanitation facilities.\n\n\n10. **Water supply and sanitation services for refugees are managed by UNHCR and partner non-governmental**\n**organizations (NGO)** . The M’Bera refugee camp is equipped with five boreholes from which water is pumped into\ntwo separate piped systems supplying 12 stand posts. Although the systems are operational, they were\nconstructed in haste, with frequent turnover among contractors, leading to a jumbled system. Consequently,\nthere are many technical issues, such as lack of pressure, which hinder the quality of service and leads to some\nresidents not receiving reliable water supply, particularly at the tail end of the distribution network. As for\nsanitation, the camp has been equipped with double ventilated improved pit (VIP) latrines 10 combined with a\nshower, but the current ratio of four", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000106:13:0:0", "start": 295, "end": 326, "surface": "2016\ninventory of piped systems", "probe_tag": "keep", "probe_score": 0.9399, "luna_label": 1, "luna_reason": "Existing inventory supports concrete pumping-use percentages."}, {"key": "refugee_pads:000106:13:0:1", "start": 756, "end": 780, "surface": "Joint Monitoring Program", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1, "luna_reason": "Named source for reported sanitation access figures"}]}, {"key": "paddy-123", "text": "\nfor cybersecurity. To handle incidents and attacks, Uganda has both a national Computer Emergency Response\nTeam (CERT) at NITA-U and a Communications Sector CERT at the Uganda Communications Commission (UCC).\nThe country benefitted from the Cybersecurity Maturity Model assessment that was undertaken in 2016 and\nupdated in 2020; the emanating recommendations, for instance on capacity building and awareness raising, are\nreflected in the present project’s design. Uganda was nominated as the regional lead on cybersecurity under the\nEast African Northern Corridor Infrastructure Regional Memorandum of Understanding (MoU). In 2018, Uganda\nranked 7 th in Africa in the ITU’s Global Cybersecurity Index, and 65 th globally. 38 [^38: International Telecommunications Union, 2018., _Global Cybersecurity Index 2018_ . https://www.itu.int/dms_pub/itu-d/opb/str/D-STRGCI.01-2018-PDF-E.pdf] The country’s next challenges for\ncybersecurity are therefore to expand technical capacity, implement best practice governance, and move toward\neffective implementation and sustainability.\n\n\n**16.** **In the area of data protection, Uganda is in the early stages of operationalizing a recently adopted Data**\n**Protection Law.** This landmark legislation, passed in 2019, made Uganda the first East African country to recognize\nprivacy as a fundamental human right, as enshrined in Article 27 of the 1995 Uganda Constitution. It aims to\nprotect individuals and their personal data by regulating processing of personal information by state and nonstate actors, within and outside Uganda. The law expands the rights of individuals to control how their personal\ndata are collected and processed, placing a range of obligations on those processing it, both public bodies and\ncompanies. A year since its enactment, the law remains in need of accelerated implementation and enforcement,\nwith observers reporting that personal data continue to be collected in violation of the law’s principles. <", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "sample:refugee_pads:000075:18:1:0", "start": 687, "end": 713, "surface": "Global Cybersecurity Index", "probe_tag": "keep", "probe_score": 0.9404, "luna_label": 1, "luna_reason": "Named index supports Uganda’s reported regional and global rankings."}]}, {"key": "paddy-124", "text": "|3|4|4|4|Annual|Project Progress Reports|PCU|\n|Number of persons trained (disaggregated by gender)||Number|0|400|900|1,200|1,700|2,000|Annual|Project progress reports| PCU|\n|Number of persons trained, of which women (%)||Percentage Sub-Type Supplement al|0|20|20|20|25|30|Annual|Project progress reports| PCU|\n\n\n.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n39 No specific targets can be established for this indicator since the nature of works has not been determined. The outputs will be measured as they occur in the\nannual reports and based on the contracts for work undertaken in the LGs under financed by the CPG.\n40 No specific targets can be established for this indicator since the nature of works has not been determined. This indicator will be measured annually based on\nthe progress reports from the LGs and consolidated by the PCU.\n\n\n26", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000166:40:1:0", "start": 809, "end": 838, "surface": "progress reports from the LGs", "probe_tag": "keep", "probe_score": 0.9027, "luna_label": 0, "luna_reason": "Future indicator measurement relies on planned progress reporting."}]}, {"key": "paddy-125", "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_paddy", "spans": [{"key": "refugee_pads:000097:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Survey data support reported enrollment-rate disparities across expenditure groups."}, {"key": "refugee_pads:000097:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1, "luna_reason": "Household Survey data supports the finding that parents withdraw girls from school."}]}, {"key": "paddy-126", "text": "up> These gains are likely to be reversed due to the COVID-19 pandemic and\nits associated containment measures. The challenges of poverty reduction can be further affected by\nclimate change and disaster risk-related vulnerabilities. 4 [^4: Climate change is expected to exacerbate extreme weather events in Pakistan, thereby increasing the vulnerability of people, assets and\ninfrastructure to-climate induced disasters. Balochistan is highly vulnerable to major natural disasters and has suffered significantly from\nvarious crises, including drought in 2000-02, and Cyclone Yemyin in 2007.] The economic contraction is expected to\ncontribute to a sizeable increase in poverty, reversing the trend of sustained poverty reduction observed\nover the 14 years. Urban workers employed in the informal sector and daily wage workers employed in\nthe formal sector will bear the brunt of the slowdown. In rural areas, expected decline in off-farm\nemployment opportunities is also likely to increase vulnerability to shocks of households relying on\nagriculture. It is important that the government prioritizes investments to ensure poverty reduction and\nhuman capital losses are quickly offset to bounce back strongly.\n\n\n3. **Human capital accumulation is low and the impact of COVID-19 pandemic puts at risk some of**\n**the gains made in recent years.** According to the World Bank Human Capital Index (HCI), if no\nimprovements in health and education service delivery take place, a Pakistani child born today is expected\nto be only 40 percent as productive as s/he could be by age 18. With a large share of births taking place\noutside health facilities (33.8 percent), and low immunization rates (65.6 percent) children are deprived\nof a strong start to life. High rates of malnutrition and low learning outcomes contribute to the country’s\nlow HCI: 37.6 percent of Pakistani children under age five are stunted; and learning poverty is very high\nwith 75 percent of Pakistani children not being able to read and understand a short age-appropriate text\nby age 10.\n\n\n4. **Pakistan has", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000126:10:1:0", "start": 1373, "end": 1403, "surface": "World Bank Human Capital Index", "probe_tag": "keep", "probe_score": 0.9529, "luna_label": 1, "luna_reason": "Named index supports the attributed human-capital productivity finding."}]}, {"key": "paddy-127", "text": "s=NE)\n[2 World Bank Group. 2025. Niger. https://www.worldbank.org/en/country/niger/overview#3.](https://www.worldbank.org/en/country/niger/overview#3)\n3 Recent data from the World Food Program indicates that over 3.3 million individuals were classified as acutely food insecure during the 2023 season (June–August). An estimated 4.3 million people (2.4 million of whom are\nchildren) require humanitarian assistance. Additionally, Niger’s global acute malnutrition rate is estimated at 12.7 percent, and 42 percent of children under 5 years old are stunted. Severe recorded food crises in 1980, 1988,\n[1990, 1997, 2001, 2005, 2009, 2011–-.https://openknowledge.worldbank.org/handle/10986/37620).](https://openknowledge.worldbank.org/handle/10986/37620)\n[4 World Bank Group. 2021. Data. Poverty headcount ratio at US$2.15 a day (2017 PPP) (% of population) - Niger. https://data.worldbank.org/indicator/SI.POV.DDAY?locations=NE](https://data.worldbank.org/indicator/SI.POV.DDAY?locations=NE)\n5 In the transport sector women hold less than one percent of jobs. Although data on women in technical roles is unavailable, their share is likely lower due to inadequate skills and strong gender norms.\n6 When referring to host communities in this document, internally displaced persons are considered part of the host population unless noted otherwise.\n7 P. Thenkabail et al. 2016. Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000184:11:3:1", "start": 1374, "end": 1416, "surface": "Global Food Security Support Analysis Data", "probe_tag": "keep", "probe_score": 0.9406, "luna_label": 0, "luna_reason": "Standalone bibliography entry without shown analytical use"}]}, {"key": "paddy-128", "text": "**The World Bank**\nEducation Quality Improvement Project (P179363)\n\n\nproject manager, FM specialist, PS, M&E specialist, environmental specialist and social development\nspecialist with experience in GBV prevention and response, civil works engineers, and other technical\nspecialists to ensure timely, quality, transparent, and effective implementation of the civil works activities.\nFurther details will be provided in the POM. The fiduciary assessment of the PMT of the MoER and NORLD\nhas been completed and is reflected in the fiduciary sections.\n\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n\n68. The PDO-level results indicators and intermediate results indicators will be monitored using the\nfollowing data: (a) data on education institutions and students generated by EMIS and e-Catalogue/eRegister (disaggregated by gender, urban-rural divide, students with disabilities, and refugee students);\n(b) results from the nationally representative national and international assessments of student\nperformance and classroom observations; (c) regular survey data and administrative data of the MoER\nand NORLD; and (d) semiannual monitoring reports prepared by the MoER under support of the PMT. The\nMoER through the PMT will carry out the day-to-day coordination of M&E activities. It will bring together\nconsultants and representatives of various MoER departments to ensure the provision of timely and\naccurate information required for monitoring of the progress toward attainment of the PDOs and results\nof implementation of project activities. Progress reports will be prepared during implementation,\nparticularly before implementation support missions, midterm review, and before project closing\n(completion report).\n\n69. The project will also finance two impact evaluations of (a) the tutoring or accelerated learning\nprogram for disadvantaged students, with a statistically valid control group to compare differences in\naverage progress in student learning, and (b) the interventions in preschool education to accommodate\nmore children from disadvantaged and refugee families to assess the impact on", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000185:35:0:0", "start": 728, "end": 771, "surface": "data on education institutions and students", "probe_tag": "confusion", "probe_score": 0.8957, "luna_label": 1, "luna_reason": "Existing EMIS data are used to monitor education results indicators."}, {"key": "refugee_pads:000185:35:0:1", "start": 794, "end": 815, "surface": "e-Catalogue/eRegister", "probe_tag": "confusion", "probe_score": 0.8186, "luna_label": 1, "luna_reason": "Named registry systems provide data used to monitor project results."}, {"key": "refugee_pads:000185:35:0:2", "start": 1054, "end": 1097, "surface": "regular survey data and administrative data", "probe_tag": "confusion", "probe_score": 0.8408, "luna_label": 0, "luna_reason": "Planned project monitoring use, not an already analyzed data finding."}]}, {"key": "paddy-129", "text": " and evaluation\n(M&E), the impact o f project interventions on ‘higher order’ outcome indicators (such as\nimproved language capacity, cognitive abilities o f children etc.) will be initially difficult to\nassess. Therefore, this project has made a strategic decision to focus on monitoring project\nprogress and assessing project impact through the use o f simple output indicators (such as\nenrollment increase). These would be the indicators for which the project would be\naccountable (see Results Framework). The project, however, aims to build capacity in the\narea o f M&E, after which the MOE is expected to also assess impact through more difficultto-measure outcome indicators. Outcome indicators for the sector, toward which this project\nwill aim to build the capacity in MOE are presented in the PIP.\n\n\n22. While monitoring project progress and evaluating project impact, indicators will be\ndisaggregated as far as possible by income levels, gender, and inclusion in ‘disadvantaged’\ngroups. Such a focus in monitoring and evaluation will ensure better targeting. Enrollment\nrates in KG programs are low for all children, but girls fare poorly given a GER - f 12.8\npercent as opposed to 13.5 percent for boys. Disparities in KG enrollment rates are also large\nin poorer, rural governorates - KG enrollment is approximately 10 percent o f children, as\ncompared to KG enrollment rates o f 25 to 42 percent o f children for the relatively wealthier,\nurban governorates. The project targets 152 administrative units (in 18 Governorates) which\nrepresent the bottom third rankings in terms o f the Human Development Index (HDI; UNDP,\n2003) - full list available in PIP. The units represent some 48 percent o f the Egyptian\npopulation.\n\n\n**4.** **Project components**\n\n\n23. The proposed project aims to achieve its development objective through the following three\ncomponents: (1) Increase Access; (2) Improve Quality; and (3)", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000040:10:1:0", "start": 1601, "end": 1624, "surface": "Human Development Index", "probe_tag": "confusion", "probe_score": 0.7307, "luna_label": 1, "luna_reason": "HDI rankings informed targeting of the project's administrative units."}]}, {"key": "paddy-130", "text": " low purchasing power\n\n- subsidies are for farmers cultivating up to five hectares. The project takes deliberate measures to ensure that\nwomen farmers receive e-vouchers to access improved inputs through the e-voucher enrollment survey, with\ndedicated advisory services to ensure their effective use/application. For R H C, direct distribution of inputs will be\nuse if constraints on receiving vouchers are observed. Climate resilient infrastructure, improved seeds/breeds,\nand sustainable soil management will reduce producers’ vulnerability to extreme weather, and land degradation.\n\n\n**COMPONENT 3:** **ACCESS TO MARKETS,** **FINANCE, AND VALUE ADDITION (US$** **93.41 MILLION EQUIVALENT OF WHICH US$60.82**\n**MILLION-IDA;** **US$6.37 MILLION-WHR;** **US$6.47 MILLION** **BENEFICIARIES;** **AND** **US$19.75 MILLION-PARTNERS** **FINANCIAL**\n**INSTITUTIONS (PFIS)**\n\n\n**_Subcomponent 3.1: Increasing Chad’s Agribusiness Sector Marketing Capacity (US$14.44 million IDA)_**\n\n\n30. This subcomponent will finance constructing/rehabilitating wholesale markets with modern climate-smart\nwarehouses; cleaning, packaging and cold storage facilities equipped with solar energy which will contribute to\nreducing post-harvest food losses; and efficient water harvesting and management. It will also support a range of\nclimate-informed business development services (BDS) for agri-enterprises (including, marketing, negotiation,\nadvertising, certification),", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000183:23:1:0", "start": 227, "end": 254, "surface": "e-voucher enrollment survey", "probe_tag": "confusion", "probe_score": 0.3781, "luna_label": 0, "luna_reason": "Names an enrollment mechanism without showing data use or an attributed finding."}]}, {"key": "paddy-131", "text": "\n(42). The two Hodhs together make up 50 percent of the needs. Following this initial list, a feasibility study was\nconducted as part of project preparation to verify parameters such as population size and technical feasibility\nand from this basis, the final list of infrastructures per region was determined.\n\n3. As for urban and semi-urban centers, SNDE expressed its specific needs of infrastructure rehabilitation\ninvestments which cover 19 semi-urban centers (localities of more than 5,000 inhabitants), formerly managed\nby ONSER but which were transferred to SNDE, following the multiple failures of ONSER management. Seven\nof these centers will benefit from the project through rehabilitation and expansion, after which they will be\ndelegated to private operators.\n\n4. The activities related to sanitation were developed based on the needs expressed by the DA, in\nconsultation with the Ministries of Health and Education. According to the data presented by DA, 1,746 of the\n2,283 schools surveyed do not currently have latrines (256 in Assaba, 303 in Gorgol, 193 in Guidimakha, 500 in\nHodh Ech Echargui and 494 in Hodh El Gharbi). With regard to health centers, the DA indicated the latrine\nrequirements for 25 centers in Assaba, none in Gorgol, 20 in Guidimakha, 86 in Hodh Ech Echargui and 53 in\nHodh El Gharbi.\n\n5. The selection of water and sanitation activities in the M’Bera refugee camp is based on assessments of the\nexisting facilities. A diagnostic report of water and sanitation infrastructure, commissioned by UNHCR,\nprovided useful background information on the profile of the camp, its infrastructure and the surrounding\ncommunities. In addition, a World Bank team visited the refugee camp in March 2019 and in November 2019\nfor field assessments as part of project preparation. Both the report and the field visits helped to inform the\ndesign of the water and sanitation interventions in the", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000106:52:1:0", "start": 946, "end": 966, "surface": "data presented by DA", "probe_tag": "confusion", "probe_score": 0.3635, "luna_label": 1, "luna_reason": "DA-presented data supports the concrete finding on schools lacking latrines."}, {"key": "refugee_pads:000106:52:1:1", "start": 1455, "end": 1511, "surface": "diagnostic report of water and sanitation infrastructure", "probe_tag": "confusion", "probe_score": 0.6706, "luna_label": 1, "luna_reason": "Existing UNHCR-commissioned report informed intervention design."}]}, {"key": "paddy-132", "text": "**The World Bank**\nEnhancing Community Resilience and Local Governance Project Phase II (P177093)\n\n\nreturn to Sudan in the near or medium term. 26 As humanitarian assistance in refugee situations tends to\ndecline over time, there is a need for development efforts that promote local integration as a durable\nsolution, consolidate humanitarian gains, and capacitate government to assume responsibility for service\ndelivery, with a focus on addressing the particular challenges that women and girls face.\n\n\n**Figure 1. Locations of Refugees in South Sudan**\n\n\n_Source:_ Adapted from UNHCR (2021) South Sudan Refugees and Asylum-Seekers by State, April 30, 2021. 27 [^27: South Sudan: Refugee and Asylum Seeker Population, UNHCR, April 30, 2021.\nfile:///C:/Users/wb374705/Downloads/Refugee%20and%20Asylum%20Seekers%20Population_30April.pdf]\n\n\n12. **The World Bank Group (WBG), following consultation with UNHCR, confirms that the protection**\n**framework for refugees in South Sudan is adequate.** UNHCR has provided the WBG with an overall\npositive assessment of South Sudan’s protection framework while highlighting a set of protection-related\nchallenges. In addition to the legal framework in place, the Government has maintained its policy of\ngranting refugees access to its territory and installing practical arrangements for their initial reception and\nregistration. Refugees are granted freedom of movement and in principle are free to settle anywhere in\nthe country. The Commission for Refugee Affairs (CRA) has played an important role in coordinating\ngovernment policy and establishing a presence in key refugee-affected areas despite capacity challenges\nrelated to a lack of technical, human, and financial resources. The World Bank will closely engage with\nUNHCR to ensure that South Sudan’s refugee protection framework remains adequate, including through\n\n\n26 World Bank staff conducted these focus group discussions with multiple refugee groups in Pariang and Maban during a joint WHR eligibility\nmission with UNHCR in", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000192:17:0:0", "start": 605, "end": 653, "surface": "South Sudan Refugees and Asylum-Seekers by State", "probe_tag": "confusion", "probe_score": 0.3655, "luna_label": 1, "luna_reason": "UNHCR source data underlies the figure showing refugee locations by state."}]}, {"key": "paddy-133", "text": ", Cameroon has had numerous\ndisease outbreaks. These include leishmaniosis (2017), polio (2014, 2019), yellow fever (2013), measles\n(2015, 2019), and cholera (2011, 2014, 2020). Despite Cameroon’s vulnerability to outbreaks, particularly\nin the Far North, there has been minimal investment in strengthening communicable disease surveillance\nand response systems.\n\n\n16. **Cameroon** **ranked 115/195 on the Global Health Security Index (GHSI) with an overall score of 34.4** **4**\n**in contrast to Senegal (37.9), Nigeria (37.8), Côte d’Ivoire (35.5), Ghana (35.5), and Liberia (35.1)** . The\nmost recent WHO-supported Joint External Evaluation in 2017, which assessed the Republic of\n\n\n4 Global Health Security Index, Building Collective Action and Accountability, October 2019.\n\n\nPage 10 of 56", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000169:14:2:1", "start": 406, "end": 434, "surface": "Global Health Security Index", "probe_tag": "confusion", "probe_score": 0.7551, "luna_label": 1, "luna_reason": "Named index cited as evidence for Cameroon’s ranking and score."}]}, {"key": "paddy-134", "text": "implementation, and the monitoring and evaluation of the cash transfer program, as a template for\nother social safety net interventions.\n\n34. **Building on the database of eligible households, these modules will include:** (i)\nprogram beneficiary lists with an eventual registration of complementary activities, (ii) payment\nmodules (payroll and the reconciliation from the payment provider(s)), (iii) operational tracking\nof program, and (iv) basic monitoring and evaluation, including beneficiary feedback and\ngrievance redress mechanisms when operational. The program beneficiary lists will start with the\ncash transfer beneficiary list and track beneficiaries’ participation in the complementary activities\nset-up by the program. While initially, participation will be required but payments will not be\nconditional on participation, the system will provide the functionality to set up conditionalities in\nthe future. The payment system will include the quarterly/monthly payroll based on beneficiary\nlists, the amounts transferred to the payment agency(ies), the beneficiary receipts and the\nreconciliation of accounts. The operational tracking module would provide an operational\ndashboard to enable program managers to plan and track activities, human and material resources\nand other inputs at the central, provincial and communal levels. The M&E system would track\nfinancial outlays, key program results (including those core indicators that would be common\nacross programs within the SP system), impacts and beneficiary feedback as inputs to guide\nprogram management in the implementation of the programs. The grievance redress mechanism\nwould track grievances linked to targeting, receipt of transfers and implementation of the\ncomplementary activities.\n\n35. **The project will also finance the development and management of a grievance redress**\n**mechanisms** to respond to complaints and ensure a high level of accountability across program\noperations. These mechanisms include: in-person complaints to program commune focal point,\nSMS-based system to a third-party grievance manager (conditional on finding a trusted and\ncompetent agent and for possibilities for social control of a Government", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000157:58:0:0", "start": 160, "end": 191, "surface": "database of eligible households", "probe_tag": "confusion", "probe_score": 0.5547, "luna_label": 1, "luna_reason": "Existing household database is used as the basis for program modules."}, {"key": "refugee_pads:000157:58:0:1", "start": 609, "end": 639, "surface": "cash transfer beneficiary list", "probe_tag": "confusion", "probe_score": 0.0772, "luna_label": 1, "luna_reason": "Existing beneficiary list supports program registration and participation tracking."}]}, {"key": "paddy-135", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n|Location|Male (%)|Female (%)|All (%)|\n|---|---|---|---|\n| Poorer | 0.22 | 0.01 | 0.18 |\n| Middle | 0.54 | 0.05 | 0.46 |\n| Richer | 1.52 | 1.52 | 1.52 |\n| Richest | 40.86 | 29.79| 38.50|\n\n\n\n3. **Access to electricity for cooking.** Most households in Chad rely on wood for cooking (87.8\npercent) followed by charcoal (6.5 percent of all households). Only 2.9 percent of households use\nelectricity, LPG, or natural gas as their main fuel for cooking. About 3.1 percent of male-headed\nhouseholds rely on either of these fuels compared to 2.1 percent of female-headed households.\n\n4. **Income and productive asset ownership.** Women see their productive activities constrained due\nto high fertility and limited agency and access to resources. Chad has one of the highest fertility rates in\nthe world (5.8 births per women), which severely affects women’s capacity to participate in the labor\nmarket. 44 Women also lack agency for personal decisions; only 23 percent of women were", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000193:77:0:0", "start": 19, "end": 54, "surface": "Chad Energy Access Scale Up Project", "probe_tag": "confusion", "probe_score": 0.8981, "luna_label": 0, "luna_reason": "Project title only; it does not identify or use a data resource."}]}, {"key": "paddy-136", "text": "A key component of the project is the development of SIPs corresponding to set targets. The SIPs aim at\nguiding service providers towards providing higher service level and improve financial sustainability. The\nSIPs outline the steps that M/VCs and JSCs need to take to meet the set OBA Targets. The actions in the\nSIPs are based on specific issues that different M/VCs face to manage their SWM system, allowing\nM/VCs to address their unique challenges differently while working towards common goals for the entire\nproject area.\n\n**_Mechanism for independent output verification_**\n\nThe OBA grant will be subject to independent verification to assess the scores for each OBA Target and\nits associated indicators. The IVA will review progress annually semi-annually and evaluate\nachievements against the agreed target for the indicators identified. Each review will result in a score\nagainst which the payment is prorated assuming the minimum passing score is achieved for each\nindicator. At the JSC level, the IVA will review the MIS records to check that scores have been calculated\ncorrectly and subsequently select a sample of that data entered in the MIS to verify whether it has been\nrecorded accurately. Acceptable verification will trigger the transfer of the corresponding OBA grant to\nJSC-H&B. The scorecard will be used for both independent verification and overall project’s M&E\npurposes. Figure 4 illustrates the anticipated timeline for implementing the SIPs, and achieving the OBA\nTargets.\n\n**Figure 2 SIP implementation timeline**\n\n\n\n\n\n\n\n\n\n\n\n22", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000056:21:0:0", "start": 1030, "end": 1041, "surface": "MIS records", "probe_tag": "confusion", "probe_score": 0.2566, "luna_label": 0, "luna_reason": "Planned verification of project monitoring records, not substantive reuse of an existing dataset."}]}, {"key": "paddy-137", "text": "**The World Bank**\nUganda: Roads and Bridges in the Refugee Hosting Districts Project (P171339)\n\n\n19. **According to UDHS, distance to health facilities in Uganda was reported as a serious problem to access**\n**health facilities and this is more acute in refugee-hosting districts in the West Nile sub-region.** Mortality\nrate is still an issue in Uganda. Fertility and mortality rates, from the 2016 UDHS, forecasted that 2 percent\nof women in Uganda would die from maternal causes at existing rates. Moreover, it is documented that\nhealth care services during pregnancy and childbirth and after delivery are important for the survival and\nwell-being of both the mother and infant. This situation worsens when roads are affected by natural\nhazards. As per the UDHS, 40.9 percent of women between 35-49 years old reported that they have serious\nproblems in accessing health facilities for themselves when they are sick because of distance to health\ncare. 27 [^27: https://dhsprogram.com/pubs/pdf/FR333/FR333.pdf] This situation worsens because of natural hazards like flooding, erosion and mudslides that disrupt\nroads, cause accidents or make roads impassable, and these are expected to increase in frequency due to\nclimate change.\n\n20. **Whereas 3,503 road fatalities were reported in the year 2016, the World Health Organization (WHO)**\n**estimated over 12,000 fatalities and a fatality rate of 29 per 100,000 population, higher than the average**\n**fatality rate of 26.6 percent for Africa** **28** [^28: World Health Organization, Global Status Report on Road Safety 2018] **.** With an average rate of 27.5 deaths per 100,000 population, the\nrisk of a road traffic death is more than three times higher in low-income countries than in high-income\ncountries where the average rate", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000146:17:0:0", "start": 396, "end": 405, "surface": "2016 UDHS", "probe_tag": "keep", "probe_score": 0.9688, "luna_label": 1, "luna_reason": "UDHS data support the maternal mortality forecast."}, {"key": "refugee_pads:000146:17:0:1", "start": 762, "end": 766, "surface": "UDHS", "probe_tag": "confusion", "probe_score": 0.7604, "luna_label": 1, "luna_reason": "UDHS data support the reported 40.9 percent access difficulty finding."}]}, {"key": "paddy-138", "text": " that will allow the implementing institutions to manage\nand monitor the implementation of their programs.\n\n65. **Monitoring will mostly rely on regular monitoring based on the MIS as well as**\n**supervision in the field** . In addition, the proposed Project will support a series of specific\nactivities, including process evaluations that review the implementation of the program and\nidentify bottlenecks; and bi-yearly spot checks to assess the quality of implementation and respect\nof procedures in all _collines_ on a rolling basis. The team is evaluating the possibility to contract an\nNGO for the spot checks, GRM management, with direct report to the operational coordinator. In\naddition, an evaluation of the impact of the cash transfers with BCC activities component of the\nprogram is planned to provide for a proof of concept for Burundian policy-makers and donors.\nFinally, the implementing support unit will organize annual financial audits for the Project, annual\nreviews of progress, and a mid-term evaluation to guide the Project implementation. The mid-term\nreview will involve Project’s stakeholders and civil society in the review of performance,\nintermediary results, and outcomes.\n\n_Role of Partners_\n\n66. UNICEF will provide technical assistance for the design of the curriculum of BCC\nactivities related to health, nutrition and early childhood component based on the materials\ndeveloped for the _Alimentation and Nutrition du Jeune Enfant_, and _Pratiques Familiales_\n_Essentielles_ in West Africa. The package will include materials for the beneficiaries and for the\nfacilitators. The project will support printing and dissemination. In addition, UNICEF may\nprovide assistance in the implementation of the related promotion activities and additional ones,\nnotably on peace-building and quality control.\n\n\n71", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000157:81:1:0", "start": 177, "end": 180, "surface": "MIS", "probe_tag": "confusion", "probe_score": 0.7568, "luna_label": 0, "luna_reason": "Future project monitoring will rely on MIS; planned monitoring activity is promissory."}]}, {"key": "paddy-139", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\n\n\n\n\n\n\n\n|Col1|working on LIPW under sub- component 1.1 on behalf of beneficiary HH, of which are refugees and host communities. Refugees are defined as forcibly displaced HHs originating from a country other than South Sudan and registered as refugees in South Sudan by the UNHCR. Host communities are defined as local population groups living in counties with a high concentration of refugees.|measured at a minimum on a quarterly basis|SNSOP MIS|updated over the course of the project. Payment data will also be periodically updated in the MIS|Col6|\n|---|---|---|---|---|---|\n|Number of beneficiary households receiving Direct Income Support who have a female primary beneficiary (Number)|Total number of beneficiary households under comopnent 1.2 that have a primary beneficiary, as registered in the SNSOP MIS, who is a woman.|This indicator will be measured at least on a quarterly basis |SNSOP MIS |This data will be collected through registration and payments |Implementing Partner |\n|Number of beneficiaries receiving<br", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000153:62:0:1", "start": 643, "end": 655, "surface": "Payment data", "probe_tag": "confusion", "probe_score": 0.0503, "luna_label": 0, "luna_reason": "Payment data will be periodically updated, indicating planned future monitoring activity."}]}, {"key": "paddy-140", "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": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000013:29:1:0", "start": 862, "end": 876, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.2118, "luna_label": 1, "luna_reason": "Named NaCSA monitoring data listed as a project-report source."}, {"key": "refugee_pads:000013:29:1:1", "start": 961, "end": 969, "surface": "M&E data", "probe_tag": "confusion", "probe_score": 0.1144, "luna_label": 0, "luna_reason": "Listed as a project monitoring source, without an existing finding or analyzed use."}]}, {"key": "paddy-141", "text": "i) well designed and timely information campaign which is\nexpected to be instrumental in raising awareness among the beneficiaries; (ii) utilization\nof existing NPTP system and individualized photo identification cards will ensure that the\nproject reaches the targeted poor and avoids potential enrollment errors, and minimizes\nfraud.\n\n - **_Institutional capacity for implementation and sustainability implementation:_** To\n\nmitigate this risk, MoPH is already undertaking measures which will be further supported\nby the project as follows: (i) providing a lump sum upfront budget to PHCCs as part of\ntheir contracts to advance the implementation readiness and provide flexibility to recruit\nadditional health workers and provide training as needed; (ii) completing a facilities\nsurvey that will help to identify needs and gaps of the PHCCs to allow for a more\ntargeted capacity building activities including preparing PHCCs for contracting,\nimplementation readiness activities and helping with preparing for new staff selection;\n(iii) establishing quotas for the number of patients per facility to ensure provision of\nadequate support for optimal PHCC utilization; (iv) building consensus and sharing\nproject achievements throughout the implementation among all the stakeholders to ensure\ntheir ownership of the program in keeping the commitment of key stakeholders to sustain\nthe program; and (v) preparing and maintaining a disbursement plan which will be based\non the overall budget and the procurement plan.\n\n\n23", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000020:22:1:0", "start": 770, "end": 787, "surface": "facilities\nsurvey", "probe_tag": "confusion", "probe_score": 0.6069, "luna_label": 0, "luna_reason": "Planned survey production to identify future facility needs and gaps."}]}, {"key": "paddy-142", "text": " **whose successful**\n**achievements would greatly rely on the digital sector.** NDP III, which covers FY21–25, notes that digital\ntechnology can play a key role in catalyzing the nation’s objectives, from raising agricultural productivity to\nimproving broadband infrastructure, workforce competitiveness, and service delivery. The project will also\ncontribute to the enhancement of refugee protection in accordance with NDP III’s Governance and Security\nProgram Implementation Action Plan. This project is being designed to support the implementation of the GovNet\ninitiative, which is the government’s flagship initiative that contributes to the objectives of Digital Uganda Vision\nand the Digital Transformation Program under NDP III. The project is also directly aligned with the Digital\nTransformation for Africa initiative of the African Union, which aims to have every African individual, business,\n\n\n40 2020 UN e-Government Development Index.\n41 Third National Development Plan (NDP III) 2020/21– – 2024/25, National Development Authority\nhttps://www.fowode.org/publications/research/40-national-development-plan-3/file.html\n\n\nPage 7 of 76", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000075:19:2:0", "start": 917, "end": 955, "surface": "2020 UN e-Government Development Index", "probe_tag": "confusion", "probe_score": 0.7882, "luna_label": 0, "luna_reason": "Standalone numbered reference-list entry, not demonstrated data use."}]}, {"key": "paddy-143", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\nprotective support to HHs and investment in resilience building community assets will help sustain livelihoods, strengthen\nresilience, and prevent the most vulnerable from falling into destitution or being forcibly displaced. It will also directly\nsupport the Government’s Community Empowerment and Socioeconomic Development Strategy for Refugee Hosting\nAreas in South Sudan, with cash transfers promoting section 4.6 of the strategy on creation of livelihood and income\ngenerating opportunities given the lack of employment prospects in refugee-hosting environments.\n\n37. **In the absence of an enabling environment for widescale mobile payment systems, beneficiaries will receive**\n**physical cash at the time of payment, except for Juba where mobile money payment will be piloted.** A financial service\nprovider (i.e., paying agent), which will be competitively selected by the MAFS, will deliver cash to beneficiaries. The\nMAFS will provide the recipient list and amount of money to the financial service provider, and the list of beneficiaries\nwill be generated from the MIS. The MIS will capture beneficiaries' biometric data, which will be used to ensure that only\nthe eligible individuals will receive the cash transfer. The financial service provider pays beneficiaries verifying them\nbiometrically. In addition, implementing partners (i.e., UNOPS and NGOs contracted by MAFS to implement the project)\nand community leaders will be present and monitor the transfer process to ensure transparency and accountability.\nBased on the findings of a recently concluded analytical work, the project will pilot the use of mobile money payments\nin Juba. Mobile money payments would help strengthen transparency and safety and were assessed to be feasible in\nlarge urban center like Juba under the SSSNP. This pilot would inform potential future scale up of mobile money payments\nin urban areas.\n\n**Sub-component 1.1: Cash for Labor-Intensive Public Works and Complementary Social Measures** *", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000153:23:0:0", "start": 1169, "end": 1172, "surface": "MIS", "probe_tag": "confusion", "probe_score": 0.1415, "luna_label": 1, "luna_reason": "MIS data generates the beneficiary list for cash-transfer targeting."}]}, {"key": "paddy-144", "text": " supply service constraints faced by the municipalities in delivering\neffective and efficient services, including: (a) frequent pipe breakage and subsequent contamination of water due\nto severe corrosion of existing pipes (cast iron, asbestos cement), and high pressure fluctuations – water utility\nrecords show about 200 calls per month, with about 30 percent of these related to the main network and 60\npercent to customer connections; and (b) non-revenue water is very high at 56 percent, of which 49 percent are\nreal losses.\n\n\n33. **Osmaniye Centrum Sewerage project.** Osmaniye sewerage project aims renewal of 403 km of wastewater\ncollection network. The existing collection network was mainly constructed in 1985 and is prone to deficiencies\ndue to aging and connection types used at that time. The high groundwater level also creates problems such as\nhigh amount of infiltration to the pipes and entrance of soil granules to network, which creates additional costs\nfor operation. It is stated that approximately 100 failure calls are received from customers due to failure and/or\ncollapse of pipes.\n\n\n34. The project is expected to lead to significant efficiency improvements for the operation of water distribution\nnetwork and wastewater collection system for Osmaniye Municipality. The investments in the water distribution\nnetwork will decrease energy consumption, water losses and non-revenue water. This will also allow the\nMunicipality to meet water demand until water is received from Aslantaş dam. Also, failure calls received for both\nwater and wastewater pipelines will be decreased. The improvement in wastewater collection network is also\nexpected to reduce possible groundwater infiltration to the system thus may reduce the wastewater flow to the\nWWTP. The proposed investments for Osmaniye are summarized as follows (Table 2.6):\n\n\n**Table 2.6: Osmaniye investment components**\n**No** **Investment name** **Scope", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000125:69:1:0", "start": 285, "end": 306, "surface": "water utility\nrecords", "probe_tag": "confusion", "probe_score": 0.8112, "luna_label": 1, "luna_reason": "Utility records provide concrete call volumes and network-related shares."}]}, {"key": "paddy-145", "text": "Number of female staff provided with trainings |Number of female staff provided with trainings |\n|Share of project beneficiaries who report that the project has established effective engagement processes|The indicator will measure satisfaction of beneficiaries with the engagement processes established under the project. Engagement processes are designed to serve two objectives - ensure local demands and concerns are accounted for during the project design and improve accountability in provision of services. Overall satisfaction of beneficiaries (households in|Every citizen engageme nt channel wi ll be monitored with the appropriat e frequency. Consolidat ed informatio|Multiple sources - community mobilization company reports, PMU data, self- reported data, reports of target utilities, survey data. |Methodology for each CE channel will be reported separately as prescribed in the POM. |MEWR, KMK, PMU |\n\n\nPage 58 of 89", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000162:61:4:0", "start": 966, "end": 977, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.0626, "luna_label": 0, "luna_reason": "Survey data is listed for future monitoring rather than cited as existing evidence."}]}, {"key": "paddy-146", "text": " boxes at the _colline_ -level in the\ncare of a civil society organization, possibly a hotline at central-level. Complaints received through\nSMS, phone or boxes will be logged in the MIS. A results indicator to track the progress of the\nimplementation of the GRM system has been included in the results framework.\n\n\n**_Subcomponent 2.3: Monitoring and evaluation (US$2.8 million equivalent)_**\n\n43. **Since the project is supporting new interventions and processes in Burundi, and in**\n**order to ensure transparency, the third sub-component will support process evaluations of**\n**the key program processes and an impact evaluation including beneficiary surveys** . The\nprocess evaluations will focus on the core operational processes: targeting, payment, delivery of\ncomplementary activities. The process evaluation in the first phase communes will inform the\nexpansion in the second phase but also provide key input in the design of the operating processes.\nThe process evaluations will continue in the second phase to provide real-time information about\nscale-up and implementation in different provinces. The process evaluation will be complemented\nby regular beneficiary surveys to help map out implementation successes and issues, externalities,\nand community dynamics and contribute to the establishment of the grievance redress mechanism.\n\n44. **The impact evaluation will focus on key poverty, welfare, and human development**\n**indicators at the household and community-levels for the cash transfers** . The random\nselection at the _colline_ -level will support a randomized control trial design based on a sample of\nparticipating and non-participating _collines_ . Baseline data will be collected prior to the first\ntransfer. A mid-term data collection will take place at 24 months. To evaluate the sustainability of\nimpacts, the end-line data collection will take place six months after the end of the program\nactivities (at 42 months).\n\n\n13", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000157:23:1:0", "start": 643, "end": 662, "surface": "beneficiary surveys", "probe_tag": "confusion", "probe_score": 0.3341, "luna_label": 0, "luna_reason": "Beneficiary surveys are planned as part of the project’s future impact evaluation."}, {"key": "refugee_pads:000157:23:1:2", "start": 1677, "end": 1690, "surface": "Baseline data", "probe_tag": "drop", "probe_score": 0.0298, "luna_label": 0, "luna_reason": "Baseline data will be collected in the future as part of the project."}]}, {"key": "paddy-147", "text": "**Annex 6: Implementation Arrangements**\n\n**West Bank and Gaza: Social Safety Net Reform Project**\n\n\nImplementation arrangements have been kept as simple as possible and build on the successful\nimplementation experience gained under the Emergency projects. The Ministry of Social Affairs will\nimplement the project using the administrative structure of the existing SHC program, and will coordinate\nprogram operations with MOH, MOEHE, MOF, MOPT, PCBS and UNRWA. Chart 1 shows the main\nagencies participating in the implementation of the SSNRP. An abbreviated description of the agencies’\nroles and responsibilities i s given below. Detailed descriptions are given in Annex 6 and in the\nOperational Manual.\n\nThe M O F will be responsible for allocating the financial resources for the program (World Bank, Donors\nand PA funds) and will monitor financial aspects of the project for reporting to donors and for internal\nuse. M O H and MOEHE will provide support on beneficiary compliance with conditions and related\npolicy aspects. UNRWA will coordinate with MOSA beneficiary targeting and compliance with\nconditions. PCBS will provide assistance on poverty mapping and statistical data on income and\nexpenditure for the application o f the targeting instrument and verification. MOSA will coordinate with\nthe M O F and MOPT on the allocation of funds to the Post Banks for cashing checks issued by MOSA to\nbeneficiaries (also see Annex **7** for details).\n\nCoordination of project implementation will take place at the following two levels:\n\n\n(i) The first level of coordination would consist of an ad hoc Policy Coordination Committee with\nthe ministers or their designated representatives of MOSA, MOF, MOH, and MOEHE to discuss\nand agree on beneficiary targeting and payments policy.\n(ii) The second level of coordination will take place at the muderiats or district, where the local\nrepresentatives of the ministries of MOSA, M O H and MOEHE will coordinate data on\nbeneficiary compliance with conditions. Coordination on operational matters with UNRWA will", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000047:43:0:0", "start": 1167, "end": 1183, "surface": "statistical data", "probe_tag": "drop", "probe_score": 0.0261, "luna_label": 1, "luna_reason": "Existing statistical data informs poverty targeting and verification."}]}, {"key": "paddy-148", "text": " a v\nManufacturIng 5 7 3.7 4 7 5 0 / s \\\\t oo Services 475 272 190 201\n\nPrfvate consumpffon 90 7 81 9 938 95 1 20\nGeneral govemment consumption 7 0 9 6 146 17.2 =c ~GDFDl\nImpons of goods and servrcea 39 7 23 6 33 4 373 -3GW_G_____\n\n\n_(average_ _annual growth)_ 1981-9 1991401 2000 2001 Growth ot exports and Imports (%)\n\nAgrutumre -0 6 -2 6 2 2 3 8 oo\nIndustry 0.1 -41 51 5 6 s0\nManUnacturIng 6.9 . .\nServices -5 7 -5.4 4 0 51 \nPrivate oonsumpffon -2 0 -19 10 4 100 -50\nGeneral govemment consumption -5.1 -0 2 41.3 27 9 -100\nGross domestic Investment -06 3 0 50 - EOpois -tr-ports\nImports of goods and services -2 2 -151 85 0 61 3\n\n\nNote 2001 data are pretirrinary eastliates\n'The diamonds show four Key Indicators in the country (in bold) conipared with itS income-group average It data are missing, the diantond wiUt be rrconrrlte\n\n\n-56", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000017:60:2:0", "start": 639, "end": 648, "surface": "2001 data", "probe_tag": "drop", "probe_score": 0.0128, "luna_label": 0, "luna_reason": "Bare date-qualified data phrase lacks a named source or demonstrated data use."}]}, {"key": "paddy-149", "text": " have been satisfactorily resolved out of the total complaints received|\n|Frequency|Annual|\n|Data source|Fayda GRM system|\n|Methodology for Data Collection|Project progress report|\n|Responsibility for Data Collection|NIDP|\n|**Establishing scalable and secure Fayda ICT infrastructure**|**Establishing scalable and secure Fayda ICT infrastructure**|\n|**Proportion of up-time (over a one-year period) for the registration module of the ID system (Percentage) (Percentage)**|**Proportion of up-time (over a one-year period) for the registration module of the ID system (Percentage) (Percentage)**|\n|Description|The proportion of time that the Fayda registration module is available and working over a one- year period|\n|Frequency|Annual|\n|Data source|Fayda system KPI|\n|Methodology for Data Collection|Fayda data analytics platform|\n|Responsibility for Data Collection|NIDP|\n|**Number of penetration tests conducted to prevent cyber-attacks and loss of data (Number)**|**Number of penetration tests conducted to prevent cyber-attacks and loss of data (Number)**|\n|Description|At least 4 penetration tests conducted each year during the 5-year project duration, intended to proactively identify and address gaps in security (cyber and physical) of personal data to prevent unauthorized access to or loss of data.|\n|Frequency|Annual|\n|Data source|Test reports by specialized firms|\n|Methodology for Data Collection|Project progress report|\n|Responsibility for Data Collection|NIDP|\n|**Inclusive and sustainable ID issuance**|**Inclusive and sustainable ID issuance**|\n|**Percentage of population", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000005:40:1:0", "start": 108, "end": 124, "surface": "Fayda GRM system", "probe_tag": "drop", "probe_score": 0.0476, "luna_label": 0, "luna_reason": "Names a GRM system without showing its data informing analysis or decisions."}, {"key": "refugee_pads:000005:40:1:1", "start": 754, "end": 770, "surface": "Fayda system KPI", "probe_tag": "confusion", "probe_score": 0.2285, "luna_label": 0, "luna_reason": "Names a KPI source without showing its data used for analysis or findings."}]}, {"key": "paddy-150", "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": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000029:27:0:0", "start": 529, "end": 544, "surface": "baseline survey", "probe_tag": "drop", "probe_score": 0.0491, "luna_label": 0, "luna_reason": "Baseline survey is a future covenant, planned to generate student distribution data."}]}, {"key": "paddy-151", "text": " vaccination and subsidies will be targeted nationally so the only way to manage this risk is through the independent qualified private external auditor who will be engaged to audit the project’s accounts, quarterly and annually, according to TORs acceptable to the WB. The TOR will include a special provision for the Auditor to check Ministry of Health and Ministry of Finance beneficiary databases to validate proper receipt of services and subsidies. |**Risk Management:**The vaccination and subsidies will be targeted nationally so the only way to manage this risk is through the independent qualified private external auditor who will be engaged to audit the project’s accounts, quarterly and annually, according to TORs acceptable to the WB. The TOR will include a special provision for the Auditor to check Ministry of Health and Ministry of Finance beneficiary databases to validate proper receipt of services and subsidies. |**Risk Management:**The vaccination and subsidies will be targeted nationally so the only way to manage this risk is through the independent qualified private external auditor who will be engaged to audit the project’s accounts, quarterly and annually, according to TORs acceptable to the WB. The TOR will include a special provision for the Auditor to check Ministry of Health and Ministry of Finance beneficiary databases to validate proper receipt of services and subsidies. |**Risk Management:**The vaccination and subsidies will be targeted nationally so the only way to manage this risk is through the independent qualified private external auditor who will be engaged to audit the project’s accounts, quarterly and annually, according to TORs acceptable to the WB. The TOR will include a special provision for the Auditor to check Ministry of Health and Ministry of Finance beneficiary databases to validate proper receipt of services and subsidies.", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000064:58:1:0", "start": 380, "end": 409, "surface": "Finance beneficiary databases", "probe_tag": "drop", "probe_score": 0.0479, "luna_label": 1, "luna_reason": "Existing beneficiary databases are checked to validate receipt of services and subsidies."}]}, {"key": "paddy-152", "text": " least once from project-supported groups. The data is disaggregated by gender, youth (18-30 years) and refugee/host community status.|\n|Frequency|Quarterly|\n|Data source|Project MIS.|\n|Methodology for Data Collection|Monitoring project implementation.|\n|Responsibility for Data Collection|IA|\n|**New or improved jobs generated through the project (Number)**|**New or improved jobs generated through the project (Number)**|\n|Description|Quantitative indicator counting number of jobs created through all three main project components.|\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-supported groups trained on climate-resilient practices and technologies (Percentage)**|**Project-supported groups trained on climate-resilient practices and technologies (Percentage)**|\n|Description|Quantitative indicator counting percentage of project-supported groups under component 3 who receive capacity-building support from the project on climate-resilient business planning, value chains, market assessments, etc., and on climate-smart technologies, such as drought-resistant seeds, etc.|\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-supported groups still operational one year after project support (Percentage)**|**Project-supported groups still operational one year after project support (Percentage)**|\n|Description", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000186:47:1:0", "start": 174, "end": 185, "surface": "Project MIS", "probe_tag": "drop", "probe_score": 0.0247, "luna_label": 0, "luna_reason": "Project MIS is merely listed as a monitoring source without shown substantive data use."}, {"key": "refugee_pads:000186:47:1:1", "start": 580, "end": 620, "surface": "Project MIS and Project Progress Reports", "probe_tag": "drop", "probe_score": 0.0462, "luna_label": 0, "luna_reason": "Project monitoring reports are planned administrative reporting outputs, not substantive reused data."}]}, {"key": "paddy-153", "text": "approximately 100ha) to be developed as part\nof the urban extension of Balbala South. This extension zone will cover about 4 to 5 years\nof the prevention policy in addition to accommodating households resettled as part of\nthe slum restructuring operations. The urban development plan will help better integrate\nthe area within the city, taking into account transport, climate adaptation and disaster\nrisk reduction, as well as economic development.\n\nc) **Slum restructuring and upgrading policy.** This activity will prepare the following:\n\ni) a framework for environmental and social safeguards, as well as a resettlement policy\nspecific to the ZSP;\nii) restructuring and upgrading plans for two slums. Priority will be given to the slum\nnamed Balbala Ancien, targeted for upgrading investments as part of ISUP (see\nComponent 2). The choice of another slum will be decided during the first year of the\nproject in line with the priorities of the strategy. These plans will be developed on the\nbasis of (a) the technical standards and priorities established by the strategy, (b) a\ndiagnostic of each site, and (c) a thorough and inclusive consultation with the local\npopulation, with a special attention to some groups such as women, youth, and refugees\nand displaced populations, to better identify and assess their particular needs.\niii) the slum restructuring plans will be completed by a broader urban study for Balbala\nNorth, which will provide a clear urban plan for transport and economic development,\nincluding the identification of secondary urban centers, which will contribute to the\nintegration of slums into the urban fabric;\niv) the creation of a land information system in line with the Land Directorate instruments\nto compile the different tenure security types, and populating it with data regarding\nBalbala Ancien, and eventually, other selected urban areas. . SIF will facilitate land\n\n\nPage 43 of 65", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000015:46:1:1", "start": 1801, "end": 1830, "surface": "data regarding\nBalbala Ancien", "probe_tag": "drop", "probe_score": 0.033, "luna_label": 0, "luna_reason": "Data will populate a newly created land information system."}]}, {"key": "paddy-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_paddy", "spans": [{"key": "refugee_pads:000161:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "drop", "probe_score": 0.0402, "luna_label": 0, "luna_reason": "The sentence states that the Planning Unit generates the statistical data."}]}, {"key": "paddy-155", "text": "\n(b) Factories receive a draft of the full assessment report regarding compliance on all assessment\n\nquestions, including information on whether the issues subject to public reporting are in\nnoncompliance.\n\n\n(c) When the assessment report is finalized, the factory’s compliance with the 29 publicly\n\nreported issues is published online, on the Better Work Transparency Portal (for all factories\nthat have had at least two assessments).\n\n\n(d) In response, factories can upload documents and photos on the public reporting website\n\n(including information from assessment reports).\n\n\n(e) A factory’s compliance findings remain on the website until a new assessment report is\n\npublished, at which point the website is updated to reflect the factory’s most recent assessment\ndata.\n\n\n(f) Every time a new assessment is completed for a factory, new compliance data replaces old\ndata.\n\n\n(g) Compliance data on factories that had not yet had two assessments when public reporting was\n\nlaunched is published following a factory’s second assessment.\n\n\n27", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000137:35:1:0", "start": 759, "end": 774, "surface": "assessment\ndata", "probe_tag": "drop", "probe_score": 0.0363, "luna_label": 0, "luna_reason": "Assessment data is mentioned only as website update content, without substantive analytical use."}, {"key": "refugee_pads:000137:35:1:1", "start": 842, "end": 857, "surface": "compliance data", "probe_tag": "confusion", "probe_score": 0.1127, "luna_label": 0, "luna_reason": "Describes website data replacement, not substantive use of compliance data."}]}, {"key": "paddy-156", "text": " _Economiques et Démographiques)_ will be involved in developing the\nPMT tools and conducting household-level surveys for the targeting process\n\n\n - Transfer of payment will be done by a payment agency (micro finance and/or mobile\nphone company) hired by the project. The CFS will transfer the funds directly to the\npayment agency based on the information provided in the MIS (such as personal\nidentifying data and amount of transfer). Payments will then be made by the\nagencies to the beneficiaries in selected locations. Detailed procedures for the\npayment process will be defined at later stage of project preparation and described in\nthe POM.\n\n\n - NGOs or other entities with the required technical expertise, qualifications, and\nexperience will be in charge of the implementation of the accompanying measures.\nThe implementation modalities for the accompanying measures will be defined in a\nview to maximize coordination with existing structures and programs.\n\n\n5. **Cash-for-Work.** In N'Djamena, the CFS will hire a specialist to serve as focal point for\nthe activities of the CfW component. The registration process, payment, and accompanying\nmeasures under the CfW program will follow similar contractual arrangements as described\nabove for the CT program. In addition to those contractual arrangements, the CFS will also\npartner local NGOs and/or the local municipalities of N'Djamena for the implementation of the\nCfW activities, particularly to organize, manage, and supervise the teams of workers on the field.\n\n\n37", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000028:48:1:0", "start": 375, "end": 378, "surface": "MIS", "probe_tag": "drop", "probe_score": 0.0381, "luna_label": 1, "luna_reason": "MIS data informs payment transfers to beneficiaries."}]}, {"key": "paddy-157", "text": " 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|\n|||||||||\n|**Name:**Grievances registered related to delivery of project benefits addressed||Percentage|40.00|75.00|Bi-annual |Grievance database |PMU |\n|Description:Percentage of grievances registered related to the delivery of project benefits that were addressed|Description:Percentage of grievances registered related to the delivery of project benefits that were addressed|Description:Percentage of grievances registered related to the delivery of project benefits that were addressed|Description:Percentage of grievances registered related to the delivery of project benefits that were addressed|Description:Percentage of grievances registered related to the delivery of project benefits that were addressed|Description:Percentage of grievances registered related to the delivery of project benefits that were addressed|Description:Percentage of grievances registered related to the delivery of project benefits that were addressed|Description:Percentage of grievances registered related to the delivery of project benefits that were addressed|\n|||||||||\n|**Name:**Hospital Assessment carried out||Text|NA|Assessment completed|Once |MoPH |MoPH/PMU |\n|Description:Hospital Assessment carried out|Description:Hospital Assessment carried out|Description:Hospital Assessment carried out|Description:Hospital Assessment carried out|Description:Hospital Assessment carried out|Description:Hospital Assessment carried out|Description:Hospital Assessment carried out", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000108:45:1:0", "start": 517, "end": 535, "surface": "Grievance database", "probe_tag": "drop", "probe_score": 0.0041, "luna_label": 0, "luna_reason": "Logframe verification database indicates planned recurring grievance monitoring."}]}, {"key": "paddy-158", "text": "Chapter 4\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**Demographics of displacement**\n\n\nBased on the United States Customs and Border\nProtection arrivals figures, **87** the demographics of\nthose arriving continues to shift over time. Between\n2018 and 2020, the proportion of unaccompanied\nand separated children from El Salvador, Guatemala\nand Honduras arriving at the United States border\ndecreased slightly from 15 per cent to 11 per cent. In\n2018 and 2019, most arrivals were families. In 2020,\ndue to movement restrictions, there was a 75 per\ncent decrease in overall arrivals, notably families, with\nnearly three-quarters recorded as single adults.\n\n\nData recorded by the Mexican National Migration\nInstitute (MNMI) reflects a similar trend. Prior to 2018,\n\n\n\napproximately half of the children from El Salvador,\nGuatemala and Honduras were unaccompanied,\ncompared to 32 per cent in 2018 and 25 per cent\nin 2019. These figures indicate that people fleeing\nthese three countries were increasingly travelling\nas families. In 2020, the overall number of children\nfrom these three countries recorded in the MNMI\ndata decreased by nearly 80 per cent, primarily\ndue to movement restrictions enforced to contain\nCOVID-19, and the proportion of unaccompanied\nchildren increased to 44 per cent. The overall number\nof asylum claims in Mexico also surged from 3,400 in\n2015 to 70,400 in 2019 before dropping 41 per cent\nto 41,200 in 2020.\n\n\n\n**87** See [https://www.cbp.gov/newsroom/stats/southwest-land-border-encounters#](https://www.cbp.gov/newsroom/stats/southwest-land-border-encounters)\n\n\nUNHCR > **GLOBAL TRENDS 2020** 33", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000757:32:0:0", "start": 85, "end": 145, "surface": "United States Customs and Border\nProtection arrivals figures", "probe_tag": "keep", "probe_score": 0.9574, "luna_label": 1, "luna_reason": "Arrivals figures support analysis of shifting displacement demographics."}, {"key": "reliefweb:000757:32:0:1", "start": 1104, "end": 1113, "surface": "MNMI\ndata", "probe_tag": "keep", "probe_score": 0.9692, "luna_label": 1, "luna_reason": "MNMI data supports the reported nearly 80 percent decrease."}]}, {"key": "paddy-159", "text": "\npossess a deeper understanding of local needs. They\nsuggested a more inclusive approach to planning\nand execution to ensure a fairer and more effective\ndistribution. This concern over exclusion was\nechoed in the survey findings, where 33.34% of\nrespondents acknowledged that humanitarian\norganizations were effective in reducing the risk of\nexclusion for minority groups, while 30.48%\nindicated they were only somewhat effective, and\n21.4% felt that exclusion mitigation efforts were not\neffective at all.\n\n\nThe FGDs also highlighted issues of **fairness versus**\n**effectiveness** **in** **aid** **delivery.** While some\nparticipants felt that the aid distribution was fair in\ntheory, they pointed out disparities in practice due\nto logistical issues and favoritism, resulting in certain\nregions or communities receiving disproportionate\namounts of assistance. This sentiment aligns with\nthe survey data showing that 39.85% of\nrespondents reported being inadequately informed\nabout available aid, suggesting that aid is not\nreaching those who need it most. Additionally, key\ninformants reinforced this view, with 53.85% rating\nthe current aid distribution process as only\n“somewhat effective” and 23.08% rating it as “not\neffective at all” for marginalized groups.\n\n\n\nUNHCR Somalia / June 2025 8", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000151:7:2:1", "start": 894, "end": 905, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9712, "luna_label": 1, "luna_reason": "Survey data supports a concrete finding about inadequate aid information."}]}, {"key": "paddy-160", "text": "\nadditional 58 settlements located between 5 to 20km from the contact line\n(see map below). The latter group corresponds to 23% of the total number\nof settlements (248) located in the 5 to 20km area, and at least 11% of the\ntotal population living in the same zone 1 . Of the monitored settlements, 95\nare located in Donetsk Oblast and 61 in Luhansk Oblast.\n\n\nThe monitored settlements represent a total population of 360,700 residents,\nincluding 232,800 persons (65 per cent) living in the 98 isolated settlements\n0 to 5km from the contact line. Because the last population census in\nUkraine was conducted in 2001 and considering population movements\nand forced displacement as a result of the conflict, the demographics and\nprofile of the population presented in this report are based on reports and\ninformation gathered from local authorities and/or key informants. Therefore,\nthe information on age, gender and diversity profiles of the population living\nin the monitored settlements is limited and based on non-official estimates.\n\n\nAccording to key informants (KIs), the population living in the monitored\nsettlements include over 48,000 children (13 per cent). Although data on\nthe breakdown of age and gender of residents was not available to KIs in all\nmonitored settlements, the latest Humanitarian Needs Overview for Ukraine\nsuggest that, in the area where the protection monitoring was conducted, the\naffected population includes 37 per cent older persons, 55 per cent women\nand 15 per cent of persons with disabilities 2 [^2: Humanitarian Needs Overview, Ukraine, 2021.] . The presence of some minority\ngroups (mainly Roma, but also Greek, Tatar and German) were reported in\n13 settlements.\n\n\n1 Total population n the zone of 5-20 km from the Line of Contact is 1,098,099 residents (source: 2001 population census)\n\n\n2 [Humanitarian Needs Overview, Ukraine, 2021.](https://www.humanitarianresponse.", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001413:4:1:0", "start": 575, "end": 592, "surface": "population census", "probe_tag": "keep", "probe_score": 0.9534, "luna_label": 1, "luna_reason": "Ukraine's 2001 census supports population figures and motivates reliance on newer local estimates."}]}, {"key": "paddy-161", "text": " to sampling constraints and the\nnon-probabilistic selection of respondents, the\nresults may not fully represent the entire refugee\npopulation from Ukraine. Additionally, the choice of\nsampling locations may have introduced a bias\ntoward more vulnerable segments of the\npopulation. Variations in sampling approaches and\ndata collection periods across countries can also\naffect comparability.\n\n\n\n**Net enrolment rates.** The questionnaire of the\n2024 SEIS contained more detailed questions on\neducation of refugee household members than the\n2023 MSNA, but needs to be interpreted with care\nknowing that any data on enrolment of children\nstemming from the survey needs to be contrasted\nto net enrolment rates as calculated according to\ninternationally accepted standard methods used by\nUNESCO and the World Bank. 15\n\n\nEnrolment rates represent the ratio of refugee\nchildren and youth from Ukraine who are of school\nage and who are enrolled in a host country’s\neducation system. 16 The 2023 MSNA and SEIS data\nallows for a calculation of enrolment rates based on\nwhat households reply to the surveys with respect\nto the enrolment status of individual children and\nyouth. However, this calculation differs from the\nglobal standard method of calculating a net\nenrolment rate. The global standard mostly relies on\nadministrative data and household surveys of\nschool age population and corresponds to\nenrolment in the relevant level of education. The\n2023 MSNA and the 2024 SEIS have a slightly\ndifferent focus and gauge whether or not children\nand youth are enrolled in education in host\ncountries, and whether or not children are engaged\nin different types of remote or online learning\nwithout collecting detailed information relating the\nenrolment at different levels of education. An\nadditional limitation is that SEIS is only available for\nten countries. Compulsory school age brackets also\nvary across Europe, making comparison of\nenrolment rates complex.\n\n\n**Refugee population data.** The data used to\nestimate the child and youth refugee populations\nhas limitations related to disaggregation and\npotential double counting across", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001564:4:1:1", "start": 1005, "end": 1014, "surface": "2023 MSNA", "probe_tag": "keep", "probe_score": 0.9576, "luna_label": 1, "luna_reason": "Named survey data used to calculate refugee enrolment rates."}, {"key": "reliefweb:001564:4:1:2", "start": 1019, "end": 1028, "surface": "SEIS data", "probe_tag": "keep", "probe_score": 0.9685, "luna_label": 1, "luna_reason": "SEIS data are used to calculate refugee enrolment rates."}]}, {"key": "paddy-162", "text": "**\n\n**o grupos de personas que se han**\n**visto forzados a huir de sus casas**\n\n**o lugares de residencia habitual,**\n**especialmente como resultado de**\n\n**o con el fin de evitar un conflicto**\n**armado, situaciones de violencia**\n**generalizada, violaciones de derechos**\n**humanos o desastres naturales o**\n**provocados por la mano del hombre,**\n**y que no han traspasado frontera**\n\n\n\n**de las estadísticas de ACNUR,**\n**esta categoría de población sólo**\n**incluye a los desplazados internos**\n**por conflictos a los que la Agencia**\n**extiende su protección y/o asistencia.**\n**La población desplazada también**\n**incluye a personas en situación**\n\n\n**Refugiados retornados son ex**\n**refugiados que han regresado a su**\n**país de origen espontáneamente**\n\n**o de manera organizada pero que**\n**no se han integrado plenamente**\n**todavía. Este retorno sólo tendría**\n**lugar normalmente en condiciones**\n**de seguridad y dignidad. A efectos**\n**de este informe, se incluyen**\n**exclusivamente los refugiados que**\n**retornaron entre enero y diciembre**\n**de 2012. Sin embargo, en la práctica,", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001451:36:1:0", "start": 398, "end": 419, "surface": "estadísticas de ACNUR", "probe_tag": "keep", "probe_score": 0.9226, "luna_label": 1, "luna_reason": "UNHCR statistics support the stated population-category definition."}]}, {"key": "paddy-163", "text": "### Chapter 9 **UNICEF Pulse Check**\n\n\n##### \n\nAUDIENCE:\nAll staff\n\n\n##### \n\nTIME:\n1-2 minutes, on a\nquarterly basis\n\n\n##### \n\nOBJECTIVE:\nTo measure the impact of work on\norganizational and workplace culture\nin UNICEF offices.\n\n\n##### \n\nMETHOD:\nOnline\nquestionnaire\n\n\n\nAs many organizations work on defining and reshaping their institutional culture to fit the values they uphold in\ncombatting sexual exploitation and abuse and sexual harassment, one area that remains difficult is measuring the\nimpact of efforts around organizational culture change. UNICEF has taken up this challenge by developing a tool\nto assist in regularly tracking progress made and the impact resulting from the organization’s many  initiatives on\nculture change among its staff.\n\n\nThe Pulse Check, which was piloted in seven offices in March, is intended to complement the statistically\nrepresentative data on workplace culture collected through UNICEF’s Global Staff Survey and Pulse Survey—\nbiennial surveys that collect data from all employees on a wide range of issues such as personal empowerment,\ndiversity and inclusion, career development, work-life balance, and job satisfaction and motivation. In contrast, the\nnew Pulse Check will aim to provide more frequent real-time data on the progress made and shifts in workplace\nculture across UNICEF teams and offices. In addition to providing a gauge on where each UNICEF office stands\non progress in specific aspects of their workplace culture, the Pulse Check will also serve as a useful management\ntool, giving managers insights into the impact of shifts in management decisions and workplace behaviours on staff,\nteams, and offices, allowing for necessary actions to be taken faster to address any concerns coming to light.\n\n\nThe Pulse Check will be part of an accountability tool for managers and Heads of Offices, called Office Scorecards,\nwhich include several KPIs for all offices. It is intended to provide a transparent indication of", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000896:24:0:2", "start": 968, "end": 980, "surface": "Pulse Survey", "probe_tag": "keep", "probe_score": 0.9058, "luna_label": 1, "luna_reason": "Named existing survey declared as source of representative workplace-culture data."}]}, {"key": "paddy-164", "text": ", Türkiye se encuentra en Europa, mientras que Siria, en el Medio Oriente y Norte de**\n**África. Hay datos disponibles desde 1961, si bien es sabido que los datos anteriores a 1975 están incompletos.**\n**44** **[Ver el Pacto Mundial sobre los Refugiados, ACNUR.](https://www.acnur.org/pacto-mundial-sobre-los-refugiados)**\n\n\nACNUR > **TENDENCIAS GLOBALES 2022** 11", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001047:10:3:0", "start": 101, "end": 129, "surface": "datos disponibles desde 1961", "probe_tag": "confusion", "probe_score": 0.4856, "luna_label": 0, "luna_reason": "Only states data availability; no analyzed finding or substantive data use."}, {"key": "reliefweb:001047:10:3:1", "start": 157, "end": 180, "surface": "datos anteriores a 1975", "probe_tag": "confusion", "probe_score": 0.5366, "luna_label": 0, "luna_reason": "Availability statement notes incomplete historical data without analyzed finding or substitute use."}]}, {"key": "paddy-165", "text": "**Education, Economic Opportunities, and Health**\n\n\nUNHCR’s AGD Policy requires **that women and girls have equal access to economic**\n\n**opportunities, including decent work, quality education and health services.**\n\n\n\n\n\n\n\n**~~Overview~~**\n\n\nUNHCR’s efforts to systematically integrate gender\nequality across the agency are having results in areas\nsuch as livelihoods, health and education. For example,\nUNHCR uses age- and sex-disaggregated health data\nto identify and analyse disparities in health care access,\nutilization and health outcomes to inform gendersensitive responses. Access to sexual and reproductive\nhealth services for women and girls was maintained\nduring the COVID-19 pandemic. The [Accelerated](https://www.unhcr.org/accelerated-education-working-group.html)\n[Education Working Group](https://www.unhcr.org/accelerated-education-working-group.html) 8 at the global level, which\nis led by UNHCR, completed an evidence review in\n2020 gathering knowledge on gender-transformative\napproaches to successfully deconstruct constraints\nimposed on female learners, and changing knowledge,\nattitudes and practices on the education of adolescent\ngirls, to enable their integration in educational\nprogrammes. Nonetheless, COVID-19 further increased\ninequalities to the detriment of women and girls. For\nexample, despite UNHCR’s efforts towards genderequal engagement in remote primary learning, many\ngirls were excluded from this and were at greater risk\nof falling behind. UNHCR responded by maintaining\nintense efforts in these areas.\n\n\n\n**~~Status of UNHCR’s Progress and Practices~~**\n\n\nOperations made sure to **understand the situations**\n**of diverse women and girls** for their analysis,\nprogramming and advocacy. In particular, UNHCR\nthrough advocacy and capacity", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000271:47:0:0", "start": 416, "end": 454, "surface": "age- and sex-disaggregated health data", "probe_tag": "confusion", "probe_score": 0.3162, "luna_label": 1, "luna_reason": "Health data are analyzed to identify disparities in access, utilization, and outcomes."}]}, {"key": "paddy-166", "text": " Government’s encampment policy and resulting lack of free movement, along\nwith resource concerns, delays in status determination, and limited options for engaging in the labour market\n(and therefore pay into contributory systems).\n\n\nPresently, refugees are not included in the Kenya National Safety Net Programme, and support to those\nin need continues to be delivered through parallel systems and aligned assistance programmes. For\ninstance, UNHCR is working with the State Department for Social Security and Protection on a small pilot\ncash transfer programme targeting 70+ refugees in urban areas, which is aligned to the Government’s Inua\nJamii 70+ programme.\n\n\nForums for social protection and humanitarian activities still remain fairly separate. However, the Cash\nWorking Group is working to address this at national level. The group is chaired by the Kenya Red Cross\nand the National Drought Management Authority, and the State Department of Social Protection attends\nfrom time to time.\n\n\nAs part of the drought response, efforts have been made to encourage partners to utilize the data in the\nEnhanced Single Registry (ESR), which includes social registry data for several drought affected counties,\nthough the refugee population is not yet included in the system. The collaborative work with the State\nDepartment for Social Security and Protection and relevant line ministries is key to the inclusion of refugees\nin national social protection programmes. The Government has agreed to the inclusion of refugees in the\nESR, which is a gateway for eligible refugees to access a range of national social protection programmes.\nA roadmap for inclusion has been agreed between the Government and UNHCR, though significant\ninvestment will be required to support the process.\n\n\nThe Sector Group for Social Protection is a key coordination forum for development partners and\ngovernment counterparts that engage in policy- and programming-related discussions. The group includes\nthe Government of Kenya, the World Bank, the UK’s Foreign, Commonwealth and Development Office,\n\n\n18 R E F U G E E P O L I C Y R E V I E W F R A M E W", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001582:17:1:1", "start": 1150, "end": 1170, "surface": "social registry data", "probe_tag": "confusion", "probe_score": 0.208, "luna_label": 1, "luna_reason": "Registry data are identified as used for drought-response social protection efforts."}]}, {"key": "paddy-167", "text": " una oportunidad para el desarrollo\n\n\ninformación detallada sobre las\ncaracterísticas de la vivienda y\nlos miembros del hogar. Se utiliza\npara monitorear la evolución de los\nindicadores de pobreza, bienestar\ny condiciones de vida en Perú. La\nENAHO es representativa a nivel\nnacional, urbano, rural y regional,\nasí como de las regiones naturales\n(costa, sierra y selva). Debido a\nla pandemia del COVID-19, las\nencuestas se realizaron de manera\npresencial y telefónica en el 2020 y\n2021. El presente informe limitó las\ncomunidades de acogida a las zonas\nurbanas, donde reside la mayoría de\nlos venezolanos (solo en Lima reside\nel 75 por ciento de los venezolanos\nen Perú según la ENPOVE 2022). El\nresultado es una muestra restringida\nde 53,711 individuos. La encuesta de\nnacionales se realizó varios meses\nantes que la encuesta de venezolanos\n(2021 versus inicios del 2022), lo\nque puede derivar en condiciones\neconómicas más favorables para los\nmigrantes debido al mejoramiento de\nlas condiciones económicas.", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001322:55:3:0", "start": 678, "end": 689, "surface": "ENPOVE 2022", "probe_tag": "keep", "probe_score": 0.9887, "luna_label": 1, "luna_reason": "Named 2022 survey supports the reported Venezuelan population concentration."}, {"key": "reliefweb:001322:55:3:2", "start": 817, "end": 840, "surface": "encuesta de venezolanos", "probe_tag": "confusion", "probe_score": 0.7609, "luna_label": 1, "luna_reason": "Existing Venezuelan survey is compared by timing to explain differing economic conditions."}]}, {"key": "paddy-168", "text": " Mediterranean Sea from\nLibya to Europe, a decrease of 10% compared to 2022, but still far below the numbers of annual crossings between\n2014 and 2017. Among those who attempted the journey, 73% were disembarked in Italy, 1.6% in Tunisia, 0.5% in Malta,\nand 1.1% in Greece,⁹ while 24% were intercepted or rescued and disembarked in Libya. According to data available to\n\n\nIOM and UNHCR I JOINT ANNUAL OVERVIEW FOR 2023 3", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000246:2:2:0", "start": 352, "end": 385, "surface": "data available to\n\n\nIOM and UNHCR", "probe_tag": "confusion", "probe_score": 0.3471, "luna_label": 1, "luna_reason": "Available data from IOM and UNHCR supports the reported crossing statistics."}]}, {"key": "paddy-169", "text": "Appendix A Sources of Data, by Country\n\n\n56\n\n\nurban households, where almost\nall Venezuelans live. The sample\nincludes 447,888 Colombians and\nthe survey conducted a few months\nbefore the migrant survey (2022\nrather than 2021).\n\n\nEcuador\n\n\n**The analysis for Ecuador is based**\n**on the fourth round of the HFPS**\n**conducted** **in** **2022** **and** **the**\n**2019 Human Mobility and Host**\n**Communities** **Survey** **or** **EPEC** .\nThe fourth round of the HFPS was\na joint effort by the World Bank\nand UNDP to measure the impact\nof the COVID-19 pandemic on\nhouseholds in the LAC region. It\nalso gathered data on Venezuelan\nhouseholds to assess whether the\npandemic had a differential effect\non migrants. The phone survey is\nrepresentative of Venezuelans in\nEcuador and Ecuadorians 18 and\nolder who had an active cellphone\nnumber or a landline at home. Given\nthat most phone companies require\na national ID, it is likely that the\nsample underrepresents irregular\nmigrants. However, over 54 percent\nof respondents reported an irregular\nstatus. The unweighted sample\nincluded 503 Ecuadorians and 356\nVenezuelans living in Ecuador.\n\n\n**Most indicators come from the**\n**HFPS, some come from the EPEC** .\nThe 2019 EPEC—a joint effort\nby the World Bank and six other\norganizations (UNICEF, UNHCR, IOM,\nUNDP, the World Food Programme,\nand the United Nations Population\nFund)—was a face-to-face survey\nthat collected information on human\nmobility to evaluate the impact of\nVenezuelan migrants and refugees in\nEcuador. It is nationally representative\nand covers Venezuelan migrants and\nrefugees who entered the Ecuador\nsince 2016 and registered at a\n\n\n\ncheckpoint upon entry. For this report,\nmigrants and individuals in host\ncommunities are defined based on\ntheir place", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001190:55:0:1", "start": 306, "end": 310, "surface": "HFPS", "probe_tag": "confusion", "probe_score": 0.5268, "luna_label": 1, "luna_reason": "Analysis is based on the fourth-round HFPS survey data."}]}, {"key": "paddy-170", "text": " 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. 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. ", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001058:17:3:0", "start": 323, "end": 335, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.4452, "luna_label": 0, "luna_reason": "Generic data phrase appears within a table description."}]}, {"key": "paddy-171", "text": " explained that the dispersion\nof people of concern throughout the country at times\nmakes persons with disabilities difficult to be identified\nin remote locations, which might explain the low rate of\ndisability (0.55%).\n\n\n46 UNHCR, Global Focus UNHCR Operations Worldwide, data retrieved Jan\n2019, http://reporting.unhcr.org/node/28\n47 In Turkey, the government of Turkey maintains the registration data of the\npeople of concern.\n48 “Research has shown that if men and women received similar nutrition, medical\nattention, and general health care, women would live longer than men This is\nbecause women, on a whole, are more resistant to diseases and less prone to\ndebilitating genetic conditions” cited in https://en.wikipedia.org/wiki/Gender_\ndisparities_in_health\n\n\n\n\n- The rates of identification of mental illness are very\nlow in all locations, and likely due to the challenges in\ndefinitions and stigma associated with disclosure.\n\n\n- The number of males aged between 18-59 years with\na disability code is double that of females. This may be a\nresult of conflict acquired injuries and due to increased\nisolation of women with disabilities as well as higher\nrates of stigma that they experience. Whilst it is well\nrecognised that men have poorer health outcomes than\nwomen (in situations with equal access to health care) 48,\nthis does not account for the significant disparity, and\nthus, it is likely that a larger proportion are due to conflict\nacquired injuries and isolation and stigma experienced by\nwomen with disabilities.\n\n\n15", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001114:16:1:0", "start": 386, "end": 428, "surface": "registration data of the\npeople of concern", "probe_tag": "confusion", "probe_score": 0.8267, "luna_label": 0, "luna_reason": "Names maintained registration data without showing its use for analysis or decisions."}]}, {"key": "paddy-172", "text": " care for a family\nmember who became sick with the AIDS\nvirus.\n\n2) Would buy fresh vegetables from a vendor\nwhom they knew was HIV�/.\n\n3) Female teacher who is HIV�/ but not sick\nshould be allowed to continue teaching in\nschool.\n\n4) Would not want to keep the HIV�/ status\nof a family member a secret.\n\nPractices\n\n\nCondom use 1) Percent of men and women (aged 15 �24)\nwho used a condom at last sex with a\nnon-marital, non-cohabiting partner, of\nthose who have had sex with a non-marital,\nnon-cohabiting partner in the last 12\nmonths. bc\n\n\n\nPEPFAR\n\n\nPEPFAR\n\n\nPEPFAR\n\n\nPEPFAR\n\n\nUNGASS, MDG, PEPFAR\n\n\n\na prior to the UNGASS indicators in 2002, the UNAIDS stated indicators did not specify youth\n(15 �24 years).\nb the MDG indicator replaces ‘have’ with ‘transmit’.\nc the MDG indicators do not specify ‘non-marital, non-cohabiting’ but add ‘high risk’. PEFPAR\nuses 15 �49 years.\n\n\nwith the surrounding host population response and a sub-regional approach\nundertaken in order to take into account the displacement cycle (UNHCR 2005).\n\nTimely and accurate data are needed to provide targeted and effective\ninterventions in conflict and post-conflict settings. Unfortunately, due to unstable", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001584:2:1:0", "start": 645, "end": 662, "surface": "UNGASS indicators", "probe_tag": "confusion", "probe_score": 0.8981, "luna_label": 1, "luna_reason": "Named indicator series cited to explain its 2002 youth-specification change."}]}, {"key": "paddy-173", "text": "The Costs of Fuelling Humanitarian Aid\n\n\n\nSustainable\nUN and UN\nEnvironment\nManagement\nGroup (EMG)\n\n\nUN High\nCommissioner\nfor Refugees\n(UNHCR)\n\n\nUnited Nations\nChildren’s Fund\n(UNICEF)\n\n\nWorld Food\nProgramme\n(WFP)\n\n\nWorld Vision\n(UK)\n\n\n\nSustainable United Nations (SUN) came into existence as a result of the UN’s\ncommitment to climate neutrality in 2007. It has been working to measure and reduce\nthe environmental footprint of UN facilities and operations. UN entities are attempting\nto measure and report climate and environmental indicators, to undertake efforts to\nholistically reduce environmental impacts, and to achieve climate neutrality by 2020.\nProgress towards this is captured in the Greening the Blue vii platform, which presents\nannual greenhouse gas emissions data reported by all UN agencies.\n\n\nUNHCR published Environmental Guidelines in 1996 that ‘lay a basis for incorporating\nenvironmental factors into specific UNHCR guidelines’. viii\n\n\nUNHCR began implementing a Green Procurement Policy in 2012, which built upon\nearlier efforts in the Environmentally Friendlier Procurement Guidelines (1997). ix This policy\ntries to make sure that social and environmental factors are combined with financial\nconsiderations when UNHCR is making purchases.\n\n\nIn 2015 UNHCR produced the UNHCR, the Environment & Climate Change x report, which\noutlined the challenges that climate change presents for its operations and the measures\ntaken in response.\n\n\nUNICEF’s Strategy on Environmental Sustainability 2016–2017 xi aims to consolidate work\non environmental sustainability across its operating practices. Priorities include improving\nguidance on environmental sustainability and incorporating environmental sustainability\nmanagement into the organization.\n\n\nWFP’s approach to environmental sustainability was first laid out in WFP and the\nEnvironment in 1998. It was one of the first UN agencies to report greenhouse gas\nemissions, in 2008, and it has done so annually since then. It also", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000796:15:0:0", "start": 697, "end": 714, "surface": "Greening the Blue", "probe_tag": "confusion", "probe_score": 0.1708, "luna_label": 1, "luna_reason": "Named platform presents annual greenhouse-gas emissions data used to capture progress."}, {"key": "reliefweb:000796:15:0:1", "start": 755, "end": 791, "surface": "annual greenhouse gas emissions data", "probe_tag": "confusion", "probe_score": 0.362, "luna_label": 1, "luna_reason": "Platform presents existing annual emissions data reported by UN agencies."}]}, {"key": "paddy-174", "text": "_Mixed Migration Routes and Dynamics in Libya in 2018, June 2019_ **10**\n\n#### **Methods Note**\n\nThis report is based on a longitudinal analysis of six research cycles on mixed migration routes and dynamics\nconducted in Libya over the course of 2018. These included:\n\n- [Mixed migration routes and dynamics in Libya: The impact of EU migration measures on mixed migration in](http://bit.ly/2GGJvCf)\n[Libya, April 2018;](http://bit.ly/2GGJvCf)\n\n- [Access to cash and the impact of the liquidity crisis on refugees and migrants in Libya, June 2018;](http://bit.ly/2NU4p8H)\n\n- [Refugees’ and migrants’ access to food, shelter and NFIs, WASH and assistance, September 2018;](http://bit.ly/2N1tEmt)\n\n- [Mixed migration routes and dynamics: May – December 2018, December 2018;](http://bit.ly/2SeR2OC)\n\n- [From hand to hand: the migratory experience of refugees and migrants from East Africa across Libya, April](http://bit.ly/2Fn6jJp)\n2019;\n\n- Libyan refugees’ and asylum seekers’ irregular boat migration to Europe in 2018, July 2019.\n\nThe research thematics explored were selected on a rolling basis with emerging information needs identified in\ncollaboration with humanitarian partners over the course of 2018. Topics were selected on the basis of available\nsecondary data and identified information gaps", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000576:9:0:0", "start": 1304, "end": 1318, "surface": "secondary data", "probe_tag": "confusion", "probe_score": 0.6317, "luna_label": 1, "luna_reason": "Existing secondary data informed selection of research topics."}]}, {"key": "paddy-175", "text": " Data for 2008-2010 includes repeat applications.\nSpain. Includes applications lodged at Spanish embassies.\nSwitzerland. Figures exclude repeat applications.\nTurkey. Source: UNHCR. \nUnited States.\nStates. Figures include (1) statistics from the US Department of Homeland \nSecurity (DHS), based on the number of cases and multiplied by 1.4 to reflect the \nestimated number of individuals; and (2) the number of new (“defensive”) requests \nlodged with the Department of Justice, Executive Office for Immigration Review \n(EOIR), based on the number of individuals.\nb. Regional classification\nEU-“old” (15). Austria, Belgium, Denmark, Finland, France, Germany, \nGreece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden \nand United Kingdom.\nEU-“new” (12). Bulgaria, Cyprus, Czech Republic, Estonia, Hungary, Latvia, \nLithuania, Malta, Poland, Romania, Slovakia and Slovenia.\nEU-Total (27). EU-“old” and EU-“new”.\nNordic countries (5). Denmark, Finland, Iceland, Norway and Sweden.\nWestern Europe (19). EU-“old” plus Iceland, Norway, Liechtenstein and Switzerland.\nSouthern Europe (8). Albania, Cyprus, Greece, Italy, Malta, Portugal, Spain \nand Turkey.\nFormer Yugoslavia (6). Bosnia and Herzegovina, Croatia, Montenegro, Serbia \n(and Kosovo: S/RES/1244 (1999)), Slovenia, and The former Yugoslav Republic \nof Macedonia.\nTotal Europe (38). All European countries listed.\nSource for national population: United Nations, Population Division, \n“World Population Prospects: The 2010 Revision”, New York, 2011.\nSource for Gross Domestic Product (PPP): International Monetary Fund, \nWorld Economic Outlook Database, October 2012 (accessed 5 March 2013).\nNotes\n21\nAsylum Levels and Trends in Industrialized Countries - 2012 21", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000259:20:1:1", "start": 1460, "end": 1486, "surface": "World Population Prospects", "probe_tag": "confusion", "probe_score": 0.0792, "luna_label": 1, "luna_reason": "Named UN population dataset cited as the source for national population figures."}]}, {"key": "paddy-176", "text": "In Sudan, the number of IDPs**\n**protected or assisted by UNHCR**\n\n\n\n**Libya |**\n**Displacement in**\n**Libya:** Misrata,\nBenghazi and Tobru…\n\n\n\n**20** UNHCR Global Trends 2011 **UNHCR Global Trends 2011** **21**", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001077:10:3:0", "start": 151, "end": 175, "surface": "UNHCR Global Trends 2011", "probe_tag": "confusion", "probe_score": 0.0592, "luna_label": 0, "luna_reason": "Standalone report title with no shown data use or cited finding."}]}, {"key": "paddy-177", "text": " 1,794 11,202 756 - 4 4,768 15,095 80 85 1,079 188 46 1,993 - 4,820 3,140 1 1,177 2,737 298 42,426 10,552 455,863|\n\n\n\n**52** UNHCR Global Trends 2014", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000809:51:4:0", "start": 194, "end": 218, "surface": "UNHCR Global Trends 2014", "probe_tag": "confusion", "probe_score": 0.1717, "luna_label": 1, "luna_reason": "Named report cited alongside table data as the underlying evidence source."}]}, {"key": "paddy-178", "text": "The same study stated that in Saida, the main source of drinking water (95%) and water for domestic use\n(98%) is city water. However, 91% reported obstacles to safe water collection due to the high dependence on\ncity water and the unreliability of the system (regular shutdowns). (World Vision, January 2013)\n\n_Mount Lebanon and Beirut_\n\nAccording to one survey covering Chouf, Aley, and Baabda, 59% of registered Syrian refugee respondents\nreport no access to public water, or a supply of less than three times per week. The same report stated that\nUNHCR reports that 75% have access to water from a well or from the public water system. However, nearly\n90% of all survey respondents purchase water.(Global Communities, November 2013)\n\nAnother assessment of registered Syrian refugees specified that in Chouf, among the 240 households\ninterviewed, 38% are not satisfied with the quantity of water received. In this assessment, however, only 37%\nof the interviewed households stated that they have to buy water from trucks or purchase bottled water, mainly\nbecause they do not receive enough water through the public water systems (local boreholes and Barouk\nspring sources), adding an important burden on families with very limited cash resources. Those buying water\nfrom trucks spend an average of LBP 60,000 per month. Families staying in rented houses have to pay a\nyearly fee of LBP 235,000 (approx. LBP 90,000 per month) to be connected to the public water network.\nAround 69% of the interviewed families live in a rented house in the assessed areas. (CARE InternationalDPNA-ACA, October 2013)\n\nThe same assessment found that a full quarter (25%) of the families living in the collective centres and/or\ninformal settlements face a storage problem. Families staying in rented houses can be considered to have a\nsufficient storage capacity (they use mainly 1000-2000 liter PVC", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000826:19:0:0", "start": 745, "end": 785, "surface": "assessment of registered Syrian refugees", "probe_tag": "confusion", "probe_score": 0.8985, "luna_label": 1, "luna_reason": "Assessment reports concrete household water-access and expenditure findings."}]}, {"key": "paddy-179", "text": "Chapter 6\n\n\nFigure 22 | **Number of reported stateless people by region** | end-2024\n\n\nAsia and the Pacific\n\n\nWest and\nCentral Africa\n\n\nEurope\n\n\nMiddle East and\n\nNorth Africa\n\n\n\nEast and Horn of Africa,\n\nand Great Lakes\n\n\nSouthern Africa\n\n\nAmericas\n\n\n\n0 500K 1.0M 1.5M 2.0M 2.5M\n\nNumber of people\n\n\n\nBased on the available age- and sex-disaggregated\ndemographic data for 76 per cent of the 4.4 million\nstateless people, **188** children account for 44 per cent\nof stateless people, women for 30 per cent and men\nfor 26 per cent.\n\n\n**Where does data on stateless**\n**people come from?**\n\n\nThe above figures were derived from data for 101\ncountries on known stateless populations reported to\nUNHCR in 2024, six more than the previous year. **189**\nJust under half of all countries do not report any data\non statelessness to UNHCR, including several with\nsignificant known stateless populations. In addition,\nsome countries provide data for only a portion of the\nstateless population within their borders. As a result,\nthe true global number of stateless people is likely\nmuch higher.\n\n\nFor those countries that have reported stateless\npopulations, in some cases, governments report\n\n\n\nfigures to UNHCR based on administrative population\nregisters, while asylum processing systems may\nidentify stateless individuals among asylum-seekers\nand refugees hosted in the country. However, in many\ncountries, administrative data on statelessness is\nunavailable and information must instead be drawn\nfrom population censuses, surveys or estimates,\nprovided by governments or UNHCR and its partners.\n\n\nIt is crucial that, when using quantitative and\nqualitative studies to estimate stateless populations,\nefforts are made to distinguish between those\nunder UNHCR’s statelessness mandate and\nindividuals who lack valid nationality documentation\nbut do not face structural barriers—such as\ndiscriminatory nationality laws or practices—to\nacquiring citizenship or", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001242:57:0:0", "start": 323, "end": 366, "surface": "age- and sex-disaggregated\ndemographic data", "probe_tag": "confusion", "probe_score": 0.8724, "luna_label": 1, "luna_reason": "Existing demographic data supports reported sex and age composition findings."}, {"key": "reliefweb:001242:57:0:3", "start": 1515, "end": 1534, "surface": "population censuses", "probe_tag": "confusion", "probe_score": 0.8766, "luna_label": 1, "luna_reason": "Censuses are identified as sources for estimating stateless populations."}]}, {"key": "paddy-180", "text": " citizen of Ukraine\nduring the month therefrom. If a person did not\ninsert the photo within the period specified by\nlaw, the passport is considered invalid. 7,8 In such a\ncase, as well as in the case of damage or loss the\npassport of a citizen of Ukraine of the sample of\n1994, issued before 2014 in the territory which\nis currently non-government controlled, for the\npurpose of further documentation of a person,\nthe territorial unit of the SMS must identify them\nand confirm his/her belonging to the citizenship\nof Ukraine.\n\n\n**IDENTIFICATION OF THE PERSON**\n\n\nUsually the person is identified on the basis of\ninformation obtained from the database of the\nUnified State Demographic Register (USDR) and\nother state and unified registers, information\ndatabases. 9 However, these registries may not\nhave information about all the persons. 10 Prior to\n\n\n\n7. Paragraph 8 of the Regulation on the Passport of a Citizen of Ukraine. Available at the link: https://zakon.rada.gov.ua/laws/show/2503-12\n8. According to the explanations posted on the official website of the State Migration Service, the person cannot be identified using the passport of a citizen of Ukraine\nof 1994 sample (booklet), since this document is “morally outdated” and also the one that can be easily forged. In particular, according to the SMS explanations, when\na person applies for documentation, starting from 2013, the data of the passport is verified with the data of the SMS unit which issued the passport. In fact, for persons\nfrom the territory temporarily not under control of the Government of Ukraine this means that even with a valid passport of a citizen of Ukraine, an individual may be\nforced to undergo the procedure for establishment the person and verification of", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001442:6:1:1", "start": 1480, "end": 1500, "surface": "data of the SMS unit", "probe_tag": "confusion", "probe_score": 0.0673, "luna_label": 1, "luna_reason": "Existing SMS administrative data are used to verify passport information."}]}, {"key": "paddy-181", "text": "10 de 11 de 2019). Requisitos para residencia de inversionista.\n\nObtenido de Dirección General de Migración: www.migracion.gob.do/Menu/SubList/44\n\n\nX **X** Oficina Nacional de Estadística- ONE (2017) Segunda Encuesta Nacional De Inmigrantes –ENI-2017–\n\nRepública Dominicana\n\n\nX **X** Oficina Nacional de Estadística- ONE (2019) Panorama Estadístico Migración Laobral hacia República\n\nDominicana: evolución ocupacional del origen al destino\n\n\nX **X** Organización Internacional del Trabajo (2019). Formalización de trabajadores independientes en\n\nParaguay.\n\n\nX **X** Organización Internacional del Trabajo (2018) Cabrera Donna. Estudio sobre un marco comparativo\n\nde experiencias y casos de buenas prácticas regionales y extra regionales sobre el vínculo entre\nnecesidades del mercado laboral y política migratoria.\n\n\nX **X** Organización Internacional del Trabajo. (2011). Desarrollo de Cadenas de Valor para trabajo decente.\n\nGinebra, Suiza: Organización Internacional del Trabajo. Recuperado el 2019", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001066:80:1:1", "start": 249, "end": 257, "surface": "ENI-2017", "probe_tag": "confusion", "probe_score": 0.2849, "luna_label": 0, "luna_reason": "Bibliographic entry naming a survey without showing data use."}]}, {"key": "paddy-182", "text": " transparency – maximizing the availability of disaggregated data – confdentiality, and privacy to ensure personal data is not abused, misused, or putting anyone at risk of identifcation or discrimination, in accordance with national laws and the Fundamental Principles of Ofcial Statistics.|\n|5|Human and technical capacity to collect, analyse, and use disaggregated data must be improved, including through adequate and sustainable fnancing|We recognize that collecting and analysing disaggregated data requires the development of specifc skills. We recognize the need to fnance data collection, analysis, and use appropriately and sustainably so that high-quality data can be collected and used by governments as well as by businesses, civil society, and citizens.|\n\n\n\n_Source:_ Global Partnership for Sustainable Development Data, 2022\n\n\nOne clear pathway for increasing the availability of data\non refugees is intentionally including them in existing\nDCEs rather than conducting additional ad hoc DCEs\nor having them included in an unrepresentative way in\nlarger DCEs covering their area of residence. Historically,\nthe use of proxies in capturing refugee status has been\ncommon practice in research on refugees. Assessing\nthe research landscape on economic integration,\nDonato and Ferris (2020, p. 24) state that ‘most studies\ndifferentiate refugees from other immigrants without\nobserving refugee status directly. Instead, they use\nnational origin to approximate refugee status or create\nsynthetic cohorts’. Similar methods have been used\nwith OECD’s Programme for International Student\nAssessment (PISA) data to distinguish between the\nperformance of ‘natives’ and ‘migrants’ (Behr and\nFugger, 2020), and nationality is also used in many\nEMIS as a proxy for refugee status (UNESCO, 2023;\nUIS and UNHCR,", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000382:17:1:0", "start": 50, "end": 68, "surface": "disaggregated data", "probe_tag": "drop", "probe_score": 0.0224, "luna_label": 0, "luna_reason": "Generic data availability principle, without cited use, finding, or analysis."}]}, {"key": "paddy-183", "text": " hecha las personas refugiadas y solicitantes\nde asilo en Ciudad de Panamá destacaron un alto nivel de\ndiscriminación que se manifiesta en diferentes contextos\ncomo las escuelas, los centros de salud y los centros de policía,\nentre otros.\n\n\nEn general, los prejuicios relacionados con la nacionalidad\ncolombiana hacen que muchos refugiados sufran una doble\nestigmatización no solo por ser refugiados sino también por ser\ncolombianos y ser tildados como narcotraficantes, guerrilleros,\n\n\n\n20 Diagnóstico Participativo 2014-2015 Diagnóstico Participativo 2014-2015 21", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000280:9:5:1", "start": 491, "end": 526, "surface": "Diagnóstico Participativo 2014-2015", "probe_tag": "drop", "probe_score": 0.0491, "luna_label": 0, "luna_reason": "Repeated page header, not a cited or analyzed data resource."}]}, {"key": "paddy-184", "text": "Photo Credit: © UNHCR\nMAIN OUTCOMES OF CASH \nASSISTANCE IN 2021 \nFindings from Post Distribution Monitoring", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001217:0:0:0", "start": 79, "end": 107, "surface": "Post Distribution Monitoring", "probe_tag": "drop", "probe_score": 0.0462, "luna_label": 0, "luna_reason": "Generic monitoring label lacks a concrete attributed finding or demonstrated data use."}]}, {"key": "paddy-185", "text": "\nThe assessment used both quantitative and qualitative methods. The quantitative method used a\nrepresentative sample survey of households, schools and students. The qualitative method used Focus\nGroup Discussions (FGD) and Key Informant Interviews (KII). The assessment used random and\npurposive sampling. Random sampling was used in the survey to select households and students.\nPurposive sampling was used for selection of schools, and for the qualitative methods of the\nassessment. Sampling was done at three levels: camp, community/household and school. A total of\n1058 individuals; 548 females and 410 males, participated in the assessment, representing 451\nhouseholds, 476 students from 18 schools. The field work was carried out between 20th November\nand 8 December 2017.\n\n\nCollection and analysis of data was done based on the five domains and thematic Areas, as presented\nin the INEE Minimum Standards.\n\n##### 2.3 Limitations\n\nThe assessment took place during the end of the school term (November/December) which coincides\nwith end of term exams and the rainy season, which restricts mobility in the region. These factors\ncontributed to delays in survey data collection and the FGDs with teachers and students. As such, the\n\n\n2 The JENA had a 3rd objective “to inform the roll out of the CRRF” which was removed following the GOT\nwithdrawal from CRRF in January 2018.\n\n\n6", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000901:6:1:0", "start": 95, "end": 123, "surface": "representative sample survey", "probe_tag": "drop", "probe_score": 0.0214, "luna_label": 1, "luna_reason": "Survey provided quantitative assessment evidence and participant findings."}, {"key": "reliefweb:000901:6:1:1", "start": 1156, "end": 1167, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.1105, "luna_label": 0, "luna_reason": "Span describes data collection activity, not use of existing data."}]}, {"key": "paddy-186", "text": "br>114,109 34 6,655 451,210 2,033 321,756 141 - 310 37,494 4,547 1,810 787 287 102 11,014 2,186 529,061 36,631 18,295 170 101,564 246,749 48 -|\n\n\n\nUNHCR > **GLOBAL TRENDS 2018** 65", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000649:63:3:0", "start": 229, "end": 247, "surface": "GLOBAL TRENDS 2018", "probe_tag": "drop", "probe_score": 0.047, "luna_label": 0, "luna_reason": "Report title or table header, not a cited data resource"}]}, {"key": "paddy-187", "text": "other hand, the same vastness means that searching for valuable and applicable information can\nresemble a search for a needle in a haystack [7].\n\n\nTo assess the information in Big (Crisis) Data that is valuable for predictive models of forced displacement, we evaluate each data source by using three criteria: (A)ccuracy, (B)ias, and (S)calability\n(ABS).\n\n\n - Accuracy: Big Data generally suffer from a low signal-to-noise ratio [8, 3]. Especially content\nfrom social network sites is prone to contain false, misleading, or irrelevant information: bots\nwith sales ads that use trending hashtags to gain traction and actors with political agendas or\ntrolls who post deceptive or false information all contribute to to the noise on social network\nsites. Likewise, satellite images require intensive training of advanced deep learning algorithms\nto extract usable information from pixels, and advanced natural language processing algorithms\nare needed to extract relevant information from text sources in various languages and dialects\nwhich often contain spelling and grammatical errors.\n\n\nIn short, the effort of filtering the signal from the noise can, in some instances, become substantial and can thereby impact the scalability of the data source.\n\n\n - Bias: To produce user-generated content, exhaust data on the internet or CDRs requires some\nform of access to electronic devices. However, although the global penetration rate for cell\nphones and the internet increases every year, it hasn’t reached full saturation yet. Studies\nhave shown that this lack in saturation is not equally distributed across all demographics but\nleads to a user demographic that is more Western, more urban, more educated, and more male\n\n[8, 9, 10]. Although cell phone penetration rates at the household level are high in developing\ncountries, male household members have immediate access to the device and often exclude\nwomen and minors. Younger and less educated people prefer communication channels with\ndirect communication, like Instagram, Pinterest, and Facebook, whilst Twitter and LinkedIn", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001630:7:0:0", "start": 176, "end": 193, "surface": "Big (Crisis) Data", "probe_tag": "drop", "probe_score": 0.0215, "luna_label": 0, "luna_reason": "Introduces a broad data category without citing specific data or showing concrete analytical use."}]}, {"key": "paddy-188", "text": "Venezolanos en Chile, Colombia, Ecuador y Perú\n\n - una oportunidad para el desarrollo\n\n\n\n8\n\n\n**ACNUR** Alto Comisionado de las Naciones Unidas para los Refugiados\n\n\n**ALC** América Latina y el Caribe\n\n\n**CPP** Carnet de Permiso Temporal de Permanencia\n\n\n**DTM** Matriz de Seguimiento del Desplazamiento\n\n\n**ENAHO** Encuesta Nacional de Hogares\n\n\n**ENPOVE** Encuesta Dirigida a la Población Venezolana que reside en el País\n\n\n**EPEC** Encuesta a Personas en Movilidad Humana y en Comunidades Receptoras en Ecuador\n\n\n**EPTV** Estatuto Temporal de Protección para Venezolanos\n\n\n**FAO** Organización de las Naciones Unidas para la Alimentación y la Agricultura\n\n\n**FMI** Fondo Monetario Internacional\n\n\n**GEIH** Gran Encuesta Integrada de Hogares\n\n\n**HFPS** Encuestas Telefónicas de Alta Frecuencia\n\n\n**INEI** Instituto Nacional de Estadística e Informática\n\n\n**IPE** Identificador Provisorio Escolar\n\n\n**OIM** Organización Internacional para las Migraciones\n\n\n**OIT** Organización Internacional del Trabajo\n\n\n**ONU** Organización de Naciones Unidas\n\n\n**PNUD** Programa de las Naciones Unidas para el Desarrollo\n\n\n**PEP** Permiso Especial de Permanencia\n\n\n**PIB** Producto Interno Bruto\n\n\n**PPT** Permiso por Protección Temporal\n\n\n**R4V** Plataforma de Coordinación Interagencial para Refugiados y Migrantes de", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001322:7:0:0", "start": 320, "end": 348, "surface": "Encuesta Nacional de Hogares", "probe_tag": "confusion", "probe_score": 0.1513, "luna_label": 0, "luna_reason": "Survey is defined in a glossary without evidence of data use."}, {"key": "reliefweb:001322:7:0:3", "start": 759, "end": 799, "surface": "Encuestas Telefónicas de Alta Frecuencia", "probe_tag": "drop", "probe_score": 0.0463, "luna_label": 0, "luna_reason": "Survey term is defined in a glossary without evidence of data use."}]}, {"key": "paddy-189", "text": "Refugee women founded the Heriyetu Foundation at Nakivale settlement in Uganda – a group that has launched a wine-making business, pharmacy and savings and\nloans programme. © UNHCR/Esther Ruth Mbabazi\n\n\n~~X~~ **~~Inclusive Programming~~**\n\n\n\n\n\n\n\n**3** Core Action 1 of the AGD Policy is applicable to UNHCR primary data collection activities. It applies only to operational data, defined as data and information that\npertains to a crisis/situation, the persons affected by the crisis/situation, and the response to the crisis/situation.\n\n\n14", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000271:12:0:0", "start": 362, "end": 378, "surface": "operational data", "probe_tag": "drop", "probe_score": 0.0281, "luna_label": 0, "luna_reason": "Defines a generic data category without showing existing data use."}]}, {"key": "paddy-190", "text": " protect participant anonymity.\n\n\nIn Zone 3, the closest village to the cluster was Jomorogo Village. Focus group discussions were\nheld in Mengo Primary School with the host community, and the Methodist church inside Zone\n3, village 3 with the refugee community. In Zone 5, the closest village to the selected cluster was\nOkubani village.\n\n#### 4. Kampala workshop\n\nOn 6 September 2019, a workshop was held by the JEU and NRC at the Silver Springs Hotel in\nKampala to present the NEAT+ and preliminary findings from the Bidibidi field test, and to\nengage participants in broader discussions about different aspects of screening and assessing\nenvironmental risk in humanitarian settings. Twenty-five representatives from the government,\ncivil society organizations and UN agencies attended the half-day workshop (see participant list\nin Annex B). The aim of the workshop was to promote the use of the NEAT+ and to have a broader\ndiscussion on screening for environmental risk and the use of environmental data in\nhumanitarian action. These discussions have informed the recommendations for NRC,\nparticularly the section on advocacy and support to Government. 13\n\n\n11 Note: FGDs are not necessary for the completion of the NEAT+.\n12 To request access to the MapX “NEAT+ Uganda” data project as a member, contact\n[theresa.dearden@un.org](mailto:theresa.dearden@un.org)\n13 See “Recommendations” section.", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001154:11:1:0", "start": 990, "end": 1008, "surface": "environmental data", "probe_tag": "drop", "probe_score": 0.0117, "luna_label": 0, "luna_reason": "Generic data is discussed conceptually without citing or analyzing an actual dataset."}]}, {"key": "paddy-191", "text": "article databases. Whilst both data sources could be used for the early detection of existing events,\ndata from news article databases could additionally be used to try to build prediction models for future\nevents.\n\n#### **Nexus between events and forced displacement**\n\n\nThe question of the characteristics of and circumstances under which events lead to sizable forced\ndisplacement is closely interconnected with the previous and the following research question. Identifying the relevant factors through a classification model allows (i) a more targeted search for, and\npotentially prediction of, relevant events, and (ii) more targeted modeling of prediction models for the\nmagnitude, demographic, and direction of forced displacement. According to the system of forced\ndisplacement, as outlined in Figure 2, both factors at the macro-level, meso-level, and micro-level 5\n\nplay in at this stage.\n\n\nEvent characteristics can be derived from either traditional data sources, e.g., ACLED, or from data\nsources described in the previous section. Likewise, data from variables at the meso- and microlevel can, to a large extent, be derived from traditional data sources. However, Big (Crisis) Data can\nsupplement with valuable and timely information at this stage, that otherwise wouldn’t be accessible.\nData sources that track and reflect the decision to migrate, as well as the planning process before\nmigration, can add further information to traditional data sources. E.g., data from internet searches\nor social media sites that inquire about flight routes or host countries or sentiment analyses on social\nmedia that reflect how threatened individuals feel by the event. 6\n\n\n**Relevant sources of Big (Crisis) Data**\n\n\n**Internet searches** Google Trends tracks its user’s search queries and IP-addresses and calculates\naggregated trends for searched keywords based on the location information of the IP-address. These\ntrend", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001630:17:0:2", "start": 1189, "end": 1206, "surface": "Big (Crisis) Data", "probe_tag": "confusion", "probe_score": 0.1364, "luna_label": 0, "luna_reason": "Generic data resource is described as potentially useful, without demonstrated data use or finding."}]}, {"key": "paddy-192", "text": " National Education’s capacity at all levels in crisis- sensitive data collection, and analysis and dissemination|-|-|\n|Output 3.2 Capacity of education sector actors and institutions are strengthened the utilization of data collection|3.2.2 Conduct sector-wide assessment of barriers to education that includes gender- sensitive analysis and age-disaggregation of data/vulnerability and protection sensitive data|-|-|\n|Output 3.2 Capacity of education sector actors and institutions are strengthened the utilization of data collection|3.2.3 Support Ministry of National Education’s capacity to strengthen assessment, monitoring and evaluation system to track learning, attendance and early school leaving data|-|-|\n\n\n|Col1|3.2.3 Support Ministry of National Education’s capacity to strengthen assessment, monitoring and evaluation system to track learning, attendance and early school leaving data|-|-|\n|---|---|---|---|\n|Output 3.3 Capacity of education sector actors and institutions are strengthened through the utilization of data to support evidence-based policy making|3.3.1 Build the capacity of education personnel and partners in evidence- based policy development and planning|-|-|\n|Output 3.3 Capacity of education sector actors and institutions are strengthened through the utilization of data to support evidence-based policy making|3.3.2", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000157:40:1:1", "start": 742, "end": 767, "surface": "early school leaving data", "probe_tag": "drop", "probe_score": 0.0187, "luna_label": 0, "luna_reason": "Planned monitoring system will track future school-leaving data."}]}, {"key": "paddy-193", "text": "**3.3.2 Vulnerability and Health in Zaatari Camp**\n\n\nIn Zaatari camp the health partners and UNHCR have used the Specific Needs Codes (group approach\nto determining vulnerability). There are two factors to consider in terms of vulnerability analysis and\nHealth in the camp:\n\n\n - The need to identify individuals with specific needs\n\n - The multi-dimensional nature of vulnerability associated with health (e.g. the interaction\nwith other sectors such as WASH and protection)\n\n\nCurrently targeting of assistance is predominantly based on the ability of individuals/families being\nable to access services. This suggests that in fact UNHCR and partners don’t know if they are reaching\nthe most vulnerable individuals/families in the camp.\n\n\nGroup vulnerability, is one dimensional and does not say why a person is vulnerable, e.g. one\ndisabled person is not necessarily as vulnerable as another vulnerable person who doesn’t have\nfamily support.\n\n\nThere is also no recognition currently that a family may have more than one member with specific\nneeds making that family more vulnerable than another family who only has one member with\nspecific needs.\n\n\nMedical forms in use by partners 10 [^10: Note IOM does assess vulnerability at the reception centre. This is done in order to prioritize referral of\nindividuals to services.] do not collect any socio-economic data that can enable\nvulnerability analysis. In addition service locations are not necessarily the best location to collect\nsuch data since it is safe to assume that those accessing services are less vulnerable than others that\nare unable to access services. This suggests that a household level data collection process is required\nin order to understand vulnerability in the camp.\n\n\nThere are two potential approaches to collecting household/individual level vuInerability data. It may\nbe possible to collect data during the planned re-verification exercise for the camp. This data could\nthen be recorded directly into RAIS. This would form a god baseline. Monitoring could then be held\non a quarterly basis by the IRD Community Health Volunteers. Should it", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001568:6:0:0", "start": 1358, "end": 1377, "surface": "socio-economic data", "probe_tag": "drop", "probe_score": 0.0351, "luna_label": 0, "luna_reason": "States data are not collected, without using existing data or substitute estimates."}]}, {"key": "paddy-194", "text": "**Table 1. Summary statistics of bank conditions, credit performance, and portfolio allocations for the U.S. banking industry**\n**and U.S. G-SIBs over 2019:Q4 to 2020:Q4.**\n\nAll variables shown are ratios to Gross Total Assets (GTA), expressed in percent, where GTA equals total assets plus the allowance for\nloan and lease losses and the allocated transfer risk reserve (a reserve for certain foreign loans). All Banks refers to data on all banks\ncompleting a U.S. Call Report during the quarter, and G-SIBs refers to Call Report data on the largest or only commercial bank in each\n[bank holding company (BHC) in the eight U.S. Globally Systemically Important Banks (G-SIBs) designated by the Financial Stability](https://en.wikipedia.org/wiki/Financial_Stability_Board)\n[Board (Citigroup, JP Morgan Chase, Bank of America, Bank of New York Mellon, Goldman Sachs, Morgan Stanley, State Street, and](https://en.wikipedia.org/wiki/Financial_Stability_Board)\nWells Fargo).\n\n\n**_Panel A -_** **2019: Q4** **All Banks** **G-SIBs**\n\n**N** **Mean** **SD** **P10** **P25** **P50** **P75** **P90** **N** **Mean** **SD** **Min** **P50** **Max**\n\n**", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000098:81:0:1", "start": 519, "end": 535, "surface": "Call Report data", "probe_tag": "confusion", "probe_score": 0.4026, "luna_label": 1, "luna_reason": "Call Report data underpin the table’s reported banking statistics."}]}, {"key": "paddy-195", "text": " financial account can lead to a number of positive development outcomes for\n\naccount holders. Research on the topic shows relationships between expanded account ownership\n\nand reductions in poverty (Beck, Demirgüç-Kunt, and Levine 2007); increased consumption\n\n(Karlan and Zinman 2012; Burgess and Pande 2005); increased productivity (Allen et al. 2016;\n\nBruhn and Love 2009); greater investment in preventive health (Dupas and Robinson 2013; and\n\nCramer 2023); and overall higher levels of savings (Karlan, Ratan, and Zinman 2014).\n\n\nPartially through its facilitation of increased savings, formal financial inclusion holds significant\n\nimplications for financial health during economic downturns. Owning an account allows adults\n\n\n1 Unless otherwise indicated in the text or through a footnote, all data in this paper is from the Global Findex 2021.\nThe Global Findex 2021 Database, the fourth edition since 2011, includes comparable indicators of financial\ninclusion for 139 economies, including 15 economies in 2022. The data is collected through nationally\nrepresentative surveys of about 1,000 adults per country. To download the complete database and for more\ninformation on the survey methodology, see: https://globalfindex.worldbank.org.\n\n\n3", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001324:4:1:2", "start": 1052, "end": 1085, "surface": "nationally\nrepresentative surveys", "probe_tag": "confusion", "probe_score": 0.2791, "luna_label": 1, "luna_reason": "Existing Global Findex survey source is declared as the paper’s data basis."}]}, {"key": "paddy-196", "text": "u> NO YES NO YES NO YES\n_Note:_ Dependent variable is the log CES-D index collected in wave 3 of the GHS panel. Conflict variables are cumulative for the years 2010–16. All regressions are\nconducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Household variables include controls for waves 1, 2, and 3,\nand geographical controls are for wave 3. Standard errors clustered at the local government area level.\nSignificance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1\n\n\n39", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000193:40:1:0", "start": 143, "end": 152, "surface": "GHS panel", "probe_tag": "confusion", "probe_score": 0.8894, "luna_label": 1, "luna_reason": "Panel data provide the regression’s dependent variable from wave 3."}]}, {"key": "paddy-197", "text": "After applying the basic background noise filters, the total luminosity mask contains 76,754 pixels. To\n\nconstruct the mask of civilian nighttime lights, we subtract 1,324 pixels identified as military bases,\n\nwhereas 1,110 pixels come from the OSM-military dataset. The paper results use the sum of all the\n\nlight captured in each pixel of the civilian and military masks, which, in the case of the VIIRS, data, is\n\navailable at monthly intervals and yearly for the VIIRS-like series. This pixel-level processing allows\n\nus to produce civilian and military luminosity series at any level of geographical aggregation.\n\n\n**4.** **Results**\n\n\nOur analysis is divided into three main components. As a first step, we show that civilian night lights\n\ncorrelation with GDP is higher than for total luminosity (including light from military bases).\n\nMoreover, we show that the spatial distribution of civilian and total nighttime lights differs due to the\n\nconcentration of military bases in strategically important areas. Using these two results, we argue\n\ncivilian lights provide a better benchmark for local economic activity, especially after the withdrawal\n\nof the international military presence began in 2014.\n\n\nOur second set of results compares the contraction of luminosity and GDP in 2021 and its evolution\n\nin the following years. We show that most of the reduction in total luminosity would have occurred\n\nin the absence of any economic dislocation since two-thirds of the contraction was driven by the\n\nshutdown of foreign military installations. Tracking the evolution in civilian nighttime light post-2021,\n\nwe find that civilian luminosity by 2023 is above its 2020 levels, a result we interpret as local economic\n\nactivity also displaying a significant recovery. We further validate this result using a synthetic control\n\napproach that benchmarks civilian luminosity in Afghanistan with that of neighboring countries.\n\nFinally, we explore the spatial changes in the distribution of economic activity. Results highlight that\n\nall regions experienced the 2021 shock (and its latter recovery) equally. We conclude by arguing for\n\nthe need to", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001629:12:0:0", "start": 245, "end": 265, "surface": "OSM-military dataset", "probe_tag": "keep", "probe_score": 0.9667, "luna_label": 1, "luna_reason": "Named dataset supplies pixels used to construct military-light masks."}]}, {"key": "paddy-198", "text": " (0.039)\nR-squared 0.014 0.018 0.035\n\n\nMean Untreated Group 0.274 0.365 0.786\nObservations (All Panels) 1,130 1,130 1,130\n\n\n_Notes:_ Dependent variables: (i) Request PPT is an indicator [=1] if the individual reported having requested the PPT, or requested\nor attended the biometric appointment in the last survey contact. (ii) Start Registration Process is an indicator [=1] if the individual\nreported starting the RUMV census in the last survey contact. (iii) Intention to Register is an indicator [=1] if the individual reported\nthe intention to start the RUMV census in the last survey contact. The experiment had 1,375 individuals registered. This table excludes\nfrom the sample 245 individuals who did not answer any of the four WhastApp surveys. Standard errors are reported in parentheses\nand False Discovery Rate (FDR) q-values are reported in brackets. _∗∗∗_ significant at the 1%, _∗∗_ significant at the 5%, _∗_ significant at\nthe 10%.\n\n\n54", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001137:55:1:0", "start": 472, "end": 483, "surface": "RUMV census", "probe_tag": "drop", "probe_score": 0.0183, "luna_label": 0, "luna_reason": "Names a census activity, but does not show its data being used."}]}, {"key": "paddy-199", "text": "_2_2**|\n| HE_04_5_3| Liquid fuels| HE_A_04_5_3|\n| **HE_04_5_3_0**| **Liquid fuels**| **HE_A_04_5_3_0**|\n| HE_04_5_4| Solid fuels| HE_A_04_5_4|\n| **HE_04_5_4_1**| **Coal**| **HE_A_04_5_4_1**|\n| **HE_04_5_4_9**| **Other solid fuels**| **HE_A_04_5_4_9**|\n| HE_04_5_5| Heat energy| HE_A_04_5_5|\n| **HE_04_5_5_0**| **Heat energy**| **HE_A_04_5_5_0**|\n\n\n\n_Source: HBS Questionnaire provided by the NSI (2023)._\n\n\n38", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001414:39:1:0", "start": 421, "end": 438, "surface": "HBS Questionnaire", "probe_tag": "confusion", "probe_score": 0.6173, "luna_label": 1, "luna_reason": "Named questionnaire cited as the source of the preceding data table."}]}, {"key": "paddy-200", "text": "#### **ENABLING CONDITIONS FOR SECOND PILLARS OF PENSION** **SYSTEMS**\n\n**By**\n**Heinz Rudolph and Roberto Rocha** **1**\n\n\n1 World Bank, FPDFS.", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:004085:2:0:0", "start": 148, "end": 153, "surface": "FPDFS", "probe_tag": "drop", "probe_score": 0.021, "luna_label": 0, "luna_reason": "Standalone footnote citation with no shown data use or attributed finding."}]}, {"key": "paddy-201", "text": "Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.\n\n\nCDR data to track short-term mobility in the hours and days after Cyclone Mahasen hit Bangladesh in\nMay 2013 (Lu et al., 2016).\n\n\n**B. Methodological innovations and CDR data**\n\nThe most commonly used methods for processing CDR data are traditional data mining techniques.\nThese include frequency-based analysis, data clustering using unsupervised machine learning, and geovisualization techniques by mapping geolocation (Calabrese, Ferrari, & Blondel, 2014). In recent years,\nresearchers have used de-identified CDR data to compute Origin-Destination matrices in order to better\nmap patterns in travel behavior (Calabrese, Di Lorenzo, Liu, & Ratti, 2011). In combination with\nadditional administrative or survey data, supervised machine learning methods can inform the\nprediction of outcomes of interest-based solely on patterns in the cell network (Sundsøy, Johannes,\nReme, Iqbal, & Jahani, 2016).\n\nThe pre-processing of the raw CDR data is essential to accommodate positioning errors in data\ncollection and the first step for processing. The oscillation problem of the user’s location is the leading\ncause of noise in position data collected from the cellular network as they transfer calls to the nearest\nbase station for traffic management, creating imprecise and overlapping Voronoi polygons (Chen, Ma,\nSusilo, Liu, & Wang, 2016). The time-based filter is used to ignore oscillation and agglomerative\n(hierarchical clustering) in methods to extract truthful location data from raw CDR.\n\nHowever, handling such sensitive data requires appropriate protocols to address concerns around data\nprivacy. While anonymizing data is necessary, Kondor et al. (2015) show that it is theoretically possible\nto identify users based on their mobility patterns alone (Dániel Kondor et al., 2015).", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:002215:5:0:2", "start": 770, "end": 799, "surface": "administrative or survey data", "probe_tag": "confusion", "probe_score": 0.2086, "luna_label": 1, "luna_reason": "Existing data are identified as inputs to supervised prediction methods."}]}, {"key": "paddy-202", "text": " 3 3\nDjibouti 1 1 0 2 3 3\nEgypt, Arab Rep. 1 1 0 2 3 3\nIran, Islamic Rep. 1 2 2 2 3 3\nIraq 2 1 1 1 2 2\nJordan 1 1 2 2 2 2\nLebanon 1 0 1 2 2 3\nLibya 0 0 0 1 2 2\nMorocco 1 0 0 2 2 3\nSyrian Arab Republic 0 0 0 1 1 1\nTunisia 1 2 0 2 2 3\nWest Bank and Gaza 1 1 2 2 2 3\nYemen, Rep. 0 0 0 1 2 1\nSource\n\n\n\nAlgeria, for example, conducted its latest HBS", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001854:27:1:0", "start": 779, "end": 782, "surface": "HBS", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "The sentence only states the survey was conducted, without showing data use or findings."}]}, {"key": "paddy-203", "text": "_Appendix II: Impacts of Tax Interventions_\n\nBelow is the list of papers included in Figure 5. Details on the results included are provided in the\nonline replication dataset.\n\n\n**Tax officials: incentives/deployment**\n\nAmodio, Francesco, Jieun Choi, Giacomo De Giorgi, and Aminur Rahman. \"Bribes vs. taxes:\nMarket structure and incentives.\" _Journal of Comparative Economics_ 50, no. 2 (2022): 435-453.\n\nBalan, Pablo, Augustin Bergeron, Gabriel Tourek, and Jonathan L. Weigel. \"Local elites as state\ncapacity: How city chiefs use local information to increase tax compliance in the democratic\nrepublic of the Congo.\" _American Economic Review_ 112, no. 3 (2022): 762-797.\n\nBasri, M. Chatib, Mayara Felix, Rema Hanna, and Benjamin A. Olken. \"Tax administration versus\ntax rates: evidence from corporate taxation in Indonesia.\" _American Economic Review_ 111, no. 12\n(2021): 3827-3871.\n\nBergeron, Augustin, Pedro Bessone, John Kabeya Kabeya, Gabriel Z. Tourek, and Jonathan L.\nWeigel. “Optimal assignment of bureaucrats: Evidence", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001060:32:0:0", "start": 166, "end": 192, "surface": "online replication dataset", "probe_tag": "drop", "probe_score": 0.0123, "luna_label": 1, "luna_reason": "Replication dataset provides details on results included in the figure."}]}, {"key": "paddy-204", "text": "**REFERENCES**\n\n\nAdams RM, 1989. Global climate change and agriculture: An economic perspective. _American_\n\n_Journal of Agricultural Economics_ 71(5): 1272–79.\n\n\nAlexandrov VA & Hoogenboom G, 2000. The impact of climate variability and change on crop\n\nyield in Bulgaria. _Agriculture and Forest Meteorology_ 104: 315–27.\n\n\nBarron EJ, 1995. Climate models, how reliable are their predictions? Consequences.\n\n[http://www.gcrio.org/CONSEQUENCES/fall95/mod.html](http://www.gcrio.org/CONSEQUENCES/fall95/mod.html)\n\n\nBefekadu D & Nega B, The Ethiopian Economic Association. Annual Report on the Ethiopian\n\nEconomy, Vol. I. 1999/2000. Addis Ababa, Ethiopia.\n\n\nCSA (Central Statistical Authority), 1995, Agricultural sample Survey for 1994/95, report on area\n\nand production for major crops. Statistical Bulletin 132, Vol. I Addis Ababa, Ethiopia.\n\n\nCSA (Central Statistical Authority), 1998. agricultural survey of farm management practices.\n\nAddis Ababa, Ethiopia.\n\n\nChang C, 2002. The potential impact of climate change on Taiwan’s agriculture. _Agricultural_\n\n_Economics_ 27: 51–64.\n\n\nCline WR, 1996. The impact of global warming on agriculture: Comment. _American Economic_\n\n_Review_ 86: 1309–12.\n\n\nDeke O, Hooss KJ, Kasten C & Springer K, 2001. Economic impact of climate change:\n\nSimulations with a regionalized climate-economy model. Kiel working paper No. 1065,\nKiel Insitute of World Economics, Kiel.\nhttp://www.ideas.repec.org/p/wop/kieliw/1065.html\n\n\nDinar A & Beach H", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:003549:22:0:0", "start": 705, "end": 743, "surface": "Agricultural sample Survey for 1994/95", "probe_tag": "confusion", "probe_score": 0.4377, "luna_label": 0, "luna_reason": "Survey title appears within a bibliography reference, not as evidence used in analysis."}]}, {"key": "paddy-205", "text": " Sector Director.\n\n\nGiven duplication and the number of ways in which staff may choose to have their names recorded\n(full name on HR’s staffing record, but only first and last name on PAD – for example), the most reliable\nidentifier is a staff’s Unique Personal Identification number (UPI), which the data set required in order to\n\n\n1 These shares are determined as of July 2017 when the data set was created. The missing 10% of the universe of IPF\nlending approvals between FY1995 and 2009 is mainly due to projects that while approved, were cancelled within\npermitted timeframes and without sufficient disbursement to warrant an IEG evaluation.\n\n40", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000027:41:2:0", "start": 301, "end": 309, "surface": "data set", "probe_tag": "confusion", "probe_score": 0.2952, "luna_label": 0, "luna_reason": "Generic dataset is named, but its data are not shown informing analysis or a finding."}]}, {"key": "paddy-206", "text": " (2015 to 2018) for which panel\ndata are available in the LSMS are the years 2013 and 2019. Our DiD analysis thus uses the survey from\n2013 as the pre-program baseline and the one from 2019 as the post-program endline. Starting with the\nfull sample of households, we exclude households that moved or split off during this period. This leaves us\nwith a balanced panel of 1,480 households (2,960 observations across 2013 and 2019). Figure 2 illustrated\nthe geographical locations and corresponding treatment status of the households.\n\nSeveral household-level outcomes are considered. The outcome variable _Access to Electricity_ is a\ndummy that equals 1 if the household reported to have electricity working in their dwelling, and 0\notherwise. The variable _Amount Paid for Electricity_ is the reported amount paid by the household per month\nfor electricity (converted into US dollars using yearly average exchange rates from the World Development\nIndicator database). The variable _Regular Blackouts_ is a dummy that equals 1 if the household reported to\n\n\n4", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001659:6:1:1", "start": 58, "end": 62, "surface": "LSMS", "probe_tag": "confusion", "probe_score": 0.3389, "luna_label": 1, "luna_reason": "LSMS survey data are used for the 2013–2019 difference-in-differences analysis."}]}, {"key": "paddy-207", "text": "education by 10-year-age bands. These variables are only available for the years that cor\n\nrespond to a population census. Thus, for the years with missing information, we assign\n\n\nthe value of the previous closest year.\n\n##### **2.2 Descriptive Statistics**\n\n\nTable A2 summarizes the characteristics of the resulting sample with and without con\n\ntrols, which differ in size due to the availability of marriage and education variables. The\n\n\nsample without controls, which results from merging the WBL dataset with the labor indi\n\ncators from ILOSTAT, covers 80 countries and is an unbalanced panel of 918 country-year\n\n\nobservations, where each country is observed for 11.5 years on average. Among these\n\n\ncountries, 79 percent had enacted a law regulating childcare by 2022, and we observe 60\n\n\npercent of them both before and after the law enactment in our data. The full estima\n\ntion sample, which includes controls related to marriage and education, has 54 countries\n\n\nand 760 country-year observations. This smaller panel is more balanced, however, and\n\n\ncountries appear, on average, for 14 years in the panel. Tables A3-A5 detail the countries\n\n\nincluded and show some characteristics of the sample by country.\n\n\nAs explained in the next section, we also construct cohort-specific datasets where we\n\n\ninclude countries that enact a childcare law in a given year and its appropriate control\n\n\nunits. The resulting stacked dataset has 11,899 and 9,497 country-year-stack observa\n\ntions for the baseline (without socio-demographic controls) and the full specification (with\n\n\nsocio-demographic controls), respectively.\n\n\nSummary statistics of key variables used in our analysis are reported in Table 1. The\n\n\n9", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001024:11:0:0", "start": 104, "end": 121, "surface": "population census", "probe_tag": "keep", "probe_score": 0.938, "luna_label": 1, "luna_reason": "Existing census data determine variable availability and subsequent imputation."}]}, {"key": "paddy-208", "text": ",�, as its level in period 𝑡 is chosen in period 𝑡−1. The control function approach is the most widely used production function estimation method\nthat corrects for this endogeneity issue. This approach was first developed by Olley and Pakes (1996), and has been improved upon by other seminal\npapers in the literature, such as Levinsohn and Petrin (2003), Wooldridge (2009), and Ackerberg et al. (2015). The methodology developed by\nAckerberg et al. allows to correct for endogeneity in the estimation of productivity, using materials as an instrumental variable, as well as correcting\nfor entry and exit.\n8 This paper classifies firms into 4 size categories following the size groupings of the World Bank Enterprise Survey: 1) micro firms have less than\n5 workers, including self-employed workers, 2) small firms have between 5 and 19 workers, 3) medium-sized firms have between 20 and 99\nworkers, and 4) large firms have more than 100 workers.\n9 Labor productivity can be decomposed into capital deepening (capital-labor ratio) and revenue TFP, to determine whether labor productivity\nlevels or its changes are explained by the accumulation of factors (capital deepening) or technical efficiency. A similar decomposition can be made\nfor sales per worker, by also including materials as another component in the decomposition in addition to capital deepening and revenue TFP.\n10 This paper classifies firms into 5 age categories: 1) less than five years of age, 2) between five and nine years of age, 3) between 10 and 14 years\nof age, 4) between 15 and 19 years of age, and 5) 20 or more years of age.\n\n\n9", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001258:10:1:0", "start": 695, "end": 723, "surface": "World Bank Enterprise Survey", "probe_tag": "keep", "probe_score": 0.9331, "luna_label": 1, "luna_reason": "Named survey provides the firm-size groupings used for classification."}]}, {"key": "paddy-209", "text": "on unemployment was far less than in the US and UK context.\n\n\nCompared to the voluminous literature from high-income countries, there is scant evi\n\ndence from the developing world on the labor market impacts of COVID-19, largely due to\n\n\nlack of data. Studies in high-income countries use a variety of data sources, ranging from\n\n\nsurveys to government administrative data sets, from private sector transactions to data\n\n\nfrom social media and search engine companies. With some exceptions, work in developing\n\n\ncountries needs to rely on survey evidence that would have to be collected for the specific\n\n\npurpose of studying the crisis and addresses specific questions. Recent work has found\n\n\nsubstantial impacts on employment and energy consumption in India (Beyer, Bedoya and\n\n\nGaldo, 2020; Deshpande, 2020; Dhingra and Machin, 2020; Lee, Sahai, Baylis and Green\n\nstone, 2020), on family businesses in Nigeria (Avenyo and Ndubuisi, 2020), and simulated\n\n\naggregate consumption in Uganda (von Carnap et al., 2020). While these papers consider\n\n\nindividual countries and various specific situations in these countries, our data allow us to\n\n\nconsider a much wider set of countries and to compare countries to each other.\n\n\nSeveral sets of papers offer regional or even global assessments of the impacts of the\n\n\ncrisis on different dimensions, often based on firm surveys, simulations, or Google search or\n\n\nmobility data. For example, Adian et al. (2020) find that small and medium enterprises in\n\n\n13 countries were more affected by the crisis than larger firms. Apedo-Amah et al. (2020)\n\n\nconfirm this using data from a wider set of countries, and also find that most adjustments\n\n\noccurred on the intensive margin of hours reductions or temporarily work stoppage. Bachas,\n\n\nBrockmeyer and Semelet (2020) simulate the shock on firms using administrative data from\n\n\n10 countries and predict an annual payroll reduction of 5%-10%.\n\n\nA", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:002183:8:0:4", "start": 1391, "end": 1423, "surface": "Google search or\n\n\nmobility data", "probe_tag": "confusion", "probe_score": 0.788, "luna_label": 1, "luna_reason": "Google search and mobility data are identified as bases for regional crisis assessments."}]}, {"key": "paddy-210", "text": "## **Regulatory Governance and Sector** **Performance: Methodology and** **Evaluation for Electricity Distribution in** **Latin America**\n\n_Luis ANDRES, José Luis GUASCH, and Sebastián LOPEZ AZUMENDI*_\n\n\n- Luis A. Andres is Infrastructure Economist, Latin America and Caribbean Region, World Bank, Jose Luis\nGuasch is Senior Adviser on Regulation and Competitiveness, Latin America and Caribbean Region, World\nBank and Professor of Economics, University of California, San Diego, and Sebastian Lopez Azumendi is\nconsultant, Latin America and Caribbean Region, World Bank. They are grateful to Daniel Benitez,\nGeorgeta Dragoiu, Antonio Estache, Martin Rossi, and Tomas Serebrisky. They are particularly indebted to\nPaulo Correa for sharing his data on Brazilian regulatory agencies and Mariam Dayoub and Aires da\nConceicao for their assistance collecting the data in Brazil. Georgeta Dragoiu and Julio A. Gonzalez\ncontributed in the collection of the performance data. The findings, interpretations and conclusions expressed\nherein do not necessarily reflect the views of the Board of the Executive Directors of the World Bank or the\ngovernments they represent.", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:003701:2:0:1", "start": 950, "end": 966, "surface": "performance data", "probe_tag": "confusion", "probe_score": 0.1704, "luna_label": 0, "luna_reason": "The sentence describes contributors collecting the performance data."}]}, {"key": "paddy-211", "text": " 2017). Similarly, a study using the Jordanian Labor\nMarket Panel Survey found that female labor force participation increases with internet\nadoption, mainly among older skilled women, a result researchers attributed to their access to\nonline job search (Viollaz and Winkler, 2020).\n\n\n4", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001202:5:2:0", "start": 37, "end": 72, "surface": "Jordanian Labor\nMarket Panel Survey", "probe_tag": "keep", "probe_score": 0.9515, "luna_label": 1, "luna_reason": "Named survey supports a reported finding on female labor force participation."}]}, {"key": "paddy-212", "text": "., citing data from WHO Global Database on Child Growth and Malnutrition\n[ix UNdata, Under five mortality rate (U5MR), 2009; http://data.un.org/Data.aspx?d=SOWC&f=inID%3A17](http://data.un.org/Data.aspx?d=SOWC&f=inID%3A17)\nx The Republic of Uganda, Uganda Nutrition Action Plan, 2011-16: Scaling up multi-sectoral efforts to establish a strong nutrition\n[foundation for Uganda’s development. 2011. http://www.ugan.ug](http://www.ugan.ug/)\nxi Uganda National Household Survey of 2009-10\nxii Uganda National Household Survey of 2009-10\nxiii Republic of Uganda, Ministry of Agriculture, Animal Industry & Fisheries, 2010. Agriculture for Food and Income Security.\nAgriculture Sector Development Strategy and Investment Plan: 2010/11-2014/15. July,\n[http://www.agriculture.go.ug/index.php?page=publications&id=313](http://www.agriculture.go.ug/index.php?page=publications&id=313)\nxiv Uganda Nutrition Action Plan 2011-2016;\n[http://www.fantaproject.org/downloads/pdfs/Uganda_NutritionActionPlan_Nov2011.pdf](http://www.fantaproject.org/downloads/pdfs/Uganda_NutritionActionPlan_Nov2011.pdf)\nxv Government of Uganda (2003) The Uganda Food and Nutrition Policy.\n[http://www.fao.", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:013651:19:2:1", "start": 442, "end": 485, "surface": "Uganda National Household Survey of 2009-10", "probe_tag": "confusion", "probe_score": 0.5488, "luna_label": 1, "luna_reason": "Named national household survey cited as an existing data source."}]}, {"key": "paddy-213", "text": "Liberia|LTTP II|English|2|0|6.4|9.1|2.7|4.8|14.2|9.4|6.7|\n|Liberia|LTTP II|English|3|0|18.9|24.3|5.4|7.6|20.2|12.6|7.2|\n|Malawi|MTPDS|Chichewa|2|0|0.38|0.24|-0.14|0.21|8.95|8.74|8.88|\n\n\n32", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:007245:33:3:2", "start": 128, "end": 133, "surface": "MTPDS", "probe_tag": "confusion", "probe_score": 0.0586, "luna_label": 0, "luna_reason": "Standalone table cell fragment, not an independently used data resource."}]}, {"key": "paddy-214", "text": "I\n\nIntroduction\n\n\n\n_This report summarizes patterns and trends in the number of individual asylum claims_ **(2)**\n_submitted in Europe and selected non-European countries during_ 2014 _. The data presented are_\n_based on information available as of_ **14 March 2015** _, unless otherwise indicated. The report covers_\n_the_ 38 _European and six non-European States that currently provide monthly asylum statistics_\n_to UNHCR. Figures are mostly based on official asylum statistics, reflecting national laws and_\n_procedures. In addition, UNHCR conducted refugee status determination under its mandate in_\n_a number of countries included in this report._ **(3)** _Annex Table_ 2 _provides trends in selected Eastern_\n\n\n\n_a number of countries included in this report._ **(3)** _Annex Table_ 2 _provides trends in selected Eastern_\n\n_European countries, based on annual data._\n\n\nhe group of coun- **may not reflect the actual number**\ntries\u0003 **analysed is referred** **of new asylum-seekers.** **(5)**\n# **T**\n\n\n\n(2) An asylum-seeker is an individual who has\nsought international protection and whose claim\nfor refugee status has not yet been determined. As\npart of internationally recognized obligations to\nprotect refugees on their territories, countries are\nresponsible for determining whether an asylumseeker is a refugee or not. This responsibility is\nderived from the 1951 Convention relating to the\nStatus of Refugees and relevant regional instruments,\nand is often incorporated into national legislation.\n\n(3) During 2014, UNHCR conducted refugee status\ndetermination under its mandate in Turkey (see notes\nin Annex Table 1 for more details).\n\n(4) See Annex Table 1 for a list", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000195:4:0:0", "start": 465, "end": 491, "surface": "official asylum statistics", "probe_tag": "confusion", "probe_score": 0.1041, "luna_label": 1, "luna_reason": "Official asylum statistics underpin the report’s presented figures."}]}, {"key": "paddy-215", "text": "|Col1|Indicator 2.4: Teacher feedback on training and certification system monitored, analyzed, and included in the annual monitoring and progress reports developed by ETC|Col3|No|Yes/No|No|Yes|Annually|MOE|Teacher surveys|\n|---|---|---|---|---|---|---|---|---|---|\n|Reformed student assessment and certification system |Indicator 3.1: Grade 3 diagnostic test on early grade reading and math implemented|7.2|No|Yes/No|No|Yes|Annually|MOE|Assessments records for a sample of schools|\n|Reformed student assessment and certification system |Indicator 3.2: Legal framework for the_Tawjihi_ exam has been adopted so that its secondary graduation and certification function is separated from its function as a screening mechanism for university entrance|7.4|No|Yes/No|No|Yes|Annually|MOE||\n|Reformed student assessment and certification system |Indicator 3.3: Student and Teacher Feedback on first phase_Tawjihi_ reform inform the", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000127:39:0:0", "start": 216, "end": 231, "surface": "Teacher surveys", "probe_tag": "confusion", "probe_score": 0.0853, "luna_label": 0, "luna_reason": "Standalone table cell naming a survey source, not an independently used data mention."}, {"key": "refugee_pads:000127:39:0:1", "start": 465, "end": 484, "surface": "Assessments records", "probe_tag": "confusion", "probe_score": 0.1264, "luna_label": 0, "luna_reason": "Standalone table-cell fragment, not an independently used data mention."}]}, {"key": "paddy-216", "text": "- Develop infrastructure (database software and computers) to strengthen the MOL\nhotline (grievance redress system) and establish IT linkages between inspectors and\nhotline.\n\n\n - Strengthen existing grievance redress mechanisms at the MOL to allow for confidential\nand effective resolution of complaints between affected people, communities, and\nmanagers of SEZs.\n\n\n - Complete a memorandum of understanding between the Ministry of Health and the\nMOL to enable MOL to take responsibility and accountability for workers’ dormitories\ninspection and compliance.\n\n\n - Continue to support a singular integrated inspection database across MOL, MoEnv, and\nother relevant ministries, which aggregates current compliance rates for individual\nfactories across several inspection agencies. This is in order for risk-based inspections\nto increase in parallel with a lower average of yearly inspections per factory.\n\n\n - Build an M&E system to assess percentage of case resolution\n\n\n - Redeploy and train existing Inspectorate Unit staff to increase technical expertise, while\nchanging mindset from ‘policing’ to providing incentives to comply with standards.\n\n\n - Conduct awareness-raising and communications activities to increase the knowledge of\ngrievance system available to workers.\n\n\n - Provide legal advisors (to advice on the labor law) with diversity of language skills to\nmanage cases of migrant workers.\n\n\n**(d)** **Support Ministry of Environment Capacity More Strategically**\n\n\n - Focus MoEnv Inspectorate capacity on designing and implementing—in priority,\npollution hotspots—air pollution abatement plans, containing: (i) targets for selected\nenvironmental improvement objectives, (ii) a clear assignment of roles and\nresponsibilities for the different stakeholders involved, and (iii) incentive mechanisms\n(including soft loans) to encourage industries to comply with environmental regulation.\n\n\n - Promote pollution control through a combination of (i) positive incentives (including\nsoft loans and technical assistance) to encourage the use of cleaner production processes,\nand (ii) gradual phasing-in of negative incentives (pollution levy for industrial emissions\nexceeding a given standard) to induce firms to meet effluent/ambient standards (for\nexample", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000045:92:0:0", "start": 601, "end": 631, "surface": "integrated inspection database", "probe_tag": "confusion", "probe_score": 0.3076, "luna_label": 1, "luna_reason": "Database compliance rates inform risk-based inspection targeting."}]}, {"key": "paddy-217", "text": "**The World Bank** Implementation Status & Results Report\nOne WASH—Consolidated Water Supply, Sanitation, and Hygiene Account Project (One WASH—CWA) (P167794)\n\n\n\nComments **:**\n\n\n\nThe indicator measures accessibility and use of the water resources monitoring system. It measures the\nuse of the water resource data (meteorology, hydrology and groundwater) to inform design and\nmanagement of water supply systems delivered under the Project.\n\n\n\n**Intermediate Results Indicators by Components**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n12/3/2021 Page 6 of 11", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "sample:fcv_pads_east_africa:007709:5:0:0", "start": 294, "end": 313, "surface": "water resource data", "probe_tag": "confusion", "probe_score": 0.2032, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-218", "text": "#### Globally Engaged Firms in the COVID-19 Crisis ∗\n\n\n\nCristina Constantinescu (World Bank)\nAna Margarida Fernandes (World Bank)\n\n\n\nArti Grover (World Bank) †\n\n\n\nStavros Poupakis (University College London)\n\n\n\nSantiago Reyes (World Bank)\n\n\n\n**JEL** **classification** : D22, F14, L20, L25, O10.\n\n\n**Keywords** : COVID-19, Crisis, Firms, Recovery, Trade, Exporters, Global Value Chains.\n\n\n∗The authors are immensely grateful to the many World Bank Group colleagues and counterpart institutions that\nhave supported the Business Pulse Survey (BPS) data collection, without whom this work would not have been possible.\nLikewise, we thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the\nWorld Bank Group for making the Enterprise Survey data available. We are thankful to the members of the EITFE team\nfor their efforts in coordinating BPS data collection. We thank Gene Grossman, Mona Haddad, Denis Medvedev, Antonio\nNucifora, Marcelo Olarreaga and Daria Taglioni for their guidance and strategic support of the BPS initiative, and the\nUmbrella Facility for Trade trust fund (financed by the governments of the Netherlands, Norway, Sweden, Switzerland\nand the United Kingdom), as well as the USAID, the IFC, and the World Bank for their financial support. We thank\nBishakha Barman, Yewon Choi, and Shwetha Eapen for their research assistance. The authors are also grateful to the\nfollowing colleagues for their feedback at the concept stage of this project: Asya Akhlaque, Caroline Freund, Mary\nHallward-Driemeier, Leonardo Iacovone, Michele Ruta, Daria Taglioni, Gonzalo Varela and Erik von Uexkull. We thank\nBeata Javorcik for kindly sharing the data on letter of credit intensity", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001120:2:0:1", "start": 772, "end": 794, "surface": "Enterprise Survey data", "probe_tag": "confusion", "probe_score": 0.8792, "luna_label": 0, "luna_reason": "Acknowledges data availability without showing analysis, finding, or substantive use."}]}, {"key": "paddy-219", "text": "estimates range between 59\nand 64 per cent), less likely to be children (between\n33 and 39 per cent) and more likely to be elderly\n(between 7 and 9 per cent). **52**\n\n\n\n**50** See [UNHCR population categories explained.](https://www.unhcr.org/refugee-statistics/insights/explainers/forcibly-displaced-pocs.html)\n**51** These models are generated using the available demographic data for a country of origin as a starting point. Where data for a particular\ncountry of asylum is missing, the values are estimated using statistical modelling from the available data for the same origin country in nearby\ncountries of asylum.\n**52** The ranges expressed represent the upper and lower 95 per cent uncertainty intervals. Note that the proportion of children amongst\nrefugees from Ukraine (33 to 39 per cent) is almost double the proportion amongst the Ukrainian population as a whole (18 per cent).\n\n\nUNHCR > **GLOBAL TRENDS 2022** 17", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001233:16:3:0", "start": 384, "end": 400, "surface": "demographic data", "probe_tag": "confusion", "probe_score": 0.8678, "luna_label": 1, "luna_reason": "Existing demographic data are used as inputs for statistical modeling."}]}, {"key": "paddy-220", "text": "**The World Bank**\nUganda: Roads and Bridges in the Refugee Hosting Districts Project (P171339)\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Percentage of women employed in construction and maintenance of Project road|Percentage of women employed in construction and maintenance of the project road|Semi- annually|Supervision Consultant's Reports|Contractor's and consultant's data|Supervision Consultants, Monitoring and evaluation consultants, UNRA|\n|---|---|---|---|---|---|\n|Health and Safety Management Plans|Health and Safety Management Plans for the Project road works|semi- annually |Monthly progress reports |Police records, data collected by supervision consultants and contractor |UNRA |\n|Percentage of Project Affected People that received full compensation and all R&R assistance dis-aggregated by gender, refugees, hosts|Project Affected People that received full compensation and all R&R assistance|Semi- annually |Reports of RAP implementin g agency |Surveys, study |Monitoring and evaluation consultants, UNRA |\n|Grievances responded and/or resolved within the stipulated service standards for response times (dis-aggregated by gender, refugees, hosts)| Percentage of", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000146:61:0:0", "start": 644, "end": 658, "surface": "Police records", "probe_tag": "confusion", "probe_score": 0.0602, "luna_label": 0, "luna_reason": "Source cell within a monitoring table; not an independently extracted data-use mention."}]}, {"key": "paddy-221", "text": "cleanliness, lighting and ventilation, prices, POS system, quality,\nwillingness to partnership, line of credit);\n\n - Post-qualification;\n\n - Agreement with merchants was signed with the NGO (WFP guarantor);\n\nthen this was modified to having WFP signing the agreement.\n\niv. Commercial bank BDL & Merchants (regulation of relationship):\n\n - The bank signs contracts with merchants to cover commercial relationship\n\n(General conditions) and WPA project parameters (Special conditions);\n\n - Merchants should be cleared by the central bank (Banque Du Liban);\n\n - The BLF provides regular updates to WFP and advises on fraudulent\n\nsuspicions, leaving to WFP the authority to act.\n\nv. Field visit outcomes:\n\n - Field visitors are in charge of (i) Distribution e-card, (ii) household\n\nsurveys (monthly level of effort of 40 person days with an average\ncoverage of 16 households per day), (iii) shop monitoring (once per month\nminimum).\n\n - Invoices are itemized and attached to the card receipt.\n\n31. Record keeping; Inventory: Both agencies have a satisfactory system.\n\n\na. The PCM/FOT has an extensive experience in record keeping and the accounting\n\nsystem contain an inventory field.\n\nb. WFP conducts physical count once yearly. In 2008, the software IPSAS/ Wings was\n\nimplemented for inventory purposes. For food procured items, a separate system (that\nis in the process to be integrated in Wings) records the commodity transaction. In the\ncurrent project WFP is implementing, the commercial selected bank tracks the e-cards\nin order to monitor the transferred moneys. Monthly financial reports are produced.\n\n32. Current Staffing:\n\n\na. At the PCM, two committees process procurement: Supply Committee (head of\n\ncommittee, one engineer, one administrative, one IT) and Acceptance committee (head\nof committee and 5 members). Housed at PCM, FOT had customized the procurement\nresources and financial management as per the needs of the ESP", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000047:51:0:0", "start": 808, "end": 826, "surface": "household\n\nsurveys", "probe_tag": "confusion", "probe_score": 0.1679, "luna_label": 0, "luna_reason": "Field visitors are assigned to conduct recurring household surveys, indicating planned data production."}]}, {"key": "paddy-222", "text": "Annex 1\nPage 3 **of** 3\n\n\n**Key Performance**\n**Hierarchy of Objectives** **Indicators** **Monitoring &** **Critical Assumptions**\n**Evaluation**\n**Project Components / Sub-** **Inputs: (budget for each** **Project reports:** **(from Components to**\n**components:** **component)** **Outputs)**\n\n\nImprove Access: provision of US$5.8 million MOE monitoring Capacity within the\nclassrooms. Number of schools reports construction sector to handle\nconstructed per year; the volume of school\nimproved design and construction.\nefficiency.\nCreate Conditions for Quality US$1.1 million School surveys; student Good textbook distribution;\nImprovement: access to Number of textbooks per learning achievement management training\neducational materials; student; autonomous school reports (MOE effectiveness; Government\nimproved school management; salaries paid on monitoring reports). commitment to paying\nmanagement; teacher a timely basis teacher salaries.\nmotivation.\n\n\nImprove Government's US$4.1 million Project monitoring Purpose and integrity\nCapacity to Manage Sector: Project effectively reports; study reports. maintained within project\ncapacity building within the implemented and management; stakeholder\nMOE and its related services; management improved; participation in pilot studies.\npilot studies. reports with implementable\nresults.\n\n\n**Annexe 1 Attachment: Program and Project Monitorin** **Tar** **ets**\n**_Year_** _2001-02 2002-03_ **_2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10_**\nPrimary Enrollment Boys 19,125 21,506 24,300 26,627 29,867 31,696 34,457 37,217 40,129\nPrimary Enrollment Girls 14,875 17,994 20,", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:009704:31:0:0", "start": 577, "end": 591, "surface": "School surveys", "probe_tag": "confusion", "probe_score": 0.1078, "luna_label": 0, "luna_reason": "Generic survey phrase appears as a monitoring-table entry, not cited existing evidence."}]}, {"key": "paddy-223", "text": " regression, where the coefficients and significances (t-values) are those of the variable indicated in the row title. The\ndependent variables are based on a country-specific heterogeneous panel SVAR estimation for 104 countries (25 advanced economies, 61 EMDEs, and 18 LICs).\nEMDEs here exclude LICs. GDP refers to national GDP measured in U.S. dollars using purchasing power parity (not market) exchange rates. Inflation targeting\nregimes are defined as in IMF (2016). Central bank transparency data are based on Dincer and Eichengreen (2014). Exchange rate regimes are based on Shambaugh\n(2004). Labor market flexibility is based on the estimates compiled by the Fraser Institute, with a higher value representing a more flexible labor market. The\nmeasures of trade and capital account openness are, respectively, trade (exports plus imports)-to-GDP ratios (in percent) and the index compiled by Chinn and Ito\n(2018). Dependent variables are based on mean values over the country-specific sample periods. The numbers in brackets refer to t-statistics. EMDEs = emerging\nmarket and developing economies; GDP = gross domestic product; LICs = low-income countries; SVAR = structural vector autoregression. *** p < 0.01, ** p <\n0.05, *p < 0.1 significance levels.\n\n38", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001705:39:3:0", "start": 471, "end": 501, "surface": "Central bank transparency data", "probe_tag": "confusion", "probe_score": 0.4473, "luna_label": 1, "luna_reason": "Data are used in the analysis and attributed to Dincer and Eichengreen (2014)."}]}, {"key": "paddy-224", "text": ",146,800\n\n(91%)\n\n\n\n(91%)\n\n\n\nAfghans protected and/\n\n\n\nor assisted by UNHCR\n\n\n\n**5,740,000**\n\n(90%)\n\n\n\n(90%)\n\n\n\nAfghan refugees and\npeople in refugee-like\n\n\n\nsituations\n\n\n\n**61,500**\n\n(21%)\n\n\n\n(21%)\n\n\n\nAfghan asylum-seekers\n\n\n\n**3,222,400**\n\n\n\ninternally displaced\n\n\n\nAfghans\n\n\n\n2023 Asia & the Pacific Regional Trends on Forced Displacement and Statelessness 21", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001559:19:1:0", "start": 283, "end": 358, "surface": "Asia & the Pacific Regional Trends on Forced Displacement and Statelessness", "probe_tag": "confusion", "probe_score": 0.3136, "luna_label": 1, "luna_reason": "Named existing regional trends report presenting displacement and statelessness figures."}]}, {"key": "paddy-225", "text": " 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 adverse effects of the situation on\nthe students. The keeping of photographic archives was begun during this mission.\n\n\nThe impacts considered will not be due solely to the project but also to the prevailing situation, which is\ncharacterized by significant degradation of the existing facilities. Since a particular aim of the project is\nto increase the capacity of schools, the main environmental measure to be included in the project will be\nto construct or rehabilitate the sanitary facilities of these schools, and to design a system of upkeep and\nmaintenance that will ensure the appropriate functioning of the schools.", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000175:65:1:0", "start": 363, "end": 384, "surface": "photographic archives", "probe_tag": "confusion", "probe_score": 0.2148, "luna_label": 0, "luna_reason": "Photographic archives were begun during the mission, indicating project-created documentation."}]}, {"key": "paddy-226", "text": " More than 70 percent of\nschool-aged children are not receiving education. 21 [^21: UNESCO et. al. (2018). “Global Initiative on Out of School Children, South Sudan Country Study”.] Three fourths of the population lack access to improved sanitation\nfacilities and 30 percent lack access to safe water. The country has the lowest road density in Africa with less than 2\npercent of the primary network paved, constraining access to the schools and health facilities that do exist. 22 [^22: World Bank 2018a.] Seasonal\nrain and periodic floods regularly leave large parts of the country inaccessible for months at a time. 23 [^23: Pape, U. Benson, M. Ebrahim, M. Lole, J. (2017) Reducing poverty through improved agro-logistics in a fragile country: findings from a trader survey\nin South Sudan. Washington, D.C.: World Bank Group.] Low trust in\ngovernment institutions and social divisions wrought by way have weakened social cohesion in the country and will impact\ngovernance in South Sudan for the foreseeable future. 24 [^24: World Bank (2016) “ _Peoples of South Sudan: A Country Social Analysis of Ethnic Diversity in South Sudan (Vol 3: Inclusive Development: The Social_\n_and Institutional Context”._] Women face a disproportionate burden of poor access to services.\nThe situation is likely to worsen as the numbers of returnees increases and where humanitarian assistance may be limited.\nUNOCHA estimate that US$1.5 billion will be needed in 2019 to accommodate the increased demand on food security,\nbasic service delivery and IDP/refugee return support. 25 [^25: UNOCHA (2019) “Humanitarian Appeal 2019”.]\n\n12. **Local institutions – both local governments and community institutions – are mandated to play an", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000014:5:1:0", "start": 796, "end": 824, "surface": "trader survey\nin South Sudan", "probe_tag": "confusion", "probe_score": 0.8094, "luna_label": 1, "luna_reason": "Cited trader survey provides evidence for reported South Sudan access constraints."}]}, {"key": "paddy-227", "text": " qualify as improved sanitation facilities. 26 The 2016 Ethiopia\n\n\n17 Stunting, or low height for age, indicates failure to reach growth potential, often associated with long-term insufficient nutrient intake (low\ndietary diversity) and frequent infections.\n18 Tasic, Hana & Akseer, et. al. (2020). Drivers of stunting reduction in Ethiopia: a country case study. 10.1093/ajcn/nqaa163.\n19 Changes in Child Undernutrition Rates in Ethiopia, 2000-2016, Hiroven et al, April 2018.\n20 All Hands-on Deck: Reducing Stunting through a Multi-Sectoral Approach in SSA and Ethiopia, Emmanuel Skofias, 2018, World Bank.\n21 Wasting, or low height for age, indicates recent and/or severe weight loss, often associated with acute starvation and/or severe disease.\n22 https://www.who.int/nutgrowthdb/about/introduction/en/index2.html.\n23 Ethiopia Mini DHS 2019.\n24 Myatt M, Khara T, Schoenbuchner S, et al. Children who are both wasted and stunted are also underweight and have a high risk of death:\ndescriptive epidemiology of multiple anthropometric deficits using data from 51 countries. _Arch Public Health_ . 2018;76:28. Published 2018 Jul 16.\ndoi:10.1186/s13690-018-0277-1\n25 Ibid.\n26 As defined by the WHO/UNICEF Joint Monitoring Program, an improved sanitation facility is one that hygienically separates human excreta from\n\n\nNov 19, 2020 Page 6 of 19", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:011472:5:7:1", "start": 1082, "end": 1104, "surface": "data from 51 countries", "probe_tag": "confusion", "probe_score": 0.2276, "luna_label": 1, "luna_reason": "Existing cross-country data underpin a cited descriptive epidemiological analysis."}]}, {"key": "paddy-228", "text": "**Appendix 2: Details of Sampling Procedure**\n\n\nSampling protocols for inside and outside the market were different:\n\n- For Danktopa market, we used a precise map of the market made by the public company\n\n\nmanaging markets in Benin (SOGEMA). This map allowed to divide geographically the\n\n\nmarket in small areas. We then randomly selected areas in the markets in which 50% of the\n\n\nbusinesses (with fixed location) where sampled for the survey. 16 [^16: Few areas were excluded from the sampling frame because they almost exclusively included businesses selling\nillegal products (i.e. taint oil, medicine, and voodoo products) or by large formal businesses.]\n\n- For other neighborhoods of Cotonou, we were able to obtain detailed maps of each of the\n\n\n144 neighborhoods in Cotonou. Those maps allowed the easy identification of _ilots_ (blocks),\n\n\nthe official administrative unit within a neighborhood. We used this administrative unit as\n\n\na reference for the listing survey sampling. We then used information given by the tax\n\n\nadministration (and confirmed by the survey company) in order to characterize\n\n\nneighborhoods as high or low firm density areas. We randomly sampled 38% of _ilots_ in\n\n\nhigh density neighborhoods and 10% of the _ilots_ in low density neighborhoods. In each _ilots_\n\n\n68% of businesses where sampled for the survey in average.\n\n\nOverall, 19,246 businesses were listed. The listing survey allowed us to estimate the total\n\n\nnumber of businesses operating in Cotonou (with a fixed location, excluding international and\n\n\nnationwide businesses and liberal professions) to approximately 68,500, including around 5,000\n\n\nin Dantokpa market. 17 [^17: Some sections of Dantokpa market were not included in the listing survey. Therefore, the total number of\nfirms in Dantokpa is probably significantly higher.] Among those 19,246 businesses, 9,938 businesses were randomly\n\n\nselected to be surveyed. 7,945 (80%) businesses were successfully surveyed,", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "sample:prwp:006903:55:0:0", "start": 151, "end": 176, "surface": "precise map of the market", "probe_tag": "confusion", "probe_score": 0.6603, "luna_label": 1, "luna_reason": null}, {"key": "sample:prwp:006903:55:0:1", "start": 973, "end": 987, "surface": "listing survey", "probe_tag": "confusion", "probe_score": 0.8137, "luna_label": 1, "luna_reason": null}, {"key": "sample:prwp:006903:55:0:2", "start": 1414, "end": 1428, "surface": "listing survey", "probe_tag": "confusion", "probe_score": 0.7685, "luna_label": 1, "luna_reason": null}]}, {"key": "paddy-229", "text": "DTM Mali – Avril 2023\n\n\nLa plupart des abris sur 41 pour cent des sites sont toujours en bon état. Toutefois, dans plus\nde la moitié des sites évalués (52%), la majorité des abris sont endommagés ou complètement\ndétruits pour seulement 2 pour des sites. Les cercles les plus touchées par les abris\nendommagés ou détruits sont principalement Ménaka, Gao, Djenne, Bamako, Ansongo,\nBourem, Ségou et Niono.\n\n\nDes risques d'expulsion de personnes de leurs abris dans les six mois suivant la période\nd'évaluation sont mentionnés dans 28 pour cent des sites. Ceux-ci sont plus concentrés dans\nles cercles de Mopti, Niono, Gourma-Rharous, Bamako, Bandiagara, Bankass, Gao, Bourem,\nSan et Ansongo.", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000580:29:0:0", "start": 0, "end": 8, "surface": "DTM Mali", "probe_tag": "confusion", "probe_score": 0.2541, "luna_label": 1, "luna_reason": "Named DTM Mali source reports shelter-condition and eviction-risk findings."}]}, {"key": "paddy-230", "text": ", these\ninvestment can be compromised or at the very least less effective, unless there i s some\nregional cohesion.\n\n\nEconomic Benefits:\n\n\nThe GLIA Support Project will generate direct benefits to: a) those populations in selected\nrefugee camps, affected surrounding areas, and returnees in terms o f reduced HIV/AIDS\nprevalence and heightened awareness o f ways to avoid contracting the virus; b) long-haul\ntruckers and associated individuals who utilize the selected health facilities and knowledge\nrooms and who are also expected to have reduced rates o f infection; and c) strengthened\nPLWHA networks which will allow these vulnerable groups to better support their\nconstituencies.\n\n\nIndirect benefits are expected for all GLIA populations as a result o f improved and more\neffective HIV/AIDS and related health policies, protocols, and service delivery. This will\noccur as good practices in one GLIA country are shared with others, from information\nexchange and harmonization o f approaches, and knowledge generated from GLIA financed\nstudies and surveys which are customized to benefit the Great Lakes region. The underlying\npremise o f GLIA i s that good ideas will be integrated into national AIDS programs, and vice\nversa. Benefits will also be incurred for those refugee populations served by UNHCR as\nUNHCR adopts service delivery lessons learned in GLIA selected sites and applies them\nelsewhere in the region and beyond.\n\n\n61", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000021:64:1:0", "start": 1026, "end": 1059, "surface": "GLIA financed\nstudies and surveys", "probe_tag": "confusion", "probe_score": 0.7942, "luna_label": 1, "luna_reason": "Existing GLIA-financed studies and surveys provide knowledge informing regional policy practices."}]}, {"key": "paddy-231", "text": ". The Recipient will prepare a Security Management Plan (SMP) by project effectiveness and a\nsummary will be disclosed in-country and on the World Bank’s external website.\n\n110. **The risk associated with institutional capacity on environmental and social management is**\n**considered Substantial** because the Recipient has little experience or capacity to manage some of the\nsocial risks identified in the ESF, and significant efforts will be required to help build the Recipient’s\ncapacity with the expanded social and environmental remit of the ESF. The Recipient also prepared,\nconsulted upon, and approved an ESCP 37 [^37: _[https://documents1.worldbank.org/curated/en/099115102012230317/pdf/P17449507045c20b70a0b20cbd9ac3ae22d.pdf](https://documents1.worldbank.org/curated/en/099115102012230317/pdf/P17449507045c20b70a0b20cbd9ac3ae22d.pdf)_], which details the material measures and actions to be\nundertaken by the Recipient during project implementation to ensure compliance with the provisions of\nthe ESF as well as the timeline and the responsible party.\n\n111. **The project has been screened for SEA/SH risks using the World Bank SEA/SH risk screening tool**\n**for projects with civil work.** The assessment concluded that the SEA/SH risks are Substantial. Drivers of\nrisk in the context include high rates of child marriage and female circumcision, general social acceptability\nof GBV, conflict, high risks of human trafficking, and lack of legislation on domestic violence and sexual\nharassment. GBV is highly prevalent, and it is estimated that 28.6 percent of women nationwide have\nexperienced physical or sexual violence by an intimate partner at some point in their lives. 38 [^38: Chad, Demographic Health Survey (DHS), 2014–15 (in French).] SEA/SH\nrequirements have been reflected in the ESCP, in contracts, and in the contractor’s ESCP", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000051:46:1:0", "start": 1727, "end": 1752, "surface": "Demographic Health Survey", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Survey cited as source for intimate-partner violence prevalence estimate."}]}, {"key": "paddy-232", "text": " The sustainability of the sanitation measures will be carefully assessed from the point of view\nof good practices, cost effectiveness, affordability and the Djibouti water shortage environment.\n\n\n6. **Social**\n\n\n_6.1 Summarize key social issues relevant to the project objectives, and specify the project's social_\n_development outcomes._\n\n\nDjibouti is a small country and many key social issues were identified in the 1997 Poverty\n\nAssessment. The issues raised included the percentage of the population classified as poor in 1996\n(50-80% reaching the upper-bound when refugees, nomads and homeless are taken into account); large\nnumbers of refugees, nomads, and homeless populations; the majority of the poor live in urban areas\n(85%) even if the incidence of extreme poverty is overwhelmingly rural. Urban households can take\nadvantage of safety nets derived from the commodity market and services, and job opportunities are\nnot available in rural areas. The key problems faced by children include: (a) the high number of street\nchildren who have fled war ravaged Somalia and Ethiopia; (b) late entrance into school by poorer\nchildren (one out of four starts school at age 9 and leaves school at age 14); (c) health issues (diarrhea\n\nand malnutrition) are a leading cause of death for children under age 5. Other problems affecting the\nwhole population include respiratory infections on the increase (due to malnutrition); endemic health\nproblems (AIDS, tuberculosis, malaria, cholera); the widespread practice of Female Genital Mutilation\n(FGM); sanitation costs are high for poorer households not connected on the main water network as", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:008855:23:1:0", "start": 420, "end": 444, "surface": "1997 Poverty\n\nAssessment", "probe_tag": "confusion", "probe_score": 0.8166, "luna_label": 1, "luna_reason": "Cited poverty assessment provides concrete social and poverty findings."}]}, {"key": "paddy-233", "text": "Table 8: Self-Reported Reasons for Not Using Consulting Services in Control Group Firms\n\n% of enterprises mentioning\nReasons for not using consulting services\nthis reason (multiple mention)\n\nWould be a good investment, but don't have funds 46.3\nDon't know what the benefits would be 22.2\nSimply hadn't considered it 18.5\nDidn't need the services 13.9\nOther 11.1\nDidn't know these services existed 7.4\nNot worth the cost 5.6\nN 108\n\n\nNote: This table includes all control group firms that, at the time of the follow-up survey,\nreported never having used consulting services.\n\n\n30", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:005657:31:0:0", "start": 549, "end": 565, "surface": "follow-up survey", "probe_tag": "confusion", "probe_score": 0.7037, "luna_label": 1, "luna_reason": "Table reports control firms’ responses based on the completed follow-up survey."}]}, {"key": "paddy-234", "text": "# 2. PRINCIPAUX RÉSULTATS\n\n###### 2.1 Nutrition\n\n\n\nDe l’analyse des données secondaires, il ressort que la prévalence de la malnutrition dans l’Adamaoua et l’Est du Cameroun est élevée. Elle touche toutes les couches de la population, en particulier les enfants de 6-59 mois, les femmes enceintes et allaitantes qui sont en effet les plus vulnérables. En\neffet, outre les enfants de moins de 5 ans qui en souffrent (33\n% de malnutrition chronique et 14 % sous la forme sévère)\ndans les deux régions 3 [^3: UNICEF, MSP, _EDS-MICS 2011_, pp. 159-161], les personnes vivant avec le VIH\n(PVVIH) constituent une catégorie de population qui présente\nun taux de dénutrition préoccupant (14,1 % de PVVIH sous\ntraitement ARV sont dénutries 4 ). Par ailleurs, l’enquête SMART\n2010 révèle un taux de prévalence de la malnutrition aigüe\nglobale (MAG) de 12 % chez les réfugiés centrafricains de l’Est\net de l’Adamaoua (Graphique 1). La même enquête indique\nune prévalence de maigreur et de maigreur extrême chez les\nfemmes (respectivement 54,5 % et 11,3 %) ainsi qu’un taux de\nprévalence de l’anémie à 48,1% et de mortalité de 0,62 décès/10000 personnes/jour. Sur la même cible, l’enquête\nSMART 2011 a in", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000948:10:0:0", "start": 68, "end": 87, "surface": "données secondaires", "probe_tag": "confusion", "probe_score": 0.4929, "luna_label": 1, "luna_reason": "Analyse de données existantes produisant un constat concret sur la malnutrition."}]}, {"key": "paddy-235", "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_paddy", "spans": [{"key": "refugee_pads:000104:36:0:1", "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 activities, not demonstrated existing data use."}]}, {"key": "paddy-236", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\n**Figure 3: Implementation Arrangements**\n\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n\n73. **The proposed SNSOP will develop a comprehensive M&E framework and plan, building on the existing ones**\n**under the SSSNP.** The SNSOP will employ an innovative M&E system that relies primarily on electronic data collection to\nbe stored and managed in the MIS, building on the M&E system using the Geo-Enabling Initiative for Monitoring and\nSupervision (GEMS) developed under SSSNP to allow for real time data collection and analysis, thus improving the\nefficiency and reducing cost of M&E. M&E activities will also be embedded in project activities where possible to minimize\nthe burden on field-based staff. These flexible, remote arrangements allow the M&E system to adapt to various\ncircumstances in South Sudan’s FCV context. Key M&E activities will include Registration Lessons Learned surveys that\nwill assess the effectiveness of targeting and registration and identify areas for improvement. These surveys will provide\nbaseline information on key demographics and socioeconomic indicators that will be tracked over the course of the\nproject. There will also be Post Distribution Monitoring to monitor project implementation, mainly on payments under\ncomponents 1 and 2. LIPW and complementary social measure activities will be routinely monitored to ensure quality\nand assess results. Citizen engagement indicators will be monitored routinely, and the M&E plan will ensure the use of\ndiverse tools and methods (i.e., surveys, Focus Group Discussions, Key Informant Interviews) to develop a strong feedback\n\n\nPage 35 of 74", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000057:39:0:0", "start": 962, "end": 998, "surface": "Registration Lessons Learned surveys", "probe_tag": "confusion", "probe_score": 0.0788, "luna_label": 0, "luna_reason": "Planned surveys will generate baseline and monitoring information."}]}, {"key": "paddy-237", "text": "**_Procurement of Second Hand Goods_** _as specified under paragraph 5.11_ of the Procurement\nRegulations – is allowed for those contracts identified in the Procurement Plan tables _“Not_\n_Applicable”_\n\n\n**_Domestic preference_** _as specified under paragraph 5.51_ of the Procurement Regulations **_(Goods_**\n**_and Works)_** .\n\n\nGoods: is not applicable;\n\n\nWorks: is not applicable\n\n\n**Other Relevant Procurement Information.**\n\n\n_None._", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:009146:1:0:0", "start": 157, "end": 180, "surface": "Procurement Plan tables", "probe_tag": "confusion", "probe_score": 0.1289, "luna_label": 0, "luna_reason": "Procurement planning paperwork is routine project administration, not substantive data reuse."}]}, {"key": "paddy-238", "text": " spraying, so they had no experience in the supportive supervision or\n\n\ncommunity mobilization aspects of the intervention.\n\n\nBesides implementer quality, another key determinant of performance is the availability of re\n\nsources for intervention activities. In Table 7, we examine the average monthly budget dedicated to\n\n\nthis project as reported by the NGOs. 7 Since NGO-M implemented the project in two sub-districts\n\n\nin Mayurbhanj, and NGO-S1 and NGO-S2 implemented it in one block each in Sundargarh, we\n\n\nreport the average monthly per-village budget. As the budget breakdown shows, various items\n\n\n6The higher levels of ASHA motivation and job satisfaction conveyed in Table 5 may also speak to more responsive\nand effective health system management in Mayurbhanj district. Since we don’t directly observe this information, it\ncannot be ruled out as a potential dimension of difference contributing to differential performance.\n\n7The NGO-M budget data are for the periods January 2010 to September 2010 and October 2010 to March 2011.\nJanuary, February, and March 2011 were not included in the intervention; hence, we apportion the share of different\nbudget items in NGO-M’s October 2010 to March 2011 budget according to their share in the budget from January\n2010 to September 2010, and calculate the average monthly budget based on the data for January 2010 to September\n2010.\n\n\n14", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:006039:16:1:1", "start": 1358, "end": 1397, "surface": "data for January 2010 to September\n2010", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Existing budget data are used to calculate the average monthly budget."}]}, {"key": "paddy-239", "text": " the specific share attribu-_\n_ted to CP._\n\n**•** **“CP component (target > CP PIN**\n**target)”** = Funding reported under the\nProtection sector where Child Protection\nactivities is one component and is coupled\nwith activities from different sectors\nnot exclusively focusing on children, but\nCP activities are clearly identified. One\nexample is gender-based violence and CP\nactivities focusing on women and children.\n\n**•** **“Multiple sectors (shared)”** = Funding\nwith a CP component but with multiple\ndestination sectors – as no disaggregated\nsectoral data is available, the share of\nfunding for CP is unknown.\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**50** **•** **STILL UNPROTECTED: HUMANITARIAN FUNDING FOR CHILD PROTECTION** **STILL UNPROTECTED: HUMANITARIAN FUNDING FOR CHILD PROTECTION** **•** **51**", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "sample:reliefweb:001376:25:4:0", "start": 532, "end": 559, "surface": "disaggregated\nsectoral data", "probe_tag": "confusion", "probe_score": 0.1835, "luna_label": 0, "luna_reason": null}]}]