rafmacalaba/data-use-annotate

1[{"key": "aivin-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_aivin", "spans": [{"key": "fcv_pads_east_africa:018645:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Existing survey data supports enrollment-rate comparisons, despite lacking education-level separation."}]}, {"key": "aivin-001", "text": "\ncontrol soil erosion and enhance soil fertility. The survey results show that 92.8 percent of interviewed\nhouseholds have constructed bunds on their farmlands, which was by far larger than the baseline year. The\nfarmland area treated with bunds was also higher than the baseline year. The survey data reports that check\ndams were constructed on at least 44.6 percent of interviewed households’ land in contrast to 15.7 percent\nduring the baseline year. Integrating cutoff drains with bunds and terraces was also one of the conservation\nactivities aiming at the sustainability of bunds and terraces and preventing soil erosion. The results of\nhousehold surveys reveal that about 30 percent of households have implemented cutoff drains. The\nproportion of farmers who carried out this conservation structure has increased in the past six years.\n\n\n9. **Gully prevention and controlling measures.** Different kinds of gully prevention and controlling\nmeasures have been implemented to reduce impacts and increase production and productivity of the project\narea. The prevention measures included property development (such as locate and construct roads, fences,\nand laneways so that they cause minimal concentration and diversion of runoff); grazing management (such\nas fence off and exclude livestock from land vulnerable to gully erosion); and cropping management (such\nas control erosion on slopping cultivated land by stubble retention and the construction and maintenance of\ncontour banks and waterways). Gully erosion control measures include use of vegetation, gully reshaping\nand filling, controlling gully heads, and gully floor stabilization. Both prevention and control measures\nhave been implemented in all five sub-watersheds since 2008.\n\n\n10. **Forestry and agroforestry.** New community forests and household woodlots are planted, and\nbackyard plantation has been enhanced through the project support. In the past six years, plantation of trees\naiming at income generation, enrichment of soil fertility, and control of soil erosion and production of\nlivestock fodder was widely practiced. The results showed that about 94.9 percent of", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:018181:73:1:0", "start": 290, "end": 301, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9604, "luna_label": 1, "luna_reason": "Survey data reports concrete household construction percentages."}, {"key": "fcv_pads_east_africa:018181:73:1:1", "start": 643, "end": 660, "surface": "household surveys", "probe_tag": "keep", "probe_score": 0.9449, "luna_label": 1, "luna_reason": "Past household surveys support an attributed 30 percent finding."}]}, {"key": "aivin-002", "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_aivin", "spans": [{"key": "fcv_pads_east_africa:019321:62:2:1", "start": 758, "end": 796, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.9824, "luna_label": 1, "luna_reason": "Named database cited as source for the presented economic data table."}]}, {"key": "aivin-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_aivin", "spans": [{"key": "fcv_pads_east_africa:015645:38:1:1", "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 gaps and education-level comparison."}]}, {"key": "aivin-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_aivin", "spans": [{"key": "fcv_pads_east_africa:012429:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Existing survey data underpin enrollment comparisons and a stated disaggregation limitation."}, {"key": "fcv_pads_east_africa:012429:38:1:2", "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 a concrete finding about girls’ school attendance."}]}, {"key": "aivin-005", "text": "age 12 months, while the overall under-5 mortality rate was 64 deaths per 1,000 live\nbirths. The most at risk populations regarding HIV/ AIDS in Eastern Uganda include\nfemale sex workers (mostly youths). Death due HIV/AIDs leads to an increase in\nnumber of orphans as well as children in extremely poor households are at high risk\nof dropping out of school and becoming working children. Many children, with low\nhuman capital and in poor health, tend to grow up to become at-risk and\nunemployed youth. The proposed project, poses a risk of child labour due to a\nsignificant number of unemployed youths and children in the area.\n\nChild abuse takes many forms including child labour. Child labour (children below 17\nyears) within the district of Mbale is recorded at 25, 560 cases (24.7% of the total\nnumber of children), and in Busia District 24,919 (34.7%). (National Population and\nHousing Census, Area Specific Profiles, 2014). This indicates that a significant\npercentage of children is already working and as such, there is a risk that the proposed\nproject will employ children if the risk is not guarded against.\n\n\nEconomic Activities\n\nIn the eastern region, majority of the population depend on subsistence agriculture\nfor food, income and employment. According to the Population and Housing Census,\n2002, the main economic activities for majority of the households in the project areas\nwere dealing in crop farming as their main economic activity. The crops grown\ninclude millet, vegetables, sweet potatoes maize, sugar cane, and sorghum. Most of\nthe population in most of the districts, notably, in the age group 20 to 24 is involved\nin unpaid family work, followed by those self-employed commonly found in the age\ngroups 18 to 19 years.\n\nIn study area, people grow a variety of food and cash crops. 85% of the farmers are\nengaged in crop production as their main activity, 12% are", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:018691:33:0:1", "start": 1275, "end": 1304, "surface": "Population and Housing Census", "probe_tag": "keep", "probe_score": 0.9673, "luna_label": 1, "luna_reason": "2002 census data supports claims about households’ main economic activities."}]}, {"key": "aivin-006", "text": "sup>recently</sup> <sup>come</sup> <sup>under Government control.</sup>\n\n\n\n**2.** **Main sector** issues **and** <sup>**Government strategy:**</sup>\n\n\n\n_Sector Issues._\n_Poverty in Sierra Leone._ Sierra <sup>Leone has</sup> <sup>the lowest</sup> <sup>Human Development Index</sup> <sup>**in**</sup> <sup>the world</sup>\n\n\n\nand has a GNP per capita <sup>of only US$130</sup> <sup>compared</sup> <sup>to the average</sup> <sup>for Sub-Saharan</sup> <sup>Africa</sup> <sup>of $470.</sup>\n\n\n\nOver 82% of the population <sup>currently</sup> <sup>lives below</sup> <sup>the poverty line and life expectancy is only</sup> <sup>38 years.</sup>\n\n\n\nFertility, infant and child <sup>mortality are</sup> <sup>high</sup> <sup>and over</sup> <sup>a third</sup> <sup>of children and</sup> <sup>a fourth</sup> <sup>of adults</sup> <sup>are</sup>\n\n\n\nmalnourished. The pnmary <sup>school enrollment</s", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:013795:7:1:0", "start": 251, "end": 274, "surface": "Human Development Index", "probe_tag": "confusion", "probe_score": 0.8186, "luna_label": 1, "luna_reason": "Index supports a concrete lowest-in-world development ranking."}]}, {"key": "aivin-007", "text": "**Independent Evaluation Group (IEG)** Implementation Completion Report (ICR) Review\nET - Health SDG Program for Results (P123531)\n\n\n**8. M&E Design, Implementation, & Utilization**\n\n\n**a. M&E Design**\n\nThe objective of the program was clearly stated and reflected by the selected indicators. M&E\narrangements were well embedded institutionally and would use HSDP IV information system\ncomplemented by household and health facility surveys (p. 21). For population-based surveys, the program\nwould utilize the reliable data of the Demographic and Health Survey whose implementation is\ncoordinated by the Central Statistical Agency, an entity independent of FMOH. The provision of data would\nbe coordinated by FMOH and the Ethiopian Health and Nutrition Research Institute, assisted by the Gates\nFoundation, UNICEF and WHO. The PforR was planned to support ongoing government efforts to further\nstrengthen HMIS.\n\n\n**b. M&E Implementation**\n\nM&E Implementation was adequate overall, but with challenges encountered during the latter years of\nimplementation in view of the COVID-19 pandemic combined with security concerns that made\npopulation-based surveys more challenging. The balanced score card was dropped because the scheme\nwas replicated nationwide at an early stage (ICR, p. 27). Regular reporting facilitated verification\nprocesses and disbursements.\n\n\nThere were other surveys and studies conducted during the life of the program and they provided\npolicymakers with valuable information for decision making, such as the nutrition reports with a wide\nrange of data and recommendations for improving interventions supported by the program, e.g., Iron\nFolic Acid utilization, Vitamin A supplementation, and Growth Monitoring and Promotion.\n\n\n**c. M&E Utilization**\n\nM&E findings were used for program monitoring, including to: (i) take stock of progress on DLIs and\nidentify remedial actions if needed; (ii) identify areas requiring technical assistance; (iii) hold", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:001913:16:0:0", "start": 359, "end": 385, "surface": "HSDP IV information system", "probe_tag": "confusion", "probe_score": 0.7432, "luna_label": 0, "luna_reason": "Future planned use of the information system, not demonstrated existing data use."}, {"key": "fcv_pads_east_africa:001913:16:0:1", "start": 402, "end": 439, "surface": "household and health facility surveys", "probe_tag": "confusion", "probe_score": 0.1044, "luna_label": 0, "luna_reason": "Planned M&E use of surveys, not an existing survey cited as used."}]}, {"key": "aivin-008", "text": " (in **E** number 4.7 4.7 4.7 4.9 **5.3** **5.3** Monthly Progress MEMD/REA\nthousands).\n\n\n\n**1.3** Number of households using Solar E number 12 **16.3** **17** 20 **27** **32** Monthly Progress MEMD/REA\nPV systems (in thousands). report\n\n1.4 Capacity of solar PV systems sold\n**by** private companies supported **by** **E** kW 1400 **1738** **1750** **1800** 2000 2400 Monthly Progress MEMD/REA\nERT-2 (kW) report\n**1.5** Percent reduction in large Progress\nindustrial/commercial loads in target H percent **0** **30** **30** **30** **30** **30** Monthly report MEMD/REA\nlocations **(%)**\n2.1 Number of Community Progress\nInformation Centers established in un- H number **1533** **1533** **1533** **1833** **2083** **2083** Monthly report **PCU/UCC**\nserved and underserved areas\n2.2 Number of sub-counties with\npublic broadband Internet access **E** number **0** **0** **0** **7** **16** **16** Monthly Progress **PCU/UCC**\npoints\n\n\n**17** Para **10** of the text explains the breakdown of the connections as funded **by IDA,** and the OBA program.\n\n\n**_", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:016331:20:1:2", "start": 546, "end": 579, "surface": "Monthly report MEMD/REA\nlocations", "probe_tag": "confusion", "probe_score": 0.618, "luna_label": 0, "luna_reason": "Planned project monitoring report, not an independently analyzed data resource."}]}, {"key": "aivin-009", "text": "|USO|KSH|USO<br>CUMMULATIVE<br>|\n|OPENlNGBALANCEATlSTJULY2013|<br>4,479,006|<br>385,529,080|-|\n|||||\n|Add;||||\n|Totalamountdepositedbv WorldBank|8,406,256<br>|723,l 24,880<br>|18,065,709|\n|Totalinterestearnedifdepositedinaccount|-|-|-<br>|\n|Amountrefundedfor ineligibleexpenditure|-|-|~~-~~|\n|TOTAL|12,885,262|1,108,419,960|18,065,709|\n|Deduct;||||\n|Totalamountwithdrawn:||||\n|TransferredtoProject Account|2,660,111|228,828,676|7,840,558|\n|InTransitMEWNR|8,912,322|766,658,807|<br>8,912,322|\n|Total servicechar~e|-|-|-|\n|||||\n|CLOSINGBALANCEAT30JUNE2014|1,312,829|112,932,477|1,312,829|\n\n\nT'·e cumulative figures have been reconciled and agree to the Bank's Loan/Grants records as per Client\n\n~ nnection. The inf01mation used to prepare this Statement is obtained from Statement of special Aecom\nActivity prepared and signed by Treasury and Central Bank of Kenya (CBK) and attached as Annex 4 1\ntJ !Se financial statements. The conversion of the figures to Kenya shillings equivalent and the compilation<\n: ~nulative figures have been done by the Project based", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:012447:14:1:1", "start": 769, "end": 804, "surface": "Statement of special Aecom\nActivity", "probe_tag": "confusion", "probe_score": 0.6034, "luna_label": 0, "luna_reason": "Project financial account statement used for routine financial reporting"}]}, {"key": "aivin-010", "text": "The application of digital data collection regarding sidewalk conditions and user surveys, combined with\n\n\nthe use of several analytical tools, including a georeferenced inventory database and global walkability\n\n\nindex, helped the team test the innovative method in the studied corridor and allowed for the revealing\n\n\nof a comprehensive picture of sidewalk conditions and walkability from an urban design, road safety,\n\n\nand user experience perspective.\n\n\nIt is reassuring that the sidewalk network within the study area is adequately wide, with 39% over 2.5\n\n\nmeters, and secondly, 29% between 1.5 meters and 2.5 meters. Street lighting can be found in 48% of\n\n\nthe sidewalk network. The urban inventory also showed that sidewalk features are clearly better in the\n\n\nrecently built segments.\n\n\nNevertheless, even in this bustling urban corridor with LRT service, walkability is far from ideal for\n\n\npedestrians and public transport users. Considering key sidewalk features, nearly two-thirds of\n\n\ncrossings are inaccessible, and it is important to remember that most dangerous pedestrian behaviors\n\n\noccur at crossings. More than half of the network in this urban center corridor has no tactile pavement\n\n\nfor visually impaired users and no adequate street lighting. **As a result, 67% of the network falls**\n\n\n**under the C and D category levels of the Global Walkability Index, confirming the deficient,**\n\n\n**unsafe sidewalk conditions** .\n\n\nUsers further corroborated analytical findings with their own experience, citing that sidewalks are\n\n\ninaccessible (75%), unsafe in terms of infrastructure quality (66%) and unsafe in terms of exposure to\n\n\ntraffic (63%), and the majority of sidewalk users (70%) considers that streets are obstructed by street\n\n\nvendors, parking, or other obstacles.\n\n\nThese analytical findings will feed into the study’s formulation of short-term strategies and guidelines for\n\n\nsidewalk design and maintenance.\n\n\n**_Page 40 of 72_**", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:006691:45:0:0", "start": 156, "end": 188, "surface": "georeferenced inventory database", "probe_tag": "confusion", "probe_score": 0.8753, "luna_label": 1, "luna_reason": "Database data informed sidewalk analysis and produced concrete infrastructure findings."}, {"key": "fcv_pads_east_africa:006691:45:0:1", "start": 193, "end": 219, "surface": "global walkability\n\n\nindex", "probe_tag": "confusion", "probe_score": 0.8468, "luna_label": 1, "luna_reason": "Global Walkability Index data supports the reported sidewalk-condition finding."}]}, {"key": "aivin-011", "text": "**The World Bank**\nHealth Sustainable Development Goals Program-for-Results (P123531)\n\n\naudit backlog from a decade earlier). <sup>25</sup> [^25: Soon after the Program got off the ground there was a need to conduct a level 2 restructuring to extend the closing for the PFSA audits for\nFY2002 and FY2002 as the client was unable to comply in a timely manner (ISR#3).] There was also strong alignment with indicators in key government documents\n(i.e., GTP, HSDP) as well as the World Bank’s country assistance strategies for Ethiopia. One shortcoming noted was the\narticulation of the DLI on financial protection (‘functionality of the CBHI scheme’) which could have been more clearly\nstated.\n\n\n**M&E Implementation**\n\n\n46. **M&E Implementation was generally well conducted.** Regular DHS were carried out and funded through various\nsources; annual rapid health facility readiness surveys were integrated with the SPA+ surveys; and a balanced score card\nwas planned but had to be dropped as the scheme was replicated nationwide. There were other surveys and studies\nconducted during the life of the Program which provided policymakers valuable information for enhanced decision\nmaking, such as the nutrition reports which generated national or regional data on a wide range of indicators and\nrecommendations on improving critical interventions supported by the Program (i.e., IFA, VAS, GMP).\n\n\n47. **During the initial phase of implementation Program monitoring went smoothly with consistent data availability,**\n**strong compliance with DLIs, and a solid disbursement record** . During the phase following the approval of the AF, M&E\nimplementation experienced more difficulties and delays, as the Program covered a wider range of areas and as the\nCOVID-19 pandemic combined with security concerns made it difficult to conduct population-based surveys. On a\ngeneral note, the MOH/MDGPF/SDGPF produced regular reports to verify compliance with DLIs and enable the World\nBank team", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:007106:32:0:1", "start": 913, "end": 925, "surface": "SPA+ surveys", "probe_tag": "confusion", "probe_score": 0.6346, "luna_label": 1, "luna_reason": "Named existing survey integrated into the program’s M&E implementation."}]}, {"key": "aivin-012", "text": "* The project aimed at establishing four sectoral\nregulatory agencies in communication, electricity, transport and water and sewerage sectors, but only two (the\nUganda Communications Commission and the Electricity Regulatory Authority ) were established by project closure.\nFurthermore, while by close of the project an effort was underway to establish the regulatory institution for transport,\nit was decided that the water and sewerage sector would continue to be regulated by the line ministry responsible for\nthe sector. In effect, the design of the project in this area was over -ambitious and failed to recognize the capacity\n<u>constraints and time required to build consensus in reforms of this kind .</u>\n\n\n**5. Efficiency (not applicable to DPLs):**\nBecause of data limitations, the ICR could not calculate the NPV and the ERR using the original appraisal\nmethodology for all components. Instead, the NPV and the ERR were calculated at project completion for\ncomponents of the project related to severance payments and redeployment support for retrenched workers, which\naccounted for about 63 percent of total project costs (including both IDA and GOU). The estimated ex-post NPV is\nUS$114m for a 12 percent discount rate and ERR of 76 percent, compared to appraisal NPV of US$8.6m and ERR\nof 14.9 percent respectively for similar components. The large disparity between the appraisal and ex -post estimates\nof NPV/ERR could be explained by the poor quality of data at appraisal relative to those generated by the monitoring\nsystem set-up for the project (especially the Retrenched Employees Tracer Study and the Privatization Impact\nAssessment). Financial rates of return were not calculated.\n\n\n**a. If available, enter the Economic Rate of Return (ERR)/Financial Rate of Return** **(FRR) at appraisal and the**\n\n**re-estimated value at evaluation** **:**\n\n\nRate", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:012452:2:2:1", "start": 1623, "end": 1654, "surface": "Privatization Impact\nAssessment", "probe_tag": "confusion", "probe_score": 0.7304, "luna_label": 0, "luna_reason": "Assessment data were generated by the project’s monitoring system."}]}, {"key": "aivin-013", "text": "**1.3 Procurement exclusions**\n\n\n4. There are no activities or high value contracts in the Government’s program that should be\nexcluded from the PforR component in accordance with the World Bank's Policy and Directive on\nProgram-for-Results Financing. Therefore, the Program procurement does not involve procurements\nwithin the World Bank Operations Procurement Review Committee (OPRC) thresholds.\n\n\n**2. Scope**\n\n\n5. The scope covers MoE and TSC to determine whether the fiduciary systems provide reasonable\nassurance that there are in place systems that ensure increased transparency and efficiency in\nprocurement processes and contract management, and financial management to undertake the PforR\nprogram, the program under preparation for the Global Partnership for Education (GPE) for Kenya Basic\nEducation Equity in Learning Program (P176867).\n\n\n6. The PforR will focus resources on specific outcomes or results which are relevant for the Country\nto achieve equitable and quality basic education.\n\n\n**3. Review of Public Financial Management Cycle**\n\n\n**3.1 Planning and Budgeting**\n\n\n**3.1.1 Planning & Budgeting**\n\n\nOverall FM objective - the Program budget is realistic, is prepared with due regard to government policy\n<u>and is implemented in an orderly and predictable manner.</u>\n\n7. The government budgeting is anchored in the PFM Act 2012 and PFM Act Regulation 2015. The\nannual budget estimates are captured in the IFMIS using the standard chart of accounts (SCOA). The\nprogram budget and expenditures will be captured and reported through the specific SCOA codes in the\nIFMIS. The staff in Planning and Finance are well qualified and experienced.\n\n\n8. The program expenditure framework selected expenditures under State Department of Early\nLearning & Basic Education- primary education and cross cutting issues in all state departments focusing\non primary school, school capitation grants in lagging counties and camp-based refugee schools; activities\nsupported under primary education", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:006419:1:0:0", "start": 1430, "end": 1435, "surface": "IFMIS", "probe_tag": "confusion", "probe_score": 0.1116, "luna_label": 0, "luna_reason": "IFMIS records routine budget estimates and program expenditures for financial management."}]}, {"key": "aivin-014", "text": " At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA M&E data - NaCSA maintains lean and\n**community** **sub-projects and** decision-making by NaCSA; efficient organizational\n**NSAP** **partners monitored** structure\n**and evaluated** **in order to**\n**improve** **program**\n**effectiveness.**\n\n\n**3(d)** **Technical** **Assistance** 3d. 1 NaCSA staff indicate - IDA aide-memoires and\n**services** **effectively** **provided** satisfaction with technical project status reports\n**to support program** assistance, including skill\n**implementation** transfer activities\n\n\n**3(e)** NaCSA **management** 3e. 1 Project management - IDA Project Status reports\n**systems** **functioning** costs (NaCSA staff salaries at (including disbursement\n**effectively** **to ensure** all levels as well as operating reports);\n**program success** expenditures) are 13.5% or - NaCSA proposed annual\nless than total budgeted annual work program and budget\nexpenditures;                 - Annual audit reports;\n3e.2 NaCSA staff and                  - GOSL semi-annual PETS\npartners indicate satisfaction reports\nwith the performance of\nNaCSA's management;\n3e.3 NaCSA performance in\n\n\n                                  - 27", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:017677:31:1:0", "start": 170, "end": 184, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.149, "luna_label": 0, "luna_reason": "Names project monitoring data without showing an attributed finding or substantive analysis."}]}, {"key": "aivin-015", "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 <sup>least 90%</sup> <sup>of</sup> - 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 <sup>missions;</sup> - 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_aivin", "spans": [{"key": "fcv_pads_east_africa:019228:30:0:0", "start": 1658, "end": 1672, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Verification entry embedded in a logframe table, not an independent data-use mention."}]}, {"key": "aivin-016", "text": "**2.5** **Training of Asset Surveyors and Field Enumerators**\nThe training was conducted by the Sociologist and Resettlement Specialist, and covered the\nfollowing topics:\n\n  - Background to the Project;\n\n  - Principles of quantitative and qualitative research;\n\n  - Interviewing ethics and techniques, including exercises and role-playing;\n\n  - Questionnaire content;\n\n  - Practical use of equipment – GPS and camera; and\n\n  - Fieldwork logistics.\n\nThe training concluded with a practical application of the Socio-Economic survey, which also\nfunctioned as the pilot study. The group was responsible for interviewing one selected PAP –\nwith enumerators taking turns to ask questions. Thereafter all met in plenary with the trainers, to\nraise points for discussion and clarification before starting the full survey, and to edit the\nquestionnaire based on appropriateness to the social environment. Interviewers then worked in\npairs for a full day before working individually with the PAP. The additional enumerators were\ngiven personalized intensive training in preparation for the work at the time of employment.\n\n**2.6** **Data Analysis and Quality Control**\nQuantitative PAP census data was analyzed using the Statistical Package for Social Sciences\n(SPSS). The quantitative data has been presented in the form of descriptions, frequencies, tables\nand percentages.  Qualitative data from community dialogues and key institutional stakeholders\nwas manually analyzed around the major themes/objectives of the RAP.\n\n\n11", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:014651:34:0:1", "start": 1172, "end": 1187, "surface": "PAP census data", "probe_tag": "confusion", "probe_score": 0.5389, "luna_label": 1, "luna_reason": "Existing census data were analyzed and presented through frequencies, tables, and percentages."}, {"key": "fcv_pads_east_africa:014651:34:0:3", "start": 1367, "end": 1383, "surface": "Qualitative data", "probe_tag": "confusion", "probe_score": 0.7589, "luna_label": 1, "luna_reason": "Qualitative data from dialogues and stakeholders was analyzed for RAP themes."}]}, {"key": "aivin-017", "text": " any other measure of student performance\nwith/without the project. According to the ICR (p. 15), when the Uganda National Examinations Board\nconducted the EGRAs it did not include a control group of students whose teachers had not received\nEGR training. Furthermore, the ICR stated that a direct comparison of the EGRA results with results\nfrom UNICEF’s School Health and Reading Program (which was implemented between 2012 and\n2017 in 31 districts) and USAID’s Literacy Achievement and Retention Activity (LARA, which was\nimplemented between 2015 and 2020 in 28 districts) was not possible because the local languages\nand orthographies differed across the impacted regions under the three programs. Looking at results\ntrends, the ICR noted that, for USAID’s LARA program, there was a jump in the percentage of\nstudents reading 20 or more words per minute from 1 percent at P1 to 5.4 percent at P2 to 24.2\npercent at P3, which is a slightly lower level of improvement than that demonstrated by the cohort\nmeasured under this project. However, it is not clear for what time period the LARA program data was\nprovided and how the LARA program activities were comparable to this project, making it challenging\nto draw conclusions from the comparison. Overall, the data on student outcomes do not allow\nconvincing attribution of observed results to the project's interventions.\n\n\nAlthough there were achievements at the output level (training of teachers, improved teacher attendance, and\nschool inspections) and intermediate outcome level (student-textbook ratios), no information is provided on\nthe results of the school inspections with regard to teacher effectiveness (which measured, among other\nthings, teacher preparation and quality of teaching). As a result, there is limited evidence on the extent to\n\n\nPage 8 of 22", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:020317:7:1:1", "start": 1261, "end": 1285, "surface": "data on student outcomes", "probe_tag": "confusion", "probe_score": 0.1855, "luna_label": 1, "luna_reason": "Existing student-outcome data are assessed as insufficient for attribution."}]}, {"key": "aivin-018", "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": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:008267:65:1:0", "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 new data production."}]}, {"key": "aivin-019", "text": " the Registrar General in\nimplementation and coordination of all elements under this component to ensure that the silo approach to implementation is eliminated for better results.\nThe WBG technical team will support the PIU and URSB to re-focus implementation on the results framework and in particular, the outcomes. The\nmission team has also agreed to support the URSB in identifying quick wins that can be targeted for recognition in the 2017 Doing Business\nassessment. The team also noted that licensing reforms are largely focused on introduction of an ICT solution and very little has been done to ensure\nthat the administrative reforms are implemented in tandem with the ICT improvements for realization of the desired goal.\n_Component 3: Tourism Competitiveness Development_ has begun in a newly enhanced institutional environment including the strengthening of UTB.\nTORs for significant activities including the feasibility study and business plan for the Hotel and Tourism Training Institute have been prepared.\n_Component 4: Matching Grant Facility has progressed._ The PCU has completed the recruitment of MFG Unit staff and they have reported to work. They\ncomprise of: Fund Manager (1); Monitoring and Evaluation Officer (1); Business Advisors (4) – one for agriculture (coffee, edible oils, grains and pulse\nand horticulture; one for tourism; one for fisheries and One for ICT); and Accountant (1). Other staff to be shared with CEDP/PCU include:\nCommunication Officer and Environmental Officer. Additional 4 M&E regional staff will be hired on need-basis for monitoring of enterprise funded\nactivities. In the next 6 months, the studies to be undertaken include (i) Update the value chain analysis on the seven targeted subsectors, to be\ncompleted by end August, 2015. (ii) Undertake Baseline Data collection: which will be completed by end August, 2015 and following the availability of\nthe Baseline data, the Business Advisors will identify key performance indicators for their subsectors which will enable the MGF KPIs", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:012703:1:1:0", "start": 1800, "end": 1813, "surface": "Baseline Data", "probe_tag": "confusion", "probe_score": 0.1465, "luna_label": 0, "luna_reason": "Baseline data collection is planned for completion by August 2015."}, {"key": "fcv_pads_east_africa:012703:1:1:1", "start": 1908, "end": 1921, "surface": "Baseline data", "probe_tag": "confusion", "probe_score": 0.5417, "luna_label": 0, "luna_reason": "Baseline data collection is planned for future production."}]}, {"key": "aivin-020", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:011711:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": 0.1197, "luna_label": 0, "luna_reason": "Standalone source entry within a results-framework table"}]}, {"key": "aivin-021", "text": "|Actual (Previous)|Actual (Current)|Actual (Current)|Closing Period|Closing Period|\n|Indicator Name|Result|Month/Year|Result|Date|Result|Date|Result|Month/Year|\n|Update and Implement National<br>Health Adaptation Plan to Climate<br>Change (Yes/No)|No|Jun/2022|Yes|27-Mar-2025|Yes|27-Mar-2025|Yes|Jun/2026|\n|Update and Implement National<br>Health Adaptation Plan to Climate<br>Change (Yes/No)|Comments on achieving targets|Comments on achieving targets|• National HNAP to climate change finalized and endorsed • Training provided to regional climate change focal<br>persons • Regional HNAP prepared|• National HNAP to climate change finalized and endorsed • Training provided to regional climate change focal<br>persons • Regional HNAP prepared|• National HNAP to climate change finalized and endorsed • Training provided to regional climate change focal<br>persons • Regional HNAP prepared|• National HNAP to climate change finalized and endorsed • Training provided to regional climate change focal<br>persons • Regional HNAP prepared|• National HNAP to climate change finalized and endorsed • Training provided to regional climate change focal<br>persons • Regional HNAP prepared|• National HNAP to climate change finalized and endorsed • Training provided to regional climate change focal<br>persons • Regional HNAP prepared|\n\n\n**Disbursement Linked Indicators (DLI)**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|DLI Name|Col2|Baseline|Actual (Previous)|Col5|Actual (Current)|Col7|End Target|\n|---|---|---|---|---|---|---|---|\n|DLI Name|DLI", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:002859:15:1:0", "start": 1334, "end": 1364, "surface": "Disbursement Linked Indicators", "probe_tag": "confusion", "probe_score": 0.0725, "luna_label": 0, "luna_reason": "Standalone table heading, not an independently used data resource."}]}, {"key": "aivin-022", "text": "**_ABBREVIATED RESETTLEMENT ACTION PLAN (ARAP) REPORT_**\n**_FOR PROPOSED WATAMU SANITATION FACILITIES_**\n**_<u>FOR MAWASCO</u>_**\n\n\n**_5.1.2 Cut-off Date_**\n\n\nThe practical Cut-off Date for implementation of the ARAP which is the date of the commencement of the PAPs census and the socioeconomic\nsurvey was initiated and is as detailed below:\n\n\n**_Table 5-1: Cutoff Dates_**\n\n**Name of Facility** **Cutoff Date**\n\nMatsangoni AB 9 <sup>th</sup> of November 2020\n\nGede Mkt AB 11th of April 2019\n\nNo structure, businesses or any other asset established in the Project-Affected Area after the said dates shall be eligible for compensation.\n\nIt is worth noting that the Matsangoni site slightly changed to an adjacent plot following the construction of the Matsangoni Market under the County\nGovernment of Kilifi which consumed part of the identified site resulting to a slight change of PAPS. However, additional consultations were done\nduring the COVID-19 period through focused group discussions to adhere to the Government of Kenya rules aimed at minimizing the spread of the\npandemic necessitating for the change of the cut-off date from 30 <sup>th</sup> November 2018 to 09 <sup>th</sup> November 2020 where a fresh PAPS Census and social\neconomic survey was carried out and report updated accordingly.\n\n\n**_5.1.3 Eligibility_**\n\n\nAssets, including structures which were surveyed in the Project-Affected Area by the Cut-Off Date are eligible for compensation. People residing in\nthe Project-Affected Area by the Cut-Off Date are eligible for compensation even", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:007334:18:0:0", "start": 262, "end": 273, "surface": "PAPs census", "probe_tag": "confusion", "probe_score": 0.7441, "luna_label": 0, "luna_reason": "The census is being initiated and carried out for the ARAP."}, {"key": "fcv_pads_east_africa:007334:18:0:2", "start": 1217, "end": 1255, "surface": "PAPS Census and social\neconomic survey", "probe_tag": "confusion", "probe_score": 0.6523, "luna_label": 0, "luna_reason": "Sentence states the census and survey were carried out as project data production."}]}, {"key": "aivin-023", "text": ">collected by<br>Kenya<br>Continuous<br>Household<br>Survey (KCHS)|<br>KCHS poverty<br>estimates<br>produced and<br>disseminated|<br>Kenya<br>Integrated<br>Household<br>Budget Survey<br>KIHBS<br>2020/21<br>fieldwork<br>started|Annual|<br>Program<br>Progress<br>Report|<br>KNBS|\n|<br>**Produce better**<br>**real and external**<br>**sector economic**<br>**data**|<br>☐|<br>☒|<br>Yes/No|<br>Insufficient<br>capacity to meet<br>SDDS criteria in<br>all real and<br>external sector<br>data categories|Foreign<br>Investment<br>Survey (FIS)<br>conducted and<br>report produced<br>and available<br>on-line|Business<br>register<br>upgraded and<br>updated|Rebased<br>Consumer Price<br>Index (CPI)<br>series produced<br>and available<br>on-line|Rebased<br>Producer Price<br>Index (PPI)<br>series produced<br>and available<br>on-line|Rebased<br>National<br>Accounts<br>produced and<br>report available<br>on-line|Annual|<br>Program<br>Progress<br>Report/<br>SDDS<br>expert|<br>KNBS|\n|<br>**Improve access to**<", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:011647:43:3:0", "start": 186, "end": 191, "surface": "KIHBS", "probe_tag": "confusion", "probe_score": 0.1206, "luna_label": 0, "luna_reason": "Table cell identifies KIHBS while reporting ongoing fieldwork, not used existing data."}]}, {"key": "aivin-024", "text": " investigating<br>COVID-19 that are returned<br>within 72 hours of sample|<br> <br>Quarterly/<br>Annual<br>|Weekly IDSR<br>reports,<br>Periodic<br>Surveillance<br>Reports<br>|Desk review for<br>retrospective data,<br>periodic data collection.<br>|Ministry of Health<br>Integrated<br>Epidemiology, Epidemic<br>preparedness &<br>Response<br>|\n\n\nPage 42 of 55", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:020825:46:1:0", "start": 194, "end": 212, "surface": "retrospective data", "probe_tag": "drop", "probe_score": 0.0061, "luna_label": 0, "luna_reason": "Planned desk review and periodic collection, not demonstrated use of existing data."}]}, {"key": "aivin-025", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:008440:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0, "luna_reason": "Logframe M&E verification source, not existing data used for analysis"}, {"key": "fcv_pads_east_africa:008440:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": 0.1197, "luna_label": 1, "luna_reason": "Named administrative data source listed for program performance indicators."}]}, {"key": "aivin-026", "text": "including information on targeting, environment and social impacts and the GRM.\n\n**Enhanced Social Assessment and Consultation (ESAC)**\nThe project will conduct consultations to capture the views of disadvantaged and vulnerable members of\nthe community. Due to the Covid-19 pandemic, consultations that were scheduled to take place prior to\nappraisal have been postponed. Phase one social assessment with primary focus on desk review has been\nconducted. Following the lifting of State of Emergency, additional consultations planned to be conducted\nin selected new and old woredas taking into consideration COVID 19 restriction measures.\n\n**Communication materials**\nWritten information will be disclosed to the public through a variety of communications materials,\nincluding brochures, flyers, posters, etc. The communications materials will be produced by the\nFSCD.FSCD will also create a webpage on the Ministry of Agriculture’s website, to be updated regularly\nwith key project updates and reports on the project’s performance. The website will also provide\ninformation about the grievance mechanism for the project’s GRM.\n\n**Information table at the woreda level**\nInformation tables at the Woreda Food Security Desk will provide information to local residents, PAPs and\nstakeholders on SEASN’s project interventions and contact details of the stakeholder engagement focal\npoint. Brochures and fliers on various project related social and environmental issues will be made available\nat these information tables.\n\n**Program Review and Monitoring Surveys**\nFSCD will organize a number of surveys to assess the quality of program implementation. These will\ninclude: Impact Assessments, PW and Livelihoods Review, GRM Reviews, PW impact assessment, and\nGSD and nutrition (see Table 6).\n\n**Grievance Redress Mechanism**\nIn compliance with the World Bank’s ESS10, a project- specific grievance mechanism will be set up for\nthe project to handle complaints and issues (see Chapter 8). Detailed communications materials\n(specifically a GRM brochure or", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:013523:21:0:0", "start": 1519, "end": 1556, "surface": "Program Review and Monitoring Surveys", "probe_tag": "drop", "probe_score": 0.0477, "luna_label": 0, "luna_reason": "Surveys will be organized by the project to assess implementation."}]}, {"key": "aivin-027", "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": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:020600:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "drop", "probe_score": 0.0402, "luna_label": 0, "luna_reason": "Planning unit generates the statistical data; it is project-produced data."}]}, {"key": "aivin-028", "text": "|OP 4.12|Kenyan Legislation|Comparison|Recommendation to<br>Address Gap|\n|---|---|---|---|\n|claim to such land or<br>assets—provided<br>that<br>such<br>claims<br>are<br>recognized<br>under<br>the<br>laws of the country or<br>become<br>recognized<br>through<br>a <br>process<br>identified<br>in<br>the<br>resettlement<br>plan<br>(see<br>Annex 10 A, para. 7(f));<br>and19<br>(c) those who have no<br>recognizable legal right<br>or claim to the land they<br>are occupying<br>_To determine eligibility:_<br>Carry out resettlement<br>census. Cut off date for<br>eligibility<br>is<br>the<br>day<br>when the census begins.|‘occupants of land even if<br>they do not have titles’ and<br>payment made in good faith<br>to those occupants of land.<br>However,<br>this<br>does<br>not<br>include those who illegally<br>acquired land<br> <br> <br>Land Act 2012 provides for<br>census<br>through<br>NLC<br>inspection<br>and<br>valuation<br>process<br>|has an obligation to pay<br>in<br>good<br>faith<br>when<br>compulsory acquisition<br>is made.<br> <", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:019521:45:0:1", "start": 529, "end": 535, "surface": "census", "probe_tag": "drop", "probe_score": 0.0143, "luna_label": 0, "luna_reason": "The recommendation calls for carrying out a future resettlement census."}]}, {"key": "aivin-029", "text": " be disaggregated<br>by sex. Citizen engagement is an indicator. Annual targets.<br>The survey for the beneficiary satisfaction has not been done, due to the initial delay in<br>disbursing grants to the CIGs. However, Phases I, II and III of the grants have been disbursed<br>to the CIGs, and the survey is scheduled by February 2026.|This indicator estimates demand-side social accountability through engagement with project<br>beneficiaries and the extent to which project activities and outcomes are meeting<br>beneficiaries' demands. This is based on a perception survey administered to a<br>representative sample of beneficiaries including, VMGs and focus on fisheries and<br>aquaculture management and livelihood diversification. Survey results will be disaggregated<br>by sex. Citizen engagement is an indicator. Annual targets.<br>The survey for the beneficiary satisfaction has not been done, due to the initial delay in<br>disbursing grants to the CIGs. However, Phases I, II and III of the grants have been disbursed<br>to the CIGs, and the survey is scheduled by February 2026.|This indicator estimates demand-side social accountability through engagement with project<br>beneficiaries and the extent to which project activities and outcomes are meeting<br>beneficiaries' demands. This is based on a perception survey administered to a<br>representative sample of beneficiaries including, VMGs and focus on fisheries and<br>aquaculture management and livelihood diversification. Survey results will be disaggregated<br>by sex. Citizen engagement is an indicator. Annual targets.<br>The survey for the beneficiary satisfaction has not been done, due to the initial delay in<br>disbursing grants to the CIGs. However, Phases I, II and III of the grants", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:000382:5:1:1", "start": 843, "end": 882, "surface": "survey for the beneficiary satisfaction", "probe_tag": "drop", "probe_score": 0.041, "luna_label": 0, "luna_reason": "The beneficiary satisfaction survey has not been conducted and remains scheduled for the future."}]}, {"key": "aivin-030", "text": "**_Leased Assets_** _as specified under paragraph 5.10_ of the Procurement\nRegulations: 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)_** .\n\n\nGoods: is applicable for those contracts identified in the Procurement Plan\ntables;\n\n\nWorks: is applicable for those contracts identified in the Procurement Plan\ntables\n\n\n**Other** **_Relevant Procurement Information._**\n\n\n_None_", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:002007:1:0:0", "start": 258, "end": 281, "surface": "Procurement Plan tables", "probe_tag": "drop", "probe_score": 0.0388, "luna_label": 0, "luna_reason": "Routine procurement planning tables, not substantive data reuse."}]}, {"key": "aivin-031", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:019177:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0, "luna_reason": "Logframe verification data, not an already analyzed data use."}, {"key": "fcv_pads_east_africa:019177:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Listed as planned indicator verification data, not shown as already analyzed or used."}]}, {"key": "aivin-032", "text": "**_Leased Assets_** _as specified under paragraph 5.10_ of the Procurement\nRegulations: 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)_** .\n\n\nGoods: is applicable for those contracts identified in the Procurement Plan\ntables;\n\n\nWorks: is applicable for those contracts identified in the Procurement Plan\ntables\n\n\n**Other** **_Relevant Procurement Information._**\n\n\n_None_", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:012143:1:0:0", "start": 258, "end": 281, "surface": "Procurement Plan tables", "probe_tag": "drop", "probe_score": 0.0388, "luna_label": 0, "luna_reason": "Procurement planning tables are routine administrative paperwork, not substantive data use."}]}, {"key": "aivin-033", "text": "|Col1|c) Ensure that transaction level budget monitoring system is<br>strengthened at all implementing entities; maintain in excel<br>spread sheet,<br>d) Provide specific explanation for major budget variances by<br>component and implementing entities.<br>e) Consider supplementary budget if actual expenditure<br>exceeds proclaimed budget|d) As part of 3rd quarter IFR<br>e) June 30, 2024|d) PIU/MinT<br>e) PIU/MInT|\n|---|---|---|---|\n||**_Accounting_**<br>a) Share project FM manual to MoF<br>b) Consider incorporating segment reporting representing<br>implementing entity into the Peachtree chart of account to<br>facilitate identification of expenditures and balances of by<br>project implementing entities.<br>|<br>a) Immediate<br>b) Immediate|<br>a) PIU/MInT<br>b) PIU/MInT<br> <br>|\n||**_Internal control_**<br>a) Perform bank reconciliation monthly at MoF<br>b) All payment supporting documents to be marked as<br>“PAID”<br>c) Update fixed asset register to include newly acquired asset<br>and complete missing key information<br>d) Follow up receivable balances focusing on balances aged<br>b/n six months to one year for timely settlement. Follow<br>up supplier advance guarantees for timely renewal.<br>e) Follow up on travel advance for timely settlement.<br>f) <br>Reconcile<br>the<br>difference<br>b/n<br>", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:002366:23:0:0", "start": 942, "end": 962, "surface": "fixed asset register", "probe_tag": "drop", "probe_score": 0.0315, "luna_label": 0, "luna_reason": "Routine fixed-asset bookkeeping record, not substantive data reuse."}]}, {"key": "aivin-034", "text": " environmental impacts of this urban improvement project are less substantial,\nEIAs and/or EMPs will be prepared for all sub-projects, and a screening process will be\nfollowed to ensure no sub-projects are located in the immediate vicinity of Nairobi\nNational Park or any other National Parks in the NMR. Sub-projects that would involve\ndestruction of the limited forest cover in the NMR will be excluded from NaMSIP.\nWhile assistance on resettlement and environmental plans will be provided by existing\nlocal authorities/counties, the MoNMD and particularly local authorities/county\nenvironmental and social officers will require substantial help and additional training to\nundertake the socio-economic surveys, censuses, and Resettlement Action Plans that will\nbe necessary under NaMSIP. This can be achieved through additional MoNMD staff or\nexternal consultants (the former being the preferred option). Cut-off dates will be\nestablished as soon as project sites are definitively selected and the censuses are ready to\ncommence.", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:014908:4:1:1", "start": 713, "end": 721, "surface": "censuses", "probe_tag": "drop", "probe_score": 0.0478, "luna_label": 0, "luna_reason": "Censuses are planned to be undertaken under the project."}]}, {"key": "aivin-035", "text": " for baseline in 56 cities<br>(especially new cities and<br>cities that have not used<br>consultancy) on asset<br>management, asset<br>inventories baseline and for<br>revenue enhancement<br>strategies, plans and<br>implementation<br>|IDA / D2770||Post|Quality And Cost-<br>Based Selection|Open - International<br>||250,000.00|0.00|Pending<br>Implementation|2018-05-10||2018-05-31||2018-07-14||||2018-08-11||2018-09-10||2018-10-15||2018-11-19||2019-04-18||\n|ET-MUDH-60160-CS-QCBS /<br>computerized comprehensive<br>municipal revenue file<br>management and data base<br>formation<br>|IDA / D2770||Prior|Quality And Cost-<br>Based Selection|Open - International<br>||1,500,000.00|1,611,730.75|Signed|2018-06-08|2019-06-21|2018-06-29|2019-07-16|2018-08-12|2020-02-11|||2018-09-09|2020-03-27|2018-10-09|2020-06-29|2018-11-13|2020-11-16|2018-12-18|2020-12-01|2020-06-10||\n|ET-MUDH-56602-CS-QCBS /<br>Strategic TA/Studies - Gender<br>(ULGs gender audit, ULGs<", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:018029:4:1:0", "start": 514, "end": 536, "surface": "municipal revenue file", "probe_tag": "drop", "probe_score": 0.0048, "luna_label": 0, "luna_reason": "Fragment from a procurement table entry, not evidence of revenue data use."}]}, {"key": "aivin-036", "text": "could match 275 to the 1928 census.\n\n\n**4.1.3** **Integration outcomes of refugees**\n\n\nI measure the integration of refugees by examining different generations of refugees: (i) those\n\n\nborn in turkey who were forcibly resettled in Greece at a different age (ii) those born in Greece to\n\n\nrefugee parents, i.e. the second-generation.\n\n\n**First-generation** I measure the socio-economic integration of first-generation refugees by compar\n\ning the educational attainment of refugees displaced at a different age, i.e., either when adults\n\n\n(above 15) or when children (below 15). I rely on the 1928 census to obtain the literacy rate of\n\n\nrefugees at arrival, and focus on those older than 15 years in 1923. To measure the socio-economic\n\n\noutcomes of refugees at a later period, I unfortunately cannot draw on population censuses as no\n\n\ninformation on country of birth is available. The earliest census to provide this information is the\n\n2001 census and I use the 10 percent extract provided by IPUMS. <sup>19</sup> I identify as refugees the 3,486\n\n\nsampled individuals born in Turkey between 1908 and 1922, who represent about 9% of the pop\n\nulation born in the same period. Reassuringly, the share of individuals born in Turkey drops to\n\n\nless than 2% among cohorts born after 1922, while the share of foreign-born (in other countries\n\n\nthan Turkey) remains stable across cohorts and start increasing only after the 1950s (see Figure\n\n\nA.1 in the appendix). The 2001 census also allows to construct the rate of intermarriage between\n\n\nrefugees and natives. Among the 2,102 refugee women in the sample, 293 report to be married\n\n\nwith a currently co-residing husband - the other women being mostly widowed. When the hus\n\nband belongs to the surveyed household, I can identify mixed refugee-native couple", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001815:14:0:0", "start": 23, "end": 34, "surface": "1928 census", "probe_tag": "keep", "probe_score": 0.9533, "luna_label": 1, "luna_reason": "Census data provide refugees’ literacy rates at arrival."}, {"key": "prwp:001815:14:0:1", "start": 808, "end": 827, "surface": "population censuses", "probe_tag": "keep", "probe_score": 0.96, "luna_label": 1, "luna_reason": "Census data absence motivates use of the later 2001 census."}]}, {"key": "aivin-037", "text": " on indicators for each country, with Malawi as the base case, along with an indicator equal to 1 if the\nhousehold was located in an urban area, as opposed to a rural area. Data come from the most recent round of phone\nsurvey data available. This is round 3 in Ethiopia and Nigeria, round 2 in Malawi, and round 1 in Uganda. Robust\nstandard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).\n\n\n\nRelied on\n\n\n\nSale of\n\n<u>Assets</u>\n\n\n\nFood\n<u>Consumption</u>\n\n\n\nReduced\nNon-Food\n<u>Consumption</u>\n\n\n\n<u>Saving</u>\n\n\n\n51", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002105:52:3:0", "start": 213, "end": 230, "surface": "phone\nsurvey data", "probe_tag": "keep", "probe_score": 0.9334, "luna_label": 1, "luna_reason": "Existing phone survey rounds are declared as the source for reported indicators."}]}, {"key": "aivin-038", "text": " data from CIESIN (2018).<br>_he figure shows the average infant mortality rate at the cell level for 2015 using a gr_|**Figure A4:** Infant Mortality Rate in 2015<br>_Source:_ Authors’ analysis using data from CIESIN (2018).<br>_he figure shows the average infant mortality rate at the cell level for 2015 using a gr_|**Figure A4:** Infant Mortality Rate in 2015<br>_Source:_ Authors’ analysis using data from CIESIN (2018).<br>_he figure shows the average infant mortality rate at the cell level for 2015 using a gr_|**Figure A4:** Infant Mortality Rate in 2015<br>_Source:_ Authors’ analysis using data from CIESIN (2018).<br>_he figure shows the average infant mortality rate at the cell level for 2015 using a gr_|**Figure A4:** Infant Mortality Rate in 2015<br>_Source:_ Authors’ analysis using data from CIESIN (2018).<br>_he figure shows the average infant mortality rate at the cell level for 2015 using a gr_|**Figure A4:** Infant Mortality Rate in 2015<br>_Source:_ Authors’ analysis using data from CIESIN (2018).<br>_he figure shows the average infant mortality rate at the cell level for 2015 using a gr_|**Figure A4:** Infant Mortality Rate in 2015<br>_Source:_ Authors’ analysis using data from CIESIN (2018).<br>_he figure shows the average infant mortality rate at the cell level for 2015 using a gr_|**", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000171:51:202:0", "start": 1, "end": 17, "surface": "data from CIESIN", "probe_tag": "keep", "probe_score": 0.946, "luna_label": 1, "luna_reason": "CIESIN data underpin authors’ analysis of infant mortality rates."}]}, {"key": "aivin-039", "text": "the Indonesian Classification of Occupation 1982 that is used in the Sakernas.</mark>\nIn this process we mapped 899 occupations from the 914 occupation that were originally in the\nO*Net database. Using the ISCO 2008 we identify the same six occupational groups we identify\nin the STEP survey and that can be match to the Sakernas survey: <mark>administrative and managerial</mark>\n<mark>workers; professionals and technicians; clerical workers; sales and services workers; agriculture,</mark>\n<mark>animal husbandry, forestry workers, fishermen and hunters; and production workers, transport and</mark>\n<mark>equipment operators, and laborers.</mark>\n\n\n55", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000535:56:2:0", "start": 180, "end": 194, "surface": "O*Net database", "probe_tag": "confusion", "probe_score": 0.7933, "luna_label": 1, "luna_reason": "Database occupations were mapped to support occupational classification analysis."}]}, {"key": "aivin-040", "text": "**Figure** **2:** **Variation** **in** **correlation** **between** **wealth** **and** **pollution** **across** **countries–**\n**Panel a** plots the coefficients from country-level regressions of pollution measured as the average\nover the sample estimated against RWI. The unit of observation is a grid-cell within a country.\ngrid-cells are weighted by the inverse of the reported error in the RWI data. Grey spikes indicate\nthe 99% CI. The inset ( **Panel** **d** ) shows how the coefficients change when using the raw PM2 _._ 5\ndata (diamonds) versus the PM2 _._ 5 data with dust and sea salt removed (squares). **Panel** **b**\nmaps the coefficients from the same regression using raw PM2 _._ 5 data and **Panel** **c** maps the\nsame using data from the dust and sea salt removed PM2 _._ 5 data. The general pattern is robust to\ndefining pollution based on median and highest monthly exposure. In the maps the coefficients\nare bottom and top coded to -4, 4 respectively.\n\n\n8", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001241:9:0:2", "start": 682, "end": 700, "surface": "raw PM2 _._ 5 data", "probe_tag": "confusion", "probe_score": 0.3752, "luna_label": 1, "luna_reason": "Raw PM2.5 data are used in regressions mapping pollution coefficients."}]}, {"key": "aivin-041", "text": " A5 in the Appendix that confirm the robustness of these\n\n\nresults to the inclusion of market baseline controls or keeping randomization strata alone.\n\n\n**IV.2.1** **What** **did** **facilities** **invest** **in?**\n\n\nOne concern is that, in the absence of data on health outcomes, improvements in the JHIC score\n\n\ncould have been cosmetic with little likelihood of affecting downstream outcomes. As Section 3\n\n\nof the [Supplemental](https://www.dropbox.com/s/ygw3z4q98cmsd6i/KePSIE_Supplemental_Material.pdf?raw=1) Material shows, several checklist items could be fulfilled simply by printing\n\n\n18", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000910:19:2:0", "start": 256, "end": 279, "surface": "data on health outcomes", "probe_tag": "confusion", "probe_score": 0.3433, "luna_label": 0, "luna_reason": "States data absence without presenting analysis or substitute estimates."}]}, {"key": "aivin-042", "text": "farmers. <sup>16</sup> [^16: In the pre-treatment survey, only 31 farmers accurately reported their plot size; this increases to 125 in the posttreatment survey .] Of those who over- (under-)estimated initially, 76 (48) percent still over- (under-)\n\nestimated post-treatment.\n\n\n**Table 5: Information-induced changes in reporting behavior (treatment group plots only)**\n\n\n\nSR: SR: SR: number of\n\n<u>Post-planting reporting behavior</u> <u>Overestimate</u> <u>Underestimate</u> <u>Accurate</u> <u>observations</u>\n\nSR: Overestimated 76.0 15.5 8.5 647\nSR: Underestimate 36.5 48.4 15.1 304\n<u>SR: Accurate</u> <u>22.6</u> <u>0.00</u> <u>77.4</u> <u>31</u>\n<u>Total number of observations</u> <u>610</u> <u>247</u> <u>125</u>\n**<u>Notes:</u>** <u>Values are shares or percentage of plots over(under)-estimated across the post-planting and post-harvest rounds.</u>\n\n\n\n<u>Post-planting reporting behavior</u>\n\n\n\n<u>Post-harvest reporting behavior</u> <u>Total</u>\nSR: SR: SR: number of\n<u>Overestimate</u> <u>Underestimate</u> <u>Accurate</u> <u>observations</u>\n\n\n\nSR:\n<u>Underestimate</u>\n\n\n\nSecond, the response of farmers to accurate information appears highly asymmetric. Those\n\nwho overestimated their plot size pre-treatment", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001922:20:0:0", "start": 36, "end": 56, "surface": "pre-treatment survey", "probe_tag": "confusion", "probe_score": 0.3757, "luna_label": 1, "luna_reason": "Pre-treatment survey provides reported plot-size findings with observed counts."}]}, {"key": "aivin-043", "text": "coverage that allows obtaining results in 4 of the 5 regions, in which the national territory is divided:\nAtlantic, Eastern, Central, Pacific and finally the capital cities of departments of the Amazon and Orinoco.\nAdditionally, the survey is representative of the 32 capital cities of the country's departments and the\nfollowing 6 prioritized municipalities: Rionegro, Soledad, San Andres de Tumaco, Barrancabermeja,\nBuenaventura, and Yumbo.\n\n**_Administrative Data_**\n\n\nAdditional information was required to complement the household survey information. Some of the\nsources used in the development of the model are the following <sup>28</sup> [^28: For more information on the administrative data used in each section see the annexes in Nuñez et, al. (2019).] : (i) DIAN administrative data: Amount\nin returns for each of the income types and exempt income; (ii) Utilities Information System; (iii) Energy\nand gas rate bulletins; (iv) Investment Project monitoring system; (v) School Meals Program Report for\nJanuary-December 2017; (vi) The information available in the Pension Fund; (vii) Local Fiscal Account; (viii)\nInformation from the General System of Transfers; (ix) Resolution 6411 of 2016; (x) The tax statute in\nforce in 2017 which is Law 1819 of December 29, 2016; (xi) The 2010 input-output matrix on 2005 basis <sup>29</sup> [^29: In this version of the document, the 2010 input-output matrix was used since that for 2015 had not yet been published by DANE.] ;\n(xii) The matching between the products of the ENPH, the CPC 2.0 and the national accounts; and (xiii)\nThe reference prices of alcoholic beverages and tobacco products for the year 2017.\n\n###### ii. Methodology\n\nFor the incidence analysis of each one of the fiscal interventions, and the impact on poverty and inequality\nof each one of the taxes", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002179:15:0:0", "start": 768, "end": 792, "surface": "DIAN administrative data", "probe_tag": "confusion", "probe_score": 0.8701, "luna_label": 1, "luna_reason": "Named administrative data source used to develop the fiscal incidence model."}, {"key": "prwp:002179:15:0:5", "start": 1092, "end": 1112, "surface": "Local Fiscal Account", "probe_tag": "confusion", "probe_score": 0.7835, "luna_label": 1, "luna_reason": "Named administrative source listed among data used to develop the model."}]}, {"key": "aivin-044", "text": "#### ONLINE APPENDICES **A Additional Figures and Tables**\n\nFigure A.1: Test of differential pre-trends in under-5 mortality by firstborn sex separately for boys\nand girls\n\n\nNOTES: This figure tests for parallel trends in under-5 mortality by firstborn sex during the pre-ultrasound\nperiod (1973-1984) for male and female births of parity _>_ 1. The graphs plot the coefficients of the _First_ _girl_\nx _Year_ indicator variables (and the 95% confidence intervals) from the regression of under-5 mortality on the\nfull set of interactions between indicators for _First_ _girl_ and _Year_, without other controls, for girls in the top\nfigure and for boys in the bottom figure. The omitted year is 1973. Data: NFHS.\n\n\n47", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001935:49:0:0", "start": 707, "end": 711, "surface": "NFHS", "probe_tag": "confusion", "probe_score": 0.7179, "luna_label": 1, "luna_reason": "NFHS data underpin the figure testing mortality pre-trends."}]}, {"key": "aivin-045", "text": "Gunasekera et al., 2015; Rentschler and Salhab, 2020; Ward et al., 2020). Gross Domes\ntic Product (GDP) is a critical economic indicator in the measurement and monitoring of\n\nan economy in a country that is typically only available at national and occasionally sub\nnational levels. Regional indicators play a key role in the necessary variation to forecast\n\nregional GDP (Lehmann and Wohlrabe, 2015) and food security (Andree et al., 2020). Pre\nvious efforts to estimate local GDP use high resolution spatial auxiliary information such as\n\nluminosity or population data to provide local variation. Methods by Kummu, Taka, and\n\nGuillaume (2018), Murakami and Yamagata (2019), Nordhaus (2006), and World Bank and\n\nUNEP (2011) took advantage of gridded population data, which is the result of a model\n\ndisaggregating the most detailed level population data into grids (e.g. see review in Leyk et\n\nal., 2019). However, wealth is not evenly distributed among people nor infrastructure (Berg,\n\nBlankespoor, and Selod, 2018). In fact, the divide between the rich and poor is even widening\n\nin our time (Dabla-Norris et al., 2015). The method used in World Bank and UNEP (2011)\n\nstratify the population by rural and urban, yet definition of these geographic areas can vary\n\nbased on the selection of the population model (Leyk et al., 2019). These measurements\n\nmatter in application to stylized facts such as the strong negative correlation of the level of\n\nurbanization with the size of its agricultural sector (Roberts et al., 2017). Also, the uniform\n\ndistribution of labor in agriculture is another key concern (Gollin, Lagakos, and Waugh,\n\n2014). Other methods used land cover such as vegetation and built-up indices, however did\n\nnot incorporate", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000596:4:0:1", "start": 742, "end": 765, "surface": "gridded population data", "probe_tag": "confusion", "probe_score": 0.6356, "luna_label": 1, "luna_reason": "Existing modeled population data were used to provide local GDP variation."}]}, {"key": "aivin-046", "text": " which\nenables us to study the heterogeneity of implications of school closures for mobility across different parts of\nthe world. Second, unlike Neidhofer et al. (2021), which is based on pre-pandemic survey data and relies on\nassumptions of pandemic learning modalities based on policies deployed by different countries and certain\nhousehold characteristics, we are able to draw from some observed data from the World Bank’s high-frequency\nphone surveys on what types of learning (if any) children from different types of households are reported to\nhave engaged in during periods of school closures, and the latest learning-loss simulation scenarios from\nAzevedo et al (2022) which now include observed actual school closure data from the <u>[UNESCO school](https://en.unesco.org/covid19/educationresponse#durationschoolclosures)</u>\n<u>[closures tracker.](https://en.unesco.org/covid19/educationresponse#durationschoolclosures)</u>\n\n\nThe main findings of the paper are that the learning losses associated with the extensive school closures around\nthe world due to COVID-19 are likely to translate into economically significant reductions in both absolute\nand relative educational mobility across generations. We estimate that in high-income and in upper middleincome countries the average share of children with more years of education than their parents would decline\nby 8-9 percentage points, with smaller average declines estimated for low-income countries. The results imply\nthat the impact of the learning losses associated with the pandemic would worsen the pre-existing trend of\ndeclining absolute mobility in Upper-Middle Income (henceforth, UMIC) and High-Income (HIC) groups of\ncountries, and reverse the improvements in absolute mobility for Low-Income (LIC) and Lower-Middle\nIncome (LMIC) country groups. <sup>3</", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001124:4:1:0", "start": 188, "end": 212, "surface": "pre-pandemic survey data", "probe_tag": "confusion", "probe_score": 0.7916, "luna_label": 1, "luna_reason": "Existing survey data is identified as the basis of a cited study."}]}, {"key": "aivin-047", "text": " school closures, partial closures<br>are assumed to affect 85% of the student population.<br>|Schools assumed to be closed uniformly for 5 months<br>in a 10-month school year.<br>|\n|School closure assumptions:<br>Pessimistic|Observed country-level school closures, partial closures<br>are treated as full closures.|Schools assumed to be closed uniformly for 7 months<br>in a 10-month school year.|\n|<br>School closure assumptions:<br>Very Pessimistic|<br>N/A|<br>Schools assumed to be closed uniformly for 9 months<br>in a 10-month school year.|\n\n\n\n15 We impute missing values for share of school system closed by using the regional average by income level for countries with\nlearning data (LAYS, LP, or PISA). The countries are: Belarus; Burundi; the Democratic Republic of Congo; the Republic of Congo;\nthe Arab Republic of Egypt; Hong Kong SAR, China; the Islamic Republic of Iran; the Republic of Korea; Kosovo; Macao SAR,\nChina; Micronesia, Fed Sts.; Nauru; St Kitts and Nevis; St Lucia; St Vincent and the Grenadines; Tajikistan; and the Repbulci of\nYemen.\n\n\n6", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000989:7:3:2", "start": 698, "end": 700, "surface": "LP", "probe_tag": "drop", "probe_score": 0.0313, "luna_label": 0, "luna_reason": "Acronym alone names no eligible data resource or independent source."}]}, {"key": "aivin-048", "text": "**Table A.4.2: Comparing weighted Internet Survey data with GMD for the model variables, Arab**\n\n|Col1|Republic|of Egypt|Col4|Col5|\n|---|---|---|---|---|\n||GMD|RIWI-wave1|RIWI-wave2|RIWI-wave3|\n|Age between 15 and 24|31.71%|30.00%|35.13%|43.59%|\n||(29.91,33.51)|(25.40,34.60)|(27.64,42.63)|(33.30,53.88)|\n|Age between 25 and 54|51.93%|52.14%|56.92%|52.95%|\n||(47.53,56.32)|(35.02,69.27)|(38.75,75.09)|(44.16,61.74)|\n|Age between 55 and 100|16.36%|17.86%|7.95%|3.46%|\n||(10.45,22.27)|(0.25,35.47)|(-3.89,19.78)|(-4.77,11.69)|\n|Female|49.25%|51.31%|43.23%|44.87%|\n||(47.95,50.55)|(40.75,61.87)|(34.20,52.26)|(23.39,66.35)|\n|Urban|66.15%|72.91%|72.10%|72.82%|\n||(", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000811:33:0:0", "start": 25, "end": 54, "surface": "weighted Internet Survey data", "probe_tag": "confusion", "probe_score": 0.628, "luna_label": 0, "luna_reason": "Span is part of a table title/header, not an independent data mention."}, {"key": "prwp:000811:33:0:1", "start": 182, "end": 192, "surface": "RIWI-wave3", "probe_tag": "drop", "probe_score": 0.0188, "luna_label": 0, "luna_reason": "Standalone table column header fragment."}]}, {"key": "aivin-049", "text": "Jain, M., P. Mondal, G. L. Galford, G. Fiske, and R. S. DeFries (2017a), “An automated approach to map\n\nwinter cropped area of smallholder farms across large scales using MODIS imagery.” _Remote_ _Sensing_, 9,\n566, URL `https://doi.org/10.3390/rs9060566` .\n\n\nJain, M., P. Mondal, G. L. Galford, G. Fiske, and R. S. DeFries (2017b), “India annual winter cropped area,\n\n2001-2016.” URL `https://doi.org/10.7927/H47D2S3W` .\n\n\nMerfeld, Joshua D (2019), “Spatially heterogeneous effects of a public works program.” _Journal_ _of_ _Develop-_\n\n_ment_ _Economics_, 136, 151–167.\n\n\nModanesi, Sara, Christian Massari, Stefania Camici, Luca Brocca, and Giriraj Amarnath (2019), “Perfor\nmance of a drought standardized soil moisture index based on ESA CCI Soil Moisture product: validation\nin India using crop data.” In _Geophysical_ _Research_ _Abstracts_, volume 21.\n\n\nMukherjee, Abhijit, Dipankar Saha, Charles F Harvey, Richard G Taylor, Kazi Matin Ahmed, and Soumen\ndra N Bhanja (2015), “Groundwater systems of the Indian sub-continent.” _Journal_ _of_ _Hydrology:_ _Regional_\n_Studies_, 4, 1–14.\n\n\nMuralidharan, Karthik, Paul Niehaus, and Sandip Sukhtankar (2016), “Building state capacity: Evidence\n\nfrom biometric smartcards in India.” _American_ _Economic_", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001627:28:0:0", "start": 793, "end": 802, "surface": "crop data", "probe_tag": "drop", "probe_score": 0.0193, "luna_label": 0, "luna_reason": "Generic phrase within a bibliography entry, not an independently used data source."}]}, {"key": "aivin-050", "text": " 19<br>20 − 99<br>100+<br>Keep formal account|\n|0<br>20<br>40<br>60<br>80 100<br>Burkina Faso (2015)<br>1 − 4<br>5 − 9<br>10 − 19<br>20 − 99<br>100+<br>Keep formal account (SYSCOA)|0<br>20<br>40<br>60<br>80 100<br>Cameroon (2008)<br>1 − 4<br>5 − 9<br>10 − 19<br>20 − 99<br>100+<br>Tax registeration|0<br>20<br>40<br>60<br>80 100<br>Ghana (2013)<br>1 − 4<br>5 − 9<br>10 − 19<br>20 − 99<br>100+<br>Tax registeration|0<br>20<br>40<br>60<br>80 100<br>Rwanda (2013)<br>1 − 4<br>5 − 9<br>10 − 19<br>20 − 99<br>100+<br>Tax registeration|\n\n\n\n52", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000985:53:1:0", "start": 281, "end": 298, "surface": "Tax registeration", "probe_tag": "drop", "probe_score": 0.0049, "luna_label": 0, "luna_reason": "Standalone chart label, not an independently cited or used data resource."}]}, {"key": "aivin-051", "text": ">Stocktaking of Global Forced Displacement Data.</mark>_ <mark>Policy Research working paper,</mark>\n<mark>no. WPS 7985; Policy Research Working Paper; No. 7985. World Bank, Washington, DC. ©</mark>\n<mark>[World Bank. URL: https://openknowledge.worldbank.org/handle/10986/26183](https://openknowledge.worldbank.org/handle/10986/26183)</mark> <mark>License: CC BY</mark>\n<mark>3.0 IGO.</mark>\n\n\nSingh, N. S., et al. (2021). _Research in forced displacement: guidance for a feminist and decolonial_\n_approach._ [The Lancet, 397(10274), 560-562. URL: https://doi.org/10.1016/ S0140-](https://doi.org/10.1016/S0140-6736%0x2821%0x2900024-6)\n<u>[6736(21)00024-6](https://doi.org/10.1016/S0140-6736%0x2821%0x2900024-6)</u>\n\n\nShemyakina, O. (2011). The effect of armed conflict on accumulation of schooling: Results from\nTajikistan. Journal of Development Economics, 95(2), 186-200. URL:\n<u>[https://www.sciencedirect.com/science/article/abs/pii/S0304387810000441](https://www.sciencedirect.com/science/article/abs/pii/S0304387810000441)", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000892:22:1:0", "start": 16, "end": 47, "surface": "Global Forced Displacement Data", "probe_tag": "drop", "probe_score": 0.0435, "luna_label": 0, "luna_reason": "Standalone publication title in a bibliography entry"}]}, {"key": "aivin-052", "text": "**Table A.12:** Effects on Self-Reported Bureaucratic Knowledge\n\n\n<u>Training</u> <u>Village Law</u> <u>Knowledge index</u>\n\n\n(1) (2) (3)\n\n\nNew village head -0.089 0.019 0.088\n(0.117) (0.107) (0.119)\n\n\nObservations 1067 1065 1065\nControl mean 0.61 0.76 0.12\n\n\nRobust p-value 0.313 0.886 0.391\nMSE-opt. bandwidth 19.1 17.8 28.4\nEffective obs. 500 476 662\n\n\n_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al.\n(2014). Units of observation are bureaucrats in all columns. The dependent variable is: in column 1, a dummy equal to 1 if\nthe bureaucrat received any training in the past 12 months; in column 2, a dummy equal to 1 if the bureaucrat reports being\ninformed about Village Law regulations; in column 3, a standardized index of self-reported knowledge across 5 topics:\ndevelopment management & accountability, financial management, village regulations, drafting development plans, and\nthe Village Law. See Section 4 for details.\n\n- p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.\n\n\n**Table A.13:** Effects on Village Transfers and Budgets\n\n\n**Administrative data** **Village head survey**\n\n\nVillage Funds: <u>Allocated</u> <u>Utilized</u> <u>% Spent</u> <u>Fully Spent</u> <u>Budget</u", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001457:54:0:0", "start": 1189, "end": 1208, "surface": "Administrative data", "probe_tag": "drop", "probe_score": 0.023, "luna_label": 0, "luna_reason": "Standalone table header naming an administrative data source."}]}, {"key": "aivin-053", "text": " to 9; higher scores indicate greater protection against<br>noncommunicable diseases. Data are for individuals ≥15 years of age from the 2021 Gallup World Poll for<br>2022<br>|World Gallup Poll<br>(GDQP) / Food systems<br>dashboard<br>|\n|**NCD-Risk** score among adults ≥ 15<br>|NCD-Risk scores are on a scale of 0 to 9; higher scores indicate greater risk for noncommunicable diseases.<br>Data are for individuals ≥15 years of age from the 2021 Gallup World Poll for 2022<br>|World Gallup Poll<br>(GDQP) / Food systems<br>dashboard<br>|\n|Prevalence of**overweight & obesity** <br>among women (20 - 49 years) %<br>|Percentage of women 20–49 years of age with a BMI greater than or equal to 25 kg/m2 in 2016<br>|UNICEF Global Database<br>(based on NCD-RisC)<br>|\n|Prevalence of**underweight** among<br>women (20 - 49 years) %<br>|Percentage of women 20–49 years of age with a BMI less than 18.5 kg/m2 in 2016<br>|UNICEF Global Database<br>(based on NCD-RisC)|\n|Adult**diabetes**prevalence<br>|Proportion of adults aged 18+ years with diabetes. Diabetes is defined as having a fasting glucose of 7.0<br>mmol/L or higher, being on medication for raised blood glucose, or having a past diagnosis of diabetes. Data<br>for 2014, except 1 country 2003, 1 country 2005, 2 countries 2010, 1 country 2012", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001312:33:1:3", "start": 747, "end": 755, "surface": "NCD-RisC", "probe_tag": "confusion", "probe_score": 0.0823, "luna_label": 1, "luna_reason": "Named source underlying the reported overweight and obesity prevalence indicator."}]}, {"key": "aivin-054", "text": "---|\n||Sentinel-5P<br> <br>|Sentinel-5P<br> <br>|Sentinel-5P<br> <br>|Google Traffic|Google Traffic|\n||CAMS-3<br>|<br>Aerosol<br>Prediction<br>|NO2<br>Prediction<br>|<br>CAMS-3<br>|<br>Prediction<br>|\n|N <br>|<br>362<br>|<br>434<br>|<br>406<br>|<br>34<br>|<br>93<br>|\n|<br>min<br>|<br>11<br>|<br>13<br>|<br>14<br>|<br>14<br>|<br>21<br>|\n|<br>p10<br>|<br>22<br>|<br>25<br>|<br>25<br>|<br>23<br>|<br>23<br>|\n|<br>p25<br>|<br>35<br>|<br>31<br>|<br>31<br>|<br>31<br>|<br>26<br>|\n|<br>p50<br>|<br>55<br>|<br>43<br>|<br>44<br>|<br>84<br>|<br>36<br>|\n|<br>p75<br>|<br>87<br>|<br>70<br>|<br>70<br>|<br>125<", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000489:15:1:4", "start": 70, "end": 84, "surface": "Google Traffic", "probe_tag": "drop", "probe_score": 0.0381, "luna_label": 0, "luna_reason": "Standalone table header naming a traffic data source."}]}, {"key": "aivin-055", "text": "br>**Location**<br>India (state of Andhra Pradesh)<br>US<br>Australia<br>US<br>**Sample restrictions/**<br>**Mother characteristics**<br>Sample restricted to households  in rural<br>areas in both periods<br>**Sample**|**Data source**<br>YLS<br>NLSY<br>HILDA<br>ECLS-K<br>**Years**<br>2007 & 2009-2010<br>biannually from 1986-1998<br>2,007<br>fall 1998, spring 1999, fall 1999, spring<br>2000, spring 2002, spring 2004<br>**Sample size**<br>3,725<br>16,650 child-year observations from 6283<br>mothers; sample size for FE ranges from<br>4,159 for child FE to 7919 for sibling FE<br>907 (IV), 430 (mother FE)<br>18,990 person-years for 6,330 indivdiuals<br>**Location**<br>India (state of Andhra Pradesh)<br>US<br>Australia<br>US<br>**Sample restrictions/**<br>**Mother characteristics**<br>Sample restricted to households  in rural<br>areas in both periods<br>**Sample**|**Data source**<br>YLS<br>NLSY<br>HILDA<br>ECLS-K<br>**Years**<br>2007 & 2009-2010<br>biannually from 1986-1998<br>2,007<br>fall 1998,", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001080:25:3:0", "start": 237, "end": 240, "surface": "YLS", "probe_tag": "drop", "probe_score": 0.0126, "luna_label": 0, "luna_reason": "Survey name appears as a standalone table cell under Data source."}, {"key": "prwp:001080:25:3:2", "start": 252, "end": 257, "surface": "HILDA", "probe_tag": "drop", "probe_score": 0.0126, "luna_label": 1, "luna_reason": "Named survey listed as the study's data source."}]}, {"key": "aivin-056", "text": "M. Regulatory substance- quality of supply index for the Philippines and comparators, 2015\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Col1|Colombia|Peru|Philippines|Vietnam|International<br>benchmark|\n|---|---|---|---|---|---|\n|**Quality Regulation**<br>|100%|100%|92%|71%|75%|\n|<br>**Quality of Service Standards**|100%|100%|100%|75%|82%|\n|<br>Requirement to meet quality of service standards<br>|||||100%|\n|Specific quality of service standards are formally<br>written and publicly available for- quality of the<br>product, quality of the service and customer<br>service|||||97%|\n|Performance on quality of service standards is<br>public<br>|||||71%|\n|Fines for failing to meet quality of service<br>standards<br>|||||59%|\n|**Quality of Service Enforcement**|100%|100%|83%|67%|68%|\n|<br>Requirement to report technical data on a<br>periodic basis|||||100%|\n|<br>Regulator specifies how to collect technical<br>performance data|||||71%|\n|<br>Regulator reviews or validates technical<br>performance data|||||47%|\n|<br>Automated information management systems<br>are required to measure the quality or reliability<br>of the power supply|||||71%|\n|Measurements of the quality or reliability of<br>power supply are made public|||", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001786:69:0:1", "start": 883, "end": 899, "surface": "performance data", "probe_tag": "drop", "probe_score": 0.007, "luna_label": 0, "luna_reason": "Standalone table row fragment, not an independently used data resource."}]}, {"key": "aivin-057", "text": "sup>9</sup> . 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% <sup>10</sup> ), suggesting an overall improvement in the economic well-being of Ukrainian refugees over time.\n\n\n**<u>REFUGEE VERSUS HOST POVERTY RATES BY COUNTRY</u>**\n\n\nUkrainian refugees (2024) Host country nationals (2023)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Hungary Moldova Poland Romania Slovakia Estonia Latvia Lithuania Region\n\n\nNote: Poverty rates for all countries apart from the Republic of Moldova are based on a calculation that follows Eurostat’s at-risk-of-poverty (AROP)\nmethodology with the at-risk-of-poverty threshold set at 50% of the national median disposable income after social transfers. Refugee disposable\nincome has been computed based on survey data. For the Republic of Moldova, the poverty threshold was taken to be the 4Q23 absolute poverty line\nreported by the National Bureau of Statistics of Moldova.\n\n\nSource: Survey data, <u>[Eurostat,](https://ec.europa.eu/eurostat)</u> <u>[National Bureau of Statistics of Moldova, SAG estimates](https://statistica.gov.md/en)</u>\n\n\n7. The MSNA, which ran in 7 countries: Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and Slovakia\n8. Equivalized as per <u>[Eurostat methodology. Essentially income per person, but with household members beyond the first one](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Equivalised_income)</u>\nassigned weights less than", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:3:1:0", "start": 872, "end": 883, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9577, "luna_label": 1, "luna_reason": "Survey data underlies the computed refugee disposable-income measure."}]}, {"key": "aivin-058", "text": "\nand accommodation type. All interviews were conducted face to face. As a probabilistic selection of\nrespondents could not be ensured, the primary goal was to collect a diverse sample that would reflect the\npopulation’s composition as closely as possible.\n\n\nFor the regional analysis, weights were applied based on the most up-to-date estimates of the number of\nrefugees staying in each country. This allowed calculated indicators to more accurately represent the broader\nrefugee population across the region.\n\n\nAppropriate measures were implemented to ensure the protection of personal data and to guarantee\nconfidentiality in all data collection and processing activities. Consent was requested and recorded for all\nselected participants, providing clear information on the purpose, and expected use of the data.\n\n\nWith the exception of Republic of Moldova, the poverty line for each country was defined as 50% of the median\n<u>[equivalized disposable income (after social transfers) as reported by](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Equivalised_disposable_income)</u> <u>[Eurostat for 2023. This figure was indexed](https://ec.europa.eu/eurostat)</u>\ntowards 2024 using national annual wage inflation data for the third quarter of 2024. As the Republic of\nMoldova does not currently run the SILC survey, the absolute poverty line as reported by the country’s National\nBureau of Statistics was used instead. Host poverty rates were based on the same poverty threshold. With the\nexcept of Republic of Moldova, these were equal to the at-risk-of-poverty (AROP) rate reported by Eurostat. On\n\n\n**14**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:13:1:0", "start": 1215, "end": 1250, "surface": "national annual wage inflation data", "probe_tag": "keep", "probe_score": 0.9279, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:13:1:1", "start": 1336, "end": 1347, "surface": "SILC survey", "probe_tag": "keep", "probe_score": 0.9283, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-059", "text": ".\nRegionally, the Ukraine refugee NEET rate tends to\nincrease with age, as it becomes more affected by\nunemployment, which is high for the 15 – 24 cohort\n(at 17%).\n\n\n\nOther\n\n\nNo longer\n\nemployed\n\n\nConstruction\n\n\nEducation\n\n\nAdministrative\n\nand\nsupport\n\nservice\nactivities\n\n\n\n16 Likely to be a bit overstated, as the survey did not inquire about activities of 15-year-olds outside of school enrollment. Also,\nsome respondents were not asked about distance learning in Moldova\n\n17 [Reference indicators were taken from the OECD dataset for 2022 with the exception of Moldova, where 2023](https://data.oecd.org/youthinac/youth-not-in-employment-education-or-training-neet.htm) <u>[data reported by](https://statistica.gov.md/en/youth-neet-in-the-republic-of-moldova-for-the-second-9430_60713.html)</u>\n<u>[the national statistics office](https://statistica.gov.md/en/youth-neet-in-the-republic-of-moldova-for-the-second-9430_60713.html)</u> was used\n\n\n**8**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000000:7:1:0", "start": 521, "end": 533, "surface": "OECD dataset", "probe_tag": "keep", "probe_score": 0.9945, "luna_label": 1, "luna_reason": "OECD dataset supplied reference indicators used in the analysis."}]}, {"key": "aivin-060", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Box 2.** Labour productivity of refugees in relation to the rest of population\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nTo gauge the labour productivity of Ukrainian refugees in relation to the rest of the population we **Chart 14.** Distribution of workers (refugees from Ukraine vs. natives and other immigrants) by average earnings in a poviat\nperformed back-of-the-envelope calculations. Unfortunately, publicly available data for the refugee and _Persons registered for social security on 30.09.2023_\ngeneral populations is gathered differently and for different time periods. Assuming that such results\n\nUkrainian refugees\n\nare broadly correct – educational attainment and geographical distribution imply higher earnings (a\nproxy for labour productivity) of refugees than the general population, while their employer firm sizes, 10%\n\n\n9%\n\n\n\nUkrainian refugees\n\n\n\n10%\n\n\n\n9%\n\n\n\n\n|Col1|Col2|Col3|Col4|Warszaw|a|Col7|Col8|\n|---|---|---|---|---|---|---|---|\n|||Nearly**10% of re**<br>work in Warsaw|**  fugees**|||||\n|||||||||\n||||Wrocław|||||\n|||||||||\n|||||Kraków||||\n|||Łodź|Poznań||||R² = 0,20|\n|||Szczecin<br>||Gdańsk||||\n||Poznański|Bydgoszcz||~~Katowice~~||||\n|||||Łęczy", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:16:0:0", "start": 522, "end": 545, "surface": "publicly available data", "probe_tag": "keep", "probe_score": 0.9864, "luna_label": 1, "luna_reason": "Existing data supports calculations comparing refugee and general-population labour productivity."}]}, {"key": "aivin-061", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nRefugees from Ukraine are not yet\nutilising their full potential in the Polish\nlabour market. In May 2022, 46% of\narriving refugees declared that they had\nno knowledge of the Polish language\n(NBP, 2023). Over time, this percentage\nimproved, with data from November 2022\nshowing a result of 21%, though this still\nrepresents a large group of people who\ndo not know the local language <sup>24</sup> [^24: Due to possible differences in methodologies data from this surveys should not be directly compared], placing\nthem at risk of limiting possible jobs to\nthose below their educational level. Nearly\n40% of Refugees from Ukraine insured\nat ZUS on 30th June 2023 in Poland were\nemployed in elementary occupations <sup>25</sup> [^25: 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\n\n\nwhile for all employed persons in Q2 2023\nthis percentage was only 5%. Furthermore,\nthe Deloitte Ukraine Refugee Pulse report\nindicates that 50% of respondents point\nto language barriers as an obstacle to\naccessing services to meet basic needs\n(Deloitte, 2023). Meanwhile, in the MSNA\nPoland 2023 survey, when asked about\nencountered barriers for accessing the\nlabour market, 34% of respondents\npointed to lack of language knowledge.\n\n\nSmooth inclusion of refugees on labour\nmarket thus far was enabled by proper\npolicies. In response to the war escalation\n\n\n\nin Ukraine and the influx of refugees into\nthe EU, prompt actions were taken both at\nEU and national level.\n\n\nRefugees from Ukraine in the European\nUnion are covered by the Temporary\nProtection Directive TPD, which was\nactivated on 4th March 2022 (European\nCouncil, 2022). The regulation aims to\nsupport EU Member States’ asylum\nschemes and to ensure harmonised\nrights for the incoming", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:11:0:0", "start": 322, "end": 345, "surface": "data from November 2022", "probe_tag": "keep", "probe_score": 0.9875, "luna_label": 0, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:11:0:1", "start": 1185, "end": 1222, "surface": "Deloitte Ukraine Refugee Pulse report", "probe_tag": "keep", "probe_score": 0.9841, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:11:0:2", "start": 1377, "end": 1400, "surface": "MSNA\nPoland 2023 survey", "probe_tag": "keep", "probe_score": 0.9876, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-062", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n\n# Health analysis\n\n### **Health remains a** **priority need**\n\nAccess to health services has remained a priority\nneed and is ranked among the top three priorities\nby 33% of households, second only to employment\nand livelihoods support. The identification of health\nas a priority is consistent with 2023, where 34% of\nhouseholds ranked health among their top three\nneeds.\n\n\nVariations exist across countries, health care was\nthe top priority need for respondents in half the\ncountries (Bulgaria, Hungary, Moldova, Romania,\nand Latvia). In 2024, health care became a top\npriority for 49% of households in Romania, a shift\nfrom 38% in 2023, potentially indicating barriers and\nchanges in the level of access.\n\n\n\n(2023 N=9,466, 2024 N=7,140)\n\n- Not included in 2023 survey\n\n\n\n**<u>TOP 10 PRIORITY NEEDS (OUT OF THOSE WHO REPORTED)</u>**\n\n\n2023 2024\n\n\n\nEmployment,\n\nlivelihoods\n\n\nHealthcare\n\nservices\n\n\nAccommodation\n\n\nLanguage course\n\n\nEducation for\n\nchildren\n\n\nMedicines\n\n\nFood\n\n\nTrainings,\neducation adults\n\n\nLegal status*\n\n\nRegistration,\nlegal assistance\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                                   - Not included in 2023 survey\n\nRegionally, women and men prioritized health care\nnearly equally with respectively 34% and 32%\nidentifying it as a priority need. For women, health was the second highest priority after employment\ncompared to men who prioritized it third after\nemployment and accommodation.\n\n\n\n**<u>TOP 10 PRIORITY NEEDS (OUT OF THOSE WHO REPORTED), BY COUNTRY</u>**\n\n\nTop 1 Top 2 Top 3 priority\n\n\n\n|Col1|Regional|Bulgaria|Czechia|Estonia|Hungary|Latvia|Lithuania|Moldova|Poland|Romania|Slovakia|\n|---|---|---|---|---|---|---|", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000004:9:0:0", "start": 829, "end": 840, "surface": "2023 survey", "probe_tag": "keep", "probe_score": 0.9978, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-063", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n\n# Executive summary\n\nThe war in Ukraine, now in its third year, continues\nto have devastating effects on the Ukrainian\npopulation, triggering one of the largest\ndisplacement crises in Europe since World War II. As\nof December 2024, over 6.2 million Ukrainian\nrefugees have been recorded across Europe, the\nmajority of whom are women, children, and older\npersons. The European Union extended the\nTemporary Protection Directive until March 2026,\ngranting Ukrainian refugees access to essential\nhealth services, education, and other critical\nsupport. The Republic of Moldova followed this\nmodel and also introduced Temporary Protection for\nUkrainian refugees.\n\n\nIn 2024, to assess the health and mental health\nsituation of Ukrainian refugees, their access to\nservices, and the barriers they face across\ncountries, Regional Refugee Response Plan (RRP)\nhealth and mental health and psychosocial support\n(MHPSS) partners conducted a regional analysis of\nthe Socio-Economic Insights Survey (SEIS) data from\n10 refugee-hosting countries: Bulgaria, Czechia,\nEstonia, Hungary, Latvia, Lithuania, Poland, Republic\nof Moldova, Romania, and Slovakia. The analysis\nincludes a comparison with key indicators collected\nin 2023.\n\n\nKey finding from the regional analysis include:\n\n\n\n\n\n\n\n\n\n\n\n\n\n**3**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000004:2:0:0", "start": 1024, "end": 1054, "surface": "Socio-Economic Insights Survey", "probe_tag": "keep", "probe_score": 0.9776, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-064", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 15. Ukrainian refugees wages median net wage by age group**\n\n\nMonthly net wage (PLN) Percengate of all workers total economy average\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 16. Median net wages of Ukrainian refugees median net wage by sector**\n\n\nin PLN Percentage of total economy average\n\n\n\n126%\n\n\n\n\n\n\n\n\n\n\n\n15 to 24 25 to 34 35 to 44 45 to 54 55 to 64 15 to 24 25 to 34 35 to 44 45 to 54 55 to 64\n\n\nUkrainian refugees All workers\n\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey and GUS data.\n\n\n\n**The Ukrainian refugee groups to earn**\n**the highest wages compared to the**\n**wages in the economy as a whole are**\n**the younger age groups.** Ukrainian\nrefugee incomes from employment are\nhighest in the 25–34 and 35–44 age\ngroups. However, relative to the economy\nas a whole, the wages of Ukrainian\nrefugees are the highest in the youngest\nage groups and decrease with age. This\ncan be explained by three facts. First,\nwages in the youngest age groups are\nmost compressed, because they diverge\nwith time and accumulated professional\nexperience. Second, younger persons have\nless experience and so lose least from\nmigration. Third, younger persons often\nfind it easier to learn the language of the\nhost country.\n\n\n\n**The earnings of Ukrainian refugees**\n**differ across sectors in nominal**\n**terms and as a percentage of the**\n**economy as a whole – only education**\n**ranked lowest on both measures.**\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 re", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:10:0:0", "start": 604, "end": 621, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9906, "luna_label": 1, "luna_reason": "Named survey cited as the basis for wage analysis and reported findings."}, {"key": "sample:jad_paddy_docs:000001:10:0:1", "start": 626, "end": 634, "surface": "GUS data", "probe_tag": "keep", "probe_score": 0.9429, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-065", "text": "/data2.unhcr.org/en/situations/ukraine](https://data2.unhcr.org/en/situations/ukraine)</u>\n\n\n14\n\n\n\n**Source:** Deloitte own elaboration based on the results of the MSNA Poland 2023 survey\n\n\n\n15", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:7:3:0", "start": 164, "end": 187, "surface": "MSNA Poland 2023 survey", "probe_tag": "keep", "probe_score": 0.9996, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-066", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n\n**<u>TOP 5 REFUGEE EMPLOYMENT SECTORS SPLIT BY</u>**\n**EMPLOYEE BACKGROUND (2024)**\n\n\n\n**<u>TOP EMPLOYMENT BARRIERS FOR EMPLOYED REFUGEES</u>**\n**(2024)**\n\n\n\nEmployed in the same\nsector in Ukraine\n\n\nManufacturing\n\n\nHospitality\n\n\nConstruction\n\n\nWholesale\n\n\nIT\n\n\n\nLack of local language\n\nknowledge\n\nCannot find a job with\n\ndecent pay\n\nFew jobs for my skills or\n\nexperience\n\nLack of jobs with a suitable\n\nschedule\n\nDegree or skills recognition\n\nissues\n\n\nSource: Survey data\n\n\n\n17%\n\n\n15%\n\n\n\n35%\n\n\n\n5%\n\n\n2%\n\n\n4%\n\n\n\nEmployed in a different\nsector in Ukraine\n\n\n16%\n\n\n9%\n\n\n5%\n\n\n\n24%\n\n\n\n\n\n\n\n\n\n6%\n\n\n\n4%\n\n\n\n1%\n\n\n\nNote: Percentages are based on the distribution of relevant responses\nin the survey\n\n\nSource: Survey data\n\n\nMoreover, in the latest survey round, nearly 35% of employed refugees identified inadequate pay, a lack of\npositions that match their skill set, and challenges in having their qualifications recognized as barriers to\nemployment. All of these answers also suggest a mismatch between qualifications and job placement.\nConsistent with the findings on having to shift to jobs outside of previous employment backgrounds, working\nwomen reported the above employment barriers more frequently than men, supporting the hypothesis that\nthey may be facing underemployment more often.\n\n# **Methodology**\n\nThe approach to data collection in each of the countries included in the report differed depending on the\navailability of sampling frames and information on the distribution of the refugee population by geographic area\nand accommodation type. All interviews were conducted face to face. As a probabilistic selection of\nrespondents could not be ensured, the primary goal was to collect a diverse sample that would reflect the\npopulation’s composition as closely as possible.\n\n\nFor the regional analysis,", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:13:0:0", "start": 568, "end": 579, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9579, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-067", "text": " enough money to\nmeet their needs, the refugees were nearly\nequally split (around 0.5% more reported\nno difficulties), with an additional 8% not\nknowing or refusing to answer. According to\nthe National Bank of Poland survey carried\nout in November 2022, 28% of refugees\nsaid they spend less than half of their\nincome on daily expenses, most spend\nbetween 50% and 80%, and 19% spend 80100% of their income.\n\n\n###### Currently between 225 and 350 thousand of refugees from Ukraine are working in Poland. The lower bound is the number from social security, while the higher bound is the product of employment rate from the surveys and working age population with PESEL numbers.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Box 1.** Inflow of savings from Ukraine\n\n\n\n**Chart 13.** Income structure of Ukrainian refugee households\n\n\n**5%** **1%**\n\n\n\nWork (regular-, temporary-,and self-employment in Poland,\nremote employment in Ukraine\n\n\nRemittances from friends/relatives\n\n\nPolish government benefits (Family 500+, cash benefits,\ndisability grants)\n\n\nUkrainian government benefits (pensions, disability grants,\nparental benefits)\n\n\nOther (humanitarian organizations, other sources)\n\n\n\n**Source:** Deloitte elaboration based on the MSNA Poland 2023. The work category includes regular employment, temporary work, self-employment\nand remote work in Ukraine.\n\n\n31 <u>[Cudzoziemcy w polskim systemie ubezpieczeń społecznych (zus.pl)](https://www.zus.pl/documents/10182/2322024/Cudzoziemcy+w+polskim+systemie+ubezpiecze%C5%84+spo%C5%82ecznych_2022.pdf/)</", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:14:2:0", "start": 193, "end": 223, "surface": "National Bank of Poland survey", "probe_tag": "keep", "probe_score": 0.9996, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:14:2:1", "start": 620, "end": 673, "surface": "surveys and working age population with PESEL numbers", "probe_tag": "keep", "probe_score": 0.9248, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:14:2:2", "start": 1263, "end": 1279, "surface": "MSNA Poland 2023", "probe_tag": "keep", "probe_score": 0.9021, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-068", "text": " 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\nbenefits. In economic terms, refugees\nfrom Ukraine in Poland are not receivers\nof social services and charity, but primarily\nconsumers, employees, and entrepreneurs.\n\n\nIn Chapter 3, we estimate the economic\nimpact of refugees from Ukraine in Poland\nin general equilibrium Deloitte D.Climate\nmodel. This is the gold standard of\neconomic modelling on the macro-level,\nwhich comprehensively accounts for the\nsupply side of the economy including\nlabour supply and productivity, and the\ndemand side including consumption\nas well as taxation. Unfortunately, such\nmacroeconomic models cannot account for\nhow exactly refugees and other migrants\nenable native workers to specialise in\nbetter paid professions (occupational\nupgrading), or how firms allow for new\nskills by adapting different production\ntechnologies. As such, our estimates\nshould be treated as a conservative lower\nbound of the effect.\n\n\n\nFurthermore, in Chapter 4, we move\nbeyond theoretical modelling, to examine\nempirical studies on how immigrants\nand refugees impact not just output, but\nlabour productivity as well. This effect\ncomes not from traditional supply-demand\nanalysis, but from increased specialisation.\nImmigrants enable occupational upgrading\nof residents, supply new skills to firms,\nenter household works services that allow\nhighly productive native women to increase\nlabour supply, and exhibit high rates of\nentrepreneurship.", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:5:1:0", "start": 20, "end": 67, "surface": "MultiSector Needs Assessment Poland 2023\nsurvey", "probe_tag": "keep", "probe_score": 0.9999, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-069", "text": "-union-statistics-on-income-and-living-conditions)</u>\n<u>[survey, which the OECD uses as the basis for host country poverty](https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions)</u>\nassessments\n\n2. Results for the Czech Republic not individually presented due to\nsampling limitations\n\n3. Accommodation rent support has been calculated as the difference\nbetween the actual equivalized accommodation expense and the\nmedian equivalized market rent in the region\n\n\n\n8 A household is defined to fall below the poverty line if its members fall below the poverty line on individual basis (the\nequivalized income of all household members is the same)\n\n\n\n9 Its magnitude can be computed as the difference between the median equivalized market rent in the region and the actual\nequivalized accommodation expense\n\n\n\n10 Those that are below the poverty line\n\n\n\n**5**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000000:4:2:0", "start": 1, "end": 49, "surface": "union-statistics-on-income-and-living-conditions", "probe_tag": "keep", "probe_score": 0.9359, "luna_label": 0, "luna_reason": null}]}, {"key": "aivin-070", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\n**The only reliable timeseries of the**\n**number of Ukrainians in Poland over**\n**the past decade is social insurance**\n**data on insured Ukrainian nationals,**\n**though it accounts only for workers.**\nThe focus on workers rather than on\nthe entire group does not change much\nin the data from before February 2022,\nas the previous influx consisted mainly\nof Ukrainians seeking employment in\nPoland. However, the actual number\nof employed Ukrainian nationals must\nhave been higher. First, certain types\nof legal work often undertaken by\ntemporary employees do not require\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 4. Age and gender structure of Ukrainian refugees**\n\n\n\n**Most of the refugees from Ukraine**\n**currently living in Poland are women**\n**and children, though over half of**\n**the total population is of working**\n**age.** The best population data available\nis the regularly updated active PESEL\ndatabase. <sup>4</sup> [^4: Available in the repository maintained by the government <u>https://dane.gov.pl/pl/dataset/2715</u> as well as UNHCR data portal <u>https://app.powerbi.com/</u>\n<u>view?r=eyJrIjoiODhkOGZiMzctZTliMi00NzA5LTgyM2QtZGZhM2IwZjBiZDk2IiwidCI6ImU1YzM3OTgxLTY2NjQtNDEzNC04YTBjLTY1NDNkMmFmODBiZSIsImMiOjh9</u>] According to the registry, 61.5%\nof the registered are women and 38.5%\nare men. The database also includes the\nage of PESEL UKR holders, which indicates\nthat over half (57.4%), i.e. more than\n560 thousand people, are of working age\n(18-65). While the male and female shares\nof people below 18 years of age (20% and\n19%, respectively) and 66+ (1%", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:4:0:0", "start": 198, "end": 233, "surface": "data on insured Ukrainian nationals", "probe_tag": "keep", "probe_score": 0.9623, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:4:0:1", "start": 1020, "end": 1034, "surface": "PESEL\ndatabase", "probe_tag": "keep", "probe_score": 0.9643, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-071", "text": ", to account\nfor spending of savings from Ukraine, data\nfrom the National Bank of Ukraine on\ncash withdrawals and retail transactions\nfrom Ukrainian bank cards in Poland was\nused <sup>55</sup> and then calibrated to data for\nprivate consumption in Poland. Additionally,\nthe same data was used to calibrate the\nnegative shock to investment in Eastern\nEurope.\n\n\n\nFor productivity two options were tested.\nFirstly, neutral impact: no shock to\nproductivity. Secondly, lower productivity of\nworkers from Ukraine negatively impacting\ntotal labour productivity. These shocks were\ncalibrated to match the difference in data\nbetween refugees income from labour in\nUNHCR survey and average wage in Poland\nfrom Statistics Poland weighted by refugees\nshare in total workforce.\n\n\nAs one period in the model is set to one\nyear and refugees started coming to Poland\nby the end of February 2022 it was assumed\nthat their impact on economy started being\nfelt starting from II quarter of the year.\nAs such shocks were set so that ¾ of shocks\nwere calibrated to data from 2022 and ¼ to\ndata for 2023 <sup>56</sup> .\n\n\n\n51  Computational General Equilibrium.\n52  Global Trade Analysis Project, <u>[GTAP Models: Current GTAP Model (purdue.edu).](https://www.gtap.agecon.purdue.edu/models/current.asp)</u>\n53  Region combined from: Russia, Belarus, Ukraine, Moldova, Czechia, Slovakia, Hungary, Romania, and Bulgaria.\n54  All shocks are percent deviations.\n55 <u>[Oversight of financial market infrastructures (bank.gov.ua)](https://bank.gov.ua/en/payments/oversite)</u>\n56  E.g. for total increase in number of workers of 350 thousand around 262.5 thou. was set to have happened in 2022 while rest in 2023.", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:21:1:0", "start": 51, "end": 89, "surface": "data\nfrom the National Bank of Ukraine", "probe_tag": "keep", "probe_score": 0.9987, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:21:1:1", "start": 216, "end": 254, "surface": "data for\nprivate consumption in Poland", "probe_tag": "keep", "probe_score": 0.9774, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:21:1:2", "start": 655, "end": 667, "surface": "UNHCR survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:21:1:3", "start": 1043, "end": 1057, "surface": "data from 2022", "probe_tag": "keep", "probe_score": 0.9704, "luna_label": 0, "luna_reason": null}]}, {"key": "aivin-072", "text": " have relatively better labour\nmarket situations. In countries with lower\nunemployment, refugees fare better in\nthe labour market. Since women make\nup the majority of refugees of working\nage, the situation of women on the labour\nmarket is especially important. As such,\nfemale unemployment rates explain 36%\nof the variation in refugees from Ukraine\nemployment rates in studies from 11 EU\nMember States <sup>22</sup> [^22: After excluding Germany and Switzerland as outliers. 23  Due to possible differences in methodologies data from this surveys should not be directly compared]\n\n\n**Chart 10.** Female unemployment and refugees from Ukraine employment in Europe\n\n\n70%\n\n\n\n**Chart 11.** Education attainment of Poles and Ukrainians\n\n\n**Eurostat** Poland LFS 2022\n\n\n\n**UNHCR**\n\n**(2023)**\n\n\n**NBP**\n**(2022)**\n\n\n**Ukrstat**\n\n\n\n11%\n\n\n56%\n\n\n48%\n\n\n46%\n\n\n\n30%\n\n\n\nRefugees from Ukraine VII-VIII 2023\n\n\nRefugees from Ukraine XI 2022\n\n\nPre-2022 migrants from Ukraine XI 2022\n\n\nUkraine LFS 2020\n\n\n\n29%\n\n\n20%\n\n\n17%\n\n\n29%\n\n\n\n60%\n\n\n14%\n\n\n33%\n\n\n37%\n\n\n\n37%\n\n\n\n60%\n\n\n50%\n\n\n40%\n\n\n30%\n\n\n20%\n\n\n10%\n\n\n0%\n\n\n\n\n|PL XI<br>PL VII-VIII|2022<br>2023 U|K**|Col4|Col5|\n|---|---|---|---|---|\n||~~CZ~~<br> <br>|LT<br>|SE||\n|||DK<br>NL||EE|\n||||FR|**R = 0,36**|\n|||IE<br>|||\n||DE<br>|~~H*~~||~~IT~~|\n||||||\n\n\n\n2% 4% 6%\n\n**Female unemployment rate**\n\n\n\n34%\n\n\n\n\n\n\n\n\n\n\n\nThe", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:10:1:0", "start": 747, "end": 762, "surface": "Poland LFS 2022", "probe_tag": "keep", "probe_score": 0.9924, "luna_label": 1, "luna_reason": "Named Polish labor force survey cited as the source for charted education data."}, {"key": "sample:jad_paddy_docs:000007:10:1:1", "start": 792, "end": 795, "surface": "NBP", "probe_tag": "keep", "probe_score": 0.9496, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:10:1:2", "start": 813, "end": 820, "surface": "Ukrstat", "probe_tag": "keep", "probe_score": 0.9496, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-073", "text": "## **HELPING HANDS** THE ROLE OF HOUSING SUPPORT AND EMPLOYMENT FACILITATION IN ECONOMIC VULNERABILITY OF REFUGEES FROM UKRAINE\n#### An inter-agency exploration of socio-economic data April 2024", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000000:11:0:0", "start": 164, "end": 183, "surface": "socio-economic data", "probe_tag": "confusion", "probe_score": 0.5559, "luna_label": 0, "luna_reason": "Generic data phrase in a title, with no attributed finding or demonstrated use."}]}, {"key": "aivin-074", "text": " shock\nto the total factor productivity growth. This\nyields GDP higher by 1.5-2.6% by 2030,\nsignificantly higher than our long-term\nestimate – the higher result is likely due to\nthe increase in TFP that we do not include\nin our modelling. Fourth, Monitor Deloitte\n(2022) provided an early wide estimate of\nthe economic impact of Ukrainian refugees.\nThis would translate into 0.9-2.4% higher\nGDP, also due to the possibility of a positive\nproductivity shock. We review the relevant\nliterature on the impacts of immigration on\nproductivity in Chapter 4.\n\n\n\nrefugees, we have adjusted all the results\nproportionally to a 1.4-2.2% employment\ngrowth. First, Gradzewicz, Jabłon\nowski, Sasiela, and Żółkiewski (2021)\nuse the NBP CGE model to estimate the\neconomic impact of pre-2022 Ukrainian\nmigrants over the 2015-2018 period,\ntreating their inflow as a positive unskilled\nlabour shock. If the Ukrainian refugee\ninflow had the same characteristics, it\nwould yield a 0.5-0.8% higher GDP – the\nlower bound of our estimate. Second,\nStrzelecki, Growiec, and Wyszyński (2022)\nperform a growth accounting exercise to\ngauge the economic impact of the pre-2022\nUkrainian migrants over the 2014-2018\nperiod, accounting for hours and worker\ncharacteristics to arrive at a productivityadjusted labour supply. If Ukrainian\nrefugees had the same characteristics, they\n\n\n© UNHCR / Anna Liminowicz\n\n\n\nAs such **4 scenarios** were calculated with options corresponding to different mixes\nof low and high estimation for employment and productivity while keeping stable\nimpact on consumption: high or low employment level, similar (baseline) or lower\n(conservative) productivity. In the conservative productivity scenario,\nwe assumed approximately 10% lower refugee productivity estimated based\non incomes reported in MSNA Poland 2023 and data from Statistics Poland.\n\n\n**Table 2", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "jad_paddy_docs:000007:17:1:0", "start": 1795, "end": 1811, "surface": "MSNA Poland 2023", "probe_tag": "keep", "probe_score": 0.9388, "luna_label": 1, "luna_reason": "Named survey data informs the estimated refugee productivity assumption."}, {"key": "jad_paddy_docs:000007:17:1:1", "start": 1816, "end": 1843, "surface": "data from Statistics Poland", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Statistics Poland data informs the estimated 10% lower refugee productivity."}]}, {"key": "aivin-075", "text": "%\n\n\n\n\n\n\n\n62%\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Estonia Hungary Latvia Lithuania Moldova Poland Romania Slovakia Region\n\n\nNote: For comparability, employment rates for host countries have been recalculated assuming a similar gender distribution to that of refugees\n\n\nSource: ILO, survey data\n\n\n14. The employment rate is defined as the number of employed or self-employed individuals of working age (15-64) as a share of\nthe total number of people in this age group\n15. The labor force is defined as the number of people that are either employed or unemployed\n\n\n**10**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "jad_paddy_docs:000010:9:1:0", "start": 278, "end": 294, "surface": "ILO, survey data", "probe_tag": "confusion", "probe_score": 0.869, "luna_label": 1, "luna_reason": "ILO survey data is cited as the source for employment-rate figures."}]}, {"key": "aivin-076", "text": "-and-</u>\n<u>salaries-by-occupations-for-october-2022,4,8.html</u>\n\n\nGUS (2024b). Education in the school year 2023/2024 (preliminary data),\nStatistical Office in Gdańsk, <u>https://stat.gov.pl/en/topics/education/education/</u>\n<u>education-in-the-school-year-20232024-preliminary-data,13,2.html</u>\n\n\nHeller, B. H., & Mumma, K. S. (2023). Immigrant integration in the United States:\nthe role of adult English language training. American Economic Journal: Economic\nPolicy, 15(3), 407-437.\n\n\nJaumotte, M. F., Koloskova, K., & Saxena, M. S. C. (2016). Impact of migration on\nincome levels in advanced economies. International Monetary Fund.\n\n\nKleiner, M. M., & Krueger, A. B. (2013). Analyzing the Extent and Influence of\nOccupational Licensing on the Labor Market. Journal of Labor Economics, 31(2),\nS173–S202. <u>https://doi.org/10.1086/669060</u>\n\n\n46\n\n\n\nLessem, R., & Sanders, C. (2020). Immigrant wage growth in the United States:\nThe role of occupational upgrading. International Economic Review, 61(2),\n941-972.\n\n\nLewandowski, P., Górny, A., Krząkała, M., & Palczyńska, M. (2025). The Role of Job\nTask Degradation in Shaping Return Intentions: Evidence from Ukrainian War\nRefugees in Poland. IBS working paper, 01/2025. <u>https", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:23:2:0", "start": 82, "end": 120, "surface": "Education in the school year 2023/2024", "probe_tag": "confusion", "probe_score": 0.2256, "luna_label": 0, "luna_reason": null}]}, {"key": "aivin-077", "text": " net wage of\na Ukrainian refugee (estimated based on\nthe SEIS UNHCR survey in chapter 2) from\n80% to 98% of the median in the economy\nas a whole (or from 80% to 93% according\nto Ukrainian refugee’s median in the NBP’s\n2024 survey), almost closing the gap to the\neconomy as a whole in these terms. It is in\nfact higher than the PLN 500 median net\nwage premium of the pre-war Ukrainian\nmigrants over Ukrainian refugees in the\nNBP (2024) survey, even though 68% of the\nformer and only 28% of the latter said they\nhad a high level of fluency in Polish.\n\n\n31\n\n\n\nprofessions (physicians, dentists, nurses,\nand midwives) have opened to migrants\nand refugees to reduce shortages, and\nwhile the shares for all of them stand at\njust 0.9% compared to 1.9% for Polish\ncitizens, the gap is actually very narrow for\nphysicians and dentists, who constitute\n0.7% of Ukrainian refugees and 0.8% of\nPolish citizens. In legal professions (legal\ncounsels, barristers, notaries, and bailiffs),\nthe gap remains wide, although in addition\nto occupational licensing, the likely causes\nmay be the differences between the Polish\nand Ukrainian legal systems as well as\nnew entrants’ struggles with attracting\nclients due to advertising restrictions. In\neffect, just 0.02% of Ukrainian refugees\nwork in legal professions, compared to\n0.4% of Polish citizens. There are also\nhigh contrasts across the remaining\nregulated professions, which employ\n0.8% of Ukrainian refugees and 3.3% of\nPolish citizens. These are construction\nengineers (0.02% Ukrainian refugees),\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", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:15:3:0", "start": 57, "end": 74, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9287, "luna_label": 1, "luna_reason": "Survey data estimates refugee wages and supports the reported comparison."}, {"key": "sample:jad_paddy_docs:000001:15:3:1", "start": 212, "end": 229, "surface": "NBP’s\n2024 survey", "probe_tag": "confusion", "probe_score": 0.533, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000001:15:3:2", "start": 424, "end": 441, "surface": "NBP (2024) survey", "probe_tag": "confusion", "probe_score": 0.0598, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-078", "text": " workers in the\neconomy, but also accounts for increased\nproductivity brought about by more\nspecialization. Considering all aspects, the\noverall impact of the refugees has been\npredominantly positive, and it moved the\nPolish economy to a higher growth path.\nRefugees contribute to the economy\nslightly more than their employment\nshare – they increase the labour supply\nas both workers and entrepreneurs and\nexpand demand as consumers. The rise\nin productivity due to more specialization\nacross the labour force further boosts\nthe economy. The impact of refugees\nis lowered by a temporary decrease in\nthe capital-to-labour ratio (companies\nneed time to invest in equipment and\nmachines to match the rise in the number\nof workers), as well as an increase in\ncompetition on the labour market.\n\n\n**The figures are higher than those in**\n**the previous Deloitte (2024) report,**\n**which – due to scant data available**\n**at the time – did not account for the**\n**positive impact on labour productivity.**\nIn the earlier report, the overall positive\nimpact of refugees was reduced based on\nconservative assumptions, in the absence\nof available/clear data on the increased\ncompetition in the labour market. The\n\n\n\nmodel notably considered lower wages\nand higher unemployment among native\nworkers. However, recent data indicates\nthat these concerns did not materialize.\nInstead, Polish workers have moved on to\nbetter paid occupations, and the economy\nhas benefited from a larger pool of talent,\nenabling deeper specialization and\nincreased productivity growth.\n\n##### Remaining challenges\n\n\n**Despite significant progress in**\n**integrating refugees into the labour**\n**market, several challenges persist.**\nRefugees are half as likely to have an\nemployment contract as Polish citizens\nand few of them achieve high incomes.\nAlthough refugees have been moving on\nto more desirable professions at a faster\nrate than other groups in the economy,\ntheir jobs continue to be disproportionately\nskewed towards elementary occupations.\nThese issues are most evident among\nthose with", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:2:3:0", "start": 1299, "end": 1310, "surface": "recent data", "probe_tag": "confusion", "probe_score": 0.4682, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-079", "text": " additionally a positive\nimpact on labour productivity that cannot\nbe accounted for using the available data.\nThese effects could stem from the growth\nin specialisation due to additional workers\nwith different skillsets appearing on the\nlabour market, e.g., occupational upgrading\nof native workers.\n\n\n\n**Immigrants could possess complementary skills that**\n**make native workers more productive as they specialise.**\nThese skills do not even need to be advanced to be\ncomplementary. Natives are likely to deal better with\ncommunications-intensive tasks and have better networking, all\nof which may be better paid and not easily transferable between\ncountries. OECD (2016) gives an example of a native carpenter,\nwho employs an immigrant to do his previous manual tasks\nand himself focuses on marketing and business development.\nSuch occupational upgrading has been first shown in a seminal\npaper by Peri and Sparber (2009) in the USA data, but has been\nquickly extended to other countries. From this perspective\nFoged and Peri (2016) look at refugees in Denmark in the 19912008 period. They find that inflow of low-skill refugees caused\nless educated native workers to pursue less manual-intensive\ntasks - improving their wages, employment, and occupational\nmobility. These effects are causal, as the authors exploit the\nrefugee dispersal system, which is orthogonal to economic\nopportunities.\n\n\n**Immigrants could enter childcare, elderly care, and**\n**housekeeping services, allowing highly educated and**\n**productive native women to increase their labour supply.**\nA caveat in the case of refugees from Ukraine is that this could\nmean working below their qualifications or in the informal\nsector, making the overall effect for productivity unclear.\nNevertheless, as availability of care and housework services\nincreases, it becomes easier for women mainly to combine\nfamily and professional lives and increase labour supply.\nSuch effects may be stronger for countries with less accessible\nchildcare. Furtado (2015) reviews this literature, finding evidence\nfrom Australia, Hong", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:20:1:0", "start": 931, "end": 939, "surface": "USA data", "probe_tag": "confusion", "probe_score": 0.8086, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-080", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nTo account for refugees impact on\neconomy shocks for economy were\ncalibrated according to existing data.\nAs the refugees started coming to Poland\nby the end of February and in March\n2022 it was assumed that their impact\non the economy should be seen starting\nfrom the second quarter of 2022. As such\ntheir primary impact on yearly data was\ndivided as between 2022 and 2023 setting\n¾ of it in 2022 and ¼ in 2023. The total\nnumber of refugees was set according to\nthe newest data from the PESEL registry\n(Chart 3. in Chapter 1). Their employment\nwas calibrated to match data presented\nin Chapter 2. Changes in population and\n\n\n\nlabour supply in Poland were offset by\nequivalent changes for Eastern Europe\nregion <sup>41</sup> [^41: Aggregate region in model consisting of Ukraine, Russia, Belarus, Moldova, Czechia, Slovakia, Hungary, Romania and Bulgaria.] . It was also assumed that refugees\nshould have higher spending needs which\nmeans a lower saving rate than natives.\nMoreover, data shows that it is partially\nfinanced by savings they have in Ukrainian\nbanks which was calibrated as them having\nnegative saving rate while being offset\nby lowering investment levels in Eastern\nEurope <sup>42</sup> [^42: 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\nconsumption in Poland.] . Lastly, their productivity may\ndiffer from productivity of natives (Box 2)\nwhich also was taken into account.\nIn total there were three major sources\nof uncertainty: total level of employment,\nproductivity, and impact on consumption.\n\n\n\nwould increase GDP by 0.9-1.3% – the mid\nto higher bound of our estimate.\nThird, Urban (2022) uses an Oxford\nEconomics model to estimate the impact of\nUkrainian refugees on the potential GDP by\n2030", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:17:0:0", "start": 476, "end": 487, "surface": "yearly data", "probe_tag": "confusion", "probe_score": 0.8706, "luna_label": 0, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:17:0:1", "start": 639, "end": 653, "surface": "PESEL registry", "probe_tag": "keep", "probe_score": 0.9697, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-081", "text": ". The complete questionnaires, along with the\nconsolidated anonymized dataset, are available in\nthe <u>[UNHCR Microdata Library.](https://microdata.unhcr.org/index.php/catalog/?page=1&from=2023&to=2024&region%5B%5D=3&ps=100)</u>\n\n\n# Limitations\n\nThis analysis has several limitations that should be\nconsidered when interpreting the findings. First, due\nto sampling constraints (lack of complete sampling\nframe) and the non-probabilistic selection of\nrespondents, the results may not fully represent the\nentire Ukrainian refugee population. Additionally,\nthe choice of sampling locations may have\nintroduced a bias toward more vulnerable segments\nof the population. Variations in sampling\napproaches and data collection periods across\ncountries can also affect comparability.\n\n\nThe findings on disability, chronic illness, and\nvaccination are based on self-reports and were not\nverified against medical records, which may impact\ntheir accuracy.\n\n\nA high non-response rate was observed for\nsensitive questions related to mental health,\npsychosocial well-being, protection, income and\nexpenditure which could affect the completeness of\nthe data. Additionally, the survey results for certain\nindicators, such as infant and young child feeding\nand SRH barriers, should be interpreted with\ncaution due to the small sample size or low\nresponse rates. As a result, some indicators could\nnot be further analyzed to assess how factors such\nas gender, age, disability, or place of residence\nimpact access to health and MHPSS services.\n\n\nIt is also important to note that there were slight\ndifferences in the questionnaire across countries\nand years, such as adjustments to answer options.\nTherefore, the regional trend analysis was limited to\nquestions that were consistently used across all\nparticipating countries and years to ensure\ncomparability. Furthermore, certain indicators were\nexcluded from the regional analysis due to\ninsufficient sample size or the unavailability of data\nacross all countries.\n\n\n\n7. <u>[", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000004:6:1:0", "start": 46, "end": 77, "surface": "consolidated anonymized dataset", "probe_tag": "confusion", "probe_score": 0.1552, "luna_label": 0, "luna_reason": null}]}, {"key": "aivin-082", "text": "’s\nRegulated Professions Database. This can\nbe a problem, as occupational licensing is\ncited in the literature among the reasons\nfor occupational downgrading of migrants.\nCassidy and Dacass (2021) found that in the\nUnited States, immigrants were significantly\nless likely to have a license than similar\n\n\n\n**Chart 21. Share of regulated professions by citizenship and legal status, Q2 2024**\n\n\n\n**The educational premium seems**\n**to be lower for Ukrainian refugees**\n**compared to the general workforce**\n**in Poland.** According to the SEIS survey,\nUkrainian refugees with master’s and\nPhD degrees earn a 22% higher median\nnet wage than those with only secondary\neducation. This appears to be a small\ngain, even accounting for the fact that,\ngenerally, the differences between median\nwages are less pronounced than between\naverage wages (which are pulled higher\nby top incomes) and that our method of\nwage estimation based on SEIS household\nincomes <sup>26</sup> [^26: As described in chapter 2, SEIS measures incomes on the household level. Using it to estimate individual wages likely underrepresents lowest and highest incomes.\nDetails are available in the Online Technical Appendix.] flattens the distribution.\nAccording to the most recent estimate in\nOctober 2022, in the economy as a whole,\nthe average gross wage of master’s and\nPhD degree holders was 84% higher than\nthose with only secondary education. <sup>27</sup> [^27: Note that the GUS (2024) data for the host population is not exactly comparable, as it does not include microenterprises, it covers an earlier period and focuses on\ngross and average wages.]\n\n\n\n**Most Ukrainian refugees work in**\n**a different sector than previously**\n**in Ukraine, which also points to**\n**occupational downgrading.** The SEIS\nsurvey indicates that 34% of Ukrainian\nrefugees currently employed in Poland\nwork in the same sector", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:14:2:0", "start": 538, "end": 549, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.9814, "luna_label": 1, "luna_reason": "SEIS survey supports attributed wage and employment-sector findings."}, {"key": "sample:jad_paddy_docs:000001:14:2:1", "start": 928, "end": 950, "surface": "SEIS household\nincomes", "probe_tag": "confusion", "probe_score": 0.8237, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-083", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n# Non-technical summary\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nSwift legal action facilitated labour market\nintegration of refugees. After the beginning\nof the full-scale war on 26th February\n2022, the European Union activated the\nTemporary Protection Directive on 4th\nMarch 2022, and the Polish parliament\npassed a special act to facilitate refugee\nintegration on 12th March 2022. Ukrainian\nrefugees in Poland were granted instant\naccess to the job market, health care, and\neducation. The authorities granted people\nescaping Ukraine legal residency for a\nperiod of eighteen months and enabled\nthem to access digital services and basic\nadministrative systems like PESEL.\nThus, the policies that have previously\nhampered job market integration of\nrefugees in other contexts, such as\ntemporary labour market bans (Fasani,\nFrattini, & Minale, 2021) and forced\ndispersals (Fasani, Frattini, & Minale, 2022),\nhave been avoided.\n\n\nThe high employment rate of refugees\nin Poland covers not only employees,\nbut also entrepreneurs. Five percent of\nUkrainian refugees registered for social\nsecurity have set up a business or work as\nfreelancers. Similar results can be gleaned\nfrom the Multi-Sector Needs Assessment\nPoland 2023 survey results, which show\nthat slightly more than 5% of respondent\nhouseholds receive income from selfemployment or similar activities.\n\n\n\nAll broad sectors of the economy saw an\nincrease in the number of workers with\nUkrainian citizenship and social insurance\nsince 2021, apart from storage and\ntransportation. The number increased\nthe most in manufacturing (almost by\n34 thousand), accommodation and food\n(more than 18 thousand), and wholesale\nand retail trade (more than 18 thousand). <sup>11</sup>\nWhile public data does not distinguish\nbetween refugees entering these sectors\nand pre-2022 Ukrainian workers changing\njobs, it is largely consistent with the MSNA\nPoland 2023 survey, in which the most\nrefugees are employed", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:3:0:0", "start": 1290, "end": 1338, "surface": "Multi-Sector Needs Assessment\nPoland 2023 survey", "probe_tag": "confusion", "probe_score": 0.886, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:3:0:1", "start": 1841, "end": 1852, "surface": "public data", "probe_tag": "keep", "probe_score": 0.9754, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:3:0:2", "start": 1994, "end": 2017, "surface": "MSNA\nPoland 2023 survey", "probe_tag": "confusion", "probe_score": 0.8983, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-084", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n# **2.** Situation of refugees from Ukraine on the labour market in Poland\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 7.** Unemployment rate\n\n\n12%\n\n\n10%\n\n\n8%\n\n\n6%\n\n\n4%\n\n\n2%\n\n\n0%\n\n\n**Source:** Harmonized data, Eurostat, <u>[Statistics | Eurostat (europa.eu)](https://ec.europa.eu/eurostat/databrowser/view/une_rt_m/default/table?lang=en)</u>\n\n\n##### Refugees from Ukraine fit well into the needs of the Polish labour market.\n\nUkrainians arrived on a labour market that\nstructurally needs more workers, as the\ndomestic population is ageing rapidly, while\nthe economy is growing. High levels of\neducation, cultural proximity and previous\nconnections to Poland helped refugees\nadapt to the labour market. Furthermore,\nPoland made an important and strategic\npolicy decision by promptly opening\nthe labour market and supporting their\ninclusion.\n\n\n16\n\n\n\n17\n\n\n\n**Chart 6.** Working age population with Polish citizenship (20-64 years old)\n\n\n\n25\n\n\n24\n\n\n23\n\n\n22\n\n\n21\n\n\n20\n\n\n\n\n\n\n\n\n\n\n\nThe number of people of working age\nin Poland is shrinking and is expected\nto keep declining. Although substantial\nmigrations from Poland after EU accession\nin 2004 <sup>19</sup> [^19: Statistics Poland data, <u>[https://stat.gov.pl/en/topics/population/internationa-migration/information-on-the-size-and-directions-of-emigration-for-](https://stat.gov.pl/en/topics/population/internationa-migration/information-on-the-size-and-directions-of-emigration-for-temporary-stay-from-poland-between-2004-2020,8,14.html)</u>\n<u>[temporary-stay-from-poland-between-2004-2020,8,14.html](https://stat.gov.pl/en/topics/population/internationa-migration/information-on-the-size-and-directions-of-emigration-for-temporary-stay-from-poland-between-2004-2020,8,14.html)</u>] distort population data, the\ndomestic", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:8:0:0", "start": 309, "end": 324, "surface": "Harmonized data", "probe_tag": "keep", "probe_score": 0.993, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:8:0:1", "start": 1275, "end": 1297, "surface": "Statistics Poland data", "probe_tag": "keep", "probe_score": 0.9929, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:8:0:2", "start": 1857, "end": 1872, "surface": "population data", "probe_tag": "confusion", "probe_score": 0.6838, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-085", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nImportantly, most of the income of\nUkrainian nationals living in Poland, both\nrefugees and pre-2022 migrants, comes\nfrom their work. Our calculations based\non the UNHCR MSNA Poland 2023 survey\nresults show that 80% of refugee income\ncomes from employment, with other\nsources on average playing a much lesser\nrole. The income brackets presented in the\nsurvey show that 20% of households earn\nless than 3000 PLN, 41% earn between\n3,000 and 6,000 PLN and 12% earn more\nthan 6,000 PLN, while 27% of respondents\npreferred not to answer. Meanwhile, in the\nNBP (2023) survey conducted in November\n2022 the net income of refugees oscillated\nbetween 2,000 and 3,000 PLN, while the\nnet income of pre-2022 migrants was\ncloser to between 3,000 and 4,000 PLN.\nIn the case of Ukrainians that were out\nof work, the monthly income was more\nvaried, especially because there were fewer\nprevious migrants in this situation.\nThe median income for non-employed prewar immigrants was around 2,500 PLN <sup>32</sup> .\nAmong refugees from Ukraine remaining\nout of work at the time of the survey, the\nmedian income equaled around 600 PLN.\n\n\nThe standard of living of refugees from\nUkraine may be significantly lower than\nnatives, even at similar incomes due to\ntheir lack of housing capital. 87% of the\npopulation in Poland resided in owneroccupied housing in 2021 and 2022,\naccording to Eurostat. As most refugees\nfrom Ukraine do not possess housing of\ntheir own in Poland, they need to rent in a\nrelatively tight market, especially when they\nreside in large metropolitan areas that offer\nthe most opportunities. Initially, in the first\nmonth after the outbreak of the full-scale\nwar in Ukraine, the number of renting\noffers in the OLX and Otodom portals\ndropped by approximately 60%, though it\nlater", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:14:0:0", "start": 239, "end": 268, "surface": "UNHCR MSNA Poland 2023 survey", "probe_tag": "keep", "probe_score": 0.9899, "luna_label": 1, "luna_reason": "Survey results support calculations and income-distribution findings."}, {"key": "sample:jad_paddy_docs:000007:14:0:1", "start": 626, "end": 643, "surface": "NBP (2023) survey", "probe_tag": "confusion", "probe_score": 0.8845, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-086", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n# **1.** Inflow of refugees from Ukraine into Poland\n\n##### Even before the 2022 full-scale war in Ukraine the number of Ukrainians in Poland was substantial and growing, although their precise number is difficult to measure.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 1.** Ukrainian workers with social insurance.\n\n\nRate of change (right axis) Number (left axis)\n\n\n\n**Source:** Deloitte own elaboration based on ZUS data.\n\n\n\n1000\n\n\n750\n\n\n500\n\n\n250\n\n\n0\n\n\n\n\n\n\n\n120%\n\n\n100%\n\n\n80%\n\n\n60%\n\n\n40%\n\n\n20%\n\n\n0%\n\n\n-20%\n\n\n\n\n\n\n\n\n\nA steady inflow of migrants could be\nobserved in the last decade since 2014,\nwhen armed conflict erupted in Eastern\nUkraine. As the Ukrainian economy\nsuffered and its currency lost value, many\nUkrainians came to Poland looking for\nwork. Most of them came on a guest\nworker basis, enabled by a law from 2011,\nthat allowed Ukrainians and five other\nnations to work in Poland for 6 months\nduring a year without a work permit, based\non employers' declaration. This was a\ncircular migration, in which they returned to\nUkraine once the 6 month period expired.\nIn effect most stayed in Poland for less\nthan twelve months and therefore were\nnot included in the resident population or\nother national population estimates.\nA National Bank of Poland research paper\nestimates that between 2014 and 2018,\nbetween one and two million Ukrainian\nworkers arrived in the country (Strzelecki,\nGrowiec and Wyszyński, 2022).\nHard data on the number of Ukrainian\nworkers in Poland before 2022 are limited\n\n\n12\n\n\n\nand might understate their presence.\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", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:6:0:0", "start": 530, "end": 538, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9775, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:6:0:1", "start": 1671, "end": 1680, "surface": "hard data", "probe_tag": "confusion", "probe_score": 0.7393, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-087", "text": " 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\nthe one employed by the EU SILC and the Republic of Moldova’s Household Budget Survey, which may limit\ncomparability of this data to that of host populations.\n\n\nIt is also important to highlight that there were slight differences in the questionnaire across countries. Not all\nquestions were consistently included in all country-level surveys, and some answer options were individually\nadjusted.\n\n\nLastly, the survey was conducted during the summer months, coinciding with both host country and Ukraine\nschool holidays. This period often sees many households temporarily visiting Ukraine, which impacted the\naccessibility of households and posed challenges in meeting targets, particularly in certain countries and\ngeographic locations.\n\n\n**15**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:14:1:0", "start": 347, "end": 370, "surface": "Household Budget Survey", "probe_tag": "drop", "probe_score": 0.0183, "luna_label": 1, "luna_reason": null}]}, {"key": "aivin-088", "text": "\neducational attainment from November 2022\nsurvey) to 19% (July-August 2023) higher\n\nearnings than the general population. <sup>36</sup>\n\n\nPoviat-level geographical distribution of\nUkrainian refugees is more concentrated in\nhigh productivity agglomerations, implying\n7% higher earnings than for other workers\nregistered for social security. <sup>37</sup>\n\n\nThere is no publicly available hard data on the\nsectoral distribution of Ukrainian refugees,\nwhich we proxy by the growth in employees\nwith Ukrainian citizenship registered for social\nsecurity since the outbreak of the full-scale\nwar in Ukraine (unfortunately this to some\nextent will be Ukrainian workers previously\npresent in Poland who changed jobs) – this\nimperfect proxy implies 7% lower earnings\nthan general workers. <sup>38</sup>\n\n\n\nSmaller firm sizes of employers\nof Ukrainian refugees imply their\n8% lower productivity (and thus earnings)\nfrom the general population. <sup>39</sup>\n\n\nMost of all, occupational distribution of\nUkrainian refugee workers from ZUS implies\n21% lower earnings from the general\npopulation when measured by nine large\noccupational groups and 23% lower by\ndetailed occupations. <sup>40</sup>\n\n\n\n5000 6000 7000 8000 9000 10000 11000 12000\n\n\n**Average monthly gross earnings in enterprise sector in poviat of employment in 2022**\n\n\nNatives and other immigrants\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n36  Note that these percentages are only indicative, as Ukrainian refugee educational attainment is assumed on the basis of November 2022 NBP\n(2023) survey for 18+ age group and July-August 2023 UNHCR (2023) survey for 15+ age group, while the general population is taken from the 2022\nLabour Force Survey for 15-74 (broadest available) age group, and earnings by educational attainment are taken from the GUS (2022) “Structure of\nwages and salaries by occupations in October 2020”.\n37  Note that the numbers of Ukrainian refugees and all workers", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:16:2:0", "start": 1504, "end": 1521, "surface": "NBP\n(2023) survey", "probe_tag": "confusion", "probe_score": 0.0869, "luna_label": 1, "luna_reason": "Survey data underpin assumed Ukrainian refugee educational attainment."}, {"key": "sample:jad_paddy_docs:000007:16:2:1", "start": 1561, "end": 1580, "surface": "UNHCR (2023) survey", "probe_tag": "drop", "probe_score": 0.002, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:16:2:2", "start": 1647, "end": 1671, "surface": "2022\nLabour Force Survey", "probe_tag": "confusion", "probe_score": 0.5191, "luna_label": 1, "luna_reason": "Survey data provide the general-population basis for comparative earnings analysis."}]}, {"key": "aivin-089", "text": "529 refugees within the region (Arua district is about 50\nkm from Koboko and connected to South Sudan by the KYM road, Adjumani district is about 30 km from\nMoyo and connected to DRC border at Oraba by KYM road). It is a gravel road with poor geometry in a few\nplaces; passes through low-lying areas and seasonal swamps; and constrained to provide all-weather\nconnectivity. It has narrow, damaged and non-functional cross-drainage structures and the transport\nsituation worsens during rainy season when some of the rivers and their tributaries flood and cut off access.\nThis road corridor connects DRC and South Sudan through Uganda and is an alternate route to reach the\n\n\n32 UNRA Diagnostic Study funded by the World Bank – DFID multi-donor trust fund\n33 The host and refugee population of Koboko district are 258,000 and 5,423 (2 percent of the host population) respectively, the host\nand refugee population of Yumbe district are 663,600 and 232,109 (35 percent of host population) respectively, and the host and\nrefugee population of Obongi district, which is a new district carved out of Moyo district, are 48,300 and 122,645 (254 percent of\n[host population) respectively – Source: https://data2.unhcr.org/en/country/uga](https://data2.unhcr.org/en/country/uga)\n34 Bidibidi resettlement concentration Zone 2, Lobule and Palorinya settlements are about 9 km, 6 km and 18 km away respectively\nfrom the KYM road and there are access roads to reach the settlements from the KYM road corridor.\n[35 https://data2.unhcr.org/en/documents/download/74603](https://data2.unhcr.org/en/documents/download/746", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000050:18:2:0", "start": 677, "end": 698, "surface": "UNRA Diagnostic Study", "probe_tag": "keep", "probe_score": 0.9168, "luna_label": 1, "luna_reason": "Named diagnostic study cited as an existing external assessment."}]}, {"key": "aivin-090", "text": " specified package of benefits to all</mark>\n<mark>members of a society with the end goal of providing financial risk protection, improving access to health</mark>\n<mark>services and health outcomes</mark> .” <sup>9</sup> Financed from the Lebanon Syria Multi-Donor Trust Fund, the project\naims to strengthen and improve access to PHC services, especially for the low-income host communities\ncrowded out by the increased demand for PHC services from refugees. The project strengthens the\ncapacity of 75 MoPH network centers, expands the package of services provided, and subsidizes the cost\nof care to 150,000 poor Lebanese enrolled in the NPTP (see Box 2). However, strengthening the capacity\nof the network clinics also extends benefits to low-income non-subsidized Lebanese and displaced\nSyrians covered by the international community. The latest MoPH data show that improving the\ncapacity of the network centers through the EPHRP is having a positive impact on access to services for\nhost communities and displaced Syrians alike. While before the project access to PHC services was\nrelatively low, especially for host communities in areas with high concentration of displaced Syrians, it\nincreased steadily after the start of the project for both poor Lebanese (28 percent) and displaced\nSyrians (47 percent). <sup>10</sup> The project demonstrates that strengthening the integrated PHC model benefits\nboth communities.\n\n\n9 WHO, SDGs, 2016.\n10 Ministry of Public Health data, 2017.\n\n\nPage 13 of 54", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000032:15:1:0", "start": 850, "end": 859, "surface": "MoPH data", "probe_tag": "keep", "probe_score": 0.9351, "luna_label": 1, "luna_reason": "Ministry of Public Health data support reported access improvements."}, {"key": "jdc_operational:000032:15:1:1", "start": 1448, "end": 1478, "surface": "Ministry of Public Health data", "probe_tag": "keep", "probe_score": 0.9268, "luna_label": 1, "luna_reason": "Ministry data support reported access increases for Lebanese and displaced Syrians."}]}, {"key": "aivin-091", "text": "**The World Bank**\nCHAD Improving Learning Outcomes Project (P175803)\n\n\nof parents of children aged 7-14 years report that the school management committee includes parent members; and\nonly one fifth report participating in meetings of the committee. The majority (62 percent) receive report cards, but only\n26 percent report discussing their child’s progress with a teacher. <sup>27</sup> [^27: Data for this and the following two paragraphs are taken from MICS 2019.]\n\n\n18. **Children enter school poorly prepared for learning** **and are taught in a language** **they do not speak.** Many\nchildren start Grade One after years of debilitating health episodes and inadequate cognitive stimulation. Roughly 40\npercent of children under five years old are either moderately or severely stunted. Only one percent of children aged\n36-59 months attend a pre-school program, while only 2 percent of Grade One students have attended a pre-school in\nthe previous year. Also, the existing preschool program dates from 1994 and is no longer relevant, and there are no\nstandards regulating this sub-sector. Further, almost all children are taught in a language they cannot speak.\n\n\n19. **Education budgets are inadequate, and financial mechanisms can be strengthened** . Public spending on\neducation was the equivalent of 2 percent of GDP and education expenditures constituted only 11 percent of total\ngovernment expenditures in 2018, <sup>28</sup> [^28: Chad Public Expenditure Analysis 2019: Fiscal Space for Productive Social Sectors Expenditure, June 2019, pp. 55 ff.] well below those recommended by the Global Partnership for Education, 5 percent\nand 20 percent respectively. They are also low by regional standards. Over the past fifteen years, Chad’s public\nexpenditures on education as a percentage of GDP have on average been 0.8 percentage points lower than the Sahelian\naverage. <sup>29</sup> Education expenditures are fragile and susceptible to shocks. In", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000009:12:0:0", "start": 457, "end": 466, "surface": "MICS 2019", "probe_tag": "keep", "probe_score": 0.9788, "luna_label": 1, "luna_reason": "Named MICS 2019 data source supports the reported education findings."}]}, {"key": "aivin-092", "text": ", communities, and societies as a whole. Several benefits from educating girls should be highlighted here\n(see Wodon et al., 2019, for details):\n\n\n\n<u>(2%)</u>\n\n\n\n\n    - **Child marriage and early childbearing:** The prevalence of child marriage (32.5 percent among girls aged\n18-22 according to the latest Demographic and Health Survey) and early childbearing (26.0 percent)\nremain high in Uganda. Girls from rural areas and disadvantaged socio-economic backgrounds tend to\nhave worse outcomes. Keeping girls in secondary school until they graduate is one of the best ways to end\nchild marriage and early childbearing. Each additional year of secondary education is associated with a\nsubstantial reduction in the risks of child marriage and early childbearing and universal secondary\neducation for girls could virtually eliminate child marriage and thereby also reduce the prevalence of early\nchildbearing by half (because about half of all instances of early childbearing appear to be due to child\nmarriage).\n\n    - **Fertility and population growth:** Women who have children earlier (including when they are still children\nthemselves) tend to have more children over their lifetime. By reducing the risks of child marriage and\nearly childbearing, as well as providing agency for women, universal secondary education could reduce\nfertility rates by more than a third (36 percent) in Uganda. This, in turn, would reduce population growth,\naccelerate the demographic transition, and potentially generate a large demographic dividend which\ncould help in raising standards of living and reducing poverty. For example, levels of human capital wealth\nper capita would increase.\n\n    - **Women’s health:** Finally, analysis suggests that universal secondary education for girls would increase\nwomen’s health knowledge and their ability to seek care, improve their psychological well-being, and\nreduce the risk of intimate partner violence from partners, and reduce risks associated with having\nchildren at an early age.\n\n7. **Education for children", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000018:95:1:0", "start": 307, "end": 336, "surface": "Demographic and Health Survey", "probe_tag": "keep", "probe_score": 0.9615, "luna_label": 1, "luna_reason": "Survey data support the reported child-marriage prevalence figure."}]}, {"key": "aivin-093", "text": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n\nthrough out-of-pocket payments. This poses major challenges in terms of the equity, the efficiency and the\nsustainability of the country’s health financing architecture.\n\n15. **Health facilities have low readiness levels to deliver quality health services** . The number of health facilities\nin Chad is low and more than 3,000 facilities are needed to reach WHO target of two facilities per 10,000\ninhabitants. Further, according to the most recent SARA survey, one in three health facilities had access to\nelectricity and two in three had access to improved water sources. The availability of essential medical\nequipment (scales, thermometers, stethoscopes, etc.) and laboratory capacity were also substandard\n(WHO, 2019). In terms of health professionals, in 2017 there were less than 10,000 professionals in all Chad.\nShortages are particularly acute for doctors and specialized health professionals (0.38 per 10,000\npopulation), and there are important disparities in the distribution of health professionals between\nprovinces.\n\n16. **The coverage of essential health services is low** . The above-mentioned constraints, together with the\nsignificant geographic barriers enhanced by the poor transport infrastructure, lead to the low coverage of\nessential health services such as reproductive, maternal, neonatal and child health services and nutrition.\nIn 2017, one in four children under five received all required vaccines. According to the DHS 2014/2015,\nonly 25 percent of women attended at least four antenatal care visits and less than 30 percent delivered at\na health facility. These coverage rates reflect a low demand for health services and great difficulties\ndelivering health services through outreach.\n\n17. **Weakness in core capacities to enforce International Health Regulation (IHR) can increase the risk of**\n**emergencies** . Chad signed on to the International Health Regulations (RSI,", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000039:14:0:0", "start": 538, "end": 549, "surface": "SARA survey", "probe_tag": "keep", "probe_score": 0.9432, "luna_label": 1, "luna_reason": "Existing SARA survey supports a concrete health-facility access finding."}, {"key": "jdc_operational:000039:14:0:1", "start": 1537, "end": 1550, "surface": "DHS 2014/2015", "probe_tag": "keep", "probe_score": 0.9315, "luna_label": 1, "luna_reason": "DHS survey supports antenatal-care and facility-delivery coverage findings."}]}, {"key": "aivin-094", "text": ". Besides assisting these communities with inclusive improved all-season access to opportunities,\nservices and markets, the Project will generate employment opportunities.\n\n\n7. **Uganda is the land “bridge” for the rest of the Great Lakes region** <sup>**6**</sup> [^6: Consists of Burundi, Democratic Republic of Congo, Kenya, Malawi, Rwanda, Tanzania and Uganda] **, connecting its landlocked neighbors**\n**to the coastal countries.** Regional integration is indispensable for Uganda and provides great opportunity\nto foster trade with its neighbors. Uganda is ranked 102 in the Logistics Performance Index 2018 and\ninfrastructure and tracking and tracing are the two areas it is ranked very low with a ranking of 124 and 123\nrespectively <sup>7</sup> [^7: https://openknowledge.worldbank.org/bitstream/handle/10986/29971/LPI2018.pdf] . The efficiency of the transit traffic performance in the major road corridors is critical for\nsupporting and sustaining competitive international trade in the sub-region as well as supporting the\nrefugee settlements and host communities through the facilitation of logistics services from the various\nhumanitarian agencies. Inefficiencies in logistics systems can have dire consequences especially during\nemergency situations like the spread of a pandemic (e.g. COVID-19) when uninterrupted flow of essential\ngoods like food and medical supplies is imperative.\n\n8. **Uganda has a longstanding history of hosting refugees, since 1942 and is currently hosting the largest**\n**number of refugees in Africa and the third largest number in the world.** By the end of February 2020, there\nwere 1.4 million refugees and asylum seekers in Uganda and their numbers have been increasing since\nOctober 2018. South Sudanese (62 percent) make up the largest refugee population followed by refugees\nfrom the Democratic Republic of Congo (DRC) (29 percent), Burundi (3.4 percent), Somalia (2.8 percent) and\nothers", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000050:12:1:0", "start": 581, "end": 613, "surface": "Logistics Performance Index 2018", "probe_tag": "keep", "probe_score": 0.94, "luna_label": 1, "luna_reason": "Named index cited as evidence for Uganda’s logistics ranking and performance weaknesses."}]}, {"key": "aivin-095", "text": " profession and to effectively manage teachers in improving teacher classroom practices.\nWith renewed political commitment, along with the directives outlined in the National Human\nResource Development Strategy (NHRDS) (2016–2025), the MOE is working toward formalizing and\nimplementing the NTPSF. The overall aim of the NTPSF is to tackle the low status, social prestige, and\nquality of the professional performance of Jordanian teaching staff, and to expand preservice training.\n\n\n13 According to MOE data for the 2015‐2016 academic year, enrollment is similar for girls and boys.\n14 Latest (2012) EGRA and EGMA scores for Jordan.\n15 PISA 2015\n\n4", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000041:11:2:0", "start": 499, "end": 507, "surface": "MOE data", "probe_tag": "confusion", "probe_score": 0.6975, "luna_label": 1, "luna_reason": "MOE data supports the enrollment comparison for 2015–2016."}]}, {"key": "aivin-096", "text": "43 \nthe Customs \nGolden List  \nfollow expedited clearance including lowered \nguarantees, green channel streaming, cursory \ndocument review, and minimal to no physical \ninspections \n  \nDepartment of \ncustom services  \nAutomated \nsystem for \ncustoms data \ndatabase \n \na print from the \ncustoms IT system \n(automated system \nfor customs data) to \nverify the number \nof entries by those \ncompanies and \nwhat, if any, \nexaminations \noccurred.  \nAB will confirm the \ncounting and \nperform random \nchecks as needed \nand issue its report \nwithin 2 months \nfollowing PMU \nnotification.  \nMOPIC will submit \nrelated \ndocumentation \nconfirming \nachievement of \nresults along with \nthe AB report. \nDLI 5 \nNumber of \ninvestments \nbenefitting \nfrom \ninvestment \nfacilitation by \nthe JIC  \nDLR 5.1: Removing the minimum capital \nrequirements for foreign investments (Prior \nResult) \nDLR 5.2: Number of investments benefiting \nfrom investment facilitation by JIC = 530 \n(cumulative).  \n \nThis includes the following:  \n(a) Basic communication/investor inquiries \n(b) Site visits facilitated \n(c) Secured investment commitment \nYes \nThe JIC’s CRM \ndatabase \nAB \nAt the end of each \nCY, the JIC will \nprovide to the PMU \nits CRM database. \nAB will review and \nconfirm the \ncounting and \nperform random \nchecks of the \nfacilitated \ninvestments as \nneeded. The AB", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000045:51:0:0", "start": 1120, "end": 1139, "surface": "JIC’s CRM \ndatabase", "probe_tag": "confusion", "probe_score": 0.5222, "luna_label": 0, "luna_reason": "Database provision and review are planned future verification activities."}, {"key": "jdc_operational:000045:51:0:1", "start": 1207, "end": 1219, "surface": "CRM database", "probe_tag": "confusion", "probe_score": 0.1012, "luna_label": 0, "luna_reason": "Future database submission for DLI verification is planned monitoring machinery."}]}, {"key": "aivin-097", "text": " part of the Government’s COVID-19 response.** In response to the\ndanger posed by the pandemic to Ugandan students, the Government announced the closure of all schools from\nMarch 20, 2020 for a period of 30 days in a bid to avoid the possible rapid spread of new infections of COVID-19.\nThe closure of schools remains in place and it is not clear when this could be lifted. This has impacted more than\n67,516 schools affecting more than 15 million students and over 400,000 teachers.\n\n\n**<u>Table 1. Enrollment by education level (public and private, and refugees)</u>**\n\n|Level of education|# of Schools|# of Students|# of Teachers|\n|---|---|---|---|\n|Pre-primary|28,208|2,050,403|90,742|\n|Primary|36,314|10,777,846|315,787|\n|Lower Secondary|2,994|1,779,550|114,859|\n|**Total **|**67,516 **|**14,607,799 **|**406,529 **|\n\n\n\nSources: Report on the Master List of schools in Uganda (MEIU) Uganda Bureau of Statistics (UBOS) 2019\n\n13. **The COVID-19 outbreak and school closures are expected to have a wide range of impacts on students,**\n**teachers and households.** Prolonged school closures are expected to lead to a loss in learning. Households will\nface increasing economic difficulties with rising unemployment and income losses. This could impact the\nlikelihood of children staying in school and transitioning to the next level of education once the schools reopen,\nincreasing the number of out-of-school children. Parents’ ability to contribute to educational inputs may also be\nmore limited", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000034:12:1:0", "start": 848, "end": 880, "surface": "Master List of schools in Uganda", "probe_tag": "confusion", "probe_score": 0.8, "luna_label": 1, "luna_reason": "Named source cited for enrollment table figures."}]}, {"key": "aivin-098", "text": "sup>12</sup> The percentage of women in leadership roles was highest\nin Western Bahr el Ghazal state (30.3 percent) and lowest in Warrap State (4.9 percent). <sup>13</sup> In addition,\nthere are limited income generating opportunities for women. When women do generate income,\n\n\n7 World Bank 2021.\n8 UNDP (United Nations Development Programme). 2020. _Human Development Report_ .\n9 World Bank 2021.\n10 HDI’s life-course gender gap compiles 12 indicators that analyze gender gaps in choices and opportunities across the life-span including\neducation, labor and work, political representation, time use, and social protection. HDI’s women’s empowerment dashboard compiles 13\nwoman-specific empowerment indicators in three categories: reproductive health and family planning, violence against women and girls, and\nsocioeconomic empowerment.\n11 UNDP. 2018. _Human Development Indices and Indicators: 2018 Statistical Update - South Sudan._\n12 Kenwill International Limited. 2015 _. Fortifying Equality and Economic Diversification (FEED): Improved Livelihoods in South Sudan._ Gender\nAssessment Report, World Vision.\n13 Kenwill International Limited 2015 _._\n\n\nPage 9 of 73", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000028:14:2:0", "start": 631, "end": 660, "surface": "women’s empowerment dashboard", "probe_tag": "confusion", "probe_score": 0.63, "luna_label": 0, "luna_reason": "Dashboard is named, but its data are not shown informing analysis or a finding."}]}, {"key": "aivin-099", "text": "**The World Bank**\nStrengthening Lebanon’s Covid-19 Response (P178587)\n\n\n79 **. The World Bank will support various efforts to actively engage with citizens to collect feedback on the NDVP and**\n**project performance, through social media surveys, the TPM mechanism, and through the use of Iterative**\n**Beneficiary Monitoring (IBM).** In February 2021, the World Bank team administered a Facebook survey to assess\nbeliefs and attitudes towards COVID-19 vaccination. Responses from more than 15,000 participants showed that\nonly 28 percent of them intended to take the vaccine when it becomes available, and 48 percent were still unsure\nat the time of the survey. Moreover, as part of the TPM mechanism, feedback from vaccine recipients and health\nproviders was collected and shared regularly with the MoPH, the National Vaccination Committee, and the Vaccine\nExecutive Committee for corrective action as needed. Additionally, performance score cards were produced and\nshared with vaccination sites to monitor performance and identify challenges. An IBM approach is also being\nplanned as an iterative feedback loop that collects information directly from beneficiaries and identifies challenges\nat the local level that can be addressed by project teams. In addition to improving project efficiency, this approach\nincreases beneficiary engagement and satisfaction by creating positive, self-reinforcing cycles of improvement.\nFindings will be used to improve the communication campaign and citizen engagement. Through the IBM as well\nas social media surveys, engagement with community, especially in remote areas, will ensure the inclusion of their\nongoing feedback in the rollout and implementation of the COVID-19 vaccination campaign to strengthen targeting\naccuracy and increase uptake. To ensure citizen engagement, the project will: (a) target messages to areas where\nvulnerable groups, including refugees and IDPs, reside to inform them about safety measures and benefits; (b)\ntailor messages to the elderly and those with medical risks including their target family members and health care", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000000:39:0:0", "start": 226, "end": 246, "surface": "social media surveys", "probe_tag": "confusion", "probe_score": 0.6578, "luna_label": 0, "luna_reason": "Future project-supported surveys are planned to collect feedback."}, {"key": "jdc_operational:000000:39:0:1", "start": 389, "end": 404, "surface": "Facebook survey", "probe_tag": "keep", "probe_score": 0.9452, "luna_label": 1, "luna_reason": "Past survey produced vaccination-attitude findings from over 15,000 participants."}]}, {"key": "aivin-100", "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|Enrolment at lower secondary education<br>for refugees|The project will finance<br>construction of about<br>15,000 new places in lower<br>secondary schools in the<br>refugee hosting areas. The<br>target is set based on the<br>assumption that the current<br>proportion in enrollment<br>between refugees and hosts<br>is maintained (33% to 67%).<br>However, the overall<br>enrolment will depend on<br>the refuges influx and<br>internal migration during<br>the project life, which is<br>beyond the project control.<br>Enrolment in private and<br>public schools is counted as<br>reliable disagregated<br>baseline data is not<br>available.<br>AEP enrolment is not<br>included and measured<br>under a dedicated indicator.|Midterm and<br>end of<br>project|EMIS|Enrollment data by<br>refugee settlement and<br>hosting communities.|MOES|\n|---|---|---|---|---|---|\n|Enrolment at lower secondary education<br>for host communities|<br>The project will finance<br>construction of about<br>15,000 new places in lower<br>secondary schools in the<br>refugee hosting areas. The<br>target is set based on the<br>assumption that the current<br>proportion in enrollment|Data will be<br>reported at<br>midterm and<br>end of<br>project.<br>|Baseline", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000018:57:0:0", "start": 842, "end": 857, "surface": "Enrollment data", "probe_tag": "confusion", "probe_score": 0.1286, "luna_label": 0, "luna_reason": "Standalone table cell, not an independently used data source"}]}, {"key": "aivin-101", "text": "**The World Bank**\nSouth Sudan Emergency Food and Nutrition Security Project (P163559)\n\n\nwill develop detailed checklists to ensure consistent and compliant Project procurement.\n\n   - The PIU will also develop a contract management system to ensure that all contracts under the\nProject are effectively and efficiently managed. This will include the tracking of key contract\nmilestones and performance indicators as well as capturing all procurement and contract records.\n\n\n**Table A2.3. Procurement risk analysis and mitigation**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Risk Description|Description of Mitigation|Risk owner|\n|---|---|---|\n|**(i) Operational context:**  volatile political<br>situation<br>and<br>weak<br>macro‐economic<br>projection; current constraint in making<br>payments to another party outside of country<br>due to foreign exchange shortages.|Direct payments to suppliers at the request<br>of the Government.|Borrower|\n|**(ii) Fiduciary risk:**    corruption and bribery<br>concerns with regards to internal controls<br>within the Ministry and broader context of the<br>country;  South  Sudan  is  ranked  the  second<br>most corrupt country in the world on<br>transparency corruption perception index.|Project to be implemented with the support<br>of  UN  Agencies  as  the  main  suppliers  of<br>goods and services. In addition, direct<br>payments are proposed.|Borrower|\n|**(iii)Lack of complaints and resolution of**<br>**disputes system:**  the Government does not<br>have ", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000038:59:0:0", "start": 1158, "end": 1198, "surface": "transparency corruption perception index", "probe_tag": "confusion", "probe_score": 0.166, "luna_label": 1, "luna_reason": "Index ranking is cited as evidence of South Sudan’s corruption risk."}]}, {"key": "aivin-102", "text": " percent. More than 70 percent of school-age children are not receiving education. <sup>37</sup> [^37: UNESCO, et. al. 2018. _Global Initiative on Out of School Children, South Sudan Country Study._] The country\nhas the lowest road density in Africa with less than 2 percent of the primary network paved, constraining access to the\nschools and health facilities that do exist. <sup>38</sup> [^38: World Bank 2018.] The situation would likely worsen if the number of returnees (both from within\nand outside the country) increased to areas where humanitarian assistance is limited, as the impacts of COVID-19 become\nmore widespread, and in areas where continuing violence triggers short-term, localized displacement.\n\n\n13. **Local institutions—both local governments and community institutions—are mandated to play an integral role**\n**in providing services.** Under the Transitional Constitution of South Sudan 2011 as well as the Local Government Act (LGA)\n2009, the Government is organized into three tiers **—** at the national, state, and local levels. The local government, in turn,\n\n\n30 Ibid.\n31 IOM 2021; and United Nations Mission in South Sudan (UNMISS) (August 2019) ( _Mimeo_ ).\n32 IDMC (International Displacement Monitoring Center 2021. 2019 figures from UNOCHA Humanitarian Needs Overview, p.13 and IOM DTM, p.4\n33 IOM “Draft Population Movement Analysis October 2019.”\n34 IOM DTM. The movements of the remaining 7 percent of returnees could not be clearly determined.\n35 IOM DTM. Some displaced have moved to settle more permanently in urban centers (as in Wau or Malakal) or may remain in cities because they are unable to return\nto their villages due to security concerns, or the occupation of their land and houses by other groups (for example, Bor or Bentiu).\n36 UNOCHA Humanitarian Needs Overview (2018).\n37 UNESCO, et.", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000027:6:2:0", "start": 1267, "end": 1301, "surface": "UNOCHA Humanitarian Needs Overview", "probe_tag": "confusion", "probe_score": 0.8924, "luna_label": 1, "luna_reason": "Named humanitarian overview cited as source for 2019 figures."}, {"key": "jdc_operational:000027:6:2:1", "start": 1312, "end": 1319, "surface": "IOM DTM", "probe_tag": "keep", "probe_score": 0.9493, "luna_label": 1, "luna_reason": "Named IOM displacement data source cited for reported 2019 figures."}]}, {"key": "aivin-103", "text": ", is the very low share of\nhouseholds that have access to means of communication, thus making it difficult to reach large sections of the\ncountry using new technologies. In order to ensure continuity in the teaching and learning process, multiple\neducation delivery modalities will need to be used to reach the maximum number of students. These modalities\ninclude paper-based resources (printed materials, books, etc.), mobile phones, radio/audio interaction;\nvideo/television; and online teaching and learning platforms. This sub-component will mainly target vulnerable\nhouseholds using data from the recent household survey.\n\n\n**_Sub-Component 1.2: Teacher training to prepare and deliver educational content through a multi-modal_**\n**_distance learning system (US$0.7 million)._**\n\n\n14. Sub-component 1.2 will support training of approximately 5,000 teachers, particularly female teachers, in\nonline and distance learning methodologies to ensure teachers can effectively play a key role in supporting\nremote teaching and learning programs. Teachers will be trained to design formative questions, tests, or\nexercises to closely monitor students’ learning processes. Teachers will also engage with parents and design\nsimple audio clips for parents who may not have gone to school to support them in home schooling, be on call\nfor interaction with learners and parents, or hosting online-or phone-based group learning conversations. To\nachieve this, the proposed Project will support: (i) technical assistance for teacher training programs to adapt the\ncurrent curriculum to distance learning strategies; (ii) a platform for all teachers (WhatsApp for instance) to\nengage among themselves, or with the administration, students, and parents; and (iii) printing and distribution\n(including digital) of guidance materials for teachers on the management of daily home-based learning practices.\n\n\nAugust 14, 2020 Page 7 of 13", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000031:6:1:0", "start": 609, "end": 625, "surface": "household survey", "probe_tag": "confusion", "probe_score": 0.757, "luna_label": 1, "luna_reason": "Existing household survey data are used to target vulnerable households."}]}, {"key": "aivin-104", "text": "from Decision Review Decision Note)**|\n|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|Will the review of Safeguards be deferred? [ ] Yes   [ X ]  No|\n|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000003:6:1:0", "start": 863, "end": 885, "surface": "Project Financing Data", "probe_tag": "confusion", "probe_score": 0.1108, "luna_label": 0, "luna_reason": "Standalone table header, not a substantive data-use mention."}]}, {"key": "aivin-105", "text": "<br>|\n|<br>N’Djamena<br>|<br>51.0<br>|<br>43.8<br>|<br>49.6|\n\n\n2. The gap in electricity access is also visible when looking at quintiles of wealth, although it only\nbecomes sizable for the highest quintile. From all male-headed households in the top quintile, 40.9\npercent have access to electricity while the same figure is 29.79 percent for female-headed households.\n\n\n\n\n\n\n|Location|Male (%)|Female (%)|All (%)|\n|---|---|---|---|\n|<br>Poorest|<br>0.01|<br>0.03|<br>0.01|\n\n\n\n43 The data on the distribution of household heads were updated by a survey on ability and willingness of households to pay for\nelectricity services, completed in 2021. Details are provided in annex 6.\n\n\nPage 71 of 87", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000051:76:2:0", "start": 484, "end": 527, "surface": "data on the distribution of household heads", "probe_tag": "confusion", "probe_score": 0.5761, "luna_label": 1, "luna_reason": "Survey data updated the household-head distribution used in the analysis."}, {"key": "jdc_operational:000051:76:2:1", "start": 546, "end": 593, "surface": "survey on ability and willingness of households", "probe_tag": "keep", "probe_score": 0.9392, "luna_label": 1, "luna_reason": "Completed survey updated household-head distribution data used in analysis."}]}, {"key": "aivin-106", "text": " in volume and\nhave an 18-month maturity so as to be available over the COVID-19 period only and so as not to distort the market\nfor loan capital. All participants along the chain will still maintain positive margins, even if reduced ones. Burden\nsharing means costs are reduced or absorbed, or revenues (margins) are compressed while staying moderately\npositive and different stakeholders assume their respective shares. Those financial intermediaries benefiting from\nthese credit lines will determine how to provide the COVID-19 support to their clients so as not to disrupt their\n\n\n47 Surveys will be conducted in person or over the phone and could be substituted by surveys conducted by UBOS.\n48 The objective is to develop an actional M&E system that is used as instrument to monitor and improve project effectiveness along\nimplementation rather than just a system for ex-post accountability.\n\n\nPage 31 of 92", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000021:36:2:0", "start": 670, "end": 695, "surface": "surveys conducted by UBOS", "probe_tag": "confusion", "probe_score": 0.7968, "luna_label": 0, "luna_reason": "Future surveys are planned data production by UBOS."}]}, {"key": "aivin-107", "text": "openknowledge.worldbank.org/handle/10986/34401 License: CC BY 3.0 IGO\n14 The referenced study uses a definition of CCIs based on the World Intellectual Property Organization (WIPO) copyright definition: Visual arts,\nMusic, Performing arts, Cinema, Photography, Television and Radio, Videogames, Books and Press, Advertising and Communication and\nSoftware.\n15 IOF. “The Contribution of Cultural and Creative Industries to the Lebanese Economy - Executive Summary.” Accessed December 20, 2021.\nhttp://www.institutdesfinances.gov.lb/publication/the-contribution-of-cultural-and-creative-industries-to-the-lebanese-economy-executivesummary/.\n16 World Bank Group; European Union; United Nations. 2020. Beirut Rapid Damage and Needs Assessment. World Bank, Washington, DC. ©\nWorld Bank. https://openknowledge.worldbank.org/handle/10986/34401 License: CC BY 3.0 IGO\n17 Socio-economic vulnerability: The project will prioritize the poorest and the most vulnerable households affected by the blast (e.g. lowincome, FHH, refugees), based on a socioeconomic field survey. The vulnerability criteria considers social (i.e. presence of elderly, female\nheaded households, people with disabilities, refugees, building located in an area of higher social vulnerability) and economic vulnerability (i.e.\nhousehold receiving rental support, presence of CCI, level of income).\n18 World Bank Group; European Union; United Nations. (2020). _Beirut Rapid Damage and Needs Assessment_ . Washington, DC.: World Bank\nGroup.\n\n19 The terms \"housing\", \"units\", \"apartments\" are used equally throughout the document to refer to the individual residential unit. The term\n\"building\" refers to the urban infrastructure that", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000012:13:2:0", "start": 1033, "end": 1059, "surface": "socioeconomic field survey", "probe_tag": "confusion", "probe_score": 0.1634, "luna_label": 1, "luna_reason": "Existing survey data informs planned vulnerability-based household prioritization."}]}, {"key": "aivin-108", "text": " the integrated data platform. Data from each implementing agency will be\naggregated with the data of others and used as the basis of quarterly progress reports.\n\n82. **Data generation and reporting** . The data to track the key performance indicators come from (a) project-specific\nsurveys and questionnaires, (b) project service providers (women’s entrepreneurship platform managers, trainers, PFIs,\nfacility managers); (c) local governments; (d) consultant reports; and (e) supervising engineers’ reports on construction\nprogress. The MGLSD PIT is responsible for preparing the quarterly financial and progress report, consolidating\ninformation from each and from the integrated data platform. It will submit the submit quarterly project progress reports\nto the World Bank, the PTC, the PSC, and to other the stakeholders within 45 days of the end of each quarter. The\ngovernment and World Bank will prepare a comprehensive midterm review of the project implementation and results in\n2024/25, during which the target values will be reviewed and any required adjustments to project design agreed.\n\n83. **Capacity building for M&E** . The project will provide support to strengthen capacity for M&E of the MGLSD and of\nthe national and subnational PITs. Specifically, the project will finance consultants who will work with the MGLSD to\nprepare a detailed M&E and reporting system plan, provide on-the-job and other training for M&E specialists (at both\nthe MGLSD and other implementing agencies), and provide other capacity support required to establish and operate an\neffective M&E system. The project will also finance follow-on training and workshops with M&E specialists to ensure that\nnormal staff turnover does not disrupt the M&E effort.\n\n**C. Sustainability**\n\n\n84. **The project aims to support female entrepreneurs grow and transform their micro and small enterprises to**\n**larger more profitable firms", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000025:36:1:0", "start": 5, "end": 29, "surface": "integrated data platform", "probe_tag": "confusion", "probe_score": 0.3854, "luna_label": 1, "luna_reason": "Platform data are consolidated and used for quarterly progress reporting."}, {"key": "jdc_operational:000025:36:1:1", "start": 266, "end": 309, "surface": "project-specific\nsurveys and questionnaires", "probe_tag": "confusion", "probe_score": 0.2756, "luna_label": 0, "luna_reason": "Project-specific surveys are planned M&E data sources, not existing external data."}]}, {"key": "aivin-109", "text": "**The World Bank**\nChad Rural Mobility and Connectivity Project (P164747)\n\n\n**Figure 2: Theory of Change**\n\n\n\nPage 18 of 76", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000048:21:0:0", "start": 19, "end": 63, "surface": "Chad Rural Mobility and Connectivity Project", "probe_tag": "confusion", "probe_score": 0.0644, "luna_label": 0, "luna_reason": "Project title names an operation, not an existing data resource or data use."}]}, {"key": "aivin-110", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n\n1. To inform the design of the project subcomponent aiming to electrify households through SHSs,\na survey on ability and willingness of Chad rural households to pay for electricity services was conducted\nin the first half of 2021. Due to time and budget limitations, as well as security constraints, the survey was\nimplemented in the rural areas of three Chadian provinces that were selected with the objective of\nobtaining representative data that can be extrapolated to the rest of the rural areas of the country. The\npoverty incidence, together with homogeneity/differences between provinces, played a key role in the\nstratification of the sample. Table 6.1. summarizes information on the three selected provinces and\nsample size, while figure 6.1. shows a Chad map with the names of provinces.\n\n|Province|Poverty Incidence (%)|Sample Size|\n|---|---|---|\n|<br>Guéra <br>|<br>60.0 <br>|<br>248 <br>|\n|<br>Kanem <br>|<br>27.7 <br>|<br>241 <br>|\n|<br>Logone Occidental <br>|<br>43.5 <br>|<br>239 <br>|\n|<br>Total|<br> <br>|<br>728|\n\n\n\n2. Select results of the survey by province and the average for the three provinces are summarized\nin Table 6.2.\n\n\nPage 84 of 87", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000051:89:0:0", "start": 1127, "end": 1145, "surface": "survey by province", "probe_tag": "confusion", "probe_score": 0.7453, "luna_label": 1, "luna_reason": "Survey results by province are presented as evidence in Table 6.2."}]}, {"key": "aivin-111", "text": " The sub-component will\nsupport the implementation of the overall project by PSFU. The sub-component will support regular auditing,\nfinancial reporting, all safeguards assessments and monitoring and evaluation. Establishment of a web platform\nto enable the project to reach all beneficiaries (particularly refugees and host communities) and be accessible\nfrom all locations in Uganda. Monitoring and evaluation (M&E) activities undertaken as part of this component\nwill focus on data collection, survey implementation, and evaluating the economic impact of the program through\na structured impact evaluation at the conclusion of the project. The monitoring component of the M&E approach\nwill require data collection across different dimensions of the project: (a) performance tracking data (for example,\nsales, employment, wages, transactions); (b) activity tracking data reflecting the theory of change; (c) key results\ndata (for example, value of private investment in manufacturing firms, formal employment in manufacturing\nfirms); and (d) tracking of key risks (for example, project implementation performance, NPL ratio of banks, and\nportfolio at risk [PAR] of MFIs). The evaluation component will build on the data collected under the monitoring\ncomponent but additionally focus on implementing a structured impact evaluation process to measure the impact\nand attribution of the program.\n\n\n**C. Project Beneficiaries**\n\n\n52. The project targets direct beneficiaries, 140,000 MSMEs and 120,000 refugees, of these at least 40,000\nare expected to be women-led microenterprises. The project will seek to have a focus on the manufacturing\nand/or exporting supply chains. Other larger-size firms will also benefit from project interventions, for instance\nindirectly benefiting from the receivables financing under sub-component 1.3. The program will also focus on\neconomic opportunities <sup>42</sup> for RHDs by seeking to catalyze investments that enhance economic activity as well as\n\n\n40 This group of firms will typically include", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000021:29:1:0", "start": 764, "end": 789, "surface": "performance tracking data", "probe_tag": "confusion", "probe_score": 0.0766, "luna_label": 0, "luna_reason": "Data are to be collected for project monitoring, so they are project-produced."}, {"key": "jdc_operational:000021:29:1:2", "start": 909, "end": 925, "surface": "key results\ndata", "probe_tag": "confusion", "probe_score": 0.0912, "luna_label": 0, "luna_reason": "Project monitoring data collection is planned, not existing data use."}]}, {"key": "aivin-112", "text": "/Abdeh<br>area – Akkar,<br>North Lebanon|89,100|20,315|\n|Union of Municipalities of Al<br>Shafat|11|Halba area –<br>Akkar, North<br>Lebanon|84,800|13,810|\n|Total|166**||1,095,729|250,373|\n\n\n*Number of Syrians registered with UNHCR in a given union as of March 31, 2014. Source: _UNHCR/CDR_\n** Given the limitations of the current available funding envelope, a number of municipalities in the targeted unions\nmay not initially benefit from Project interventions.\n\n\n**A.** **Project Components**\n\n\n24. The Project consists of three components: (i) Emergency Response; (ii) Rehabilitation of\nCritical Infrastructure; and (iii) Project Implementation Support.\n\n25. **_Component 1: Emergency Response (US$ 3.5 million)._** This component will finance the\nprovision of high priority municipal services (Subcomponent 1A) and initiatives that promote\nsocial interaction and collaboration (Subcomponent 1B) in the eleven participating unions of\nmunicipalities. Allocation of resources among the participating unions of municipalities will be\nrelated to the number of Syrians hosted, considering the number of Syrian refugees as a proxy of\nthe additional stress on local communities. Initiatives will be selected in consultation with\nmunicipalities and communities, with a decentralized approach to decision-making. These\ninitiatives will provide non-exclusionary benefits addressing some of the most immediate service\nneeds in the unions most affected by the crisis and will be aimed at: (i) improving safety and\nmobility; (ii) mitigating the increasing health and environmental risks associated with the\ndeterioration of water, waste, and sanitation services; and (iii) increasing collaboration and\ninteraction amongst the communities.\n\n26. _Subcomponent 1A_ _Service Delivery_ will focus", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000003:17:1:0", "start": 191, "end": 230, "surface": "Number of Syrians registered with UNHCR", "probe_tag": "confusion", "probe_score": 0.5862, "luna_label": 1, "luna_reason": "UNHCR registration figures support refugee-hosting-based resource allocation."}]}, {"key": "aivin-113", "text": "## **Annex 5: Fiduciary Systems Assessment – Addendum**\n\n1. **As part of the USMID additional financing Program preparation and in accordance with**\n**OP/BP 9.0, a fiduciary assessment was carried out that evaluated the Program Procurement,**\n**Financial Management, Governance and Anti-corruption systems.** The assessment determined that\nexisting systems can provide reasonable assurance that the Program expenditures will be used appropriately\nto achieve their intended purpose with due attention to the principles of economy, efficiency, effectiveness\nand transparency. There is sufficient evidence under the current original USMID financing, as documented\nin the five years of independent Annual Performance Assessments and audits, that funds have been used as\nintended.\n\n2. **The Fiduciary Systems Assessment has identified the various implementation and Program**\n**risks and their potential impact on the ability of the Program to attain its development objective.** As\ndiscussed in detail below, the Program Implementation framework is characterized by the following main\nrisks: (i) low staffing levels, (ii) limited staff knowledge about systems and lack of full systems availability\nwhere one MC - Mubende is not on IFMS, (iii) Poor internet connectivity, (iv) An environment of low\nlevels of compliance to laws and regulations with weak enforcement regimes, (v) Planning and budgeting\nfigures are not consistently adhered to with mismatch between annual budgeted funds and annual\nassessment results resulting in releases that have not been appropriated, (vi) weak contract management,\n(vii) Low bidder participation and poor quality bids, (viii) Poor record keeping, and (ix) Weak cash flow\nmanagement. The gaps in the functioning of monitoring and oversight systems like internal audit and\nprocurement audits regarding capacity and limited coverage of MCs also pose additional risks. Based on\nthe above analysis of the risks, the overall fiduciary risk of the operation is rated as Substantial.\n\n3. **Key design features of", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000006:71:0:0", "start": 682, "end": 735, "surface": "independent Annual Performance Assessments and audits", "probe_tag": "drop", "probe_score": 0.044, "luna_label": 1, "luna_reason": "Existing assessments and audits provide evidence that funds were used as intended."}]}, {"key": "aivin-114", "text": "**The World Bank**\nUganda Secondary Education Expansion Project (P166570)\n\n\n\n|Col1|Col2|Col3|Col4|(about 590,000) and<br>50% of students<br>enrolled in private<br>schools (about 322,000)<br>and students enrolled<br>in new schools. The<br>training of the teachers<br>will be done gradually:<br>25% trained in Y2, 50%<br>by Y3, 75% by Y4 and<br>100% by Y5. Students<br>from the new schools<br>are included as well.|Col6|\n|---|---|---|---|---|---|\n|Students benefiting from direct<br>interventions to enhance learning -<br>Female||Same as for<br>the core<br>indicator<br> <br>|Same as for<br>the core<br>indicator<br>|<br>Same as for the core<br>indicator<br>|Same as for the core<br>indicator<br>|\n|Enrolment at public lower secondary<br>schools in targeted districts, girls|The indicator will measure<br>the number of female<br>students who will annually<br>enroll in public school in the<br>targeted districts (2017 is<br>the baseline year).<br>|<br>Measuremen<br>t will be<br>taken at<br>project<br>midterm and<br>end of<br>project.<br>|<br>EMIS<br>|Headcount<br>|MoES<br>|\n|Enrolment at", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000018:56:0:0", "start": 1042, "end": 1046, "surface": "EMIS", "probe_tag": "drop", "probe_score": 0.0228, "luna_label": 0, "luna_reason": "Standalone table cell naming EMIS, with no shown use of its data."}, {"key": "jdc_operational:000018:56:0:1", "start": 1065, "end": 1069, "surface": "MoES", "probe_tag": "drop", "probe_score": 0.0228, "luna_label": 0, "luna_reason": "Agency name appears as a source cell within an indicator table."}]}, {"key": "aivin-115", "text": "**The World Bank**\nSouth Sudan Enhancing Community Resilience and Local Governance Project (P169949)\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Col1|include realization of local<br>hazard protection measures<br>(e.g. levees) and climate<br>change adaptation (e.g.<br>flood-proof/heat-resistant<br>buildings).|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|Percentage of county government officials<br>trained.|<br>Percentage of county<br>government officials trained<br>in areas described in the<br>PAD.|Biannual<br>|UNOPS/IOM<br>|Regular monitoring<br>|UNOPS/IOM<br>|\n|Percentage of grievances appropriately<br>responded to within the pre-determined<br>timeframe.||Quarterly<br>|Project MIS<br>|Regular monitoring<br>|UNOPS<br>|\n|Percentage of counties for which the<br>required information is uploaded to the<br>MIS in a timely manner to monitor results.<br>|<br>Geo-referenced data on<br>subproject progress<br>uploaded in the MIS in a<br>timely manner for project<br>management to monitor<br>results.|Quarterly<br>|Project MIS<br>|Regular monitoring<br>|UNOPS<br>|\n\n\nPage 56 of 94", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000049:61:0:0", "start": 837, "end": 856, "surface": "Geo-referenced data", "probe_tag": "drop", "probe_score": 0.0366, "luna_label": 0, "luna_reason": "Span is a data phrase embedded within a monitoring table."}]}, {"key": "aivin-116", "text": "**The World Bank**\nUganda Digital Acceleration Project – GovNet (P171305)\n\n\nto the national development priorities and institutional mandates to ensure that the different activities are fully\nsupported. Institutional strengthening of NITA-U has already taken place during implementation of RCIP-5 and the\nuse of alternative delivery models involving partnerships with the private sector and NGOs to complement\ngovernment efforts will also be applied under this project. Despite these mitigation measures, the residual\nstakeholder risk remains substantial for the time being.\n\n**100.** **Refugee protection is an ‘other’ risk that is rated as Moderate.** The WB, in consultation with UNHCR, has\nconfirmed that Uganda’s protection framework is adequate for accessing funding under the IDA19 WHR. Uganda\nis adopting comprehensive humanitarian and development programs aimed at mitigating protection risks faced\nby refugees, including the managed arrival of refugees despite COVID-19 border closures. However, there is a\nmoderate risk that Uganda’s asylum space and refugee policies could become more restrictive in response to the\nstrain on services and the natural environment, continuing refugee population growth, and COVID-19-related and\npolitical pressure. Additional refugee-specific risks include the high proportion of women and girls and other\nvulnerable people within the refugee population, which poses specific protection challenges, including GBV;\nchallenges to the ongoing allocation of land to refugees; and administrative and informal barriers for refugees to\naccess productive employment, finance, and market opportunities. Another protection risk that this project will\nmanage relates to ensuring the ongoing adequacy and management of refugee registration data. Over the three\nyears, the WB has undertaken analytical studies in Uganda across refugees and RHDs such as on GBV,\ndeforestation and environmental management, and socioeconomic status informing refugee policy. The findings\nof these are being operationalized through WHR-financed projects including this one. These risks are then being\nmanaged jointly through effective in-country coordination mechanisms which include the", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000023:48:0:0", "start": 1751, "end": 1776, "surface": "refugee registration data", "probe_tag": "drop", "probe_score": 0.0397, "luna_label": 0, "luna_reason": "Mentions managing registration data but shows no substantive analytical or decision use."}]}, {"key": "aivin-117", "text": "**The World Bank**\nUganda: Roads and Bridges in the Refugee Hosting Districts Project (P171339)\n\n\nbuses, motorbikes, cars, 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 (days)\n(iv) Percentage increase in trade volumes using the Project road (dis-aggregated by refugees, hosts)\n\n(b) enhance the capacity of UNRA to manage environmental, social and road safety risks\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 (yes/no)\n(vii) Annual Number of fatalities or serious injuries involving construction vehicles or at construction\n\nsites\n\n\n**B. Project Components**\n\n\n43. The project will have the following components as detailed below.\n\n\n44. **Component 1: Road Upgrading Works (Total US$145.8 million; IDA: US$125.8 million equivalent, GoU:**\n**US$20 million).**\n\n\n1(a) Upgrading - to bituminous paved road standard - and widening of about 105 km of KobokoYumbe-Moyo road corridor.\n1(b) Carrying out supervision of civil works under part 1(a) of the Project.\n1(c) Carrying out environmental and social risks management (including implementation of action\nplans to address among others gender-based violence, sexual exploitation and abuse, violence\nagainst children and HIV/AIDS), monitoring and evaluation, third party integrated performance\naudits and road user satisfaction surveys.\n1(d) Preparation of Project’s environmental and social risk management documents as well as\ndetailed engineering designs of civil works and carrying out land acquisition, and resettlement\nand rehabilitation associated with upgrading works under Part 1(a) and maintenance of the road\ncorridor for five years post-construction.\n\n45. This", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000050:25:0:0", "start": 535, "end": 545, "surface": "Crash data", "probe_tag": "drop", "probe_score": 0.0446, "luna_label": 0, "luna_reason": "Planned monitoring indicator for future crash-data entry and reporting"}, {"key": "jdc_operational:000050:25:0:1", "start": 1453, "end": 1483, "surface": "road user satisfaction surveys", "probe_tag": "drop", "probe_score": 0.0463, "luna_label": 0, "luna_reason": "Project will carry out future road user satisfaction surveys."}]}, {"key": "aivin-118", "text": " enrolment, teacher training,<br>socio‐emotional learning program, school maintenance,<br>student assessment, etc.|MOE (OpenEMIS)|Third Party|The verification agency will check<br>the number of Syrian refugee<br>children enrolled in target schools<br>and will conduct site visits and spot<br>checks in a sample of randomly<br>selected schools to verify<br>enrollment numbers.|\n|**DLR#2**Number of additional<br>children enrolled in public and<br>private KG2|<br> <br> <br>Number of students enrolled in public or freely provided<br>private KG2. Data should be reported disaggregated by type of<br>school, directorate, gender, and nationality.|MOE (OpenEMIS)|Third Party|Enrollment data and disaggregation<br>is provided to the verification<br>agency. The verification agency will<br>conduct site visits and spot checks<br>in a sample of randomly selected<br>schools to verify enrollment<br>numbers. The sampling framework<br>should be acceptable to the WB.|\n|**DLR#3.1**Comprehensive and<br>harmonized quality assurance", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000041:43:1:0", "start": 670, "end": 685, "surface": "Enrollment data", "probe_tag": "drop", "probe_score": 0.0021, "luna_label": 0, "luna_reason": "Enrollment data appears in planned DLR verification machinery, not substantive analysis."}]}, {"key": "aivin-119", "text": "**The World Bank**\nSouth Sudan Emergency Food and Nutrition Security Project (P163559)\n\n\n\n\n\n\n\n\n\n\n|Indicator Name|Core|Unit of<br>Measure|Baseline|End Target|Frequency|Data Source/Methodology|Responsibility for<br>Data Collection|\n|---|---|---|---|---|---|---|---|\n|<br>beneficiaries <br>|||||||FAO<br>|\n|Percent of which are<br>female||Number|0.00|55.00|Quarterly<br>|M&E System<br>|MAFS/WFP/FAO<br>|\n|<br> <br>Description:|<br> <br>Description:|<br> <br>Description:|<br> <br>Description:|<br> <br>Description:|<br> <br>Description:|<br> <br>Description:|<br> <br>Description:|\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Indicator Name|Core|Unit of<br>Measure|Baseline|End Target|Frequency|Data Source/Methodology|Responsibility for<br>Data Collection|\n|---|---|---|---|---|---|---|---|\n|**Name:**Amount of general<br>food  rations availed to<br>beneficiaries||Metric<br>ton|0.00|15000.00|Monthly<br>|Progress reports<br>|MAFS/WFP<br>|\n|<br>Description:|", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000038:37:0:0", "start": 19, "end": 76, "surface": "South Sudan Emergency Food and Nutrition Security Project", "probe_tag": "drop", "probe_score": 0.048, "luna_label": 0, "luna_reason": "Project title identifies an operation, not an existing data resource or data use."}]}, {"key": "aivin-120", "text": "gating the Impact of COVID-19 with a Focus on the Manufacturing and Exporting**\n**Sectors Driving Economic Transformation, including Refugee and Host Districts (IDA US$79 million and US$5**\n**million from the IDA-19 Window for Host Communities and Refugees, WHR).** The objective of this component is\nto ease liquidity constraints on MSMEs, including women led and refugee MSMEs. For the reasons discussed\nabove, the component will seek to prioritize the manufacturing and exporting sectors driving economic\ntransformation, with the vision of connecting lower income regions, like RHDs with more viable and sustainable\nmarkets. This component will operate three different windows designed to assist MSMEs to better manage the\nCOVID crisis by easing the cost of finance and the availability of liquidity by working with Participating Financial\nInstitutions (PFIs) to reach the MSMEs. PFIs will be required to provide gender-disaggregated data on eligible\nMSMEs in order to address the lack of data on women-led firms as well as data on refugee or host community\nstatus to ensure that intersectional issues of exclusion are sufficiently addressed.\n\n38. **Window 1.1** will support loans that have been restructured under the Bank of Uganda COVID-19 response\napproach, primarily in the manufacturing and exporting sectors by covering part of the added financial cost due\nto the restructuring. The window will be available to qualifying MSMEs who received an extension of the\namortization period on their loans, to reduce the MSMEs incremental cost or debt servicing liability. The cost for\nthis rebate will be shared with PFIs to ensure that PFIs participate in burden sharing through lower margins than\nusual. <sup>38</sup> The window will focus on MSMEs in the manufacturing and exports sectors, and strongly encourage the\ninclusion of women-led firms. The window can also be used by Microfinance Institutions (MFIs) or Savings and", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000021:25:1:0", "start": 916, "end": 959, "surface": "gender-disaggregated data on eligible\nMSMEs", "probe_tag": "confusion", "probe_score": 0.6084, "luna_label": 0, "luna_reason": "PFIs will provide this data prospectively; it is planned data production."}, {"key": "jdc_operational:000021:25:1:1", "start": 1027, "end": 1067, "surface": "data on refugee or host community\nstatus", "probe_tag": "drop", "probe_score": 0.019, "luna_label": 0, "luna_reason": "PFIs are required to provide this data as a planned collection activity."}]}, {"key": "aivin-121", "text": "**PAD DATA SHEET**\n\n\n_Lebanon_\n\n\n_Lebanon Municipal Services Emergency Project (P149724)_\n\n\n**PROJECT APPRAISAL DOCUMENT**\n\n\n_MIDDLE EAST AND NORTH AFRICA_\n\n\n_MNSSU_\n\n\nReport No.: PAD1018\n\n|Basic Information|Col2|Col3|\n|---|---|---|\n|Project ID|EA Category|Team Leader|\n|P149724|B - Partial Assessment|Chantal Reliquet|\n|Lending Instrument|Fragile and/or Capacity Constraints [ X ]|Fragile and/or Capacity Constraints [ X ]|\n|Investment Project Financing|- Fragile within a non fragile<br>country<br>|- Fragile within a non fragile<br>country<br>|\n||Financial Intermediaries [  ]|Financial Intermediaries [  ]|\n||Series of Projects [  ]|Series of Projects [  ]|\n|Project Implementation Start Date|Project Implementation End Date|Project Implementation End Date|\n|20-June-2014|30-June-2017|30-June-2017|\n|Expected Effectiveness Date<br>Expected Closing Date|Expected Effectiveness Date<br>Expected Closing Date|Expected Effectiveness Date<br>Expected Closing Date|\n|1-July-2014<br>30-Dec-2017|1-July-2014<br>30-Dec-2017|1-July-2014<br>30-Dec-2017|\n|Joint IFC<br> <br>|Joint IFC<br> <br>|Joint IFC<br> <br>|\n|No<br> <br>|No<br> <br>|No<br> <br>|\n|Sector Manager<br>Sector Director<br>Country Director<br>Regional Vice President|Sector Manager", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000003:5:0:0", "start": 2, "end": 16, "surface": "PAD DATA SHEET", "probe_tag": "drop", "probe_score": 0.0499, "luna_label": 0, "luna_reason": "Standalone document/table heading, not a cited or used data resource."}]}, {"key": "aivin-122", "text": " Procurement Documents:** The World Bank’s Standard Procurement Documents (SPDs) shall be used for\nprocurement of goods, works, and non-consulting services under Open International Competitive Procedures. National\nBidding documents as set forth in the Public Procurement and Disposal Act, 2003 may be used under Open National\ncompetitive as well as for the Request for Quotation method subject to the inclusion of the universal eligibility and ES\nprovisions. Selection of consultant firms shall use the World Bank’s SPDs, in line with procedures described in the\nProcurement Regulations.\n\n\n37. In accordance with paragraph 5.3 of the Procurement Regulations, the request for bids/request for proposals\ndocument shall require that Bidders/Proposers submitting Bids/Proposals to present a signed acceptance at the time of\nbidding, to be incorporated in any resulting contracts, confirming application of, and compliance with, the World Bank’s\nAnti-Corruption Guidelines, including without limitation the World Bank’s right to sanction and the World Bank’s\ninspection and audit rights.\n\n\n38. **Record keeping and management.** All records pertaining to award of tenders, including bid notification, register\npertaining to sale and receipt of bids, bid opening minutes, bid evaluation reports and all correspondence pertaining to\nbid evaluation, communication sent to/with the World Bank in the process, bid securities, and approval of\ninvitation/evaluation of bids will be retained by the respective agencies in electronic or hard copy and uploaded in STEP.\n\n\n40 (a) open advertising of the procurement opportunity at the national level; (b) the procurement is open to eligible firms from any country; (c) the\nrequest for bids/request for proposals document shall require that Bidders/Proposers submitting Bids/Proposals present a signed acceptance at the\ntime of bidding, to be incorporated in any resulting contracts, confirming application of, and compliance with, the World Bank’s Anti-Corruption\nGuidelines,", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000025:70:1:0", "start": 1196, "end": 1243, "surface": "register\npertaining to sale and receipt of bids", "probe_tag": "drop", "probe_score": 0.023, "luna_label": 0, "luna_reason": "Procurement bid register retained for recordkeeping, not substantive data analysis."}]}, {"key": "aivin-123", "text": "|\n|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|**Expected Disbursements (in USD Million)**|\n|Fiscal Year|2015|2016|2017||||||||||||||\n|Annual|9.00|6.00|3.00||||||||||||||\n|Cumulative|9.00|15.00|18.00||||||||||||||\n|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000007:6:1:0", "start": 877, "end": 895, "surface": "Institutional Data", "probe_tag": "drop", "probe_score": 0.0363, "luna_label": 0, "luna_reason": "Standalone table header, not an actual cited or analyzed data resource."}]}, {"key": "aivin-124", "text": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Uganda Support to<br>Municipal Infrastructure<br>Development Program<br>Additional Financing<br>(P163515)<br>Financing: US360 million<br>WHR: US50 million|Enhance institutional performance of program<br>local governments for urban service delivery.<br>This includes planning, land tenure security,<br>and small-scale infrastructure investments<br>within refugee-hosting urban centers and their<br>wider districts. It will support eight refugee-<br>hosting districts. The focus is secondary cities.|1. Collaboration with the Ministry of Land<br>and Urban Development for the adequate<br>inclusion of common-user facilities the<br>cross-sectoral fiscal plans for local<br>development (only the framework has been<br>developed).<br>2. Complementary infrastructure<br>investments in RHDs (for example, USMID<br>roads, lighting, and markets in RHDs can be<br>supplemented by GROW business<br>infrastructure)<br>3. Both focus on strengthening district level<br>implementation and management of<br>infrastructure needs in RHDs.|\n|---|---|---|\n|**GBV and Violence Against**<br>**Children Prevention and**<br>**Response Services in**<br>**Uganda’s Refugee-Hosting**<br>**Districts Report**<br> <br>**Financing:**US$0.5 million <br>|To mitigate GBV and prevent violence against<br>children through engagement", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000025:75:0:0", "start": 1300, "end": 1316, "surface": "Districts Report", "probe_tag": "drop", "probe_score": 0.0397, "luna_label": 0, "luna_reason": "Standalone table fragment, not a data source or data-use statement."}]}, {"key": "aivin-125", "text": " among small scale farmers**|** Enhanced production and income generation among small scale farmers**|\n|Description|The number of small-scale farmers supported to improve production|\n|Frequency|Annual|\n|Data source|Project progress report, Ministry of Agriculture|\n|Methodology for Data<br>Collection|Biannual review, number of trainings and grants provided|\n|Responsibility for Data<br>Collection <br>|MoHAIS <br>|\n\n\n\n**<u>Monitoring & Evaluation Plan: Intermediate Results Indicators by Components</u>**\n\n\n\n\n\n\n\n|Monitoring & Evaluation Plan: Intermediate Result Indicators|Col2|\n|---|---|\n|**Number of legislative reforms instituted in support of policy objectives**|**Number of legislative reforms instituted in support of policy objectives**|\n|Description|The indicator will measure the number of legal and policy reforms identified for amendment to support implementation<br>of the Refugee Policy by addressing inconsistencies in existing laws that prevent refugees and former refugees from<br>accessing basic services and regularizing their immigration status.|\n|Frequency|Biannual|\n|Data source|Project progress report, cabinet memos|\n|Methodology for Data<br>Collection|Data collected through minutes of the interministerial committee (MORHCSA) meetings, Parliamentary committee<br>reports|\n|Responsibility for Data<br>Collection <br>|MoHAIS <br>|\n|**Stakeholder consultations convened and priority measures identified**|**Stakeholder consultations convened and priority measures identified**|\n|Description|Number of high-level consultations held with relevant government ministries and civil society organizations to deliberate<br>on measures identified for legislative and regulatory reform, implementation performance, roles and responsibilities of|\n\n\nPage 35", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000187:38:1:0", "start": 215, "end": 238, "surface": "Project progress report", "probe_tag": "keep", "probe_score": 0.9178, "luna_label": 1, "luna_reason": "Data-source citation identifies the progress report underlying the monitoring indicator."}]}, {"key": "aivin-126", "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_aivin", "spans": [{"key": "refugee_pads:000102:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1, "luna_reason": "Survey data supports enrollment-rate comparisons across expenditure quintiles."}, {"key": "refugee_pads:000102:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Existing survey data supports enrollment-gap findings and clarifies education-level separation."}, {"key": "refugee_pads:000102:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1, "luna_reason": "Existing household survey data supports a concrete finding about girls leaving school."}]}, {"key": "aivin-127", "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": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000022:19:0:0", "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": "aivin-128", "text": " Food Programme (WFP), the United Nations Children’s Fund (UNICEF), the\nFood and Agriculture Organization, the Islamic Development Bank, the United States Agency for Development, and the\nNorwegian Refugee Council) which were mainly focused on providing food to vulnerable populations. At present, the\nscale and funding of SSN programs remains inadequate to protect most poor and vulnerable groups. According to the\nlatest available data, only 32.7 percent of the poorest 20 percent of households are covered by any SSN program. In\naddition, the Government’s share of spending in SSN is quite limited, as Djibouti only spends 0.18 percent of its GDP on\n\n\n10 Hallegatte et al, “Shockwaves: Managing the Impacts of Climate Change on Poverty”, World Bank, 2016.\n11 Wooden et al, “ _Impact of Weather Shocks on MENA Households_ ”, World Bank, 2014.\n12 _Djibouti’s First NDC_, August 2015, p2.\n\n\nPage 8 of 44", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000063:12:2:0", "start": 415, "end": 436, "surface": "latest available data", "probe_tag": "keep", "probe_score": 0.9131, "luna_label": 1, "luna_reason": "Available data supports the reported SSN coverage figure."}]}, {"key": "aivin-129", "text": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n\n19. The current economic analysis presents estimates of the efficiency gains in basic education to 2021, based on\nenrollment estimates employing UN population projections, average values from recent years for intake into\nGrade 1 of basic education (from the School Census) relative to population, and recent trends in promotion\nand retention in each grade of basic school. The analysis employs the same projections as the current\nEducation Sector Strategic Plan (ESSP) including for the GDP growth (IMF/World Bank), share of domestic\nresources spent on education (a 0.5 p.p. annual increase from 9.8 percent in 2017/18).\n\n\n20. _Gains from improved Internal Efficiency_ . The project objective is to sustain enrollment in public schools\nmeaning that the enrollment in target schools is to increase at the rate of the population growth – 2.5 percent\nper annum: from 5.40 million pupils to 5.54 million in 2021. The analysis is built on the assumption that survival\nand repetition rates will remain unchanged.\n\n21. We compare inefficient government spending under the expected scenario and a scenario, under which there\nis no increase in the student enrollment. The following formula is used to estimate inefficient spending:\n\n\n\n𝟖\n\n\n\n𝒙 <sup><u>𝒊</u></sup> ∗𝑮𝒙𝒊\n\n\n\n𝒙\n\n𝑰𝒏𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒕 𝑺𝒑𝒆𝒏𝒅𝒊𝒏𝒈 = ∑( <sup>𝑫𝒊</sup>\n\n\n\n𝑮𝒊\n\n\n\n𝒙 ) ∗𝑺 <sup>𝒙</sup>,\n\n\n\n𝒙𝒊\n\n\n\n𝒊=𝟏\n\n\n\nwhere 𝑫𝒊𝒙 is the dropout rates in grade 𝒊 in year 𝒙 in target schools; 𝑮𝒙𝒊 is the number of pupils enrolled in\n\ngrade 𝒊 in year 𝒙 ; 𝑺 <sup>𝒙</sup> is the projected government spending per pupil in year 𝒙 .\n\n22. According to the", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000024:43:0:0", "start": 224, "end": 249, "surface": "UN population projections", "probe_tag": "keep", "probe_score": 0.9507, "luna_label": 1, "luna_reason": "UN projections underpin enrollment estimates in the economic analysis"}, {"key": "refugee_pads:000024:43:0:1", "start": 337, "end": 350, "surface": "School Census", "probe_tag": "keep", "probe_score": 0.9062, "luna_label": 1, "luna_reason": "School Census data inform enrollment estimates used in efficiency analysis."}]}, {"key": "aivin-130", "text": "The World Bank\nMauritania Water and Sanitation Sectoral Project (P167328)\n\n\n8. The consumer surplus is equal to the increase of water consumption multiplied by the difference of\nthe water price paid before and after the project and by the price elasticity (0.5).\n\n\n**Table 4.4: Consumption and Water Prices with and without Project**\n\n\n\n\n\n|Col1|Without project|Col3|With project|Col5|\n|---|---|---|---|---|\n|**Current/ future source of supply**|**Consumption**<br>**(lpcd)**|**Cost**<br>**(MRU/m3) **|**Consumption**<br>**(lpcd)**|**Cost**<br>**(MRU/**<br>**m3) **|\n|Distant sources/ stand posts (small water systems)|3|159|15|50|\n|Distant sources/ connection (regular watersources)|3|159|20|25|\n|Neighbor/ own connection|Neighbor/ own connection|Neighbor/ own connection|Neighbor/ own connection|Neighbor/ own connection|\n|-7 SNDE centers|8.9|40|20|25|\n|Kiffa|16|40|30|25|\n\n\n_Source: ONSER surveys, SNDE and World Bank estimates._\n\n\n\n\n\n\n\n9. **Sensitivity Analysis** . A range of scenarios has been developed to test the sensitivity of the EIRR to the\nmain elements of the economic cash-flows. The variables tested were: (i) investment costs; (ii) operating\ncosts; and (iii) water demand. The outcome of the scenarios is given in", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000106:60:0:0", "start": 885, "end": 898, "surface": "ONSER surveys", "probe_tag": "keep", "probe_score": 0.9015, "luna_label": 1, "luna_reason": "Named surveys cited as the source for tabled consumption and water-price estimates."}]}, {"key": "aivin-131", "text": " their own<br>indicators but also on the geographical location where<br>they live<br>- <br>The Social Registry will have the capacity to refer<br>beneficiaries affected by climate change to existing social<br>programs|\n|**Component 4:**<br>**Integration of**<br>**refugee and host**<br>**communities into**<br>**national social**<br>**protection systems**<br>**(US$ 20 million)**<br> <br>Cash transfers<br>Productive inclusion<br>Sensitization<br>on<br>climate<br>change<br>adaptation<br>and<br>mitigation, including links<br>between<br>nutrition<br>and<br>climate change<br>- <br>Reduces the pressure of refugees on resources from host<br>communities<br>- <br>Livelihood diversification (alternative to agriculture)<br>- <br>Reduce reliance on deforestation as a livelihood<br>- <br>Beneficiaries will receive extension services on crops that<br>are more adapted to changing climatic conditions<br>- <br>Behavior change interventions with information on timing<br>of rainy and lean seasons or which kinds of crops to<br>diversify out of or into; (ii) savings interventions to<br>generate a buffer to absorb climate shocks, enable<br>investments in adaptation or adjustment of livelihood<br>portfolios; (iii) skills training and coaching to support<br>diversification of livelihoods|**Component 4:**<br>**Integration of**<br>**refugee and host**", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000130:88:4:0", "start": 95, "end": 110, "surface": "Social Registry", "probe_tag": "confusion", "probe_score": 0.2967, "luna_label": 0, "luna_reason": "Names a registry without showing its data being used."}]}, {"key": "aivin-132", "text": "**The World Bank**\nStrengthening Institutions for Refugee Administration Project (P165542)\n\n\n\n\n\n\n\n\n\n|Women employed in CCAR and CARs|Measures combined share<br>of CCAR and CAR employees<br>that are women|Annually|CCAR/CAR<br>HR records|Reports from HR data<br>bases of CCAR and CAR|CCAR and CAR|\n|---|---|---|---|---|---|\n|Host communities with functioning<br>complaints handling mechanisms|This indicator measures the<br>proportion of refugee<br>communities countrywide<br>that have established local<br>level complaint handling<br>mechanisms.|Semi-<br>Annual<br>|Refugee<br>hosting<br>communities<br>|Review of the list<br>describing the host<br>communities and the<br>nature of their<br>Grievance Redress<br>Mechanism<br>|CCAR<br>|\n|Women employed in professional<br>positions in the Operations Support Unit<br>to support project implementation|Measures proportion of<br>professional women<br>employees in the OSU.<br>Targets are cumulative.|Annual<br>|Payroll of the<br>OSU<br>|Review of payroll data<br>and head count of<br>professional women<br>employees<br>|OSU<br>|\n|Quarterly progress reports submitted<br>within 15 days after the end of the<br>reporting cycle<br>|Measures submission of<br>progress reports on a<br>quarterly basis. Each year, at<br>least four reports expected.|<br>Quarterly<", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000009:32:0:1", "start": 249, "end": 256, "surface": "HR data", "probe_tag": "confusion", "probe_score": 0.5467, "luna_label": 1, "luna_reason": "HR data supports the reported share of women employed in CCAR and CARs."}, {"key": "refugee_pads:000009:32:0:2", "start": 992, "end": 1004, "surface": "payroll data", "probe_tag": "confusion", "probe_score": 0.2336, "luna_label": 0, "luna_reason": "Routine payroll records used for project staffing headcount, not substantive external data reuse."}]}, {"key": "aivin-133", "text": "its Public Expenditure\nTracking Survey (PETS)\n\nscorecard inproves each\nsemester\n\n\n\n**Project Components /** **Inputs:** **(budget for** each Project reports: (from Components to\nSub-components: component) Outputs)\n\n\n\n1. Community Driven - US$ 28.0 million (IDA $25.0 <sup>- M&E data</sup> - Cormnunities emnpowered\n\n\n\n**Program** (CDP) million) - Quarterly progress reports; and responsible for\nimplementing sub-projects;\n\n                                                  - Implemnenting partners <sup>are</sup>\ncomnpetent to support\n**2.** Pilot and Special\n\n\n\n**Programs in** Newly cormmunities;\n\n                                                - Implementing partners\n**Accessible** **Areas**\n\n                - US$ 2.0 mnillion (ODA <sup>$1.75</sup> participate in capacity building\n\n\n\n**(a)** **Rural Public Works** million) activities;\n\n                - US$ 2.0 million (IDA <sup>$1.75</sup>                - Operations Manual and\n\n\n\n**(b)** **Shelter** Program **for** million) annexed Handbooks for Direct\n\nFinancing to Communities <sup>and</sup>\n**Vulnerable** **Groups**\n\n               - US$ 10.0 million (IDA <sup>$6.5</sup>                - NaCSA and IDA Public Works are thorough,\n\n\n\n3. **Project** **Manaeem2nt** <sup>**and**</sup> <sup>million)</sup> administrative data appropriate, and clear in\n\n\n\ndefining the and\n**In", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000096:32:0:0", "start": 4, "end": 38, "surface": "Public Expenditure\nTracking Survey", "probe_tag": "confusion", "probe_score": 0.8166, "luna_label": 1, "luna_reason": "Named expenditure-tracking survey is referenced as an existing data resource."}]}, {"key": "aivin-134", "text": " 2017 and was reconfirmed under IDA19 in September 2020, with the\napproval of the Additional Financing to the Refugees and Host Communities Support Project (P164748).\n\n6. **The World Bank, following consultations with UNHCR, confirms that the protection framework**\n**for refugees is adequate in Chad** **(UNHCR update of August 8, 2021).** It has an adequate institutional and\nmonitoring framework to ensure the implementation of the refugee protection framework, <sup>5</sup> [^5: See annex 2 for details.] including (a)\na dedicated agency, National Commission for Reception and Reintegration of Refugee and Returnees\n( _Commission Nationale d'Accueil de Réinsertion des Réfugies,_ CNARR) set up within the GoC to manage\nrefugee protection; (b) an action plan to implement a Comprehensive Refugee Response Framework; (c)\na ministerial-level high committee integrating representatives of all sectors contributing to the refugee\nagenda; and (d) the Asylum Law enacted in December 2020. In 2020–2021, a reduction of food assistance\nin some refugee camps and the COVID-19 restrictions temporarily heightened protection risks and access\nto socioeconomic services but these have since been reduced or mitigated.\n\n7. **In terms of gender equality, Chad ranks 147 out of 153 countries for the Global Gender Gap**\n**Index and 187 out of 189 for the Gender Inequality Index with significantly worsening trends in the past**\n**few years.** <sup>6</sup> [^6: Human Development Report: _[http://hdr.undp.org/sites/default/files/hdr2020.pdf](http://hdr.undp.org/sites/default/files/hdr2020.pdf)_] Women are disadvantaged for productive activities due to limited agency, access to resources,\nand employment opportunities as well as high fertility rates that can exacerbate these challenges. In\naddition, female-headed households are on average", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000193:16:1:0", "start": 1342, "end": 1365, "surface": "Gender Inequality Index", "probe_tag": "confusion", "probe_score": 0.7192, "luna_label": 1, "luna_reason": "Named index cited for Chad's gender inequality ranking, sourced to the Human Development Report."}]}, {"key": "aivin-135", "text": " économique et sociale – PRES_ ), underpinned by a dedicated\nSolidarity Fund at the Central Bank of Western African States – FORCE-COVID-19. The main objectives of PRES are to\nupgrade the health system and mitigate the economic fallout, while providing targeted support to vulnerable\nhouseholds and firms. In particular, it allowed the GoS to pay for the electricity bills of the most vulnerable\nhouseholds of Senegal (975,000) between May and June 2020 – for a total cost of US$34 million. In addition, the GoS\nissued on September 5, 2020, a new version of its Priority Action Plan - Adjusted and Accelerated (PAP 2A). The PAP\n2A, spanning 2021-23, seeks to ensure that the economy is on track to meet the objectives of the National\n\n[25] Enquête Harmonisée sur les Conditions de Vie des Ménages (EHCVM) 2018/19.\n\n[26] Between 2005 and 2015 poverty reduction in several countries in SSA exceeded 1 p.p. per year: Tanzania (-2.6 p.p.); Rwanda (-1.5 p.p.) and Ghana (-1.3\np.p.). Over the same period, Senegal reduced poverty at an annual rate of 0.43 p. p. and only by 0.65 p.p. per year between 2011 and 2018. Source: World\nBank staff calculations using _[PovCalNet](http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx)_ 2020 harmonized surveys and Macro Poverty Outlook Fall 2019 for the evolution between 2011 and 2018.\n\n[27] Sub-Saharan Africa is 0.894.\n\n[28] UNDP, 2020. Human Development Report, Senegal. http://hdr.undp.org/sites/default/files/Country-Profile", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000116:11:2:0", "start": 1156, "end": 1165, "surface": "PovCalNet", "probe_tag": "confusion", "probe_score": 0.7439, "luna_label": 1, "luna_reason": "PovCalNet harmonized surveys underpin World Bank staff poverty calculations."}, {"key": "refugee_pads:000116:11:2:1", "start": 1227, "end": 1250, "surface": "2020 harmonized surveys", "probe_tag": "confusion", "probe_score": 0.587, "luna_label": 1, "luna_reason": "Harmonized surveys are cited as sources for World Bank poverty calculations."}]}, {"key": "aivin-136", "text": "6\n\n\nThere is strong support in the Government for increasing resources for education, and the Government\nmade a commitment to increase education's share of budget from 16% in 2001-02 to 25% in 2009-10.\n\n\nOne of the reasons for choosing an APL with a ten-year perspective is that the education budget\nshortages will continue to be a constraint in the next few years. Over this period, Government\n\nexpenditures in non-priority areas will be brought under control and Government expenditures on\neducation can be expected to increase significantly. Despite the manageability in the long-run, the\nshort-run prospects on the budget are more challenging and donors will need to finance some recurrent\ncosts. The proposed APL will be implemented in three phases with distinct triggers (see Section B. 4).\nAs a result, a 10-year projection of enrollments and education costs has been developed (which is the\noverall framework for the APL), and a detailed five year plan and project proposals have been\nprepared (which is the framework for the first phase of the APL).\n\n\n3. Sector issues **to be addressed by the project and strategic choices**\n\nThe project will directly address all the issues below except for higher education.\n\n\n_Issues/Sector Problems_ _Government strategy and project proposal_\n\n**School Places**\n\nThe immediate problem in Djibouti City and The Government's strategy includes a combination\nsurrounding suburbs and other towns is the lack of of building more schools and continuing with the\nschool places due to the strong demand for schooling. double-shifting policy. The project will finance new\nclassrooms, sanitation services, and school furniture.\n\n**Equity, Gender, Disparities**\n\nChildren from poorer families, rural children, and The Government will construct schools in underespecially girls do not always attend school. The served areas, particularly in poorer parts of Djiboutirecent Household Expenditure Survey states that Ville where almost 70% of the population lives.\nmajor reasons for the", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000093:9:0:0", "start": 1906, "end": 1934, "surface": "Household Expenditure Survey", "probe_tag": "confusion", "probe_score": 0.8798, "luna_label": 1, "luna_reason": "Recent survey supports a concrete population-distribution finding."}]}, {"key": "aivin-137", "text": "**Annex 10: IBRD Map 39860 showing Local Governments included under LGDP**\n\n\n                       - 81", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000166:95:0:0", "start": 12, "end": 26, "surface": "IBRD Map 39860", "probe_tag": "confusion", "probe_score": 0.6365, "luna_label": 1, "luna_reason": "Named IBRD map presents the included local governments under LGDP."}]}, {"key": "aivin-138", "text": "No**|**Investment name**|**Scope**|**Budget**<br>**(million EUR)**|\n|C-1|Extension of Kayseri<br>Wastewater<br>Treatment Plant|• <br>Extension of existing WWTP (2nd phase) and installation of solar sludge<br>drying facility; replacement of existing belt filters with decanter units.|25|\n\n\n\n**.Konya Municipality**\n\n\n22. **Konya context.** The refugee population in Konya consists mainly of Syrians and Afghans. In 2018, Konya province\nhad a total population of about 2,205,609 <sup>52</sup> [^52: As per the address-based census for 2018] people out of which 109,124 (or 4.95 percent of the total population)\nwere SuTP’s <sup>53</sup> [^53: Calculations based on PID for Konya and projected estimates for the year 2020.] . There are no refugee camps in Konya <sup>54</sup> [^54: UNHCR Turkey: Syrian Refugee Camps and Provincial Breakdown of Syrian Refugees Registered in South East Turkey (January 2020):\n_[https://data2.unhcr.org/en/documents/details/73300](https://data2.unhcr.org/en/documents/details/73300)_, accessed on January 15, 2020], refugees are mainly living in the central districts of Konya.\nSince 2014, water supply and sanitation services for the metropolitan municipality of Konya are provided by Konya\nWater and Sewerage Administration General Directorate (KOSKİ). Due to high seasonal variations in the availability\nof water resources and the high NRW (57 percent) <sup>55</sup> [^55: According to PID for Konya], KOSKİ has developed the following investment project for\nAkşehir district.\n\n\n23. **Konya Sub-Project.", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000125:67:1:0", "start": 508, "end": 537, "surface": "address-based census for 2018", "probe_tag": "keep", "probe_score": 0.9427, "luna_label": 1, "luna_reason": "Existing census provides the cited 2018 population figure."}, {"key": "refugee_pads:000125:67:1:2", "start": 663, "end": 676, "surface": "PID for Konya", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "PID data underlies calculated refugee population estimates."}]}, {"key": "aivin-139", "text": "- **_Reducing pesticides_** . Farmers currently rely on application of chemical pesticides, with little\n\nknowledge about their handling and management. The project will control the use of pesticides\non rational basis according to IPM tactics and practices on farm. This component aims to\n**reduce pesticides application by about 40-50 percent** of their current use in targeted crops\n(FAO 2015).\n\n109. **Activities** _._ IPM activities will be implemented through the FFS methodology, which is a\nproven methodology based on a participatory approach to train and empower farmers on the use of\nIPM techniques and on the proper handling and disposal of pesticides. Similar participatory approaches\nwill be used to train farmers on alternative methods and practices for sustainable fertilizer use in the\nproject area. Baseline surveys and regular farm visits will be conducted to monitor the use of\nagrochemicals by targeted farmers as well as the sales of these chemicals in the project area.\n\n110. **Steps** _._ The component is based on four steps:\n\n(a) Assessing current agricultural practices and developing GAP-IPM program. Review previous\n\nsurveys, undertake chemical analysis of soil and water, and identify essential practices to\naddress existing gaps (that is, activities, target farmers, and crops).\n(b) Train professionals and facilitators in GAP-IPM practices. Train field technical staff from\n\nMoA, other stakeholders, and facilitators on extension and FFS methods to promote the\npractices identified above.\n(c) Implement the GAP-IPM program at the farm. Identify the FFS curriculum and agricultural\n\ninputs needed and establish and run the FFS based on crops and target areas.\n(d) Evaluate and monitor for sustainability. Provide the monitoring, review, and follow-up\n\nstrategy for sustainability of the project outcomes.\n\n\n111. Given that the FAO has been implementing a Regional Integrated Pest Management Program\nin the Near East since 2004 that covers ten countries (including Lebanon)", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000036:38:0:0", "start": 814, "end": 830, "surface": "Baseline surveys", "probe_tag": "confusion", "probe_score": 0.0648, "luna_label": 0, "luna_reason": "Future baseline surveys will be conducted by the project."}]}, {"key": "aivin-140", "text": " an increase by 600,000 from\nthe previous year. <sup>10</sup> [^10: United Nations Office for the Coordination of Humanitarian Affairs (UN OCHA). 2022. _South Sudan Humanitarian Needs Overview 2022._\n11Integrated Food Security Phase Classification (IPC) -the severity and magnitude of food insecurity\nclassifying units of analysis in five distinct phases: (1) Minimal/None, (2) Stressed, (3) Crisis, (4) Emergency, (5) Catastrophe/Famine] In the current IPC analysis period of February to March 2022, an estimated 6.83 million people (55.3\npercent of the population) are facing Crisis (IPC Phase 3) or worse acute food insecurity, of which 2.37 million people are\nfacing Emergency (IPC Phase 4) acute food insecurity. An estimated 55,000 people are classified in Catastrophe (IPC Phase\n5) acute food insecurity. It is expected that food insecurity levels will remain elevated due to the impact of severe flooding\nand drought on livelihoods, conflict, and persistent macroeconomic challenges. The percentage of the population that\nhas experienced crisis (IPC3 <sup>11</sup> ) or emergency (IPC4) conditions has increased significantly over the last few years (Figure\n1). <mark>In addition, an estimated 2 million people, including 1.3 million children under the age of five, and 676,000</mark>\n<mark>pregnant and lactating women, are expected to be acutely malnourished in 2022.</mark> <sup>12</sup> [^12: OCHA- South Sudan Humanitarian Needs Overview 2022 (February 2022)]\n\n\n5 Climate induced natural disasters have increased in recent years in terms of intensity, frequency, and complexity, such that in 2017,\nSouth Sudan ranked among the five worst-performing countries in the world in tackling the impact of climate change, according to the\nClimate Change Vulnerability Index.\n6 World Bank. 2021", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000153:12:1:0", "start": 1744, "end": 1778, "surface": "Climate Change Vulnerability Index", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Index supports the stated ranking of South Sudan's climate-change performance."}]}, {"key": "aivin-141", "text": "**The World Bank**\nUganda: Investment for Industrial Transformation and Employment (P171607)\n\n\nindicated that they struggled to make ends meet. <sup>12</sup> [^12: Federation of Small and Medium Sized Enterprises in Uganda (August 2021).] Of those MSMEs in Kampala, 45 percent reported closing their\nbusiness as a direct consequence of the pandemic. They were facing both demand (willingness to spend) and\nsupply (labor force disruptions) constraints. Socioeconomically depressed districts, such as those hosting refugees,\nare expected to be even further negatively impacted. The poorest quintiles and vulnerable communities, including\nrefugees, are already showing significant income reductions and increased food insecurity.\n\n\n**B. Sectoral and Institutional Context**\n\n\n10. **COVID-19 remains a significant threat to emerging economic transformation in Uganda and**\n**puts prospects of new jobs in danger.** Data from the June 2020 <sup>13</sup> [^13: Uganda Bureau of Statistics June 2020 conducted with the support of the World Bank.] Uganda Bureau of Statistics (UBOS) high\nfrequency phone survey on the impact of the COVID-19 pandemic show that the following sectors lost the highest\nnumber of workers: services 43 percent, commerce 43 percent, and transport 39 percent. It is expected that the\nhardest-hit firms will be exporters to international markets, manufacturing companies, and start-ups. The\nfloriculture industry, for example, which employs over 10,000 people, is still facing severe disruptions in its supply\nchains and cancellation of orders. Restarting or continuing economic transformation will require the provision of\nnew loans and products in the market, leaner and more efficient firms and rapid adaptation to new market\nconditions, and the capacity to identify new markets and sources of demand, particularly for new and exporting\nfirms. This is consistent with the World Bank Group’s economic response including the <mark>Green, Resilient, and</mark>\n<mark>Inclusive Development (</mark>", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000073:16:0:0", "start": 911, "end": 934, "surface": "Data from the June 2020", "probe_tag": "confusion", "probe_score": 0.6016, "luna_label": 0, "luna_reason": "Bare date-only qualifier cannot inherit the cited survey's source or finding."}, {"key": "refugee_pads:000073:16:0:1", "start": 1075, "end": 1102, "surface": "high\nfrequency phone survey", "probe_tag": "confusion", "probe_score": 0.7738, "luna_label": 1, "luna_reason": "UBOS June 2020 phone survey provides sectoral worker-loss findings."}]}, {"key": "aivin-142", "text": "**The World Bank**\nIntegrated Cash Transfer and Human Capital Project (P166220)\n\n\nindicating forecasts and discrepancies relative to the actual budget; and (v) a comprehensive\nlist of all fixed assets.\n\nThe IFRs should be produced by SEAS every quarter and sent to the World Bank within 45 days from the end of\neach quarter. PFS should be produced annually. The PFS should include (i) a cash flow statement; (ii) a closing\nstatement of financial position; (iii) a statement of ongoing commitments; (iv) an analysis of payments and\nwithdrawals from the project’s account; (v) a statement of cash receipts and payments by category and\ncomponent; (vi) reconciliation statement for the balance of the project’s DA; (vii) statement of cash payments\nmade using SOE basis; and (viii) the yearly inventory of fixed assets acquired under the project.\n\n_Flow of funds:_ payments will be instructed by three signatures: The Secretary General of SEAS, the Director of the\nExternal Financing Department at the Ministry of Finance and the Director of the Debt Department at the Ministry\nof Budget.\n\nFunds will be transferred from the World Bank based on withdrawal applications submitted by SEAS. The funds\nwill be channeled from the World Bank through a segregated DA in US$ opened in a commercial bank in Djibouti\nacceptable for the World Bank. Advances from the IDA account will be disbursed to the DA to be used for the\nproject expenditures.\n\n_Internal control:_ For purpose of the project, SEAS will prepare a POM which will define the roles, functions and\nresponsibilities for the implementing agency. The POM will contain a separate financial management chapter\ndetailing the financial management and accounting procedures and will include internal controls procedures. The\nmanual will be prepared no later than one month following effectiveness.\n\n_Additional Control Arrangements_\n\nThe project will be financing expenditures covering works, goods, consultants’ services, non-consultant services,\nsoft conditional", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000063:44:0:0", "start": 781, "end": 813, "surface": "yearly inventory of fixed assets", "probe_tag": "confusion", "probe_score": 0.2152, "luna_label": 0, "luna_reason": "Routine project fixed-assets inventory for financial reporting."}]}, {"key": "aivin-143", "text": "br>delivery, including private-sector service providers [Number].|\n|Frequency|Annually.|\n|Data source|Annual reports on DPI implementation by MODEE and public- and private-sector relying parties.|\n|Methodology for Data<br>Collection|Indicator values will be collected from the administrative data of MODEE and public- and private-sector relying parties<br>on the use of transactional digital services that incorporate trusted, people-centric DPI, and cross-checked by the IVA<br>through spot surveys.|\n|Responsibility for Data<br>Collection|MODEE.|\n|||\n|**Improving trusted, people-centric data sharing**|**Improving trusted, people-centric data sharing**|\n|Description|Availability of trusted, people-centric data sharing [Yes/No].|\n|Frequency|Annually|\n|Data source|(a) Annual reports on DPI implementation from MODEE, sectoral ministries, and the private sector, (b) MODEE’s<br>software documentation and testing reports, and (c) Third-party assessment reports.|\n|Methodology for Data<br>Collection|Indicator values will be collected from (a) DPI implementation reportsreleased by MODEE, sectoral ministries, and the<br>private sector, (b) MODEE’ssoftware documentation, and testing reports, and (c) Official reports submitted by third-<br>party assessment bodies recruited to carry out the Privacy Impact Assessments. All indicator values will be cross-<br>checked by the IVA.|\n|Responsibility for Data<br>Collection|MODEE.|\n|||\n|**Enhanced management of medical records**|**Enhanced management of medical records**|\n|Description|Promoting digital transformation in health servcie delivery by scaling up", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000181:48:1:0", "start": 277, "end": 305, "surface": "administrative data of MODEE", "probe_tag": "confusion", "probe_score": 0.6366, "luna_label": 0, "luna_reason": "Data will be collected for indicator measurement."}]}, {"key": "aivin-144", "text": "|4,513<br>5,028|28,601<br>104,972|\n\n\n2. **The economic activity slow down caused by COVID-19 has affected Uganda’s ability to generate**\n**jobs for those living in vulnerable situations, including refugees and host communities.** Despite the\nconcerted efforts to integrate refugees within the ecosystems of their host communities, refugeehosting districts (RHDs) remain less developed areas. Low levels of disposable incomes have resulted\nin low demand and limited access to labor markets, leaving those residents with some access to land\nwith no alternative but to live off subsistence agriculture and humanitarian aid. These areas were less\ndeveloped even before the inflow of refugees and remain decoupled from resilient and viable supply\nchains in the economy. For example, the average value of assets among all households (both refugee\nand host) in the district of Arua <sup>64</sup> is 560,000 Ugandan shillings (US$ 144), which is only 10 percent of\ncomparable asset values in the Kampala region.\n\n\n62 Uganda Comprehensive Refugee Response Portal ( _[https://data2.unhcr.org/en/country/uga](https://data2.unhcr.org/en/country/uga)_ ) 31 October 2021\n63 Calculation based on district-level firm data from Census of Business Establishments (COBE), and refugee and host\ncommunity household data from the Refugee and Host Community Household Survey\n64 Arua was until recent sub-divisions of the district considered a refugee hosting district.\n\n\nPage 71 of 92", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000073:76:3:0", "start": 1211, "end": 1244, "surface": "Census of Business Establishments", "probe_tag": "keep", "probe_score": 0.9193, "luna_label": 1, "luna_reason": "Named census data underpin a district-level calculation."}, {"key": "refugee_pads:000073:76:3:1", "start": 1257, "end": 1298, "surface": "refugee and host\ncommunity household data", "probe_tag": "confusion", "probe_score": 0.8897, "luna_label": 1, "luna_reason": "Household data from a named survey supports a district-level economic calculation."}]}, {"key": "aivin-145", "text": " employment. Proof of legal identity\nis also the basis for exercising rights, such as property ownership, and nationality. For governments and businesses, ID\nsystems can serve as a platform for more effective and efficient service delivery by enabling the unique identification and\nverification of persons. Importantly, ID systems can promote greater inclusion by de-risking and reducing the costs of\n\n\n8 UNHCR's Ethiopia Update on the Total Number of Refugees and Asylum Seekers as of August 31, 2023.\n9 In Tigray, new internal displacement data has been reported, including 1,021,798 IDPs (250,468 households) in 643 sites across six zones (excluding\n20 _woredas_ /districts hard to reach due to security or environmental factors).\n10 IOM. 2023. Ethiopia National Displacement Report 16 - Site Assessment Round 33 and Village Assessment Survey Round 16: Nov 2022 - Jun 2023.\nhttps://reliefweb.int/report/ethiopia/ethiopia-national-displacement-report-16-site-assessment-round-33-and-village-assessment-survey-round16-november-2022-june-2023.\n11 Foundational ID systems are primarily created to provide credentials to the general population as proof of identity for a wide variety of public and\nprivate sector transactions. Common types of foundational ID systems include civil registries, national ID systems, and population registers.\n\n\nPage 2 of 39", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000005:12:2:0", "start": 520, "end": 546, "surface": "internal displacement data", "probe_tag": "confusion", "probe_score": 0.2249, "luna_label": 1, "luna_reason": "Reported displacement data supports concrete IDP and household counts."}]}, {"key": "aivin-146", "text": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n\n\n\n\n\n\n\n\n|Monitoring & Evaluation Plan: PDO Indicators|Col2|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Indicator Name **|**Definition/Description **|**Frequency **|**Datasource **|**Methodology for Data**<br>**Collection **|**Responsibility for Data**<br>**Collection **|\n|Student enrollment in targeted schools|The enrollment will be<br>monitored through the<br>annual school census|Annual<br>|Annual<br>School<br>Census<br>|Census of schools key<br>data collected yearly<br>|Ministry of Education<br>and PCU<br>|\n|Girls enrolment in targeted schools<br>|Number of girls enrolled in<br>targeted schools|Annual<br>|Annual<br>School<br>Census<br>|Census of school key<br>data collected annually<br>|Ministry of Education<br>and PCU<br>|\n\n\n|Monitoring & Evaluation Plan: Intermediate Results Indicators|Col2|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Indicator Name **|**Definition/Description **|**Frequency **|**Datasource **|**Methodology for Data**<br>**Collection **|**Responsibility for Data", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000024:33:0:2", "start": 730, "end": 750, "surface": "Census of school key", "probe_tag": "confusion", "probe_score": 0.1335, "luna_label": 0, "luna_reason": "Fragment inside a monitoring table methodology field; not an independent data mention."}]}, {"key": "aivin-147", "text": " (DLI 3).|\n|Data source/ Agency|Annual reports on the usage of digital services from MODEE, sectoral ministries, and the private sector.|\n|Verification Entity|KACE.|\n|Procedure|Indicator values will be collected from MODEE’s administrative data on the usage of transactional digital<br>services that use trusted, people-centric DPI and cross-checked by the IVA through spot surveys.|\n\n\n\n\n\n\n\n|DLI 2: Number of individuals adopting people-centric digital identity|Col2|\n|---|---|\n|Formula|The DLI will disburse US$3 for each unique individual activating people-centric digital identity, up to a total<br>3.5 million individuals, in the limit of US$10.5 million. Moreover, it will disburse the following additional<br>amounts:<br>• <br>US$4 for each woman activating people-centric digital identity, up to 1.75 million women, in the<br>limit of US$7 million<br>• <br>US$5 for each elder activating people-centric digital identity, up to 200,000 elders, in the limit of<br>US$1 million<br>• <br>US$15 for each refugee activating people-centric digital identity, up to 100,000 refugees, in the<br>limit of US$1.5 million|\n|Description|The Program disburses against the number of unique individuals activating people-centric digital identity,<br>disaggregated by type of user (women, elders, refugees).|\n|Data source/ Agency|Annual reports on digital ID implementation by MODEE.|\n|Verification Entity|KACE.|\n\n\nPage | XLI", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000181:50:2:0", "start": 217, "end": 244, "surface": "MODEE’s administrative data", "probe_tag": "confusion", "probe_score": 0.6243, "luna_label": 0, "luna_reason": "Future collection of indicator values is planned from administrative data."}, {"key": "refugee_pads:000181:50:2:1", "start": 1319, "end": 1371, "surface": "Annual reports on digital ID implementation by MODEE", "probe_tag": "confusion", "probe_score": 0.8198, "luna_label": 1, "luna_reason": "Named annual reports cited as the data source for DLI verification."}]}, {"key": "aivin-148", "text": "based\ndata sharing from authoritative sources (data sharing). The sharing of personal data should be, where relevant and possible,\nunder the control or consent of the data subject or person to whom that data relates. A person’s data is not necessarily all\nin one place or in one database, and in most cases, it is maintained by an authoritative source, such as a government\nministry or a public entity. An example is a record of achievement in education (proof that the person has a qualification,\nsuch as a degree), a medical record (details of the person’s current medication and conditions), or an entitlement\ndocument, such as a national identity card (identifying the person’s legal name). Holding and managing all this data in a\nsingle database is impractical and creates security vulnerabilities. Sharing this data securely and reliably under the consent\nof the individual offers many advantages over silos of data and functionality. People should have the ability to manage their\nconsent with a particular service or dataset, including the ability to review and revoke consent as necessary. Taking a\n\n\n27 Digital Public Infrastructure (DPI) refers to digital ID, payment, and data exchange capabilities that are fundamental to enabling\nservice delivery at scale and supporting innovation in the digital economy. DPI provides reusable and foundational digital platforms\nthat allow public and private sector service providers to build and innovate their products and services.\n\n\nPage | 52", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000181:61:2:0", "start": 419, "end": 453, "surface": "record of achievement in education", "probe_tag": "confusion", "probe_score": 0.491, "luna_label": 0, "luna_reason": "Merely names an example document; no existing data use, finding, or analysis is shown."}]}, {"key": "aivin-149", "text": "**The World Bank**\nCash for Jobs Project (P175327)\n\n\n\n\n\n\n\n\n\n|in the National Social Registry - households that are Social from refugee social registry<br>refugees, disaggregated by gender registered in the national Registry households will be<br>social registry collected and inserted<br>in the social registry|Col2|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|Households in targeted areas included<br>in the National Social Registry - host<br>communities, disaggregated by<br>gender<br>Households registered in<br>the Social Registry that live<br>collines hosting refugees<br>Annual<br> <br>National<br>Social<br>Registry<br> <br>The Social Registry will<br>inform on the number<br>of households that<br>were registered in<br>collines hosting<br>refugees<br> <br>Agency handling the<br>social registry<br>|Households in targeted areas included<br>in the National Social Registry - host<br>communities, disaggregated by<br>gender<br>Households registered in<br>the Social Registry that live<br>collines hosting refugees<br>Annual<br> <br>National<br>Social<br>Registry<br> <br>The Social Registry will<br>inform on the number<br>of households that<br>were registered in<br>collines hosting<br>refugees<br> <br>Agency handling the<br>social registry<br>|Households in targeted areas included<br>in the National Social Registry - host<br>communities, disaggregated by<br>gender", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000130:66:0:1", "start": 295, "end": 310, "surface": "social registry", "probe_tag": "drop", "probe_score": 0.0123, "luna_label": 0, "luna_reason": "Standalone table content naming a registry; no eligible existing-data use shown."}]}, {"key": "aivin-150", "text": " as a<br>result of the project<br>interventions|Baseline,<br>MTR and End<br>of project<br>|Independent<br>consulting<br>firms and the<br>project M&E<br>system<br>|baseline, MTR and end<br>of project surveys and<br>project M&E system<br>|independent firms and<br>PCUs<br>|\n|Land area under sustainable landscape<br>management practices|The indicator measures, in<br>hectares, the land area for<br>which new and/or improved<br>sustainable landscape<br>management practices have<br>been introduced. Land is the<br>terrestrial biologically<br>productive system|<br>Quarterly<br>|Quarterly<br>progress<br>reports,<br>|Routine data<br>collection/GEMS<br>|PCUs<br>|\n\n\n\nPage 57 of 80", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000113:62:1:0", "start": 191, "end": 206, "surface": "project surveys", "probe_tag": "drop", "probe_score": 0.0055, "luna_label": 0, "luna_reason": "Project surveys are project-generated data collection, not existing data use."}, {"key": "refugee_pads:000113:62:1:1", "start": 613, "end": 625, "surface": "Routine data", "probe_tag": "drop", "probe_score": 0.011, "luna_label": 0, "luna_reason": "Standalone table-cell phrase naming routine data collection"}]}, {"key": "aivin-151", "text": "**The World Bank**\nDjibouti Integrated Slum Upgrading Project (P162901)\n\n\neffectiveness, and will be composed of a seasoned civil engineer, an environmental and social specialist, a\nmonitoring and evaluation specialist, a procurement specialist, and a financial management specialist. The\nPCU will be reinforced as needed by a communications officer, an urban planner, and international experts.\nProject coordination will remain with the ARULOS Director. In line with the Government decision to reinforce\npublic institutions, PCU members will be selected among existing ARULOS staff or through external hiring\nbased on terms of reference acceptable to the IDA and described in the Project Implementation Manual\n(PIM).\n\n\n7. **Other institutions involved in project implementation.** For certain activities, the ARULOS will rely on\nother institutions (DATUH, Land Directorate, and the municipal institutions) within their range of\ncompetencies; though fiduciary responsibility will be retained by ARULOS. For those partnerships, each\ninstitution will designate a focal point. The PIM defines in detail the responsibilities and obligations of each\nparty.\n\n\n8. DATUH will coordinate the preparation of the urban plans. For these activities, it will elaborate the\nterms of reference for envisaged studies and will monitor the work of consulting firms to ensure timely and\nhigh-quality deliverables.\n\n\n9. The Land Directorate will be charged with steering studies on land registration and, in collaboration\nwith the prefectures, will ensure the creation of a land information system and inventory of land properties,\nstarting first in Balbala Ancien. The Land Directorate will benefit from technical support, the purchase of\nneeded equipment, and capacity building in order to undertake its tasks.\n\n\n10. The Municipality of Djibouti and the Commune of Balbala will play a critical role in achieving the social\ncohesion around the infrastructure investments within the neighborhood. They will support community\nmobilization and preservation of public spaces identified to receive infrastructure and services as part of\nupgrading plans. They will notably ensure that new", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000015:54:0:0", "start": 1553, "end": 1576, "surface": "land information system", "probe_tag": "drop", "probe_score": 0.0445, "luna_label": 0, "luna_reason": "The system will be created by the project, so its data does not yet exist."}]}, {"key": "aivin-152", "text": " analytics (ASA) (P176730), a payment\nsystems assessment is being carried out. The assessment will evaluate the performance of the payment system used under Merankabandi and will\nassess its scalability. In addition, the assessment will identify other existing e-payment options in the country that could complement the phonebased system in the case this is not scalable at national level.\n29 The _colline_ (“hill”) is the lowest administrative unit in Burundi\n30 This amount might be revised during the project implementation based on the Consumer Price Index.\n31 A mapping exercise of NGOs implementing human capital development and productive inclusion activities was carried out during project\npreparation. The assessment showed that there is an important number of international and national NGOs operating in all provinces of the\ncountry.\n\n\nPage 21 of 86", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "sample:refugee_pads:000130:25:2:0", "start": 539, "end": 559, "surface": "Consumer Price Index", "probe_tag": "drop", "probe_score": 0.0021, "luna_label": 1, "luna_reason": "Existing price index used to revise the project amount."}]}, {"key": "aivin-153", "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_aivin", "spans": [{"key": "refugee_pads:000012:36:0:0", "start": 1493, "end": 1511, "surface": "NSAP baseline data", "probe_tag": "drop", "probe_score": 0.0301, "luna_label": 0, "luna_reason": "Establishment of NSAP baseline data is a planned project data-production activity."}, {"key": "refugee_pads:000012:36:0:1", "start": 1743, "end": 1778, "surface": "public expenditure\ntracking surveys", "probe_tag": "confusion", "probe_score": 0.3091, "luna_label": 0, "luna_reason": "Planned incorporation into future monitoring surveys, not cited existing data use."}]}, {"key": "aivin-154", "text": "**INDONESIA**\n**EMERGENCY REHABILITATION OF THE DRAINAGE AND FLOOD PROTECTION**\n\n**SYSTEM OF BANDA ACEH PROJECT**\n\n\n**Annex** 3: **Projects Results Summary**\n\n\n\n**Project Intervention**\n\n**Logic**\n\n\nSustainable post-tsunan\nProject Purpose\nRe-establish\nsustainable urban\nenvironment in Banda\nAceh to provide a less\nrisky physical\nenvironment for the\neconomic recovery,\nfacilitating investment\nand long term\nsustainability of the\naffected areas.\n**Objective 1**\nFast track protection\nagainst tidal incursion\nto vulnerable areas.\n\n\n**Objective 2**\nEmergency protection\nagainst stormwater and\ntidal incursion to\nvulnerable areas.\n\n\n**Objective 3**\nSustainable operation\nand maintenance of\ndrainage system in\nBanda Aceh\n\n\n\nChronic tidal\nincursion in the\nproject area is\nprevented within three\nmonths.\nChronic tidal and\nstorm water incursion\nin the project area is\nprogressively\nprevented over an 10 \n15 month period\nPumps and valves\noperate correctly as\nand when required.\n\n\nMarine incursions are\nprevented\n\n\nStorm water i s\nremoved without\ncausing flooding\n\n\n\n**Verifiable Indicators** **Means of Verification** **Assumptions**\n**of Achievement**\n\n\n\nreconstruction and dew\n\n100% protection of\npopulation against\ndesign intensity storm\nwater flooding and\ntidal flooding\n\n\n\npment in Aceh\nAbsence of visual\nevidence; reports o f\nflooding events associated\nwith design intensity\nevents.\n\n\nAbsence of visual evidence\nand reports of flooding\nevents associated with\ndesign intensity events.\n\n\nAbsence of visual evidence\nand reports of flooding\nevents associated with\ndesign intensity events.\n\n\nRecords of pump\noperation.\n\n\nVisual inspection of valve\noperation.\n\n\nAbsence of visual evidence\nand reports of flooding\nevents associated with\ndesign intensity events.\n\n\n22\n\n\n\nSystem designs are\nappropriate.\n\n\nOperation and\nmaintenance of the\nsystems are sustained.\n\n\nLong-term drainage\nplanning is appropriate\nto the growing needs of\n\nBanda Aceh\n\n\nSystem designs are\nappropriate.\n\n\nConstruction i s\nappropriate.\nSystem designs are\nappropriate.\n\n\nConstruction i s\nappropriate.\n\n\nOperators maintain\nrecords.\n\n\nManagers inspect\nrecords.", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000122:27:0:0", "start": 1583, "end": 1608, "surface": "Records of pump\noperation", "probe_tag": "drop", "probe_score": 0.0425, "luna_label": 0, "luna_reason": "Logframe verification record, not an existing dataset used for analysis."}]}, {"key": "aivin-155", "text": "|MoLG|Ministry of Local Government|\n|---|---|\n|MoLHUD|Ministry of Lands, Housing and Urban Development|\n|MoWT|Ministry of Works and Transport|\n|NDP|National Development Plan|\n|NEMA|National Environmental Management Agency|\n|NERAMP|Northeastern Road Corridor Asset Management Project|\n|NGO|Non-Governmental Organization|\n|NR|National Road|\n|NSP|Nominated Service Provider|\n|OPM|Office of the Prime Minister|\n|PAP|Project Affected Person|\n|PDU|Procurement and Disposal Unit|\n|PIU|Project Implementation Unit|\n|PPSD|Project Procurement Strategy Document|\n|PSC|Project Steering Committee|\n|PSI|Project Safety Impact|\n|RAMS|Road Asset Management System|\n|RAP|Resettlement Action Plan|\n|ReHOPE|Refugee and Host Population Empowerment|\n|ROW|Right of Way|\n|RSW|Sub-Window for Refugees and Host Communities|\n|RSSAT|Road Safety Screening and Analysis Tool|\n|SEA|Sexual Exploitation and Abuse|\n|SEP|Stakeholder Engagement Plan|\n|SH|Sexual Harassment|\n|SIDA|Swedish International Development Cooperation Agency|\n|SOP|Standard Operating Procedures|\n|SPD|Standard Procurement Document|\n|STA|Settlement Transformation Agenda|\n|STEP|Systematic Tracking of Exchanges in Procurement|\n|TAC|Third-party Audit Consultant|\n|TOR|Terms of Reference|\n|TSDP|Transport Sector Development Project|\n|UDHS|Uganda Demographic and Health Survey|\n|UNHCR|United Nations High Commissioner for Refugees|\n|UNRA|Uganda National Roads Authority|\n|UPDF|Uganda Peoples’ Defense Forces|\n|URF|Uganda Road Fund|\n|USAID|United States Agency for International Development|\n|US$|United States Dollar|\n|UXO", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000146:3:0:0", "start": 1276, "end": 1312, "surface": "Uganda Demographic and Health Survey", "probe_tag": "drop", "probe_score": 0.0221, "luna_label": 0, "luna_reason": "Survey is only defined in a glossary; no data use or finding is shown."}]}, {"key": "aivin-156", "text": "ية**||\n\n\n|لخدملا|Col2|Col3|\n|---|---|---|\n||**  البيانات الذاتية**|**  البيانات الذاتية**|\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nالمصدر: الوكالة الوطنية لنظام معلومات الدولة\n\n\n.إعادة تصميم الخدمات اإللكترونية المختارة المستهدفة في إطار هذا المشروع وسيتمهنا في يتمثل الهدف . **هيكلة العمليات التجارية إعادة** .15\n\nتنفيذ األنشطة التالية: (أ) تحليل شامل للخدمات يشمل الخطوات القانونية والتقنية واإلدارية واإلجرائية التي تحتوي على روابط وأنظمة\n\" وخطة عمل لخدمات إعادة الهيكلة ؛ (ج) وضع معاييرالحقةات \"العمليللولوجيا المعلومات المطلوبة )؛ (ب وضع خرائطوتغييرات تكن\n\nالرصد والتقييم ، باإلضافة إلى أنظمة اإلبالغ لقياس آثار إعادةو )؛ (هـالحقة\"داعمة لتنفيذ الخرائط \"ال (د) إعداد وثائق؛ الخدمة لكل خدمة\n\nهيكلة. ال\n\n\nية ي", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000140:39:3:0", "start": 48, "end": 64, "surface": "البيانات الذاتية", "probe_tag": "drop", "probe_score": 0.0273, "luna_label": 0, "luna_reason": "Standalone table cell/header; source citation appears separately and does not make it data use."}]}, {"key": "aivin-157", "text": " / LHW and PMU,<br>DOH<br>|\n|Children under 1 year immunized with the<br>first dose of measles vaccination in target<br>districts|<br>Percentage of children<br>under 1 immunized at EPI<br>centres, PHC, and in the<br>community in target<br>districts.|Bi-Annually<br>|DHIS, EPI MIS<br>|Routine HMIS<br>|DHIS/EPI<br>|\n|Women receiving iron/folic acid<br>supplementation during pregnancy in<br>target districts|Percentage of all pregnant<br>women receiving iron/folic<br>acid supplementation at<br>PHC facilities, or in the<br>community in target<br>districts.|Bi-Annually<br>|DHIS, LHW<br>MIS<br>|Routine HMIS<br>|IMU, PMU, DOH<br>|\n|Health professionals (doctors, nurses,<br>non-medical staff) receiving refresher and<br>on-the-job training|Number provincial and<br>district staff trained<br>(Cumulative number).|Bi-Annually<br>|PMU<br>|PMU Records<br>|IMU, PMU, DOH<br>|\n\n\n\nPage 38 of 48", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000126:42:1:0", "start": 835, "end": 846, "surface": "PMU Records", "probe_tag": "drop", "probe_score": 0.047, "luna_label": 0, "luna_reason": "Standalone table cell naming a project monitoring record source."}]}, {"key": "aivin-158", "text": "Annex 1\nPage 2 of 3\n\n\n**Project Development** **Outcome / Impact** **Project reports:** **(from Objective to Purpose'**\n**Objective:** **Indicators:**\nExpand access to basic Increased number of school Project Reports. It is assumed that\neducation. places. Government's current fiscal\nsituation will be resolved\n\n(salary payment to civil\nservants and teachers).\nEnrollment increases in MOE reports. Expansion of facilities and\nprimary schools from 35,000 quality will contribute to\nto 80,000 increased enrollment\nincluding among girls.\nIncreased availability of It is assumed that the\ntextbooks. Government maintains\ndouble-shifting.\n\n\nTrained primary school head Headteachers have autonomy\nteachers. and authority in managing the\nschools.\nBetter trained contractual Contractual teachers are\nteachers recruited early enough before\nthe school year to allow time\nfor training.\n\n\n**Output from each** **Output Indicators:** **Project reports:** **(from Outputs to Objective)**\n**Component:**\nIncreased number of school 226 classrooms will be built Monthly disbursement Availability of school places\nplaces. increasing capacity by over summary. will increase enrollment.\n20,000 based on double\nshifting.\nProvide textbooks. Numbers of textbooks per Semi-annual Provision of textbooks will\npupil increases. supervision reports. improve learning.\nTrained primary school head- Primary school head-teachers Annual audit reports; Better trained head-teachers\nteachers. trained and Guidebook for site visits. will improve school\nschool management prepared efficiency.\nand distributed.", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000100:30:0:0", "start": 385, "end": 396, "surface": "MOE reports", "probe_tag": "drop", "probe_score": 0.0342, "luna_label": 1, "luna_reason": "MOE reports are cited as evidence of increased enrollment."}]}, {"key": "aivin-159", "text": " implementation of project activities as per the implementation work plan\n\nin a timely and high quality manner;\n\n\nb) Monitoring the project performance with regard to achievement of project activities and\n\nif necessary, modify and/or redirect activities to maximize the potential impact of the\nproject;\n\n\nc) Establishing a basis for evaluating the project with regard to achievement of the overall\n\ndevelopment objective;\n\n\nd) Establishing working partnerships with PHCCs, Prime Minister’s (PM) office responsible\n\nfor the NPTP program, Hospitals (OPD) to gather and/or access the relevant data to\noptimize the M&E outputs for each component and sub-component under an integrated\nM&E plan;\n\n\ne) Organizing data collection from different stakeholders and facilitating verification,\n\nanalysis of the data/information received from various stakeholders;\n\n\nf) Liaising with implementing agencies and partners required for implementation of key\n\ntechnical instruments (i.e. facility surveys, beneficiary assessments etc.); and\n\n\n53", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000020:52:1:0", "start": 969, "end": 985, "surface": "facility surveys", "probe_tag": "drop", "probe_score": 0.0181, "luna_label": 0, "luna_reason": "Planned facility surveys are listed as project monitoring instruments, not existing data used."}, {"key": "refugee_pads:000020:52:1:1", "start": 987, "end": 1010, "surface": "beneficiary assessments", "probe_tag": "confusion", "probe_score": 0.1875, "luna_label": 0, "luna_reason": "Assessment implementation is planned project data production, not use of existing findings."}]}, {"key": "aivin-160", "text": "**The World Bank**\nUganda: Roads and Bridges in the Refugee Hosting Districts Project (P171339)\n\n\nbuses, motorbikes, cars, 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 (days)\n(iv) Percentage increase in trade volumes using the Project road (dis-aggregated by refugees, hosts)\n\n(b) enhance the capacity of UNRA to manage environmental, social and road safety risks\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 (yes/no)\n(vii) Annual Number of fatalities or serious injuries involving construction vehicles or at construction\n\nsites\n\n\n**B. Project Components**\n\n\n43. The project will have the following components as detailed below.\n\n\n44. **Component 1: Road Upgrading Works (Total US$145.8 million; IDA: US$125.8 million equivalent, GoU:**\n**US$20 million).**\n\n\n1(a) Upgrading - to bituminous paved road standard - and widening of about 105 km of KobokoYumbe-Moyo road corridor.\n1(b) Carrying out supervision of civil works under part 1(a) of the Project.\n1(c) Carrying out environmental and social risks management (including implementation of action\nplans to address among others gender-based violence, sexual exploitation and abuse, violence\nagainst children and HIV/AIDS), monitoring and evaluation, third party integrated performance\naudits and road user satisfaction surveys.\n1(d) Preparation of Project’s environmental and social risk management documents as well as\ndetailed engineering designs of civil works and carrying out land acquisition, and resettlement\nand rehabilitation associated with upgrading works under Part 1(a) and maintenance of the road\ncorridor for five years post-construction.\n\n45. This", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000146:25:0:1", "start": 1453, "end": 1483, "surface": "road user satisfaction surveys", "probe_tag": "drop", "probe_score": 0.0463, "luna_label": 0, "luna_reason": "Project will carry out these planned surveys; no existing survey data are used."}]}, {"key": "aivin-161", "text": "PRS : LIBERIANS IN COTE D’IVOIRE\n\n\n35. The demographic structure of the refugee population is somewhat difficult\nto evaluate, because the age groups used are not the same as those in the national\npopulation census (Table 2). However, demographic tables allow an estimate of the\nage structure of the refugee population according to the standard classification: the\nproportion in the age group 0-14 would be 45.2 per cent and for the 15-59 bracket\n50.0 per cent. These data clearly point to a population with a higher dependency ratio\nthan the national one: more women, more children, and more old people. However,\nit must be borne in mind that the national figure is strongly influenced by the fact\nthat so many residents of Côte d’Ivoire are foreign nationals, many of whom are\ntemporary migrants and these are disproportionately male and working-age adult.\nThe male: female ratio for Ivorian citizens is only 98.3. Furthermore, the rural areas\nalso have a higher percentage of children and elderly (46.1 and 4.9 per cent,\nrespectively). All this indicates that the age structure of the refugee population may\nnot really be all that different from the Ivorian one; there remains, however, a noted\npreponderance of women, which may be due to the differential impact of the civil\nwar on the sexes. The consequence of such a demographic structure is a somewhat\nlower capacity for economic self-sufficiency.\n\n\n**Table 2. Demographic structure**\n\n\n\n\n\n\n|Liberians (1997 refugee census)|Col2|All residents (1998 population census)|Col4|\n|---|---|---|---|\n|0-4 years old|14.8 %|||\n|5-17|38.9 %|0-14|42.9 %|\n|18-59|41.2 %|15-59|53.2 %", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001267:15:0:0", "start": 187, "end": 213, "surface": "national\npopulation census", "probe_tag": "keep", "probe_score": 0.9038, "luna_label": 1, "luna_reason": "Existing census data are compared with refugee demographic estimates and population figures."}]}, {"key": "aivin-162", "text": "-Sub-Saharan-Africa)</u>\n\n\nThis paper **examines individual-level deprivations of women and men in forcibly**\n\n**displaced households and host communities, as well as intrahousehold**\n\n**inequalities, building on prior analysis of the tailored Multidimensional Poverty**\n\n**Index (MPI) in Ethiopia, Northeast Nigeria, Somalia, South Sudan, and Sudan** .\n\nThe MPI measures deprivations in education, health, living standards, and financial\n\nsecurity, by combining 15 indicators across these four dimensions. For the six\n\nindividual-level indicators (years of schooling, school attendance, pregnancy care,\n\nearly marriage, legal identification, and unemployment) spanning the education,\n\nhealth, and financial security dimensions, the authors identify which household\n\nmembers are deprived: their gender and their age, and what proportion of eligible\n\nhousehold members are deprived. The analysis draws on household survey data in\n\nthe five countries <sup>13</sup> [^13: In Ethiopia, the Skills Profile Survey (2017) sampled refugees in and around camps in the Tigray, Afar, Gambella,\nBenishangul Gumuz, and Somali regions. In Nigeria, the IDP Survey (2018) sampled IDPs and host communities in six\nnortheastern states (Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe). In Somalia, the High Frequency Survey (2017)\nsampled IDPs and host communities in secure parts of the country. In Sudan, the IDP Profiling Survey (2018) sampled IDPs\nand neighboring host communities in the Abu Shouk and El Salam camps, in Al-Fashir. And in South Sudan, the High\nFrequency Survey Wave 4 (2017) sampled IDPs and host communities in urban areas of seven of the ten pre-war states\n(Western Equatoria, Central Equatoria, Eastern Equatoria, Northern Bahr-El-Ghazl, Western Bahr-El-Ghazal, Warrap, Lakes\nstate).] .\n\n\nMain results:\n\n\n- In Ethiopia, there are statistically significant gender gaps in school attendance\n\namong the refugee population (3 percentage points for all refugee households, 5\n\npercentage points for MPI poor", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000595:28:1:0", "start": 904, "end": 925, "surface": "household survey data", "probe_tag": "keep", "probe_score": 0.9253, "luna_label": 1, "luna_reason": "Existing household surveys underpin the analysis; footnote identifies country-specific survey rounds."}, {"key": "reliefweb:000595:28:1:1", "start": 986, "end": 1007, "surface": "Skills Profile Survey", "probe_tag": "keep", "probe_score": 0.9358, "luna_label": 1, "luna_reason": "Named survey data underlies the paper’s household-level deprivation analysis."}]}, {"key": "aivin-163", "text": "Chapter 6\n\n\nResettlement is a way to save lives and safeguard\nhuman rights by assisting refugees in countries that\ncannot provide them with appropriate protection\nand support. Of all cases submitted by UNHCR in\n2020, 86 per cent were for survivors of torture and/\nor violence, people with legal and physical protection\nneeds, and particularly vulnerable women and\ngirls. <sup>**112**</sup> Just over half (51 per cent) of all resettlement\nsubmissions concerned children.\n\n\nAccording to government statistics, in 2020 the\nUnited States welcomed 9,600 resettled refugees\nfrom 51 countries, predominantly refugees originating\nfrom the Democratic Republic of the Congo (25 per\ncent), Ukraine (18 per cent) and Myanmar (17 per\ncent). A further 9,200 refugees were resettled to\nCanada, most commonly Syrians, Iraqis and Eritreans.\nResettlement to both countries dropped precipitously\nfrom 2019, when 30,100 refugees were resettled in\nCanada and 27,500 in the United States. In 2020,\nEuropean countries collectively welcomed 11,600\nresettled refugees.\n\n\nOverall, Syrians accounted for one-third of resettled\nrefugees in 2020, followed by Congolese (12%). The\nother resettled refugees were from 82 countries\nof origin, including Iraq, Eritrea, Myanmar, Ukraine,\nSudan and Afghanistan.\n\n\nUNHCR is calling on more countries to expand thirdcountry solutions like resettlement. It is also urging\nthem to resettle more refugees, where possible,\nand to make family reunification and complementary\npathways more accessible to refugees.\n\n\n**Local integration**\n\n\nWhen repatriation and resettlement are not viable\noptions, some refugees are able to achieve a third\ndurable solution: building a new life in their country\nof asylum. There are millions of refugees around the\nworld who live in protracted situations with little hope\nof ever returning home. Local integration of refugees\ncan include the provision of legal status, including\nappropriate alternatives under domestic regulations\non long-term residence, and naturalization.\n\n\n\nRefugees must be prepared to adapt to their\nnew country", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000757:47:0:0", "start": 486, "end": 507, "surface": "government statistics", "probe_tag": "keep", "probe_score": 0.9459, "luna_label": 1, "luna_reason": "Government statistics support concrete refugee resettlement figures."}]}, {"key": "aivin-164", "text": "\nin their efforts to protect and assist displaced\npopulations. In 2024, sub-national data on IDPs was\nreported for 31 countries and covered almost all\n(98 per cent) of the total IDP population protected/\nassisted by UNHCR. Based on the available data, at\nleast 42 per cent of IDPs reside in urban areas, <sup>**159**</sup>\n\n\n\nand approximately one-quarter live in settlements,\nincluding camps, <sup>**160**</sup> both in urban and rural settings.\n\n\nAge- and sex-disaggregated data for IDPs was\navailable for 16 countries, 2 fewer than the\nprevious year. This represents 58 per cent of the\nIDP population reported by UNHCR, while sex\ndisaggregation was available for 60 per cent of the\nIDP population.\n\n\nWomen and girls made up 53 per cent of all IDPs,\nwhile children accounted for 46 per cent of IDPs\nworldwide. Countries with the highest proportion\nof internally displaced children included Somalia\n(66 per cent) and Burkina Faso (56 per cent), while\nthe lowest proportion of children were reported in\ncountries such as Mexico (19 per cent) and Ukraine\n(24 per cent). <sup>**161**</sup>\n\n\n**Displacement in the context of**\n**disasters**\n\n\nIn addition to conflict and violence, people were\ndisplaced within their countries due to disasters, with\nrecord levels reported in 2024. Disasters include\npeople displaced due to extreme weather events,\nsuch as floods and storms, and those displaced due\nto geophysical events, such as earthquakes. The\nglobal forced displacement does not include people\ndisplaced due to disasters.\n\n\nDuring the year, 45.8 million internal displacements\ndue to disasters were reported, with 9.8 million\npeople remaining displaced within their own\ncountry at the end of 2024, according to the Internal\nDisplacement Monitoring Centre. <sup>*", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001242:43:1:0", "start": 449, "end": 489, "surface": "Age- and sex-disaggregated data for IDPs", "probe_tag": "keep", "probe_score": 0.9061, "luna_label": 1, "luna_reason": "Existing disaggregated data supports reported coverage and demographic findings."}]}, {"key": "aivin-165", "text": ".\nThey are to be added to the multiple other refugee\npopulations that Jordan hosts, including 66,362 Iraqis,\nand more than 7,972 from Sudan, Somalia, and other\ncountries.\nThe continuously worsening economic situation in\nJordan due to COVID-19 was one of the motives to\nreturn to the country of origin. According to CARE2021\nAnnual Needs Assessment, refugees expressed\ntheir beliefs that reduction in assistance – caused by\nunderfunding humanitarian programs and the absence\nof one refugee approach implementation - is a strategy\nto persuade them to return to their countries of origin <sup>3</sup> .\nAccording to data of the Department of Statistics in\nJordan for the third quarter of 2021, unemployment rate\nhas reached 23.2% (21.2% for males and 30.8% for\nfemales), which represents a decrease in employment\nrates by 1.5% for males and 2.3% for females\n\n\n3 2021 Annual Needs Assessment – Care, Jordan, Page 5 https://data.\nunhcr.org/en/documents/details/92107\n\n\n\ncomparing to the second quarter of the same year. The\nstatistics reflect a constant impact of COVID-19 on the\neconomic situation of Jordan and labor market with a\nslight enhancement by 0.7% compared to the second\nquarter of 2020.\nRefugee occupations are generally limited to either\ninformal work in the field of agriculture, construction,\nmanufacturing, or incentive-based volunteering\nopportunities which are mainly available in refugee\ncamps. Confining opportunities to these sectors and\nthe expensive fees of issuing work permits are the main\nchallenges that limited refugees from participating in\nthe labor market and may expose them to significant\nrisks of detention and exploitation. Moreover, only\nSyrian refugees in Jordan are legally allowed to work.\nThose from other countries, including Iraq, Yemen,\nSudan and Somalia, are not able to apply for permits <sup>4</sup> .\nAs of September 2021, the number", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001068:4:1:0", "start": 315, "end": 347, "surface": "CARE2021\nAnnual Needs Assessment", "probe_tag": "keep", "probe_score": 0.9734, "luna_label": 1, "luna_reason": "Named assessment supports refugees’ reported beliefs about reduced assistance."}, {"key": "reliefweb:001068:4:1:1", "start": 613, "end": 649, "surface": "data of the Department of Statistics", "probe_tag": "keep", "probe_score": 0.9471, "luna_label": 1, "luna_reason": "Department of Statistics data supports reported unemployment and employment-rate findings."}]}, {"key": "aivin-166", "text": "**UNHCR News**\n\n\nHQP100\nP.O. Box 2500\nCH-1211 Geneva 2\nTel +41 22 739 85 02\nFax +41 22 739 73 14\nwww.unhcr.org\n@RefugeesMedia\n\n\n# PRESS RELEASE\n\nand highly visible consequences of the world’s conflicts and the terrible suffering they\ncause has been dramatic growth in numbers of refugees seeking safety by undertaking\ndangerous sea journeys, including on the Mediterranean, in the Gulf of Aden and Red\nSea, and in Southeast Asia.\n\n\n**Half are Children**\n\n\nUNHCR’s Global Trends report shows that in 2014 alone 13.9 million became newly\ndisplaced – four times the number in 2010. Worldwide there were 19.5 million refugees\n(up from 16.7 million in 2013), 38.2 million were displaced inside their own countries (up\nfrom 33.3 million in 2013), and 1.8 million people were awaiting the outcome of claims\nfor asylum (against 1.2 million in 2013). Alarmingly, over half the world’s refugees are\nchildren.\n\n\n“With huge shortages of funding and wide gaps in the global regime for protecting\nvictims of war, people in need of compassion, aid and refuge are being abandoned,”\nsaid Guterres. “For an age of unprecedented mass displacement, we need an\nunprecedented humanitarian response and a renewed global commitment to tolerance\nand protection for people fleeing conflict and persecution.”\n\n\nSyria is the world’s biggest producer of both internally displaced people (7.6 million) and\nrefugees (3.88 million at the end of 2014). Afghanistan (2.59 million) and Somalia (1.1\nmillion) are the next biggest refugee source countries.\n\n\nEven amid such sharp growth in numbers, the global distribution of refugees remains\nheavily skewed away from wealthier nations and towards the less wealthy. Almost nine\nout of every 10 refugees (86 per cent) were in regions and countries considered\neconomically", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000905:1:0:0", "start": 464, "end": 484, "surface": "Global Trends report", "probe_tag": "keep", "probe_score": 0.918, "luna_label": 1, "luna_reason": "UNHCR report is cited as showing displacement and refugee figures."}]}, {"key": "aivin-167", "text": "Information Management as Coordination Support\n\n\n\n3RP 2017-18 reporting\n\n\n\nActivityInfo: an online Inter-Agency 3RP reporting platform\n\n\n\nTo support coordination, **an online**\n**platform is rolled-out to collect reports on the**\n**8 Sectors’ activities** carried out by about 80\npartners. Reporting on ActivityInfo enables each\npartner/user to:\n\n- Collect, Manage, analyse and geo-locate their\nown activities.\n\n- View and extract reports on all the activities of\nother agencies in the response.\n\n- Integrate their activities within the entire\nresponse.\n\n- Reinforce partnerships and reduce costs and\ntime on reporting.\nTo familiarize the partners with the tool, training\nsessions were provided to more than 500 staff of\nall agencies with users access to the databases.\n\nA time line for reporting is also agreed upon as shown below:\n\n\n\nA screen-shot of ActivityInfo, **www.activityinfo.org** while partners are\nentering achievement data on their activities:\n\n\n\n2017-18: Information flow/roles and responsibilities/timeframes for monthly reporting on ActivityInfo\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|Col1|Col2|\n|---|---|\n|||\n\n\n\n**irqerbim@unhcr.org**\n\n\n\n22", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001003:19:0:0", "start": 920, "end": 936, "surface": "achievement data", "probe_tag": "confusion", "probe_score": 0.0699, "luna_label": 0, "luna_reason": "Data are being entered into a project reporting platform, not analyzed as existing evidence."}]}, {"key": "aivin-168", "text": "_Source: Authors’ calculation of 2024 survey data._\n\n\n**Livelihood opportunities for the Shona community have improved significantly since gaining citizenship,**\n\n**particularly in terms of financial access and job prospects – as well as protection outcomes, such as reduction in**\n\n**harassment by law enforcement.** According to the survey data, over half of the Shona community reported that\n\ncitizenship has enhanced their access to banking and mobile wallet services, making it easier for them to save,\n\ninvest, and manage their finances. Increased job opportunities were cited by 47 percent of respondents, reflecting\n\nhow the ability to present formal identification has opened new avenues for employment and economic\n\nparticipation. Importantly, 46 percent of the Shona noted a reduction in harassment by law enforcement, which\n\nhad previously hindered their movement and limited their economic activities. This improvement has enabled more\n\nindividuals to seek employment and conduct business without fear of arbitrary detentions or fines.\n\n\n_Figure 6: Impacts on receiving citizenship on livelihood opportunities_\n\n\n_Source: Authors’ calculation of 2024 survey data._\n\n\n**[www.unhcr.org](http://www.unhcr.org/)** 8", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000886:7:0:0", "start": 33, "end": 49, "surface": "2024 survey data", "probe_tag": "confusion", "probe_score": 0.8806, "luna_label": 1, "luna_reason": "2024 survey data underlies authors’ calculations and reported livelihood findings."}, {"key": "reliefweb:000886:7:0:1", "start": 335, "end": 346, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9694, "luna_label": 1, "luna_reason": "Survey data supports reported percentages and concrete livelihood findings."}]}, {"key": "aivin-169", "text": "identified. This poses a challenge from a financial viewpoint. As a consequence, this ends up\nbeing often neglected.\n\n\n➔ _Coordination between environmental and humanitarian actors_\n.\nThere is not too much exchange of information between the two communities. The workshop\noffered an opportunity to bring together humanitarians and environmentalists within the GIFMM\nto discuss how to strengthen this collaboration beyond the workshop itself.\n\nSome collaborations exist, for example, WWF has been working with Oxfam on issues of natural\nresource management and has had a great experience of partnership. WWF and WFP have also\nbeen collaborating in some parts of Colombia where there is geographical overlap of their\nactivities. This collaboration has focused on food security and climate change, and where WFP\nlack in environmental data and technical expertise, WWF and the MoE has helped to fill the gap.\n\nThe WWF representative suggested that it would be beneficial to create a national directory of\nlocations where all organizations are working, in order to encourage more partnerships across\nthe humanitarian, development and conservation communities.\n\n**Part 2: Environmental data in humanitarian contexts**\n\nThe second part of the workshop focused on environmental data use in humanitarian contexts,\nincluding a discussion on data sharing and geospatial data. The discussion followed a\npresentation of MapX and some results from participatory mapping exercises held with the\nChichituy indigenous community (see presentation in Annex B). The questions posed to\nparticipants included:\n\n## ➔ What are the types of environmental data your organization uses for operations? ➔ Which are the main sources of these data? Are these data accessible? ➔ Do you use remote sensing data from platforms such as MapX to overcome data\n\nlimitations?\n\nParticipants were interested in learning more about MapX and connections of spatial data to the\nNEAT+. To summarize the discussions specifically about MapX and NEAT+:\n\n\n➔ _Types of environmental data_\n\nType of environmental data used by some of the organizations attending the workshop include\ndata on climate, WASH,", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000102:54:0:0", "start": 979, "end": 1010, "surface": "national directory of\nlocations", "probe_tag": "confusion", "probe_score": 0.163, "luna_label": 0, "luna_reason": "Proposed directory creation; the data resource does not yet exist."}]}, {"key": "aivin-170", "text": "Among the Somalis and Ethiopians who reach South Africa, an estimated fifty percent\ncontinue their journey onward to destinations beyond the African continent (IOM\n2009). The southward flow from the Horn of Africa, of course, is only part of the\npicture. Others head north, while many move east across the Gulf of Aden to Yemen.\nAvailable statistics from the Mixed Migration Task Force <sup>5</sup> [^5: The Mixed Migration Task Force (MMTF) was formed in 2007 to address the needs of migrants,\nrefugees and asylum seekers crossing the Gulf of Aden. The Task Force members include the Danish\nRefugee Council (DRC), Norwegian Refugee Council (NRC), IOM, UNHCHR, UNHCR, UNICEF\nand UNOCHA.] indicate that in 2008, more\nthan 50,000 people made the perilous voyage in smugglers‟ boats. At least 590\ndrowned and another 359 were reported missing along the different East Africa\nmigration routes.\n\n_The Great Lakes region_\n\nMixed movements from the Great Lakes region are to date poorly documented. The\ncycle of violence in the DRC since the mid-1990s has generated large numbers of\nrefugees and asylum seekers. Tanzania, Uganda, Rwanda and Burundi all host\nsizeable Congolese refugee populations (approximately 60,000 each in Tanzania,\nUganda and Rwanda, and over 20,000 in Burundi).\n\nAlthough the 2006 elections in the DRC, following the peace agreement that brought\nthe second Congolese war to an end, have brought relative stability to some areas of\nthe country, other regions continue to suffer from violence and displacement. Attacks\nby the Lord‟s Resistance Army in north-eastern DRC, complex conflicts related to\nidentity, ethnicity and nationality, as well as rising levels of sexual violence in the\nKivus, have exacerbated the humanitarian crisis in that country. There is a concern\nthat the lack of protection experienced by many Congolese citizens may be\nperpetuated and reinforced by a possible withdrawal of MONUSCO forces in 2011.\n\nSince the 1990s, there has been a considerable growth in the movement of people", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000802:9:0:0", "start": 339, "end": 385, "surface": "statistics from the Mixed Migration Task Force", "probe_tag": "confusion", "probe_score": 0.8993, "luna_label": 1, "luna_reason": "Task Force statistics support the reported 2008 migration figures."}]}, {"key": "aivin-171", "text": "Chapter 9\n\n\nThere are notable differences in the availability\nof detailed age- and sex-disaggregated data by\npopulation group and country of asylum. Detailed\ndemographic data is available for 79 per cent of\nrefugees, <sup>**140**</sup> but just 36 per cent of asylum-seekers.\n\n\nRegional variations in the availability of age- and\nsex-disaggregated demographic data are significant.\nThey range from 6 per cent in Southern Africa to\n68 per cent in Asia and the Pacific. Europe and\nthe Americas, which host about three-quarters\nof the global asylum-seeking population, provide\ndetailed demographics only for 51 and 11 per cent\nof their asylum-seeking population respectively.\nWhile some demographic data for asylum-seekers\nis available for many European countries, the data is\noften not directly interoperable with UNHCR’s due\nto inconsistent age cohorts. UNHCR’s age cohorts\nhave been designed to align, where possible, to UN\nconventions and to capture information to support\nthe monitoring of critical benchmarks in humanitarian\nprogramming. <sup>**141**</sup>\n\n\nNotable improvements in the demographic coverage\nin 2020 included data for Venezuelans displaced\nabroad, for whom the availability of age and sex\ndata soared from just one per cent in 2019 to 47 per\ncent in 2020, due to newly available official data in\nColombia. Continuing to improve the availability of\ndemographic data is a priority for UNHCR, which is\nexploring alternative data sources to estimate missing\ndata with statistical modelling (e.g. the regional\nmodelling presented in Chapter 2).\n\n**a. Sub-national coverage**\n\n\nRecording the locations of forcibly displaced and\nstateless populations is crucial to ensure effective\nhumanitarian responses within countries. In large\ncountries such as Syria and the Democratic Republic\nof the Congo, new displacement can be highly\nlocalized, sometimes within a specific region or city.\nUNHCR documents the locations of forcibly displaced\npopulations in its annual statistical reporting. In 2020,\nsome 102 of", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000757:67:0:0", "start": 158, "end": 174, "surface": "demographic data", "probe_tag": "confusion", "probe_score": 0.3763, "luna_label": 1, "luna_reason": "Demographic data supports a concrete 79-percent availability finding."}, {"key": "reliefweb:000757:67:0:2", "start": 1128, "end": 1165, "surface": "data for Venezuelans displaced\nabroad", "probe_tag": "confusion", "probe_score": 0.6739, "luna_label": 1, "luna_reason": "Data coverage finding for Venezuelans is quantified and attributed to official Colombian data."}]}, {"key": "aivin-172", "text": " y en el marco del\ntrabajo interagencial con ONUSIDA, se desarrolló un\nfolleto informativo sobre acceso a la salud integral y\nprevención de VIH, enfocado en población extranjera\nen el país, el que fue ofrecido en unos exhibidores\nespecialmente diseñados, que cuentan en su costado\ncon información sobre los Espacios de Apoyo en Chile.\nPara garantizar la protección de grupos especialmente\nvulnerables, ACNUR cuenta con una alianza de trabajo\ncolaborativo con el Servicio Nacional de la Mujer y\nla Equidad de Género, que permite la derivación a la\nred especializada en temas de violencia basada en\ngénero (VBG), en todas las regiones, cuando un caso\nsea identificado por una agencia socia. Así, durante\n2021, 208 personas sobrevivientes de VBG recibieron\nservicios especializados en esta materia. En este\ncontexto, también se está elaborando un folleto sobre\nacceso a servicios para personas sobrevivientes de\nVBG.\n\n\nAsimismo, durante 2021 se realizaron 8 diagnósticos\nparticipativos, en formato híbrido, en los cuales\nparticiparon 80 personas, entre 14 a 65 años de edad.\nLos resultados de estos diagnósticos son un insumo\nclave para orientar la planificación del año entrante y\najustar las estrategias de trabajo. De los testimonios\n\n\n\n30 CAPÍTULO 4 / **PRINCIPALES ACTIVIDADES E INICIATIVAS DE ACNUR EN 2021**", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001529:28:1:0", "start": 955, "end": 982, "surface": "diagnósticos\nparticipativos", "probe_tag": "confusion", "probe_score": 0.1349, "luna_label": 1, "luna_reason": "Completed participatory assessments informed subsequent planning and strategy adjustments."}]}, {"key": "aivin-173", "text": " such as reading glasses were\nhighlighted as the recurrent concerns linked to access to\nassistive devices.\n\n\nThe participants of the focus group discussion in Jordan\nand Lebanon highlighted the link between assistive\ndevices and access to education. One young Syrian boy\nin Jordan shared how he had waited for eye glasses.\nOnce received, he was able to start school. A girl who had\ninitially been waiting for medical boots had later been\nenrolled in school. A Syrian boy in Lebanon explained\nhow significant the impact that an electric wheelchair\ncan have on quality of life and ability to function\nindependently.\n\n\nAs highlighted by the key informants, the current\nchallenges and barriers to accessing devices include:\nlack of availability of specialised devices (prosthetics,\nmedical boots, white canes) in many countries; lack of\nfunding for specialised devices with higher costs, such as\nelectric wheelchairs. Additionally, there is currently no\nquantitative data available on specific types and numbers\nof devices required that can be used to inform annual\nprogram planning for. In Jordan, the closure of one of the\nNGO projects in Zaatari camp (not UNHCR funded) has\nleft a significant gap in the provision of hearing aids in\nthe camp.\n\n\nIn Turkey, there is potential for persons with disabilities\nto receive government funded devices. However, at the\ntime of the mapping, information about the number of\npersons with disabilities supported with devices was\nnot available. In Lebanon, Algeria, Morocco, Jordan and\n\n\n33", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001114:34:1:0", "start": 950, "end": 967, "surface": "quantitative data", "probe_tag": "confusion", "probe_score": 0.749, "luna_label": 0, "luna_reason": "States quantitative data is unavailable without analyzing a finding or substitute estimate."}]}, {"key": "aivin-174", "text": "/lawethiopia.com/index.php/volume-3/6747-proclamation-no-1263-2021-definition-of-powers-and-duties-of-the-executive-organs-proclamation)</u>\nWhile RRS has, in effect, maintained its traditional mandate, it does not yet have an established Regulation\ndetailing its powers, responsibilities and functions, including how it seeks to foster coordination. Having\na legally defined power, structure and accountability would assist RRS in clearly articulating and strengthening\nits protection mandate.\n\n\nThe refugee community governance structures have continued to function and provide refugee inputs\nand feedback to the Government in the prescribed period. However, the level of functionality of other\nassociations such as women’s associations, youth associations and religious leaders’ associations has\ncontinued to vary from area to area. These associations are registered by RRS but lack legal personality.\nThe <u>[Grievances and Appeals Handling Directive No 03/2019, which sets out grievance mechanisms relating](https://www.coursehero.com/file/77988168/Refugee-Draft-Directive-2pdf/)</u>\nto misconduct committed by RRS has not yet been functional.\n\n\nA national population census has not been conducted since 2007, although refugees have been included\nin administrative data collection. RRS has continued to provide refugee vital events data to the ICS and\nrefugee education data to the Ministry of Education (MoE). The steps taken at the sectoral level to include\nrefugees in education sector planning have been further strengthened. Refugee data have been included in\nthe Rapid Justice Sector Assessment that was conducted from July 2020-July 2021 at the national level by\nthe government Justice Sector Steering Committee commissioned by UN agencies (UN Women, UNODC,\nUNDP, UNICEF, and OHCHR).\n\n\n**2.4** **Access to", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001338:5:1:1", "start": 1316, "end": 1341, "surface": "refugee vital events data", "probe_tag": "confusion", "probe_score": 0.0859, "luna_label": 1, "luna_reason": "Existing refugee vital-events data are provided to ICS as an institutional data resource."}, {"key": "reliefweb:001338:5:1:2", "start": 1357, "end": 1379, "surface": "refugee education data", "probe_tag": "confusion", "probe_score": 0.1819, "luna_label": 0, "luna_reason": "Data are merely provided to the ministry, with no shown analytical or decision use."}]}, {"key": "aivin-175", "text": "# PROVINCES DE KWANGO, KWILU, MAÏ-NDOMBE\n\n\n\n\n\n\n\n\n\n\n\n|Col1|Violations et abus de droits en décembre 2023|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Provinces**|**Droit à la**<br>**propriété**|**Droit à**<br>**l’intégrité**<br>**physique**|**VBG**|**_Total_**|**_% _**|\n|**Kwilu**|15|0|16|**_31_**|**_48_**|\n|**Maï-Ndombe**|12|13|9|**_34_**|**_52_**|\n|**TOTAL**|**27**|**13**|**25**|**_65_**|**_100_**|\n\n\n\n- Contrairement au mois de novembre avec 207 violations et abus de droits,\nce sont **65** violations et abus des droits de l’homme qui ont été\ndocumentées durant le mois de décembre <sup>**6**</sup> dans les provinces de Kwilu\n\n\n6 <u>Rapport mensuel de monitoring de protection Bandundu mois de décembre 2023 Kadima Foundation</u>\n<u>et UNHCR</u>\n\n\n\net Maï-Ndombe : 27 violations du droit à la", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001633:11:1:0", "start": 645, "end": 688, "surface": "Rapport mensuel de monitoring de protection", "probe_tag": "confusion", "probe_score": 0.8646, "luna_label": 1, "luna_reason": "Named monitoring report cited for documented human-rights violations."}]}, {"key": "aivin-176", "text": " cycle permanent de vulnérabilité et de violence.\n\n\nA cela s’ajoute les effets du changement climatique (inondations et sècheresses), la réduction et/ou la dégradation des\nressources socio-économiques des ménages, la propagation des maladies et épidémies, ainsi qu’un accès limité aux moyens\nde subsistance (faible production agricole, détérioration des échanges), à l’eau potable et aux soins de santé primaires. D’après\nl’Indice de Risque Climatique pour les enfants publié par l’UNICEF en 2021, le Tchad est le 2ème pays au monde où les enfants\nsont les plus exposés aux risques des effets du changement climatique. Dans ce contexte, les femmes et filles sont de plus en\nplus exposées aux risques accrus de violences par leur partenaire ou sont contraintes d’adopter des mécanismes négatifs de\nsurvie.\n\n\nLa situation de protection au Tchad devient de plus en plus préoccupante, notamment en raison des incursions des groupes\narmés non étatiques (GANE) et des individus armés, ainsi que du faible accès des populations aux services essentiels. Selon les\ndonnées du Projet 21, un mécanisme interagence de monitoring de la perception des populations sur l’évolution de leur\nenvironnement et des risques de protection, 5 657 incidents individuels de violations graves des droits humains ont été\ncollectés entre janvier et octobre 2024. Bien que toutes les tranches d’âge soient touchées, la population active de 18", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000315:2:1:0", "start": 424, "end": 468, "surface": "Indice de Risque Climatique pour les enfants", "probe_tag": "confusion", "probe_score": 0.8789, "luna_label": 1, "luna_reason": "UNICEF's 2021 index supports Chad's ranking for children's climate-risk exposure."}]}, {"key": "aivin-177", "text": "15%), child headed-HHs (11%), persons with disabilities\n(11%), elderly person headed-HHs (10%), single male headed-HHs\n(7%), persons with life-threatening health issues (7%) and\nunaccompanied and separated children (6%). The main reasons for\nbeing unable to access these services are being unable to pay for the\nservice (22%), lacking documentation (19%), facing\ndiscrimination/exclusion (15%), assistance not reaching people in\nneed (16%), and assistance not being what people need (14%).\n\nProtection monitoring data highlighted that women and girls face\nincreased barriers to accessing services, with their freedom of\nmovement being limited. This is further compounded by the low\n\n\n\nrates of women’s access to civil documentation, with 35% of FGDs\nstating that women and girls lack the Tazkera, compared to only 1%\nof FGDs stating the same for men. In Households Surveys, 22,8% of\nfemale respondents reported lacking documentation compared to\n9,9% of male respondents.\n\n**2.2.3** **Feeling of Safety**\nThe feeling of safety was impacted during this reporting period, with\na 10% decrease in the percentage of those that responded that they\nfeel safe (69% in Q3 compared to 79% in Q2). In Q3, 27% of\nrespondents stated that there was no change in their security\nsituation (30% decrease from Q2 and 36% decrease Q1), 37%\nmentioned that the security situation had worsened (7% increase\nfrom Q2 and 9% decrease Q1) and 36% mentioned that the security\nsituation had improved (23% increase from Q2 and 37% increase\nfrom Q1). The contributing factors indicated for the worsening\nsecurity situation data are increased conflict between government\nand anti-government", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001240:8:1:0", "start": 491, "end": 517, "surface": "Protection monitoring data", "probe_tag": "confusion", "probe_score": 0.8918, "luna_label": 1, "luna_reason": "Existing monitoring data supports concrete findings about barriers faced by women and girls."}, {"key": "reliefweb:001240:8:1:3", "start": 854, "end": 872, "surface": "Households Surveys", "probe_tag": "keep", "probe_score": 0.9411, "luna_label": 1, "luna_reason": "Household survey findings report documentation gaps by respondent sex."}, {"key": "reliefweb:001240:8:1:4", "start": 1573, "end": 1596, "surface": "security situation data", "probe_tag": "confusion", "probe_score": 0.4939, "luna_label": 1, "luna_reason": "Data are used to identify contributing factors behind worsening security conditions."}]}, {"key": "aivin-178", "text": "**Processing:** Removing irrelevant or inaccurate information, reformatting contents to\nbe interpretable by an analytic software, and otherwise validating the data collection.\n\n\n\n\n\n\n|Developing<br>Storage<br>Guidelines|To be useful, data collected from surveys and traditional data collection methods,<br>FRRM, and “Child Friendly Spaces” and from other MHPSS programmes/<br>interventions needs to be stored in a safe way for responsible data use and<br>reuse and to maintain confidentiality of personal/identifying information.<br>However, the RD4C team was not able to verify how data collected on MHPSS<br>was stored by UNICEF, UNHCR, partners and the Government of Uganda. Little<br>knowledge is available on whether there were backup systems, deletion<br>practices, or reviews of storage procedures.|\n|---|---|\n|**Building Robust**<br>**Security**|The RD4C team was not able to verify whether there were** policies or**<br>**procedures  in place to prevent unauthorized access, data breaches, data loss,**<br>**and data misuse.** Interviews with key stakeholders did not reveal any specialized<br>training on data handling and data security.|\n|**Establishing**<br>**Internal Access**<br>**& Security**<br>**Protocols**|The RD4C team was not able to verify whether there were**particular restrictions**<br>**regarding data access**—including tiered access to raw data, password protocols,<br>secure server rooms, change history and audit trails, and internal processing<br>safeguards.|\n|**Categorizing**<", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000499:29:0:0", "start": 582, "end": 605, "surface": "data collected on MHPSS", "probe_tag": "confusion", "probe_score": 0.1252, "luna_label": 0, "luna_reason": "Data storage could not be verified; no analyzed finding or substantive use is shown."}]}, {"key": "aivin-179", "text": "|GOAL 2: Ensure that alternatives to detention are available in law and implemented in practice|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|2013 detention baseline – UNHCR Global Strategy<br>– Beyond Detention 2014-2019|2013 detention baseline – UNHCR Global Strategy<br>– Beyond Detention 2014-2019|Canada|Hungary|Indonesia|Israel|Lithuania|Malaysia|Malta|Mexico|Thailand|United Kingdom|United States|Zambia|\n|**SUB-GOAL 1:** \u0007Legal and policy frameworks include alternative(s) to immigration detention.|**SUB-GOAL 1:** \u0007Legal and policy frameworks include alternative(s) to immigration detention.|**SUB-GOAL 1:** \u0007Legal and policy frameworks include alternative(s) to immigration detention.|**SUB-GOAL 1:** \u0007Legal and policy frameworks include alternative(s) to immigration detention.|**SUB-GOAL 1:** \u0007Legal and policy frameworks include alternative(s) to immigration detention.|**SUB-GOAL 1:** \u0007Legal and policy frameworks include alternative(s) to immigration detention.|**SUB-GOAL 1:** \u0007Legal and policy frameworks include alternative(s) to immigration detention.|**SUB-GOAL 1:** \u0007Legal and policy frameworks include alternative(s) to", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000853:37:0:0", "start": 226, "end": 249, "surface": "2013 detention baseline", "probe_tag": "confusion", "probe_score": 0.848, "luna_label": 0, "luna_reason": "Standalone table header naming a baseline column, not an independently used data source."}]}, {"key": "aivin-180", "text": "Darwanaji and Teferi Ber) received theoretically 37% and 12% of all returnees during\nthe period under consideration, but in real terms they received only 27% and 5%.\n\nConversely, Hargeisa and Gabiley received \"officially\" 41% and 9% of the returnees\nwhile \"actually\" the percentages were 55% and 13%. We should however still bear in\nmind that the greatest impact of all was during the spontaneous or self-repatriation\nphase. These differences were not caused - as commonly assumed during the period\nunder consideration (1997-99) - by an \"urbanisation\" of refugees in the camps, i.e.\nrefugees of rural or pastoral origin who got used to easy access to social services in\nthe camps and perceive greater job opportunities in urban areas and as a result decide\nnot to repatriate to their ancestral areas in the bush.\n\nTo be sure, we cannot exclude that this phenomenon played some role in this period\nand maybe a greater role subsequently in other camps, such as the Aware camps <sup>21</sup> [^21: Refugees in the Aware camps (Camaboker, Rabasso and Daror) mainly originate from Burao and the\nrural areas between Burao and Hargeisa, such as Salahley and Odweyne However there was also a\nsubstantial minority from Hargeisa. Repatriation from these camps started only after the period under\nconsideration.] .\nBut the view commonly held by many members of the international community and\nmany \"Hargeisawis\" (Hargeisa dwellers) that this people were illegitimately returning\nto Hargeisa instead of the countryside was not supported by evidence.\n\nFirst we can recall how a 1994 survey conducted in the camps by a Somali\nanthropologist graduated from the LSE concluded that \"many urban poor remain in\nHartasheikh…\" <sup>22</sup> [^22: \"Going Back Home\", _op. cit._, see above for the full quote.] . Second, an unpublished \"Social Assessment of Somali Returnees\nin Awadal and Waqooyi Galbeed Regions of NW Somalia\" conducted in 1998 on", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000627:24:0:0", "start": 1565, "end": 1599, "surface": "1994 survey conducted in the camps", "probe_tag": "keep", "probe_score": 0.9423, "luna_label": 1, "luna_reason": "Past survey provides a concrete finding about urban poor in Hartasheikh."}, {"key": "reliefweb:000627:24:0:1", "start": 1814, "end": 1851, "surface": "Social Assessment of Somali Returnees", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Named 1998 assessment cited as evidence about Somali returnees."}]}, {"key": "aivin-181", "text": "DTM Mali – Avril 2021\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Régions-Cercles<br>-Communes|Enfant ( < 18 ans )|Col3|Col4|Col5|Adulte (18 - 59 ans )|Col7|Col8|Personne âgée ( > 59 ans )|Col10|Col11|Total<br>Femme|Total<br>Homme|Total<br>Individus|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|**Régions-Cercles**<br>**-Communes**<br>|**Ménages**|**F **|**M **|**Total**<br>**Enfant**|**F **|**M **|**Total**<br>**Adulte**|**F **|**M **|**Total**<br>**Personne**<br>**âgée**|**Total**<br>**Personne**<br>**âgée**|**Total**<br>**Personne**<br>**âgée**|**Total**<br>**Personne**<br>**âgée**|\n|**TOMBOUCTOU**|**2250**|**3485**|**3473", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001352:50:0:0", "start": 0, "end": 21, "surface": "DTM Mali – Avril 2021", "probe_tag": "confusion", "probe_score": 0.2262, "luna_label": 0, "luna_reason": "Standalone table title introducing tabulated DTM figures"}]}, {"key": "aivin-182", "text": " of Refugees.\n\nThe IASC typology can be considered a work in progress. There are other effects of\nclimate change not explicitly dealt with, such as increases in certain diseases and\nepidemics. Some of these effects are related to the ―natural‖ disasters while others can\nperhaps be considered either sudden-onset or slow-onset disasters in themselves. <sup>13</sup>\n\nThere are many other complex links that also need consideration: disasters and\ndegradation can trigger displacement and conflicts, and conflicts and displacement, in\nturn, often cause further environmental degradation.\n\nWe could also add another category to the IASC typology, namely displacement linked to\nmeasures to mitigate or adapt to climate change. For example, biofuel projects and forest\nconservation could lead to displacement if not carried out with full respect for the rights\nof indigenous and local people. <sup>14</sup> [^14: See for example _Resisting Displacement by Combatants and Developers: Humanitarian_\n_Zones in North-West Colombia,_ Geneva: Internal Displacement Monitoring Centre, 2007.]\n\n9 Gleditsch, N P, 2003. Environmental Conflict: Neomalthusians vs. Cornucopians, in _Security and the_\n_Environment in the Mediterranean: Conceptualising Security and Environmental Conflicts_, Berlin:\nSpringer.\n10 Homer-Dixon, T, 1994. Environmental scarcities and violent conflict: evidence from cases. International\nSecurity 19(1): 5-40.\n11 Gleditsch, N P, 1998. Armed Conflict and the Environment: A Critique of the Literature. Journal of\nPeace Research 35(3): 381–400.\n12 German Advisory Council on Global Change, 2007. _Climate Change as a Security Risk_, available at:\n<u>[http://www.wbgu.de/wbgu_jg2007_engl.html](http://www.wbgu.de/wbgu_jg2007_engl.html)</u>\n<u>[13 See for example the Emergency Events Database categories at www.em-dat.be]", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000711:4:1:0", "start": 1775, "end": 1800, "surface": "Emergency Events Database", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Named database cited as source for disaster-category classification."}]}, {"key": "aivin-183", "text": " persecution were the mostoften reported threats towards women, albeit in a relatively low proportion (2%), whereas for\nchildren the perception of the threat of violence in the community was reported as a risk for\nboys (2%) and girls (3%), alike. Gaps in terms of access to extracurricular activities to children\n\n\n1 UNHCR - Operational data portal, Ukraine refugee situation. Available <u>[online.](https://data.unhcr.org/en/situations/ukraine)</u>\n2 UNHCR - Operational data portal, Ukraine refugee situation. Available <u>[online.](https://data.unhcr.org/en/country/mda)</u>\n3 REACH Area Monitor. Available upon request.", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001671:3:2:0", "start": 317, "end": 348, "surface": "UNHCR - Operational data portal", "probe_tag": "confusion", "probe_score": 0.8981, "luna_label": 1, "luna_reason": "Source line identifies UNHCR portal underlying the reported refugee-risk findings."}, {"key": "reliefweb:001671:3:2:1", "start": 580, "end": 598, "surface": "REACH Area Monitor", "probe_tag": "keep", "probe_score": 0.9065, "luna_label": 1, "luna_reason": "Named monitoring report cited as the source for reported protection-risk findings."}]}, {"key": "aivin-184", "text": "-from-)\n<u>south-sudan-1431344971. MSF, “MSF calls on warring parties to respect medical facilities in South Sudan as the</u>\n[humanitarian organization is forced to evacuate staff again”, 9 May 2015, www.msf.org/article/south-sudan-msf-calls-](http://www.msf.org/article/south-sudan-msf-calls-)\n<u>warring-parties-respect-medical-facilities-south-sudan-humanitarian.</u>\n51 UN News Centre _,_ “South Sudan: heavy fighting and reported ‘atrocities’ in northeast force UN to evacuate staff”, 11 May\n[2015, www.un.org/apps/news/story.asp?NewsID=50818#.VZ6VSRawRUY.](http://www.un.org/apps/news/story.asp?NewsID=50818#.VZ6VSRawRUY.)\n52 OCHA, _South Sudan Humanitarian Bulletin: Biweekly Update 30 June 2015_ [, http://reliefweb.int/report/south-](http://reliefweb.int/report/south-)\n<u>sudan/south-sudan-humanitarian-bulletin-biweekly-update-30-june-2015.</u>\n53 South Sudan NGO Forum, _Access survey summary findings_, 20 June 2015. The survey took place between 5 and 13 June.\n81 NGOs (61 INGOs and 20 NNGOs) responded. It did not include UN agencies funds and programmes.\n54 The May and June 2015 Access Snapshots had not been finalized at the time of writing this report.\n\n\n17", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000585:23:2:0", "start": 1079, "end": 1113, "surface": "May and June 2015 Access Snapshots", "probe_tag": "confusion", "probe_score": 0.8839, "luna_label": 0, "luna_reason": "States snapshots were unfinished, without using findings or data."}]}, {"key": "aivin-185", "text": "|-|101,760|97,012|-|-|-|3,790|-|-|202,562|\n|Cayman Islands|36|-|36|13|-|-|-|-|-|52|101|\n|<br>Central African Rep.|7,175|-|7,175|311|46,523|669,906|90,672|-|-|-|814,587|\n|<br>Chad|442,672|-|442,672|3,759|308|170,278|-|-|122,359|-|739,376|\n|Chile|2,053|-|2,053|8,545|-|-|-|-|2,073|452,712|465,383|\n|China, Hong Kong SAR|130|-|130|-|-|-|-|-|-|-|130|\n\n\n72 UNHCR > **GLOBAL TRENDS 2019**", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001657:71:5:0", "start": 362, "end": 380, "surface": "GLOBAL TRENDS 2019", "probe_tag": "drop", "probe_score": 0.023, "luna_label": 0, "luna_reason": "Standalone report/table heading, not a cited or analyzed data resource."}]}, {"key": "aivin-186", "text": "Venezuelans in Chile, Colombia, Ecuador and\n\nPeru – A Development Opportunity\n\n\n8\n\n\n**CPP** Carnet de Permiso Temporal de Permanencia (Temporary Stay Permit)\n\n\n**DTM** Displacement Tracking Matrix\n\n\n**ENAHO** Encuesta Nacional de Hogares (National Household Survey on Living Conditions and Poverty)\n\n\n**ENPOVE** Encuesta Dirigida a la Población Venezolana que reside en el País (Survey Targeted at the Venezuelan Population Residing in Peru)\n\n\n**EPEC** Encuesta a Personas en Movilidad Humana y en Comunidades Receptoras en Ecuador (Human Mobility and Host Communities Survey)\n\n\n**EPTV** Estatuto Temporal de Protección para Migrantes Venezolanos (Temporary Protection Statute for Venezuelan Migrants)\n\n\n**FAO** Food and Agricultural Organization\n\n\n**GDP** Gross Domestic Product\n\n\n**GEIH** Gran Encuesta Integrada de Hogares (Great Integrated Household Survey)\n\n\n**HFPS** High-Frequency Phone Survey(s)\n\n\n**ILO** International Labor Organization\n\n\n**IMF** International Monetary Fund\n\n\n**INEI** Instituto Nacional de Estadística e Informática (National Institute of Statistics and Information)\n\n\n**IPE** Identificador Provisorio Escolar\n\n\n**IOM** International Organization for Migration\n\n\n**LAC** Latin American and the Caribbean\n\n\n**PEP** Permiso Especial de Permanencia (Special Permit of Permanence)\n\n\n**PPT** Permiso por Protección Temporal (Temporary Protection Status)\n\n\n**R4V** Interagency Coordination Platform for Refugees and Migrants from Venezuela\n\n\n**SERMIG** Servicio Nacional de Migraciones de Chile\n\n\n**UN** United Nations\n\n\n**UNDP** United Nations Development Programme\n\n\n*", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001190:7:0:3", "start": 533, "end": 575, "surface": "Human Mobility and Host Communities Survey", "probe_tag": "confusion", "probe_score": 0.309, "luna_label": 0, "luna_reason": "Glossary definition names a survey without showing data use or findings."}, {"key": "reliefweb:001190:7:0:6", "start": 873, "end": 900, "surface": "High-Frequency Phone Survey", "probe_tag": "drop", "probe_score": 0.0186, "luna_label": 0, "luna_reason": "Glossary expansion only; no survey data are cited or used."}]}, {"key": "aivin-187", "text": "br>2<br>138,586<br>622<br>13,352<br>1<br>211<br>38,323<br>24,107<br>5,488<br>35<br>10<br>1,240<br>802<br>720,307<br>2<br>2,132<br>40|\n\n\n\n70 UNHCR > **GLOBAL TRENDS 2018**", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000649:68:3:0", "start": 150, "end": 168, "surface": "GLOBAL TRENDS 2018", "probe_tag": "drop", "probe_score": 0.0494, "luna_label": 0, "luna_reason": "Standalone report title without shown data use"}]}, {"key": "aivin-188", "text": " <sup>18</sup> compared to Brazilian cohort and the relative\nprobability of registered Venezuelans to be _Bolsa_ _Familia_ (PBF) <sup>19</sup> beneficiaries compared to their\nBrazilian counterpart.\n\n\n\n_Rc_ = <sup>_<u>V enezuelansCadastroUnico/V enezuelansP opulation</u>_</sup>\n\n_BraziliansCadastroUnico/BraziliansP opulation_\n\n\n_Rp_ = <sup>_<u>V enezuelansP BF /V enezuelansCadastroUnico</u>_</sup>\n\n_BraziliansP BF /BraziliansCadastroUnico_\n\n\n\n(3)\n\n\n(4)\n\n\n\nThis relative probability index has an easy interpretation. A _Ri_ of 0.5 means that Venezuelans are\nhalf as likely as Brazilian to be found in sector _i_ . Abramitzky et al. (2020), Carneiro et al. (2020)\nand Fryer and Levitt (2004) point out that the relative probability index is sensitive to outliers and\nadvocates the use of F-Index, which is a monotonic transformation of the relative probability index.\nThe _F_ _−_ _Index_ is measured by the following expression:\n\n_<u>Ri</u>_\n_Fi_ = 100 _⇤_ 1 + _Ri_ (5)\n\n\nwhere _i_ can be _e_, _f_, _c_ and _p_ . This paper reports the F-Index in the main body of the paper. The\nrelative probability index is reported in the appendix. The _F_ _−_ _Index_ runs from 0 to 100, with\nhigher number signalling more integration. A F-index of 0 means that Venezuelans are not present\nat all", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000075:12:1:0", "start": 217, "end": 242, "surface": "V enezuelansCadastroUnico", "probe_tag": "drop", "probe_score": 0.0347, "luna_label": 0, "luna_reason": "Formula variable fragment names no eligible data resource."}]}, {"key": "aivin-189", "text": "Improving Data Collection and Analysis\n\n#### IMPROVING DATA COLLECTION AND ANALYSIS\n\n\nData collection and analysis is the backbone of results-based SGBV programming. It is critical to\nthe effectiveness of targeted service delivery, advocacy, policy development, and accountability\nand monitoring. Survivors are often afraid of being stigmatized, thus, actual prevalence figures\nare hard to identify. Efforts in **Syria** are currently underway to generate service provision data\nto better analyse SGBV trends and patterns through needs assessments, feedback from health\nand psycho-social service providers, and reports from mobile teams. UNHCR supported the\ngovernment of **Turkey** in conducting a survey which identified early marriages, polygamous\nmarriages and domestic violence as leading problems affecting Syrian refugees. Data and\nfeedback collected from Participatory Assessments in **Yemen** inform UNHCR’s SGBV programmes.\nUNHCR authored a report, entitled “Women Alone”, on Syrian refugee women who are heads of\nhousehold in **Jordan**, **Lebanon**, and **Egypt** focusing on the issues of housing, food, health, work\nand financial security, changed roles, isolation, and sexual and gender-based violence.\n\n\nGBV Information Management System\n\n\nUNHCR has supported the rollout of the Gender-Based Violence Information Management\nSystem (GBVIMS) to ensure the safe, ethical, and confidential collection, management and\nsharing of SGBV data in various operations. GBVIMS is a data management system that enables\nservice providers working with SGBV survivors to collect, store, analyze, and share data related\nto reported incidents of SGBV in a safe and confidential manner. GBVIMS is designed to allow\nfor the aggregation of non-identifiable (anonymized) data on reported SGBV cases to inform\nprevention and response programming, policy development and advocacy, resource mobilization,\nmonitoring and evaluation. It also helps to identify gaps in follow up and service delivery. As part\nof UNHCR’s", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000871:37:0:0", "start": 456, "end": 478, "surface": "service provision data", "probe_tag": "confusion", "probe_score": 0.4032, "luna_label": 0, "luna_reason": "The sentence states efforts are underway to generate this data."}, {"key": "reliefweb:000871:37:0:2", "start": 1440, "end": 1449, "surface": "SGBV data", "probe_tag": "drop", "probe_score": 0.0355, "luna_label": 0, "luna_reason": "Data collection is described, not use of an existing dataset."}]}, {"key": "aivin-190", "text": "**ARRA** Agency for Refugee and Returnee\n\nAffairs\n**CRRF** Comprehensive Refugee Response\n\nFramework\n**CTE** College of Teacher Education\n**DEED** Digital Education Enrolment Data\n**DFID** Department for International\n\nDevelopment of UK Government\n**ECCE** Early Childhood Care and Education\n**ECW** Education Cannot Wait\n**<mark>ESDP</mark>** <mark>Education Sector Development Plan</mark>\n**ETWG** Education Technical Working Group\n**GCR** Global Compact on Refugees.\n**GEQIP-E** General Education Quality\n\nImprovement Programme for Equity\n**GER** Gross Enrolment Rate\n\n**GPE** Global Ppartnership for Education\n**GPI** Gender Parity Index\n**GRF** Global Refugee Forum\n**ICT** Information Communication\nTechnology\n**<mark>IDP</mark>** <mark>Internally Displaced Person</mark>\n**IGAD** Intergovernmental Authority on\n\nDevelopment\n\n\n\n**KPI** Key Performance Indicator\n**MoE** Ministry of Education.\n**MoS** HE Ministry of Science and Higher\n\nEducation.\n**MoU** Memorandum of Understanding\n\n\n**NGO** Non- Governmental Organization\n**OOS** Out-of-School\n**REB** Regional Education Bureau\n**SDG** Sustainable Development Goal.\n**TVET** Technical and Vocational Education\n\nand Training\n**UASC** Unaccompanied and Separated\n\nChildren\n**UNESCO** United Nations Educational,\n\nScience and Cultural Organization\n**UNICEF** United Nations Children’s Fund\n**UNSDCF** United Nations Sustainable\n\nDevelopment Cooperation\nFramework\n**USAID** United States Agency for\nInternational Development\n**WASH** Water, Sanitation and Hygiene\n\n\n2", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000503:1:0:0", "start": 147, "end": 179, "surface": "Digital Education Enrolment Data", "probe_tag": "drop", "probe_score": 0.0129, "luna_label": 0, "luna_reason": "Glossary definition only; no data use, analysis, finding, or cited figures."}]}, {"key": "aivin-191", "text": " colombianos que residen en Ecuador, la\n\nRepública Bolivariana de Venezuela, Costa Rica y Panamá, a quienes se\nconsidera que están en una situación similar a la de los refugiados.\n\n\n**(11)** Alrededor de 13.300 congoleños que llegaron a Uganda fueron\n\nconsiderados refugiados prima facie, mientras que 13.700 presentaron una\nsolicitud individual de asilo. Los que llegaron a Burundi y Kenia pasaron por\nla determinación individual de la condición de refugiado.\n\n\n\n<mark>TABLA 1</mark> **Poblaciones de refugiados por regiones de ACNUR \u0003|** 2014\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Regiones de ACNUR<br>- África Central y Grandes Lagos<br>- Este y Cuerno de África<br>- África del Sur<br>- África Occidental|Principio-2014|Col3|Col4|Final -2014|Col6|Col7|Variación (total)|Col9|\n|---|---|---|---|---|---|---|---|---|\n|Regiones de ACNUR<br>- África Central y Grandes Lagos<br>- Este y Cuerno de África<br>- África del Sur<br>- África Occidental|Refugiados<br> 508.600<br> 2.003.400<br> 134.500<br> 242.300|Personas en<br>situación<br>similar a<br>la de los<br>refugiados<br> 7.400<br> 35", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000206:9:3:0", "start": 487, "end": 534, "surface": "Poblaciones de refugiados por regiones de ACNUR", "probe_tag": "drop", "probe_score": 0.0419, "luna_label": 0, "luna_reason": "Table title introducing refugee population data, not an independently usable data mention."}]}, {"key": "aivin-192", "text": "##### **4. Objectives of the Gender Analysis**\n\nThe specific objectives of the gender analysis were to:\n\n1. Analyze refugee population demographics.\n2. Assess refugee community practices and cultural patterns for household and care arrangements.\n3. Identify the concerns and needs of women, girls, boys and men in relation to shelter.\n4. Establish what needs to be considered in the provision of Shelter services.\n\n##### **5. Methodology**\n\n\nAlthough both quantitative and qualitative methods were used to answer the gender analysis questions,\nthe study was designed to be more qualitative <sup>1</sup> than quantitative to allow for respondents (from the openended nature of the qualitative inquiry) to describe their shelter related needs, experiences, challenges,\nbehaviors, cultural practices, etc., for better understanding of their gender specific needs. Data was\ncollected using different methods including desk review (extracting both quantitative and qualitative\ninformation); key informant interviews (KIIs) during the regular monthly shelter working group meetings\nand focus group discussions (FGDs) were conducted at Zaatari and Azraq camps on the 23rd and 26th\nof October 2016 in coordination with the M&E team in NRC.\n\n##### **5.1 Data Analysis**\n\n\nQualitative data from KIIs and FGDs was categorized at gender analysis objective level with analysis of\ntrends in each objective - by grouping similar responses on each gender dimension.\nQuantitative methods were used to analyze the data with tabulations and frequencies to supplement the\nqualitative data. Triangulation of these methods was used to confirm validity of data and reliability was\nensured through use of standard data collection tools.\n\n##### **5.2 Constraints and Challenges of the Gender Analysis**\n\nThe analysis was conducted taking in consideration the impact of the changing context of the ongoing\nSyrian Crisis. Under these circumstances, the lack of", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000927:8:0:0", "start": 1263, "end": 1279, "surface": "Qualitative data", "probe_tag": "confusion", "probe_score": 0.092, "luna_label": 1, "luna_reason": "Existing qualitative interview and focus-group data were analyzed for gender-related trends."}, {"key": "reliefweb:000927:8:0:1", "start": 1552, "end": 1568, "surface": "qualitative data", "probe_tag": "drop", "probe_score": 0.0231, "luna_label": 1, "luna_reason": "Qualitative data is explicitly analyzed using tabulations and frequencies."}]}, {"key": "aivin-193", "text": "................................................................... 32**\n\n\n**Population Data Analysis** Mid-Year Review Regional Bureau for Southern Africa | June 2024", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001265:2:7:0", "start": 77, "end": 92, "surface": "Population Data", "probe_tag": "drop", "probe_score": 0.0055, "luna_label": 0, "luna_reason": "Generic title phrase with no cited data use, finding, or source."}]}, {"key": "aivin-194", "text": "|ANNEXE: Liste des incidents|Col2|Col3|Col4|Col5|\n|---|---|---|---|---|\n|N <br>Localités<br>Descripton de l’incident<br>Personnes affectées par l’incident<br>Actons entreprises et/ou<br>préconisées|N <br>Localités<br>Descripton de l’incident<br>Personnes affectées par l’incident<br>Actons entreprises et/ou<br>préconisées|N <br>Localités<br>Descripton de l’incident<br>Personnes affectées par l’incident<br>Actons entreprises et/ou<br>préconisées|N <br>Localités<br>Descripton de l’incident<br>Personnes affectées par l’incident<br>Actons entreprises et/ou<br>préconisées|N <br>Localités<br>Descripton de l’incident<br>Personnes affectées par l’incident<br>Actons entreprises et/ou<br>préconisées|\n|Région du Nord<br>|Région du Nord<br>|Région du Nord<br>|Région du Nord<br>|Région du Nord<br>|\n|1. <br>|Région du<br>Nord/Province<br>du<br>Yatenga/Tanga<br>ye|Enlèvement<br>d’un<br>pay", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000792:12:0:0", "start": 9, "end": 28, "surface": "Liste des incidents", "probe_tag": "drop", "probe_score": 0.0385, "luna_label": 0, "luna_reason": "Standalone table title introducing an incident list"}]}, {"key": "aivin-195", "text": "including routine immunization for women and children, as well as polio vaccinations and\nVitamin A for children under five. Measures with respect to education concern primary and\nsecondary levels, literacy classes, including educational material for adult men and women,\nconstruction/rehabilitation and furnishing of schools, and the recruitment and training\nof teachers. Vocational training and business development training services will include\nlinkages to micro-credit schemes, essential employment toolkits upon graduation, a survey to\ncatalogue skills related to employment creation under relevant National Priority Programmes\n(NPP) and bilateral programmes, with training and educational programmes that link skills\nwith jobs. Other interventions concern the provision of access to clean drinking water and\nsanitation. During Phase I, MoRR capacity will be enhanced to enable it to effectively provide\nsupport, and oversight. Provincial MoRR offices will receive infrastructure and capacity\ndevelopment support, and senior staff will be trained in various management fields.\n\n\nProjects to empower young people (roughly 25% of returnees) will be undertaken to equip\nthem with effective life skills, provide training and study opportunities, and encourage their\nactive involvement in the social, cultural, and economic life of their communities.\n\n\nSince monitoring and evaluation (M&E) are an integral part of the Solutions Strategy; MoRR\nand UNHCR have initiated discussions with independent local research institutes as potential\npartners for this task. Such partners will have no role in site selection or implementation. Before\nan intervention is undertaken at a site, baseline data will be collected, including information\nabout the community, access to services, livelihoods, ethnic and geographic data, and will\ninclude more qualitative information through random interviews with households. These\ndata will be used to develop qualitative and quantitative indicators by which progress towards\nachieving parity among returning refugees and their host community can be measured.\n\n\nThe M&E partners will not only participate in a mid-term and final evaluation, but also\nundertake regular monitoring, analysis, making recommendations on progress against agreed\nindicators, identifying constraints and good practice, and documenting lessons", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001138:13:0:0", "start": 1678, "end": 1691, "surface": "baseline data", "probe_tag": "drop", "probe_score": 0.0278, "luna_label": 0, "luna_reason": "Baseline data will be collected before intervention, so it is planned production."}, {"key": "reliefweb:001138:13:0:1", "start": 1787, "end": 1813, "surface": "ethnic and geographic data", "probe_tag": "drop", "probe_score": 0.044, "luna_label": 0, "luna_reason": "Baseline data will be collected before intervention, making this planned data production."}]}, {"key": "aivin-196", "text": "ANNEXES\n\n\n**Annex tables 3 through 29 can be downloaded from the UNHCR website at:**\nhttp://www.unhcr.org/globaltrends/2016-GlobalTrends-annex-tables.zip\n\n\n58 UNHCR > **GLOBAL TRENDS 2016**", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000712:57:0:0", "start": 169, "end": 187, "surface": "GLOBAL TRENDS 2016", "probe_tag": "drop", "probe_score": 0.0195, "luna_label": 0, "luna_reason": "Report title is merely displayed, with no cited finding or demonstrated data use."}]}, {"key": "aivin-197", "text": " subsidies. Wealthier deciles included the largest share\nof total subsidy beneficiaries (blue bars) and the largest share of all households within each\ndecile that are beneficiaries (red bars). The differences between the top three deciles and the\nremaining ones are remarkable in all countries, although less pronounced in Nigeria. As noted\nabove, in Niger, the poor (i.e., the first four deciles) do not benefit from the subsidy at all;\nUganda shows a similar pattern, and in Mali only the fourth decile shows a very small amount\nof beneficiaries. We conclude that in the five African countries analyzed, and using the\nhousehold data, tariff data, and the methodology described, consumption subsidies are\nregressive (albeit relatively less so in Nigeria).\n\n\n12", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002124:13:1:0", "start": 621, "end": 635, "surface": "household data", "probe_tag": "confusion", "probe_score": 0.5484, "luna_label": 1, "luna_reason": "Household data are explicitly used to conclude consumption subsidies are regressive."}]}, {"key": "aivin-198", "text": "largest 4 countries in our sample. <sup>13</sup> Regression results for the reduced sample are provided in Table\n\n\n8. For brevity, only some of the specifications are shown. Table 8 clearly shows that the qualitative\n\n\nnature of the relationship between corruption (overall corruption, incidence and depth of petty\n\n\ncorruption) and _Population_ remains intact.\n\n\n3.6 _Additional controls_\n\n\nStarting with our final baseline specification (column 4, Table 4), we experimented by adding\n\n\nsome more controls for extra robustness. The controls include dummy variables for the region\n\n\n(region fixed effects) <sup>14</sup> ; religious affiliation of countries captured by the proportion of population\n\n\nthat is Catholic, Muslim and Protestant (omitted category is all other religions) <sup>15</sup> ; a measure of the\n\n\nindependence of the judiciary taken from Freedom House’s Economic Freedom of the World\n\n\ndatabase (Judicial independence); and a measure of the functioning of the government based on\n\n\nwhether elected officials determine the policies of the government, steps taken by the government\n\n\nto combat corruption and whether the media and public can freely express their views regarding\n\n\ncorruption, and if the government is answerable to the electorate between elections and if it works\n\n\nwith transparency, taken from Freedom House’s Economic Freedom of the World database\n\n\n(Functioning of the government). <sup>16</sup> There is no qualitative change in our main results due to these\n\n\ncontrols (Table A3 in the Appendix provides the full results).\n\n\n13 The largest 4 counties excluded have a population of more than 190 million and include Brazil, China, India, and\nIndonesia. We experimented with alternative samples such as excluding the largest 5 percent and the smallest 5 percent\nof the countries, but this did not change the qualitative nature of the results discussed in this section.\n14 The regions are Sub-Saharan Africa, East Asia and the Pacific", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000204:26:0:0", "start": 874, "end": 914, "surface": "Economic Freedom of the World\n\n\ndatabase", "probe_tag": "keep", "probe_score": 0.9674, "luna_label": 1, "luna_reason": "Database measure is used as a robustness control in regression analysis."}]}, {"key": "aivin-199", "text": " acquisition and or exercise market power, e.g., by taxing or requiring\n\nelectronic auction if use right concentration exceeds a certain level, may be gainfully explored.\n\n\n_Informality:_ Most studies assume that the benefits from formalization accrue to those directly affected and\n\nassess the impact of informal land rights by comparing parcels with and without rights using discontinuities\n\nin space (Ali _et al._ 2014; Hawley _et al._ 2018) or time (Beg 2022) for identification although rights may\n\nreturn to informality (Galiani and Schargrodsky 2016; Gutierrez and Molina 2020) if the cost of subsequent\n\nregistration is high (Ali _et al._ 2021). Informal rights can substitute for formal ones to some extent (Lanjouw\n\nand Levy 2002). They offer short-term advantages (Niu _et al._ 2021) but impose costs in the short (Tellman\n\n_et al._ 2021) and the medium term (Henderson _et al._ 2021; Navarro and Turnbull 2014). Yet, if it yields rents\n\nfor intermediaries (Krishna _et al._ 2020), informality may be difficult to eliminate politically. <sup>1</sup>\n\n\nWe depart from a view of informality as a purely private issue by using fields as a basic unit of agricultural\n\nland use. This allows estimating external effects of three types of informality that we expect to affect land\n\nprices via different channels. First, parcels not mapped in the cadaster or registered in the registry of rights\n\n\n1 A detailed model and discussion of costs and benefits of informality in the labor market for Mexico (Bobba _et al._ 2022); biased response to\ninfrastructure improvement in Ethiopia (Perra _et al._ 2024), and externalities due to tax reform in Pakistan (Waseem 2018).\n\n\n3", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001374:4:1:1", "start": 1380, "end": 1398, "surface": "registry of rights", "probe_tag": "confusion", "probe_score": 0.4269, "luna_label": 1, "luna_reason": "Registry status defines parcels used to estimate land-price effects of informality."}]}, {"key": "aivin-200", "text": "), slightly below\n\n\nthe minimum wage for a full-time worker in most sectors. Wage work was eight times more common\n\n\nthan self-employment. Most work was relatively short-term, with median and mean tenures of 2\n\n\nand 7 months respectively.\n\n\nOf the sample, 97% searched for work in the week before the baseline. In that week, they\n\n\nspent on average 17 hours and 242 South African rand (40 USD PPP) searching. The relatively\n\n\nhigh search costs suggest that welfare gains for workseekers are possible from improved search\n\n\ntargeting. Workseekers submitted on average 10 applications in the preceding month and received\n\n\n1.2 offers, though the medians for both measures are zero. The job search and application process is\n\n\nsomewhat formal: 38% of the candidates employed at endline reported that they submitted written\n\n\napplications for their current job and 47% reported that they had a formal interview.\n\n\n**2.5** **Assessments**\n\n\nWe conduct six assessments with workseekers: communication, concept formation (similar to a\n\n\nRaven’s test), focus, grit, numeracy, and planning. Firms have demonstrated interest in the results\n\n\nof these assessments, though they obviously also use other information in hiring decisions. Client\n\n\nfirms have paid Harambee to screen roughly 160,000 prospective workers using these assessments.\n\n\nAppendix A describes each assessment in detail, their psychometric properties, and how some\n\n\nHarambee client firms use them in hiring.\n\n\nEach assessment session is led by two or three industrial psychologists, who manage a team of\n\n\n12See Garlick et al. (2019) for an experimental validation of labor market data from phone surveys in this setting.\n\n\n10", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001763:11:1:0", "start": 1627, "end": 1644, "surface": "labor market data", "probe_tag": "confusion", "probe_score": 0.8833, "luna_label": 1, "luna_reason": "Cited phone-survey data are used in an experimental validation."}]}, {"key": "aivin-201", "text": " the statistical determination of a break could benefit from knowledge of the economic\nhistory of a country. However, except for well-known events, an in-depth understanding of the timing of\nactual events, shocks, or crises is beyond this study's scope (given the number of countries). In the\nestimation, we also consider a dummy when the time patterns of the right- and left-side variables begin to\ndiverge over their past behavior, combining breakpoint unit root tests, visual inspection, and, if available,\nrelevant economic information <sup>13</sup> to determine possible timings.\n\n\n13 Such as country reports from the World Bank, IMF, Economist Intelligence Unit (EIU), or Wikepedia.\n\n\n11", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000629:12:2:0", "start": 598, "end": 613, "surface": "country reports", "probe_tag": "confusion", "probe_score": 0.6009, "luna_label": 1, "luna_reason": "Reports from named institutions inform breakpoint timing determination."}]}, {"key": "aivin-202", "text": " not\nsystematic, we would risk bias in the analysis if we included only the available data.\n\n**We use data for the 2003 to 2017 period, from Eurostat, the Organisation for Economic**\n**Co-operation and Development (OECD), and** **_fDi Markets_** **(Financial Times).** Given our\nempirical goal, and considering data availability constraints, we have collected the largest possible\nset of data at the NUTS-3 level from three main sources to cover the longest possible time period,\nnamely from 2003 to 2017. On economic growth and industrial structure, we use Eurostat data from\nthe _Regio_ database on GDP, population, employment, land area, and sectoral Gross Value Added\n(GVA) – for agriculture, industry (mining; electricity; manufacturing), construction, market\nservices, <sup>13</sup> [^13: Wholesale and retail trade; transport; accommodation and food service activities; information and communication; financial and\ninsurance activities; real estate activities; professional, scientific and technical activities; administrative and support service activities.] and non-market services. <sup>14</sup> [^14: Public administration and defence; compulsory social security; education; human health and social work activities; arts,\nentertainment and recreation; other service activities; activities of household and extra-territorial organisations and bodies.] Missing values in the NUTS-3 level regional series have been\nfilled in by linearly interpolating NUTS-2 level data. On innovation, we use microdata on patents\nfiled under the Patent Co-operation Treaty (PCT) from the _REGPAT_ database provided by the OECD,\naggregated at the NUTS-3 level by priority year and inventor’s residence using the fractional count\ncriterion. On FDI, we use data on inward ‘greenfield’ FDI from the _fDi Markets_ database provided\nby the Financial Times. Of particular interest for our purposes, the _fDi Markets_ database collects\ninformation on individual investment projects in terms of year, destination region at the NUTS-3 (or\ncity) level", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000335:6:1:1", "start": 1459, "end": 1476, "surface": "NUTS-2 level data", "probe_tag": "confusion", "probe_score": 0.146, "luna_label": 1, "luna_reason": "Existing regional data are used to interpolate missing NUTS-3 values."}, {"key": "prwp:000335:6:1:4", "start": 1786, "end": 1808, "surface": "_fDi Markets_ database", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Named database provides inward greenfield FDI data used in analysis."}]}, {"key": "aivin-203", "text": " are\nincluded to enable the actual tax liabilities of taxpayers to be modelled, including non-standard reliefs\nwhere the required information is included in the survey. To examine the VAT or excise taxes, household\nbudget survey data can be used as these surveys typically provide a breakdown of total consumption\nacross a large number of consumption categories, enabling differentiation between consumption subject\nto different VAT rates and different excise tax rates.\n\n\nBefore calculating progressivity or other metrics, some adjustments (in addition to basic data cleaning)\nmay be made to the microdata to adjust for limitations associated with that data. For example, a common\nproblem with income survey data is that incomes at the top end are often underreported, and various\nmethods can be applied to adjust for this (see, e.g., Blanchet et al., 2022; Ruiz and Woloszko, 2016).\nMeanwhile, in the absence of income survey data, if consumption survey data is being used to model the\nPIT, adjustments based on savings patterns can be made to proxy better for income data. A parallel paper\nexamines such techniques (including greater use of tax return microdata) in detail. Further discussion of\nthese techniques is left to that paper.\n\n\nAdditional adjustments may also be made to specify the welfare metric and the unit of analysis. For PIT\nanalysis, where income data is available it will be the preferred starting point in calculating a welfare\n\n\n8", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001110:9:1:5", "start": 1063, "end": 1074, "surface": "income data", "probe_tag": "confusion", "probe_score": 0.748, "luna_label": 0, "luna_reason": "Generic income data is mentioned as a proxy target without an attributed finding."}]}, {"key": "aivin-204", "text": ", particularly in dryland areas (Rohde et al., 2017). Second, GDEs\nare important for the livelihoods of populations in rural areas that are typically more vulnerable. An unfettered provision of solar\npumping could lead to a deterioration of GDEs on which pastoralists rely for their cattle while also expanding agricultural land to\nareas previously used for transhumance and grazing, heightening tensions between farmers and pastoralists (Rodella, Zaveri and\nBertone, 2023). Third, beyond their importance for wildlife – including for migratory birds – GDEs play an essential role in carbon\nstorage (Mendonça et al. 2017). Countries seeking to reduce their emission by switching groundwater pumping to solar, owe to\nconsider the carbon impacts of compromising the role GDEs play as carbon sink.\n\n\nFor those reasons, the results highlight the necessity for policymakers and development partners to collaborate upstream on largescale solar energy deployment in regions with high irrigation potential, such as SSA, that will facilitate easier and cheaper access to\nmuch-needed solar pumping irrigation but require enforceable policy and regulation to protect against unintended consequences,\nsuch as the deterioration or destruction of GDEs. Our analysis also highlights the need for more data on GDEs from location to the\nservices they provide to people and the environment to monitor better their health and address situations of poor groundwater\nmanagement that could threaten their existence. The proper monitoring of GDEs can thus be an efficient strategy to contribute early\nwarning information on the mismanagement of groundwater resource and help prevent or remedy impacts.\n\n##### 7. Conclusion\n\n\nWe proposed an analytical hierarchy process to evaluate the risk posed to groundwater dependent ecosystems (GDEs) by an\nuncontrolled expansion of access to groundwater through photovoltaic pumping in sub-Saharan Africa. More specifically, we\nevaluated the risk of over exploitation of groundwater resources which could endanger GDEs, and we considered the following\ninput data: global horizontal irradiance, renewable groundwater resources", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001392:11:1:0", "start": 1286, "end": 1298, "surface": "data on GDEs", "probe_tag": "confusion", "probe_score": 0.3977, "luna_label": 0, "luna_reason": "States a need for more data, without citing existing data or an analyzed finding."}]}, {"key": "aivin-205", "text": " Endogenous variables are 𝒀𝒀𝒕𝒕 = [𝚫𝚫𝒒𝒒𝒕𝒕, 𝚫𝚫𝒚𝒚𝒕𝒕, 𝒑𝒑𝒕𝒕] <sup>′</sup>, where 𝚫𝚫𝒒𝒒𝒕𝒕, 𝚫𝚫𝒚𝒚𝒕𝒕, 𝒑𝒑𝒕𝒕 denote the loggrowth is only available on a quarterly basis, the exercise is conducted with quarterly averages of\nmonthly data. Other variables that have been used in forecasting commodity prices include\nindicators of global economic conditions, world output gap, capacity utilization, industrial\nproduction and exchange rates (Baumeister, Korobilis and Lee 2022; Dées et al. 2007; Kaufmann\net al. 2004; Tang and Hammoudeh 2002; Zamani 2004; Lalonde, Zhu and Demers 2003; Ye, Zyren\nand Shore 2006).\n\n\n\nand Shore 2006).\n\nIn the prediction step, 1-step-ahead prediction at the time origin 𝒉𝒉, 𝒀𝒀 <sup>�</sup> 𝒉𝒉(𝟏𝟏), and the associated\nforecast error, 𝒆𝒆𝒉𝒉(𝟏𝟏) are:\n\n𝟒𝟒\n\n𝒀 𝒉 𝟏\n\n\n\n𝟒𝟒\n\n𝒀 𝒉 𝟏\n\n\n\n𝟒𝟒\n\n<u>𝒀</u> <sup><u>�</u></sup> <u>𝒉𝒀</u> 𝒉(𝟏𝟏) = �𝑨𝑨𝒊𝒊𝒀𝒀𝒉𝒉+𝟏𝟏−𝒊𝒊,\n\n𝒊𝒊=𝟏𝟏\n\n\n\n𝒀 𝒉 𝟏\n\n𝒊𝒊=𝟏𝟏\n\n\n\n𝒆𝒆𝒉𝒉(𝟏𝟏) = 𝒖𝒖𝒉𝒉+𝟏𝟏.\n\n\n\nFoodstuffs include butter, cocoa beans, corn, cottonseed oil, hogs, lard, steers, sugar, and wheat. It is developed by\nthe Commodities Research Bureau.\n12 We adopt a log-level commodity price model following Kilian and Murphy (2014). As they suggested, it is not clear\nwhether commodity prices should be modeled in log-levels or log-differences. The advantage of the level specification\nis that impulse responses of log-level price models are consistent with sign-restriction", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000851:13:1:0", "start": 211, "end": 223, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.7058, "luna_label": 1, "luna_reason": "Existing monthly data are used as quarterly averages in the forecasting exercise."}]}, {"key": "aivin-206", "text": "fatigue is crucial to inform the inferential consequences of response fatigue, we assess whether the\n\nplacement of the women’s food consumption module differs by maternal and household\n\ncharacteristics.\n\nWe find significant impacts of fatigue: respondents who received the dietary diversity\n\nmodule earlier in the survey report significantly higher dietary diversity score. Delaying the arrival\n\nof the dietary diversity module by about 15 minutes leads to 8-17 percent underestimation in\n\ndietary diversity score. This is a striking result given the relatively short difference in the timing\n\nof the food consumption module between the treatment and control groups. The impact of fatigue\n\nappears to be more pronounced for food groups that are infrequently consumed, consistent with\n\nevolving evidence that these food groups are prone to other sources of measurement error. We also\n\nfind significant and intuitive heterogeneity in respondents’ vulnerability to fatigue. For example,\n\nolder women and less educated women are prone to fatigue than younger and more educated ones.\n\nMoving the module on diet diversity had a larger effect on responses provided by mothers in larger\n\nhouseholds, consistent with the idea that women in larger households might face more distractions\n\nwhen answering phone surveys or are simply more vulnerable to the effects of fatigue. Relatedly,\n\nthis systematic underreporting of dietary diversity for larger households may contribute to the\n\ninverse relationship between food demand and household size documented in developing countries\n\n(Lanjouw and Ravallion, 1995; Deaton and Paxson, 1998; Gibson, 2002; Gibson and Kim, 2007).\n\nAlthough our findings come from a specific rural women sample and phone surveys in\n\nrural Ethiopia, the implication and relevance of our findings are likely to extend to FTF surveys\n\nand other settings involving phone surveys. While the impact of fatigue in choice experiments and\n\ndiaries is well-documented (e.g., Bradley and Daly, 1994; Savage and Waldman, 2008; Silberstein\n\nand Scott, 2011; Beegle et al., 2012; Hess et al., 2012; Schündeln, 2018; Battistin et al", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000074:6:0:1", "start": 1833, "end": 1844, "surface": "FTF surveys", "probe_tag": "confusion", "probe_score": 0.2507, "luna_label": 0, "luna_reason": "Refers to a survey category without citing its data or attaching a finding"}]}, {"key": "aivin-207", "text": " the survey rounds. This data provides insight into potential\n\nchanges through which market or non-market transactions land was acquired. For women, we see a\n\nsharp increase in the probability that a parcel was purchased: this increases by 7.5pp in our pooled\n\nestimates, roughly a 35 percent increase relative to the round 3 control mean. This seems to be\n\nreplacing sharecropping as a mode of acquisition, which drops by 3.6pp in our pooled estimates\n\n(significant at 10 percent), with a particularly sharp drop in round 3. For men, we also see a\n\nsignificant increase in purchases of 7.1pp, with declines not only in sharecropping, but renting as well\n\n(the latter significant at 10 percent). Taken together, these results suggest that both men and women\n\nare taking advantage of the clarification of property rights that certification provides to move from\n\nrental contracts of various forms to land purchases.  It is also worth noting that there is a significant\n\nincrease of 10 percentage points, in round 3, in the acquisition of land through inheritance for men.\n\n\nIn Table 7, Panel A we examine how improved security translates into changes in overall\n\nlandholdings controlled by women and men. Since we do not have a plot panel, we cannot accurately\n\nmeasure sales, but the overall level of holdings reveal a significant amount of land being released.\n\nWomen reduce their number of plots in the pooled estimates by 0.068, with the largest reduction\n\nhappening in round 3. They also end up having significantly smaller plots, a reduction of about 0.14\n\nhectares using the round 3 estimates. Putting these two measures together at the owner level, we can\n\nsee a reduction in aggregate individual land holdings of about a quarter of a hectare in round 3.\n\n\n18", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002191:19:1:0", "start": 1227, "end": 1237, "surface": "plot panel", "probe_tag": "confusion", "probe_score": 0.2755, "luna_label": 0, "luna_reason": "States missing panel data without analyzing or substituting estimates."}]}, {"key": "aivin-208", "text": " but even\nin that context incorporating geospatial data led to an increase in precision roughly equivalent to\ndoubling the sample size. These results are only suggestive, however, and an important outstanding\nresearch agenda is to compare small area estimates of monetary poverty obtained using geospatial data\nto those obtained from standard census-based poverty maps directly from census data. This can confirm\nthat the encouraging results presented above apply to monetary poverty.\n\n\nA final caveat, which applies to small area estimation in general, is the challenge of measuring local\ntransient shocks. In the context of geospatial data, key indicators used to generate model predictions,\nsuch as building counts, are not necessarily affected by local economic shocks. This also poses an issue\nfor traditional census-based poverty maps, as the census predictions tend to rely heavily on indicators\nsuch as educational attainment and household size that may also fail to track local shocks. Empirical Best\nmethods partly address this issue by incorporating welfare information from the sample. Nonetheless,\nfuture research is needed to explore which geospatial indicators better reflect local economic shocks.\n\n\nOverall, the results from an evaluation exercise using data from two countries demonstrate major\nefficiency gains from combining survey data with geographically comprehensive geospatial data to\ngenerate small area estimates of non-monetary poverty. These efficiency gains came at no cost to\ncoverage in Sri Lanka and a moderate cost in coverage rates in Tanzania, and the latter can be addressed\nthrough further refinements to the methodology. The financial cost of incorporating geospatial data is\nlow, relative to the cost of achieving similar efficiency gains by surveying additional households. While\nthere is room for further methodological improvement, these techniques can currently be applied at\nmodest cost to generate more granular and informative estimates of non-monetary welfare.\n\n\n43 Chambers and Das (2017) propose a similar strategy of adjusting traditional ELL estimates to correct for biased\nestimates of mean squared error.\n44 See Belghith et al, 2020. This corresponds to the marginal R2 reported in Table 6.\n\n\n42", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002095:43:1:0", "start": 343, "end": 368, "surface": "census-based poverty maps", "probe_tag": "confusion", "probe_score": 0.8909, "luna_label": 0, "luna_reason": "Referenced only in a proposed future comparison, not an actual data use."}]}, {"key": "aivin-209", "text": " identify goods with\nlinear Engel curves given prevailing prices, under different prices this\nlinearity may fail.\n\nHowever, from a practical point of view it may be possible to identify\ngoods with linear Engel curves in one period, and then we may get\nlucky: in a subsequent period perhaps relative prices will not have\nchanged in such a way that linearity will be compromised, and our\nsub-aggregate will work for measuring poverty.\n\n\n4.1. **Data.** In the remainder of this paper we test our luck using data\nfrom expenditure surveys in Rwanda, Uganda, and Tanzania. <sup>5</sup> All\nsurveys are large multipurpose household consumption surveys, representative at both national and urban/rural levels with large sample\nsizes. In Uganda about 3000 households were interviewed each year, in\nTanzania between 3000 and 4000 each year, and in Rwanda the number of households increased substantially from 6900 to 14300 household\nobservations per year.\n\n\n5. Rwanda: Enquete Int´egrale sur les Conditions de Vie des m´enages de Rwanda\n(EICV1) 2001 and (EICV2) 2006 . Uganda: Uganda National Household Survey\n(UNHS) 2005/06 and 2009/10. Tanzania: Tanzania National Panel Survey (NPS)\n2008/09 and 2010/11.", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002089:16:2:2", "start": 1138, "end": 1168, "surface": "Tanzania National Panel Survey", "probe_tag": "keep", "probe_score": 0.925, "luna_label": 1, "luna_reason": "Named panel survey data are used to test the poverty measurement approach."}]}, {"key": "aivin-210", "text": "_Source_ : Authors' estimates using 2021 GEIH data, information from the CEQ Institute, and the World Bank.\nNote: The results for all countries other than Colombia were obtained by applying the old CEQ methodology, which\namong other things did not include indirect effects of indirect taxes and accounted for in-kind income differently, while\nthe results for Colombia and Ecuador were obtained by applying the new CEQ methodology.\n\n#### **6. The 2022 tax reform: Exploring its potential impact**\n\n\nIn November 2022, the Tax Reform for Equality, Social Justice, and Fiscal Adjustment\nConsolidation Act, which will take effect in fiscal year 2023, was introduced and approved by the\ncongress. This tax reform included changes in income tax and complementary taxes for both\nindividuals and companies, especially through the reduction of deductions and tax exemptions in\nthe personal income tax, changes in rates for some sources of income and adjustments in the\ndeductibility of royalties, as well as changes in discounts for investments made in technological\ndevelopment and innovation.\n\nThe reform also contemplated the creation of a permanent wealth tax. In addition, it included\nchanges in the rates on the national carbon tax and the creation of the national tax on single-use\nplastic products used for packaging or wrapping goods. Finally, the approved law created health\ntaxes, which impose tariffs on ultra-processed sugary beverages, as well as on ultra-processed\nindustrially produced food goods with a high content of added sugars, sodium, or saturated fats.\n\nIn order to evaluate the redistributive impacts of the reform using the microsimulation tool\ndescribed throughout this paper, the specific sections of the reform that can be captured by the\nhousehold surveys used in the model are simulated, in particular the items of the reform\ncorresponding to: (i) the reduction in the cap on personal income tax exemptions and deductions;\n(ii) the homogenization under the general schedule rate paid on dividend income; (iii) the\nintroduction of", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000584:36:0:1", "start": 1758, "end": 1775, "surface": "household surveys", "probe_tag": "confusion", "probe_score": 0.597, "luna_label": 1, "luna_reason": "Existing household surveys are used in the microsimulation model."}]}, {"key": "aivin-211", "text": " _new_ toilet.\n\n\nTable 4: Reported loan use\n\n\nSanitation &\nNew toilet Upgrade Repair Other only Total\n\n<u>other</u>\n\n146 7 2 31 14 200\nSL\n(73%) (4%) (1%) (16%) (7%) (100%)\n\n\n14 0 0 5 1 20\nControl\n(70%) (0%) (0%) (25%) (5%) (100%)\n\n\n160 7 2 36 15 220\nTotal\n<u>(73%)</u> <u>(3%)</u> <u>(1%)</u> <u>(16%)</u> <u>(7%)</u> <u>(100%)</u>\n\n\n_Notes_ : Data source: Client survey and administrative data. Sanitation loan usage was reported for those\nclients who took a sanitation loan according to administrative data from the MFI and confirmed it during the\ninterview. Hence, information is missing for clients who did not confirm to have taken a sanitation loan in the\nsurvey.\n\n\nThe household survey collected information on toilet ownership, whether it was not in use and the\n\n\nreasons for this, and a number of dimensions of toilet quality, which allow us to directly test whether\n\n\nhouseholds made a variety of sanitation investments. We focus on three types of sanitation investments:\n\n\n(i) construction of new toilets; (ii) repair of existing toilets; and (iii) upgrade of toilets. We measure\n\n\nthe construction of new toilets through (i) toilet ownership, regardless of whether it was functioning,\n\n\n40Even though the evidence presented above suggests that the loan label might have played a role in affecting the\ndemand for the sanitation loan, this does not imply that all households that took a sanitation loan intended to use it for\nsanitation investments. Some of these households could be less sensitive to the label effect and take the", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001768:27:1:2", "start": 489, "end": 521, "surface": "administrative data from the MFI", "probe_tag": "confusion", "probe_score": 0.7522, "luna_label": 1, "luna_reason": "MFI administrative data identify sanitation-loan clients underlying reported usage findings."}]}, {"key": "aivin-212", "text": " of the 19 <sup>th</sup> ICLS standards requires household surveys that are used to measure\n\nlabor market engagement – e.g. dedicated labor force surveys (LFS) and multi-topic household surveys –\n\nto differentiate between individuals engaged in own-use production work and individuals engaged in\n\nemployment. While own-use production of goods can take many forms (e.g. collection of firewood or water,\n\nfood preservation, etc. for family use), the revised standards have the largest implications for the\n\nmeasurement of agricultural work, where surveys need to distinguish between a farmer producing for\n\nown/family use or for sale. This, however, is difficult to measure, and historically, there has been scant\n\nempirical evidence and guidance on measurement practices. While the 2007 Standard Classification of\n\nOccupations (ISCO-08) distinguishes “subsistence farmers, fishers, hunters and gatherers” (sub-major\n\ngroup 63) and “market-oriented skilled agricultural workers and forestry, fishery and hunting workers”\n\n(sub-major groups 61 and 62) (ILO 2013a), this separation has had limited practical relevance, as\n\noccupational breakdowns rarely go below the level of major groups (i.e. combining all agricultural\n\n\n1 The International Conference of Labour Statisticians (ICLS) is a standard-setting body in labor statistics, hosted every five years\nby the International Labour Organization (ILO). As per the tripartite structure of the ILO, its participants include experts from\ngovernments (typically officials from the ministries dealing with labor issues and national statistical offices), employers' and\nworkers' associations.\n2 The 19th ICLS also introduced a new ‘forms-of-work’ framework.\n\n\n2", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002436:3:1:2", "start": 164, "end": 193, "surface": "multi-topic household surveys", "probe_tag": "confusion", "probe_score": 0.3026, "luna_label": 0, "luna_reason": "Generic survey category is mentioned conceptually without an attributed finding or existing data use."}]}, {"key": "aivin-213", "text": "golanos ou\nestrangeiros, e completar o registo de adultos não-registados, quebrando a cadeia de adultos nãoregistados e de crianças não-registadas;\n\n - Facilitar a naturalização de apátridas ou pessoas em risco de apatridia;\n\n - Criar mecanismos e procedimentos para resolver casos complexos de disputas relativas à nacionalidade,\nparticularmente para os refugiados que regressam e que não têm provas de ligações a Angola, possivelmente\nem coordenação com os antigos Estados de acolhimento;\n\n - Atribuir cartões de identidade e passaportes aos refugiados e requerentes de asilo;\n\n - Considerar a regularização da situação dos migrantes irregulares de longa duração.\n\n\n## 1. INTRODUÇÃO\n#### 1.1. Metodologia e agradecimentos\n\nEste estudo baseia-se na revisão documental da legislação angolana, documentos da ONU, dados quantitativos\ne históricos e na investigação realizada em Angola entre meados de Novembro de 2019 e finais de Fevereiro de\n2020. O Ministério da Justiça e Direitos Humanos, o Serviço de Migração e Estrangeiros no Ministério do Interior,\n\n- Ministério das Relações Exteriores, e o Ministério da Acção Social, Família e Promoção da Mulher partilharam\ninformações valiosas sobre políticas, situações e números relativos à apatridia em geral e pessoas em risco de\napatri", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000443:6:1:0", "start": 812, "end": 844, "surface": "dados quantitativos\ne históricos", "probe_tag": "confusion", "probe_score": 0.6627, "luna_label": 1, "luna_reason": "Quantitative and historical data are declared as sources underpinning the study."}]}, {"key": "aivin-214", "text": "its Public Expenditure\nTracking Survey (PETS)\n\nscorecard inproves each\nsemester\n\n\n\n**Project Components /** **Inputs:** **(budget for** each Project reports: (from Components to\nSub-components: component) Outputs)\n\n\n\n1. Community Driven - US$ 28.0 million (IDA $25.0 <sup>- M&E data</sup> - Cormnunities emnpowered\n\n\n\n**Program** (CDP) million) - Quarterly progress reports; and responsible for\nimplementing sub-projects;\n\n                                                  - Implemnenting partners <sup>are</sup>\ncomnpetent to support\n**2.** Pilot and Special\n\n\n\n**Programs in** Newly cormmunities;\n\n                                                - Implementing partners\n**Accessible** **Areas**\n\n                - US$ 2.0 mnillion (ODA <sup>$1.75</sup> participate in capacity building\n\n\n\n**(a)** **Rural Public Works** million) activities;\n\n                - US$ 2.0 million (IDA <sup>$1.75</sup>                - Operations Manual and\n\n\n\n**(b)** **Shelter** Program **for** million) annexed Handbooks for Direct\n\nFinancing to Communities <sup>and</sup>\n**Vulnerable** **Groups**\n\n               - US$ 10.0 million (IDA <sup>$6.5</sup>                - NaCSA and IDA Public Works are thorough,\n\n\n\n3. **Project** **Manaeem2nt** <sup>**and**</sup> <sup>million)</sup> administrative data appropriate, and clear in\n\n\n\ndefining the and\n**In", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000017:32:0:0", "start": 4, "end": 38, "surface": "Public Expenditure\nTracking Survey", "probe_tag": "confusion", "probe_score": 0.8166, "luna_label": 1, "luna_reason": "Named Public Expenditure Tracking Survey is explicitly referenced."}]}, {"key": "aivin-215", "text": " newly\nestablished and young women entrepreneurs and provide spaces and opportunities for women entrepreneurs to\nenhance their voice and agency in legal and policy processes. For refugees, the local platform chapters will target specific\nbarriers to voice and agency (such as language and specific cultural norms) and the additional barriers women refugees\nhave to business information (such as lack of access to formal business channels, mentors, and inputs). This\nsubcomponent implementation is aligned to the PDM Pillar 5: Community Mobilization and Mindset Change.\n\n35. **The subcomponent will finance:** (a) mobilization costs for the establishment of local platform chapters (20–25\nwomen per platform); (b) establishment of a digital platform for women entrepreneurs and its linkage to other existing\n\n\n34 The poverty incidence (headcount ratio) figures are based on the Uganda National Household Survey (UNHS) 2019/2020.\n\n\nPage 17 of 77", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000025:21:2:0", "start": 877, "end": 909, "surface": "Uganda National Household Survey", "probe_tag": "confusion", "probe_score": 0.886, "luna_label": 1, "luna_reason": "Survey data underpin reported poverty incidence figures."}]}, {"key": "aivin-216", "text": " the Panel discovered significant communication issues, creating\nradically different understandings of what had been decided or agreed, and why. Whether this\ncould be attributed to language problems in the absence of using the Maa language, the failure to\nadequately disclose critical documents, issues of representation, or other reasons, the fact remains\nthis caused major confusion and contributed to mistrust towards the authorities, as well as tension\nwithin the community.\n\n174. Among good-practice approaches and related Bank Policies that were not followed, or\ncould have been followed more rigorously, several stand out. They constitute lessons for the future:\n\n     - The use of the affected peoples’ language, in this case the Maa language, in the conduct\nof the census and other consultations with the community, including in written\ndocumentation, such as the RAP and census results.\n\n     - Inclusion of traditional structures of authority, specifically the group of Elders.\n\n     - Presentation of wider options to PAPs, for example with regard to housing construction\ntypes, materials, and size, to fit different preferences better.\n\n     - Implementation of necessary livelihood restoration programs.\n\n     - Establishment of a comprehensive baseline of key socioeconomic indicators covering\nall PAPs, and a monitoring system to assess progress in achieving resettlement goals\nthroughout execution of the resettlement plan and to permit adjustments as needed.\n\n\n175. Another factor of relevance and special interest to the PAPs is an expected new Kenyan\nlaw regarding the benefit sharing of certain commercial investments. While this is part of the\n\n\n49", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:010946:57:1:0", "start": 881, "end": 895, "surface": "census results", "probe_tag": "confusion", "probe_score": 0.0709, "luna_label": 1, "luna_reason": "Existing census results are explicitly referenced as community documentation."}]}, {"key": "aivin-217", "text": "IEA, 2020. CO2 Emissions from fuel combustion database. [online]. Available at:\nhttps://www.iea.org/data-and-statistics/data-product/co2-emissions-from-fuel-combustion-highlights\n\n[accessed 14 June 2021].\n\n\nILO, 2019. _<mark>Skills for a greener future: A global view” based on 32 country studies</mark>_ <mark>. Geneva: International Labour</mark>\n<mark>Organization.</mark>\n\n\nIPCC, 2014: Summary for Policymakers. In: Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani,\nS. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S.\nSchlömer, C. von Stechow, T. Zwickel and J.C. Minx, eds. _Climate Change 2014: Mitigation of Climate Change._\n_Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change._\nNew York: Cambridge University Press.\n\n\nIPCC, 2019: Summary for Policymakers. In: P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte,\nH.- O. Pörtner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S.\nLuz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi,\nJ. Malley, eds. _Climate Change", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001287:43:0:0", "start": 11, "end": 54, "surface": "CO2 Emissions from fuel combustion database", "probe_tag": "confusion", "probe_score": 0.4148, "luna_label": 0, "luna_reason": "Bibliographic database entry without shown data use in the passage."}]}, {"key": "aivin-218", "text": "##### **Monitoring of Refugee Inclusion in the National System Indicator 22 .**\n\n|Activity|2020|2021|2022|2023|2024|\n|---|---|---|---|---|---|\n|Refugee education reflected five-year - ESDP at national and sub-<br>national level[1].|0|Target 6 plans<br>(1 national and<br>5 sub-national.||||\n|Refugees education reflected yearly (sub-national plans scale-1-5)|0|Target 5|Target<br>5|Target 5|Target 5|\n|Refugee education reflected in education law/ MoE strategies|0|X||||\n|Budget allocation reflected in MoE/REB plan for refugee education.<br>(regional hosting) Scale 1-5.|0|5|5|5|5|\n|Camp-based schools  receiving support from REB: Inspection,<br>supervision, EMIS data collection, standard school classification|80%|90%|100%|100%|100%|\n|Refugee schools fully managed at field level by REB/MoE (no parallel<br>education systems)|1|2|4|5|5|\n\n\n\n22 The indicators on the table will measure strategic objective 1: Inclusion of refugee education in the National system.", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000503:39:0:0", "start": 660, "end": 669, "surface": "EMIS data", "probe_tag": "confusion", "probe_score": 0.0742, "luna_label": 0, "luna_reason": "Span is embedded in a table indicator describing support activities."}]}, {"key": "aivin-219", "text": "br>**procurement performance:**<br> <br>Piloting individual performance contract approach in the<br>procurement system<br> <br>RRI to support procurement process performance in the pilot|**3.3m**||\n|**Improved**<br>**decision-**<br>**making process**<br>**based on**<br>**reliable**<br>**statistical data**|**Component 4: Enhancing the use of statistics**<br>**for policy making**<br> <br>Timely production of reliable statistical<br>data<br> <br>Statistics widely disseminated|**Subcomponent 4.1: Improvement of poverty-related data**<br> <br>Production of a series of Poverty Notes (based on ECAM 4 and high-<br>frequency surveys)<br> <br>Production of ECAM 5<br> <br>Analysis of the population census<br> <br>Production of the LFS|**5.4m**||\n|**Improved**<br>**decision-**<br>**making process**<br>**based on**<br>**reliable**<br>**statistical data**|**Component 4: Enhancing the use of statistics**<br>**for policy making**<br> <br>Timely production of reliable statistical<br>data<br> <br>Statistics widely disseminated|**Subcomponent 4.2: Strengthening the national accounts production**<br> <br>Quarterly production of improved national accounts", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "sample:refugee_pads:000044:82:1:0", "start": 290, "end": 306, "surface": "statistical data", "probe_tag": "confusion", "probe_score": 0.1435, "luna_label": 0, "luna_reason": "Generic statistical data is mentioned without an attributed finding or concrete analysis."}, {"key": "sample:refugee_pads:000044:82:1:1", "start": 599, "end": 605, "surface": "ECAM 4", "probe_tag": "confusion", "probe_score": 0.4584, "luna_label": 1, "luna_reason": null}, {"key": "sample:refugee_pads:000044:82:1:2", "start": 661, "end": 667, "surface": "ECAM 5", "probe_tag": "confusion", "probe_score": 0.0927, "luna_label": 0, "luna_reason": null}, {"key": "sample:refugee_pads:000044:82:1:3", "start": 693, "end": 710, "surface": "population census", "probe_tag": "confusion", "probe_score": 0.7417, "luna_label": 1, "luna_reason": null}, {"key": "sample:refugee_pads:000044:82:1:4", "start": 738, "end": 741, "surface": "LFS", "probe_tag": "keep", "probe_score": 0.9679, "luna_label": 0, "luna_reason": null}]}, {"key": "aivin-220", "text": "**The World Bank**\nUganda Skills Development in Refugee and Host Communities (P176263)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1 See “Tackling the Demographic Challenge in Uganda (World Bank, 2020).\n2 See https://www.worldbank.org/en/country/uganda/overview.\n[3 State of Skills, ILO, 2020 (https://www.ilo.org/skills/pubs/WCMS_736694/lang--en/index.html)](https://www.ilo.org/skills/pubs/WCMS_736694/lang--en/index.html)\n4 This figure is higher than the regional average of 80%.\n5 United Nations Office for Coordination and Human Affairs. https://www.unocha.org/southern-and-eastern-africarosea/uganda#:~:text=Uganda%20is%20currently%20host%20to,refugees%20are%20from%20South%20Sudan.\n6 World Bank. 2019. Informing the Refugee Policy Response in Uganda. Results from the Uganda Refugee and Host Communities. 2018\nHousehold Survey.\n7 REACH household survey for the World Bank, 2019.\n8 World Bank. 2019. Informing the Refugee Policy Response in Uganda. Results from the Uganda Refugee and Host Communities. 2018\nHousehold Survey.\n9 Ibid.\n10 World Bank. 2019. Informing the Refugee Policy Response in Uganda. Results from the Uganda Refugee and Host Communities.\n2018 Household Survey.\n11 Ibid.\n12 Monitoring Social and Economic Impacts of COVID-19 on Refugees in Uganda: Results from the High-Frequency Phone Survey. 2021.\nWorld Bank and Uganda Bureau of Statistics (UBOS).\n13 The GoU’s TVET Policy (2019) presents a policy vision to prepare a “coordinated, labour-market responsive TVET system", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000016:12:0:1", "start": 812, "end": 834, "surface": "REACH household survey", "probe_tag": "confusion", "probe_score": 0.8059, "luna_label": 1, "luna_reason": "Named household survey cited as an existing source."}]}, {"key": "aivin-221", "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": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:017697:36:0:1", "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 cited existing survey data."}]}, {"key": "aivin-222", "text": " in the IFLS was calculated based\non highest grade completed in an education level. Primary completion takes the value of 1 if a child has completed 6 or more years of schooling and\n0 otherwise. Family background is measured by father’s years of schooling. The Inverse Probability Weighting (IPW) estimates are calculated using\n1975 district level density interacted with time trend. Data sources: Indonesia’s full count census 2000, Duflo (2001), IFLS and IFLS-East.\n\n\n70", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000774:72:3:1", "start": 448, "end": 452, "surface": "IFLS", "probe_tag": "confusion", "probe_score": 0.5985, "luna_label": 1, "luna_reason": "Named survey listed as a data source for the analysis."}]}, {"key": "aivin-223", "text": "> 8<br>_ .._<br> 36<br> 38<br> 21<br> 5<br> 13<br> 27<br> 2<br> 7<br> 3|\n\n\n\n**<mark>Total (44)</mark>** **<mark>360,950 443,590 488,020 596,660 866,020 2,755,240</mark>** **<mark>45%</mark>** **<mark>0.7</mark>** **<mark>2.3</mark>**\n\n\nNotes\n\nSource: Governments, UNHCR. See notes on next page for information on applications registered with UNHCR.\nThis table includes final data for 2010 to 2013 and provisional data for 2014. In the following tables, the 2013 figures are based on the monthly database. This results in some discrepancies.\nAll figures in this table have been rounded to the closest ten.\nA dash (“-”) indicates that the value is zero or not available. Two dots (“..”) indicate that the value is not available.\n\n- This refers to Gross Domestic Product (GDP), Purchasing Power Parity (PPP), per capita.\n\n\n**20** UNHCR Asylum Trends 2014", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000195:19:9:0", "start": 487, "end": 503, "surface": "monthly database", "probe_tag": "confusion", "probe_score": 0.6202, "luna_label": 1, "luna_reason": "2013 figures are explicitly based on this existing database."}]}, {"key": "aivin-224", "text": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n|Col1|open-source data set|\n|---|---|\n|Methodology for Data Collection|Heat map of all registration locations|\n|Responsibility for Data Collection|NIDP|\n|**Improving service delivery**|**Improving service delivery**|\n|**Number of entities using Fayda services for improved delivery of benefits and services (Number)**|**Number of entities using Fayda services for improved delivery of benefits and services (Number)**|\n|Description|Number of entities that are using at least one of the several services offered by Fayda to verify<br>the identity of their beneficiaries/clients, including but not only unique number seeding, yes/no<br>authentications, and sharing of ID attributes|\n|Frequency|Biannual|\n|Data source|Number of API connections to the Fayda Platform|\n|Methodology for Data Collection|Fayda data analytics platform|\n|Responsibility for Data Collection|NIDP|\n|**Number of whom are public entities (Number)**|**Number of whom are public entities (Number)**|\n|Description|Number of public entities that are using Fayda services that are a state, regional, or local<br>authority; a body governed by public law; or a private entity mandated to provide public services|\n|Frequency|Biannual|\n|Data source|Number of API connections to the Fayda Platform|\n|Methodology for Data Collection|Fayda data analytics platform|\n|Responsibility for Data Collection|NIDP|\n|**Number of who are private entities (Number)**|**Number of who are private entities (Number)**|\n|Description|Number of private entities using Fay", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000005:41:0:0", "start": 19, "end": 73, "surface": "Ethiopia Digital ID for Inclusion and Services Project", "probe_tag": "confusion", "probe_score": 0.1172, "luna_label": 0, "luna_reason": "Project title, not a cited or used data resource"}]}, {"key": "aivin-225", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n\n**ANNEX 2: Refugees and Host Communities**\n\n\n\n1. As of December 2021, Chad was hosting more than 560,000 refugees and asylum seekers settled\nin 20 camps and a city in the East, South, and Lake Chad regions across nine provinces, as detailed in Table\n1.1 and the UNHCR map provided at the end of the annex. Five provinces host about 80 percent of the\nrefugees. These include four eastern provinces: Ouaddai (26 percent), Wadi Fira (24 percent), and Sila (12\npercent), Ennedi Est (6 percent), and a southern province: Logone Oriental (11 percent). In the East,\naround 376,000 Sudanese refugees are settled along the border having fled violence in Darfur, many living\nthere for more than a decade. In the south, Chad hosts around 123,880 refugees from the Central African\nRepublic, the majority of whom have been in exile for more than a decade. In Lake Chad, some 19,650\nNigerian refugees who fled Boko Haram now reside in the area on Chad’s western border with Nigeria,\nNiger, and Cameroon. More recently, intercommunity tensions led to the arrival (August 2021–January\n2022) of about 35,900 refugees from Cameroon. An additional number of about 4,970 refugees of\ndifferent origins live scattered in the different camps as well as around 406,570 IDPs in the Lake Chad\narea. Camps are managed by CNARR and the UNHCR.\n\n2. CNARR works with the UNHCR and other partners to register new arrivals, issue documentation,\nand administer refugee camps and sites. It also serves as a technical adviser to the GoC on durable\nsolutions, notably on voluntary repatriation agreements or resettlement. Refugees receive support from\na variety of donors including the World Bank; European Union’s Humanitarian Office; the United States", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000051:72:0:0", "start": 329, "end": 338, "surface": "UNHCR map", "probe_tag": "confusion", "probe_score": 0.8707, "luna_label": 1, "luna_reason": "UNHCR map supports the reported refugee locations and distribution."}]}, {"key": "aivin-226", "text": "u>11. Other Information</u>**\n\nThe following data sources, project background reports and studies, relevant publications, and other items\nwill be made available to the consultant:\n\n  - Project Concept Note\n\n  - Pre-feasibility Studies\n\n  - Feasibility studies\n\n  - Population and Land Use Projections,\n\n  - Land Use Plans\n\n  - Drainage and Flood Control Service Needs Surveys,\n\n  - Traffic Management Studies\n\n**<u>11.1 Equipment, Logistics and Facilities</u>**\nThe Consultant shall be responsible for the provision of all the necessary resources to carry out the\nServices; and shall make arrangements for the establishment of office, supporting office equipment and\nfurniture, vehicles, accommodation, utilities, communications, and any other required resources.\n\n**<u>11.2 Duration and Timing of the Services</u>**\nThe time period required for the provision of the services is envisaged to be Four months from the date of\nthe contract.", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:014297:9:1:1", "start": 327, "end": 375, "surface": "Drainage and Flood Control Service Needs Surveys", "probe_tag": "confusion", "probe_score": 0.1294, "luna_label": 0, "luna_reason": "Survey materials are listed for future consultant access, not shown as used."}]}, {"key": "aivin-227", "text": "1999)) most textile mills were founded by foreigners, who owned 62%\n\n\nof wholesale textile trade in Rio, and foreigners soon dominated the manufacturing activities\n\n\nclosely linked to their commercial activities. In both S˜ao Paulo and Rio, the first electricity\n\n\ngenerating companies were founded by foreigners. Birchal (1999) documents that Juis de\n\n\nFora, Minas Gerais, a major steel and manufacturing area from 1858-1912, immigrants were\n\n\nresponsible for 66% of industries (Birchal, 1999, p.26).\n\n\nIn Chile, 70% of steam powered businesses were started by immigrants, roughly 12 times\n\n\ntheir share in the male population. Again, this conforms with other historical accounts.\n\n\nSilva Vargas (1977a) notes that “the lack of entrepreneurs and of qualified national workers\n\n\n6We are very grateful to Richard Sutch for the census sample data and to he and Larry Neal for extremely\nhelpful discussions.\n\n7", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001790:9:1:0", "start": 826, "end": 844, "surface": "census sample data", "probe_tag": "confusion", "probe_score": 0.5023, "luna_label": 1, "luna_reason": "Existing census sample data are acknowledged as a research source."}]}, {"key": "aivin-228", "text": " characterization of the\nIDP population by a variety of factors, has national\ncoverage, and has temporal and geographical levels\nof disaggregation.\n\n\nSince 2012, the GEIH migration module has been\nexpanded and modified to allow better characterization\nof the population (DANE, 2019). As of mid-2020, the\n\n\n\n**4. COUNTRIES IN FOCUS**\n\n\nGEIH was being redesigned, and two more questions\nwere expected to be added to the migration module.\n\n\nThe National Population and Housing Census (CNPV,\nSpanish acronym) has the purpose of counting\nand characterizing the universe of persons and\nhouseholds residing in Colombia, and it is the\nstatistical operation with the greatest possibilities\nto obtain disaggregated data by population groups,\nbased on their location, gender, migration status,\nand other characteristics. Thus, the CNPV allows the\ngeneration of statistical information that supports\ndecision-making in a variety of areas relevant to the\ninterests for FDPs and it is useful to evaluate the\nscope of development policies targeting specific\ngroups of the population (DANE, 2019).\n\n\nThe official methodological document of CNPV 2018\nconsiders that the census, as a statistical operation\nthat reaches all parts of the country, may be quite\nappropriate for collecting recent information on\ninternal mobility, which cannot be captured through\nother surveys. The variables incorporated into the\nCNPV 2018 allowed for the characterization of the\ninternally displaced population and international\nmigrants (DANE, 2018).\n\n\nIn 2018, the implementation of digital technology\nstrengthened DANE’s data collection processes\nand helped facilitate census processing, especially\nwith the first electronic census (eCensus). Other\ninnovations implemented in this census included the\nprocess of consultation and concertation with ethnic\ngroups, the monitoring and control of the operation,\nand the use of administrative records as input to\nevaluate the census.\n\n\nIn addition to the CNPV and the GEIH, the following\nstatistical operations capture information on IDPs:\n\n**•** Production and analysis of annual migration\nstatistics\n\n**", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000141:25:1:2", "start": 1888, "end": 1910, "surface": "administrative records", "probe_tag": "confusion", "probe_score": 0.1922, "luna_label": 1, "luna_reason": "Administrative records are used as input to evaluate the census."}]}, {"key": "aivin-229", "text": " new\neducation law (approved in August 2000) sets in place the conditions for broadening participation in\nDjibouti's education system. It provides for setting up school management committees with parent\n\nand community involvement. The law also provides for the creation of conditions to increase private\nsector participation in education.\n\n\n_6.3 How does the project involve consultations or collaboration with NGOs or other civil society_\n_organizations?_\n\n\nThe National Educational Forum consulted all stakeholders including NGOs and civil society during\nthe initial preparation. In addition, the project foresees the increased involvement of parent\nassociations or community-based associations in the management of project activities on the ground\n(i.e., operations & maintenance).\n\n\n_6.4 What institutional arrangements have been provided to ensure the project achieves its social_\n_development outcomes?_\n\n\nThe DGEN will be responsible for monitoring the gender gap in enrollment issues, and the gap\nbetween the poorest and the richest quintiles, and related education services available to them. The\ndata collection on enrollment will be strengthened by the capacity building support provided to the\nMinistry of Education's planning unit - thus over time these issues can be effectively monitored.\nTriggers are included in the APL phasing to ensure that various social development goals are met e.g.\ndecreasing the enrollment gap between the rich and the poor, decreasing the gender gap and increasing\ncommunity participation in school management.\n\n\n_6.5 How will the project monitor performance in terms of social development outcomes?_\n\n\nThe MOE planning unit will monitor enrollment paying attention to gender gaps, socioeconomic gaps\n\nand performance of students by socioeconomic class through use of surveys of students.", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000147:24:1:0", "start": 1811, "end": 1830, "surface": "surveys of students", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Future project monitoring proposes using student surveys; no existing finding is cited."}]}, {"key": "aivin-230", "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\n\n\n\n\n|Verification Protocol Table: Performance-Based Conditions|Col2|\n|---|---|\n|<br>**PBC 1**|<br>Number of newly constructed, equipped and operationalzed lower secondary schools and schools benefiting from<br>additional infrastructure <br>|\n|**Description**|<br>Number of newly constructed and equipped lower secondary schools and schools benefiting from additional infrastructure<br>furnished, equipped and with teachers deployed by the MoES, The PBC 1 is attached to construction of school<br>infrastructure, provision of furniture for classes, equipment, instructional materials, and new teachers.|\n|**Data source/ Agency**|MoES monitoring reports|\n|**Verification Entity**|Independent Verification Agent (IVA) <br>|\n|**Procedure **<br> <br>|The PBC include the following Results: finalization of selection of construction sites at Year 1; procurement for construction<br>concluded at Year 1 & 2; construction commences at Year 1 & 2; construction completed in batches (Year 2 & 3); quality<br>control of completed construction undertaken (Year 3 & 4); and schools fully operationalized with furniture and equipment<br>installed and teachers deployed (Year 3, 4 & 5). The facilities will be utilized as they are completed from Year 3. Details<br>provided in the PAD PBC section.<br> <br>|\n|<br> <br>**PBC 2**|<br>Number of Schools with Child Friendly School Program implemented <br>|\n|**Description**|The PBC 2 is attached to Component 1 and is specifically on subcomponent 1.2. dealing with improving school safety and", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000018:63:0:0", "start": 717, "end": 740, "surface": "MoES monitoring reports", "probe_tag": "confusion", "probe_score": 0.3248, "luna_label": 0, "luna_reason": "Source entry appears as a fragment inside a verification protocol table."}]}, {"key": "aivin-231", "text": "**Annex 3. Economic Costs and Benefits**\n\n\nA standard ERR was not estimated in the SAR, as is often the case for projects with a primary emphasis on\ncapacity strengthening and institutional reform. The lack of economic data from monitoring and evaluation\nof various project activities makes _ex post_ estimation of an ERR difficult, should it be deemed desirable.\nThe section below provides indicative socio-economic data and a discussion of quantitative results achieved\ncompared to the potential benefits highlighted in the SAR.\n\n\nAmong the gross benefits expected in the SAR are avoided losses related to decline in fishery as a result of\nover-fishing and deterioration in water quality, impacts of water hyacinth infestation, poor quality of water\nsupply for domestic and animal uses, and continued degradation of wetlands.\n\n\n**Economic Importance of the Resource:**\nThe Lake Basin economy is driven by Agriculture and Fisheries (70 percent), including a number of cash\ncrops (including fish exports) and a high level of subsistence fishing and agriculture. It produces in the\norder of USD 5 billion annually (2000-04), increasing from the estimated USD 3-4 billion, in 1996.\nPopulation in the Lake Basin, in that time, has gone up from 25 million to an estimated 30 million people,\nof whom 3 million depend directly or indirectly on fish and fisheries. General standards of living are\nbetween USD 90-270 per capita per annum (based on national figures). It is estimated that fisheries\ncontribute about 3 percent to the riparian economies. The quality of the environment and the status of the\nnatural resources are therefore critical factors in the maintenance and growth of incomes, livelihoods and\npoverty alleviation opportunities in these countries.\n\n\n**Fisheries Sector**\nFish production for the whole lake is currently estimated to be between 400,000 to 600,000 metric tons\nworth USD 400 to 600 millions annually. It is estimated that a majority of this production is artisanal\nfishery. In Uganda, 200,000", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:010748:36:0:0", "start": 210, "end": 223, "surface": "economic data", "probe_tag": "confusion", "probe_score": 0.3464, "luna_label": 0, "luna_reason": "States missing monitoring data without directly using an attributed finding."}, {"key": "fcv_pads_east_africa:010748:36:0:1", "start": 402, "end": 421, "surface": "socio-economic data", "probe_tag": "confusion", "probe_score": 0.3394, "luna_label": 0, "luna_reason": "Generic data reference lacks an attributed finding tied directly to it."}]}, {"key": "aivin-232", "text": " <u>[.pe/sala_situ](https://covid19.minsa.gob.pe/sala_situacional.asp)</u> which include variables for age and\n\n<u>[acional.asp](https://covid19.minsa.gob.pe/sala_situacional.asp)</u> sex, among other COVID-19-related\n\ndata and statistics.\n**Philippines** 1,116 Yes June Department <u>[https://bit.ly/](https://bit.ly/DataDropPH)</u> The Philippines' Department of\n18, of Health <u>[DataDropPH](https://bit.ly/DataDropPH)</u> Health shares to the public daily\n2020 metadata of COVID-19 cases\n\n\n\nMinistry of\nHealth\n\n\n\n<u>[https://covid](https://covid19.minsa.gob.pe/sala_situacional.asp)</u>\n<u>[19.minsa.gob](https://covid19.minsa.gob.pe/sala_situacional.asp)</u>\n<u>[.pe/sala_situ](https://covid19.minsa.gob.pe/sala_situacional.asp)</u>\n<u>[acional.asp](https://covid19.minsa.gob.pe/sala_situacional.asp)</u>\n\n\n\n**Philippines** 1,116 Yes June Department <u>[https://bit.ly/](https://bit.", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000211:27:2:0", "start": 460, "end": 491, "surface": "2020 metadata of COVID-19 cases", "probe_tag": "confusion", "probe_score": 0.1646, "luna_label": 0, "luna_reason": "Table-cell metadata description, not evidence of data use."}]}, {"key": "aivin-233", "text": "Looking beyond the current situation, the full socioeconomic impact of COVID-19 will\n\n\nplay out over the medium- to long-term, especially for developing countries. This\n\n\nhighlights the need for robust tracking mechanisms that collect regular and reliable\n\n\ndata on vulnerable groups, following common standards. High-frequency phone\n\n\nsurveys with multiple rounds, such as those presented in this paper, are examples of\n\n\nsuch a data collection mechanism that can produce much-needed data (as a panel or\n\n\nrepeated cross-section) on how the forcibly displaced fare over time and space in\n\n\nvarious settings. Once harmonized, the data from these surveys will allow for cross\n\ncountry and pooled analyses, and hence can inform global comprehensive\n\n\napproaches to alleviate the socioeconomic repercussions of the pandemic for the most\n\n\nvulnerable.\n\n\nIn most countries affected by forced displacement, including in the eight surveyed in\n\n\nthis paper, those forcibly displaced are rarely represented in national statistics. This is\n\n\nfrequently due to a variety of—often practical—capacity reasons impeding national\n\n\nstatistical offices from addressing this particular population group through their regular\n\n\nwork. For humanitarian and development policy, inclusion of FDPs is a conscious step\n\n\nthat these offices need to take—notwithstanding the technical and financial challenge\n\n\nit poses—over their regular approaches to population statistics. Targeted support and\n\n\ncapacity building from the humanitarian and development community, working more\n\n\nclosely with national statistical offices, will allow for greater visibility of FDPs in\n\n\nsocioeconomic data.\n\n\nThe interim findings in this paper constitute a springboard to deeper analysis through\n\n\ndiscussion of potential drivers of the observed results, analysis of the country-specific\n\n\npolicies and responses in the pandemic in coordination with the socioeconomic\n\n\nmicrodata collected, and review of the trends in these and more countries as\n\n\nharmonized data become available. Important insights could be gained by further\n\n\npursuing questions such as: the degree to which initial economic and social conditions\n\n\nare associated with FDP and host outcomes and convergence; exploration of the\n\n\nexplanatory factors of the (few) examples of positive outcomes or faster recovery; the", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000604:42:0:0", "start": 1644, "end": 1662, "surface": "socioeconomic data", "probe_tag": "confusion", "probe_score": 0.3244, "luna_label": 0, "luna_reason": "Generic data reference lacks an attributed finding or concrete claim."}, {"key": "reliefweb:000604:42:0:1", "start": 1911, "end": 1936, "surface": "socioeconomic\n\n\nmicrodata", "probe_tag": "confusion", "probe_score": 0.4136, "luna_label": 0, "luna_reason": "Mentioned as input to potential future analysis, not currently used evidence."}]}, {"key": "aivin-234", "text": "investments include roads, solid waste management, street lighting, greenery, servicing industrial land and\ntourist sites, parks for micro-enterprises and cottage industries, youth centers and incubators, etc.\nCompliance with the investment menu will be a minimum condition and be verified by the APA each\nProgram year. If a municipal LG has not invested Program funds in full compliance with the investment\nmenu, it will be sanctioned in the following year. Municipal LGs will be required to identify and prepare\ninvestments in a participatory manner and use the screening tools developed under USMID and the new\nDDEG guidelines. Participatory approaches and proper planning and budgeting will be promoted through\nthe annual assessments, and municipal LGs will be incentivized to involve the divisions, the municipal\ndevelopment forum as well as the private sector in the prior dialogue on planning and budgeting as well as\nmonitoring of execution.\n\n10. **Institutional strengthening for infrastructure provision will continue in USMID AF, targeting**\n**both the participating municipalities as well as MoLHUD and MDAs** . Firstly, the participating\nmunicipalities will receive grants to strengthen their capacities to execute their mandates for delivering a\nwide range of urban infrastructure and services. Each municipal LG will be required to develop a\ncomprehensive institutional strengthening plan that will respond to its capacity gaps especially those that\nwill be unearthed by the annual assessments under the Program and to ensure that the plan is executed on\neligible expenditures. The activities will continue to focus on: tooling, discretionary capacity building\n/institutional strengthening, and career development. US$ 10 million will be provided as Municipal\nInstitutional Strengthening Grant (ISG) as part of the overall DDEG allocation. Secondly, MoLHUD, which\nis responsible for the oversight of Program implementation as well as other MDAs, will also receive support\nto perform their mandate for urban development as well as for providing supply driven institutional support\nto the municipalities for activities which can be pooled together for economies of scale and that cut across\nall municipalities", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000168:55:0:0", "start": 719, "end": 737, "surface": "annual assessments", "probe_tag": "confusion", "probe_score": 0.2727, "luna_label": 0, "luna_reason": "Refers to program assessments used as future institutional planning mechanisms."}]}, {"key": "aivin-235", "text": " moves to higher\nlevels, up to upper secondary: about 70 percent of children attending pre-primary were at the appropriate age; 63\npercent for primary; 37 percent for lower secondary; and 22 percent for upper secondary.\n\n\n3 United Nations High Commissioner for Refugees (UNHCR), Operational Data Portal.\n4 Pre-primary education services are the responsibility of MFPPE. However, the Ministry of National Education and Civic Promotion (MENPC) is\nresponsible for pre-primary pedagogy.\n5 46 percent were public schools and 15 percent private (Statistics Education Yearbook 2019-2010)\n\n\nAug 03, 2021 Page 4 of 27", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000009:3:2:0", "start": 540, "end": 579, "surface": "Statistics Education Yearbook 2019-2010", "probe_tag": "confusion", "probe_score": 0.4674, "luna_label": 1, "luna_reason": "Named yearbook cited as source for school ownership percentages."}]}, {"key": "aivin-236", "text": "did not have significant impact on the outcome indicators as they were minor\ninterventions (in terms of scope as well as funding) as compared to the others.\n\n\n**3.3** **Efficiency**\n\n**_Rating: Substantial_**\n\n105. The Bank’s disbursement record shows that the project managed to complete its\nplanned activities with a total cost of about SDR 9.87 million (equivalent of US$14.9\nmillion) against the budget for both credit and grant, after the reallocation of SDR 4.46\nmillion (equivalent of US$ 7 million) to the Global Food Crisis Initiative. The\ndisbursement rate as of April 29, 2013 was 92percent. Even though resources were\nreduced from the project budget, the devaluation of the local currency by about 20percent\nduring the project implementation period had provided more Ethiopian Birr per one\nUS$ to carry out project activities. In addition, there was some saving from the\ncompensation payments under the Voluntary Retrenchment Plan (VRP) as the number of\nemployees affected under the plan was lower than originally anticipated. Even though the\nproject was extended for additional 18 months, the operating cost for the project was\nwithin budget and only 81percent of the allocated budget was utilized at the end of the\nproject. The cost efficiency of running the project may be as a result of prudent utilization\nof funds and/ or the government’s contribution for project management.\n\n**Component one –** _Accelerating the Implementation of privatization program_\n\n106. For the economic analysis done during the design, the PAD indicates that due to\nlack of reliable time series data, proxy estimates are used when explicit prices and\ncalibrations factors were not available. Pre-privatization data collected from the\nPrivatization and Public Enterprises Agency (PPESA) does not relate to the numbers used\nin the analysis during design. To carry out a robust quantitative economic analysis for\nthis component for the ICR, complete", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:013446:35:0:0", "start": 1577, "end": 1593, "surface": "time series data", "probe_tag": "confusion", "probe_score": 0.6426, "luna_label": 1, "luna_reason": "Data absence motivates use of proxy estimates in the economic analysis."}]}, {"key": "aivin-237", "text": " Labor Organization.** “International standard classification of occupations: Structure, group\ndefinitions and correspondence tables.” _International Labor Office, Geneva_ (2012).\n\n**National Institute of Statistics and Censuses.** “Encuesta de Hogares de Propósitos Múltiples.” San Jose,\nCosta Rica (2009).\n\n**National Institute of Statistics and Censuses.** “Encuesta Nacional de Hogares.” San Jose, Costa Rica\n(2020).\n\n\n48", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002138:49:2:0", "start": 233, "end": 276, "surface": "Encuesta de Hogares de Propósitos Múltiples", "probe_tag": "confusion", "probe_score": 0.5641, "luna_label": 0, "luna_reason": "Bibliographic entry naming a survey without showing substantive data use."}]}, {"key": "aivin-238", "text": "nie par le gouvernement local (gouvernement de Borno) à la réhabilitation\ndes logements. Certains étaient retournés s'inscrire pour obtenir de l'aide. En effet, il y a une liste d'attente\ngérée par un département qui visite les logements affectés (pour prendre des photos et enregistrer d'autres\ninformations clé). Il y a également eu des rapports sur la réhabilitation de logements de la part de la CroixRouge. Malgré le fait que la plupart des réfugiés aient perdu leur documentation relative au LTP, ils estimaient\nqu’il n’y aurait aucun problème à prouver leur propriété par rapport au bien concerné. A l'instar des personnes\ndéplacées, il n'y avait pas de préférence marquée pour l'aide à la réhabilitation du logement ou aux activités\nde subsistance parmi les réfugiés. La plupart des réfugiés n'ont pas pu sauver leurs biens mobiliers, bien que\ncertains réfugiés aient déclaré avoir été contactés par l'armée afin de rentrer et récupérer leurs biens (réfugiés\nde Mallam-Fatori à Ngagam).\n\n<u>4) Perte de la documentation relative au LTP</u>\n\n\n4", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000230:3:3:0", "start": 172, "end": 187, "surface": "liste d'attente", "probe_tag": "confusion", "probe_score": 0.5497, "luna_label": 0, "luna_reason": "Waiting list is merely mentioned, with no demonstrated data use or targeting."}]}, {"key": "aivin-239", "text": " every three years, this\nformula will be updated regularly. Furthermore, the verification instruments are simple and take into\naccount the lessons learned from experience in other countries. Privacy of beneficiary information i s\nrespected and the time consumed in filling out the pertinent forms i s minimal.\n\nOutcome and output indicator monitoring takes into account lessons learned from other CCT projects and\nwill augment the information gathered. The forms to verify education, health, and training/awareness\nconditions will include basic indicators to facilitate monitoring.\n\n(i) **Improving targeting**\n\n\n_How_ _is_ _it auurouriate to the borrower’s needs?_ Under the existing MOSA systems, the\nassessments made by individual social workers were the sole determinant of eligibility. This\napproach i s too discretionary and can result in errors of inclusion. The new beneficiary evaluation\nand selection process will improve targeting and will also free the social workers to spend more\ntime assisting beneficiary households. Roles, responsibilities, and time allocations of social\nworkers under the new system will be better delineated.\n\n\n(ii) <sup>**Will reverse declining school and health allocations outcomes of children**</sup>\n\n\nPassing grades in Arabic have declined from 71 percent to 38 percent and in math from 54\npercent to 26 percent and dropout rates have increased precipitously. Approximately, 34 percent\nof children under five years old suffer mild anemia and about _9_ percent suffer _acute_ proteincalorie malnutrition. Data on the first population decile in the West Bank and Gaza i s scant, the\ndegree of decline i s likely to have been greater for children in the poorest households. The\nSSNRP program will provide the government and households with an instrument to help mitigate\nthese declines.\n\n\n16", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000047:19:1:0", "start": 1546, "end": 1581, "surface": "Data on the first population decile", "probe_tag": "confusion", "probe_score": 0.619, "luna_label": 0, "luna_reason": "States data are scant without an analyzed finding or substitute estimate."}]}]