queue_annotate_rafael_part2.json

[{"key": "rafael2-000", "text": "micro-level decision makers, each household in the household survey is linked directly to\n\nthe corresponding representative household in the CGE model. Changes in representative\n\nhouseholds’ consumption in the CGE model component are passed down to their corre\nsponding households in the survey data. Only commodities used in the calculation of the\n\npoverty lines are considered.\n\nIn the next step, real total and per capita consumption expenditures are recalculated for\n\neach household in the survey. This new level of per capita expenditure is compared to the\n\nexogenously given poverty line and standard poverty measures are recalculated. Poverty\n\nchanges are evaluated using the standard Foster–Greer–Thorbecke (FGT) poverty measures.\n\nRepresentative households have been disaggregated across three dimensions:\n\n\n- Regional distinction: the Coast region and the rest of Kenya\n\n- Settlement pattern (urban and rural)\n\n- Disaggregation by consumption quintiles\n\n\nMapping between the CGE model representative households and those in the survey were\n\nnecessary to connect the households in the two sets of data. First, survey households were\n\ndistinguished by region: Coast region and the rest of Kenya. Second, within each group,\n\nurban households were distinguished from the rural. Finally, within each group, households\n\nwere classified by consumption quintile. A second level of mapping matched the commodi\nties in the CGE model and SAM with the commodities used in the calculation of the poverty\n\nline. This means that although total household consumption may have been notably affected\n\nin the economywide analysis, the composition in different commodities determines gains in\n\npoverty reduction.\n\nOur analysis, therefore, accounts for the poverty impact in each region across rural and\n\nurban households, each disaggregated by consumption quintile. This allows us to capture the\n\neffect on the bottom 40 (B40) percent of the wealth quintile of the Kenyan population, which\n\nis considered as an indicator of shared prosperity. Although shared prosperity refers to income,\n\nin many cases, household consumption must be used as a proxy for household income, partic\nularly when", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:fcv_pads_east_africa:018516:27:0:0", "start": 51, "end": 67, "surface": "household survey", "probe_tag": "keep", "probe_score": 0.9775, "luna_label": 1, "luna_reason": null}, {"key": "sample:fcv_pads_east_africa:018516:27:0:1", "start": 288, "end": 299, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9318, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-001", "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_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:016131:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1, "luna_reason": "Household Survey data support the finding that parents withdraw girls from school."}]}, {"key": "rafael2-002", "text": "37\n\n\n**The Nuer**\nThe Nuer people, who live on the plains around the Baro River in the Gambella region of Ethiopia,\nare traditional cattle herders, although they sometimes resort to small farming, hunting, and fishing.\nTheir language belongs to the Nilo-Saharan African language family like their neighbors the Anuak.\nThe Nuer people are largely livestock dependent and are mostly found in Akobo, Jikawo and parts of\nItang _woredas_ . During rainy seasons, Akobo and Jikawo become flooded and the people therefore\nmigrate to the highlands with their cattle until the riverbanks recede. According to the 2007 census,\nthe population of the Region is about 300,000, and 46% of which are the Nuer.\n\n\nThe Nuer are preeminently pastoral, though they grow more millet and maize than is commonly\nsupposed. They not only depend on cattle for many of life’s necessities but they have pastoral\nmentality and the herdsman’s outlook. Cattle are their dearest possession and they gladly risk their\nlives to defend their herds or to pillage those of their neighbours. The attitude of Nuer towards, and\ntheir relations with, neighboring peoples are influenced by their love of cattle and their desire to\nacquire them.\n\nThe Nuer living pattern changes according to the seasons of the year. As the rivers flood, the people\nhave to move farther back onto higher ground, where the women cultivate millet and maize while\nthe men herd the cattle nearby. In the dry season, the younger men take the cattle herds closer to\nthe receding rivers. Parallel to territorial divisions are clan lineages and they trace their lineage\nthrough the male line from a single ancestor. These lineages are significant in the control and\ndistribution of resources, and tend to coalesce with the territorial sections. Marriages must be\noutside one's own clan and are made legal by the payment of cattle by the man's clan to the\nwoman", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:015974:36:0:0", "start": 603, "end": 614, "surface": "2007 census", "probe_tag": "keep", "probe_score": 0.9639, "luna_label": 1, "luna_reason": "Census supports the stated regional population and Nuer share."}]}, {"key": "rafael2-003", "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_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:011459:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1, "luna_reason": "Survey data supports reported enrollment-rate disparities and separation limitations."}, {"key": "fcv_pads_east_africa:011459:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1, "luna_reason": "Household survey data support the finding that parents withdraw girls from school."}]}, {"key": "rafael2-004", "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_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:010177:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1, "luna_reason": "Survey data supports concrete enrollment-rate comparisons by expenditure quintile."}]}, {"key": "rafael2-005", "text": " of books and materials. They prefer to use\ntheir constrained resources for their boys who they feel have a better labor market potential. Finally,\nthe data from the Household Survey, showed that even if girls go to school, their parents pull them\nout at an age when they think they can help around the household.", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:011734:38:2:0", "start": 166, "end": 182, "surface": "Household Survey", "probe_tag": "keep", "probe_score": 0.966, "luna_label": 1, "luna_reason": "Survey data directly supports a concrete finding about girls’ schooling and household labor."}]}, {"key": "rafael2-006", "text": ".\n\n\n\n**_2.5.1 Progress on Harmonizing Systems within_**\n**_the NSNP and Moving Towards Government_**\n**_Management of the NSNP_**\n\nThe Single Registry. As a first step towards the\nharmonization integration of all cash transfer\nprogrammes, the NSNP created a Single Registry\nof Beneficiaries in early 2015. The Single Registry\nwill make it possible to build a picture of\nNSNP beneficiaries as a whole and will enable\npolicymakers and programme managers to verify\nthat beneficiaries fit the eligibility criteria, are not\nreceiving more than one cash transfer, and have\na valid ID number. The Single Registry has several\nuseful features. First, it is housed on servers in the\nSocial Protection Secretariat, but the MISs of each\nindividual cash transfer programme can instantly\nand automatically input information into the\nSingle Registry. Second, it is able to cross-check\nbeneficiaries from each cash transfer programme\nto ensure that no households are benefitting\nfrom more than one NSNP programme. Third,\nthe Single Registry is linked to the Integrated\nPopulation Registration Service (IPRS) to enable\neach programme to cross-reference the national\nID number for all beneficiaries with the IPRS\ndatabase to determine if it is a valid ID number\nand if the person meets the age criteria for the\ncash transfer programmes. Figure 7 shows the links\nbetween the Single Registry and the individual\nprogramme MISs.\n\n\n\n28 _Inua Jamii_ Towards a More Effective National Safety Net for Kenya", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:020590:36:1:0", "start": 258, "end": 290, "surface": "Single Registry\nof Beneficiaries", "probe_tag": "confusion", "probe_score": 0.8782, "luna_label": 0, "luna_reason": "The sentence states the NSNP created the registry."}, {"key": "fcv_pads_east_africa:020590:36:1:2", "start": 1190, "end": 1203, "surface": "IPRS\ndatabase", "probe_tag": "confusion", "probe_score": 0.6872, "luna_label": 1, "luna_reason": "IPRS database is used to validate beneficiary IDs and age eligibility."}]}, {"key": "rafael2-007", "text": ">full<br>documentation<br>for all changes<br>made to<br>personnel<br>records each<br>month and<br>checked<br>against the<br>previous<br>month's<br>payroll data.<br>Staff hiring and<br>promotion is<br>controlled by a<br>list of<br>approved staff<br>positions.|Col7|value of<br>procurement,<br>and who has<br>been awarded<br>contracts; (iii)<br>Approved staff<br>lists, personnel<br>database, and<br>payroll are<br>directly linked<br>to ensure<br>budget<br>control, data<br>consistency,<br>and monthly<br>reconciliation.|Col9|\n|---|---|---|---|---|---|---|---|---|\n||Comments on<br>achieving targets|Comments on<br>achieving targets|On track|On track|On track|On track|On track|On track|\n|**Enhanced External Audit and Oversight**|**Enhanced External Audit and Oversight**|**Enhanced External Audit and Oversight**|**Enhanced External Audit and Oversight**|**Enhanced External Audit and Oversight**|**Enhanced External Audit and Oversight**|**Enhanced External Audit and Oversight**|**Enhanced External Audit and Oversight**|**Enhanced External Audit and Oversight**|\n|Indicator Name|Baseline|Baseline|Actual (Previous)|Actual (Previous)|Actual (Current)|Actual (Current)|Closing Period", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:fcv_pads_east_africa:003145:7:1:0", "start": 147, "end": 159, "surface": "payroll data", "probe_tag": "confusion", "probe_score": 0.4954, "luna_label": 0, "luna_reason": "Payroll data is used for routine personnel reconciliation and budget-control bookkeeping."}]}, {"key": "rafael2-008", "text": "br>nine months performance report)<br>**4.1**<br>Addis<br>Ababa<br>(Percentage)<br> <br>1.04<br>1.29<br>1.05<br>1.05<br>TBC<br>Based<br>on<br>AAWSA<br>updated<br>report, FY2015<br>**4.2**<br>Secondary<br>cities<br>(Percentage)<br> <br>1.09<br>1.34<br>2.17<br>2.17<br>1.72<br>Based on the bassline report of<br>SCs collected from 22 utilities<br>(FY2015) Tt|**4 **<br>Operation<br>cost<br>coverage<br>ratio<br>(Percentage)<br> <br>Ratio<br>1.04<br>1.29<br>0.525<br>1.052<br> <br> The figure has been collected<br>based on the data base updated<br>and reported by MoWE(in the<br>nine months performance report)<br>**4.1**<br>Addis<br>Ababa<br>(Percentage)<br> <br>1.04<br>1.29<br>1.05<br>1.05<br>TBC<br>Based<br>on<br>AAWSA<br>updated<br>report, FY2015<br>**4.2**<br>Secondary<br>cities<br>(Percentage", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:000324:17:12:1", "start": 525, "end": 542, "surface": "data base updated", "probe_tag": "confusion", "probe_score": 0.8312, "luna_label": 1, "luna_reason": "Updated MoWE database underlies the reported operation cost coverage figure."}]}, {"key": "rafael2-009", "text": "\ntarget of 80 percent of Government services online (equivalent to around 5,000 services). The country ranks 113th\non the UN’s eGovernment Development Index 2022, and only 10 <sup>th</sup> in Africa. Kenya is ranked in the second category\nof countries (B) in the 2022 GovTech Maturity Index of the World Bank. Furthermore, with the expanding digital\neconomy and increasing complexity of threats to cybersecurity and data protection and privacy, having the\nrequisite talent base to support appropriate mitigation and response measures is key.\n\n\n_Need to enhance Kenya’s capacity to drive regional data integration_\n\n\n19. **<mark>A thriving digital market requires enabling frameworks that ensure that data can be securely,</mark>**\n**<mark>seamlessly, and cost-effectively exchanged</mark>** <mark>. While Kenya ranks relatively high on enablers under the Global Data</mark>\n<mark>Regulation Diagnostic Survey of 2020–21 in the region, it scores lower than other African peers on their data</mark>\n<mark>safeguards. C</mark> hallenges to a trusted online environment not only limit the uptake, accessibility, and user\nexperience of digital services in Kenya, but also create a bottleneck for expanding Kenyan digital services in the\nregion and beyond. While regulatory and institutional mechanisms such as the Data Protection Act of 2019 and\nthe Office of Data Protection Commissioner (ODPC) have been set in place, their operationalization is still in its\nearly stages, and budget is lacking. Strengthening the GoK’s capacity, in line with international best practice and\nwith the requirements for regional digital integration, will not only support the update of digital (public and\nprivate) services in Kenya but also across the whole region. <mark>Different data regimes are emerging for cross-border</mark>\n<mark>data exchange around the", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:004519:7:1:1", "start": 263, "end": 290, "surface": "2022 GovTech Maturity Index", "probe_tag": "keep", "probe_score": 0.943, "luna_label": 1, "luna_reason": "Named World Bank index supports Kenya’s maturity-category ranking."}, {"key": "fcv_pads_east_africa:004519:7:1:2", "start": 880, "end": 908, "surface": "Regulation Diagnostic Survey", "probe_tag": "confusion", "probe_score": 0.7274, "luna_label": 1, "luna_reason": "Named 2020–21 survey supports a regional data-safeguards comparison."}]}, {"key": "rafael2-010", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:013795:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": 0.1197, "luna_label": 0, "luna_reason": "Names administrative data as a verification source without showing substantive use."}]}, {"key": "rafael2-011", "text": "**The World Bank**\nUganda Multisectoral Food Security and Nutrition Project (P149286)\n\n\n  - **The position of District Nutrition Officer (DNO) was institutionalized** and mainstreamed into district structures\nalong with a dedicated budget.\n\n  - **Some districts established a District Nutrition Policy that makes the establishment of kitchen gardens mandatory**\n**for all households in rural and peri-urban areas** . National guidelines were developed for the establishment and\nmaintenance of school demonstration gardens and the Ministry of Finance issued a directive to schools to provide\nfacilitation for school demonstration gardens.\n\n  - **Training provided to Head Teachers and the relevant school committees resulted in improved institutional**\n**efficiency** <sup>**26**</sup> [^26: Efficiency is measured in terms of timely production and utilization of workplans including procurement plans, timely execution of procurements, timely\npayments to service providers, display of funds received and expended, timely accountability for the funds received and spent, timely production of financial\nreports.] **, financial discipline, and transparency in management of resources provided to the primary schools.**\nThe technical capacity of the School Nutrition Committee/School Procurement Committee was strengthened to\neffectively plan, procure, and account for funds of their school nutrition projects and other projects. Data obtained\nfrom the project districts’ Department of Education showed that 99 percent of the participating schools reported\nimproved books of accounts and 98 percent of them, efficiently utilized resources because of the training they\nreceived through the project.\n\n  - **The capacity of the Village Health Teams (VHTs) to carry out their core functions was strengthened** through\ntraining, and many have been integrated into the health system as community health extension workers.\n\n  - **The project also enabled the advancement of the implementation of the national School Health Policy (SHP** ),\nthat resulted in weekly school visits by health workers to provide health and nutrition talks and interventions to\nstudents demonstrating the relevance and impact of the", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:003462:20:0:0", "start": 1426, "end": 1491, "surface": "Data obtained\nfrom the project districts’ Department of Education", "probe_tag": "confusion", "probe_score": 0.6258, "luna_label": 1, "luna_reason": "District education data supports the reported 99-percent school finding."}]}, {"key": "rafael2-012", "text": "_Sierra Leone_\n\n\nPRICES and GOVERNMENT FINANCE\n\n1981 1991 2000 2001 Inflation (%)\n_Domest)c_ _pHces_\n_(% change)_ c 1\nConsumer prices 16 7 102 7 -0.9 3 0 30 _< _\nImplicit GDP deflator 8 7 128 8 6 2 6 1 20\n\n_Govemment finance_ _10_\n_(% of GDP,_ _includes curent_ grants) 0\nCurrentrevenue .. 112 182 178 .10. 98 97 93 99 00 01\nCurrent budget balance .. -5 8 -4.5 -7 1 - GDP deflator _ CPI\nOverall surplus/deficit -10 4 -10.6 -12 3\n\n\nTRADE\n\n1981 1991 2000 2001 Export and Import levels (USS mnIll.)\n_(US$ millions)_\nTotal exports (fob) 147 176 75 78 400\nRutile . 72\nDiamonds (recorded) 32 10 21 300\nManufactures\nTotal imports (cHf) 317 158 161 303 20\nFood . 53 66 72 100\nFuel and energy 26 29 3d _ _8_\nCaptal goods .. 38 18 22  - __\n96 D6 97 9o 99 00 01\nExport price index _(1995=100)_ 90 86 87\nImport price Index (1995=100) 93 93 92 mExports ***Mrrports**\nTerms of trade (1995-100) 97 93 94\n\n\n\nBALANCE of PAYMENTS\n\n\n\n1981 1991 2000 2001 Curmnt account balance to GDP _(%)_\n_(US$_ _millions)_\nExports of goods and services 163 244 110 116 0\nImports of goods and services 349 226 212 252\nResource balance -186 18 -102 -137 .a*\n\n\n\nExports of goods and services 163 244 110 116 0\nImports of goods and services 349 226 212 252\nResource balance -186 18 -102 -137 .a*\n\nNet income -28 -60 -18 -20\nNet current", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:fcv_pads_east_africa:010464:61:0:0", "start": 751, "end": 769, "surface": "Export price index", "probe_tag": "confusion", "probe_score": 0.209, "luna_label": 0, "luna_reason": "Standalone table row label with associated numeric values."}, {"key": "sample:fcv_pads_east_africa:010464:61:0:1", "start": 792, "end": 810, "surface": "Import price Index", "probe_tag": "confusion", "probe_score": 0.8072, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-013", "text": "The World Bank\n\n\n\nReport No: ISR8854\n\n\n\n~~demand side survey. Supply Side Survey: The supply-side survey collects and analyzes the data from officials in local jurisdictions on aspects like financial autonomy, institutional~~\ncapacity and coordination, service delivery, practice of participation,and openness of the local government affairs to citizens. Demand Side Survey: The demand side survey collects\nand analyzes the views, opinions and perceptions of citizens with regard to service delivery, openness of the local governments including access to government information,\nparticipation and accountability. The demand side survey uses three tools (citizens’ Report Card using household survey), Focus Group Discussion with citizens representatives and\nKey Informant Interviews with civil society organizations). Coverage of the survey: The first round of the Woreda and City Benchmarking was carried out in 2005/6 and established a\nbaseline in a limited number of jurisdictions (10%). In the subsequent three rounds the sample size increased to 38%, 50%, and 33% respectively including the initial (10%)\njurisdiction which are used for time series comparison. In addition the demand side of the survey covers more than 11,000 households in every round. The survey result indicates a\nconfidence interval of +/- 3 % at 95% confidence interval.\n\n|Locations|Col2|Col3|Col4|Col5|\n|---|---|---|---|---|\n|Country|First Administrative Division|Location|Planned|Actual|\n|Ethiopia|Not Entered|Federal Democratic Republic of Ethiopia|||\n\n\n\n**<mark>Results</mark>**\n\n\n**<u><mark>Project Development Objective Indicators</mark></u>**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Indicator Name|Core|Unit of Measure|Col4|Baseline|Current|End Target|\n|---|---|---|", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:015966:3:0:1", "start": 42, "end": 60, "surface": "demand side survey", "probe_tag": "confusion", "probe_score": 0.5273, "luna_label": 0, "luna_reason": "Heading merely names a generic survey without tying it to an attributed finding."}]}, {"key": "rafael2-014", "text": "**The World Bank** Implementation Status & Results Report\nEthiopia Economic Opportunities Program (P163829)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**Disbursement Linked Indicators**\n\n\n\n\n\n11/15/2020 Page 10 of 17", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:012141:9:0:0", "start": 127, "end": 157, "surface": "Disbursement Linked Indicators", "probe_tag": "confusion", "probe_score": 0.0653, "luna_label": 0, "luna_reason": "Standalone section or table heading, not a substantive data mention."}]}, {"key": "rafael2-015", "text": " data. Therefore, only milk data are shown as a representative measure of livestock productivity.|**Comment:** The project achieved 65 percent of its target. Impact analysis showed that project beneficiaries have 50<br>percent higher yields than non-project beneficiaries. Reported data on yield covers the drought period of 2016, in<br>which yields were on average lower than in a typical agricultural year. There were complications in collecting other<br>livestock product data. Therefore, only milk data are shown as a representative measure of livestock productivity.|\n|**Indicator**<br>**2**: <br>Percentage increase<br>in the real value of<br>marketed agricultural<br>products<br>(including<br>livestock)<br>per<br>household|Ethiopian birr<br> <br> <br>Total: 4,951<br>FHH: 3,242<br>YHH: 5068<br>|Percentage<br>change<br>since baseline<br> <br>Total: 8,731 (21.7%)<br>FHH: 7,509 (21.7%)<br>YHH: 8,785 (21.7%)|N/A|Percentage<br>change<br>since<br>baseline<br> <br>Total: 25% (6,181)<br>FHH: 32% (4,678)<br>YHH: -3% (4,914)|\n\n\n\n8 Key commodities are defined as those comprising the bulk of current agricultural commodities in the selected _woredas,_ including\nthose selected in the agribusiness value-chain activity.\n\n62", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:017280:66:3:0", "start": 23, "end": 32, "surface": "milk data", "probe_tag": "confusion", "probe_score": 0.3568, "luna_label": 1, "luna_reason": "Existing milk data are used as a representative livestock productivity measure."}, {"key": "fcv_pads_east_africa:017280:66:3:1", "start": 457, "end": 479, "surface": "livestock product data", "probe_tag": "confusion", "probe_score": 0.7354, "luna_label": 0, "luna_reason": "Sentence describes collecting the data, not using an existing dataset."}]}, {"key": "rafael2-016", "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": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:010560:24:1:0", "start": 1811, "end": 1830, "surface": "surveys of students", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Planned future monitoring through student surveys, with no existing finding cited."}]}, {"key": "rafael2-017", "text": "**[individually** <sup>**4**</sup> [^4: Instruction to the Recipient: Use this bracket if any one of the authorized persons may sign; if this is not applicable,] **/** **jointly** <sup>**5**</sup> [^5: Instruction to the Recipient: Use this bracket only if several individuals must jointly sign each Application; if this] **]** to deliver the Applications and evidence in support thereof on the terms and\nconditions specified by the Bank.\n\n\nThis Authorization also confirms that **the Recipient** is authorizing such persons to accept Secure\nIdentification Credentials (SIDC) and to deliver the Applications and supporting documents to the Bank\nincluding by electronic means. The Bank shall rely upon such representations and warranties, including\nthe representations and warranties contained in the _Terms and Conditions_ _of Use_ _of Secure Identification_\n_Credentials_ _in_ _connection with_ _Use_ _of Electronic Means_ _to_ _Process Applications and Supporting_\n_Documentation_ (\"Terms and Conditions of Use of SIDC\"), the Recipient represents and warrants to the\nBank that it will cause such persons to abide by those terms and conditions.\n\n\nThis Authorization replaces and supersedes any Authorization currently in the Bank records with\nrespect to the Agreement(s) referred to in the subject line of this Authorization.\n\n|Signatory Details Name|Position|Email ID|\n|---|---|---|\n|**Name**<br>|**Position**<br>|**Email ID**<br>|\n|<br>**[Signatory Name]**|<br>**[Title]**|<br>**[Email]**|\n||||\n||||\n||||\n\n\n|Spedmen Signatures Signatory Name|Signature|Signature 2|Signature 3|\n|---|---|-", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:000334:5:0:0", "start": 1226, "end": 1238, "surface": "Bank records", "probe_tag": "confusion", "probe_score": 0.2406, "luna_label": 0, "luna_reason": "Routine Bank records reference in an authorization, with no substantive data use."}]}, {"key": "rafael2-018", "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_rafael_part2", "spans": [{"key": "sample:fcv_pads_east_africa:012749:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "confusion", "probe_score": 0.7323, "luna_label": 1, "luna_reason": null}, {"key": "sample:fcv_pads_east_africa:012749:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.1461, "luna_label": 1, "luna_reason": "Existing survey data underpin reported enrollment-rate inequalities and separation caveat."}, {"key": "sample:fcv_pads_east_africa:012749:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "confusion", "probe_score": 0.1121, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-019", "text": " the development of the Guidance Note, which was updated during\nthe first year of implementation by WBG TA. During the second year of implementation, the WBG\nprovided TA to support the counties in developing the eight emergency operations plans. However, there\nwere further delays in their adoption as it depended on the county assemblies, which was outside the\nmandate of the technical teams that developed them. The Council of Governors (CoG) was instrumental\nin convening county technical teams to develop the respective CEOPs and aided with the finalization of\nthe plans, ensuring that they were adapted to the appropriate county context, and the approval process.\nWhen the Operation closed in September 2021, only 2 CEOPs were approved, however the GoK made\n\n\n12 Evidence, the County Assembly proceedings showing that the CEOPs were discussed and approved, was provided to the Bank\nfor 3 counties (Lamu, Kisumu, Muranga) and is pending for 3 counties (Kakamega, Turkana and Isiolo).\n\n\nPage 19 of 46", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:007856:22:2:0", "start": 782, "end": 809, "surface": "County Assembly proceedings", "probe_tag": "confusion", "probe_score": 0.6628, "luna_label": 1, "luna_reason": "Proceedings provide evidence that county emergency plans were discussed and approved."}]}, {"key": "rafael2-020", "text": " child was still\nlacking. However, the situation prevailing in Wakiso district was a pointer that a lot still\nneeds to be done. Wakiso District has the highest number of women in the entire\ncountry, but the social economic survey established that women only 20.2% of the\nhouses were owned by women.\nWomen were exposed to incidences of domestic violence, and 12.2% of households\nwere headed by single mothers. Efforts have been made to emphasize gender\nmainstreaming in all government programs within the district. However, a lot needs to\nbe done and due to lack of skills, women and the youth were predominantly engaged in\ninformal trade like markets, stone quarrying, and roadside petty trade.\n\nThe electrification project will therefore have a direct positive impact on both women\nand men in the sense that they will be able to start new businesses or and boost existing\n\n\n40 | P a g e", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:017466:43:1:0", "start": 207, "end": 229, "surface": "social economic survey", "probe_tag": "confusion", "probe_score": 0.8839, "luna_label": 1, "luna_reason": "Past survey attributed a concrete finding about women’s house ownership."}]}, {"key": "rafael2-021", "text": " of the reform work is set for June 2004.\n\n\nThe first labor market survey was conducted in 1999. The changes made in the job structure and\ngrading system, and the fluctuations that resulted in the labor market following the civil service\nemployee, salary increments made in 2001/02 the need to carry a labor market survey became\nevident. In 2002 the second labor market survey was conducted with a concurrent family budget\nsurvey. Analysis of the survey data however, is delayed apparently for lack of resources. A draft\npolicy paper on pay, benefits and conditions of work is finalized. There is considerable\ninterfacing between the Job Evaluations and Grading and Remuneration and Conditions of\nService (RCS) projects, which requires integration. The remuneration reform is to be finalized\nby June 2004.\n\n\n**<u>2.3.4. Service Delivery Policy</u>**\n\n\nThe Service Delivery Policy was adopted by the Council of Ministers in 2001. Federal Civil\nService institutions have started implementing the policy. Following the adoption of the policy,\nmost regional states have adapted and/or adopted their service delivery policies. Most federal\ncivil service reform offices have established Customer Services and Complaints Handling units\nand have finalized the preparation of service standards. They have started receiving and handling\nclients feedback report cards. Encouraging results have been registered and growing clients\n\n\n                            - 45", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:011141:49:1:0", "start": 302, "end": 321, "surface": "labor market survey", "probe_tag": "confusion", "probe_score": 0.2339, "luna_label": 0, "luna_reason": "The sentence identifies a needed survey, not existing data used or analyzed."}, {"key": "fcv_pads_east_africa:011141:49:1:3", "start": 447, "end": 458, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.5653, "luna_label": 1, "luna_reason": "Existing survey data is identified as the subject of delayed analysis."}]}, {"key": "rafael2-022", "text": "**Independent Evaluation Group (IEG)** Implementation Completion Report (ICR) Review\nET: Agricultural Growth Program (P113032)\n\n\ndrought that impacted the country, which was described by the ICR \"as the worst in 50 years.\" On the\npositive side the results reported in the ICR (p. 20, para 41) showed that the agricultural yield index for the\naverage AGP beneficiary was 56% higher than for the average household that did not benefit from the\nproject. Compared to the average non-beneficiary household, the crop and milk yields for project\nbeneficiaries were 58 and 43% higher, respectively. Similarly, female-headed households who benefited\nfrom AGP interventions had 52% percent higher crop yields and 41% higher milk yields compared to nonbeneficiary female-headed households. While youth headed households who benefited from the project\nshowed a 44% increase in milk yields compared to non-beneficiary youth headed households, the results\ndid not show a significant impact on agriculture productivity.\nWith regards to increasing participation of women and youth. The impact of the project on women was\nexpected to be captured through a specific analysis of women’s activities (dairy; sheep and goats; poultry;\nand possibly backyard vegetables). While the project did a commendable effort to record gender\ndisaggregated data for various activities, \"many livestock-related activities, such as animal fattening, were\nnot assessed because they were not captured in the household data\" (ICR, p. 19, para 40). Women\nparticipation in training activities was generally lower than expected. For example, only 57 women\nparticipated in an experience sharing event after crop harvest which was planned for 556 women. Similarly,\nonly 603 women attended a capacity building training designed for 1,139 women. The ICR (p. 50, para 24)\nattributed the low participation of women to the time constraint faced by women given their multiple roles in\nthe household, including cooking", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:011498:7:0:1", "start": 1469, "end": 1483, "surface": "household data", "probe_tag": "confusion", "probe_score": 0.5343, "luna_label": 1, "luna_reason": "Existing household data explains why livestock activities were not assessed."}]}, {"key": "rafael2-023", "text": "**The World Bank**\nEthiopia General Education Quality Improvement Program for Equity (P163050) ICR DOCUMENT\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|ANNEX 6. ORIGINAL BOUNDARIES OF THE PforR PROGRAM|Col2|\n|---|---|\n|**Government ESDP V Program**|**PforR Program**|\n|**1. Capacity development for improved management** <br>|**1. Capacity development for improved management** <br>|\n|1.1 Develop a relevant structure, with a clear distribution of mandates and responsibilities at all levels  <br>1.1.1 Improving the education sector’s organizational structure<br>1.1.2 Managing the implementation of cross-cutting programs|<br> <br>|\n|1.2 Provide regular gathering, processing and sharing of information to inform decision making <br> <br>1.2.1   Gathering and processing education performance data<br>1.2.2   Gathering and processing financial data  <br>1.2.3   Sharing information to inform decision making|<br>   <br>   <br>|\n|<br>1.3 Promote good coordination and communication within and across levels<br>1.3.1    Job specifications and operational handbook<br>1.3.2    Improved use of existing documentation centers and sharing platforms|<br> <br> <br>|\n|<br>1.4 Ensure adequate supply of staff with the right mix of technical and leadership skills in each post/level  <br>1.4.1    Profiles and recruitment  <br>1.4.2    Professional development: mentoring, training and on-the-job support|<br> <br>|\n|1.5 Improve resources and conditions of work||\n|**2. Improve quality of general education", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:000638:79:0:1", "start": 820, "end": 834, "surface": "financial data", "probe_tag": "confusion", "probe_score": 0.1482, "luna_label": 0, "luna_reason": "Span is a data-activity entry inside a program table."}]}, {"key": "rafael2-024", "text": " and Monitoring and Evaluation**|**Project Management and Monitoring and Evaluation**|\n|**Percentage of project-related grievances addressed (Percentage) **|**Percentage of project-related grievances addressed (Percentage) **|\n|Description|This indicator will be tracked by PIU through data collected through the Project Management Information System (MIS).|\n|Frequency|**Quarterly**|\n|Data source|**MSEA’s and KDC’s registry of project benefeciary firms**|\n|Methodology for Data<br>Collection|**Project progress reports**|\n|Responsibility for Data<br>Collection|** PIU**|\n|**of which women (Percentage) **|**of which women (Percentage) **|\n|Description||\n|Frequency|**Quarterly**|\n|Data source|**MSEA’s and KDC’s registry of project benefeciary firms**|\n|Methodology for Data<br>Collection|**Project progress reports**|\n\n\n\nNov 16, 2023 Page 42 of 60", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:fcv_pads_east_africa:005133:46:2:0", "start": 313, "end": 350, "surface": "Project Management Information System", "probe_tag": "confusion", "probe_score": 0.1482, "luna_label": 0, "luna_reason": null}, {"key": "sample:fcv_pads_east_africa:005133:46:2:1", "start": 400, "end": 454, "surface": "MSEA’s and KDC’s registry of project benefeciary firms", "probe_tag": "drop", "probe_score": 0.0376, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-025", "text": "**The World Bank** Implementation Status & Results Report\nUganda COVID-19 Response and Emergency Preparedness Project (P174041)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**Overall Comments IR**\n\n\n\n\n\nThere has been no change in the targets reported for 4 out of 10 intermediate results indicators. This is because the IDA credit portion of the\nProject, which accounts for more than 70 percent of the total Project cost is not yet effective. The progress seen in the four indicators reporting\nimprovements was attributable to the PEFF funds.\n\n\n**Performance-Based Conditions**\n\n\n**Data on Financial Performance**\n\n\n**Disbursements (by loan)**\n\n\nProject Loan/Credit/TF Status Currency Original Revised Cancelled Disbursed Undisbursed % Disbursed\n\n\nNot\nP174041 IDA-67620 USD 12.50 12.50 0.00 0.00 13.00 0%\nEffective\n\n\n4/23/2021 Page 5 of 7", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:018643:4:0:0", "start": 552, "end": 581, "surface": "Data on Financial Performance", "probe_tag": "drop", "probe_score": 0.0194, "luna_label": 0, "luna_reason": "Standalone financial performance table heading, not a data-use mention."}]}, {"key": "rafael2-026", "text": "Project** **Manaeem2nt** <sup>**and**</sup> <sup>million)</sup> administrative data appropriate, and clear in\n\n\n\ndefining the and\n**Innovative Activities**\n\n\n\nresponsibilities of all parties;\nNaCSA retains competent\n**(a) Capacity Building**\n\n\n\nstaff;\n\n                                             - Other governnent and donor\n**(b)** **Information and**\n\nsupport mobilized for\n\n\n\nsupport mobilized for\n**Sensitization**\ndecentralization to\ncomplement NaCSA efforts;\n(c) **Monitoring and**\n\n\n\n**Evaluation**\n\n\n\n**(d)** **Technical Assistance**\n\n\n(e) **Operating** Expenses\n\n\n\n-28", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:018259:32:1:0", "start": 64, "end": 83, "surface": "administrative data", "probe_tag": "drop", "probe_score": 0.0277, "luna_label": 0, "luna_reason": "Generic administrative data is mentioned without showing substantive use or an attributed finding."}]}, {"key": "rafael2-027", "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_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:019421:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "drop", "probe_score": 0.0227, "luna_label": 1, "luna_reason": "Existing 1999 data are identified and characterized as preliminary estimates."}, {"key": "fcv_pads_east_africa:019421:62:2:1", "start": 758, "end": 796, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.9824, "luna_label": 1, "luna_reason": "Named database provides data presented in the table."}]}, {"key": "rafael2-028", "text": "- _The rights for the Bank to review procurement documentation and_\n_activities;_\n\n\nWhen other national procurement arrangements other than national open\ncompetitive procurement arrangements are applied by the Borrower,\nsuch arrangements shall be subject to paragraph 5.5 of the Procurement\nRegulations.\n\n\n**_Leased Assets_** _as specified under paragraph 5.10_ of the Procurement\nRegulations: Leasing may be used for those contracts identified in the\nProcurement Plan tables. _“Not Applicable”_\n\n\n**_Procurement of Second Hand Goods_** _as specified under paragraph 5.11_ of\nthe Procurement Regulations – is allowed for those contracts identified in the\nProcurement Plan tables _“Not Applicable”_\n\n\n**_Domestic preference_** _as specified under paragraph 5.51_ of the Procurement\nRegulations **_(Goods and Works)_** . _“Not Applicable”_\n\n\nGoods: [is not applicable/is applicable for those contracts identified in the\nProcurement Plan tables];\n\n\nWorks: [is not applicable/is applicable for those contracts identified in the\nProcurement Plan tables]\n\n\n**Proposed Procedures for CDD Components** (as per paragraph. 6.52 and\nAnnex III, paragraphs 6.9 and 6.10 of the World Bank Procurement\nRegulations for IPF Borrowers. “Not Applicable””\n\n\n**Other Relevant Procurement Information** _. “None”._", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:004094:1:0:0", "start": 452, "end": 475, "surface": "Procurement Plan tables", "probe_tag": "drop", "probe_score": 0.0311, "luna_label": 0, "luna_reason": "Routine procurement planning tables, not substantive data analysis or evidence."}]}, {"key": "rafael2-029", "text": "**4.5** **Notes To** **Financial Statements**\n\n\n**4.5.1** **Accounting** **Policies**\n\n\n**a)** **Basis** **of preparation of Financial Statements**\nThe financial statements have been prepared using the modified accrual basis of accounting.\nRevenue (grant income) is recognised when received. Expenses are recognised when incurred\nleading to recognition of payables for all obligations incurred but not paid for **by** the end of the\nreporting period.\n\n\n**b)** **Receipts** **and payments**\nThe receipts are recorded on the date received and payments recorded when actaully paid.\nUnsettled obligations are recognised in the Statement of Financial Position as payables.\n\n\nc) **Fixed** **assets**\nIn line with asset accounting policies **2023,** assets have been included in statement of financial\nposition. **A** fixed assets register is also maintained for the record and safeguard of assets\nacquired **by** the project.\n\n\n**d)** **Reporting Currency**\nFunds from the **IDA** are translated into the reporting currency **(UGX)** using the Bank of Uganda\nruling rates on the respective dates of transactions. Payment transactions in foreign currency\nare translated in reporting currency at **BOU** transfer rate to which the transactions relate. Hence\nthe average of transfer rates is used to convert transactions in **US** Dollars into the **UGX**\nequivalent amounts. Exchange differences are realized in the Statement of Comprehensive\nIncome in the year in which they arise. The financial statements are prepared in Uganda\nShillings, but also indicating the equivalent in **US** dollars. The **IDA** Designated Account is\nmaintained in **", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:000523:32:0:0", "start": 811, "end": 832, "surface": "fixed assets register", "probe_tag": "drop", "probe_score": 0.049, "luna_label": 0, "luna_reason": "Routine project asset bookkeeping record, not substantive data reuse."}]}, {"key": "rafael2-030", "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_rafael_part2", "spans": [{"key": "sample:fcv_pads_east_africa:016663:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "confusion", "probe_score": 0.582, "luna_label": 0, "luna_reason": null}, {"key": "sample:fcv_pads_east_africa:016663:16:0:1", "start": 1487, "end": 1500, "surface": "random survey", "probe_tag": "drop", "probe_score": 0.012, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-031", "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": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:010290:30:0:0", "start": 385, "end": 396, "surface": "MOE reports", "probe_tag": "drop", "probe_score": 0.0342, "luna_label": 0, "luna_reason": "Logframe verification reports represent planned future monitoring evidence."}]}, {"key": "rafael2-032", "text": "00|14-Jun-2024|337,733|31-Jan-2025|211,962.00|Mar/2025|\n|Enrolment at public lower<br>secondary schools in<br>targeted districts, girls<br>(Number)|Comments on<br>achieving targets|Comments on<br>achieving targets|Total enrolment is drawn from 1,416 government schools (S1-S4) as of September 2024.<br>Values exceed the end targets because the Results Framework (RF) is based on data from<br>2017, since which time the MoES has been updating the comprehensive Education<br>Management Information System (EMIS). Enrollments in target areas have increased in the<br>interim for reasons outside the influence of the project.|Total enrolment is drawn from 1,416 government schools (S1-S4) as of September 2024.<br>Values exceed the end targets because the Results Framework (RF) is based on data from<br>2017, since which time the MoES has been updating the comprehensive Education<br>Management Information System (EMIS). Enrollments in target areas have increased in the<br>interim for reasons outside the influence of the project.|Total enrolment is drawn from 1,416 government schools (S1-S4) as of September 2024.<br>Values exceed the end targets because the Results Framework (RF) is based on data from<br>2017, since which time the MoES has been updating the comprehensive Education<br>Management Information System (EMIS). Enrollments in target areas have increased in the<br>interim for reasons outside the influence of the project.|Total enrolment is drawn from 1,416 government schools (S1-S4) as of September 2024.<br>Values exceed", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:001118:2:1:1", "start": 379, "end": 396, "surface": "data from<br>2017", "probe_tag": "drop", "probe_score": 0.028, "luna_label": 1, "luna_reason": "2017 data underpins the Results Framework and explains target deviations"}]}, {"key": "rafael2-033", "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_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:020949:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "drop", "probe_score": 0.0402, "luna_label": 0, "luna_reason": "The Planning unit is generating the statistical data, so it is project-produced."}, {"key": "fcv_pads_east_africa:020949:16:0:1", "start": 1487, "end": 1500, "surface": "random survey", "probe_tag": "drop", "probe_score": 0.0148, "luna_label": 0, "luna_reason": "Staff will carry out the survey to establish a future baseline and repeat it."}]}, {"key": "rafael2-034", "text": " Service Delivery (LGMSD)**\n\n\n    - **DLI 5** : Strengthening LGMSD and improving the weakest performing LGs was measured by a combined average\nperformance assessment score. The Independent Verification Agent (IVA) reported achievement across all\nassessment areas, unlocking USD 7 million.\n**Result Area 4: Improvement in the Effectiveness and Efficiency of Service Delivery**\n\n\n    - **DLI 6** : Service delivery performance was strengthened, and the IVA reported this DLI as achieved, unlocking a\ntotal of USD 14 million.\n\n\n**4.** **DATA ON FINANCIAL PERFORMANCE**\n\n\n**4.1** **Disbursements (by loan)**\n\n\nLoan/Credit/TF Status Original Revised Cancelled Disbursed Undisbursed % Disbursed\n\n\n**4.2** **Key Dates (by loan)**\n\n\nApr 19, 2025 Page 2 of 35", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:001920:1:1:0", "start": 535, "end": 564, "surface": "DATA ON FINANCIAL PERFORMANCE", "probe_tag": "drop", "probe_score": 0.0141, "luna_label": 0, "luna_reason": "Standalone table section header, not a data-use mention."}]}, {"key": "rafael2-035", "text": "**_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)_** shall apply for those contracts identified in\nthe Procurement Plan tables\n\n\n**Activities where Rated Criteria is applied** _as specified under paragraphs_\n_5.50_ of the Procurement Regulations is Applicable.", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:005148:1:0:0", "start": 157, "end": 180, "surface": "Procurement Plan tables", "probe_tag": "drop", "probe_score": 0.0339, "luna_label": 0, "luna_reason": "Routine procurement planning documentation, not substantive data use."}]}, {"key": "rafael2-036", "text": "br>(12.94)|\n|Education of head of household<br>|4.47<br>(2.77)|7.14<br>(4.81)|7.03<br>(5.02)|\n|Head of household is male<br>|0.85<br>(0.36)|0.81<br>(0.39)|0.77<br>(0.42)|\n|Head of household is literate<br>|0.83<br>(0.38)|0.87<br>(0.34)|0.91<br>(0.29)|\n|Head of household is indigenous<br>|0.16<br>(0.36)|0.08<br>(0.28)|0.17<br>(0.38)|\n|**Child-level variables**||||\n|Age<br>|11.41<br>(3.09)|10.86<br>(3.14)|11.72<br>(3.46)|\n|Mean years of completed schooling<br>|4.41<br>(2.60)|4.13<br>(2.83)|5.08<br>(3.03)|\n|_Note_: The table presents means and standard deviations.  Calculations from the 1998/99 LSMS and the 2001 Census are<br>limited to households with children ages 6-17; calculations from the census refer only to parishes included in the impact<br>evaluation sample.|_Note_: The table presents means and standard deviations.  Calculations from the 1998/99 LSMS and the 2001 Census are<br>limited to households with children", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:003143:24:1:0", "start": 591, "end": 603, "surface": "1998/99 LSMS", "probe_tag": "keep", "probe_score": 0.9423, "luna_label": 1, "luna_reason": "Named household survey used for table calculations and reported summary statistics."}, {"key": "prwp:003143:24:1:1", "start": 612, "end": 623, "surface": "2001 Census", "probe_tag": "keep", "probe_score": 0.944, "luna_label": 1, "luna_reason": "Census calculations provide analyzed household and child-level statistics."}]}, {"key": "rafael2-037", "text": "7%<br>19.4%<br>11.1%<br>19.5%<br>16.4%<br>37.7%<br>44.5%<br>2.3%|11,745<br>2,835<br>7,075<br>6,405<br>8,815<br>20,933<br>38,844<br>27,348<br>29,904<br>38,433|24,700<br>12,394<br>22,081<br>17,625<br>19,329<br>31,955<br>44,195<br>34,045<br>35,426<br>37,304<br>2,972|95%<br>70%<br>56%<br>62%<br>66%<br>77%<br>60%<br>77%<br>61%<br>86%<br>29%|\n\n\nSources: Dexia:2008 and Mohanty et alia: 2007, World Development Indicators.\n\n\nComparing all these countries to India shows it to be an outlier in most respects, even when taking into\n\n\naccount the low level of income in India. The country in the EU whose finances appear to most resemble\n\n\nIndia’s is Malta, a tiny country with a population of only 400,000, a mere neighborhood in one of India’s\n\n\nmajor cities. Bulgaria’s per capita income is six times that of India, but spending at the municipal level is\n\n\n18 times that of a sample of 35 of the larger municipal corporations in India. India’s municipal spending\n\n\nas a share of sub-national spending, at only about 5%, is a small fraction of even the most federalized\n\n\ncountry in this", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:004660:9:2:0", "start": 388, "end": 416, "surface": "World Development Indicators", "probe_tag": "keep", "probe_score": 0.9244, "luna_label": 1, "luna_reason": "Named indicator source cited for the presented comparative country data."}]}, {"key": "rafael2-038", "text": " -0.729 -0.414 0.083\n\n\n<u>Zimbabwe</u> <u>-25.998</u> <u>3.721</u> <u>-9.584</u> <u>-0.586</u> <u>-0.833</u> <u>-0.570</u> <u>-1.148</u>\nSource: Infrastructure, Telephones and Phones are the indexes calculated by the authors. Control of\nCorruption, Government Efficiency, Rule of Law and Regulatory Quality were obtained from Kauffmann,\nD.(2003). The information used to construct the airport infrastructure index was obtained from CIA World\nFact Book, 1990 - 2001, and the World Bank World Development Indicators (2002). The indexes of Roads\nand Telephones were contructed using the information of phones, roads, surface and population available in\nthe World Development Indicators (2002), World Bank.\n\nThe first three variables are in logarithms. All the indexes reported are only for year 2000.\n\n\n30", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:002631:29:2:2", "start": 654, "end": 682, "surface": "World Development Indicators", "probe_tag": "keep", "probe_score": 0.9604, "luna_label": 1, "luna_reason": "Indicators supplied information used to construct the airport infrastructure index."}]}, {"key": "rafael2-039", "text": "child height on these more precise measures of exposure to density of open defecation using\n\n\ndistrict and survey round fixed effects. As in Sastry and Hussey (2003), we use geographic\n\n\nfixed effects because they control for time-invariant properties of place at the level of the\n\n\nfixed effect, in this case the district. <sup>1</sup> The magnitude of the interaction that we identify in\n\n\nthe Bangladesh dataset is quantitatively similar to what is predicted for Bangladesh by a\n\n\nsemi-parametric model fit to the international data.\n\n\nThis paper proceeds in three sections. First, section 2 presents background on global\n\n\nsanitation and summarizes evidence from the literature about why poor sanitation would\n\n\nbe expected to have a larger effect on infant mortality and child height where population\n\n\ndensity is higher. Section 3 describes the analysis and presents results from the international\n\n\ndataset. Section 4 describes the analysis and presents results from the Bangladesh dataset.\n\n\nSection 5 discusses the findings. We point out that although, taken at face value, our results\n\n\nmight seem to recommend concentrating policy efforts on improving sanitation in urban\n\n\nareas, the distributions of sanitation coverage and population density in the world today\n\n\nshow that many of the places on earth where open defecation is most densely practiced are\n\n\nactually classified as rural. Indeed, our findings, combined with these empirical distributions,\n\n\nhighlight the threats to child health posed by the enduring density of open defecation in _rural_\n\n\nSouth Asia.\n\n#### **2 Background: Population density, sanitation and** **disease externalities**\n\n\nRural places have lower population density than urban places on average, but also have\n\n\nmore open defecation than urban places and lower quality sanitation, on average. Although\n\n\n1We do not present multi-level models because, as explained by Sastry and Hussey (2003), these models\nrequire the assumption that the random effects that are used in the models be independent of measured\ncovariates. This independence criterion is not met in this case; for example, more", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:prwp:006203:6:0:0", "start": 396, "end": 414, "surface": "Bangladesh dataset", "probe_tag": "keep", "probe_score": 0.931, "luna_label": 1, "luna_reason": null}, {"key": "sample:prwp:006203:6:0:1", "start": 517, "end": 535, "surface": "international data", "probe_tag": "keep", "probe_score": 0.9527, "luna_label": 1, "luna_reason": null}, {"key": "sample:prwp:006203:6:0:2", "start": 890, "end": 913, "surface": "international\n\n\ndataset", "probe_tag": "keep", "probe_score": 0.9873, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-040", "text": "Penh, exporting firms, and those with poor access to formal finance suffered more losses of sales and falls\n\nin profits. The deeper impact on the profits for less productive firms suggests a well-functioning market\n\nwhere more efficient firms weather downturns better than less efficient firms. The mechanism at play is\n\nthat less efficient firms had to cut output prices to maintain market shares since they could not compete\n\non quality, hence leading to a deeper impact on profits. <sup>5</sup> [^5: We have run similar regressions for changes in output prices and find that firms in the bottom tercile of the\nproductivity distribution cut their prices by 4% relative to firms in the top two terciles of the distribution.]\n\n\nBased on the CRBS 2009 survey, an estimated 4 percent of formal firms went bankrupt since the previous\n\nICS 2007/2008 survey, with another 5 percent closing for seasonal or other reasons (weighted figures).\n\nThe large number of bankruptcies is at odds with the lack of formal processes to close a business (in the\n\n“Doing Business” publication, Cambodia ranks 149 out 183 of countries in the ‘resolving an insolvency’\n\nindicator, World Bank, 2011)). This suggests that a number of firms closed without due process (and with\n\nsome overdue wage payments). In the garment sector, the likelihood of bankruptcy was 18.5 percent. <sup>6</sup> [^6: Weighted figure. This is in line with Ministry of Commerce data, showing that the number of factories declined\nfrom 308 in June 2008 to 257 in June 2009 (a fall of 16.3 percent; personal communication to the authors).] In\n\ncolumns (5) and (6) we report the marginal effects from a descriptive Probit regression for the probability\n\nthat a firm went bankrupt between the 2007/2008 and 2009 surveys. Tourism businesses and more\n\nproductive firms were less likely to go bankrupt, but larger firms were, surprisingly, more likely to go\n\nbankrupt. The", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:005384:9:0:0", "start": 741, "end": 757, "surface": "CRBS 2009 survey", "probe_tag": "keep", "probe_score": 0.9661, "luna_label": 1, "luna_reason": "Survey data support the reported bankruptcy estimate."}]}, {"key": "rafael2-041", "text": "### **3. Summary of Key Findings**\n\nThis section highlights some of the key characteristics of informal entrepreneurs and their\n\nbusinesses based on the data covering seven countries and twenty-four cities. Informal businesses\n\ndiffer from formal firms along several key dimensions, which we also explore for countries where\n\nthe survey data is available covering formal firms of comparable size. In five of the countries\n\nincluded in the analysis (India, Mozambique, Somalia, Zambia, and Zimbabwe), surveys of micro\nenterprises, (i.e., formally registered business with fewer than five employees) were also\n\nconducted roughly at the same time as the Informal Sector Enterprise Surveys. <sup>7</sup> [^7: While the survey for Iraq also covers micro-enterprises, data collection for the micro-enterprises survey has not\nbeen finalized at the time of this writing, and thus not used in the computation of subsequent formal sector\ncomparisons.] We use this data,\n\nwhere possible, to provide an overall comparison of informal businesses with micro-enterprises.\n\nTable 2 provides details and results of these simple comparisons across multiple dimensions. The\n\nanalysis in this section is purely exploratory without aiming to uncover causal relations underlying\n\nthe patterns that we highlight.\n\n###### **3.1 Characteristics of Informal Business Owners**\n\n\nWe find that owners of informal businesses across the seven countries are on average in\n\ntheir late 30s and early 40s, with an overall median age of about 36 years. <sup>8</sup> [^8: In what follows, estimates at the city level are calculated using sampling weights. When aggregating across cities\nand countries, each country is weighted as one.] Only 3% of owners are\n\nover 60 years old. About half of the business owners are 35 years old or younger, ranging from\n\n61% in Somalia to about 35% in India. There is no difference in age between female and male\n\nowners, with each averaging around 38 years of age, respectively. We", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000667:12:0:1", "start": 330, "end": 341, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.943, "luna_label": 1, "luna_reason": "Survey data are used for formal-firm comparisons where available."}]}, {"key": "rafael2-042", "text": "**Table D11. Results of simulation reported for the full sample with LAYS Data and the subsample with PISA Data**\n\n\n<u><mark>Changes in</mark></u> <u><mark>Optimistic</mark></u> <u><mark>Intermediate</mark></u> <u><mark>Pessimistic</mark></u>\n**<u><mark>Full sample (174 Countries)</mark></u>**\n\n6.9 6.7 6.7\n<u>Learning Adjusted Years of Schooling (LAYS)</u>\n\n-1,289 -1,598 -1,743\n<u>Per student average earning loss in annual terms ($)</u>\n\nPer student average lifetime earning loss at present\n-23,514 -29,162 -31,800\n<u>value ($)</u>\n\nAggregate economic cost of forgone earnings at\n16.6 20.6 22.8\npresent value ($ trillions)\n\nAggregate economic cost as a share of total\n3.9 4.6 4.9\nspending on basic education (%)\n\n**<u><mark>PISA Subsample (92 Countries)</mark></u>**\n\n8.4 8.2 8.1\n<u>Learning Adjusted Years of Schooling (LAYS)</u>\n\n-1,802 -2,262 -2,475\n<u>Per student average earning loss in annual terms ($)</u>\n\nPer student average lifetime earning loss at present\n-32,882 -41,276 -45,160\nvalue ($)\n\nAggregate economic cost of forgone earnings at\n15.3 19.1 21.2\npresent value ($ trillions)\n\nAggregate economic cost", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000989:46:0:0", "start": 69, "end": 78, "surface": "LAYS Data", "probe_tag": "confusion", "probe_score": 0.7438, "luna_label": 0, "luna_reason": "Span occurs in a table title introducing simulation results."}, {"key": "prwp:000989:46:0:1", "start": 102, "end": 111, "surface": "PISA Data", "probe_tag": "confusion", "probe_score": 0.2524, "luna_label": 0, "luna_reason": "Span occurs in a table title describing simulation results."}]}, {"key": "rafael2-043", "text": "We considered all observational research including, but not limited to, studies using quantitative\n\n\ndata that is longitudinal or cross-sectional, individual or aggregate (e.g., at the level of schools) on\n\n\nthe effect of the COVID-19 pandemic on learning loss among students.\n\n\nTo be included in our sample, the outcome measure must be a valid measure of learning progress.\n\n\nThese include measures using scores from school-based tests or from assessments administered by\n\n\nresearchers or international assessment programs, including EGRAs, and any study that can be\n\n\nscaled to the Harmonized Learning Outcomes (HLO) (Angrist et al 2021). In addition, studies must\n\n\nhave measured learning loss with pre-COVID control data, and during or post-COVID treatment\n\n\ndata. Studies using projections and simulations were excluded.\n\n\nSecondary research such as systematic reviews were excluded from the analysis but used to\n\n\nidentify further relevant studies. Given the focus on the COVID-19 pandemic, we restricted our\n\n\nsearch to papers published between March 1, 2020, and March 1, 2022.\n\n\nStudies were screened according to the following selection criteria: Studies that conducted student\n\n\nanalyses and reported impacts on learning progress (either positive, negative, or insignificant)\n\n\nbecause of COVID-19 school disruptions were included. Factors for rejecting studies from our\n\n\nreview included the absence of student analyses or recorded impacts on learning progress, analyses\n\n\noccurring before the onset of COVID-19, or hypothesized results. We collected study details such\n\n\nas author name(s), sample size, information on the age of students in sample, length of closures,\n\n\nmeasures of social background recorded, outcomes measured, follow-up period(s), and the effect\n\n\nsizes. We recorded the data in an MS Excel spreadsheet. We assessed the methodological rigor of\n\n\neach study and subsequently rated the robustness of the overall body of evidence across all\n\n\noutcomes.\n\n\n5", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000588:6:0:1", "start": 702, "end": 724, "surface": "pre-COVID control data", "probe_tag": "confusion", "probe_score": 0.1864, "luna_label": 1, "luna_reason": "Existing control data used to measure COVID-era learning loss."}]}, {"key": "rafael2-044", "text": "##### **References**\n\nAiken, E. L., Bedoya, G., Blumenstock, J. E., and Coville, A. (2023). Program targeting with machine\n\n\nlearning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan. _Journal_ _of_\n\n\n_Development_ _Economics_, 161:103016.\n\n\nAthey, S., Tibshirani, J., and Wager, S. (2019). Generalized random forests.\n\n\nAtkinson, A. B. (2019). _Measuring_ _Poverty_ _around_ _the_ _World_ . Princeton University Press.\n\n\nBattese, G. E., Harter, R. M., and Fuller, W. A. (1988). An error-components model for prediction of county\n\n\ncrop areas using survey and satellite data. _Journal_ _of_ _the_ _American_ _Statistical_ _Association_, 83(401):28–36.\n\n\nBeegle, K., Christiaensen, L., Dabalen, A., and Gaddis, I. (2016). _Poverty_ _in_ _rising_ _Africa_ . World Bank.\n\n\nBesley, T. and Kanbur, R. (1991). _The_ _principles_ _of_ _targeting_ . Springer.\n\n\nBlumenstock, J., Cadamuro, G., and On, R. (2015). Predicting poverty and wealth from mobile phone\n\n\nmetadata. _Science_, 350(6264):1073–1076.\n\n\nBlumenstock, J. E. (2016). Fighting poverty with data. _Science_, 353(6301):753–754.\n\n\nButar, F. B. and Lahiri, P.", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:001079:45:0:0", "start": 581, "end": 606, "surface": "survey and satellite data", "probe_tag": "confusion", "probe_score": 0.1072, "luna_label": 0, "luna_reason": "Generic phrase appears within a bibliography reference title."}]}, {"key": "rafael2-045", "text": "**Figure 2: Estimated levels of urbanization using different methods and data sets**\n\n\n100%\n\n90%\n\n80%\n\n70%\n\n60%\n\n50%\n\n40%\n\n30%\n\n20%\n\n10%\n\n0%\n\n\n\nUN AI - LandScan AI - GHSPop EC - LandScan\n\n(All urban\n\nclusters)\n\n\n\nEC - LandScan\n\n(HD clusters\n\nonly)\n\n\n\nEC - GHSPop\n\n(All urban\n\nclusters)\n\n\n\nEC - GHSPop\n\n(HD clusters\n\nonly)\n\n\n\nWorld LAC SSA SAR ECA MENA EAP N. America\n\n**Note:** EC refers to the cluster method for the consistent measurement of urbanization. “All urban clusters” refers to the\nshare of population living in all urban clusters, while “HD clusters” refers to the share of population living just in highdensity clusters.\n\nThe conclusion to be drawn from the above is that, when measured consistently, LAC appears far\nless urban relative to other regions than we are generally led to believe. This finding is consistent\nwith the use of more relaxed standards for classifying areas as urban in LAC versus non-LAC\ncountries (see Section 2). It also confirms findings reported by Uchida and Nelson (2010) and the\nWorld Bank (2008) using the original version of the AI.\n\n_4.2. Re-assessing LAC’s position in relation to the basic stylized facts_\n\nTables 2 and 3 show results from two basic sets of regressions. In Table 2, the (natural log) of a\ncountry’s GDP per capita level is regressed on its level of urbanization. Meanwhile, in Table 3, a\ncountry’s level of urbanization is regressed on the share of its national GDP that is generated by\nagriculture. In both tables, columns (1) – (3) report results obtained when using the WUP data,\nwhich are based on national definitions, to measure levels of urbanization, while columns (4) – (6)\nreport the corresponding results when instead using the AI with _LandScan", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:007011:14:0:0", "start": 218, "end": 226, "surface": "LandScan", "probe_tag": "confusion", "probe_score": 0.7209, "luna_label": 0, "luna_reason": "Standalone figure label, not an independently cited data-use mention."}, {"key": "prwp:007011:14:0:2", "start": 256, "end": 262, "surface": "GHSPop", "probe_tag": "confusion", "probe_score": 0.8993, "luna_label": 1, "luna_reason": "Named population dataset used to estimate urbanization levels in the figure."}]}, {"key": "rafael2-046", "text": "Figure 7. Unconditional β regional population-weighted convergence or divergence\n\n\n(β coefficient with the 95 confidence interval)\n\n\nUnited States China India\n\n\n\n0.1 ~~0~~\n\n\n0.0 ~~0~~\n\n\n-0.1 ~~0~~\n\n\n-0.2 ~~0~~\n\n\n\n**Years**\n\n\n\n0.0 ~~4~~\n\n\n0.0 ~~2~~\n\n\n0.0 ~~0~~\n\n\n-0.0 ~~2~~\n\n\n-0.0 ~~4~~\n\n\n\n**Years**\n\n\n\n\n\n0.1 ~~2~~\n\n\n0.0 ~~8~~\n\n\n0.0 ~~4~~\n\n\n0.0 ~~0~~\n\n\n-0.0 ~~4~~\n\n\n\n\n\n**Years**\n\n\n\n0.0 ~~4~~\n\n\n0.0 ~~2~~\n\n\n0.0 ~~0~~\n\n\n-0.0 ~~2~~\n\n\n-0.0 ~~4~~\n\n\n\nIndonesia Brazil\n\n\n0.04\n\n\n0.02\n\n\n\n\n\n0.00\n\n\n-0.02\n\n\n-0.04\n\n\n\n\n\n**Years**\n\n\n\n**Years**\n\n\n\nNote: the regression is ROG(t-5,t)=β0+β1 log GDP (t-5) (population weighted) where ROG=annualized per\ncapita growth rate between t-5 and t, and GDP = real GDP per capita in year t-5 (all expressed in constant\ninternational 1995 dollars). Indonesian data exclude oil and gas portion of GDP.\n\n\n31", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:002913:30:0:0", "start": 770, "end": 785, "surface": "Indonesian data", "probe_tag": "confusion", "probe_score": 0.591, "luna_label": 1, "luna_reason": "Indonesian data underpin the reported GDP convergence regression analysis."}]}, {"key": "rafael2-047", "text": " Expenditure Survey, as in the\n\n\n\ndata for the years 2007 and 2014 from the High Commission for Planning (HCP). To\n\nharmonize the DHS data and HCP Household Consumption Expenditure Survey, as in the\n\nlatter there is no information on the number of births by women, we construct variables on\n\n\n\nharmonize the DHS data and HCP Household Consumption Expenditure Survey, as in the\n\nlatter there is no information on the number of births by women, we construct variables on\n\nchildren based on the age of the youngest member of the household. As with DHS data, only\n\n\n\nlatter there is no information on the number of births by women, we construct variables on\n\nchildren based on the age of the youngest member of the household. As with DHS data, only\n\nmarried, widowed or divorced women are taken into account.\n\n\n\nmarried, widowed or divorced women are taken into account.\n\n\n\nmarried, widowed or divorced women are taken into account.\n\nNext, to study longitudinal effects of unilateral divorce reforms on women’s labor outcomes,\n\nwe build a pseudo-panel based on repeated cross-sectional surveys for several periods\n\n\n\nNext, to study longitudinal effects of unilateral divorce reforms on women’s labor outcomes,\n\nwe build a pseudo-panel based on repeated cross-sectional surveys for several periods\n\nbefore and after the date of the reform. We follow cohorts of women that share two time\n\n\nwe build a pseudo-panel based on repeated cross-sectional surveys for several periods\n\nbefore and after the date of the reform. We follow cohorts of women that share two time\ninvariant characteristics, namely gender and year of birth, and make sure that each cohort is\n\n\n\nbefore and after the date of the reform. We follow cohorts of women that share two time\ninvariant characteristics, namely gender and year of birth, and make sure that each cohort is\n\nlarge enough to reduce measurement errors (see Verbeek and Nijman, 1992, 1993; Deaton\n\n\n\ninvariant characteristics, namely gender and year of birth, and make sure that each cohort is\n\nlarge enough to reduce", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:001171:19:3:3", "start": 1057, "end": 1089, "surface": "repeated cross-sectional surveys", "probe_tag": "confusion", "probe_score": 0.1585, "luna_label": 1, "luna_reason": "Existing surveys underpin construction of a pseudo-panel for labor-outcome analysis."}]}, {"key": "rafael2-048", "text": "line. BPL households are APL energy and fixed charges if their consumption exceeds 50 kWh of\nconsumption during a billing cycle.\n\nThe IBT schedule seeks to provide a minimum amount of electricity at an affordable cost to\nlow-income/low-consumption households. IBTs also enable a utility to achieve its revenue goals, by\nderiving a larger share of its revenue from high-income and high-consumption households. From the\nperspective of balancing affordability and revenue sufficiency goals, designing an efficient IBT schedule\namounts to separating households into distinct groups based on their willingness and ability to pay for\nelectricity and using observable household characteristics to optimally setting consumption blocks at\nan appropriate marginal price.\n\nIBTs are not unique to India and the estimation of residential electricity demand under IBT\nschedules has been studied extensively in developed country settings. Reiss and White (2001) use a\nrepresentative sample of California households, and summarize how the structure of electricity demand\nvaries across customers. <sup>6</sup> The model is then used to analyze the effect of tariff changes on changes in\nconsumption and the share total monthly expenditure a household spends on electricity.\n\nMcRae (2015) is one of the few papers that conducts a similar exercise in a developing country\nsetting, <sup>7</sup> by building an asset ownership model using Colombian census data and pairing it with a\nutility’s administrative billing data, to show that government subsidies for electricity programs\ndisincentivizes greater investments in electricity infrastructure and ensnares poorer households in a\nlow-level subsidy trap. McRae (2015) uses the demand estimation under non-linear pricing econometric\nmodeling framework developed by Hanemann (1984) <sup>8</sup> to recover the parameters of household-level\npreference functions.\n\nMore recently, Wolak (2016) applies an enriched version of this modeling framework to water\nutility customers in California and uses it to find price schedules that ‘’optimally", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000020:7:0:0", "start": 953, "end": 999, "surface": "representative sample of California households", "probe_tag": "confusion", "probe_score": 0.8465, "luna_label": 1, "luna_reason": "Sample is used to analyze electricity demand and tariff-change effects."}, {"key": "prwp:000020:7:0:1", "start": 1418, "end": 1439, "surface": "Colombian census data", "probe_tag": "confusion", "probe_score": 0.8728, "luna_label": 1, "luna_reason": "Census data used to build an asset ownership model and support subsidy findings."}]}, {"key": "rafael2-049", "text": "**3.1 Data and descriptive statistics**\n\n\nOur data are from two nationally-representative surveys in Malawi, namely the Fourth Integrated\n\nHousehold Survey 2016/17 (IHS4), a cross-sectional survey of 12,480 households, and the Integrated\n\nHousehold Panel Survey 2016 (IHPS), a longitudinal survey of 2,508 households that had been followed\n\nsince 2010. <sup>7</sup> The IHS4 asked the most knowledgeable household member to provide information on\n\nhousehold members’ ownership of and rights to agricultural parcels and other assets. <sup>8</sup> On the other hand,\n\nthe IHPS aimed to conduct private interviews with each adult household member on his/her personal\n\nownership of and rights to the residential and each agricultural parcel, based on a common roster of parcels\n\nthat is used across all private interviews in each household. In contrast to the IHS4, the IHPS questions on\n\nland rights for a parcel were asked only if the respondent identified him or herself as a (co)-owner. <sup>9</sup>\n\n\nThe mean values for key variables at the household- and parcel-level from the analysis sample of parcels\n\nthat were reported to be owned are provided in Table 1 for the IHPS (column 1) and the IHS4, split into the\n\ntotal sample (column 2) and the sample of respondents claiming to be owner or co-owner of the parcel in\n\nquestion (column 3). The average household has about 5 members, with a 44-year old head. Eighty percent\n\nof heads of households are Christian; approximately 30 percent are female; 70 percent can read and write\n\nChichewa; and have an average of 5.5 years of education. The mean area for agricultural parcels is 0.4\n\nhectare and the incidence of organic fertilizer application and cash crop cultivation is 23 percent and 6\n\npercent, respectively. The", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:001750:10:0:2", "start": 1171, "end": 1175, "surface": "IHPS", "probe_tag": "confusion", "probe_score": 0.8792, "luna_label": 1, "luna_reason": "Named panel survey data provide mean values reported in the analysis table."}]}, {"key": "rafael2-050", "text": "<u>Table 5: Changes in Assets for households in other urban areas in Kenya, 1993-2003.</u>\n\n\n1993 to 1998 to 1993 to\n\n<u>WMS 1997</u> <u>1997</u> <u>2003</u> <u>2003</u>\n\n_<u>Household Demographics</u>_\n<mark>Dependency Ratio</mark> <mark>0.410</mark> <mark>0.24</mark> <mark>-0.005</mark> <mark>0.000</mark> <mark>-0.003</mark>\n_<u>Household Education</u>_\n<mark>Dummy: HH head with primary education</mark> <mark>0.330</mark> <mark>0.47</mark> <mark>0.01</mark> <mark>0.09</mark> <mark>**</mark> <mark>0.10</mark> **\nDummy: HH head with post secondary education 0.085 0.28 0.04 ** 0.07 ** 0.11 **\n_Housing & Assets_\nDummy: Flush toilet 0.357 0.48 -0.14 ** -0.15 ** -0.28 **\nDummy: Owns a radio 0.775 0.42 0.04   - 0.02 0.10 **\nDummy: Owns a TV 0.308 0.46 0.00 0.02 0.09 **\nDummy: Owns a refrigerator 0.136 0.34 -0.03 + -0.04 ** -0.06 **\n_<u>Cluster & District Characteristics</u>_\n<mark>Cluster", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:003024:38:0:0", "start": 121, "end": 129, "surface": "WMS 1997", "probe_tag": "confusion", "probe_score": 0.1117, "luna_label": 0, "luna_reason": "Standalone table header fragment, not an independently used data citation."}]}, {"key": "rafael2-051", "text": " build a data set of individual microentrepreneurs (MEI) using the registry of firms from\nthe Brazilian tax authority (SRF) for 2012 through 2020. MEI is a type of firm with a simplified\nregistration process, created in 2008 by Complementary Law 128. The only tax MEIs need to pay\nis a flat monthly fee below USD 15, whose major component is a contribution to the public pension\nsystem. An MEI is subject to a revenue cap of BRL 81,000 (about USD 17,000) per year and can\nhire at most one employee. Registration as an MEI (as opposed to operating without registration)\nhas the advantage that it allows entrepreneurs to provide receipts to their customers, increasing the\npool of partners with whom they can conduct business. The name of an MEI is automatically\ngenerated as the name of its owner concatenated with the CPF, allowing us to match the MEI\ndatabase with our sample via the CPF. At the end of 2019, the SRF registry of firms included 12.8\nmillion MEIs, representing about 42 percent of all firms in the SRF registry. <sup>9</sup> [^9: For the pre-2018 period, we drop 1.37 million MEIs that were determined to be inactive in a 2018 audit. Including\nthese MEIs in the analysis does not change our findings.] We do not use data\non other types of firms in our analysis since they have identification numbers that are not clearly\nlinked to the CPF of the owner.\n\nFourth, we use data on formal employment from RAIS, <sup>10</sup> [^10: RAIS is the Annual Report of Social Information (Relação Anual de Informações Sociais).] a database maintained by the Ministry\nof Labor. In RAIS, all employers in Brazil are required to report their employees who have a\nwritten contract. RAIS also includes information on wages, education, gender, sector, and type of\noccupation. However, RAIS is not designed to capture business owners or the self-employed", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000903:12:1:0", "start": 848, "end": 860, "surface": "MEI\ndatabase", "probe_tag": "confusion", "probe_score": 0.3248, "luna_label": 1, "luna_reason": "MEI database is used to match the sample via CPF."}, {"key": "prwp:000903:12:1:2", "start": 1014, "end": 1026, "surface": "SRF registry", "probe_tag": "keep", "probe_score": 0.9558, "luna_label": 1, "luna_reason": "SRF firm registry data are used to build and match the analysis dataset."}]}, {"key": "rafael2-052", "text": "Figure 2: Distribution of High-Capacity Highways by Government Entity, 2015\n\n\n_Notes:_ Authors’ calculations based on official road statistics from INEGI (2015).\nHighways under the responsibility of the federal government includes selfadministered roads and concessions to the private sector.\n\n###### **B. Election Data**\n\n\nTo understand the politics of road investment in Mexico, we complement our road\n\n\ndata with legislative election outcomes. We focus on the single ballot plurality election\n\n\nresults for deputies using municipality-level data compiled by the Federal Electoral In\n\nstitute (IFE). There were six legislative electoral cycles between 1993 and 2012 held in the\n\n\nyears 1994, 1997, 2000, 2003, 2006, and 2009. To combine the election outcomes with the\n\n\nroad data, we match each sequential pair of the difference in road length to the earliest\n\n\nelection that occurred in the period. This leaves us with the following relevant election\n\n\nyears (and corresponding road data periods): 1994 (1993-1998), 2000 (1998-2003), 2003\n\n\n(2003-2008), and 2009 (2008-2012). We depict in Figure 3 the legislative election dates,\n\n\nhighlighting (in red) the years we match to the road data. Given our primary goal to\n\n\nestimate the effect of the president’s party status on road construction, we contrast mu\n\nnicipalities where the president’s party won versus locations where the president’s party\n\n\nfailed to secure the simple majority. Furthermore, in the spirit of the RDD, we construct\n\n\n9", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:007617:11:0:1", "start": 981, "end": 990, "surface": "road data", "probe_tag": "confusion", "probe_score": 0.7532, "luna_label": 1, "luna_reason": "Road data are matched to election periods in the investment analysis."}]}, {"key": "rafael2-053", "text": "rate for a model. The measure is computed across all instances of a given model’s estimation with\n\n\na unique pair of a base survey and a target survey in a given country. These models include the\n\n\ncore Models 1 to 9 (shown in Tables A <mark>.1 to A.6) a</mark> nd four additional models where we further add\n\n\ngeospatial variables to Models 2 and 9.\n\n\nRegarding headcount poverty, Figure 1 suggests that for the first nine models, Models 3 and 9\n\n\nperform better than average with an imputation accuracy of, respectively, 65 and 69 percent,\n\n\nfollowed by Model 8 (50 percent). Adding agricultural soil quality and geospatial characteristics,\n\n\nsuch as soil index and distance to facilities, significantly improved the prediction of Model 9 up to\n\n\n70 and 75 percent, respectively, but it does not help to improve Model 3. On the other hand, adding\n\n\ngeospatial nightlight information to Model 2 increases the accuracy of the prediction up to 67\n\n\npercent for Viet Nam. Moreover, adding nightlight information to Model 9 increases accuracy up\n\n\nto 83 percent for Viet Nam.\n\n\nRegarding near-poverty, both Model 3 and Model 9 perform better than average with an\n\n\nimputation accuracy of, respectively, 77 and 69 percent, followed by Models 8 and 5 (both up to\n\n\n65 percent). However, adding geospatial characteristics, such as soil quality and distance to\n\n\nfacilities to Model 9, marginally improves imputation accuracy up to 70 percent. Adding nightlight\n\n\nto Model 9 for Viet Nam does not help to improve Model 9.\n\n\nRegarding extreme poverty, Model 3 has the highest imputation accuracy for across all the\n\n\ndifferent models tested – about 69 percent, followed by Model 9 (54 percent) and Model 4 (46\n\n\npercent). Model 3 also has the highest imputation accuracy for the poverty gap, as it raises the\n\n\nimputation accuracy above the average model performance to 65 percent.\n\n\n20", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:001549:21:0:0", "start": 119, "end": 130, "surface": "base survey", "probe_tag": "confusion", "probe_score": 0.3621, "luna_label": 1, "luna_reason": "Base survey is used as an input to poverty-imputation models."}]}, {"key": "rafael2-054", "text": "_3.4 Limitations of the study_\n\n\nThis assessment of the impact of the JCLIS relies on simple comparisons of the average outcomes\n\n\nof interest between participants of the lift irrigation system and their neighbors in the same village\n\n\nor in a neighboring village who do not participate. We use self-reported recall data collected from\n\n\nfarmers through in-person interviews with them. JCLIS sites and their beneficiaries were not\n\n\nselected randomly. Site selection was guided by hydrological and topological characteristics.\n\n\nFarmers with land closer to favorable sites may differ from their neighbors in skills, motivation,\n\n\nsocial capital, and other qualities, any of which can also affect agricultural outcomes. Farmers\n\n\nwere moreover required to pay a small one-time Rs. 1,100 fee and join the local water user group\n\n\nto irrigate their land from the JCLIS, and their decision to do so may also differentiate them from\n\n\nothers who chose to stay out or wait longer before joining the group. The comparisons made are\n\n\ntherefore qualified as suffering from omitted variables bias. Nor did the study benefit from baseline\n\n\ndata on variables of interest prior to the intervention. Attempts to collect recall data from farmers\n\n\nwith questions about cropping areas, patterns, and yields before the JOHAR irrigation systems\n\n\nwere introduced led to unreliable data, particularly for earlier years, and were discontinued. We\n\n\nalso considered revisiting farmers who were interviewed in the baseline survey carried out by\n\n\nOxford Policy Management (OPM), but at the time of our survey (August-September 2021), very\n\n\nfew villages in the baseline survey had a functioning JCLIS. We compare beneficiary and non\n\nbeneficiary farmers across economic variables that presumably do not change over time or as a\n\n\nresult of the JCLIS irrigation. These include average land holding size and asset holdings. We find\n\n\nthat the two are not significantly different across the two groups, providing some evidence of\n\n\nbaseline comparability. In an alternate specification, we drop the control farmers from the\n\n\nneighboring villages and compare JCLIS beneficiaries only with their geographical neighbors\n\n\nwhile", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:prwp:001011:8:0:0", "start": 295, "end": 320, "surface": "self-reported recall data", "probe_tag": "confusion", "probe_score": 0.6422, "luna_label": 0, "luna_reason": null}, {"key": "sample:prwp:001011:8:0:1", "start": 1120, "end": 1135, "surface": "baseline\n\n\ndata", "probe_tag": "confusion", "probe_score": 0.6875, "luna_label": 0, "luna_reason": "States baseline data were unavailable, without analyzed findings or substitute use."}, {"key": "sample:prwp:001011:8:0:2", "start": 1208, "end": 1219, "surface": "recall data", "probe_tag": "confusion", "probe_score": 0.376, "luna_label": 0, "luna_reason": null}, {"key": "sample:prwp:001011:8:0:3", "start": 1494, "end": 1509, "surface": "baseline survey", "probe_tag": "confusion", "probe_score": 0.7531, "luna_label": 1, "luna_reason": null}, {"key": "sample:prwp:001011:8:0:4", "start": 1641, "end": 1656, "surface": "baseline survey", "probe_tag": "confusion", "probe_score": 0.8726, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-055", "text": "across the different sections of the Excel file, the user is provided with a button for the\n\n\n_Navigation Tree_, where the analyst can click on the element of interest (see Figure B.1).\n\n\nIn addition, (advanced) users can add new country data sets to ISIM and change other aspects of\n\n\nthe GEM‐Core program by editing relevant files in the ISIM\\GEM‐Core installation folder, using\n\n\nExcel and a text editor.\n\n\n**The reference scenario**\n\n\n<mark>The first step in performing counterfactual simulations with ISIM is the construction of a</mark>\n\n\n<mark>(dynamic) baseline scenario. To help the user carry out this task, ISIM includes, for each country</mark>\n\n\n<mark>data set, a pre‐defined reference scenario. Key parameters of this scenario can be changed</mark>\n\n\n<mark>inside ISIM, including t</mark> he GDP growth rate, all model elasticities (including those related to\n\n\ntrade, household expenditure, the reservation wage, and the impact of share of trade in real\n\n\nGDP on total factor productivity), as well as closures and other rules, the latter covering the\n\n\ngovernment budget, the balance of payments, the savings‐investment balance, factor\n\n\nmarkets, and various payments, split into government and non‐government depending on\n\n\nwhether the government is involved or not. The rule chosen for any payment is overwritten\n\n\nif, according to the related closure setting, the payment in question is a free variable; for\n\n\nexample, the specification that direct taxes are determined on the basis of exogenous tax\n\n\nrates is overwritten if, according to the government closure rules, changes in direct taxes\n\n\nclear the government budget. In addition, the user can configure the ISIM Poverty Module\n\n\n(see Lofgren and Cicowiez (2017b)). Specifically, the parameters of the Poverty Module allow\n\n\nthe user to change: (1) the approach to", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:007230:58:0:0", "start": 230, "end": 247, "surface": "country data sets", "probe_tag": "confusion", "probe_score": 0.7217, "luna_label": 0, "luna_reason": "Users can add new datasets; the sentence describes data creation, not existing data use."}]}, {"key": "rafael2-056", "text": "## **Welfare Effects of Introducing Competition in** **the Telecom Sector in Djibouti***\n\n###### Xavier Decoster, Gabriel Lara Ibarra, Vibhuti Mendiratta and Marco Santacroce\n\n**_Key words:_** Digital Economy, telecom, market structure, welfare\n\n\n**_JEL codes:_** D60, D40\n\n\n*We are grateful to Carlos Rodriguez Castelan, Eduardo Malasquez and Axel Rifon Perez for\ntheir insightful comments. The authors also acknowledge the collaboration with the\nDirectorate for Statistics and Demographic Studies in Djibouti that made the microdata of the\nEDAM 2017 survey available for this work. Contact the authors at:\n<u>glaraibarra@worldbank.org</u> and vmendiratta@worldbank.org.", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:002400:2:0:0", "start": 542, "end": 558, "surface": "EDAM 2017 survey", "probe_tag": "confusion", "probe_score": 0.6636, "luna_label": 1, "luna_reason": "Named existing survey microdata made available for the analysis."}]}, {"key": "rafael2-057", "text": "###### Case data\n\nGeoreferenced cholera case data were available for the Harare area collected by the Ministry of\nHealth and Child Care (MoHCC) following the World Health Organization (WHO) guidelines.\nThe first case was recorded on September 4, 2018, and the last on January 9, 2019. <mark>WHO criteria</mark>\n<mark>for case diagnosis state that any patient aged 5 years or more presenting with acute watery diarrhea</mark>\n<mark>and severe dehydration where cholera is not known to be occurring, or any patient 2 years or older</mark>\n<mark>presenting with acute watery diarrhea where cholera is known to be occurring can be suspected as</mark>\n<mark>a cholera case. Confirmation is achieved by sampling a subset of patients during the course of the</mark>\n<mark>epidemic.</mark> <sup>2</sup> [^2: It is standard practice in epidemic response that ‘Once an outbreak is declared, there is no need to confirm all\n[suspected cases. The clinical case definition is sufficient to monitor epidemiological trends’. [26]](https://www.zotero.org/google-docs/?Xdbp3i)] <mark>Date of diagnosis, gender, age and geo-location of the case’s dwelling are also</mark>\n<mark>collected.</mark> For the purpose of spatio-temporal analyses, cases were aggregated across 7-day bins\nfrom September 1, 2018. Age and gender of the patients is not taken into account in this analysis.\nA total of 9,890 cases were included in the analysis after cleaning.\n\n###### Population Characteristics Data\n\n[Population density data were available from Facebook HDX[27]. Small Area Estimates (SAEs)](https://www.zotero.org/google-docs/?47LRrU)\nfor poverty produced by the World Bank were used for", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:001560:5:0:0", "start": 18, "end": 49, "surface": "Georeferenced cholera case data", "probe_tag": "confusion", "probe_score": 0.6018, "luna_label": 1, "luna_reason": "Existing case data were aggregated and analyzed for spatio-temporal analysis."}, {"key": "prwp:001560:5:0:1", "start": 1439, "end": 1470, "surface": "Population Characteristics Data", "probe_tag": "confusion", "probe_score": 0.0999, "luna_label": 0, "luna_reason": "Standalone section heading, not a cited or used data resource."}]}, {"key": "rafael2-058", "text": " training data in Burkina Faso, as seen\nin the clumped pattern of survey-measured asset wealth shown in\nthe right panel of Figure 1d. This difference is reflected in the\nresults. As shown in Figure 1a, a naive transformer model that\ndoes not condition on geo-features consistently outperforms other\nmodels across the Malawi, Mozambique, and Madagascar\ndatasets. In those countries, predictions using the transformer\n\nwhen trained on the full census extract. In Burkina Faso, because\nmodel achieve of the smaller effective sample size, XGBoost using satellite 𝑅𝑅 <sup>2</sup> values of 0.83, 0.70, and 0.62, respectively,\nimagery and geospatial features achieves the best average\nperformance among the models (62.9% of variation explained). A\nnaive transformer, when using only satellite imagery, remains\ncompetitive (57.4% of variation explained). We also limit the\nnumber of training samples to 1%, 5%, 10%, 25%, and 50% of the\noriginal training dataset to analyze how model performance varies\nwith training sample size. Based on the results from these four\ncountries, we empirically identify 10% as a critical inflection point\nfor model performance, below which the accuracy of the estimates\ndeteriorates rapidly.\n\nAnother key factor in reducing training sample collection costs\nfor wealth prediction is the number of households aggregated per\nsample. To analyze this factor, we randomly sample 10 households\nper administrative area to construct the training sample, yielding\na “10-household” training dataset for each country. We then train\na naive transformer model on this “10-household” training set\nwhile still evaluating its performance on the original full household test set. The results (Figure 1b) indicate that our transformer\nmodels trained with the 10-household data exhibit comparable\nperformance to those trained with data on all households. Using\nMalawi as an example, the 10-household data only include\napproximately 23% of all surveyed households", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:001373:3:2:0", "start": 1891, "end": 1908, "surface": "10-household data", "probe_tag": "confusion", "probe_score": 0.6409, "luna_label": 1, "luna_reason": "Training data are used for model evaluation and linked to a concrete household share."}]}, {"key": "rafael2-059", "text": "Bank (2005) data on sectoral value added, <sup>4</sup> [^4: The World Bank (2005) data on sectoral value added is complemented with statistics from the InterAmerican Development Bank and the United Nations.] and Purdue University’s Global Trade\n\n\nAnalysis Project database (GTAP, 2005) on labor shares.\n\n\nWe focus on changes occurring over long horizons, where the poverty reduction\n\neconomic growth relationship is most stable. For this reason we use only one spell per\n\n\ncountry, where the duration of the spell corresponds to the longest period for which initial\n\n\nand final poverty data exist for the country. The rest of the variables (e.g., value added\n\n\ngrowth rates and labor ratios) are calculated over the corresponding period per country.\n\n\nThe dependent variable is the proportional change in poverty over a period of\n\n\ntime (spell) per country. Specifically, this is the annualized change in poverty as\n\nproportion to average poverty over the period. <sup>5</sup> [^5: That is, proportional poverty change = <sup><u>1</u></sup>\n_T_ ( _PF_ + _PI_ ) / 2] Given its importance in the literature, the\n\n\nbenchmark poverty measure in the paper is the headcount poverty index, defined as the\n\n\nfraction of the population with income below a given poverty line. In robustness\n\n\nexercises, however, we use alternative measures of poverty, comprising other members of\n\n\nthe Foster-Greer-Thorbecke class of measures (the poverty gap and the squared poverty\n\n\ngap) and the Watts index. Following convention for cross-country comparability, the\n\n\npoverty line is set to $1 per person per day, converted into local currency using a\n\n\npurchasing-power-parity adjusted exchange rate.\n\n\nRegarding the explanatory variables, we work with growth rates of sectoral value\n\nadded and employment data at two levels of disaggregation. The first is the traditional\n\n\nsectoral division of agriculture, industry, and services. The second one disaggregates\n\n\nindustry further into mining, manufacturing, utilities, and", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:003290:9:0:0", "start": 12, "end": 40, "surface": "data on sectoral value added", "probe_tag": "confusion", "probe_score": 0.8014, "luna_label": 1, "luna_reason": "World Bank data support sectoral value-added analysis and are complemented by named statistics."}]}, {"key": "rafael2-060", "text": " sexual abuse. A more recent study of violence in\nBrazilian schools found that 8% of students from 5 <sup>th</sup> to 8 <sup>th</sup> grade had witnessed sexual\nviolence within the school environment (Abramovay and Franco, 2004). <sup>37</sup> [^37: The study covered middle schools in 14 Brazilian state capitals. The percentage of students who had\nwitnessed sexual violence ranged from a low of 5% in Vitoria (Espirito Santo) and Fortaleza (Ceara) to a high\nof 12% in Cuiaba (Mato Grosso).  The way in which the question was formulated does not permit identifying\nwhat percentage of girls were victimized by sexual violence.] Data on sexual\nviolence, however, remain spotty for Latin America and the Caribbean.\n\n\n35 An unsafe environment in school may dissuade parents from enrolling girls in school or may lead to\nincreased rates of school abandonment (WCRW, n/d). While this has been documented for the African context,\nit may or may not be important in Latin America and the Caribbean, where girls’ enrollment rates typically\nexceed boys’ rates.\n36 According to recent studies in six African countries, between 16% and 47% of girls in primary and\nsecondary schools report sexual abuse or harassment, with both male fellow students and male teachers\nresponsible for the abuse (Leach et al., 2003). In Botswana, 20% of female students reported having being\nasked by teachers for sexual relations (Rosetti, 2001, cited in Leach, 2003). In Cameroon, 8% of sexual abuse\ntowards girls was accounted for by teachers (Mbassa Menick, 2001, reported in Leach, 2003). The DHS survey\nin South Africa, surveying women between 15 and 49 years of age, found that 37.7% of all rape victims\nidentified a teacher or principal as the rapist (Medical Research Council, 2000). At the same time, girls in\nSouth Africa are more", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:prwp:002693:46:1:0", "start": 628, "end": 651, "surface": "Data on sexual\nviolence", "probe_tag": "drop", "probe_score": 0.0038, "luna_label": 0, "luna_reason": null}, {"key": "sample:prwp:002693:46:1:1", "start": 1566, "end": 1576, "surface": "DHS survey", "probe_tag": "keep", "probe_score": 1.0, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-061", "text": "**_Appendix A_**\n\n\n**_Appendix Table A.1: Sectoral Classification of Services_**\n\n\nSub-sectors in KLEMS\nSub-Sector Description\n<u>database: NACE 2-digit</u>\n\n40 Electricity, gas, steam and hot water supply (Non-ICT) [High skill]\n41 Collection, purification and distribution of water (Non-ICT) [High skill]\n50 Sale, maintenance and repair of motor vehicles and motorcycles; retail sale services of automotive fuel (Non-ICT) [Low skill]\n51 Wholesale trade and commission trade, except of motor vehicles and motorcycles (ICT user) [Low skill]\n52 Retail trade, except of motor vehicles and motorcycles; repair of personal and household goods (ICT user) [Low skill]\n55 Hotels and restaurants (Non-ICT) [Low skill]\n60 Land transport; transport via pipelines (Non-ICT) [Low skill]\n61 Water transport (Non-ICT) [Low skill]\n62 Air transport (Non-ICT) [High skill]\n63 Supporting and auxiliary transport activities; activities of travel agencies (Non-ICT) [High skill]\n64 Post and telecommunications (ICT producer) [High skill]\n65 Financial intermediation, except insurance and pension funding (ICT user) [High skill]\n66 Insurance and pension funding, except compulsory social security (ICT user) [High skill]\n67 Activities auxiliary to financial intermediation (ICT user) [High skill]\n70 Real estate activities (Non-ICT) [High skill]\n71 Renting of machinery and equipment without operator and of personal and household goods (ICT user) [High skill]\n72 Computer and related activities (ICT producer) [High skill]\n73 Research and development (ICT user) [High skill]\n74a* Legal, technical, and advertising (ICT user) [High skill]\n<u>74b*</u> <", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:003564:43:0:0", "start": 140, "end": 152, "surface": "NACE 2-digit", "probe_tag": "drop", "probe_score": 0.0087, "luna_label": 0, "luna_reason": "Standalone database header within an appendix table."}]}, {"key": "rafael2-062", "text": "**ABBREVIATIONS AND ACRONYMS**\n\n\nACB Akiba Commercial Bank\nBARA Business Activities Registration Act\nBDS Business Development Service\nBEST Business Environment Strengthening for Tanzania\nBOT Bank of Tanzania\nBRAC Bangladesh Rural Advancement Committee, Tanzania\nBRELA Business Registration Licensing Authority\nCEM Country Economic Memorandum\nCRDB Cooperative Rural Development Bank\nCSSC Christian Social Services Commission\nDDSDP Demand-driven Skill Development Program\nFBO Faith-based Organizations\nFDC Focal Development Colleges\nFGD Focus Group Discussion\nFINCA Foundation for International Community Assistance\nFSDT Financial Sector Deepening Trust\nGEMA Gender Education Management Association\nGTZ _Deutsche Gesellschaft für Technische Zusammenarbeit_\nHBS Household Budget Survey\nHE Household Enterprise\nISIC International Standard Industrial Classification\nIST Informal Sector Training\nILFS Integrated Labor Force Survey\nITEP Individual Training Evaluation Program\nLGA Local Government Authority\nMDA Ministries, Departments, and Agencies\nMFI Microfinance Institutions\nMITM Ministry of Industry and Trade, Marketing\nMKUKUTA _Mkakati wa Kukuza Uchumi na Kupunguza_ Umaskini Tanzania\n(National Strategy for Growth and Poverty Reduction)\nMKURABITA _Mpango wa Kurasimisha Rasilimali na Biashara za Wanyonge_\nTanzania (National Business and Property Formalization Program)\nMSME Micro- and Small and Medium Enterprises\nMNRT Ministry of Natural Resources and Tourism\nM&E Monitoring and Evaluation\nNACTE National Council for Technical Education\nNSGRP National Strategy for Growth and Reduction of Poverty\nNBS National Bureau of Statistics\nNGO Nongovernmental Organization\nNMP National Microfinance Policy\nPIN Personal Identification Number\nPMO Prime Minister‟s Office\nPTF Presidential Trust Fund\nPPA Participatory Poverty Assessment\n\n\nv", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:005058:7:0:1", "start": 890, "end": 924, "surface": "ILFS Integrated Labor Force Survey", "probe_tag": "drop", "probe_score": 0.0288, "luna_label": 0, "luna_reason": "Survey is only defined in an acronym list, with no data use or finding."}]}, {"key": "rafael2-063", "text": " the productivity of the transport sector or the costs of\n\n\ntransportation, and examine the four dimensions of trade facilitation noted above. We use a\n\n\ngravity model of bilateral trade flows for our estimations, rather than a computable general\n\n\nequilibrium (CGE) approach. The scenarios examined here do not assume that all countries\n\n\nin our sample (those that have acceded to the European Union or candidate members)\n\n\nimprove capacity by the same amount. To keep our scenarios realistic, we assume that\n\n\ncountries improve their trade facilitation capacity half-way to the EU15 level– the countries\n\n\n4 The data on port efficiency, customs regimes, regulatory policy and information technology\ninfrastructures for Cyprus, Malta and Croatia are not available. Given the relatively small economic\nsize of Cyprus and Malta, we focus on the study of the other eight new member countries of EU. Data\nfor Croatia are not currently available.\n\n\n5", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:prwp:003046:4:1:0", "start": 614, "end": 637, "surface": "data on port efficiency", "probe_tag": "confusion", "probe_score": 0.4694, "luna_label": 0, "luna_reason": null}, {"key": "sample:prwp:003046:4:1:1", "start": 897, "end": 913, "surface": "Data\nfor Croatia", "probe_tag": "drop", "probe_score": 0.0299, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-064", "text": "_WATER WHEN IT COUNTS_ _39_\n\n\nExperimentation and Communication.” _Review of Economic Studies_ .\n\n\n**Braca, Giovanni.** 2008. “Stage-discharge relationships in open channels: Practices and\n\n\nproblems.” _FORALPS Technical Report, 11_,, (1): 44.\n\n\n**Buytaert,** **W.,** **and** **et** **al.** 2014. “Citizen Science in Hydrology and Water Resources:\n\n\nOpportunities for Knowledge Generation, Ecosystem Service Management, and Sus\n\ntainable Development.” _Frontiers in Earth Science_, 2.\n\n\n**Climate** **Prediction** **Center,** **National** **Centers** **for** **Environmental** **Prediction,** **Na-**\n\n\n**tional** **Weather** **Service** **NOAA** **U.S.** **Department** **of** **Commerce.** 2015. “NOAA\n\n\nNCEP CPC CMORPH daily mean morphed cmorph: estimated precipitation data from\n\n\n23 Feb 2005 to present.”\n\n\n**Cole,** **S.,** **and** **A.** **N.** **Fernando.** 2016. “‘Mobile’izing Agricultural Advice: Technology\n\n\nAdoption, Diffusion and Sustainability.” Notre Dame University Working Paper.\n\n\n**Deichmann,** **Uwe,** **Aparajita** **Goyal,** **and** **Deepak** **Mishra.** 2016. “Will digital tech\n\nnologies transform agriculture in developing countries?” _Agricultural_ _", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:007294:40:0:0", "start": 699, "end": 747, "surface": "NOAA\n\n\nNCEP CPC CMORPH daily mean morphed cmorph", "probe_tag": "drop", "probe_score": 0.0425, "luna_label": 0, "luna_reason": "Bibliography entry naming a precipitation dataset without demonstrated analytical use."}]}, {"key": "rafael2-065", "text": " works, such as cleaning the sides of roads. When youths completed the two‐month\nemployment, they completed the program. This component of the Project was offered to\napproximately 12,500 eligible urban youths over the course of seven years, equivalent to\napproximately 80 percent of total program participants.\n\n\n(vi) Meanwhile, participants in Component 2 were provided with: i) one month (equivalent to 160\nhours) of pre‐employment training (PET) that aims to prepare them for employment in the\nprivate, public, or civil society sectors, and: ii) five months of on‐the‐job training (OJT) with a\nprivate or public company. The PET has two program tracks that participants can choose from\n‐‐ the first focuses on trade‐, industrial‐ and commerce‐related jobs, while the second focuses\non basic book keeping, data entry, business practices, and information technology skills. Each\nPET program track is expected to train about 2,000 eligible youths over the life of the Project.\n\n\nThe UYEP enrollment process is summarized in Table 1.\n\n\n9 In general, about 90% of youth screened through ESS are deemed eligible as the eligibility criteria are generally\ncommunicated by the Project and are known in the community before the enlisting takes place.\n\n\n7", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:007023:8:1:0", "start": 1085, "end": 1088, "surface": "ESS", "probe_tag": "drop", "probe_score": 0.0458, "luna_label": 1, "luna_reason": "ESS screening data support the reported 90 percent eligibility finding."}]}, {"key": "rafael2-066", "text": "Ordered probit,<br>conditional fe logit and<br>re probit|Oredered Probit|Ordered logit|Ordered logit|Ordered probit|Ordered Logit|\n|**Data**|Ad-hoc questionnaire in<br>two villages in Israel|US-GSS 1989-1996|8 countries study 1972-<br>1994|GSOEP 1985-1998|BHPS 1991-2002<br>(Employed)|WVS ( 1980 – 1982,<br>1990 – 1991, and 1995<br>– 1997 waves)|US-GSS 1972-1997|EU Eurobarometer<br>1975-1992|RLMS 1994-2000|Latinobarometro 2004,<br>17 LAM countries|\n|**Study**|Morawetz et al., 1977|Hagerty 2000|Hagerty 2000|Schwarze and Harpfer,<br>2002|Clark, 2003|Helliwell, 2003|Alesina et al. 2004|Alesina et al. 2004|Senik, 2004|Graham and Felton,<br>2006|\n\n\n\n27", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:004758:29:3:4", "start": 191, "end": 197, "surface": "US-GSS", "probe_tag": "drop", "probe_score": 0.0247, "luna_label": 1, "luna_reason": "Named survey dataset listed as the data source for the study's analysis."}, {"key": "prwp:004758:29:3:5", "start": 363, "end": 379, "surface": "EU Eurobarometer", "probe_tag": "confusion", "probe_score": 0.2673, "luna_label": 1, "luna_reason": "Named Eurobarometer survey series identified as the study's data source."}]}, {"key": "rafael2-067", "text": " students<br>assessed in all or in<br>selected grades of<br>primary and secondary<br>school every year.<br>|4|3.6|\n|**4.2 Use of**<br>**school**<br>**assessments**<br>**for making**<br>**school**<br>**adjustments**<br>|Schools are<br>not assessed.<br>Adjustments<br>made with<br>different<br>criteria.<br>|Ministry of<br>Education<br>analyzes school<br>assessment and<br>results sent to the<br>schools. Results<br>used in<br>pedagogical and<br>operational<br>adjustments.<br>|Ministry of Education<br>analyzes school<br>assessments and makes<br>direct recommendations<br>to regional and local<br>offices and to schools.<br>Schools use the<br>information to make<br>pedagogical and<br>operational<br>adjustments.<br>|Ministry of Education<br>or municipal<br>governments analyzes<br>school assessments.<br>Results easily<br>accessible to schools<br>and the public. Schools<br>use the information to<br>make pedagogical,<br>personnel, and<br>operational<br>adjustments.<br>|4|3.2|\n|**4.3**<br>**Frequency**<br>**of**<br>**standardize**<br>**d student**<br>**assessments**<br>", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:006115:32:1:0", "start": 781, "end": 799, "surface": "school assessments", "probe_tag": "drop", "probe_score": 0.0276, "luna_label": 1, "luna_reason": "Assessments are analyzed and used for pedagogical and operational adjustments."}]}, {"key": "rafael2-068", "text": "|Table 1 – Construction of Variables|Col2|Col3|Col4|Col5|\n|---|---|---|---|---|\n|<br>|**Risk Type**<br>**I, II, III**|**If variable appears**<br>**in the data set**|**If variable appears**<br>**in the data set**|**Used in**<br>**the**<br>**cluster**<br>**analysis**|\n|<br>|**Risk Type**<br>**I, II, III**|**Chile**|**Mexico**|**Mexico**|\n\n\n**<u>Behaviors/outcomes</u>**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n24", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:003584:25:0:0", "start": 154, "end": 162, "surface": "data set", "probe_tag": "drop", "probe_score": 0.0185, "luna_label": 0, "luna_reason": "Standalone generic phrase inside a table cell; not an independent data mention."}]}, {"key": "rafael2-069", "text": "**<u>Regression results for Q3: How do you think the general economic situation in the country has changed during</u>**\n**<u>the previous 12 months?</u>**\n\n\n_Table A13. Heterogeneity of responses for_ **_Q3_** _along dimensions of livelihood outcomes and shocks_\n\n\n\n**<u>Dummy:</u>**\n**household reports**\n\n**that the country**\n**economic situation**\n\n**has gotten worse**\n**<u>over the past year</u>**\n\n\n\n**<u>Dummy:</u>**\n**household reports**\n\n**that the country**\n**economic situation**\n\n**has gotten worse**\n**<u>over the past year</u>**\n\n\n\n**<u>Dummy:</u>**\n**household reports**\n\n**that the country**\n**economic situation**\n\n**has gotten worse**\n**<u>over the past year</u>**\n\n\n\n**<u>Dummy:</u>**\n**household reports**\n\n**that the country**\n**economic situation**\n\n\n\n**Dependent**\n**variable (Y)**\n\n\n\n**<u>Dummy:</u>**\n**household reports**\n\n**that the country**\n**economic situation**\n\n**has gotten worse**\n**<u>over the past year</u>**\n\n\n\n**has gotten worse** **has gotten worse** **has gotten worse** **has gotten worse** **has gotten worse**\n*", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000971:26:0:0", "start": 286, "end": 303, "surface": "household reports", "probe_tag": "drop", "probe_score": 0.0398, "luna_label": 0, "luna_reason": "Fragment within a regression-results table, not a standalone data resource."}]}, {"key": "rafael2-070", "text": ", & Ermon, S. (2021). Using satellite imagery to understand\nand promote sustainable development. _Science_, _371_ (6535).\n\n\nButar, F. and Lahiri (2002), On the measures of uncertainty of empirical Bayes small-area\nestimators, Journal of Statistical Planning and Inference, 112, 63-76.\n\n\nChi, G., Fang, H., Chatterjee, S., & Blumenstock, J. E. (2022). Microestimates of wealth for all\nlow-and middle-income countries. _Proceedings of the National Academy of Sciences_, _119_ (3),\ne2113658119.\n\n\nCorral, P. Himelein, K. McGee and I. Molina (2021). A map of the poor or a poor map?\n_Mathematics, 9(21)_, 2780.\n\n\nElbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro-level estimation of poverty and\ninequality. _Econometrica_, _71_ (1), 355-364.\n\n\nEngstrom, R., Hersh, J., & Newhouse, D. (2022). Poverty from space: Using high resolution\nsatellite imagery for estimating economic well-being. _The World Bank Economic Review_, _36_ (2),\n382-412.\n\n\nFay, R. E., & Herriot, R. A. (1979). Estimates of income for small places: an application of JamesStein procedures to census data. _Journal of the American Statistical Association_, _74_ (366a), 269277.\n\n\n35", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000814:36:1:0", "start": 1063, "end": 1074, "surface": "census data", "probe_tag": "drop", "probe_score": 0.0124, "luna_label": 0, "luna_reason": "Generic phrase appears within a bibliography entry, not as cited data use."}]}, {"key": "rafael2-071", "text": " Credit? Evidence from European Data.</mark> _<mark>Journal of</mark>_\n_<mark>Banking & Finance</mark>_ <mark>80 (C): 119-134.</mark>\n\n\n31", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000833:32:4:0", "start": 23, "end": 36, "surface": "European Data", "probe_tag": "drop", "probe_score": 0.0492, "luna_label": 0, "luna_reason": "Fragment of a journal citation, not evidence of data use."}]}, {"key": "rafael2-072", "text": "s tend to have higher rates of entrepreneurship**\n**than native workers.** There may be different reasons for this\nas perhaps immigrants have character traits that make them\nmore inclined towards entrepreneurial activity (unclear in the\ncase of refugees), or maybe it is a way to avoid occupational\ndowngrading as immigrants find it difficult to prove their\neducational and professional credentials from home countries.\nNotwithstanding the reason, such effects are observed across\nthe OECD countries (OECD, 2011). In a recent paper, Anelli,\nBasso, Ippedico, and Peri (2023) look at Italian data, finding that\none standard deviation increase in emigration rate generates\n4.8% decline of business formation in the municipality of origin.\nThe authors use existing migration networks to ensure causality\nof the results. Nevertheless, it is unclear whether immigrantcreated businesses are more successful from the native\nones, but one could speculate that a higher rate of trying new\nbusiness ideas spurs more successes.\n\n\n\nThe escalatation of war 2022 in Ukraine\ncaused a large inflow of refugees into\nPoland. While some refugees from Ukraine\nhave since returned to Ukraine or gone\nfurther to Germany or other countries,\nin October 2023 close to 957 thousand\nremained in Poland (identified by active\nPESEL UKR numbers). They consist\nprimarily of children and working aged\nwomen. Despite the forced nature of\ndisplacement, war trauma, and caregiving\nresponsibilities, refugees from Ukraine\nvery quickly entered the labour market\nas employees and entrepreneurs. The\nprecise number of refugees from Ukraine\nworking in Poland remains uncertain,\nwith our estimates ranging from 225 to\n350 thousand. The lower bound is the\nnumber of social security registrations and\nunderstates the actual figure, as some jobs\nmay not require social contributions or\nremain in the informal sector. The higher\nbound is the product of employment rates\nfrom surveys of refugees from Ukraine,\nand their working age population from\nthe active PESEL UKR database. By JulyAugust 2023 Ukrainian refugee households\nsupported themselves, with 80% of", "source": "jad_paddy_docs", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jad_paddy_docs:000007:20:3:0", "start": 582, "end": 594, "surface": "Italian data", "probe_tag": "keep", "probe_score": 0.9915, "luna_label": 1, "luna_reason": "Italian data support the reported business-formation finding."}, {"key": "sample:jad_paddy_docs:000007:20:3:1", "start": 1296, "end": 1305, "surface": "PESEL UKR", "probe_tag": "keep", "probe_score": 0.9915, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-073", "text": "\npensions)\n\n\n\nHumanitarian\n\ncash\n\n\n\nEquivalized\n\n\n\nEmployment\n\nincome\n\n\n\nOther Equivalized\n\n\n\nincome\n\n\n\n2024\n\n\n\nincome\n\n\n\n2023\n\n\n\nNote: Only includes data from the 7 countries surveyed in both rounds (Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and\nSlovakia)\n\n\nSource: Survey data, SAG estimates\n\n\n12. Household disposable income adjusted for size, as per Eurostat’s methodology\n13. The 2024 survey probed for income from family in Ukraine more explicitly\n\n\n**9**", "source": "jad_paddy_docs", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jad_paddy_docs:000010:8:1:0", "start": 298, "end": 309, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9985, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000010:8:1:1", "start": 416, "end": 427, "surface": "2024 survey", "probe_tag": "keep", "probe_score": 0.9861, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-074", "text": " Poles. Visible\n\n\n\ndifferences are identified in 15-19 and\n20-24 age groups, with much higher\nemployment rates for Ukrainian refugees\ndue perhaps to the fact that the Ukrainian\nschool system ends at 17 while the Polish\none at 19. <sup>13</sup> [^13: Children in Ukraine start school at 6 years of age, while in Poland at 7. School system lasts for 11 years in Ukraine and 12 years in Poland. After recent reform,\nUkrainian children who started school in or after 2018 will receive 12 years of schooling.] However, employment rates\ndrop for Ukrainian female refugees in the\n25-29, 30-34, and 35-39 age groups when\ncompared to younger and older groups, as\nwell as to Polish women, which may be due\nto insufficient access to childcare services.\nEmployment rates for Ukrainian female\nrefugees are also substantially lower in the\n55-59 age group, perhaps due to the fact\nthat female retirement age in Ukraine has\nonly recently (in 2021) become 60. <sup>14</sup> [^14: <u>https://www.social-protection.org/gimi/gess/Media.action;jsessionid=U1dm6XAPF38pPk5_pzhfaGoS_rOACMDDVv4w5uevMsKBeQEC5-_g!284293951?id=15680</u> 15 Percentage calculated based on GUS average for the economy as a whole in the months of the SEIS survey.]\n\n\n\n**With the current level of integration**\n**of Ukrainian refugees into the labour**\n**market and the jobs they perform,**\n**their median net earnings are about**\n**four-fifths of the national median**\n\n**– although likely lower in terms of**\n**average or gross earnings.** The median\nnet earnings of Ukrainian refugees in\nQ2 2024 were PLN 4,000 in SEIS, and\nPLN 3,767 in NBP (2024) surveys. This\nis 84% and 79% of the national median,\nrespectively. This estimate would be\nmost likely lower, if data allowed us to\nlook at", "source": "jad_paddy_docs", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jad_paddy_docs:000001:9:1:0", "start": 1204, "end": 1215, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.9934, "luna_label": 1, "luna_reason": "SEIS survey data underpin reported employment and earnings calculations."}, {"key": "sample:jad_paddy_docs:000001:9:1:1", "start": 1592, "end": 1610, "surface": "NBP (2024) surveys", "probe_tag": "confusion", "probe_score": 0.6828, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-075", "text": "|~~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 high level of education of Ukrainians\ncoming to Poland helped them access the\nlabour market. The percentage of higher\neducation for refugees and pre-2022\nmigrants from Ukraine in NBP and UNHCR\nsurveys is significantly higher than for the\nPolish population and even higher than\nfor Ukraine, according to the Labour Force\nSurvey (LFS) in Poland, and its equivalent\nin Ukraine. According to NBP survey from\n2022 the percentage of refugees with\nhigher education was at 48%, while MSNA\n\n\n\n\n\n8% 10%\n\n\n\n\n\n\n\n\n\n\n\n**Source:** Deloitte own elaboration based on Eurostat, <u>[https://nbp.pl/wp-content/uploads/2023/04/Sytuacja-zyciowa-i-ekonomiczna-](https://nbp.pl/wp-content/uploads/2023/04/Sytuacja-zyciowa-i-ekonomiczna-migrantow-z-Ukrainy-w-Polsce_raport-z-badania-2022-r.pdf)</u>\n<u>[migrantow-z-Ukrainy-w-Polsce_raport-z-badania-2022-r.pdf, UNHCR survey conducted from 13.07.2023 to 21.08.2023, and State Statistics](https://nbp.pl/wp-content/uploads/2023/04/Sytuacja-zyciowa-i-ekonomiczna-migrantow-z-Ukrainy-w-Polsce_raport-z-badania-2022-r.pdf)</u>\nService of Ukraine (2021).", "source": "jad_paddy_docs", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jad_paddy_docs:000007:10:2:0", "start": 395, "end": 414, "surface": "Labour Force\nSurvey", "probe_tag": "confusion", "probe_score": 0.8876, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:10:2:1", "start": 476, "end": 486, "surface": "NBP survey", "probe_tag": "keep", "probe_score": 0.9825, "luna_label": 1, "luna_reason": "NBP survey provides the reported 48% higher-education finding."}, {"key": "sample:jad_paddy_docs:000007:10:2:2", "start": 924, "end": 936, "surface": "UNHCR survey", "probe_tag": "keep", "probe_score": 0.9833, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-076", "text": " heatlh and social work activities\n\n\nAdministrative and support service activities\n\n\nEducation\n\n\nInformation and communication\n\n\nProfessional, scientific and technical activities\n\n\nPublic administration and defense; compulsory social security\n\n\nConstruction\n\n\nFinancial and insurance activities\n\n\nAgriculture, forestry and fishing\n\n\nReal estate activities\n\n\nWater supply: sewerage, waste management and remediation activities\n\n\nElectricity, gas, steam and air conditioning supply\n\n\nMining and quarrying\n\n\nTransportation and storage\n\n\n\n**-1**\n\n\n\n**34**\n\n\n\n\n\n\n\n**Chart 8.** Share of companies reporting vacancies\n\n\n50%\n\n\n40%\n\n\n30%\n\n\n20%\n\n\n10%\n\n\n0%\n\n\n\n\n\n\n\nC\n\n\nI\n\n\nG\n\n\nQ\n\n\nN\n\n\nP\n\n\nJ\n\n\nM\n\n\nO\n\n\nF\n\n\nK\n\n\nA\n\n\nL\n\n\nE\n\n\nD\n\n\nB\n\n\nH\n\n\n\n\n\n**Source:** Deloitte own elaboration based on ZUS data.\n\n\n\n(more than 18 thousand), and wholesale\nand retail trade (more than 18 thousand).\nThe only sector that has seen a decline\nwas transportation and storage (by more\nthan 1 thousand). Unfortunately, public\ndata does not report how much of this\nchange is due to the refugees from Ukraine\nentering these sectors, or how much due\nto pre-2022 Ukrainian workers changing\ntheir jobs.\n\n\n\n2006\n-Q4\n\n\n\n2008\n\n-Q2\n\n\n\n2009\n\n-Q4\n\n\n\n2011\n\n-Q2\n\n\n\n2012\n-Q4\n\n\n\n2014\n-Q2\n\n\n\n2015\n\n-Q4\n\n\n\n2017\n-Q2\n\n\n\n2018\n\n-Q4\n\n\n\n2020\n-Q2\n\n\n\n2021\n-Q4\n\n\n\n2023\n-Q2\n\n\n\nThe number of workers with Ukrainian\ncitizenship since the beginning of the\nfull-scale war in Ukraine grew in all NACE <sup>21</sup>\nsectors apart from transportation and\nstorage. Since 2021, just before the\nescalation of war in 2022 in Ukraine,\nthe number of workers with Ukrainian\ncitizenship and social security insurance\ngrew the most in manufacturing (almost by\n34 thousand), accommodation and food\n\n\n\n**Source:** Deloitte own elaboration based on estimated data from <u>[https://nbp.pl/publikacje/cykliczne-materialy-analityczne-nbp/]", "source": "jad_paddy_docs", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jad_paddy_docs:000007:9:1:0", "start": 770, "end": 778, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9555, "luna_label": 1, "luna_reason": null}, {"key": "sample:jad_paddy_docs:000007:9:1:1", "start": 977, "end": 988, "surface": "public\ndata", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 0, "luna_reason": "States public data lacks attribution; no analyzed finding or substitute use."}]}, {"key": "rafael2-077", "text": " quality and condition of the network.|<br>Description:The visual survey involves camera recording of road network condition, in addition to other visual measures. The collected data will then be decoded in<br>accordance of a road inspection manual that associates various aspects of the recording with a rating of the quality and condition of the network.|<br>Description:The visual survey involves camera recording of road network condition, in addition to other visual measures. The collected data will then be decoded in<br>accordance of a road inspection manual that associates various aspects of the recording with a rating of the quality and condition of the network.|\n|<br>|**Name:**Number of wheel<br>loaders purchased||Number|0.00|15.00|Bi‐Annual<br>|Progress report compiled by<br>CDR. Bank implementation<br>support mission.<br>|CDR<br>|\n|<br>|<br>Description:Necessary equipment for emergency road repairs.|<br>Description:Necessary equipment for emergency road repairs.|<br>Description:Necessary equipment for emergency road repairs.|<br>Description", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000008:49:8:0", "start": 59, "end": 72, "surface": "visual survey", "probe_tag": "keep", "probe_score": 0.9932, "luna_label": 0, "luna_reason": null}, {"key": "sample:jdc_operational:000008:49:8:1", "start": 377, "end": 390, "surface": "visual survey", "probe_tag": "keep", "probe_score": 0.9932, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-078", "text": "4 per 100,000) double that among Lebanese (15.8 per 100,000). <sup>5</sup> [^5: Ministry of Public Health; Presentation, Biostatistics Department, March 2017.]\n\n11. **Lebanon also faces epidemiological risks, the reemergence of some diseases that had been**\n**controlled before the Syrian crisis, and a growing need for mental health services.** Despite intensive\nvaccination campaigns, outbreaks of measles, mumps, and waterborne diarrheas are increasing, mainly\nin areas with high concentrations of refugees. While the vulnerable population in Lebanon shares a\ncommon disease burden, especially from chronic illnesses, the disease burden among displaced Syrians\nis largely concentrated around maternal and child health, communicable diseases, and mental health.\nThe majority of displaced Syrians visit providers for infections and communicable diseases (40 percent). <sup>6</sup> [^6: LCRP 2015-2016.]\nThere is also a significant demand for antenatal care. According to an assessment conducted in 2015, 20\npercent of displaced Syrian households have either a pregnant or a lactating woman, compared to 6.5\npercent among Palestinian refugees from Syria. <sup>7</sup> [^7: LCRP 2015-2016; WFP, UNICEF, and UNHCR, Vulnerability Assessment of Syrian Refugees in Lebanon, 2015.] There is also a growing need for specialized mental\nhealth services for both Lebanese and displaced Syrians. A research study conducted in 2016 reported a\nclear increase in mental health disorders among the displaced Syrian youth and adult population. <sup>8</sup> [^8: Lebanon: Mental health system reform and the Syrian crisis. Elie Karam et al. _BJPSYCH International_ 13 (4). November 2016.]\nPrevalence rates of depression were found to be 16.8 percent among displaced Syrians and 13.3 percent\namong Lebanese. Similarly, prevalence rates for anxiety were found to be 56 percent among displaced\nSyrians and 50.7 percent", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000032:14:1:0", "start": 975, "end": 1003, "surface": "assessment conducted in 2015", "probe_tag": "keep", "probe_score": 0.9869, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-079", "text": " economic recovery, which started in 2018. Chad entered into\nrecession in 2020 as the economy contracted by 0.9 percent. Both the fiscal and current account balances\ndeteriorated substantially, and difficulties in financing the fiscal deficit may have led to further domestic\narrears’ buildup. The pandemic has highlighted Chad’s oil dependence and vulnerability to multiple and\noften concurrent shocks. The passing of the Chad President in April 2021 deepened the slowdown in\neconomic activity in the second quarter of 2021, as the new authorities shifted public resources from\ncritical investments toward the political transition and security-related spending to curb sociopolitical\ntensions. Economic growth is projected to gradually rise, due to the recovery in global oil markets,\ninternational trade, and economic activity in agriculture and industry. Chad experienced another year of\nrecession, as the economy contracted by 1.2 percent in 2021 (-4.1 percent in per capita terms), after the\n2020 growth contraction of 1.6 percent, due to political and security developments and a two-month\nsuspension of oil production in Esso plants, accounting for one-fourth of the total oil production in Chad.\n\n3. **Security risks originating in neighboring countries have persistently destabilized the regional**\n**economy and created a situation of acute humanitarian needs and large refugee inflows into Chad.** Over\nthe past 25 years, the number of refugees in Chad continued to grow, and many refugees have been in\nthe country for more than a decade. By December 2021, the United Nations High Commissioner for\nRefugees (UNHCR) data indicated that Chad was hosting 560,000 refugees and asylum seekers, accounting\nfor more than 3 percent of the population in the country. <sup>3</sup> [^3: UNHCR data as of December 31, 2021. _[https://data2.unhcr.org/en/country/tcd,](https://data2.unhcr.org/en/country/tcd)_ accessed on January 21, 2022.]", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000051:15:1:0", "start": 1787, "end": 1797, "surface": "UNHCR data", "probe_tag": "keep", "probe_score": 0.927, "luna_label": 1, "luna_reason": "UNHCR data supports the reported refugee and asylum-seeker count."}]}, {"key": "rafael2-080", "text": "**The World Bank**\nUganda Secondary Education Expansion Project (P166570)\n\n\nthat 40 percent of teachers in schools have been placed there based on factors other than the class time\nrequired by students. <sup>9</sup> [^9: UNESCO 2014, Teacher Issues in Uganda: A shared vision for an effective teachers’ policy.] Old curricula (replaced in 2020) used to further complicate teacher allocation\nacross schools by imposing too many subjects that required specialized teachers. This inefficiency was\nresolved by the new curricula.\n\n\n8. **In spite of increased access to schooling, the average level of education of the work force remains low**\n**and does not meet labor market requirements.** Uganda has been absorbing 600,000 new entrants to the labor\nmarket each year since 2014. In order to sustainably increase welfare, these entrants must find productive\nemployment. <sup>10</sup> [^10: Uganda job diagnostics/strategy, World Bank, 2018, draft.] Estimates from the Uganda National Household Survey (UNHS) (2016) show that only entrants\nwith post-secondary education can escape informal sector work. In order to increase the employability and\nproductivity of the expanding workforce, supply of quality education, especially for low-income, rural households\nand girls, is critical. According to the UNHS, only one in five people aged 15 and above completed full secondary\neducation. Thus, a large number of youth enter the job market without foundational skills of basic literacy and\nnumeracy, as well as generic skills essential for life and work.\n\n\n9. **Uganda is a pioneer in SSA in terms of setting the goal of achieving universal access to secondary**\n**education.** The secondary education sub-sector in Uganda is centrally managed and comprises six grades, Senior\n1 (S1) to Senior 6 (S6). S1-S4 is categorized as ordinary (‘O’) level, or lower secondary, while S5-S6 is Advanced\n(‘A’", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000018:14:0:0", "start": 964, "end": 996, "surface": "Uganda National Household Survey", "probe_tag": "keep", "probe_score": 0.9795, "luna_label": 1, "luna_reason": null}, {"key": "sample:jdc_operational:000018:14:0:1", "start": 1296, "end": 1300, "surface": "UNHS", "probe_tag": "keep", "probe_score": 0.9429, "luna_label": 1, "luna_reason": "UNHS supports the finding that one in five completed secondary education."}]}, {"key": "rafael2-081", "text": "**The World Bank**\nUganda Digital Acceleration Program (P171305)\n\n\nthe National Information Technology Authority in Uganda (NITA-U), 70.9% of Ugandans own mobile phones <sup>22</sup> . The\nownership rates are higher among urban residents compared to rural residents (78.5% and 65.7% respectively) and\nmore males than females (81.6% and 63.2%). <sup>23</sup> The telecommunications market includes two major operators – MTN\nand Airtel – that control market shares (in terms of mobile subscriptions) at 37% and 45% respectively, <sup>24</sup> and two\nother mobile operators such as Uganda Telecom and Africell with market shares below 10% each. <sup>25</sup> The increased\naccess to mobile phones and mobile services in Uganda enabled the take-up of related services such as mobile\nbanking: in 2014, 18.5 million Ugandans used mobile money services in transactions valued at UGX 18 trillion –\nequivalent to US$6.2 billion – whereas less than a third of this number hold bank accounts at traditional financial\ninstitutions. <sup>26</sup> The 2017 Global Findex database shows that 50% of adults have mobile money accounts in Uganda. <sup>27</sup>\n\n6. **Affordability remains a key barrier to the take-up of mobile broadband, despite widespread adoption of mobile**\n**phones.** Mobile devices provide the main platform for internet use (as opposed to fixed access), with about half of\nall mobile subscribers in Uganda using mobile internet services. The penetration rate of mobile broadband was 23%\ncompared to 1% only for fixed-line internet access by June 2018. <sup>28</sup> High prices hamper", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000063:4:0:0", "start": 1051, "end": 1066, "surface": "Findex database", "probe_tag": "keep", "probe_score": 0.9535, "luna_label": 1, "luna_reason": "2017 Global Findex database supports the reported mobile-money account finding."}]}, {"key": "rafael2-082", "text": " violent death of the former Chad President in April 2021. Underscored by the 2021 Chad\nRRA, the underlying drivers of fragility, conflict, and violence include the elite capture of power and\nresources, geographical and social exclusion, lack of security and justice, or tension around access to\nresources. Since the first Boko Haram attack in Chad in 2015, there has been a dramatic rise in violence,\nparticularly in the border areas with targeted and indiscriminate attacks on local authorities and leaders,\nsecurity forces, and civilians with the Lake Chad region particularly affected. Inter and intracommunal\nconflicts, notably between farmers and herders, is also on the rise particularly in the south and east.\nConsidering the security and fragility dimensions that in particular affect the poor, accounting for 42\npercent of the Chad population, the bulk of access will be provided by SSSs that have proved to be one of\nthe most adapted electrification options in high security risk and fragile environments with high poverty\n\n\nPage 46 of 87", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000051:51:2:0", "start": 78, "end": 91, "surface": "2021 Chad\nRRA", "probe_tag": "keep", "probe_score": 0.947, "luna_label": 1, "luna_reason": "2021 Chad RRA provides evidence for identified fragility drivers."}]}, {"key": "rafael2-083", "text": "**The World Bank**\nUganda: Investment for Industrial Transformation and Employment (P171607)\n\n\n77. **Project Advisory Committee for Refugees (PACR).** The PSFU, in agreement with MoFPED, will establish a\nProject Advisory Committee, consisting of OPM, UNHCR, and selected representatives of other implementing\nagencies. The purpose of the PACR is to ensure coordination and complementarity with other implementing\nagencies, taking lessons learned from other project and policy initiatives, and providing feedback in the\nimplementation of programmed RHD activities.\n\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n\n78. The M&E, which will be led by the PSFU on behalf of MoFPED in collaboration with all collaborating\nagencies, will apply a Results Framework which includes baseline measurements and annual targets to monitor\nresults and progress. The BoU M&E data and reporting will be consolidated by PSFU into an overall project report.\nThe economic impact of program activities will be measured through data collection and survey implementation\nand through a structured impact evaluation at the conclusion of the project. The impact evaluation will include\nbaseline data collection, done through a survey of potential participants at the registration phase, and two follow\nup surveys <sup>47</sup> [^47: Surveys will be conducted in person or over the phone and could be substituted by surveys conducted by UBOS.] 6 and 18 months respectively after the conclusion of the pilot. The design of the impact evaluation\nwill be done in agreement with the collaborating agencies and is expected to rely on a rigorous methodology (i.e.,\nRandomized Controlled Trial or similar). Additionally, all project beneficiaries will be linked with firms surveyed\nby UBOS for allowing long-term follow up. This M&E process will involve the teams in the implementing agencies.\nUgandan research institutions will be a key partner in implementing the M&E framework building on existing data\nreports and in partnership with local research institutions. The PSFU PIU will work closely with Ugandan research\ninstitutions for three reasons. First", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000021:36:0:0", "start": 859, "end": 871, "surface": "BoU M&E data", "probe_tag": "confusion", "probe_score": 0.2115, "luna_label": 0, "luna_reason": "Future consolidation of project monitoring data into reporting"}, {"key": "jdc_operational:000021:36:0:1", "start": 1209, "end": 1241, "surface": "survey of potential participants", "probe_tag": "confusion", "probe_score": 0.6996, "luna_label": 0, "luna_reason": "Planned baseline survey data collection for the project's impact evaluation."}]}, {"key": "rafael2-084", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\nat the time of sub-project selection, as appropriate.\n\n\n**d.** **Bi-monthly cash transfer payments** . During the SSSNP, payments were often delayed due to a number of\n\noperational challenges, such as lack of access due to flooding and insecurity. This led to dissatisfaction among\nbeneficiaries and implementation risks on the ground. As such, many beneficiaries expressed a preference for bimonthly payments. Consequently, SNSOP will employ a bi-monthly payment schedule which is expected to\nminimize payment delays as well as meet the preferences of beneficiaries.\n\n**e.** **The role of Cash “Plus” activities in behavior change** . Lessons learned from the SSSNP suggest that cash “plus”\n\nactivities were associated with positive behavior change among beneficiaries. Beneficiaries reported increasing\nsavings and access to income for women in HHs, there was increased awareness of the importance of child\nnutrition and early development, as well as the adoption of safe sanitary and hygiene practices. For instance,\npreliminary findings from SSSNP suggest that the number of HHs that saved increased by 13 percentage points\nand the percentage of HHs that adopted safe sanitary and hygiene practices increased by 20 percentage points.\nIn recognition, the SNSOP will expand the cash “plus” delivery in two ways. First, complementary social measures\nfor Cash Transfer beneficiaries will provide in-depth training and activities covering WASH, nutrition and ECD to\nselected eligible female beneficiaries, while continuing to provide light touch messaging to all. In addition,\nComponent 2 will build on the SSSNP experience by providing financial literacy trainings through life and business\nskills curricula, as well as by forming savings groups. These are expected to contribute to increased investment in\nand development of human capital.\n\n\n**f.** **Inclusion of men in gender mainstreaming** . Though the SSSNP had", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000057:35:0:0", "start": 1113, "end": 1144, "surface": "preliminary findings from SSSNP", "probe_tag": "confusion", "probe_score": 0.8872, "luna_label": 1, "luna_reason": "SSSNP findings support concrete percentage-point claims about beneficiary outcomes."}]}, {"key": "rafael2-085", "text": "**The World Bank**\nUganda: Investment for Industrial Transformation and Employment (P171607)\n\n\ncapturing off budget support to refugees and host communities; (iv) ensuring provision\nof additional support to develop human capital to refugee hosting communities; (v)\ndeveloping targeted agricultural led interventions for both refugees and Cost community;\nand (vi) strengthening compilation of statistics of refugees by the Uganda Bureau of\nStatistics. This comprehensive inclusion of refugees into NDP III demonstrates Uganda’s\ncomprehensive policy approach. NDP III is well aligned to IDA 19 WHR objectives of\nsupporting projects that create medium to long term development opportunities for both\nrefugees and host community.\n\nIn the context of COVID, the Government remains committed to working with partners in\nthe short-term to provide emergency response focusing on life- saving assistance to\nrefugees. In parallel to this the Government will continue integrating refugee plans and\nprograms into all levels of planning, service delivery, provision of infrastructure and\nstrengthening human capital to build self-reliance and ensure a strong enabling\nenvironment for employment creation for refugees and host communities. World Bank\nWHR finance will be key in supporting this agenda. In particular, investments in refugee\nhosting district enabling a more rapid economic recovery to drive employment for both host\ncommunities and refugees. The areas of roads, energy, settlement physical planning, the\nsettlement shelter strategy for refugees and host communities are among the gaps\nidentified in NDPIII which World Bank support can assist to ensure the socio-economic\nrecovery of these communities impacted by COVID are addressed. World Bank finance to\nbuild human capital, to drive employment generation and strengthen self-reliance is\nessential to build the prosperity of these communities following the impacts of COVID.\n\nOn behalf of the Government of Uganda, I wish to extend my appreciation to the World\nBank for its continued support of the Country’s national development agenda and policies\nto strengthen support to refugees and host communities through IDA19 and the WHR.\n\n\nPage 91 of 92", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000021:96:0:0", "start": 392, "end": 414, "surface": "statistics of refugees", "probe_tag": "confusion", "probe_score": 0.0757, "luna_label": 0, "luna_reason": "Sentence describes strengthening compilation, a data-production activity rather than using existing statistics."}]}, {"key": "rafael2-086", "text": "**The World Bank**\nCHAD Improving Learning Outcomes Project (P175803)\n\n\n  - **Interruptions in learning are common** . A 2019 survey indicated that 70 percent of school children aged 7-14 years\nexperienced classroom stoppages during the school year due to teacher absence; the other most common reasons\ngiven were strikes (66 percent) and natural catastrophe (20 percent). In the 2015-16 school year, the fall in oil\nprices led to a sharp reduction in salary payments, resulting in the closure of one quarter of primary schools.\n\n\n_Figure 1: Pupil-teacher ratio by province_\n\n\nSource: Education Statistics Yearbook, 2018-2019\n\n\n  - **The quality of teaching can be improved** . Aside from the difficulties of teaching effectively to overcrowded\nclassrooms, teachers have weak pedagogical skills and often do not master course content themselves. In a 2019\ncompetency test, primary teachers had the second lowest score (421) in reading comprehension among 14\nparticipating countries, and 38 percent were at Level 1 or below (vs. 17 percent for all countries). In terms of the\npedagogy of reading comprehension, Chad’s score was again the second lowest. With respect to mathematical\ncompetencies, primary teachers had the lowest score (419), with 69 percent of teachers operating at Level 1 or\nbelow (vs 35 percent for all countries). Pedagogical competencies in mathematics were third lowest. There was\nlittle or no difference in performance between teachers based on their years of work experience, while teachers\nwith a university degree performed markedly better than those with a secondary diploma. <sup>13</sup> [^13: PASEC 2019]\n\n  - **Remedial programs for children returning to school are not fully functional** . The Education Act No. 16 of March\n2006 introduced two types of education as part of the education system to help absorb out-of-school children: a\n4-year non-formal basic education program which targets children aged 9-", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000009:7:0:0", "start": 121, "end": 132, "surface": "2019 survey", "probe_tag": "confusion", "probe_score": 0.55, "luna_label": 1, "luna_reason": "Survey provides the attributed finding that 70 percent experienced classroom stoppages."}, {"key": "sample:jdc_operational:000009:7:0:1", "start": 585, "end": 614, "surface": "Education Statistics Yearbook", "probe_tag": "keep", "probe_score": 0.9959, "luna_label": 1, "luna_reason": null}, {"key": "sample:jdc_operational:000009:7:0:2", "start": 851, "end": 871, "surface": "2019\ncompetency test", "probe_tag": "confusion", "probe_score": 0.5194, "luna_label": 1, "luna_reason": "Competency test results provide teacher scores and are identified by PASEC 2019."}]}, {"key": "rafael2-087", "text": "measures, and specific arrangements for the proposed project; and procurement arrangements\nand procedures.\n\n\n\n\n\n\n\n**B. The Public Financial Management Environment in Chad**\n\n\n6. The recent Country Financial Accountability Assessment, considered as an essential tool\nby the Government of Chad, identified the following weaknesses in the public financial\nmanagement environment in Chad: (i) the lack of a computerized accounting and budget\nexecution system; (ii) the absence of unified tracking of public expenditures, (iii) weak capacity\nof the control bodies; and (iv) insufficiencies in the management of petroleum resources. The\n\n\n32", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000007:41:0:0", "start": 189, "end": 232, "surface": "Country Financial Accountability Assessment", "probe_tag": "confusion", "probe_score": 0.7803, "luna_label": 1, "luna_reason": "Assessment identified weaknesses in Chad’s public financial management environment."}]}, {"key": "rafael2-088", "text": " purchase equipment\n\nusing” (i) Request for bids (RFB) for both international (replacing ICB) and national\nmarkets (replacing NCB), (ii) Request for quotations (or Shopping); and (iii) Direct\nselection (old Direct contracting).\n(b) _Consulting services._ The project is expected to use Request for Proposals with the\n\nfollowing methods: Quality- and Cost-Based Selection (QCBS), Fixed Budget-based\nSelection (FBS), Least-Cost-based Selection (LCS), Consultants’ Qualification-based\nSelection (CQS), Direct Selection (formerly, single sourcing); and Selection of Individual\nConsultants.\n(c) _Particular contracts._ The PHCCs will continue to use a performance-based contract\n\nunder which they will account for enrolled patients receiving services, and for the\nreturn of the same patients to the center based on their satisfaction with the services.\n\n\n     - **Prior review thresholds.** Based on the procurement assessment risk rating, the project will\nbe subject to moderate risk prior review thresholds as defined under NPF.\n\n84. **STEP.** The Government leads the development of the Project Procurement Strategy\nDevelopment (PPSD), which will define the market approach options, the selection methods and\ncontractual arrangements, and the WB’s reviews. The main outcome of the PPSD is a well-informed\nprocurement plan for the life of the project. A preliminary procurement plan was prepared as part of the\nLoan Agreement and will be uploaded in the Systematic Tracking of Exchanges in Procurement (STEP)\nsystem. Procurement activities shall be packaged in an efficient and economic manner.\n\n\nPage 37 of 54", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000032:39:1:0", "start": 1451, "end": 1498, "surface": "Systematic Tracking of Exchanges in Procurement", "probe_tag": "confusion", "probe_score": 0.8533, "luna_label": 0, "luna_reason": "Names STEP as a procurement system without showing use of its data."}]}, {"key": "rafael2-089", "text": " in armed conflict may facilitate substantial population**\n**movement.** About 1.2 million people have returned from displacement within and outside of South Sudan since 2016 to\ndate. Among them, over 534,000 people (45 percent) returned since the signing of the revitalized peace agreement in\nSeptember 2018 until March 2019. <sup>13</sup> [^13: IOM DTM] This demonstrates that more people returned in a shorter period of time since the\nsigning of the agreement. According to both the UNHCR and IOM’s recent intention surveys, major pull factors for return\nare improved security, family reunification, access to basic services, and livelihood opportunities. For refugees, 30 percent\nof them in neighboring countries consider returning but majority are waiting to see how the situation unfolds. Slightly\nhigher numbers of IDPs have longer term return intentions to home areas. The prevailing security and basic living\nconditions are not yet conducive to more widespread return movements among both populations, however. In addition,\nthere have been new/secondary displacements triggered by intensifying inter-communal clashes in areas such as Unity,\nWarrap, Lakes, Western Bahr-el-Ghazal, Central Equatoria and Jonglei. <sup>14</sup> [^14: UNMISS (August 2019) ( _Mimeo_ )] Population movement thus remains volatile,\nwhere temporary visits by single family members to look after assets and property in home areas predominate. Should\nthe unity government be formed in November 2019 and security situation improve, however, the return of some of the\n4.2 million displaced (both refugees and IDPs) is likely to accelerate.\n\n9. **Unlike in other countries where significant numbers of returnees gravitate to major cities, in South Sudan the**\n**evidence suggests that IDPs and refugees are likely to return to their original villages or in their vicinity.** Existing data\nshows that a large majority (87 percent) of IDP and refugee returns are primarily to areas of habitual residence while\nrelocations to third areas is quite", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000014:4:1:0", "start": 347, "end": 354, "surface": "IOM DTM", "probe_tag": "keep", "probe_score": 0.9374, "luna_label": 1, "luna_reason": null}, {"key": "sample:jdc_operational:000014:4:1:1", "start": 509, "end": 526, "surface": "intention surveys", "probe_tag": "confusion", "probe_score": 0.8434, "luna_label": 1, "luna_reason": "UNHCR and IOM surveys support identified return-factor findings."}]}, {"key": "rafael2-090", "text": "first step through the installation of traffic signals and a traffic control center, as well as park meters\nto regulate parking, and building grade separation infrastructure at critical intersections. Within the\nscope of the UTDP, follow up studies and projects were planned to introduce reliable public\ntransport as a second step to ease traffic congestion.\n\n11. Recently, GOL has been increasingly considering public transport and mass transit\nsolutions for Lebanon. Financed by the UTDP loan, the Ministry of Transport had prepared a bus\nstudy for Greater Beirut which has identified about 20 regular bus routes. The Government of\nLebanon had approved the purchase of 250 buses as a start, however political challenges had stalled\nthe allocation of funds. Meanwhile, MOT is also assessing introducing a freight and passenger\nrailway on the old railway alignment between Beirut, Tripoli and the Syrian border in the north.\nHowever such a project will be both financially and technically difficult (due to dense urban\ndevelopments) to implement, and will have to be implemented in stages over a long period, starting\nnorth and going south to Beirut. Given the long term nature of such a project, it was decided to go\nahead with a comprehensive public transport program for GBA that will focus on Bus/BRT\nsolutions for the medium term, to be upgraded to rail on certain sections in the long term when\nfunds become available.\n\n12. This proposed project therefore represents a first phase in a comprehensive public transport\nprogram for GBA, primarily focused on introducing a BRT line on the most congested Northern\napproach to Beirut (Tabarja to Beirut) and possibly BRT and/or bus lines extensions within Beirut\nto distribute commuters efficiently. Follow up projects will look into extending the BRT line to the\nsouthern and eastern approaches to Beirut as needed, and to further improving public transport\ncoverage within Beirut. The BRT line would begin in the Tabarja-Jounieh area, a major populated\narea and feeder for Beirut, and pass through the densely inhabited northern suburbs of Beirut before\nending in", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000004:3:0:0", "start": 537, "end": 565, "surface": "bus\nstudy for Greater Beirut", "probe_tag": "confusion", "probe_score": 0.3664, "luna_label": 1, "luna_reason": "Existing study identified about 20 regular bus routes for transport planning."}]}, {"key": "rafael2-091", "text": "up>14</sup> At the lower secondary level, barely any progress has been made\nto increase enrollment since 2000 (Figures 4 and 5). This is considerably worse than most countries in the region.\nUganda’s GER for lower secondary has not moved beyond 35 percent since 2010 (Figure 4). Very low enrollment\nrates in secondary education and the lack of progress require an urgent, emergency-like response.\n\n\n9 UNESCO 2014, Teacher Issues in Uganda: A shared vision for an effective teachers’ policy.\n10 Uganda job diagnostics/strategy, World Bank, 2018, draft.\n11 Bashir S., Lockheed M., Ninan Dulvy E., Tan J.P. Facing Forward: Schooling with Learning in Africa. World Bank, Washington DC, 2017.\n12 While in December 2019, Uganda Bureau of Statistics completed a mapping exercise of all education institutions in the country, until now the data has\nnot been officially released and is still being validated, and therefore was not included in this document. Instead, the 2017 EMIS is used as the source of\nthe most recent government data.\n13 World Development Report, 2018.\n14 UNESCO Institute of Statistics.\n\n\nPage 9 of 96", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000018:14:2:0", "start": 962, "end": 971, "surface": "2017 EMIS", "probe_tag": "confusion", "probe_score": 0.6537, "luna_label": 1, "luna_reason": "Named EMIS is used as the source of most recent government data."}]}, {"key": "rafael2-092", "text": "**The World Bank**\nSouth Sudan Enhancing Community Resilience and Local Governance Project (P169949)\n\n\nsubprojects; and (d) continued accessibility and permissive security. Allocations to the _payam_ level will\nfollow the Government’s fiscal transfer formula of 60 percent equal allocation and 40 percent based on\npopulation (utilizing IOM’s DTM projections) whereas all _bomas_ within target _payams_ will receive equal\namount of funding as no population data are available <sup>52</sup> [^52: Population figures for urban areas would be calculated based on a headcount or by complementing 2008 census with other data sources (for\nexample, DTM.)] (see annex 2 for details).\n\n\n33. **Use of community labor.** The project will encourage contractors to utilize local labor in the\ninfrastructure construction or rehabilitation to the extent possible. Emphasis will be placed on the\ninclusion of various social groups facing marginalization or barriers to participation (for example, women,\nyouth, returnees, ethnic minority groups, and people with disabilities) and ensuring their access to daily\nwage labor opportunities. It will be especially important to include women in the design and construction\nof WASH facilities, for instance, to ensure these facilities are rehabilitated in ways that promote security\nand effective management on completion. The project will harmonize, to the extent possible, the labor\nprovisions adopted by the World Bank’s SSSNP and coordinate salary levels with UN coordination cluster\nstandards.\n\n\n**Component 2. Local Institution Strengthening (US$14.17 million equivalent)**\n\n\n34. **Subcomponent 2.1. Community Institution Strengthening.** This subcomponent supports the\nparticipatory planning process for the identification of subprojects that will be financed under Component\n1, monitoring of the construction and O&M of subprojects, and capacity building of the community\ninstitutions. Specific activities under this subcomponent include (a) community mobilization; (b)\nparticipatory risk mapping/analysis and risk mitigation training; (c) support for community institutions on\nparticipatory development", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000049:26:0:0", "start": 342, "end": 357, "surface": "DTM projections", "probe_tag": "keep", "probe_score": 0.9374, "luna_label": 1, "luna_reason": null}, {"key": "sample:jdc_operational:000049:26:0:1", "start": 445, "end": 460, "surface": "population data", "probe_tag": "confusion", "probe_score": 0.662, "luna_label": 1, "luna_reason": "Absence of population data motivates substitute census and DTM-based estimates."}]}, {"key": "rafael2-093", "text": "Syrian businesses and removing barriers to Syrian entrepreneurship fulfills a commitment of the Jordan\nCompact.\n\n\n64. **Regulatory simplification process to reduce the burden on SMEs.** As indicated by the\nDoing Business report and other indicators, the regulatory environment in Jordan is cumbersome and\nsometimes unpredictable in its implementation. This hurts primarily SMEs which cannot cope with\nuncertainty and the cost of regulatory burden. There is a need for the private sector and the Government\nto work together in a consultative manner to identify key regulatory and licensing reforms that could\nreduce such burden. Such a public-private dialogue process will be put in place to identify, within 12\nmonths, at least two regulatory reform areas that are deemed important to the business community, and\nthat the Government commits to pursue. The goal will be, on one hand, to initiate a culture of publicprivate dialogue to identify priority reforms and to act upon them, and on the other hand, to reduce the\nregulatory burden on firms in a concrete and palpable way by businesses (in the form of reduced time,\ncost, and process complexity to comply with a given regulation—as measured by a composite index of\nthese three dimensions of regulatory burden).\n\n\n_Trade and Customs_\n\n\n65. Delays on the clearance process for goods either entering, exiting, or transiting Jordan impacts\nthe competitiveness of the country to grow the economy through trade. Faster, more efficient trade frees\nup inventory, improves cash flow, increases port capacity, attracts foreign investment, and reduces costs\nto trade and for consumers. The more companies that are part of the Customs Golden List (to be\nexpanded into a Trusted Trader Program), the fewer the number of physical inspections, the greater the\nnumber of pre-arrival releases, the greater the opportunity for regional mutual recognition programs,\nthe lower the customs guarantees required, and the greater the coordination with Golden Lists from the\nother government agencies with border controls.\n\n\n**_<mark>Investment Promotion</mark>_", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000045:71:0:0", "start": 206, "end": 227, "surface": "Doing Business report", "probe_tag": "confusion", "probe_score": 0.6606, "luna_label": 1, "luna_reason": "Named report cited as evidence that Jordan's regulatory environment is cumbersome."}]}, {"key": "rafael2-094", "text": "* **Beyond the physical damage of the blast, housing speculation is threatening the preservation of the**\n**cultural identity of the central areas of Beirut.** The following HLP issues have been identified: owners’ plans to\nrepurpose or upgrade property; the refusal of owners to rehabilitate damaged property, particularly in cases\n\n\n103 Norwegian Refugee Council (NRC)’s multisectoral needs assessment\n104 UNHCR, UNICEF and WFP, “VASyR 2019”; UNHCR, UNICEF and WFP, “VASyR 2020.”\n105 UNHCR, UNICEF and WFP, “VASyR 2019.”\n106 UNHCR, UNICEF and WFP, “VASyR 2020.”\n\n\nPage 55 of 66", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000012:60:1:0", "start": 373, "end": 403, "surface": "multisectoral needs assessment", "probe_tag": "confusion", "probe_score": 0.2034, "luna_label": 1, "luna_reason": "NRC’s cited needs assessment is an existing external assessment resource."}]}, {"key": "rafael2-095", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n\n**ANNEX 4: Climate and Hazard Considerations**\n\n\n**Climate Change and Natural Hazard Risks and Adaptation Opportunities**\n\n1. Resilient infrastructure development in the Republic of Chad includes consideration of existing\nnatural hazards <sup>51</sup> and ongoing climate change. Three key risks in this project include wildfire, flooding,\nand extreme heat, which are expected to increase due to climate change.\n\n\nInternal calculations using data from NASA NEX-GDDP CMIP5 data [50].\n\n2. **Wildfire is recognized as a ‘high’ risk in Chad** under current climate conditions, and climate\nchange is expected to exacerbate this risk. <sup>52</sup> [^52: Liu, Y., J. A. Stanturf, and S. L. Goodrick. 2009. “Trends in Global Wildfire Potential in a Changing Climate.” _Forest Ecology and_\n_Management_ 259 (4): 685–697. _[https://doi.org/10.1016/j.foreco.2009.09.002](https://doi.org/10.1016/j.foreco.2009.09.002)_ .] However, this risk is concentrated in the southern part of the\ncountry, particularly along the southeastern corner where the average annual area of land that is burned\nis 20–50 percent or more (Figure 4.1). These data are calculated based on an annualized average from a\nhistorical 25-year period. <sup>53</sup> [^53: Giglio, L., J. Randerson, and G. van der Werf. 2013. “Analysis of Daily, Monthly, and Annual Burned Area Using the Fourth‐\nGeneration Global Fire Emissions Database (GFED4).” _Journal of Geophysical Research: Bio geosciences_ 118 (1): 317–328.] Over a 30-year period, however, even", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000051:79:0:0", "start": 519, "end": 543, "surface": "NASA NEX-GDDP CMIP5 data", "probe_tag": "keep", "probe_score": 0.9264, "luna_label": 1, "luna_reason": "Named climate dataset used for internal calculations."}, {"key": "jdc_operational:000051:79:0:1", "start": 1430, "end": 1460, "surface": "Global Fire Emissions Database", "probe_tag": "confusion", "probe_score": 0.8178, "luna_label": 1, "luna_reason": "Named database cited as basis for historical burned-area calculations."}]}, {"key": "rafael2-096", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n\n**41.** **UN-Habitat will be responsible for results monitoring and will ensure the frequent monitoring of project**\n**implementation through regular follow-up with its local partners, site visits.** UN-Habitat will ensure that results\nmonitoring is responsive to the changing circumstances on the ground. It will assign a full time M&E officer to collect\nthe relevant data at baseline (i.e., at grant effectiveness) and over monitoring phases in coordination with the other\nproject management team experts. As part of the reporting process, UN-Habitat will provide updated geographic\ninformation system (GIS) maps of the project areas to help monitor progress of infrastructure-related activities. The\nlocal partners will prepare quarterly progress reports that will be reviewed by UN-Habitat and shared with the World\nBank. UN-Habitat will submit technical and financial progress reports on project activities to the World Bank every six\nmonths in accordance with an agreed template.\n\n\n**C. Sustainability**\n\n\n**42.** **The World Bank’s value proposition is to create an enabling environment for a sustainable program-wide**\n**approach to housing and recovery of the culture sector.** The project has been designed with sustainability in mind, so\nthat interventions, institutions, and individuals can continue to benefit after the conclusion of the project. Although\nthe project will intervene in a small portion of the identified reconstruction and recovery needs, it is scalable and can\nbe replicated and expanded throughout the post-explosion recovery of Beirut. The project has the potential to catalyze\nthe revitalization of the neighborhoods’ vibrancy, while establishing an integrated reconstruction framework to scale\nup operations once additional funding becomes available. Building rehabilitation under the BBB approach will ensure\nlong", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000012:24:0:0", "start": 737, "end": 766, "surface": "geographic\ninformation system", "probe_tag": "confusion", "probe_score": 0.4413, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-097", "text": " designs and\ntargeting mechanisms, with the assistance of the donor community. The proposed\nproject has a simple design based on approaches that worked in similar\ncircumstances.\n\n    - **_Despite the Bank’s efforts to respond to crises when they occur, tangible results will_**\n\n**_not be achieved unless governments improve the institutional and administrative_**\n**_context and are able to reach affected groups promptly_** . In this particular case, the\nGovernment of Chad has responded to the crisis using its own resources while in\nparallel seeking the donor community’s support.\n\n\n    - **_Good results frameworks and sound M&E arrangements are essential for_**\n\n**_emergency operations._** It is important to work with client countries and development\npartners to identify practical mechanisms (including indicators) for monitoring\nnutritional and welfare outcomes and impacts of programs to mitigate food crises, and\nto work with them to implement those mechanisms and report the results. The\nproposed project will establish a robust M&E system that takes advantage of\nestablished periodic data collection and reports by WFP and FAO to monitor project\nimplementation.\n\n\n10", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000007:19:1:0", "start": 1089, "end": 1125, "surface": "periodic data collection and reports", "probe_tag": "confusion", "probe_score": 0.6922, "luna_label": 1, "luna_reason": "Existing WFP and FAO data collection and reports inform project monitoring."}]}, {"key": "rafael2-098", "text": " the cereals price index 11 percent, and the export price index 64 percent.\nWheat prices retreated somewhat from the peak reached on March 7, 2022, but they are still nearly 36 percent\nhigher than in early February 2022, before the war in Ukraine, and 60 percent higher since January 2021. Maize\nprices are about 20 percent above the early February level and 48 percent higher than in January 2021, while rice\nprices have continued to remain remarkably stable. Meanwhile, fertilizer prices surged in March 2022, up nearly\n20 percent since January and almost 3 times higher compared to a year ago, whereas energy prices had surged\n63.4 percent year on year.\n\n\n7 Winkler, Hernan; Gonzalez, Alvaro. 2019. “Jordan Jobs Diagnostic.” Jobs Series; No. 18. World Bank, Washington, DC.\n8 In 2019, FDI inflows fell to the lowest point over the past two decades, accounting for only 1.5 percent of GDP.\n9 International freight charges peaked around US$11,109 in September 2021 from US$1,500 pre-pandemic. These currently stand around\nUS$ 9,430 (source: Bloomberg).\n10 Jordan’s fiscal adjustment remains supported by the IMF. The Third Review of the IMF EFF program was completed on December 20,\n2021. This allowed IMF to disperse almost US$335 million (or cumulatively US$1.23 billion) under the program). According to the IMF,\nsound policies have helped maintain macroeconomic stability, while the government remains on track to narrow its fiscal deficit, and\nreserves remain at a comfortable level.\n\n\nPage 7 of 54", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000024:11:2:0", "start": 5, "end": 24, "surface": "cereals price index", "probe_tag": "confusion", "probe_score": 0.784, "luna_label": 0, "luna_reason": "Bare price index without a named source"}, {"key": "jdc_operational:000024:11:2:1", "start": 45, "end": 63, "surface": "export price index", "probe_tag": "confusion", "probe_score": 0.8387, "luna_label": 0, "luna_reason": "Index value lacks a named source or attributable data resource."}]}, {"key": "rafael2-099", "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": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000051:16:1:0", "start": 1342, "end": 1365, "surface": "Gender Inequality Index", "probe_tag": "confusion", "probe_score": 0.7192, "luna_label": 1, "luna_reason": "Index ranking is cited as evidence, with Human Development Report source."}]}, {"key": "rafael2-100", "text": " between 2019 and 2020, with 18-to34-year-olds consistently accounting for more than half of this number but the share of other age brackets\ngrowing. <sup>37</sup> The call volume continued to increase in the first half of 2021, with 565 calls received in\nJanuary, 602 in February, 760 in March, and more than 1000 in April and May. <sup>38</sup>\n\n17. **An exacerbating economic and political crisis, COVID-19 and the POB blast have further heightened**\n**negative feelings and experiences for many, particularly vulnerable people in Beirut.** Traumatizing\nevents, loss, separation, GBV, financial struggles or drastic changes in social and living conditions are likely\nto lead to people experiencing several distressing psychological reactions, which might have short or longterm impacts on people’s mental health and psychosocial wellbeing. The explosion as well as its political\nand economic shockwaves have affected most families, their community structures, schools and\nworkplaces, increased risks and exacerbated pre-existing vulnerabilities and inequalities, particularly\naround gender. The results of several surveys undertaken since the blast have shown that a significant\nnumber of respondents have experienced and continue to experience mental health issues. <sup>39</sup> Many refer\nto this as negatively impacting on their personal well-being and their sense of social inclusion and\nconnection to their families and communities.\n\n18. **The mental health sector in Lebanon has been undergoing a major reform, initiated by the National**\n**Mental Health Programme in 2015.** Progress has been made as shown by the external mid-term\nevaluation conducted in 2018, <sup>40</sup> for the implementation of the National Mental Health and Substance Use\n\n\n34 <mark>BMC Women’s Health Continuum of sexual and gender-based violence risks among Syrian refugee women and girls in</mark", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000002:7:1:0", "start": 1117, "end": 1124, "surface": "surveys", "probe_tag": "confusion", "probe_score": 0.7872, "luna_label": 1, "luna_reason": "Existing surveys provide findings on respondents’ mental health issues."}]}, {"key": "rafael2-101", "text": "**The World Bank**\nPromoting Financial Inclusion Policies and Regulations in Jordan ( P163719 )\n\n\n2017; (iii) continuing to champion the financial education program into the existing Jordanian school\ncurriculum from class 7 to class 11 by 2020; (iv) enhancing interoperability among the payments systems in the\nkingdom by end 2018; (v) ensuring efficient and responsible growth of microfinance sector as part of the\nformal financial system; (vi) providing the refugees and non-nationals with access to digital financial services;\n(vii) ensuring the provision of an enabling legislative and regulatory environment for digital financial services;\n(viii) upgrading financial inclusion data collection and measurement to align with AFI's network to produce\ncomparable indicators by 2018; and (ix) increasing the financial inclusion access of Jordan’s youth (15-22 years)\nby 25 annually by 2020.\n\n   - **Moreover, CBJ has worked in the last four years on building an infrastructure for payment systems, including**\n\n**interoperable platforms for mobile money and bill payments.** The CBJ has also progressively modernized its\ninternal systems to support a greater shift of Government payments into electronic payments. An ongoing\nproject with GIZ called “Digi#ances” aims to target low income Jordanians and refugees through JoMoPay to\nprovide them with digital wallets to receive money and transact. This comes along with financial literacy\nprograms and outlook for digital cross-borders remittances routes.\n\n   - **The Government of Jordan has announced its commitment to digitizing money transfers.** This is of particular\n\nimportance as the World Bank Universal Financial Access (UFA) data portal estimates that 0.2 million adults can\nbe reached in Jordan by exploiting the country opportunity of digitizing G2P payments. Currently, 160 out of\n260 government services are paid electronically in the system where government transactions in the system\nare growing exponentially in terms of volume and number.\n\n   - **With the objective of attaining a", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000013:4:0:0", "start": 662, "end": 686, "surface": "financial inclusion data", "probe_tag": "drop", "probe_score": 0.0226, "luna_label": 0, "luna_reason": null}, {"key": "sample:jdc_operational:000013:4:0:1", "start": 1651, "end": 1695, "surface": "Universal Financial Access (UFA) data portal", "probe_tag": "confusion", "probe_score": 0.3084, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-102", "text": " targeting formula that\ngives additional weight to vulnerability criteria along key dimensions, including by sex and gender head of\nhousehold.” The project will address the food security needs of women, who are usually responsible for food\nmanagement at the household level. Households are affected by higher food prices in different ways, with femaleheaded households resorting to harmful coping strategies, such as reducing their own nutrition consumption to\nfeed members of their family or taking on risky jobs to acquire food as a result of having limited access to assets\nand markets, and fewer pathways out of the crisis as compared to their male counterparts. In general, women are\nusually responsible for food management at the household level yet few work in Jordan, with female\nunemployment among those seeking work nearly twice that of men. Moreover, those women who do work tend\nto earn less than men do for comparable jobs limiting their access to resources during times of crises (World Bank\n2021). The project design includes surveys and other monitoring and evaluation activities to inform food security\n\n38 REACH 2020\n\n\nPage 26 of 54", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000024:30:2:0", "start": 1041, "end": 1048, "surface": "surveys", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-103", "text": " emergency procedures while deferring exact projects/roads selection\nand associated safeguards to implementation;\n\n2. Agree on the first‐year road works program to speed up implementation;\n\n3. Include the purchase of necessary equipment which can be implemented quickly; and\n\n4. Introduce retroactive financing to support timely project implementation and initiate\nprocurement activities and required studies.\n\n  - **Deliver good quality infrastructure and asset management practices:**\n\n1. Prepare procurement strategy and packages to ensure a wider participation of local contractors\n(hence, broader benefits in different areas/communities) while maintaining well qualified\ncontractors to guarantee proper rehabilitation works in accordance with existing Lebanon’s high\nroad rehabilitation design standards;\n\n2. Introduce proper and objective road prioritization measures through the visual survey of the\nnetwork’s condition and safety, which will also later inform the creation of a new and integrated\nroad asset management system for Lebanon;\n\n3. Introduce road safety and climate resilient improvements to improve existing road design and\nconstruction standards and practices in Lebanon; and\n\n4. Introduce routine maintenance contracts as an important and efficient asset preservation\nmeasure (including possibly the piloting of performance‐based contracts).\n\n  - **Create significant number of short‐term jobs for Lebanese and Syrians:**\n\n      - Select road sections with required civil works such as drainage and slope stabilization structures\nto increase the labor content of contracts;\n\n\nPage 22 of 90", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000008:25:1:0", "start": 886, "end": 899, "surface": "visual survey", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 0, "luna_reason": "Proposed survey activity, not an existing dataset used for an attributed finding."}]}, {"key": "rafael2-104", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\n\n|Col1|project will undertake<br>proactive efforts to<br>communicate the service<br>standard to address and<br>respond to feedback that<br>will be received.|Col3|Col4|mechanism|Col6|\n|---|---|---|---|---|---|\n|Beneficiaries reporting satisfaction with<br>project activities<br>|Percentage of beneficiaries<br>in component 1 satisfied<br>with project application,<br>grant disbursement,<br>implementation, and<br>technical support.<br>Beneficiaries in component<br>2 reporting improved<br>community cohesion,<br>enhanced social inclusion,<br>and neighborhood<br>revitalization.<br>The findings of these<br>surveys will be published<br>and/or that the survey<br>findings will be used by the<br>implementing entity to<br>generate an action plan to<br>address the feedback<br>acquired through the<br>surveys.|At mid-<br>point of<br>project and<br>project<br>closure<br>|The scope of<br>the GRM will<br>include<br>complaints<br>and other<br>types of<br>feedback<br>such as<br>suggestions,<br>queries (e.g.<br>Quality of<br>Life<br>Survey) and<br>compliments<br>|A survey will be carried<br>out with direct<br>beneficiaries of the<br", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000012:46:0:0", "start": 712, "end": 719, "surface": "surveys", "probe_tag": "drop", "probe_score": 0.0087, "luna_label": 0, "luna_reason": null}, {"key": "sample:jdc_operational:000012:46:0:1", "start": 903, "end": 910, "surface": "surveys", "probe_tag": "drop", "probe_score": 0.0022, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-105", "text": "verification agent<br> <br>|PIU of the Ministry of<br>Petroleum and Energy<br>|\n|Community forest resources under<br>integrated and participative management||Quaterly<br>|<br>Reports of<br>verification<br>agents,<br>progress<br>reports of|Data provided by PIU<br>and verified by an<br>independent<br>verification agent<br>|PIU of the Ministry of<br>Petroleum and Energy<br>|\n\n\nPage 57 of 87", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000051:62:1:0", "start": 239, "end": 259, "surface": "Data provided by PIU", "probe_tag": "drop", "probe_score": 0.0294, "luna_label": 0, "luna_reason": "Project monitoring data used for verification reporting, not an external analytical resource."}]}, {"key": "rafael2-106", "text": "147,500 (indirect)<br>|\n|<br>**Sub-component 1A: **<br>**Supporting creation**<br>**and strengthening of **<br>**women platforms,**<br>**community**<br>**mobilization, and **<br>**mindset change**(IDA <br>US$5 million, including<br>WHR US$450,000) <br>|<br>• Mobilization of women and girls in<br>target districts to establish<br>platforms at district level of new<br>and existing women entrepreneurs.<br>• Setting up a national digital<br>platform for women entrepreneurs<br>• Setting up a database of women-<br>owned/managed businesses<br>• Communication and outreach<br>campaign<br>• Service provider is contracted to<br>conduct sessions on social<br>norms/women safety<br>• Advocacy on policy issues impacting<br>women entrepreneurs|• Women and adolescent<br>girls<br>• Existing women<br>entrepreneurs<br>• Refugee women<br>• Men, male partners,<br>community leaders<br>benefiting from<br>participating in behavior<br>change interventions.<br>•  Women business leaders|150,000 women and<br>adolescent girls<br> <br>Estimated 1,147,500 men,<br>male partners, communities,<br>and household members<br>indirectly benefit from<br>platform and communication<br>campaign<br>|\n\n\n\nPage 54 of 77", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000025:58:2:0", "start": 491, "end": 508, "surface": "database of women", "probe_tag": "drop", "probe_score": 0.0219, "luna_label": 0, "luna_reason": "Database is being set up by the project, so it is future data production."}]}, {"key": "rafael2-107", "text": " will be implemented first to ensure timely project implementation. Should any counties\nbe deemed unfeasible, they will be replaced by ‘replacement counties’ on the long list. In some cases,\ncertain _payams_ within selected counties will be inaccessible or difficult to access for a number of reasons.\nThey may also be among the most deserving of project investments due to the vulnerability of their\npopulations. In such cases, efforts will be made to adapt a participatory methodology for the\ncircumstances at hand, to engage surrounding _payams_ and counties in investment and resolution\nstrategies for such locations, or to defer investment until a later round when conditions improve.\n\n\n**Figure 2.10. Subproject Budget Allocation**\n\n\n8. **Use of community labor.** The project will encourage contractors to use local labor in the\ninfrastructure construction or rehabilitation to the extent possible. Emphasis will be given to the inclusion\nof various social groups facing marginalization or barriers to participation (for example, women, youth,\nreturnees, ethnic minority groups, and people with disabilities) and ensuring their access to community\ninfrastructure and daily wage labor opportunities. It will be especially important to include women in the\n\n\n80 Population figures for urban areas would be calculated based on a headcount or by complementing 2008 census with other data sources (for\nexample, DTM).\n\n\nPage 71 of 94", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000049:76:1:0", "start": 1363, "end": 1374, "surface": "2008 census", "probe_tag": "confusion", "probe_score": 0.0902, "luna_label": 1, "luna_reason": null}, {"key": "sample:jdc_operational:000049:76:1:1", "start": 1413, "end": 1416, "surface": "DTM", "probe_tag": "drop", "probe_score": 0.0021, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-108", "text": "):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|\n|**4.**Municipal** r**oads built<br>or rehabilitated with<br>related infrastructure<br>using urban LDG|√|3|Km<br>Targets|53.02|Measured<br>Annually|Measured<br>Annually|Measured<br>Annually|Measured<br>Annually|Measured<br>Annually|Annually|Municipal reports|Participating<", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000006:36:4:0", "start": 1379, "end": 1396, "surface": "Municipal reports", "probe_tag": "drop", "probe_score": 0.0017, "luna_label": 0, "luna_reason": "Annual verification source in project results framework, not demonstrated existing data use."}]}, {"key": "rafael2-109", "text": " earn their livelihoods, is key to achieving the\ngoals. Fertility rates remain high (at five births per woman), as does the population growth rate, meaning that most\nhouseholds have few income earners and several dependents, and much of the care responsibilities fall on women. In this\ncontext, supporting women micro-entrepreneurs to move further up the value chain or into other, more profitable\nsectors, will help harness women's contributions to the economy and contribute to greater growth overall.\n\n\n25. **This project is also aligned with Bank-financed operations aimed at promoting gender inclusion and WEE.** These\ninclude the Skills Development Project (P145309); the Agricultural, Technological, and Advisory Services Project\n(P109224); the Reproductive, Maternal and Child Health Services Improvement project, and NUSAF. The project’s work\nwith refugees will build on and partner closely with DRDIP project in refugee-hosting districts.\n\n\n**C. Proposed Development Objective(s)**\n\n\nTo enhance the economic and social empowerment of women entrepreneurs in Uganda\n\n\n26. **The proposed outcome indicators to measure achievement of the PDO are** :\n\n\n    - Increase in household income, including the contribution of women\n\n    - Increase in productive assets\n\n    - Increase in the number of women-led enterprises in project locations\n\n    - Increase in household welfare\n\n    - Increase in women’s decision-making.\n\n\n27. Potential outcome indicators will be explored during preparation to ensure that baseline data exist, that they are\nmeasurable, and that approaches to measure them are available to provide accurate results at reasonable cost.\n\n\nJun 15, 2021 Page 9 of 13", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000005:8:1:0", "start": 1510, "end": 1523, "surface": "baseline data", "probe_tag": "drop", "probe_score": 0.0001, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-110", "text": " the NRs and the respective\ndistrict, municipal and local authorities manage the secondary network.\n\n15. **Only 3.3 percent of the road network is paved; 52 percent are gravel roads and another about 45**\n**percent are earthen roads, which do not provide all-weather connectivity and are difficult to maintain.**\nOnly about 100-400 km of gravel NRs are being upgraded every year to paved roads. Some of the river\ncrossings on NRs do not have bridges and traffic movement on these highways is dependent on the ferry\ncrossings, which operate for only few hours in a day.\n\n\n16. **The road network is prone to disruption by floods, is not resilient, and has only few redundancies** <sup>**26**</sup> [^26: Redundancies are alternate routes] **but**\n**uncertainty about the future and limited resources make it difficult to plan robust mitigating measures.**\nUganda is exposed to a variety of natural hazards (droughts, flooding, landslides, heat waves). Each year,\nfloods impact nearly 50,000 people and over US$62 million in GDP. Uganda experiences both flash floods\nand slow-onset floods. Areas most prone to floods are the capital city, Kampala, and the northern and\neastern areas of the country.\n\n\n17. **According to UDHS, distance to health facilities in Uganda was reported as a serious problem to access**\n**health facilities and this is more acute in refugee-hosting districts in West Nile sub-region.** Mortality\nrate is still an issue in Uganda. Fertility and mortality rates, from the 2016 UDHS, forecasted that 2\npercent of women un Uganda would die from maternal causes at existing rates. Moreover, it is\ndocumented that health care services during pregnancy and childbirth and after delivery are important\nfor the survival and well-being of both the mother and infant. This situation worsens when roads are", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000001:7:1:0", "start": 1217, "end": 1221, "surface": "UDHS", "probe_tag": "drop", "probe_score": 0.0457, "luna_label": 1, "luna_reason": null}, {"key": "sample:jdc_operational:000001:7:1:1", "start": 1492, "end": 1501, "surface": "2016 UDHS", "probe_tag": "keep", "probe_score": 0.9013, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-111", "text": " under the regional No Lost\nGeneration strategy. <sup>45</sup> The work also aligns with the national Child Protection Policy, developed by the\nMinistry of Education and Higher Education, in the form of its education personnel and psychosocial\nsupport counsellors. A combination of United Nations High Commissioner for Refugees (UNHCR), World\nHealth Organization (WHO), United Nations Children’s Emergency Fund (UNICEF) and others provide\nthese services to both refugees from Syrian and vulnerable Lebanese, while United Nations Relief and\nWorks Agency for Palestine Refugees United Nations Relief and Works Agency for Palestine\nRefugees (UNRWA) provides these services to Palestinian refugees.\n\n**Persons with Disabilities and Older Persons**\n\n\n20. **Conditions for persons with disabilities and Older Persons (OPs) had been deteriorating since 2019 as**\n**the compound crises affected these groups disproportionately.** <sup>46</sup> The POB explosion only exacerbated\nthese vulnerabilities further. In fact, at the end of 2019, the Ministry of Social Affairs – which had\npreviously delivered services to persons with disabilities – rescinded its support services due to budgetary\nshortages. In turn, civil society partners providing services to persons with disabilities and OPs recorded a\nsignificant increase in their Lebanese caseload over the course of 2020. <sup>47</sup> In late May 2020, a Rapid Needs\nAssessment conducted by HelpAge International in Beirut identified that 68% of people aged 50 and above\nhad at least one disability or impairment. <sup>48</sup> While reliable data quantifying the impact of the blast on these\nnumbers does not exist, an August 2021 Human Rights Watch investigation estimated that 150 people\n\n\n[41https://www.moph.gov.lb/userfiles/files/Mental%20Health%20and%20Substance%20Use%20Strategy%20for%20", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000002:8:1:0", "start": 1400, "end": 1422, "surface": "Rapid Needs\nAssessment", "probe_tag": "drop", "probe_score": 0.0014, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-112", "text": "**The World Bank**\nSouth Sudan Emergency Food and Nutrition Security Project (P163559)\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>Description:|<br>Description:|<br>Description:|<br>Description:|<br>Description:|<br>Description:|<br>Description:|<br>Description:|\n\n\n\n\n\n\n\n\n\n\n|Col1|Name: Number of<br>pregnant and lactating<br>women consuming<br>blended supplementary<br>food in the intended<br>quantities|Col3|Number|0.00|80000.00|Monthly|Progress reports|MAFS/WFP|Col10|\n|---|---|---|---|---|---|---|---|---|---|\n||<br>Description:|<br>Description:|<br>Description:|<br>Description:|<br>Description:|<br>Description:|<br>Description:|<br>Description:|<br>Description:|\n\n\n|Col1|Name: Number of<br>individuals provided with<br>WASH services|Col3|Number|0.00|200000.00|Quarterly|Progress reports|MAFS/UNICEF|Col10|\n|---|---|---|---|---|---|---|-", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000038:36:0:0", "start": 19, "end": 76, "surface": "South Sudan Emergency Food and Nutrition Security Project", "probe_tag": "drop", "probe_score": 0.0443, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-113", "text": "Table **3:** Impact of **NPTP** on extreme poverty\n\n\nExree ovrt **rte7.19** **5.06** **2.13** **29.6%**\nExree ovrt gp1.40 **0.87** **0.53** **37.9%**\n\nExrmePvet svriy0.43 0.24 **0.19** 44.1%\nGini coefficient **39.25** **39.00** **0.26** **0.7%**\n_Source:_ Author's calculations using 2004 Household Budget Survey and ADePT\n\n\n**19.** Thus, with perfect coverage, **NPTP** can be expected to reduce the extreme poverty rate in\nLebanon from **7.19** percent to **_5.06_** percent or **by** **30** percent (Table **3** **).** It is important to note that\nextreme poverty gap and extreme poverty severity would decrease **by** even more in percent terms\n**(38** and 44 percent, respectively). Needless to say, these are significant gains in terms of poverty\nreduction.\n\n\n20. Here, we perform sensitivity analysis to relax our strong assumption of perfect coverage.\nFor each level of coverage, this would consist of drawing a certain share (say, **10** percent) of\nextremely poor households in the 2004 HBS, adding the average value of **NPTP** to their\nconsumption, calculating the extreme poverty rate with **NPTP,** and repeating this sufficient\nnumber of times **(100** times) to generate an average impact on extreme poverty", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000109:89:0:0", "start": 284, "end": 312, "surface": "2004 Household Budget Survey", "probe_tag": "keep", "probe_score": 0.9861, "luna_label": 1, "luna_reason": null}, {"key": "sample:refugee_pads:000109:89:0:1", "start": 317, "end": 322, "surface": "ADePT", "probe_tag": "keep", "probe_score": 0.983, "luna_label": 0, "luna_reason": null}, {"key": "sample:refugee_pads:000109:89:0:2", "start": 992, "end": 1000, "surface": "2004 HBS", "probe_tag": "keep", "probe_score": 0.9895, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-114", "text": "EP observed in\nrecent projects; (b) assessments of the rehabilitation needs that were carried out in the audit of ONSER\nsystems in the three southern wilayas; (c) SNDE’s assessments of the rehabilitation/expansion needs of\nselected centers in its coverage zone and of the Kiffa water system; and (d) sanitation costs related to\nconstructing additional latrines. Investment costs consist of: (i) the direct costs of activities of Subcomponents 1.1, 1.2, 2.1, and 2.2; (ii) design and supervision costs; and (iii) the cost of Component 3\nactivities that may be partially allocated to the execution of water activities.\n\n\n4. **Incremental costs** : Estimates of energy consumption are drawn from Water Master Plans. The unit\ncosts per kWh are based on ARE estimates (for ONSER and small SNDE systems that use diesel-driven\ngenerators) and SNDE estimates for Kiffa. The annual maintenance costs of solar pumping are estimated\non the basis of similar facilities in the sub-region. As for other O&M costs (Table 4.1), chlorination costs\nand commercial costs are estimated on the basis of similar facilities in the sub-region; staff cost estimates\nare drawn from ARE databases; and maintenance costs are estimated on the basis of similar projects in\nthe sub-region.\n\n\n**Table 4.1: Overview of Project Operating Costs**\n\n\n\n\n\n\n\n\n\n\n\n|Item|Mini water<br>system|Regular water<br>system|ONSER<br>system|7 SNDE centers|Kiffa|\n|---|---|---|---|---|---|\n|Energy||||||\n|Thermal pumping (MRU/m3 produced)|None|7.4|7.4|7.4|2.5|", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000106:58:1:0", "start": 1156, "end": 1169, "surface": "ARE databases", "probe_tag": "keep", "probe_score": 0.9576, "luna_label": 1, "luna_reason": "ARE database data inform staff cost estimates."}]}, {"key": "rafael2-115", "text": " 335,000 hectares that require only modest\ninvestments in infrastructure to become productive. This resource, coupled with the country’s\nagro-ecological diversity, creates huge potential for crop diversification.\n\n\n16. The livestock subsector contributes significantly to the Chadian economy, although its\ncontribution to export revenues is suboptimal owing to the enormous uncontrolled migration of\nherds to markets in Nigeria each year. The dominant herding practices in Chad, affecting 80\npercent of livestock, are transhumance and extensive herding. Cattle herding is the primary\nagricultural activity in the Sahel and a secondary activity in the Sudanian zone, although the\nrelatively greater availability of food, agricultural by-products, and agro-industrial operations in\nthe Sudanian zone attract many herders from the north. In some instances, the herders’\ncoexistence with the agro-pastoralists poses land management problems and can be a source of\nconflict.\n\n\n17. Food security is volatile in Chad because agricultural production systems rely largely on\nrainfall rather than irrigation. Numbers of food-insecure individuals more than triple when\ndroughts deplete harvests and food prices soar. In southern Chad, where most of the refugees and\nreturnees are being settled, agriculture is dominated by small-scale farmers producing a\nmoderate, locally marketed surplus. Cross-border trade with Cameroon and CAR is also\nimportant. While local trade with CAR has been significantly curbed by the conflict, markets in\nthe areas where vouchers are being implemented continue to be well stocked and no unusual\nprice movements have been observed in the past few months since voucher transfers became\noperational. A January 2014 market assessment indicated that food production surpluses were\n\n\n4", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000019:13:1:0", "start": 1719, "end": 1749, "surface": "January 2014 market assessment", "probe_tag": "keep", "probe_score": 0.9415, "luna_label": 1, "luna_reason": "Existing assessment provides evidence of food production surpluses."}]}, {"key": "rafael2-116", "text": "**The World Bank**\nUganda: Roads and Bridges in the Refugee Hosting Districts Project (P171339)\n\n\non the other sectors by enhancing connectivity of the host population and refugees to markets. The existing\nroad infrastructure in West Nile Sub-Region is of poor quality and not motorable especially during rains.\nAccess to health facilities and referral medical units is also encumbered by the dilapidated road network.\n\n\n11. **Uganda was ranked 127** <sup>**th**</sup> **out of 162 countries in the 2018 Gender Inequality Index** <sup>11</sup> . Prevalence rates of\ngender-based violence (GBV) in Uganda are high. According to the Uganda Demographic and Health Survey\n(UDHS) <sup>12</sup>, 56 percent of women have experienced spousal violence and 22 percent sexual violence. The\nfigures for Violence Against Children (VAC) are also high, with 59 percent of females and 68 percent of males\nreporting experiencing physical violence during childhood. <sup>13</sup> Adolescent girls in Uganda are more likely to\nbe poor, miss out on school and are at a greater risk of contracting HIV. <sup>14</sup> Of Ugandans ages 13-17 years,\none in four girls and one in ten boys reported sexual violence in 2015. <sup>15</sup> Nearly a quarter of teenage girls in\nUganda become pregnant. <sup>16</sup> The intersection with poverty and lack of access to education is the greatest\nrisk to violence against adolescent girls, particularly in rural areas. Refugee women/girls are at high risk of\nseveral forms of GBV including sexual exploitation and abuse (SEA), rape, forced and child marriage and\nintimate partner violence (IPV) <sup>17</sup>", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000146:14:0:0", "start": 499, "end": 527, "surface": "2018 Gender Inequality Index", "probe_tag": "keep", "probe_score": 0.9487, "luna_label": 1, "luna_reason": "Named index provides Uganda's ranking among 162 countries."}, {"key": "refugee_pads:000146:14:0:1", "start": 631, "end": 667, "surface": "Uganda Demographic and Health Survey", "probe_tag": "keep", "probe_score": 0.9706, "luna_label": 1, "luna_reason": "Survey provides cited violence prevalence findings for Ugandan women."}]}, {"key": "rafael2-117", "text": "br>|\n\n\n\n9. **Reduced compliance costs for taxpayers.** For the taxpayers, the major economic benefits include\na reduction in the time to comply with taxes and the time spent on dealing with tax inspections. The latter\nwill be achieved mainly through a strengthened compliance risk management mechanism and the fact\nthat tax inspectors would now need to spend less time on gathering and processing information, as well\nas doing surveillance during field audits. The 2015 Doing Business Report provides a baseline for the\naverage time needed to comply with taxes. The indicator is reported for a hypothetical medium-size\ncompany, which needs, on average, about 82 hours per year (or approximately 10 days) to comply with\nmajor types of taxes and contributions in Djibouti. The indicator can be subdivided further into the time\nneeded to comply with the corporate income tax (30 hours or 3.75 days per year), labor taxes (36 hours\nper 4.5 days), and consumption taxes (16 hours per 2 days).\n\n\n**Table 4.4. Compliance Costs**\n\n\n\n\n\n\n\n\n\n|Type of taxpayer|Number|Days|Days|Days|Days|Days|Days|Days|Days|\n|---|---|---|---|---|---|---|---|---|---|\n|||2018|2022|2023|2024|2025|2026|2027|2028|\n|Personal income taxpayers|3,224|4.5|4.25|4.2|4.15|4.1|4.05|4.025|4|\n|Corporate income taxpayers|470|3.75|3.5|3.4|3.3|3.2|3.1|3.2|3|\n|Value added taxpayers|320|2|1.9|1.", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000142:67:2:0", "start": 465, "end": 491, "surface": "2015 Doing Business Report", "probe_tag": "keep", "probe_score": 0.989, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-118", "text": "**The World Bank**\nAfghanistan: Eshteghal Zaiee - Karmondena (EZ-Kar) (P166127)\n\n\ncommittee (PSC) and IAs. This subcomponent will also finance operational planning; capacity building;\nmanagement information and reporting systems; support the rapid selection of Business Gozar sub‐projects under\nComponent 2.2; grievance redress mechanisms (GRM); human resource management; communications; donor\nand field coordination; financial management (FM) and procurement functions; and safeguards oversight.\n\n**C. Project Beneficiaries**\n\n42. The EZ‐Kar project will reach Afghan refugees living in Pakistan and Afghans in cities such as Jalalabad (Nangarhar\nProvince), Kabul (Kabul Province), Kandahar (Kandahar Province), Herat (Herat Province), Puli Khumri (Baghlan\nProvince), Maimana (Faryab Province), Firozkoh (Ghor Province), Khost (Khost Province), Asadabad (Kunar\nProvince), Kunduz (Kunduz Province), Mihtarlam (Laghman Province), Taloqan (Takar Province) and Paroon\n(Nuristan). These cities have been selected based on the influx of returnees and IDPs with data from GoIRA\n(National Statistics and Information Authority: NSIA) sources. (Additional cities can be added if additional financial\nresources are made available) (see Annex 8 for the population figures).\n\n43. Available survey data indicate that once returned to Afghanistan, most refugees return to their province of origin\nto be in proximity to family and friends or, if they return elsewhere, they do so for safety and economic reasons.\nAfghan returnee households are large and although most families have at least one person working for pay, they\nhave low job stability and low wages. According to survey data, most returnees work as daily wage laborers in\nnon", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000123:26:0:0", "start": 1279, "end": 1290, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9217, "luna_label": 1, "luna_reason": "Available survey data supports findings about refugees’ return patterns and employment."}]}, {"key": "rafael2-119", "text": "**The World Bank**\nBurundi - Jobs and Economic Transformation Project- PRETE (P177688)\n\n\n<u><mark>Collection</mark></u>\n\n|Number of new NBFIs connected to Bi-Switch and are interoperable within the Burundi Financial System (Number)|Col2|\n|---|---|\n|Description <br>The indicator captures the number of new NBFIs that become interoperable within the Burundi Financial System by<br>connecting to Bi-Switch.|Description <br>The indicator captures the number of new NBFIs that become interoperable within the Burundi Financial System by<br>connecting to Bi-Switch.|\n|Frequency<br>Quarterly|Frequency<br>Quarterly|\n|Data Source<br>Project records|Data Source<br>Project records|\n|Methodology for Data<br>Collection<br>The data will be collected from the agency overseeing the Bi-Switch connection to determine the new connections to Bi-<br>Switch. A follow-up will also be conducted with NBFIs that received support from the project to connect to the Bi-Switch.|Methodology for Data<br>Collection<br>The data will be collected from the agency overseeing the Bi-Switch connection to determine the new connections to Bi-<br>Switch. A follow-up will also be conducted with NBFIs that received support from the project to connect to the Bi-Switch.|\n|Responsibility for Data<br>Collection <br> PIU|Responsibility for Data<br>Collection <br> PIU|\n|**Guaranteed loans disbursed to MSMEs with support of the PPCG fund (Amount [US$])PBC**|**Guaranteed loans disbursed to MSMEs with support of the PPCG fund (A", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000004:43:0:0", "start": 626, "end": 641, "surface": "Project records", "probe_tag": "keep", "probe_score": 0.9271, "luna_label": 0, "luna_reason": null}, {"key": "sample:refugee_pads:000004:43:0:1", "start": 657, "end": 672, "surface": "Project records", "probe_tag": "confusion", "probe_score": 0.6689, "luna_label": 0, "luna_reason": "Indicator data collection is planned from project records."}]}, {"key": "rafael2-120", "text": ". Orientation, counseling and skills training activities will focus on providing a foundational\nset of skills to ex-combatants as they begin the transition to civilian life. The survey on signatory\nmovements found that traditional authorities, leaders of signatory movements; as well as\ncombatants recognize the importance of having skills in the aftermath of the DDR program. In\neach community, there will be an ex-combatant focal point who will support the project with\nfeedback, concerns and suggestions, as well as monitoring purposes.\n\n25. In addition, for those combatants identified as requiring psychosocial support during\nscreening in cantonments, the Project will provide counseling during the first six months.\nPsychosocial suffering and mental and behavioral disorders are risk factors for socio-economic\n\n14 Exact duration will be determined depending on the type of the skills training.\n\n\n6", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000148:16:1:0", "start": 178, "end": 207, "surface": "survey on signatory\nmovements", "probe_tag": "confusion", "probe_score": 0.3776, "luna_label": 1, "luna_reason": "Past survey reports a concrete finding about authorities, leaders, and combatants."}]}, {"key": "rafael2-121", "text": ",\nbut the most serious threats come from agriculture because of uncontrolled pumping and the use\nof pesticides and fertilizers that could cause significant pollution of ground water aquifers.\n\n\n27. The baseline conditions of the site are suitable for landfill construction. The site is\nlocated in semi-desert land with low annual precipitation; the area is very sparsely populated.\nThe site is underlain by fresh water aquifer, which constitutes the main source of drinking and\nirrigation water for southern Gaza Strip. The ESIA reported that this aquifer is of low\nvulnerability to pollution. The BESIA report indicates that excessive withdrawal <sup>48</sup> of ground\nwater at drinking water wells has created major cones of depression and the cone of influence is\nsignificant in the vicinity and approach areas to the well fields. However, in the area in the\nvicinity to the proposed Al-Fukhari (Sofa) landfill, the effect of ground water withdrawal is\ninsignificant, which can be construed to indicate that the potential impact of the proposed\nsanitary landfill at Al-Fukhari (Sofa) would not cause any immediate threat to existing water\n\n47 Environmental and Social Impact Assessment for Solid Waste Management in Gaza, Baseline Report, and draft\ndated December 2011 carried out by EcoConServ Environmental Solutions (international Egyptian consultant) and\nUniversal Group-Gaza (local Gaza consultant).\n48 The cones of depression are due to an imbalance in the rate of pumping that exceeds the rate of recharge from\nrainfall. Hydrological studies for years 2000/2001 and 2006/2007 report two large cones of depression, one in the\nnorth GS (in the vicinity to Beit Lahia) and the other in the south GS (in the vicinity to Rafah city).\n\n\n68", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000035:77:1:0", "start": 1532, "end": 1552, "surface": "Hydrological studies", "probe_tag": "confusion", "probe_score": 0.6378, "luna_label": 1, "luna_reason": "Existing studies report groundwater cones of depression for specified years."}]}, {"key": "rafael2-122", "text": "indicators for the monitoring and tracking system of the SEP/CNPS. The use of these modules is\nexpected to improve program design and implementation, foster future coordination between\nprograms, and the monitoring and evaluation of the cash transfer program as a template for other\nsocial safety net interventions.\n\n41. **Building on the database of eligible households, these modules will include:** (i)\nprogram beneficiary lists, and eventually a registration of complementary activities; (ii) payment\nmodules (limited to the payroll and the reconciliation from the payment provider(s)); (iii)\noperational tracking of programs; and (iv) basic monitoring and evaluation, including beneficiary\nfeedback and grievance redress system. The payment system will include the quarterly/monthly\npayroll based on beneficiary lists, the amounts transferred to the payment agency(ies), the\nbeneficiary receipts and the reconciliation of accounts. The operational tracking module would\nprovide an operational dashboard to enable program managers to plan and track activities, human\nand material resources and other inputs at the central, provincial and communal levels. The M&E\nsystem would track financial outlays, key program results (including those core indicators that\nwould become common across programs), impacts and beneficiary feedback as inputs to guide\nprogram management in the implementation of the programs.\n\n\n42. **The focus on citizens’ engagement includes a robust grievance redress mechanism**\n**(GRM).** The grievance redress mechanism would track grievances linked to targeting, receipt of\ntransfers and implementation of the complementary activities. It will use several mechanisms: inperson complaints to program commune focal point, SMS-based system to a third-party grievance\nmanager (conditional on finding a trusted and competent agent), boxes at the _colline_ -level in the\ncare of a civil society organization, possibly a hotline at central-level. Complaints received through\nSMS, phone or boxes will be logged in the MIS. A results indicator to track the progress of the", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000157:23:0:0", "start": 338, "end": 369, "surface": "database of eligible households", "probe_tag": "confusion", "probe_score": 0.4806, "luna_label": 1, "luna_reason": "Existing household database is used as the basis for beneficiary and monitoring modules."}, {"key": "refugee_pads:000157:23:0:1", "start": 804, "end": 821, "surface": "beneficiary lists", "probe_tag": "confusion", "probe_score": 0.301, "luna_label": 1, "luna_reason": "Existing beneficiary lists determine payroll payments and transferred amounts."}]}, {"key": "rafael2-123", "text": " other regional offices in the East, South, and Lake Chad areas, fully staffed\nand trained. These offices will have the authority to procure goods and services up to a\npredetermined threshold and to manage related financial transactions, as well as manage\nimplementation of safeguards, coordination, and monitoring activities. These institutional\narrangements will rely on strong involvement of local government authorities and deconcentrated\ntechnical services.\n\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n\n57. **As the first Government-led project in Chad to focus on refugees through a**\n**development lens**, **the project will provide important lessons to guide future investments in**\n**this emerging area** . For these reasons, the project will put in place innovative monitoring\narrangements that will measure progress on outputs and outcomes and on the evolution of the\npolicy and protection agenda for refugees.\n\n58. **The project will rely on its internal resources and on several partners to help monitor**\n**progress in different areas.** For Component 1, the project will adapt the existing MIS of the CFS\nto capture progress in rehabilitation and new construction of facilities and full operationalization\nof basic services. Because the CFS does not have expertise in the implementation of works, the\nproject will hire specialized technical consultants (for example civil engineers) to supervise\nactivities under Component 1. The project will also rely on feedback from local authorities and\ncommunities during the identification, implementation, and operationalization phases. In areas that\nare difficult to reach, the project will consider using geo-enabling and remote sensing technologies\n(enhanced M&E) to monitor implementation progress of ongoing investments and community use\n\n32 Marcel Ferland, Rapport d’évaluation Institutionnelle du PARCA (Projet d’Appuis aux Refugiés et aux Communautés\nd’Accueil), Mimeo, Avril 2018.\n\n\nPage 27", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000112:31:1:0", "start": 1118, "end": 1132, "surface": "MIS of the CFS", "probe_tag": "confusion", "probe_score": 0.1913, "luna_label": 0, "luna_reason": "Future adaptation for project monitoring, not existing data use."}]}, {"key": "rafael2-124", "text": "**C.** **Sectoral and Institutional Context**\n\n\n9. Even prior to the onset of the Syrian conflict and the inflow of large numbers of Syrian\nrefugees, poverty in Lebanon was significant and regional disparities in living conditions were\nacute. It is estimated that nearly 27 percent of the Lebanese population, or 1.2 million people, are\npoor, living on less than US$4 per day, and seven percent, or 300,000 people, are extremely\npoor, living on less than US$2.40 per day (UNDP, 2008). <sup>3</sup> [^3: In 2013, the poverty rate was updated using the Consumer Price Index to US$3.84 for the lower (food) poverty line.] Poverty is significantly higher in\nsome regions, with the highest concentration of poor people found in the North governorate (52.5\npercent), followed by the South governorate (42 percent) and the Beka’a (29 percent).\n\n10. The Syrian conflict is projected to increase the poverty headcount of those below the\nupper poverty line by 170,000 people by end 2014. Simulations using household expenditures\ndata show that, between 2012 and 2014, poverty in Lebanon was projected to continue its\ndownward path in the absence of the Syrian conflict. In its presence, however, about 120,000\nLebanese are estimated to have been pushed into poverty in 2013, which is approximately three\npercent of the Lebanese (pre-conflict) population. If the current patterns were to continue, by\n2014 another 50,000 are expected to join the ranks of the poor. By 2014, the rate of poverty\nincidence in Lebanon would therefore be four percent higher due to the impact of the Syrian\nconflict. At the same time, the existing poor (about one in seven Lebanese) would be pushed\ndeeper into poverty through the impact on lower wages and higher unemployment rates.\n\n11. Geographically, the majority of the Syrian refugees", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000139:13:0:0", "start": 996, "end": 1023, "surface": "household expenditures\ndata", "probe_tag": "confusion", "probe_score": 0.8508, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-125", "text": " of public\nschools. <sup>7</sup>\n\n\n10. Prior to the onset of the Syrian crisis, Lebanon’s adjusted primary net enrollment rates\nwere slightly above the regional average at 96 percent. However, secondary net enrollment rates\nin Lebanon at 67 percent lagged behind the MENA average of 72 percent. Even when compared\nwith countries with similar level of development, Lebanon’s secondary net enrollment rate was\nsignificantly lower than the average of 81 percent. <sup>8</sup>\n\n\n11. Public education in Lebanon tends to serve the poor at low levels of quality. Public\nschools educate about 31 percent of students in Lebanon, despite being free. This revealed\npreference reflects the overall poor quality of public schools, particularly at the primary level,\nand has large and negative implications for the poor. The higher quality associated with private\nschools means that public-school students are likely to learn less and face more difficult job\nprospects upon graduation. This sets up inter-generational transmission of both lower learning\nlevels and lower income. <sup>9</sup> Public schools exhibit lower academic outcomes in international and\nnational assessments. The level of public school students was 10 percent lower than that of\nprivate schools in the 2011 Trends in International Mathematics and Science Study (TIMSS)\nresults. Indeed, based on the 2004 household survey, poverty and education are highly correlated\nin Lebanon.\n\n\n5 Lebanon’s inequality-adjusted HDI is 20.8 percent lower than its HDI, among the largest losses in the group of\ncountries in the high human development category.\n6 World Economic Forum’s 2013 Human Capital Index\n7 Further information about the level of private sector investments is expected from a forthcoming Education\nExpenditure Review.\n8 World Bank Ed Stats\n9 “Poverty, Growth and Income Distribution in Lebanon,” August 2008.\n\n\n3", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000099:11:1:0", "start": 1267, "end": 1320, "surface": "Trends in International Mathematics and Science Study", "probe_tag": "keep", "probe_score": 0.9973, "luna_label": 1, "luna_reason": null}, {"key": "sample:refugee_pads:000099:11:1:1", "start": 1359, "end": 1380, "surface": "2004 household survey", "probe_tag": "confusion", "probe_score": 0.8236, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-126", "text": " is defined as the competencies of teachers and school\nmanagers, teaching practices in classrooms, and the physical and digital environment in schools and\npreschools. The proposed project’s investments would primarily benefit the educational institutions\nserving the larger number of students from lower socioeconomic backgrounds. The following categories\nof disadvantaged students would be supported under the project: students from poor families, students\nfrom rural areas, girls, Roma students, refugees from Ukraine, at-risk students falling behind academically,\nand children with disabilities. The focus on providing equal educational opportunities for disadvantaged\nstudents and thus promoting shared prosperity is at the core of the project design. To harness the\nopportunities of digital transformation, the project will support as a cross-cutting area efforts to embrace\nsystemic changes that promote digitalization practices in education.\n\n25. PDO level indictors are as follows:\n\n    - Participating teachers with improved teaching practices\n\n    - Improved learning outcomes of students benefitting from project-supported tutoring program\n(disaggregated by gender, urban/rural, refugee/vulnerability status)\n\n    - Annual education statistics reports produced and publicly disseminated using data generated by\nthe integrated EMIS for education sector management and refugee response\n\n\n24 From Learning Recovery to Education Transformation: Insights and Reflections from the Fourth Survey on National Education\nResponses to COVID-19 School Closures. https://openknowledge.worldbank.org/handle/10986/38112\n25 UNICEF; the United Nations Educational, Scientific and Cultural Organization (UNESCO); and UNESCO’s Institute for Statistics.\n26 World Bank Group. 2022. Moldova – Digital Education Readiness Assessment 2021-22. Washington, D.C.: World Bank Group.\n27 Navigating Multiple Crises, Staying the Course on Long-term Development: The World Bank Group’s Response to the Crises\nAffecting Developing Countries (English), Washington, DC, World Bank Group.\n\n\nPage 10 of 68", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000185:19:1:0", "start": 1304, "end": 1341, "surface": "data generated by\nthe integrated EMIS", "probe_tag": "confusion", "probe_score": 0.1047, "luna_label": 1, "luna_reason": "Integrated EMIS data are used to produce education statistics reports."}]}, {"key": "rafael2-127", "text": "**The World Bank**\nLebanon Health Resilience Project (P163476)\n\n\n**Box 2. Lebanon Emergency Primary Healthcare Restoration Project (EPHRP)**\n**Objective**\nThe objective of the EPHRP is to assist the GoL in reducing the social, economic, and health impacts of the Syrian\ncrisis on poor Lebanese by subsidizing a package of essential health care services.\n**Beneficiaries**\nThis project targets 150,000 of the 340,000 poor Lebanese identified by the NPTP as living below the poverty\nline, using a proxy means testing targeting mechanism.\n**Essential Health Care Package**\nThe project provides beneficiaries with a package of essential health care services comprising the following:\n(i) three age- and gender-specific wellness packages (age 0-18, females 19 years and above, males 19 years and\nabove); (ii) two care packages for the most common non-communicable diseases in Lebanon, diabetes and\nhypertension; and (iii) an antenatal package.\n**Providers**\nServices are provided to beneficiaries through 75 of the 204 MoPH network centers. Network facilities are\nmanaged by NGOs (67 percent), local municipalities (20 percent), MoPH (11 percent), and MoSA (2 percent).\nProvider participation is voluntary and is governed by the legal agreement between the MoPH and the managing\nentity.\n**Quality of Care**\nQuality of care is monitored through the PHCC accreditation program implemented by the MoPH in collaboration\nwith Accreditation Canada International. Currently, all 75 PHCCs are within the accreditation program. The\nquality of clinical care is also monitored by the MoPH through clinical indicators captured in the Health\nInformation System.\n**Contracting and Provider Payment Mechanism**\nThe MoPH purchases the package of services for the beneficiary population from PHCCs. Provider payment is\nbased on capitation and is output-based. The average per", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000108:16:0:0", "start": 1617, "end": 1642, "surface": "Health\nInformation System", "probe_tag": "confusion", "probe_score": 0.5564, "luna_label": 1, "luna_reason": "System data are used to monitor clinical care through captured indicators."}]}, {"key": "rafael2-128", "text": "\n2. The standard CBA methodology is used to analyze the likely benefits of the Project, based on a comparison of the\nProject plans versus the status quo. The analysis uses simulations to calculate passenger demand, vehicle operating costs,\ntravel times, and emissions.\n\n3. **Demand forecast model:** The CBA was based on a four-step macrosimulation transport model that was calibrated\nusing information from the Origin and Destination (OD) Survey carried out with mobile data from cell phones, in order to\nbetter represent current and projected displacement patterns. Based on a series of inferences, it was possible to update\nthe trip matrix for the metropolitan region for the base year of 2019, prior to the impacts of COVID-19, characterizing trips\nbased on the mode of transport used, reason, times and volumes. The assumptions for the demand forecast model were\nhighly conservative (i.e., a low annual growth rate of 1 percent for public transport). The demand forecast uses a\ndisaggregated behavior model to estimate marginal income utility based on revealed preference data sampled from users\nof the influence area that have access to mode choice between car and bus using the future corridor. The network model\nwas carried out using a macrosimulation model (EMME) that estimates indicators for data on operational parameters such\nas headways, operation speeds, frequencies, embarking and lighting times, and vehicle mileage during a typical week day,\nboth with and without the Project. Two separate scenarios for the river tunnel were considered: one in which the tunnel\nis completed in the seventh year of BRT operations, and one without the tunnel.\n\n4. **Outputs of the demand forecast model:** The BRT is expected to attract about 70,000 passengers per day in the\nfirst year of the tunnel’s operation. In the scenario without the tunnel, operating with an integrated system of passenger\ncrossing by fast barges, this demand would be reduced by only 5.8 percent and travel time increased", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000182:72:1:1", "start": 464, "end": 492, "surface": "mobile data from cell phones", "probe_tag": "confusion", "probe_score": 0.8957, "luna_label": 1, "luna_reason": "Existing mobile data informed calibration of the transport demand model."}, {"key": "refugee_pads:000182:72:1:2", "start": 1057, "end": 1081, "surface": "revealed preference data", "probe_tag": "confusion", "probe_score": 0.8371, "luna_label": 1, "luna_reason": "Existing revealed-preference data are used as the model’s estimation basis."}]}, {"key": "rafael2-129", "text": "**The World Bank**\nSouth Sudan Health Sector Transformation Project (HSTP) (P181385)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Percentage of children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)|Col2|\n|---|---|\n|Description|Proportion of surviving infants who have received 1st & 3rd dose of the combined diphtheria, tetanus toxoid,<br>pertussis, Hepatitis B and Homophiles influenza type b vaccine|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH and UNICEF; Measures subcomponent 1.1 Under UNICEF|\n|**Percentage of refugee children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)**|**Percentage of refugee children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)**|\n|Description|Proportion of surviving infants who have received 1st & 3rd dose of the combined diphtheria, tetanus toxoid,<br>pertussis, Hepatitis B and Homophiles influenza type b vaccine|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH and UNICEF; Measures subcomponent 1.1 Under UNICEF|\n|**Percentage of HC children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)**|**Percentage of HC children under one year of age who have received 1st & 3rd dose of pentavalent", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000006:48:0:0", "start": 471, "end": 476, "surface": "DHIS2", "probe_tag": "confusion", "probe_score": 0.1362, "luna_label": 0, "luna_reason": "DHIS2 is named as a data source, but no actual data use or finding is shown."}]}, {"key": "rafael2-130", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n\n\n**Figure 6.3. Monthly Expenditure of Households for Lighting, Phone Charging, and Batteries for Radio**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n5. The survey outcomes highlighted that poverty incidence is higher for female-headed households\nthan for the male-headed ones: 49 percent of female-headed households are in the bottom 40 percent\ncompared with 43 percent of male-headed households, as shown in Figure 6.4.\n\n\n**Figure 6.4. Distribution of Male- and Female-Headed Households Expenditure Quintiles**\n\n\n\n\n\n\n\n_Source:_ World Bank staff calculation.\n\n\n\nPage 86 of 87", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000193:91:0:0", "start": 19, "end": 54, "surface": "Chad Energy Access Scale Up Project", "probe_tag": "confusion", "probe_score": 0.7594, "luna_label": 0, "luna_reason": "Project title, not a data resource or evidence of data use."}]}, {"key": "rafael2-131", "text": " on\nthe 2012 household survey for the city of Djibouti (EDAM 3). In the case of household\nconsumption, the estimation takes into account the impact of neighborhood characteristics such\nas the overall unemployment rate. In the case of property values, the estimation focuses on both\nthe characteristics of the dwellings and benchmarking with those of surrounding neighborhoods.\nThese two relationships, estimated for the entire city of Djibouti, can then be applied to Q7,\ntaking into account the household, dwelling, and neighborhood characteristics reported in the\nPopulation and Housing Census of 2009 (RGPH).\n\n_Table 1. Summary of economic impacts_\n\n\n\n\n\n\n\n\n\n\n\n\n\n_<u>In US$ million</u>_\n\n\n\nInvestment net of\n1.89\nmaintenance costs\n\n\n\nDirect demand for\nlabor, goods, and\nservices\n\n\n\n0.63\n<u>building</u>\n<u>Implementation, supervision</u> <u>0.59</u>\n\n\n\nPro-poor hiring and\npurchase of inputs,\n\nInvestment net of\n1.89 unskilled workers, and\nmaintenance costs\n\ncobblestones\n\n<u>-0.27</u> <u>Maintenance costs</u>\n\nInstitutional capacity\n\n\n\n<u>-0.27</u>\n\n\n\nResilience Flood control 0.48 0.48\n\n\n\nImpact on health, property\ndamages, missed\neducation, and work\n<u>opportunities</u>\n\n\n\nLivelihoods\n\n\n\nPotential of pro-poor\nEmployment 2.16 2.16 growth in retail, catering\n\nand local services of\n<u>Economic activity</u> <u>0.27</u> <u>0.27</u> <u>maintenance and repair</u>\n\n\n\n<u>Capital gains</u> <u>Property values</u> <u>0.28</u> <u>0.28</u> <u>Improved environment</u>\n\n\n58", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000067:67:1:0", "start": 8, "end": 54, "surface": "2012 household survey for the city of Djibouti", "probe_tag": "confusion", "probe_score": 0.7151, "luna_label": 1, "luna_reason": "Existing EDAM 3 household survey informs the city-level estimation."}, {"key": "refugee_pads:000067:67:1:1", "start": 566, "end": 603, "surface": "Population and Housing Census of 2009", "probe_tag": "confusion", "probe_score": 0.8222, "luna_label": 1, "luna_reason": "Census characteristics inform neighborhood-based impact estimations."}]}, {"key": "rafael2-132", "text": "|Conditional Performance Grants to LGs|Col2|\n|---|---|\n|Participating LGs under the project that produce timely<br>and acceptable final accounts at the end of FY|According to existing financial regulations and requirements|\n|Participating LGs that receive audits that are either<br>unqualified or if qualified only with minor comments|As per the audit reports produced on an annual basis|\n|Number of person-days employment created under LG's<br>sub-projects|Calculated based on contracts for sub projects.<br> <br>No specific targets can be established for this indicator since the nature of works has<br>not been determined. The outputs will be measured as they occur in the annual reports<br>and based on the contracts for work undertaken in the LGs under financed by the CPG.|\n|Disaggregated list of investments by type and sector|Based on physical progress report from each LG.<br> <br>No specific targets can be established for this indicator since the nature of works has<br>not been determined. This indicator will be measured annually based on the progress<br>reports from the LGs and consolidated by the PCU.|\n|Number of mobile teams achieving at least 90% of their<br>performance targets per the contracts agreed with the<br>PCU|According to criteria and outputs agreed in contracts|\n|Number of persons trained (disaggregated by gender)|Persons trained under capacity support program|\n|Number of persons trained, of which women (%)|Persons trained under capacity building program (women)|\n\n\n28", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000166:42:0:0", "start": 843, "end": 880, "surface": "physical progress report from each LG", "probe_tag": "confusion", "probe_score": 0.6719, "luna_label": 0, "luna_reason": "Planned annual indicator monitoring based on future LG progress reports."}]}, {"key": "rafael2-133", "text": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Number of communication campaigns<br>about COVID-19 broadcast to<br>communities|Number of awareness<br>communications<br>campaigns conducted|weekly|COVID-19<br>report|routine data|MOPH|\n|---|---|---|---|---|---|\n|<br>National COVID-19 risk communication<br>and community engagement strategy<br>established|<br>Establishment of a risk<br>communication and<br>engagement strategy in<br>Chad <br>|once<br>|<br>COVID-19<br>report<br>|routine data<br>|MOPH<br>|\n|Number of technical crisis coordination<br>meetings issuing an official report on<br>epidemic surveillance and response|<br>Number of meetings<br>conducted/official reports<br>issued by the emergency<br>crisis committee|weekly<br>|COVID-19<br>report<br> <br>|routine data<br>|MOPH<br>|\n|Number of treatment, isolation &<br>quarantine centers preparing daily report <br>|<br>Number of centers<br>preparing daily reports|weekly<br>|COVID-19<br>report<br> <br>|routine data<br>|MOPH<br>|\n|Number of centers assessed monthly<br>(using check list) treatment, isolation &<br>quarantine|Number of centers<br>assessed monthly|monthly<br>|<br>COVID-19<br>report<br> <", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000124:38:0:0", "start": 277, "end": 289, "surface": "routine data", "probe_tag": "confusion", "probe_score": 0.4384, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-134", "text": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda (P176747)\n\n\n\n|Col1|including childcare and<br>those community and<br>household members.|Col3|Col4|the infrastructure<br>facilities.|Col6|\n|---|---|---|---|---|---|\n|Women beneficiaries (percentage)|<br>|||<br>||\n|Women in RHD||||||\n|Refugee women||||<br>||\n|Value of credit provided to women<br>enterprises (Amount)|This indicator measures the<br>value of credit provided by<br>the PFIs under the project,<br>disaggregated by refugee<br>status, district, age, and<br>disability status.|Continuous.<br>|PFI data.<br>|The PFIs will maintain<br>databases of the value<br>of the credit disbursed,<br>disaggregated by<br>refugee status, district,<br>age, and disability<br>status.<br>|The MGLSD to collect<br>the data from the PFIs<br>each month, and<br>compile and report it.<br>|\n|Women enterprises in RHDs||||<br>||\n|Refugee-owned enterprises||<br>||||\n|Beneficiaries of job-focused interventions||Enterprise<br>baseline<br>survey,<br>annual<br>surveys from<br>year 2.<br>|Surveys of<br>enterprises.<br>|This indicator measures<br>", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000088:50:0:0", "start": 599, "end": 607, "surface": "PFI data", "probe_tag": "confusion", "probe_score": 0.3346, "luna_label": 0, "luna_reason": null}, {"key": "sample:refugee_pads:000088:50:0:1", "start": 805, "end": 823, "surface": "data from the PFIs", "probe_tag": "confusion", "probe_score": 0.8123, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-135", "text": "|\n|Frequency|Quarterly|\n|Data source|DHIS|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH and UNICEF; Measures subcomponents 1.1 and 1.2 Under UNICEF|\n|**BHI training material revised to include refugee sensitive health interventions (Yes/No) **|**BHI training material revised to include refugee sensitive health interventions (Yes/No) **|\n|Description|BHI training materials for boma health workers revised to include refugee sensitive health interventions|\n|Frequency|Quarterly|\n|Data source|TPM Report|\n|Methodology for Data<br>Collection|TPM|\n|Responsibility for Data<br>Collection|TPM / PMU; Measures subcomponents 1.1 and 1.2 Under UNICEF|\n|**Percentage of women receiving four ANC visits (Percentage)**|**Percentage of women receiving four ANC visits (Percentage)**|\n|Description|Percentage of women at childbearing age with a live birth in a given time period who received antenatal care,<br>four times or more times from any provider.|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH and UNICEF; Measures subcomponent 1.1 Under UNICEF|\n|**Percentage of refugee women receiving four ANC visits (Percentage)**|**Percentage of refugee women receiving four ANC visits (Percentage)**|\n|Description|Percentage of refugee women at childbearing age with a live birth in a given time period who received antenatal<br>care, four times or more times from any provider.|\n\n\nPage 43 of 68", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000006:46:1:0", "start": 534, "end": 544, "surface": "TPM Report", "probe_tag": "confusion", "probe_score": 0.142, "luna_label": 0, "luna_reason": "Planned results-framework monitoring source, not evidence of already-used data."}]}, {"key": "rafael2-136", "text": " system to improve learning outcomes of the most vulnerable including the\npoorest. The development benefits of education also extend to more environmentally friendly behavior.\nInvestments in quality education lead to more rapid and sustainable economic growth and development.\n\n\n**Figure 2.1. Change in Reading Performance over 2009–2018**\n\n\n_Source:_ OECD PISA 2009 and 2018 data.\n\n\nPage 53 of 68", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000185:62:1:0", "start": 352, "end": 380, "surface": "OECD PISA 2009 and 2018 data", "probe_tag": "confusion", "probe_score": 0.675, "luna_label": 1, "luna_reason": "Source data for a figure comparing reading performance across 2009–2018."}]}, {"key": "rafael2-137", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\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|Beneficiaries of social safety net programs||This indicator<br>will be<br>measured at<br>least on a<br>quarterly<br>basis during<br>missions and<br>ISRs<br>|SNSOP MIS<br>which hosts<br>beneficiary<br>registration<br>and payment<br>data<br>|The implementing<br>partner will collect<br>beneficiary data during<br>targeting and<br>registration. The<br>payment service<br>provider will document<br>payment data and<br>share with the<br>implementing partner<br>|Implementing Partner<br>|\n|Beneficiaries of social safety net<br>programs - Female||This indicator<br>will be<br>measured at<br>least on a<br>quarterly<br>basis during<br>missions and<br>ISRs<br>|SNSOP MIS<br>which hosts<br>beneficiary<br>registration<br>and payment<br>data<br>|The implementing<", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000153:55:0:0", "start": 655, "end": 671, "surface": "beneficiary data", "probe_tag": "confusion", "probe_score": 0.0877, "luna_label": 0, "luna_reason": null}, {"key": "sample:refugee_pads:000153:55:0:1", "start": 765, "end": 777, "surface": "payment data", "probe_tag": "drop", "probe_score": 0.0486, "luna_label": 0, "luna_reason": "Payment data will be documented by the provider, indicating planned production."}]}, {"key": "rafael2-138", "text": "136. To address the shortcomings of the cash transfer cycle as identified above, **_Sub-component_** _1.1_\nwill support the services of a long-term advisor to assist SWF to develop and test procedures for each step\nof the cycle. The long-term advisor will provide “hands-on” training and further development of the\nOperations Manual and its technical annexes as required. All 1,600 SWF staff (at all three levels) will be\ntrained in the cash transfer procedures and processes specific to their area of responsibility by technical\nexperts identified by the long-term advisor. Study tours as well as exchange visits with other relevant\ncountries would be implemented.\n\n\n137. An essential cash transfer program component as identified in the cash transfer cycle above is a\nresponsive MIS that links data entered at the district, governorate and head office to program operations.\nAn effective MIS would provide up-to-date information for case management (e.g., beneficiaries\nrequiring recertification, etc.) as well as information for managers on the status of program elements, such\nas applications, enrolments, payments, recertifications, and beneficiary development program\nparticipation. At the present time, the existing MIS system does not receive inputs from operations\ndepartments, highlighting the lack of connection between all cash transfer operations and the IT system.\n\n\n138. The sub-component provides the services of an MIS expert to put an effective MIS in place\nmodule by module. Training for all program staff will cover every aspect of the SWF data collection,\ninput, application of the PMT, analysis and utilization of results, and reporting. An MIS manual would be\n\n\n40", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000080:45:0:0", "start": 1556, "end": 1564, "surface": "SWF data", "probe_tag": "drop", "probe_score": 0.0284, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-139", "text": " capita -O 2 -0 5 Lower-middle-income _group_\nESports of goods and servic\"s\n\n\n**STRUCTURE ofthe ECONOMY**\n\n**1979** **1989** **1998** **1999** **Growth rates of output and Investment ()**\n_{%I ol GOP)_\nAgriculture 3.4 _.._ **_2._**\nIndustry 21.0 O.Manufacturing 5.6 **_-2_** **_94_** _as_ _se_ _s_\nServices **75.6** 6\n\nPrivate consumption **-r.**\nGeneral government consumplion **G**\nImports of goods and services\n\n\n\n**1979-89** **1989-99** **1998** **1999**\n_(average annual orowth)_\nAgriculture\n\n\n\nIndustry\n\n\n\nManufacturing\nServices\n\n\n\nPrivate consumption\nGenerai government consumption\nGross domestic investment\nImports of goods and services\nGross national product 1 7 **1.4**\n\n\nNote. 1999 data are preliminary estimates.\nThis table was produced from the Development Economics central database.\n\nThe diamonds show four kev midicators in the country (in bold) compared with its income-group average. 11 data are missing, Ihe diamond will", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000062:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "drop", "probe_score": 0.0227, "luna_label": 0, "luna_reason": "Generic data phrase lacks an attributed finding or concrete claim."}]}, {"key": "rafael2-140", "text": " through which resources will be pooled<br>• Coordinate donor and Government inputs into project documents and<br>monitoring reports<br>• Provide fiduciary, technical, and management oversight for health service<br>delivery financed by IDA and linked MDTF|\n|High Level<br>Steering<br>Committee|Donors, MoH, MoFP, WB,<br>SMoH, UNICEF|• Provide high level direction for the project<br>• Meet every six months<br>• Review project data, identify needed actions, and follow-up on actions during<br>meetings|\n|Operational<br>steering<br>committee|MoH, PMU, World Bank,<br>Donors, UNICEF|• Provide routine oversight and operational direction in line with overall<br>direction from the HSC<br>• Meet on a quarterly basis<br>• Identify and discuss needed actions<br>• Review project data, identify needed actions, and follow-up on actions during<br>meetings|\n|UNICEF|UNICEF contracted by the<br>PMU|• Sub-contract NGOs<br>• Supervise and support NGOs<br>• Sub-contract procurement and logistics agency<br>• Supervise and support logistics agency<br>• Develop capacity of CHDs|\n|Contracted<br>Service<br>Providers|NGOs sub-contracted by<br>UNICEF|• Deliver health services<br>• Engage with communities to support health service delivery|\n\n\n\nPage 58 of 68", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000006:61:1:0", "start": 419, "end": 431, "surface": "project data", "probe_tag": "drop", "probe_score": 0.0004, "luna_label": 0, "luna_reason": null}, {"key": "sample:refugee_pads:000006:61:1:1", "start": 766, "end": 778, "surface": "project data", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-141", "text": " a v\nManufacturIng 5 7 3.7 4 7 5 0 / s \\\\t oo  Services 475 272 190 201\n\nPrfvate consumpffon 90 7 81 9 938 95 1 20\nGeneral govemment consumption 7 0 9 6 146 17.2 =c ~GDFDl\nImpons of goods and servrcea 39 7 23 6 33 4 373 -3GW_G_____\n\n\n_(average_ _annual growth)_ 1981-9 1991401 2000 2001 Growth ot exports and Imports (%)\n\nAgrutumre -0 6 -2 6 2 2 3 8 oo\nIndustry 0.1 -41 51 5 6 s0\nManUnacturIng 6.9 . .\nServices -5 7 -5.4 4 0 51 \nPrivate oonsumpffon -2 0 -19 10 4 100 -50\nGeneral govemment consumption -5.1 -0 2 41.3 27 9 -100\nGross domestic Investment -06 3 0 50 - EOpois -tr-ports\nImports of goods and services -2 2 -151 85 0 61 3\n\n\nNote 2001 data are pretirrinary eastliates\n'The diamonds show four Key Indicators in the country (in bold) conipared with itS income-group average It data are missing, the diantond wiUt be rrconrrlte\n\n\n-56", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000117:60:2:0", "start": 639, "end": 648, "surface": "2001 data", "probe_tag": "drop", "probe_score": 0.0128, "luna_label": 1, "luna_reason": "Year-specific data are identified as preliminary estimates in the table note."}]}, {"key": "rafael2-142", "text": "**Annex 2: Detailed Project Description**\n\n\n**Chad: Safety Nets Project**\n\n\n1. **The PDO** is to pilot cash transfers and cash-for-work interventions to the poor and lay\nthe foundations of an adaptive safety nets system.\n\n\n2. **The project proposes to focus on the development of a SSN delivery system that will**\n**initially include two interventions:** a CfW program in urban and peri _-_ urban areas of the capital\ncity, N'Djamena, and a CT program in rural areas. Both programs will strive to reach the poorest\nhouseholds (at least 40 percent of the food poverty gap in the respective regions). These two\nprograms would also offer accompanying measures to the beneficiary households, concentrating\non behavior change communication and nutrition and hygiene education for mothers in the CT\nprogram; and basic financial literacy and savings for the CfW program.\n\n\n3. **The project will also support the incremental development of key administrative**\n**systems** such as a targeting and data collection system, a social registry, a payment system, a\ngrievance system, and a computerized MIS. These systems will be developed and tested in the\ntwo pilot programs and will allow effective management and coordination of safety net programs\nas they are scaled up in the future.\n\n\n4. **The intention is to build an ‘adaptive’ safety net system.** The project will support\ndevelopment and use of critical service delivery instruments and institutional arrangements\nessential to building a safety net system that will ultimately be capable of expanding program\ncoverage in response to shocks, especially for households vulnerable to climatic and seasonal\nshocks and temporary food-insecurity. The expansion can be achieved by: (a) increasing the\nlevel of transfer given to current beneficiaries; (b) expanding the number of beneficiaries of the\nCT or the CfW programs only for specified period of time; and, in a later phase;", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000028:38:0:0", "start": 1015, "end": 1030, "surface": "social registry", "probe_tag": "drop", "probe_score": 0.027, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-143", "text": "**Activity Monitoring and Evaluation.** Activities on any level would be monitored following structured\nreporting and assessment forms and procedures. Given the program’s large scope o f interventions and\ndecentralized nature of activities, it would be necessary to incorporate a coherent and consistent set o f\nindicators into all contracts/agreements funded by the project. For example, the health centers or\nprefectoral hospitals would be required to submit their plans following the logical framework outline\nlinking inputs, process, outputs. Agreements/ contracts would be performance-based and would thus\nidentify all yearly indicators which those front-line health structures plan on achieving. These indicators\nwould be compiled and aggregated in the annual report o f the Task Force.\n\n\n**Outcome and Impact Monitoring and Evaluation.** Another aspect - f the M&E system would be the\nmonitoring o f the outcome and impact; this would be done by a Demographic and Health Survey at the\nbeginning and end o f the project. In addition, data on deaths avoided would be calculated through\noperational research contracted to a specialized institution which would use DHS estimates as well as\nhealth structures records on coverage. Quality o f services would be checked yearly based on a simple\nchecklist which describes the standards expected and which would be designed with the help o f GTZ.\nSuch quality check would be contracted out to consultants. Smaller surveys and operations research might\nalso be needed every time a problem i s identified and its solution i s not obvious.\n\n\n**Financial Monitoring and Evaluation.** Lastly, financial management monitoring o f the utilization o f\nresources and funds by the public sector, private sector and civil society would be combined with\nprogram monitoring to provide a basis for cross checking financial and activity data and establishing the\nrelation between disbursement and activities. Audits would be carried out by the FMA as well as external\nauditors, at all levels. A yearly health expenditures tracking survey would provide information on how\nmuch the M O H i s", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000092:29:0:0", "start": 955, "end": 984, "surface": "Demographic and Health Survey", "probe_tag": "confusion", "probe_score": 0.8912, "luna_label": 0, "luna_reason": "Future project monitoring survey is planned, not an existing data source used."}, {"key": "refugee_pads:000092:29:0:1", "start": 1193, "end": 1230, "surface": "health structures records on coverage", "probe_tag": "confusion", "probe_score": 0.3367, "luna_label": 1, "luna_reason": "Coverage records are used with DHS estimates to calculate deaths avoided."}, {"key": "refugee_pads:000092:29:0:2", "start": 1847, "end": 1874, "surface": "financial and activity data", "probe_tag": "drop", "probe_score": 0.0346, "luna_label": 0, "luna_reason": "Routine financial and activity monitoring used for project administration and cross-checking."}, {"key": "refugee_pads:000092:29:0:3", "start": 2035, "end": 2070, "surface": "health expenditures tracking survey", "probe_tag": "confusion", "probe_score": 0.5223, "luna_label": 0, "luna_reason": "Proposed survey would provide future financial monitoring information."}]}, {"key": "rafael2-144", "text": "DJIBOUTI\nSchool Access and Improvement Program\n\n\n**Project Appraisal Document**\n\n\nMiddle East and North Africa Region\n\nMNSHD\n\n\n\nDate: November 17, 2000 Team Leader: Qaiser M. Khan\n\n\n\nCountry Director: Inder K. Sud Sector Director: Baudouy\nProject **ID:** P044585 Sector(s): EP - Primary Education, ES - Secondary\n\n\n\n. Education\nLending Instrument: Adaptable Program Loan <sup>(APL)</sup> Theme(s): Education; Gender and development\n\n\n\nPoverty Targeted Intervention: N\n\n\n\nProgram Fin ncing Data\n\n\n\nEstimated\nAPL Indicative Financing Plan Implementation Period (Bank FY) Borrower\n\n\n\n**IBRD** Others **Total** **COMMITMENT** **Closing**\n**US$** m % US$ m US$ m Date Date\nAPL 1 10.00 75.8 3.20 13.20 03/31/2001 06/30/2005 Republic of\n\n\n\nLoan/ Credit Djibouti\nCredit Ministry of\n\n\n\n________________ Education\n\n\n\nAPL 2 10.0 65.8 5.20 15.20 07/01/2005 06/30/2008 Republic of\n\n\n\nLoan/ Credit Djibouti\n\n\n\nCredit Ministry of\n\n\n\ni_________ ________________ Education\n\n\n\nAPL 3 10.00 41.0 14.40 24.40 07/01/2008 06/30/2011 Republic of\n\n\n\nLoan/ Credit Djibouti\nCredit Ministry of\n\n\n\nEducation\n\n\n\nTotal 30.00 22.80 52.80\nProject Financing Data Credit\nFor Loans/Credits/Others: Amount (US$m): 10.0\n\n\n\nProposed Terms: Standard Credit\n\n\n\nGrace period (years): 10 Years to maturity: 40\nCommitment fee: 0.50% (0% for FY01) Service charge: 0.75", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000149:4:0:0", "start": 1106, "end": 1128, "surface": "Project Financing Data", "probe_tag": "drop", "probe_score": 0.0462, "luna_label": 0, "luna_reason": "Standalone project financing table header, not a substantive data-use mention."}]}, {"key": "rafael2-145", "text": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n\n      - The establishment of an Emerging Companies Market at Borsa Istanbul where the shares of\nthe SMEs are exclusively traded. Since 2011 SMEs have access to the capital markets via this\nchannel.\n\n      - Angel investment program has been launched in 2013 to encourage angel investment as a\nnew instrument for SMEs at their early stages.\n\n      - Promoting SME financing with the regulations that allows the use of movable assets as\ncollateral.\n\n      - The Portfolio Guarantee System has been included into the scope of Treasury-backed\nguarantee mechanism enabling Credit Guarantee Fund to access more firms and to perform\nmore efficiently.\n\n      - State-backed credit insurance has been launched in order to cover the losses of SMEs.\n\n\n15. **SMEs make more use of internal funds to finance investments and working capital and face**\n**higher collateral requirements than their peers in Eastern Europe and Central Asia.** <sup>13</sup> [^13: Enterprise Surveys (database), International Finance Corporation and World Bank, Washington, DC,\nhttps://www.enterprisesurveys.org/.] The share of SMEs\nin total credit declined by 5 percentage points to slightly more than 20 percent in the aftermath of the\nglobal crisis in 2008–2009 and peaked at 28 percent in mid-2018 following the economic upturn and the\nhighly utilized credit guarantee scheme. However, it declined to 23 percent, in 2019, despite several statefinanced policy stimulus programs. This fluctuation demonstrates how SMEs are among the first and most\naffected frontiers of the financing cycle.\n\n\n16. **SMEs account for most firms in Turkey, but they have been facing difficulties in growing and**\n**expanding.** Firm composition is highly skewed toward microenterprises and small firms. Firms with fewer\nthan 10 employees represent 84 percent of all firms that have employees; yet, they employ only 20\npercent of the workforce. Firms with fewer than 50 employees represent 97 percent of all firms and\nemploy 44 percent of the workforce,", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000025:16:0:0", "start": 1012, "end": 1041, "surface": "Enterprise Surveys (database)", "probe_tag": "drop", "probe_score": 0.0001, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-146", "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": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000073:29:1:0", "start": 764, "end": 789, "surface": "performance tracking data", "probe_tag": "drop", "probe_score": 0.0024, "luna_label": 0, "luna_reason": null}, {"key": "sample:refugee_pads:000073:29:1:1", "start": 849, "end": 871, "surface": "activity tracking data", "probe_tag": "drop", "probe_score": 0.0126, "luna_label": 0, "luna_reason": null}, {"key": "sample:refugee_pads:000073:29:1:2", "start": 909, "end": 925, "surface": "key results\ndata", "probe_tag": "drop", "probe_score": 0.0019, "luna_label": 0, "luna_reason": "Data will be collected through planned project monitoring activities."}]}, {"key": "rafael2-147", "text": ">basis<br>|SNSOP MIS<br>|This data will be<br>collected through<br>registration and<br>payments<br>|Implementing Partner<br>|\n|Number of beneficiaries receiving<br>economic opportunities who are youth|Number of beneficiaries<br>receiving economic<br>opportunities under<br>Component 2, in accordance<br>with the Project Operations<br>Manual, of which are youth,<br>defined as people between<br>the ages of 18 and 35 years,<br>and have receive at least 1|<br>This<br>indicator<br>will be<br>measured,<br>at a<br>minimum,<br>on a<br>quarterly<br>basis|SNSOP MIS<br>|Beneficiary data will be<br>gathered at registration<br>and will be updated<br>over the course of<br>project<br>implementation.<br>Payment data will be<br>regularly updated in the<br>SNSOP MIS|The Implementing<br>Partner in charge of<br>Component 2 will be<br>responsible for data<br>collection<br>|\n\n\nPage 58 of 74", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000153:62:1:0", "start": 11, "end": 20, "surface": "SNSOP MIS", "probe_tag": "drop", "probe_score": 0.0313, "luna_label": 0, "luna_reason": "Data will be collected through future registration and payment processes."}]}, {"key": "rafael2-148", "text": "br>|900.00|1,200.00|1,200.00|\n|Number of new high<br>schools constructed in<br>priority areas that meet<br>infrastructure requirements<br>under national quality<br>assurance standards<br>(Number)||0.00|0.00<br>|0.00|2.00<br>|3.00|3.00|3.00|\n|**Strengthening the Capacity for Education Sector Management and Refugee Response**|**Strengthening the Capacity for Education Sector Management and Refugee Response**|**Strengthening the Capacity for Education Sector Management and Refugee Response**|**Strengthening the Capacity for Education Sector Management and Refugee Response**|**Strengthening the Capacity for Education Sector Management and Refugee Response**|**Strengthening the Capacity for Education Sector Management and Refugee Response**|**Strengthening the Capacity for Education Sector Management and Refugee Response**|**Strengthening the Capacity for Education Sector Management and Refugee Response**|**Strengthening the Capacity for Education Sector Management and Refugee Response**|\n|Number of sector staff<br>participating in capacity<br>building trainings under the<br>project (Number)||0.00|100.00<br>|250.00|500.00<br>|750.00|1,000.00|1,000.00|\n|Monitoring tool with<br>reliable gender-<br>disaggregated data to<br>identify disadvantaged<br>students to receive<br>accelerated learning||No|No<br>|Yes|Yes<br>|Yes|Yes|Yes|\n\n\nPage 36 of 68", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000185:45:1:0", "start": 1210, "end": 1228, "surface": "disaggregated data", "probe_tag": "drop", "probe_score": 0.0128, "luna_label": 0, "luna_reason": "Planned monitoring tool data, not an existing dataset used for analysis."}]}, {"key": "rafael2-149", "text": "Venezuelans in Chile, Colombia, Ecuador and\nPeru – A Development Opportunity\n\n\n\n6 Labor Market Outcomes for Venezuelan\nMigrants and Hosts\n\n\n41\n\n\n\n**Figure 6.5 Monthly wages of hosts and Venezuelans in Colombia and Peru, by education**\n**level**\n\n\nMonthly wages (US dollars, 2017 PPP)\n\n\n\n0 200 400 600 800 1,000\n\n\n**Colombia**\n\n\nHosts\n\n\n\n1,200\n\n\nTertiary\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nVenezuelans\n\n\nHosts\n\n\nVenezuelans\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTertiary\n\n\n**Peru**\n\n\nTertiary\n\n\n\nTertiary\n\n\n\n\n\n\n\n\n\nSources: Colombia: Pulso de la Migración (DANE 2022) and Gran Encuesta Integrada de Hogares (DANE 2021). Peru: Encuesta dirigida a la población venezolana\nque reside en el país (INEI 2022) and Encuesta Nacional de Hogares (INEI 2021).\n\n\nNote: Wages are measured in US dollars (PPP 2017) and censored at 2000 dollars.", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:reliefweb:001190:40:0:0", "start": 497, "end": 518, "surface": "Pulso de la Migración", "probe_tag": "keep", "probe_score": 0.9965, "luna_label": 1, "luna_reason": "Named survey cited as source for wage comparisons in the figure."}, {"key": "sample:reliefweb:001190:40:0:1", "start": 535, "end": 569, "surface": "Gran Encuesta Integrada de Hogares", "probe_tag": "keep", "probe_score": 0.9994, "luna_label": 1, "luna_reason": "Named household survey cited as the source for Colombia wage figures."}, {"key": "sample:reliefweb:001190:40:0:2", "start": 589, "end": 654, "surface": "Encuesta dirigida a la población venezolana\nque reside en el país", "probe_tag": "keep", "probe_score": 0.9999, "luna_label": 1, "luna_reason": null}, {"key": "sample:reliefweb:001190:40:0:3", "start": 671, "end": 699, "surface": "Encuesta Nacional de Hogares", "probe_tag": "keep", "probe_score": 0.9983, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-150", "text": "Regular Surveys on Social Tensions throughout Lebanon: Wave IV September 2018\n\n\n3.2.3 Access to Services\n\n\nRespondents were asked about their use of a number of public services in\n\n\nthe previous three months. Syrian respondents were more likely to have\n\nutilised services like a ‘primary health centre’ (14.8% of Syrians), relative\n\n\nto their Lebanese counterparts (2.1%), with Lebanese more likely to use\n\nprivate alternatives for, e.g. school or healthcare. Nevertheless, reports of\n\n\nall service use declined marginally over the period between the Wave III\n\nand Wave IV surveys, for both Lebanese and Syrians. Of Lebanese, 70.1%\n\n\nsaid they had not used any public service in Wave IV, compared to 65.6%\n\nin Wave III. And amongst Syrians, 37.1% said they had not used in public\n\n\nservice in Wave IV, compared to 30.2% in Wave III.\n\n\n**Figure 9:** Aggregate measure of _greater_ perception of the capability and fairness of\nassistance, by governorate and wave, as a per cent of scale maximum.\n\n\nARK DMCC | 28", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000696:28:0:0", "start": 0, "end": 53, "surface": "Regular Surveys on Social Tensions throughout Lebanon", "probe_tag": "keep", "probe_score": 0.9364, "luna_label": 1, "luna_reason": "Named survey series supports reported service-use comparisons across Lebanon waves."}]}, {"key": "rafael2-151", "text": " The most notable national level analysis is the 2018 World Bank skills survey, but there are also other studies\nconducted at the regional level, including the recent Refugee Studies Centre reports on Dollo Ado and Addis Ababa,\nand a number of more specific assessments in Jigjiga. The World Bank skills survey provides a range of findings\nabout the level of education and skill of refugees, as well as their current level of economic engagement and poverty\nstatus in Ethiopia (see Table 1). The skills survey highlights both the significant variation in educational attainment\namong different refugee cohorts and the relatively small percentage of completion of secondary school education.\nThe educational challenges have been clearly recognised by the government to the extent that increasing educational\nattainment is a key commitment under the 2016 pledges and the draft NCRRS.\n\n\n28 UNHCR (2017a). Bringing the New York Declaration to Life Applying the Comprehensive Refugee Response Framework (CRRF).\n\n29 UNHCR (2005). Handbook for Self Reliance; see: https://www.unhcr.org/44bf7b012.pdf\n\n30 Available through the ReDSS website; see: https://www.dropbox.com/sh/8k983otoua3xucz/AADPmAkA-twjGyQDmzguMPMwa/Module%205%20Self%20reli<u>ance?dl=0&preview=Module+5+ODI+checklist.doc</u>\n\n31 Holzaepfel and Tadesse (2015). Evaluation: Evaluating the Effectiveness of Livelihoods Programs for Refugees in Ethiopia; Samuel Hall (2014). Living Out of\nCamp: Alternative to Camp-Based Assistance for Eritrean Refugees in Ethiopia; Samuel Hall (2018). Local Integration Focus: Refugees in Ethiopia. Gaps and\nOpportunities for Refugees Who Have Lived in Ethiopia for 20 Years or More.\n\n32 GoE (2019).\n\n33 Woldetsa", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001587:14:2:1", "start": 496, "end": 509, "surface": "skills survey", "probe_tag": "keep", "probe_score": 0.9486, "luna_label": 1, "luna_reason": "Existing World Bank survey supports findings on refugees’ educational attainment."}]}, {"key": "rafael2-152", "text": "DTM Mali – Décembre 2021\n\n|Mali - Indice de Stabilité|Décembre 2021|\n|---|---|\n|<br>**Source : Direction Nationale du Développement Social - DNDS** <br>|<br>**Source : Direction Nationale du Développement Social - DNDS** <br>|\n\n\n##### Perception de la stabilité\n\n\nAu niveau des 305 localités, les informateurs clés ont affirmé qu’un peu plus de trois quart (87%) des communautés\nse sentaient en sécurité dans leurs lieux de déplacement. Cependant, au niveau de 13% des localités évaluées, les\ncommunautés ne se sentaient pas en sécurité. Ceux-ci concernent principalement les localités évaluées dans les\ncercles de Bankass, Douentza, Bandiagara, Tenenkou et Ansongo.\n\n\nGraphique 25 : Sentiment de la communauté sur la situation sécuritaire\n\n\n\n\n##### **35**", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:reliefweb:001542:40:0:0", "start": 0, "end": 24, "surface": "DTM Mali – Décembre 2021", "probe_tag": "keep", "probe_score": 0.9937, "luna_label": 1, "luna_reason": null}, {"key": "sample:reliefweb:001542:40:0:1", "start": 27, "end": 53, "surface": "Mali - Indice de Stabilité", "probe_tag": "keep", "probe_score": 0.9885, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-153", "text": " complementary pathways** : While resettlement remains a strategic tool for achieving\nprotection and solutions, there is a sharp declining trend in availability of resettlement places, constraining\nUNHCR ability to respond to resettlement priorities. In the 2026 Resettlement Planning, the Sudan Operation\nprojected a population of 61,330 would need resettlement, based on an assessment of profiles with specific\nneeds that could benefit from resettlement as a protection/solution intervention. <sup>8</sup> In line with commitments\ncontained in the New York Declaration, efforts will be taken to support the establishment or expansion of\n\n\n8 [UNHCR, 2026 Projected Global Resettlement Needs https://www.unhcr.org/publications/2026-projected-global-resettlement-needs-pgrn,](https://www.unhcr.org/publications/2026-projected-global-resettlement-needs-pgrn)\nresettlement needs relevant to Sudan (61,330).\n\n\nPage 31 of 36                                                               JUNE 2025-DECEMBER 2027", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000548:30:2:0", "start": 651, "end": 691, "surface": "2026 Projected Global Resettlement Needs", "probe_tag": "keep", "probe_score": 0.9243, "luna_label": 1, "luna_reason": "Named UNHCR report supports the projected resettlement-needs figure."}]}, {"key": "rafael2-154", "text": ". The previous census, conducted in 1996,\nprovided a benchmark of sorts, however our target areas have experienced massive\ndemographic changes since 1996, making that census much less useful to us. Even if\nthe 2001 census data were available, they are unlikely to reflect the true composition\nof the population living in our target areas. While the census asked questions about\nnationality, it is unlikely that all immigrants would reveal their status to a\nrepresentative of the South African government, and census officers were not allowed\nto ask for identity papers.\n\n\nIn order to work around this problem and to ensure a reasonably representative and\nrandom sample, we used a combination of multi-stage cluster and snowball sampling.\nWe began with discussions with key informants who helped us identify those\nneighborhoods with high densities of our target populations. The City of\nJohannesburg’s existing administrative demarcations divide these neighborhoods into\nsmaller areas, called ‘enumerator areas’ (EAs). Within each of the targeted\nneighborhoods, we randomly selected 100 EAs (30%). In neighborhoods with fewer\n\n\n14", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000839:15:1:0", "start": 210, "end": 226, "surface": "2001 census data", "probe_tag": "keep", "probe_score": 0.9539, "luna_label": 1, "luna_reason": "Existing census data are assessed as unlikely to represent current population composition."}]}, {"key": "rafael2-155", "text": "vant sources of Big (Crisis) Data**\n\n\n**Internet searches** Google Trends tracks its user’s search queries and IP-addresses and calculates\naggregated trends for searched keywords based on the location information of the IP-address. These\ntrend data are frequently used in migration studies to assess the intention of potential migrants to\nleave, as well as the host countries migrants are interested in. In this way, analysts gain insights into\nindividuals’ planning processes before the migration [4].\n\n\nData from Google Trends is free and easily accessible [38]. Additional to Google Trends, Google\nalso offers Google Correlate, which is a tool that allows finding keywords that are frequently searched\ntogether. The easy access has made Google Trends a popular tool in migration studies. Wladyka [39]\nuses search data from Google Trends to assess South Americans’ migration intentions to Spain. He\nfinds a moderate to strong co-integration between lagged search queries and real migration numbers\n\n\n5Factors at the micro-level should not be confused with micro-level data. These can still be measured at the macrolevel, e.g., through demographic data, size of the agricultural sector, etc.\n\n6Unfortunately, we found no applied study that explored social media sites to collect such information.\n\n\n18 UNHCR", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001630:17:1:0", "start": 238, "end": 248, "surface": "trend data", "probe_tag": "confusion", "probe_score": 0.7428, "luna_label": 1, "luna_reason": "Google Trends data are used to assess migrants’ intentions and destinations."}, {"key": "reliefweb:001630:17:1:1", "start": 505, "end": 528, "surface": "Data from Google Trends", "probe_tag": "keep", "probe_score": 0.9607, "luna_label": 1, "luna_reason": "Named Google Trends data resource is cited and used for migration-intention analysis."}]}, {"key": "rafael2-156", "text": "br>**** Combination of number of persons (EOIR) and cases (DHS).<br>|<br><br> <br><br><br> <br><br> <br><br> <br><br><br> <br><br> <br><br> <br><br><br> <br><br> <br><br><br> <br>Origin<br>Poland<br>Portugal<br>Rep. of<br>Korea<br>Romania<br>Serbia<br>Slovakia<br>Slovenia<br>Spain<br>Sweden<br>Switzerland<br>TfYR<br>Macedonia<br>Turkey<br>United<br>Kingdom<br>United<br>States****<br>Iraq<br>66<br> <br>*<br>*<br>133<br> <br>12<br> <br>45<br> <br>-<br> <br>61<br> <br>6,083<br> <br>1,440<br> <br>*<br>6,904<br> <br>2,030<br> <br>835<br> <br>Somalia<br>*<br>*<br>-<br> <br>8<br> <br>*<br>-<br> <br>-<br> <br>195<br> <br>3,361<br> <br>2,014<br> <br>*<br>647<br> <br>1,575<br> <br>311", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000427:18:80:0", "start": 59, "end": 62, "surface": "DHS", "probe_tag": "confusion", "probe_score": 0.1856, "luna_label": 1, "luna_reason": "DHS figures are combined with EOIR counts in the presented data."}]}, {"key": "rafael2-157", "text": " <sup>35</sup> [^35: UNFPA (2013) UNFPA Response to the Syrian Humanitarian Crisis in Lebanon\nJanuary - April 2013. Available at:\nhttp://www.unfpa.org/webdav/site/global/shared/documents/news/2013/Humanitarain%20Factsheet%202.pdf]\n\nInternational Medical Corps <sup>36</sup> [^36: International Medical Corps (2013) Deliveries analysis. September to December 2013.] (IMC) conducted an analysis of their reproductive health data (September to\nDecember 2013) across the 17 national hospitals within which they operate (Table 5).\n\n_Table [5]: Hospital admissions and deliveries SRs, September to December 2013 at IMC supported hospitals_\n\n|Hospital|Hospital<br>Admissions1|Deliveries|Percentage|\n|---|---|---|---|\n|Al Beqaa|328|35|10.7%|\n|Al Rayyan|627|239|38.1%|\n|Chtoura|466|85|18.2%|\n|Dar al Amal|4|0|0.0%|\n|Farhat|722|287|39.8%|\n|Hermel Gov|358|113|31.6%|\n|Taanayel|2599|1454|55.9%|\n|Dar Al Chifaa|713|338|47.4%|\n|Islamic|226|105|46.5%|\n|NDPaix –Qbayat|1311|619|47.2%|\n|Tripoli Gov|1547|672|43.4%|\n|Bent Jbeil Gov|196|90|45.9%|\n|Lebanese Italian|581|297|51.1%|\n|Marjeyoun Gov|190|78|41.1%|\n\n\n\n31 Huster K (2013) ‗Caesarean section rates among the Syrian refugee population in Lebanon: possible causes, implications and\nrecommendations going forward‘", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001622:29:1:0", "start": 402, "end": 426, "surface": "reproductive health data", "probe_tag": "confusion", "probe_score": 0.3662, "luna_label": 1, "luna_reason": "IMC analyzed existing reproductive health data and presented hospital delivery findings."}]}, {"key": "rafael2-158", "text": "|employers and private sectors other operational situation Participatory<br>/chambers partners working in and Assessments<br>the areas (e.g., GIZ funding)<br>-collecting individual person-specific Economic Inclusion<br>skills profile data from interested Project Team (with<br>refugees and host communities across key components on<br>all 6 camps, refugee hosting districts labour, skills, and<br>and urban areas through call for employment),<br>expression of interest to register INKOMOKO,<br>themselves (online/paper-based) in their Indego Africa,<br>relevant skills categories with any Maison Shalom,<br>supporting documentations. WVI, Alight, Kepler)<br>-Refugee data may then be tagged with<br>the socio-economic profile module of<br>ProGres v4.<br>Adding skills questions during next<br>verification|Col2|Col3|Col4|Col5|Col6|Col7|\n|---|---|---|---|---|---|---|\n|Organizing a Livelihoods Sector<br>Working Group<br> <br>An open forum for regular discussion,<br>information sharing, mapping exercise,<br>new programming and fund mobilization<br>approach, review of targeting and<br>tracking<br>results<br>in<br>line<br>with<br>global/country<br>plans/strategies<br>(GoR/UNHCR/Partner agencies).<br>UNHCR Livelihoods<br>Team in partnership<br>with MINEMA, Ips,<br>DPs,<br>Other<br", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:reliefweb:001259:58:0:0", "start": 219, "end": 238, "surface": "skills profile data", "probe_tag": "confusion", "probe_score": 0.2199, "luna_label": 0, "luna_reason": null}, {"key": "sample:reliefweb:001259:58:0:1", "start": 659, "end": 671, "surface": "Refugee data", "probe_tag": "confusion", "probe_score": 0.3615, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-159", "text": " asylum trends in\nindustrialized countries, see _Asylum Levels and Trends_\n_in Industrialized Countries, 2011_, UNHCR Geneva, March\n2012, available at: http://www.unhcr.org/4e9beaa19.\nhtml.\n\n\n\n**26** Despite the fact that statistical reporting on new\nasylum-seekers has improved in recent years, in particular\nin Europe, it should be borne in mind that the data for\nsome countries include a significant number of repeat\nclaims, i.e. the applicant has submitted at least one\nprevious application in the same or another country.\n\n\n\n<mark>TABLE 2</mark> **New and appeal applications received**\n\n\n\n\n\n**These asylum-seekers,** who were rescued by\nthe Italian coastguard, are lucky to be alive.\nTheir boat sank on its way from North Africa\nto the Italian island of Lampedusa.\n\n\n\n\n\n\n\n**Greece | In the**\n**waiting line** Every\nFriday, asylum-seekers\ncrowd around the police\nstation in Athens …\n\n\n\n**Greece | Beyond**\n**the border** The Turkish\nborder with Greece\nbecame the main entry\npoint…\n\n\n\nURING 2011, **some** 876,100 <sup>**(24)**</sup>\n**individual applications for**\n**asylum or refugee status**\n**were submitted to Govern-**\n**ments or UNHCR offices**\n## Din 171 countries or territories. This\n\n**constituted a** 3 **per cent increase com-**\n**pared to the previous year (** 850,300\n**claims) and was in line with increases**\n**observed in industrialized countries**\n**in** 2011 **.** <sup>**(25)**</sup> **", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001077:12:3:1", "start": 357, "end": 380, "surface": "data for\nsome countries", "probe_tag": "confusion", "probe_score": 0.2069, "luna_label": 1, "luna_reason": "Existing country data are qualified by a concrete finding about repeat claims."}]}, {"key": "rafael2-160", "text": " peer violence experienced by refugee learners.\nThe literature suggests that children who are\nperceived as different from their peers, in terms of\ntheir physical appearance, socio-economic status\n(Elgar et al., 2009), school performance (Thornberg,\n2011), or nationality (Alsharabti and Lahoud, 2016),\nare more likely to be bullied. However, some of these\naspects are not systematically measured in the EMIS,\nand data availability mostly comes from studies on\nthe school experiences of refugees that are limited\nto small samples (Cate and Glock, 2018; Liebkind,\nJasinskaja-Lahti, and Solheim, 2004; D’hondt et al.,\n2016).\n\n\nThis report contributes to and advances efforts to\nquantify the extent of these challenges and data\ngaps. It will focus on showing the breadth and\ndepth of refugee identification questions that are\nasked, including where and how they are asked,\nas well as looking at the spectrum of education\nindicators available for both refugee and nonrefugee populations. The report concludes with key\nrecommendations and actionable ways for data\nproducers and users to improve the availability of\nrefugee education data, and further inclusion of\nrefugees in national data systems going forward.", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000382:21:1:0", "start": 403, "end": 407, "surface": "EMIS", "probe_tag": "confusion", "probe_score": 0.3656, "luna_label": 0, "luna_reason": "Names an education information system without showing its data being used."}]}, {"key": "rafael2-161", "text": "**Recommendations**\n\n\nWhile the review shows that there is still a long way to go to ensure refugee inclusion in education data systems\nthere are some actionable ways forward to improve data availability for refugee education:\n\n\ny **Include refugees within sampling frames and include refugee identification questions in existing data**\n\n**collection instruments.** Findings from this review show substantial gains in refugee education data could\nbe made by wider inclusion in existing data collection, especially on access to schools, quality learning,\nand safety for refugees (see Figure 1). This inclusion is in line with the mandate of the Expert Group on IDP,\nRefugee, and Statelessness Statistics and their recommendations for refugee statistics (see the IRRS,\np. 44-45).\n\n\n - **To National Statistical Offices and Ministries of Education/Higher Education:** Work with and use\nthe existing guidance and refugee identification standards developed by the EGRISS to include and\ndisaggregate refugees and other FDPs by status within national data systems, including education\ndata. For administrative data, disaggregating students by nationality or refugee status is easier where\nindividual-level EMIS is already in place.\n\n\n - **To organizations engaged in collecting or funding education data or funding data collection:**\nAdvocate for the inclusion of refugees in sampling frames where contextually relevant and raise awareness\nof the EGRISS standards among national actors. Further, apply the EGRISS recommendations to include\nrefugee identification questions in surveys, in addition to existing disaggregation by gender, age, and\ndisability. For example, the report identified that including refugee identification questions in international\nand regional learning assessments could contribute to significant increases in data availability on the\nlearning of refugees. The inclusion of refugees in the Multiple Indicators Cluster Surveys, the Demographic\nand Health Surveys and any other national-level surveys that are important sources of data for global\neducation figures would also provide much-needed information on the status of refugee learners.\n\n\nWhile the inclusion of", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000382:7:0:0", "start": 426, "end": 440, "surface": "education data", "probe_tag": "confusion", "probe_score": 0.2499, "luna_label": 0, "luna_reason": "Generic education data is mentioned in a recommendation without a concrete attributed finding."}, {"key": "reliefweb:000382:7:0:1", "start": 1068, "end": 1082, "surface": "education\ndata", "probe_tag": "confusion", "probe_score": 0.0636, "luna_label": 0, "luna_reason": "Recommendation to improve future disaggregation within national education data systems"}, {"key": "reliefweb:000382:7:0:4", "start": 1949, "end": 1979, "surface": "Demographic\nand Health Surveys", "probe_tag": "confusion", "probe_score": 0.8599, "luna_label": 0, "luna_reason": "Proposed inclusion would generate future information, not cite existing survey data."}]}, {"key": "rafael2-162", "text": " instruments\nand opportunities as, a survey found in March 2018\nthat half of IDPs were unaware of the government’s\nstrategy for integration of IDPs.\n\n\nMany civil society and international organizations\nare already implementing peacebuilding and\nreconciliation activities in Ukraine. These activities\ninclude initiatives to build dialogue, promote good\ngovernance and empower displaced communities.\nProtection Cluster partners are working with IDPs\nto raise their awareness about these processes and\nthe opportunities they offer for participation in\ndecision-making, planning and budgeting processes\nat community level. A comprehensive support\nprogramme to the government for implementation\nof the Women, Peace and Security agenda includes\nintegration of gender-sensitivity and responsiveness\nto security reform, defence reform, and mediation.\nAn economic and social recovery project to increase\nemployment and rebuild infrastructure in the Donbas\nregion is also underway. There is also an ongoing\nsupport for decentralization and strengthening local\ngovernance.\n\n\n\nGPC CONFERENCE REPORT 9", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000586:7:2:0", "start": 37, "end": 63, "surface": "survey found in March 2018", "probe_tag": "confusion", "probe_score": 0.7244, "luna_label": 1, "luna_reason": "Past survey is tied to the concrete finding that half of IDPs were unaware."}]}, {"key": "rafael2-163", "text": " ¿Por qué te gustaría\nsolicitar asilo en México? ¿Quieres ser devuelto a\ntu país? ¿Hay alguna razón por la que no deberían\ndevolverte a tu país? ¿Consideras que tu vida,\nseguridad e integridad están en riesgo si regresas\na tu país?\n\n\n\nLos datos obtenidos revelan la necesidad de\nadoptar una tipología para el análisis de las formas\nde violencia que experimentan los NNAS en sus\npaíses: 1) la violencia vivida en el ámbito privado, que\ncorrespondería al hogar o grupo domestico; y 2) la\nviolencia vivida en el ámbito público, exaltada en la\ncolonia, barrio o departamento.\n\n\nComo se puede inferir, ambos espacios donde se\nviven estas diversas violencias, se ubican dentro del\nentorno inmediato de la niñez centroamericana y\npor tanto dentro de su cotidianidad.\n\n\nDe acuerdo a los datos de este estudio son los\nadolescentes y niños quienes mayormente son\nvíctimas de la violencia del entorno comunitario,\nmientras que las adolescentes y niñas padecen\nla violencia del espacio doméstico. También son\nlos adolescentes varones (de 13 a 17 años) más\nsusceptibles a ser reclutados por los grupos de\ndelincuencia locales, mientras que los niños y niñas\ncuyas edades no exceden los 12 años, pueden sufrir\nviolencia inter-personal al interior del hogar.\n\n\n\n**42** **arrancados de raíz** **Causas que originan el desplazamiento transfronterizo de niños, niñas y adolescentes no acompañados", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001434:21:3:0", "start": 779, "end": 800, "surface": "datos de este estudio", "probe_tag": "confusion", "probe_score": 0.3328, "luna_label": 1, "luna_reason": "Study data support concrete findings about violence affecting children and adolescents."}]}, {"key": "rafael2-164", "text": " to accommodate just 13%\nof the adolescent population (UNHCR, 2015c).\n\n\nRefugee sites have had varied success in\nmeeting education needs\n\n\nSince the mid-2000s, education for refugee children has\nprogressed in some countries but stagnated in others,\nas is shown by new analysis of data from nine refugee\nsites (Figure 3). A range of factors contribute to this wide\ndivergence in access to education and education quality,\nincluding differences in refugees’ rights to education and\ncertification according to national legislation, the difficulty\nin dealing with large influxes of displaced people, language\ndifferences, and the difficulty of sustaining education in\nprotracted refugee situations.\n\n\n|Enrolment rate Enrolment rate|Col2|Col3|\n|---|---|---|\n|2004–2007<br>2013–2015<br>Enrolment rate<br>improved<br>Enrolment rate<br>worsened|2004–2007<br>2013–2015<br>Enrolment rate<br>improved<br>Enrolment rate<br>worsened|2004–2007<br>2013–2015<br>Enrolment rate<br>improved<br>Enrolment rate<br>worsened|\n|<br>|||\n\n\n\nNote: The size of the marker is proportional to the size of the population\nin each site.\n\n\nSource: Analysis based on 2014 UNHCR data.\n\n\n\n\n\n\n\n\n\n**FIGURE 3:**\nDifferent sites in different contexts have followed different\ntrajectories in getting refugee children and adolescents\ninto schools\n_Enrolment rates of refugee children aged 5–17, selected refugee_\n_sites in selected countries, 2004–2007 and 2013–2015_\n\n\n\n\n\n\n\n\n\n\n\n80\n\n\n60\n\n\n40\n\n\n20\n\n\n0\n\n\n\n\n\n4", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001012:3:1:0", "start": 280, "end": 308, "surface": "data from nine refugee\nsites", "probe_tag": "confusion", "probe_score": 0.7301, "luna_label": 1, "luna_reason": "Analysis uses data from nine refugee sites to show enrollment trajectories."}, {"key": "reliefweb:001012:3:1:1", "start": 1133, "end": 1148, "surface": "2014 UNHCR data", "probe_tag": "confusion", "probe_score": 0.6643, "luna_label": 1, "luna_reason": "UNHCR data are explicitly analyzed to produce Figure 3 enrollment findings."}]}, {"key": "rafael2-165", "text": "**PROTECTION BRIEF III SLOVAKIA**\n\n**July 2023 – March 2024**\n\n\nmaking the corresponding unemployment contribution to the Social Insurance Agency, without being\nable to claim this benefit. According to the official statistics, as of March 2024, approximately 30,000\nrefugees from Ukraine with Temporary Protection were employed in Slovakia. <sup>51</sup> [^51: Central Office of Labour, Social Affairs and Family, Zamestnávanie cudzincov na území Slovenskej republiky za rok 2024.] Their mandatory\ncontributions to the Social Insurance Agency are equivalent to those of employed Slovak nationals,\nincluding the unemployment insurance contribution which finances precisely the Unemployment\nAllowance. <sup>52</sup> [^52: Social Insurance Agency (2024), Tabuľky platenia poistného od 1. januára 2024.] Employees in Slovakia are generally entitled to this allowance if they paid the respective\ncontribution for at least two years, and they are registered in the Office of Labour, Social Affairs and\nFamily database of job applicants. Nevertheless, Temporary Protection holders are unable to register for\nthis due to their lack of temporary or permanent residence. Therefore, if the approximately 30,000\nemployed Temporary Protection holders were earning only a minimum wage (while many of them\ncertainly earn more), it can be estimated that they are contributing more than €337,000 per month to a\nsocial benefit which they cannot access. Exploring the expansion of this and the other social protection\nbenefits which are currently not available to Temporary Protection holders presents an important\nopportunity to strengthen the social protection of the most vulnerable.\n\n\nWhen it comes specifically to refugees with disabilities, enabling their access to different compensation\nbenefits including for personal assistance, transportation, adaptation of accommodation, or purchase of\ndisability aids would also significantly support this particularly vulnerable group. As shared by a refugee\nduring one of the focus group discussions with UNHCR:\n\n\n_“Persons with disabilities cannot work in many cases; they need extra cash assistance and support_\n_for example to cover the costs of prothesis,", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000476:5:0:0", "start": 206, "end": 225, "surface": "official statistics", "probe_tag": "confusion", "probe_score": 0.8831, "luna_label": 1, "luna_reason": "Official statistics support the attributed employment estimate of approximately 30,000 refugees."}]}, {"key": "rafael2-166", "text": " las\nexperiencias antes de partir, determinan los efectos y\nlos itinerarios de su desplazamiento.\n\nLas y los niños y adolescentes **no acompañados**\n**son aquellos** que no se encuentran con sus\nprogenitores ni con el resto de sus parientes y no\nse hallan al cuidado de un adulto que, por ley o\ncostumbre, sea el responsable de ellos. Los niños o\nniñas y adolescentes **separados** son aquellos que\nestán separados de ambos padres, de su anterior\ntutor legal o la persona que acostumbra cuidarlos pero\nno necesariamente de otros parientes. Esta categoría\nincluye a niños y niñas y adolescentes acompañados\npor otros adultos de su familia. <sup>2</sup> En la práctica, pocos\npaíses han adoptado la categoría de separados. En\nMéxico, las estadísticas oficiales sobre niños, niñas\ny adolescentes no acompañados, retomadas en esta\ninvestigación, también incluyen a aquellos que se\nencuentran separados.\n\n\n\n\n\n**Tasa de crecimiento de 2011-2012 del número**\n\n**de los NNAS (varones) atendidos por el**\n\n**módulo del DIF en la EM Tapachula**\n\n\n**Tasa de crecimiento de 2012-2013 del número**\n\n**de los NNAS (varones) atendidos por el**\n\n**módulo del DIF en la EM Tapachula**\n\n\nIntegral de la Familia (Sistema DIF) en la Estación Migratoria de Tapachula,", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001434:10:1:0", "start": 736, "end": 758, "surface": "estadísticas oficiales", "probe_tag": "confusion", "probe_score": 0.8136, "luna_label": 1, "luna_reason": "Official statistics are reused to establish who is included in the category."}]}, {"key": "rafael2-167", "text": "**DEPARTAMENTO DE ARAUCA** | Agosto de 2024\n\n\n**Metodología**\n\n\nLa metodología de esta actualización de análisis de protección ha combinado monitoreo periódicos del Equipo Local de\nCoordinación de Arauca, el subgrupo de trabajo de Niñez y el Subgrupo de trabajo de VBG, al igual que insumos cualitativos\nde las reuniones y consultas con los socios locales, informantes clave y población afectada. El proceso de análisis ha seguido\nla metodología de severidad y las estimaciones de Personas en Necesidad (PIN) y el Marco Analítico de Protección (PAF).\n\n\n**Limitaciones**\n\n\nEl presente análisis ha seguido una lógica de análisis cualitativo y cuantitativa derivado de datos oficiales para posterior\ninterpretación por parte de expertos. Por otra parte, para evitar los potenciales riesgos que se podrían llegar a generar\npara las comunidades, se limitó el encuentro con las mismas.\n\n\nPor lo tanto, los ejercicios de recolección de información y análisis de la situación humanitaria se centraron en datos\nsecundarios y entrevistas con referentes en el territorio.\n\n\nPara obtener más información, póngase en contacto con: **Sebastián Díaz Parra** <u>[diazj@unhcr.org |](mailto:diazj@unhcr.org)</u> **Gabriela Villota** <u>[gabriela.villota@drc.ngo](mailto:", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:reliefweb:000549:9:0:0", "start": 666, "end": 681, "surface": "datos oficiales", "probe_tag": "confusion", "probe_score": 0.4966, "luna_label": 1, "luna_reason": "Official data is declared as the basis for qualitative and quantitative analysis."}, {"key": "sample:reliefweb:000549:9:0:1", "start": 996, "end": 1013, "surface": "datos\nsecundarios", "probe_tag": "confusion", "probe_score": 0.5031, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-168", "text": ".\nThe basis for harmonization has been set by the\nIRRS and IRIS, and practical steps towards making it\neasier to use those definitions have already started\namong international agencies working with FDPs.\nHowever, in our exploration of surveys and datasets,\n\n\n\nwe found that FDPs tend not to be a priority, and\nthe sampling of national surveys is rarely designed\nto allow estimation of statistics for FDPs. This could\nbe due to lack of capacity, political sensitivities, or\nprotection concerns. More details on specific issues\nare presented below.\n\n\n**Issues with sample designs**\n\n\nLarge-scale surveys, designed to represent the\npopulation of a country, generate data suitable to\nestimate statistics, including SDG indicators and\nother indicators, for the national population. They\nare the approach of choice for many SDG indicators.\nHowever, they are normally designed with objectives\nthat do not prioritize estimation for forcibly displaced\npopulations. As a consequence, the resulting sample\nsizes for FDPs tend to be small and the statistics\nproduced have low precision and may be biased.\n\n\nUnder some conditions, a case can be made for these\nsurveys to attempt over-sampling of FDPs. However,\nwhile the resulting sample size will be larger, it can\nbe difficult to derive sampling weights in cases where\nreliable information about the overall size of the\nforcibly displaced population in the country is not\navailable.\n\n\n**Sampling weights**\n\n\nThe data that allows for the derivation of appropriate\nweights for FDPs is rarely available. In our exploration\nof datasets, these weights were not available at all.\nAs a consequence, the extent to which estimates\nof disaggregated SDG indicators for FDPs can be\nconsidered representative of all FDPs in the country\nremains difficult to establish. While in some cases\nestimates can be calculated for FDPs, they are likely\nto be biased and cannot be interpreted without\ncaveats about who they apply to. In the case of SDG\nindicators, this is risky (and not recommended), as\nthe general expectation is that SDG", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000141:18:1:0", "start": 326, "end": 342, "surface": "national surveys", "probe_tag": "confusion", "probe_score": 0.6735, "luna_label": 1, "luna_reason": "Survey sampling is analyzed and found rarely designed for displaced-population estimates."}]}, {"key": "rafael2-169", "text": " to the constitution. The children of refugees are specifically excluded\nfrom acquiring citizenship by birth; and refugees are also excluded from accessing citizenship by\n\n\n285 Registration of Persons Act, sec.30.\n\n\n[286 See information at NIRA website: https://nira.go.ug/fees.](https://nira.go.ug/fees)\n\n\n287 Registration of Persons Act, sec. 39.\n\n\n288 Interviews, NIRA, 2021.\n\n\n289 Van Der Straaten, Jaap, Victoria Esquivel Korsiak, and Luda Bujoreanu, Identification for Development (ID4D)\nCountry Diagnostic: Uganda. Washington, D.C.: World Bank 2018, chapter 2.2.1.\n<u>[http://documents.worldbank.org/curated/en/921761542144309171/Identification-for-Development-ID4D-](http://documents.worldbank.org/curated/en/921761542144309171/Identification-for-Development-ID4D-Country-Diagnostic-Uganda)</u>\n<u>[Country-Diagnostic-Uganda.](http://documents.worldbank.org/curated/en/921761542144309171/Identification-for-Development-ID4D-Country-Diagnostic-Uganda)</u>\n\n\n[290 As reported in the Uganda Demographic and Health Survey 2016 https://dhsprogram.com/where-we-work/Country-](https://dhsprogram.com/where-we-work/Country-Main.cfm)\n<u>[Main.cfm.](https://dhsprogram.com/where-we-work/Country-Main.cfm)</", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000434:81:2:0", "start": 989, "end": 1030, "surface": "Uganda Demographic and Health Survey 2016", "probe_tag": "confusion", "probe_score": 0.8837, "luna_label": 1, "luna_reason": "Named survey cited as the source of a reported claim."}]}, {"key": "rafael2-170", "text": " cuya nacionalidad no se estableció**\n**con la independencia ni antes de que en 1972 la ley sobre nacionalidad**\n**cambiara. La estimación proviene en parte de casos en los que se denegó la**\n**inscripción en el censo electoral en 2010 porque las autoridades electorales**\n**no pudieron determinar su nacionalidad en ese momento. El cálculo se ha**\n**ajustado para reflejar el número de personas que adquirieron la nacionalidad**\n**mediante el procedimiento especial de “adquisición de nacionalidad por**\n**declaración” hasta el final de 2018. El cálculo no incluye a las personas de**\n**padres desconocidos que fueron abandonadas en su infancia y que no son**\n**consideradas nacionales según la legislación de Côte d’Ivoire.**\n\n**20 Casi todas las personas registradas como apátridas tienen residencia**\n**permanente y disfrutan de más derechos que lo previsto en la Convención**\n**sobre el Estatuto de los Apátridas de 1954.**\n\n**21 La información provista por la Federación Rusa incluye datos estadísticos**\n**sobre la República Autónoma de Crimea y la ciudad ucraniana de Sebastopol,**\n**temporalmente ocupada por la Federación Rusa. Las cifras de apatridia se**\n**refieren a las cifras del censo de 2010 ajustadas para reflejar el número de**\n**personas a", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000519:34:18:0", "start": 212, "end": 235, "surface": "censo electoral en 2010", "probe_tag": "confusion", "probe_score": 0.8038, "luna_label": 1, "luna_reason": "2010 electoral census figures inform the estimate of stateless persons"}, {"key": "reliefweb:000519:34:18:1", "start": 990, "end": 1008, "surface": "datos estadísticos", "probe_tag": "confusion", "probe_score": 0.6208, "luna_label": 1, "luna_reason": "Statistical data underpin adjusted 2010 census figures on statelessness."}]}, {"key": "rafael2-171", "text": " gauge\nor set the qualitative range of indicators at sectoral\nlevel.\n\n\nIssues related to M&E were also frequently raised\nby key informants during interviews. Several\nrespondents noted that cash Post-Distribution\nMonitoring (PDM) exercises typically focus on\nexpenditure or market-related issues and rarely\ndelve into outcomes of cash. The fact that multipurpose cash cuts across sectors adds a layer of\ncomplication and means that monitoring a range of\noutcomes across several sectors is needed. This is\nhowever largely perceived as difficult and unusual,\n\n\n\nand in the words of a key informant, “we don’t see\nmuch of this type of monitoring coming from the\nfield”.\n\n\nIndeed, there were frequent mentions that\nregardless of the fact that multi-purpose cash\nand related monitoring can enable a better\nunderstanding of household need prioritization\npatterns, and of the effect of cash injections across\ndifferent aspects of well-being, M&E indicators for\nmulti-purpose cash are largely limited to those with\nwhich agencies are more familiar and are easier to\ncollect (e. g. food security indicators). Other, more\ncomplex and unusual indicators, such as access\nto housing, are typically neglected. In addition,\ncash monitoring systems tend to predominantly\nrely on economic benchmarks, often neglecting\nto consider the environmental externalities\nassociated with these solutions. For example, in\nthe WASH sector there is a need to understand\nnot only whether domestic water was purchased\nand in what quantity, but also assess the quality of\nwater purchased, the sources from which it was\nobtained (water kiosk, truck, etc. ) and associated\nenvironmental risks.\n\n\nIn Greece, the few available PDMs showed very\nlimited, if any, disaggregation of cash expenditures\n(e. g. hygiene), and some reports grouped and\npresented different, largely unrelated cash\nexpenditures categories together (e. g. food and\nclothes). From available PDM reports it was not\npossible to gain a comprehensive picture of cash\nexpenditures and outcomes, or draw comparisons\nbetween cash expenditures and outcomes in\ncamps versus", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000069:74:1:0", "start": 1072, "end": 1096, "surface": "food security indicators", "probe_tag": "confusion", "probe_score": 0.4742, "luna_label": 0, "luna_reason": "Generic indicator category is mentioned without an attributed finding or concrete result."}]}, {"key": "rafael2-172", "text": "\nUNHCR, or persons whose\nasylum claims had been\nfinally rejected by UNHCR.\nFor our purposes, we\nincluded only the registered\nAfghans and the asylum\nseekers in our analysis.\n\n\n�� Registered refugees (n=112) and\nasylum seekers (n=64) from\nAfghanistan: 15.8% (n=176)\n\n�� Refugees from Myanmar :\n39% (n=435)\n\n\n�� Refugees from Somalia :\n5.7% (n=64)\n\n\n�� Indians : 34.8% (=388)\n\n\nDefining the most appropriate sampling\napproach for the household survey\nposed some challenges. Firstly, the\nsettlement patterns of the three target\ngroups were highly heterogeneous.\nMyanmarese refugees were clustered\n\n\n\n**III. METHODOLOGY**\n\n\n_Household survey_\nOur survey began by identifying\nthe main areas of Delhi where the\nrefugee populations were living.\nWe did this by working with key\ninformants from UNHCR and its\npartner Bosco, our implementing\npartner DAJI and from the refugee\ncommunities. In addition, we used\nthe data from UNHCR’s registration\ndatabase (known as proGRES).\nWe then stratified the identified\nareas (wards) according to high,\nmedium and low densities of refugee\npopulations. Fourteen high-density\nareas were selected for the survey:\nBurari, Tilak Nagar, Vikaspuri East,\nJanakpuri West, Sitapuri, Hastsal,\nBindapur, Nizammuddin, Lajpat\nNagar, Bhogal, HauzKhaz, Madangir,\nEast of Kailash and Laxmi Nagar.\n\n\nprimarily in specific neighbourhoods\nof West Delhi, while Somalis were\nhighly clustered in South Delhi and in\nWazirabad, North Delhi. Afghans were\nmore dispersed in South Delhi and\nin Wazirabad, North Delhi. Secondly,\nthe size of the refugee populations\nvaried a lot (from about 200 Somalis\nto more than 9,000 Myanmarese).\n\n\nA simple random sampling in each\nward would therefore not yield enough\nrefugee respondents. Moreover, a\nsingle sampling strategy", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000087:17:1:1", "start": 913, "end": 942, "surface": "UNHCR’s registration\ndatabase", "probe_tag": "confusion", "probe_score": 0.8807, "luna_label": 1, "luna_reason": "Existing UNHCR database informed refugee-area identification and survey stratification."}]}, {"key": "rafael2-173", "text": " credibility. When available, video material, lists of victims and supplementary\ninformation from protection cluster partners is incorporated.\n\nCIMP monitors civilian impact that occurs after an incident of armed violence have taken place, thus CIMP numbers on\n\ndisplacement, loss of livelihood and restriction of movements/obstruction to flight only covers households that have experienced a\ndirect impact from armed violence, e.g. a house destroyed or a vehicle hit. Therefore, CIMP data does not include full numbers of\npeople being displaced, loosing livelihood or experiencing restricted freedom of movement/obstruction to flight, where numbers are\nnaturally much higher than what is captured by CIMP.\n\nCivilian impact incidents recorded by CIMP are divided into direct and indirect impact, with associated direct and indirect protection\nimplications. Direct impact includes incidents in which individuals or households are directly affected by the incident, e.g. damage to\nhouses and farms, damage to markets and local businesses, impact on vehicles or as well as exposure to UXOs and armed conflict\ngenerating casualties. Indirect impact can broadly be defined as incidents of armed violence impacting on infrastructure and basic\nservices and in turn restricting access of civilians to various vital services, infrastructure and goods, e.g. healthcare, education, food\nand water and transport infrastructure. Due to the nature of the indirect impact, the number of households impacted is often much\nhigher than during direct impact.\n\nAs CIMP aims to collect and disseminate data on civilian impact that occurs as a result of armed conflict, some incidents are\nexcluded. This includes incidents related to crime, domestic violence and small arms fire incidents that occurs away from areas of\nactive conflict and have less than two casualties. Small arms fire incidents are always included when they occur in areas of active\nconflict.\n\n\n###### **3**", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:reliefweb:000783:4:1:0", "start": 480, "end": 489, "surface": "CIMP data", "probe_tag": "confusion", "probe_score": 0.3748, "luna_label": 1, "luna_reason": null}, {"key": "sample:reliefweb:000783:4:1:1", "start": 1581, "end": 1604, "surface": "data on civilian impact", "probe_tag": "drop", "probe_score": 0.0067, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-174", "text": "of progressive steps. Progress along a pathway\nto inclusion begins with assistance delivered\ntotally in parallel with government programmes\nrepresenting traditional ‘care and maintenance’\nprogramming. This assistance is aligned to\ngovernment delivery approaches where there\nis some progress on the enabling factors for\ninclusion. With further progress on these factors,\nharmonised area-based programmes covering\nhost and displaced populations becomes\npossible, which replicate many of the features\nof the government delivery approach. However,\nthese programmes may not yet facilitate refugee\nenrolment to social registries or other forms of\nformal recognition by the government.\n\n\nThe following options, based on the ten enabling\nfactors for inclusion identified in this study, can\nbe chosen and adapted according to the specific\ncontext and type of forcibly displaced population\ntargeted for inclusion:\n\n**1. Work to narrow the gap between** **_de_**\n**_jure_** **and** **_de facto_** **access to government**\n**systems** by strengthening the application of\ninternational and government legal and policy\ninstruments, backed by a solid business case\nto governments for inclusion.\n\n**2. Scale up multilateral and development**\n**funding** for refugee-hosting areas that\npromotes an area-based approach that\nincreases coverage for host and displaced\npopulations.\n\n**3. Scale up local and central government**\n**capacity** offering support that enables forcibly\ndisplaced persons to reach government\nprogrammes and for government agencies to\nreach forcibly displaced persons.\n\n**4. Support the preparation and scale up of**\n**government social registries** to enrol forcibly\ndisplaced persons including reinforcing the\ncollection and analysis of relevant socioeconomic data.\n\n**5. Ensure that forcibly displaced persons can**\n**meet eligibility criteria** for social registry and\nbenefit targeting including reinforcing access\nto civil documentation and digital IDs.\n\n**6. Accompany referrals and monitor the impact**\n**of inclusion on forcibly displaced persons", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001334:3:0:1", "start": 1752, "end": 1770, "surface": "socioeconomic data", "probe_tag": "drop", "probe_score": 0.0394, "luna_label": 0, "luna_reason": "Data collection and analysis are recommended activities, not use of existing data."}]}, {"key": "rafael2-175", "text": "2021<br>2022<br>2023<br> <br>* Figures have been revised in June by removing individuals found in<br>VolRep and Data Transfer Request.<br> <br>*** The flows<br>first country<br>|", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000515:3:2:0", "start": 101, "end": 133, "surface": "VolRep and Data Transfer Request", "probe_tag": "drop", "probe_score": 0.0258, "luna_label": 1, "luna_reason": "Named data records used to revise figures by removing duplicated individuals."}]}, {"key": "rafael2-176", "text": "**Types of program Implementation, monitoring and evaluation data**\nDifferent types of data help humanitarian actors understand the situation of people living with\ndisabilities and the progress of the response.\n\n\n1. Data at the **individual level** that identifies disability, needs, and barriers individuals may face as well\nas the capacities they may have. Individual data allows the population to be differentiated, providing\nan insight into size of the population of persons with disabilities that allows meaningful planning\ntargets to be set and evaluations to occur. Individual data can be obtained in two ways:\n\n\n  - Data that **extrapolates for the whole population** such as a national census or large-scale sample\nsurvey helps determine prevalence, and is useful to shape programmatic interventions. This type\nof individual data is better gathered in advance of the crisis, and where it exists this type of data\nprovides an excellent baseline against which to assess the response during an evaluation.\n\n\n  - **Administrative processes** where data from individuals is collected during the course of a\nhumanitarian response can also be used effectively to understand how people with disabilities\nare being reached. Data such as collected by UNHCR when a refugee is registered that is entered\ninto the “ProGres” database can be used by the humanitarian community to understand the\nprevalence of persons with disabilities. Administrative data of this type has limitations if it was\nimproperly captured, if individuals were “unregistered”, or their disabilities were “unidentified”.\n\n\n2. **Service level data** on the availability of inclusive services (or barriers to be addressed) does not track\nindividuals, but the proportion of services, facilities or activities in terms of accessibility to persons\nwith disabilities. Data of this type can be used for program planning, setting targets, measuring\nprogress and evaluations. During a humanitarian action, this kind of data may be easier to obtain.\nThis kind of data looks at the proportion of WASH facilities are accessible, for example", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:reliefweb:001044:14:0:0", "start": 686, "end": 701, "surface": "national census", "probe_tag": "confusion", "probe_score": 0.1314, "luna_label": 1, "luna_reason": null}, {"key": "sample:reliefweb:001044:14:0:1", "start": 705, "end": 730, "surface": "large-scale sample\nsurvey", "probe_tag": "drop", "probe_score": 0.0326, "luna_label": 0, "luna_reason": null}, {"key": "sample:reliefweb:001044:14:0:2", "start": 1311, "end": 1318, "surface": "ProGres", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1, "luna_reason": "UNHCR registration data in ProGres is used to understand disability prevalence."}, {"key": "sample:reliefweb:001044:14:0:3", "start": 1430, "end": 1449, "surface": "Administrative data", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1, "luna_reason": null}, {"key": "sample:reliefweb:001044:14:0:4", "start": 1596, "end": 1614, "surface": "Service level data", "probe_tag": "confusion", "probe_score": 0.2674, "luna_label": 0, "luna_reason": "Generic data type is described, but no attributed finding or concrete claim is provided."}]}, {"key": "rafael2-177", "text": " refugee children have little or no access\nto specialized education programmes and tertiary education, even though the Education Policy and the\n2018–2030 Education and Training Roadmap provide for them.\n\n\n**4.2** **Healthcare**\n\n\nThe Refugees Proclamation provides refugees with access to available health services in Ethiopia under\nthe same conditions as nationals. This includes access to available national sexual and reproductive\nservices for refugee women and girls. There are no further regulations or guidance on how to facilitate this\naccess.\n\n\nData on the number of refugees accessing the national health-care system is not available. Generally,\nrefugees living out of camp access health care provided for by government institutions under the same\nconditions as nationals, but many need financial support for health care. The Government provides\nrefugees living in camp settings with HIV testing and treatment, TB, leprosy, and vaccination services,\nincluding both routine and campaign-based vaccination services, all free of charge. For other health\nservices, refugees in camp settings generally use the camp-based health system but in some contexts\nalso access the national health system in the host community. Correspondingly, on average, 10 per cent of\nthe users of the camp-based health system are host community members. In locations where the host\ncommunity’s health-care facilities are particularly poor, this can reach 30 per cent.\n\n\nA community-based health insurance (CBHI) scheme exists, but it is still in its pilot phase and non-existent\nin most refugee-hosting woredas. The extent to which refugees have the right to be enrolled is unclear.\nThere is no system in place for financing refugee health-care costs in the publicly financed health care\nsystem.\n\n\n**4.3** **Social protection**\n\n\nThe Refugees Proclamation stipulates that ARRA shall ensure that refugees and asylum-seekers with\nspecific needs are provided with special protection commensurately with their needs. There is no further\npolicy that provides guidance on how this is to be", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:reliefweb:000182:11:1:0", "start": 553, "end": 583, "surface": "Data on the number of refugees", "probe_tag": "drop", "probe_score": 0.0006, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-178", "text": "**\n**rapid gender analysis** should be conducted as part of the needs assessment at the immediate onset of a disaster\nto inform emergency programming. It is important to collect sex and age disaggregated data throughout all\nphases of programming, but data collection must be **accompanied by analysis in order to be effective in practice** .\nClusters and agencies should work with gender technical advisors where possible to collect and use sex and age\ndisaggregated data to adapt programming as needed.\n\n\n7 The IASC resources provide guidance on gender mainstreaming in humanitarian response. See also: Inter-Agency Minimum Standards for Gender-Based Violence in\n\nEmergencies Programming; Interagency Gender-Based Violence Case Management Guidelines; Inter-Agency PSEA-CBCM Best Practice Guide; Policy on Protection in\nHumanitarian Action; and IASC Revised Commitments on Accountability to Affected Populations and Protection from Sexual Exploitation and Abuse, 2017.\n\nPAGE 5", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000705:4:2:0", "start": 178, "end": 208, "surface": "sex and age disaggregated data", "probe_tag": "drop", "probe_score": 0.0407, "luna_label": 0, "luna_reason": "Recommends collecting data during programming rather than using existing data."}]}, {"key": "rafael2-179", "text": " continued to meet regularly to ensure regional coherence at the\nstrategic and operational levels. At the country level, inter-agency mechanisms continued to implement planning, response and\nmonitoring activities, with approximately 40 working groups across the five response countries meeting regularly to coordinate\nimplementation of the plan in an efficient and effective manner. Meanwhile, as an accountability measure to donors, reports\nwere published on a regular basis at both the country and regional levels, including quarterly sectoral dashboards and financial\ntracking by sector and component. Countries also continued to implement their M&E frameworks to better articulate progress and\nstrengthen linkages between outputs and overall strategic objectives. This also offered support to Governments in achieving the\nstrategic objectives of national chapters.\n\n\nDuring 2017, there were examples across the region of the adoption of strong monitoring and two way communication approaches.\nFood Security partners are providing increased accountability to all parties via cutting-edge technological advancements. In\nMay 2017, the Building Blocks pilot was launched in Azraq camp in Jordan, the first large-scale humanitarian use of Blockchain\ntechnology. Building Blocks provides a real-time view into food purchase transactions and allows partners to have increased\noperational oversight and management of cash-based transfer (CBT) operations, ensuring data security and reducing the risk of\nfraud.\n\n\nThe food sector in Jordan also utilizes a Triangulation Database; a system which compiles data from various sources and\nsynthesizes it into actionable intelligence, dashboards, and mapping. The Triangulation Database gathers real time financial\ndata from Building Blocks, financial service providers, retailers, and the sector’s internal data systems. From this data, the sector\ncan conduct monthly financial reconciliation, identify anomalies, generate real time data on expenditure patterns, and ensure\nmaximum transparency and accountability.\n\n\nIn Lebanon, food security partners work to empower Syrian and Lebanese families to have as much control as possible over their\nshopping. Launched in November, the smartphone application ‘Dalili’ enables them to do just this. It collates and displays the", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:reliefweb:001030:11:2:0", "start": 1550, "end": 1572, "surface": "Triangulation Database", "probe_tag": "confusion", "probe_score": 0.1378, "luna_label": 1, "luna_reason": null}, {"key": "sample:reliefweb:001030:11:2:1", "start": 1702, "end": 1724, "surface": "Triangulation Database", "probe_tag": "drop", "probe_score": 0.0163, "luna_label": 1, "luna_reason": null}, {"key": "sample:reliefweb:001030:11:2:2", "start": 1733, "end": 1757, "surface": "real time financial\ndata", "probe_tag": "drop", "probe_score": 0.0422, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-180", "text": " gender-based violence (GBV), unaccompanied or separated children, and lesbian, gay, bisexual,\ntransgender, intersex and queer (LGBTIQ+) persons. Despite challenges related to social distancing, almost\n**1.4 million new registrations** were facilitated in 2021.\n\n\nAccording to the preliminary findings of the Global Survey on Registration, Biometrics, and Digital Identity,\napproximately **85 per cent of operations** reported that women were issued with their own individual identity\ncredentials.\n\n\nOverall, **81,000 stateless persons** acquired a nationality or had it confirmed in 2021. **Nineteen operations** reported\nacting on documentation and registration of women and girls. Of these, **13** have reportedly succeeded in providing\nwomen and girls with access to non-discriminatory registration and documentation.\n\n\nRecognizing that direct access to, and control over, the assistance had positive effects on women’s empowerment\nand more equitable divisions of roles and responsibilities, many operations in 2021 continued to prioritize women\nwhen providing assistance.\n\n\nCurrently, in **46 countries** using UNHCR’s cash management system CashAssist, some 52 per cent cash recipients\nare female. <sup>5</sup> [^5: UNHCR, “Digital payments to refugees – A pathway towards financial inclusion” (2020). Available from www.unhcr.org/5fdcd8474.] Globally, most countries prioritize cash for people with specific needs.\n\n\nData on equal access to economic opportunities, including decent work and quality education and health services\nvaried greatly in 2021, with access to health services being the least reported on. The focus was primarily on\nacknowledging harmful gender norms, roles and inequalities that impact health-seeking behaviour and access;\nadvocacy on women’s and girls’ right to make decisions about their health and health care; and integrating\nconsiderations related to the protection risks and needs of men and women of different ages in health-care\nprovision. Reference was also made to efforts to increase the number of female medical and nursing staff", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000524:8:1:0", "start": 1424, "end": 1470, "surface": "Data on equal access to economic opportunities", "probe_tag": "drop", "probe_score": 0.0456, "luna_label": 1, "luna_reason": "Data are used to report variation in access during 2021."}]}, {"key": "rafael2-181", "text": "**[International](https://uis.unesco.org/en/glossary)**\n**[Migrant](https://uis.unesco.org/en/glossary)**\n\n\n**International**\n**Learning**\n**Assessments**\n**(ILAs)**\n\n\n**[National Learning](https://uis.unesco.org/en/glossary)**\n**[Assessment](https://uis.unesco.org/en/glossary)**\n\n\n\n**Key Terms**\nPaving pathways for inclusion: A global overview of refugee education data\n\n\n‘For the specific purposes of global statistics on international migration, the United\nNations (UN DESA) defines an international migrant as any person who changes their\ncountry of usual residence (excluding short-term movement for purposes of recreation,\nholiday, visits to friends and relatives, business, medical treatment or religious\npilgrimage)…[However, there is no universally accepted definition of the term migrant,\nand the term is not defined by international law.]’\n\n\n‘...international assessments and surveys that aim to produce internationally comparable\ndatasets. To ensure international comparability, large-scale international surveys are\nhighly standardised for all phases of the study, ranging from framework and instrument\ndevelopment, translation and verification procedures, test design, sample design,\nfield operations, scaling methodology, data processing and management to quality\n[assurance’ (Cresswell, Schwantner, and Waters, 2015, p. 38). For learning assessments, it](https://www.oecd-ilibrary.org/education/a-review-of-international-large-scale-assessments_9789264248373-en)\nis critical that they have common ‘analytical framework for cross-national comparisons\nof subject-specific", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000382:12:0:0", "start": 350, "end": 372, "surface": "refugee education data", "probe_tag": "drop", "probe_score": 0.037, "luna_label": 0, "luna_reason": "Generic data phrase in a title, with no attributed finding or demonstrated use."}]}, {"key": "rafael2-182", "text": " be assured.\nSection II, clearly illustrates that there is insufficient information on IDP and HIV\nprogrammes to provide a clear picture of their needs and the gaps. A recent report\nin Northern Uganda reports that basic HIV services are lacking for IDPs. A\ncomprehensive HIV/AIDS needs assessment, combined with assessments from\nother sectors in a multi-sectoral fashion, is needed in all 8 IDP priority countries.\n\n**6.** **Prevention**\nThe same points as for recommendation 5.\n\n**7.** **Support, Care and Treatment**\nThe same points as for recommendation 5. As antiretroviral therapy (ART)\nbecomes available to IDP surrounding host communities, we must ensure that\nIDPs also have access.\n\n**8.** **Assessment, Surveillance, Monitoring and Evaluation**\nSections II and III clearly show a lack of data on HIV and IDP situations. As\nmentioned in recommendation 5, a comprehensive multi-sectoral assessment\nshould occur in all 8 IDP priority countries. Baseline data must be collected to\nallow for monitoring and evaluation of HIV interventions over time. In countries\nundertaking HIV sentinel surveillance or population-based HIV biological and/or\nbehavioural surveys, sample size should provide sufficient power to disaggregate\nbetween IDPs and non-displaced populations, as well as gender and age.\n\n\n8", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:reliefweb:001616:7:1:0", "start": 951, "end": 964, "surface": "Baseline data", "probe_tag": "drop", "probe_score": 0.0037, "luna_label": 0, "luna_reason": null}, {"key": "sample:reliefweb:001616:7:1:1", "start": 1108, "end": 1166, "surface": "population-based HIV biological and/or\nbehavioural surveys", "probe_tag": "confusion", "probe_score": 0.1936, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-183", "text": " plusieurs facteurs aggravants, notamment, l’utilisation des espaces agricoles pour accueillir les réfugiés, la limitation de\nmise en œuvre des moyens d’existence, la vulnérabilité accrue des femmes et des enfants filles et garçons, la mendicité\ninfantile, la délinquance et les actes de banditisme, les mécanismes d’adaptation néfastes, les tensions et les conflits\nintercommunautaire etc.\n\n\n**<u>IMPACT SUR LES PROGRAMMES HUMANITAIRES REGULIER</u>**\n\n\nDepuis le début de l’urgence, il a été observé un chamboulement au niveau des programmes réguliers. Pour répondre à\nl’urgence et sauver des vies, la quasi-totalité des partenaires par solidarité déploient régulièrement le peu de ressources\nlogistiques, humaines et financières et les assistances pour faire face à cet afflux massif sans précédent à l’Est Tchad. Les trois\nprovinces de l’Est, Ouaddaï, Wadi Fira et le Sila faisaient déjà face à plusieurs défis opérationnels, avant cette nouvelle crise.\n\n\n**<u>IMPACT LIE A L’UTILISATION DE LA MODALITE DE TRANSFERT DES FONDS</u>**\n\n\nLe rapport de l’étude de PNUDiii cité ci-haut et complétée par l’analyse sur la fonctionnalité des marchés du PAMiv en juillet\n2023 et le monitoring des marchés réalisé par le HCRv en août 2023 déterminent les effets de conflits du", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:reliefweb:000417:3:3:0", "start": 1175, "end": 1197, "surface": "monitoring des marchés", "probe_tag": "drop", "probe_score": 0.0031, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-184", "text": "This integrated package is meant to link with the\nsocial-protection-related initiatives led by other\ngovernment ministries including the Ministry of\nFood and Agriculture, <sup>13</sup> the Agricultural Sector\nInvestment Programme <sup>14</sup> and the Ministry of\nHealth. <sup>15</sup> The national health financing system\ncovers around 12 million people. <sup>16</sup>\n\n\n**Cameroon:** The National Social Protection Policy\n(2017) outlines four areas of support:\n\n- Social transfers whose common thread is the\nstrengthening of non-contributory systems.\nThese aim to strengthen the human capital\nof vulnerable populations by improving their\naccess to basic social services and improving\nthe satisfaction of their basic needs. Their\npriority targets are orphans and vulnerable\nchildren, women in difficult circumstances,\nthe elderly, chronically poor households, small\nfarmers, victims of accidents and disasters,\npeople with serious medical conditions, the\ninternally displaced and refugees.\n\n- Social security, with the objective to\nguarantee social and health coverage to all\nsegments of the population, especially the\nmost vulnerable.\n\n- Social action services through the protection\nand promotion of groups with specific and\ncyclical vulnerabilities. They aim to improve\nthe access of these groups to social action\nservices through the fight against exclusion\nand the implementation of support and\nsupervision initiatives.\n\n- Promotion of the economic integration\nof vulnerable people. As a major lever for\npoverty reduction, inclusion and social justice,\nemployment has, since 2010, been placed at\nthe heart of public authorities’ development\nstrategy. This axis is about improving the\naccess of vulnerable populations to economic\nactivities.\n\n\nThe government is still planning to introduce a\nunified social registry that would include all the\nabove programmes.\n\n\n**Republic of Congo:** The National Policy for Social\nAction (2014) sets the frame of the national\nsocial protection system, comprising two pillars:\nthe first, to harmonise historically fragmented\ncash and in-kind social", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001334:6:0:0", "start": 1797, "end": 1820, "surface": "unified social registry", "probe_tag": "drop", "probe_score": 0.0406, "luna_label": 0, "luna_reason": "Registry introduction is planned, so its data do not yet exist for use."}]}, {"key": "rafael2-185", "text": ">community consultation,<br>indigenous land<br>management practices,<br>grievance settlement<br>mechanisms|Woreda<br>officials,<br>experts, kebele<br>officials and<br>development<br>agents|Mobilization strategies; capacity<br>constraints, formal and informal<br>institutions, capacity of local<br>institutions, indigenous land<br>management knowledge, self-help<br>and mutual support groups,<br>vulnerable groups in the area,<br>implementation and monitoring,<br>grievance handling mechanism, etc.|\n\n\n\nAmong the secondary data, the Ethiopian government laws and regulations related to land\nexpropriation and compensation, equity and inclusion, World Bank social safeguard policies, project\nappraisal documents, SLMP-II social assessment report (SA) and RPF, periodic reports as well as\nother World Bank flagship programs' safeguard instruments were the major ones. Consultative\nWorkshop was conducted from January 11-21, 2018 with regional environment and social safeguard\nspecialists and representative from regional Environment, Forest and climate change Bureaus.\n\n\n13", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:fcv_pads_east_africa:010201:17:1:0", "start": 711, "end": 743, "surface": "SLMP-II social assessment report", "probe_tag": "confusion", "probe_score": 0.4619, "luna_label": 1, "luna_reason": "Named existing assessment report cited as a major secondary data source."}]}, {"key": "rafael2-186", "text": "The trade data are reported in current US dollars, and are deflated by the US CPI. We recognize\n\n\nthat ideally we would use bilateral price indices to deflate trade between different country pairs\n\n\nbut such bilateral deflators are not available. However, the presence of the very general fixed\n\n\neffects has the consequence also of implicitly deflating the trade data. The data are implicitly\n\n\ndeflated by prices that vary by importer, product and time; by importer, product and exporter;\n\n\nand by exporter, product and time. They are, however, not deflated by prices that vary along all\n\n\nfour dimensions (importer, exporter, product, and time).\n\n\nExchange rate data are from the IMF’s International Financial Statistics (IFS) database. In the\n\n\ntheoretical framework, the key price that determines the transmission mechanism of exchange\n\n\nrate changes is the price in the importing market charged by Chinese exporters. Equation (6)\n\n\nsuggests that this price is determined by the domestic price of the good in China, the China\n\nimporter bilateral exchange rate and the passthrough. Since we parameterize the passthrough, the\n\n\nrelevant changes to focus on are those stemming from changes in domestic prices in China and\n\n\nthe exchange rate. Hence, our bilateral exchange rate is deflated by China’s CPI.\n\n\nBefore we present the econometric results, it is worth looking at some basic data. Figure 2 plots\n\n\nChina’s average index of competition (where the average is over all exporters and products). The\n\n\nindex is measured in two ways consistent with the discussion in the analytical section. Both the\n\n\nVBI and the CBI rise over time, consistent with China becoming a bigger and more diverse\n\n\nexporter. The CBI shows in particular that by 2008, on average, China occupies nearly all the\n\n\nproduct space of other developing country exporters. Figures 3a and 3b plot the same indices but\n\n\ndisaggregated by region. These charts show that China’s overlap with all regions has risen\n\n\nsteadily over time, with the level of the overlap", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:005164:15:0:1", "start": 4, "end": 14, "surface": "trade data", "probe_tag": "confusion", "probe_score": 0.6638, "luna_label": 1, "luna_reason": "Existing trade data are used as an analytical input and deflated by CPI."}]}, {"key": "rafael2-187", "text": "**Abstract**\n\n\nThis study investigates Congolese refugees’ economic activities in Durban in order to\n\n\nunderstand why some refugees adapt and integrate in the local economy whereas\n\n\nothers fail and migrate to refugee camps outside South Africa. I use the concept of\n\n\nsocial exclusion to understand refugee action, and highlight the importance of social\n\n\nnetworks as a form social capital among refugees.\n\n\nQuantitative data revealed that Congolese refugees are skilled and heterogeneous\n\n\nfrom various viewpoints. Their survival is based on a wide range of economic\n\n\nactivities in both the informal and the formal economy. Income is also generated via\n\n\nthe transfer of monies between Durban and other South African Cities, and between\n\n\nSouth Africa and other countries including the DRC.\n\n\nQualitative data further revealed that social networks are key to their livelihood\n\n\nstrategies. These social networks may be positive or negative. Social exclusion,\n\n\nexploitation and xenophobia are the main problems that Congolese refugees face on a\n\n\ndaily basis. Xenophobia is a result of perceived or real competition over scarce\n\n\nresources. Mistrust is an Achilles heel of this community for numerous reasons and\n\n\nrepresents a permanent threat and source of community fragmentation. The\n\n\nproliferation of small churches, ethnical political parties and self-help projects is\n\n\nsymptomatic of this fragmentation.\n\n\n3", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001619:2:0:0", "start": 409, "end": 426, "surface": "Quantitative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Quantitative data supports a concrete finding about refugees’ skills and heterogeneity."}, {"key": "reliefweb:001619:2:0:1", "start": 796, "end": 812, "surface": "Qualitative data", "probe_tag": "confusion", "probe_score": 0.481, "luna_label": 1, "luna_reason": "Qualitative data directly supports the finding about refugees’ livelihood strategies."}]}, {"key": "rafael2-188", "text": " due to irregularities and lack of transparency in local tax administration, obstruction of\ninvestments by some unscrupulous politicians, and a general perception that LGs do not offer useful support,\nwithin their mandates, for local private sector development <sup>11</sup> . In addition, little support is provided to local\nfirms by municipal LGs, even though they have the mandate to provide support to micro-enterprises and\nother firms through the Commercial Office. There is also an absence of meaningful public private dialogue,\nparticularly in terms of consulting the private sector in the development of local development plans. The\nstudy made three main recommendations to LGs in Uganda: (i) to make infrastructure investments that are\nbetter prioritized according to local economic potentials – building on the major recent investments in roads\nand connectivity to transition to other strategic investments in tourism site development, market\n\n4 Arch Design Ltd, 2012 – Municipal Assets Inventory and Conditions Assessment Final Report\n5 From UGX 37 million in 1993/4 to UGX1.6 trillion in 2011/12.\n6 Local Government Finance Commission (2012) **.** Review of Local Government Financing: Financing Management and\nAccountability for Decentralized Service Delivery.\n7 FDA section 7.2\n8 DDEG has replaced the Local Development Grant (LDG) as part of the broader GoU IGFT reform\n9 For example, unit costs of paving 1 km of urban road ranges between US$800,000 to US$1 million and for a primary drainage\n(with box culverts at road crossings and armoflex linings) about US$500,000 to US$1.1 million per km.\n10 USAID (2015) Ugandan Decentralization Policy and Issues Arising in the Health and Education Sectors: A Political Economy\nStudy. October 2015.\n11 World Bank 2016. _Uganda - Repositioning local governments for economic growth_ . Washington, DC: World Bank.\n\n\n2", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000168:9:2:0", "start": 980, "end": 1032, "surface": "Municipal Assets Inventory and Conditions Assessment", "probe_tag": "confusion", "probe_score": 0.6535, "luna_label": 1, "luna_reason": "Named assessment report cited as an existing data resource."}]}, {"key": "rafael2-189", "text": "**The World Bank**\nSouth Sudan Enhancing Community Resilience and Local Governance Project (P169949)\n\n\n24. Combined with quick wins, the ECRP will target both conflict-affected and more stable counties\nas shown below.\n\n\n**Figure 2. Vulnerable Counties with Quick Wins**\n\n\n25. **Subproject budget allocation.** The subproject budget allocation for the new counties will be\ncalculated on a per capita basis using humanitarian agencies’ latest population data. There will be two\nrounds of allocations per county to maximize communities’ learning by doing. Counties will need to meet\na set of basic performance indicators to be eligible for the second allocation. These include (a)\nparticipation rate of women, youths, IDPs, and returnees in the subproject planning and implementation;\n(b) satisfactory collaboration during community mobilization; (c) timely implementation of the\nsubprojects; and (d) continued accessibility and permissive security. Allocations to the _payam_ level will\nfollow the Government’s fiscal transfer formula of 60 percent equal allocation and 40 percent based on\npopulation (utilizing IOM’s DTM projections) whereas all _bomas_ within target _payams_ will receive equal\namount of funding as no population data are available. <sup>47</sup> [^47: Population figures for urban areas would be calculated based on a headcount or by complementing 2008 census with other\ndata sources (for example, DTM.)]\n\n\n26. **Use of community labor.** The project will encourage contractors to utilize local labor in the\ninfrastructure construction or rehabilitation to the extent possible. Emphasis will be placed on the\ninclusion of various social groups facing marginalization or barriers to participation (for example, women,\nyouth, returnees, ethnic minority groups, and people with disabilities) and ensuring their access to daily\nwage labor opportunities. It will be especially important to include women in the design and construction\nof WASH facilities, for instance, to ensure these facilities are rehabilitated in ways that promote security\nand effective management on completion. The project will harmonize", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000058:11:0:0", "start": 441, "end": 456, "surface": "population data", "probe_tag": "keep", "probe_score": 0.9692, "luna_label": 1, "luna_reason": null}, {"key": "sample:jdc_operational:000058:11:0:1", "start": 1116, "end": 1131, "surface": "DTM projections", "probe_tag": "keep", "probe_score": 0.9413, "luna_label": 1, "luna_reason": null}, {"key": "sample:jdc_operational:000058:11:0:2", "start": 1219, "end": 1234, "surface": "population data", "probe_tag": "confusion", "probe_score": 0.7169, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-190", "text": "* Popular Benchmarks\n**children.**\n\n\n**Project** **Development** **Outcome** **/** **Impact** **Project reports:** **(from** **Objective** **to Goal)**\n**Objective:** **Indicators:**\n**Assist** **war affected** - Improved social capital and - Initial Social Assessment - Communities in the NSAP\n**communities** **to restore** organizational development; (to establish indicators and target areas are assisted to\n**infrastructure,** **services** **and** - Increased access to and use methodologies for social ensure a reduced risk of\n**build** **local** **capacity for** of social and economic capital and organizational conflict\n**collective** **action.** Priority infrastructure and services development)\nwill be given to areas not - Proportion of NSAP - Annual social assessments; - NACSA complements and\npreviously serviced by investments targeted to newly - NaCSA M&E data; extends the work of other\ngovernment, newly accessible accessible areas, & areas - M&E data of relevant line agencies and rninistries\nand the most vulnerable previously not served, and mninistries; in support of the PRSP's\npopulation groups within those vulnerable people within these - Beneficiary Assessment poverty reduction and\nareas. areas; (BAs) biannually; decentralization objectives\n\n                  - Proportion of sub-projects                   - Participatory evaluation\nthat reflect priorities of reports for a random sample of                   - NACSA is fully integrated\ntargeted communities and assisted conmmunities; into national planning and\nbeneficiaries; and                  - Technical audits resource allocation\n\n                  - Proportion of sub-projects frameworks (such as the\noperative 24emonths after National Recovery Strategy,\ncorpletion. the PRSP, and", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:020681:29:1:0", "start": 862, "end": 876, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.2118, "luna_label": 0, "luna_reason": "Project monitoring data is listed without an attributed finding or substantive analysis."}]}, {"key": "rafael2-191", "text": ">capita per day.<br> <br>**Extreme Poverty Line:**<br>Official poverty lines follow the formula𝐶𝐵𝐴𝑝𝑐∗𝑁. In this sense, for not being consider<br>poor a household in Montevideo in 2012 needed 69 pesos per capita per day or 2.6 USD<br>PPP (2005) per-capita per day. Extreme poverty lines also vary between regions but in a<br>lower extend: a Household in Interior Urbano needed only 2.44 USD PPP (2005) per-<br>capita per day for not to be considered extreme poor while a household in rural areas<br>needed 2.1 USD PPP (2005) per-capita per day.<br> <br>For a detailed explanation of the poverty methodology please refer to LÍNEAS DE<br>POBREZA E INDIGENCIA 2006: Metodologia y Resultados. (INE 2006)<br>|\n|Welfare<br>measure<br>|<br>Measured by per-capita family income, which considers: labor income, transfers, pensions,<br>imputed rent and capital income. Since official income cannot be completely constructed<br>from this source, and adjusted vector was constructed. It replicated official poverty<br>numbers.<br> <br>When an indicator required used data previous to 2006 in order to guarantee sample<br>comparability only Montevideo and Interior urbano were used.<br>|\n|Conversion<br>to real values<br>|<br>Regional prices are deflated to Montevideo using the ratio of average poverty lines. The<br>resulting values are then converted to 2005 prices using CPI values<br>|\n|<br>Data Source|<br>-ECH (2003-2011) from INE.<br> <br>-", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:005967:31:1:0", "start": 1399, "end": 1402, "surface": "ECH", "probe_tag": "confusion", "probe_score": 0.0588, "luna_label": 1, "luna_reason": "Named household survey source for poverty and welfare analysis"}]}, {"key": "rafael2-192", "text": " 4.|**Table 22. Top-10 nationalities of asylum applicants by country of asylum, 2007**<br>Covering 43 countries which provided monthly asylum data to UNHCR. An asterisk (*) indicates that the value is between 1 and 4.|**Table 22. Top-10 nationalities of asylum applicants by country of asylum, 2007**<br>Covering 43 countries which provided monthly asylum data to UNHCR. An asterisk (*) indicates that the value is between 1 and 4.|**Table 22. Top-10 nationalities of asylum applicants by country of asylum, 2007**<br>Covering 43 countries which provided monthly asylum data to UNHCR. An asterisk (*) indicates that the value is between 1 and 4.|**Table 22. Top-10 nationalities of asylum applicants by country of asylum, 2007**<br>Covering 43 countries which provided monthly asylum data to UNHCR. An asterisk (*) indicates that the value is between 1 and 4.|**Table 22. Top-10 nationalities of asylum applicants by country of asylum, 2007**<br>Covering 43 countries which provided monthly asylum data to UNHCR. An asterisk (*) indicates that the value is between 1 and 4.|\n|Albania|Albania|Australia|Australia|Austria|Austria|Belgium|Belgium|Bosnia and H.|Bosnia and H.|\n|Serbia|10<br>|China|1,213<br>|Russian Fed.|2,673<br>|Russian Fed.|1,436<br>|Serbia|555<br>|\n|Nepal|9<br>|Sri Lanka|445<br>|Serbia|1,762<br>|Serbia**|1,223<br>|China|*|\n|Czech Rep.|*|", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001058:34:1:0", "start": 127, "end": 146, "surface": "monthly asylum data", "probe_tag": "confusion", "probe_score": 0.2616, "luna_label": 0, "luna_reason": "Data phrase appears in a table caption describing the displayed asylum table."}]}, {"key": "rafael2-193", "text": "a. .. ..\nn.a. .. .. MIGA\n\nGross exposure                                       -                                       Stock market capitalization (% of GDP) .. .. New guarantees - <mark>Bank capital to asset ratio (%)</mark> <mark>..</mark> <mark>..</mark>\n\n\n\nTime required to start a business (days) - 37 IFC _(fiscal year)_\nCost to start a business (% of GNI per capita) - 222.0 Total disbursed and outstanding portfolio - Time required to register property (days) - 49 of which IFC own account - Disbursements for IFC own account                                        -                                        Ranked as a major constraint to business Portfolio sales, prepayments and\n(% of managers surveyed who agreed) repayments for IFC own account  -  n.a. .. ..\nn.a. .. .. MIGA\n\n\n\nNote: Figures in italics are for years other than those specified. 2006 data are preliminary. 9/28/07\n.. indicates data are not available. – indicates observation is not applicable.\n\n\nDevelopment Economics, Development Data Group (DECDG).\n\n\n70", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000163:69:2:0", "start": 856, "end": 865, "surface": "2006 data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Table note marking preliminary data, not a substantive data-use mention."}]}, {"key": "rafael2-194", "text": " country’s long-term\nstability and social peace. General elections are scheduled for May 15, 2022. Talks with the International Monetary\nFund (IMF) have been relaunched with a mission in January, February, and March 2022, aiming to develop a\nstabilization program and secure foreign currency financing. Critical elements of a stabilization program comprise:\na national budget for 2022 and a medium-term fiscal framework, a new monetary policy (starting from the process\nto unify the multiple exchange rates and an audit of the BdL), recognition and distribution of the banking sector\nlosses (LEM Spring 2021, LEM Fall 2021), growth enhancing reforms, and comprehensive, well-targeted social\nprotection programs.\n\n\n**B. Sectoral and Institutional Context**\n\n\n11. **The food security situation continues to deteriorate in Lebanon and has reached alarming levels for**\n**some population groups, particularly refugees.** It is estimated that 34 percent of Lebanese people and 50 percent\nof refugees were food insecure in 2021 (World Food Programme - WFP, 2022). More than half of adults—53\npercent—reported that they lacked money for food at some time in the previous 12 months (Loschky, 2021).\nConsumer prices in Lebanon have continued to climb significantly, and the overall yoy consumer price index rose\nby more than 240 percent in January 2022, the largest spike since the onset of Lebanon’s current economic crisis.\nPrices of food and non-alcoholic beverages jumped by 483 percent in January 2022. These represent the biggest\nupticks since the onset of the crises and are an outlier globally.\n\n12. **Bread is an essential staple in the poorest people’s diet.** Bread is the single largest item in the survival\nand minimum expenditure basket (SMEB) in Lebanon, as calculated by the WFP in 2020. Bread needs are\nestimated at 234 grams/person/day, or 632 kcal out of a needed 2,", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000019:13:1:0", "start": 1273, "end": 1297, "surface": "yoy consumer price index", "probe_tag": "confusion", "probe_score": 0.6216, "luna_label": 0, "luna_reason": "Bare index figure lacks a named or generic source noun."}]}, {"key": "rafael2-195", "text": "**Media** . The Media has a significant role in informing and creating awareness and has contribution in\n\nshaping public views and attitudes. Building partnerships with the Media is key to keep the public regularly\ninformed about the outbreak and the measures put in place for mitigating risks for the population, their\nbenefits and to motivate families and communities to seek urgently care in case people develop symptoms.\nTherefore, as the parent project based on a database contacts of print and electronic media journalists, the\nMedia will be kept informed through press release and press conference on the situation and any plans,\nprograms and decisions. An updated information package with documents including FAQs around COVID19 and vaccination will be also shared with the media professionals.\n\n\nTV and radio spots will continue to be used to disseminate specific information about preventive measures\n\nhealth-seeking behaviours, mental health and psychosocial support, and on information on the vaccination.\nMoreover, radio and TV interactive programs with the active engagement of community members and\nrepresentative of institutions will position the community as active participant to the response and facilitate\nopen discussions with health experts to respond to the public specific needs for information.\n\n\nFor reducing misconceptions, stigma, and rumors on vaccine, the interventions of health experts on mass\n\nmedia will be multiplied at different levels, based on the key messages validated at federal and regional\nEOC to maintain consistency. The format of the messaging, even though delivered by health experts, will\nbe adapted and simplified so that it can be easily understood by the general public.\n\n\n**Using trusted sources.** The rapid assessment conducted by GoE analyzed preferences regarding\n\ncommunication channels. Ministry of Health, PM Office, Regional president or Mayor, health care\nproviders and to some degree, TV and radio stations are the most trusted sources. Social media contents\noriginating from these Institutions or portraying these Institutions’ representatives might have also a high\nimpact. In order to respond as much as possible to the questions and concerns from the community, while\nkeeping broadcasting COVID-19 key", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:006649:27:0:0", "start": 469, "end": 528, "surface": "database contacts of print and electronic media journalists", "probe_tag": "confusion", "probe_score": 0.6696, "luna_label": 1, "luna_reason": "Existing journalist-contact database underpins parent project media outreach."}]}, {"key": "rafael2-196", "text": " adoption of antidumping (and other trade remedy) law, provides a\n\n\ndiscussion of how the law has been used, and in some cases, how it has even been reformed during this\n\n\ntime period. A common theme from many of the country-level case studies is that antidumping helped\n\n\nprovide an escape valve that domestic policymakers used to manage an overall program of trade\n\n\nliberalization during this time period. The theory is that antidumping may positively affect the\n\n\nsustainability of a country’s overall liberalization commitment and/or increase a country’s ex ante\n\n\nwillingness to take on more extensive liberalization commitments than it would take on without such an\n\noption. <sup>31</sup>\n\n\nmarginal effects estimates, and the means and standard deviations of the underlying data are reported in the upper\nhalf of table 4.\n30 See specifically the Finger and Nogués (2005) chapters by Nogués and Baracat (Argentina); Kume and Piani\n(Brazil); Reina and Zuluaga (Colombia); Reyes de la Torre and González (Mexico); and Webb, Camminati, and\nThorne (Peru).\n\n31 Our evidence from table 5b is broadly consistent with this theory. If trade liberalization leads to additional import\ncompetition, our results are that industries with a more rapid increase in its average yearly percent change in import\npenetration receive more protection from imports via antidumping over the 1995-2003 period. For a broader\ndiscussion of the theory behind this issue, see Hoekman and Kostecki (2001, chapter 9).\n\n\n20", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:003227:21:1:0", "start": 771, "end": 786, "surface": "underlying data", "probe_tag": "confusion", "probe_score": 0.1522, "luna_label": 1, "luna_reason": "Underlying data support reported marginal effects, means, and standard deviations."}]}, {"key": "rafael2-197", "text": " a\ngreater blast radius. During this quarter, 30 people were killed and\nmaimed, including 10 children, yet only two of those people were\ninjured by landmines and the remainder by UXO. An analysis of\naccident information shows a link to young boys engaged in the\nscrap-metal trade, prompting specific messaging to be incorporated\ninto risk education sessions.\n\n2: In 2015, risk education teams were embedded in all UNMAS\nclearance teams and were able to use their information sessions to\nengage communities and encourage the reporting of explosive\nhazards. With clearance teams in close proximity, communities can see\nthat information they provide leads to the immediate reduction of\nhazards – recognition that has improved community reporting.\n\n3: Increased population movement results in new hazards being found\nand reported. It is anticipated that this upward trend will continue into\nthe next quarter, during which UNMAS is planning to keep as many of\nits teams operational as is feasible during the wet season.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSource: Information Management System of Mine Action (IMSMA).\n\n- MRE: Mine Risk Education, UXO: Unexploded Ordnance.\n\n\n\n_6_", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000827:6:1:0", "start": 199, "end": 219, "surface": "accident information", "probe_tag": "confusion", "probe_score": 0.8853, "luna_label": 1, "luna_reason": "Accident information is analyzed to identify links informing risk-education messaging."}]}, {"key": "rafael2-198", "text": " The GoB\nalso does not have a digital registry of health care providers with basic data to manage human resources,\nsuch as job titles or professional profiles including education, work experience, and in‐service trainings.\nSimilarly, the supply chain management process is based on paper systems, except for two vertical\nprograms (immunization and family planning), often leading to delays in the delivery of drugs, extended\nperiods of medicine stockouts at public HFs, and high incidence of expired drugs.\n\n\n17. **In Balochistan, children suffer from suboptimal learning outcomes and large disparities by**\n**gender.** Despite significant efforts by the Secondary Education Department (SED), Balochistan performs\npoorly compared to the national average across all education outcomes. In FY16/17, 64 percent of boys\nand 78 percent of girls (between the ages of 5 and 16) were not enrolled in primary and secondary schools\nin Balochistan, compared to 40 percent of boys and 49 percent of girls at the national level. <sup>22</sup> [^22: In Pakistan, primary schools cover grades 1 through 5 and secondary schools cover grades 6 to 10 with middle schools for grades 6 to 8 and\nhigh schools for grades 9 and 10. Higher‐secondary schools cover grades 11 and 12.] The overall\nnet enrollment and effective transition rates, from primary to middle school and middle to high school,\nwere low compared to national rates, especially among girls. When it comes to student learning metrics,\nchildren in Balochistan also perform poorly in comparison to the same age groups in rural Pakistan\ncommunities. For example, approximately 60 percent of children in grade 5 could not perform a two‐digit\ndivision problem. The 2018 ASER report also highlighted a wide gender gap in student learning, with 31\npercent of boys and 20 percent of girls (ages 5 to 16 years) being able to read second‐grade level sentences\n\n\n19 Expanded Program on Immunization (EPI), Tuberculosis, Malaria and Vector Borne Diseases Control", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000085:14:1:0", "start": 1704, "end": 1720, "surface": "2018 ASER report", "probe_tag": "confusion", "probe_score": 0.7947, "luna_label": 1, "luna_reason": "Report provides learning-gap evidence with gender-specific reading results."}]}, {"key": "rafael2-199", "text": " rural areas and\nwork in the agricultural sector, which accounts for 70 percent <sup>6</sup> [^6: World Bank Data.] of total employment (75 percent of all women in\nthe labor force) and around a quarter of the country’s gross domestic product (GDP). <sup>7</sup> [^7: World Bank. 2020. _Uganda Economic Update_, _Strengthening Social Protection to Reduce Vulnerability and Promote Inclusive Growth_,\n2020. World Bank.] This renders a significant\nportion of the workforce vulnerable to climate change and weather shocks and demonstrates the need for\neconomic diversification and alternative sources of employment in higher productivity industries.\n\n2. **The Coronavirus Disease 2019 (COVID-19) pandemic is putting Uganda’s growth trajectory at risk,**\n**exacerbating structural constraints and increasing pressure on the poor and vulnerable, including people living**\n**in Refugee Hosting Districts (RHDs).** Uganda’s real GDP grew at 2.9 percent in FY20, less than half the 6.8 percent\nrecorded in FY19, <sup>8</sup> [^8: World Bank. 2020. _Uganda Economic Update, 1_ 6 _th Edition, December 2020:_ Investing in Uganda’s Youth. December 2020.] due in large part to the effects of the COVID-19 pandemic. As of February 2021, there have\nbeen almost 40,000 cases of COVID-19 in the country. The expected revenue loss from COVID-19 measures is\nestimated at 0.18 percent of GDP in FY20 while tax revenues, more broadly, are expected to fall to 11.6 percent\nof GDP for FY20—1 percentage point less than FY19. Economic activity stalled during the latter part of the fiscal\nyear due to a domestic lockdown that lasted over four months, border closures, and the spillover effects of\ndisruption in global demand and supply chains. This resulted in a sharp contraction in public investment and\ndeceleration in private consumption. The", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000023:13:1:0", "start": 98, "end": 113, "surface": "World Bank Data", "probe_tag": "confusion", "probe_score": 0.8945, "luna_label": 1, "luna_reason": "World Bank Data sources the attributed 70 percent employment statistic."}]}, {"key": "rafael2-200", "text": "Some selected indicators from the project were adopted with minor modifications and\nincluded with the HMIS.\n\n\nApproaches have been changed as strategies, etc. were revised – for example the use of\nVCHW was stopped and GMP is being done by HEWs; new objectives/ indicators were\nadded when deemed necessary – e.g. Training of regional and federal level mid-level\nnutrition managers.\n\n\nThe implementation arrangements of both components were reasonable. Community\nbased nutrition and micronutrient interventions were delivered through the existing\nHealth system. At community level, the HEP platform was used, as nutrition was one of\nthe 16 packages of HEWs. This has strengthened the health system as well as the delivery\nof nutrition services at community level.\n\n\nInstitutional strengthening – HR deployment at FMOH and RHBs to support NNP has\nfacilitated the implementation of the NP.\n\n\nCapacity Building – there have been many in-service and pre-service trainings and\nresearch conducted through the NP. These included CBN training for health workers and\nHEWs; training of VCHW (at the beginning); training on M & E and financial\nmanagement; and Master’s level training for MOH and RHB staff.\n\nAll these have been well incorporated in existing systems and therefore have contributed\nto the sustainability of the Nutrition program in Ethiopia.\n\n**Achievements of Project Development Objectives**\n\nCurrently, CBN activities are implemented in 372 woredas. Mothers/caregivers with\nchildren under two years of age are monthly weighed and counseled based on the\nchildren nutritional status by HEWs. In March 2013, a total of 919,409 children were\nweighed with an average participation rate of 44.1% and an underweight prevalence of\n7%. In March 2014, there was an increase in the number of children weighed (1,144,348)\nas well as in the participation rate (49.0%), with a decrease in underweight prevalence\n(5%). The program has shown", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:019152:67:0:0", "start": 102, "end": 106, "surface": "HMIS", "probe_tag": "confusion", "probe_score": 0.19, "luna_label": 0, "luna_reason": "HMIS is merely named as an incorporated system, without shown data use."}]}, {"key": "rafael2-201", "text": "findings. Another notable observation is that financial constraint, _Finance obstacle_, is only\n\nsignificantly detrimental to the survival and revenue growth of private firms, whereas it plays\n\nno significant role in publicly listed firms’ survival and growth during the pandemic. This is\n\nnot surprising: listed firms have better access to financing via equity markets.\n\n\n**_[Table 5 here]_**\n\n\n**4.2. The effects of firm/country characteristics during normal versus pandemic times**\n\n\nAn advantage of our study over the extant literature on corporate immunity is that we are able\n\nto combine the pre-pandemic WBES with the WBES-COVID surveys via a firm’s unique\n\nidentifier. The combined dataset contains firm-level information for both the pre-pandemic\n\nperiod (“normal times”) and the pandemic period (“pandemic times”). This allows us to\n\nexamine if, what, and how firm/country characteristics affect firm growth differently in normal\n\nversus pandemic times. Since by definition, the pandemic follow-up survey is conditional on\n\nfirm existence in the last survey—that is, _Closure_ is always zero for WBES—we do not analyze\n\nthe closure outcome when comparing behavior in normal times and during the pandemic.\n\nInstead, we examine firm sales and employment growth in these two periods. The regression\n\nresults are reported in Table 6, with columns (1) and (3) for pandemic times, and (2) and (4)\n\nfor normal times.\n\nMost of the firm characteristics affect firm growth _differently_ in pandemic versus in\n\nnormal times. In particular, large firms exhibit a low employment growth during the pandemic\n\n(column (3)) but a high employment growth in normal times (column (4). These results provide\n\nadditional support for the notion that large firms may have taken advantage of the crisis to shed\n\nthemselves of excessive workers, which may be politically untenable during normal times.", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:001943:16:0:0", "start": 598, "end": 615, "surface": "pre-pandemic WBES", "probe_tag": "confusion", "probe_score": 0.8945, "luna_label": 1, "luna_reason": "Pre-pandemic WBES is combined with COVID surveys for comparative regression analysis."}]}, {"key": "rafael2-202", "text": "-sufficient training of staff to properly interact with persons with disabilities. In\naddition to that there is the difficulty of accessing residence permits and business licenses.\nHowever, it is true that this latter issue also affects persons without disabilities but for those\nwith disabilities, access is almost impossible.\n\n\nThe livelihoods sector is also impacted by the identification issue. Indeed, by missing\nadequate information on persons with disabilities beyond the numbers and disability types\noverall in the operation it is hazardous to attribute adequate budget and consider supportive\nsolution. In this regard, local staff is asking to capture data on skills and capabilities which\nwould be helpful in enabling livelihood partners tailor design of interventions to their most\npriority needs.", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000175:36:1:0", "start": 661, "end": 692, "surface": "data on skills and capabilities", "probe_tag": "confusion", "probe_score": 0.14, "luna_label": 0, "luna_reason": "Staff request future capture of skills and capabilities data."}]}, {"key": "rafael2-203", "text": "**The World Bank**\nDjibouti Integrated Slum Upgrading Project (P162901)\n\n\nof financial position; (c) a statement of ongoing commitments; (d) an analysis of payments and withdrawals\nfrom the project’s account; (e) a statement of cash receipts and payments by category and component; (f)\nreconciliation statement for the balance of the project’s Designated Accounts; (g) statement of cash\npayments made using SOE basis; and (h) the yearly inventory of fixed assets acquired under the project.\n\n\n24. _Flow of funds_ : Payment will be authorized by three signatures: by the Director of ARULOS, the Director\nof the External Financing Department at the Ministry of Finance, and the Director of the Debt Department at\nthe Ministry of Budget. Funds will be transferred based on Withdrawal Applications submitted by ARULOS.\nThe funds will be channeled from the IDA through one segregated Designated Account (DA) in US dollars\nopened at a commercial bank in Djibouti acceptable for the IDA. Advances from the IDA account will be\ndisbursed to the Designated Accounts to be used for the project expenditures.\n\n\n_Additional Control Arrangements_\n\n25. The project will be financing works, goods, consultants’ services, non-consultants’ services,\ncommunity development sub-grants and operational costs.\n\n\n26. For the category of works and to ensure proper quality in execution, the scope of the external\nauditor’s terms of references will be expanded to include qualitative on-site checks of the works done in\ninfrastructure under Component 2 in the zone of Balbala. The auditor will provide a special purpose report\non the progress and quality of the works done. The special purpose audit report will be submitted with the\naudit report on the financial statements.\n\n\n27. For the community development sub-grants, the project will be providing small grants to community\nassociations. The following control arrangements will be applied under this category:\n\n\n   - ARULOS will prepare a specific small grants", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000015:57:0:0", "start": 430, "end": 462, "surface": "yearly inventory of fixed assets", "probe_tag": "confusion", "probe_score": 0.0536, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-204", "text": " other regional offices in the East, South, and Lake Chad areas, fully staffed\nand trained. These offices will have the authority to procure goods and services up to a\npredetermined threshold and to manage related financial transactions, as well as manage\nimplementation of safeguards, coordination, and monitoring activities. These institutional\narrangements will rely on strong involvement of local government authorities and deconcentrated\ntechnical services.\n\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n\n57. **As the first Government-led project in Chad to focus on refugees through a**\n**development lens**, **the project will provide important lessons to guide future investments in**\n**this emerging area** . For these reasons, the project will put in place innovative monitoring\narrangements that will measure progress on outputs and outcomes and on the evolution of the\npolicy and protection agenda for refugees.\n\n58. **The project will rely on its internal resources and on several partners to help monitor**\n**progress in different areas.** For Component 1, the project will adapt the existing MIS of the CFS\nto capture progress in rehabilitation and new construction of facilities and full operationalization\nof basic services. Because the CFS does not have expertise in the implementation of works, the\nproject will hire specialized technical consultants (for example civil engineers) to supervise\nactivities under Component 1. The project will also rely on feedback from local authorities and\ncommunities during the identification, implementation, and operationalization phases. In areas that\nare difficult to reach, the project will consider using geo-enabling and remote sensing technologies\n(enhanced M&E) to monitor implementation progress of ongoing investments and community use\n\n32 Marcel Ferland, Rapport d’évaluation Institutionnelle du PARCA (Projet d’Appuis aux Refugiés et aux Communautés\nd’Accueil), Mimeo, Avril 2018.\n\n\nPage 27", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000035:31:1:0", "start": 1118, "end": 1132, "surface": "MIS of the CFS", "probe_tag": "confusion", "probe_score": 0.1913, "luna_label": 0, "luna_reason": "Existing MIS will be adapted for future project monitoring, not used data."}]}, {"key": "rafael2-205", "text": " appropriate) to reach out to these groups under the AF Project will\ninclude:\n\n\n(a) Making AF Project-related documentation accessible for people with sensory disabilities, for\n\ninstance, through engaging a sign language interpreter at a consultation meeting, as appropriate.\n(b) In cases where stakeholder’s literacy levels are low such as Project-affected HUCs in Afar Region\n\nof Ethiopia, additional formats like location sketches, physical models, and video presentations\nmay be useful to communicate relevant Project information. EEP and other AF Project\nimplementing entities including MoF, MoWE, MoM, and IPPs will help the Project-affected\ncommunities with low literacy level to understand technical documents, for instance, through the\npublication of simplified summaries (integrated Project information document), and\nnontechnical background explanations, or access.\n(c) Identify leaders of vulnerable and marginalized groups to reach-out to these groups.\n(d) Through the existing associations, maintain a database of marginalized groups, e.g., Federation\n\nof Physically Disabled Persons in particular Project locations. The SA developed for PRIME-1 will\nalso support in availing database and local information on vulnerable groups and HUCs and their\nrepresentative organizations.\n(e) Engage community leaders, CSOs and NGOs working with vulnerable groups and HUCs.\n(f) Organize face-to-face focus group discussions with these populations.\n(g) Women focused groups (at site specific level): The Project will facilitate formation of a focus\n\ngroup for women, which will be led by a female facilitator, and will provide a platform to discuss\nany issues and concerns that the women may have regarding the AF Project development. This\nwill particularly ensure that female Project workers have the opportunities to participate in and\nbenefit from the Project. The Project teams will put maximum efforts to address the genuine\nconcerns of the women group.\n\n\n**17**\n\n\nOfficial Use Only", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:002454:20:1:0", "start": 1016, "end": 1047, "surface": "database of marginalized groups", "probe_tag": "confusion", "probe_score": 0.2137, "luna_label": 0, "luna_reason": "Planned database maintenance, with no demonstrated use of its data."}]}, {"key": "rafael2-206", "text": " description of the</mark>\n<mark>Roma according to the Household Budget Survey 2016. Journal of Community Positive Practices,</mark>\n<mark>[XIX(1), 18-42. https://doi.org/10.35782/JCPP.2019.1.03](https://doi.org/10.35782/JCPP.2019.1.03)</mark>\n\n\nChronic Poverty Research Center (2011). Tackling chronic poverty. Policy brief, 28. Chronic Poverty\nResearch Center\n\n\n<mark>Copeland, P. (2023). Poverty and social exclusion in the EU: third-order priorities, hybrid governance</mark>\n<mark>and the future potential of the field. Transfer: European Review of Labour and Research, 29(2), 219-</mark>\n<mark>233.</mark> <u><mark>[https://doi.org/10.1177/10242589231171091](https://doi.org/10.1177/10242589231171091)</mark></u>\n\n\n<mark>Copeland, P., & Daly, M. (2012). Varieties of poverty reduction: Inserting the poverty and social</mark>\n<mark>exclusion target into Europe 2020. Journal of European Social Policy, 22(3), 273-287.</mark>\n<u><mark>[https://doi.org/10.1177/0958928712440203](https://doi.org/10.1177/0958928712440203)</mark></u>\n\n\nCuesta, J., López-Noval, B., & Niño-Zarazúa, M. (2024). Social exclusion concepts", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:001536:41:2:0", "start": 55, "end": 83, "surface": "Household Budget Survey 2016", "probe_tag": "confusion", "probe_score": 0.7492, "luna_label": 0, "luna_reason": "Embedded in a bibliography citation rather than used as evidence or analysis."}]}, {"key": "rafael2-207", "text": " Vacuna de COVID-19**\n\n\n**<mark>Porcentaje de encuestados vacunados</mark>**\n\nDel total de personas\n\nencuestadas, el 75% (265\n\npersonas) indicó que contaba\n\ncon el esquema de\n\nvacunación de 2 dosis, 10%\n\ncon un esquema de\n\nvacunación de una sola dosis,\n\ny el 14% indicó no estar\n\nvacunado.\n\n\n87% de los encuestados\n\nseñalaron que todos los\n\nmiembros mayores de 16\n\naños del hogar, elegibles para\n\nla aplicación de la vacuna al\n\nmomento de la encuesta, se\n\nencontraban vacunados. 5%\n\n\n\nde los encuestados indicaron\n\n\n\n_Fuente: HFS3_\n\n\n\n_17 Esta muestra se basa en 358 hogares encuestados._\n\n\nUNHCR / Febrero 2022 40", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001181:38:1:0", "start": 526, "end": 538, "surface": "HFS3_\n\n\n\n_17", "probe_tag": "confusion", "probe_score": 0.845, "luna_label": 1, "luna_reason": "Source citation for survey vaccination findings based on 358 households."}]}, {"key": "rafael2-208", "text": "Description <br>|Total number of children immunized among the host community in Garissa and Turkana. <br>|\n|Frequency <br>|Every six months <br>|\n|Data source|KHIS|\n|Methodology for Data<br>Collection|Routine HMIS data collection|\n|<br>Responsibility for Data<br>Collection <br>|MoH <br>|\n|**Number of children immunized among refugees in Garissa and Turkana (Number)  ** <br> <br>|**Number of children immunized among refugees in Garissa and Turkana (Number)  ** <br> <br>|\n|Description <br>|Total number of children immunized among refugees in Garissa and Turkana. <br>|\n|Frequency <br>|Every six months <br>|\n|Data source|UNHCR reports|\n|<br>Methodology for Data<br>Collection <br>|<br>Routine UNHCR data collection|\n|Responsibility for Data<br>Collection|MoH|\n\n\n\nFeb 21, 2024 Page 34 of 43", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000000:39:2:0", "start": 159, "end": 163, "surface": "KHIS", "probe_tag": "confusion", "probe_score": 0.3864, "luna_label": 0, "luna_reason": "KHIS is listed as a data source without shown use of its data."}, {"key": "refugee_pads:000000:39:2:1", "start": 209, "end": 229, "surface": "HMIS data collection", "probe_tag": "confusion", "probe_score": 0.5363, "luna_label": 0, "luna_reason": "Describes routine collection methodology, not use of existing HMIS data."}]}, {"key": "rafael2-209", "text": "**The World Bank**\nRoads and Employment Project (P160223)\n\n\nthe project investment value. Given that the selection of road sections is not yet finalized, the analysis\nconsidered typical road sections per road category (primary, secondary, and tertiary). For each road\ncategory, unit costs and type of works (reconstruction, overlay, drainage, and so on) were provided, as\nwell as the average condition index (International Roughness Index [IRI]), and traffic volume and\ncomposition. The economic analysis was done with a 6 percent discount rate and a 25‐year evaluation\nperiod, in line with the Bank’s guidance. The cost‐benefit analysis compared a do minimum scenario to\nthe following two project scenarios: (a) applying periodic maintenance (6 cm overlay) on a project road,\nor (b) partially reconstructing a project road (assuming around 30 percent of road length requires full\nreconstruction and remaining 70 percent requires only overlay). Both project alternatives include annual\nroutine maintenance and future periodic maintenance in the form of placing an overlay every 10 years.\n\n\n65. **The results of the analysis show strong net economic benefits under various scenarios that are**\n**robust to variations in project costs and benefits.** The economic cost‐benefit analysis of the proposed\nroad works yielded an Economic Internal Rate of Return (EIRR) higher than 6 percent for all project\nalternatives, indicating they all are economically justified (table 6). The partial reconstruction option yields\nthe highest net present value (NPV) for all road classes and compares favorably in the incremental IERR\nanalysis, and thus is the preferred project alternative. This indicates that doing periodic maintenance\n(overlays) on bad roads with structural problems, while economically justified, is not the preferred\neconomical option nor a good practice. The project alternative of partial reconstruction yields a total\nproject NPV of US$407 million, at a 6 percent discount rate, and an EIR", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000008:38:0:0", "start": 409, "end": 438, "surface": "International Roughness Index", "probe_tag": "confusion", "probe_score": 0.8192, "luna_label": 1, "luna_reason": "Named road-condition index used as an input to the economic cost-benefit analysis."}]}, {"key": "rafael2-210", "text": " <sup>th</sup> percentile.\n\n\n_Capital stock (excluding land and buildings):_ Measures the firm’s total capital stock as the\nsum of the monetary values of tools and utensils, machinery, animals, furniture, transport\nequipment, and other physical assets, winsorized at the 99 <sup>th</sup> percentile.\n\n\n_Took out a loan in the past 12 months:_ Dummy taking the value one if the entrepreneur took\nout a loan for the business in the past 12 months in either of the three follow-up surveys.\n\n\n_Amount of loans taken out:_ Sum of loans the entrepreneur took out for the business across\nthe three follow-up surveys.\n\n\n_Business practice index:_ the proportion of the following business practices employed, based\non McKenzie and Woodruff (2017):\n\n\n  - Marketing Practices: Coded as one for each of the following that the business has\ndone in the last 3 months\n\n`o` M1: Visited at least one of its competitor’s businesses or websites to see what\n\nprices its competitors are charging. Coded as zero if entrepreneur says they\nhave no competitors,\n\n\n60", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:006085:61:1:0", "start": 468, "end": 485, "surface": "follow-up surveys", "probe_tag": "confusion", "probe_score": 0.5425, "luna_label": 1, "luna_reason": "Existing follow-up surveys provide data for defining the loan-taking indicator."}]}, {"key": "rafael2-211", "text": "a large drag in the capacity of rural areas to produce income, is at historically low levels, see Figure 7 panel\n\na.\n\n\n\n\n\n\n\nPanel b. Trends in nominal and real wages, unskilled and skilled labor\n\nUnskilled labor\n\n\n\nSkilled labor\n\n\n\n130\n\n\n120\n\n\n110\n\n\n100\n\n\n90\n\n\n80\n\n\n70\n\n\n\n\n\n\n\n130\n\n\n120\n\n\n110\n\n\n100\n\n\n90\n\n\n80\n\n\n70\n\n\n|Col1|R1|Col3|R2|Col5|R3|\n|---|---|---|---|---|---|\n|||||||\n|||||||\n|||||||\n|||||||\n|||||||\n\n\n|Col1|R1|Col3|R2|Col5|R3|\n|---|---|---|---|---|---|\n|||||||\n|||||||\n|||||||\n|||||||\n|||||||\n\n\n\n_Source: World Bank based on ACLED_ and WFP food monitoring data\n_Note: conflict 2023 data reported as in March._\n\n### Conclusion\n\n\nAfghanistan has experienced significant economic and social changes since the regime change in late\n\n2021. However, how these multiple shocks have affected monetary poverty has yet to be assessed since\n\nup-to-date household and budget survey data has not been available, a situation unlikely to change in the\n\nshort term. Under these circumstances, round 3 of the AWMS phone survey was expanded to include\n\nquestions to predict monetary poverty through survey-to-survey imputation techniques. The resulting\n\n\n19", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:001116:20:0:0", "start": 544, "end": 568, "surface": "WFP food monitoring data", "probe_tag": "confusion", "probe_score": 0.6792, "luna_label": 1, "luna_reason": "Source line attributes chart data to WFP food monitoring data."}, {"key": "prwp:001116:20:0:1", "start": 576, "end": 594, "surface": "conflict 2023 data", "probe_tag": "confusion", "probe_score": 0.8854, "luna_label": 1, "luna_reason": "Conflict data underpin the figure and are sourced to ACLED and WFP monitoring data."}]}, {"key": "rafael2-212", "text": "\n\n\n\n**Forcibly displaced**\n**people 117.3 million** <sup>**12**</sup>\n\n\n\n**Population UNHCR protects and/or**\n**assists 122.6 million**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**12** See footnote 1.\n\n\nUNHCR > **GLOBAL TRENDS 2023** 5", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001204:4:0:0", "start": 211, "end": 229, "surface": "GLOBAL TRENDS 2023", "probe_tag": "confusion", "probe_score": 0.0866, "luna_label": 1, "luna_reason": "Named UNHCR report associated with presented displacement statistics."}]}, {"key": "rafael2-213", "text": " of books and materials. They prefer to use\ntheir constrained resources for their boys who they feel have a better labor market potential. Finally,\nthe data from the Household Survey, showed that even if girls go to school, their parents pull them\nout at an age when they think they can help around the household.", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:refugee_pads:000147:38:2:0", "start": 166, "end": 182, "surface": "Household Survey", "probe_tag": "confusion", "probe_score": 0.4582, "luna_label": 1, "luna_reason": "Survey data support a concrete finding about girls’ schooling and household decisions."}]}, {"key": "rafael2-214", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n\ncookstoves more affordable for households. The outcomes of this subcomponent will largely be beneficial\nto women who are the primary users of these appliances. Switching to improved biomass stoves only (a\nconservative scenario) is expected to result in net emission reduction of 0.135 million tCO2eq over the\nproject lifetime. Beyond reduction in emissions and health benefits, due to increased efficiency, the use\nof improved biomass stoves is expected to reduce consumption of woody biomass by up to 91,500 tons\nof wood. Reduced consumption of woody biomass can lead to a decrease in deforestation rates and thus\npromote building climate resilience by maintaining forest cover. Considering that forest covers help\nprotect communities against the impact of climate hazards, these interventions can further improve the\nadaptive capacity of communities. Another mechanism through which this subcomponent helps build\nadaptive capacity is by increasing the disposable income of households switching to improved biomass\nstoves. Depending upon the current fuel use and tier level of improved biomass stoves, the annual savings\nper household are estimated at US$28–US$37. Finally, by prioritizing solar, biogas, and improved biomass\nstoves, clean cooking solutions will reduce fuelwood consumption. Moreover, biomass to feed improved\nstoves will be sourced from forests that are sustainably managed under Subcomponent 3.3 and in line\nwith the Environmental and Social Framework (ESF)/Environmental and Social Standards (ESS) 6\nrequirements.\n\n55. **Subcomponent 3.3: Restoration and sustainable natural resource management - Phase 2 (IDA**\n**WHR grant: US$11 million).** This subcomponent will support two main sets of activities: (a) integrated\nand participative community forest resources management through community management of natural\nresources and restoration of degraded forests and (b) technical support and capacity building through (i)\ninstitutionalization of forestry-based geographical information system in the Directorate of", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000051:30:0:0", "start": 2032, "end": 2078, "surface": "forestry-based geographical information system", "probe_tag": "confusion", "probe_score": 0.0521, "luna_label": 0, "luna_reason": null}]}, {"key": "rafael2-215", "text": "**ANNEX 1** **BASELINE CONDITIONS AND POTENTIAL IMPACTS**\n\n\n**1.1** **Introduction**\n\nThe decentralization process instigated by the GoU has delivered a number of improvements to its\ncitizens, offering increased levels of self-determination and governance. However the process has\ninevitably been beset by a number of challenges leading to sub-optimal environmental and social\n\n\nslopes. Common environmental degradation identified through surveys with municipal officers included:\nwetland encroachment, noise and air pollution in 86% of the 14 MCs, inadequate funding of environment\ndepartment hence poor monitoring and supervision, political interference in environmental management\nleading to wetland degradation, poor sanitation and pollution of water sources especially in slums.\n\nThe expansion of these Municipalities is occurring at the expense of the environment in and around the\nurban centers. From the field surveys done, it was observed that the urban centers are typically\nsurrounded by a ribbon of wide green valleys with swamps, wetlands and forest reserves extending into\n\n1 Source: Interview MinLG 27.03.12, Annex 6\n\n\n4", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:015460:7:0:0", "start": 439, "end": 470, "surface": "surveys with municipal officers", "probe_tag": "confusion", "probe_score": 0.3741, "luna_label": 1, "luna_reason": "Surveys provided identified environmental degradation findings and prevalence figures."}]}, {"key": "rafael2-216", "text": " Of\n\n\n5To truncate the dataset to Arabic, we first obtained the language of a message using _langid_ (Lui and\nBaldwin (2012)), an off the shelf tool for detecting language from text. The tool is based on pre-trained\nmachine learning models and can detect over 90 languages. Before beginning data analysis, we performed\nbasic cleaning on the text data using Nielsen’s stemmer (Nielsen (2017)), but prevented the stemming of\nproper nouns like key locations and political figures. Next, we removed stopwords using a base list of Arabic\nstop words and some words in Syrian dialect (my brother, where, why, how, etc.), and the words ‘channel’,\n‘subscribe’, ‘Telegram’ and ‘Twitter’. The base list of Arabic stop words is from Mohatahar Arabic Stopword,\n_Github_, `[https://github.com/mohataher/arabic-stop-words/blob/master/list.txt](https://github.com/mohataher/arabic-stop-words/blob/master/list.txt)`\n\n6 `[http://www.geonames.org/](http://www.geonames.org/)`\n7We removed any locations which had the same names as governorates to improve granularity. A few\ntop locations which were disproportionately represented due to false positive matches, ‘mil’, ‘san’, and ‘ada’,\nwere removed.\n\n\n10", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000955:10:2:0", "start": 341, "end": 350, "surface": "text data", "probe_tag": "confusion", "probe_score": 0.8559, "luna_label": 1, "luna_reason": "Existing text data are cleaned as part of the stated analysis workflow."}]}, {"key": "rafael2-217", "text": " by UNHCR \n | 2004 - 2013*\nseen in the previous decade. Accord-\nseen in the previous decade. Accord-\ning to UNHCR estimates, the total \ning to UNHCR estimates, the total \nnumber of persons seeking protec-\nnumber of persons seeking protec-\ntion within or outside the borders of \ntion within or outside the borders of \ntheir countries during the first half \ntheir countries during the first half \nof \nof 2013\n2013 exceeded the \n exceeded the 5.9 million mark.\n million mark.\nConflicts such as those in the Syr-\nConflicts such as those in the Syr-\nian Arab Republic, Central African \nian Arab Republic, Central African \nRepublic, Democratic Republic of \nRepublic, Democratic Republic of \nthe Congo, and Mali forced more \nthe Congo, and Mali forced more \nthan \nthan 1.5 million individuals to seek \n million individuals to seek \nrefuge, predominantly in neighbour-\nrefuge, predominantly in neighbour-\ning countries. In addition, at least \ning countries. In addition, at least \n456\n456,000\n000 persons submitted individu-\n persons submitted individu-\nal asylum applications during the pe-\nal asylum applications during the pe-\nriod under review. \nriod under review. 2 Where UNHCR\n Where UNHCR\nworked with IDP populations, offices \nworked with IDP populations, offices \nreported close to four million \nreported close to four million 3 newly \n newly \nGlobal Trends\nI\n3\nUNHCR Mid-Year Trends 2013", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001071:2:2:0", "start": 1362, "end": 1388, "surface": "UNHCR Mid-Year Trends 2013", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Named UNHCR report presenting existing refugee and asylum statistics."}]}, {"key": "rafael2-218", "text": "**Indicator Description**\n\n\n**Project Development Objective Indicators**\n\n\n\n\n\n\n\n|Indicator Name|Description (indicator definition etc.)|Frequency|Data Source / Methodology|Responsibility for<br>Data Collection|\n|---|---|---|---|---|\n|Direct project beneficiaries|Eligible NPTP beneficiaries who enroll<br>with contracted providers and are eligible<br>for the essential healthcare services<br>package.|Bi-annually|NPTP Database|PMU|\n|Female beneficiaries|Percentage of direct project beneficiaries<br>that are female.|Bi-annually|NPTP Database|PMU|\n|User Satisfaction (percent)|Share of users satisfied by the received<br>health care services.|Bi-annually|User Satisfaction Survey|External Technical<br>Audit|\n|Utilization of services: average<br>no of visits per beneficiary per<br>year (number)|Utilization of services provided by health<br>care service providers that will be<br>calculated as a weighted average of the<br>number of visits per beneficiary per year.|Bi-annually|HIS|PMU|\n\n\n32", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000020:31:0:0", "start": 413, "end": 426, "surface": "NPTP Database", "probe_tag": "confusion", "probe_score": 0.7797, "luna_label": 1, "luna_reason": "Named database supplies the direct beneficiary indicator in the table."}, {"key": "refugee_pads:000020:31:0:1", "start": 655, "end": 679, "surface": "User Satisfaction Survey", "probe_tag": "confusion", "probe_score": 0.4528, "luna_label": 1, "luna_reason": "Survey is the stated data source for the user satisfaction indicator."}]}, {"key": "rafael2-219", "text": " economic development and wealth. To illustrate this concept, the World Bank developed the\nHuman Capital Index (HCI), which measures the impact of underinvesting in human capital on the productivity\nof the next generation of workers. It is defined as the amount of human capital that a child born today can\nexpect to achieve in view of the risks of poor health and poor education currently prevailing in the country\nwhere that child lives.\n\n9. Education is a major component of the HCI, and Africa is the region of the world with the highest economic\nreturns to education. The key drivers of these returns are the quality of education and the average years of\nschooling that a child may benefit from. Analyses of HCI indexes among developing countries show that\nUganda is underinvesting in the future productivity of its citizens. A child born in Uganda today will be only 38\npercent “as productive when she grows up as she could be if she enjoyed complete education and full health.”\nA child born today in Uganda is expected to complete only seven years of education combined by age 18,\ncompared to a regional average of 8.1. Because of the low levels of learning achievement in Uganda, this is\nonly equivalent to 4.5 years of learning, with 2.5 years considered as lost due to poor quality. Even though\nUganda is ranked among the countries in the lowest quartile of the HCI distribution, with an index slightly\nlower than the average for the SSA region, the index has increased over time. The current pandemic\nrepresents a risk to any gains associated with education and, therefore, the importance of this Project to\nmitigate those risks.\n\n\n10. Private returns to education are high in SSA, where one additional year of education represents on average\na 12.4 percent increase in expected income, higher than the global average of 9.7 percent. These returns\n\n\nPage 41 of 43", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000034:45:1:0", "start": 91, "end": 110, "surface": "Human Capital Index", "probe_tag": "confusion", "probe_score": 0.7639, "luna_label": 0, "luna_reason": "Introduced and defined as a concept without showing its data used in analysis."}]}, {"key": "rafael2-220", "text": "Bl.f Project Management Reports In order to prepare quarterly BEPE 31 December 2001\n(PMRs) PMRs, one must be able to print a\nbudget situation on an automatic\nand punctual basis, to conduct\nanalyses on variances, establish\nunit costs for different program\nindicators, and plan commitments\nand expenditures.\n\n\n\nThe BEPE will prepare the first\ndraft PMR for IDA review.\nFollowing comments/approval, the\nfinal report will be prepared and\nused by the BEPE on a reporting\nbasis only.\n**_B.2._** _Procedures manualfor_\n_financial management_\n\nB2.a General Operations Manual Organization guide which would BEPE 30 June 2001\ndefine the roles and responsibilities\nof the BEPE in order to facilitate\ncommunication and exchange of\nproject inforrnation.\nB2.b Formalizing procedures by Description of each operation: BEPE 30 June 2001\nan Organizational Procedures - supporting documents for\nManual. inputs/outputs\n\n                         - information flow\n\n                         - roles of each agency\n\n                         - data needed\n\n                         - control mechanisms\nB2.c Formal presentation of Project indicators (following data BEPE 30 June 2001\nmanagement reports. consolidation): internal, external\naudit, IDA.\n\n**_R3. Audit_**\n\n\nB3.a Appointment of an auditor. Selection of an auditor whose BEPE 31 March 2001\ncontract would be renewable based\non satisfactory performance.\n\n**C. PROCUREMENT**\n\n\n**_C.l._** _BEPEFunction_\nCl .a No procedures manual exist. Prepare a detailed procedures Technical 30 June 2001\nmanual which would outline the unit/\nfunction of the organization, the BEPE\nvarious activities being\n\nundertaken, the steps for each\nactivity and the respective\n_ responsibilities.\nC .b Procurement function lacks a Recruit a qualified and seasoned MEN/ 30 June 2001\nsenior officer. procurement officer, and train BEPE\nexisting junior procurement staff in", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:008131:57:0:0", "start": 1139, "end": 1180, "surface": "data BEPE 30 June 2001\nmanagement reports", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0, "luna_reason": "Fragmented table content, not a standalone data-use mention."}]}, {"key": "rafael2-221", "text": "Without reference to Beck and Webb (2003), Erbas and Sayers (2006) focus their research on the\nlink between non-life insurance development and institutional quality. Based on experimental findings\nby other authors, they argue that institutional quality is a good indicator of uninsurable uncertainty,\nwhich is detrimental to insurance demand. This is because a country’s institutional framework\ndetermines the reliability of norms, which in turn determine the subjective evaluation of economic risks,\nfor example the risk that a legitimate insurance claim will be denied without recourse. With 1994-2003\ndata from 70 countries and the World Bank Governance indicators, they find that institutional quality,\nand the levels of transparency and uncertainty it entails, explains insurability (which they measure as\ninsurance penetration) better than income level for non-life insurance, and suggest that “an examination\nof life insurance penetration across countries along the lines of the present paper is left for future\nresearch.”\n\n\nThis is the point of departure for the present paper, which establishes correlations of the different\ndrivers of insurance market development discussed in the literature with insurance penetration for both\nlife and non-life insurance based on a data set that is much more extensive in terms of countries and\nyears of observation.\n\n\n**3.** **Data**\n\n\nWe use 4 main sets of cross-country data in our analysis: income per capita, insurance market data,\nnational governance indices and financial market development measures. All the data are available\nyearly and most of the variables span roughly 180 countries for a period of 20 years. Two of the three\nmeasures of financial market development are more limited in terms of geographical and time coverage,\nas described below. Summary statistics are presented in Table 1. Online Appendix Table 1 shows\nsummary statistics for low and lower-middle income countries and higher-middle and high income\ncountries separately.\n\n\n<u>Income.</u> The income measure used is annual GDP per capita provided by the World Bank for", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000111:8:0:3", "start": 1440, "end": 1457, "surface": "income per capita", "probe_tag": "confusion", "probe_score": 0.5439, "luna_label": 1, "luna_reason": "Cross-country income data are explicitly used as an analytical dataset."}]}, {"key": "rafael2-222", "text": " <sup>13</sup> However, the possibility that refugees, host state, and non-governmental actors\nmay not view UNHCR as important or even relevant to justice administration is not considered.\n\n\nThirdly, the study prioritized breadth over depth. Even with the incorporation of examples from\nsurvey responses, the highly general nature of Da Costa’s study risks essentializing refugees and\ntheir experiences. A senior official observed that the Da Costa study “lacked the anthropological\nperspective” needed to truly contribute to a nuanced understanding of justice administration in\nrefugee camps and settlements. <sup>14</sup>\n\n\nGriek’s work offers the case study depth that is lacking in Da Costa’s study and draws attention\nto the important human rights and protection concerns that can arise for victims of violence\nseeking justice in refugee camps. However, she too focuses on the forms and operations of\njustice institutions without seeking a deeper understanding of the perceptions and preferences\nthat sustain them. Though Griek did engage with refugees through informal conversations and\nfive focus groups, <sup>15</sup> [^15: Griek 2007, 95. She does not specify how many individuals were present in each group, how they were selected,\nor what questions they were asked.] she spent just two weeks in each camp, a limitation she acknowledges. <sup>16</sup> [^16: Ibid, 25]\n\n\n**Theoretical framework**\n\n\nUNHCR policy documents, Da Costa, Griek, and to some extent the IRC study analyzed justice\ninstitutions primarily in terms of their legal rules and procedures. Griek justifies her focus on\n\n\n10 IRC 2006, 1\n11 Ranger 1994, 281\n12 They were: “What is the general reputation or view of [refugee dispute resolution] mechanisms by the targeted\nrefugee population?” and “When are refugees most interested in pursuing cases in the state legal system?” (Da Costa\n2006, 77-78)\n13 Ibid, 1\n14 Stakeholder Interview 1\n15 Griek 2007, 95. She", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000359:5:1:0", "start": 287, "end": 303, "surface": "survey responses", "probe_tag": "confusion", "probe_score": 0.8352, "luna_label": 0, "luna_reason": "Generic survey responses are mentioned without an attributed finding or concrete claim."}]}, {"key": "rafael2-223", "text": "sup>\n\n\n\n180. The assumptions considered for this cost-benefit analysis are based on prior international\nevidence and estimations, estimates from Turkey’s Labor Force Survey (LFS), and qualitative information\ncollected from the field. To estimate the project returns, we assume the project beneficiaries have similar\ncharacteristics to the average low-skilled agricultural worker or farmer observed in the LFS\n\n\n181. The analysis is broken down in two parts where the effects of the project are separately estimated\nfor the supply side (agricultural workers) and demand side (farmers). Regarding the demand side, the\nproject expects to increase the demand for labor through (a) farms’ productivity increase, (b) production\nexpansion, and (c) reduced costs of formal employment through wage subsidies. On the workers’ side,\nwelfare gains are expected as a result of (a) productivity and wage gains through skill training and\ncertification, (b) gains from formalization productivity of workers by improving their skills, and (c) access\nto additional farm employment for currently not employed individuals. Labor productivity gains will\nincrease farmers’ revenues, and the assumption is that part of this additional surplus will be transferred\nto workers. Regarding formalization, workers and farmers incur private costs to formally register,\nincluding SSI contributions and potential social benefit losses. In the long term, it entitles workers to\nretirement and health care benefits, in addition to severance pay in the short term. <sup>70</sup> [^70: However, within the design of the project, the ACC has no responsibility or liability for severance payments and other labor\nreceivables.] Finally, for the SSI\nof Turkey, more formal workers will increase the revenue and potentially reduce the spending on social\nassistance. The sections below explore the different potential impacts.\n\n\n**1.** **Supply Side (Agricultural Workers)**\n\n\n182. As the vast majority of agricultural workers earn way below the minimum wage, formalization is\nan achievable target only for a subgroup of workers.", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000114:76:1:0", "start": 405, "end": 408, "surface": "LFS", "probe_tag": "confusion", "probe_score": 0.8748, "luna_label": 1, "luna_reason": "Turkey’s Labor Force Survey informs beneficiary characteristics in the cost-benefit analysis."}]}, {"key": "rafael2-224", "text": " than that for male beneficiaries. Women will be targeted for inclusion in planning\ncommittees, where relevant, and they will be incentivized to participate in and use all projectfunded services and activities.\n\n116. **The project will also support women’s employment in the energy sector.** Employment\nopportunities will be created for female technicians and engineers in O&M activities. Limited sexdisaggregated data are available, but <mark>global data indicate that women are underrepresented in both</mark>\n<mark>technical and nontechnical roles and that the sector as a whole is male dominated. Field</mark> evidence further\nreports a near-complete lack of women in similar operations in Chad, which explains the baseline to be\nassessed as zero. The key barriers for accessing these jobs were assessed to stem primarily from lack of\nskills/education and social norms/lack of targeted recruitment. Therefore, private O&M companies will\nbe mandated to train and employ female professionals to boost women’s opportunities for joining the\nsector and help them overcome the key barrier of school-to-work transition and access to technical skills\nand men-dominated jobs. They will also be expected to accommodate women through a more equitable\nemployment policy, which is currently lacking in most operations and limits employment possibilities.\n\n117. **M&E.** Several indicators will monitor project progress with respect to gender, including\n(a) A percentage of female-headed households provided with electricity access under the project.\n\n\n\nThis indicator will track progress toward raising the share of electrified households that are female\nheaded to 15 percent. This percentage is based on the level of their prevalence recently measured\nby the survey on the ability and willingness of households to pay for electricity, provided in annex\n6, updating earlier data. This indicator will be applied to Component 1 and Subcomponent 2.3;\n(b) A percentage of women technicians employed in O&M under Component 1 and Subcomponents\n\n\n\n2", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000051:48:1:0", "start": 397, "end": 418, "surface": "sexdisaggregated data", "probe_tag": "confusion", "probe_score": 0.1156, "luna_label": 0, "luna_reason": null}, {"key": "sample:jdc_operational:000051:48:1:1", "start": 444, "end": 455, "surface": "global data", "probe_tag": "confusion", "probe_score": 0.2257, "luna_label": 1, "luna_reason": null}]}, {"key": "rafael2-225", "text": "NR to carry out the baseline survey did not participate in the mission, which was an\nopportunity lost.\n\n\n**Global Environmental Objective Indicators**\n\n\nPHINDGEOTBL\n\n\n 2. Total area of restored or reforested/afforested on both individual and communal land (Hectare(Ha), Custom)\n\n\nBaseline Actual (Previous) Actual (Current) End Target\n\n\nValue\n300000.00 314539.00 330310.00 910000.00\n\n\nDate\n01-Dec-2014 21-May-2015 05-Nov-2015 31-Dec-2018\n\n\nComments\nSame as indicator 1 in PDO.\n\n\nPHINDGEOTBL\n\n\n 3. Increase in the amount of biomass in the intervention areas (Ton/ha) (Hectare(Ha), Custom Supplement)\n\n\nBaseline Actual (Previous) Actual (Current) End Target\n\n\nValue\n0.00 0.00 0.00 0.00\n\n\nOverall Comments\nFor indicator 3 above, it is the same as indicator 3 in PDO.\n\n\n**Intermediate Results Indicators**\n\n\n2/21/2016 Public Disclosure Copy Page 5 of 13", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:014700:4:1:0", "start": 20, "end": 35, "surface": "baseline survey", "probe_tag": "confusion", "probe_score": 0.0839, "luna_label": 0, "luna_reason": "Planned baseline survey activity; data production is not yet existing or used."}]}, {"key": "rafael2-226", "text": "Several existing studies analyze the effect of the COVID-19 pandemic on LGBTI people. These\n\n\nstudies primarily focus on assessing the impact of the pandemic on LGBTI people’s health and access to\n\n\nhealth care services (for example, see: United Nations Human Rights 2020; Banerjee and Nair 2020; Gato\n\n\net al. 2021; Jarrett et al. 2021; Santos et al. 2021). There are also a few studies on the economic impact of\n\n\nthe pandemic on LGBTI people in the United States (US) (Movement Advancement Project 2020;\n\n\nWhittington, Hadfield, and Calderón 2020; Sears, Conron, and Flores 2021). The studies, which are based\n\n\non US data, find that in addition to a greater risk of health complications during the pandemic, LGBTI\n\n\npeople in the US are more likely than non-LGBTI people to live in poverty, lose jobs, experience financial\n\n\nconstraints, and lack access to necessities. More widely, a report by OutRight Action International using\n\n\nqualitative in-depth interviews conducted in 38 countries concludes the same (Bishop 2020).\n\n\nNevertheless, due to the lack of available quantitative data, little is known about the magnitude\n\n\nof the economic impact of the COVID-19 pandemic on LGBTI people globally. Using multi-national cross\n\nsectional data from the first round of the 2020 COVID-19 Disparities Survey, this paper examines the\n\n\nimpact of the COVID-19 pandemic and subsequent pandemic-control measures on the income,\n\n\nconsumption, and mental well-being of individuals while accounting for their self-reported gender identity\n\n\n(cismen, ciswomen, transgender, and non-binary). More precisely, the paper investigates the differential\n\n\neconomic impacts faced by transgender and non-binary populations due to existing systemic\n\n\nvulnerabilities combined with the COVID-19 imposed public health crisis.\n\n\nThe paper is organized as follows: Section 2 describes the data source and the methodology.\n\n\nSection 3 discusses the main findings under two headings; the first focusing on", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:000961:4:0:0", "start": 618, "end": 625, "surface": "US data", "probe_tag": "confusion", "probe_score": 0.7288, "luna_label": 1, "luna_reason": "US data underpin studies finding health and economic impacts on LGBTI people."}]}, {"key": "rafael2-227", "text": "###### **Vulnerabilities of the Affected Community**\n\nThis prolonged monsoon flood\ncreate two fold impacts when its\nassociated with existing\nvulnerabilities and aggravate\ndistress to the affected community.\n\n\nFor assessing the severity of\nimpacts relevant demographic and\nsocio economic vulnerability\nindicators of the affected areas\nwere consider and indexed as per\nthe Global <u>INFORM</u> <u>[risk](https://drmkc.jrc.ec.europa.eu/inform-index)</u> <u>index</u>\nguideline. The analyzed\nvulnerability indicators are\n\n\n\n\n**Table : Data table of vulnerability indicators for the affected community**\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|Division|District Name|Number of<br>Affected<br>child (0-to<br>59<br>months)|Number of<br>Affected<br>Elderly<br>(60+) age|Number of<br>Person<br>with<br>Disability<br>Affected|Number of<br>Poor HH<br>Affected|Number of<br>Extreme<br>Por HH<br>Affcted|Number of<br>Women<br>Headed<br>HH Affcted|Number of<br>People in<br>Affected<br>Women<br>Headed<br>HH|Number of<br>People in<br>Affected<br>Agri labor<br>Depended<br>HH|Number of<br>Katcha<br>and Jhupri<br>HH<br>Affected|\n|---|---|---|---|---|---|---|---|---|---", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001664:24:0:1", "start": 531, "end": 569, "surface": "Data table of vulnerability indicators", "probe_tag": "confusion", "probe_score": 0.6895, "luna_label": 0, "luna_reason": "Table caption introducing vulnerability indicator data"}]}, {"key": "rafael2-228", "text": "arissa and Turkana, during the<br>reporting period<br>|\n|Frequency <br>|Every six months <br>|\n|Data source <br>|UNHCR reports|\n|Methodology for Data<br>Collection <br>|Routine UNHCR data collection|\n|Responsibility for Data<br>Collection <br>|MoH <br>|\n|**Percentage of children immunized with three doses of Pentavalent vaccine (Percentage)  **|**Percentage of children immunized with three doses of Pentavalent vaccine (Percentage)  **|\n|Description <br>|Numerator: Number of children under 1 year who have received three doses of the Pentavalent vaccine<br>Denominator: Total number of surviving children under 1 year <br>|\n|Frequency <br>|<br> Every six months <br>|\n|Data source <br>|KHIS|\n|Methodology for Data<br>Collection <br>|Routine HMIS data collection <br>|\n|Responsibility for Data|MoH|\n\n\n\nFeb 21, 2024 Page 30 of 43", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000000:35:2:0", "start": 113, "end": 126, "surface": "UNHCR reports", "probe_tag": "confusion", "probe_score": 0.8397, "luna_label": 1, "luna_reason": "Named UNHCR reports are cited as the table indicator's data source."}]}, {"key": "rafael2-229", "text": "</sup> 25.00 27.70 0.11 0.02 27.70 27.70\n\n\nMale Incomplete primary 8.00 5.60 ‐0.30 ‐0.06 3.93 2.60\n\n<sup>Incomplete lower secondary</sup> 22.00 23.00 0.05 0.01 21.33 20.00\n\n<sup>Incomplete upper secondary</sup> 34.00 32.40 ‐0.05 ‐0.01 30.73 29.40\n\nCompleted upper secondary but\nnot post‐secondary 12.00 13.50 0.13 0.03 0.01 18.50 0.02 22.50\n<u>Post‐secondary</u> <u>24.00</u> <u>25.50</u> <u>0.06</u> <u>0.01</u> <u>25.50</u> <u>25.50</u>\nSource: Income and Expenditure Survey, 2010, Department of Statistics and own calculations.\n\n\n59", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000041:66:1:0", "start": 447, "end": 476, "surface": "Income and Expenditure Survey", "probe_tag": "confusion", "probe_score": 0.8732, "luna_label": 1, "luna_reason": "Named survey cited as source for tabulated figures and calculations."}]}, {"key": "rafael2-230", "text": "- 37 \n\n10. “Disbursement Linked Indicator” or “DLI” means in respect of a given Category,\nthe indicator related to said Category as set forth in the table in Section IV.A.2 of\nSchedule 2 to this Agreement.\n\n\n11. “Disbursement Linked Result” or “DLR” means in respect of a given Category,\nthe result under said Category as set forth in the table in Section IV.A.2 of Schedule\n2 to this Agreement, on the basis of the achievement of which, the amount of the\nFinancing allocated to said result may be withdrawn in accordance with the\nprovisions of said Section IV.\n\n\n12. “EFY” means the Ethiopian Fiscal Year, the fiscal year of the Recipient which\ncommences on July 8 and ends on July 7.\n\n\n13. “Eligible Crisis or Emergency” means an event that has caused, or is likely to\nimminently cause, a major adverse economic and/or social impact to the Recipient,\nassociated with a natural or man-made crisis or disaster.\n\n\n14. “Eligible Refugee Incentive Teachers” mean the refugee incentive teachers\ndeemed eligible to receive in-service skill upgrading Training on an annual basis:\n(i) in line with the eligibility criteria elaborated in the Operations Manual; and (ii)\nbased on data received from UNHCR.\n\n\n15. “Eligible Refugee Primary Schools” mean refugee primary schools which are\noperational in the five main refugee-hosting regions, based on data received from\nUNHCR, on an annual basis as further elaborated in the Operations Manual.\n\n\n16. “Eligible Refugee Secondary Schools” mean refugee secondary schools which are\noperational in the five main refugee-hosting regions, based on data received from\nUNHCR, on an annual basis as further elaborated in the Operations Manual.\n\n\n17. “Emergency Action Plan” means the plan referred to in Section I.F of Schedule 2\nto this Agreement, detailing the activities, budget,", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:014916:37:0:0", "start": 1171, "end": 1195, "surface": "data received from UNHCR", "probe_tag": "confusion", "probe_score": 0.7821, "luna_label": 1, "luna_reason": "Existing UNHCR data determines eligibility for refugee incentive teachers."}]}, {"key": "rafael2-231", "text": " national accounts and household surveys averages move differently—\nrepresents one of the most important areas for further research. Bhalla (2001) must be credited through his,\nat times single-minded, insistence on using National accounts data for highlighting this issue.\n\n8 And with global inequality conventionally defined as inequality in relative, not absolute, incomes and\nusing the conventional measures of inequality like the Lorenz curve, Gini coefficient or Theil index. The\nfocus on absolute inequality however has its own uses (see Atkinson and Brandolini, 2004; Svedberg,\n2003; Ravallion 2004). Similarly, relative income inequality with the use of different inequality aversion\nparameters (reflecting in principle different welfare judgments) will produce ambiguous results even where\nconventional statistics yield a clear outcome (see Capeau and Decoster, 2004, Table 5).\n\n\n5", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:003078:4:1:0", "start": 23, "end": 40, "surface": "household surveys", "probe_tag": "confusion", "probe_score": 0.862, "luna_label": 1, "luna_reason": "Household survey averages are compared as evidence of differing movement."}]}, {"key": "rafael2-232", "text": "828 other nationalities**\n\n\n**_*Source: UNHCR registration data_**\n**_December 31st 2018_**\n\n\n\n|No.|Population group|Total Population|\n|---|---|---|\n|1|**Camp refugees**|126,041|\n|2|**Non-camp refugees**|545,609|\n|3|**Other affected population**|600,00013|\n|4|**Refugee children under five**|102,000|\n|5|**Refugee women of reproductive age**|151,000|\n|6|**Adolescents**|121,000|\n|7|**Pregnant women and lactating women**|33,550|\n|8|**Refugees with impairment and disabilities**|54,000|\n\n\n_Table 2 – Estimated target populations among Syrians based on end of 2018 projections_\n\n\n**iii. Coordination**\nCoordination is an essential part of the humanitarian response, with the aim of avoiding\nunnecessary duplication of service delivery and identifying gaps where services are most needed.\nCoordination platforms at national and field levels have been strengthened with increasing\nutilization of data and survey results to ensure gaps and emerging needs are addressed. In\ntransitioning from humanitarian relief in the Syrian refugee context there is a need to link with the\nbroader development initiatives in-country. This will entail stronger coordination both within and\nbetween the humanitarian and development sectors at all levels; health sector mapping of all\ndevelopment initiatives and the relationship between the humanitarian effort and development\nefforts, and elaboration of longer-term plans to strengthen gaps highlighted by the humanitarian\nsituation.\n\n\n12 Such as Zarqa, Maadaba, Balqa, Maan, Karak and Tafilah\n13 This include Non UNHCR registered Refugees\n\n\n11 | P a g e", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:001452:10:1:1", "start": 892, "end": 915, "surface": "data and survey results", "probe_tag": "confusion", "probe_score": 0.6487, "luna_label": 0, "luna_reason": "Generic data resources are mentioned without an attributed finding or concrete analysis."}]}, {"key": "rafael2-233", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000050:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0, "luna_reason": "Names monitoring data without showing substantive analysis or decision use."}, {"key": "refugee_pads:000050:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": 0.1197, "luna_label": 1, "luna_reason": "NaCSA administrative data is cited as a verification source for program indicators."}, {"key": "refugee_pads:000050:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1, "luna_reason": "Named administrative data source listed for program-awareness indicators."}]}, {"key": "rafael2-234", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\nLebanese to about 2.7 million. <sup>6</sup> [^6: Income headcount poverty rates based on upper poverty line of $14 income per day and lower poverty line of $8.5 per day. For the lower\npoverty line, the corresponding increase is from 8.2 percent to 23.2 percent, bringing the total number of poor Lebanese to 1.1 million for the\nlower poverty line and 2.7 million for the upper one. Source: Fakih, Ali, Makdissi, Paul, Marrouch, Walid, Tabri, Rami V., Yazbeck, Myra,\n“Confidence in Public Institutions and the Run up to the October 2019 Uprising in Lebanon,” Working Paper, 2020.] The middle-income group has contracted from over 57 percent in 2019 to less than 40\npercent in 2020, while the affluent group has also shrunk significantly from 15 to 5 percent of the population.\nThe _[Beirut RDNA (2020)](https://openknowledge.worldbank.org/handle/10986/34401)_ reported vulnerabilities and needs among the poor and the vulnerable populations to be\nsignificantly exacerbated following the PoB explosion, especially among children, women, persons with disabilities,\nthe elderly, refugees and migrant workers.\n\n**4.** **This socio-economic deprivation has driven a wedge between the people and the state.** Weak governance is\nboth a root cause and a major impediment to the effective management of today’s crises. Deteriorating public\ngovernance, compounded by limited fiscal space, has severely worsened the delivery of public services and the quality\nof infrastructure in virtually all sectors. Pervasive political gridlock has so far prevented the swift formulation of policies\nand reform agendas to ameliorate the fallout of the compounded crises. While trust in governmental institutions has\nbeen declining for several years, the inadequate management of the impact of the explosion, combined with", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "jdc_operational:000012:12:0:0", "start": 889, "end": 900, "surface": "Beirut RDNA", "probe_tag": "confusion", "probe_score": 0.7147, "luna_label": 1, "luna_reason": "Named assessment reports vulnerabilities and needs among affected populations."}]}, {"key": "rafael2-235", "text": "KNBS\n\nKrEpiyeA:Ifre\nKenya National Bureau of Statistics\n\n\n**V.** **The** **Director General's Report**\n\n\nAccording to the Statistics Act **2006,** the Kenya National Bureau of Statistics is the\n\nprincipal agency of the national government for the collection, compilation, analysis <sup>and</sup>\ndissemination of official statistics for planning, policy making and <sup>monitoring</sup> <sup>and</sup>\nevaluation. It is also mandated to ensure best standards and methods in the production\nof statistics across the National Statistical System **(NSS).**\n\n\nThis annual financial report, therefore, highlights achievements made **by** **KNBS.**\n\n\n**Kenya** **Integrated** **Household** **Budget Survey** **(2015/2016** **KIHBS)**\nDuring the **2015-2016** Financial Year, major activities were completed. Key among <sup>them</sup>\n\nwas training of Research Assistants who were to undertake data collection. Data\n\nCollection commenced on 1s September **2015** and continued throughout <sup>the</sup> <sup>year.</sup>\nQuarterly data collection reports were produced.\n\n\n\n**National** **Strategy for Development** **of Statistics**\nThe process of developing National Strategy for Development of Statistics <sup>**(NSDS)**</sup> a\nstrategy to guide the generation of statistics across **NSS** which commenced <sup>in</sup> <sup>the</sup> FY\n2014-2015 continued. During the FY **2015-2016,** a workshop with sectors across <", "source": "fcv_pads_east_africa", "subset": "annotate_rafael_part2", "spans": [{"key": "fcv_pads_east_africa:016623:17:0:1", "start": 718, "end": 723, "surface": "KIHBS", "probe_tag": "confusion", "probe_score": 0.6268, "luna_label": 0, "luna_reason": "Survey data collection was being undertaken by the reporting agency."}]}, {"key": "rafael2-236", "text": "|Table A3: Gender Indices|Col2|Col3|Col4|\n|---|---|---|---|\n|**Gender Index**|**Description**|**Country coverage**|**Years**|\n|Social Institutions and Gender<br>Index (SIGI)|Measures long-lasting social institutions defined<br>as societal practices and legal norms. 5 subindices<br>- Family code<br>- Civil liberties<br>- Physical integrity<br>- Son preference<br>- Ownership rights|160 economies for<br>SIGI 2014|2009,<br>2012,<br>2014|\n|Gender-related Development<br>Index (GDI) - UNDP|Measures gender gap in human development<br>achievements in three basic dimensions of<br>human development: health, measured by female<br>and male life expectancy at birth; education,<br>measured by female and male expected years of<br>schooling for children and female and male mean<br>years of schooling for adults ages 25 and older;<br>and command over economic resources,<br>measured by female and male estimated earned<br>income. Part of HDI.|142 countries|introduced<br>in 1995|\n|Gender Empowerment Measure<br>(GEM) - UNDP|Designed to measure \"whether women and men<br>are able to actively participate in economic and<br>political life and take part in decision-making\"<br>The GEM is determined using three basic<br>indicators: Proportion of seats held by women in<br>national parliaments, percentage of women in<br>economic decision making positions (incl.<br>administrative, managerial, professional and<br>", "source": "general_prwp", "subset": "annotate_rafael_part2", "spans": [{"key": "prwp:006813:42:0:0", "start": 404, "end": 413, "surface": "SIGI 2014", "probe_tag": "confusion", "probe_score": 0.2708, "luna_label": 0, "luna_reason": "Standalone table cell, not an independently used data mention."}]}, {"key": "rafael2-237", "text": "population were no longer a target of attacks or violence emanating from the majority community\nand they had relative freedom of movement although their mobility continued to be dictated by\ntheir ability to converse in Albanian. The use of Bosniak language in public was still limited.\nMany Kosovo Bosniak business premises and apartments are still illegally occupied by Kosovo\nAlbanians. So as not to generate tensions with the occupants, most Bosniaks did not initiate legal\nprocedures for repossession of their property. Bosniak children including those from Mazgit had\naccess to primary and secondary schools in Prishtine/Pristina.\n\nNotwithstanding, there were no Bosniak returns to the region. Instead the community continued\nto experience more departures after property transactions and relocation to Bosnia-Herzegovina\nor Sandzak. Unlike the communities in Peje/Pec and Prizren regions, the Bosniak communities in\nthis region have lost the critical mass in the recent period to anchor their community, making it\nextremely difficult to return to the pre-war conditions. Moreover, the Bosniak leadership in\nPrishtine/Pristina municipality failed to have a significant political platform to have their voice\nheard and to normalise the situation.\n\n**C.2.** **Prizren region**\n\nIn Prizren region, Kosovo Bosniaks enjoyed freedom of movement within the Zhupa valley,\nPodgor area and Prizren town - where the majority of the Bosniak returns in the region took place\n-, but were reluctant to venture further because of the language barrier. During the reporting\nperiod, a total of 275 Bosniaks returned to Prizren region <sup>83</sup> [^83: This includes UNHCR monitored returns from Western Europe, of which 97 returned voluntarily and] and most returned to their places or\nvillages of origin, to mixed or mono-ethnic villages or rural areas. The majority of the returnees\ndid not report any security incidents or immediate concerns upon arrival, with a few exceptions. <sup>84</sup>\nOver", "source": "reliefweb", "subset": "annotate_rafael_part2", "spans": [{"key": "reliefweb:000287:25:0:0", "start": 1654, "end": 1697, "surface": "UNHCR monitored returns from Western Europe", "probe_tag": "confusion", "probe_score": 0.8144, "luna_label": 1, "luna_reason": "UNHCR-monitored returns substantiate the reported total and voluntary-return breakdown."}]}, {"key": "rafael2-238", "text": "* Popular Benchmarks\n**children.**\n\n\n**Project** **Development** **Outcome** **/** **Impact** **Project reports:** **(from** **Objective** **to Goal)**\n**Objective:** **Indicators:**\n**Assist** **war affected** - Improved social capital and - Initial Social Assessment - Communities in the NSAP\n**communities** **to restore** organizational development; (to establish indicators and target areas are assisted to\n**infrastructure,** **services** **and** - Increased access to and use methodologies for social ensure a reduced risk of\n**build** **local** **capacity for** of social and economic capital and organizational conflict\n**collective** **action.** Priority infrastructure and services development)\nwill be given to areas not - Proportion of NSAP - Annual social assessments; - NACSA complements and\npreviously serviced by investments targeted to newly - NaCSA M&E data; extends the work of other\ngovernment, newly accessible accessible areas, & areas - M&E data of relevant line agencies and rninistries\nand the most vulnerable previously not served, and mninistries; in support of the PRSP's\npopulation groups within those vulnerable people within these - Beneficiary Assessment poverty reduction and\nareas. areas; (BAs) biannually; decentralization objectives\n\n                  - Proportion of sub-projects                   - Participatory evaluation\nthat reflect priorities of reports for a random sample of                   - NACSA is fully integrated\ntargeted communities and assisted conmmunities; into national planning and\nbeneficiaries; and                  - Technical audits resource allocation\n\n                  - Proportion of sub-projects frameworks (such as the\noperative 24emonths after National Recovery Strategy,\ncorpletion. the PRSP, and", "source": "refugee_pads", "subset": "annotate_rafael_part2", "spans": [{"key": "refugee_pads:000012:29:1:0", "start": 862, "end": 876, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.2118, "luna_label": 0, "luna_reason": "Listed as project monitoring verification data, not an already-used external resource."}]}, {"key": "rafael2-239", "text": "53. Under the Uganda rainfall condition, the frequency of flooding in most municipalities is between\n10 to 15 times in a year lasting 3 to 4 hours per flooding. Private and commercial vehicles are disrupted\nleading to loss of time and income. Improved drainage also leads to improvements to the environment and\nhealth benefits from reduced incidence of water borne disease. The internal rate of return obtained in\nprevious studies in similar environments was used to estimate the stream of benefits generated by improved\ndrainage. The EIRR of drainage under USMID was calculated at 6%.\n\n**Increase in Property Values**\n\n54. Improvements in urban roads in all the municipalities sampled has led to increases in value of\nproperties (land, buildings) and rental prices of properties in the adjacent areas of the constructed roads\nranging from 20% to 100% as per the table below. In Hoima municipality, the sharp increase in both rent\nand land values can also be attributed to speculations about oil extraction impact on the local economy.\n\n**Table 9: Changes in Rent and Land values**\n\n\n**Employment Creation**\n\n55. Construction of urban roads created direct and indirect jobs during construction. However, most of\nthe urban road infrastructure projects visited had been completed or partially completed. It was only\nNyakana road in Fort Portal where construction was still ongoing and therefore data on employment was\nobtained. The construction of Nyakana road in Fort Portal with a length of 0.94km, was directly employing\n56 workers out of which, 10% were highly skilled, 10% were skilled and 80 % unskilled. The highly skilled\nworkers, skilled workers and unskilled workers were earning UGX35,000, UGX25,000 and UGX12,000\nper day respectively. The construction of the road was also indirectly employing approximately 70 workers.\nThe construction of the road was expected to last for one year and three months.\n\n56. It can therefore be estimated from this data that construction of one kilometer creates", "source": "jdc_operational", "subset": "annotate_rafael_part2", "spans": [{"key": "sample:jdc_operational:000062:69:0:0", "start": 1393, "end": 1411, "surface": "data on employment", "probe_tag": "confusion", "probe_score": 0.4287, "luna_label": 0, "luna_reason": null}]}]