queue_annotate_sample.json

[{"key": "p000", "text": "**The World Bank**\nBalochistan Human Capital Investment Project (P166308)\n\n\n(FMS), and an environmental and social safeguards specialist/officer. <sup>47</sup> [^47: During the early phase of implementation, the Governance and Policy Program (GPP) PMU will provide back‐up support.] The PMUs will be fully authorized\nto implement the planned activities approved by the Project Steering Committee (PSC).\n\n\n46. **A Project Coordination Committee (PCC) will be set up to coordinate project implementation**\n**and a PSC will be set up to provide strategic guidance and oversight.** The PCC, co‐chaired by Secretaries\nHealth and Secondary Education, will meet quarterly. The PSC, chaired by the Additional Chief Secretary,\nwill meet biannually (see figure 2).\n\n\n**Figure 2. Institutional and Implementation Arrangements**\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**B. Results Monitoring and Evaluation Arrangements**\n\n\n47. **Building on the results chain, the M&E framework identified indicators to track project**\n**implementation progress and impact.** The PDO‐level health indicators are taken from the RMNCHN\nindicators in the DHIS, while digitization and integration of various HMIS is an intermediate indicator. The\neducation indicators are taken from the EMIS. Where possible, relevant indicators will be disaggregated\nby gender. Discussions with the GoB and the UNHCR have confirmed, however, that beneficiary data by\nnationality will not be routinely collected or publicly released.\n\n\n48. **The project M&E will leverage and strengthen existing routine information systems, and finance**\n**the generation of user‐friendly evidence for efficient service delivery.** Routine surveys will be used to\ncollate data from target facilities, which will be triangulated through the existing management\ninformation system within the Health and Secondary Education Departments. The remote monitoring\nsystem within the SED uses technology‐based data management solutions with a dashboard to display\nthe broader analysis. The project will support the", "source": "refugee_pads", "subset": "annotate_sample", "spans": [{"key": "refugee_pads:000085:25:0:0", "start": 1085, "end": 1102, "surface": "RMNCHN\nindicators", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing RMNCHN indicators from DHIS are used as PDO health indicators.", "human_verdict": null, "human_note": ""}, {"key": "refugee_pads:000085:25:0:1", "start": 1110, "end": 1114, "surface": "DHIS", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing DHIS indicators are used as project health monitoring indicators.", "human_verdict": null, "human_note": ""}, {"key": "refugee_pads:000085:25:0:2", "start": 1241, "end": 1245, "surface": "EMIS", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Education indicators are sourced from the named EMIS information system.", "human_verdict": null, "human_note": ""}, {"key": "refugee_pads:000085:25:0:3", "start": 1385, "end": 1416, "surface": "beneficiary data by\nnationality", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "States data will not be routinely collected or released, without substantive use.", "human_verdict": null, "human_note": ""}]}, {"key": "p001", "text": "home-based learning programs,\" or IRI 1.1,\n“Percentage of children provided access to programs and sensitization campaigns that aim at minimizing\nthe negative impacts of school closure like psychological impacts and gender-based violence.”\nNevertheless, these indicators were measured using a telephone survey implemented by the Bank, which\nwas originally intended as a secondary source for validation of the REB data (ICR, p. 32).\n\n\n**c. M&E Utilization**\n\nThe project M&E data were used for assessing progress on project activities and indicators, keeping\nproject implementation on track, informing the two project restructurings and reallocations, and serving\nas a basis for implementation support missions. Additionally, the project-supported strengthening of\nsupervision capacity at federal and REB levels contributed to utilization of the M&E system. Also,\nfollowing the second restructuring, in order to improve REBs’ familiarity with the M&E design, the Task\nTeam presented the RF and M&E findings to REB officials at a workshop that aimed to support both\nprocurement and environmental and social management (ICR, pp. 32-33).\n\n\n**M&E Quality Rating**\nSubstantial\n\n\n**10. Other Issues**\n\n\n**a. Safeguards**\n\nThe Environmental and Social (E&S) risk was rated Moderate throughout the project’s life. The activities\nfinanced by the project were not expected to cause irreversible environmental and social impacts,\nconversion of natural habitats, degradation of biodiversity, or loss of forest resources, as neither large-scale\n\n\nPage 17 of 21", "source": "fcv_pads_east_africa", "subset": "annotate_sample", "spans": [{"key": "fcv_pads_east_africa:001527:16:1:0", "start": 293, "end": 333, "surface": "telephone survey implemented by the Bank", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Past telephone survey data measured indicators and validated REB data.", "human_verdict": null, "human_note": ""}, {"key": "fcv_pads_east_africa:001527:16:1:2", "start": 462, "end": 478, "surface": "project M&E data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Project monitoring data used for implementation management, not an independent evidence resource.", "human_verdict": null, "human_note": ""}]}, {"key": "p002", "text": "Figure A7: Distributions of poor populations across urban versus rural areas by country, $3.65\npoverty line\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSource: International Urban Poverty Database.\nNote: For the DOU and DB methods, WorldPop 250m is used.\n\n\nFigure A8: Comparison of urban and rural poverty rates\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSource: International Urban Poverty Database.\nNote: WorldPop 250m is used for the DOU and DB methods. Dashed lines are 45-degree lines. Urban areas\ninclude the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and\n“Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.\n\n\n32", "source": "general_prwp", "subset": "annotate_sample", "spans": [{"key": "prwp:000732:33:0:2", "start": 211, "end": 224, "surface": "WorldPop 250m", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "WorldPop population data are used in the DOU and DB methods.", "human_verdict": null, "human_note": ""}]}, {"key": "p003", "text": " Global\nPartnership for Education (GPE). The COVID-19 crisis is expected to put further strain on the Government budget.\nIt is also despite the large volumes of foreign aid allocated to the education sector, as Uganda has been one of\nfive top recipients of foreign aid at US$1.6 billion disbursed between 2002 and 2014 (World Bank 2017).\n\n\n1 Uganda CPF FY16-21, 2.\n2 Uganda Economic Update #14, 2020.\n3 National Population and Housing Census 2016.\n4 Uganda CPF FY16-21, 2.\n5 UNESCO 2014, Teacher Issues in Uganda: A shared vision for an effective teachers’ policy.\n\n\nPage 6 of 96", "source": "refugee_pads", "subset": "annotate_sample", "spans": [{"key": "refugee_pads:000060:11:2:0", "start": 403, "end": 446, "surface": "National Population and Housing Census 2016", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named national census cited as an existing data source.", "human_verdict": null, "human_note": ""}]}, {"key": "p004", "text": " allocated to the end target was allocated to a new DLR<br>13.3d: Conduct national assessment on the integration of youth friendly health services into PHCU at select health facilities.  Result achieved and<br>disbursement was made fully.|<br>**Comments (achievements against targets):**  <br>The government could not conduct the SARA and SPA surveys in 2021 due to factors associated with the COVID-19 outbreak and security problems in the<br>country. As a result, the achieved result for this indicator could not be verified. Hence, the fund allocated to the end target was allocated to a new DLR<br>13.3d: Conduct national assessment on the integration of youth friendly health services into PHCU at select health facilities.  Result achieved and<br>disbursement was made fully.|<br>**Comments (achievements against targets):**  <br>The government could not conduct the SARA and SPA surveys in 2021 due to factors associated with the COVID-19 outbreak and security problems in the<br>country. As a result, the achieved result for this indicator could not be verified. Hence, the fund allocated to the end target was allocated to a new DLR<br>13.3d: Conduct national assessment on the integration of youth friendly health services into PHCU at select health facilities.  Result achieved and<br>disbursement was made fully.|\n|||||||\n|**Indicator Name**|**Unit of Measure**|**   Baseline**|**Original Target**|**Formally Revised**<br>**Target**|**Actual Achieved at**<br>**Completion**|\n|Improved transparency of<br>Pharmaceutical Fund and<", "source": "fcv_pads_east_africa", "subset": "annotate_sample", "spans": [{"key": "fcv_pads_east_africa:007106:49:2:0", "start": 330, "end": 350, "surface": "SARA and SPA surveys", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Sentence states the government could not conduct these surveys; they were project activity.", "human_verdict": null, "human_note": ""}]}, {"key": "p005", "text": "**The World Bank**\nUganda Climate Smart Agricultural Transformation Project (P173296)\n\n\nprocurement of machinery; installation of irrigation systems for post control evaluation; seed inspection,\nverification and certification; and procurement and deployment of seed traceability systems.\n\n\n_Subcomponent 1.3. Strengthening Agro-Climate Monitoring and Information Systems (US$10.6 million -_\n_IDA)_\n\n\n20. The subcomponent will finance the generation and timely transmission of accurate weather data\nand climate information thereby strengthening weather forecasting and its dissemination tools. Financing\nwill be for:(a) acquisition and establishment of functional automated weather stations and related\nequipment in locations where gaps have been identified to improve agro-meteorological forecasting and\nmonitoring; (b) rehabilitation and upgrading of existing weather stations in project areas; (c) acquisition\nand utilization of big data to develop a climate smart, agro-weather information system and advisories;\n(d) establishing partnerships with local and international institutions to support the generation of climate\ninformation using global data sources such as satellites; (e) upgrading and operationalizing the weather\ninformation dissemination system; (f) building the technical capacity of MAAIF and extension staff for\nagro-meteorological observation and forecasting and real-time delivery of weather information and\nadvisories to target farmers in project districts including RHDs and refugee settlements; (g) development\nof agroclimatic and climate smart digital tools to facilitate access to early warning, agroclimatic, and pest\nand disease surveillance information; (h) establishment of soil organic carbon monitoring reporting and\nverification of GHG removals including lab analysis for tracking application, adoption, and impact of TIMPs;\nand (i) facilitating partnership with Uganda National Meteorological Authority (UNMA) to build capacity\nof MAAIF and local governments in agro-met data collection, management, analysis and dissemination;\nand (j) enhancement of UNMA’s capacity in agro-met data collection, management, analysis and\ndissemination.\n\n\n_Sub", "source": "refugee_pads", "subset": "annotate_sample", "spans": [{"key": "refugee_pads:000001:20:0:1", "start": 931, "end": 939, "surface": "big data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Planned acquisition and utilization for a future information system, not existing data use.", "human_verdict": null, "human_note": ""}, {"key": "refugee_pads:000001:20:0:2", "start": 1998, "end": 2011, "surface": "agro-met data", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Data collection and management are planned project activities.", "human_verdict": null, "human_note": ""}]}, {"key": "p006", "text": "**IBRD Map 43032**\n\n\n83", "source": "refugee_pads", "subset": "annotate_sample", "spans": [{"key": "sample:refugee_pads:000144:96:0:0", "start": 2, "end": 10, "surface": "IBRD Map", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p007", "text": " SRH\nservices and one in five did not trust local\nhealthcare providers, in addition to facing language\nbarriers.\n\n\nWomen with a disability reported more barriers with\n11% across the region (N=29) compared to those\nwithout disability (5%).\n\n\nAn in-depth assessment is needed to better\nunderstand sexual and reproductive health (SRH)\nneeds, the role of SRH access barriers in decisions\nto visit Ukraine, and how these barriers vary among\nwomen of different age groups, pregnant and\nbreastfeeding women, and women with disabilities.\n\n\n**Support services for survivors of gender-based**\n**violence**\nServices for survivors of gender-based violence\nencompass a range of functions, including safety\nand security, legal assistance, healthcare, mental\nhealth and psychosocial support. A critical\ncomponent is access to clinical management of\nrape to ensure timely medical treatment and care.\nAs this service is provided by the health care sector,\nas part of SRH, it is included in this analysis.\n\n\nThe SEIS identified gaps in awareness about on\navailable GBV services. In 2024, 38% of\nrespondents were unaware of health services\nproviding support to GBV survivors in their area,\nwhile 58% were unaware of available psychosocial\nsupport services. Respondents were less aware of\nhealth services in rural areas (45%) compared to\nurban areas (37%). Key barriers to accessing\nGBV-related services in general included lack of\nawareness (58%), language and cultural barriers\n(53%) and stigma/ shame (46%). This indicates that\nadditional coordinated efforts between the health,\nprotection and GBV working groups and partners\nare required to enable access to all lifesaving\nGBV-related services including clinical management\nof rape.\n\n\n**17**", "source": "jad_paddy_docs", "subset": "annotate_sample", "spans": [{"key": "sample:jad_paddy_docs:000004:16:1:0", "start": 994, "end": 998, "surface": "SEIS", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p008", "text": "Chapter 5\n\n\nFigure 6 **| Forcibly displaced persons from the Sahel region |** 2010 to mid-2020\n\n\n\n2,000,000\n\n\n1,800,000\n\n\n1,600,000\n\n\n1,400,000\n\n\n1,200,000\n\n\n1,000,000\n\n\n800,000\n\n\n600,000\n\n\n400,000\n\n\n200,000\n\n\n\n\n\n0\n\n2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 mid-2020\n\n\n\nSince an initial outbreak of armed conflict in northern\nMali in 2011, fighting has spread to central Mali,\nNiger and Burkina Faso. As living conditions further\ndegrade and livelihoods disappear, more people are\nlikely to be displaced within the region and possibly\nsouthward to coastal countries such as Benin, Côte\nd’Ivoire, Ghana and Togo or northward to North\nAfrica and Europe.\n\n\nThe recent statistics show a sharp increase in\nforced displacement within and from each of\nthese five countries. The total number of refugees,\n\n\n\nasylum-seekers and IDPs from these countries\nrose from 742,000 in 2018 to 1.4 million in 2019 and\nreached almost two million by mid-2020. Internal\ndisplacement accounts for the largest proportion of\nnew displacement, with IDP numbers in the Sahel\ngrowing from 489,000 in 2018 to 1.1 million in 2019.\nDisplacement increased by a further 50 per cent in\nthe first half of this year to reach 1.7 million by mid2020. Similarly, the number of refugees originating\nfrom countries in the Sahel rose 8 per cent in the first\nsix months of 2020, up from 228,000 at end-2019.", "source": "reliefweb", "subset": "annotate_sample", "spans": [{"key": "reliefweb:000911:19:0:0", "start": 666, "end": 683, "surface": "recent statistics", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Statistics support reported displacement increases and totals.", "human_verdict": null, "human_note": ""}]}, {"key": "p009", "text": "**The World Bank**\nLebanon: Wheat supply emergency response project (P178866)\n\n\nTo address price risks and monitor access to affordable bread, the project will finance third-party monitoring agency\n(TPMA), such as mobilization of Red Cross Volunteers with the Lebanese Red Cross/International Federation of Red Cross\nand Red Crescent Societies at community level/downstream. The TPMA will facilitate price data collection for flour at the\nmill, for bread at the bakeries, and for bread at retail outlets, on a weekly basis and for a sample of outlets across all\ngovernorates. At the same time, the project will finance high frequency ‘Listening to Poor and Vulnerable Household\nSurveys’, entailing data collection on bread prices and consumption for the poor and vulnerable households, by\nconducting random sampling and surveying (biweekly) using UNHCR and WFP beneficiary lists. This information will be\ntriangulated at MOET level with information consolidated from the consumer protection agency, GM, and WFP price\nmonitoring system, and used to adopt appropriate remedies, such as activating the preferential distribution clause\nforeseen in the Framework Agreement, for bakeries in areas where most of the poor and vulnerable groups are located.\nAll monitoring reports will be published on Central Inspection’s IMPACT online platform.\n\n35. **The component will also support consultancy services and technical assistance that will strengthen MOET’s**\n**oversight function as well as capacity** **to manage the gradual transition from the current wheat subsidy system to a**\n**more market-oriented system** . This will include developing a better price monitoring and data system (both for wheat\nand bread); developing an implementation plan for gradually removing wheat subsidies and bread prices and potentially\nincreasing importers’ financial participation in wheat import purchases, to be informed by the complementary activities\ndescribed below; conducting an adequate stakeholder outreach and communication about these reforms; and\nstrengthening regional cooperation around food security and risk management in the", "source": "jdc_operational", "subset": "annotate_sample", "spans": [{"key": "sample:jdc_operational:000019:21:0:0", "start": 635, "end": 685, "surface": "Listening to Poor and Vulnerable Household\nSurveys", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:jdc_operational:000019:21:0:1", "start": 847, "end": 878, "surface": "UNHCR and WFP beneficiary lists", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing beneficiary lists are used to draw a random survey sample.", "human_verdict": null, "human_note": ""}, {"key": "sample:jdc_operational:000019:21:0:2", "start": 1007, "end": 1034, "surface": "WFP price\nmonitoring system", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:jdc_operational:000019:21:0:3", "start": 1648, "end": 1680, "surface": "price monitoring and data system", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p010", "text": " The interim\nICR (December 2022) used the 2019 survey data to assess achievement of the project outcomes as of 2021\nand judged efficacy to be High, noting that the project had exceeded all applicable (unadjusted) outcome\ntargets at that time. The interim ICR Review, however, rated efficacy as “Substantial, with moderate\nshortcomings,” arguing that, although the targets had been met, it was “unwise to assume that the results\nwould have been maintained, much less improved through 2021, considering the outbreak of the COVID-19\npandemic beginning in 2020.”\n\n\nThe final ICR (July 2025) acknowledges the absence of outcome indicator data after 2019, but explains that\nconducting subsequent impact evaluation surveys had become increasingly difficult and costly: (a) the\nCOVID-19 pandemic made data collection impossible for the survey scheduled in 2021; (b) the conflict in\nnorthern Ethiopia during 2020-2022, followed by renewed conflict in the Oromia and Amhara regions,\ndisrupted activities across the country; and (c) a new impact evaluation survey would now cost an estimated\nUS$1 million and take several years to complete.\n\n\nIn view of these limitations, this ICR Review considers the output indicators to be sufficient substitutes for the\noutcome indicators in assessing the project’s efficacy. The project delivered substantial outputs, providing\nover 29,000 loans to women-owned MSEs, training more than 43,000 women entrepreneurs, and mobilizing\nresources from donors and PFIs amounting to nearly twice the US$150 million IDA credit. Accordingly,\nefficacy is rated Substantial, with moderate shortcomings.\n\n\n**Rating**\nSubstantial\n\n\nPage 9 of 20", "source": "fcv_pads_east_africa", "subset": "annotate_sample", "spans": [{"key": "sample:fcv_pads_east_africa:001330:8:1:0", "start": 42, "end": 58, "surface": "2019 survey data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:fcv_pads_east_africa:001330:8:1:1", "start": 615, "end": 637, "surface": "outcome indicator data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:fcv_pads_east_africa:001330:8:1:2", "start": 1028, "end": 1052, "surface": "impact evaluation survey", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p011", "text": ">sanitation service delivery and support long-term<br>investments in infrastructure development, in RHDs<br>in the West Nile and Northern region.<br>Locations targeted for solar based water pumping<br>have already been identified in Arua, Yumbe, Moyo,<br>Adjumani, Lamwo, and Kiryandongo<br>|Provide digital enabling environment for<br>remove water monitoring and strengthen<br>efficiencies and effectiveness of water<br>management systems.<br> <br> <br> <br> <br>|\n|**Gender Based Violence**<br>**and Violence Against**<br>**Children Prevention and**<br>**Response Services in**<br>**Uganda’s Refugee-**<br>**Hosting Districts Report**<br> <br>_Status: Analysis_<br>_completed,_|Total<br>0.5<br> <br> <br>RSW/<br>WHR<br>N/A|To mitigate GBV and prevent violence against children<br>through engagement in productive activities in 4<br>RHDs.|Increased<br>access<br>to<br>more<br>affordable<br>connectivity will also increase likelihood of GB<br>online risks. Project will support the project<br>objective indirectly by including awareness<br>and mitigation measures in digital skills<br>training.<br> <br>Digital connectivity will strengthen case<br>management for GBV and violence against|\n\n\nPage 64 of 76", "source": "jdc_operational", "subset": "annotate_sample", "spans": [{"key": "jdc_operational:000023:76:3:0", "start": 610, "end": 634, "surface": "Hosting Districts Report", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Fragment of a table title, not an independently used data resource.", "human_verdict": null, "human_note": ""}]}, {"key": "p012", "text": " the Deloitte D.Climate model, economic\nimpact of Ukrainian refugees amounted\nto a higher real GDP by 1.5% in 2022, as\nthey initially entered the labour market.\nWith more refugees finding employment,\ntheir impact grew to 2.3% GDP in 2023,\nand further to 2.7% GDP in 2024. This\ncorresponds to GDP being higher by\nPLN 98.7 billion in 2024. In the long term, as\nthe refugees acquire more country-specific\nskills and firms invest to restore their\ncapital-to-labour ratio, the impact will grow\nto 3.2% GDP by 2030. Refugees contribute\nto the economy by increasing the labour\nsupply as both workers and entrepreneurs,\nand by boosting demand as consumers.\nThe increase in GDP is not directly\n\n\n\nproportional to the increase in population\nor employment. <sup>20</sup> [^20: As outlined in the Appendix on modelling strategy, the total number of refugees was set at 2.6% of the total population, while their share in total employment as\ngrowing from 1.5% in 2022 to 2.4% in 2024.] On the one hand,\nincrease in productivity further boosts\nthe economy, on the other net benefits\nare lowered both due to a decrease in\nthe capital-to-labour ratio, as well as an\nincrease in competition in the labour\nmarket. Moreover, the increase in demand\nin tight labour market conditions work in\nthe direction of higher inflation and lower\nprice competitiveness of Polish products\nwhich decrease its overall positive impact.\n\n\n**The results are in line with the**\n**optimistic scenario from the**\n**previous Deloitte (2024) report.** The\ncurrent report is different from the one\nfrom 2024 in that we account for the\npositive productivity shock reflected in\nthe labour market data, which further\n\n\n\n**The impact of Ukrainian refugees**\n**on the Polish economy is estimated**\n**with the Deloitte D.Climate general**\n**equilibrium model** <sup>", "source": "jad_paddy_docs", "subset": "annotate_sample", "spans": [{"key": "sample:jad_paddy_docs:000001:11:3:0", "start": 1635, "end": 1653, "surface": "labour market data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing labour market data are used to reflect a productivity shock in the model.", "human_verdict": null, "human_note": ""}]}, {"key": "p013", "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_sample", "spans": [{"key": "refugee_pads:000012:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Logframe verification data represent planned project monitoring, not existing analytical use.", "human_verdict": null, "human_note": ""}, {"key": "refugee_pads:000012:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Listed as a future indicator-verification source, not evidence actually analyzed.", "human_verdict": null, "human_note": ""}, {"key": "refugee_pads:000012:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "NaCSA administrative data are listed as a verification source for program awareness indicators.", "human_verdict": null, "human_note": ""}]}, {"key": "p014", "text": "The sampling approach for\nMyanmarese refugees entailed two\nsteps. Firstly, we used UNHCR’s\nregistration database (proGRES)\nand a population proportional to size\n(PPS) strategy to randomly select\n20 enumeration areas out of the 30\nmapped Sample Sites <sup>9</sup> . PPS is useful\nwhen sampling units vary in size\nbecause it assures that households in\ndenser enumeration areas have the\nsame probability of getting into the\nsample as those in smaller sites, and\nvice versa. Secondly, we randomly\nselected 20 households in each of the\n20 Sample Sites, for a total sample\nof 400. This selection occurred by\nrandomly selecting 6-7 apartment\nbuildings in each enumeration area,\nthen going to these locations and\nrandomly selecting the nearest 3-4\ndwellings with refugees from Myanmar\nplus 2-3 Indian/non-refugee dwellings\nfor interviews. Our final sample of\nMyanmarese totalled 434 households.\n\n\nThe sampling approach for Somali\nrefugees entailed enumeration. In view\nof the very limited number of Somali\nrefugees living in Delhi (around 200\nindividuals in total) we targeted all of\nthem. We identified their addresses\n\n\n\nusing UNHCR’s registration database,\nbeneficiaries’ lists of implementing\npartners and field visits with key\ninformants. We ended up with a\ntotal of 64 Somali households.\n\n\nThe sampling strategy for the Afghan\ncommunities had to address the\nvery low densities in Delhi’s wards.\nWe randomly identified addresses\nfrom UNHCR’s registration database\n(using a random number generator).\nDuring the field visits, if no Afghan\nnationals were living in the selected\napartment building, the enumerators\nselected up to two replacements from\nneighbouring blocks or buildings.\n\n\nOnce an Afghan household was\nidentified, enumerators used a\n‘snowball’ technique (often used\nin hidden populations which are\ndifficult to access) by asking the\nidentified Afghan respondent to\npoint us to other Afghans in the\nneighbourhood. In order to limit bias\nwe only selected up to four households\nthrough snowballing to complete the\ndesired sample. Indian households\nwere", "source": "reliefweb", "subset": "annotate_sample", "spans": [{"key": "reliefweb:000087:19:0:4", "start": 1121, "end": 1150, "surface": "UNHCR’s registration database", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing UNHCR database used to identify addresses for refugee sampling.", "human_verdict": null, "human_note": ""}, {"key": "reliefweb:000087:19:0:3", "start": 1152, "end": 1197, "surface": "beneficiaries’ lists of implementing\npartners", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing beneficiary lists were used to identify addresses for refugee sampling.", "human_verdict": null, "human_note": ""}]}, {"key": "p015", "text": "-Poverty-in-Contexts-of-Forced-Displacement](http://documents.worldbank.org/curated/en/492181635479693932/A-Multi-Country-Analysis-of-Multidimensional-Poverty-in-Contexts-of-Forced-Displacement)</u>_\n\nThis paper **develops a Multidimensional Poverty Index (MPI) to examine patterns**\n\n**of multidimensional poverty among IDPs and refugees, with comparisons to**\n\n**host populations, in five African countries** . The MPI is disaggregated to analyze\n\nvariations in deprivation by displacement status of the household and gender of the\n\nhousehold head. The analysis draws on household survey data from Ethiopia, Nigeria,\n\nSomalia, South Sudan, and Sudan <sup>11</sup> [^11: In Ethiopia, the Skills Profile Survey (2017) sampled refugees in and around camps in the Tigray, Afar, Gambella,\nBenishangul Gumuz, and Somali regions. In Nigeria, the IDP Survey (2018) sampled IDPs and host communities in six\nnortheastern states (Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe). In Somalia, the High Frequency Survey (2017)\nsampled IDPs and host communities in secure parts of the country. In Sudan, the IDP Profiling Survey (2018) sampled IDPs\nand neighboring host communities in the Abu Shouk and El Salam camps, in Al-Fashir. And in South Sudan, the High\nFrequency Survey Wave 4 (2017) sampled IDPs and host communities in urban areas of seven of the ten pre-war states\n(Western Equatoria, Central Equatoria, Eastern Equatoria, Northern Bahr-El-Ghazl, Western Bahr-El-Ghazal, Warrap, Lakes\nstate).] .\n\n\n[10 This work is part of the program “Building the Evidence on Forced Displacement: A Multi-Stakeholder Partnership”. The](https://www.worldbank.org/en/programs/building-the-evidence-on-forced-displacement)\nprogram is funded by UK aid from the United Kingdom's Foreign, Commonwealth, and Development Office (FCDO). It is\nmanaged by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for\nRefugees (UNHCR). This", "source": "reliefweb", "subset": "annotate_sample", "spans": [{"key": "sample:reliefweb:000595:24:1:0", "start": 573, "end": 594, "surface": "household survey data", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:reliefweb:000595:24:1:1", "start": 689, "end": 710, "surface": "Skills Profile Survey", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:reliefweb:000595:24:1:2", "start": 841, "end": 851, "surface": "IDP Survey", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:reliefweb:000595:24:1:3", "start": 987, "end": 1008, "surface": "High Frequency Survey", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:reliefweb:000595:24:1:4", "start": 1096, "end": 1116, "surface": "IDP Profiling Survey", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:reliefweb:000595:24:1:5", "start": 1245, "end": 1273, "surface": "High\nFrequency Survey Wave 4", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p016", "text": "**The World Bank**\nSupport for Social Recovery Needs of Vulnerable Groups in Beirut (P176622)\n\n\nand most recently, to 157.9% in March 2021.” <sup>5</sup> Importantly, inflation is a highly regressive tax, affecting\nthe poor and vulnerable disproportionately, as well as people on fixed income, such as pensioners. <sup>6</sup>\n\n\n3. **Compounded by the global economic shock presented by COVID-19, disruptions in international food**\n**supply chains and trade networks exacerbate Lebanon’s food security vulnerabilities.** Lebanon's\nremittances dropped by 20%, from 3.9 billion U.S. dollars in the first half of 2019 to 3.1 billion dollars in\nthe first half of 2020, according to Bank Byblos' Lebanon This Week report released on Tuesday February\n9, 2021. <sup>7</sup> Furthermore, the restrictions on movement to combat the pandemic have hindered foodrelated logistic services, disrupting food supply chains and jeopardizing food security for millions of\npeople. The higher levels of export restrictions particularly leave food-importing countries vulnerable to\ncommodity price fluctuations. The World Bank’s Spring 2021 LEM found average food inflation to have\ngrown by a record 254 percent over 2020. <sup>8</sup> Meanwhile, the World Food Program (WFP) reported that the\nConsumer Price Index (CPI) experienced annual inflation of 133% between October 2019 and November\n2020, representing an all-time high since it began monthly price monitoring in 2007. <sup>9</sup> This is particularly\nrelevant as Lebanon imports at least 80% of its food supplies (ESCWA 2016). Real GDP growth is estimated\nto have contracted by 20.3 % in 2020, on the back of a 6.7 % contraction in 2019.\n\n4.", "source": "jdc_operational", "subset": "annotate_sample", "spans": [{"key": "jdc_operational:000002:3:0:0", "start": 1109, "end": 1124, "surface": "Spring 2021 LEM", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named World Bank report supports the stated 2020 food-inflation finding.", "human_verdict": null, "human_note": ""}, {"key": "jdc_operational:000002:3:0:1", "start": 1274, "end": 1294, "surface": "Consumer Price Index", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "WFP-reported CPI inflation provides a concrete finding for Lebanon.", "human_verdict": null, "human_note": ""}]}, {"key": "p017", "text": "|4,513<br>5,028|28,601<br>104,972|\n\n\n2. **The economic activity slow down caused by COVID-19 has affected Uganda’s ability to generate**\n**jobs for those living in vulnerable situations, including refugees and host communities.** Despite the\nconcerted efforts to integrate refugees within the ecosystems of their host communities, refugeehosting districts (RHDs) remain less developed areas. Low levels of disposable incomes have resulted\nin low demand and limited access to labor markets, leaving those residents with some access to land\nwith no alternative but to live off subsistence agriculture and humanitarian aid. These areas were less\ndeveloped even before the inflow of refugees and remain decoupled from resilient and viable supply\nchains in the economy. For example, the average value of assets among all households (both refugee\nand host) in the district of Arua <sup>64</sup> is 560,000 Ugandan shillings (US$ 144), which is only 10 percent of\ncomparable asset values in the Kampala region.\n\n\n62 Uganda Comprehensive Refugee Response Portal ( _[https://data2.unhcr.org/en/country/uga](https://data2.unhcr.org/en/country/uga)_ ) 31 October 2021\n63 Calculation based on district-level firm data from Census of Business Establishments (COBE), and refugee and host\ncommunity household data from the Refugee and Host Community Household Survey\n64 Arua was until recent sub-divisions of the district considered a refugee hosting district.\n\n\nPage 71 of 92", "source": "jdc_operational", "subset": "annotate_sample", "spans": [{"key": "jdc_operational:000021:76:3:0", "start": 1211, "end": 1244, "surface": "Census of Business Establishments", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named census data underlies the district-level firm-data calculation.", "human_verdict": null, "human_note": ""}, {"key": "jdc_operational:000021:76:3:1", "start": 1257, "end": 1298, "surface": "refugee and host\ncommunity household data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Household data from the named survey supports a district-level calculation.", "human_verdict": null, "human_note": ""}, {"key": "jdc_operational:000021:76:3:2", "start": 1308, "end": 1351, "surface": "Refugee and Host Community Household Survey", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named household survey data used in a district-level firm-data calculation.", "human_verdict": null, "human_note": ""}]}, {"key": "p018", "text": "**The World Bank**\nUG Teacher and School Effectiveness Project (P133780)\n\n\nof National Assessment of Progress in Education (NAPE) in 2015 and Early Grade Reading Assessment (EGRA) in 2016, though both are\nrunning behind their respective year 2 targets.\n\n\n**Table 2. Progress on Intermediate Results Indicator**", "source": "fcv_pads_east_africa", "subset": "annotate_sample", "spans": [{"key": "fcv_pads_east_africa:015421:6:0:0", "start": 78, "end": 122, "surface": "National Assessment of Progress in Education", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Assessment appears as a project progress target, not as analyzed existing data.", "human_verdict": null, "human_note": ""}]}, {"key": "p019", "text": "**Sub-Program C—Transparency and Accountability** ( _IDA US$10 million equivalent)._ This SubProgram will support the following three sets of activities\n\n\n(a) **_Part C1: Monitoring the broader framework of accountability reforms_**\nPart C1 of Sub-Program C will monitor, within the PBS Results Framework, the progress of selected\naccountability-related reforms financed and implemented outside PBS but relevant to the effective\ndelivery of services at the local level, including the Good Governance package of the District Level\nDecentralization Program (DLDP) and other activities that are linked with Public Sector Capacity\nBuilding Program (PSCAP). No funding will be allocated from PBS to Sub-Program C Part C1.\n\n(b) **_Part C2: Targeted support to Public Financial Management_**\nBuilding on the experience of Component 3 of PBS I, part 2 of Sub-Program C will finance a series of\nactivities designed to strengthen public financial management at the Federal and regional levels, to help\ncreate a system that effectively supports the delivery of high-quality basic services. These will include:\n(i) support and training for the Office of the Federal Auditor General (OFAG) and (Office of Regional\nAudit General (ORAGs), to strengthen fiscal accounting and reporting capacity; (ii) enhancing PFM\ncapacity at woreda levels (IBEX and so forth); (iii) financing the PBS accountants?; (iv) Continuous\nAudits reformed so that (among other things) they include “transactions testing” (verification of reported\nfiscal data); (v) other capacity building activities to support Public Finance Management (PFM)\nstrengthening at Federal and regional levels.\n\n(c) **_Part C3: Financial Transparency and Social Accountability_** .\nThis activity aims to strengthen and deepen Financial Transparency and Accountability (FTA) and social\naccountability initiatives which", "source": "fcv_pads_east_africa", "subset": "annotate_sample", "spans": [{"key": "fcv_pads_east_africa:013690:4:0:0", "start": 1507, "end": 1518, "surface": "fiscal data", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Fiscal data is verified as part of routine financial auditing and reporting.", "human_verdict": null, "human_note": ""}]}, {"key": "p020", "text": "12</sup>\n(henceforth noted as the 3YS) was\n\n\n\na multi-stakeholder, multi-sectoral blueprint\non the process of GCR objective of expanding\nthird country solutions. The 3YS, which evolved\ninto the “Third Country Solutions for Refugees:\nRoadmap 2030,” (hereafter the Roadmap 2030) <sup>13</sup>\nwas published in June 2022. Sound data on\ncomplementary pathways is needed to track\nprogress of the Roadmap 2030 target of 2.1\nmillion on complementary pathways, and yet, a\nkey issue identified in the 3YS and Roadmap 2030\nis the lack of data on the availability and use of\ncomplementary pathways. This finding highlights\nthe need for systematic and harmonised\ndata collection to build the evidence base on\ncomplementary pathways. As such, the “Safe\nPathways for Refugees” report series is a flagship\npublication on complementary pathways data\nallowing us to more effectively assess the impact\nof pathways programmes and to centre evidencebased programming.\n\n\n**LOOKING FORWARD**\nRoadmap 2030 provides updated information on\nenabling actions, along with short- and mediumterm activities, and more in-depth review of the\nthird country solutions of family reunification,\neducation pathways, labour mobility, and other\ncomplementary pathways. In 2023 and 2024, the\nRoadmap 2030 notes the need for stock-taking\nand data reporting, including a review at the\n<u>[2023 Global Refugee Forum (GRF)](https://www.unhcr.org/global-refugee-forum-2023)</u> scheduled in\n<u>December, and an update on the Global Action</u>\nPlan (currently Roadmap 2030). With clear and\ncontinued need for data on complementary\npathways, the “Safe Pathways for Refugees”\nreport remains essential to informing stakeholders\nabout progress towards achieving the Roadmap\n2030 vision of admitting 2.1 million refugees by\n2030.\n\n\n\n[First edition: OECD-UNHCR, “Safe pathways for refugees", "source": "reliefweb", "subset": "annotate_sample", "spans": [{"key": "reliefweb:001039:6:1:0", "start": 806, "end": 833, "surface": "complementary pathways data", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Names a data topic without presenting an attributed finding or concrete analytical use.", "human_verdict": null, "human_note": ""}]}, {"key": "p021", "text": "outcomes of individuals in this narrow age group below and above the cut-off age would have\n\nevolved similarly in the absence of the reform.\n\n\nOur sample contains DI recipients belonging to the affected benefit categories who were\n\naged 56 or 57 in December 2011. Those who were 56 (just below the cut-off) in December\n\n2011 make up the treatment group, while those who were 57 (just above the cut-off) make up\n\nthe control group. We restrict the sample to individuals claiming DI throughout 2011 who\n\nwere alive in January 2012. We restrict the control age group to age 57 at the end of 2011\n\nto exclude individuals close to the old-age retirement age in order to improve comparability\nacross the control and treatment age groups. <sup>9</sup> We focus on men below 62, the statutory\n\nretirement age for the oldest cohorts, allowing us to use data up to 2015. Our focus on men\n\nis motivated by the “Women 40” policy which since 2011 gives an early retirement option to\n\nwomen with 40 years of work credits, regardless of age. This policy could affect the control\n\nand treatment age groups differently, potentially confounding our results for women. Finally,\n\nthose who died during the observed time period are included in the sample until the last\n\nyear they were alive.\n\n\nSummary statistics for the control and treatment groups are displayed in Table 2. The\n\ntwo groups are quite similar to each other on most dimensions. They have approximately the\n\nsame employment rate (24.3% vs 24.9%) while receiving benefits in 2011 and each group has\n\nbeen receiving benefits for 11 years on average. Despite being a year younger, the 56-year\nold treatment group may be slightly less healthy with average prescription drug spending of\n\n533 euros vs 512 euros among the 57-year-old control group. Importantly for labor market\n\noutcomes, the two groups live in geographic areas with similar economic environments as\n\nevidenced by the average unemployment rate of their micro-", "source": "general_prwp", "subset": "annotate_sample", "spans": [{"key": "prwp:000704:10:0:0", "start": 844, "end": 859, "surface": "data up to 2015", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Generic data availability timeframe without an identified source or analyzed finding.", "human_verdict": null, "human_note": ""}]}, {"key": "p022", "text": "data leaves important considerations unaccounted for, such as reverse causality and the migration\ntransition’s longitudinal dimension, as the transition takes place over an extended time period in a given\norigin country. Other studies have tested for a hump shape using panel data (Mayda, 2010; Bertoli and\nHuertas-Moraga, 2013). However, these papers use a limited number of country-time points, which restricts\nthe empirical strength of their results. Other papers test the inverted-U relationship using solely migration\nflows to OECD destinations (Lull, 2016; Benček and Schneiderheinze, 2019). These studies, however,\nexclude the possibility that migrants from low-income countries can also migrate to other low- or mediumincome countries. Since the average share of migration from all origins to non-OECD destinations is 50%\nover the 1960-2017 period, <sup>4</sup> [^4: Computed using the World Bank’s Global Bilateral Migration (Özden _et al._, 2011) and the United Nations’ Trends in International] we include such migration flows in order to incorporate all migration corridors\nin the analysis.\n\n\nThe aim of this paper is to test for the inverted U-shape between emigration and development using\na large panel database. We employ a comprehensive global panel data set with 180 origin and destination\ncountries on a 50-year timeframe (1970-2020). <sup>5</sup> [^5: Data on international migrant stocks in 2019 is used as a proxy for 2020.] This allows us to empirically test for bilateral migration\ndynamics not only across countries but also across time with a relatively large number of observations.\nBecause of its large longitudinal dimension, it is well suited for testing the migration transition hypothesis’\ncentral prediction, which is a long-run phenomenon per origin country (De Haas, 2010). Our empirical\nspecification is based on the random utility-maximization (RUM) model, which provides the microfoundations for a migration version of the gravity model. <sup>6</sup> We employ a gravity-migration specification with\na large number of", "source": "general_prwp", "subset": "annotate_sample", "spans": [{"key": "prwp:000028:4:0:0", "start": 270, "end": 280, "surface": "panel data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing panel data supported prior empirical tests of the migration hump hypothesis.", "human_verdict": null, "human_note": ""}, {"key": "prwp:000028:4:0:2", "start": 1240, "end": 1275, "surface": "comprehensive global panel data set", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing panel dataset is explicitly used for empirical migration analysis.", "human_verdict": null, "human_note": ""}]}, {"key": "p023", "text": ", ECRP-II will scale up to include refugees in the two refugee-hosting\ncounties that were already targeted under ECRP-I <sup>49</sup> and two new flood-prone vulnerable counties to\nsupport Subcomponent 1.2 activities, leading to a total of 12 counties. Five counties have been shortlisted for the flood risk reduction subcomponent based on predetermined selection criteria (see subcomponent 1.2). Based on budgetary availability, additional counties may be included based on\nconsultation with the Government. Alternatively, some may be replaced should security or accessibility\nbecome an issue during implementation. It was confirmed that the project will use the administrative\ndivision of 10 states, 79 counties, and 2,008 _payams_ just as under ECRP-I in the absence of more recent\nofficial boundaries _._ <sup>50</sup> Any discrepancies on the ground will be resolved in consultation with the\nGovernment.\n\n\n48 The 10 counties were originally selected based on a composite vulnerability index that the World Bank team developed and which the\nGovernment agreed to. The index included (a) concentration of returnees, (b) access to basic services, (c) food insecurity, (d) incidents of violence,\n(e) remoteness, and (f) exposure to natural disasters. The long list of the most vulnerable counties was short-listed based on security and\naccessibility.\n49 As refugees are exclusively in refugee camps, ECRP-I only targeted host communities and not the refugees. Under ECRP-II, the project will target\nboth refugees and host communities in the two refugee-hosting counties of Maban and Pariang.\n50 Such practice is in line with how the national budget is being allocated and how other development partners are operating.\n\n\nPage 20 of 73", "source": "jdc_operational", "subset": "annotate_sample", "spans": [{"key": "jdc_operational:000028:25:1:0", "start": 966, "end": 995, "surface": "composite vulnerability index", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Index data informed county selection using documented vulnerability criteria.", "human_verdict": null, "human_note": ""}]}, {"key": "p024", "text": "19. The foundations of an adaptive safety nets system will be measured by:\n\n\n     - Establishment and functioning of a Safety Net Unit.\n\n\n     - Design, testing and validation of a targeting system of Chad safety nets.\n\n\n     - Design, development, utilization and assessment of a Management Information\nSystem (MIS).\n\n\n     - Share of beneficiaries with information stored in the new social registry.\n\n\n**III.** **PROJECT DESCRIPTION**\n\n\n20. **The proposed project includes three components.** The first component of the proposed\nproject will provide income opportunities to poor households in three different areas of Chad,\nwith CTs and CfW activities; the second component will support the development and use of\nnew service delivery instruments and new institutional arrangements in Chad, including\nidentification, registration, and payment systems; and the third component will support the\nestablishment of the CFS with strong implementation capacity.\n\n\n21. **Four years of project duration is deemed necessary to start designing and**\n**implementing essential systems to support the safety nets, to deliver on the safety net pilots**\n**and achieve the PDO.** It is envisioned that Components 2 and 3 will be implemented\nimmediately following effectiveness through project completion (years one – four), while\nComponent 1 will likely begin implementation from year two onward (years two – four),\nalthough if conditions are met, some CfW activities will start during year one.\n\n\n**Component 1: Safety Net Pilots (US$6.5 million equivalent – IDA (US$3.1 million**\n**equivalent) and Adaptive Social Protection Multi-Donor Trust Fund (ASP MDTF) (US$3.4**\n**million) Financed).**\n\n\n22. **This component will support the design and piloting of CT programs to serve as a**\n**cornerstone of Chad’s future safety net.** As most cash and food transfers are", "source": "refugee_pads", "subset": "annotate_sample", "spans": [{"key": "refugee_pads:000028:17:0:0", "start": 385, "end": 400, "surface": "social registry", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "New registry storage is a future project measurement target, not existing data use.", "human_verdict": null, "human_note": ""}]}, {"key": "p025", "text": " additionally a positive\nimpact on labour productivity that cannot\nbe accounted for using the available data.\nThese effects could stem from the growth\nin specialisation due to additional workers\nwith different skillsets appearing on the\nlabour market, e.g., occupational upgrading\nof native workers.\n\n\n\n**Immigrants could possess complementary skills that**\n**make native workers more productive as they specialise.**\nThese skills do not even need to be advanced to be\ncomplementary. Natives are likely to deal better with\ncommunications-intensive tasks and have better networking, all\nof which may be better paid and not easily transferable between\ncountries. OECD (2016) gives an example of a native carpenter,\nwho employs an immigrant to do his previous manual tasks\nand himself focuses on marketing and business development.\nSuch occupational upgrading has been first shown in a seminal\npaper by Peri and Sparber (2009) in the USA data, but has been\nquickly extended to other countries. From this perspective\nFoged and Peri (2016) look at refugees in Denmark in the 19912008 period. They find that inflow of low-skill refugees caused\nless educated native workers to pursue less manual-intensive\ntasks - improving their wages, employment, and occupational\nmobility. These effects are causal, as the authors exploit the\nrefugee dispersal system, which is orthogonal to economic\nopportunities.\n\n\n**Immigrants could enter childcare, elderly care, and**\n**housekeeping services, allowing highly educated and**\n**productive native women to increase their labour supply.**\nA caveat in the case of refugees from Ukraine is that this could\nmean working below their qualifications or in the informal\nsector, making the overall effect for productivity unclear.\nNevertheless, as availability of care and housework services\nincreases, it becomes easier for women mainly to combine\nfamily and professional lives and increase labour supply.\nSuch effects may be stronger for countries with less accessible\nchildcare. Furtado (2015) reviews this literature, finding evidence\nfrom Australia, Hong", "source": "jad_paddy_docs", "subset": "annotate_sample", "spans": [{"key": "sample:jad_paddy_docs:000007:20:1:0", "start": 931, "end": 939, "surface": "USA data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p026", "text": "**~~PERSONS~~**\n\n\ntatelessness, the situation when a person\ndoes not have the nationality of any country,\nrestricts the enjoyment of fundamental hu# man, social, and political rights, such as access to S\n\neducation, health care and freedom of movement.\nUNHCR estimates that at least 10 million people globally are stateless, while the current statistical data\ncover 3.7 million stateless persons in 78 countries.\n\nCollecting comprehensive data on stateless populations presents a particular challenge because stateless\nindividuals frequently live in precarious situations on the\nmargins of society. Identifying stateless people, however, remains key to addressing the difficulties they face\nand to enabling the efforts of governments, UNHCR,\nand others to prevent and reduce statelessness.\n\nIn November 2014, UNHCR launched the #IBelong\nCampaign to End Statelessness and its accompanying Global Action Plan to End Statelessness: 20142024. The plan sets out a guiding framework of 10\nactions to be taken to end statelessness within 10\nyears. Successful implementation of the plan requires\nimproved baseline population data, and two of the\nplan’s actions relate to the identification of stateless\npersons and to improving data on the situation of\nstateless people.\n\nVarious methods may be used to gather data on\nstateless people, including civil registries, surveys,\n\n\n**46** UNHCR Global Trends 2015\n\n\n\nand population censuses. Population censuses are a\nparticularly important source of data, given that they\nare intended to enumerate the entire population of\na country and the majority of countries implement\na census approximately once a decade. The United\nNations’ recommendations on population censuses\nunderscore the importance of including questions\nrelated to citizenship and on statelessness. <sup>**46**</sup> Where\ncountries have published statistics on stateless people derived from their censuses, such data have been\nincluded in this report.\n\nIn addition, UNHCR collaborates with the different\nparts of the United Nations, in particular at the regional level, to further refine these recommendations\non stateless persons", "source": "reliefweb", "subset": "annotate_sample", "spans": [{"key": "reliefweb:001367:44:0:0", "start": 343, "end": 359, "surface": "statistical data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Statistical data support the concrete count of 3.7 million stateless persons.", "human_verdict": null, "human_note": ""}, {"key": "reliefweb:001367:44:0:1", "start": 1338, "end": 1354, "surface": "civil registries", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Presented as a method for gathering data, not as existing data used.", "human_verdict": null, "human_note": ""}, {"key": "reliefweb:001367:44:0:2", "start": 1374, "end": 1398, "surface": "UNHCR Global Trends 2015", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named existing report referenced as the source containing statelessness data.", "human_verdict": null, "human_note": ""}, {"key": "reliefweb:001367:44:0:3", "start": 1406, "end": 1425, "surface": "population censuses", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Censuses provide published statistics incorporated into the report.", "human_verdict": null, "human_note": ""}, {"key": "reliefweb:001367:44:0:4", "start": 1427, "end": 1446, "surface": "Population censuses", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Censuses are identified as important sources for reported statelessness data.", "human_verdict": null, "human_note": ""}]}, {"key": "p027", "text": "**The World Bank**\nUganda Investing in Forests and Protected Areas for Climate-Smart Development Project (P170466)\n\n\nable to demonstrate benefits fast—considering that landholdings in Uganda are fairly small\nand most landowners cannot allocate significant land areas to tree planting.\n\n(e) **Strengthening women’s land tenure rights and participation in forests (especially**\n\n**community forests) is critical for sustainable (community) forest conservation and**\n**management.** Plans and intervention should be matched by budgets. Male champions and\ntheir involvement are important factors for strengthening women’s rights and\nempowerment.\n\n(f) **Promoting gender-responsive approaches in the forest sector lacks qualitative and gender-**\n**disaggregated data/information.** Therefore, the project will support efforts toward\ndisaggregating data by sex, to sufficiently track progress on men and women involved in\nvarious forest-related businesses and value chains and gender-sensitive benefit sharing and\nleadership.\n\n\n**III.** **IMPLEMENTATION ARRANGEMENTS**\n\n**A. Institutional and Implementation Arrangements**\n\n77. **Implementation of the project activities will be carried out by the MWE, NFA, and UWA, with**\n**the close cooperation of the MTWA in tourism-related activities and OPM** <sup>**59**</sup> [^59: OPM will coordinate closely with the UNHCR.] **in activities in the refugee-**\n**hosting areas.** Overall coordination of the project will be led by the MWE on behalf of the Government.\nStrategic guidance and oversight will be provided by the Project Steering Committee, co-chaired by the\nMWE and MTWA. Annex 1 includes detailed information on the implementation arrangements. The PIM\nwill include financial and administrative policies and procedures for managers, administrators, staff, and\nconsultants responsible for project implementation. It covers aspects related", "source": "refugee_pads", "subset": "annotate_sample", "spans": [{"key": "refugee_pads:000043:35:0:0", "start": 743, "end": 761, "surface": "disaggregated data", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "States missing disaggregated data without citing existing data or substitute estimates.", "human_verdict": null, "human_note": ""}]}, {"key": "p028", "text": ", while addressing climate challenges.\n\n\n\n**SUSTAINABLE COMMUNITIES' PRACTICES**\n\n\nExpanding on the best practices implemented at the\nSustainability Centre in Boa Vista, Roraima, UNHCR will\n**further develop sustainable communities’ practices in**\n**other parts of the country.** Training sessions will be\nprovided on sustainable practices tailored to the needs of\nrefugee and indigenous communities. These sessions will\nfocus on eco-friendly practices, resource conservation,\nclimate resilience, green job skills, and sustainable resource\nand waste management, all aimed at enhancing their\neconomic empowerment.\n\n\n**INDIGENOUS COMMUNITIES**\n**ENGAGING IN SUSTAINABLE FARMING**\n\n\nThe city of Cantá, Roraima, hosts some 140 Warao and Kariña\nindigenous persons living in a self-organized community\nbased on subsistence farming. To address the needs that the\ncommunity expressed during participatory consultations,\nUNHCR and the University of Aalto, Finland, **conducted**\n**capacity strengthening sessions on composting and piloted**\n**a rainwater gathering system,** providing water for personal\nhygiene and irrigation purposes. Participants produced a\nbooklet to disseminate these best practices, and the lessons\nthey learned throughout the process, among other\nindigenous and rural communities.\n\n\n##### **Emergency response to severe floodings** **and landslides in Rio Grande Do Sul**\n\n\n\nUNHCR supported the climate emergency response to the\nsevere floods and landslides of May 2024 affecting 478 cities\nin Rio Grande do Sul state. According to official data, more\nthan 2,3 million people were affected by the floods, with over\n580,000 people having been displaced. An estimate of\n43,000 refugees and others in need of international\nprotection were living in the state when 96 per cent of its\nterritory was severely impacted by the floods.\n\n\nFollowing the Government's request for support, UNHCR\nestablished an emergency operation focused on three areas\nmore severely impacted and hosting the highest", "source": "reliefweb", "subset": "annotate_sample", "spans": [{"key": "reliefweb:000331:4:1:0", "start": 1547, "end": 1560, "surface": "official data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Official data supports reported figures on people affected and displaced.", "human_verdict": null, "human_note": ""}]}, {"key": "p029", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n## **1.** Ukrainian refugees in Poland\n\n##### The large influx of refugees since February 2022, added to a hefty and growing Ukrainian migrant population in Poland (1.1). Refugees have changed the demographics of the local Ukrainian residents, with most of them being women and children and many households led by women alone (1.2). In the past year, refugee household income sources have become more Poland-based (1.3).\n\n#### **1.1 Influx since February 2022**\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 1. Poland-Ukraine border movement balance and registered/active PESEL data**\n\n\n\n**Prior to the 2022 conflict in Ukraine,**\n**the population of Ukrainians in**\n**Poland was already significant and**\n**on the rise, but the exact number of**\n**migrants was challenging to quantify.**\nSince the onset of the armed conflict\nin eastern Ukraine in 2014, there was a\nconsistent influx of Ukrainians into Poland.\nMany of them sought work as Ukraine’s\neconomy declined and the currency\ndevalued. Most of the migrants came as\nguest workers, a status brought in by a\n2011 law enabling Ukrainians and five other\nnations to work in Poland for six months\n\n\n\nSource: Deloitte own elaboration based on Polish\nBorder Guard Headquarter and PESEL data.\n\n\n\n\n\nPESEL-UKR data Total entries-exits of the\nPolish-Ukrainian border\n\n\n\n\n\nduring a year without a work permit,\nbased on an employer’s declaration. This\nwas a circular migration, with Ukrainians\ncoming to Poland for half of the year,\nthen returning to Ukraine for another six\nmonths, and coming back to Poland. The\ndata on employers’ declarations do not\nreveal the actual number of Ukrainian\ncitizens who followed this system – a", "source": "jad_paddy_docs", "subset": "annotate_sample", "spans": [{"key": "jad_paddy_docs:000001:3:0:0", "start": 686, "end": 696, "surface": "PESEL data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "PESEL data underpin the chart analyzing Poland-Ukraine border movements.", "human_verdict": null, "human_note": ""}, {"key": "jad_paddy_docs:000001:3:0:1", "start": 1360, "end": 1374, "surface": "PESEL-UKR data", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named PESEL-UKR data are presented in a chart of border entries and exits.", "human_verdict": null, "human_note": ""}]}, {"key": "p030", "text": ">in the refugee beneficiaries’<br>communities|Bi-annually,<br>MTR,EOP<br>|Project MIS<br>|H/H survey -<br>beneficiaries<br>assessment<br>|NPCU<br>|\n|Host Community beneficiaries|This will assess the<br>increased hectare<br>developed for sustainable<br>land management practices<br>in the host communities|Bi-annually,<br>MTR, EOP<br>|Project MIS<br>|H/H survey -<br>beneficiaries<br>assessment<br>|NPCU<br>|\n|National beneficiaries|This will assess the<br>increased hectare|Bi-annually,<br>MTR, EOP|Project MIS<br>|HH surveys -<br>evaluations -|NPCU<br>|\n\n\n\nPage 46 of 81", "source": "refugee_pads", "subset": "annotate_sample", "spans": [{"key": "refugee_pads:000001:51:1:0", "start": 350, "end": 360, "surface": "H/H survey", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Planned beneficiary assessment survey for project monitoring, not existing data use.", "human_verdict": null, "human_note": ""}, {"key": "refugee_pads:000001:51:1:1", "start": 515, "end": 525, "surface": "HH surveys", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Survey phrase appears within a table monitoring row.", "human_verdict": null, "human_note": ""}]}, {"key": "p031", "text": "**Data disaggregated by AGD enriched the assessment**\n**and analysis of needs, capacities and programming**\n**gaps.** Most UNHCR operations reported gathering and\nanalysing AGD-disaggregated data in their consultations\nwith persons of concern as part of assessment\nprocesses.\n\n\nIn **Sudan,** data disaggregated by AGD demonstrated\nthat efforts to include persons with disabilities and\nolder persons in interventions needed to go beyond the\nprovision of assistive devices. It also showed that better\nunderstanding of the attitudinal, physical and systemic\nbarriers experienced by persons with disabilities is\nneeded to ensure that the operation’s programming\naddresses such barriers in an optimum way.\n\n\nThe **Argentina** Multi-Country Office based its planning\nexercise for 2021 on a joint needs assessment of\nVenezuelan refugees’ and migrants’ humanitarian\nneeds, conducted in October 2019.\n\n\nIn **Lebanon,** UNHCR used AGD-disaggregated data,\ngenerated through inter-agency coordination, to inform\njoint situation analysis and sectoral strategies, and to\ncoordinate sectoral activities.\n\n\n**AGD approaches and disaggregated data improved**\n**planning and the prioritization of interventions.**\nUNHCR operations used the findings of assessments\nand consultations with persons of concern to inform\nthe design of programmes, to set out their strategic\ndirections and to incorporate responses that address\nAGD-related risks and barriers, including those created\nor exacerbated by the COVID-19 pandemic.\n\n\nFor example, in **Ethiopia,** consultations with persons\nof concern and the use of disaggregated data from\nthe Profile Global Registration System (proGres) led to\nthe prioritization of emergency one-off food or cash\ndistributions to refugee female-headed households at\nheightened protection risk.\n\n\nIn **Nepal,** the analysis of AGD-disaggregated data about\nschool-age children highlighted the need to facilitate,\nsupport and strengthen the enrolment of children in\npre-primary and secondary level public schools. The", "source": "reliefweb", "subset": "annotate_sample", "spans": [{"key": "reliefweb:000271:16:0:0", "start": 2, "end": 27, "surface": "Data disaggregated by AGD", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Disaggregated data enriched an assessment and analysis of needs and programming gaps.", "human_verdict": null, "human_note": ""}, {"key": "reliefweb:000271:16:0:1", "start": 173, "end": 195, "surface": "AGD-disaggregated data", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing disaggregated data were analyzed in consultations and assessment processes.", "human_verdict": null, "human_note": ""}, {"key": "reliefweb:000271:16:0:4", "start": 1614, "end": 1648, "surface": "Profile Global Registration System", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "ProGres data informed prioritization of assistance to high-risk refugee households.", "human_verdict": null, "human_note": ""}, {"key": "reliefweb:000271:16:0:5", "start": 1832, "end": 1880, "surface": "AGD-disaggregated data about\nschool-age children", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Analysis of disaggregated data highlighted school enrollment needs.", "human_verdict": null, "human_note": ""}]}, {"key": "p032", "text": "* **IMPLEMENTATION PLANS**\n\n\n**Resettlement Action Plans**\n\nThe LGs shall make sure that, following the census of PAPs, a comprehensive\nResettlement Action Plan is prepared for each project activity that triggers\nresettlement. In this undertaking, LGs may, if need be contract the services of a\n\n#### 56", "source": "fcv_pads_east_africa", "subset": "annotate_sample", "spans": [{"key": "fcv_pads_east_africa:009406:56:1:0", "start": 104, "end": 118, "surface": "census of PAPs", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing census informs preparation of the Resettlement Action Plan.", "human_verdict": null, "human_note": ""}]}, {"key": "p033", "text": " whom are also women; see charts\nbelow).\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\nIn the model, shocks were calibrated using\ndata for 2022-2024. In the case of 2022,\nwe adjusted the data to reflect refugee\narrivals after February, fixing their share\nat roughly 2.6 percent of the population\nper PESEL registry figures. We then\ncalibrated refugee employment to match\nthe NBP’s 2022 survey (NBP, 2024), the\nUNHCR’s 2023 MSNA, and the 2024 SEIS\nsurvey - implying their employment share\nrose from 1.5 percent to 2.4 percent of\ntotal employment in Poland. To keep the\nregional labour supply balance, equivalent\noffsets were applied in the broader\nEastern Europe aggregate. <sup>35</sup> [^35: Aggregate region in the D.Climate model, that consists of Ukraine, Russia, Belarus, Moldova, Czechia, Slovakia, Hungary, Romania, and Bulgaria.] Furthermore,\nit was assumed that refugees have higher\nspending needs and thus a lower saving\nrate than other earners in Poland for\n2022 and 2023. Moreover, according\nto National Bank of Ukraine data, the\nconsumption of Ukrainian refugees has\nbeen partially financed by savings in\nUkrainian banks in 2022 and 2023, which\nwas modelled as them having a negative\nsaving rate, while being offset by lowering\ninvestment levels in Eastern Europe. <sup>36</sup> [^36: In other words, it was assumed that money that would be spent e.g. through credit action for investments in Eastern Europe were spent for consumption in Poland.] By\n2024, it was assumed that their situation on\nthe labour market had stabilised and that\nthere had been no further changes in their\nsavings.\n\n\n\nDeloitte D.Climate, <sup>32 33</sup> is a general\nequilibrium model that uses consumer\nand producer optimisation to calculate\nchanges in the economy in response\nto shocks. This enables the impact of\nshocks to be assessed by considering\nsupply and", "source": "jad_paddy_docs", "subset": "annotate_sample", "spans": [{"key": "jad_paddy_docs:000001:21:1:0", "start": 332, "end": 354, "surface": "PESEL registry figures", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Registry figures calibrate the modeled refugee population share.", "human_verdict": null, "human_note": ""}, {"key": "jad_paddy_docs:000001:21:1:1", "start": 407, "end": 424, "surface": "NBP’s 2022 survey", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Survey data calibrate refugee employment share in the economic model.", "human_verdict": null, "human_note": ""}, {"key": "jad_paddy_docs:000001:21:1:2", "start": 450, "end": 459, "surface": "2023 MSNA", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "UNHCR survey used to calibrate refugee employment in the model.", "human_verdict": null, "human_note": ""}, {"key": "jad_paddy_docs:000001:21:1:3", "start": 469, "end": 485, "surface": "2024 SEIS\nsurvey", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Survey data calibrated refugee employment and supported the employment-share estimate.", "human_verdict": null, "human_note": ""}, {"key": "jad_paddy_docs:000001:21:1:4", "start": 1039, "end": 1068, "surface": "National Bank of Ukraine data", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named bank data supports modeling refugee consumption financed by Ukrainian savings.", "human_verdict": null, "human_note": ""}]}, {"key": "p034", "text": "**<u>Asylum Levels and Trends in Industrialized Countries 2007</u>**\n\n|Table 20. Asylum applications lodged in 43 industrialized countries by origin, fourth quarter 2007<br>Covering 29 major asylum countries which provided monthly data to UNHCR. Values between 1 and 4 have been replaced with an asterisk.<br>See Annex I for country codes used.|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|Origin<br>|AUL<br><br>|AUS<br><br>|BEL<br><br>|BUL<br><br>|CAN<br><br>|CYP<br><br>|CZE<br><br>|DEN<br><br>|FIN<br><br>|FRA<br><br>|GBR<br><br>|GFR<br><br>|GRE<br><br>|HUN<br><br>|IRE<br><br>|\n|Iraq<br>|46<br> <br><br>|124<br> <br><br>|218<br> <br><br>|221<br> <br><br>|93<br> <br><br>|58<", "source": "reliefweb", "subset": "annotate_sample", "spans": [{"key": "sample:reliefweb:001058:31:0:0", "start": 223, "end": 235, "surface": "monthly data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p035", "text": "’ for Ksh193,965) and two other significant cheques which\nwere 6 months old (‘Scangraphics’ for Ksh286,262.90 and ‘Ramco Printings’ for\nKsh700,258.60).\n\n79. INT determined that district bank account balances were not necessarily minimal or nil as\nat the beginning or end of a financial period (see Wajir example below). Even the\ncashbook reported balances as reported in Part Six of the FMR were not insignificant\n(e.g. as at 31 March 2007 total cashbook balance was Ksh540.6 million).  INT also\ndetermined that significant numbers of unpresented cheques existed on project bank\naccounts, many of which should have been ‘written back’ or cancelled (this would be\ngood business practice), which raised concerns about the validity or correctness of the\noriginal payment voucher.  INT also identified cheques presented on bank accounts\nwhich did not appear in the cashbook.  Considering the controls and role of the District\nAccountant (one of the mandatory dual signatures required to operate the bank account\nand sign cheques) in the operation of district project bank accounts, such issues are\nindicators that a significant degree of collusion or mutual ineptness existed on a systemic\nbasis, as these problems were identified across a number of the districts sampled.\n\n80. Wajir district’s bank balance, as per its own district FMR (FY06/07: Ksh816,665,\nFY07/08: Ksh1,189,695) had balances that were significantly less than the actual balance\nas per the local banks’ records for both FY06/07 (Ksh16,898,926) and FY07/08\n(Ksh22,148,996), the reported FMR balances were approximately 5% of the local banks’\nrecords.\n\n\nPage 29 of 73", "source": "fcv_pads_east_africa", "subset": "annotate_sample", "spans": [{"key": "sample:fcv_pads_east_africa:009492:28:1:0", "start": 1320, "end": 1332, "surface": "district FMR", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Financial management report used for routine accounting balance reconciliation.", "human_verdict": null, "human_note": ""}, {"key": "sample:fcv_pads_east_africa:009492:28:1:1", "start": 1455, "end": 1475, "surface": "local banks’ records", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:fcv_pads_east_africa:009492:28:1:2", "start": 1593, "end": 1613, "surface": "local banks’\nrecords", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p036", "text": "d) Support to LGs in the LGs/communities hosting refugee for improved planning, land tenure security\n\n\n\nand infrastructure investments to benefit both refugees and host communities: **US$60 million.**\n\nThe table provides the summary of the funds allocation under the three investments areas (a) – (c) above\nwhich will be funded under the Program.\n\n**Table 6:** **USMID AF Disbursement Projections over 5-year Period**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|No|Description|Total<br>funding<br>(US$ mn)|% of<br>total<br>funding|Project Disbursements (US$ millions)|Col6|Col7|Col8|Col9|\n|---|---|---|---|---|---|---|---|---|\n|**No**|**Description**|**Total**<br>**funding**<br>**(US$ mn)**|**% **<br>**of**<br>**total**<br>**funding**|**Year 1**<br>**FY18/19**|**Year 2**<br>**FY19/20**|**Year 3**<br>**FY20/21**|**Year 4**<br>**FY21/22**|**Year 5**<br>**FY22/23**|\n|**_A. _**<br>**_Funds for Infrastructure Development in Local Governments_**|**_A. _**<br>*", "source": "jdc_operational", "subset": "annotate_sample", "spans": [{"key": "sample:jdc_operational:000006:23:0:0", "start": 363, "end": 396, "surface": "USMID AF Disbursement Projections", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p037", "text": " urban areas in 2013). <sup>6</sup> [^6: World Bank World Development Indicators (2013).]\n\n2. **Uganda faces several challenges including a recent economic slowdown** <sup>**7**</sup> [^7: Uganda’s economy slowed from an average of 7% annual GDP growth in the early 2000s to 4.5% in the 5 years leading up to 2017.] **, which could impede the country’s**\n**progress toward middle-income status by 2020 – a goal outlined in its second National Development Plan (NDPII)** .\nThis slowdown is attributed to various factors including adverse weather conditions and meagre harvests, private\nsector credit constraints, conflict and unrest in neighboring countries, and underperformance in public sector project\nimplementation. <sup>8</sup> [^8: Uganda: Driving inclusive socio-economic progress through mobile-enabled digital transformation, GSMA, 2019.] Uganda’s relatively low productivity in the agricultural sector, which employs the bulk of its\nworkforce, and in the private sector further impedes its growth potential: in the past years, growth in agricultural\nincomes has been largely driven by expanding cultivation areas and exogenous variables such as good weather and\nhigh commodity prices rather than a significant increase in the use of modern production technologies and other\nproductivity-enhancing factors. In addition to agriculture, the manufacturing sector led by micro, small and mediumsize enterprises (MSMEs) contributes a significant share of GDP at 20%. In parallel, the digital sector is growing at a\nfast pace (see Section B below), and newly found oil and gas reserves are driving recent investments in the energy\nsector. <sup>9</sup> [^9: Ibid.]\n\n\n1 World Bank World Development Indicators (2017).\n2 Ibid.\n3 Diagnostic study n°5.1 to 5.3 to support the mid-term review of Uganda’s 2nd National Development Plan (", "source": "jdc_operational", "subset": "annotate_sample", "spans": [{"key": "sample:jdc_operational:000063:2:1:0", "start": 41, "end": 80, "surface": "World Bank World Development Indicators", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p038", "text": "br>**procurement performance:**<br> <br>Piloting individual performance contract approach in the<br>procurement system<br> <br>RRI to support procurement process performance in the pilot|**3.3m**||\n|**Improved**<br>**decision-**<br>**making process**<br>**based on**<br>**reliable**<br>**statistical data**|**Component 4: Enhancing the use of statistics**<br>**for policy making**<br> <br>Timely production of reliable statistical<br>data<br> <br>Statistics widely disseminated|**Subcomponent 4.1: Improvement of poverty-related data**<br> <br>Production of a series of Poverty Notes (based on ECAM 4 and high-<br>frequency surveys)<br> <br>Production of ECAM 5<br> <br>Analysis of the population census<br> <br>Production of the LFS|**5.4m**||\n|**Improved**<br>**decision-**<br>**making process**<br>**based on**<br>**reliable**<br>**statistical data**|**Component 4: Enhancing the use of statistics**<br>**for policy making**<br> <br>Timely production of reliable statistical<br>data<br> <br>Statistics widely disseminated|**Subcomponent 4.2: Strengthening the national accounts production**<br> <br>Quarterly production of improved national accounts", "source": "refugee_pads", "subset": "annotate_sample", "spans": [{"key": "sample:refugee_pads:000044:82:1:0", "start": 290, "end": 306, "surface": "statistical data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Generic statistical data is mentioned without an attributed finding or concrete analysis.", "human_verdict": null, "human_note": ""}, {"key": "sample:refugee_pads:000044:82:1:1", "start": 599, "end": 605, "surface": "ECAM 4", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:refugee_pads:000044:82:1:2", "start": 661, "end": 667, "surface": "ECAM 5", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:refugee_pads:000044:82:1:3", "start": 693, "end": 710, "surface": "population census", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:refugee_pads:000044:82:1:4", "start": 738, "end": 741, "surface": "LFS", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p039", "text": " of workers – mostly made up\nof the youngest refugees who might not\nhave worked back in Ukraine. Employment\nin one’s pre-displacement sector appears\nto boost the median net wage by about\n6%, when compared to those who switch\nfields – a back-of-the-envelope calculation\nputs their median net earnings at 89% of\nthe overall median. However, this likely\noverstates the true effect of staying in the\nsame sector, since the higher-paid groups\n(for example, the majority of IT specialists)\n\n\n\nwere more likely to remain in their original\nindustry.\n\n\n**Highly skilled Ukrainian refugees**\n**are likely to suffer from significant**\n**downgrading.** With the median wages\nof Ukrainian refugees estimated at 84%\n(SEIS survey) or 80% (NBP 2024 survey)\nof the national median (see chapter 2),\nthe difference for average wages may be\neven larger. This is because medians are\ninsensitive to high earners who typically\ninflate average earnings. Such high earners\nmay suffer significant downgrading\nconsidering the unfavourable occupational\nstructure of Ukrainian refugees. There are\nmany reasons to this, from occupational\nlicensing to a high level of language fluency\nrequired by high-skill jobs. <sup>28</sup> [^28: Lessem and Sanders (2020) modelled immigrant wage growth in the United States, finding that in a counterfactual model eliminating barriers to occupational entry\nwould lead to only small earnings increase for the average immigrant, but a substantial increase for the most highly skilled.]\n\n\n\nUkrainian\n\nrefugees\n\n\n\n\n\n\n\n\n\n\n\n\n\nPre-war\nUkrainians\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOther\nforeigners\n\n\n\nPolish\ncitizens\n\n\n\nDrivers (truck, bus)\n\n\nTeachers\n\n\nMedical professions\n(physician, dentist, nurse, midwife)\n\n\nLegal professions\n(legal counsel, barrister, notary, bailiff)\n\n\nTaxi drivers\n\n\nOthers\n\n\nSource: Deloitte own elaboration based on ZUS data\non June 30th 2024.\n\n\n\n26 As described in chapter 2, SEIS measures incomes on the household level. Using it to estimate individual wages likely underrepresents lowest and highest incomes.\nDetails are available in the Online Technical Appendix.\n\n27 Note that the GUS (2024) data for", "source": "jad_paddy_docs", "subset": "annotate_sample", "spans": [{"key": "sample:jad_paddy_docs:000001:14:3:0", "start": 703, "end": 714, "surface": "SEIS survey", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:jad_paddy_docs:000001:14:3:1", "start": 724, "end": 739, "surface": "NBP 2024 survey", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:jad_paddy_docs:000001:14:3:2", "start": 1825, "end": 1833, "surface": "ZUS data", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:jad_paddy_docs:000001:14:3:3", "start": 1886, "end": 1890, "surface": "SEIS", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p040", "text": "sup>9</sup> . This figure is almost double that of host country nationals (12%), implying a\nlarge gap in economic vulnerability. Compared to 2023, poverty rates have decreased substantially (from\n36% <sup>10</sup> ), suggesting an overall improvement in the economic well-being of Ukrainian refugees over time.\n\n\n**<u>REFUGEE VERSUS HOST POVERTY RATES BY COUNTRY</u>**\n\n\nUkrainian refugees (2024) Host country nationals (2023)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Hungary Moldova Poland Romania Slovakia Estonia Latvia Lithuania Region\n\n\nNote: Poverty rates for all countries apart from the Republic of Moldova are based on a calculation that follows Eurostat’s at-risk-of-poverty (AROP)\nmethodology with the at-risk-of-poverty threshold set at 50% of the national median disposable income after social transfers. Refugee disposable\nincome has been computed based on survey data. For the Republic of Moldova, the poverty threshold was taken to be the 4Q23 absolute poverty line\nreported by the National Bureau of Statistics of Moldova.\n\n\nSource: Survey data, <u>[Eurostat,](https://ec.europa.eu/eurostat)</u> <u>[National Bureau of Statistics of Moldova, SAG estimates](https://statistica.gov.md/en)</u>\n\n\n7. The MSNA, which ran in 7 countries: Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and Slovakia\n8. Equivalized as per <u>[Eurostat methodology. Essentially income per person, but with household members beyond the first one](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Equivalised_income)</u>\nassigned weights less than", "source": "jad_paddy_docs", "subset": "annotate_sample", "spans": [{"key": "sample:jad_paddy_docs:000010:3:1:0", "start": 872, "end": 883, "surface": "survey data", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Survey data underlies the computed refugee disposable-income measure.", "human_verdict": null, "human_note": ""}]}, {"key": "p041", "text": " much needed capital inflows, and catalyze job creation for women and\nmen. The new Government, formed on January 31, 2019, promised to contain public spending and\nimplement needed reforms that could unlock pledged aid and loans. Since the new government was\nformed, domestic bank deposits are forecast to grow by US$7-8 billion in 2019 compared with US$5.6\nbillion in 2018. The Council of Ministers (COM) submitted to the Parliament the draft 2019 budget that\n\n\n3 World Economic Forum, Global Competitiveness Index 2017-2018.\n4 Vulnerability Assessment for Syrian Refugees in Lebanon (VASyR-2018) by the United Nations Children’s Fund (UNICEF), United Nations High\nCommissioner for Refugees (UNHCR) and the United Nations World Food Programme (WFP).\n\n\nJun 21, 2019 Page 4 of 14", "source": "jdc_operational", "subset": "annotate_sample", "spans": [{"key": "jdc_operational:000010:3:2:0", "start": 486, "end": 524, "surface": "Global Competitiveness Index 2017-2018", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named competitiveness index cited as an existing data resource.", "human_verdict": null, "human_note": ""}]}, {"key": "p042", "text": " Firstly, taxpayers perhaps need up to 3-4 years to learn and effectively use\n\nthe e-filing system (as was the case in South Africa’s experience). However, some of\n\n\n\nanalysis. Firstly, taxpayers perhaps need up to 3-4 years to learn and effectively use\n\nthe e-filing system (as was the case in South Africa’s experience). However, some of\n\nthe surveys were performed in the first or second year of the policy and this may\n\n\n\nthe e-filing system (as was the case in South Africa’s experience). However, some of\n\nthe surveys were performed in the first or second year of the policy and this may\n\nunderestimate the benefits from e-filing, while focusing excessively on its cost.\n\n\n\nthe surveys were performed in the first or second year of the policy and this may\n\nunderestimate the benefits from e-filing, while focusing excessively on its cost.\n\nNevertheless, this also helped us to observe possible short-run consequences of the\n\n\n\nunderestimate the benefits from e-filing, while focusing excessively on its cost.\n\nNevertheless, this also helped us to observe possible short-run consequences of the\n\npolicy (e.g. Nepal and Ukraine) versus long-run (e.g. South Africa). Yet, due to\n\n\n\nNevertheless, this also helped us to observe possible short-run consequences of the\n\npolicy (e.g. Nepal and Ukraine) versus long-run (e.g. South Africa). Yet, due to\n\n\n\n53", "source": "general_prwp", "subset": "annotate_sample", "spans": [{"key": "prwp:005776:54:3:1", "start": 345, "end": 352, "surface": "surveys", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Past surveys inform analysis of e-filing benefits and costs.", "human_verdict": null, "human_note": ""}]}, {"key": "p043", "text": "Appendix Α: Information on data sources\n\n\nTable A1: List of candidate geospatial variables.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Variable|Source|Approximate<br>Resolution|Year|\n|---|---|---|---|\n|Population structure<br>|WorldPop (https://www.worldpop.org)<br>|100 m<br>|2018<br>|\n|Population density|WorldPop<br>|100 m<br>|2018<br>|\n|Temperature<br>|TerraClimate<br>(https://www.climatologylab.org/terraclimat<br>e.html)|4 km<br>|2018<br>|\n|Palmer Draught<br>Severity Index<br>(PSDI)<br>|TerraClimate|4 km<br>|2018<br>|\n|Distance to OSM<br>major roads<br>|WorldPop<br>|100 m<br>|2016<br>|\n|Radiance of night-<br>time lights|VIIRS<br>(https://eogdata.mines.edu/products/vnl/) <br>|500 m<br>|2018<br>|\n|Net primary<br>production|FAO Remote Sensing for Water<br>Productivity (WaPOR) 2.1<br>(https://data.apps.fao.org/wapor/?lang=en)<br>|240 m<br>|2018<br>|\n|Rainfall|Climate Hazards Group InfraRed<br>Precipitation with Station data (CHIRPS)<br>(https://www.chc.ucsb.edu/data/chirps) <br>|5", "source": "general_prwp", "subset": "annotate_sample", "spans": [{"key": "prwp:001372:28:0:0", "start": 220, "end": 228, "surface": "WorldPop", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Source entry embedded in a candidate-variable table, not an independent data-use mention.", "human_verdict": null, "human_note": ""}, {"key": "prwp:001372:28:0:2", "start": 350, "end": 362, "surface": "TerraClimate", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Standalone source entry within a table of candidate geospatial variables.", "human_verdict": null, "human_note": ""}, {"key": "prwp:001372:28:0:4", "start": 727, "end": 755, "surface": "FAO Remote Sensing for Water", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named geospatial data source listed for net primary production.", "human_verdict": null, "human_note": ""}]}, {"key": "p044", "text": " Polish women.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 29. Effect of a 1 pp. change in employment share of Ukrainian refugees on gross wage change**\nCross-section model of all 380 poviats in 2023. Results are statistically significant at a 0.01 level.\n\n\n200\n\n\n150\n\n\n100\n\n\n50\n\n\n0\n\nOLS IV (pupils) IV (2019) IV (both)\n\nSource: Deloitte own elaboration based on GUS and ZUS data, as well as data for instrumental variables from Public Employment Services Portal and Open Data\ngovernmental portal. OLS is the standard Ordinary Least Squares model, IV are Instrumental Variables Two Stage Least Squares models with instrumental variables of\nthe share of Ukrainian pupils in Polish schools, distribution of Ukrainians from declarations on entrusting work to a foreigner in Poland in 2019, or both. For details see\nthe Online Technical Appendix.\n\n\n\nFurthermore, consistent with a positive\nproductivity shock, Polish citizens have\nbeen moving to better-paid occupations –\nas predicted by the theory of occupational\nupgrading (e.g., Beerli and Peri, 2018; Foged\nand Peri, 2016; Peri and Sparber, 2009).\nDeloitte has acquired quarterly data on\nthe occupational groups of Polish citizens\nwho are insured with ZUS from Q1 2022 to\nQ2 2024. This data is not exhaustive as ZUS\nonly began requiring such information in\n\n\n\ntowards higher-paid occupational groups.\nThe share of Poles in the two lowest\nsalary brackets (earning less than gross\nPLN 4,000 and PLN 4,000-6,000 in 2022)\ndecreased by 0.4 and 1.8 percentage\npoints, respectively, while the subsequent\nhigher salary brackets increased by\n1.4 pp. (PLN 6,000-8,000), 0.6 pp.\n(PLN 8,000-10,000), and 0.3 pp. (above\nPLN 10,000) (see", "source": "jad_paddy_docs", "subset": "annotate_sample", "spans": [{"key": "sample:jad_paddy_docs:000001:20:1:0", "start": 405, "end": 408, "surface": "GUS", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:jad_paddy_docs:000001:20:1:1", "start": 413, "end": 421, "surface": "ZUS data", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:jad_paddy_docs:000001:20:1:2", "start": 434, "end": 465, "surface": "data for instrumental variables", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Data from named portals supports the chart's instrumental-variable analysis.", "human_verdict": null, "human_note": ""}]}, {"key": "p045", "text": " stronger increase in\nlabour productivity than was assumed. As\na result, the positive impact of Ukrainian\nrefugees on the economy is greater than\npreviously expected.\n\n\n\n**As Ukrainian refugees entered the**\n**labour market, the economy adapted,**\n**resulting in more specialization and**\n**higher productivity.** In a simplistic\nsupply-demand framework, the influx\nof Ukrainian refugees should have\ncaused some Polish workers to become\nunemployed or leave the labour force, or\nreal wages to fall. This has not happened.\nFirst, among Polish citizens employment\nrates have grown, and unemployment\nrates have fallen. Second, poviats in which\nemployment share of Ukrainian refugees\nhas grown by 1 pp., saw 0.5 pp. higher\nemployment rates among Polish citizens,\nand 0.3 pp. lower unemployment rates.\n\n\n\nThird, there is no evidence of lowered\nwages, in fact the limited available data\nsuggests that Ukrainian refugees may have\ncaused higher wage growth in poviats\nwhich they have moved to. These are\ncommon findings well documented in\nacademic literature, that as immigrants\nenter the labour market, native workers\nspecialize in complementary, higher\nvalue tasks, which we see empirically in\nPolish workers moving to more attractive\noccupational groups. This can be seen in\nthe data, as Polish citizens are moving to\nbetter paid occupations. It constitutes a\npositive shock to productivity which is what\ncounterbalances labour market pressures. <sup>17</sup> [^17: For the literature review, underlying empirical evidence, and model calibration refer to the appendix on modelling strategy.]\n\n\n\nSource: Deloitte D.Climate estimates. For details see\nthe Online Technical Appendix.\n\n\n**The main impact of Ukrainian refugees**\n**is expanding the economy and putting**\n**it on a higher growth path.** According\nto the Deloitte D.Climate model, economic\nimpact of Ukrainian refugees amounted\nto a higher real GDP by 1.5% in 2022, as\nthey initially entered the labour market.\nWith more", "source": "jad_paddy_docs", "subset": "annotate_sample", "spans": [{"key": "sample:jad_paddy_docs:000001:11:2:0", "start": 857, "end": 879, "surface": "limited available data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p046", "text": "DFA) in Afghanistan postponed the resumption of</mark>\n<mark>schooling for girls above grade six, which directly contradicted the multiple assurances from</mark>\n<mark>the DFA that all girls will not be prevented from accessing education. This denial of access to</mark>\n<mark>education for girls continues to be a major concern of the humanitarian community in</mark>\n<mark>Afghanistan as well as the civil society. In addition, the various restriction imposed on women</mark>\n<mark>and girls, including restriction on their freedom of movement, hinders women and girls from</mark>\n<mark>accessing assistance and services as well as hinders the mobility of female humanitarian actors,</mark>\nleading to limited outreach to women and girls in the field.\n\n\n<mark>Protection monitoring also shows that protection needs vary based on population groups (IDPs,</mark>\n<mark>IDP returnees, etc.), whether households are headed by women or men, and on geographical</mark>\n<mark>locations, which means that types of population groups, gender of HHs, and geographical</mark>\n<mark>locations have also to be considered when identifying protection issues. In this regard, a specific</mark>\n<mark>protection risk that emerges for IDPs is the threat of eviction owing to an inability to pay rent</mark>\n<mark>or residing in informal settlements. The risk of eviction continues for many vulnerable Afghans.</mark>\n<mark>Residents of informal settlements, displaced people, and low-income renters face particularly</mark>\nsevere risks of eviction.\n\n\n<mark>Return figures of undocumented returnees from neighboring countries for the first quarter of</mark>\n<mark>2022 have increased compared to the end of last year, with deportations from the Islamic</mark>\n<", "source": "reliefweb", "subset": "annotate_sample", "spans": [{"key": "reliefweb:000979:2:1:0", "start": 762, "end": 783, "surface": "Protection monitoring", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Monitoring findings show protection needs vary across population groups.", "human_verdict": null, "human_note": ""}]}, {"key": "p047", "text": "**The World Bank**\nSupport for Social Recovery Needs of Vulnerable Groups in Beirut (P176622)\n\n\nnational poverty line. <sup>15</sup> [^15: Lebanon Economic Monitor, Spring 2021. World Bank] These developments increase pressures for emigration, especially among the\nmiddle class. Such deprivations have further degraded the relationship between people and the state.\nGrievances with the political system and dissatisfaction with the state’s mismanagement of the economy\nand its entrenched corruption resulted in nationwide protests in late 2019. Since, intermittent social\nunrest highlights the needs for a new social contract between citizens and the government. In a survey\nconducted by the World Bank among victims of the blast, the overwhelming majority of respondents\nreport having “no trust at all” in political parties, the Council for Development and Reconstruction, or\nmunicipalities. <sup>16</sup> [^16: Ranking on a 5-point scale, where 1 = “no trust at all” and 5= “complete trust.” Average score was 1.2 for political parties, 1.5 for CDR, and 1.7\nfor municipalities. Survey not strictly representative due to its design. Source:\nhttp://documents1.worldbank. org/curated/en/899121600677984471/pdf/Beirut-Residents-Perspectives-on-August-4-Blast-Findings-from-aNeeds-andPerception-Survey.pdf]\n\n6. **The formation of a new government of “determination and hope” in September 2021, and the**\n**subsequent vote of parliamentary confidence this received, lays an important potential foundation for**\n**solving these challenges** . However, even in a political best case scenario, the deep socio-economic\nimpacts of the above crises upon the people of Lebanon will take considerable time and investment in\npublic sector service delivery reform to address sustainably. As such, stop-gap measures to meet the\nimmediate socio-economic needs of vulnerable groups remains important for both alleviating emergency\nhardship and setting the stage for longer-term recovery.\n\nSectoral and Institutional Context\n\n7. **The World Bank Group (WBG)", "source": "jdc_operational", "subset": "annotate_sample", "spans": [{"key": "jdc_operational:000002:4:0:0", "start": 139, "end": 163, "surface": "Lebanon Economic Monitor", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named World Bank monitor cited as the source for the poverty-line context.", "human_verdict": null, "human_note": ""}, {"key": "jdc_operational:000002:4:0:1", "start": 668, "end": 702, "surface": "survey\nconducted by the World Bank", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "World Bank survey provides reported trust findings among blast victims.", "human_verdict": null, "human_note": ""}]}, {"key": "p048", "text": "there is no strong evidence of convergence in agriculture and services. More importantly, their re\n\nsults show that the rate of convergence in manufacturing and mining has been strong enough to lead\n\n\naggregate convergence across regions, albeit at a slower pace because of high employment shares of\n\n\nagriculture and services. <sup>8</sup> [^8: They also report lack of convergence in poverty rates across regions because of limited labor mobility toward converging\nsectors and high employment of the poor in sectors exhibiting weak convergence.]\n\n\nIn the case of Brazil, Azzoni and Castro (2023) examined regional disparities from 2002 to 2019.\n\n\nTheir findings indicate a general increase in _σ_ -convergence in per capita income across regions.\n\n\nMoreover, periods of economic crisis, such as the Great Recession of 2008 and the economic crisis of\n\n\n2014, were linked to more pronounced convergence. The study also reveals evidence of conditional\n\n\nconvergence in regional per capita income and wages (serving as a proxy for labor productivity)\n\n\nduring these crisis periods.\n\n\nOverall, this observed (or lack thereof) convergence and its speed call for further evidence that\n\n\naccounts for the drivers of factor accumulation, sectoral composition, technology diffusion, and pro\n\nduction externalities within and across regions and countries.\n\n###### **3 Empirical approach**\n\n\n**3.1** **Data**\n\n\nGiven data availability, we focus on the period from 2002 to 2018. The main dataset comes from\n\n\nthe Relação Anual de Informações Sociais (RAIS), which is a matched employer-employee dataset\n\n\nput together by the Ministry of Labor and Employment (Ministério do Trabalho e Emprego). RAIS\n\n\ncovers all registered firms and formal employees in which a firm is identified by its registration\n\n\nnumber (CNPJ) and a worker by its identification number (PIS). Most importantly, both the PIS and\n\n\nCNPJ do not change over time, even when a worker stops to work in the formal sector. These unique\n\n\nfeatures allow us to track workers and firms over time. The", "source": "general_prwp", "subset": "annotate_sample", "spans": [{"key": "prwp:001577:9:0:2", "start": 1683, "end": 1687, "surface": "RAIS", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named administrative dataset used to track workers and firms over time.", "human_verdict": null, "human_note": ""}]}, {"key": "p049", "text": "is higher is due to the exclusion of China, which has both a large population and low rate of extreme\n\n\npoverty. Nonetheless, the data in our analysis is representative of nearly 5.9 billion people or more\n\n\nthan 3/4 <sup>th</sup> of the world’s population. The coverage of people estimated to be living in extreme\n\n\npoverty, the population that is relevant for this analysis, is much larger – the data cover 98 percent\n\n\nof this population.\n\n\nFor each country, the surveys have been conducted in different years and the data are\n\n\nreported in local currency units in current prices. Following the methodology used to report on\n\n\nSDG 1.1, we convert all income and consumption data into 2011 constant local prices using\n\n\nConsumer Price Indices from each country, and then convert the resulting vector into an\n\n\ninternationally comparable US dollars using 2011 purchasing power parity exchange rates (PPPs). <sup>9</sup> [^9: For more details on the CPI series, see: <u>[https://worldbank.github.io/PIP-Methodology/convert.html#CPIs.](https://worldbank.github.io/PIP-Methodology/convert.html#CPIs)</u>\nFor more details on the PPPs, see: <u>[https://worldbank.github.io/PIP-Methodology/convert.html#PPPs .](https://worldbank.github.io/PIP-Methodology/convert.html#PPPs)</u>]\n\n\nThe CPIs are used to estimate real changes in income and consumption over time, while the PPPs\n\n\naccount for relative price differences across countries. In addition to using the same CPI and PPP\n\n\ndata as used by the World Bank for global poverty monitoring, we also use the same population\n\n\nand national accounts data. For more details on how the World Bank estimates poverty, see World\n\n\nBank (2020a).\n\n\nIn the second part of the analysis, we use", "source": "general_prwp", "subset": "annotate_sample", "spans": [{"key": "sample:prwp:001052:10:0:0", "start": 654, "end": 681, "surface": "income and consumption data", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:001052:10:0:1", "start": 722, "end": 744, "surface": "Consumer Price Indices", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:001052:10:0:2", "start": 1460, "end": 1478, "surface": "CPI and PPP\n\n\ndata", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:001052:10:0:3", "start": 1557, "end": 1596, "surface": "population\n\n\nand national accounts data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p050", "text": "**_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. _NA_\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 _NA_\n\n\n**_Domestic preference_** _as specified under paragraph 5.51_ of the Procurement\nRegulations **_(Goods and Works)_** . _Specify for each_\n\n\nGoods: is applicable for those contracts identified in the Procurement Plan\ntables\n\n\nWorks: is not applicable\n\n\n**Hands-on Expanded Implementation Support (HEIS)** _NA_\n\n\n**Other Relevant Procurement Information. NA**", "source": "fcv_pads_east_africa", "subset": "annotate_sample", "spans": [{"key": "fcv_pads_east_africa:002568:1:0:0", "start": 146, "end": 169, "surface": "Procurement Plan tables", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Routine procurement planning tables identify contracts; no substantive data analysis is shown.", "human_verdict": null, "human_note": ""}]}, {"key": "p051", "text": "16\n\n\ntraditional gross or net enrollment ratios. Their most recent version of the data set also\ntakes into account changes in school duration over time within countries.\n\nThe second global data set for average years of schooling is that of Cohen and\nSoto (2001), which covers 95 countries and spans the period 1960 to 2000 on a decade\nbasis. This data set uses 3 main sources of data. They are the OECD database on\neducation, national censuses or surveys published by UNESCO’s Statistical Yearbook\nand censuses obtained directly from national statistical agencies’ web pages. Based on\nreports from its members and other non-member countries, the OECD has published\ndetailed information on educational attainment, beginning at the end of the 1980s. This\ninformation refers to the population aged 15 to 64 broken up in different age groups and\nthis is the cornerstone of the Cohen-Soto data set for high-income countries. The main\nadvantage of the OECD data set is that the information is presented in a standardized\nform across countries. Cohen and Soto extend the study performed by the OECD to\nmissing periods and countries.\n\n\nOne key difference between the Cohen-Soto dataset is in the methodology for\nextrapolating the missing data. Barro and Lee extrapolate missing data for the whole\npopulation either backwards or forwards to obtain educational attainment for missing\nyears. As opposed to using the whole population, Cohen and Soto utilize estimates for\nage-specific groups, which they argue tend to result in more reliable estimates. Cohen\nand Soto also claim that for some countries, they had more recent census information\nthan that used by Barro and Lee.\n\n\nIn order to obtain the broadest coverage of countries for data on educational\nattainment, we combined the information from the Barro-Lee and Cohen-Soto data sets to\nobtain the resultant data set that covers 144 countries for the period 1960 to 2000. If\neducational attainment data on a specific country is contained in both data sets, then we\nfollow Bosworth and Collins (2003) and take the", "source": "general_prwp", "subset": "annotate_sample", "spans": [{"key": "sample:prwp:002639:15:0:0", "start": 398, "end": 424, "surface": "OECD database on\neducation", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:002639:15:0:1", "start": 873, "end": 892, "surface": "Cohen-Soto data set", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:002639:15:0:2", "start": 946, "end": 959, "surface": "OECD data set", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:002639:15:0:3", "start": 1159, "end": 1177, "surface": "Cohen-Soto dataset", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:002639:15:0:4", "start": 1613, "end": 1631, "surface": "census information", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:002639:15:0:5", "start": 1794, "end": 1803, "surface": "Barro-Lee", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:002639:15:0:6", "start": 1920, "end": 1947, "surface": "educational attainment data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p052", "text": "they sought protection. However, government\nstatistics on first-time residence permits or other\nadministrative data sources in general do\nnot enable refugees, persons in a refugee-like\nsituation or persons with a refugee background\nto be identified unless they hold an asylum\nor humanitarian-related permit. <sup>13</sup> The figures\npresented in this report may therefore include\npeople who were admitted directly from one of\nthe seven countries of origin (Afghanistan, Eritrea,\nIran, Iraq, Somalia, Syria, Venezuela) and thus may\nnot have crossed an international border to meet\nthe definition of a refugee prior to arrival in an\nOECD country.\n\nGiven the high asylum recognition rates of\nnationals from the seven countries of origin\nconsidered (see Annex V for details), it can\nhowever be assumed that a large number of\nindividuals covered in this study would have a\nwell-founded refugee claim.\n\nFurther, this data collection exercise focuses\non first-time permits granted, excluding permit\nrenewals or status changes in the destination\ncountry to avoid double-counting individuals in\nthe data. Nevertheless, double-counting may\noccur in a few cases where the renewals and\nstatus changes could not be extracted from a\ncountry’s permit data (see Annex I for details).\nFurther, a few countries count native-born\n\n\n\nchildren of foreign nationals under the residence\npermit of their parents, although to our knowledge\nthis is not likely to lead to a large overestimation.\n\nThe definitional focus on first permits issued for\nentry into the host country excludes individuals\nwho obtain a visa or status regularization following\ntheir entry into the country—which is notably\nthe case for a large population of Venezuelans\nresiding in Chile and Colombia. Finally, while\ndata availability improves year to year, some\ngaps remain for specific country-year figures, but\nthese represent a relatively small percentage of\nundercoverage.\n\n\nThe figures in this report are based on the latest\navailable data in each reporting country. As a\nresult, some past figures have been", "source": "reliefweb", "subset": "annotate_sample", "spans": [{"key": "sample:reliefweb:000620:7:0:0", "start": 33, "end": 86, "surface": "government\nstatistics on first-time residence permits", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing government statistics are discussed as a data source and limitation.", "human_verdict": null, "human_note": ""}, {"key": "sample:reliefweb:000620:7:0:1", "start": 96, "end": 123, "surface": "administrative data sources", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:reliefweb:000620:7:0:2", "start": 1230, "end": 1241, "surface": "permit data", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p053", "text": " uniform data standards and\nclassification. Some of these key databases are; the maternal and neonatal registry, cancer registry,\ncommunicable disease surveillance registry, primary health care information system, and hospital\nutilization and billing system (visa billing system).\n\nThe aim of this component is to design a database that will allow for interoperability and serve as a\nmain repository of health sector data ready for analysis and dissemination. The database will not\nonly link the key MoPH databases, but will also pull relevant data from other sources such as CAS\nhousehold surveys (including Demographic and Health survey), NHA, and national hospital\nmorbidity and mortality reporting.\n\nThis component will work on the following activities:\n\n➢❨¢ Develop key performance indicators for assessing the performance of the health sector\nincluding disease burden, efficiency and equity of the health sector, PHC and hospital key\nperformance indicators, health economics indicators, as well as setting a plan to extract and monitor\nthe progress in the health and health-related Sustainable Development Goals indicators\n➢❨¢ Design an ICT system for ensuring interoperability of the existing databases including\nunification of patient identification numbers to facilitate statistical analyses for evidence based\ndecision making in the implementation of the national health sector plan\n\n\nPage 4 of 7", "source": "jdc_operational", "subset": "annotate_sample", "spans": [{"key": "jdc_operational:000046:3:1:0", "start": 130, "end": 172, "surface": "communicable disease surveillance registry", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Registry is listed as an existing system, but its data are not shown informing analysis or decisions.", "human_verdict": null, "human_note": ""}, {"key": "jdc_operational:000046:3:1:1", "start": 576, "end": 597, "surface": "CAS\nhousehold surveys", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Future database integration plans to pull survey data; no completed analytical use is shown.", "human_verdict": null, "human_note": ""}, {"key": "jdc_operational:000046:3:1:2", "start": 609, "end": 638, "surface": "Demographic and Health survey", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing survey data will be integrated for health-sector analysis and dissemination.", "human_verdict": null, "human_note": ""}, {"key": "jdc_operational:000046:3:1:3", "start": 641, "end": 644, "surface": "NHA", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Future database integration plans to pull NHA data; no completed use is shown.", "human_verdict": null, "human_note": ""}, {"key": "jdc_operational:000046:3:1:4", "start": 650, "end": 701, "surface": "national hospital\nmorbidity and mortality reporting", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing hospital reporting data will be integrated for health-sector analysis.", "human_verdict": null, "human_note": ""}]}, {"key": "p054", "text": "**2. Trends in economic growth, inequality, and poverty**\n\n\n**2.1. Data**\n\n\nWe compile data from the Vietnam Household Living Standards Surveys (VHLSSs), which\n\n\nhas been widely employed by the government, the international community, and academic\n\n\nresearchers for poverty and inequality analysis for the country. The VHLSSs have been conducted\n\n\nby the General Statistics Office of Vietnam with technical support from the World Bank every two\n\n\nyears since 2002. We compile data on all 58 provinces and five centrally controlled municipalities\n\n\nand supplement this data with other data that we collected. <sup>2</sup> In particular, provincial government\n\n\nspending data for the period 2018-2020 is currently unavailable for all the 63 provinces in any\n\n\nofficial document. To get the most updated data, we manually collected the state spending and\n\n\ninvestment spending data for 2018-2020 from several sources including the Ministry of Finance’s\n\n\nwebsite, provincial finance departments’ websites, and relevant official documents.\n\n\nThe VHLSSs contain detailed data on individuals and households. Household-level data are\n\n\ncollected on durables, assets, production, income, and participation in government programs.\n\n\nIndividual-level data are collected on demographics, education, employment, health, and\n\n\nmigration. The 1999 Population and Housing Census was used as the sampling frame of the\n\n\nVHLSSs during 2002-2008, while the 2009 and 2019 Population and Housing Censuses were used\n\n\nas the sampling frame of the VHLSSs respectively for 2010-2016 and 2018-2020. Around 3,100\n\n\ncommunes were chosen as the primary sampling units out of the list of 10,000 communes for the\n\n\n2 These municipalities are Can Tho, Da Nang, Hai Phong, Hanoi, and Ho Chi Minh City.\n\n5", "source": "general_prwp", "subset": "annotate_sample", "spans": [{"key": "sample:prwp:001252:6:0:0", "start": 101, "end": 143, "surface": "Vietnam Household Living Standards Surveys", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:001252:6:0:1", "start": 636, "end": 673, "surface": "provincial government\n\n\nspending data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:001252:6:0:2", "start": 833, "end": 878, "surface": "state spending and\n\n\ninvestment spending data", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:001252:6:0:3", "start": 1329, "end": 1363, "surface": "1999 Population and Housing Census", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Existing named census used as the VHLSS sampling frame.", "human_verdict": null, "human_note": ""}, {"key": "sample:prwp:001252:6:0:4", "start": 1439, "end": 1484, "surface": "2009 and 2019 Population and Housing Censuses", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p055", "text": " / LHW and PMU,<br>DOH<br>|\n|Children under 1 year immunized with the<br>first dose of measles vaccination in target<br>districts|<br>Percentage of children<br>under 1 immunized at EPI<br>centres, PHC, and in the<br>community in target<br>districts.|Bi-Annually<br>|DHIS, EPI MIS<br>|Routine HMIS<br>|DHIS/EPI<br>|\n|Women receiving iron/folic acid<br>supplementation during pregnancy in<br>target districts|Percentage of all pregnant<br>women receiving iron/folic<br>acid supplementation at<br>PHC facilities, or in the<br>community in target<br>districts.|Bi-Annually<br>|DHIS, LHW<br>MIS<br>|Routine HMIS<br>|IMU, PMU, DOH<br>|\n|Health professionals (doctors, nurses,<br>non-medical staff) receiving refresher and<br>on-the-job training|Number provincial and<br>district staff trained<br>(Cumulative number).|Bi-Annually<br>|PMU<br>|PMU Records<br>|IMU, PMU, DOH<br>|\n\n\n\nPage 38 of 48", "source": "refugee_pads", "subset": "annotate_sample", "spans": [{"key": "refugee_pads:000126:42:1:0", "start": 835, "end": 846, "surface": "PMU Records", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Standalone table cell naming a project monitoring record source.", "human_verdict": null, "human_note": ""}]}, {"key": "p056", "text": "When it comes to census data, how old is too old? Or, put another way, at what age do census-based\npredictions become less accurate than current geospatial predictions? It is difficult to know, but\nNewhouse et al. (2022) offer one small piece of evidence on this point. When evaluated against Mexican\n2015 small area poverty estimated based on the intercensus, 2010 census-based estimates are more\naccurate than 2015 estimates based on geospatial indicators (correlation of 0.91 vs 0.86). However, this\nis only representative of one context, and regional patterns of poverty in Mexico may have been more\nstatic during this time than in other contexts.\n\n###### d. Prediction accuracy is very sensitive to the training data\n\n\nMany of the papers discussed above predict poverty rates or average asset indices estimated at the\nvillage level. Since these are often derived from surveys with a limited number of observations per\nvillage, this raises the issue of noise in the dependent variable. In fact, correlations between predicted\nvalues and census-based estimates depend critically on the extent of noise in the training data, which\nwill reduce measured accuracy. For example, Engstrom et al. (2022) considered how the accuracy of\npredictions depends on the size and nature of the sample used to estimate average per capita\nconsumption at the GN division level. That analysis correlated interpretable geospatial features with\npredicted per capita consumption imputed into a census. Model R <sup>2</sup> fell from 0.61 when using the mean\nover all census households, to 0.55 when using the mean over thirty households, and further to 0.40\nwhen taking the mean over 8 households per enumeration area. Differences in the extent of noise\npresent in the training data, as well as the reference evaluation measure, will not necessarily affect the\nranking of different types of models within the same context. But it explains much of the wide variation\nin R <s", "source": "general_prwp", "subset": "annotate_sample", "spans": [{"key": "prwp:001122:11:0:2", "start": 1749, "end": 1762, "surface": "training data", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Training data are analyzed as a source of prediction noise affecting model accuracy.", "human_verdict": null, "human_note": ""}]}, {"key": "p057", "text": ", of which the most recent one is the 2019/20 UNHS. In addition,\nthe Bureau initiated a multi-topic, four-year annual Uganda National Panel Survey (UNPS) program in 2009.\nThe 2009/10 UNPS was funded with support from the Kingdom of the Netherlands while the subsequent\nsurveys (2010/11, 2011/12 and 2013/14) were funded largely by the World Bank. The GoU contributed\ntowards the implementation of the UNPS rounds and provided resources that facilitated the UNPS program\nimplementation.\n\n\n3. The first phase of the UNPS ended in 2015. According to the plan, the UNPS was carried out annually\nover a 12-month period on a nationally representative sample with the aim to produce annual estimates of\noutcomes and outputs in the key policy areas and to provide a platform for the experimentation and\nassessment of national policies and programs. One of the primary uses of the UNPS was to inform policy in\nadvance of the Budget and to provide an analytical framework to inform National Budget preparations and\ngovernment annual reviews. The first phase of panel surveys ensured regular availability of data.\n\n\n4. The success achieved during the first phase of UNPS implementation asked for a second phase of\nimplementation with the goal of consolidating the capacity.\n\n\n5. The first motivation for the second phase of the UNPS 2016 – 2021 was to consolidate and enhance\nthe technical capacity already developed, the efficiency in data collection and processing to continue to\nproduce on an annual basis the key outcome indicators (poverty, service delivery, governance, employment\namong other indicators) to monitor the NDP. The second motivation was to continue supporting the program\nof annual national panel household surveys over the period of 2016-2020, and maintaining the links to at least\na sub-sample of households that have been surveyed in the period of 2009-2015. In such a way the second\nphase intended to facilitate analysis of long-term, slow-moving processes such as agricultural transformation", "source": "fcv_pads_east_africa", "subset": "annotate_sample", "spans": [{"key": "sample:fcv_pads_east_africa:005339:6:1:0", "start": 38, "end": 50, "surface": "2019/20 UNHS", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:fcv_pads_east_africa:005339:6:1:1", "start": 118, "end": 146, "surface": "Uganda National Panel Survey", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named existing survey program described as informing policy and budget analysis.", "human_verdict": null, "human_note": ""}, {"key": "sample:fcv_pads_east_africa:005339:6:1:2", "start": 1051, "end": 1064, "surface": "panel surveys", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:fcv_pads_east_africa:005339:6:1:3", "start": 1684, "end": 1723, "surface": "annual national panel household surveys", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p058", "text": " earned by Ukrainian refugees than\n\n\n\nthe general population. UNHCR (2025b),\nusing a different method than the one in\nthis report, estimated average instead\nof median net wages based on the SEIS\ndata. The average net wage of a Ukrainian\nrefugee they arrived at was PLN 4,214, only\nslightly higher than calculated above. This\nwould yield 72% of the national average net\nwage. <sup>15</sup> Average is not used here, because\nmedian is more relevant for discussing\neconomic impact, as it is not disrupted\nby bottom or top earnings. Furthermore,\nthere are no outside estimates to compare\nit to. Second, as discussed previously, there\nare much fewer Ukrainian refugees with\nemployment contracts than Poles, and\nthis lowers their social contributions and\nthus gross earnings. Unfortunately, there\nis no such data available, as both SEIS and\nNBP (2024) measure only net earnings.\n\n\n\n**Chart 13. Polish citizens and Ukrainian refugees’ employment rates by age group**\n\n\nMale Female\n\n\n\n**Chart 14. Ukrainian refugee median net wage estimates in Q2 2024**\n\n\nMonthly net wage (PLN) Percengate of all workers total economy average\n\n\n84%\n\n\nUNHCR NBP UNHCR NBP\n15 May - 24 June 2024 6 May - 5 July 2024 15 May - 24 June 2024 6 May - 5 July 2024\n\n\nSource: Deloitte own elaboration based on SEIS survey, NBP (2024) survey. Monthly GUS median wage in the general economy has been recalculated to reflect the\nspecific time periods of SEIS UNHCR and NBP (2024) surveys.\n\n\n\nSource: Deloitte own elaboration based on Eurostat Labour Force Survey data SEIS UNHCR survey\nconducted in May and June 2024. For Polish citizens reference period is Q2 2024.\n\n\n\nPolish citizens Ukrainian refugees\n\n\n\n**The wages of Ukrainian refugees are**\n**higher for men than women, but the**\n**gap is not wider than in the economy**\n*", "source": "jad_paddy_docs", "subset": "annotate_sample", "spans": [{"key": "sample:jad_paddy_docs:000001:9:2:0", "start": 190, "end": 199, "surface": "SEIS\ndata", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:jad_paddy_docs:000001:9:2:1", "start": 826, "end": 830, "surface": "SEIS", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Named survey supports the statement that gross-earnings data are unavailable.", "human_verdict": null, "human_note": ""}, {"key": "sample:jad_paddy_docs:000001:9:2:2", "start": 1275, "end": 1286, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:jad_paddy_docs:000001:9:2:3", "start": 1288, "end": 1305, "surface": "NBP (2024) survey", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}, {"key": "sample:jad_paddy_docs:000001:9:2:4", "start": 1496, "end": 1529, "surface": "Eurostat Labour Force Survey data", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "Eurostat survey data source an employment-rate comparison and chart.", "human_verdict": null, "human_note": ""}, {"key": "sample:jad_paddy_docs:000001:9:2:5", "start": 1530, "end": 1547, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": null, "human_verdict": null, "human_note": ""}]}, {"key": "p059", "text": " approaches that<br>support structural strengthening, like roofing material that can sustain strong winds and rains, incorporating<br>defense mechanisms against floods, landslides, and soil erosion (US$3M); iii) introduce or improve rainwater<br>harvesting systems (US$4M) and drainage and sanitation systems (US$4M) where appropriate; iv) all<br>beneficiary schools will need to plant trees, enabling instruction to continue in the shade during high heat<br>days (US$2M); v) implement an awareness campaign on climate change (US$1M), including information on<br>school-specific evacuation protocols at the onset of climate-related emergencies; vi) the child-friendly schools<br>interventions will include content on environmental safety and protocols at the onset of climate change-<br>induced emergencies like flash floods, increasing the capacity of teachers, pupils, school management, and<br>community’s capacity to address climate change-related issues (US$1.2M); vii) the updated BRMS will include<br>design considerations for schools to be used as shelters during climate related and other emergencies (such as<br>use of wind-resistant materials, emergency lighting, and backup power supply) (US$2M).<br> <br>The proposed climate interventions will be financed by IDA. All infrastructure activities under DLI #5<br>(US$189M) are to address existing vulnerabilities due to climate change and extreme climate events expected<br>in the future.|\n|RA 3: Supporting<br>Use of Data for<br>Improved<br>System<br>Management|<br>DLI #6. Percentage<br>of schools with<br>student-level data<br>in EMIS|**_M", "source": "fcv_pads_east_africa", "subset": "annotate_sample", "spans": [{"key": "fcv_pads_east_africa:002824:29:6:0", "start": 1568, "end": 1586, "surface": "student-level data", "probe_tag": "drop", "probe_score": null, "pred": null, "luna_label": 0, "luna_reason": "Standalone indicator fragment within a DLI table", "human_verdict": null, "human_note": ""}, {"key": "fcv_pads_east_africa:002824:29:6:1", "start": 1593, "end": 1597, "surface": "EMIS", "probe_tag": "confusion", "probe_score": null, "pred": null, "luna_label": 1, "luna_reason": "EMIS data underpin the stated percentage indicator for schools with student-level records.", "human_verdict": null, "human_note": ""}]}]