[{"key": "aivin-000", "text": "**The World Bank**\nKenya Inclusive Growth and Fiscal Management DPO 2 (P172321)\n\n\n**PDO3/Pillar 3: Create Fiscal Space and Crowd in the Private Sector to Advance the Government’s Inclusive**\n**Growth Agenda**\n\nDPO1: PA7 & PA8. DPO2: PA5, PA6, & PA7\n\n26. Under DPO1 PA7, value added tax (VAT) exemptions were rationalized through the passage by\nParliament of the Finance Act 2018, which contained a provision for the removal of VAT exemptions on\npetroleum products and through the adoption of a governance framework that limited the number of tax\nexemptions that were granted to arrest the decline in VAT tax revenues. Under DPO2, PA5 an additional\nmeasure to reverse the decline in tax revenues was promoted by the Cabinet approving for submission to\nParliament the Income tax bill 2019 that also limited the number of tax income exemptions.\n\n27. DPO1 PA8 addressed corruption issues by establishing procedures for the investigation of collusive\nbehavior in public procurement bids. It also provided for the verification and reconciliation of external debt\nrecords in order to improve the accuracy of Kenya’s electronic debt registry. DPO2 PA6 increased the\ntransparency of public procurement through publishing online, details of contracts and tenders, including\ntender documents. It also would publish details of companies that had been awarded tenders as well as the\nnames of the directors of the companies. The Cabinet also approved under this PA new regulations that\nallowed for standard bidding documents to be revised in order to provide greater details on bidders.\nParliament also enacted the Competition Amendment Act that included penalties for collusive behavior with\nrespect to public procurement. DPO2 PA7 contained a provision for Cabinet approval of the Debt and\nBorrowing Policy that allowed for the Central Bank to launch the Treasury Mobile Direct System which\nprovided for institutional and retail investors to participate through an electronic online platform in the\nissuance of government securities that would enhance the transparency", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "sample:fcv_pads_east_africa:000459:18:0:0", "start": 1043, "end": 1064, "surface": "external debt\nrecords", "probe_tag": "confusion", "probe_score": 0.183, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:000459:18:0:1", "start": 1109, "end": 1133, "surface": "electronic debt registry", "probe_tag": "drop", "probe_score": 0.0169, "luna_label": 0}]}, {"key": "aivin-001", "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_aivin", "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": 0.946, "luna_label": 1}, {"key": "fcv_pads_east_africa:001527:16:1:2", "start": 462, "end": 478, "surface": "project M&E data", "probe_tag": "confusion", "probe_score": 0.8128, "luna_label": 0}]}, {"key": "aivin-002", "text": ".\n\n\n**Coordinated, community-linked interventions can reduce barriers to education for**\n**marginalized groups.** IERCs combined with itinerant teacher training and community\noutreach increased enrollment of children with special needs, while gender clubs in emerging\nregions supported girls' retention and were replicated beyond program schools. These experiences\nshowed that structured approaches linking education authorities, families, and service providers —\nsupported by capacity-building and awareness-raising — can meaningfully improve access and\nretention for the most excluded learners.\n\n\n**12. Assessment Recommended?**\n\n\nNo\n\n\n**13. Comments on Quality of ICR**\n\n\n**Quality of Evidence.** Evidence provided in the ICR was drawn from systematically collected administrative and\nmonitoring data, including strengthened national systems such as EMIS, textbook tracking, national and earlygrade assessments, and school inspections. Achievement of DLI/DLR targets was independently validated,\nenhancing the credibility of reported results. The ICR appropriately triangulated project monitoring data with\nexternal sources, including the World Bank’s 2025 Global Education Policy Dashboard for Ethiopia. While some\nindicators in the ICR’s results framework lacked baseline, target, or achievement values, this information was\navailable elsewhere in project documentation, including the ICR’s Efficacy section, the PAD, restructuring\n\n\nPage 24 of 25", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:002925:23:1:1", "start": 1081, "end": 1104, "surface": "project monitoring data", "probe_tag": "confusion", "probe_score": 0.0898, "luna_label": 0}, {"key": "fcv_pads_east_africa:002925:23:1:2", "start": 1155, "end": 1193, "surface": "2025 Global Education Policy Dashboard", "probe_tag": "keep", "probe_score": 0.9327, "luna_label": 1}]}, {"key": "aivin-003", "text": "**g)  Failure to transfer Land into the Custody of ULC**\nI noted that out of the titled 1,909 land tittles held by 42 of the 45 MDAs\nin the sample, 15 MDAs did not transfer 167 land titles into the name\nand custody of the Uganda Land Commission for the benefit of the user\nMDAs. The Accounting Officers of public Universities and URA explained\nthat Universities were body corporate with a right to own and manage\ntheir own land.\n\n\nI advised the Attorney General to give a general guidance to corporate\ngovernment entities in regard to the custody of entity land with ULC.\n\n\n**h)  Recording and Reporting of Government Land**\nI noted that out of the 57 sampled entities, 20 entities did not record a\ntotal of 636 pieces of land measuring approximately 19,275.25 hectares\nin their respective Land registers rendering it difficult to confirm the\ncompleteness of their Land inventory.\nI also noted that 26 entities did not record a total of 1,355 pieces of land\nmeasuring approximately 21,603.08 hectares of land in their respective\nGFMIS fixed asset module thus affecting the accuracy of the nonproduced assets in the financial statements.\n\n\nThe non-recording of land in the land register and GFMIS Asset module\naffects the Government’s ability to keep track of all its land which could\neasily lead to loss and /or misstatement of the non-produced asset in the\nfinancial statements.\n\n\nI advised the Accountant General to ensure that the asset module under\nthe GFMIS is functioning properly. In addition, I advised the respective\nAccounting Officer to update both the fixed asset registers and the asset\nmodule under the GFMIS.\n\n\n**i)  Utilization of Government Land for Delivery of Service**\nI noted that out of the 57 sampled entities, 29 entities did not utilize a\ntotal of 258 pieces of land measuring approximately 10,066.8 hectares\nof land held by 30th June 2022. Non-utilization and use of land for", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:003028:61:0:3", "start": 1190, "end": 1208, "surface": "GFMIS Asset module", "probe_tag": "keep", "probe_score": 0.918, "luna_label": 0}, {"key": "fcv_pads_east_africa:003028:61:0:4", "start": 1564, "end": 1585, "surface": "fixed asset registers", "probe_tag": "confusion", "probe_score": 0.8225, "luna_label": 0}]}, {"key": "aivin-004", "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_aivin", "spans": [{"key": "prwp:000028:4:0:0", "start": 270, "end": 280, "surface": "panel data", "probe_tag": "confusion", "probe_score": 0.8931, "luna_label": 1}, {"key": "prwp:000028:4:0:2", "start": 1240, "end": 1275, "surface": "comprehensive global panel data set", "probe_tag": "keep", "probe_score": 0.9288, "luna_label": 1}]}, {"key": "aivin-005", "text": "combines these indicators according to the bureau’s judgment of what factors are important (using a\n\n\ndecision rule not trained on data). Subscribers were matched to their financial histories based on an\n\n\nencrypted, anonymized identifier.\n\n\nThe mobile phone data include metadata for each call and SMS, with identifiers for the other party,\n\n\ntime stamps, tower locations, and durations. It does not include top-ups, balances, data access, charges,\n\n\nhandset models used, or mobile money transactions; thus performance is expected to improve with richer\n\n\ndata. The data do not include any information on the content of any communication.\n\n\nWe aim to predict default based on the information available at the time credit was granted, and so\n\n\ninclude only mobile phone transactions that precede the date of plan switching. Descriptive statistics for\n\n\nthe sample are presented in Table 1. Although 85% of our sample has a file at the credit bureau, many of\n\n\nthese files are thin: 59% have at least one entity currently reporting an account, 31% have at least two, and\n\n\nonly 16% have at least three. By construction, 100% of the sample has a prepaid mobile phone account.\n\n\nThe median individual places 26 calls per week, speaking 32 minutes, and sends 24.4 SMS. The data\n\n\nincludes the median individual’s phone usage for 16 weeks; an implementation that can obtain longer\n\n\nhistories is likely to perform better.\n\n\n**3.** **METHOD**\n\n\nThe goal is to predict the likelihood of repayment using behavioral features derived from mobile\n\n\nphone usage. We consider a sample of completed plan transitions, and consider whether information that\n\n\nwas available at the time the credit was extended could have predicted its repayment. Because this sample\n\n\nof individuals did obtain credit, risk is reported among those who received credit based on the selection\n\n\ncriteria at the time, which was relatively permissive and spanned the distribution of credit histories\n\n\n(including no history at all).\n\n\nThe credit data provide an indicator for whether", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000103:10:0:0", "start": 246, "end": 263, "surface": "mobile phone data", "probe_tag": "confusion", "probe_score": 0.8786, "luna_label": 1}, {"key": "prwp:000103:10:0:1", "start": 2001, "end": 2012, "surface": "credit data", "probe_tag": "keep", "probe_score": 0.919, "luna_label": 1}]}, {"key": "aivin-006", "text": "Lastly, the education policy literature reports the importance of community support in\nsustaining reform efforts. In particular, support from local stakeholders is a strong positive\npredictor of successful institutionalization of school reforms (Bryk et al. 2010).\n\nThis paper considers each of these factors together rather than individually. As such, it\ncontributes not only to the literature on sustainability of development projects but also offers\noperational lessons for project designers. We leverage rich panel data on playgroups in rural\nIndonesia to demonstrate how characteristics of the playgroups during the funded project period\npredict their sustainability several years after donor funding ends. This is an improvement from\nexisting research on sustainability in health and education, which typically uses cross-sectional\ndata (Scheirer and Dearing 2011). While the focus of this paper is on the sustainability of services\nestablished under an early childhood education project, the findings also provide lessons and\ninsights for designing other types of community-based development projects.\n\nWe present six key findings in this paper. First, we show that the vast majority (86%) of\nplaygroups established under the project remained open three years after project funding ended. In\nparticular, those that remained open balanced the reduction in donor funding by introducing\nstudent fees. Both our survey data and qualitative interview data show that the playgroups that\nclosed were those that struggled to find the financial and human resources needed to continue\noperating.\n\nSecond, we show that the playgroups that allocated a larger share of expenditure to teacher\nsalaries during the project period were significantly more likely to remain in operation after the\nproject ended. We find evidence that playgroups reallocated their resources towards teacher\nsalaries by cutting down on supplementary services such as weekly food programs.\n\nThird, we find that the quality of the project playgroups – as measured by direct observation\nof the playgroup – is a key predictor of sustainability. We find that such observations capture\nelements of the quality of these playgroups that are predictive of sustainability, over and above\nwhat can be predicted", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000125:6:0:0", "start": 513, "end": 556, "surface": "panel data on playgroups in rural\nIndonesia", "probe_tag": "keep", "probe_score": 0.9641, "luna_label": 1}, {"key": "prwp:000125:6:0:1", "start": 822, "end": 842, "surface": "cross-sectional\ndata", "probe_tag": "confusion", "probe_score": 0.499, "luna_label": 1}]}, {"key": "aivin-007", "text": "qualitative data is collected, for instance, to understand patterns of well-being across a large country\nwhich would follow the familiar logic of stratified probability sampling (Alexander, 2017).\n\n4) Integrating qualitative and quantitative work can be done in different ways. Following classical\ninferential logic, small-N qualitative work can be conducted using the case study method to develop\nhypotheses, which can then be tested for their generalizability, possibly mediated by a theoretical model,\nwith quantitative data collected from a representative sample of respondents (Rao, 1997b). The integrated\ncollection and analysis of qualitative and quantitative information can also be analyzed using Bayesian\ninference (Humphries and Jacobs 2015).\n\n5) Machine learning and Natural Language Processing are a double-edged sword. They offer tremendous\nadvantages in moving us towards analyzing narrative data at scale. Yet, supervised methods that rely on\nbiased training sets, such as sentiment dictionaries developed for western contexts applied to non-western\nlinguistic cultures, can result in substantial bias. Furthermore, as Woolcock (2021) has argued, when\nmachines are used for analyzing data, narratives have the danger of being analyzed out of context and\nwithout nuance, resulting in misinterpretation. In other words, relying on machines without sensitive\nhuman intervention has the danger of turning reflexively collected data into non-reflexive analysis.\n\n6) Process, studied carefully with qualitative methods, can be extremely valuable to understand the\nmechanisms of change and thus complement time-variant quantitative studies, particularly impact\nevaluations. Understanding process matters not just for research but also for policy, where an exclusive\nemphasis on policies that can only be assessed using experiments or impact evaluations can sharply limit\nour capacity to imagine and create a better world (Rao, 2019).\n\n7) The potential for the direct participation of “respondents,” “beneficiaries,” and “subjects” in research is\nvastly unexplored. If our purpose as researchers is to assist in the process by which people", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000135:20:0:1", "start": 510, "end": 527, "surface": "quantitative data", "probe_tag": "confusion", "probe_score": 0.5204, "luna_label": 0}, {"key": "prwp:000135:20:0:2", "start": 897, "end": 911, "surface": "narrative data", "probe_tag": "drop", "probe_score": 0.0381, "luna_label": 0}]}, {"key": "aivin-008", "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_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:20:1:0", "start": 405, "end": 408, "surface": "GUS", "probe_tag": "drop", "probe_score": 0.0061, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:20:1:1", "start": 413, "end": 421, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.969, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:20:1:2", "start": 434, "end": 465, "surface": "data for instrumental variables", "probe_tag": "confusion", "probe_score": 0.8616, "luna_label": 1}]}, {"key": "aivin-009", "text": " the economy. It provides information\non the total impact of shocks on various\naspects of the economy, including the\nlabour market, government revenue, as\nwell as key economic aggregates. It is the\nmost appropriate tool for accounting for\nthe multi-layered impact of Ukrainian\nrefugees. A counterfactual analysis was\nperformed using the latest data on the\nPolish economy to account for the effects\nof other shocks in the economy. The model\nenabled a counterfactual analysis to be\nconducted by isolating the refugee influx\nfrom all other economic shocks, e.g. the\nother macroeconomic consequences of the\nwar in Ukraine. The results were calculated\nfor 2022, 2023 and 2024 with an additional\nlong-term analysis up to 2030 to assess the\neconomy’s long-term adaptation (assuming\nno new shocks, including no countershocks <sup>34</sup> [^34: E.g. refugees keep having lower productivity rather than adapt to level of natives.] ).\n\n\n32 <u>Economics of climate change | Deloitte Australia</u>\n\n\n\n**Chart 32. Average weekly hours in main job**\n\nWomen in 15-64 age group\n\n\n\n68.4%\n\n\n2023-Q2\n\n\n\n68.8%\n\n\n2024-Q2\n\n\n\n2021-Q2\n\n\n\n67.5%\n\n\n2022-Q2\n\n\n\n40.3\n\n\n\n40.3\n\n\n\n40.4\n\n\n\n40.4\n\n\n\n\n\n\n\n\n\n\n\nSource: Deloitte own elaboration based of Eurostat\ndata (Labour Force Survey).\n\n\n\nSource: Deloitte own elaboration based of Eurostat\ndata (Labour Force Survey).\n\n\n\nPart-time\n\nFull-time\n\n\n\n31 Deloitte has not received data that would be detailed as to citizenship, poviat, sex, age group, occupational group, and ZUS insurance code that would be suitable\nfor econometric approach.\n\n\n42\n\n\n\n33 <u>https://www2.deloitte.com/pl/pl/pages/risk/solutions/analiza-ryzyk-klimatycznych-badanie-scenariuszy-z-modelem-DClimate.html34</u>\n\n34 E.g. refugees keep having", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:21:2:0", "start": 344, "end": 370, "surface": "data on the\nPolish economy", "probe_tag": "confusion", "probe_score": 0.4794, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:21:2:1", "start": 1215, "end": 1228, "surface": "Eurostat\ndata", "probe_tag": "drop", "probe_score": 0.006, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:21:2:2", "start": 1230, "end": 1249, "surface": "Labour Force Survey", "probe_tag": "keep", "probe_score": 0.9867, "luna_label": 1}]}, {"key": "aivin-010", "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_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:14:3:0", "start": 703, "end": 714, "surface": "SEIS survey", "probe_tag": "confusion", "probe_score": 0.5162, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:14:3:1", "start": 724, "end": 739, "surface": "NBP 2024 survey", "probe_tag": "confusion", "probe_score": 0.8219, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:14:3:2", "start": 1825, "end": 1833, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9997, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:14:3:3", "start": 1886, "end": 1890, "surface": "SEIS", "probe_tag": "drop", "probe_score": 0.0004, "luna_label": 1}]}, {"key": "aivin-011", "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_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:9:2:0", "start": 190, "end": 199, "surface": "SEIS\ndata", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:9:2:1", "start": 826, "end": 830, "surface": "SEIS", "probe_tag": "drop", "probe_score": 0.001, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:9:2:2", "start": 1275, "end": 1286, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.999, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:9:2:3", "start": 1288, "end": 1305, "surface": "NBP (2024) survey", "probe_tag": "confusion", "probe_score": 0.8582, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:9:2:4", "start": 1496, "end": 1529, "surface": "Eurostat Labour Force Survey data", "probe_tag": "keep", "probe_score": 0.9804, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:9:2:5", "start": 1530, "end": 1547, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9549, "luna_label": 1}]}, {"key": "aivin-012", "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_aivin", "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": 0.1039, "luna_label": 0}, {"key": "sample:jdc_operational:000019:21:0:1", "start": 847, "end": 878, "surface": "UNHCR and WFP beneficiary lists", "probe_tag": "keep", "probe_score": 0.9488, "luna_label": 1}, {"key": "sample:jdc_operational:000019:21:0:2", "start": 1007, "end": 1034, "surface": "WFP price\nmonitoring system", "probe_tag": "confusion", "probe_score": 0.5167, "luna_label": 1}, {"key": "sample:jdc_operational:000019:21:0:3", "start": 1648, "end": 1680, "surface": "price monitoring and data system", "probe_tag": "drop", "probe_score": 0.0244, "luna_label": 0}]}, {"key": "aivin-013", "text": " the NRs and the respective\ndistrict, municipal and local authorities manage the secondary network.\n\n15. **Only 3.3 percent of the road network is paved; 52 percent are gravel roads and another about 45**\n**percent are earthen roads, which do not provide all-weather connectivity and are difficult to maintain.**\nOnly about 100-400 km of gravel NRs are being upgraded every year to paved roads. Some of the river\ncrossings on NRs do not have bridges and traffic movement on these highways is dependent on the ferry\ncrossings, which operate for only few hours in a day.\n\n\n16. **The road network is prone to disruption by floods, is not resilient, and has only few redundancies** <sup>**26**</sup> [^26: Redundancies are alternate routes] **but**\n**uncertainty about the future and limited resources make it difficult to plan robust mitigating measures.**\nUganda is exposed to a variety of natural hazards (droughts, flooding, landslides, heat waves). Each year,\nfloods impact nearly 50,000 people and over US$62 million in GDP. Uganda experiences both flash floods\nand slow-onset floods. Areas most prone to floods are the capital city, Kampala, and the northern and\neastern areas of the country.\n\n\n17. **According to UDHS, distance to health facilities in Uganda was reported as a serious problem to access**\n**health facilities and this is more acute in refugee-hosting districts in West Nile sub-region.** Mortality\nrate is still an issue in Uganda. Fertility and mortality rates, from the 2016 UDHS, forecasted that 2\npercent of women un Uganda would die from maternal causes at existing rates. Moreover, it is\ndocumented that health care services during pregnancy and childbirth and after delivery are important\nfor the survival and well-being of both the mother and infant. This situation worsens when roads are", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000001:7:1:0", "start": 1217, "end": 1221, "surface": "UDHS", "probe_tag": "drop", "probe_score": 0.0457, "luna_label": 1}, {"key": "sample:jdc_operational:000001:7:1:1", "start": 1492, "end": 1501, "surface": "2016 UDHS", "probe_tag": "keep", "probe_score": 0.9013, "luna_label": 1}]}, {"key": "aivin-014", "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_aivin", "spans": [{"key": "jdc_operational:000002:3:0:0", "start": 1109, "end": 1124, "surface": "Spring 2021 LEM", "probe_tag": "keep", "probe_score": 0.9428, "luna_label": 1}, {"key": "jdc_operational:000002:3:0:1", "start": 1274, "end": 1294, "surface": "Consumer Price Index", "probe_tag": "confusion", "probe_score": 0.7491, "luna_label": 1}]}, {"key": "aivin-015", "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_aivin", "spans": [{"key": "jdc_operational:000002:4:0:0", "start": 139, "end": 163, "surface": "Lebanon Economic Monitor", "probe_tag": "confusion", "probe_score": 0.8792, "luna_label": 1}, {"key": "jdc_operational:000002:4:0:1", "start": 668, "end": 702, "surface": "survey\nconducted by the World Bank", "probe_tag": "keep", "probe_score": 0.9754, "luna_label": 1}]}, {"key": "aivin-016", "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_aivin", "spans": [{"key": "refugee_pads:000001:20:0:1", "start": 931, "end": 939, "surface": "big data", "probe_tag": "confusion", "probe_score": 0.1158, "luna_label": 0}, {"key": "refugee_pads:000001:20:0:2", "start": 1998, "end": 2011, "surface": "agro-met data", "probe_tag": "drop", "probe_score": 0.0205, "luna_label": 0}]}, {"key": "aivin-017", "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_aivin", "spans": [{"key": "refugee_pads:000001:51:1:0", "start": 350, "end": 360, "surface": "H/H survey", "probe_tag": "drop", "probe_score": 0.021, "luna_label": 0}, {"key": "refugee_pads:000001:51:1:1", "start": 515, "end": 525, "surface": "HH surveys", "probe_tag": "confusion", "probe_score": 0.0678, "luna_label": 0}]}, {"key": "aivin-018", "text": "HDs. z\n\nThere is strong progress on: the commitment to integrate refugee services into national service delivery systems.\nAs outlined in the National Development Plan III (NDP III), refugee planning is integrated into national, sectoral and\nlocal government plans and data collection. The CRRF has developed sectoral plans for refugees and host\ncommunities and included both groups under the Uganda Intergovernmental Fiscal Transfer to support service\nprovision through district development plans. Refugees and hosts have been included in the 2022 Uganda\nDemographic Health Survey. Although a refugee sample was not collected in the Uganda National Household\nSurvey that was conducted in 2019/20 due to COVID-19 pressures, the Uganda Bureau of Statistics remains\ncommitted to include refugees in national data exercises.\n\nOn the commitments to: ensure access for refugees and host population to quality, efficient and integrated basic\nsocial services; and enhance social infrastructure in refugee hosting areas, strong progress is being made on health\nand education service provision. The second Education Response Plan for Refugees and Host Communities was\nendorsed in September 2022. Under the Uganda intergovernmental Fiscal Transfer (UgIFT) program WHR, refugee\nchildren have been included in the education capitation grant for RHDs and 51 schools are being transferred from\nhumanitarian partners into the national education system. The Uganda Secondary Education Expansion Project\n(USEEP) will strengthen infrastructure in 61 secondary schools in RHDs.\n\nAccording to the just concluded Health Sector Integrated Refugee Response Plan a mid-term review, under the\nUgIFT, 15 health centers are being transitioned from humanitarian partners into national systems and refugee\npopulations have been included in the per capita recurrent cost allocations to RHDs. Two blood banks to service\nRHDs are also being constructed under UgIFT. Refugees were integrated into the National COVID-19 Health\nPrevention and Response Plan. The Uganda COVID 19 Response and Emergency Preparedness Project is supporting\n\n\nPage 79 of 81", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000001:84:1:0", "start": 543, "end": 580, "surface": "2022 Uganda\nDemographic Health Survey", "probe_tag": "keep", "probe_score": 0.9092, "luna_label": 1}, {"key": "refugee_pads:000001:84:1:1", "start": 633, "end": 665, "surface": "Uganda National Household\nSurvey", "probe_tag": "confusion", "probe_score": 0.8341, "luna_label": 1}]}, {"key": "aivin-019", "text": "\neconomic participation and opportunities and to 133 for educational attainment. <sup>6</sup> The 2022 Global Findex Survey <sup>7</sup> found\n\n\n1 International Monetary Fund, 2023. Website, accessed November 14th: imf.org/en/Countries/ETH#featured\n2 Drought is the most destructive climate-related natural hazard. Through 2100, there is a likely 20 percent increase in extreme high rainfall events.\nFlash floods and seasonal river floods are becoming more frequent and widespread. World Bank, 2021, Ethiopia Climate Risk Profile.\n3 This is particularly due to dependence on key sectors that are highly affected by climate change such as agriculture, water, tourism, and forestry\n(World Food Programme. Ethiopia Annual Country Report 2022).\n4 Ethiopia Country Climate Development Report, 2023, draft, World Bank.\n5 World Bank, 2021, Ethiopia Climate Risk Profile.\n6 World Economic Forum. 2022. Global Gender Gap Report, published July 2022. URL: https://www3.weforum.org/docs/WEF_GGGR_2022.pdf.\n7 World Bank. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the age of COVID-19.\nhttps://www.worldbank.org/en/publication/globalfindex#sec1.\n\n\nPage 1 of 39", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000005:11:2:0", "start": 98, "end": 123, "surface": "2022 Global Findex Survey", "probe_tag": "keep", "probe_score": 0.9687, "luna_label": 1}, {"key": "refugee_pads:000005:11:2:1", "start": 1026, "end": 1046, "surface": "Findex Database 2021", "probe_tag": "confusion", "probe_score": 0.8074, "luna_label": 1}]}, {"key": "aivin-020", "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_aivin", "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": 0.7888, "luna_label": 1}, {"key": "sample:reliefweb:000620:7:0:1", "start": 96, "end": 123, "surface": "administrative data sources", "probe_tag": "drop", "probe_score": 0.0163, "luna_label": 1}, {"key": "sample:reliefweb:000620:7:0:2", "start": 1230, "end": 1241, "surface": "permit data", "probe_tag": "keep", "probe_score": 0.9301, "luna_label": 1}]}, {"key": "aivin-021", "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_aivin", "spans": [{"key": "reliefweb:000271:16:0:0", "start": 2, "end": 27, "surface": "Data disaggregated by AGD", "probe_tag": "confusion", "probe_score": 0.5952, "luna_label": 1}, {"key": "reliefweb:000271:16:0:1", "start": 173, "end": 195, "surface": "AGD-disaggregated data", "probe_tag": "drop", "probe_score": 0.0406, "luna_label": 1}, {"key": "reliefweb:000271:16:0:4", "start": 1614, "end": 1648, "surface": "Profile Global Registration System", "probe_tag": "keep", "probe_score": 0.9217, "luna_label": 1}, {"key": "reliefweb:000271:16:0:5", "start": 1832, "end": 1880, "surface": "AGD-disaggregated data about\nschool-age children", "probe_tag": "confusion", "probe_score": 0.7524, "luna_label": 1}]}, {"key": "aivin-022", "text": " weaving, embroidery and clothes-making.\n\n\nScarce and poorly paid livelihood opportunities\nwere often indicated as having prompted further\nmigration. In FGDs both in Kabul and Mazar,\nreturnees explained that several male youth,\nmarried and unmarried, in their community had\ndecided to return to Pakistan soon after return\nbecause of extremely limited job opportunities.\nOne male returnee in Kabul estimated that around\n60% of male youth had returned to Pakistan.\nOther FGD participants added that they had male\nrelatives who were currently in Pakistan but “were\ntrying to go to Iran and even Europe”. In parallel,\nthe great majority of returnees stated that they\nwere receiving regular remittances from relatives\nabroad, including males that had returned to\nPakistan. This phenomenon is also captured in the\nreturnee monitoring report of November 2017. Due\nto lack of livelihood opportunities, 8% of the sample\n\n\n\n15", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "sample:reliefweb:000080:16:2:0", "start": 153, "end": 157, "surface": "FGDs", "probe_tag": "drop", "probe_score": 0.0001, "luna_label": 1}, {"key": "sample:reliefweb:000080:16:2:1", "start": 808, "end": 834, "surface": "returnee monitoring report", "probe_tag": "confusion", "probe_score": 0.1948, "luna_label": 1}]}, {"key": "aivin-023", "text": "Though the voluntary character of the return was emphasized by the authorities during the\nplanning phase, the Protection cluster believed in the importance to follow the SOPs on\nreturn as a guide for the humanitarian when providing support to the authorities in the\nreturn process and in the importance of consulting the population to better ascertain the\nintentions and needs of the population.\n\n\n**II.** **Methodology**\n\n\nBased on the information received from FDMA on the denotification of the 50 tehsil, data\nwithin UNHCR database of registered IDPs were compared and 4549 were found as from Ali\nSherzai tribe.\n\n\nThe protection cluster established a representative sample capturing a significant portion of\nthe displaced population from Central Kurrum from Ali Sherzai tribe. IVAP provided the\nphone numbers of the registered families and IOM conducted phone calls with the IDPs.\n\n\n259 families were consulted and the RIS was conducted by 7 enumerators who contacted\ndisplaced families from a call centre using the contact information available through the\nIVAP records. This RIS was conducted using a specific tool/questionnaire developed in 2012\nfor previous consultations and slightly adapted in 2013. <sup>1</sup> [^1: See Annex1]\n\n\nThe enumerators were trained on 23rd January by protection cluster coordinator on the\ntool as well as on basic principles of confidentiality, informed consent and interviewing\ntechniques. The data collection was conducted from 29.1. to 5.2.\n\n\nIn total, 259 interviews were conducted- 98 families currently residing in Hangu, 94 in\nKohat, 42 in Peshawar, 17 in New Durrani, 4 in Nowshera, 3 in Charsadda, 1 in Tank.\n\n\n**III.** **Main findings of the Return Intention Survey 2014**\n\n\n**1)** **Profile of the interviewed population**\n\n\nMostly heads of families were interviewed during the return intention survey (89%), only in\n28 cases other family members were interviewed, depending on", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000116:1:0:0", "start": 520, "end": 553, "surface": "UNHCR database of registered IDPs", "probe_tag": "keep", "probe_score": 0.9607, "luna_label": 1}, {"key": "reliefweb:000116:1:0:1", "start": 1061, "end": 1073, "surface": "IVAP records", "probe_tag": "confusion", "probe_score": 0.8956, "luna_label": 1}]}, {"key": "aivin-024", "text": " from one stage to another. It is also driven in part by the curriculum which is geared to\npreparing students for the French baccalaureate examnination and may be contextually difficult for\nDjiboutians from less educated families.\n\n\n**3. Income and Gender Gaps in Enrollment Rates**\n\nEven though the main constraint at present appears to be school places, there is already evidence of\ngender and income gaps which cannot be explained by lack of school places alone. These are\nexpected to become more prominent over time as enrollment rates rise.\n\n\nAccording to the household expenditure survey data, in urban areas, the Net Enrollment Rate in\nPrimary Enrollment is 50% higher for the highest expenditure quintile compared to the lowest\nexpenditure quintile. The inequity is even worse in secondary education (lower secondary education\nis part of basic education but the survey data did not separate the two), where the NER of the highest\nquintile is 420% higher than the NER of the lowest quintile. The problem in urban areas is access demand exists among all groups but the rationing of sets ends up benefiting the better off who live in\nareas where schools have historically been located. Any further expansion of places will help the\npoorer segments of the population more particularly if care is taken to site the schools in areas where\nthe poor live.\n\n\nThere are also significant gender gaps and research indicates that educated mothers play a key role in\nthe country's overall development. There is a shortage of school places and any rationing works to\nthe detriment of girls enrollment. Parents are less willing for their girls to attend school because in\npar., they may view the curriculum as foreign. In addition, despite the fact the education is officially\nfree, poor families still have difficulty paying the cost of books and materials. They prefer to use\ntheir constrained resources for their boys who they feel have a better labor market potential. Finally,\nthe data from the Household Survey, showed that even if girls go to school, their parents pull them\nout at an", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:017781:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1}]}, {"key": "aivin-025", "text": "**The World Bank**\nKenya: Integrated Devolution and Urban Support Program (P177048)\n\n\n\n\n\n**B. Introduction and Context**\n\n\nCountry Context\n\n\n**1.** **Kenya experienced steady economic growth and declining poverty levels up to 2019 but the COVID-19 pandemic**\n**has had a severe impact on the economy and poverty levels since then.** From 2004 until 2019, Kenya’s GDP grew at an\nannual average of 5.3 percent, with absolute poverty numbers declining. <sup>1</sup> [^1: Draft Kenya County Partnership Framework FY 22-27, World Bank Group] The share of the population living below the\nnational poverty line decreased from 46.8 percent in 2005 to 36.8 percent in 2016 and to 33.4 percent in 2019. Kenya’s\nHuman Development Index rose by 9.1 percent to 0.60 in the decade leading up to 2019, as a result of significant\ninvestments in health care and basic education. GDP contracted by 0.3 percent in 2020. <sup>2</sup> [^2: GoK (2021) Economic Survey 2021, Kenya National Bureau of Statistics] Poverty increased sharply in\n2020 due to jobs and earnings losses. Poverty levels increased by 4 percentage points during 2020.\n\n\n**2.** **However, Kenya’s economy has been recovering and real gross domestic product (GDP) is expected to increase**\n**by an estimated 5.0 percent in 2021 and by 4.9 percent over the period 2022/23, close to its pre-pandemic rate** . <sup>3</sup> [^3: The World Bank Group (2021), Kenya Economic Update: From Recovery to Better Jobs] This\nis supporting renewed job creation and poverty reduction. Government revenues have also rebounded, facilitating fiscal\nconsolidation efforts. However, some sectors (such as tourism) still face major difficulties, and further COVID-19", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "sample:fcv_pads_east_africa:007536:2:0:0", "start": 701, "end": 724, "surface": "Human Development Index", "probe_tag": "keep", "probe_score": 0.9254, "luna_label": 1}, {"key": "sample:fcv_pads_east_africa:007536:2:0:1", "start": 919, "end": 950, "surface": "GoK (2021) Economic Survey 2021", "probe_tag": "keep", "probe_score": 0.9985, "luna_label": 1}]}, {"key": "aivin-026", "text": "**The World Bank**\nUganda Investing in Forests and Protected Areas for Climate-Smart Development Project (P170466)\n\n\n4. **Poverty reduction has not yet achieved set targets.** The percentage of people living below the\nnational poverty line (US$1 per day) was 21.4 percent in FY2017/18 compared to the NDP 2 target of\n14.2 percent. For the first time in more than a decade, there was a reversal in the poverty levels in 2017\nlargely due to droughts and crop failures. Average life expectancy is 63 years <sup>2</sup>, and the fertility rate is\namong the highest in the world. Uganda ranked 127th out of 162 countries in the 2018 Gender Inequality\nIndex. <sup>3</sup> Prevalence rates of gender-based violence (GBV) in Uganda are high compared to both global and\nregional averages. <sup>4</sup> The Government of Uganda recognizes GBV as a serious problem and approved a\nNational Policy on the Elimination of GBV in October 2016.\n\n\n5. **Poor and vulnerable people typically live in rural areas, have large families, and derive their**\n**income predominantly from farming, with high dependency on forests.** One in four rural Ugandans lives\nin poverty compared to just one in ten urban Ugandans. Rural poverty has become further entrenched as\na disproportionate percentage of the population remains trapped in subsistence agriculture. A heavy\nreliance on rain-fed and subsistence agriculture continues to expose the economy to risks from adverse\nweather, and these risks are likely to grow under most scenarios for future climate change. <sup>5</sup> Uganda is\nvulnerable to climate change, and its impacts are already being experienced in the region. <sup>6</sup> Increased\noccurrences of drought conditions and reduced or more variable rainfall across", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:019014:3:0:0", "start": 623, "end": 651, "surface": "2018 Gender Inequality\nIndex", "probe_tag": "keep", "probe_score": 0.9548, "luna_label": 1}]}, {"key": "aivin-027", "text": "3\n\n\n**2. Project development objective** (see Annex 1)\n\nThe objective of the project is to enhance the quality of education and to increase enrollment in\nprimary schools.\n\n\n**3. Key performance indicators:** (see Annex 1)\n\nThe key performance indicator is an increased number of students enrolled in grades 1-9, especially\namong girls.\n\n\nB. **STRATEGIC CONTEXT**\n\n\n**1. Sector-related Country Assistance Strategy (CAS) goal supported by the project** (see Annex 1)\n\n\n**Document number:** P 7403 DJI **Date of latest CAS discussion:** (scheduled for) 12/19/00\n\nThe CAS has been prepared in the context of the country's economic difficulties and deepening\npoverty. Despite Djibouti's relatively high nominal per capita income (US$790 versus an average of\nUS$510 for Sub-Saharan Africa, and US$100 for Ethiopia), Djibouti has one of the poorest social\nindicators in the world (poverty, illiteracy, maternal and infant mortality, and morbidity), according to\nthe UNDP Human Development Index, ranking 157th among 174 countries.\n\n\nThe Republic of Djibouti has very few natural resources and the economy is mainly dependent on the\nport, external financial assistance, the French military and associated services. However, with the\n\ndecreased amount of external assistance, and deepening structural problems, the country has suffered\neconomic stagnation over the past decade and a half. As a result, per capita Gross Domestic Product\n(GDP) declined by 50% in real terms since 1985. The switch of Ethiopia's transit traffic from Assab in\nEritrea to Djibouti in mid-1998 and the consequent four-fold increase in port traffic has opened new,\nas yet not fully exploited, opportunities for investment and growth. Djibouti's open economic policies\nand relative stability, characterized by a liberal trade policy and exchange system, which operates free\n\nof capital or", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:015648:6:0:0", "start": 959, "end": 987, "surface": "UNDP Human Development Index", "probe_tag": "keep", "probe_score": 0.9623, "luna_label": 1}]}, {"key": "aivin-028", "text": " from one stage to another. It is also driven in part by the curriculum which is geared to\npreparing students for the French baccalaureate examnination and may be contextually difficult for\nDjiboutians from less educated families.\n\n\n**3. Income and Gender Gaps in Enrollment Rates**\n\nEven though the main constraint at present appears to be school places, there is already evidence of\ngender and income gaps which cannot be explained by lack of school places alone. These are\nexpected to become more prominent over time as enrollment rates rise.\n\n\nAccording to the household expenditure survey data, in urban areas, the Net Enrollment Rate in\nPrimary Enrollment is 50% higher for the highest expenditure quintile compared to the lowest\nexpenditure quintile. The inequity is even worse in secondary education (lower secondary education\nis part of basic education but the survey data did not separate the two), where the NER of the highest\nquintile is 420% higher than the NER of the lowest quintile. The problem in urban areas is access demand exists among all groups but the rationing of sets ends up benefiting the better off who live in\nareas where schools have historically been located. Any further expansion of places will help the\npoorer segments of the population more particularly if care is taken to site the schools in areas where\nthe poor live.\n\n\nThere are also significant gender gaps and research indicates that educated mothers play a key role in\nthe country's overall development. There is a shortage of school places and any rationing works to\nthe detriment of girls enrollment. Parents are less willing for their girls to attend school because in\npar., they may view the curriculum as foreign. In addition, despite the fact the education is officially\nfree, poor families still have difficulty paying the cost of books and materials. They prefer to use\ntheir constrained resources for their boys who they feel have a better labor market potential. Finally,\nthe data from the Household Survey, showed that even if girls go to school, their parents pull them\nout at an", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:010150:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1}, {"key": "fcv_pads_east_africa:010150:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1}]}, {"key": "aivin-029", "text": "### 1.8 11\n\n\n\nCurrent account balance . **.2**\n\n\n\nFinancing items (net)\nChanges in net reserves .. .\n_Memo:_\nReserves including gold _(US$ millions)_\nConversion rate _(DEC, locaW/US$)_ 177.7 177.7 177.7\n\n\n**EXTERNAL DEBT and RESOURCE FLOWS**\n\n**1979** **1989** **1998** **1999**\n_(US$ millions)_ **Compositlon of total debt, 1998 (USS milIlona)**\nTotal debt outstanding and disbursed 26 **179** 288\nIBRD 0 0 0\nIDA 0 26 50 49 G 15 B\n\nTotal debt service 2 15 6\nIBRD 0 0 0 C: 9\nIDA 0 0 1 1\n\nComposition of net resource flows E: 119\nOfficial grants 5 32 48\nOfficial creditors -1 3 2\nPrivate creditors 0 -1 0\nForeign direct investment 0 0 6 D: **95**\nPortfolio equity 0 0 0\n\nWorld Bank program\n\nCommitments 0 9 3 A  - IBRD E - Bilateral\nDisbursements 0 2 2 1 B - IDA D - Other rrultilateral F **-** Private\nPrincipal repayments 0 0 0 0 C- IMF G - Short-term\nNet flows 0 2 2 1\nInterest payments 0 0 0 0\nNet transfers 0 2 1 1\n\n\nNote: This table was produced from the Development Economics central database. 9/13/00", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:021237:63:1:0", "start": 960, "end": 998, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.946, "luna_label": 1}]}, {"key": "aivin-030", "text": " Substation</u> <u>Substation Land has been fully paid</u> <u>&</u> <u>acquired by last year</u>\n\n\n\nThe enumerated number of cases requiring relocation was fmalised as 65 (5 between Kale and Gulu,\nand 60 between Gulu .and Arua). Fifty four (54) relocation sites have so far .been secured whilst\narrangements to finalise the remaining 11 are underway. The contract for construction of resettlement\nhouses was signed on 8th April 2020, the contractor is yet to submit their advance/ performance\nguarantees before construction works can commence as at 30 <sup>1</sup> h June 2020.\n\n\n\nd. Vulnerable group.\nA total of 145 Cases as highlighted in the RAP report are to be verified before rolling out vulnerability\nsupport initiatives. These will be verified before and a framework to guide on interventions will be\ncrafted.\n\n\n\n..\n11", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:006048:25:4:0", "start": 645, "end": 655, "surface": "RAP report", "probe_tag": "confusion", "probe_score": 0.2969, "luna_label": 1}]}, {"key": "aivin-031", "text": ".\n\n\n\n**_2.5.1 Progress on Harmonizing Systems within_**\n**_the NSNP and Moving Towards Government_**\n**_Management of the NSNP_**\n\nThe Single Registry. As a first step towards the\nharmonization integration of all cash transfer\nprogrammes, the NSNP created a Single Registry\nof Beneficiaries in early 2015. The Single Registry\nwill make it possible to build a picture of\nNSNP beneficiaries as a whole and will enable\npolicymakers and programme managers to verify\nthat beneficiaries fit the eligibility criteria, are not\nreceiving more than one cash transfer, and have\na valid ID number. The Single Registry has several\nuseful features. First, it is housed on servers in the\nSocial Protection Secretariat, but the MISs of each\nindividual cash transfer programme can instantly\nand automatically input information into the\nSingle Registry. Second, it is able to cross-check\nbeneficiaries from each cash transfer programme\nto ensure that no households are benefitting\nfrom more than one NSNP programme. Third,\nthe Single Registry is linked to the Integrated\nPopulation Registration Service (IPRS) to enable\neach programme to cross-reference the national\nID number for all beneficiaries with the IPRS\ndatabase to determine if it is a valid ID number\nand if the person meets the age criteria for the\ncash transfer programmes. Figure 7 shows the links\nbetween the Single Registry and the individual\nprogramme MISs.\n\n\n\n28 _Inua Jamii_ Towards a More Effective National Safety Net for Kenya", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:020590:36:1:0", "start": 258, "end": 290, "surface": "Single Registry\nof Beneficiaries", "probe_tag": "confusion", "probe_score": 0.8782, "luna_label": 0}, {"key": "fcv_pads_east_africa:020590:36:1:2", "start": 1190, "end": 1203, "surface": "IPRS\ndatabase", "probe_tag": "confusion", "probe_score": 0.6872, "luna_label": 1}]}, {"key": "aivin-032", "text": " and Maternal Care Practices and Increased Utilization_**\n\n**_of key Micronutrients:_**\n\nThis is particularly reflected in the impressive achievement of the PDO indicators added\nafter restructuring in 2012 (to better capture results of project activities). Improvements\nin these indicators (often exceeding the target values) show that today more than 55m\npeople have access to a basic package of nutrition services (up from 0 persons prior to the\nproject) and 42 % of children 0-23 months being routinely weighed and monitored, by\nHealth Extension Workers (HEWs), a starting point to engage the community in actions\nthat promote child growth including optimal breastfeeding and complementary feeding.\nIn addition, 2 million more children receive a dose of vitamin A every 6 months, and the\nsame amount are screened quarterly for acute malnutrition through community health\ndays. The percentage of pregnant women receiving iron and folate supplementation\nincreased from 17% to 89% during the project duration, and the percentage of infants\nexclusively breastfed increased by about 1% over the duration of the Project (albeit this\nindicator reflects national level data – an external evaluation carried out by Tulane in\nCBN woredas only, which received much of the focus of this project and is discussed\nbelow– revealed much better improvements). Addressing household provision of\nadequately iodized salt (an indicator dropped in 2012) was an unrealistic goal, given the\n\n\n18", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:019152:29:1:0", "start": 157, "end": 171, "surface": "PDO indicators", "probe_tag": "confusion", "probe_score": 0.8876, "luna_label": 0}]}, {"key": "aivin-033", "text": "**The World Bank**\nTHE NORTHEASTERN ROAD-CORRIDOR ASSET MANAGEMENT\nPROJECT 2 - (NERAMP 2) (P514937)\n\n\n\nPROJECT APPRAISAL DOCUMENT\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|People in host communities supported with accessing and utilizing services and livelihoods – Female (Number of people)|Col2|\n|---|---|\n|Description|Female in host communities supported with accessing and utilizing services and livelihoods|\n|Frequency|Annual|\n|Data source|UNHCR|\n|Methodology for Data <br>Collection|Observe and conduct annual surveys for host communities along the corridor|\n|Responsibility for Data <br>Collection|MoWT PIU|\n|**People in host communities supported with accessing and utilizing services and livelihoods – Youth (Number of people)**|**People in host communities supported with accessing and utilizing services and livelihoods – Youth (Number of people)**|\n|Description|Youth in host communities supported with accessing and utilizing services and livelihoods|\n|Frequency|Annual|\n|Data source|UNHCR|\n|Methodology for Data <br>Collection|Observe and conduct annual surveys for host communities along the corridor|\n|Responsibility for Data <br>Collection|MoWT PIU|\n|**Kilometers of town roads upgraded to climate resilient standards (in the refugee hosting districts of Koboko, Yumbe, and Moyo) (Kilometers) **|**Kilometers of town roads upgraded to climate resilient standards (in the refugee hosting districts of Koboko, Yumbe, and Moyo) (Kilometers) **|\n|Description|Length of town urban roads upgraded in Koboko, Yumbe, and Moyo|\n|Frequency|Quarterly|\n|Data source|Supervision reports|\n|Methodology for Data <br>Collection|Engineering measurement and field verification|\n|Respon", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:002275:47:0:1", "start": 501, "end": 555, "surface": "annual surveys for host communities along the corridor", "probe_tag": "confusion", "probe_score": 0.2086, "luna_label": 0}]}, {"key": "aivin-034", "text": " capita -O 2 -0 5 Lower-middle-income _group_\nESports of goods and servic\"s\n\n\n**STRUCTURE ofthe ECONOMY**\n\n**1979** **1989** **1998** **1999** **Growth rates of output and Investment ()**\n_{%I ol GOP)_\nAgriculture 3.4 _.._ **_2._**\nIndustry 21.0 O.Manufacturing 5.6 **_-2_** **_94_** _as_ _se_ _s_\nServices **75.6** 6\n\nPrivate consumption **-r.**\nGeneral government consumplion **G**\nImports of goods and services\n\n\n\n**1979-89** **1989-99** **1998** **1999**\n_(average annual orowth)_\nAgriculture\n\n\n\nIndustry\n\n\n\nManufacturing\nServices\n\n\n\nPrivate consumption\nGenerai government consumption\nGross domestic investment\nImports of goods and services\nGross national product 1 7 **1.4**\n\n\nNote. 1999 data are preliminary estimates.\nThis table was produced from the Development Economics central database.\n\nThe diamonds show four kev midicators in the country (in bold) compared with its income-group average. 11 data are missing, Ihe diamond will", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "sample:fcv_pads_east_africa:012073:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "confusion", "probe_score": 0.5672, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:012073:62:2:1", "start": 758, "end": 796, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.9023, "luna_label": 1}]}, {"key": "aivin-035", "text": " contracts identified in the\nProcurement Plan tables **_Not Applicable._**\n\n\n**_Domestic preference_** _as specified under paragraph 5.51_ of the Procurement\nRegulations **_(Goods and Works)_** .\n\n\nGoods: is not applicable for those contracts identified in the Procurement\nPlan tables;\n\n\nWorks: is not applicable for those contracts identified in the Procurement\nPlan tables\n\n\n**Other Relevant Procurement Information.**\n\n\nThe project has a CDD component and the project’s procurement\narrangements for community-based procurement will be in line with the\nprovisions of the Procurement Regulations and “Guidance Note for Design and", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:002589:1:1:0", "start": 29, "end": 52, "surface": "Procurement Plan tables", "probe_tag": "confusion", "probe_score": 0.1481, "luna_label": 0}]}, {"key": "aivin-036", "text": " At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA M&E data - NaCSA maintains lean and\n**community** **sub-projects and** decision-making by NaCSA; efficient organizational\n**NSAP** **partners monitored** structure\n**and evaluated** **in order to**\n**improve** **program**\n**effectiveness.**\n\n\n**3(d)** **Technical** **Assistance** 3d. 1 NaCSA staff indicate - IDA aide-memoires and\n**services** **effectively** **provided** satisfaction with technical project status reports\n**to support program** assistance, including skill\n**implementation** transfer activities\n\n\n**3(e)** NaCSA **management** 3e. 1 Project management - IDA Project Status reports\n**systems** **functioning** costs (NaCSA staff salaries at (including disbursement\n**effectively** **to ensure** all levels as well as operating reports);\n**program success** expenditures) are 13.5% or - NaCSA proposed annual\nless than total budgeted annual work program and budget\nexpenditures;                 - Annual audit reports;\n3e.2 NaCSA staff and                  - GOSL semi-annual PETS\npartners indicate satisfaction reports\nwith the performance of\nNaCSA's management;\n3e.3 NaCSA performance in\n\n\n                                  - 27", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:019432:31:1:0", "start": 170, "end": 184, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.149, "luna_label": 0}]}, {"key": "aivin-037", "text": "*|**1.7**|\n|**Grand Total**|**10.0**|\n\n\n\n47. **Retroactive Financing** . All eligible expenditures that would be incurred on or after April\n21, 2014 up to the date of the signing of the financing agreement, up to an aggregate amount not\nto exceed US$500,000 equivalent, would be eligible for retroactive financing. This retroactive\nfinancing is intended to finance the following activities: inter alia, (a) hiring of FM and\nprocurement experts and for set up of project management office; (b) hiring of construction\nexperts to meet the needs of the ESMF; and (c) hiring of consultant to prepare an updated user\nsatisfaction index.\n\n48. **Status of NSDS Implementation:** The implementation of the NSDS began in 2009.\nOver the past three years, the CSA conducted major surveys, which are included in the NSDS\ndocument. These include the Household Income and Expenditure Consumption Survey, the\nWelfare Monitoring Survey, and the Demographic and Health Survey. The CSA also regularly\nconducted the Annual Agricultural Sample Survey. In addition, the CSA implemented several\nproject-based surveys and studies within the NSDS framework. On the physical and IT\ninfrastructure front, the construction of the headquarters building in Addis Ababa and the\nnetworking of branch offices are in progress. The CSA has also acquired some GPS and CAPI\nmachines that are being used for pilot activities in selected enumeration areas.\n\n\n14", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:018905:24:1:1", "start": 893, "end": 918, "surface": "Welfare Monitoring Survey", "probe_tag": "confusion", "probe_score": 0.1226, "luna_label": 1}, {"key": "fcv_pads_east_africa:018905:24:1:2", "start": 928, "end": 957, "surface": "Demographic and Health Survey", "probe_tag": "confusion", "probe_score": 0.4134, "luna_label": 1}]}, {"key": "aivin-038", "text": "\noutside of the NAADS framework, the adverse consequences for large numbers of Ugandan smallholders\nwould most likely have been severe.\n\n\n**2.3 Monitoring and Evaluation (M&E) Design, Implementation and Utilization**\n\n**The NAADS design is predicated on the assumption that participatory M&E (PM&E) will ensure**\n**that** the quality of service provision meets the standards required to achieve the program’s intended\noutcomes (PAD, Annex 11). While the program undertook M&E, the work was not part of a clearly\ndefined M&E framework with program objectives and the relevant indicators of outcome, output/result,\nand activity level clearly set out (ITAD 2008, p. 2008, p. 117). The ICR team has not been able to find\ndocumentation to show that NAADS did conduct regular, broad, and systematic PM&E. The ITAD\nreport concludes that the PM&E system was not routinely followed (ITAD, 2008, p. 116). There are no\nbaseline indicators until 2004. The NAADS team conducted household surveys in 2004 and 2007 and\nimpact evaluations using this survey data.  These efforts provide more rigorous and detailed information\nat two points in time than is available for many projects, but do not provide the more mundane times\nseries of key indicators, particularly for the period between 2007 and closing. The project did track and\nreport annually on the number of districts and sub-counties participating, the number of registered groups\nand their members disaggregated by gender, the number of farmers’ fora at various levels, and the\nrecruitment of service providers.  See Annex 2 on M&E weaknesses (from GoU ICR, p. 34-35).\n\n\n**2.4 Safeguard and Fiduciary Compliance**\n\n**_Safeguards_**\nThe project correctly identified the two environmental safeguard policies triggered at preparation: OP/BP\n4", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:019701:19:1:1", "start": 1034, "end": 1045, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.8372, "luna_label": 1}]}, {"key": "aivin-039", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:018783:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "aivin-040", "text": "* Popular Benchmarks\n**children.**\n\n\n**Project** **Development** **Outcome** **/** **Impact** **Project reports:** **(from** **Objective** **to Goal)**\n**Objective:** **Indicators:**\n**Assist** **war affected** - Improved social capital and - Initial Social Assessment - Communities in the NSAP\n**communities** **to restore** organizational development; (to establish indicators and target areas are assisted to\n**infrastructure,** **services** **and** - Increased access to and use methodologies for social ensure a reduced risk of\n**build** **local** **capacity for** of social and economic capital and organizational conflict\n**collective** **action.** Priority infrastructure and services development)\nwill be given to areas not - Proportion of NSAP - Annual social assessments; - NACSA complements and\npreviously serviced by investments targeted to newly - NaCSA M&E data; extends the work of other\ngovernment, newly accessible accessible areas, & areas - M&E data of relevant line agencies and rninistries\nand the most vulnerable previously not served, and mninistries; in support of the PRSP's\npopulation groups within those vulnerable people within these - Beneficiary Assessment poverty reduction and\nareas. areas; (BAs) biannually; decentralization objectives\n\n                  - Proportion of sub-projects                   - Participatory evaluation\nthat reflect priorities of reports for a random sample of                   - NACSA is fully integrated\ntargeted communities and assisted conmmunities; into national planning and\nbeneficiaries; and                  - Technical audits resource allocation\n\n                  - Proportion of sub-projects frameworks (such as the\noperative 24emonths after National Recovery Strategy,\ncorpletion. the PRSP, and", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:010417:29:1:0", "start": 862, "end": 876, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.2118, "luna_label": 0}, {"key": "fcv_pads_east_africa:010417:29:1:1", "start": 961, "end": 969, "surface": "M&E data", "probe_tag": "confusion", "probe_score": 0.1144, "luna_label": 1}]}, {"key": "aivin-041", "text": " this has spread to all other surveys currently being\nconducted in the country. Technical assistance has also been received from this team in various areas.\n\n\nThe UNPS was intended to be a multi-sector project providing data to conduct analysis on different sectors\nincluding health, education, agriculture, etc. Overall, the project aimed to contribute to the Bank’s objective\nof making data free and publicly available to allow for evidence-based policymaking.\n\n\nIn 2016, we received a special grant from the bank which was very useful in continuing and consolidating the\ngains from the first phase. This was useful as it was used to collect data for a number of waves in addition to\nprocuring a few vehicles to replace the old ones.\n\n\nThe outputs of these surveys have been exceptionally valuable for policy design and monitoring among policy\nmakers, analysts and the public. In recent times, the Uganda Household Panel has been the most reliable\nsource of information for understanding the dynamics of agricultural households in the country. For example,\nwe are able to look at poverty dynamics among others. The survey has also provided useful up to date general\ninformation on households in Uganda, filling existing gaps in household data.\n\n\nIn the actual conduct of the surveys, the experience and skills gathered by UBOS staff has been vast. UBOS is\nnow much more knowledgeable and experienced in managing complex household-based surveys. We are now\napplying the latest methodologies and standards in conducting and analyzing all our household survey.\nWorking closely with the LSMS-ISA Team, UBOS staff have gained useful knowledge and experience which\nhave been applied in carrying out other surveys conducted by the Bureau. There has been lots of training on\nSurvey Solutions which we greatly appreciate.\n\n\nGenerally, this has been a very appreciated and vital exercise for the UBOS and the entire statistical system\nsince its inception in Uganda, and UBOS is committed to continue working with the Bank.\n\n\nUBOS appreciates the substantial financial support and the technical support from the World Bank LSMS team", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:005339:20:1:1", "start": 1230, "end": 1244, "surface": "household data", "probe_tag": "confusion", "probe_score": 0.3176, "luna_label": 1}]}, {"key": "aivin-042", "text": "The World Bank\n\n\n\nReport No: ISR15652\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|Student to classroom ratio|Col2|Text|Value|64:1|54:1|72:1|\n|---|---|---|---|---|---|---|\n|Student to classroom ratio||Text|Date|27-Nov-2008|29-May-2014|31-Jul-2014|\n|Student to classroom ratio||Text|Comments||This indicator is based on the<br>Annual Head Count data for<br>the EMIS; which targets all<br>public and private schools<br>that participate in the delivery<br>of the universal secondary<br>education reform program.||\n|Student to teacher ratio||Text|Value|27:1|27:1|33|\n|Student to teacher ratio||Text|Date|27-Nov-2008|31-Oct-2013|31-Jul-2014|\n|Student to teacher ratio||Text|Comments||Data for 2014 still under<br>validation, but will be<br>concluded by September<br>2014 for inclusion in the ICR.||\n|Permanent Classrooms||Number|Value|4788.00|8000.00||\n|Permanent Classrooms||Number|Date|27-Nov-2008|29-May-2014|31-Jul-2014|\n|Permanent Classrooms||Number|Comments||3743 classrooms of the total<br>is contributed by the Project.|No end target was defined|\n|Textbooks purchased||Number|Value||3898902.00|1700000.00|\n|Textbooks purchased||Number|Date||30-Jun-2014|31-Jul-2014|\n|Text", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:020479:4:0:0", "start": 314, "end": 336, "surface": "Annual Head Count data", "probe_tag": "confusion", "probe_score": 0.3193, "luna_label": 1}]}, {"key": "aivin-043", "text": " droughtprone nature of many districts, with projections indicating that climate change will further increase the\nfrequency and severity of drought. Even though some have since been restored, many post-conflict areas and\nIDP settlements remain dependent on water supply by mobile tankers, with budgetary impacts on already\n\n\n13 The World Bank Group. 2021. Climate Risk Profile: Ethiopia.\n14 Yearly breakdown of internal displacement trends available at: Ethiopia: Country Information, Internal Displacement Monitoring\n[Centre. URL: https://www.internal-displacement.org/countries/ethiopia.](https://www.internal-displacement.org/countries/ethiopia)\n15 IOM. 2021. Ethiopia National Displacement Report 10 (August-September 2021), published 13 December 2021. URL:\n_https://displacement.iom.int/reports/ethiopia-national-displacement-report-10-august-september-2021._\n16 Ibid.\n[17 URL: https://www.unicef.org/press-releases/children-northern-ethiopia-dire-need-protection-and-assistance.](https://www.unicef.org/press-releases/children-northern-ethiopia-dire-need-protection-and-assistance)\n18 IOM estimated that there were 2,091,387 IDPs in Ethiopia in December 2020/January 2021 (as reported in its Ethiopia National\nDisplacement Report 7 _)_ and noted that there were 4,239,636 IDPs in Ethiopia in September 2021 (as reported in its Ethiopia National\n[Displacement Report 10, which is the most recent data available). Reports are available at https://displacement.iom.int/ethiopia.](https://displacement.iom.int/ethiopia)\n19 Ibid.\n20 World Bank. 2020", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:007039:5:2:0", "start": 391, "end": 439, "surface": "Yearly breakdown of internal displacement trends", "probe_tag": "confusion", "probe_score": 0.8017, "luna_label": 1}, {"key": "fcv_pads_east_africa:007039:5:2:1", "start": 1333, "end": 1374, "surface": "Ethiopia National\n[Displacement Report 10", "probe_tag": "confusion", "probe_score": 0.3864, "luna_label": 1}]}, {"key": "aivin-044", "text": " 1997 to 47 percent in 2006. Extreme poverty was\nestimated at 19 percent of the population in 2006. Almost one in three of Kenya’s poor population was\nbetween 15-29 years of age. Half of these were youth between 15-19 years of age and who had a poverty\nrate of 51% - which was larger than the national average. The economic slowdown and the series of\ndifferent crises have negatively affected the population, and poverty levels are expected to start increasing\nagain. The 2006 data show that inequality in household consumption is high, with the consumption\ndecile ratios of the top 10 percent to the bottom 10 percent stood at 20:1 in urban areas and 12:1 in rural\nareas <sup>1</sup> .\n\n\n1 This compares to a national average of 5:1 in Tanzania and only 3.3:1 in Ethiopia.", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:018216:0:2:0", "start": 472, "end": 481, "surface": "2006 data", "probe_tag": "confusion", "probe_score": 0.8663, "luna_label": 0}]}, {"key": "aivin-045", "text": " - Efficiency of drainage structures Supervising Engineer MoRPW Maintenance Unit (o) routine maintenance and\nroad condition survey\nSoil erosion - _Control earthworks_ Supervising Engineer and (c) inspection (c) daily;\n\n_-_ _Install drainage structures_ _properly_ Contractor (o) routine maintenance and erosion control measures:\n\n_-_ _Install erosion control measures_ road condition survey during construction and on\n\n               - _grouted stone pitching and rock fill_ completion of each measure\n_gabion_ _works will be necessary_ _to_ (o) once a year\n_protect_ _culvert inlets and outlets_\n\n_-_ _For drainage_ _ditches along the road_\n\n\n\n_scouring checks_ _will be_ _necessary_\n\n_-_ _Management_ _of excavation_ _activities_\n\n_-_ _In slopes and other suitable places_\n\n\n\n_along_ _the roadside,_ _shrubs should be_\n_planted_ _along the first_ _15 Km._\n\n\n\n\n- Impact on erosion (on road, off road, Supervising Engineer MoRPW Maintenance Unit (c) inspection (c) once a month\nembankments, riverbanks, etc.) (o) routine maintenance and (o) once in 6 months to\n\n- Efficiency of erosion control road condition survey account for seasonal variations\nmeasures over 3-5 years", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:013240:66:1:0", "start": 369, "end": 390, "surface": "road condition survey", "probe_tag": "confusion", "probe_score": 0.3066, "luna_label": 0}]}, {"key": "aivin-046", "text": " by Kaabong District to the north, the Republic of Kenya to the east,</mark>\n<mark>by Amudat District to the south, Nakapiripirit District to the southwest, Napak District to the west</mark>\n<mark>and by Kotido District to the northwest It lies on the foot of Mt. Moroto. The district headquarters</mark>\n<mark>at Moroto, are located approximately 210 km (130 mi), by road, northeast of Mbale the nearest</mark>\nlarge city. The coordinates of district are: 02 32N, 34 40E.\n\n<mark>Moroto District is part of the larger Karamoja Sub-region which consists of: Abim, Amudat,</mark>\n<mark>Kaabong, Kotido, Moroto, Nakapiripirit and Napak Districts. In 2002, the population of</mark>\n<mark>Karamoja sub-region was estimated at approximately 800,000, by the National Population and</mark>\n<mark>Housing census conducted that year.</mark>\n\nMoroto District is a plain covered by the Savannah grassland and some low lying rocky hills. It\ncomprises two counties: _Matheniko County_ and _Moroto Municipality_ . It is inhabited by the\nKarimojong, a distinctive ethnic group that highly cherishes its traditions. One peculiar\ncharacteristic of the Karimojong is their dress code which includes a long cultural woven **_suuka_**\nmade by the Masaai in Kenya which is tied on the shoulder sometimes with an under pant or none\nfor the men and the women, especially girls, make skirts out of them which they keep swinging\nfrom one side to another as they walk.\n\n\n**5.3.1** **Population**\n\n\n<mark>In 1991, the National Population and Housing census", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "sample:fcv_pads_east_africa:019593:73:1:0", "start": 751, "end": 770, "surface": "National Population", "probe_tag": "keep", "probe_score": 0.9823, "luna_label": 1}, {"key": "sample:fcv_pads_east_africa:019593:73:1:1", "start": 788, "end": 802, "surface": "Housing census", "probe_tag": "confusion", "probe_score": 0.8116, "luna_label": 1}]}, {"key": "aivin-047", "text": "CURRENCY EQUIVALENTS\n(Exchange Rate Effective August 31, 2021)\n\n<u>Currency Unit = Kenyan Shilling (Ksh)</u>\n<u>109.90 Ksh = US$1.00</u>\n\n<u>US$ 1.42 = SDR 1</u>\n\n\nFISCAL YEAR\nJuly 1 - June 30\n\n\n**ABBREVIATIONS AND ACRONYMS**\n\n\nARC Advance Release Calendar\nBAPS Busan Action Plan for Statistics\nCAPI Computer Assisted Personal Interview\nCOVID-19 Corona Virus Disease 2019\nCPI Consumer Price Index\nCPS Country Partnership Strategy\nCRA Corruption Risk Assessment\nCoA Chart of Accounts\nDLI Disbursement Linked Indicator\nDLR Disbursement Linked Result\nDQAF Data Quality Assessment Framework\nEACC Ethics and Anti-Corruption Commission\nESSA Environmental and Social Systems Assessment\nFIS Foreign Investment Survey\nFM Financial Management\nFMIS Financial Management Information System\nFY Fiscal Year\nGoK Government of Kenya\nICR Implementation Completion and Results Report\nICT Information and Communication Technology\nIDA International Development Association\nIMF International Monetary Fund\nIRA Intermediate Results Area\nISM Implementation and Support Mission\nISO International Standards Organization\nISR Implementation Status Reports\nISS Integrated Survey of Services\nKCHS Kenya Continuous Household Survey\nKeNADA Kenya National Data Archive\nKIHBS Kenya Integrated Household Budget Survey\nKNBS Kenya National Bureau of Statistics\nKNBS SP Kenya National Bureau of Statistics Strategic Plan\nKPHC Kenya Population and Housing Census\n\n\n\nOfficial Use", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:011818:1:0:1", "start": 1237, "end": 1283, "surface": "KIHBS Kenya Integrated Household Budget Survey", "probe_tag": "confusion", "probe_score": 0.1502, "luna_label": 0}]}, {"key": "aivin-048", "text": " capita -O 2 -0 5 Lower-middle-income _group_\nESports of goods and servic\"s\n\n\n**STRUCTURE ofthe ECONOMY**\n\n**1979** **1989** **1998** **1999** **Growth rates of output and Investment ()**\n_{%I ol GOP)_\nAgriculture 3.4 _.._ **_2._**\nIndustry 21.0 O.Manufacturing 5.6 **_-2_** **_94_** _as_ _se_ _s_\nServices **75.6** 6\n\nPrivate consumption **-r.**\nGeneral government consumplion **G**\nImports of goods and services\n\n\n\n**1979-89** **1989-99** **1998** **1999**\n_(average annual orowth)_\nAgriculture\n\n\n\nIndustry\n\n\n\nManufacturing\nServices\n\n\n\nPrivate consumption\nGenerai government consumption\nGross domestic investment\nImports of goods and services\nGross national product 1 7 **1.4**\n\n\nNote. 1999 data are preliminary estimates.\nThis table was produced from the Development Economics central database.\n\nThe diamonds show four kev midicators in the country (in bold) compared with its income-group average. 11 data are missing, Ihe diamond will", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:012828:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "drop", "probe_score": 0.0227, "luna_label": 0}]}, {"key": "aivin-049", "text": "**Annex 1:** **Project** **Design** **Summary**\n\n**SIERRA LEONE:** **NATIONAL** **SOCIAL ACTION** **PROJECT**\n\n\n. **Hierarchy o.Qbijctives -'**  - . **diator** **r**, t **,P** **jCitidaI** **r!** **As's-umptIons,Y-**\n**Sector-related** **CAS** **Goal:** **Sector** **Indicators:** **Sector/ country reports:** **(from** **Goal** **to Bank** **Mission)**\nMitigate the risk of renewed 1. National conflict/security- - UNHCR/OCHA reports - Continued peace and\n**conflict** **and lay foundation** related indicators - Household Income and regional security\n**for** **poverty reduction and** 2. Inter-regional disparities in Expenditure Surveys - Economic and political\n**improvements** **in nutrition,** I-PRSP & PRSP core - PETS surveys stability\n**health, education** **and** indicators - Strategic Planning and\n**targeting** **the rural** 3. Inter-regional disparities in Action Process (SPP) reports\n**population,** women **and** Popular Benchmarks\n**children.**\n\n\n**Project** **Development** **Outcome** **/** **Impact** **Project reports:** **(from** *", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "sample:fcv_pads_east_africa:008446:29:0:0", "start": 590, "end": 639, "surface": "Inter-regional disparities in Expenditure Surveys", "probe_tag": "drop", "probe_score": 0.0005, "luna_label": 1}, {"key": "sample:fcv_pads_east_africa:008446:29:0:1", "start": 721, "end": 733, "surface": "PETS surveys", "probe_tag": "drop", "probe_score": 0.0465, "luna_label": 1}]}, {"key": "aivin-050", "text": "13\n\n\n**_Monitoring and Evaluation_**\n\n\nMonitoring will be done according to the development indicators given in the attachment to Annex 1.\nThe project will strengthen the capacity of CNOSEGE, and the Planning Unit of the Ministry so that\nmonitoring reports on the implementation of the reform can include key progress and impact\nindicators. Currently the Planning unit generates statistical data on all aspects of the education sector,\nhowever this can be further strengthened to monitor progress on key reform objectives such as access,\nequity and quality. In addition, during the donors round-table UNESCO offered support to develop an\nEducation Management Information System (EMIS). If this is not in place by the end of Phase I of the\nAPL, this would be a priority item for Phase II.\n\n\nEvaluation of the impact of the reforms will be done by CNOSEGE by recruiting experts in this field\n\nand an initial evaluation will be done at the end of Phase I. Particular areas of impact assessment will\nbe student performance and success in reaching out to disadvantaged groups. Normally, student\nperformance would be measured by overall test results but as the pool of students widens to include\nstudents from less advantaged socioeconomic groups, there will be a downward pressure on test\nscores. The Planning Unit of the Ministry will be strengthened to monitor progress in reaching out to\ndisadvantaged groups and test scores of students by socioeconomic background. Staff will carry out a\nrandom survey (5 to 10% sample) of students by socioeconomic background in 2001 to establish a\nbaseline. To keep the survey simple, the socioeconomic background questions will be limited to easily\nidentified categories such as day-laborers, civil servants, shopkeepers etc. The survey will be repeated\nin 2005 and 2110.\n\n\n**D.** PROJECT RATIONALE\n\n\n**1. Project alternatives considered and reasons for rejection**\n\nOriginally, the project was designed as a Sector Investment Loan, however, given the Government's\ncommitment to the education sector, and the", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:009841:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "drop", "probe_score": 0.0402, "luna_label": 0}]}, {"key": "aivin-051", "text": "2. Broader social capital building outcome: Capacity building interventions for implementing\npartners and local authorities would include conflict resolution training, and emphasize sub-project\ndesign and supervision processes which build social capital and mutual accountability between\ncommunities and the array of support organizations and institutions.\n\n\n3. Intra-community equity outcomes: To address intra-community equity issues in a more\nsystematic manner, NaCSA would revise its Operations Manual to enhance procedures for community\nneeds identification and sub-project selection. The enhanced guidelines would include, inter alia, a\nrequirement for implementing partners to fill out a Community Assessment Form. This would include\ninformation on community population (by gender and age group), basic infrastructure, main income\ngenerating activities, housing, organization, forms and levels of outside assistance, and particularly\nvulnerable groups.\n\n6.5 How will the project monitor performance in terms of social development outcomes?\n\nA Social Assessment is under preparation. It is being designed so as to ensure that the project\nresponds to social development concerns by identifying the opportunities, constraints and social risks\n\n\n\ninherent in the project and translating these ideas into practical design and implementation measures.\nThe World Bank's Social Capital Assessment Tool (SOCAT) and instruments for analyzing local\ninstitutions will be used in the Social Assessment. Subsequent iterative rounds of social analysis using\nsimple locally adapted variants of these tools would continue during the duration of the project to collect\nand analyze data on social development issues. These would include associational memberships and\ntrust, the process of creation and destruction of social capital and data on groups and networks,\n\n\n\nsubjective well-being, political engagement, sociability, community activities, cohesion and\ncommunication. The Assessment would thus provide baseline data on social issues, recommend and\n\n\n\napply social capital measurement tools, and provide a framework for continuing social analysis\nthroughout the project.\n\n\n\n7. Safeguard Policies:\n7.1 Are any of the following safeguard policies triggered by the prcect?\n\n     - '& **~** P19 ~ f _-", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:014165:24:0:0", "start": 1670, "end": 1703, "surface": "data on social development issues", "probe_tag": "drop", "probe_score": 0.0226, "luna_label": 0}]}, {"key": "aivin-052", "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_aivin", "spans": [{"key": "sample:fcv_pads_east_africa:009492:28:1:0", "start": 1320, "end": 1332, "surface": "district FMR", "probe_tag": "confusion", "probe_score": 0.1903, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:009492:28:1:1", "start": 1455, "end": 1475, "surface": "local banks’ records", "probe_tag": "drop", "probe_score": 0.0099, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:009492:28:1:2", "start": 1593, "end": 1613, "surface": "local banks’\nrecords", "probe_tag": "drop", "probe_score": 0.0001, "luna_label": 0}]}, {"key": "aivin-053", "text": " entrance.\n\n  - Safety measures training done during tool box meetings. The tool box meetings are\nconducted periodically.\n\n  - There is a signage showing a warning on 'Construction Site' and control on speed at\nthe entries/exits to the site at the main roads.\n\n  - Labour; There are mechanisms in place to ensure hiring from the local community is\nencouraged through the engagement of Community Liason Officers (CLOs)\n\n  - Recruitment gives equal opportunity to women and persons living with disabilities\nthrough the engagement of the **CLO** and inclusion of the criteria in the engagements\nwith the community elders during recruitment.\n\n  - The workers sign engagement contracts on employment as per requirement **by**\n\nLabour Laws. The contracts are clear on employment terms including working hours\nand compensations where appropriate. There are daily registers for workers which\ninclude ID numbers and Time In and Time Out. Recruitment through IDs ensures\nthat workers are of age.\n\n  - Workers are frequently sensitized on **HIV/AIDS** and other sexually transmitted\n\n\n\ndiseases. Condoms are provided for the workers. Sensitizations are included in the\ntoolbox meetings as a topic on **HIV/AIDS** and signages on **HIV/AIDS** awareness are\n\n\n\ndisplayed on site.\n\n- Gender Based Violence (GBV) and Sexual harassment **_;_** The Catholic Archdiocese of\nLodwar has been providing support on issues related to GBV.\n\n- The site has provided separate ablution facilities for both Genders which are well\n\n\n\nlabled.\n\n- Social Safeguards Specialist; There is a safeguards specialist on site at all times.\n\n\n\nXxvi", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:000716:28:1:0", "start": 850, "end": 877, "surface": "daily registers for workers", "probe_tag": "drop", "probe_score": 0.0183, "luna_label": 0}]}, {"key": "aivin-054", "text": "\nrespectively. Where an asset is acquired <sup>in</sup> <sup>a non-exchange transaction</sup> <sup>for nil</sup> <sup>or nominal consideration</sup>\n\nand the fair value of the asset can be reliably established, <sup>a contra</sup> <sup>transaction</sup> <sup>is</sup> <sup>recorded</sup> <sup>as</sup> <sup>receipt</sup> <sup>and</sup>\n\nas a payment.\n\n\n**A** fixed asset register is maintained **by** each public <sup>entity</sup> <sup>and</sup> <sup>a</sup> <sup>summary</sup> <sup>provided</sup> <sup>for</sup> <sup>purposes</sup> <sup>of</sup>\n\nconsolidation. This summary is disclosed <sup>as</sup> <sup>an annexure</sup> <sup>to the</sup> <sup>consolidated</sup> <sup>financial</sup> <sup>statements.</sup>\n\n\ne) **In-kind donations**\n\n\nIn-kind contributions are donations that are made <sup>to</sup> <sup>the</sup> <sup>Project</sup> <sup>in</sup> <sup>the</sup> <sup", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:006157:40:2:0", "start": 359, "end": 379, "surface": "fixed asset register", "probe_tag": "drop", "probe_score": 0.0359, "luna_label": 0}]}, {"key": "aivin-055", "text": "|OP 4.12|Kenyan Legislation|Comparison|Recommendation to<br>Address Gap|\n|---|---|---|---|\n|claim to such land or<br>assets—provided<br>that<br>such<br>claims<br>are<br>recognized<br>under<br>the<br>laws of the country or<br>become<br>recognized<br>through<br>a <br>process<br>identified<br>in<br>the<br>resettlement<br>plan<br>(see<br>Annex 10 A, para. 7(f));<br>and19<br>(c) those who have no<br>recognizable legal right<br>or claim to the land they<br>are occupying<br>_To determine eligibility:_<br>Carry out resettlement<br>census. Cut off date for<br>eligibility<br>is<br>the<br>day<br>when the census begins.|‘occupants of land even if<br>they do not have titles’ and<br>payment made in good faith<br>to those occupants of land.<br>However,<br>this<br>does<br>not<br>include those who illegally<br>acquired land<br> <br> <br>Land Act 2012 provides for<br>census<br>through<br>NLC<br>inspection<br>and<br>valuation<br>process<br>|has an obligation to pay<br>in<br>good<br>faith<br>when<br>compulsory acquisition<br>is made.<br> <", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:019521:45:0:1", "start": 529, "end": 535, "surface": "census", "probe_tag": "drop", "probe_score": 0.0143, "luna_label": 0}, {"key": "fcv_pads_east_africa:019521:45:0:2", "start": 529, "end": 535, "surface": "census", "probe_tag": "confusion", "probe_score": 0.6346, "luna_label": 0}]}, {"key": "aivin-056", "text": " strategy in<br>draft form|Policy ratified by Cabinet|Framework fully developed<br>(through consultative process)<br>and established|\n|Social Protection strategy and policy<br>framework established||Text|Date|31-Mar-2009|31-May-2012|31-Dec-2013|\n|Social Protection strategy and policy<br>framework established||Text|Comments||||\n\n\n\n**<u>Data on Financial Performance (as of 01-May-2012)</u>**\n\n\n**<u>Financial Agreement(s) Key Dates</u>**\n\n|Project|Ln/Cr/Tf|Status|Approval Date|Signing Date|Effectiveness Date|Original Closing Date|Revised Closing Date|\n|---|---|---|---|---|---|---|---|\n|P111545|IDA-45530|Effective|31-Mar-2009|08-May-2009|03-Jul-2009|31-Dec-2013|31-Dec-2013|\n|P111545|TF-97272|Effective|08-Jul-2010|20-Jul-2010|20-Jul-2010|31-Dec-2013|31-Dec-2013|\n\n\n\n**<u>Disbursements (in Millions)</u>**\n\n|Project|Ln/Cr/Tf|Status|Currency|Original|Revised|Cancelled|Disbursed|Undisbursed|% Disbursed|\n|---|---|---|---|---|---|---|---|---|---|\n|P111545|IDA-", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:009643:5:1:0", "start": 337, "end": 366, "surface": "Data on Financial Performance", "probe_tag": "drop", "probe_score": 0.0309, "luna_label": 0}]}, {"key": "aivin-057", "text": "|Contract Completion<br><br>|Contract Completion<br><br>|\n|Activity Reference No. /<br>Description|Loan / Credit<br>No.|Component|Review Type|Method|Market Approach|Contract Type|Actual Amount<br>(US$)|Process Status|Planned|Actual|Planned|Actual|Planned|Actual|Planned|Actual|Planned|Actual|Planned|Actual|Planned|Actual|Planned|Actual|Planned|Actual|\n|ET-DBE-116473-CS-CQS /<br>Implementing of Biogas User<br>Survey (BUS)<br>|/|Biogas user survey under<br>National Biogas Program|Post|Consultant<br>Qualification<br>Selection|Open - National<br>||0.00|Canceled|2019-06-23|2019-10-02|2019-07-14|2019-12-30|2019-08-09|2019-12-08|||||||2019-08-16|2019-12-02|2019-08-24||2019-12-31||\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|INDIVIDUAL CONSULTANTS|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17|Col18|Col19|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:008651:3:10:0", "start": 430, "end": 448, "surface": "Biogas user survey", "probe_tag": "drop", "probe_score": 0.0276, "luna_label": 0}]}, {"key": "aivin-058", "text": "\n|18|**% OF CLIENT HOUSEHOLDS REPORTING THAT THEIR**<br>**LIVELIHOOD HAS BENEFITTED FROM PW CREATED ASSETS**|**% OF CLIENT HOUSEHOLDS REPORTING THAT THEIR**<br>**LIVELIHOOD HAS BENEFITTED FROM PW CREATED ASSETS**|||0||\n||RAW DATA<br>SOURCES:||||||\n||COMMENTS||Will be available after midline survey evaluation in Sept 2018|Will be available after midline survey evaluation in Sept 2018|Will be available after midline survey evaluation in Sept 2018|Will be available after midline survey evaluation in Sept 2018|\n|19<br>|**% OF PSNP HHS REPORTING NEW INCOME SOURCE AS A RESULT**<br>**OF PROGRAM SUPPORT - DISAG BY SEX **|**% OF PSNP HHS REPORTING NEW INCOME SOURCE AS A RESULT**<br>**OF PROGRAM SUPPORT - DISAG BY SEX **|||0||\n||RAW DATA<br>SOURCES:||||||\n||COMMENTS<br>|<br>|||||\n|20|**% OF HH THAT SAVE REGULARLY **|**% OF HH THAT SAVE REGULARLY **|885,375|1,135,065|78.0%||\n||RAW DATA<br>SOURCES:||||||\n||COMMENTS||EFY 2010 Annual Report based on savings through MFIs<br>RUSSACOS and VSLA only. Note that during the MTR, it<br>was agreed that PSNP clients may have accounts in more than<br>one financial institution, by tracking savings through each of<br>these accounts may result in double", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:017191:54:1:0", "start": 221, "end": 229, "surface": "RAW DATA", "probe_tag": "confusion", "probe_score": 0.1138, "luna_label": 0}, {"key": "fcv_pads_east_africa:017191:54:1:1", "start": 221, "end": 229, "surface": "RAW DATA", "probe_tag": "drop", "probe_score": 0.043, "luna_label": 0}, {"key": "fcv_pads_east_africa:017191:54:1:2", "start": 221, "end": 229, "surface": "RAW DATA", "probe_tag": "confusion", "probe_score": 0.1817, "luna_label": 0}]}, {"key": "aivin-059", "text": "24\n\n\n**During negotiations the following assurances were received:**\n\n\n1. Agreement on triggers for subsequent phases\n2. Agreement on monitoring and impact assessment studies\n3. Agreement on project monitoring indicators\n4. Agreement on finalized bidding documents for the first batch of schools\n\n\n**Actions to be included in Development Credit Agreement:**\n\n\n_Financial_\n\n\n1. Audits and Project Management Reports.\n2. Dated covenant on the selection of the auditor before March 31, 2001.\n\n\n_Management_\n\n\n1. Daied covenant on a baseline survey to establish gender and social class distribution of students\nbefore December 31, 2001.\n2. Provide regular reports on monitoring indicators and prepare a draft midterm report for review\nwith IDA before September 15, 2002.\n\n\n**In addition the following Management conditions are included in supplemental letters attached**\n**to the Developinent Credit Agreement:**\n\n\n1. Triggers from Phase I to Phase II in APL\n2. Triggers from Phase II to Phase III in APL\n\n\nH. READINESS **FOR IMPLEMENTATION**\n\n\nL. a) The engineering design documents for the first year's activities are complete and ready for the\n\nstart of project implementation.\nD b) Not applicable.\n\n3 2. The procurement documents for the first year's activities are complete and ready for the start of\nproject implementation.\n\n\nK/ 3. The Project Implementation Plan has been appraised and found to be realistic and of satisfactory\n\nquality.\n##### D 4. \"he following 7tems are lacking and are discussed under loan conditions (Section G):", "source": "fcv_pads_east_africa", "subset": "annotate_aivin", "spans": [{"key": "fcv_pads_east_africa:011003:27:0:0", "start": 529, "end": 544, "surface": "baseline survey", "probe_tag": "drop", "probe_score": 0.0491, "luna_label": 0}]}, {"key": "aivin-060", "text": "**4.2. Robustness checks**\n\n_Cost-of-living differences_\n\nThe consumption aggregates across the TZNPS survey rounds are expressed in 2022 prices\n\n\nusing the World Bank Global Database of Inflation (Ha _et al._ 2021), as discussed earlier (Section\n\n\n2.2). To assess the sensitivity of our results to the approach to standardizing prices – namely the\n\n\ndegree of disaggregation of price adjustments by types of commodities, and by frequency of\n\n\nadjustment – alternative approaches were evaluated. Our preferred models relying on deflating\n\n\nprices using annual inflation at the level of commodity groups – food, utilities, and other non-food\n\n\ncommodities – are compared to models relying on deflation by the traditionally used overall CPI\n\n\ndeflators provided by the International Monetary Fund. <sup>13</sup> [^13: To access the database, visit: https://data.imf.org/?sk=4ffb52b2-3653-409a-b471-d47b46d904b5.] Deflation using the annual CPI\n\n\ndisaggregated by groups is also compared to that using the quarterly CPI between the midpoints\n\n\nof survey rounds (Q1-2009, Q1-2011, Q2-2013, Q2-2015, Q2-2019, Q2-2021, and Q2-2022) and\n\n\nthe monthly CPI (3/2009, 3/2011, 6/2013, 6/2015, 7/2019, 6/2021, 6/2022). <sup>14</sup> [^14: Quarterly and monthly inflation rates for food and energy are not available prior to February 2010. For years 2009–\n2010, annual inflation rates for those sub-aggregates are used.]\n\n\nFigures 3–5 reveal that, across all models and pairs of base-target survey rounds (with a single\n\n\nexception for Model 4 in imputing into TA 1), annual commodity-disaggregated deflation\n\n\nperforms not worse than and frequently better than non-disaggregated deflation. The advantage of\n\n\ncommodity-disaggregated deflation is particularly notable in Models 8-9 when imputing into TA\n\n\n1, Model 8 when imputing into TA 2, and Model 2 when imputing into TA 3.\n\n\nRegarding the frequency of deflation, there appears to be no advantage to going below", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001579:22:0:1", "start": 157, "end": 196, "surface": "World Bank Global Database of Inflation", "probe_tag": "keep", "probe_score": 0.9715, "luna_label": 1}]}, {"key": "aivin-061", "text": ">29.7<br>(5.3)<br>29.7<br>(6.8)|39.2<br>(3.4)<br>40.9<br>(4.5)<br>40.0<br>(1.1)<br>41.9<br>(4.3)<br>44.1<br>(5.5)<br>43.8<br>(1.3)<br>31.0<br>(5.3)<br>31.2<br>(7.3)<br>28.4<br>(1.9)|\n\n\n_Source:_ Authors’ calculations using 2000 and 2005 HIES data.\n\na. A simplified method, which is described in detail in section II, is used.\n\n\nFinally, we examined whether updating poverty lines using a subsample of the 2005 HIES data has a\nlarge impact on poverty estimates derived from the Subsample Imputation. To determine this impact, we\ncompared poverty estimates from poverty lines and inflation rates estimated from subsamples of 2005\nHIES data with those estimated from the full sample of 2005 HIES data.\n\n\nIn the Subsample Imputation described in section III, inflation rates and poverty lines are estimated using\nthe full 2000 and 2005 HIES samples with only the consumption models developed using a subsample\nfrom the 2005 HIES. Here inflation rates, poverty lines, and consumption models are all estimated using a\nsubsample from the 2005 HIES. To reduce the computational burden of this analysis, we adopted the\nsimplified projection model (described in detail in section II) rather than a full-fledged ELL method when\ncreating consumption models. Also, to see the effect of sample size, we conducted this analysis with\nsubsamples of 320 and 640 households.\n\n\nTable", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:006020:19:2:1", "start": 405, "end": 419, "surface": "2005 HIES data", "probe_tag": "keep", "probe_score": 0.9765, "luna_label": 1}, {"key": "prwp:006020:19:2:4", "start": 915, "end": 924, "surface": "2005 HIES", "probe_tag": "keep", "probe_score": 0.9549, "luna_label": 1}]}, {"key": "aivin-062", "text": ".\n\nWithin any given context, seemingly contradictory views may exist. For example, in urban\n\nareas, tolerance levels are generally lower and refugees are more likely to be perceived as an\n\neconomic burden, and yet hosts are more likely to hold progressive (cosmopolitan) attitudes\n\nto refugee rights. The Turkana hosts around Kakuma see refugees as a security threat while\n\ngenerally regarding them as friendly and valuing their economic contribution. Ugandan\n\nlandowners in Nakivale view refugees as both competitors and contributors. These ‘mixed’\n\nsentiments – positive and negative – are not mutually exclusive, but rather co-existent in host\n\nsociety. However, we also observe some clear patterns across contexts. For example, the role\n\nof ethno-linguistic proximity (notably revealed through the so-called ‘Somali bond’) plays\n\na role in shaping attitudes, the importance of neighbourhood and household-level attitude\n\nformation, and the role of a household’s own socio-economic position in relation to refugees\n\n(landlord or tenant, employer or employee, landowner or non-landowner) are important\n\nthemes across context. Furthermore, a clear theme is the distinction between urban and\n\nrural contexts: through our Uganda data there is evidence that direct interactions may play\n\na causal role in shaping attitudes in cities, but not in camp-like settings. Further, we find\n\nevidence across the countries that attitudes in camp-like settings are more positive than in\n\n\n21These two statements were asked to hosts in all sites except the Kakuma Camps in Kenya.\n\n\n28", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "sample:prwp:002099:30:1:0", "start": 1221, "end": 1232, "surface": "Uganda data", "probe_tag": "keep", "probe_score": 0.9637, "luna_label": 1}]}, {"key": "aivin-063", "text": "that leads to individual occupational status. This model was empirically applied using path\nanalysis.\n\nAs mentioned above, we propose an adaptation to such model that includes additional\ndeterminants of occupational attainment that have been suggested in relevant literature.\nSpecifically, we agree with the proposal of Sewell, Haller and Portes (2001 [1969]) that considers\npsychosocial variables, although we only aggregate cognitive abilities (from the provider and the\nyouth), in the absence of information to build the other variables used (academic performance,\neducational and labor aspirations, influence of third parties). <sup>51</sup> Furthermore, instead of following\npath analysis, we apply a model of structural equations, which incorporates latent variables.\nFigure 1 is a graphic representation of our proposed analytical model as a starting point of the\nanalysis, which was subject to empirical tests that resulted in the selection of a more parsimonious\nmodel that will be presented in section VI.\n\n\n**Figure 1. Structural model of the determinants of occupational attainment**\n\n\nSource: Own elaboration.\n\n\n51 We carried out the exercise of including a latent variable for social capital in the model. However, the manifest\nvariable related to social capital in ENCEL 2017 was not adequate, neither theoretically, nor statistically. The latent\nvariable had a bad fit, with a low level of composite reliability (  = 0.22).\n\n\n29", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001818:30:0:0", "start": 1280, "end": 1290, "surface": "ENCEL 2017", "probe_tag": "keep", "probe_score": 0.9293, "luna_label": 1}]}, {"key": "aivin-064", "text": "**_Control Variables_**\n\nThe study has extracted the control variables from various sources, including the World Development\nIndicators (WDI) and other relevant sources. The set of control variables includes the political regime or\ndemocracy measure, including Polity2 of the Polity V dataset <sup>14</sup> [^14: The Polity V project codes the authority characteristics of states in the world system for purposes of comparative,\nquantitative analysis.] as political regime characteristics may lead\nto differentiated effects across countries. Following the literature (Hilger et al., 2020; Vicari et al., 2015),\nthe study also uses a set of control variables from WDI, including socio-economic and demographic\ncharacteristics (e.g., the share of the population aged between 15 and 44, the human capital index, and\nGDP per capita <sup>15</sup> [^15: This allows for the removal of the implicit income effects and reduces the gap between the values of the different variables.] ). The study also uses the power distributed by social group variable from V-Dem. In\naddition, the study uses the fixed internet broadband subscriptions per 100 inhabitants variable as a proxy\nfor internet penetration (Boulianne, 2009; Howard, 2006; Putman, 2000) from the International\nTelecommunication Union (ITU) dataset.\n\n\n**2.** **Overview of the Adoption of GovTech Platforms and the Level of Citizen Engagement**\n**Worldwide**\n\nIn the context of this study, a statistical analysis (including stylized facts) was conducted related to the\nadoption of GovTech platforms and the level of citizen engagement worldwide, focusing on the 198\ncountries covered by the GTMI. Table 1 summarizes information on the implementation of digital\nplatforms. In the sample of 198 countries, 82 (41.4%) have Implemented a digital platform allowing\ncitizens to participate in policy decision-making, and 75 countries (37.8%) have implemented a platform\nenabling citizens to provide feedback. However, 112", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "sample:prwp:001592:8:0:0", "start": 107, "end": 135, "surface": "World Development\nIndicators", "probe_tag": "keep", "probe_score": 0.9626, "luna_label": 1}, {"key": "sample:prwp:001592:8:0:1", "start": 276, "end": 292, "surface": "Polity V dataset", "probe_tag": "keep", "probe_score": 0.9696, "luna_label": 1}, {"key": "sample:prwp:001592:8:0:2", "start": 663, "end": 666, "surface": "WDI", "probe_tag": "keep", "probe_score": 0.9825, "luna_label": 1}, {"key": "sample:prwp:001592:8:0:3", "start": 1050, "end": 1055, "surface": "V-Dem", "probe_tag": "keep", "probe_score": 0.993, "luna_label": 1}, {"key": "sample:prwp:001592:8:0:4", "start": 1248, "end": 1299, "surface": "International\nTelecommunication Union (ITU) dataset", "probe_tag": "keep", "probe_score": 0.9977, "luna_label": 1}, {"key": "sample:prwp:001592:8:0:5", "start": 1642, "end": 1646, "surface": "GTMI", "probe_tag": "keep", "probe_score": 0.9763, "luna_label": 1}]}, {"key": "aivin-065", "text": "of Mexico where municipalities that had an Azteca branch in the fourth quarter of 2002 are\n\n\nmarked in dark blue, and municipalities that had a different bank branch, but no Azteca\n\n\nbranch, are marked in light blue. Both types of municipalities are distributed throughout\n\n\nMexico.\n\n\nSince our analysis applies to potential Banco Azteca clients who could be\n\n\neconomically active, we keep only individuals of working age (between the ages of 20 and\n\n\n65) in our sample. We then construct three of our main outcome variables by creating\n\n\ndummy variables for each person in the sample, indicating whether the person a) owns an\n\ninformal business, b) owns a formal business, and c) is a wage earner <sup>14</sup> [^14: A detailed description of how these variables were constructed is available in Bruhn (2008).] . These are the three\n\n\npossible occupations for somebody who is employed. People who do not fall into either of\n\n\nthese categories are not employed (they are either unemployed or not in the labor force).\n\n\nAll dummies are defined for the whole sample, meaning that they denote the\n\n\npercentage of all individuals in the sample who fall into each category. For example, a value\n\n\nof 0.08 of the formal business owner dummy means that 8 percent of all people between the\n\n\nages of 20 and 65 own a formal business.\n\n\nFinally, we also use monthly income as an outcome variable. While the dummy\n\n\nvariables are defined for everybody in our sample, income is available only for the gainfully\n\n\nemployed and is missing for the unemployed, for individuals who are out of the labor force,\n\n\nand for unpaid workers. Unfortunately, the income data from the ENE is probably not as\n\n\nreliable as the occupation data since it is not the focus of the survey. The survey only\n\n\nincludes one question on income, asking for the earnings from the individual’s main\n\n\noccupation. 4.2 percent of employed individuals do not answer this question. These\n\n\nindividuals are then asked to report their income", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:004175:11:0:1", "start": 1700, "end": 1715, "surface": "occupation data", "probe_tag": "keep", "probe_score": 0.9174, "luna_label": 1}]}, {"key": "aivin-066", "text": "Table 2 shows the mean growth rates of external and internal imports before\n\nand after 1993. While several factors beyond AFTA have probably affected trade\n\ngrowth rates in the ASEAN countries (the currency crisis of 1997-1998 being only the\n\nmost prominent one), a few regularities are worth noting. First, intra-AFTA trade\n\ngrows faster than external trade in five of the six original members even in the _pre_ \nagreement period (the only exception being Indonesia). This suggests that the choice\n\nof members was not random, but rather influenced by their growing trade ties. In the\n\npost-agreement period, internal trade growth was faster than external import growth in\n\nall countries, and by a significantly larger margin than it was before 1993.\n\n\nAlthough using aggregate trade data obviously makes the sample very small,\n\nwe can still perform t-tests on the difference in the means of the pre-union and post\nunion growth rates and also between the growth rate of internal trade and trade with\n\nnonmembers. As the results reported in table 2 show, the t-tests of the difference in\n\nthe mean growth rates before and after the agreement are not conclusive. On the other\n\nhand, the difference in the mean growth rates of imports from members and\n\nnonmembers are different enough for Indonesia and the Philippines in the post-AFTA\n\nperiod that the t-test on their difference is statistically significant, despite the very\n\nsmall sample.\n\n\nFurthermore, if increased trade with bloc members displaced imports from\n\nthird countries, we would expect to see a decline in the correlation between internal\n\nand external trade growth subsequent to the formation of AFTA. However, as shown\n\nin the last column of table 2, the correlation between external trade growth and\n\ninternal trade goes up in five out of the six countries—and by a large amount in\n\nThailand and the Philippines. The correlation falls in the post-1993 period only in\n\nBrunei, and the drop was minimal.\n\n\n**IV. The effect of tariffs on", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:004155:15:0:0", "start": 768, "end": 788, "surface": "aggregate trade data", "probe_tag": "confusion", "probe_score": 0.64, "luna_label": 1}]}, {"key": "aivin-067", "text": " of field-level records from crop</mark>\n\ninsurance (Seifert _et al._ 2017). By highlighting spatial heterogeneity in effect size and a significant role of\n\ncrop varietal traits, climate (temperature and soil moisture) and management (treatment of crop residue,\n\ntillage options, and planting dates), such studies suggest that real-world data can add significant insights to\n\nwhat is available from agronomic trials.\n\n\nA second avenue is to rely on information derived from remotely sensed imagery. For the United States,\n\nthe cropland data layer (CDL), based on 30 m pixels from Landsat imagery that are available for all 48\n\nstates since 2008 (Boryan _et al._ 2011) are a frequently used source. With proper methodology (Lark _et al._\n\n2017), the CDL can be used for field-level analysis. Wang and Ortiz-Bobea (2019) use 2005-14 CDL data\n\nfor the US midwest to show that the 2005-09 biofuel boom triggered a significant increase in maize\n\nmonocropping and GM maize adoption, especially in the vicinity of newly established biofuel plants. <sup>3</sup> [^3: Economic factors may also lead producers to forgo the yield advantage of rotating crops for higher profitability from monoculture as has indeed\nbeen observed in the United States where major crops are increasingly grown in monoculture patterns (Plourde _et al._ 2013).]\n\nHendricks _et al._ (2014) use CDL data to argute that estimates of supply elasticities based on aggregate data\n\nmay be biased and to show that use of field-level data that take into account crop rotation benefits results\n\nin estimates that are larger in the short than the long run, contrary to standard models.\n\n\n2 For example, comparing a two-year standard maize-soybeans rotation with more diverse a three- or four-year rotations that includes small grains\nand leguminous fodder crops (clover or alfalfa) led to equal or greater profit, reduced input use, and significantly lower toxicity (Davis _et al._ 2012).\n3 Economic factors may also lead producers to forgo the yield advantage", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002379:6:1:1", "start": 327, "end": 342, "surface": "real-world data", "probe_tag": "confusion", "probe_score": 0.1426, "luna_label": 0}, {"key": "prwp:002379:6:1:5", "start": 1480, "end": 1496, "surface": "field-level data", "probe_tag": "confusion", "probe_score": 0.5522, "luna_label": 1}]}, {"key": "aivin-068", "text": ", similar levels of materials, and less capital and land than rural plants do. There is a\n0.43 correlation between the urban-rural intensity difference and the urbanization rate for labor\nusage; the correlation to materials usage differentials is likewise high at 0.44. Capital intensity\ndifferentials are less correlated at 0.26, and land usage differentials are not correlated with\noverall urbanization levels. <sup>6</sup>\n\n\nThese descriptive tables refine one of the key trends noted in the introduction. Overall,\nmanufacturing activity is becoming more urbanized as measured by employment and plants,\nwhile it is becoming less urbanized as measured by output. These trends are mainly explained by\nthe share of organized and unorganized sectors for each metric and the differences in the\nurbanization trends for these sectors. The details in Tables 2-3b also show that urbanization\nincreases are concentrated, rather than broad-based, even for employment. Only 8 of the 17\nstates and 7 of the 22 industries exhibit an increase in urbanization from 1994-2005. The\n\n\n6 These metrics are developed using data for the year 2000 from the organized sector. We use the year 2000\nto match our district traits taken from the 2001 Census described in Section 4. Several of these variables are not\nconsistently available for unorganized sector. These correlations and our analyses below are robust to winsorizing\nthese industry traits at their 5%/95% values.\n\n\n7", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:005431:8:1:0", "start": 1105, "end": 1127, "surface": "data for the year 2000", "probe_tag": "confusion", "probe_score": 0.6895, "luna_label": 1}]}, {"key": "aivin-069", "text": "Huizinga, Harry and Lars Jonung. 2005. _The Internalization of Asset Ownership in Europe_ .\n(Cambridge Univerity Press).\n\nIwaisako, Tokuo. 2003. “Household Portfolios in Japan.” NBER Working Paper 9647.\n\nJäntti, Markus and Eva Sierminska. 2006. “Wealth Distribution in OECD Countries.” Paper\nprepared for WIDER Project.\n\nJohnson, S., McMillan, J. and Woodruff, C. 2002. ”Property Rights and Finance”. _American_\n_Economic Review_ 92(5): 1335–1356.\n\nKapteyn, Arie and Constantijn Panis. 2003. “The Size and Composition of Wealth Holdings in\nthe United States, Italy, and the Netherlands,” RAND Labor and Population Program Working\nPaper 03-05 (DRU-3002)\n\nKar, Pratip, M.T. Raju and S.P. Batra. 2003. “Survey of Indian Investors.” (Mumbai: Securities\nand Exchange Board of India)\n\nKelly, Morgan. 1995. “All their Eggs in One Basket: Portfolio Diversification of US\nHouseholds.” _Journal of Economic Behavior and Organization_ 27(1): 87-96.\n\nKennickell, Arthur B. 2003. “A Rolling Tide: Changes in the Distribution of Wealth in the U.S.,\n1989-2001” _Levy Economics Institute of Bard College_ Working Paper 393.\n\nKing, Mervyn A., and Jonathan I. Leape, 1998, “Wealth and Portfolio Composition:\nTheory and Evidence.” _Journal of Public Economics_ 69, 155-193.\n\nLevine, Ross. E. 2005. ‘Finance and Growth: Theory, Mechanisms and Evidence’, in Aghion, P.\nand Durlauf, S.N. (eds.). _Handbook of Economic Growth_ . Elsevier.\n\nLi", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:003178:30:0:0", "start": 700, "end": 726, "surface": "Survey of Indian Investors", "probe_tag": "confusion", "probe_score": 0.2419, "luna_label": 0}]}, {"key": "aivin-070", "text": " rate|Ratio (base 2010 = 100) of an index of a currency's period-average<br>exchange rate to a weighted geometric average of exchange rates<br>for currencies of selected countries and the euro area|JPMorgan Nominal Broad<br>Effective Exchange Rate Index|\n|Real exchange rate|Computed as nominal exchange rate*US consumer price index/<br>consumer price index|exchange rate from IFS; CPI<br>from WDI|\n|Inflation|CPI inflation calculated as % change over previous year. (% yoy)|IFS|\n|Inflation targeting|dummy variable takes a value of 1 after a country moves to an<br>inflation targeting regime and 0 before that||\n|External liabilities|External liabilities include portfolio equity, FDI and debt liabilities.|Lane and Milesi-Ferretti (2007)|\n|G4-money supply|Sum of US, UK, Japan and Euro area money supply (M2)|Haver|\n\n\n32", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:006671:33:2:0", "start": 224, "end": 253, "surface": "Effective Exchange Rate Index", "probe_tag": "confusion", "probe_score": 0.2722, "luna_label": 0}, {"key": "prwp:006671:33:2:1", "start": 309, "end": 332, "surface": "US consumer price index", "probe_tag": "confusion", "probe_score": 0.146, "luna_label": 1}]}, {"key": "aivin-071", "text": "**Annex 1. Respondents to the WB-ASBA Survey**\n\n\n\n\n\n\n\n\n\n\n|Country|Head of<br>Supervision|Head of Research<br>Central Bank|\n|---|---|---|\n|Argentina<br>Bolivia<br>Brazil<br>Colombia<br>Costa Rica<br>Chile<br>Ecuador<br>El Salvador<br>Guatemala<br>Honduras<br>Mexico<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Dominican Republic<br>Uruguay<br>Venezuela<br>Aruba<br>Bahamas<br>Barbados<br>Belize<br>Cayman Islands<br>Haiti<br>Netherland Antilles<br>Suriname<br>Trinidad and Tobago<br>British Virgin Islands<br>Guyana<br>Jamaica<br>Eastern Caribbean Islands|X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X|X <br>X <br>X <br>X <br>X <br>X <br>X <br>X <br>X|\n\n\n\n32", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:005116:33:0:0", "start": 30, "end": 44, "surface": "WB-ASBA Survey", "probe_tag": "confusion", "probe_score": 0.0891, "luna_label": 0}]}, {"key": "aivin-072", "text": " budget\nsurveys. Typically, only one price is recorded for each item in each community. A major disadvantage\nof market price surveys is the lack of detailed quality information. Without detailed information about\nitem descriptions, it is not possible to compare prices of the same products across regions. Gibson, Le,\nand Kim (2017) explain that Vietnam’s price survey was carefully designed to record detailed\ninformation for each product.\n\nAs stated earlier, CPI price data are another potential source for spatial price adjustment. Unlike\nmarket price surveys, CPI price data exist in almost all countries (Berry et al. 2019). Since the CPI is\nan indicator of inflation, it does not indicate spatial differentials in costs of living. <sup>10</sup> However, it is\npossible to calculate the spatial price index by using the CPI price data (not CPI values per se). The\nproblem is that many countries collect price information only in urban areas (see Table A2 in Appendix\nA). Nevertheless, if the geographic coverage of the data is adequate, using the same price data for\nspatial and temporal price adjustment can be an efficient approach in terms of the data collection costs,\nand it could also enhance consistency between temporal and spatial price patterns. <sup>11</sup> [^11: Inconsistency in temporal and spatial price movements is not uncommon. Let us consider a spatial price index\nat year 0 (𝑆 **𝑆** 𝑆𝑆𝑡𝑡0) and year 1 (𝑆 **𝑆** 𝑆𝑆𝑡𝑡1). By applying the regional CPIs between _t_ 0 and _t_ 1 to 𝑆 **𝑆** 𝑆𝑆𝑡𝑡0, one can obtain] In addition,\ncompared to market price surveys, a wide variety of food and non-food price information is collected\nin the CPI data, and the data collection is frequent (for example, monthly). A well-known issue is that\nthe basket used for the calculation of the CPI is often skewed toward higher-income households. For\n\n\n\n10 Some countries report", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000539:8:2:0", "start": 112, "end": 132, "surface": "market price surveys", "probe_tag": "confusion", "probe_score": 0.6807, "luna_label": 1}]}, {"key": "aivin-073", "text": " imagery and remote sensing</mark>\n<mark>techniques have the potential to provide timely insights into conditions on the ground and can fill</mark>\n<mark>gaps in agricultural monitoring and statistics (Nakalembe, 2020).</mark>\n\nSatellite-based approaches to mapping agricultural outcomes, such as crop-specific estimates of\ncultivated areas and yields, require data for training and validating the underlying remote sensing\nmodels. The quality and spatial resolution of satellite-based estimates is directly impacted by the\ndata used for model training and validation (Lobell et al., 2019, 2020). Recent earth observation\nresearch that has focused on low-income countries has relied largely on two sources of training\nand validation data: (i) manually-labeled optical imagery (DeFourny et al., 2019; Xiong et al.,\n2017; Wei et al., 2020), <sup>2</sup> and (ii) ground data collection, including as part of household and farm\nsurveys (Hegarty-Craver et al., 2020; Jin et al., 2017, 2019; Kerner et al., 2020; Lambert et al.,\n2018; Richard et al., 2017). <sup>3</sup> Our paper is related to earth observation applications that rely on\ngeoreferenced survey data to meet model training and validation needs.\n\n\n2 In order to map cropland across the African continent, researchers have manually labeled land cover types for sample\npoints using high-resolution optical imagery as a reference. For example, Xiong et al (2017) created a training data\nset covering Africa by labeling homogenous crop/non-crop areas with a minimum area of 90 x 90 meters (0.81 ha).\nSeveral satellite-based cropland data sets have been created for the African continent (using manually-labeled satellite\nimagery - see Wei et al.’s (", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002366:3:1:0", "start": 906, "end": 932, "surface": "household and farm\nsurveys", "probe_tag": "confusion", "probe_score": 0.4161, "luna_label": 1}, {"key": "prwp:002366:3:1:1", "start": 1134, "end": 1159, "surface": "georeferenced survey data", "probe_tag": "confusion", "probe_score": 0.3964, "luna_label": 1}]}, {"key": "aivin-074", "text": "**Figure 1: Number of Job Listings on Babajob and Average Number of Applciations per Listing by**\n\n|Col1|Col2|Occupational Category, 2015|Col4|\n|---|---|---|---|\n|||||\n|Total number of ads (bar)|||Applicants per ad|\n||Managers<br>Professionals|Clerical support<br>workers<br>Service workers<br>Sales workers<br>Skilled<br>agricultural<br>workers<br>Construction,<br>Craft and related<br>trades workers<br>Plant and<br>machine<br>operators,<br>Assemblers,<br>Drivers<br>Elementary<br>occupations||\n\n\n\nSource: Authors’ calculations using Babajob data\n\n\n**5.** **Using Big Data to Analyze Labor Market Dynamics in India: the Case of Babajob**\n\n\nA review of the existing literature and a thorough analysis of Babajob data revealed five ways in which\n\n\nonline job-portal data can be used to formulate and refine labor market policies. These include (i) labor\n\n\nmarket monitoring and analysis, (ii) assessing demand for workforce skills, (iii) observing job-search\n\n\nbehavior and improving skills matching, (iv) predictive analysis of skills demand, and (v) experimental\n\n\nstudies.\n\n\n**5.1.** **Labor Market Monitoring and Analysis**\n\n\nOnline job-portal data can be used to monitor and analyze labor market trends in real time. Big data can\n\n\ncomplement official government statistics and other traditional forms of employment information-gathering,\n\n\nsuch as sample-based labor force and enterprise surveys, to provide richer and more granular data. Labor\n\n\nforce surveys capture information on the personal characteristics, wages and experience levels of employees", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:006962:9:0:3", "start": 1367, "end": 1401, "surface": "labor force and enterprise surveys", "probe_tag": "confusion", "probe_score": 0.1075, "luna_label": 0}]}, {"key": "aivin-075", "text": "42 0.1928* 0.1746* 0.1535* 0.1309* 0.0038 1\n<u>9. commodityexporter</u> <u>0.1811*</u> <u>-0.0416</u> <u>-0.2165*</u> <u>-0.1099*</u> <u>-0.0383</u> <u>-0.1039</u> <u>0.2697*</u> <u>-0.0128</u>\n\nPerson’s correlation coefficients are examined using five-year average data. * indicates significance at 5% level.\n\n\n**Annex Table A5-3: Person’s correlations between manufacturing share and determinants**\n\n\n<u>Variables</u> <u>1</u> <u>2</u> <u>3</u> <u>4</u> <u>5</u> <u>6</u> <u>7</u> <u>8</u> <u>9</u> <u>10</u>\n\n\n1. manuf._share 1\n2. ln_hhcredit -0.1755* 1\n3. ln_nfccredit -0.1717* 0.6932* 1\n4. fdi -0.2204* 0.1583* 0.3575* 1\n5. enrolment -0.2897* 0.5140* 0.4168* 0.1911* 1\n6. ln_initialgdppc -0.3214* 0.6438* 0.5748* 0.2056* 0.77", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:007067:26:2:0", "start": 248, "end": 270, "surface": "five-year average data", "probe_tag": "confusion", "probe_score": 0.6915, "luna_label": 1}]}, {"key": "aivin-076", "text": "||||\n|||||||\n|||||||\n|||~~Taiwan, Ch~~|<br>~~ ina~~|~~Japan~~<br>||\n|||<br>Korea|Hong Ko<br><br>|ng, China||\n||||~~Singapore~~|||\n|||||||\n\n\n0 10,000 20,000 30,000 40,000 50,000\n\nGDP per capita ($US)\n\n\n\n\n\n\n\n\n\n\n- Data refer to 2001 the latest year for which complete data are available. The predicted figures are from a regression of the\nnatural logarithm of total health expenditure (expressed as a share of GDP) on the natural logarithm of GDP per capita. Data are\nfrom the 2005 World Health Report annexes in the cases of Japan, Korea and Singapore. The per capita income data for Hong\nKong (China) and Taiwan (China) are from the World Bank’s World Development Indicators. Data on health spending are from\n<u>http://www.hwfb.gov.hk/statistics/download/dha/en/table1.pdf in the case of Hong Kong (China) and from</u>\n<u>http://www.doh.gov.tw/ufile/doc/200411_Statistic%20of%20Expenditure%20for%20Health,%201991-2003.pdf</u> in the case of\nTaiwan (China).", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:003004:3:1:1", "start": 555, "end": 577, "surface": "per capita income data", "probe_tag": "confusion", "probe_score": 0.8892, "luna_label": 1}, {"key": "prwp:003004:3:1:3", "start": 675, "end": 698, "surface": "Data on health spending", "probe_tag": "confusion", "probe_score": 0.65, "luna_label": 1}]}, {"key": "aivin-077", "text": "The purpose of the study is to examine gender and inclusion differences in education, and to suggest\npolicy actions as well as future analytical and operational work to address these differences.\n### 2. Methodology\n\nThe research design for the study was informed by several sources of data. Data examination of the\nNational Social-economic Household Survey (Susenas), the National Labor Force Survey (Sakernas),\nDapodik, EMIS, and the 2010 Population Census was conducted to collect gender-disaggregated data on\nstudent enrollment rates, and the composition of teacher and administrative staff in the workforce, as\nwell as disability information. This review and analysis of existing data sources allowed for the\nidentification of areas of subnational gender variation in education attainment and other indicators.\n\nSusenas collects data at the household and individual levels on many aspects of social and economic\ncharacteristics, such as: consumption, labor, health and other household variables. Sakernas is a survey\nthat is specifically designed for labor data collection. Both Susenas and Sakernas are nationally\nrepresentative surveys conducted by BPS ( _Biro Pusat Statistik_ /Central Bureau of Statistics) and used by\nthe government for national planning documents. The population census, also conducted by BPS, records\nthe number, composition, distribution, and selected characteristics of the population with national\ncoverage.\n\nAdministrative data used in this study is the MoEC’s Dapodik and the MoRA’s EMIS. Dapodik records selfreported information on: (i) school-level data (public-private, ownership status, establishment date,\naccreditation status, availability of Internet, the number of school facilities and their condition); (ii)\nstudent-level data (sex, learning groups, parents’ information); and (iii) teacher-level data (sex,\nemployment status, certification status, education qualification). EMIS records data on Islamic schools\nunder the Mo", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001690:6:0:7", "start": 358, "end": 365, "surface": "Susenas", "probe_tag": "confusion", "probe_score": 0.7238, "luna_label": 1}]}, {"key": "aivin-078", "text": "mark>\n<mark>yield vis-à-vis their crop cutting-based counterparts, particularly in the lower half of the plot area</mark>\n<mark>distribution.</mark> <mark>Though our findings contribute to a larger (and renewed) body of literature</mark>\n<mark>questioning the inverse scale-productivity relationship based on omitted explanatory variables or</mark>\n<mark>alternative formulations of the agricultural productivity measures, the analysis, together with</mark>\n<mark>Desiere and Jolliffe (2017), is the first documenting how the IR could be a statistical artifact, driven</mark>\n<mark>by errors in self-reported survey data on crop production.</mark>\n\n\nThe paper is organized as follows. Section 2 discusses the conceptual basis for errors in farmerreported crop production and their potential implications for the IR. Section 3 provides relevant\nbackground on the Ugandan context. Section 4 describes the data. Section 5 lays out the empirical\nstrategy. Section 6 presents the results. Section 7 concludes.\n\n\n**2.** **Conceptual Basis for Potential Errors in Farmer-Reported Crop Production**\n\n\nThe quality of farmer-reported estimates of production is degraded by a myriad of issues,\nsummarized in Table 1. Human performance and behavior are subject to a number of constraints,\none of them being finite memory. Recall bias can, therefore, pose a serious threat to the quality of\nfarmer-reported estimates of production. This is especially relevant if there is partial green harvest\n(for immediate consumption or sale) or if permanent or extended-harvest crops, such as cassava,\n\n\n4", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000209:5:1:0", "start": 595, "end": 620, "surface": "self-reported survey data", "probe_tag": "confusion", "probe_score": 0.8249, "luna_label": 1}]}, {"key": "aivin-079", "text": "*3.2 Social Norm Survey and Implicit Association Tests**\n\n\nTo measure the attitudes women face while traveling in the public space, we administer a\n\n\nsocial norm survey and IATs on a random sample of men and women commuters on the\n\n\ntrain platform. The survey includes questions on commuting behavior, stated preferences\n\n\nand willingness to pay to use the reserved space, perceptions about harassment and attitudes\n\n\n15The estimates are generated by SuperVia transport planners, based on simulations and data from the\nstation fare gates.\n\n\n12", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000255:13:1:0", "start": 5, "end": 23, "surface": "Social Norm Survey", "probe_tag": "confusion", "probe_score": 0.6039, "luna_label": 0}, {"key": "prwp:000255:13:1:1", "start": 505, "end": 537, "surface": "data from the\nstation fare gates", "probe_tag": "confusion", "probe_score": 0.6671, "luna_label": 1}]}, {"key": "aivin-080", "text": "OCHA. 2017. Yemen: 2017 Humanitarian Needs Overview. Report, United Nations Office for\nthe Coordination of Humanitarian Affairs, Sanaa, Yemen.\n\n\nOCHA. 2018. Yemen: 2018 Humanitarian Needs Overview. Report, United Nations Office for\nthe Coordination of Humanitarian Affairs, Sanaa, Yemen.\n\n\nOCHA. 2019a. Yemen: 2019 Humanitarian Needs Overview. Report, United Nations Office\nfor the Coordination of Humanitarian Affairs, Sanaa, Yemen.\n\n\nOCHA. 2019b. Yemen: 2019 Humanitarian Response Plan. Report, United Nations Office for\n\nthe Coordination of Humanitarian Affairs, Sanaa, Yemen. https://reliefweb.int/report/yemen/2019yemen-humanitarian-response-plan-january-december-2019-enar.\n\n\nOCHA. 2020. Yemen: 2020 Humanitarian Needs Overview. Report, United Nations Office for\nthe Coordination of Humanitarian Affairs, Sanaa, Yemen.\n\n\nPeoples’ Dispatch. 2019. “Saudis Retake Aden as Reports of Talks with the Houthis Surface.”\n\nNews Report, Peoples’ Dispatch, October 16, https://peoplesdispatch.org/2019/10/16/saudisretake-aden-as-reports-of-talks-with-the-houthis-surface/.\n\n\nShemyakina, O. 2011. “The Effect of Armed Conflict on Accumulation of Schooling: Results\nfrom Tajikistan.” _Journal of Development Economics_ 95, 186-200.\n\n\nSundberg, R. and E. Melander. 2013. “Introducing the UCDP Georeferenced Event Dataset.”\n_Journal of Peace Research_ 50 (4), 523-532.\n\n\nSwee, E. 2015. “On War and Schooling Attainment: The Case of Bosnia and Herzegovina.”\n_European Journal of Political Economy_ 40, 158-172.\n\n\nTandon, S. and T. Vishwanath. 2019. Potential Famine and the Aid Response", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000036:26:0:0", "start": 1280, "end": 1312, "surface": "UCDP Georeferenced Event Dataset", "probe_tag": "confusion", "probe_score": 0.2774, "luna_label": 0}]}, {"key": "aivin-081", "text": "study their export participation and entry decisions. <sup>10</sup>\n\n\nFinally, we use two alternative measures to proxy for the regulation intensity at\n\nthe product level. First, we use the WTO Specific Trade Concerns (STC) Database,\n\nwhich provides product-level (HS4) information on trade concerns raised in the WTO’s\n\nSPS and TBT committees. The main advantage of this database is that it captures\n\nthe “revealed” stringency of standards, as perceived as important trade barriers by\n\nexporters, rather than counting the number of standards that may or may not be trade\n\nimpeding (Fontagné et al., 2015; Fontagné and Orefice, 2018). We classify products\n\nto be regulation intensive if at least a concern was raised to one of the committees.\n\nThere are 312 specific concerns raised on 124 products in the SPS committee and 317\n\nspecific concerns raised on 848 products in the TBT committee, together covering a\n\nbroad range of 863 products across different industries (see Appendix Table A.4 for the\n\ndistribution of STC-products across industries). Second, we use an alternative measure\n\nof regulation intensity based on prior notice requirements by the United States’ Food\n\nand Drug Administration (FDA). This measure covers a total of 272 HS4-products,\nmainly in food, drugs, and medical devices, which require prior notice to the FDA. <sup>11</sup>\n\n\nTo construct the data set needed for the first part of our analysis (firm-level), we\n\nexpand the initial exporters database to make it squared so that every firm-destination\nyear combination is an observation. For the second part of our analysis using the\n\nproduct-level data, we first aggregate firms’ exports data (available at HS6-level) to the\n\nHS4-level, which is the level available in the WTO STC data set. Then, we expand\n\nthe data set so that each firm-product", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:002312:7:0:2", "start": 1613, "end": 1631, "surface": "product-level data", "probe_tag": "confusion", "probe_score": 0.3298, "luna_label": 1}]}, {"key": "aivin-082", "text": "###### Case data\n\nGeoreferenced cholera case data were available for the Harare area collected by the Ministry of\nHealth and Child Care (MoHCC) following the World Health Organization (WHO) guidelines.\nThe first case was recorded on September 4, 2018, and the last on January 9, 2019. <mark>WHO criteria</mark>\n<mark>for case diagnosis state that any patient aged 5 years or more presenting with acute watery diarrhea</mark>\n<mark>and severe dehydration where cholera is not known to be occurring, or any patient 2 years or older</mark>\n<mark>presenting with acute watery diarrhea where cholera is known to be occurring can be suspected as</mark>\n<mark>a cholera case. Confirmation is achieved by sampling a subset of patients during the course of the</mark>\n<mark>epidemic.</mark> <sup>2</sup> [^2: It is standard practice in epidemic response that ‘Once an outbreak is declared, there is no need to confirm all\n[suspected cases. The clinical case definition is sufficient to monitor epidemiological trends’. [26]](https://www.zotero.org/google-docs/?Xdbp3i)] <mark>Date of diagnosis, gender, age and geo-location of the case’s dwelling are also</mark>\n<mark>collected.</mark> For the purpose of spatio-temporal analyses, cases were aggregated across 7-day bins\nfrom September 1, 2018. Age and gender of the patients is not taken into account in this analysis.\nA total of 9,890 cases were included in the analysis after cleaning.\n\n###### Population Characteristics Data\n\n[Population density data were available from Facebook HDX[27]. Small Area Estimates (SAEs)](https://www.zotero.org/google-docs/?47LRrU)\nfor poverty produced by the World Bank were used for", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001560:5:0:0", "start": 18, "end": 49, "surface": "Georeferenced cholera case data", "probe_tag": "confusion", "probe_score": 0.6018, "luna_label": 1}, {"key": "prwp:001560:5:0:1", "start": 1439, "end": 1470, "surface": "Population Characteristics Data", "probe_tag": "confusion", "probe_score": 0.0999, "luna_label": 0}]}, {"key": "aivin-083", "text": "In addition to improving the measurement of inequality, the new proposed methodology is\n\n\nuseful for assessing the distributional effects of fiscal policies on the income distribution,\n\n\nsince it also allows to recover the full unconditional distribution of income by combining\n\n\nthe conditional distributions observed in the household surveys and in the tax records.\n\n\n17", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:006305:19:0:0", "start": 326, "end": 343, "surface": "household surveys", "probe_tag": "confusion", "probe_score": 0.8995, "luna_label": 1}]}, {"key": "aivin-084", "text": ".929<br> <br>8.892***<br> <br>Household<br>880<br>0.448|-1.309<br> <br>6.590*<br> <br>Parcel<br>316<br>0.423|-1.449<br> <br>9.253***<br> <br>Parcel<br>316<br>0.454|-0.906<br> <br>8.553***<br> <br>Parcel<br>594<br>0.458|\n\n\n**Notes:** - Sample excludes plots that were subject to full-plot crop cut in MAPS II. † denotes a dummy variable. ***/**/* denote statistical significance at the 1/5/10 percent\nlevel, respectively.\n\n\n51", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000209:52:1:0", "start": 300, "end": 307, "surface": "MAPS II", "probe_tag": "drop", "probe_score": 0.0439, "luna_label": 1}]}, {"key": "aivin-085", "text": "D Thesis - The University of North Carol|\n|**Data source**<br>YLS<br>NLSY<br>HILDA<br>ECLS-K<br>**Years**<br>2007 & 2009-2010<br>biannually from 1986-1998<br>2,007<br>fall 1998, spring 1999, fall 1999, spring<br>2000, spring 2002, spring 2004<br>**Sample size**<br>3,725<br>16,650 child-year observations from 6283<br>mothers; sample size for FE ranges from<br>4,159 for child FE to 7919 for sibling FE<br>907 (IV), 430 (mother FE)<br>18,990 person-years for 6,330 indivdiuals<br>**Location**<br>India (state of Andhra Pradesh)<br>US<br>Australia<br>US<br>**Sample restrictions/**<br>**Mother characteristics**<br>Sample restricted to households  in rural<br>areas in both periods<br>**Sample**|**Data source**<br>YLS<br>NLSY<br>HILDA<br>ECLS-K<br>**Years**<br>2007 & 2009-2010<br>biannually from 1986-1998<br>2,007<br>fall 1998, spring 1999, fall 1999, spring<br>2000, spring 2002, spring 2004<br>**Sample size**<br>3,725<br>16,650 child-year observations from 6283<br>mothers; sample size for FE ranges from<br>4,159 for", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001080:25:1:0", "start": 721, "end": 725, "surface": "NLSY", "probe_tag": "drop", "probe_score": 0.0219, "luna_label": 1}, {"key": "prwp:001080:25:1:1", "start": 729, "end": 734, "surface": "HILDA", "probe_tag": "drop", "probe_score": 0.0219, "luna_label": 1}]}, {"key": "aivin-086", "text": "<u>Switzerland-Italian</u> <sup>~~2~~</sup> <u>6.8</u> <u>5.3</u> <u>5.4</u>\n\nItaly <sup>2</sup> 4.7 3.4 4.0\n\nChile <sup>1</sup> 9.3 6.9 10.0\n\nNuevo Leon <sup>1</sup> 13.4 11.4 14.8\n\nKorea <sup>2</sup> 7.2 6.5 6.9\n\n**Bermuda** <sup>**2**</sup> 8.3 5.0 5.2\n\n_Mean_ _6.7_ _5.5_ _5.3_\n\n<u>1: Using IALS</u>\n\n\n2. Using ALL\n\n\nThe two sets of estimates (using estimated coefficients β1 and δ1) suggest that the cross-country\n\n\naverage of the component of the return to schooling associated with skills acquired outside school is\n\n\nabout 70-80 percent of the gross return; that is, about 70-80 percent of what the labor market rewards\n\n\nin schooling is its non-literacy component (with the set of estimates associated with estimated\n\n\ncoefficient δ1, on average slightly lower); however, there is significant heterogeneity and a\n\n\ndichotomy between the two subgroups of countries – the educationally advanced and the less\n\n\neducationally advanced.\n\n\nIn Table 2, we divide the 14 countries in two groups of 7 after ranking the countries by average\n\n\nachievement score. We see that there is a strong negative association between country achievement\n\n\nscore and dispersion of score. Chile, Slovenia and Italy rank at the bottom in average score and at the\n\n\ntop in score dispersion; the opposite is the case for countries such as", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:005823:15:0:0", "start": 295, "end": 299, "surface": "IALS", "probe_tag": "drop", "probe_score": 0.0338, "luna_label": 1}]}, {"key": "aivin-087", "text": " costs for SSN–plus programs, compared to 18 percent of the\ntotal cost for L&J programs, which tend to be more complex, with multiple program components.\n\n**_Component Dosage and Adequacy_** <sup>**_35_**</sup> [^35: Adequacy is calculated as cost ($ 2011 PPP) of a component (e.g., grant size) divided by average annual per capita\nconsumption ($ 2011 PPP) of the bottom 20 percent of households in the relevant country.]\n\n\nThe following analysis explores component dosage and adequacy for those programs that provided\ndisaggregated data on underlying components. The sample of programs varies across different\ninterventions, and components reported by three or fewer programs are not included.\n\nCash grants (for productive investments) are provided more often, of higher value, and in more\ninstallments, in L&J programs than in SSN-plus programs. The average grant size for L&J\nprograms it is $416 (PPP 2011), <sup>36</sup> [^36: This data set excludes the project in Burkina Faso, which, by design, provides a substantially higher grant to youth\nselected through business plan competitions to create small business and microenterprises, rather than for selfemployment.] as compared to $222 (PPP 2011) for SSN–plus programs. Fewer\nSSN–plus programs provide any cash grants to their beneficiaries compared to L&J programs.\nThis may be because L&J programs aim to improve productive outcomes, while SSN-plus\ninitiatives primarily aim to improve consumption and, for a subset of beneficiaries, their productive\noutcomes as well. In addition, L&J programs tend to provide cash grants in two installments, which\n\n\n33 Delivery and staff costs refers to (1) cost of implementing certain components such as savings groups and training,\nand (2) the human resource cost of administering other components such as grants, inputs and cash transfers.\n34 Both government-led and NGO-led programs incur higher delivery-led", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000078:22:1:0", "start": 519, "end": 537, "surface": "disaggregated data", "probe_tag": "drop", "probe_score": 0.0441, "luna_label": 1}]}, {"key": "aivin-088", "text": " sector in each country is then the sum of all utilities’ subsidies in that country.\n\n\nNine countries represented in the IBNET database are estimated to have a negative subsidy. Most of these\nare developed countries, where efficiency may be pushed beyond the level assumed in the Chilean\nmodel. <sup>24</sup> Where this is the case, subsidies are assumed to be zero.\n\n\n**3.10 Estimating the subsidies for countries not in IBNET**\n\n\nTo extrapolate subsidy estimates to countries not included in the database, we first separate out China\nand India, while the remaining countries are grouped into four clusters—high income, upper middle\nincome, lower middle income, and low income—based upon the World Bank’s country classifications by\nincome for fiscal year 2019. Next, for water and sanitation separately, we calculate an average subsidy\nper person served for countries with representation in IBNET, disaggregated into the four clusters (see\nAppendix B). Then, for countries not in the IBNET database, we multiply the per person subsidy for its\ncluster by the total population served by the respective service (estimated by multiplying the country’s\ncoverage rate <sup>25</sup> [^25: Water and/or sanitation coverage data from the World Health Organization/United Nations Children’s Fund for 3 countries\n(Austria, Isle of Man, and Micronesia), in addition to the 6 previously cited with partial IBNET data, were incomplete, and were\nthus supplemented by additional data and estimates. Refer to Appendix B for details.] and its total population). Since the main drivers for these estimates are the unit cost in\nthe asset base calculations, the results presented in the report assume a +/-10 percent variation in the\nunit asset base estimates.\n\n\n22 China and India were not extrapolated due to low proportional representation in IBNET and a general lack of data availability.\n\n23 Water and/or sanitation coverage data for 6 countries (Bahrain, Fiji, Indonesia,", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "sample:prwp:000381:14:2:0", "start": 121, "end": 135, "surface": "IBNET database", "probe_tag": "confusion", "probe_score": 0.8305, "luna_label": 1}, {"key": "sample:prwp:000381:14:2:1", "start": 1183, "end": 1220, "surface": "Water and/or sanitation coverage data", "probe_tag": "confusion", "probe_score": 0.7404, "luna_label": 1}, {"key": "sample:prwp:000381:14:2:2", "start": 1877, "end": 1914, "surface": "Water and/or sanitation coverage data", "probe_tag": "drop", "probe_score": 0.0375, "luna_label": 1}]}, {"key": "aivin-089", "text": "Fifth, misclassification of goods may create huge revenue losses. Low-tax headings should\ntherefore be closely monitored.\n\n\nAgainst this backdrop, a new multipronged strategy is being implemented in Madagascar,\nwith the aim not only of curtailing fraud, but also of facilitating legal trade. A reform strategy\nfor the 2015–2018 period was adopted in September 2014.\n\nSeveral significant reform measures have recently been adopted or are in the process of being\nadopted:\n\n   - A system has been instituted whereby the performance of each customs office will be\nmonitored based on monthly indicators. This should enable better scrutiny of the\nCustoms Authority and make monitoring more effective, thus facilitating trade.\n\n   - The post-clearance audit unit has been reinforced with the recruitment of additional\nauditors, and several major importers have since been audited and have been found to\nhave committed fraud.\n\n   - A new circular has been signed by the Minister of Finance revising the criteria under\nwhich companies are eligible for fast-track customs clearance.\n\n   - Moreover, in the context of the “quick results” approach adopted, the Customs\nAuthority is making every effort to scale back physical inspections, given their\nnegligible impact.\n\n   - Finally, the risk analysis and statistics departments have been reinforced to provide\nmore targeted monitoring.\n\n**VII. Concluding Remarks**\n\nThis paper proposes a simple methodology aiming to identify some risky import operations.\nThe mirror analysis first helps to identify and target products or sectors with the greatest risk.\nAnalysis of declaration patterns (that is, mining of customs data) is then performed to identify\nand target the most high-risk companies. In the specific case of Madagascar, and within the\ncurrent context of customs reform, this paper suggests that there was sizeable fraud in the\nareas of customs valuation and tariff classification in 2014.\n\n\nRecent studies (Yang (2008), Carrillo et al. (2014)) have highlighted that many tax evaders\nrespond to the implementation of corrective measures (such as increased monitoring,\nestablishment of cross-checking systems, etc.)", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:006657:19:0:0", "start": 1647, "end": 1659, "surface": "customs data", "probe_tag": "drop", "probe_score": 0.0423, "luna_label": 1}]}, {"key": "aivin-090", "text": "## WPS6097 Policy Research Working Paper 6097\n\n# Collecting High Frequency Panel Data in Africa Using Mobile Phone Interviews\n\n##### _Kevin Croke_ _Andrew Dabalen_ _Gabriel Demombynes_ _Marcelo Giugale_ _Johannes Hoogeveen_ The World Bank Africa Region Poverty Reduction and Economic Management Unit June 2012", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:005268:0:0:0", "start": 60, "end": 85, "surface": "High Frequency Panel Data", "probe_tag": "drop", "probe_score": 0.0223, "luna_label": 0}]}, {"key": "aivin-091", "text": " 19<br>20 − 99<br>100+<br>Keep formal account|\n|0<br>20<br>40<br>60<br>80 100<br>Burkina Faso (2015)<br>1 − 4<br>5 − 9<br>10 − 19<br>20 − 99<br>100+<br>Keep formal account (SYSCOA)|0<br>20<br>40<br>60<br>80 100<br>Cameroon (2008)<br>1 − 4<br>5 − 9<br>10 − 19<br>20 − 99<br>100+<br>Tax registeration|0<br>20<br>40<br>60<br>80 100<br>Ghana (2013)<br>1 − 4<br>5 − 9<br>10 − 19<br>20 − 99<br>100+<br>Tax registeration|0<br>20<br>40<br>60<br>80 100<br>Rwanda (2013)<br>1 − 4<br>5 − 9<br>10 − 19<br>20 − 99<br>100+<br>Tax registeration|\n\n\n\n52", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000985:53:1:0", "start": 281, "end": 298, "surface": "Tax registeration", "probe_tag": "drop", "probe_score": 0.0049, "luna_label": 0}, {"key": "prwp:000985:53:1:1", "start": 281, "end": 298, "surface": "Tax registeration", "probe_tag": "drop", "probe_score": 0.0054, "luna_label": 0}]}, {"key": "aivin-092", "text": "<u>Figure 48. Credit Card EACR (household level, LITS)</u>\n\n\nSweden\n\n\nTurkey\n\n\nGreat Britain\n\n\nHungary\n\n\nFrance\n\n\nSlovenia\n\n\nItaly\n\n\nCzech Republic\n\n\nSlovak Republic\n\n\nCroatia\n\n\nGermany\n\n\nLatvia\n\n\nMacedonia\n\n\nAll\n\n\nEstonia\n\n\nMongolia\n\n\nSerbia\n\n\nUkraine\n\n\nMontenegro\n\n\nPoland\n\n\nKosovo\n\n\nAlbania\n\n\nBulgaria\n\n\nBosnia and Herzegovina\n\n\nRomania\n\n\nLithuania\n\n\nBelarus\n\n\nRussian Federation\n\n\nArmenia\n\n\nGeorgia\n\n\nKazakhstan\n\n\nAzerbaijan\n\n\nMoldova\n\n\nTajikistan\n\n\nKyrgyzstan\n\n\nUzbekistan\n\n\n\n\n\n0 10 20 30 40 50 60 70 80 90 100\n\n\nEACR Dissimilarity Coverage\n\n\n35 countries sorted by coverage rates. Coverage rates are labeled\n_<u>Sources</u>_ <u>: LITS</u>\n\n\n50", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:006585:51:0:0", "start": 636, "end": 640, "surface": "LITS", "probe_tag": "drop", "probe_score": 0.0141, "luna_label": 1}]}, {"key": "aivin-093", "text": "**Appendix A**\n\n**<u>Table 1</u>** **<u>Characteristics of sample districts</u>**\n\n\n\n|District name|Literacy rate,<br>Census 2001|% population below<br>poverty line|\n|---|---|---|\n|**Karnataka**|||\n|Chikmagalur|73|8|\n|Kolar|63|22|\n|Bellary|58|43|\n|Gulbarga|51|42|\n|**MP**|||\n|Raisen|73|38|\n|Katni|65|27|\n|Guna|60|32|\n|Dhar|53|46|\n|**UP**|||\n|Kanpur rural|67|43|\n|Hathras|63|28|\n|Pratapgarh|59|22|\n|Sitapur|49|37|\n\n\n**<u>Table 2</u>** **<u>Summary of sample school and student characteristics at baseline</u>**\n\n\n\n\n\n|Col1|UP|Col3|Col4|MP|Col6|Col7|Karnataka|Col9|Col10|\n|---|---|---|---|---|---|---|---|---|---|\n|**Student characteristics**|Treatment|Control|p*|Treatment|Control|p*|Treatments<br>1 and 2|Control|p*|\n|Age|8.69|8.76|.36|8.89|9.01|.08|10|10|.77|\n|Gender (1 if male", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:003978:28:0:0", "start": 118, "end": 129, "surface": "Census 2001", "probe_tag": "drop", "probe_score": 0.0123, "luna_label": 0}]}, {"key": "aivin-094", "text": " 12,692 fulfilled the eligibility criteria.\n\n\nAs explained above, and key to the integrity of the experimental design, baseline data\n\n\nwas collected prior to assigning localities to the three treatment and control groups. The\n\n\nbaseline questionnaire included simple variables on employment, assets, education, and\n\n\nhousehold characteristics.\n\n\nThe second public lottery took place in March 2016. The list of the 5,116 selected\n\n\nbeneficiaries was publicly released in each locality between July and September 2016. <sup>25</sup>\n\n\n24The baseline instrument was designed by the research team. NGO staff were trained as enumerators.\nAn independent team of experienced enumerators was hired to perform field supervision and data quality\nchecks. A double-blind data-entry process was set-up.\n\n25The lag between the second lottery and the release of the beneficiary lists was due to delays in the\n\n\n13", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "sample:prwp:000613:16:1:0", "start": 119, "end": 132, "surface": "baseline data", "probe_tag": "drop", "probe_score": 0.0022, "luna_label": 0}, {"key": "sample:prwp:000613:16:1:1", "start": 851, "end": 868, "surface": "beneficiary lists", "probe_tag": "drop", "probe_score": 0.0042, "luna_label": 0}]}, {"key": "aivin-095", "text": "**<u>Table 5. Interactions of Business-As-Usual Survey Approach (T1/IHS4) with Individual and Household Attributes</u>**\n\n\n\n**<u>Men</u>**\n\n\n\n**<u>Women</u>**\n\n\n\n**<u>Reported</u>** **<u>Economic</u>** **<u>SDG</u>** **<u>Reported</u>** **<u>Economic</u>** **<u>SDG</u>**\n**Owner** **Owner**\n\n**Exclusive** **Joint** **Exclusive** **Joint** **Exclusive** **Joint** **Exclusive** **Joint**\n**<u>(1)</u>** **<u>(2)</u>** **<u>(3)</u>** **<u>(4)</u>** **<u>(5)</u>** **<u>(6)</u>** **<u>(7)</u>** **<u>(8)</u>** **<u>(9)</u>** **<u>(10)</u>**\nT1/IHS4 <u>0.024</u> <u>0.027</u> <u>0.080*</u> <u>0.085</u> <u>0.018</u> <u", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:000484:26:0:1", "start": 65, "end": 72, "surface": "T1/IHS4", "probe_tag": "drop", "probe_score": 0.0306, "luna_label": 0}]}, {"key": "aivin-096", "text": "\nThe only hard data on these flows is the\nnumber of Ukrainian workers with social\ninsurance, which nevertheless understates\nthe numbers, as certain kinds of legal\nwork often undertaken by temporary\nemployees did not require it, and some\nUkrainians worked in the shadow economy.\nThe number of workers with Ukrainian\ncitizenship and social insurance increased\nfrom just 33 thousand at the end of 2013,\nto 500 thousand on 30th September\n2019, and 627 thousand at the end of\n2021. Some of this increase reflects the\ntransitioning of Ukrainians to more regular\nwork arrangements and securing of work\npermits. The yearly rate of the number of\nregistrations peaked at 106% at the end\nof 2015, and even with the recent refugee\ninflow never reached such a high pace\nagain. The most recent data from 30th\nSeptember 2023 counts 753 thousand\nworkers with Ukrainian citizenship\nregistered for social security, including 225\nthousand refugees.\n\n\n\n**Source:** Deloitte own elaboration based on ZUS data. Quarter 3 2023 shows the most recent\ndata available.\n\n13\n\n\n\nThe structure of the Ukrainian population\nin Poland changed radically after 24th\nFebruary 2022. Up until 2021, Ukrainians\nin Poland were mostly men (close to\ntwo-thirds) looking for work, often leaving\ntheir families back in Ukraine. After the\nbeginning of the full-scale war in Ukraine,\nrefugees fleeing the war started to arrive.\nThey were primarily women and children.\nThis is consistent with a change in the\nnature of migration flows, from primarily\neconomic migrants to forcefully displaced\nrefugees.\n\n\n\n**Chart 2.** Number of Ukrainian men and women registered in Poland for social security.\n\n\n800\n\n\n700\n\n\n600\n\n\n500\n\n\n400\n\n\n300\n\n\n200\n\n\n100\n\n\n0\n\n2021 Q42 022 Q4 2023 Q3\n\n\n\nNumber of insured men with\nUkrainian citizenship\n\n\n\nNumber of insured women with\nUkrainian citizenship", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "jad_paddy_docs:000007:6:1:0", "start": 979, "end": 987, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9503, "luna_label": 1}]}, {"key": "aivin-097", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n# **Summary of findings and** **recommendations**\n\nThe 2024 round of the SEIS survey indicates that while the financial vulnerability of refugees from Ukraine\nresiding in neighboring countries - specifically those included in the <u>[Regional Refugee Response Plan - has](https://www.unhcr.org/europe/publications/regional-refugee-response-plan-2025-2026)</u>\ndeclined over the past year <sup>1</sup> [^1: Compared to the <u>[2023 MSNA data](https://data.unhcr.org/en/documents/details/108068)</u>], one in five still live with an income below the poverty line <sup>2</sup> . When factoring in the\ndisproportionately high accommodation costs refugees face, largely due to the region’s high homeownership\nrates among locals, the poverty rate <sup>3</sup> rises to 40%, more than three times that of host communities <sup>4</sup> .\n\n\nThe data also shows that poverty <sup>5</sup> has a very tangible effect on living conditions and protection risks. This\ngroup feel less safe, less secure in term of accommodation tenure, more frequently misses out on needed\nhealthcare, more often has children out of school, and is much more frequently forced to resort to skipping\nmeals because of lack of funds. In addition, vulnerable populations, such as older adults and individuals with\ndisabilities or MHPSS <sup>6</sup> needs exhibit significantly higher poverty rates when compared to the refugees overall.\nThese connections are important to bear in mind when designing government, development, and humanitarian\nsupport programs.\n\n\nEmployment continues to be closely associated with lower poverty rates, though ultimately, it’s the size of\nincome that is generated by working household members that", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "jad_paddy_docs:000010:2:0:0", "start": 180, "end": 191, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.9164, "luna_label": 1}, {"key": "jad_paddy_docs:000010:2:0:1", "start": 533, "end": 547, "surface": "2023 MSNA data", "probe_tag": "keep", "probe_score": 0.9544, "luna_label": 1}]}, {"key": "aivin-098", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**<u>SHARE OF POPULATION PAYING FOR RENTED HOUSING: HOSTS VS REFUGEES</u>**\n\n\nShare of hosts living in rented housing (2023) Share of refugees fully paying for rent (2024) Share of refugees partially paying for rent (2024)\n\n\n100%\n\n\n80%\n\n\n60%\n\n\n40%\n\n\n20%\n\n\n0%\nBulgaria Czechia Estonia Hungary Latvia Lithuania Moldova Poland Romania Slovakia Region\n\n\nSource: Eurostat, survey data, SAG estimates\n\n\n**<u>HOUSING COST AS A SHARE OF HOUSEHOLD DISPOSABLE INCOME</u>**\n\n\nRefugees (2024) Hosts (2022)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Estonia Hungary Latvia Lithuania Moldova Poland Romania Slovakia Region\n\n\nSource: Eurostat, survey data, SAG estimates\n\n\n**<u>REFUGEE POVERTY RATES WITH AND WITHOUT CORRECTION FOR EXCESSIVE HOUSING COSTS</u>**\n\n\nRefugees (2024) Refugees with housing expense correction (2024) Hosts (2023)\n\n\n\n65%\n\n\n\n40%\n\n\n\n52%\n\n\n\n\n\n\n\n\n\n\n\n31%\n\n\n\n\n\n46%\n43%\n37%\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Hungary Moldova Poland Romania Slovakia Estonia Latvia Lithuania Region\n\n\nSource: Eurostat, survey data, SAG estimates\n\n\n**6**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:5:0:0", "start": 476, "end": 487, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9968, "luna_label": 1}]}, {"key": "aivin-099", "text": "zyciowa-i-ekonomiczna-migrantow-z-Ukrainy-w-Polsce_raport-z-badania-2022-r.pdf)</u>\nService of Ukraine (2021).\n\n\n\nPoland 2023 assess that around 56% <sup>23</sup>\nof refugee arrivals had completed higher\neducation. This means that Ukrainians\nwith higher education were more likely\nto relocate to Poland or leave Ukraine in\ngeneral, having the necessary means to\nachieve this. On average refugees with a\nbachelor’s degree or higher had an over\n30% higher employment rate than peers\nwithout a degree, according to MSNA\nPoland 2023 survey.\n\n\n\n\n\n22  After excluding Germany and Switzerland as outliers. 23  Due to possible differences in methodologies data from this surveys should not be directly compared\n\n\n20\n\n\n\n21", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "jad_paddy_docs:000007:10:3:0", "start": 512, "end": 535, "surface": "MSNA\nPoland 2023 survey", "probe_tag": "keep", "probe_score": 0.9622, "luna_label": 1}]}, {"key": "aivin-100", "text": "\npensions)\n\n\n\nHumanitarian\n\ncash\n\n\n\nEquivalized\n\n\n\nEmployment\n\nincome\n\n\n\nOther Equivalized\n\n\n\nincome\n\n\n\n2024\n\n\n\nincome\n\n\n\n2023\n\n\n\nNote: Only includes data from the 7 countries surveyed in both rounds (Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and\nSlovakia)\n\n\nSource: Survey data, SAG estimates\n\n\n12. Household disposable income adjusted for size, as per Eurostat’s methodology\n13. The 2024 survey probed for income from family in Ukraine more explicitly\n\n\n**9**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:8:1:0", "start": 298, "end": 309, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9985, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:8:1:1", "start": 416, "end": 427, "surface": "2024 survey", "probe_tag": "keep", "probe_score": 0.9861, "luna_label": 1}]}, {"key": "aivin-101", "text": " 12.** Structure by occupational group of all employed persons and refugees from Ukraine\n\n\nAll employed persons Ukrainian refugees\n\n\n\n\n\n\n\n40% 30% 20% 10% 0% 10% 20% 30% 40%\n\n\n\n\n\n**Source:** Deloitte own elaboration based on Statistics Poland and ZUS data.\n\n\n24  Due to possible differences in methodologies data from this surveys should not be directly compared\n25  Elementary occupations include: Cleaners and helpers; Agricultural, forestry and fishery labourers; Labourers in mining, construction, manufacturing\nand transport; Food preparation assistants; Street and related sales and services workers; Refuse workers and other elementary workers.\n\n22\n\n\n\n23\n\n\n\n26  Act of March 12, 2022 on assistance to citizens of Ukraine in connection with the armed conflict on the territory of the country", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:11:2:0", "start": 246, "end": 254, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9297, "luna_label": 1}]}, {"key": "aivin-102", "text": "-union-statistics-on-income-and-living-conditions)</u>\n<u>[survey, which the OECD uses as the basis for host country poverty](https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions)</u>\nassessments\n\n2. Results for the Czech Republic not individually presented due to\nsampling limitations\n\n3. Accommodation rent support has been calculated as the difference\nbetween the actual equivalized accommodation expense and the\nmedian equivalized market rent in the region\n\n\n\n8 A household is defined to fall below the poverty line if its members fall below the poverty line on individual basis (the\nequivalized income of all household members is the same)\n\n\n\n9 Its magnitude can be computed as the difference between the median equivalized market rent in the region and the actual\nequivalized accommodation expense\n\n\n\n10 Those that are below the poverty line\n\n\n\n**5**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000000:4:2:0", "start": 1, "end": 49, "surface": "union-statistics-on-income-and-living-conditions", "probe_tag": "keep", "probe_score": 0.9359, "luna_label": 0}]}, {"key": "aivin-103", "text": "\nincrease in Polish citizens' employment\nrates by 0.5% and a decrease in the\nunemployment rate by 0.3%, in the\npreferred specification with dummy\nvariables for all quarters. All results are\nstatistically significant at a 0.01 level.\n\n\n\n**Chart 26. Polish citizens employment rate** **Chart 27. Polish citizens unemployment rate**\n\nEurostat survey data, 20-64 age group Eurostat survey data, 20-64 age group\n\n\n\n3.6%\n\n\n\n\n\n83.8%\n\n\n\n83.6%\n\n\n\n\n\n83.2%\n\n\n\n**Chart 28. Effect of a 1 pp. change in employment share of Ukrainian refugees on**\n\nPanel model of all 380 poviats quarterly data from Q1 2022 to Q2 2024. Results are statistically significant at a 0.01 level.\n\n\nPolish citizens employment rate Unemployment rate\n\n\n1.0 1.0\n\n\n0 0\n\n\n-1.0 -1.0\n\n\n-2.0 -2.0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n2021-Q2 2022-Q2 2023-Q2 2024-Q2 2021-Q2 2022-Q2 2023-Q2 2024-Q2\n\n\n\nSeasonal Quarterly No time Seasonal\ndummies dummies dummies dummies\n\n\n\nQuarterly\ndummies\n\n\n\nNo time\ndummies\n\n\n\nSource: Deloitte own elaboration based\nof Eurostat data (Labour Force Survey).\n\n\n38\n\n\n\nMales Source: Deloitte own elaboration based Males\n\n\n\nSource: Deloitte own elaboration based\nof Eurostat data (Labour Force Survey).\n\n\n\nFemales of Eurostat data (Labour Force Survey). Females\n\n\n\nSource: Deloitte own elaboration based on GUS and ZUS data. All continuous variables have been regressed in first differences to account for non-stationarity Polish\ncitizens employment rates and registered unemployment rates. For details see the Online Technical Appendix.\n\n\n39", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:19:2:0", "start": 331, "end": 351, "surface": "Eurostat survey data", "probe_tag": "keep", "probe_score": 0.9506, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:19:2:1", "start": 989, "end": 1002, "surface": "Eurostat data", "probe_tag": "keep", "probe_score": 0.9995, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:19:2:2", "start": 1004, "end": 1023, "surface": "Labour Force Survey", "probe_tag": "keep", "probe_score": 0.9886, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:19:2:3", "start": 1271, "end": 1274, "surface": "GUS", "probe_tag": "keep", "probe_score": 0.9985, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:19:2:4", "start": 1279, "end": 1287, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9836, "luna_label": 1}]}, {"key": "aivin-104", "text": "THE ROLE OF HOUSING SUPPORT AND EMPLOYMENT FACILITATION IN ECONOMIC VULNERABILITY OF REFUGEES FROM UKRAINE\n\n\n**<u>SHARE OF YOUNG UKRAINIANS (AGED 15 TO 24) WHO ARE NOT ENGAGED IN EMPLOYMENT, EDUCATION, OR TRAINING</u>**\n**(NEET) BY COUNTRY, %** <sup>**1,2,3**</sup>\n\n\nHost country Refugees, excluding online education Refugees\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Hungary Moldova Poland Romania Slovakia Region\n\n\n\n1. Data reported by the national statistics service was used as a\nreference for Moldova\n\n2. With the exception of Poland and the Czech Republic, the reliability\nof data by country is hindered by a relatively low number of\nobservations\n\n[3. Host country data is based on indicators reported by the OECD for](https://data.oecd.org/youthinac/youth-not-in-employment-education-or-training-neet.htm)\n[2022](https://data.oecd.org/youthinac/youth-not-in-employment-education-or-training-neet.htm)\n\n\n**<u>UKRAINE REFUGEE YOUTH ACTIVITY BY AGE</u>**\n\n\n\n**The gender divide: female – led households are**\n**more economically vulnerable than their male –**\n**led counterparts**\nThe share of households that are composed of\nfemale only adults with or without children\nconstitute 65% of the total sample across the\nregion. Compared to households with male only\nadults, the vast majority of which are without\nchildren, they are 51% more likely to find themselves\nbelow the poverty line.\n\n\n**<u>DISTRIBUTION OF HOUSEHOLDS BY GENDER OF</u>**\n**ADULTS, %**\n\n\n\nNEET\n\n\n15", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000000:8:0:0", "start": 432, "end": 480, "surface": "Data reported by the national statistics service", "probe_tag": "keep", "probe_score": 0.9647, "luna_label": 1}]}, {"key": "aivin-105", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n\n# Executive summary\n\nThe war in Ukraine, now in its third year, continues\nto have devastating effects on the Ukrainian\npopulation, triggering one of the largest\ndisplacement crises in Europe since World War II. As\nof December 2024, over 6.2 million Ukrainian\nrefugees have been recorded across Europe, the\nmajority of whom are women, children, and older\npersons. The European Union extended the\nTemporary Protection Directive until March 2026,\ngranting Ukrainian refugees access to essential\nhealth services, education, and other critical\nsupport. The Republic of Moldova followed this\nmodel and also introduced Temporary Protection for\nUkrainian refugees.\n\n\nIn 2024, to assess the health and mental health\nsituation of Ukrainian refugees, their access to\nservices, and the barriers they face across\ncountries, Regional Refugee Response Plan (RRP)\nhealth and mental health and psychosocial support\n(MHPSS) partners conducted a regional analysis of\nthe Socio-Economic Insights Survey (SEIS) data from\n10 refugee-hosting countries: Bulgaria, Czechia,\nEstonia, Hungary, Latvia, Lithuania, Poland, Republic\nof Moldova, Romania, and Slovakia. The analysis\nincludes a comparison with key indicators collected\nin 2023.\n\n\nKey finding from the regional analysis include:\n\n\n\n\n\n\n\n\n\n\n\n\n\n**3**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000004:2:0:0", "start": 1024, "end": 1054, "surface": "Socio-Economic Insights Survey", "probe_tag": "keep", "probe_score": 0.9776, "luna_label": 1}]}, {"key": "aivin-106", "text": "\nand accommodation type. All interviews were conducted face to face. As a probabilistic selection of\nrespondents could not be ensured, the primary goal was to collect a diverse sample that would reflect the\npopulation’s composition as closely as possible.\n\n\nFor the regional analysis, weights were applied based on the most up-to-date estimates of the number of\nrefugees staying in each country. This allowed calculated indicators to more accurately represent the broader\nrefugee population across the region.\n\n\nAppropriate measures were implemented to ensure the protection of personal data and to guarantee\nconfidentiality in all data collection and processing activities. Consent was requested and recorded for all\nselected participants, providing clear information on the purpose, and expected use of the data.\n\n\nWith the exception of Republic of Moldova, the poverty line for each country was defined as 50% of the median\n<u>[equivalized disposable income (after social transfers) as reported by](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Equivalised_disposable_income)</u> <u>[Eurostat for 2023. This figure was indexed](https://ec.europa.eu/eurostat)</u>\ntowards 2024 using national annual wage inflation data for the third quarter of 2024. As the Republic of\nMoldova does not currently run the SILC survey, the absolute poverty line as reported by the country’s National\nBureau of Statistics was used instead. Host poverty rates were based on the same poverty threshold. With the\nexcept of Republic of Moldova, these were equal to the at-risk-of-poverty (AROP) rate reported by Eurostat. On\n\n\n**14**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:13:1:0", "start": 1215, "end": 1250, "surface": "national annual wage inflation data", "probe_tag": "keep", "probe_score": 0.9279, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:13:1:1", "start": 1336, "end": 1347, "surface": "SILC survey", "probe_tag": "keep", "probe_score": 0.9283, "luna_label": 1}]}, {"key": "aivin-107", "text": "\nbetween 2008 and 2017. He exploited the\nrandom assignment of refugees to French,\nGerman, and Italian cantons, as well as\ntheir prior language knowledge (e.g. French\nspeakers placed in French or German\nspeaking cantons). The results showed that\nlanguage proficiency more than doubled\nthe employment level in the first five years\nafter arrival.\n\n\n**The econometric model estimated**\n**in the SEIS UNHCR survey shows**\n**substantial wage gains from Polish**\n**language proficiency for Ukrainian**\n**refugees.** Deloitte has estimated an\neconometric model incorporating\nindividual income determinants of\nUkrainian refugees. Our study has\n\n\n\nto work, but only in a designated health\ncare center, and for no longer than five\nyears without the possibility of extension.\nIn February 2022, Ukrainian psychologists\nwere allowed to provide services to other\nUkrainian citizens, but only for 18 months.\nWhile such changes are likely to be\nextended, they do not motivate Ukrainian\nrefugees – unsure, if they will be allowed\nto continue practice in the long term – to\nacquire the necessary skills, for instance\nto improve language fluency or invest in\nprofessional courses. Instead, the refugees\nmay prefer to seek opportunities in other\ncountries or change their line of work to\none that will be more accessible, but less\nvaluable for the Polish economy.\n\n\nconfirmed the results found in international\nliterature presented above. The most\nimportant result is that Ukrainian refugees\nwho are fluent in Polish earn a net wage\npremium of about PLN 700 (+16% relative\nto refugee net median wage,\nPLN 1,000 gross wage) when compared to\nthose with beginner language skills. This\nresult is stable across different model\nspecifications. Note that such an earnings\ngain would bring the median net wage of\na Ukrainian refugee (estimated based on\nthe SEIS UNHCR survey in chapter 2) from\n80% to 98% of the median in the economy\nas a whole (or from 80% to 93% according\nto Ukrainian refugee’s median in", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "jad_paddy_docs:000001:15:2:0", "start": 391, "end": 408, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9715, "luna_label": 1}]}, {"key": "aivin-108", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n\n**<u>TOP 5 REFUGEE EMPLOYMENT SECTORS SPLIT BY</u>**\n**EMPLOYEE BACKGROUND (2024)**\n\n\n\n**<u>TOP EMPLOYMENT BARRIERS FOR EMPLOYED REFUGEES</u>**\n**(2024)**\n\n\n\nEmployed in the same\nsector in Ukraine\n\n\nManufacturing\n\n\nHospitality\n\n\nConstruction\n\n\nWholesale\n\n\nIT\n\n\n\nLack of local language\n\nknowledge\n\nCannot find a job with\n\ndecent pay\n\nFew jobs for my skills or\n\nexperience\n\nLack of jobs with a suitable\n\nschedule\n\nDegree or skills recognition\n\nissues\n\n\nSource: Survey data\n\n\n\n17%\n\n\n15%\n\n\n\n35%\n\n\n\n5%\n\n\n2%\n\n\n4%\n\n\n\nEmployed in a different\nsector in Ukraine\n\n\n16%\n\n\n9%\n\n\n5%\n\n\n\n24%\n\n\n\n\n\n\n\n\n\n6%\n\n\n\n4%\n\n\n\n1%\n\n\n\nNote: Percentages are based on the distribution of relevant responses\nin the survey\n\n\nSource: Survey data\n\n\nMoreover, in the latest survey round, nearly 35% of employed refugees identified inadequate pay, a lack of\npositions that match their skill set, and challenges in having their qualifications recognized as barriers to\nemployment. All of these answers also suggest a mismatch between qualifications and job placement.\nConsistent with the findings on having to shift to jobs outside of previous employment backgrounds, working\nwomen reported the above employment barriers more frequently than men, supporting the hypothesis that\nthey may be facing underemployment more often.\n\n# **Methodology**\n\nThe approach to data collection in each of the countries included in the report differed depending on the\navailability of sampling frames and information on the distribution of the refugee population by geographic area\nand accommodation type. All interviews were conducted face to face. As a probabilistic selection of\nrespondents could not be ensured, the primary goal was to collect a diverse sample that would reflect the\npopulation’s composition as closely as possible.\n\n\nFor the regional analysis,", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:13:0:0", "start": 568, "end": 579, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9579, "luna_label": 1}]}, {"key": "aivin-109", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n# **Background**\n\nOver three years have elapsed since the start of the full-scale invasion of Ukraine, an event which has led to\nthe largest displacement crisis in Europe since World War II. As of the end of 2024, 6.3 million refugees from\n[Ukraine were recorded across Europe, close to 2 million of whom are located in ten Regional Refugee](https://www.unhcr.org/europe/publications/regional-refugee-response-plan-2025-2026)\n<u>[Response Plan](https://www.unhcr.org/europe/publications/regional-refugee-response-plan-2025-2026)</u> countries: Bulgaria, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Republic of Moldova,\nRomania, and Slovakia. This report aims to assess the livelihood situation of this population based on the 2024\nround of data collected by the Socio-Economic Insights Survey (SEIS), which received responses from 8,723\nhouseholds containing 19,803 individuals. Figures for 2023 are derived from a similar exercise <sup>7</sup> [^7: The MSNA, which ran in 7 countries: Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and Slovakia] conducted in\n2023.\n\n# **Key findings**\n\n**Refugee poverty rates remain high, albeit improved from 2023**\n\n\nThe 2024 SEIS equivalized <sup>8</sup> disposable income data indicates that just over one in five refugees (23%) residing\nin the region are living in poverty <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>", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:3:0:0", "start": 879, "end": 909, "surface": "Socio-Economic Insights Survey", "probe_tag": "keep", "probe_score": 0.9956, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:3:0:1", "start": 1071, "end": 1075, "surface": "MSNA", "probe_tag": "keep", "probe_score": 0.9978, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:3:0:2", "start": 1301, "end": 1310, "surface": "2024 SEIS", "probe_tag": "keep", "probe_score": 0.9978, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:3:0:3", "start": 1336, "end": 1358, "surface": "disposable income data", "probe_tag": "keep", "probe_score": 0.9316, "luna_label": 1}]}, {"key": "aivin-110", "text": "\n**radically after February 24, 2022.**\nUp until 2021, Ukrainians in Poland were\nmostly men (close to two-thirds), who came\nfor work-related reasons, often leaving their\nfamilies back in Ukraine. The onset of the\nfull-scale conflict in Ukraine triggered the\narrival of individuals displaced by the war.\nThose were primarily women and children,\nwith men in Ukraine being mobilized for\nthe war effort. Social insurance data does\nnot reflect the full extent of the change,\nshowing only workers, without children and\nadults outside of employment.\n\n\n\n**Chart 3. Number of Ukrainians registered in Poland for social insurance by sex**\n\n\n2021 Q4 2022 Q4 2023 Q4 2024 Q2\n\n\n\nNumber of insured\nmen with Ukrainian\ncitizenship\n\n\n\nNumber of insured\nwomen with Ukrainian\ncitizenship\n\n\n\nNumber of insured\nwith Ukrainian\ncitizenship\n\n\n\nSource: Deloitte own elaboration based on ZUS data.\n\n\n3 Employed person is a person, who during the reference week worked for at least 1 hour for pay or profit, including contributing family workers; had a certain job\nattachment; or produced agricultural goods for sale or barter. A definition according to the Labour Force Survey: <u>https://ec.europa.eu/eurostat/statistics-explained/</u>\n<u>index.php?title=Glossary:Employed_person_-_LFS</u>\n\n\n08\n\n\n\nSource: Deloitte own elaboration based on the PESEL database as of September 2024.\n\n\n4 Available in the repository maintained by the government <u>https://dane.gov.pl/pl/dataset/2715</u> as well as UNHCR data portal <u>https://app.powerbi.com/</u>\n<u>view?r=eyJrIjoiODhkOGZiMzctZTliMi00NzA5LTgyM2QtZGZh", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:4:2:0", "start": 400, "end": 421, "surface": "Social insurance data", "probe_tag": "keep", "probe_score": 0.9986, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:4:2:1", "start": 862, "end": 870, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9734, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:4:2:2", "start": 1319, "end": 1333, "surface": "PESEL database", "probe_tag": "keep", "probe_score": 0.9802, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:4:2:3", "start": 1471, "end": 1488, "surface": "UNHCR data portal", "probe_tag": "keep", "probe_score": 0.9645, "luna_label": 0}]}, {"key": "aivin-111", "text": " elaboration based on Eurostat\n(Labour Force Survey) data.\n\n\n**Occupational downgrading is**\n**widespread among Ukrainian refugees**\n**in Poland.** SEIS data shows that 40% of\nUkrainian refugees aged 25–64 hold a\ntertiary degree – exceeding the share of\nPolish citizens in the same age bracket,\naccording to Eurostat’s 2023 annual\naverage. While ZUS data available as of\nJune 30, 2024, shows occupational groups\nfor less than half of the socially insured\nin Poland, it reflects a visible mismatch\nbetween the refugees’ education and the\njobs they perform. Only 12% of Ukrainian\nrefugees (less than one-third of the share\nin terms of tertiary education) worked in\noccupational groups which require tertiary\neducation, that is managers, specialists,\nand technicians (ISCO 1-3), compared to\n37% of Polish citizens (almost the same\nas the tertiary education share in the\n25-64 age group).\n\n\n\n2022 2023", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:13:2:0", "start": 148, "end": 157, "surface": "SEIS data", "probe_tag": "keep", "probe_score": 0.9975, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:13:2:1", "start": 346, "end": 354, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9902, "luna_label": 1}]}, {"key": "aivin-112", "text": "-and-</u>\n<u>salaries-by-occupations-for-october-2022,4,8.html</u>\n\n\nGUS (2024b). Education in the school year 2023/2024 (preliminary data),\nStatistical Office in Gdańsk, <u>https://stat.gov.pl/en/topics/education/education/</u>\n<u>education-in-the-school-year-20232024-preliminary-data,13,2.html</u>\n\n\nHeller, B. H., & Mumma, K. S. (2023). Immigrant integration in the United States:\nthe role of adult English language training. American Economic Journal: Economic\nPolicy, 15(3), 407-437.\n\n\nJaumotte, M. F., Koloskova, K., & Saxena, M. S. C. (2016). Impact of migration on\nincome levels in advanced economies. International Monetary Fund.\n\n\nKleiner, M. M., & Krueger, A. B. (2013). Analyzing the Extent and Influence of\nOccupational Licensing on the Labor Market. Journal of Labor Economics, 31(2),\nS173–S202. <u>https://doi.org/10.1086/669060</u>\n\n\n46\n\n\n\nLessem, R., & Sanders, C. (2020). Immigrant wage growth in the United States:\nThe role of occupational upgrading. International Economic Review, 61(2),\n941-972.\n\n\nLewandowski, P., Górny, A., Krząkała, M., & Palczyńska, M. (2025). The Role of Job\nTask Degradation in Shaping Return Intentions: Evidence from Ukrainian War\nRefugees in Poland. IBS working paper, 01/2025. <u>https", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:23:2:0", "start": 82, "end": 120, "surface": "Education in the school year 2023/2024", "probe_tag": "confusion", "probe_score": 0.2256, "luna_label": 0}]}, {"key": "aivin-113", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**Refugee employment rates significantly improved in 2024, moving closer to national levels**\n\n\nUkrainian refugee employment rates <sup>14</sup> [^14: The employment rate is defined as the number of employed or self-employed individuals of working age (15-64) as a share of\nthe total number of people in this age group] have experienced a sizeable increase from 2023 in most countries,\nrising by 9 percentage points year-over-year at the regional level to 64%. This indicator now stands just 7\npercentage points below the equivalently weighted mean for the host population (71%).\n\n\nRefugee employment grew both owing to a decrease in unemployment, which declined to 9% from 13% last\nyear, as well as new individuals coming into the labor force <sup>15</sup> . In fact, the 2024 labor force participation rate\namounted to 70%, which stands almost in line with the equivalent host country indicator of 73%.\n\n\n**<u>EVOLUTION OF THE REFUGEE EMPLOYMENT RATE FROM 2023 TO 2024</u>**\n\n\n70%\n\n\n60%\n\n\n50%\n\n\n40%\n\n\n30%\n\n\n20%\n\n\n10%\n\n\n\n0%\n\n\n\nEmployment\n\nrate 2023\n\n\n\nEmployment\n\nrate 2024\n\n\n\nUnclear\nlabor force\n\nstatus\n\n\n\nDecrease in\nunemployment\n\n\n\nDecrease in\n\npotential\nlabor force\n\n\n\nExpansion of\n\n\n\nforce\n\n\n\nthe labor\n\n\n\nNote: Only includes data from the 7 countries surveyed in both rounds (Bulgaria, Czech Republic, Hungary, Republic of Moldova, Poland, Romania, and\nSlovakia)\n\n\nSource: Survey data, SAG estimates\n\n\n**<u>REFUGEE VS HOST EMPLOYMENT RATES BY COUNTRY</u>**\n\n\nRefugee (2023) Refugee (2024) Host (2023)\n\n\n\n76% 76%\n\n\n\n76%\n\n\n\n\n\n\n\n62%\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Estonia Hungary Latvia Lithuania Moldova Poland Romania Slovakia Region\n\n\nNote: For comparability, employment rates for host countries have been recalculated assuming a similar gender distribution to that of refugees\n\n\nSource: ILO, survey data\n\n\n14. The employment rate is defined", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:9:0:0", "start": 1489, "end": 1500, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9928, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:9:0:1", "start": 1502, "end": 1515, "surface": "SAG estimates", "probe_tag": "keep", "probe_score": 0.9921, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:9:0:2", "start": 1916, "end": 1927, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.8192, "luna_label": 1}]}, {"key": "aivin-114", "text": " the first stage, it\nadjusts for the non-random distribution\nof Ukrainian refugees across poviats,\nand in the second stage, it calculates\nthe models. These results can be\ninterpreted causally. The two previously\nmentioned instruments were used and\nyielded statistically significant results, as\nwell as passed statistical tests on their\nappropriateness for instrumenting the\nemployment share of Ukrainian refugees.\nOne instrument is the share of Ukrainian\nchildren in Polish schools, and the other is\nthe distribution of Ukrainian citizens across\nPoland in 2019, as recorded in declarations\n\n\n\nof intent to employ foreign workers. The\nresults are similar to the OLS estimation;\nhowever, when the second instrument is\nused without the first one, it gives a higher\nresult. Instrumental variables regressions\nshow that in 2023, a 1 percentage point\nhigher employment share of Ukrainian\nrefugees caused a PLN 70 higher wage\ngrowth (0.7 pp.) when instrumented by\nschool pupils' share, PLN 132 (1.4 pp.)\nwhen instrumented by 2019 Ukrainian\nworkers distribution, and PLN 75 (0.8 pp.)\nwhen both instruments were used. That\nsaid, the results should be treated with\ncaution, as the instruments used may not\nbe sufficiently exogenous to allow for fully\ncredible causal inference.\n\n\nThe early analysis by Gromadzki and\nLewandowski (2023), mentioned previously\nalso found a statistically significant effect\nof Ukrainian refugees on earnings. In their\nestimation, the share of the Ukrainian\nrefugee population had a small positive\nand statistically significant relationship with\nthe earnings of Polish women at a 0.1 level.\nTheir results for foreign women were\nalso positive, albeit smaller and lacking\nstatistical significance.\n\n\n\n2021, so the sample size is still growing.\nHowever, it is large enough to allow for\nsome cautious inference with 4.2 million\nPolish citizens in mid-2022 and 6.2 million\nin mid-2024. This has been combined with\naverage salaries in 128 occupational groups\nin GUS data for October 2022 (the most\nrecent data).", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:20:3:0", "start": 1974, "end": 1982, "surface": "GUS data", "probe_tag": "confusion", "probe_score": 0.6318, "luna_label": 1}]}, {"key": "aivin-115", "text": " in large metropolitan areas that offer\nthe most opportunities. Initially, in the first\nmonth after the outbreak of the full-scale\nwar in Ukraine, the number of renting\noffers in the OLX and Otodom portals\ndropped by approximately 60%, though it\nlater returned to previous levels.\n\n\n\n\n\nSince the beginning of the Russian invasion,\nthe number of Ukrainian workers covered by\nsocial security insurance in Poland increased\nby 126 thousand. The true increase\nof insured refugees however is larger.\nBy checking previous insurance status,\nZUS identified 228 thousand newly\nregistered insured persons active on 31st\nMarch 2023. <sup>31</sup> [^31: <u>[Cudzoziemcy w polskim systemie ubezpieczeń społecznych (zus.pl)](https://www.zus.pl/documents/10182/2322024/Cudzoziemcy+w+polskim+systemie+ubezpiecze%C5%84+spo%C5%82ecznych_2022.pdf/)</u> 32  According to NBP (2023), there were few respondents in this situation, and they most likely had secured other sources of income.] Considering the number\nof Ukrainians with active PESEL UKR at\nthe time, this would yield an employment\nrate of 43%. As this number does not take\ninto account jobs not covered by social\ninsurance and informal work, this can\nalign with the over 60% employment rate\nnoted in the surveys. This number does\nnot take into account over 500 thousand\nUkrainians that were paying social security\ncontributions before the beginning of the\nfull-scale war and remained in Poland after\nits outbreak.\n\n\nThe refugees present in Poland, while\nnot without difficulties, are largely able\nto provide for themselves and their\nfamilies. As reported in the Deloitte\n\n\n\nUkraine Refugee Pulse, which is based on\na survey carried out between October\nand December 2022, 40% of respondents\nhave enough income to meet basic needs\nor are able to support the same lifestyle\nthey had in Ukraine, while 60% say they do\nnot have enough income or have to rely on\nsavings and welfare. At the", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:14:1:0", "start": 1601, "end": 1634, "surface": "Deloitte\n\n\n\nUkraine Refugee Pulse", "probe_tag": "confusion", "probe_score": 0.3801, "luna_label": 1}]}, {"key": "aivin-116", "text": " is more uncertain, resulting in the\n\n\n\nneed to finance their consumption using\ncapital generated outside the country e.g.\nsavings from their home countries.\n\n\nHigh employment rate of Ukrainian\nrefugees translates into more workers and\nthus additional economic growth. In July\n2022, OECD estimated the contribution of\nUkrainian refugees to the labour force and\nemployment in European host countries,\nbased on a 2014 Labour Force Survey adhoc module that includes refugee labour\nmarket outcomes and 2019 LFS with\noutcomes of recent non-EU migrants.\nThey estimated that Ukrainian refugees\nwould increase employment in Poland by\n1.2-1.8% (Dumont & Lauren, 2022).\nAs refugee employment rates are higher\nthan expected, the actual increase is higher,\nbetween 1.4% and 2.2% (estimates of 0,230,35 million relative to LFS employment).\n\n\n##### To assess the impact of refugees on the Polish economy, a simulation was performed using the Deloitte D.Climate model 33,34 .\n\nIt is a general equilibrium model using consumer and producer optimalisation to\ncalculate changes in the economy in response to shocks. This allows to assess the\nimpact of shocks considering supply and demand channels as well as connections\nbetween different sectors of economy. This provides information about their total\nimpact on many aspects of the economy including the labour market, government\nrevenue as well as main economic aggregates. It is deemed the most appropriate\ntool to account for refugees’ multi-layered impact. To account for the effects of other\nshocks happening in the economy (such as the energy crisis) counterfactual analysis\nwas performed using the newest data for the Polish economy. In other words, we\nwere able to isolate the refugee inflow from all other shocks in the economy, like\nthe other macroeconomic consequences of the war in Ukraine. The results were\ncalculated for 2022 and 2023 with additional long-term analysis to check how the\neconomy would adapt assuming no new shocks including no counter-shocks <sup>35<", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:15:2:0", "start": 411, "end": 435, "surface": "2014 Labour Force Survey", "probe_tag": "confusion", "probe_score": 0.6603, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:15:2:1", "start": 498, "end": 506, "surface": "2019 LFS", "probe_tag": "confusion", "probe_score": 0.7001, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:15:2:2", "start": 1638, "end": 1672, "surface": "newest data for the Polish economy", "probe_tag": "confusion", "probe_score": 0.8279, "luna_label": 1}]}, {"key": "aivin-117", "text": " disease prevention, detection\nand treatment programmes, increasing the health\nrisks.\n\n\n\nMental health is a significant concern, with\nparticularly high needs in populations fleeing\nconflict. WHO estimates that one in five people\naffected by conflict (22.1%) have a mental health\ncondition, including 13.0% with mild, 4.0% with\nmoderate, and 5.1% with severe conditions <sup>4</sup> .\nRefugees may have experienced conflict-related\nviolence and other adversities prior to displacement\nand likely face current stressors associated with\nadapting to new environments, languages, and\ncultures, economic and housing instability, limited\nsocial support networks and uncertainties about\ntheir future and those left behind in Ukraine. These\nfactors may contribute to mental health and\npsychosocial problems that can persist for many\nyears if left unaddressed.\n\n\nRefugee receiving and hosting countries have\nshown generosity and kept borders open while\nadapting to a protracted stay of the refugees as the\nsituation in Ukraine remains volatile and prospects\nfor refugees returning home remain grim. Under\nthe Temporary Protection Directive or other\nprotection mechanisms implemented in most\ncountries, refugees have access to health care\nbenefits, but certain groups may be left out\naccording to country-specific practices – for\nexample, those who do not apply for temporary\nprotection and those who temporarily return to\nUkraine or move from one location to another and\nare deregistered <sup>5</sup> .\n\n\nHealth systems in refugee-receiving countries have\nmade substantial efforts to meet the needs of\nUkrainian refugees. However, challenges in host\ncountry health systems, such as health workforce\nshortages, long waiting lists, and language barriers\naffect access to care and may discourage healthseeking among refugees. Refugees’ movement\nwithin and out of countries present challenges for\nlocal health providers to register and provide\nrequired services. At the same time, there are\n\n\n\n1. <u>[Ukraine Refugee Situation ( accessed 12 Dec 2024)](https://data.unhcr.org/en/situations", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "jad_paddy_docs:000004:4:1:0", "start": 1987, "end": 2012, "surface": "Ukraine Refugee Situation", "probe_tag": "confusion", "probe_score": 0.8274, "luna_label": 0}]}, {"key": "aivin-118", "text": ".\nTo maximise the positive impact\nof refugees on economy, policies\nhelping their integration into the\nlabour market that both allow\ntheir maximal employment as\nwell minimise market mismatch\nbetween demand for specific\nskills and their abilities are\ncrucial. The second part of this\nrecommendation, achieved\neither through improvements\nin utilisation of skills of refugees\nor trainings giving them abilities\ndemanded by the labour market,\nis integral as it should lower\ncosts for the local labour force.\n\n\n\n45  Based on number of economically active people in III quarter of 2023 according to labour market survey.\n46  E.g. vice-president of Polish Development Found Bartosz Marczuk estimated it at around 16 billion PLN, but this estimation also included spending of NGOs\nwhich was combined with spendings of local governments Polska pomoc dla Ukrainy 2022 - ile kosztowała? - Infor.pl.\n47  Excluding spending of private households estimated as further 10 billion PLN.\n48  Model treats general government sector as a whole, as such cost and income internal structure may differ creating institutions with financial loses while\nother may have disproportionate increase of income.", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:18:2:0", "start": 592, "end": 612, "surface": "labour market survey", "probe_tag": "confusion", "probe_score": 0.7997, "luna_label": 1}]}, {"key": "aivin-119", "text": " Poles. Visible\n\n\n\ndifferences are identified in 15-19 and\n20-24 age groups, with much higher\nemployment rates for Ukrainian refugees\ndue perhaps to the fact that the Ukrainian\nschool system ends at 17 while the Polish\none at 19. <sup>13</sup> [^13: Children in Ukraine start school at 6 years of age, while in Poland at 7. School system lasts for 11 years in Ukraine and 12 years in Poland. After recent reform,\nUkrainian children who started school in or after 2018 will receive 12 years of schooling.] However, employment rates\ndrop for Ukrainian female refugees in the\n25-29, 30-34, and 35-39 age groups when\ncompared to younger and older groups, as\nwell as to Polish women, which may be due\nto insufficient access to childcare services.\nEmployment rates for Ukrainian female\nrefugees are also substantially lower in the\n55-59 age group, perhaps due to the fact\nthat female retirement age in Ukraine has\nonly recently (in 2021) become 60. <sup>14</sup> [^14: <u>https://www.social-protection.org/gimi/gess/Media.action;jsessionid=U1dm6XAPF38pPk5_pzhfaGoS_rOACMDDVv4w5uevMsKBeQEC5-_g!284293951?id=15680</u> 15 Percentage calculated based on GUS average for the economy as a whole in the months of the SEIS survey.]\n\n\n\n**With the current level of integration**\n**of Ukrainian refugees into the labour**\n**market and the jobs they perform,**\n**their median net earnings are about**\n**four-fifths of the national median**\n\n**– although likely lower in terms of**\n**average or gross earnings.** The median\nnet earnings of Ukrainian refugees in\nQ2 2024 were PLN 4,000 in SEIS, and\nPLN 3,767 in NBP (2024) surveys. This\nis 84% and 79% of the national median,\nrespectively. This estimate would be\nmost likely lower, if data allowed us to\nlook at", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:9:1:0", "start": 1204, "end": 1215, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.9934, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:9:1:1", "start": 1592, "end": 1610, "surface": "NBP (2024) surveys", "probe_tag": "confusion", "probe_score": 0.6828, "luna_label": 1}]}, {"key": "aivin-120", "text": " additionally a positive\nimpact on labour productivity that cannot\nbe accounted for using the available data.\nThese effects could stem from the growth\nin specialisation due to additional workers\nwith different skillsets appearing on the\nlabour market, e.g., occupational upgrading\nof native workers.\n\n\n\n**Immigrants could possess complementary skills that**\n**make native workers more productive as they specialise.**\nThese skills do not even need to be advanced to be\ncomplementary. Natives are likely to deal better with\ncommunications-intensive tasks and have better networking, all\nof which may be better paid and not easily transferable between\ncountries. OECD (2016) gives an example of a native carpenter,\nwho employs an immigrant to do his previous manual tasks\nand himself focuses on marketing and business development.\nSuch occupational upgrading has been first shown in a seminal\npaper by Peri and Sparber (2009) in the USA data, but has been\nquickly extended to other countries. From this perspective\nFoged and Peri (2016) look at refugees in Denmark in the 19912008 period. They find that inflow of low-skill refugees caused\nless educated native workers to pursue less manual-intensive\ntasks - improving their wages, employment, and occupational\nmobility. These effects are causal, as the authors exploit the\nrefugee dispersal system, which is orthogonal to economic\nopportunities.\n\n\n**Immigrants could enter childcare, elderly care, and**\n**housekeeping services, allowing highly educated and**\n**productive native women to increase their labour supply.**\nA caveat in the case of refugees from Ukraine is that this could\nmean working below their qualifications or in the informal\nsector, making the overall effect for productivity unclear.\nNevertheless, as availability of care and housework services\nincreases, it becomes easier for women mainly to combine\nfamily and professional lives and increase labour supply.\nSuch effects may be stronger for countries with less accessible\nchildcare. Furtado (2015) reviews this literature, finding evidence\nfrom Australia, Hong", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:20:1:0", "start": 931, "end": 939, "surface": "USA data", "probe_tag": "confusion", "probe_score": 0.8086, "luna_label": 1}]}, {"key": "aivin-121", "text": " frames and\ninformation regarding population distribution by\n\n\n18 With both adult males and females present\n\n19 Also defined here for the 15+ age group\n\n\n**10**\n\n\n\ngeographic area and accommodation type. The\nobjective was to ensure a diverse sample\nrepresentative of the population’s composition. A\ncombination of different sampling methods was\nused, typically incorporating multiple stages and\nblending convenience sampling, cluster random\nsampling, and simple random sampling (the latter\nbeing exclusive to Romania).\n\n\nFor the regional analysis, population weights were\napplied based on the most up-to-date figures\nregarding the number of individual refugees\nrecorded in each country. This ensured the analysis\nmore accurately represented the broader refugee\npopulation across the region.\n\n\nAppropriate measures were implemented to ensure\nthe protection of personal data and guarantee\nconfidentiality in all data collection and processing\nactivities. Consent was requested and recorded for\nall selected participants, providing clear information\non the purpose, and expected use of the data.\n\n\nThe poverty line for each country was defined as\n50% of the median <u>[equivalized disposable income](https://www.oecd.org/els/soc/OECD-Note-EquivalenceScales.pdf)</u>\nas reported by the <u>[OECD (new definition since](https://stats.oecd.org/)</u>\n2012) for 2021. This figure was indexed towards\n2023 using national consumer price indexes (CPI).\nOn the survey data side, only households that did\nnot have any missing information on income\n(respondents were asked to provide both sources\nand amounts) were included in the calculation\n(households that reported having no income also\ndid not pass the filter). In addition, for households\nthat reported total expenditure above total income,\nexpenditure data was used instead, as it was\ndeemed to be more reliable. Following the OECD\nmethodology for equivalization, the resulting\ndisposable income figure for each household was\ndivided by the", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000000:9:1:0", "start": 1402, "end": 1433, "surface": "national consumer price indexes", "probe_tag": "confusion", "probe_score": 0.5394, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000000:9:1:1", "start": 1782, "end": 1798, "surface": "expenditure data", "probe_tag": "confusion", "probe_score": 0.1167, "luna_label": 1}]}, {"key": "aivin-122", "text": ", it has\ndeclined by 2.6 million. This trend is set to\ncontinue. According to the latest Eurostat\nprojections, without migration, the\npopulation (counting all nationalities) aged\n20-64 years would decrease by 4.8 million\nby 2050. <sup>20</sup> [^20: Eurostat data, <u>[https://ec.europa.eu/eurostat/databrowser/view/proj_23np__custom_8710248/bookmark/table?lang=en&bookmarkId=97472dd3-3dd2-](https://ec.europa.eu/eurostat/databrowser/view/proj_23np__custom_8710248/bookmark/table?lang=en&bookmarkId=97472dd3-3dd2-4aca-a51f-15c98be37466)</u>\n**Source:** Eurostat <u>[Statistics | Eurostat (europa.eu)](https://ec.europa.eu/eurostat/databrowser/view/lfsq_pganws__custom_8677828/bookmark/table?lang=en&bookmarkId=06047812-2bde-4cf8-a627-7d63d65f17db)</u> <u>[4aca-a51f-15c98be37466](https://ec.europa.eu/eurostat/databrowser/view/proj_23np__custom_8710248/bookmark/table?lang=en&bookmarkId=97472dd3-3dd2-4aca-a51f-15c98be37466)</u>]\n\n\n© UNHCR / Anna Liminowicz\n\n\n\n19  Statistics Poland data, <u>[https://stat.gov.pl/en/topics/population/internationa-migration/information-on-the-size-and-directions-of-emigration-for-](https://stat.gov.pl/en/topics/population/internationa-migration/information-on-the-size-and-directions-of-emigration-for-temporary-stay-from-poland-between-2004-2020,8,14.html)</u>\n<u>[temporary-stay-from-poland-between-2004-2020,8,14.html](https://stat.gov.pl/en/topics/population/internationa-migration/information-on-the-size-and-directions-of-emigration-for-temporary-stay-from-poland-between-2004-2020,8,14.html)</u>\n20  Eurostat data, <u>[https://ec.europa.eu/eurostat/databrowser/view/proj_23np__custom_8710248/bookmark/table?lang=en&bookmarkId=97472dd3-3dd2-](https://ec.", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:8:1:0", "start": 250, "end": 263, "surface": "Eurostat data", "probe_tag": "keep", "probe_score": 0.9976, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:8:1:1", "start": 965, "end": 987, "surface": "Statistics Poland data", "probe_tag": "confusion", "probe_score": 0.7274, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:8:1:2", "start": 1542, "end": 1555, "surface": "Eurostat data", "probe_tag": "confusion", "probe_score": 0.7274, "luna_label": 1}]}, {"key": "aivin-123", "text": " overall.\nThese connections are important to bear in mind when designing government, development, and humanitarian\nsupport programs.\n\n\nEmployment continues to be closely associated with lower poverty rates, though ultimately, it’s the size of\nincome that is generated by working household members that makes the biggest difference. Considering that\nthe share of working-age refugees that are employed is nearing host population levels after rising further in\n2024, attention should now turn to wages. Data on the latter, which was derived from household level\nindicators, demonstrates that refugees on average make two-thirds of what the local population does per hour\nof work. Low wage premiums for higher education levels and the fact that some 60% of current refugee\nemployees have a background in an entirely different sector of the economy, suggest the presence of\nunderemployment and skills mismatching. This assertion is corroborated by nearly 35% of employed refugees\nin the region reporting few available jobs with adequate pay, lack of positions that match their skills, or issues\nwith getting their qualifications recognized. Moreover, almost the same percentage indicate lack of local\nlanguage knowledge to be a problem, a well-acknowledged barrier to skilled employment.\n\n\n**Based on the above findings it is recommended that:**\n\n\n- Governments, development and humanitarian actors take into account poverty levels when designing their\nsupport programs for Ukrainian refugees. The quality of day to day life, safety, and level of access to key\nservices are directly tied to household income.\n\n- Poverty measures account for differences in housing costs between refugees and host populations.\n\n- A special focus is placed on supporting refugee employment at their skill level, including transition from\ncurrent low-level jobs. The difference between refugee and local population wages could be an important\nmetric to monitor on an ongoing basis\n\n\n1. Compared to the <u>[2023 MSNA data](https://data.unhcr.org/en/documents/details/108068)</u>\n2.", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000010:2:1:0", "start": 544, "end": 570, "surface": "household level\nindicators", "probe_tag": "confusion", "probe_score": 0.1331, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:2:1:1", "start": 1982, "end": 1996, "surface": "2023 MSNA data", "probe_tag": "confusion", "probe_score": 0.6904, "luna_label": 1}]}, {"key": "aivin-124", "text": "D.Climate** general equilibrium modelling\nstrategy compared to the previous Deloitte\n(2024) report, accounting for the impact\nof Ukrainian refugees on productivity.\nOver the past two decades, research by\nacademic economists has shown that\nviewing immigration solely through the lens\nof supply and demand severely constrains\nour understanding of the process\n(Peri, 2016). Such a model suggests that\nimmigration increases economic output\n\n\n36\n\n\n\nbut has negative labour market effects due\nto increased competition for jobs among\nworkers. However, little of these theoretical\nnegative labour market effects can be seen\nin empirical data. This is because immigrant\nworkers encourage further specialisation\namong native workers and firms, which\nincreases productivity and offsets the\nnegative effects. The same effects can\nbe expected in the case of refugees\nentering the Polish labour market – besides\nincreasing labour supply and competing\nwith Polish workers, they provide new skills,\nideas, and allow Polish workers to specialize\nin higher value-added tasks. These effects\nwere included in the estimates of the\nimpact of Ukrainian refugees on Polish\nGDP by Monitor Deloitte (2022) and Oxford\nEconomics (2022), but these estimates\nwere based on literature rather than Polish\nempirical data. In Deloitte’s 2024 study,\nthese effects were omitted due to a lack\nof empirical data with which to calibrate\nthem specifically. This study uses available\ndata, albeit limited, to create a conservative\nscenario that includes positive impacts on\nproductivity.", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:18:3:0", "start": 619, "end": 633, "surface": "empirical data", "probe_tag": "keep", "probe_score": 0.9246, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:18:3:1", "start": 1266, "end": 1287, "surface": "Polish\nempirical data", "probe_tag": "confusion", "probe_score": 0.7406, "luna_label": 1}]}, {"key": "aivin-125", "text": " 15,902\n\n\n**7. Szczecin** 14,318\n\n\n**8. Poznanski** 14,028\n\n\n**9. Pruszkowski** 12,206\n\n\n\n\n\n\n\n18-59 female and male\n\n\n\n\n\n60+ female and male\n\ntwo 18-59 or 60+ females\n\nother households\n\n\n\n\n\n\n\n18-59 female with one or\nmore 60+ individuals\n\n\n\ntwo 18-59\n\nfemales\n\n\n\n0.3% 7.4%\n\n\n\n\n\n**10. Katowice** 11,591\n\n\n\nNote that poviat-level population does not include Ukrainian refugees, who were added to calculate their appropriate shares.\n\nSource: Deloitte own elaboration based on the PESEL database as of September 2024 and GUS population data as of mid-2024.\n\n#### **1.3 Households income sources**\n\n\n\n18-59 female\nand male\n\n\nother\nhouseholds\n\n\n\n\n\nNote: Some groups that constituted less than 2% of households have been omitted.\n\nSource: Deloitte own elaboration based on SEIS (May-June 2024) UNHCR (2024) survey.\n\n\n\n**The majority of refugees settled in**\n**major cities, especially Warsaw and**\n**Wroclaw, and their vicinities.** Eight most\npopulous poviats in Poland <sup>5</sup> comprising\n16% of the host population, are also the\ntop eight poviats in terms of the number\nof Ukrainian refugees, 29% of whom\nreside there. This shows that Ukrainian\nrefugees are more concentrated in the\nlargest cities than the host population,\nas they migrated to the most attractive\n\n\n5 Seven cities and Poznański poviat.\n\n\n10\n\n\n\n11\n\n\n\nlabour markets, where they have better\nchances of supporting themselves.\nUkrainian refugees make up the largest\nshares of the population in the city of\nWroclaw (7.4%), Przemysl, a city on the\nUkrainian border (6.5%), and in Pruszkowski\npoviat", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:5:1:0", "start": 477, "end": 491, "surface": "PESEL database", "probe_tag": "keep", "probe_score": 0.9914, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:5:1:1", "start": 517, "end": 536, "surface": "GUS population data", "probe_tag": "keep", "probe_score": 0.9517, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:5:1:2", "start": 766, "end": 770, "surface": "SEIS", "probe_tag": "confusion", "probe_score": 0.4326, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:5:1:3", "start": 787, "end": 806, "surface": "UNHCR (2024) survey", "probe_tag": "keep", "probe_score": 0.9423, "luna_label": 1}]}, {"key": "aivin-126", "text": " immigrant inflow comprised less educated workers.\n\n\n39\n\n\n\nMacroeconomic modelling generally finds\nthat immigration increases output, while\ncross-country econometric estimates\nfind also positive impacts on labour\nproductivity. The first approach builds\na theoretical model of the economy\ncalibrated to the particular circumstances,\nwhich allows us to simulate counterfactuals\nand observe all changes in the economy.\nThe second approach relies on empirical\ndata, usually over many years and\ncountries, trying to isolate the effect of\nimmigration, but gives no information on\nthe channels through which these effects\noperate. In the present case it would not\nbe practical to follow anything else than the\nfirst approach. Unfortunately, it relies on\nthe canonical labour market model.\nAs Peri (2014) elaborates, that model\nassumes that immigration is simply a\nshift in the labour supply for a given\nlabour demand and given labour supply\nof native workers. It further assumes\nthat immigrants are essentially identical\nto natives in that they enter the same\n\n\n38\n\n\n\noccupations and perform the same tasks,\nthe native workers do not change their\noccupations and tasks, while firms do\nnot adjust (at least in the short term).\nIn effect, immigrants grow output, but\nslightly lower wages. This is the case in\nthe modelling performed (Chapter 3), as\nwell as in the NBP general equilibrium\nmodelling exercise for the pre-2022\nUkrainian workers 2013-2018 performed\nby Gradzewicz, Jabłonowski, Sasiela, and\nŻółkiewski (2021). Conversely, Peri (2014)\nsurveys 270 econometric estimates from\n27 studies published over the 1982-2013\nperiod on the impact of immigration\non native wages. He shows that effects\nfor a very large immigrant inflow of 10\npercentage points (five-times larger than\nthe refugees from Ukraine share in Poland)\non native wages range from -1% to +1%,\nwith most estimates clustered around zero,\nfrom -0.1% to +0.1%. Similarly, Gromadzki\nand Lewandowski (2023) examine\neconometrically the", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000007:19:2:0", "start": 446, "end": 460, "surface": "empirical\ndata", "probe_tag": "drop", "probe_score": 0.0095, "luna_label": 0}]}, {"key": "aivin-127", "text": " among\nproductive-age individuals, with women\n18-65 making up 40% and men only 18% of\nUkrainian refugee population. This is largely\ndue to the conscription of military aged\nmen in Ukraine.\n\n\n\n66+\n\n\n56-65\n\n\n46-55\n\n\n36-45\n\n\n26-35\n\n\n18-25\n\n\n<18\n\n\n\nsocial insurance. Second, the statistical\ndefinition of an employed person is wider\nthan just being registered with the Social\nInsurance Institution (Zakład Ubezpieczeń\nSpołecznych, ZUS) on a certain day <sup>3</sup> [^3: Employed person is a person, who during the reference week worked for at least 1 hour for pay or profit, including contributing family workers; had a certain job\nattachment; or produced agricultural goods for sale or barter. A definition according to the Labour Force Survey: <u>https://ec.europa.eu/eurostat/statistics-explained/</u>\n<u>index.php?title=Glossary:Employed_person_-_LFS</u>] . Third,\nsome Ukrainians work in the shadow\neconomy. The number of workers with\nUkrainian citizenship and social insurance\nincreased from only 33 thousand at\nthe end of 2013 to 627 thousand at the\nend of 2021 – just before the refugee\ninflux. Some of this increase reflects\nthe transitioning of Ukrainians to more\nregular work arrangements and securing\nwork permits. Following February 2022,\n\n\n\nthe numbers further increased with the\narrival of Ukrainian refugees. The data for\nmid-2024 shows 771 thousand workers\nwith Ukrainian citizenship registered for\nsocial insurance, including 247 thousand\nrefugees. Ukrainian refugees in Poland\nhave been given PESEL UKR numbers\n\n- a version of an ID number assigned to\nevery Polish national – which made high\nquality data on their demographics easily\navailable, though they may still be missing\nfrom most of the statistics compiled by the\nPolish statistical office.\n\n\n\n20% 19%\n\n\nMen Women\n\n\n\n1%\n\n\n1%\n\n\n2%\n\n\n\n3%\n\n\n4%\n\n\n\n7%\n\n\n8%\n\n\n\n4%\n\n\n4%\n\n\n\n9%\n\n\n\n12%\n\n\n\n7%\n\n\n#### **1.2 Households characteristics**\n\n\n\n**The structure of the Ukrainian**", "source": "jad_paddy_docs", "subset": "annotate_aivin", "spans": [{"key": "sample:jad_paddy_docs:000001:4:1:0", "start": 722, "end": 741, "surface": "Labour Force Survey", "probe_tag": "keep", "probe_score": 0.9286, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:4:1:1", "start": 1326, "end": 1343, "surface": "data for\nmid-2024", "probe_tag": "drop", "probe_score": 0.0039, "luna_label": 0}]}, {"key": "aivin-128", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\nprofessional artists.\n\n\n**47.** The model results show that the project benefit-cost ratio (BCR) is expected to exceed the poverty rate, i.e., the\nmost natural alternative to compare with a uniform (random) cash distribution. <sup>49</sup> According to this model, the BCR is\n0.92. This outperforms by a factor of 3.7 the threshold of 25%, i.e., the poverty rate in Lebanon <sup>50</sup> . The proposed\napproach is therefore expected to reach vulnerable households more effectively, compared to a random distribution\nof cash to the poor.\n\n\n**48.** **Cultural and creative industries are key drivers of the creative economy and represent important sources of**\n**employment, economic growth, and innovation, thus contributing to city competitiveness and sustainability.** <sup>51</sup>\nAccording to the Cities, Culture and Creativity report, in 2013 the revenues of cultural and creative industries were\nestimated to be around $2.25 trillion, i.e., three percent of global GDP. <sup>52</sup> In 2015, it was estimated that CCI in Lebanon\ncontributed almost five percent to Lebanon’s GDP and 4.5 percent to national employment, with an average annual\ngrowth rate of over eight percent. <sup>53</sup> Approximately 20 percent of the economically active population (362,000 people)\nare estimated to be working in the sector, including both formal and informal professionals. <sup>54</sup> Many CCIs also generate\na large number of non-creative jobs. A preliminary calculation using UNESCO data suggests that overall for every\ncreative job in a CCI, 1.", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000012:26:0:0", "start": 1585, "end": 1596, "surface": "UNESCO data", "probe_tag": "keep", "probe_score": 0.9511, "luna_label": 1}]}, {"key": "aivin-129", "text": " project will build a foundation for\n\n\n54 FATHOM Flooding Data [Fluvial]. The World Bank Group 2021.\n55 Hirabayashi, Y., R. Mahendran, S. Koirala, L. Konoshima, D. Yamazaki, S. Watanabe, H. Kim, and S. Kanae. 2013. “Global Flood\nRisk under Climate Change.” _Nature Climate Change_ 3: 816–821.\n56 Decadal values represent the average of the annual seven-day maximum average temperature for each year within the\ncorresponding decade. The 2030 decade represents the average value for 2030–39, for example. Values are the increase, in\ndegrees Celsius, above the 30-year historical baseline (calculated as the average of annual seven-day maximum temperatures\nfrom 1970 to 1999). Calculations completed by the authors, based on data from NASA Center for Climate Simulation, NASA\nNEX-GDDP (2019).\n57 Skoplaki, E., and J. A. Palyvos. 2009. “On the Temperature Dependence of Photovoltaic Module Electrical Performance: A\nReview of Efficiency/Power Correlation.” _Solar Energy_ 83 (5): 614–624.\n\n\nPage 75 of 87", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000051:80:2:0", "start": 722, "end": 766, "surface": "data from NASA Center for Climate Simulation", "probe_tag": "keep", "probe_score": 0.913, "luna_label": 1}, {"key": "jdc_operational:000051:80:2:1", "start": 768, "end": 781, "surface": "NASA\nNEX-GDDP", "probe_tag": "keep", "probe_score": 0.948, "luna_label": 1}]}, {"key": "aivin-130", "text": "529 refugees within the region (Arua district is about 50\nkm from Koboko and connected to South Sudan by the KYM road, Adjumani district is about 30 km from\nMoyo and connected to DRC border at Oraba by KYM road). It is a gravel road with poor geometry in a few\nplaces; passes through low-lying areas and seasonal swamps; and constrained to provide all-weather\nconnectivity. It has narrow, damaged and non-functional cross-drainage structures and the transport\nsituation worsens during rainy season when some of the rivers and their tributaries flood and cut off access.\nThis road corridor connects DRC and South Sudan through Uganda and is an alternate route to reach the\n\n\n32 UNRA Diagnostic Study funded by the World Bank – DFID multi-donor trust fund\n33 The host and refugee population of Koboko district are 258,000 and 5,423 (2 percent of the host population) respectively, the host\nand refugee population of Yumbe district are 663,600 and 232,109 (35 percent of host population) respectively, and the host and\nrefugee population of Obongi district, which is a new district carved out of Moyo district, are 48,300 and 122,645 (254 percent of\n[host population) respectively – Source: https://data2.unhcr.org/en/country/uga](https://data2.unhcr.org/en/country/uga)\n34 Bidibidi resettlement concentration Zone 2, Lobule and Palorinya settlements are about 9 km, 6 km and 18 km away respectively\nfrom the KYM road and there are access roads to reach the settlements from the KYM road corridor.\n[35 https://data2.unhcr.org/en/documents/download/74603](https://data2.unhcr.org/en/documents/download/746", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000050:18:2:0", "start": 677, "end": 698, "surface": "UNRA Diagnostic Study", "probe_tag": "keep", "probe_score": 0.9168, "luna_label": 1}]}, {"key": "aivin-131", "text": " Sahelian\naverage. <sup>29</sup> [^29: The average for Burkina Faso, Chad, Mali, Mauritania and Niger. Data from World Bank Databank – World Development Indicators.] Education expenditures are fragile and susceptible to shocks. In 2015-16, further to the collapse in petrol\nprices collapsed, roughly one quarter of all primary schools closed; <sup>30</sup> [^30: Projet de Renforcement de l’Éducation et de l’Alphabétisation au Tchad (PREAT), Avril 2019, p. 12.] and by 2017 the primary GER had plummeted to 79\npercent, wiping out the gains of the previous ten years. <sup>31</sup> [^31: World Bank Databank – World Development Indicators.] Salaries constitute roughly 90 percent of expenditures; and\ncontributions and grants about 7 percent. Only 3 percent is spent on services, goods and materials, and investments.\n\n\n20. **The mechanisms used to distribute resources face challenges.** In the case of salaries, the reach of the banking\nnetwork is such that many teachers must travel substantial (and costly) distances to get paid, while banks themselves\nface hazardous conditions in setting up decentralized pay-points. Development partners that finance community teacher\nsalaries have established mobile money systems, and there may be room for their more widespread use in areas where\nthe banking network is poorly developed. With respect to funds for school operating expenses, neither the banking\nsystem nor mobile money systems are used. Rather, the relevant budget line is disbursed to a local education office,\nwhich should then procure items (such as chalk) and distribute them physically to the schools. In the event that schools\nremain under-supplied, they must call upon the community for support.\n\n\n21. **Rapid demographic growth makes it difficult for the education system to keep up** . The average annual\npopulation growth rate is 3.3 percent, and represents an enormous challenge to the education sector. The size", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000009:12:1:0", "start": 113, "end": 132, "surface": "World Bank Databank", "probe_tag": "keep", "probe_score": 0.9783, "luna_label": 1}]}, {"key": "aivin-132", "text": " access to credit, learns about new market opportunities,\nand acquires the skills needed to successfully operate their businesses. <sup>22</sup> Studies of women entrepreneurs in Uganda find\nthat women who work closely with a mentor—often male, and usually a family member—are more likely to transition\ninto higher-profit sectors. <sup>23</sup> <sup>24</sup>\n\n15. **Additional factors that block women from developing growth-oriented enterprises in profitable sectors are**\n**related to the failure of existing business development services to address the needs of women-owned firms** .\nAccording to an enterprise survey conducted in 2014, MSMEs lacked key skills needed for business growth. Only 28\npercent of firms surveyed said they do book-keeping to track revenues and expenses; a mere 10 percent had invested in\ntraining for employees; and just 36 percent had access to the internet. Female-owned firms appear to be particularly\nlacking when it comes to the use of standard business practices. A recent microenterprise survey showed a gender gap\nof 24 percentage points on an index of adoption of good business practices. Few training courses address the specific\nchallenges of formalizing a business, including meeting tax obligations, preparing proper records, fulfilling reporting\nrequirements, and obtaining licenses. Training tends to focus on limited topics, such as financial or computer literacy,\nbut leaves out training in life skills and support for network. Yet, global evidence demonstrates that developing socioemotional skills, through psychology-based trainings, are as important to enterprise success as strengthening business\nskills. <sup>25</sup> Finally, many business development services continue to train women for sectors where women-owned firms are\nover-represented, such as small trade or food service, rather than where they could diversify their business and earn\nhigher profits.\n\n\n19 Delecourt, S. and Fitzpatrick, A. 2021.", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000025:14:1:0", "start": 603, "end": 620, "surface": "enterprise survey", "probe_tag": "keep", "probe_score": 0.9434, "luna_label": 1}, {"key": "jdc_operational:000025:14:1:1", "start": 1009, "end": 1031, "surface": "microenterprise survey", "probe_tag": "keep", "probe_score": 0.9147, "luna_label": 1}]}, {"key": "aivin-133", "text": "x|x|x|x|x|x|\n|Local (“host”) population|||x|x|x||x|\n\n\n**C. Project Components**\n\n**_Component A: Targeted Food Assistance (US$7 million IDA_** )\n\n7. Under this component, WFP provides a safety net to 31,200 refugees/returnees who\nreceive support for 12 months following their return. Safety nets are notoriously difficult to\ninitiate in crisis situations, and given its current operations in Chad, WFP is well placed to scale\nup an existing safety net that provides both vouchers (for people to purchase food) and direct\nfood transfers (consisting of basic food items as well as specialized foods for children). Targeted\nfood assistance will improve the food security of refugees and returnees and help them\nreestablish livelihoods by preventing them from having to sell their remaining productive assets.\nVouchers will be provided for eight months of the year; in the other four months (the lean\nseason), beneficiaries will receive direct transfers of food. The vouchers cover the cost of a local\nstandard food basket (currently about US$0.30 per day).\n\n8. Lists of individuals who will receive food assistance will be drawn up based on data on\nrefugees and returnees provided by agencies such as IOM, which facilitate the movement of\npeople from CAR to Chad. Informal returnees are identified locally and verified by WFP staff as\n\n\n25", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000007:34:2:0", "start": 1138, "end": 1168, "surface": "data on\nrefugees and returnees", "probe_tag": "keep", "probe_score": 0.9682, "luna_label": 1}]}, {"key": "aivin-134", "text": "investments include roads, solid waste management, street lighting, greenery, servicing industrial land and\ntourist sites, parks for micro-enterprises and cottage industries, youth centers and incubators, etc.\nCompliance with the investment menu will be a minimum condition and be verified by the APA each\nProgram year. If a municipal LG has not invested Program funds in full compliance with the investment\nmenu, it will be sanctioned in the following year. Municipal LGs will be required to identify and prepare\ninvestments in a participatory manner and use the screening tools developed under USMID and the new\nDDEG guidelines. Participatory approaches and proper planning and budgeting will be promoted through\nthe annual assessments, and municipal LGs will be incentivized to involve the divisions, the municipal\ndevelopment forum as well as the private sector in the prior dialogue on planning and budgeting as well as\nmonitoring of execution.\n\n10. **Institutional strengthening for infrastructure provision will continue in USMID AF, targeting**\n**both the participating municipalities as well as MoLHUD and MDAs** . Firstly, the participating\nmunicipalities will receive grants to strengthen their capacities to execute their mandates for delivering a\nwide range of urban infrastructure and services. Each municipal LG will be required to develop a\ncomprehensive institutional strengthening plan that will respond to its capacity gaps especially those that\nwill be unearthed by the annual assessments under the Program and to ensure that the plan is executed on\neligible expenditures. The activities will continue to focus on: tooling, discretionary capacity building\n/institutional strengthening, and career development. US$ 10 million will be provided as Municipal\nInstitutional Strengthening Grant (ISG) as part of the overall DDEG allocation. Secondly, MoLHUD, which\nis responsible for the oversight of Program implementation as well as other MDAs, will also receive support\nto perform their mandate for urban development as well as for providing supply driven institutional support\nto the municipalities for activities which can be pooled together for economies of scale and that cut across\nall municipalities", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000006:55:0:0", "start": 719, "end": 737, "surface": "annual assessments", "probe_tag": "confusion", "probe_score": 0.2727, "luna_label": 0}]}, {"key": "aivin-135", "text": " than that for male beneficiaries. Women will be targeted for inclusion in planning\ncommittees, where relevant, and they will be incentivized to participate in and use all projectfunded services and activities.\n\n116. **The project will also support women’s employment in the energy sector.** Employment\nopportunities will be created for female technicians and engineers in O&M activities. Limited sexdisaggregated data are available, but <mark>global data indicate that women are underrepresented in both</mark>\n<mark>technical and nontechnical roles and that the sector as a whole is male dominated. Field</mark> evidence further\nreports a near-complete lack of women in similar operations in Chad, which explains the baseline to be\nassessed as zero. The key barriers for accessing these jobs were assessed to stem primarily from lack of\nskills/education and social norms/lack of targeted recruitment. Therefore, private O&M companies will\nbe mandated to train and employ female professionals to boost women’s opportunities for joining the\nsector and help them overcome the key barrier of school-to-work transition and access to technical skills\nand men-dominated jobs. They will also be expected to accommodate women through a more equitable\nemployment policy, which is currently lacking in most operations and limits employment possibilities.\n\n117. **M&E.** Several indicators will monitor project progress with respect to gender, including\n(a) A percentage of female-headed households provided with electricity access under the project.\n\n\n\nThis indicator will track progress toward raising the share of electrified households that are female\nheaded to 15 percent. This percentage is based on the level of their prevalence recently measured\nby the survey on the ability and willingness of households to pay for electricity, provided in annex\n6, updating earlier data. This indicator will be applied to Component 1 and Subcomponent 2.3;\n(b) A percentage of women technicians employed in O&M under Component 1 and Subcomponents\n\n\n\n2", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000051:48:1:0", "start": 397, "end": 418, "surface": "sexdisaggregated data", "probe_tag": "confusion", "probe_score": 0.1156, "luna_label": 0}, {"key": "sample:jdc_operational:000051:48:1:1", "start": 444, "end": 455, "surface": "global data", "probe_tag": "confusion", "probe_score": 0.2257, "luna_label": 1}]}, {"key": "aivin-136", "text": "**The World Bank**\nUganda Digital Acceleration Program (P171305)\n\n\nmicrofinance and banking service providers, with 96 percent of cyberattacks being unreported or\nunresolved. As a legislative framework, the government has enacted a suite of laws that include the\nComputer Misuse Act 2011, the Electronic Signatures Act 2011 and the Electronic Transactions Act\n2011. A review of the existing cybersecurity and cybercrime legislation and an update to the\nCybersecurity Strategy are being supported under currently active RCIP-5 project and the\ngovernment has stated its intentions to accede to the Council of Europe’s Budapest convention on\ncybercrime. The Digital Uganda Vision of 2019, the National Information Security Framework (NISF)\nof 2014 and the National Information Security Strategy (NISS) of 2011 round out the strategic and\npolicy foundations for cybersecurity. To handle incidents and attacks, Uganda has both a national\nComputer Emergency Response Team (CERT) at NITA-U and a Communications Sector CERT at UCC.\nThe country has benefitted from two in-depth cybersecurity diagnostics: a Cybersecurity Maturity\nModel (CMM) assessment was undertaken in 2016 and updated in 2020; the emanating\nrecommendations, for instance on capacity building and awareness raising are reflected in the\npresent project’s design. Uganda was nominated as the regional lead on Cybersecurity under the\nEast African Northern Corridor Infrastructure Project Regional MoU. In 2018, Uganda ranked 7 <sup>th</sup> in\nAfrica in the ITU’s Global Cybersecurity Index, and 65 <sup>th</sup> globally. <sup>47</sup> [^47: International Telecommunications Union, _Global Cybersecurity Index 2018_ . https://www.itu.int/dms_pub/itu-d/opb/str/DSTR-GCI.01-2018-PDF-E.pdf] The country’s next challenges for\ncybersecurity are therefore to expand technical capacity, implement best practice governance and\nmove towards effective, steady-state implementation and sustainability.\n\n\n**14.** **In the area of Data Protection, Uganda is in the early stages of operationalizing a recently**\n**adopted", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000033:8:0:0", "start": 1521, "end": 1547, "surface": "Global Cybersecurity Index", "probe_tag": "confusion", "probe_score": 0.7891, "luna_label": 1}]}, {"key": "aivin-137", "text": ", this indicator will capture the satisfaction of the quality of the formal bus system by the passengers. It will thus reflect the improved quality<br>of public transport services.|<br>Description:Through surveys, this indicator will capture the satisfaction of the quality of the formal bus system by the passengers. It will thus reflect the improved quality<br>of public transport services.|<br>Description:Through surveys, this indicator will capture the satisfaction of the quality of the formal bus system by the passengers. It will thus reflect the improved quality<br>of public transport services.|<br>Description:Through surveys, this indicator will capture the satisfaction of the quality of the formal bus system by the passengers. It will thus reflect the improved quality<br>of public transport services.|<br>Description:Through surveys, this indicator will capture the satisfaction of the quality of the formal bus system by the passengers. It will thus reflect the improved quality<br>of public transport services.|<br>Description:Through surveys, this indicator will capture the satisfaction of the quality of the formal bus system by the passengers. It will thus reflect the improved quality<br>of public transport services.||\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Indicator Name|Core|Unit of<br>Measure|Baseline|End Target|Frequency|Data Source/Methodology|Responsibility for<br>Data Collection|\n|---|---|---|---|---|---|---|---|\n|**Name:**Number of labor days<br>of short term jobs created<br>for Lebanese and Syrians <br>||Number<br>(Thousand)|0.00|2000.00|Annual<br>|Contractors’ reports<br>compiled by CDR &<br", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000022:48:1:0", "start": 1566, "end": 1586, "surface": "Contractors’ reports", "probe_tag": "confusion", "probe_score": 0.4237, "luna_label": 0}]}, {"key": "aivin-138", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n\n**ANNEX 4: Climate and Hazard Considerations**\n\n\n**Climate Change and Natural Hazard Risks and Adaptation Opportunities**\n\n1. Resilient infrastructure development in the Republic of Chad includes consideration of existing\nnatural hazards <sup>51</sup> and ongoing climate change. Three key risks in this project include wildfire, flooding,\nand extreme heat, which are expected to increase due to climate change.\n\n\nInternal calculations using data from NASA NEX-GDDP CMIP5 data [50].\n\n2. **Wildfire is recognized as a ‘high’ risk in Chad** under current climate conditions, and climate\nchange is expected to exacerbate this risk. <sup>52</sup> [^52: Liu, Y., J. A. Stanturf, and S. L. Goodrick. 2009. “Trends in Global Wildfire Potential in a Changing Climate.” _Forest Ecology and_\n_Management_ 259 (4): 685–697. _[https://doi.org/10.1016/j.foreco.2009.09.002](https://doi.org/10.1016/j.foreco.2009.09.002)_ .] However, this risk is concentrated in the southern part of the\ncountry, particularly along the southeastern corner where the average annual area of land that is burned\nis 20–50 percent or more (Figure 4.1). These data are calculated based on an annualized average from a\nhistorical 25-year period. <sup>53</sup> [^53: Giglio, L., J. Randerson, and G. van der Werf. 2013. “Analysis of Daily, Monthly, and Annual Burned Area Using the Fourth‐\nGeneration Global Fire Emissions Database (GFED4).” _Journal of Geophysical Research: Bio geosciences_ 118 (1): 317–328.] Over a 30-year period, however, even", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000051:79:0:0", "start": 519, "end": 543, "surface": "NASA NEX-GDDP CMIP5 data", "probe_tag": "keep", "probe_score": 0.9264, "luna_label": 1}, {"key": "jdc_operational:000051:79:0:1", "start": 1430, "end": 1460, "surface": "Global Fire Emissions Database", "probe_tag": "confusion", "probe_score": 0.8178, "luna_label": 1}]}, {"key": "aivin-139", "text": " Uganda’s digital services, such as\ncitizens, businesses, government, to aid decision making and ensure commercial viability and subsequent\nincorporation in the project design.\n\nThe activities under the UDAP-GovNet are like those under RCIP-5, except the scope is much larger though the\nstaff skills requirements are the same. There is limited domestic consulting/contracting IT capacity to conduct\nprojects of this nature, complexity, and scope. Individual consultants will be hired to enhance the technical in<u>house capacity of NITA-U to support effective project implementation.</u>\n\n2. The project will use the **Systematic Tracking of Exchanges in Procurement (STEP), the World Bank’s**\n**online** planning and tracking system, which will provide data on procurement activities and delays and measure\nprocurement performance. Some NITA-U staff are already familiar with STEP, which is being used under RCIP-5.\n\n3. **Use of National Procurement System.** National procurement procedures shall only apply if the\nrequirements as required by the paragraph 5.3 Procurement Regulations <sup>70</sup> are met. In March 2017 (updated\nFebruary 2018) the PPDA issued a guideline titled “reservations to promote local content in public procurement”\n\n\n70 (a) open advertising of the procurement opportunity at the national level; (b) the procurement is open to eligible firms from any country;\n(c) the request for bids/request for proposals document shall require that Bidders/Proposers submitting Bids/Proposals present a signed\nacceptance at the time of bidding, to be incorporated in any resulting contracts, confirming application of, and compliance with, the World\nBank’s Anti-Corruption Guidelines, including without limitation the World Bank’s right to sanction and the World Bank’s inspection and\naudit rights; (d) Procurement Documents include provisions, as agreed", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000023:66:1:0", "start": 619, "end": 666, "surface": "Systematic Tracking of Exchanges in Procurement", "probe_tag": "confusion", "probe_score": 0.6822, "luna_label": 0}]}, {"key": "aivin-140", "text": ".00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|\n|Cumulati<br>ve|7.00|17.00|32.00|32.00|32.00|32.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|\n|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|**Institutional Data**|\n|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead)**|**Practice Area (Lead", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000030:5:2:0", "start": 165, "end": 183, "surface": "Institutional Data", "probe_tag": "confusion", "probe_score": 0.1218, "luna_label": 0}]}, {"key": "aivin-141", "text": "**The World Bank**\nUganda Secondary Education Expansion Project (P166570)\n\n\n11. **There are disparities in access to secondary education by region, location, wealth, and gender.** The\nNorthern region lags behind in terms of access to secondary education (Figure 6), with GER for nearly all districts\nin the region below the national average of 28 percent and many of them below 10 percent. The enrollment rates\nin urban areas of the Central region are dramatically higher than those in rural and underserved areas in the North.\nFor instance, in 2015, GER in the capital city of Kampala was over 50 percent, while in rural Kaabong (Karamoja\ndistrict), it was only 5 percent. Variations by welfare quintiles reveal that secondary school enrollment drops with\ndecreasing welfare. It is the lowest for persons in the lowest quintile (7 percent) and highest in the fifth quintile\n(41 percent). <sup>15</sup> Disparities in completion <sup>16</sup> rates are evident between rural areas, at 6.5 percent, and urban, at just\nover 14 percent. Variations in secondary completion rates persist across the country with Kampala (Central) having\n\n\n15 National Household Survey 2012/13. [Data on wealth and enrolment disparities not included in the 2016 National Household Survey]\n16 Completion rate here is defined as children completing primary seven as a proportion of children entering primary one.\n\n\nPage 10 of 96", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000018:15:0:0", "start": 1137, "end": 1162, "surface": "National Household Survey", "probe_tag": "keep", "probe_score": 0.9793, "luna_label": 1}, {"key": "sample:jdc_operational:000018:15:0:1", "start": 1173, "end": 1213, "surface": "Data on wealth and enrolment disparities", "probe_tag": "confusion", "probe_score": 0.3042, "luna_label": 0}, {"key": "sample:jdc_operational:000018:15:0:2", "start": 1234, "end": 1264, "surface": "2016 National Household Survey", "probe_tag": "keep", "probe_score": 0.9563, "luna_label": 0}]}, {"key": "aivin-142", "text": "**The World Bank**\nLebanon Health Resilience Project (P163476)\n\n\n**Box 2. Lebanon Emergency Primary Healthcare Restoration Project (EPHRP)**\n**Objective**\nThe objective of the EPHRP is to assist the GoL in reducing the social, economic, and health impacts of the Syrian\ncrisis on poor Lebanese by subsidizing a package of essential health care services.\n**Beneficiaries**\nThis project targets 150,000 of the 340,000 poor Lebanese identified by the NPTP as living below the poverty\nline, using a proxy means testing targeting mechanism.\n**Essential Health Care Package**\nThe project provides beneficiaries with a package of essential health care services comprising the following:\n(i) three age- and gender-specific wellness packages (age 0-18, females 19 years and above, males 19 years and\nabove); (ii) two care packages for the most common non-communicable diseases in Lebanon, diabetes and\nhypertension; and (iii) an antenatal package.\n**Providers**\nServices are provided to beneficiaries through 75 of the 204 MoPH network centers. Network facilities are\nmanaged by NGOs (67 percent), local municipalities (20 percent), MoPH (11 percent), and MoSA (2 percent).\nProvider participation is voluntary and is governed by the legal agreement between the MoPH and the managing\nentity.\n**Quality of Care**\nQuality of care is monitored through the PHCC accreditation program implemented by the MoPH in collaboration\nwith Accreditation Canada International. Currently, all 75 PHCCs are within the accreditation program. The\nquality of clinical care is also monitored by the MoPH through clinical indicators captured in the Health\nInformation System.\n**Contracting and Provider Payment Mechanism**\nThe MoPH purchases the package of services for the beneficiary population from PHCCs. Provider payment is\nbased on capitation and is output-based. The average per", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000032:16:0:0", "start": 1617, "end": 1642, "surface": "Health\nInformation System", "probe_tag": "confusion", "probe_score": 0.5564, "luna_label": 1}]}, {"key": "aivin-143", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\n**TABLE OF CONTENTS**\n\n\n**DATASHEET ..................................................................................................................................1**\n\n\n**I.** **STRATEGIC CONTEXT ..............................................................................................................7**\n\n\nA. Country Context ........................................................................................", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000057:4:0:0", "start": 19, "end": 80, "surface": "Productive Safety Net for Socioeconomic Opportunities Project", "probe_tag": "confusion", "probe_score": 0.0612, "luna_label": 0}]}, {"key": "aivin-144", "text": "sup>4</sup> . As of 2018, most Ugandans – 76\npercent <sup>5</sup>     - live in rural areas and work in the agricultural sector, which accounts for 70 percent <sup>6</sup> of total\nemployment (75 percent of all women in the labor force) and around a quarter of the country’s gross\n\n\n1 Uganda Economic Update, Strengthening Social Protection to Reduce Vulnerability and Promote Inclusive Growth, 2020.\nWorld Bank.\n2 Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population\nProspects: The 2015 Revision\n3 Uganda Economic Update, Strengthening Social Protection to Reduce Vulnerability and Promote Inclusive Growth, 2020.\nWorld Bank.\n4 The Uganda Poverty Assessment Report 2016. Washington, DC: World Bank.\nhttp://pubdocs.worldbank.org/en/381951474255092375/pdf/Uganda-Poverty-Assessment-Report-2016.pdf\n5 World Bank Data\n6 World Bank Data\n\n\nJan 15, 2021 Page 3 of 26", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000033:2:1:0", "start": 867, "end": 882, "surface": "World Bank Data", "probe_tag": "confusion", "probe_score": 0.2427, "luna_label": 1}, {"key": "jdc_operational:000033:2:1:1", "start": 867, "end": 882, "surface": "World Bank Data", "probe_tag": "confusion", "probe_score": 0.2437, "luna_label": 1}]}, {"key": "aivin-145", "text": " women un Uganda would die from maternal causes at existing rates. Moreover, it is\ndocumented that health care services during pregnancy and childbirth and after delivery are important\nfor the survival and well-being of both the mother and infant. This situation worsens when roads are\naffected by natural hazards.  As per the UDHS, 40.9 percent of women between 35-49 years old reported\nthat they have serious problems in accessing health facilities for themselves when they are sick because\n\n\n25 Article 61, Refugee Regulations 2010\n26 Redundancies are alternate routes\n\n\nApr 07, 2020 Page 8 of 16", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000001:7:2:0", "start": 327, "end": 331, "surface": "UDHS", "probe_tag": "confusion", "probe_score": 0.7928, "luna_label": 1}]}, {"key": "aivin-146", "text": "**The World Bank**\nDecent Employment Creation for Vulnerable Lebanese Citizens and Syrian Refugees in Livestock Value Chains\n\n\nRelationship to CPF\n\n10. The activity is aligned with the _Lebanon - Country Partnership Framework for FY17-FY22 by_\n_contributing to the_ Focus Area 2: Expand Economic Opportunities and Increase Human Capital,\nthrough improving access to finance, skills development, and strengthening safety nets for the\npoorest Lebanese farming households and Syrian refugees. It will help Lebanon mitigate the\neconomic and social impact of the Syrian crisis, financial crisis and the COVID-19 pandemic, safeguard\nthe country’s development gains, and enhance the prospects for stability and development in the\ncoming years.\n\n\n**References**\n\n\nIFC, 2020. Lebanon Agribusiness Deep Dive. Draft, IFC, 2020.\n\n\nILO and FAFO. 2020. Rapid Impact Assessment of COVID-19 on vulnerable workers and small-scale\nenterprises in Lebanon.\n\n\nUNHCR, UNICEF and WFP. 2019. VASYR 2019 - Vulnerability Assessment of Syrian Refugees in Lebanon.\nhttps://reliefweb.int/report/lebanon/vasyr-2019-vulnerability-assessment-syrian-refugees-lebanon\n\n\nWorld Bank, 2021. World Bank Indicators https://data.worldbank.org/indicator accessed 8/19/2021\n\n\nFAO. 2021. Country gender assessment of the agriculture and rural sector – Lebanon. Beirut.\nhttps://doi.org/10.4060/cb3025en\n\n\nDal, E., Díaz-González, A.M., Morales-Opazo, C. & Vigani, M. 2021. Agricultural sector review in Lebanon. FAO\nAgricultural Development Economics Technical Study No. 12. Rome, FAO. https://doi.org/10.4060/cb5157en\n\n\nWorld Bank. 2020. World Development Indicators. Washington, DC: World Bank Group. Accessed January 22,", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000036:5:0:0", "start": 1154, "end": 1175, "surface": "World Bank Indicators", "probe_tag": "confusion", "probe_score": 0.6973, "luna_label": 0}]}, {"key": "aivin-147", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\n**I.** **STRATEGIC CONTEXT**\n\n\n**A. Country Context**\n\n\n**1.** **On August 4, 2020, a massive explosion at the Port of Beirut (PoB) devastated the city, killing at least 217**\n**people, wounding more than 6,000 and displacing around 300,000 individuals.** The explosion caused widespread\ndestruction to homes, businesses, infrastructure, and disrupted economic activity. In the immediate aftermath of the\nblast, a Rapid Damage and Needs Assessment (RDNA) was undertaken to estimate the impact of the disaster on the\npopulation, changes in economic flows, physical assets, infrastructure, and service delivery in Beirut. Total damages\nwere estimated between US$3.8-4.6 billion, with the housing and culture sectors as the most severely affected. Losses\nwere estimated at US$2.9-3.5 billion, with the housing sector being the hardest hit, followed by the transport and port\nsector, and the culture sector. <sup>1</sup> [^1: World Bank Group; European Union; United Nations. 2020. Beirut Rapid Damage and Needs Assessment. World Bank, Washington, DC. ©\nWorld Bank. - https://openknowledge.worldbank.org/handle/10986/34401] A WBG survey of citizens’ perceptions conducted immediately after the explosion\nfound that the population in Zone 1 (the Port and surrounding areas most affected by the blast) and Zone 2 (Ashrafieh)\nexpressed the need for housing support above other needs. <sup>2</sup> [^2: World Bank-administered survey conducted for 3,000 online respondents a little over week after the explosion]\n\n**2.** **The PoB explosion has exacerbated the impact of Lebanon’s pre-existing economic and financial crisis.** During\n2011-17 (", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000012:11:0:1", "start": 1229, "end": 1264, "surface": "WBG survey of citizens’ perceptions", "probe_tag": "confusion", "probe_score": 0.8763, "luna_label": 1}, {"key": "jdc_operational:000012:11:0:2", "start": 1502, "end": 1532, "surface": "World Bank-administered survey", "probe_tag": "confusion", "probe_score": 0.8926, "luna_label": 1}]}, {"key": "aivin-148", "text": "2</sup> <mark>After decades of insecurity, ab</mark> out 7.2 million people (60 percent of the\npopulation) are in ‘Crisis’ (IPC <sup>3</sup> [^3: IPC = Integrated Food Security Phase Classification.] Phase 3) or even more acute situations of food insecurity. <sup>4</sup> [^4: IPC. South Sudan IPC Results October 2020–July 2021.] As of November\n2021, over 4 million people, 85 percent of them women and children, <sup>5</sup> [^5: United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) November 2021.] were displaced, many of them\nmore than once. About 2.3 million people have fled to neighboring countries in search of safety, while 2\nmillion remain to be displaced within South Sudan. <sup>6</sup> [^6: UNOCHA November 2021.]\n\n\n3. **A sharp decline in international oil prices triggered by the pandemic combined with devastating**\n**floods has eroded most of the economic gains from the peace process, but there are signs of modest**\n**recovery following rising oil prices and government reforms.** The economy contracted by an estimated\n5.4 percent in FY2020/21 due to the COVID-19 pandemic, decline in global oil prices, resurgence of\nviolence, and major flooding. In FY2021/22, South Sudan’s economy is expected to grow by 1.2 percent\nreflecting stronger oil prices, rebounding optimism in the non-oil sectors, and stronger domestic and\nregional trade following crippling COVID-19 restrictions in the previous year. The overall FY2020/21\nbudget deficit is estimated to have narrowed to about 6.9 percent of gross domestic product (GDP) from\n9.8 percent in FY2019/20. South Sudan’s fiscal position benefited from higher-than-projected oil revenue,\nimproved domestic revenue mobilization,", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000028:13:1:0", "start": 282, "end": 305, "surface": "South Sudan IPC Results", "probe_tag": "confusion", "probe_score": 0.8655, "luna_label": 1}]}, {"key": "aivin-149", "text": "**The World Bank**\nRoads and Employment Project (P160223)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Indicator Name|Core|Unit of<br>Measure|Baseline|End Target|Frequency|Data Source/Methodology|Responsibility for<br>Data Collection|\n|---|---|---|---|---|---|---|---|\n|various countries below Trunk or Primary, Secondary or Link roads, or sometimes Tertiary roads. Such roads are often described as rural access, feeder, market,<br>agricultural, irrigation, forestry or community roads.  Typically, rural roads connect small urban centers/towns/settlements of less than 2,000 to 5,000 inhabitants to each<br>other or to higher classes of road, market towns and urban centers.|various countries below Trunk or Primary, Secondary or Link roads, or sometimes Tertiary roads. Such roads are often described as rural access, feeder, market,<br>agricultural, irrigation, forestry or community roads.  Typically, rural roads connect small urban centers/towns/settlements of less than 2,000 to 5,000 inhabitants to each<br>other or to higher classes of road, market towns and urban", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000008:48:0:0", "start": 19, "end": 47, "surface": "Roads and Employment Project", "probe_tag": "confusion", "probe_score": 0.1307, "luna_label": 0}]}, {"key": "aivin-150", "text": "**The World Bank**\nChad Rural Mobility and Connectivity Project (P164747)\n\n\n**ANNEX 3: Climate Change Impact Study**\n\n\nRural Mobility and Connectivity Project in Chad:\nAssessing the Vulnerability to and Cost of Climate Change Impacts on a Road Rehabilitation Project\n\nusing IPSS\n\n\n**Overview and Background**\n\n\n1. The assessment of this road rehabilitation project included in its evaluation an engineering-based\nanalysis tool called the IPSS <sup>12</sup> to further understand the vulnerabilities of the road segments to the impacts\nof climate change. IPSS, developed by researchers out of the University of Colorado, has been used to\nidentify vulnerabilities in the road under study, to help better manage the risk associated with climate\nchange and to help better inform specific adaptation investment options for the road.\n\n\n2. IPSS utilizes stressor-response engineering data coupled with IPCC-approved climate change data\nto estimate the potential impacts from specific climate stressors, in this case flooding and precipitation.\nThe tool is then used to assess the cost of reactively addressing the impacts of climate change (fixing\ndamages) versus proactively mitigating the impacts of climate change (through upgrades in the\nconstruction process of the roads). The tool will shed light on the cost incurred by changing climate in\nterms of keeping the road maintained and will inform decisions regarding investment strategy. IPSS was\nrun for the first phase (75 km) of the Chad Rural Mobility and Connectivity Project and will be utilized to\nanalyze the remaining 325 km stretch of road as well. It is important to note that the analysis does not\nsupport any one of the climate models or support that any outcome is more likely than another. The\nresults displayed in the following paragraph show the predictions of the median model, as noted.\n\n\n3. The climate-induced hazards threatening the project include flooding and change in precipitation.\nThese pose a threat to mobility, accessibility, and thus economic growth. Flooding has the potential to\nin", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000048:58:0:0", "start": 847, "end": 881, "surface": "stressor-response engineering data", "probe_tag": "confusion", "probe_score": 0.2518, "luna_label": 1}, {"key": "jdc_operational:000048:58:0:1", "start": 895, "end": 928, "surface": "IPCC-approved climate change data", "probe_tag": "confusion", "probe_score": 0.8432, "luna_label": 1}]}, {"key": "aivin-151", "text": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n\nproject. This component will also finance coordination activities. These include meetings of Technical\nCoordination committees, Coordination’s meetings at different level of the health system and\noperation costs of Emergency Operation Center.\n\n\n.\n\n\n\n\n\n**Note to Task Teams:** This summary section is downloaded from the PAD data sheet and is editable. It should\nmatch the text provided by E&S specialist. If it is revised after the initial download the task team must manually\nupdate the summary in this section. **_Please delete this note when finalizing the document._**\n\n\n31. The main concerns relate to testing and treatment of infected persons, handling of medical samples\nand waste by medical professionals and local community health and safety. The project will finance\nequipment for selected primary health care facilities and hospitals to improve their ability to deliver\ncritical medical services including testing, treatment and hospitalization. The PIU will prepare an ESMF\nto provide clear guidance regarding the treatment of medical waste, guidelines for community\nengagement and the preparation of subproject ESMPs (if needed). The ESMF will also incorporate\ninternational protocols for community health and safety during a pandemic and measures to address\nGBV/SEA. The ESMF will be consulted with stakeholders using the modified approach currently under\npreparation and publicly disclosed per the requirements of the ESF no later than 30 days after Project\neffectiveness.\n\n\n**Note** : To view the Environmental and Social Risks and Impacts, please refer to the Appraisal Stage ESRS Document.\n**_Please delete this note when finalizing the document._**\n\n\n**E. Implementation**\n\n\nInstitutional and Implementation Arrangements\n\n\n32. **The Government of Chad has established several working groups to coordinate the response to**\n**COVID-19.** The Health Security Committee is a high-level committee formed by ministers and chaired\nby the Secretary General", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000017:9:0:0", "start": 409, "end": 423, "surface": "PAD data sheet", "probe_tag": "confusion", "probe_score": 0.8103, "luna_label": 0}]}, {"key": "aivin-152", "text": " of trainees/participants, duration, staff months, timing, and\nestimated costs will be submitted to IDA for review and approval before initiating the process. The\nappropriate methods of selection will be derived from the detailed schedule. After the training, each\nbeneficiary will be requested to submit a brief report indicating what skills have been acquired and how\nthese skills will contribute to enhance his/her performance and contribute to attainment of the PDO.\nReports by the trainees, including completion certificate/diploma upon completion of training, shall be\nprovided to the project coordinators, will be kept as parts of the records, and will be shared with the\nWorld Bank if required.\n\n9. **Operating costs.** Operating costs financed by the project are incremental expenses, incurred by\nthe PIUs or its regional representations, based on the annual work plans and budgets as approved by IDA,\non account of project implementation, management, and M&E, including office supplies; bank charges;\nvehicles operation; maintenance and insurance; maintenance of equipment and buildings;\ncommunication costs; travel and supervision costs (that is, transport, accommodation, and per diem);\ncosts related to utilities and office space rental; and salaries of contracted and temporary staff. The related\ngoods/services will be procured using the procurement procedures specified in the PIM and accepted and\napproved by the World Bank.\n\n10. **Procurement assessment of the SNE PIU.** Procurement capacity of the existing SNE PIU is overall\nsatisfactory. It is currently rated Moderately Satisfactory according to the last PRAMS. <sup>41</sup> Table 1.4\nsummarizes the main risks/issues and mitigation actions to be taken by SNE.\n\n\n41 PRAMS = Procurement Risk Assessment and Management System.\n\n\nPage 62 of 87", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000051:67:1:0", "start": 1628, "end": 1633, "surface": "PRAMS", "probe_tag": "drop", "probe_score": 0.0003, "luna_label": 1}]}, {"key": "aivin-153", "text": "**Annex III: Implementation Arrangements**\n\n\n**LEBANON**\n**Emergency National Poverty Targeting Program Project (P149242)**\n\n**Institutional and Implementation Arrangements**\n\n\n1. With respect to the institutional setup of the NPTP, the program has been managed by the\nMOSA and the Presidency of the Council of Ministers (PCM). This was deemed the best option\nat the time of appraisal of the ESPISP II project, which supported the creation of the NPTP. The\npresent institutional setup will be retained during the implementation of the Emergency NPTP.\nHowever, an external unit (FOT) under the PCM will handle the fiduciary aspects of the project.\n\n2. The SDCs will continue supporting the implementation of the NPTP by hosting the social\nworkers who register beneficiaries, administer household questionnaires, do data entry, and\noverall follow up for the NPTP.\n\n\n3. With respect to the NPTP implementation and institutional structure, the following\nexplains the overall implementation arrangements of the program:\n\n\n     - The Council of Ministers makes policy decisions related to the NPTP, allocates\n\nannual budget, and defines cut-off scores which determine benefits.\n\n     - The Social-IMC reviews progress of NPTP and makes recommendations to the\n\nCouncil of Ministers.\n\n\n     - The NPTP Project Unit in the MOSA is responsible for the following: (i)\n\nmanaging the NPTP database in MOSA; (ii) receiving household applications;\n(iii) interfacing with applicants; (iv) entering data; (v) conducting household\nvisits; (vi) checking for data errors; (vii) transmitting data to the central database\nof the NPTP CMU; (viii) verifying claims from hospitals, schools, and PHCs and\nauthorizing payments; (ix) managing the outreach campaign; (x) managing the ecard food voucher beneficiaries list, delivery of the e-cards to beneficiaries, and\nfollow up; and (xi) monitoring", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000047:41:0:0", "start": 1371, "end": 1384, "surface": "NPTP database", "probe_tag": "drop", "probe_score": 0.0017, "luna_label": 0}, {"key": "sample:jdc_operational:000047:41:0:1", "start": 1607, "end": 1615, "surface": "NPTP CMU", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 0}, {"key": "sample:jdc_operational:000047:41:0:2", "start": 1755, "end": 1792, "surface": "ecard food voucher beneficiaries list", "probe_tag": "drop", "probe_score": 0.0001, "luna_label": 0}]}, {"key": "aivin-154", "text": "ics<br>reports<br>|<br>Administrative data<br>|<br>PMU/MOPH<br>|\n|Percentage of residents of Lebanon who<br>are fully vaccinated, total and<br>disaggregated by sex, age risk, group and<br>nationality (including refugees and host<br>communities).|<br>The indicator will track the<br>number of eligible people<br>as defined among a specific<br>set of priority groups in the<br>National Deployment and<br>Vaccination Plan<br>(NVDP)/government<br>prioritization list who are<br>fully vaccinated from<br>COVID-19 using vaccines<br>that meet the Bank's<br>vaccine approval criteria.<br>This indicator will be|<br>6 months<br>|MOPH<br>reports,<br>National<br>Vaccine digital<br>platform<br>(IMPACT)<br>|Administrative data<br>|PMU/MOPH<br>|\n\n\nPage 42 of 54", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000000:46:1:0", "start": 696, "end": 715, "surface": "Administrative data", "probe_tag": "drop", "probe_score": 0.0335, "luna_label": 0}]}, {"key": "aivin-155", "text": "**The World Bank**\nChad Rural Mobility and Connectivity Project (P164747)\n\n\n\n\n\n|Col1|prioritization of the rural<br>road network|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|Number of first responders trained in<br>road safety|This indicator is linked with<br>the Road Safety Plan which<br>includes training sessions of<br>first responders (gendarmes<br>and police officers, medical<br>staff, community leaders) in<br>active road safety data<br>collection|<br> <br>Annual<br>|MITD/Gener<br>al<br>Directorate<br>for Transport<br>Safety (DGTS)<br>|Number of certificate<br>of course completion<br>given<br>|PMCU through MITD<br>|\n|Proportion of villages that have<br>implemented physical road safety<br>measures|Cumulative number of<br>villages that have benefited<br>from physical speed calming<br>measures financed by the<br>project, divided by the<br>cumulative number of<br>villages located along the<br>project roads (to be<br>determined after the APS<br>studies).|Annual<br>|AGER<br>|Reports of reception of<br>civil works<br>|AGER<br>|\n|Small common facilities provided and<br>built for women|Cumulative number of small<br>common facilities that have<br>been completed under the<br>project|Annual<br>|Every year,<br>the PCMU<br>will compile", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000048:45:0:0", "start": 421, "end": 444, "surface": "active road safety data", "probe_tag": "drop", "probe_score": 0.0121, "luna_label": 0}]}, {"key": "aivin-156", "text": " job training.\n\n   - **Sub-component 1.2:** An assessment of data gaps will be conducted to identify and develop solutions to build\n\nany required data infrastructure needed to fully design and operationalize the M&E framework. This will\ninclude: (i) T.A in improving the frequency or depth of demand-side data collected, including adding additional\nmodules to already available surveys building on the experience of the EU-GIZ demand side study of 2017;\n(ii) assisting in the development and consolidation of data currently missing; and (iii) adding relevant indicators\nor modules to supply-side data templates and supervisory templates collected by various agencies;\n\n   - **Sub-component 1.3:** Support the improvement of operating environment of the NFIS implementation unit. The\n\nunit will procure the required equipment and IT, both software and hardware in order to enhancing the data\ncollection, processing and analysis. The sub-component will cover the hiring of experts to support this\nimprovement process.\n\n   - Key outputs envisioned for this component include (i) an operational M&E framework; (ii) the delivery of\n\nmultiple trainings and workshops to build capacity; (iii) data collection templates to consolidate financial\ninclusion data; (iv) the development of a Secretariat policy toolkit (including an M&E tracker to track the\nperformance and completion of NFIS activities); and (v) facilitation of M&E reports, implementation reports\n\n\nApr 05, 2017 Page 7 of 11", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000013:6:1:0", "start": 293, "end": 309, "surface": "demand-side data", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 0}, {"key": "sample:jdc_operational:000013:6:1:1", "start": 420, "end": 444, "surface": "EU-GIZ demand side study", "probe_tag": "drop", "probe_score": 0.0187, "luna_label": 1}]}, {"key": "aivin-157", "text": "|UNRA<br>|UNRA and M&E reports<br>|Monitoring and<br>evaluation consultants,<br>UNRA<br>|\n|Annual number of fatalities or serious<br>injuries involving construction vehicles or<br>at construction sites<br>|Effectiveness of the traffic<br>management plan<br>implemented and<br>occupational health and<br>safety monitoring program<br>under the civil works|Semi-<br>annually<br>|M&E reports,<br>UNRA<br>reports<br>|Police data<br>|Monitoring and<br>evaluation consultants,<br>UNRA<br>|\n\n\n**ME PDO Table SPACE**\n\n\n\n\n\n\n\n\n\n\n\nPage 55 of 80", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000050:59:1:0", "start": 412, "end": 423, "surface": "Police data", "probe_tag": "drop", "probe_score": 0.0446, "luna_label": 1}]}, {"key": "aivin-158", "text": "- MOSA SDCs are responsible for: (i) receiving household applications and\n\ninterface with applicant; (ii) data entry into program application; (iii) conducting\nhousehold visits; (iv) checking possible data errors in application forms against\nprovided official documents; (v) transmitting households’ application data to\nMOSA central unit; and (vi) handling appeals and claims received by households.\n\n     - With respect to the implementation arrangements of the e-card food voucher, the\n\nfollowing arrangements have been agreed upon: (i) WFP will conduct training for\nNPTP field work coordinators and social workers, including on assessments,\ndistribution, monitoring and household visits; (ii) NPTP will be responsible for\ndistribution and training of beneficiaries on the use of the e-card, as well as\nassessing and monitoring of food security indicators; (iii) WFP will provide the\n_Banque Libano-Française_ (BLF) with the necessary information/data based on\nthe NPTP database and operations for the production, activation and loading of ecards; (iv) WFP will in turn share reports from the bank on transactions and\nspending patterns; and (v) WFP and its identified partner(s) will continue to\nsupport NPTP through joint reporting and monitoring in the field. (For more\ndetails on the business processes and implementation, see Annex II).\n\n**Financial Management and Disbursements**\n\n\n_Financial Management_\n\n\n4. The Bank assessed the adequacy of the project FM arrangements proposed by the\nimplementing entity. The arrangements are considered acceptable if the entity budgeting,\naccounting, internal controls, funds flow, financial reporting, and auditing arrangements: (a) are\ncapable of correctly and completely recording all transactions and balances relating to the\nproject; (b) facilitate the preparation of regular, timely, and reliable financial statements; (c)\nsafeguard the project’s assets; and (d) are subject to auditing arrangements acceptable to the", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000047:42:0:0", "start": 967, "end": 980, "surface": "NPTP database", "probe_tag": "drop", "probe_score": 0.0011, "luna_label": 1}]}, {"key": "aivin-159", "text": " model will introduce and maintain a minimum standard of quality for a lower secondary education school\nand develop schools as safe learning spaces.\n\n\n33. **Approach to targeting sub-counties** :\n\n     - There are 135 districts in Uganda (as of 2016), including 13 refugee hosting districts (RHDs). The Project\nwill target sub-counties in 96 districts with low enrollment rates, high unsatisfied demand for lower\nsecondary education, and no public secondary school. All RHDs will be supported under the Project.\n\n     - The Project will also include 84 non-refugee hosting local governments (LG). The 84 LG were selected\nusing the following approach. First, only LGs which have sub-counties without a public secondary school\n(“underserved subcounty”) were considered as Uganda implements one-public-school-per-subcounty\npolicy. There are 90 such LGs. Second, the LGs which do not have enough primary feeder schools in\nunderserved sub-counties were removed. Based on the current primary to secondary transition patterns\nand experience from private sector constructing new secondary schools, a minimum of seven primary\nschools are required to provide sufficient number of graduates to feed in a new large (eight classrooms,\ntwo stream) lower secondary school. Thus, six LG were removed from the list brining the final number to\n84 LG.\n\n     - Unfortunately, enrollment data is not available on sub-county level. Thus, the demand assessment used\ndistrict/LG level information as a proxy. In 70 out of 84 selected LGs, the GER is below average for Uganda,\nand in 14 districts it exceeds the average. However, while the GER for the district as a whole is higher than\nexpected, there are sub-counties/areas in these districts which provide limited opportunities for\ncontinuing education in secondary schools and unsatisfied demand for lower secondary education is high.\nThe demand for each subcounty will be established and verified during the", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000018:22:1:0", "start": 1356, "end": 1371, "surface": "enrollment data", "probe_tag": "confusion", "probe_score": 0.6073, "luna_label": 1}, {"key": "sample:jdc_operational:000018:22:1:1", "start": 1443, "end": 1472, "surface": "district/LG level information", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1}]}, {"key": "aivin-160", "text": "**The World Bank**\nLebanon Health Resilience Project (P163476)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPage 4 of 54", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000032:6:0:0", "start": 19, "end": 52, "surface": "Lebanon Health Resilience Project", "probe_tag": "drop", "probe_score": 0.0412, "luna_label": 0}]}, {"key": "aivin-161", "text": "**The World Bank**\nSouth Sudan Emergency Food and Nutrition Security Project (P163559)\n\n\n**MAP OF SOUTH SUDAN**\n**REPUBLIC OF SOUTH SUDAN: South Sudan Emergency Food and Nutrition Security Project**\n\n\n\nPage 80 of 80", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "jdc_operational:000038:83:0:0", "start": 91, "end": 109, "surface": "MAP OF SOUTH SUDAN", "probe_tag": "drop", "probe_score": 0.0122, "luna_label": 0}]}, {"key": "aivin-162", "text": " for trade, thereby making the cost of travel cheaper.\n\n94. The cost-benefit analysis indicates that the Economic Internal Rate of Return (EIRR) for the upgrading option\nof Asphalt Concrete pavement (with 50 mm Asphalt Concrete, 200 mm of Aggregate Base Course, and 250\nmm of Granular Subbase) is 19.7 percent when a discount rate of 12 percent is used. A sensitivity analysis\nhas been carried out by increasing or decreasing the critical factors. For the scenario with a 20 percent\nincrease in construction costs, the EIRR is 16.3 percent. For the scenario with a 20 percent decrease in traffic\nvolume, the EIRR is 14 percent. However, if both scenarios are combined, the EIRR is 9.9 percent.\n\n\n95. Reduced travel time and reduced congestion is expected to lower carbon emissions. Based on current and\nfuture traffic forecasts, upgrading of the KYM road corridor is expected to reduce GHG emissions by 42,884\ntons CO2, for the 30 years of operation.\n\n\n**B. Fiduciary**\n\n\n**(i)  Financial Management**\n\n\n96. The project’s financial and other resources will be managed through the existing financial management\narrangements in UNRA as established under the Directorate of Corporate Services in the Finance/Accounts\ndepartment regarding record keeping, accounts, reporting and disbursements. The project planning and\nbudgeting process is mainstreamed into the UNRA procedures. The UNRA Board of Directors has the\nresponsibility of approval of policy, work plans and budget of the entity operations while Bank project\nbudgets are approved in liaison with the World Bank. UNRA will dedicate a project accountant to oversee\nday-to-day financial transactions and ensure proper reporting and controls are in place for the project using\nPastel Accounting System while the GoU IFMIS is used for counterpart funding. The Executive Director of\nUNRA will be the Accounting Officer assuming the overall responsibility for accounting for the project funds.\nDuring the assessment, some risks have", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000050:41:1:0", "start": 791, "end": 827, "surface": "current and\nfuture traffic forecasts", "probe_tag": "drop", "probe_score": 0.0016, "luna_label": 1}, {"key": "sample:jdc_operational:000050:41:1:1", "start": 1764, "end": 1773, "surface": "GoU IFMIS", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 0}]}, {"key": "aivin-163", "text": "SNSOP<br>Management<br>Information<br>System (MIS)<br> <br>|Data on participation in<br>Component 2 will be<br>collected at registration<br>where based on the<br>targeting and<br>registration process<br>outlined in the Project<br>Operations Manual,<br>eligible beneficiaries<br>will be allocated to<br>Component 2. Data on<br>payment of the<br>livelihood grant will be<br>collected through the<br>SNSOP MIS that will be<br>linked with SNSOP<br>payment data<br>|The Implementing<br>Partner responsible for<br>Component 2 will be<br>responsible for data<br>collection<br>|\n|Eligible beneficiary households with<br>functional income-generating investments|<br>The total number of<br>households with functional|This indicator<br>will be|SNSOP<br>Management|Data will be collected<br>through routine M&E|Implementing Partner<br>|\n\n\nPage 53 of 74", "source": "jdc_operational", "subset": "annotate_aivin", "spans": [{"key": "sample:jdc_operational:000057:57:1:0", "start": 397, "end": 406, "surface": "SNSOP MIS", "probe_tag": "drop", "probe_score": 0.0361, "luna_label": 0}]}, {"key": "aivin-164", "text": "br>|\n\n\n\n9. **Reduced compliance costs for taxpayers.** For the taxpayers, the major economic benefits include\na reduction in the time to comply with taxes and the time spent on dealing with tax inspections. The latter\nwill be achieved mainly through a strengthened compliance risk management mechanism and the fact\nthat tax inspectors would now need to spend less time on gathering and processing information, as well\nas doing surveillance during field audits. The 2015 Doing Business Report provides a baseline for the\naverage time needed to comply with taxes. The indicator is reported for a hypothetical medium-size\ncompany, which needs, on average, about 82 hours per year (or approximately 10 days) to comply with\nmajor types of taxes and contributions in Djibouti. The indicator can be subdivided further into the time\nneeded to comply with the corporate income tax (30 hours or 3.75 days per year), labor taxes (36 hours\nper 4.5 days), and consumption taxes (16 hours per 2 days).\n\n\n**Table 4.4. Compliance Costs**\n\n\n\n\n\n\n\n\n\n|Type of taxpayer|Number|Days|Days|Days|Days|Days|Days|Days|Days|\n|---|---|---|---|---|---|---|---|---|---|\n|||2018|2022|2023|2024|2025|2026|2027|2028|\n|Personal income taxpayers|3,224|4.5|4.25|4.2|4.15|4.1|4.05|4.025|4|\n|Corporate income taxpayers|470|3.75|3.5|3.4|3.3|3.2|3.1|3.2|3|\n|Value added taxpayers|320|2|1.9|1.", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "sample:refugee_pads:000142:67:2:0", "start": 465, "end": 491, "surface": "2015 Doing Business Report", "probe_tag": "keep", "probe_score": 0.989, "luna_label": 1}]}, {"key": "aivin-165", "text": " go beyond these modest efforts to create the critical\nmass o f regional programs and policies that can make a difference in their collective effort to\nstem the HIV/AIDS pandemic. Mobile populations, networks o f populations which are highly\nmobile and/or represent high risk when mobile (PLWAs), harmonizing health policies,\nprograms and services, and sharing epidemiological, behavioral and program data, all\nrepresent potentially significant mutual benefit.\n\n\nGreat Lakes countries are particularly affected by the HIV/AIDS epidemic with more than _6_\nmillion people living with HIV/AIDS out o f a total o f some 25 million in Sub-Saharan Africa\n(UNAIDS, 2004). It i s estimated that more than 3 million children have been orphaned or\nmade vulnerable by HIV/AIDS in the GLIA countries. With already high levels o f HIV\n\ninfection in the early 199Os, the situation has deteriorated over the past decade with massive\ndisplacement of populations, resulting from conflicts, genocide, natural disasters and difficult\nsocio-economic conditions. Aggregated figures o f adults age 15-49 reflect prevalence rates\nrange from 4.1% to 8.8%. By country it is Burundi (6.0%); DRC (4.2%); Kenya (6.7%);\nRwanda (5.1%); Tanzania (8.8%); and Uganda (4.1%) (December 2003 data provided in\n2004, UNAIDS).\n\n\nHIV/AIDS i s dramatically fueling the TB epidemic with up to 75 percent o f TB patients in\nsome countries co-infected. As can be seen from the figure to the right the trend in HIV\nprevalence in Kenya mirrors closely the pattern o f new TB cases with roughly a five year lag.\nCo-infection i s a particularly serious problem amongst displaced populations in the region\nwho are living in extremely difficult conditions. It i s likely that co-infection rates are\nrelatively high amongst these groups", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "sample:refugee_pads:000021:4:1:0", "start": 361, "end": 405, "surface": "epidemiological, behavioral and program data", "probe_tag": "keep", "probe_score": 0.9767, "luna_label": 0}, {"key": "sample:refugee_pads:000021:4:1:1", "start": 1242, "end": 1260, "surface": "December 2003 data", "probe_tag": "keep", "probe_score": 0.9617, "luna_label": 0}]}, {"key": "aivin-166", "text": " with higher\nperformance cities. Poverty also holds a gender unbalance as women tend to be more exposed to poverty\nand economic shocks: men hold three jobs in every four, even though women represent 55 percent of the\n\n\n\n**Figure 1. Difference between Rural and Urban**\n\n\n\n**Welfare (World Bank 2019)**\n\n\n\n\n\n\n\n\n\n\n\n1 Source: National Statistical Office (NSO) – Demographic Forecasts 2013-2043 (May 2016). NSO considers as urban the\npopulation living in district ( _moughatas_ ) capitals and in centers of more than 5,000 inhabitants. Areas with a population of\nfewer than 5000 people are considered rural.\n2 Source: NSO – 2013 Population Census.\n3 World Bank Group, Mauritania Country Partnership Framework (CPF) Report 125012-MR\n\n\nPage 7 of 76", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000083:12:2:0", "start": 620, "end": 642, "surface": "2013 Population Census", "probe_tag": "keep", "probe_score": 0.9607, "luna_label": 1}]}, {"key": "aivin-167", "text": "term assistance to both the displaced and host communities.\nThis can help reduce real and perceived inequalities and prevent host communities from forming negative attitudes\nabout newly arriving displaced persons. Phase II of Jya Mbere continues the area-based approach of Phase I,\nproviding multi-sectoral support to respond to the impacts of forced displacement, covering refugees and host\ncommunities. The mid-term survey for Phase I indicated positive results from this approach for social relations\nbetween refugees and local residents, mirroring results from independent impact evaluations of the Development\nResponse to Displacement Impacts Project (DRDIP) in Ethiopia (P152822) and Kenya (P161067).\n\n47. **By creating new challenges and new opportunities, the presence of refugees transforms the environment**\n**in which host communities are pursuing their own development efforts.** This requires an approach that supports\nhosting areas rather than focusing on refugees alone. Investments are needed to mitigate the downside of hosting\nrefugees – overcrowded basic services, environmental degradation, competition over natural resources and,\nsometimes, over employment opportunities – while taking advantage of the benefits their presence may generate\nthrough their human capital, financial resources and the humanitarian and development assistance they can\n\n\nPage 13", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000188:28:2:0", "start": 409, "end": 436, "surface": "mid-term survey for Phase I", "probe_tag": "keep", "probe_score": 0.9208, "luna_label": 1}]}, {"key": "aivin-168", "text": " access to credit, learns about new market opportunities,\nand acquires the skills needed to successfully operate their businesses. <sup>22</sup> Studies of women entrepreneurs in Uganda find\nthat women who work closely with a mentor—often male, and usually a family member—are more likely to transition\ninto higher-profit sectors. <sup>23</sup> <sup>24</sup>\n\n15. **Additional factors that block women from developing growth-oriented enterprises in profitable sectors are**\n**related to the failure of existing business development services to address the needs of women-owned firms** .\nAccording to an enterprise survey conducted in 2014, MSMEs lacked key skills needed for business growth. Only 28\npercent of firms surveyed said they do book-keeping to track revenues and expenses; a mere 10 percent had invested in\ntraining for employees; and just 36 percent had access to the internet. Female-owned firms appear to be particularly\nlacking when it comes to the use of standard business practices. A recent microenterprise survey showed a gender gap\nof 24 percentage points on an index of adoption of good business practices. Few training courses address the specific\nchallenges of formalizing a business, including meeting tax obligations, preparing proper records, fulfilling reporting\nrequirements, and obtaining licenses. Training tends to focus on limited topics, such as financial or computer literacy,\nbut leaves out training in life skills and support for network. Yet, global evidence demonstrates that developing socioemotional skills, through psychology-based trainings, are as important to enterprise success as strengthening business\nskills. <sup>25</sup> Finally, many business development services continue to train women for sectors where women-owned firms are\nover-represented, such as small trade or food service, rather than where they could diversify their business and earn\nhigher profits.\n\n\n19 Delecourt, S. and Fitzpatrick, A. 2021.", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000088:14:1:0", "start": 603, "end": 620, "surface": "enterprise survey", "probe_tag": "keep", "probe_score": 0.9434, "luna_label": 1}, {"key": "refugee_pads:000088:14:1:1", "start": 1009, "end": 1031, "surface": "microenterprise survey", "probe_tag": "keep", "probe_score": 0.9147, "luna_label": 1}]}, {"key": "aivin-169", "text": "</u>_ .\nAccording to the latest Annual School Census, public schools enroll 30 thousand refugee students (in 1,681 schools)\nand 280 thousand IDPs (in 1,852 schools). While IDP children are concentrated in three Darfur states (68 percent of\n\n\nPage 14 of 40", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "sample:refugee_pads:000024:18:2:0", "start": 32, "end": 52, "surface": "Annual School Census", "probe_tag": "keep", "probe_score": 0.9861, "luna_label": 1}]}, {"key": "aivin-170", "text": " Project**\n\n\nThe mission by an IDA environment specialist to the Republic of Djibouti in June 2000 confirmed the\ndegraded situation of the sanitary facilities in all of the schools visited. Discussions with school staff and\nparents of students revealed the concern felt by the latter regarding the adverse effects of the situation on\nthe students. The keeping of photographic archives was begun during this mission.\n\n\nThe impacts considered will not be due solely to the project but also to the prevailing situation, which is\ncharacterized by significant degradation of the existing facilities. Since a particular aim of the project is\nto increase the capacity of schools, the main environmental measure to be included in the project will be\nto construct or rehabilitate the sanitary facilities of these schools, and to design a system of upkeep and\nmaintenance that will ensure the appropriate functioning of the schools.", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000041:65:1:0", "start": 363, "end": 384, "surface": "photographic archives", "probe_tag": "confusion", "probe_score": 0.2148, "luna_label": 0}]}, {"key": "aivin-171", "text": "second cycle of activities. The Mid Term Review will take into account the results of the\nevaluations and readjust the project activities accordingly.\n\n\n38. **The process evaluation will use several sources of data.** The MIS and regular\nmonitoring reports will produce information on whether the key elements of the safety nets\nsystem (targeting mechanism, payment, MIS) have been adequately developed. The process\nevaluation will include interviews with local implementing partners, such as payment agencies\nand NGOs in charge of the accompanying measures, to identify bottlenecks and recommend\nsolutions. A small qualitative beneficiary assessment might be carried out to complete the\nprocess evaluation to assess the satisfaction of direct beneficiaries with payment procedures,\naccompanying measures and with the benefits they will have received.\n\n\n39. **Targeting assessment.** The project targeting approach will be refined as the project starts\nimplementation and on the basis of specific studies supported by the ASP MDTF. The result of\nthe targeting assessment will support the preparation of the manuals. The proposed approach is\nas follows: Beneficiary households will be selected combining community, geographical, and\npoverty approaches; and will include a census of all village households in select areas,\ncategorical targeting (households with children under the age of 12 or with pregnant women) and\na simple PMT exercise. Data will be collected for all pre-selected households, following a list of\nvariables and based on the experience of other targeting techniques, namely the HE A. This will\nallow the calculation of a PMT score to be used to confirm preselected households, reduce\npotential inclusion errors and make the final selection. The community will then discuss the list\nand validate it if in agreement. Comparison of the poverty ranking of the actual beneficiaries\nwith the national survey poverty criteria at different levels will provide the basis for estimating\nthe extent of any inclusion and exclusion errors (ECOSIT 2011 is the most recent national\nhousehold survey, but a new one is expected to be conducted in 2016).\n\n\n40. **", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000028:59:0:0", "start": 1271, "end": 1303, "surface": "census of all village households", "probe_tag": "confusion", "probe_score": 0.7669, "luna_label": 0}, {"key": "refugee_pads:000028:59:0:2", "start": 2076, "end": 2101, "surface": "national\nhousehold survey", "probe_tag": "confusion", "probe_score": 0.5708, "luna_label": 1}]}, {"key": "aivin-172", "text": " laws and regulations on social safeguards and\nland acquisition. The RPF provides clear guidance on minimizing land acquisition and related\nphysical or economic displacement; compensating project-affected persons; rehabilitating\nlivelihoods; addressing grievances; and implementing other aspects of the RPF. If resettlement\ndoes occur in any of the activities, subproject-specific Resettlement Actions Plans (RAPs) or\nAbbreviated Resettlement Action Plans (ARAPs) will be prepared by the CFS following the\nguidelines set out in the RFP. The resettlement process is meant to be inclusive, to encompass\nvulnerable groups such as refugees, and to guarantee that they receive equitable treatment. The\nRPF and RAPs/ARAPs will be reviewed and cleared by the Bank and the Government and\ndisclosed publicly on the World Bank website and in-country.\n\n\n83. **Gender inclusion and SGBV** **_._** Gender issues are critical to the implementation of the Chad\nRefugees and Host Communities Support Project, since refugee women and children are especially\nvulnerable to sexual and gender-based violence. The project will be sensitive to the need to: (a)\nprotect women and children protection from SGBV; (b) increase women’s economic opportunities;\n(c) provide appropriate services to women; (d) establish gender-disaggregated baseline data in\norder to measure the gender impact of the project; and (e) mainstream gender-sensitive approaches\nin all project activities. Moreover, the project will sensitize key stakeholders and contractors on\nthe prevention of SGBV by including specific clauses in bidding documents and contracts. In\naddition, a social and gender assessment is being conducted to examine household dynamics in\n\n\nPage 34", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000112:38:1:0", "start": 1290, "end": 1324, "surface": "gender-disaggregated baseline data", "probe_tag": "confusion", "probe_score": 0.1203, "luna_label": 0}]}, {"key": "aivin-173", "text": " the ones with the worst indicators.\n\n\nGuinea already benefits from a Multi-Sectoral AIDS Project (MAP) which became effective in\nMarch 2003. The country also received financing from the Global Fund to address the HIViAIDS\nepidemic in 2003. Therefore, the present operation would not invest in this area. HIV/AIDS indicators are\nmonitored by the MAP. And, although those indicators would be looked at during each annual review o f\nthe Five-Year Plan, the present operation would not be held accountable for those indicators.\n\n\n**3.** **Project Components**\n\n\nIt was agreed that the Bank’s comparative advantages would be channeled into activities around the\nfollowing two components:\n\n\n9 **Component I: Strengthening Health Care Services** **_(US$18.99_** **million)**\n\n\nThis component would help strengthen health posts, health centers and hospitals in the 18\nselected prefectures to give quality services to at least one million pregnant women and 933,000\nchildren less than one year o f age. The package o f health services that would be reinforced are\nthose for prenatal care, normal and emergency delivery for women, and those o f the integrated\nmanagement o f childhood diseases, as well as control o f communicable diseases (TB, malaria).\n\n_o_ Sub-component A       - Quality o f care. Investments would be made in particular to: (i)\nreinforce the chain of care for at-risk deliveries, starting with early identification by\nvillage traditional birth attendants (TBAs); (ii) improve the norms and adherence to the\nnorms for basic as well as emergency birth delivery in public and private health centers;\n(iii) improve emergency birth delivery care at the higher levels o f the network, namely in\n‘Centres de SantC AmCliorCs”, private clinics (including NGOs) and prefectoral hospitals;\n\n\n5", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000092:10:1:0", "start": 305, "end": 324, "surface": "HIV/AIDS indicators", "probe_tag": "confusion", "probe_score": 0.2332, "luna_label": 0}]}, {"key": "aivin-174", "text": "**233.** In a series of micro-simulations on existing SWF beneficiary and survey data from **2008,** the\nPMT targeting accuracy in increasing the coverage of the poor was compared to the previous Score Card\ntargeting method used by SWF. The SWF **2008** survey included income level information as well as\nPMT/Score Card indicator data, allowing comparison of actual income reported with the PMT and Score\nCard scores for each applicant.\n\n\nThe simulations showed that using the Score Card targeting method would extend coverage to\nonly **8.4** percent of the population and reach only 10.8 percent of the lowest HBS decile (Le., the\npoorest 10 percent).\nBy applying the PMT weights, the coverage for Group A remained approximately the same **(9**\npercent), but reach was extended to cover **26.9** percent of the poorest 10 percent.\nBy including Group A&B data in the simulation, **35** percent of the Yemeni population would be\ncovered and the program would reach _77.5_ percent of the poorest 10 percent.\nIn considering budget allocation to the extreme poor, the Score Card method was found to reach\napproximately 19 percent of the poorest, while the PMT Group A method reached approximately\n**50** percent of the poorest.\n\n\nThese results suggest higher targeting errors (inclusion and exclusion errors) under the Score\nCard targeting method, leading to the recommendation that SWF use the PMT method to increase the\ntargeting accuracy of the program to include poor and vulnerable households.\n\n**_Process of beneficiary selection and enrollment_** - **_reaching out to the poor_**\n\n\n**235.** Reaching the ultra-poor is a challenge: they are often widely dispersed in small settlements, have\nno means of communication with authorities and lack basic skills (i-e., literacy", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000080:78:0:0", "start": 54, "end": 85, "surface": "SWF beneficiary and survey data", "probe_tag": "confusion", "probe_score": 0.842, "luna_label": 1}, {"key": "refugee_pads:000080:78:0:1", "start": 241, "end": 260, "surface": "SWF **2008** survey", "probe_tag": "keep", "probe_score": 0.938, "luna_label": 1}]}, {"key": "aivin-175", "text": " At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA M&E data - NaCSA maintains lean and\n**community** **sub-projects and** decision-making by NaCSA; efficient organizational\n**NSAP** **partners monitored** structure\n**and evaluated** **in order to**\n**improve** **program**\n**effectiveness.**\n\n\n**3(d)** **Technical** **Assistance** 3d. 1 NaCSA staff indicate - IDA aide-memoires and\n**services** **effectively** **provided** satisfaction with technical project status reports\n**to support program** assistance, including skill\n**implementation** transfer activities\n\n\n**3(e)** NaCSA **management** 3e. 1 Project management - IDA Project Status reports\n**systems** **functioning** costs (NaCSA staff salaries at (including disbursement\n**effectively** **to ensure** all levels as well as operating reports);\n**program success** expenditures) are 13.5% or - NaCSA proposed annual\nless than total budgeted annual work program and budget\nexpenditures;                 - Annual audit reports;\n3e.2 NaCSA staff and                  - GOSL semi-annual PETS\npartners indicate satisfaction reports\nwith the performance of\nNaCSA's management;\n3e.3 NaCSA performance in\n\n\n                                  - 27", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000013:31:1:0", "start": 170, "end": 184, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.149, "luna_label": 0}]}, {"key": "aivin-176", "text": " indicators are:**\n\n\n**3) Increased student trust in the fairness of the general secondary education examination (Tawjihi) as an**\n**outcome of the digitalization of student assessment.** A recent survey <sup>11</sup> [^11: Centre for Strategic Studies-University of Jordan. 2022. Public Opinion Poll: Tawjihi, Universities and the Unified Admission\n<u>[System. https://view.officeapps.live.com/op/view.aspx?src=https%3A%2F%2Fjcss.org%2Fwp-](https://view.officeapps.live.com/op/view.aspx?src=https%3A%2F%2Fjcss.org%2Fwp-content%2Fuploads%2F2022%2F10%2FThe-pulse-of-the-Jordanian-street31English.docx&wdOrigin=BROWSELINK)</u>\ncontent%2Fuploads%2F2022%2F10%2FThe-pulse-of-the-Jordanian-street31English.docx&wdOrigin=BROWSELINK.] reflects a widespread lack of trust in the fairness\nof the exam by most students (47 percent). It is expected that the digitalization of the exam will enhance the perception\nby students of its objectivity and fairness.\n\n**4)** **Increased representation of women in leadership positions in the civil service as a result of performance-**\n**based promotions and competitive recruitment.** In 2020, women were officially appointed to only 16 percent of\nleadership positions in the civil service. The Program aims to improve the gender balance in leadership positions with\nmore performance-based promotions and competitive recruitments as a result of digital skills enhancement and by\nleveraging the use of the Human Resource Management Information System (HRMIS) and other HRM digital platforms.\n\n**Result Area 3 on transparency and accountability through digitalization: the PDO-level indicator is:**\n\n\n**5) Improved e-participation as an outcome of enhancing e-information and of interactive statistical**\n**information.** Jordan’s rating under the e-participation indicator of the UN E-Government Development Index in 2022", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000181:21:1:0", "start": 281, "end": 300, "surface": "Public Opinion Poll", "probe_tag": "confusion", "probe_score": 0.7235, "luna_label": 1}]}, {"key": "aivin-177", "text": "\n|Frequency|Quarterly|\n|Data source|Project MIS and Project Progress Reports.|\n|Methodology for Data<br>Collection|Monitoring project implementation including reporting from beneficiaries. BDF data fed to MINEMA.|\n|Responsibility for Data<br>Collection|BDF and MINEMA|\n|**Beneficiaries of matching grant that are still operational one year after intervention (Percentage)**|**Beneficiaries of matching grant that are still operational one year after intervention (Percentage)**|\n|Description|Quantitative indicator counting number of beneficiaires that received matching grants and that are<br>operational 1 year after the intervention.|\n|Frequency|Quarterly|\n|Data source|Project MIS and Project Progress Reports.|\n|Methodology for Data<br>Collection|Monitoring project implementation including reporting from beneficiaries. BRD data fed to MINEMA.|\n|Responsibility for Data<br>Collection|BRD and MINEMA.|\n|**Households benefiting from graduation programming (Number)**|**Households benefiting from graduation programming (Number)**|\n|Description|Quantitative indicator counting number of households that benefit from graduation programming.|\n|Frequency|Quarterly|\n|Data source|Project MIS and Project Progress Reports.|\n|Methodology for Data<br>Collection|Monitoring project implementation.|\n|Responsibility for Data<br>Collection|MINEMA.|\n|**Businesses that benefit from partial credit guarantee scheme (Number)**|**Businesses that benefit from partial credit guarantee scheme (Number)**|\n|Description|Quantitative indicator counting number of businesses that benefit from the BDF partial credit guarantee<br>scheme.|\n|Frequency|Quarterly|\n|Data source|Project MIS and Project Progress Reports.|\n|Method", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000188:47:1:0", "start": 189, "end": 211, "surface": "BDF data fed to MINEMA", "probe_tag": "confusion", "probe_score": 0.6196, "luna_label": 0}, {"key": "refugee_pads:000188:47:1:1", "start": 826, "end": 848, "surface": "BRD data fed to MINEMA", "probe_tag": "confusion", "probe_score": 0.6441, "luna_label": 0}]}, {"key": "aivin-178", "text": " for transportation, supplies, and school\nfees, contributing to increasing drop-out rates T o address this, MOEHE has decided to waive school fees\n\n\n\n**21**\n\n\n**22**\n\n\n\nrelation of stunting to poverty and food deprivation i s well established. It i s essential to address stunting in the first two\nyears of life to prevent its irreversible effects. Wasting (low weight for height) i s caused by temporary food deficiencies and\nby sudden economic deprivation such as war and natural catastrophes.\n\nThe World Bank report _Twenty-Seven Months-Intifada,_ _Closures and Palestinian Economic_ _Crisis_ features a comparison\nof welfare instruments-food, cash schemes, job creation, budget support-and concludes that the PA and municipalities have\ngreater impact from the macro-economic and welfare perspectives than employment generation activities implemented thus\nfar.\nAverage for both sexes in the Palestinian Territory (West Bank and Gaza Strip) as reported by PCBS “Net Enrollment Ratio\nby Stage, Sex and Region in _2002l2003.”_\n\n\n23", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000047:26:2:0", "start": 958, "end": 962, "surface": "PCBS", "probe_tag": "confusion", "probe_score": 0.6277, "luna_label": 1}]}, {"key": "aivin-179", "text": " mobile internet or Wi-Fi signal becomes available. This\nplatform has been deployed in places like Ghana to monitor urban issues at the district level, in\nUganda to track education outcomes at the school level, and in Liberia to monitor construction of\na WB-financed highway. The integrated platform will allow for the categorization and\nvisualization of the citizen feedback data collected through _Eyano_ and _CC_ on one website.\n\n\n53", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000067:62:1:0", "start": 359, "end": 380, "surface": "citizen feedback data", "probe_tag": "confusion", "probe_score": 0.7395, "luna_label": 1}]}, {"key": "aivin-180", "text": "**The World Bank**\nSouth Sudan Health Sector Transformation Project (HSTP) (P181385)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Frequency|Quarterly|\n|---|---|\n|Data source|Quarterly Health Facility Assessment|\n|Methodology for Data<br>Collection|TPM report|\n|Responsibility for Data<br>Collection|TPM / PMU|\n|**Percentage of general service availability score for refugees.**|**Percentage of general service availability score for refugees.**|\n|Description|Service availability is described by an index using the three areas of tracer indicators (infrastructure, workforce, and<br>utilization). This is made possible by expressing the indicators as a percentage score un-weighted average of the<br>three areas|\n|Frequency|Quarterly|\n|Data source|Quarterly Health Facility Assessment|\n|Methodology for Data<br>Collection|TPM report|\n|Responsibility for Data<br>Collection|TPM / PMU|\n\n\n\n**Monitoring & Evaluation Plan: Intermediate Results Indicators by Components**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Component 1: Provision of Basic Health Services Nationwide|Col2|\n|---|---|\n|**Percentage of Gender-Based Violence Services provided (Percentage) **|**Percentage of Gender-Based Violence Services provided (Percentage) **|\n|Description|Percentage of SGBV survivors who treated for assault + SGBV cases provided with emergency contraceptives +<br>SGBV cases referred out + SGBV survivors given PEP + Clinical management of rape + OPD Rape and GBV services|\n|Frequency|Quarterly|\n|Data source|DHIS|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH and UNICEF; Measures subcomponents 1.", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000006:46:0:0", "start": 148, "end": 184, "surface": "Quarterly Health Facility Assessment", "probe_tag": "confusion", "probe_score": 0.2196, "luna_label": 1}, {"key": "refugee_pads:000006:46:0:1", "start": 1450, "end": 1454, "surface": "DHIS", "probe_tag": "confusion", "probe_score": 0.4518, "luna_label": 1}]}, {"key": "aivin-181", "text": "**The World Bank**\nMauritania Social Safety Net System Project II (P171125)\n\n\nwill be published in the UNDB online, on the World Bank’s external website, and in at least one newspaper of\nnational circulation in the Borrower’s country, or in the official gazette, or a widely used website or electronic\nportal with free national and international access. SPNs for goods and non-consulting services will be procured\nusing National Competitive Bidding (NCB). Expressions of Interest for all consulting services with a cost less\nthan US$500,000 will be published in at least one newspaper of national circulation in the Borrower’s country.\n\n\n23. **Fraud and corruption.** All procurement entities as well as bidders and service providers (that is,\nsuppliers, service providers, and consultants) shall observe the highest standard of ethics during the\nprocurement and execution of contracts financed under the project in accordance with paragraphs 1.16 and\n1.17 (Fraud and Corruption) of the Procurement Guidelines and paragraph 1.23 and 1.24 (Fraud and\nCorruption) of the Consultants Guidelines, and the _‘Guidelines on Preventing and Combating Fraud and_\n_Corruption in Projects Financed by IBRD Loans and IDA Credits and Grants’_, dated July 1, 2016 in addition to\nthe relevant articles of the national public procurement legislation.\n\n\n**_Procurement Arrangements_**\n\n\n24. The project must use procurement methods for all activities in accordance with the World Bank’s\n‘Procurement Regulations for IPF Borrowers’ (Procurement Regulations) dated July 2016 and revised in\nNovember 2017 and August 2018 under the NPF.\n\n\n**_Assessment of Procurement Capacity of the Implementing Agency and Risks_**\n\n\n25. **The procurement capacity is considered moderately satisfactory** . The _Tadamoun_ Procurement team\nhave carried out procurement activities for the first phase project (P150430", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000045:51:0:0", "start": 103, "end": 114, "surface": "UNDB online", "probe_tag": "confusion", "probe_score": 0.8125, "luna_label": 0}]}, {"key": "aivin-182", "text": ">144 76 68 34|Col11|Col12|Col13|TOTAL<br>79,668<br>29,133<br>23,091<br>12,954<br>4,534<br>1,316|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|**_Total_**|**_8,186_**<br>|**_14,022_**<br>|**_23,155_**<br>|**_22,698_**<br>|**_21,491_**<br>|**_10,724_**<br>|**_8,099_**<br>|**_4,275_**<br>|**_18,551_**<br>|**_9,069_**<br>|**_6,798_**<br>|**_3,628_**<br>|**_150,696_**<br>|\n\n\n\n_Source: NPTP Database, Ministry of Social Affairs_\n\n**Essential Healthcare Package (EHCP)**\n\n5. The project will provide beneficiaries with a package of essential healthcare services\ncomprising of the following: (i) three age specific and gender wellness packages (ages 0-18,\nfemales 19 years and above, males 19 years and above); (ii) two care packages for the two\ncommon NCDs in Lebanon; diabetes and hypertension; and (iii) a", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000020:35:1:0", "start": 397, "end": 410, "surface": "NPTP Database", "probe_tag": "confusion", "probe_score": 0.682, "luna_label": 1}]}, {"key": "aivin-183", "text": "* Popular Benchmarks\n**children.**\n\n\n**Project** **Development** **Outcome** **/** **Impact** **Project reports:** **(from** **Objective** **to Goal)**\n**Objective:** **Indicators:**\n**Assist** **war affected** - Improved social capital and - Initial Social Assessment - Communities in the NSAP\n**communities** **to restore** organizational development; (to establish indicators and target areas are assisted to\n**infrastructure,** **services** **and** - Increased access to and use methodologies for social ensure a reduced risk of\n**build** **local** **capacity for** of social and economic capital and organizational conflict\n**collective** **action.** Priority infrastructure and services development)\nwill be given to areas not - Proportion of NSAP - Annual social assessments; - NACSA complements and\npreviously serviced by investments targeted to newly - NaCSA M&E data; extends the work of other\ngovernment, newly accessible accessible areas, & areas - M&E data of relevant line agencies and rninistries\nand the most vulnerable previously not served, and mninistries; in support of the PRSP's\npopulation groups within those vulnerable people within these - Beneficiary Assessment poverty reduction and\nareas. areas; (BAs) biannually; decentralization objectives\n\n                  - Proportion of sub-projects                   - Participatory evaluation\nthat reflect priorities of reports for a random sample of                   - NACSA is fully integrated\ntargeted communities and assisted conmmunities; into national planning and\nbeneficiaries; and                  - Technical audits resource allocation\n\n                  - Proportion of sub-projects frameworks (such as the\noperative 24emonths after National Recovery Strategy,\ncorpletion. the PRSP, and", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000017:29:1:0", "start": 862, "end": 876, "surface": "NaCSA M&E data", "probe_tag": "confusion", "probe_score": 0.2118, "luna_label": 0}, {"key": "refugee_pads:000017:29:1:1", "start": 961, "end": 969, "surface": "M&E data", "probe_tag": "confusion", "probe_score": 0.1144, "luna_label": 0}]}, {"key": "aivin-184", "text": " place un projet similaire à ce dernier dans d’autres quartiers démunis de Balbala. Les\nmodalités de la collaboration entre les quatre bailleurs sont donc fondées sur la volonté du Gouvernement\nd’assigner à chaque bailleur un quartier d’action distinct, ainsi qu’un domaine d’activité en relation avec\nson domaine d’expertise dans le pays. Le Gouvernement contribuera également au programme général, et\nses contributions financeront probablement des projets d’amélioration des infrastructures, de\ndéveloppement communautaire, et de microfinance. Les contributions actuelles font partie du budget de\n2008 et d’autres participations suivront.\n\n**<u>Emplacement des programmes</u>** **<u>Source de financement</u>** **<u>Coûts estimés</u>**\n<u>Quartier 7, Commune de Boulaos, DV</u> <u>AID</u> <u>3 millions US$</u>\nDivers quartiers, Commune de Balbala, DV AFD 8 millions US$\nAli Sabieh, Arta, Dikhil, Obock, Tadjourah BAD 12 millions US$\nDivers quartiers, Commune de Balabala, DV BID 8.5 millions US$\nContributions transversales au programme Gouvernement 7 millions US$\n**<u>Total</u>** **<u>38.5 millions US$</u>**\n\n\n**20.** **Les quartiers urbains ont été choisis comme étant les plus pauvres du pays suite à la**\n**réalisation de la carte de la pauvreté de Djibouti de", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000163:10:1:0", "start": 1234, "end": 1266, "surface": "carte de la pauvreté de Djibouti", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}]}, {"key": "aivin-185", "text": "up>24</sup> and updated in 2019. In 2020, a legal assessment was carried out, which contributed to the\nPrinciples and Governance Structure of the National Identity Program published by the GoE. The ID4D technical assistance\nalso contributed to the development of the Digital ID Proclamation, now adopted. In 2021, ID4D worked with NIDP to do\na costing for a Fayda rollout, which came up with (a) registering adults ages 18 and older only (US$283 million or US$3.8\nper registrant), (b) registering adults and children ages 14 and older only (US$308 million or US$3.5 per registrant), and\n(c) registering adults and children ages 5 and older only (US$334 million or US$2.8 per registrant). The World Bank has\nalso worked with NIDP to map use cases for Fayda, including identifying complementary World Bank engagements. During\n2022 pilots, ID4D conducted an exit survey and focus group discussions among PSNP beneficiaries who registered for\nFayda to get insights on any shortcomings of registration processes to fine-tune registration during scale-up. A conflict\nanalysis and end-user survey have contributed to the design and risk mitigation measures for this project.\n\n\n23 NBE. 2021. _Requirements for Undertaking Account Based Transactions and Ensuring of Regulatory Limits Directive No. FIS/04/2021_ .\n24 World Bank. 2016. _ID4D Country Diagnostic: Ethiopia_ . https://id4d.worldbank.org/country-action/id4d-diagnostics.\n\n\nPage 6 of 39", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000005:16:2:1", "start": 1052, "end": 1089, "surface": "conflict\nanalysis and end-user survey", "probe_tag": "confusion", "probe_score": 0.3443, "luna_label": 1}]}, {"key": "aivin-186", "text": " other development partners have shown the scope for efficiency gains, notably through profitable\ninvestments in technologies and processes for resource efficiency and cleaner production.\n\n\n16. **In addition, the regulatory and enabling environment is not supportive of new growth sectors.**\nDiscussions with Ugandan private sector actors and government officials indicate significant challenges with\nregulatory enforcement, contract enforcement, and the public private interface. A countrywide survey\ncommissioned in 2017 by the Uganda National Bureau of Standards (UNBS) found that 54 percent of the products\nin the Ugandan market are either fake or counterfeit. The number of backlogged civil suits in the commercial court\nsystem (which account for most of all substantive cases) has increased. Private investors have reported that the\nprocess to obtain an investment license is cumbersome.\n\n\n17. **There is lack of investment in new production locations and markets, that is caused by inadequate**\n**access to basic private sector services.** Land officially earmarked for industrial use—with appropriate access to\ninfrastructure services and reflecting sound environmental and social (E&S) planning—is not easily accessible\noutside of the main Southern Corridor along the Kampala road to the east and west, particularly for new and/or\nforeign investors. Government services in support of new investors (permits, certification, and so on) are generally\ndifficult to access outside greater Kampala. An investment outside Kampala faces significantly longer lead times\nand higher transactions costs than comparable investments within the Kampala region. As a result, the cost of\nlocating into new regions and markets of Uganda is prohibitively high and deters potential first-movers from\ninvesting.\n\n\n**Refugees and Host Sector Specific Issues**\n\n\n18. **There are few medium-to-large enterprises in RHDs that could stimulate the local product or labor**\n**market.** The business census recorded a total of 165 enterprises in Arua district that employ more than 10 workers\n(less than 1 firm per square kilometer", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000073:18:1:0", "start": 483, "end": 522, "surface": "countrywide survey\ncommissioned in 2017", "probe_tag": "keep", "probe_score": 0.9213, "luna_label": 1}, {"key": "refugee_pads:000073:18:1:1", "start": 1971, "end": 1986, "surface": "business census", "probe_tag": "confusion", "probe_score": 0.8714, "luna_label": 1}]}, {"key": "aivin-187", "text": "**The World Bank**\nMauritania Social Safety Net System Project II (P171125)\n\n\n\n\n\n\n\n\n\n**Sub-component 3.1: Enhancing the early warning system and the shock response coordination (US$1.5**\n**million)**\n\n\n43. **Through the following three activities, the proposed project will continue to strengthen the**\n**capacity of the Food Security Observatory** ( _Observatoire de la Sécurité Alimentaire_ OSA) (one of the CSA\nDirectorates) in its mandate to run the EWS for climate-related hazards:\n\na. _Support for primary data collection and data analysis_ : The proposed project will support the Food\n\nSecurity Monitoring Survey (FSMS) data collection twice a year at the district level (rather than the\npresent regional level), and the development of a food insecurity prediction model.\nb. _Development of a communication platform for better dissemination_ : The project will strengthen OSA\n\ncapacity through communication tools such as OSA web site and intranet, map unit, information\nbulletins, etc.\nc. _Strengthening of OSA's technical capacities_ : The project will also support capacity building for the OSA\n\nby bringing in additional technical expertise.\n\n\n[13 See https://www.agendaforhumanity.org/initiatives/5358](https://www.agendaforhumanity.org/initiatives/5358)\n\n\nPage **17** of **61**", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000045:22:0:0", "start": 587, "end": 619, "surface": "Food\n\nSecurity Monitoring Survey", "probe_tag": "confusion", "probe_score": 0.5325, "luna_label": 0}]}, {"key": "aivin-188", "text": "<mark>.</mark>\n\n\n<mark>.</mark>\n\n\n\n**<u><mark>PAD DATA SHEET</mark></u>**\n\n_<u><mark>West Bank and Gaza</mark></u>_\n\n_<u><mark>Gaza Solid Waste Management Project (P121648)</mark></u>_\n\n**<u><mark>PROJECT APPRAISAL DOCUMENT</mark></u>**\n\n\n_<u><mark>MIDDLE EAST AND NORTH AFRICA</mark></u>_\n\n_<u><mark>Urban Social and Disaster Risk Management Unit</mark></u>_\n\n\n\n\n\n\n\n\n\n\n\n|Report No.: PAD84<br>.|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|\n|---|---|---|---|---|---|---|---|---|---|\n|<br>**Basic Information**|<br>**Basic Information**|<br>**Basic Information**|<br>**Basic Information**|<br>**Basic Information**|<br>**Basic Information**|<br>**Basic Information**|<br>**Basic Information**|<br>**Basic Information**|<br>**Basic Information**|\n|Project ID|Project ID|Project ID|Project ID|EA Category|EA Category|EA Category|EA Category|Team Leader|Team Leader|\n|P121648|P121648|P121648|P121648|", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "sample:refugee_pads:000035:5:0:0", "start": 46, "end": 60, "surface": "PAD DATA SHEET", "probe_tag": "drop", "probe_score": 0.049, "luna_label": 0}]}, {"key": "aivin-189", "text": "13\n\n\n**_Monitoring and Evaluation_**\n\n\nMonitoring will be done according to the development indicators given in the attachment to Annex 1.\nThe project will strengthen the capacity of CNOSEGE, and the Planning Unit of the Ministry so that\nmonitoring reports on the implementation of the reform can include key progress and impact\nindicators. Currently the Planning unit generates statistical data on all aspects of the education sector,\nhowever this can be further strengthened to monitor progress on key reform objectives such as access,\nequity and quality. In addition, during the donors round-table UNESCO offered support to develop an\nEducation Management Information System (EMIS). If this is not in place by the end of Phase I of the\nAPL, this would be a priority item for Phase II.\n\n\nEvaluation of the impact of the reforms will be done by CNOSEGE by recruiting experts in this field\n\nand an initial evaluation will be done at the end of Phase I. Particular areas of impact assessment will\nbe student performance and success in reaching out to disadvantaged groups. Normally, student\nperformance would be measured by overall test results but as the pool of students widens to include\nstudents from less advantaged socioeconomic groups, there will be a downward pressure on test\nscores. The Planning Unit of the Ministry will be strengthened to monitor progress in reaching out to\ndisadvantaged groups and test scores of students by socioeconomic background. Staff will carry out a\nrandom survey (5 to 10% sample) of students by socioeconomic background in 2001 to establish a\nbaseline. To keep the survey simple, the socioeconomic background questions will be limited to easily\nidentified categories such as day-laborers, civil servants, shopkeepers etc. The survey will be repeated\nin 2005 and 2110.\n\n\n**D.** PROJECT RATIONALE\n\n\n**1. Project alternatives considered and reasons for rejection**\n\nOriginally, the project was designed as a Sector Investment Loan, however, given the Government's\ncommitment to the education sector, and the", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "sample:refugee_pads:000100:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "confusion", "probe_score": 0.582, "luna_label": 0}, {"key": "sample:refugee_pads:000100:16:0:1", "start": 1487, "end": 1500, "surface": "random survey", "probe_tag": "drop", "probe_score": 0.012, "luna_label": 0}]}, {"key": "aivin-190", "text": ".....................................64**\n\n**Annex 5: Economic and Financial Analysis ..............................................................................69**\n\n**Annex 6: Major Related Projects Financed by the World Bank and/or other Agencies ......81**\n\n**Map IBRD 43032**\n\n\nvi", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000144:5:1:0", "start": 267, "end": 281, "surface": "Map IBRD 43032", "probe_tag": "drop", "probe_score": 0.0368, "luna_label": 0}]}, {"key": "aivin-191", "text": "Annex 1\nPage 2 of 3\n\n\n**Project Development** **Outcome / Impact** **Project reports:** **(from Objective to Purpose'**\n**Objective:** **Indicators:**\nExpand access to basic Increased number of school Project Reports. It is assumed that\neducation. places. Government's current fiscal\nsituation will be resolved\n\n(salary payment to civil\nservants and teachers).\nEnrollment increases in MOE reports. Expansion of facilities and\nprimary schools from 35,000 quality will contribute to\nto 80,000 increased enrollment\nincluding among girls.\nIncreased availability of It is assumed that the\ntextbooks. Government maintains\ndouble-shifting.\n\n\nTrained primary school head Headteachers have autonomy\nteachers. and authority in managing the\nschools.\nBetter trained contractual Contractual teachers are\nteachers recruited early enough before\nthe school year to allow time\nfor training.\n\n\n**Output from each** **Output Indicators:** **Project reports:** **(from Outputs to Objective)**\n**Component:**\nIncreased number of school 226 classrooms will be built Monthly disbursement Availability of school places\nplaces. increasing capacity by over summary. will increase enrollment.\n20,000 based on double\nshifting.\nProvide textbooks. Numbers of textbooks per Semi-annual Provision of textbooks will\npupil increases. supervision reports. improve learning.\nTrained primary school head- Primary school head-teachers Annual audit reports; Better trained head-teachers\nteachers. trained and Guidebook for site visits. will improve school\nschool management prepared efficiency.\nand distributed.", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000147:30:0:0", "start": 385, "end": 396, "surface": "MOE reports", "probe_tag": "drop", "probe_score": 0.0342, "luna_label": 0}]}, {"key": "aivin-192", "text": "13\n\n\n**_Monitoring and Evaluation_**\n\n\nMonitoring will be done according to the development indicators given in the attachment to Annex 1.\nThe project will strengthen the capacity of CNOSEGE, and the Planning Unit of the Ministry so that\nmonitoring reports on the implementation of the reform can include key progress and impact\nindicators. Currently the Planning unit generates statistical data on all aspects of the education sector,\nhowever this can be further strengthened to monitor progress on key reform objectives such as access,\nequity and quality. In addition, during the donors round-table UNESCO offered support to develop an\nEducation Management Information System (EMIS). If this is not in place by the end of Phase I of the\nAPL, this would be a priority item for Phase II.\n\n\nEvaluation of the impact of the reforms will be done by CNOSEGE by recruiting experts in this field\n\nand an initial evaluation will be done at the end of Phase I. Particular areas of impact assessment will\nbe student performance and success in reaching out to disadvantaged groups. Normally, student\nperformance would be measured by overall test results but as the pool of students widens to include\nstudents from less advantaged socioeconomic groups, there will be a downward pressure on test\nscores. The Planning Unit of the Ministry will be strengthened to monitor progress in reaching out to\ndisadvantaged groups and test scores of students by socioeconomic background. Staff will carry out a\nrandom survey (5 to 10% sample) of students by socioeconomic background in 2001 to establish a\nbaseline. To keep the survey simple, the socioeconomic background questions will be limited to easily\nidentified categories such as day-laborers, civil servants, shopkeepers etc. The survey will be repeated\nin 2005 and 2110.\n\n\n**D.** PROJECT RATIONALE\n\n\n**1. Project alternatives considered and reasons for rejection**\n\nOriginally, the project was designed as a Sector Investment Loan, however, given the Government's\ncommitment to the education sector, and the", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "sample:refugee_pads:000102:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "confusion", "probe_score": 0.582, "luna_label": 0}, {"key": "sample:refugee_pads:000102:16:0:1", "start": 1487, "end": 1500, "surface": "random survey", "probe_tag": "drop", "probe_score": 0.012, "luna_label": 0}]}, {"key": "aivin-193", "text": " the nationwide adaptive social safety net system**|**Increased effectiveness and efficiency of the nationwide adaptive social safety net system**|**Increased effectiveness and efficiency of the nationwide adaptive social safety net system**|**Increased effectiveness and efficiency of the nationwide adaptive social safety net system**|\n|Programs using the Social<br>Registry to select their<br>beneficiaries (Number)||7.00|8.00|10.00|12.00<br>|14.00|15.00|\n|SSN programs' beneficiary<br>households avoiding negative<br>coping strategies<br>(national/refugees) (Text)||N/A|N/A|60%|65%<br>|70%|70%|\n|**Increased coverage of poor and vulnerable hhs with targeted ST, including in refugee and host comm**|**Increased coverage of poor and vulnerable hhs with targeted ST, including in refugee and host comm**|**Increased coverage of poor and vulnerable hhs with targeted ST, including in refugee and host comm**|**Increased coverage of poor and vulnerable hhs with targeted ST, including in refugee and host comm**|**Increased coverage of poor and vulnerable hhs with targeted ST, including in refugee and host comm**|**Increased coverage of poor and vulnerable hhs with targeted ST, including in refugee and host comm**|**Increased coverage of poor and vulnerable hhs with targeted ST, including in refugee and host comm**|**Increased coverage of poor and vulnerable hhs with targeted ST, including in refugee and host comm**|\n|Beneficiaries of social safety<br>net programs", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000045:39:1:0", "start": 358, "end": 376, "surface": "Social<br>Registry", "probe_tag": "drop", "probe_score": 0.0275, "luna_label": 1}]}, {"key": "aivin-194", "text": " operational responsibilities for the** **Component 2: Access to**\n**Socio-Economic Infrastructure and Development of Economic Opportunities** . Each agency will manage separate\ndesignated accounts to ensure clear, effective financial management aligned with their respective components.\n\n59. **The capacities of the MASS and ADDS to manage the new proposed project were assessed carefully during**\n**project preparation** . MASS has considerable experience implementing social protection programs, including cash\ntransfers, social registries, and behavioural change communication. ADDS brings experience in managing development\nprojects in rural areas involving civil works, sustainable economic opportunities and service delivery.\n\n\n60. **A Financial Management assessment has been conducted according to IPF policy and directives to verify that**\n**the current systems provide reasonable assurance of resource allocation for their designated purpose.** The assessment\nfocuses on the capacities of the implementing agencies, leveraging insights from previous projects. Detailed arrangements\nfor budgeting, accounting, staffing, internal controls, fund flow, reporting, and auditing have been outlined during project\npreparation, with measures to mitigate fiduciary risks.\n\n\n**B. Results Monitoring, Evaluation, and Verification Arrangements**\n\n61. **The project’s monitoring and evaluation (M&E) arrangements reflect the capacity constraints and operational**\n**challenges faced by a project with two implementing agencies, which lack an integrated management information**\n**system, including the M&E.** The project will be implemented with necessary oversight at the local level, where M&E\ncapacities will be mobilized for the collection, organization and analysis of project-related data. The main instrument for\nM&E will be the Results Framework (Annex 1). The indicators at PDO and intermediate levels, as well as their respective\nbaselines, midterm, and end-of-project targets, will serve as the basis for reporting progress. The indicators, data sources,\nand data collection methods", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000180:26:1:0", "start": 1772, "end": 1792, "surface": "project-related data", "probe_tag": "drop", "probe_score": 0.0333, "luna_label": 0}]}, {"key": "aivin-195", "text": " capita -O 2 -0 5 Lower-middle-income _group_\nESports of goods and servic\"s\n\n\n**STRUCTURE ofthe ECONOMY**\n\n**1979** **1989** **1998** **1999** **Growth rates of output and Investment ()**\n_{%I ol GOP)_\nAgriculture 3.4 _.._ **_2._**\nIndustry 21.0 O.Manufacturing 5.6 **_-2_** **_94_** _as_ _se_ _s_\nServices **75.6** 6\n\nPrivate consumption **-r.**\nGeneral government consumplion **G**\nImports of goods and services\n\n\n\n**1979-89** **1989-99** **1998** **1999**\n_(average annual orowth)_\nAgriculture\n\n\n\nIndustry\n\n\n\nManufacturing\nServices\n\n\n\nPrivate consumption\nGenerai government consumption\nGross domestic investment\nImports of goods and services\nGross national product 1 7 **1.4**\n\n\nNote. 1999 data are preliminary estimates.\nThis table was produced from the Development Economics central database.\n\nThe diamonds show four kev midicators in the country (in bold) compared with its income-group average. 11 data are missing, Ihe diamond will", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000147:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "drop", "probe_score": 0.0227, "luna_label": 0}, {"key": "refugee_pads:000147:62:2:1", "start": 758, "end": 796, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.9824, "luna_label": 1}]}, {"key": "aivin-196", "text": " the IFRs on a yearly basis. The annual reports will be submitted to\nthe World Bank within six months from the closure of each fiscal year and the limited review opinion will be\nsubmitted to the World Bank along with the yearly audit report.\n\nSEAS will ensure that the recruitment of the external auditor will be done six months after project effectiveness\nin order for the auditor to start the field work early enough to deliver the audit report and management letter\nwithin the deadlines.\n\n_Technical audit of the project:_ technical audits will be carried out at mid-term and at the end of the project for the\nactivities related to the cash transfers and the sub-projects. SEAS will enter into a contract with a technical auditor\nwith ToR acceptable to the World Bank. The technical auditor will verify that all activities related to the cash\ntransfers and sub-projects are carried out as per the terms and conditions set in the POM.\n\n_Flow of information_ : SEAS will be responsible for preparing periodic reports on project implementation progress\nand on both physical and financial achievements. These reports will be based on project activity progress (by\ncomponent and expenditure category), including technical and physical information reported on a quarterly\nbasis. SEAS will maintain the project bookkeeping and will produce annual PFSs and quarterly IFRs.\n\n\nPage 41 of 44", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000154:45:1:0", "start": 1133, "end": 1158, "surface": "project activity progress", "probe_tag": "drop", "probe_score": 0.0316, "luna_label": 0}]}, {"key": "aivin-197", "text": " the situation that was agreed upon with\nthe Borrower.\n\nSigned by:\nFinancial Management v I _N_\n##### Specialist K {dO'.i1, O0\n\n(FMS-OPR) Rafika Chaouali, MNSHD Date\n\n\n**Part II:** **Procurement/Contract Management** System\n\nI have reviewed the procurement/contract management system relating to this project, including\nthe format and content of the section on Project Management Reports (PMRs) on procurement\n\nmonitoring. The objective of the review was to determine whether the procurement/contract\nmanagement system adopted by the project conforms to IDA's guidelines for procurement in\ninvestment projects. My review was based on the \"Assessment of Agency's Capacity to\nImplement Project Procurement, Setting of Prior Review Thresholds and Procurement\nSupervision Plan\" guidelines issued by IDA.\n\nI confirm that the project satisfies IDA's minimum procurement management requirements.\nHowever, in my opinion, the project does not have in place an adequate procurement/contract\nmanagement system that can provide the appropriate data on major procurement and contract\nmanagement (PMR - Section 3) as required by IDA.", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000090:54:1:0", "start": 1032, "end": 1081, "surface": "data on major procurement and contract\nmanagement", "probe_tag": "drop", "probe_score": 0.0296, "luna_label": 0}]}, {"key": "aivin-198", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\n\n\n\n\n\n\n\n|Monitoring & Evaluation Plan: PDO Indicators|Col2|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Indicator Name **|**Definition/Description **|**Frequency **|**Datasource **|**Methodology for Data**<br>**Collection **|**Responsibility for Data**<br>**Collection **|\n|Beneficiaries of social safety net programs||This indicator<br>will be<br>measured at<br>least on a<br>quarterly<br>basis during<br>missions and<br>ISRs<br>|SNSOP MIS<br>which hosts<br>beneficiary<br>registration<br>and payment<br>data<br>|The implementing<br>partner will collect<br>beneficiary data during<br>targeting and<br>registration. The<br>payment service<br>provider will document<br>payment data and<br>share with the<br>implementing partner<br>|Implementing Partner<br>|\n|Beneficiaries of social safety net<br>programs - Female||This indicator<br>will be<br>measured at<br>least on a<br>quarterly<br>basis during<br>missions and<br>ISRs<br>|SNSOP MIS<br>which hosts<br>beneficiary<br>registration<br>and payment<br>data<br>|The implementing<", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "sample:refugee_pads:000153:55:0:0", "start": 655, "end": 671, "surface": "beneficiary data", "probe_tag": "confusion", "probe_score": 0.0877, "luna_label": 0}, {"key": "sample:refugee_pads:000153:55:0:1", "start": 765, "end": 777, "surface": "payment data", "probe_tag": "drop", "probe_score": 0.0486, "luna_label": 0}]}, {"key": "aivin-199", "text": "2. Broader social capital building outcome: Capacity building interventions for implementing\npartners and local authorities would include conflict resolution training, and emphasize sub-project\ndesign and supervision processes which build social capital and mutual accountability between\ncommunities and the array of support organizations and institutions.\n\n\n3. Intra-community equity outcomes: To address intra-community equity issues in a more\nsystematic manner, NaCSA would revise its Operations Manual to enhance procedures for community\nneeds identification and sub-project selection. The enhanced guidelines would include, inter alia, a\nrequirement for implementing partners to fill out a Community Assessment Form. This would include\ninformation on community population (by gender and age group), basic infrastructure, main income\ngenerating activities, housing, organization, forms and levels of outside assistance, and particularly\nvulnerable groups.\n\n6.5 How will the project monitor performance in terms of social development outcomes?\n\nA Social Assessment is under preparation. It is being designed so as to ensure that the project\nresponds to social development concerns by identifying the opportunities, constraints and social risks\n\n\n\ninherent in the project and translating these ideas into practical design and implementation measures.\nThe World Bank's Social Capital Assessment Tool (SOCAT) and instruments for analyzing local\ninstitutions will be used in the Social Assessment. Subsequent iterative rounds of social analysis using\nsimple locally adapted variants of these tools would continue during the duration of the project to collect\nand analyze data on social development issues. These would include associational memberships and\ntrust, the process of creation and destruction of social capital and data on groups and networks,\n\n\n\nsubjective well-being, political engagement, sociability, community activities, cohesion and\ncommunication. The Assessment would thus provide baseline data on social issues, recommend and\n\n\n\napply social capital measurement tools, and provide a framework for continuing social analysis\nthroughout the project.\n\n\n\n7. Safeguard Policies:\n7.1 Are any of the following safeguard policies triggered by the prcect?\n\n     - '& **~** P19 ~ f _-", "source": "refugee_pads", "subset": "annotate_aivin", "spans": [{"key": "refugee_pads:000012:24:0:0", "start": 1670, "end": 1703, "surface": "data on social development issues", "probe_tag": "drop", "probe_score": 0.0226, "luna_label": 0}]}, {"key": "aivin-200", "text": "**\n\n**o grupos de personas que se han**\n**visto forzados a huir de sus casas**\n\n**o lugares de residencia habitual,**\n**especialmente como resultado de**\n\n**o con el fin de evitar un conflicto**\n**armado, situaciones de violencia**\n**generalizada, violaciones de derechos**\n**humanos o desastres naturales o**\n**provocados por la mano del hombre,**\n**y que no han traspasado frontera**\n\n\n\n**de las estadísticas de ACNUR,**\n**esta categoría de población sólo**\n**incluye a los desplazados internos**\n**por conflictos a los que la Agencia**\n**extiende su protección y/o asistencia.**\n**La población desplazada también**\n**incluye a personas en situación**\n\n\n**Refugiados retornados son ex**\n**refugiados que han regresado a su**\n**país de origen espontáneamente**\n\n**o de manera organizada pero que**\n**no se han integrado plenamente**\n**todavía. Este retorno sólo tendría**\n**lugar normalmente en condiciones**\n**de seguridad y dignidad. A efectos**\n**de este informe, se incluyen**\n**exclusivamente los refugiados que**\n**retornaron entre enero y diciembre**\n**de 2012. Sin embargo, en la práctica,", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001451:36:1:0", "start": 398, "end": 419, "surface": "estadísticas de ACNUR", "probe_tag": "keep", "probe_score": 0.9226, "luna_label": 1}]}, {"key": "aivin-201", "text": "UFA 1 corresponds to the Old City of Homs, a traditional,\nmiddle-income area that housed mixed-use functions,\nincluding residential, commercial and light crafts. The area\nwas severely destroyed during the war. UFAs 2 and 4 are\ncontemporary extension areas with a modern building typology\nand service networks housing mostly upper-income groups. In\nthis classification, UFA 2 refers specifically to the first planned\nexpansion outside the historic boundaries of the city, while UFA 4\nrefers to the post-1980s modern expansion of the city. Unlike the\nabove-mentioned three areas, UFAs 3 and 5 comprised mostly\ninformal housing and market areas developed at the outskirts of\nthe old city on the eastern, southern and northern sides for UFA\n3, and further away to the east for UFA 5.\n\n\n\nof confidence. Household samples for each of the five UFAs\nwere randomly drawn from the UNHCR registration database,\nwhich records sub-city place of origin in Syria, and these were\nthen geolocated in Lebanon. The number of registered cases in\nLebanon from each UFA in Homs City was determined through a\nfirst round of pre-sampling via phone interviews **(Table 1)** .\n\n\nSubsequently, both quantitative and qualitative data-collection\ntools were used. In the summer of 2017, a household (HH) survey\nwas conducted in Lebanon with a cluster of 1,514 households\n(covering 6,767 persons) from the five UFAs of Homs. The survey\ncovered questions on HLP issues in both Lebanon and Syria,\nrefugees’ civil documentation, displacement (from Syria and\nwithin Lebanon), mobility within Lebanon, perceived barriers\nto return and access to information about their HLP rights.\nIndividuals who responded to the questionnaire were asked\nto identify themselves as, or their relation to, the head of the\nhousehold. The survey enquired households about how they\naccessed shelter, what percentage of their income they spent\non rent and other housing costs, what condition their shelter", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "sample:reliefweb:000666:12:0:0", "start": 871, "end": 898, "surface": "UNHCR registration database", "probe_tag": "keep", "probe_score": 0.9853, "luna_label": 1}, {"key": "sample:reliefweb:000666:12:0:1", "start": 1259, "end": 1280, "surface": "household (HH) survey", "probe_tag": "keep", "probe_score": 0.939, "luna_label": 0}]}, {"key": "aivin-202", "text": "\ndisplaced communities to the extent that we find that\nnone feel they are being treated equally and with the same\nrights in their day-to-day co-existence in the urban space.\n\n\nThese negative dynamics clearly influence community\nmembers’ thinking and yield extreme positions\nwhen people are asked how to resolve issues facing\ntheir neighbourhoods. For example, in all FGDs with\nhost community members, participants agreed that\nsegregating displaced communities, in particular Arab\nIDPs, into separate, specifically designated places outside\nof urban areas is the most desired outcome for easing\nstrain, irrespective of the human and economic costs\nthis involves. Other more rights-based approaches, that\ninvolve inter-community dialogue and cooperation, must\nbe designed in response to such antagonism.\n\n\n\n**14**", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001035:15:1:0", "start": 367, "end": 371, "surface": "FGDs", "probe_tag": "keep", "probe_score": 0.9264, "luna_label": 1}]}, {"key": "aivin-203", "text": "##### Water, Sanitation and Hygiene\n\nIn the context of return, refugees require access to safe drinking water and adequate and gendersensitive sanitation facilities during the entire duration of their journey. WASH interventions play a\ncrucial role in limiting the risk of contracting infectious diseases and ensuring the dignity and safety of\nthe returnees, especially women, through the provision of hygiene supplies and set-up of secure and\nprivate WASH facilities.\n\nWhile the scale of the intervention depends on the level of return and the specific support needs of\nthe countries, the type of WASH assistance planned, during Phases 1 and 2, encompasses a range of\nactivities, from ensuring sanitation facilities in waiting areas according to SPHERE standards _,_ to\nensuring during the return movement adequate quantity and quality of drinking water, and upon\nreturn providing hygiene items, including diapers and women's sanitary pads.\n\nIn the case of large-scale facilitated return, preparatory steps include identifying and planning for\nadequate water and sanitation measures. Wherever possible, water and sanitation response is\ncombined with the promotion of good personal and environmental hygiene. Host and return country\noperations coordinate closely on WASH assistance in order to guarantee WASH coverage and\ncontinuity of assistance throughout the entire return journey. Planned activities also include\ndecommissioning of the distributed WASH installations in informal settlements where returning\nrefugees resided (e.g. water tanks, latrines).\n\n##### Shelter\n\nWhen asked about their intentions to return, refugees reported the need for information on\navailability of shelter and status of their property as among their top three information needs. <sup>12</sup> [^12: Refugee Perceptions and Intentions Survey (5th round, surveyed Nov. 2018 to Jan. 2018), publication forthcoming. To\nnote, the intentions surveys were carried out in Egypt, Iraq, Jordan and Lebanon.] The\ncore shelter activities, during both Phases 1 and 2, focus on aiming to ensure that those deciding to\ngo back have the needed information on the general shelter conditions in locations of anticipated\nreturn", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000391:10:0:0", "start": 1782, "end": 1823, "surface": "Refugee Perceptions and Intentions Survey", "probe_tag": "keep", "probe_score": 0.9005, "luna_label": 1}]}, {"key": "aivin-204", "text": "Regular Surveys on Social Tensions throughout Lebanon: Wave IV September 2018\n\n\ncoincided with the Wave III survey, and this most likely provoked a ‘spike’\n\n\nin public confidence. The 34.3% who said the Cabinet ‘improved life a little’\n\nand the 4.4% who said the Cabinet ‘improved life a lot’ represented about\n\n\na ten-percentage point drop in confidence, but the level of confidence in\n\nthis institution, even with the new Cabinet’s formation still pending,\n\n\nnevertheless exceeded the baseline levels of _distrust_ observed in the initial\n\nWave I and Wave II surveys. While public support for the Lebanese Armed\n\n\nForces (LAF) remained near-unanimous, increasing support both for the\n\nInternal Security Forces (ISF) and General Security (GS) brought public\n\n\nsupport the two other major Lebanese security agencies closer in line with\n\npublic support for the LAF.\n\n\n**Figure 8:** _Lesser_ trust in government institutions, by governorate and wave, as a per cent\nof scale maximum.\n\n\nARK DMCC | 27", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "sample:reliefweb:000696:27:0:0", "start": 0, "end": 53, "surface": "Regular Surveys on Social Tensions throughout Lebanon", "probe_tag": "keep", "probe_score": 0.9977, "luna_label": 1}, {"key": "sample:reliefweb:000696:27:0:1", "start": 99, "end": 114, "surface": "Wave III survey", "probe_tag": "keep", "probe_score": 0.9006, "luna_label": 1}]}, {"key": "aivin-205", "text": " referred to the evening shift in order to get the\nmedication and you have to pay.”\nFemale student, Sarbasti, host community.\n\n“There is just a morning shift in the health facility in our\nneighbourhood. During the morning, you cannot get any\nmedication or medical test at all. By the end of the day, we\nhave to go to the private services.”\nAdult woman, Hasarok, host community.\n\n**Water and electricity provision in urban**\n**areas**\n\n\nWhile most of the public services in the Kurdistan Region\nare provided for free, residents must pay service fees for\nwater and electricity. Electricity supply data for 2014\nsuggests that the amount billed is approximately 10% of\nthe total operating cost incurred by the government (not\nincluding capital investment), while collection rates were\naround just 75% <sup>17</sup> . Survey data collected for this report\nindicates that 90% of households actually pay electricity\nfees, while only 30% do so with regards to water fees. Both\nwater and electricity supply underwent shocks due to the\nsudden increase in demand on one side stemming from\nan influx of displaced populations, and lack of available\nbudget to fund the operations on the other side due to\nfinancial crisis. The following data on supply reliability\noriginate from secondary sources <sup>18</sup> [^18: REACH Initiative (2015a).] .\nIn terms of electricity provision, there is virtually\nuniversal, though not 24 hours, connection to the national\ngrid across all urban areas. Displaced families living in\ninformal settlements (3% of the total households in urban\nareas), however, may not have access to this supply. In\nErbil district centre, 46% of households reported having\n\n\n16 Disaggregated data on health access is available in Section E of the data\nannex.\n17 MERI (2016). In the best of times and the worst of times: addressing\nstructural weaknesses of the Kurdistan Region’s economy. January 2016.\n18", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001035:25:2:1", "start": 813, "end": 824, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9442, "luna_label": 0}]}, {"key": "aivin-206", "text": " partially due to sensitization\nefforts by ONARS, in collaboration with Immigration Services.\n\n\nOver the past three years, UNHCR and ONARS conducted 42 sensitization sessions on refugee rights,\ninvolving refugees, host communities, local administrative authorities, and security agents in two regions\nhosting refugees. These capacity-building efforts aimed to raise awareness and enhance understanding of\nrefugee and asylum-seekers’ rights within the national system.\n\n\nDjibouti’s asylum system, as governed by the <u>[2017 Refugee Law and the](https://migrationpolicy.unescwa.org/sites/default/files/policies/2017_Djibouti_Law_No_159.pdf)</u> <u>[2017 Decree on Asylum Procedure,](https://migrationpolicy.unescwa.org/sites/default/files/policies/2017_Djibouti_2017_409_PR_MI.pdf)</u>\nmandates individual refugee status determination (RSD) procedures for asylum-seekers unless they belong\nto groups or countries approved for prima facie refugee recognition. ONARS plays a pivotal role as the\nSecretariat, overseeing asylum-seeker registration, eligibility interviews and RSD assessments. The National\nEligibility Commission (NEC) makes decisions on refugee status based on RSD files prepared by ONARS.\nHowever, information about these procedures remains inaccessible in languages understood by all asylumseekers, leading to awareness gaps. While legal counsel is allowed by law, no asylum-seekers have utilized\nthis right thus far. The appeal procedure, initiated in 2022, is not yet fully operational.\n\n\nDuring the review period, people arriving from South Central Somalia and Yemen continued to be granted\nrefugee status through a prima facie approach, despite the absence of a formal declaration by the\ngovernment. Asylum applicants from other countries of origin went through individualized RSD procedures\n\n\nR E F U G E E P O L I C Y R E V I", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001508:2:5:0", "start": 1173, "end": 1200, "surface": "RSD files prepared by ONARS", "probe_tag": "confusion", "probe_score": 0.0723, "luna_label": 1}]}, {"key": "aivin-207", "text": " Security and Law Enforcement (MHAS), Malta. UASC figures are based\non age declared by the refugees and migrants upon arrival. Not all the persons who make\nsuch a declaration are recognised to be UASC by the authorities after the age assessment\nis conducted.\n\n8. For 2019, asylum application data is used to provide an estimate of child arrivals in\n\n2019. Data on arrivals in Bulgaria comprising apprehensions upon irregular entry, exit or\ninside the country is not disaggregated by age. The vast majority of children arriving in\nBulgaria apply for asylum.\n\n9. Under emergency regulations adopted by the Hungarian government in 2017, unaccom\npanied and separated asylum-seeking children of and above the age of 14 years are\nconfined to the transit zones for the duration of the asylum procedure.\n\n10. The Flow Monitoring Surveys (FMS) are part of the IOM’s Displacement Tracking Matrix\n(DTM) activities in the Mediterranean region, started in October 2015 and conducted within\nthe framework of IOM’s research on populations on the move through the Mediterranean\nand Western Balkan Routes to Europe. Surveys are analysed to provide information on\nprofiles, transit routes and vulnerabilities of respondents. Below are summary findings\nbased on the analysis of different nationality groups.\n\n11. Data on asylum applications and decisions are from Eurostat [migr_asyappctza, migr\n\nasyunaa, migr_asydcfsta, migr_asypenctzm], last checked on April 29 2020. Data for Spain\nand Cyprus on asylum applicants considered to be UASC were not available at the time of\nclosing the report; all data are constantly subject to revisions/adjustments.\n\n12. European Union Member States plus the United Kingdom, Iceland, Liechtenstein,\nNorway and Switzerland.\n\n13. The European Union Agency for Fundamental Rights released the\nlessons learned report on the unaccompanied children relocation\nschemes: https://fra.europa.eu/en/publication/2020/re", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001341:7:4:0", "start": 273, "end": 296, "surface": "asylum application data", "probe_tag": "confusion", "probe_score": 0.6616, "luna_label": 1}, {"key": "reliefweb:001341:7:4:1", "start": 356, "end": 384, "surface": "Data on arrivals in Bulgaria", "probe_tag": "confusion", "probe_score": 0.5896, "luna_label": 1}]}, {"key": "aivin-208", "text": "<br>-<br> <br>-<br> <br>5<br> <br>636<br> <br>Hungary<br>-<br> <br>-<br> <br>-<br> <br>-<br> <br>-<br> <br>-<br> <br>-<br> <br>-<br> <br>-<br> <br>-<br> <br>-<br> <br>-<br> <br>-<br> <br>8<br> <br>Bangladesh<br>*<br>-<br> <br>11<br> <br>7<br> <br>-<br> <br>*<br>-<br> <br>*<br>35<br> <br>8<br> <br>-<br> <br>12<br> <br>150<br> <br>46<br> <br>** October and November 2009 only due to unavailability of data for December.<br>*** Figures may include citizens of Montenegro in the absence of separate statistics available for Serbia and for Montenegro.<br>**** UNHCR estimates.<br>***** Combination of number of cases (DHS) and persons (EOIR).|\n\n\nAsylum Levels and Trends in Industrialized Countries, 2009 31", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "sample:reliefweb:000810:29:114:0", "start": 643, "end": 695, "surface": "Asylum Levels and Trends in Industrialized Countries", "probe_tag": "confusion", "probe_score": 0.2234, "luna_label": 0}]}, {"key": "aivin-209", "text": " de América\n(un aumento tres veces superior, a 730.400).\n\n\n\n**80** **Los datos de algunos países pueden incluir una cantidad considerable de solicitudes repetidas, si la persona solicitante presentó al menos**\n**una solicitud anterior en el mismo país o en otro.**\n**81** **Los datos estadísticos sobre los resultados de las apelaciones y los procedimientos judiciales en materia de asilo no se registran en las**\n**estadísticas del ACNUR, especialmente en los países de renta alta, porque los Estados a menudo no recopilan o no publican este tipo de datos.**\n**82** **En 2022, en Costa Rica, las personas nicaragüenses y venezolanas (solo de octubre a diciembre) iniciaron sus solicitudes luego de obtener**\n**una cita para formalizar su solicitud de asilo. Solo se incluyeron en las estadísticas de solicitantes de asilo quienes formalizaron sus**\n**solicitudes. El proceso para concertar citas para las personas nicaragüenses y venezolanas finalizó en diciembre de 2022.**\n**83** **Se excluyen los 3,8 millones de personas ucranianas que recibieron protección temporal.**\n**84** **Las decisiones sustantivas incluyen el estatus de la Convención, la protección complementaria y otras formas de protección, como también**\n**los casos rechazados.**\n\n\n30 ACNUR > **TENDENCIAS", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001047:29:4:1", "start": 785, "end": 822, "surface": "estadísticas de solicitantes de asilo", "probe_tag": "confusion", "probe_score": 0.2863, "luna_label": 1}]}, {"key": "aivin-210", "text": "The highest regional allocation to the **return priority** was in Western Europe (37%), although the UK and\nIreland allocated far more on average (39.8%) than other states in the region (32%).\n\nc. National allocations to AMIF priorities vs. needs in Member States\n\nStudy respondents in four MS (BE, FI, IE, IT) reported the **distribution of AMIF basic allocations across**\n**National Programme priorities as being appropriate for the predominant needs** in their contexts. In Finland,\nthe strong focus on allocating funding to asylum, reception and integration was particularly welcomed.\n\n**An undue focus on returns** in AMIF allocations to National Programme priorities was noted by respondents\nin the UK (57.4% allocation) and the Czech Republic (27.4% allocation).\n\nIn Cyprus, whilst the need to allocate substantial AMIF funding to reception capacity within the asylum\npriority was acknowledged, respondents felt that the allocation to the integration priority was insufficient to\nmeet prevailing needs.\n\nRespondents in Greece and Portugal noted **the use of outdated statistics when weighting funding across**\n**National Programme priorities**, which in Greece led to overestimated needs for the return priority. Actors\nin Spain also reported the negative impact of the reliance on data from 2011-12 to set priorities and\nallocations, noting the substantial increase in asylum arrivals since the National Programme was finalised and\na subsequent insufficient allocation to the asylum priority.\n\nRegardless of respondent views on the extent to which the distribution of AMIF funds across programme\npriorities accurately reflected national needs, **a lack of programme-level information on AMIF**\n**implementation in MS means it is not possible to judge how far AMIF spending is achieving the priorities**\n**envisaged in National Programmes** .\n\n\n\n\n\n\n\n\n\nJANUARY 2018  23", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000398:22:0:0", "start": 1065, "end": 1084, "surface": "outdated statistics", "probe_tag": "confusion", "probe_score": 0.8936, "luna_label": 1}, {"key": "reliefweb:000398:22:0:1", "start": 1289, "end": 1306, "surface": "data from 2011-12", "probe_tag": "confusion", "probe_score": 0.8285, "luna_label": 0}]}, {"key": "aivin-211", "text": " The top donors are\nUNICEF (19%), USA (16%), Canada (12%), and\nJapan and Sweden (both with 9%). In addition, a\ntotal of US$130 million is identified where CP is\none among multiple destination sectors, the two\nlargest combinations being CP/Health/Water,\nsanitation and hygiene and CP/Education/Nutrition/ Water, sanitation and hygiene.\n\n\nIn this study, we are not focusing solely on funding\nrequired and received for CP specifically in the\nCOVID-19 response but rather on CP funding in\nthe COVID-19 context, where pre-existing needs\nand newly arising CP needs are targeted. We look\nat funding received for CP against requirements\nformulated before the COVID-19 pandemic and at\ntheir revision due to the changing environment\nlinked to the pandemic.\n\n\n\n**Child Protection funding under COVID-19 in**\n**2020 humanitarian response plans**\nThe COVID-19 crisis is evolving fast; the present\nreport is merely a snapshot at one point in time.\nChild protection needs are changing rapidly, and\nthe response must be adapted to match the\nchanging context.\n\nBy triangulating CP data reported in HNOs, HRPs\nand the FTS with data collected through the CP\nAoR at country level, this study provides a more\ndetailed review of CP response in a selection of 19\nHumanitarian Response Plans for 2020. <sup>89</sup>\n\n1. Afghanistan\n2. Burkina Faso\n3. Cameroon\n4. Central African Republic\n5. Democratic Republic of Congo\n6. Ethiopia\n7. Iraq\n8. Libya\n9. Mali\n10. Myanmar\n11. Niger\n12. Nigeria\n13. Occupied Palestinian territory\n14. Somalia\n15. South Sudan\n16. Sudan\n17. Syria\n18. Ukraine\n19. Zimbabwe\n\nData collected for these response plans and\nsources are detailed in Appendix 5: Selection of\n2020 humanitarian response plans: data and\nsources.\n\n\n#####", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001376:20:1:0", "start": 1061, "end": 1068, "surface": "CP data", "probe_tag": "confusion", "probe_score": 0.4471, "luna_label": 1}]}, {"key": "aivin-212", "text": " boys aged 13-18, and 10 girls aged 12-17. Informed consent was sought from all\nparticipants prior to the data gathering activity. The FGD data are not representative, given that\nsaturation was not sought for this exercise.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n_Newer adolescent peer educators at the_\n_ARHN youth center._\n\n\n\n14", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000501:17:2:0", "start": 135, "end": 143, "surface": "FGD data", "probe_tag": "confusion", "probe_score": 0.1159, "luna_label": 1}]}, {"key": "aivin-213", "text": "MONITOREO DE PROTECCIÓN\n\n\n##### **6. Acceso a Documentación**\n\n\n\n76%\n\n\n\n\n\n\n\nSolicitante de la\n\ncondición de\n\nrefugiado\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRefugiado\nreconocido\n\n\n\nSolicitud ha sido\n\nrechazada\n\n\n\nRetiró solicitud Intenciones de\nsolicitar refugio\n\nen Panamá\n\n\n\nIntenciones de\nsolicitar refugio\n\nen otro país\n\n\n\nNo tiene\nintenciones de\nsolicitar refugio /\n\n\n\nNo sabe / No ha\n\n\n\n_Fuente: PMT, HFS1, HFS2, HFS3_ decidido*\n_*Sólo se preguntó \"No sabe /no ha decidido solicitar refugio\" en PMT 2%. Se agrega a 2% \"No tiene intenciones de solicitar refugio.\"_\n\n\n\ndecidido*\n\n\n\nUNHCR / Febrero 2022 23", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001181:21:0:0", "start": 383, "end": 386, "surface": "PMT", "probe_tag": "confusion", "probe_score": 0.3196, "luna_label": 1}, {"key": "reliefweb:001181:21:0:2", "start": 394, "end": 398, "surface": "HFS2", "probe_tag": "confusion", "probe_score": 0.4818, "luna_label": 1}]}, {"key": "aivin-214", "text": "2. REFUGEES AND PEOPLE IN REFUGEE-LIKE SITUATIONS\n\n\nAs of end-2023, 2 million refugees and people in refugee-like situations were in Pakistan (mostly Afghans),\nincluding around 1,371,400 registered refugees and around 143,900 unregistered members of registered\nfamilies (UMRFs) who were recorded through the documentation renewal and information verification\nexercise (DRIVE) and were being verified at the time of reporting. It also includes an estimated 472,900\nAfghans <sup>**12**</sup> who sought asylum in Pakistan, following transition in Afghanistan in August 2021.\n\n\nFigure 6 **|** **Number of refugees per 1,000 inhabitants | 2023**\n\n\n\nIslamic Republic of Iran\n\n\nPakistan\n\n\nBangladesh\n\n\nKyrgyzstan\n\n\nMalaysia\n\n\n\n\n\n42\n\n\n\n\n\n\n\nComparing the size of the refugee population, including people in refugee-like situations, with that of the\nhost country provides an important perspective to appreciate the full magnitude of the pressure felt by host\ncountries. The number of refugees per 1,000 estimated inhabitants gives a relative perspective of the scale\nof the burden in host countries. Independent United Nations member states with a population size of at\nleast 1,000 are included in this indicator. The top refugee-hosting countries in Asia-Pacific in relative terms\nare Iran (Islamic Republic of) (42 refugees per 1,000 inhabitants), Pakistan (8 refugees per 1,000 inhabitants),\nBangladesh (6 per 1,000), Kyrgyzstan (4 per 1,000), and Malaysia (4 per 1,000). Even though India was the\nfourth largest refugee-hosting country in the region by end-2023, it does not rank amongst the top five\ncountries due to its large population size. Globally, Lebanon had the highest number of refugees relative to\ntheir inhabitants (149 per 1,000).\n\n\nIn Asia-Pacific, refugees are hosted in one low-income country, 23 middle-income countries and", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001559:8:0:0", "start": 308, "end": 367, "surface": "documentation renewal and information verification\nexercise", "probe_tag": "confusion", "probe_score": 0.4923, "luna_label": 0}]}, {"key": "aivin-215", "text": "**Kadir serves a customer** with a freshlymade bolony – bread stuffed with\nspinach and onion. The 13-year-old\nAfghan and his family have faced many\nchallenges since returning to Kabul from\nthe Islamic Republic of Iran, including\nthe loss of his father.\n\n\n\nUNHCR Global Trends 2012 **31**", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000089:30:0:0", "start": 256, "end": 275, "surface": "UNHCR Global Trends", "probe_tag": "confusion", "probe_score": 0.1631, "luna_label": 0}]}, {"key": "aivin-216", "text": "**a.** Actions and policies need to be grounded in\nthorough knowledge and understanding of land and\nproperty dynamics prevalent in areas of planned\nrelocation. For example, multiple forms of land and\nproperty frameworks (traditional/customary, collective and individual, private and social, formal and\ninformal, local versus national, etc.) may co-exist;\nthere may be a disconnect between the formal State\nland tenure laws and the way land and property\nrelations are actually managed on the ground; and\ninformal land tenure arrangements and/or existing\ncadastres and land title registries may not be reliable,\netc.;\n\n\n**b.** Macro-level policy setting needs to be accompanied by micro-level political engagement, since land\nand property relations are highly contextual and\noften highly politicized;\n\n\n**c.** The feasibility of action should be ascertained\nfrom the outset, with policies grounded in a realistic\nassessment of institutional capacities. <sup>58</sup>\n\n\n73. Among the multiple sources of potentially valuable guidance on land, housing, and property are:\nthe FAO Voluntary Guidelines on the Responsible\nGovernance of Tenure of Land, Fisheries, and Forests in the Context of National Food Insecurity, the\nUnited Nations Principles on Housing and Property\nRestitution for Refugees and Displaced Persons, and\nguidance on evictions. <sup>59</sup>\n\n\nCOMPENSATION AND RESTITUTION\n\n\n74. Experience from DIDR, in particular, indicates\nthat cash-based compensation schemes alone have\nrarely prevented impoverishment risks and impacts.\nIn-kind compensation, however, entails less risk\nand such compensation remains one indispensable\nmeans of making reparations for what has been lost.\nCertain forms of compensation schemes can fail relocated communities, thereby compounding vulnerability. For example, when compensation is provided\n\n\n\nin the form of ‘cash for land’, safeguards need to be\nput in place to avoid the sudden and concomitant\ninfusion of money into the market from pricing out\nrelocated communities. Compensation", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000184:23:0:0", "start": 553, "end": 562, "surface": "cadastres", "probe_tag": "confusion", "probe_score": 0.6241, "luna_label": 0}, {"key": "reliefweb:000184:23:0:1", "start": 567, "end": 588, "surface": "land title registries", "probe_tag": "confusion", "probe_score": 0.1002, "luna_label": 0}]}, {"key": "aivin-217", "text": "Specialized Services for SGBV Survivors\n\n\nIn 2014, 84,566 survivors or at\nrisk of SGBV received specialized\nsupport, through interagency\nefforts, in the countries affected by\nthe Syria crisis alone. <sup>33</sup> In **Jordan**,\n7,776 SGBV survivors received\nmulti-sectorial services for intimate\npartner violence (physical assault\nand emotional abuse), early and\nforced marriage, sexual violence,\nand exploitation among other\nprotection issues.\n\n\nUNHCR and partners are also\nworking to implement a holistic\n\nLibyan refugee has access to multi-sectoral services in Egypt.\n\nprotection framework through the\nsafe spaces for women and girls in **Lebanon** .Two thousand women and girls in Lebanon benefit\neach month from services provided at safe spaces including psycho-social counseling, information\nand referral to specialized services. Through partners, UNHCR counsels Syrian refugees at risk of\nSGBV and survivors in **Turkey** on national protection mechanisms and makes referrals to needed\nservices. Services were mapped by sector in provinces hosting high numbers of Syrian refugees,\nand a listing of these services is maintained, updated and shared with survivors and partners.\nPsycho-social counseling was provided to 142 SGBV survivors in **Iraq**, while in **Egypt**, 162 Syrian\nrefugees who wished to receive multi sectoral response services received appropriate referrals and\nother assistance as needed. In addition, the newly established case management panel contributed\nto better coordination among key partners.\n\n\nIn **Yemen**, UNHCR partners support SGBV survivors through individual and family interviews,\npsycho-social counseling, legal, financial, material assistance, and referral to other services when\nneeded. In **Morocco**, UNHCR provided assistance to SGBV survivors that are found in mixed\nmigratory movements. UNHCR and partners support the distribution of “dignity kits” to women\nand girls in detention facilities, and provision of medical assistance to address their specific needs\nof SGBV survivors", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000871:34:0:0", "start": 1094, "end": 1119, "surface": "listing of these services", "probe_tag": "confusion", "probe_score": 0.5003, "luna_label": 0}]}, {"key": "aivin-218", "text": "##### FEMALE-HEADED HOUSEHOLDS ON THE RISE\n\nThere has been a high prevalence of single females heading their households within areas in the North East most\n\naffected by the conflict. The UNHCR Vulnerability Screening (December 2017) found that 63% of vulnerable households\nwere female-headed. The breakdown of the 62,113 female-headed households (FHH) screened included 2,283 women\n\nreporting to have been widowed, 1,589 reporting to be lactating and 405 reporting to be pregnant. <sup>1</sup> [^1: UNHCR North East Nigeria: Vulnerability Screening Report (December 2017), available at:\n<u>[https://reliefweb.int/report/nigeria/unhcr-north-east-nigeria-operational-vulnerability-screening-report-december-2017](https://reliefweb.int/report/nigeria/unhcr-north-east-nigeria-operational-vulnerability-screening-report-december-2017)</u>] The highest numbers\nof FHH identified in the Vulnerability Screening were in Dikwa LGA (14,142), Ngala LGA (9,520), Monguno LGA (8,537)\n\nand Damasak, Mobbar LGA (5,578) in Borno State and Gulani LGA (5,458) in Yobe State.\n\n\nIn 2018, with every new arrival of IDPs, IDP returnees and refugee returnees, protection monitors have noted high\n\nnumbers of female-headed households.\n\n##### THEMATIC REPORT METHODOLOGY\n\n\nThis thematic report analyses information gathered by UNHCR protection monitors through numerous focus group\ndiscussions with females heading their households throughout May and June 2018 in sites in which UNHCR is\n\nconducting protection monitoring. This includes interviews with 1,722 females heading their households to provide\n\ndisaggregated data analyzed in the report. This report seeks to identify particular protection issues affecting women\nheading their households to enable evidence-based programming and response.\n\n\n1 UNHCR North East Nigeria: Vulnerability Screening Report (December 2017), available at:\n<u>[https://reliefweb.int/report/nigeria/unhcr-north-east-nigeria-operational-vulnerability-screening-report-december-2017](https://reliefweb.int/report/nigeria/unhcr-north-east-nigeria-operational-vulnerability-screening", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000488:0:0:1", "start": 1580, "end": 1598, "surface": "disaggregated data", "probe_tag": "confusion", "probe_score": 0.482, "luna_label": 0}]}, {"key": "aivin-219", "text": " to accommodate just 13%\nof the adolescent population (UNHCR, 2015c).\n\n\nRefugee sites have had varied success in\nmeeting education needs\n\n\nSince the mid-2000s, education for refugee children has\nprogressed in some countries but stagnated in others,\nas is shown by new analysis of data from nine refugee\nsites (Figure 3). A range of factors contribute to this wide\ndivergence in access to education and education quality,\nincluding differences in refugees’ rights to education and\ncertification according to national legislation, the difficulty\nin dealing with large influxes of displaced people, language\ndifferences, and the difficulty of sustaining education in\nprotracted refugee situations.\n\n\n|Enrolment rate Enrolment rate|Col2|Col3|\n|---|---|---|\n|2004–2007<br>2013–2015<br>Enrolment rate<br>improved<br>Enrolment rate<br>worsened|2004–2007<br>2013–2015<br>Enrolment rate<br>improved<br>Enrolment rate<br>worsened|2004–2007<br>2013–2015<br>Enrolment rate<br>improved<br>Enrolment rate<br>worsened|\n|<br>|||\n\n\n\nNote: The size of the marker is proportional to the size of the population\nin each site.\n\n\nSource: Analysis based on 2014 UNHCR data.\n\n\n\n\n\n\n\n\n\n**FIGURE 3:**\nDifferent sites in different contexts have followed different\ntrajectories in getting refugee children and adolescents\ninto schools\n_Enrolment rates of refugee children aged 5–17, selected refugee_\n_sites in selected countries, 2004–2007 and 2013–2015_\n\n\n\n\n\n\n\n\n\n\n\n80\n\n\n60\n\n\n40\n\n\n20\n\n\n0\n\n\n\n\n\n4", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001012:3:1:0", "start": 280, "end": 308, "surface": "data from nine refugee\nsites", "probe_tag": "confusion", "probe_score": 0.7301, "luna_label": 1}, {"key": "reliefweb:001012:3:1:1", "start": 1133, "end": 1148, "surface": "2014 UNHCR data", "probe_tag": "confusion", "probe_score": 0.6643, "luna_label": 1}]}, {"key": "aivin-220", "text": "// PGRN 2023 Annex 3//\n\nDEPARTURES\n\n\n**TOP TEN: RESETTLEMENT DEPARTURES IN 2021***\n\n\n\n\n\n\n\n\n\n\n\nUnited States of America\n\n\nCanada\n\n\nGermany\n\n\nSwenden\n\n\nNorway\n\n\nFrance\n\n\nUnited Kingdom\n\n\nFinland\n\n\nBelgium\n\n\nSwitzerland\n\n\nAll Others\n\n\n\n0 2,000 4,000 6,000 8,000 10,000 12,000 14,000\n\n\n|COUNTRY OF<br>RESETTLEMENT|PERSONS|\n|---|---|\n|United States of<br>America|11,554|\n|Canada|5,825|\n|Germany|5,363|\n|Swenden|5,036|\n|Norway|2,875|\n|France|1,827|\n|United Kingdom|1,595|\n|Finland|997|\n|Belgium|960|\n|Switzerland|831|\n|All Others|2,403|\n|**Grand Total**|**39,266**|\n\n\n\n\n_* Departure figures reported by UNHCR may not match resettlement statistics published by States as Government figures may_\n_include submissions received outside of UNHCR resettlement processes,_\n\n\n\n\n\n\n\nTürkiye\n\n\nLebanon\n\n\nJordan\n\n\nEgypt\n\n\nRwanda\n\n\nUnited Rep, of Tanzania\n\n\nKenya\n\n\nUganda\n\n\nEthiopia\n\n\nMalaysia\n\n\nAll Others\n\n\n0 2,000 4,000 6,000 8,000 10,000\n\n\nSyrian Arab Rep,\n\n\nDem, Rep, of the Congo\n\n\nSudan\n\n\nAfghanistan\n\n\nEritrea\n\n\nSouth Sudan\n\n\nSomalia\n\n\nMyanmar\n\n\nIraq\n\n\nEthiopia\n\n\nAll Others\n\n\n0 5,000 10,000 15,000 20,000\n\n\n\n\n|COUNTRY OF<br>ASYLUM|PERSONS|\n|---|---|\n|Türkiye|7,382|\n|Lebanon|6,246|\n|Jordan|4,374|\n|Egypt|3,729|\n|Rwanda|2,506|\n|United Rep. of<br>Tanzania|2,115|\n|Kenya|1,517|\n|Uganda|1,451|\n|Ethiopia", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000214:58:0:0", "start": 617, "end": 640, "surface": "resettlement statistics", "probe_tag": "confusion", "probe_score": 0.4104, "luna_label": 1}]}, {"key": "aivin-221", "text": "### **SECTION 2: SIT. ANALYSIS**\n\n##### **2.1 Trend Analysis on Inclusion of Refugees in the National** **System**\n\nRefugee education in Ethiopia covers pre-primary, primary, secondary and tertiary education\n\n\nthrough formal and non-formal programme opportunities. According to MoE guidelines, children\n\n\nbetween the ages of 3-18 years are eligible for enrolment in general education before\n\n\ntransitioning to post-secondary institutions. Provision of refugee education in Ethiopia remains a\n\n\nchallenge from one refugee-hosting region to another, as the contexts differ. Low student\n\n\nparticipation and internal efficiency <sup>6</sup> within the refugee context continue to persist when\n\n\ncompared to the national indicators (Federal Democratic Republic of Ethiopia Ministry of\n\n\nEducation, 2018). Participation of girls drastically drops between the first cycle (Grade 1-4) and\n\n\nsecond cycle (Grade 5-8). The participation further reduces, when it comes to transition from\n\n\nprimary to secondary level for both girls and boys. The Gender Parity Index (GPI) at primary level\n\n\n6 <mark>Internal efficiency is the relationship between the outputs and inputs of an education system</mark>\n\n\n11", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000503:10:0:0", "start": 707, "end": 726, "surface": "national indicators", "probe_tag": "confusion", "probe_score": 0.6337, "luna_label": 1}]}, {"key": "aivin-222", "text": "statelessness-statistics-iross)</u>\n<u>[on Statelessness Statistics (IROSS)](https://egrisstats.org/recommendations/international-recommendations-on-statelessness-statistics-iross)</u> by the UN\nStatistical Commission in March 2023. These\nrecommendations help guide the production of higherquality, harmonized statistics that are consistent and\ncomparable across countries.\n\n\n\n\n**•** **20 European countries** (Albania, Austria, Czech Republic, Croatia, Denmark, Estonia, Finland, Iceland,\nIreland, Lithuania, Malta, Norway, Poland, Portugal, Serbia, Slovakia, Sweden, Switzerland, Ukraine and\nthe United Kingdom) have conducted mapping studies or surveys to better understand the size and\nprofile of the stateless population in their territory.\n\n- The 2022 censuses in **Kyrgyzsta** n and **Tajikistan** included questions related to stateless people.\n\n**•** **Panama’s** 2023 census included questions about birth registration, and the results have allowed mapping\npopulations that might be at risk of statelessness or of undetermined nationality.\n\n- The 2019 Cartography of Persons at Risk of Statelessness in **Cote d’Ivoire** (CAPRA) provided a basis\nfor reporting updated and more accurate official figures for stateless persons and persons at risk of\nstatelessness.\n\n**•** **Mozambique** is the process of conducting a qualitative study on statelessness in line with the pledge\nmade in the 2019 High-level Segment on statelessness. The study is expected to be finalized by the end\nof 2024.\n\n- Qualitative studies on statelessness have been carried out in countries including Botswana, Burkina\nFaso, Democratic Republic of the Congo, Mali, and South Africa, helping inform identification and\ndocumentation efforts, as well as advocacy and resource mobilization activities.", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "sample:reliefweb:000303:40:1:0", "start": 753, "end": 766, "surface": "2022 censuses", "probe_tag": "confusion", "probe_score": 0.8916, "luna_label": 1}, {"key": "sample:reliefweb:000303:40:1:1", "start": 873, "end": 884, "surface": "2023 census", "probe_tag": "keep", "probe_score": 0.9063, "luna_label": 1}, {"key": "sample:reliefweb:000303:40:1:2", "start": 1057, "end": 1109, "surface": "2019 Cartography of Persons at Risk of Statelessness", "probe_tag": "confusion", "probe_score": 0.8897, "luna_label": 1}]}, {"key": "aivin-223", "text": " representatives of the Contracting States to the Convention\nadopted a Declaration ‘[r]ecognizing the enduring importance of the 1951\nConvention, as the primary refugee protection instrument which, as amended by its\n1967 Protocol, sets out rights, including human rights, and minimum standards of\ntreatment that apply to persons falling within its scope’. <sup>83</sup> [^83: Declaration of States Parties to the 1951 Convention and/or its 1967 Protocol relating to the Status of\nRefugees (Geneva 13 December 2001) UN Doc HCR/MMSP/2001/09 (16 January 2002)\n<www.unhcr.ch/cgi-bin/texis/vtx/protect> (18 February 2003) Preamble [2], [4].] UNHCR has repeatedly\n\n\n77 Memo from P Weis to Mr J Mersch, UNHCR Branch Office in Luxembourg ‘Application of 1951\nConvention to Hungarian Refugees’ (28 May 1957) Ref.G.XV.7/1/8, 6/1/HUN [3], in UNHCR\nArchives Fonds 11 Sub-fonds 1, 6/1/HUN.\n78 Independent Commission on International Humanitarian Issues _Refugees: The Dynamics of_\n_Displacement_ (London 1986) 33, in G Melander ‘The Two Refugee Definitions’ Raoul Wallenberg\nInstitute of Human Rights and Humanitarian Law _Report No 4_ (Lund 1987) 14.\n79 This is the view expressed in Storey and others (n66) 14.\n80 For subsequent State practice, see eg Perluss and Hartman (n8); Goodwin-Gill (n8).\n81 ExCom ‘Summary Record of the 540th Meeting’ (Geneva 7 October 1999) UN Doc A/AC.96/SR.540\n(12 October 1999) [44]. The Nordic States’ relatively generous complementary protection is\ncounterbalanced by very low recognition rates of Convention refugees. Domestic complementary\nprotection effectively takes refugee protection outside international law. In Denmark, the ratio was\napproximately one-third Convention refugees to two-thirds de facto refugees: KU Kjær ‘The Abolition\nof the Danish _De Facto_", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000702:14:1:0", "start": 831, "end": 851, "surface": "UNHCR\nArchives Fonds", "probe_tag": "confusion", "probe_score": 0.8685, "luna_label": 0}]}, {"key": "aivin-224", "text": " access to education.|Child Protection AoR|\n|**Health**|Ensure health and volunteer staff working at the<br>different GBV entry points within health centres<br>(maternity,<br>_SAAJ-Serviços_<br>_Amigos_<br>_dos_<br>_Adolescentes e Jovens_, _UATs –Unidades de_<br>_Aconselhamento e Testagem_) are trained to be able<br>to provide survivor-centred care and safe referrals.<br> <br>Ensure that complete post-rape kits are available at<br>health centre level, including pregnancy tests,<br>emergency contraception, PEP, STD treatment,<br>Hepatitis B vaccine and that safe abortion services<br>are available and providers trained in clinical<br>management of rape. Make different family planning<br>methods are available at health centre level,<br>including condoms. Ensure that all services are<br>provided for free and complaints mechanisms are in<br>place.<br> <br>Ensure GBV screening is always conducted in safe<br>and confidential manners, and a safe and<br>confidential space to attend GBV survivors is<br>available at health centre level.<br> <br>Ensure data on GBV cases are collected in<br>confidential manner and safely stored and MISAU<br>case intake forms are available.|Health Cluster, GBV AoR|\n|**WASH**|Seek ways of increasing safe access to water at<br>household<br>and<br>neighbourhood<br>level,<br>monitor<br>protection risks for women and girls in the sale of<", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000736:10:1:0", "start": 1057, "end": 1074, "surface": "data on GBV cases", "probe_tag": "drop", "probe_score": 0.0308, "luna_label": 0}]}, {"key": "aivin-225", "text": " health related data in a\n\nstandardized manner, albeit the sampling tends to focus on a specific subgroup of the refugee\n\npopulation (households with children under 5 years old, and households with members with\n\nadolescent girls, pregnant and lactating mothers); with the exception of two rounds of SENS\n\nsurvey that is representative of the entire refugee population. In addition to the SENS survey,\n\nthe recent JPDM is another survey, that informs us regularly on the state of food security of\n\nrefugees that relies on a relatively robust Food Consumption Score (FCS) measure.\n\n\nFigure 2: Overview of data mapping on POCs in Rwanda, by sector/theme\n\n\nUNHCR / June 2022 / Version 1", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "sample:reliefweb:001182:17:1:0", "start": 1, "end": 20, "surface": "health related data", "probe_tag": "drop", "probe_score": 0.0022, "luna_label": 0}, {"key": "sample:reliefweb:001182:17:1:1", "start": 299, "end": 311, "surface": "SENS\n\nsurvey", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 1}, {"key": "sample:reliefweb:001182:17:1:2", "start": 388, "end": 399, "surface": "SENS survey", "probe_tag": "confusion", "probe_score": 0.6193, "luna_label": 1}, {"key": "sample:reliefweb:001182:17:1:3", "start": 413, "end": 417, "surface": "JPDM", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1}]}, {"key": "aivin-226", "text": "IPS), supported the training\nof Member States on including displacement\nstatistics in national statistical systems.\n\n- To improve monitoring and impact reporting,\nan overall Results Framework for the IGAD\nSupport Platform was developed. The IGAD\nSupport Platform also conducted regular\nthematic follow-up and review meetings to\ntake stock of their implementation, document\nlessons learned, and provide a platform for\nmulti-stakeholder exchange.\n\n- In 2020, the IGAD Support Platform\nlaunched the Solutions Initiative for the\ndisplacement situation in South Sudan and\nthe Sudan. Through this initiative, both\ncountries developed and adopted national\ndurable solution strategies and action plans\nto support early recovery, longer-term\npeacebuilding, and resilience of displaced\npopulations and their respective host\ncommunities.\n\n- The IGAD Support Platform helped its\nmembers develop costed plans of action\nfor the inclusion of refugees in national\neducation systems as part of the Djibouti\nDeclaration to enable them to better access\ntargeted financing.\n\n- As part of implementing the Djibouti\nDeclaration, IGAD trained 755 secondary\nschool teachers, one-third of whom were\nwomen, in pedagogy, socio-psychological\nskills, life skills, and information and\ncommunications technology (ICT), and\nimproved the quality of education for over\n12,000 students in the largest refugeehosting communities in Ethiopia, the Sudan,\nand Uganda.", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000435:37:1:0", "start": 59, "end": 82, "surface": "displacement\nstatistics", "probe_tag": "drop", "probe_score": 0.0449, "luna_label": 0}]}, {"key": "aivin-227", "text": "/www.oraro.co.ke/cbk-to-regulate-digital-lenders/)</u>\n<u>269</u> Kenya National Payments System Vision and Strategy 2021-2025.\n<u>270</u> Id.\n<u>271</u> Id. at 28.\n<u>272</u> Id. at 28.\n<u>273</u> Id. at 51. Kenya’s GSMA Mobile Money Regulatory Index score has increased – from 79.24 to 85 in 2020; however, the score for KYC\nremains low at just 32. See GSMA, Mobile Money Metrics, <u>https://www.gsma.com/mobilemoneymetrics/#regulatory-index. For details on</u>\nKenya’s existing KYC obligations with respect to customer identity, see the 2019 Kenya Country Report in Annex III.\n<u>274</u> At the beginning of 2018, the Government of Rwanda through the Ministry of Emergency Management, the Rwanda Directorate General\nof Immigration and Emigration, and the National Identification Agency in partnership with UNHCR launched a verification exercise of all\nrefugees and asylum seekers living in Rwanda with the purpose of updating information and verifying their physical presence in the country\nthrough biometric data.\n<u>275</u> The figure of 70% was provided by MINEMA and inconsistency with UNHCR’s ProGres. Refugee ID cards are renewed when they expire. It\nis worthwhile noting that the renewal process is sometimes slowed down by movement restrictions and social distancing measures put into\nplace to curve the spread of COVID-19 pandemic.\n<u>276</u> [Rwanda Utilities Regulatory Authority, Public Notice, 31 Aug. 2021, (https://rura.rw/fileadmin/publication/Public_Notice_on_SIM", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000137:31:6:0", "start": 1002, "end": 1016, "surface": "biometric data", "probe_tag": "drop", "probe_score": 0.0323, "luna_label": 1}]}, {"key": "aivin-228", "text": " poverty, labor markets,\nand other welfare indicators at the country level. Including\nthem can contribute to filling socioeconomic data gaps on\ninternational displacement, while providing crucial inputs\n\n\n\nto inform targeted responses, policies, and programs for refugees and host communities. Particularly, increasing panel\ndata across refugee and host communities would provide\na rich learning to assess how welfare and social cohesion\ntrends change over time. Investigating this hypothesis and\nothers underlines our earlier point, the need for panel data\nto monitor changes of the same household over times of war\nand forced displacement.\n\n\n**74. Socioeconomic data with a focus on the displacement**\n**trajectory can further enhance the design of solutions**\n**for displacement.** Socioeconomic surveys are essential to\nunderstand the current living conditions of households to\ninform policies, for example, on labor markets and safety\nnets as well as health and education. However, they usually\ndo not consider the specific displacement trajectory of refugees who are affected by traumatic episodes causing them to\ncross international borders. It is critical to understand refugees’ vulnerabilities and needs in order to find solutions. It is\nrecommended that a forcibly displaced module be developed\nby development organizations in collaboration with national\nstatistics offices, to serve as an input for existing surveys,\nsuch as national surveys that measure poverty, Living Standards Measurement Surveys, and beyond. A standardized\nforced displacement module that measures the vulnerabilities faced by refugees and other forcibly displaced persons\nis essential to complete the picture needed for informing\noptimal national policies and programming. A newly developed framework by the World Bank has been administered\nto displaced populations in Ethiopia, Nigeria, Somalia, South\nSudan, and Sudan—and should be considered for future data\ncollection of refugees in Kenya and other countries in the\nregion. <sup>93</sup>\n\n\n93 \u0007Pape and Sharma. 2019. Informing Durable Solutions for Internal\nDisplacement in Nigeria, Somalia, South Sudan, and Sudan.\n\n\n33", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "sample:reliefweb:000641:45:1:0", "start": 319, "end": 329, "surface": "panel\ndata", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 0}, {"key": "sample:reliefweb:000641:45:1:1", "start": 650, "end": 668, "surface": "Socioeconomic data", "probe_tag": "confusion", "probe_score": 0.0908, "luna_label": 0}, {"key": "sample:reliefweb:000641:45:1:2", "start": 785, "end": 806, "surface": "Socioeconomic surveys", "probe_tag": "confusion", "probe_score": 0.3435, "luna_label": 0}, {"key": "sample:reliefweb:000641:45:1:3", "start": 1437, "end": 1474, "surface": "national surveys that measure poverty", "probe_tag": "confusion", "probe_score": 0.2719, "luna_label": 0}, {"key": "sample:reliefweb:000641:45:1:4", "start": 1476, "end": 1512, "surface": "Living Standards Measurement Surveys", "probe_tag": "confusion", "probe_score": 0.0687, "luna_label": 0}]}, {"key": "aivin-229", "text": "#### **Services offered and taken up**\n\nRefugees primarily use their mobile wallets to\nobtain cash to pay monthly bills for rent and\nfood. Incentives are often given to withdraw\nall their money in a single transaction since\nthe initial cashing out each month is often\nexempt from fees. One report concluded:\n\n“ _Current humanitarian processes incentivise_\n_recipients to withdraw the full transfer_\n_amount. However, this undermines potential_\n_savings and trust in digital services by_\n_reinforcing the belief that only hard cash, as_\n_opposed to an electronic balance, has_\n_permanent value_ .” <sup>45</sup> [^45: _Humanitarian CT and FI lessons from Jordan, April 2020_]\n\nHowever, WFP has recently decided not to\ncover any withdrawal fees when transferring\nassistance to mobile wallets, with the\nobjective of incentivising digital transactions.\nThere are a range of other services\npotentially available through mobile wallets\nincluding:\n\n\n  - Person to person payments\n\n  - International payments and\nremittances\n\n  - Payment of bills (including phone topup)\n\n  - Savings and credit\n\n  - Insurance\n\n\n\nOne hypothesis is that refugees will become\nmore confident in the use of mobile wallets\nover time and expand the range of services\nthat they access. One potential area for\nexpansion is the payment of bills. The use of\nmobile services reduces the need and\ntransport costs for attending offices to pay\nbills. The Government significantly expanded\nthe use of eFAWATEERcom by government\ninstitutions from 21 in 2017 to 48 by the end\nof 2020. This is reflected by growth in this\nplatform but it still has very limited use by\nrefugees.\n\nAlthough non-Jordanians\nlag Jordanians in most\nmeasures of digital financial\nservices, this is not the\ncase with remittances.\n2017 data suggested that\n31.4% of non-Jordanians\nsent or received\nremittances through formal\nchannels in the previous\nyear, well above the rate of\n19.9% for Jordanians. <sup>46</sup> At\npresent mobile wallet\nproviders are", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "sample:reliefweb:001027:26:0:0", "start": 1762, "end": 1771, "surface": "2017 data", "probe_tag": "drop", "probe_score": 0.0001, "luna_label": 0}]}, {"key": "aivin-230", "text": "*Annual Report**<br>**20**<br>**Regional**<br>**Overview**<br>registered Syrian refugees<br>**506,000**<br>**129,210**<br>directly targeted members<br>of impacted communities<br>in 2019<br>**EGYPT**<br>registered Syrian refugees<br>**520,000**<br>**654,692**<br>directly targeted members<br>of impacted communities<br>in 2019<br>**JORDAN**<br>registered Syrian refugees<br>**158,110**<br>**245,810**<br>directly targeted members<br>of impacted communities<br>in 2019<br>**IRAQ**<br>registered Syrian refugees<br>**1,005,000**<br>**914,648**<br>directly targeted members<br>of impacted communities<br>in 2019<br>**LEBANON**<br>registered Syrian refugees<br>**1,800,000**<br>**3,576,369**<br>directly targeted members<br>of impacted communities<br>in 2019<br>**TURKEY**<br>**REGIONAL TOTAL**<br>**5,520,729**<br>registered refugee population<br>as of 31 December 2019<br>**Voluntary Syrian Refugee Returns**<br>**Syrian Refugee Resettlement Targets**<br>**and Submissions**<br>Population in Need<br>Achievements<br>Syrian", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000166:1:35:0", "start": 810, "end": 839, "surface": "registered refugee population", "probe_tag": "drop", "probe_score": 0.0392, "luna_label": 0}]}, {"key": "aivin-231", "text": " affected people\ntotals.\n\n\nAt a global level, the most thorough and most cited database of disaster losses that tracks these variables is EMDAT. At a national level, the expanding series of disaster\nloss databases following the DesInventar methodology\nprovide disaggregated loss figures per jurisdiction. Due\nto the fact that each DesInventar database is administered by each participating country, there are slight\nvariations in structure and more significant variations\nin coverage and low-end thresholds for inclusion. In the\npast this has made inter-country comparison difficult.\nIn 2015 IDMC is going to use the ISDR’s GAR data universe, which has been pre-screened to insure the highest\nlevel of inter-country comparison, thus largely removing this substantial limitation to previous DesInventar\ndatasets.\n\n\nIn all of these datasets, mortality data is of the highest\nquality, while homeless and affected population information can be somewhat less accurate, especially for\nsome particular types of hazard. Homeless data appears\nto be most accurately represented in earthquake events,\nand least well tracked in flood events. Storms and floods\nhave both the highest number of entries and total in\nterms of mortality and homelessness, which makes their\nindividual hazard analyses rather more robust due to\nthe larger sample sizes.\n\n\nLandslides, and smaller events in general, receive\nsubstantially less attention due to a combination of\ndifficulty in collecting data on so many events and\nproblems that can arise from a change in methodology.\nThese can include a lowering of thresholds for inclusion,\nthat would drastically increase the number of entries\nfor these types of events. For example, EM-DAT utilises\n\n\n\nDisaster-related displacement risk: Measuring the risk and addressing its drivers 39", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "sample:reliefweb:000972:36:2:0", "start": 331, "end": 351, "surface": "DesInventar database", "probe_tag": "drop", "probe_score": 0.0004, "luna_label": 1}, {"key": "sample:reliefweb:000972:36:2:1", "start": 624, "end": 641, "surface": "GAR data universe", "probe_tag": "confusion", "probe_score": 0.1778, "luna_label": 0}, {"key": "sample:reliefweb:000972:36:2:2", "start": 840, "end": 854, "surface": "mortality data", "probe_tag": "confusion", "probe_score": 0.1965, "luna_label": 1}, {"key": "sample:reliefweb:000972:36:2:3", "start": 1012, "end": 1025, "surface": "Homeless data", "probe_tag": "confusion", "probe_score": 0.0574, "luna_label": 1}, {"key": "sample:reliefweb:000972:36:2:4", "start": 1698, "end": 1704, "surface": "EM-DAT", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 0}]}, {"key": "aivin-232", "text": ") in<br>formal/informal<br>sector / # of targeted<br>PoC surveyed) x<br>100<br> <br>TBD<br>TBD<br>**Beneficiary**<br>**midline and end**<br>**line Survey (1st **<br>**cycle)** will be<br>administered by<br>partner and this<br>indicator will be<br>reported by filling in<br>the M&E report (or<br>the mid-term<br>/progress<br>performance report)<br> <br>Bi-annually or<br>Mid-term of project<br>(could be bi-annually<br>if project is 12<br>months, or annual if<br>project is 24<br>months)) <br>The partner (the<br>program manager<br>and the project<br>M&E Officer)<br>* Bi-annually (or<br>mid-term of<br>project)<br> * In the progress<br>(mid-term)<br>report/performan<br>ce report <br> <br> <br>MINEMA & UNHCR<br>will collect data from<br>partners/projects<br>and will consolidate<br>data, from mid-term<br>programme<br>performance/progre<br>ssive reports<br>Bi-annually or Mid-<br>term of programme<br>(could be bi-annually<br>if programme is 12<br>months, or annual if<br>programme is 24<br>months)<br>MINEMA/UNHCR<", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:001259:91:22:0", "start": 142, "end": 153, "surface": "line Survey", "probe_tag": "drop", "probe_score": 0.0223, "luna_label": 0}]}, {"key": "aivin-233", "text": "br>2<br>138,586<br>622<br>13,352<br>1<br>211<br>38,323<br>24,107<br>5,488<br>35<br>10<br>1,240<br>802<br>720,307<br>2<br>2,132<br>40|\n\n\n\n70 UNHCR > **GLOBAL TRENDS 2018**", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000649:68:3:0", "start": 150, "end": 168, "surface": "GLOBAL TRENDS 2018", "probe_tag": "drop", "probe_score": 0.0494, "luna_label": 0}]}, {"key": "aivin-234", "text": "rio de enumeración” <sup>36</sup> consistente\nen siete preguntas con filtros de aplicación\nque exploraban el historial migratorio del\nhogar. El objetivo del formulario fue clasificar\na la población entre hogares de estudio\n(hogares en los que al menos una persona\nreportara haber cambiado de residencia\nal interior de Honduras entre 2004 y 2014\npor causas relacionadas con violencia o\ninseguridad) y hogares de comparación. <sup>37</sup> [^37: Para el estudio, se entiende por hogar a una o varias\npersonas, unidas o no por vínculos familiares, que viven\njuntas para proveer y satisfacer sus necesidades alimenticias,\nque habitan una vivienda.]\n\n**Selección de hogares:** se determinó\nencuestar a todos los hogares de población\nde estudio encontrados en la enumeración\ny encuestar a uno de población de\ncomparación, seleccionado de forma\naleatoria, por cada tres hogares de estudio\nencontrados. En los casos en que se\nencontraron menos de tres hogares de\nestudio por segmento siempre se realizó una\nencuesta en los hogares de comparación.\n\n**Encuesta:** el instrumento de encuesta <sup>38</sup> [^38: Ver anexo II] se\ndiseñó para recopilar la siguiente información:\n\n- Datos de la vivienda: tipo, tenencia,\nservicios, etc.\n\n- Datos del hogar: activos, recursos,\nparticipación, redes sociales,\nintegración, etc.\n\n- Datos de las personas del hogar:\ncaracterísticas demográficas, migración,\nsalud, educación, empleo, etc.\n\n- Historial de migración y hechos\nvictimizantes\n\n- Intenciones futuras\n\n\n36 Ver anexo I\n37 Para el estudio, se entiende por hog", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "sample:reliefweb:001615:26:2:0", "start": 1169, "end": 1189, "surface": "Datos de la vivienda", "probe_tag": "drop", "probe_score": 0.046, "luna_label": 0}, {"key": "sample:reliefweb:001615:26:2:1", "start": 1226, "end": 1241, "surface": "Datos del hogar", "probe_tag": "confusion", "probe_score": 0.2977, "luna_label": 0}, {"key": "sample:reliefweb:001615:26:2:2", "start": 1314, "end": 1345, "surface": "Datos de las personas del hogar", "probe_tag": "confusion", "probe_score": 0.3922, "luna_label": 0}]}, {"key": "aivin-235", "text": "|Strengthen data-<br>driven approaches to<br>refugee livelihoods.|Conduct socio-economic profiling of refugee<br>households, including baseline, midline, and<br>end-line surveys, market assessments, and<br>economic database development.|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|Strengthen data-<br>driven approaches to<br>refugee livelihoods.|Collaborate with host government agencies to<br>integrate refugee data into national systems,<br>ensuring interoperability.|||||||||||||\n|Strengthen data-<br>driven approaches to<br>refugee livelihoods.|Partner with the World Bank and other<br>research-focused organizations to analyze labor<br>market trends and value chains for refugee<br>inclusion.|||||||||||||\n|Strengthen data-<br>driven approaches to<br>refugee livelihoods.|Publish reports showcasing successful<br>interventions to attract further investment and<br>partnerships.|||||||||||||\n|Strengthen data-<br>driven approaches to<br>refugee livelihoods.|Share anonymized data with partners to<br>enhance advocacy efforts and demonstrate the<br>economic contributions of refugees.|||||||||||||\n|Build partnerships to<br>scale refugee<", "source": "reliefweb", "subset": "annotate_aivin", "spans": [{"key": "reliefweb:000822:28:0:0", "start": 485, "end": 497, "surface": "refugee data", "probe_tag": "drop", "probe_score": 0.0478, "luna_label": 0}]}, {"key": "aivin-236", "text": "37\n\n\n**Table 2: Disaggregated neonatal, postneonatal, and infant mortality**\n\n\n|Col1|Mortality (per 1000 live births)|Col3|Col4|\n|---|---|---|---|\n||Neonatal|Postneonatal|Infant|\n|<br>**Overall**<br>**By survey**<br>2003 survey<br>2008 survey<br>**By malaria risk zone and ITN ownership**<br>Low malaria risk zone (LMRZ)<br>Intermediate malaria risk zone (IMRZ)<br>High malaria risk zone (HMRZ)<br>ITN ownership<br>No ITN ownership<br>ITN ownership in LMRZ (1)<br>No ITN ownership in LMRZ (1)<br>ITN ownership in IMRZ (1)<br>No ITN ownership in IMRZ (1)<br>ITN ownership in HMRZ (1)<br>No ITN ownership in HMRZ (1)<br>**By antenatal and natal care**<br>Born in a facility<br>Not born in a facility<br>Antenatal tetanus protection<br>No antenatal tetanus protection<br>Antenatal antimalarial drugs<br>No antenatal antimalarial drugs<br>Antenatal iron supplement<br>No antenatal iron supplement<br>**By access to drinking water and sanitation**<br>Surface water<br>Unimproved water source (not surface)<br>Improved water source<br>No sanitation<br>Unimproved sanitation<br>Improved sanitation<br>**By mother's education**<br>No education<br>Primary education<br>Secondary", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:005229:38:0:1", "start": 231, "end": 242, "surface": "2008 survey", "probe_tag": "confusion", "probe_score": 0.0572, "luna_label": 0}]}, {"key": "aivin-237", "text": "**Figure 4. Wage Bill vs. Fiscal Balance (2000-2011 averages)**\n\n\n\n6\n\n\n4\n\n\n2\n\n\n0\n\n\n-2\n\n\n-4\n\n\n-6\n\n\n\n**Wage Bill (% of GDP)**\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: ECA Fiscal Database, Country sources\n\n\n**3. Data and Empirical Strategy**\n\nThe data set contains public wage bill data for 26 ECA <sup>3</sup> countries, covering the period 20002011 (see Table A5 for a complete list of countries used in the analysis). <sup>4</sup> [^4: The sample period reflects the availability of data. Public wage bill data does not exist for most ECA countries\nprior to the year 2000. Data on public employment is not comprehensively available for most countries in the\nsample. Therefore, it is not possible to decompose changes in wage bill expenditure into changes in public\nemployment and in pay policies.] Nineteen Western\nEuropean countries are subsequently used as a comparator group to determine if the effects are\nless significant in non-transition economies. Table A1 in the annex reports the summary statistics\nfor all variables used in the analysis. There is considerable variation, both between and within\ncountries, which justifies the panel estimation method.\n\nWe test three related hypotheses. The first hypothesis is that wage bill growth pro-cyclically\nresponds to the business cycle and the electoral cycle. The second hypothesis is that the wage bill\nspending is characterized by downward rigidity, e.g. that spending increases are stronger in\nboom periods than wage bill decreases in downturns. The third hypothesis is that wage bill\nexpansions are associated with a deterioration of the overall fiscal position.\n\n**_H1: Cyclicality of Wage Bill Spending_**\n\nThe first hypothesis explores the cyclical behavior of wage bill spending. Periods of high growth\ntend to increase wages in the private sector, and lead to calls for higher wages or benefits in the\n\n\n3 Turkmenistan and Uzbekistan were not included due to data availability, and Armenia and Azerbaijan were\ndropped due to spurious data.\n4 The sample period", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:005885:8:0:1", "start": 280, "end": 301, "surface": "public wage bill data", "probe_tag": "confusion", "probe_score": 0.7079, "luna_label": 1}]}, {"key": "aivin-238", "text": "≤_ _PALE_ 1( _B_ ) _(Condition_ _C1)_\n\n\n_(ii)_ _There_ _exist_ _societies_ _A_ _and_ _B_ _for_ _which_ _PALEθ_ ( _A_ ) _≤_ _PALEθ_ ( _B_ ) _for_ _all_ _θ_ _≤_\n\n\n1 _even_ _though_ _H_ ( _A_ ) _<_ _H_ ( _B_ ) _._ _These_ _societies_ _are_ _such_ _that_ _H_ ( _A_ ) _<_ _H_ ( _B_ ) _and_\n\n\n_LE_ ( _A_ ) _< LE_ ( _B_ ) _._\n\n\n_Proof._ See Appendix J.\n\n\nIn Appendix K, we study the conditions under which comparisons by _EDθa_ ˆ are\n\n\nrobust to the plausible values for its parameters.\n\n\n**4** **Real** **world** **implications**\n\n\nWe now turn to data on poverty and life expectancy spanning the period 1990-2019.\n\n\nThe data come respectively from the World Bank’s Poverty and Inequality Platform\n\n\n(World Bank, 2023) and the Global Burden of Disease Project (Global Burden of\n\n\n15", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:001580:16:1:0", "start": 541, "end": 576, "surface": "data on poverty and life expectancy", "probe_tag": "confusion", "probe_score": 0.7117, "luna_label": 1}]}, {"key": "aivin-239", "text": "Equation 4 estimates the amount of FDI inflow into manufacturing industry i in year t\n\n\nmeasured at the 2-digit VSIC level. (I) is the set of other industry level characteristics that\n\nmay affect FDI inflows such as industry wise productivity and industrial tax; μ is\n## industry fixed effects and π is the error term. Similar to equations (1), (2) and (3), we\n\nalso used fixed effects for dirty and clean industries based on the Mani and Wheeler\n\n\n(1998) classification. Industry wise productivity is measured by net value-added per\n\n\nworker. We also include we also amount of taxes paid by each industry as a control\n\n\nvariable. The variable of interest is P which is the industry-wise pollution intensity. If\n\n\nFDI does not show an increase in dirty industries, then =0.\n###### δ 2\n\n\n**IV. SAMPLE AND DATA**\n\n\nAnnex 1 provides a list of variables used in the industry-level analysis, unit of analysis\n\n\nand their data sources. Data on net turnover, industrial wages, fixed capital, foreign\n\n\ninvestment and environmental abatement cost come from the General Statistical Office\n\n\n(GSO) of Vietnam. Collection of these data in Vietnam is a recent exercise. As result,\n\n\ndetailed data is not available for a longer time frame. The GSO collects these data on a\n\n\nyearly basis since the year 2000 through enterprise surveys and to the extent that this was\n\n\nthe first time such an exercise has been conducted in Vietnam, the data seem fairly\n\n\nreliable. Although, ideally we would have liked to have data for much longer timeframe,\n\n\na most comprehensive industrial survey for Vietnam is available only for the years 2000\n\n2002. Since this comes on the heels of the bilateral and multilateral trade agreements, it\n\n\nshould provide reasonable approximation of the post-openness trends in industrial\n\n\nproduction.\n\n\nThis data is organized by industry according to the Vietnamese Standard Industrial\n\nClassification (VSIC) <sup>11</sup", "source": "general_prwp", "subset": "annotate_aivin", "spans": [{"key": "prwp:003092:15:0:0", "start": 1303, "end": 1321, "surface": "enterprise surveys", "probe_tag": "confusion", "probe_score": 0.8577, "luna_label": 0}, {"key": "prwp:003092:15:0:1", "start": 1553, "end": 1582, "surface": "industrial survey for Vietnam", "probe_tag": "confusion", "probe_score": 0.8877, "luna_label": 1}]}]