queue_annotate_rafael.json

[{"key": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-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_rafael", "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": "rafael-024", "text": "**The World Bank**\nUganda Secondary Education Expansion Project (P166570)\n\n\n**Figure 3: Survival rates in primary education.**\n\n\n\n100\n\n\n90\n\n\n80\n\n\n70\n\n\n60\n\n\n50\n\n\n40\n\n\n30\n\n\n20\n\n\n10\n\n\n0\n\n\n\n<u>100</u>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGrade1 Grade2 Grade3 Grade4 Grade5 Grade6 Grade7 Grade8 Grade9\n\n\nSource: Facing Forward, 2017\n\n6. **In spite of increased access to schooling, the average level of education of the work force remains low and does**\n**not meet labor market requirements.** Uganda will need to absorb an additional 600,000 new entrants to the labor\nmarket each year between 2014-2020. In order to sustainably increase welfare, these entrants must find productive\nemployment. <sup>9</sup> [^9: Uganda job diagnostics/strategy, World Bank, 2018, draft.] Estimates from the National Household Survey (UNHS) (2016) show that only entrants with postsecondary education can escape informal sector work. In order to increase the employability and productivity of the\nexpanding workforce, supply of quality education, especially for low-income, rural households and girls, is critical.\nAccording to the UNHS, only one in five people aged 15 and above completed secondary education. Thus, a large\nnumber of youth enter the job market without foundational skills of basic literacy and numeracy, as well as generic\nskills essential for life and work.\n\n**B.** **Sectoral and Institutional Context**\n\n7. **Uganda is the pioneer in terms of introducing universal access to secondary education in Sub-Saharan Africa.** The\nsecondary education sub-sector in Uganda is centrally managed and comprises six grades, Senior 1 (S1) to Senior 6\n(S6). S1-S4 is categorized as ordinary (‘O’) level, or lower secondary, while S5-S6 is Advanced (‘A’) level, or upper\nsecondary. In 2007, Uganda introduced the Uganda", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:014224:4:0:0", "start": 764, "end": 789, "surface": "National Household Survey", "probe_tag": "keep", "probe_score": 0.9113, "luna_label": 1}, {"key": "fcv_pads_east_africa:014224:4:0:1", "start": 1088, "end": 1092, "surface": "UNHS", "probe_tag": "keep", "probe_score": 0.955, "luna_label": 1}]}, {"key": "rafael-025", "text": ",000 houses were destroyed dunng the war and only 10,000 have been\nrebuilt so far. Government is particularly concerned with the shelter needs of returnees, IDPs <sup>and</sup>\n\n\n\ngovernment employees such as health workers and teachers. The state of shelter in many <sup>areas is one</sup> <sup>of</sup>\nthe factors constraining the return of government employees and the revitalization of district and local\neconomic activity. The agricultural sector is particularly important because it currently <sup>employs 75%</sup> <sup>of</sup>\nthe country's labor force. The 2000 Baseline Service Delivery Survey reported that between <sup>65%</sup> <sup>and</sup>\n85% of the population do not have access to safe drinking water and sanitation facilities. Recent\nestimates in the most neglected communities of the \"newly accessible areas\", such as Bombali, <sup>suggest</sup>\nthat access to potable water and adequate sanitation are as low as 5% and 3%, respectively.\n\n\n\nWidespread human rights abuses during the civil war included the forced recruitment of children\nas combatants, porters and sex slaves. In addition, tens of thousands of people continue to suffer <sup>from</sup>\nthe traumatic effects of amputation and other injuries, sexual violence, loss of parents, and the general\nstress of living through a civil war. This has greatly increased the need for a kind of <sup>social service</sup>\nsupport that was not as widely needed before the war, integrating traditional health services <sup>with</sup>\npsycho-social care, counselling, foster homes and disability programs. The challenges ahead <sup>will include</sup>\n\nthe provision of community-", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:019789:9:1:0", "start": 568, "end": 605, "surface": "2000 Baseline Service Delivery Survey", "probe_tag": "keep", "probe_score": 0.9403, "luna_label": 1}]}, {"key": "rafael-026", "text": "NER) at the primary level in the\nsurvey year (1996) was 50% greater for the highest expenditure quintile compared to the lowest\nexpenditure quintile. The inequity is even more pronounced in secondary education (lower secondary\neducation is part of basic education but the survey data does not separate the two), where the NER of\nthe highest quintile was more than 420% higher than the NER of the lowest quintile. The income\ndifferences in enrollment are significantly higher than other countries in Africa. The problem in urban\nareas is access - demand exists among all groups but the rationing of school intake ends up benefiting\nthe better off. Any further expansion of places will help the poorer segments of the population. Thus,\npublic expenditure in basic education is justified both on the public good rationale and also on the\nequity rationale.\n\n\nThe ten-year program proposed by the Government will also result in efficiency gains through lowered\nrepetition and drop out rates. This will result in reducing the average number of years to graduate\nfrom the primary and middle school levels, resulting in large potential savings in recurrent and\ninvestment expenditures in the long-run. The net present value of these public expenditure savings was\nfound to be significantly higher than the net present value of the investment costs required by the\nprogram. This is based on the difference between what the Government would have had to spend in\nthe absence of system reforms to educate the same number of students to the basic education level and\nwhat the Government would spend with systemic reforms. These savings yield an internal rate of over\n\n11% which clearly justifies the investment. This rate of return is actually an understatement for the\nprogram because the benefits do not include benefits from economic development, externalities and\nreduction in enrollment differentials between the poor and the better-off.", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:012423:19:1:0", "start": 272, "end": 283, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9509, "luna_label": 1}]}, {"key": "rafael-027", "text": "**9.** **CONCLUSION AND RECOMMENDATIONS**\n\n\nThe Environment Impact Assessment for Augmentation and Rehabilitation of Gatanga\nWater Supply identified that the population pressure of Gatanga district is growing at a\nsteady 2.426% per annun form the population statistics form the census report 2009 now\nstanding at 130,000 people. The current water infrastructure can only provide 6,310\nm3/day against an estimated demand of 9880m3/day leaving a deficit of 3,570m3/day.\nThe proposed project is step towards providing water close to the people of Gatanga\ndistrict.\n\nNegative environmental impacts identified in the report can be mitigated as illustrated in\nthe Environmental Management Plan and proper monitoring throughout construction and\noperation phases of the project is advised.\n\nThere is overwhelming acceptance by the project by the local community in the areas of\nGitemi, Ndakaini, Gitiri, Gatur, Gathaithi, Rwagetha, Chomo and Gatanga sub locations.\nThe areas are experiencing inadequate water supply leaving residents with the option of\ngoing for raw water plants which is not treated therefore leaving them exposed to water\nborne diseases such as typhoid and diarrhea.\n\nRecommendation is therefore for implementation of the above project with compliance to\nrecommendations outlined in the Environment Management Plan and resident and other\nstakeholder views as described in chapter six of the report.", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:020355:91:0:0", "start": 247, "end": 268, "surface": "population statistics", "probe_tag": "keep", "probe_score": 0.9472, "luna_label": 1}, {"key": "fcv_pads_east_africa:020355:91:0:1", "start": 278, "end": 296, "surface": "census report 2009", "probe_tag": "keep", "probe_score": 0.9168, "luna_label": 1}]}, {"key": "rafael-028", "text": ".\n\n**Relevance of Implementation** is rated as **high both before and after the restructuring.**\n\nThe overall relevance of objectives, design and implementation is assessed as **high.**\n\n**3.2 Achievement of Project Development Objectives (Efficacy)**\n\nProgress of the results indicators was notable both before and after the results framework\nwas revised for the Additional Financing in 2011. The appraisal mission for PBS II\nAdditional Financing undertook a comprehensive review of the PBS program\nperformance and in terms of progress towards Development Objectives and\nImplementation Progress, PBS II was rated Satisfactory.\n\nMeasured in terms of meeting its results indicators before the Additional Financing, the\nPBS Program, while not exceptional, was progressing relatively well. This was evidenced\nby data in the project ISR before the AF, and the accompanying mission documentation.\nFor example, in education the Net Enrollment Rate for children in primary school\nincreased from 79 percent to 87.9 percent between FY07 and FY10, which represents an\nincrease of almost 1.5 million additional children attending school. The ratio of primary\nhealth workers to population at the time (beginning of 2011) reached 1:2,500 from\n1:4,369 three years earlier. Similarly, in agriculture, the number of development agents\nproviding technical advice to small-scale farmers has risen from about 50,000 in FY08 to\n63,000 in FY10, an increase of 26 percent. For water, 65.8 percent of the rural population\nhad access to potable water in FY10, which represented a substantial improvement over\nthe baseline in FY07, where 46 percent had access. Finally, there was improved\n\n\n15", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "sample:fcv_pads_east_africa:016413:30:1:0", "start": 809, "end": 832, "surface": "data in the project ISR", "probe_tag": "keep", "probe_score": 0.9403, "luna_label": 0}]}, {"key": "rafael-029", "text": ",000 houses were destroyed dunng the war and only 10,000 have been\nrebuilt so far. Government is particularly concerned with the shelter needs of returnees, IDPs <sup>and</sup>\n\n\n\ngovernment employees such as health workers and teachers. The state of shelter in many <sup>areas is one</sup> <sup>of</sup>\nthe factors constraining the return of government employees and the revitalization of district and local\neconomic activity. The agricultural sector is particularly important because it currently <sup>employs 75%</sup> <sup>of</sup>\nthe country's labor force. The 2000 Baseline Service Delivery Survey reported that between <sup>65%</sup> <sup>and</sup>\n85% of the population do not have access to safe drinking water and sanitation facilities. Recent\nestimates in the most neglected communities of the \"newly accessible areas\", such as Bombali, <sup>suggest</sup>\nthat access to potable water and adequate sanitation are as low as 5% and 3%, respectively.\n\n\n\nWidespread human rights abuses during the civil war included the forced recruitment of children\nas combatants, porters and sex slaves. In addition, tens of thousands of people continue to suffer <sup>from</sup>\nthe traumatic effects of amputation and other injuries, sexual violence, loss of parents, and the general\nstress of living through a civil war. This has greatly increased the need for a kind of <sup>social service</sup>\nsupport that was not as widely needed before the war, integrating traditional health services <sup>with</sup>\npsycho-social care, counselling, foster homes and disability programs. The challenges ahead <sup>will include</sup>\n\nthe provision of community-", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:018073:9:1:0", "start": 568, "end": 605, "surface": "2000 Baseline Service Delivery Survey", "probe_tag": "keep", "probe_score": 0.9403, "luna_label": 1}]}, {"key": "rafael-030", "text": " sourced from<br>presentation at April 2015 JRIS, which essentially represent MoE data.<br>iv. Computed multiplying number of primary teachers (Grade 1-8) by per cent of qualified teachers. The<br>statistical abstracts do not report the absolute number of qualified teachers.|Note: The targets are as per the revised results framework.<br>* The target figures for 2012-13 and 2013-14 as shown in the results framework are not consistent.<br>i. <br>Computed using GER data from Education Statistics Annual Abstract, 2013-14<br>ii. Computed dividing number of enrolled students at Grade 5-8 by total number of teacher at that grade. The<br>data are collected from Education Statistics Annual Abstract, 2013-14. The estimated figures are different<br>from the student-teacher ratios reported in the Annual Abstract and the presentations made at April 2015<br>JRIS.<br>iii. The per cent of qualified primary teachers (Grade 1-8) having diploma and above are sourced from<br>presentation at April 2015 JRIS, which essentially represent MoE data.<br>iv. Computed multiplying number of primary teachers (Grade 1-8) by per cent of qualified teachers. The<br>statistical abstracts do not report the absolute number of qualified teachers.|Note: The targets are as per the revised results framework.<br>* The target figures for 2012-13 and 2013-14 as shown in the results framework are not consistent.<br>i. <br>Computed using GER data from Education Statistics Annual Abstract, 2013-14<br>ii. Computed dividing number of enrolled students at Grade 5-8 by total number of teacher at that grade. The<br>data are collected from Education Statistics Annual Abstract, 2013-14. The estimated figures are different<br", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:010666:89:3:0", "start": 78, "end": 86, "surface": "MoE data", "probe_tag": "confusion", "probe_score": 0.1602, "luna_label": 1}, {"key": "fcv_pads_east_africa:010666:89:3:4", "start": 78, "end": 86, "surface": "MoE data", "probe_tag": "confusion", "probe_score": 0.1692, "luna_label": 1}]}, {"key": "rafael-031", "text": "40|0.68|0.32|0.30|0.51|\n|**Other**|**14,104**|0.16|45.79|**3,181**|0.32|0.44|0.29|0.34|0.08|\n|**Total**|**8,767,954**|**100.00**|**38.85**|**985,016**|**100.0**|**100.00**|**100.00**|**100.00**|**100.00**|\n|**Distribution of Households in Nairobi (%)**|**Distribution of Households in Nairobi (%)**|**Distribution of Households in Nairobi (%)**|**Distribution of Households in Nairobi (%)**|**Distribution of Households in Nairobi (%)**|**100.0**|**21.6**|**37.5**|**33.2**|**7.7**|\n\n\nSource: KNBS, 2010 <sup>17</sup> [^17: Kenya National Bureau of Statistics. 2010. 2009 Kenya Population and Housing Census: Volume II.\nMinistry of Planning, National Development and Visio 2030, Government of the Republic of\n<u>Kenya, Nairobi</u>]\n\n\n**2.1.3** **Addressing the Water and Sanitation Challenges**\n\nAddressing the two specific water and sanitation sector challenges identified in _Kenya Vision_\n_2030_, according to _Kenya Vision 2030_, will involve increasing development of water resources\nto meet the demand of an increasing population and a growing economy. It will also involve\ninvestment in infrastructure", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:016391:31:2:0", "start": 572, "end": 607, "surface": "Kenya Population and Housing Census", "probe_tag": "confusion", "probe_score": 0.6923, "luna_label": 1}]}, {"key": "rafael-032", "text": "Disbursement forecast\n\n\n\nContract number\n*Contract subject\nAwardee\n*Launching date\n-Expected delivery date\n-Non objection date\n*Expected date of final delivery\n*Bidder nationality\n*Contract allocation (general account, budget, loan\ncategory, geographic area)\n*List of contracts\nManagement of financial -Standard financial statements (balance sheet;\naccounts statement of sources and uses of funds/income\nstatement, ...)\n*LACI reports for the project duration\nFixed Assets management -Inventory of Fixed Assets (type, quantity, valuation,\ndate of service, etc.)\n\n\n\nSupplier\nAccounting category ; budgetary and accounting\nallocation of fixed assets\n\n\n\nLocation\nDepreciation\n-Disposal of Fixed assets\n\n\n\n**Module** Functions\nSorting parameters Project ID and currency used\n\n                  - Fiscal years\nCurrency\nDecentralized data entry locations\n\n\n\nChart of accounts, managerial reports, geographic\nareas of intervention, etc.\n\n\n\n\n                - Books of accounts\nDonors\n\n                 - Contracts\nCategories of disbursement\nUser Management Data storage ; restitution ; correction; cleaning; etc.\n\n                - Import/export of data to other Tempro modules\n\n\n\nIt is expected that the application would be modified to differentiate the operations from the\nprojects, as well as funding sources to allow for reporting in financial and accounting terms of the\nproject objectives and activities. The concept should allow for proper monitoring of the project\nduring the life of the credit, namely: (i) chart of accounts; (ii) by category, component, and subcomponent; (iii) by geography (type of establishment, site and district); (iv) by category of\nexpenses; and (v) in local and foreign currency. Reporting of multi-level data is planned, which\n**wiU** bring about a more dynamic approach to the management of the project, and which should", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:012527:51:0:0", "start": 1720, "end": 1736, "surface": "multi-level data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "rafael-033", "text": "report should include budget allocation and expenditure data that should be consistent with the KPI\nreport.\n\n39. While the KPI data has quality issues particularly related with the comprehensiveness of\nthe data capture, the practice if encouraging. It is understood, the system building effort is a process\nthat passes through obstacles and challenges and the end result cannot be achieved in one go. The\neffort requires continuous engagement and resource. The aim is to streamline the thinking and\nnecessity of collecting data and measuring performance of procurement through KPIs. In the past\nnone of the regulatory bodies considered this to be their basic duty, instead investing all their time\non audit and trainings. The regulatory bodies are now putting great effort in the endeavor. It requires\ncollecting and entering data at each stage of procurement process for each item. The envisaged\ncapacity and streamlining of the system take time to reach a dependable stage. The initial aim was\nto make the exercise be a catalyst and eye opener for the regulatory bodies to improve the oversight\nsystem and achieve higher. And the exercise has fulfilled this objective. The below figures indicate\nthe results of the two indicators using data collected the past three years (EFY 2010-2012).\n\nTable 8: Average Bid Process Period [days]\n\n\n\n\n\n\n\n\n\nAmhara 51 52 44\nOromia 85 76 100\nSNNP 36 66 42\nTigray 52 47 59\n\nFigure 1: Share of Open Bid\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n14", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:017679:13:0:1", "start": 123, "end": 131, "surface": "KPI data", "probe_tag": "confusion", "probe_score": 0.6392, "luna_label": 0}, {"key": "fcv_pads_east_africa:017679:13:0:2", "start": 1238, "end": 1273, "surface": "data collected the past three years", "probe_tag": "confusion", "probe_score": 0.5362, "luna_label": 1}]}, {"key": "rafael-034", "text": "**Independent Evaluation Group (IEG)** Implementation Completion Report (ICR) Review\nTransforming Health Systems (P152394)\n\n\nThe added third objective (to provide immediate and effective response to an eligible crisis or emergency)\nwas fully achieved and rated High. The aggregation of achievement of all three objectives is indicative of a\nSubstantial rating for efficacy under the revised objectives.\n\n\n**Overall Efficacy Revision 1 Rating**\n\n\nSubstantial\n\n\n**5. Efficiency**\nThe cost benefit analysis (CBA) carried out at the time of appraisal adopted the approach of economic\nevaluation of complex interventions. This approach was used because it was not possible to determine the exact\ncombination of interventions that would be carried out during the life of the project. Benefits included lives saved\n(for both mothers and children) and increased productivity, valued at USD 954.2 million. The total cost was\nestimated as USD 174.9 million, resulting in a total benefit of USD 779.2 million, that is, a benefit-to-cost ratio of\nabout 5.5:1. The sensitivity analysis showed that, even if only half of the estimated benefits were achieved, the\nproject would continue to be economically viable. The CBA did not estimate an internal rate of return.\n\n\nThe ICR did not carry out a CBA, but it argued that the evidence indicated that the type of interventions carried\nout as part of the project had significant economic impact. For example, as per UNICEF data, childhood\nvaccination would provide a return of USD 20 per USD invested in low- and middle-income countries. Also,\nresearch carried out in Kenya showed that protective equipment for healthcare workers during the COVID-19\npandemic led to an 11-fold return on investment. The project team later delivered a rigorous CBA, based on the\nsame key assumptions as the analysis at appraisal, using data from the project implementation period. This\nanalysis found, at a three percent discount rate, a net present value of benefits", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:004630:10:0:1", "start": 1850, "end": 1893, "surface": "data from the project implementation period", "probe_tag": "confusion", "probe_score": 0.8495, "luna_label": 1}]}, {"key": "rafael-035", "text": "### **Annex-6: Progress Towards Achieving MDGs**\n\nThe present evaluation has tracked progress towards MDGs (Goals 1 to 5) using counterfactual\nanalysis and based on latest available data. The findings are summarised in this annex.\n\n\n**Eradication of extreme poverty and hunger (Goal 1)** : The proportion of population below the\npoverty line declined sharply from 44.2 per cent in 1999-00 to 27.8 per cent in 2011-12 ( _Figure 1-A_ ).\nThis represents a whopping reduction by 17.7 per cent points over a period of one-and-a-half decade.\nA further reduction by 3.8 per cent points would enable the country achieve MDG target of 24 per\ncent. Given that inflation is now contained to single digit, the MDG target has possibly been achieved\nby this time. However, it should be borne in mind that even if poverty reduction touches 24 per cent\nthat would mean 22 million population still remaining below the poverty line.\n\n\n**Figure 1: Reduction in poverty and hunger**\n\n\n\n\n\n\n\n**55**\n\n\n**45**\n\n\n**35**\n\n\n**25**\n\n\n**15**\n\n\n\n**1999-00** **2004-05** **2010-11** **2011-12** **2015**\n\n\n\n**(A) Popula1on below poverty line (percent)**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe food poverty or hunger, as measured by proportion of underweight children (under 5 year), has\nalso declined from 47.2 per cent in 2000 to 28.7 per cent in 2011-12 ( _Figure 1-B_ ). If the trend\ncontinues, it is quite likely that MDG target of 22.7 per cent underweight children will be achieved in\n2015. On the whole, Ethiopia is on track to achieve MDG targets of poverty and hunger reduction", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:010666:95:0:0", "start": 165, "end": 186, "surface": "latest available data", "probe_tag": "confusion", "probe_score": 0.5756, "luna_label": 1}]}, {"key": "rafael-036", "text": "**The World Bank**\nLSMS-ISA Ethiopia Rural Socioeconomic Survey (P125475)\n\n\n**ABBREVIATIONS AND ACRONYMS**\n\n\nCAPI Computer Assisted Personal Interviewing\n\n\nCSA Central Statistical Agency, Ethiopia\n\n\nERSS Ethiopia Rural Socioeconomic Survey\n\n\nESS Ethiopia Socioeconomic Survey\n\n\nLSMS-ISA Living Standards Measurement Study- Integrated Surveys on Agriculture\n\n\nMOF Ministry of Finance, Ethiopia\n\n\nPDO Project Development Objective\n\n\nNSDS National Strategy for the Development of Statistics", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:017539:1:0:1", "start": 199, "end": 239, "surface": "ERSS Ethiopia Rural Socioeconomic Survey", "probe_tag": "confusion", "probe_score": 0.5417, "luna_label": 0}, {"key": "fcv_pads_east_africa:017539:1:0:2", "start": 242, "end": 275, "surface": "ESS Ethiopia Socioeconomic Survey", "probe_tag": "confusion", "probe_score": 0.5659, "luna_label": 0}]}, {"key": "rafael-037", "text": "- 37 \n\n10. “Disbursement Linked Indicator” or “DLI” means in respect of a given Category,\nthe indicator related to said Category as set forth in the table in Section IV.A.2 of\nSchedule 2 to this Agreement.\n\n\n11. “Disbursement Linked Result” or “DLR” means in respect of a given Category,\nthe result under said Category as set forth in the table in Section IV.A.2 of Schedule\n2 to this Agreement, on the basis of the achievement of which, the amount of the\nFinancing allocated to said result may be withdrawn in accordance with the\nprovisions of said Section IV.\n\n\n12. “EFY” means the Ethiopian Fiscal Year, the fiscal year of the Recipient which\ncommences on July 8 and ends on July 7.\n\n\n13. “Eligible Crisis or Emergency” means an event that has caused, or is likely to\nimminently cause, a major adverse economic and/or social impact to the Recipient,\nassociated with a natural or man-made crisis or disaster.\n\n\n14. “Eligible Refugee Incentive Teachers” mean the refugee incentive teachers\ndeemed eligible to receive in-service skill upgrading Training on an annual basis:\n(i) in line with the eligibility criteria elaborated in the Operations Manual; and (ii)\nbased on data received from UNHCR.\n\n\n15. “Eligible Refugee Primary Schools” mean refugee primary schools which are\noperational in the five main refugee-hosting regions, based on data received from\nUNHCR, on an annual basis as further elaborated in the Operations Manual.\n\n\n16. “Eligible Refugee Secondary Schools” mean refugee secondary schools which are\noperational in the five main refugee-hosting regions, based on data received from\nUNHCR, on an annual basis as further elaborated in the Operations Manual.\n\n\n17. “Emergency Action Plan” means the plan referred to in Section I.F of Schedule 2\nto this Agreement, detailing the activities, budget,", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:014916:37:0:0", "start": 1171, "end": 1195, "surface": "data received from UNHCR", "probe_tag": "confusion", "probe_score": 0.7821, "luna_label": 1}]}, {"key": "rafael-038", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:014600:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "rafael-039", "text": " from one stage to another. It is also driven in part by the curriculum which is geared to\npreparing students for the French baccalaureate examnination and may be contextually difficult for\nDjiboutians from less educated families.\n\n\n**3. Income and Gender Gaps in Enrollment Rates**\n\nEven though the main constraint at present appears to be school places, there is already evidence of\ngender and income gaps which cannot be explained by lack of school places alone. These are\nexpected to become more prominent over time as enrollment rates rise.\n\n\nAccording to the household expenditure survey data, in urban areas, the Net Enrollment Rate in\nPrimary Enrollment is 50% higher for the highest expenditure quintile compared to the lowest\nexpenditure quintile. The inequity is even worse in secondary education (lower secondary education\nis part of basic education but the survey data did not separate the two), where the NER of the highest\nquintile is 420% higher than the NER of the lowest quintile. The problem in urban areas is access demand exists among all groups but the rationing of sets ends up benefiting the better off who live in\nareas where schools have historically been located. Any further expansion of places will help the\npoorer segments of the population more particularly if care is taken to site the schools in areas where\nthe poor live.\n\n\nThere are also significant gender gaps and research indicates that educated mothers play a key role in\nthe country's overall development. There is a shortage of school places and any rationing works to\nthe detriment of girls enrollment. Parents are less willing for their girls to attend school because in\npar., they may view the curriculum as foreign. In addition, despite the fact the education is officially\nfree, poor families still have difficulty paying the cost of books and materials. They prefer to use\ntheir constrained resources for their boys who they feel have a better labor market potential. Finally,\nthe data from the Household Survey, showed that even if girls go to school, their parents pull them\nout at an", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "sample:fcv_pads_east_africa:019153:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "confusion", "probe_score": 0.7323, "luna_label": 1}, {"key": "sample:fcv_pads_east_africa:019153:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.1461, "luna_label": 1}, {"key": "sample:fcv_pads_east_africa:019153:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "confusion", "probe_score": 0.1121, "luna_label": 1}]}, {"key": "rafael-040", "text": "|No|Issues|Agreed Actions|Time frame|\n|---|---|---|---|\n|5.1|Recruit/assign E&S specialists for the<br>vacant regional positions.<br>|• E&S specialist positions at Dire Dawa and Southwest region to be hired and<br>deployed.<br>• MOE to assign an E&S expert to work and coordinate with the national and<br>regional PIU|• December 12, 2024<br>• December 18, 2024|\n|5.2|Capacity building|• NWCO and Federal PMUs to prepare a capacity development plan and share<br>with the Bank’s ES team<br>• NWCO and Federal PMUs to cascade the capacity development trainings for<br>woreda focal persons and relevant project workers at woreda level|• October 30, 2024<br>• By December 28,<br>2024|\n|5.3|Proper documentation of E&S instruments|• Federal and regional PMUs to assess, compile, and consolidate data on the ES<br>instruments for subprojects implemented and under implementation<br>• Federal and regional PMUs to share the available ESIA/ESMP for CR WASH<br>with the Bank E&S team|• December 28, 2024<br>• October 22, 2024|\n|5.4|Safeguards Instruments|Federal and regional PMUs: (i) to compile and share corrective action plan of the<br>internal due diligence reports from all regions; (ii) finalize, compile and share the<br>ESMPs for the identified high value contracts|December 30, 2024|\n|5.5|Independent E&S compliance audit|• Federal and regional PMUs share the independent E&S", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:004976:6:0:0", "start": 1130, "end": 1160, "surface": "internal due diligence reports", "probe_tag": "confusion", "probe_score": 0.1123, "luna_label": 0}]}, {"key": "rafael-041", "text": "21\n\n\nthey have to purchase water from trucks which costs four times as high than a household connection;\nand qat consumption poses a major social, income and productivity issue.\n\n\nIn order to capture as much of the school-age population presently out of school due to the lack of\nexisting places, the project will construct additional/rehabilitate classrooms. In addition, sanitation\n\nservices will be rehabilitated, and a study will be undertaken on which sanitation services best serve\nthe area, especially in a drought-prone area, and the most cost-effective methods of implementation\nand maintenance. The project will finance a study to analyze the factors that hinder girls' attendance\nand achievement, and of the feasibility of measures to overcome them, including the issues\n\nsurrounding access to education by the poor. The problems are cross-sectoral which the project, in\nPhase I, will not address (unemployed youth, health issues, non-Djiboutian school-age population,\netc.). See Section C above for program details.\n\n\nThe IDA's regional team will discuss with Government on updating the initial 1997 poverty\n\nassessment in order to produce a better picture of the issues as they exist now. In addition, it is\nenvisaged that IDA will discuss the rising health issues with the Government and the best ways for\naddressing these problems.\n\n\n_6.2 Participatory Approach: How are key stakeholders participating in the project?_\n\n\nKey stakeholders participated in the National Educational Forum _(Etats-Generaux de l'Education)_\nwhich was held in December 1999. This included officials, teachers, parents, students, members of\nparliament and the general public. The project is based on the outcome of the conference. The new\neducation law (approved in August 2000) sets in place the conditions for broadening participation in\nDjibouti's education system. It provides for setting up school management committees with parent\n\nand community involvement. The law also provides for the creation of conditions to increase private", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:019187:24:0:0", "start": 1107, "end": 1131, "surface": "1997 poverty\n\nassessment", "probe_tag": "confusion", "probe_score": 0.6264, "luna_label": 1}]}, {"key": "rafael-042", "text": "will include broad based community _barazas,_ radio, SMS, brochures/leaflets, megaphone\nannouncements etc. Specifically, the Beneficiary Outreach Strategy with nuanced VMG messages\nwould be used to ensure inclusion of VMGs, and would provide the appropriate mechanisms to\nascertain that VMGs are reached and that information is provided in ways that are easily\nunderstood. This may need to be tailored to different VMG groups, depending on their\nremoteness, language, literacy level, integration into the broader communities and civic\neducation.\nb. Track the number and type of complaints that are lodged with the program and the actions taken\n\nand ensure that appropriate mitigation measures are planned and implemented.\nc. Carry out periodic reviews of beneficiary and grievance data to ensure targeted locations where\n\nminorities are present are reached and recurring complaints are investigated to ensure mitigation\nmeasures.\nd. Ensure that barriers to inclusion (e.g. difficulty securing IDs) are addressed for eligible\n\npopulation, including VMGs.\n\n61. There is a need to further sensitize and build capacity of all relevant stakeholders on proper\nidentification and inclusion of different categories of VMGs. As such, the KSEIP implementing agencies\nshould review the existing VMG databases under NSNP for each KSEIP county. Furthermore, program\nofficers should be sensitized on the stigmatized conditions, and how to include such groups in KESIP. This\nwill enhance understanding of the characteristics and locations of VMGs, and create awareness about the\nVMGs among the officers for better engagement and targeting. To increase the engagement with VMGs,\ncollaboration between SDSP and NDMA and other government and civil society organisations who work with\nVMGs should be encouraged.\n\n\n62. Training and civic education of communities should be undertaken to enhance understanding of rights\nand entitlements of all, including VMGs. Training of rights can be done as part of beneficiary outreach for all\nNSNP beneficiaries and communities.\n\n63. In communicating with VMG", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:008714:15:0:0", "start": 755, "end": 785, "surface": "beneficiary and grievance data", "probe_tag": "confusion", "probe_score": 0.409, "luna_label": 0}, {"key": "fcv_pads_east_africa:008714:15:0:1", "start": 1284, "end": 1308, "surface": "VMG databases under NSNP", "probe_tag": "confusion", "probe_score": 0.7324, "luna_label": 1}]}, {"key": "rafael-043", "text": "required for local government involvement, and arrangements for maintenance, monitoring and\nevaluation (M&E). Social capital enhancing activities would be a mandatory part of all sub-projects, and\nwould be tailored to support activities chosen by the communities.\n\nSupport to Decentralized Government Structures. Most local administrations are beginning\nto operate again with a limited number of staff and other inputs. District and chiefdom authorities are\nvery weak, however, and lack the financial and human resources needed to address their concerns and\npnorities effectively. NGOs have demonstrated their ability to implement successful community-based\nsocial and economic projects and have played a key role in shelter reconstruction activities. With the\ngradual strengthening of local government capacity, partnerships between community groups and local\nauthorities are expected to increase. Upon completion of initial training, district and chiefdom authorities\nwould be required to demonstrate that they have used the training by showing that there have been some\nimprovements in their community. For instance, at the end of each training session, district authorities\nwould be required to develop a simple action plan that specifies some activities that NSAP or other\npartners could support. District and chiefdom authorities would also gain experience in implementing,\nsupporting or overseeing community development activities.\n\nHealth. The unfavorable health indicators in Sierra Leone can be attributed to several factors.\nHigh fertility, female genital mutilation and the presence of HIV/AIDS increase morbidity and mortality\nrisks for women and children. Many risk factors that have contributed to HIV/AIDS epidemics in other\nAfrican countries have long been present in Sierra Leone, and the protracted conflict has created the\nconditions for explosive growth in HIV/AIDS infection rates. The Centers for Disease Control carried\nout a survey in 2002 which found the HIV prevalence among adults (aged 1549) to be 6.1 %; in\nFreetown, 4% in rural areas and 4.9% nationwide. In response to the crisis, Government has developed\na multi-sector HIV/AIDS Program, which is being supported by various", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:018329:10:0:0", "start": 1950, "end": 1964, "surface": "survey in 2002", "probe_tag": "confusion", "probe_score": 0.7887, "luna_label": 1}]}, {"key": "rafael-044", "text": ">|SheepI Goat : Donkey<br> <br> <br>|BeeHive<br>|\n|. FartaSide<br>|2.713.64<br>|4.88<br>|3.88<br>! 1.62<br> <br>|3.88<br>! 1.62<br> <br>|7 <br>|\n|EbinatSide|2.512.66|3.84|4.4|1.88|4.1|\n\n\n\nSource: Household Survey. 2008\n\n\n3.3. Vulnerable Groups\n\n\nAs per the policy and legal framework of the Government and major donor\n\nagencies like the World Bank, vulnerable groups, like the elderly, women\nhousehold heads and those with physical and emotional impairment. are\nexpected to have special support to address part of their problems during the\nrelocation and resettlement process. These landless households are also\nexpected to have special support. The house to house survey result shows that\nabout six percent of the household heads are falling within the category of\nvulnerable groups. Those aged ones (65 and plus) and women headed who\nlost more than 25% of their land are also taken as vulnerable groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Table 3.3: Vulnerability Status amongst PAPs I Vulnerability Type|Col2|Col3|Col4|Col5|Col6|Col7|\n|---|---|---|---|---|---|---|\n|i <br>Woreda<br> <br>~~I ~~<br>Vulnerability Type<br>I <br>Old Age( 65+)<br>Hearing<br>~~I ~~Sight<br>Amputee<br>landless<br>1 household<br>Impair", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:011334:18:2:1", "start": 650, "end": 671, "surface": "house to house survey", "probe_tag": "confusion", "probe_score": 0.5725, "luna_label": 1}]}, {"key": "rafael-045", "text": "**the Bank’s right to sanction and the Bank’s inspection and audit**\n**rights. The form of the Letter of Acceptance is attached in Appendix**\n**1** .\n\n\n**_Leased Assets_** _as specified under paragraph 5.10_ of the Procurement\nRegulations: Leasing may be used for those contracts identified in the\nProcurement Plan tables: **Not Applicable.**\n\n\n**_Procurement of Second Hand Goods_** _as specified under paragraph 5.11_\nof the Procurement Regulations – is allowed for those contracts identified in\nthe Procurement Plan tables: **Not Applicable.**\n\n\n**_Domestic_** **_preference_** _as_ _specified_ _under_ _paragraph_ _5.51_ of the\nProcurement Regulations **_(Goods and Works)_** .\n\n\nGoods : **Applicable for those contracts identified in the**\n**Procurement Plan tables**\n\n\nWorks: **applicable** **for** **those** **contracts** **identified** **in** **the**\n**Procurement Plan tables**\n\n\nThis being an emergency project, increased thresholds for Requests for\nQuotations (RFQ) to US$1 million for goods and services and US$5 million for\nworks **;**\n\n\n**Other Relevant Procurement Information:-**\n\n\n**Prior Procurement Arrangements:**\n\n\nThe Procurement Arrangements as indicated in the below table and within\nthe thresholds indicated in the below tables will be used. The thresholds for\nthe Bank’s prior review requirements are also provided in the table below:\n\n\nTable: **Prior review Thresholds**\n\n|Procurement Type|High<br>Risk|\n|---|---|\n|Works|5.0|", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:000568:1:0:0", "start": 298, "end": 321, "surface": "Procurement Plan tables", "probe_tag": "confusion", "probe_score": 0.0657, "luna_label": 0}, {"key": "fcv_pads_east_africa:000568:1:0:2", "start": 298, "end": 321, "surface": "Procurement Plan tables", "probe_tag": "confusion", "probe_score": 0.0507, "luna_label": 0}]}, {"key": "rafael-046", "text": "Kenya Power and Lighting Company PLC\n\nKenya **Off** grid Solar Access Project (KOSAP)\nAnnual Report and Financial Statements for the financial year ended June **30, 2025**\n\n\n**_2.8_** Summary of Overall Project Performance:\n\n\na) Budget Performance against Actual Amounts\n\nTo date the Project has received a total Kshs **136** million, which is 2.4 **%** of the budget.\n\n**b)** Physical Progress and Achievement of the Project\n\nUnder the KPLC component, the project entails the development of **83** mini-grids and 343\nsolar stand-atone systems, targeting to connect over **37,500** households, enterprises,\nand public facilities. Implementation progress to date includes handover of project sites,\ncompletion of site surveys, and ongoing detailed design works.\n\nDuringthe year under review, the Project absorbed **5.1 %** of the budget in the FY20242025\n\n\nc) Implementation Challenges and way forward\n\nThe acquisition of land for the construction of mini grids experienced significant delays.\nInitially, the adopted approach-donation of unregistered community land-was found\nto be inconsistent with the prevailing legal framework. Following extensive consultations,\nthe World Bank approved a Com pensation-in -Kind Strategy, whereby the Project would\nprovide community infrastructure, such as the construction of classrooms or the desitting of water pans, in lieu of cash payments.\nThe Ministry subsequently mobilized the required resources and engaged the National\nLand Commission **(NLC)** to lead the land acquisition process. Key milestones, including\ngazettement of the land, grantingof eartyentry, site inspections, and public inquiries, have\n\nbeen completed. Final surveys have also been undertaken jointly **by** the Directorate of\nSurveys and technical teams from KPLC and REREC and wilt be submitted to the Ministry\nof Lands for final vesting.\nThe", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:000418:9:0:0", "start": 712, "end": 724, "surface": "site surveys", "probe_tag": "confusion", "probe_score": 0.1065, "luna_label": 0}]}, {"key": "rafael-047", "text": " the consumption of MNR crops\nand led to increased HH dietary diversity and food security <sup>7</sup> [^7: Food Security was measured in terms of the food consumption score (FCS).] in project districts **_._** The project initiated a combination\nof activities that included community nutrition education forums, cooking demonstrations, information\n\n\n3 The Quasi-Experimental evaluation used panel data from the baseline, midline and endline evaluations surveys of the project. The evaluation approach\ninterviewed the same households from baseline and midline in 5 project districts and in 3 comparison districts (non-project districts). The sample survey\ncovered a total of 2,841HHs (1,897HHs from treatment project areas and 944HHs from non-project areas). The evaluation approach corrected for potential\nselection bias due to non-random allocation of districts to control and treatment groups. During the selection process, each treatment project group was\nmatched with one or two control (non-Project) districts with similarity in location, agroecological conditions and geographical closeness to minimize on\ndistrict level confounding differences. Treatment project districts were largely comparable to control districts in several aspects. Difference in Difference\nestimation of impacts were applied with (1) Propensity Score Matching estimators with Kernel matching and nearest neighbor matching and (2) regression\nbased non-matching estimators to ensure robustness of impact results. The study used the Agricultural Scalability Assessment Tool to qualitatively\ndetermine the effects of the project on beneficiaries and spill-over effects.\n4 Impact Evaluation Report of the Multisectoral Food Security and Nutrition Project, December 2023.\n5 35 percent more households in treatment districts than in control districts had higher awareness levels of OFSP; 17 percent more households in\ntreatment districts than in control districts had higher awareness levels of Amaranthus; 15 percent more households in treatment districts than in control\ndistricts had higher awareness levels of pumpkin while 4 percent more households in treatment districts than in control districts had higher awareness\nlevels of iron rich beans.\n6 Treatment districts produced higher quantities of MNR", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:003462:14:2:0", "start": 392, "end": 402, "surface": "panel data", "probe_tag": "confusion", "probe_score": 0.8625, "luna_label": 1}]}, {"key": "rafael-048", "text": "83. _Procurement Delays:_ Cost of the civil works component at the time of bidding\nvaried substantially from that at the feasibility and detailed design stage due to delays in\nthe procurement process of about four years. The eventual bid prices could therefore not\nenable the project to be implemented as appraised and consequently some components\nhad to be dropped. In future, design reviews and updates of the cost estimates will be\nne **c** essary just before calling for bids. It would also be advisable to avoid a long\nprequalification period. It is also worthwhile to explore the possibility of commencing\nprocurement prior to credit effectiveness in order to provide for procurement lead-time.\n\n\n84. _Monitoring and Evaluation Indicators of the Projects:_ At appraisal, the baseline\ndata for the outcome indicators were not provided. These were provided in the ISR4 in\nAugust 2005. As a proxy for increased industrial and agricultural activity, the traffic\nincrease on the project roads was used to reflect the agricultural productivity benefits to\nthe rural population. The lesson learned is that baseline data should be provided at the\nappraisal stage for appropriate reading of the PDO indicators.\n\n\n**7.** **Comments on Issues Raised by Borrower/Implementing Agencies/Partners**\n\n**(a) Borrower/implementing agencies: None**\n\n**(b) Co financiers: No co-financier**\n\n**(c) Other partners and stakeholders** : **None**\n\n\n23", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:018941:34:0:0", "start": 781, "end": 794, "surface": "baseline\ndata", "probe_tag": "drop", "probe_score": 0.0445, "luna_label": 1}, {"key": "fcv_pads_east_africa:018941:34:0:1", "start": 1105, "end": 1118, "surface": "baseline data", "probe_tag": "confusion", "probe_score": 0.1153, "luna_label": 0}]}, {"key": "rafael-049", "text": "**The World Bank** Implementation Status & Results Report\nUganda Multisectoral Food Security and Nutrition Project (P149286)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**Data on Financial Performance**\n\n\n**Disbursements (by loan)**\n\n\n1/6/2019 Page 5 of 6", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:010599:4:0:0", "start": 166, "end": 195, "surface": "Data on Financial Performance", "probe_tag": "drop", "probe_score": 0.023, "luna_label": 0}]}, {"key": "rafael-050", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:012788:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0}]}, {"key": "rafael-051", "text": "br>Program (Number)|Comments on achieving targets|Comments on achieving targets|Pending UDG disbursement|Pending UDG disbursement|Pending UDG disbursement|Pending UDG disbursement|Pending UDG disbursement|Pending UDG disbursement|\n|**RA 4 - Improved private sector engagement**|**RA 4 - Improved private sector engagement**|**RA 4 - Improved private sector engagement**|**RA 4 - Improved private sector engagement**|**RA 4 - Improved private sector engagement**|**RA 4 - Improved private sector engagement**|**RA 4 - Improved private sector engagement**|**RA 4 - Improved private sector engagement**|**RA 4 - Improved private sector engagement**|\n|Indicator Name|Baseline|Baseline|Actual (Previous)|Actual (Previous)|Actual (Current)|Actual (Current)|Closing Period|Closing Period|\n|Indicator Name|Result|Month/Year|Result|Date|Result|Date|Result|Month/Year|\n|Participating urban board with up-to-<br>date business enterprise database<br>(Percentage)|0.00|Jun/2023|0.00|14-Mar-2025|0.00|14-Mar-2025|50.00|Jun/2028|\n|Participating urban board with up-to-<br>date business enterprise database<br>(Percentage)|Comments on achieving targets|Comments on achieving targets|To be tracked in APA2|To be tracked in APA2|To be tracked in APA2|To be tracked in APA2|To be tracked in APA2|To be tracked in APA2|\n|Participating urban boards operating<br>within the county government's", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:001991:14:2:0", "start": 906, "end": 934, "surface": "business enterprise database", "probe_tag": "drop", "probe_score": 0.0491, "luna_label": 0}, {"key": "fcv_pads_east_africa:001991:14:2:1", "start": 906, "end": 934, "surface": "business enterprise database", "probe_tag": "drop", "probe_score": 0.0123, "luna_label": 0}]}, {"key": "rafael-052", "text": "**_Kenya_** **_Agricultural Productivity_** **_and Sustainable Land Management Project (KAPSLMP)_**\n**_Reports_** **_and Financial Statements_**\n\n**_For the financial year ended June 30,_** **_2017_**\n\n\n**Specific** **Duties:**\n\n\n(i) Develop and **help** establish <sup>a</sup>\nsystem of routine records and\nperiodic monitoring reports at\ncommunity, district and\nproject level\n(ii) Assist in development of a\ndatabase of existing M&E\ncapacity in the districts for\ncapacity building\nrequirements\n(iii) Assist in development of\ncurriculum for training M&E\npersonnel\n(iv) Provide Technical Assistance\nto KAPAP Specialists and\nRSUs to develop\ncomprehensive monitoring\nand evaluation guidelines for\nthe various components\nobjectives. Develop\nappropriate indicators for\neffective implementation and\n\nimpact assessment at various\nlevels including community\nlevel\n(v) Coordinate and organize\nstudies/surveys and other field\nactivities in support of midterm and terminal evaluation\nof the project\n(vi) Liaise with and provide\ntechnical support to the\ndistricts evaluation needs\n(vii) Undertake project and\nthematic evaluations. This\nwill include diagnostic studies\nand in-depth reviews of\nproject interventions covering\neconomic, social and\nenvironmental impact\n(viii) Undertake impact assessment\nof project activities\n(ix) Act as the focal point for\norganizational evaluationresponsible for the\npreparation of periodic reports\n(x) Plan and implement\n\n\n**7**", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:013875:9:0:0", "start": 409, "end": 442, "surface": "database of existing M&E\ncapacity", "probe_tag": "drop", "probe_score": 0.0147, "luna_label": 0}]}, {"key": "rafael-053", "text": "- **_The procurement is open to eligible firms from any country;_**\n\n\n  - **_The request for bids/Proposal documents shall require that_**\n\n\n**_bidders/proposers submitting Bids/Proposal present a signed_**\n\n\n**_acceptance at the time of bidding, to be incorporated in any_**\n\n\n**_resulting contracts, confirming application of and compliance_**\n\n\n**_with the Bank’s Anti-Corruption Guidelines, including without_**\n\n\n**_limitation the bank’s right to sanction and the Bank’s inspection_**\n\n\n**_and audit right;_**\n\n\n  - **_Contracts with an appropriate allocation of responsibilities, risks_**\n\n\n**_and liabilities;_**\n\n\n  - **_Application of Standstill period and Publication of contract award_**\n\n\n**_information;_**\n\n\n  - **_Maintenance of records of the procurement process; and_**\n\n\n  - **_The Bank has the right to review procurement documentation_**\n\n\n**_and activities._**\n\n\nWhen other national procurement arrangements other than national open\ncompetitive procurement arrangements are applied by the Borrower, such\narrangements shall be subject to paragraph 5.5 of the Procurement\nRegulations.\n\n\n**_Leased Assets as specified under paragraph 5.10_** of the Procurement\nRegulations: Leasing may be used for those contracts identified in the\nProcurement Plan tables. **_“Not Applicable”_**\n\n\n**_Procurement of Second-Hand Goods_** **_as specified under paragraph_**\n**_5.11_** of the Procurement Regulations – is allowed for those contracts identified\nin the Procurement Plan tables _“_ **_Not Applicable”_**\n\n\n**_Domestic preference as specified under paragraph 5.51_*", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:002548:1:0:0", "start": 1250, "end": 1273, "surface": "Procurement Plan tables", "probe_tag": "drop", "probe_score": 0.0126, "luna_label": 0}]}, {"key": "rafael-054", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:011679:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0}, {"key": "fcv_pads_east_africa:011679:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "rafael-055", "text": "- To manage public land on behalf of the national and county governments;\n\n- To recommend a national land policy to the national government;\n\n- To advise the national government on a comprehensive programme for the\nregistration of Title in land throughout Kenya;\n\n- To conduct research related to land and the use of natural resources, and make\nrecommendations to appropriate authorities;\n\n- To initiate investigations, on its own initiative or on a complaint, into present or\nhistorical land injustices, and recommend appropriate redress;\n\n- To encourage the application of traditional dispute resolution mechanisms in\nland conflicts;\n\n- To assess tax on land and premiums on immovable property in any area\ndesignated by law; and\n\n- To monitor and have oversight responsibilities over land use planning\nthroughout the country;\n\n- On behalf of, and with the consent of the national and county governments,\nalienate public land;\n\n- To monitor the registration of all rights and interests in land;\n\n- To ensure that public land and land under the management of designated state\nagencies are sustainably managed for their intended purpose and for future\ngenerations;\n\n- Develop and maintain an effective land information management system at\nnational and county levels;\n\n- Manage and administer all unregistered trust land and unregistered community\nland on behalf of the county government; and\n\n- Develop and encourage alternative dispute resolution mechanisms in land dispute\nhandling and management.\n\nRelevance:\n\nThe National Land Commission will come in handy in cases of grievance solving\nmechanisms. It will also help to initiate investigations, on its own initiative or on a\ncomplaint, into present or historical land injustices that might relate to the project\nimplementation.\n\nThe commission also monitors and has oversight responsibilities over land use planning\nthroughout the country and will intervene should the proposed project go contrary to the\nland use plans.\n\n3.2.3 The Land Act 2012\n\nThis is an Act of Parliament intended to give effect to Article 68 of the Constitution, to\nrevise, consolidate and rationalize land laws; to provide for the sustainable administration\nand management of land and land based resources, and for connected purposes", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:016432:30:0:0", "start": 1201, "end": 1235, "surface": "land information management system", "probe_tag": "drop", "probe_score": 0.0351, "luna_label": 0}]}, {"key": "rafael-056", "text": "Annex 1\nPage 2 of 3\n\n\n**Project Development** **Outcome / Impact** **Project reports:** **(from Objective to Purpose'**\n**Objective:** **Indicators:**\nExpand access to basic Increased number of school Project Reports. It is assumed that\neducation. places. Government's current fiscal\nsituation will be resolved\n\n(salary payment to civil\nservants and teachers).\nEnrollment increases in MOE reports. Expansion of facilities and\nprimary schools from 35,000 quality will contribute to\nto 80,000 increased enrollment\nincluding among girls.\nIncreased availability of It is assumed that the\ntextbooks. Government maintains\ndouble-shifting.\n\n\nTrained primary school head Headteachers have autonomy\nteachers. and authority in managing the\nschools.\nBetter trained contractual Contractual teachers are\nteachers recruited early enough before\nthe school year to allow time\nfor training.\n\n\n**Output from each** **Output Indicators:** **Project reports:** **(from Outputs to Objective)**\n**Component:**\nIncreased number of school 226 classrooms will be built Monthly disbursement Availability of school places\nplaces. increasing capacity by over summary. will increase enrollment.\n20,000 based on double\nshifting.\nProvide textbooks. Numbers of textbooks per Semi-annual Provision of textbooks will\npupil increases. supervision reports. improve learning.\nTrained primary school head- Primary school head-teachers Annual audit reports; Better trained head-teachers\nteachers. trained and Guidebook for site visits. will improve school\nschool management prepared efficiency.\nand distributed.", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:012936:30:0:0", "start": 385, "end": 396, "surface": "MOE reports", "probe_tag": "drop", "probe_score": 0.0342, "luna_label": 1}]}, {"key": "rafael-057", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:008440:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0}, {"key": "fcv_pads_east_africa:008440:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": 0.1197, "luna_label": 1}]}, {"key": "rafael-058", "text": "#### I Fig 4-1a: Consultative meeting Hoima I Fig4-1b: Sensitisation meeting In Buliisa\n\n**4.1.2** **Notification of meeting dates and venues**\n\n\nIntroductory letters from REA were delivered by team members to the district and Sub\n\nCounty headquarters (for both the Bunyoro and Ankole zones) after which a schedule of\n\nmeetings was prepared in'consulta!ti6n?.:with the district and sub county leaders. Having\n\nfixed the venues and meeting days/tim.es (shown under Annex 7 and Annex 8), the local\n\ncouncils in the area undertook to inform the communities about these meetings. This\n\nwas done to ensure that the LC can mobilise in time and prepare effectively for the\n\nmeetings.\n\n\n**4.1.3** **Meetings**\n\n\nAt the sensitisation mee,tiii~s, the _Team_ Leader would introduce his/her team and the\n\nsubject matter to be covered. The language used was mostly Runyoro with some\n\ninterjections in English for the Bunyoro zone, and Mostly Runyankore _I_ Rukiga also with\n\ninterjections in English for the Ankole zone. The list of the contacted people is attached\n\nas Annex 9\n\n\n~ **Information provided** **by** **the Social Team**\n\n\nThe Social Team explained the social component particularly as it related to the human\n\nfactor at the design stage 1 during Jmpl~!)1entation and during the operational phases of\n\nthe 33 kV Distribution Line. In particular the following were highlighted:\n\n - That the surveyor would demarcate the 33 kV Distribution Line on the ground;\n\n - That the names picked by the surveyor would be the same names picked by the social\nteam as well as the valuation team to ensure that all the data on PAPs is harmonised.\n\n - That", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:019898:31:0:0", "start": 1603, "end": 1615, "surface": "data on PAPs", "probe_tag": "drop", "probe_score": 0.0286, "luna_label": 0}]}, {"key": "rafael-059", "text": "**_Leased Assets_** _as specified under paragraph 5.10_ of the Procurement\nRegulations: Not Applicable\n\n\n**Procurement of Second Hand Goods** as specified under paragraph 5.11 of\nthe Procurement Regulations – is allowed for those contracts identified in the\nProcurement Plan tables: Not Applicable\n\n\n**_Domestic preference_** _as specified under paragraph 5.51_ of the Procurement\nRegulations **_(Goods and Works)_** .\n\n\nGoods: is applicable for those contracts identified in the Procurement Plan\ntables;\n\n\nWorks: is applicable for those contracts identified in the Procurement Plan\ntables\n\n\n**Other** **_Relevant Procurement Information._**\n\n\n_None_", "source": "fcv_pads_east_africa", "subset": "annotate_rafael", "spans": [{"key": "fcv_pads_east_africa:004224:1:0:0", "start": 258, "end": 281, "surface": "Procurement Plan tables", "probe_tag": "drop", "probe_score": 0.0388, "luna_label": 0}]}, {"key": "rafael-060", "text": "diary, effectively blurring the line between consumption data collected by diary and by recall interview.\n\n\nThis mediation by interviewers is especially likely since most diary surveys instruct interviewers to return\n\n\nevery few days to update the diary in cases where it may not be completed by household members, such\n\n\nas in households with no literate adults.\n\n\nWhile well-implemented diary surveys might be expected to yield higher (and presumably closer\n\n\nto actual) levels of consumption, experimental evidence for this from developing countries is\n\n\nfragmentary. In urban Papua New Guinea, Gibson (1999) found that mean total expenditure per capita\n\n\nwas 14 percent higher (and food consumption 26 percent higher) using a diary rather than a recall survey.\n\n\nHowever, preliminary comparisons of consumption from the Bosnia and Herzegovina LSMS (recall)\n\n\nsurvey with the household budget survey (diary) in 2004 show levels to be similar (World Bank, 2006).\n\n\nThese studies pertain to household-level diaries and thus do not address an important source of\n\n\nmis-measurement – the difficulty of a sole respondent to perfectly capture total household consumption.\n\n\nPersonal diaries are generally considered better for obtaining complete household expenditure or\n\n\nconsumption data because it is unusual, in most societies, for any one household member to know the\n\n\nexpenditure/consumption of every other member, especially on items such as alcohol, tobacco, daily\n\n\ntravel, personal toiletries, daily newspapers/magazines, and meals (especially snacks and lunches) eaten\n\n\noutside the home. In Russia, as part of an effort to study the reliability of the Household Budget Survey, a\n\nrandom sample of households in the 3 <sup>rd</sup> quarter of 2003 were assigned personal diaries rather than the\n\n\nhousehold diary. The personal diary yielded expenditure levels which were 6-11 percent higher than a\n\n\nhousehold diary (World Bank, 2005). However, the personal diary was plagued with non-respondent\n\n\nproblems in this experiment; about 54 percent of households assigned the personal diary did not complete\n\n\nit. Personal diaries", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "sample:prwp:004687:7:0:0", "start": 45, "end": 61, "surface": "consumption data", "probe_tag": "keep", "probe_score": 0.9299, "luna_label": 0}, {"key": "sample:prwp:004687:7:0:1", "start": 824, "end": 851, "surface": "Bosnia and Herzegovina LSMS", "probe_tag": "keep", "probe_score": 0.9895, "luna_label": 1}, {"key": "sample:prwp:004687:7:0:2", "start": 879, "end": 902, "surface": "household budget survey", "probe_tag": "keep", "probe_score": 0.9511, "luna_label": 1}, {"key": "sample:prwp:004687:7:0:3", "start": 1662, "end": 1685, "surface": "Household Budget Survey", "probe_tag": "keep", "probe_score": 0.9011, "luna_label": 1}]}, {"key": "rafael-061", "text": ", gets a value of unity). The median of this measure across the 122 programs is 1.25. The\n\n\nhighest value is 4.0 (for Argentina’s _Trabajar_ program). By contrast, the value estimated by\n\n\nChen et al., for the DB program is 8.3, making it a better performer by this measure than all the\n\n\nprograms surveyed by Coady et al. (2004).\n\n\nNonetheless, the overall impact on poverty appears to be modest. In the same sample,\n\nChen et al., find that the poverty gap index ( _C_ _j_ / <sup>_Z_</sup> _j_ ), based on income net of DB receipts, is\n\n\n2.28%; on adding in DB payments it only falls to 2.06%. (Among participants only, the\n\n\ncorresponding figures are 19.92% and 14.23%; the higher index for participants reflects the\n\n\nprograms’ targeting to the poor.) This largely reflects weak coverage of the poor, as identified\n\n\n\nby the UHSS.\n\n\n\n18 Performance improves if one allows for measurement errors in the UHSS, and\n\n\n\n18 This echoes observations from some of the literature on targeting in developing countries that has\npointed to the inadequacies of focusing solely on the problem of avoiding leakage to the non-poor, largely\nignoring the duel problem of incomplete coverage. In particular, see Cornia and Stewart (1995) who\n\n\n13", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:003510:14:1:0", "start": 828, "end": 832, "surface": "UHSS", "probe_tag": "keep", "probe_score": 0.946, "luna_label": 1}]}, {"key": "rafael-062", "text": " is built out of the school\nquestionnaire from PISA 2012 using the methodology from Anderson [2008]. All specifications include PISA _school_ _final_ _weights_\nand country fixed effects. School controls include school location, student-teacher ratio, log of the number of students, share of\ngovernment funding relative to total school funding, ratio of computers connected to the web as a proxy for school resources,\nand average student socio-economic status. For control variables, missing variables are replaced with a value of -99 and we\ninclude an indicator variable with a value of 1 for each imputed value, for each variable with imputed values dummies are added\nto the specifications.\n\n\nApp. 2", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001847:54:1:0", "start": 21, "end": 56, "surface": "school\nquestionnaire from PISA 2012", "probe_tag": "keep", "probe_score": 0.9596, "luna_label": 1}]}, {"key": "rafael-063", "text": "**1.** **Introduction**\n\n\nThe shock associated with the COVID-19 pandemic has been large and persistent. As documented\n\nin Apedo-Amah et al. (2020) and Cirera et al. (2021), the private sector has experienced a large,\n\n\npersistent negative impact on sales across all regions of the world. Most firms had still not recovered\n\n\nthe levels of sales in 2019 more than a year after the beginning of the pandemic (Cirera et al., 2021)\n\n\nand some sectors are experiencing structural changes in the way products or services are produced\n\nand demanded. These changes and the associated reallocation of resources have already been\n\n\nidentified in the data from the United States (Barrero et al., 2021).\n\n\nA critical question for policy is how these changes will affect firm growth and aggregate produc\ntivity. Are surviving firms coming out stronger from the pandemic? The answer to this question\n\n\ndepends on many different channels that affect productivity, which in many cases are in opposing\n\n\ndirections. For example, Harris and Moffat (2021) implemented a survey of UK firms and found that,\n\non the one hand, 18% of firms have stopped and 45% reduced doing R&D but, on the other hand,\n\n40% of firms have increased ICT investments. Andrews et al. (2021) find a positive reallocation\n\neffect for three OECD countries, mainly driven by technology readiness to face the shock and the\n\nadditional investments in technology. This paper explores firms’ digitalization trends during the\n\nCOVID-19 pandemic using a novel firm-level data set of 57 countries across three periods of time\n\nsince the start of the pandemic. The paper identifies the digitalization effects on firms’ resilience,\n\n\ndifferences in adoption, and changes in market share distribution.\n\n\nThere are at least two forces associated with the pandemic, in opposite directions, driving the\n\n\ndecision of businesses to invest in and expand the use of digital technologies. On the one hand,", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "sample:prwp:000561:3:0:0", "start": 641, "end": 668, "surface": "data from the United States", "probe_tag": "keep", "probe_score": 0.9659, "luna_label": 1}, {"key": "sample:prwp:000561:3:0:1", "start": 1052, "end": 1070, "surface": "survey of UK firms", "probe_tag": "keep", "probe_score": 0.9731, "luna_label": 1}, {"key": "sample:prwp:000561:3:0:2", "start": 1508, "end": 1543, "surface": "firm-level data set of 57 countries", "probe_tag": "keep", "probe_score": 0.9809, "luna_label": 1}]}, {"key": "rafael-064", "text": " increase the savings share. Since investment is\nmodeled as being savings driven, total global investment is driven by total global savings, with\nthe amount of investment in a given country being a function of both domestic savings as well as\nthe current account balance, which is determined exogenously. The additional implication of the\nsavings driven investment assumption is that that as dependency ratios fall in a given country,\ndomestic savings will rise, which in turn will boost investment. The opposite would hold true for\na country where dependency ratios are rising.\n\n\nWhile LINKAGE provides the economy-wide effects of demographic change over time, the\nGIDD microsimulation framework (Bussolo et al., 2010; Bourguinon and Bussolo, 2013) will be\nused to generate income distributions under the various scenarios. GIDD draws on household\nlevel survey data benchmarked to 2010 to estimate income distributions by country that account\nfor demographics, household characteristics (e.g. age, gender, and education of different\nmembers), sector of employment, skill premia on wages, and income. <sup>10</sup> Using the simulated\nincome and employment under future scenarios from LINKAGE, and accounting for the\ndemographic shifts characterized in the UN (2015), GIDD is able to generate income\ndistributions by country that are consistent with both the more ‘aggregated’ changes under the\nCGE simulations and also what is known about households from survey data. In addition to\nincorporating the changes in key variables from the LINKAGE scenario results, the GIDD\nmethodology updates the household survey data for the terminal year of the simulation. This is\ndone by reweighting the population characterized by the base year household surveys using non\n\n10 Table A1 in the annex provides information on the household surveys used in the micro-simulation.\n\n\n11", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:006809:12:1:0", "start": 839, "end": 866, "surface": "household\nlevel survey data", "probe_tag": "keep", "probe_score": 0.9067, "luna_label": 1}]}, {"key": "rafael-065", "text": "##### Figures\n\n**Figure 1: Sowing/transplanting and harvesting periods of paddy in**\n\n**<u>Bangladesh</u>**\n\n\n\n\n\n\n\nSource: BBS 2004\n\n\n**<u>Figure 2: Seasonality in borrowing in 1998/99</u>**\n\n\nSource: World Bank-BIDS survey 1998/99\n\n\n32", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:006981:33:0:0", "start": 201, "end": 223, "surface": "World Bank-BIDS survey", "probe_tag": "keep", "probe_score": 0.9138, "luna_label": 1}]}, {"key": "rafael-066", "text": "from 2001 onward, the last two of these—arts and business—were removed\n(Orlale 2000, Kremer, Miguel, and Thornton 2009). As a consequence, the\nKLPS data include observations in which the maximum score is 700, and\nobservations where the maximum is 500. Throughout this paper, I normalize\nall scores to the 500-point scale.\n\nThose who are not admitted to any government school have several options if they wish to continue their education: they may repeat eighth grade\nand re-take the KCPE; they may still have access to private secondary schools\nand vocational schools; or they may travel to Uganda to enroll in school there.\n\n\n**A.1.3** **Re-taking** **the** **KCPE**\n\n\nOne clear pattern both from the survey data and the administrative records is\nthat students sometimes re-take the test. In the 2003-2005 round of surveying\n(KLPS1), the questionnaire asked not only for respondents’ KCPE score, but\nalso how many times they had taken the KCPE. Of KCPE-takers in the older\ncohorts (who had reached eighth grade before being interviewed in KLPS2),\napproximately 87 percent said they took it exactly once, 13 percent said that\nthey had taken the exam twice, and around one tenth of one percent said\nthey took it three times. The reason such a small fraction re-take such an\nimportant examination, according to my interviews with with both teachers\nand pupils, is that it is costly: they have to repeat eighth grade in order to do\nit. The survey data are in agreement: more than 98 percent of respondents\nwho report re-taking the KCPE also report repeating standard 8; conversely,\nof those who take the KCPE only once, comparatively few respondents (less\nthan 3 percent) repeat standard 8 for any reason. While a pupil’s decision to\nre-take", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "sample:prwp:006439:53:0:0", "start": 143, "end": 152, "surface": "KLPS data", "probe_tag": "keep", "probe_score": 0.921, "luna_label": 1}, {"key": "sample:prwp:006439:53:0:1", "start": 702, "end": 713, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.7988, "luna_label": 1}, {"key": "sample:prwp:006439:53:0:2", "start": 722, "end": 744, "surface": "administrative records", "probe_tag": "keep", "probe_score": 0.9405, "luna_label": 1}]}, {"key": "rafael-067", "text": ".503*** 0.877***\nHH Married 0.188 0.0615 1.192***\nHH Gender - HH Married -0.21 -0.221 -1.532**\n**Quality** **of** **Dwelling**\nNumber of Rooms 0.0614* 0.0527 -0.0260\nQuality of Walls\n\n(baseline=Low)\n\nMedium -0.175 -0.314 0.509*\nHigh -0.444*** -0.564*** -0.254\n**Assets**\nTV -0.0738 -0.0483 0.382\nFridge 0.078 0.259* -0.527\nStove -0.273*** -0.372*** -0.939**\nBicycle 0.354*** 0.408* 0.368\nCar -0.280** -0.486*** -0.319\nIron 0.0453 0.113 -0.260\n\n\nConstant 0.819*** 0.0513 -2.585***\n\n\nObservations 4,577 1,289 359\n<u>Pseudo</u> <u>R-squared</u> <u>0.141</u> <u>0.19</u> <u>0.357</u>\nRobust standard errors (not reported)\n\n*** p _<_ 0.01, ** p _<_ 0.05,              - p _<_ 0.1\nSource: Authors’ estimations based on GHS and ACLED Data\n\n\n37", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:007320:39:1:1", "start": 721, "end": 731, "surface": "ACLED Data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}]}, {"key": "rafael-068", "text": "FAO. 2008. Global Change in Net Primary Productivity (1981-2003). Rome: Food and Agricultural\nOrganization.\n\n\nFAO. 2010. Global survey of agricultural mitigation projects. Rome: Food and Agricultural Organization.\n\n[Available on the internet at http://www.fao.org/docrep/012/al388e/al388e00.pdf.](http://www.fao.org/docrep/012/al388e/al388e00.pdf)\n\n\nFAO. 2010. Land use systems of the World – East Europe and Central Asia. Available on the internet at:\n\nwww.fao.org/nr/lada/\n\n\nFAO. 2011. FAO statistical databases. Food and Agriculture Organization. Available on the internet at\n\nfaostat.fao.org\n\n\nFAO-UNESCO. 2007. Soil Map of the World: Organic carbon pool – Topsoil. Available on the internet at\n\nwww.fao.org\n\n\nGhosh, P. and J. Puri (eds). 1994. Joint implementation of climate change commitments: Opportunities and\n\napprehensions. Tata Energy Research Institute, New Delhi.\n\n\nGold Standard Foundation. 2010. The Gold Standard Premium quality carbon credits requirements. Available\n\non the Internet at www.cdmgoldstandard.org.\n\n\nGraff-Zivin, Joshua and Leslie Lipper. 2008. Poverty, risk, and the supply of soil carbon sequestration.\n\nEnvironment and Development Economics 13(3), 353-73.\n\n\nGrubb, Michael, Christiaan Vrolijk and Duncan Brack. 1999. The Kyoto Protocol: A Guide and Assessment.\n\nLondon: Earthscan/ James and James.\n\n\nGulbrandsen Lars H. and Steinar Andresen. 2004. NGO influence in the implementation of the Kyoto\n\nProtocol: Compliance, flexibility mechanisms, and sinks. Global Environmental Politics 4(4), 54-75.\n\n\nHaites, Erik, Maosheng Duan and Stephen Seres. Technology", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:005251:36:0:1", "start": 488, "end": 513, "surface": "FAO statistical databases", "probe_tag": "confusion", "probe_score": 0.3083, "luna_label": 0}]}, {"key": "rafael-069", "text": "ceij_ + µ _ijtk_,\n\n\nwhere _gtrade_ is trade growth. The _η_ s gives an estimate of _d_ γ in equation (8), which\n\n\nrepresents whether trade growth is biased toward more distant countries. It is the\n\n\npercentage point change in annual trade growth for a percentage change in distance. For\n\n\nexample, if η is -.1, it would imply that 10 percent increase in distance between two\n\n\ncountries leads to 1 percentage point slower trade growth.\n\n\nIn order to provide a benchmark, we also estimate equations (7) and (11) using\n\n\nthe aggregate bilateral trade data. This allows us to compare the average industry\n\n\ndistance elasticity with the distance elasticity from aggregate data.\n\n\n**V. Regression Results**\n\n\nWe use data on the value of bilateral trade for 766 SITC2 industries. <sup>8</sup> Within the\n\n\nindustries, we calculate average bilateral trade for three periods, 1985-89, 1990-1994,\n\n\n1995-2000. Using averaged data should reduce problems due to idiosyncratic shocks to\n\n\nan industry in any given year or exchange rate fluctuations.  We restrict the sample to be\n\n\na balanced sample. This has an advantage in making the data directly comparable across\n\n\nperiods, but a disadvantage because we are excluding new trade that may be more or less\n\n\n8 Here we dropped two industries that did not have enough observations to estimate a distance coefficient.\n\n\n14", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002587:13:1:0", "start": 523, "end": 553, "surface": "aggregate bilateral trade data", "probe_tag": "confusion", "probe_score": 0.6837, "luna_label": 1}, {"key": "prwp:002587:13:1:1", "start": 711, "end": 747, "surface": "data on the value of bilateral trade", "probe_tag": "keep", "probe_score": 0.932, "luna_label": 1}]}, {"key": "rafael-070", "text": "Source: Authors’ calculations based on predicted probability from model 3 for ELMPS 2012 and ELMPS 2018, see Table 4\n\n\nNotes: Firm age category “don’t know” was combined with 5 years or above.\n\n\n\n44", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000524:45:0:0", "start": 78, "end": 88, "surface": "ELMPS 2012", "probe_tag": "confusion", "probe_score": 0.7204, "luna_label": 1}]}, {"key": "rafael-071", "text": "18\n\n\nFigure 3: Indexes of real international prices and real producer prices of rice and wheat,\n\ndeveloping countries’ unweighted average, 1972-76 (1972 = 100).\n\n\n(a) Rice\n\n\n400\n\n\n\n350\n\n\n300\n\n\n250\n\n\n200\n\n\n150\n\n\n100\n\n\n50\n\n\n0\n\n\n(b) Wheat\n\n\n\n\n\n400\n\n\n350\n\n\n300\n\n\n250\n\n\n200\n\n\n150\n\n\n100\n\n\n50\n\n\n0\n\n\nNote: Countries included are: Argentina, Bangladesh, Brazil, Chile, Colombia, Cote d’Ivoire,\nDominican Republic, Ecuador, Ghana, India, Indonesia, Kenya, Korea, Madagascar,\nMalaysia, Mozambique, Nigeria, Pakistan, Philippines, Senegal, Sri Lanka, Sudan, Taiwan,\nTanzania, Thailand, Uganda, Zambia, and Zimbabwe.\n\n\nSource: Authors’ compilation based on data in Anderson and Valenzuela (2008).", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:004591:19:0:0", "start": 644, "end": 675, "surface": "data in Anderson and Valenzuela", "probe_tag": "confusion", "probe_score": 0.7068, "luna_label": 1}]}, {"key": "rafael-072", "text": " for these tasks. We specified a list of sanitation measures, and asked the Gram\nPanchayat members who was responsible for carrying them out, and whether these were ever\ndone in the village. Tables 1 and 2 summarize this information. To collect the direct\nobservation data, our investigators first constructed detailed maps of the village showing all\nthe roads and paths. They then assessed the condition of each road along the dimensions\ndetailed in Table 3, and noted the characteristics of the inhabitants by the side of that road. In\nparticular they noted the caste and religion of the inhabitants, whether they were GP\nmembers, and whether important village institutions such as schools, health centers, were by\nthe side of the road. Compared with the respondents’ reported perception of whether a task\nwas ever done, the direct observation data give us much more objective information on the\nactual sanitary conditions on the ground.\n\nIn addition to the sanitation outcomes, we have additional village-level information, collected\nin a previous survey conducted the previous year in 2002. This information was collected\nfrom the official census (population, literacy, area), interviews with GP members (whether\nthe village is the GP headquarter and whether the village is the GP president’s village), and\nfrom participatory appraisal methods (the fraction Scheduled Castes/Tribes in the village, the\nfraction land owned by the upper castes, and oligarchy – the extent to which the GP members\ncontrol the important functions in the village.)\n\n**4.** **Analytical Methods**\n\nTo analyze the determinants of information about GP responsibilities we use the village level\ndata. To examine the inter- and intra-village distribution of sanitation outcomes we use the road\nlevel dataset onto which we merged the village level information. Hence, the unit of observation\n\n7 The state-wise break up is AP: 69 villages, KA: 182 villages, KE: 126 wards; TN 129 villages.\n\n\n4", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:003998:6:1:0", "start": 249, "end": 272, "surface": "direct\nobservation data", "probe_tag": "confusion", "probe_score": 0.525, "luna_label": 0}, {"key": "prwp:003998:6:1:3", "start": 1659, "end": 1677, "surface": "village level\ndata", "probe_tag": "keep", "probe_score": 0.9516, "luna_label": 1}]}, {"key": "rafael-073", "text": "people may also gain from conflict in terms of income and wealth and this may explain why some people\n\n\ndo not move. The defining attributes of the alternative choices are very different from any other model\n\n\nand the task of economics is to understand what these defining attributes should be.\n\n\nIn terms of independent variables, “push” factors become more important than “pull” factors in forced\n\n\ndisplacement models. The intensity of a conflict may be more important than the income opportunities\n\n\nin potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress,\n\n\nanxiety, other traits of personality and behavioral factors in general have to be well understood and\n\n\nmeasured. Hence, one could think of four essential blocks of independent variables including individual\n\n\nor household socioeconomic characteristics, “push” factors, “pull” factors and behavioral factors. Also,\n\n\naccess to and dissemination of information related to the conflict in the place of origin but also in the\n\n\npotential places of destination may be crucial for people to make choices. This is where social psychology,\n\n\nbehavioral economics and neuroeconomics may offer insights into such choices.\n\n\nForced displacement data are also unusual in their form. Deciding on whether to flee or not to flee a\n\n\nconflict (the migration choice) can be an individual or household choice and risk coping strategies may\n\n\ninclude temporary migration, shuttling between places, migration of only selected members of the\n\n\nhouseholds or migration of the whole household. This implies that individuals may stay put throughout\n\n\nthe period observed, join or leave the household during the period, or have several episodes of out and\n\n\nimmigration. Households may decide to leave and come back several times. In econometric terms, this\n\n\nmeans that longitudinal data may be left and right censored and have spells within. They are therefore\n\n\nthe most complex set of panel data possible and require particular treatment of data and modeling.\n\n\nSurvival or duration models can usually accommodate many of these complexities but it is very rare to\n\n\nfind similar data sets used in published articles. Collecting such type of data", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:007029:15:0:0", "start": 1228, "end": 1252, "surface": "Forced displacement data", "probe_tag": "confusion", "probe_score": 0.1869, "luna_label": 1}]}, {"key": "rafael-074", "text": "s) and advanced machine-learning\ntechniques have especially promising applications in developing countries, as they can potentially\ngenerate reliable poverty data at a far lower cost than conventional household surveys.\n\n\nCDR analysis can play a vital role by filling the spatial and temporal gaps left by traditional\nresearch methods. By making inferences based on cellular network usage, CDR analysis can\nreliably project the evolution of poverty dynamics over a specific timeframe. Unlike censuses and\nhousehold surveys, CDR analysis is quick and relatively inexpensive and can be performed by a\nsmall team of statisticians using records that are already collected by Mobile Network Operators\n(MNOs).\n\n\nGuatemala offers a prime example of the limits of traditional data collection. The country’s\nmost recent Population and Housing Census, dates from 2002, and all national poverty data are\nderived from just four household surveys conducted over the past 25 years. For example, the most\nrecent 2014 household survey ( _Encuesta Nacional de Condiciones de Vida_, ENCOVI) covered\n\n\n**3**", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:006965:4:1:1", "start": 201, "end": 218, "surface": "household surveys", "probe_tag": "confusion", "probe_score": 0.8881, "luna_label": 0}]}, {"key": "rafael-075", "text": ">\ncorrelated with satellite-derived indicators that affect the probability of selection of the village\n\ninto the HIES. We therefore adjust the weights based on the predicted probability of the\n\nvillage being sampled in HIES, based on satellite indicators available for all villages (Horvitz\n\nand Thompson, 1952; Wooldridge, 2002). This probability is obtained from the following\n\nprobit model:\n\n\n_INHIESv_ = _α_ + _βlassov_ + _λv,_ (4)\n\n\nwhere _INHIESv_ is a binary indicator for whether village _v_ is sampled into HIES, and _lassov_\n\nrepresents all LASSO-selected variables from the Poisson model. We estimate this model\n\nusing data for all 1,360 villages in the 55 sub-districts. The predicted probability of the\n\nvillage selection into the HIES, _INHIES_ - _v_ is used for correcting the weights in the following\nway : _V illagePopulation X_ <u>1</u> _INHIES_ - _v_ <sup>.</sup>\nUsing these above two sets of weights, we estimate the model in HIES-villages. Then,\n\nwe predict out-of-sample densities for the remaining 946 non-HIES villages and report out\nof-sample accuracy measures by comparing them with the actual census density. To provide\n\nadditional context, we compare the accuracy of this model to predictions from a similar census\nbased model and with density estimates from the publicly available “top-down” population\n\nproducts. Further, we calculate population count predictions by multiplying these density\n\npredictions with village-level area, and report their accuracy with respect to the census pop\nulation.\n\n\n17 Out-of-bag _R_ 2 is an out-of- sample statistic that refers to the average variance explained by the random\nforest predictions, across all villages, when", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:007669:11:1:0", "start": 234, "end": 254, "surface": "satellite indicators", "probe_tag": "confusion", "probe_score": 0.6548, "luna_label": 1}, {"key": "prwp:007669:11:1:1", "start": 630, "end": 657, "surface": "data for all 1,360 villages", "probe_tag": "confusion", "probe_score": 0.7435, "luna_label": 1}]}, {"key": "rafael-076", "text": ". Achievements and challenges in Cameroon’s infrastructure sectors 8\nTable 2. Trading across borders in Central African countries 10\nTable 3. Cameroon’s road indicators benchmarked 12\nTable 4. Condition and type of road corridors passing through Cameroon 15\nTable 5. Port indicators for selected ports 17\nTable 6. Comparative performance ofCentral African railways, 2005 19\nTable 7. Benchmarking of Cameroon’s air transport indicators 21\nTable 8. Cameroon's irrigation potential 25\nTable 9. Cameroon’s water and sanitation indicators benchmarked 26\nTable 10. Cameroon’s power indicators benchmarked 31\nTable 11. AES Sonel’s hidden costs 34\nTable 12. Cameroon’s ICT indicators benchmarked 36\nTable 13. Africa’s ICT prices, 2008 39\nTable 14. Illustrative investment targets for infrastructure in Cameroon 39\nTable 15. Indicative infrastructure spending needs in Cameroon for 2006 to 2015 40\nTable 16. Financial flows to Cameroon’s infrastructure, 2001-2006 41\nTable 17. Cameroon’s potential gains from greater operational efficiency 42\nTable 18. Funding gaps by sector 46\nTable 19. Savings from innovation 47\n\n\niv", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:004998:5:1:1", "start": 661, "end": 675, "surface": "ICT indicators", "probe_tag": "confusion", "probe_score": 0.0647, "luna_label": 0}]}, {"key": "rafael-077", "text": "26\n\n\nstatus to determine the district‘s autonomy status; and where there is only a central hospital,\n\n\nwe use the central hospital‘s status to determine the district‘s autonomy status. When deriving\n\n\nour autonomy variable, we exclude specialized hospitals for mental illness, rehabilitation,\n\n\nleprosy, tuberculosis, pediatrics, and traditional medicine, because they tend to provide\n\n\nmainly subsidized or highly specialized services.\n\n\nOutcome variables\n\n\nOur outcome variables include the number of hospital outpatient visits and the\n\n\nnumber of inpatient admissions. In both cases, we also estimate models capturing simply\n\n\nwhether a visit or admission occurred. The recall period in both cases is 12 months, and the\n\n\ndata on utilization are available for every household member. In addition, we explore the\n\n\nimpacts of autonomization on total out-of-pocket spending on hospital outpatient and\n\n\ninpatient care, as well as the average out-of-pocket spending per visit and admission. The\n\n\nexpenditure data have been converted to real values using the Vietnam consumer price index.\n\n\nCovariates in the estimating equation\n\n\nOur covariates include two variables that are unlikely to be correlated with the\n\n\nautonomy status of the local hospital ―market‖ but which we include to increase the precision\n\n\nof our estimates. These include the individual‘s health insurance status (some insurance\n\n\ncoverage is specific to the individual in Vietnam, while other coverage applies to all\n\n\nhousehold members), and the household‘s per capita consumption. The latter is a\n\n\nconsumption aggregate reflecting not just market expenditures but also other elements of\n\n\nconsumption such as the value of home-grown produce and the imputed rent of an owned\n\n\nhouse. In addition to these variables, we include whether the household lives in a rural area –\n\n\nthis may be correlated with the timing of the autonomization of the hospitals in the\n\n\nhousehold‘s district.", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:005304:27:0:1", "start": 997, "end": 1013, "surface": "expenditure data", "probe_tag": "confusion", "probe_score": 0.3889, "luna_label": 1}]}, {"key": "rafael-078", "text": "22\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Survey|Title in full|Core topics|No. of countries|History|Data collection<br>method|Sample size|Further<br>information on<br>data and access|\n|---|---|---|---|---|---|---|---|\n|MCSS|Multi-Country<br>Survey Study on<br>Health and<br>Responsiveness|Adult health state descriptions,<br>health conditions, screening,<br>health state valuations, health<br>system responsiveness, adult<br>mortality|60 countries of all income<br>levels and worldwide, 35 in<br>HEFPI (postal surveys<br>excluded, several countries to<br>be added)|2000-2001|Face-to-face<br>interviews,<br>telephone<br>interviews,<br>postal survey|Typically<br>600-6,000<br>adults|http://apps.who.i<br>nt/healthinfo/syst<br>ems/surveydata/i<br>ndex.php/catalog/<br>mcss/about|\n|MICS|Multiple <br>Indicator Cluster<br>Survey|Population, health, and<br>nutrition, with a focus on <br>reproductive, maternal and<br>child health|108 LMIC with completed<br>surveys, 89 with available<br>data, 73 in HEFPI (MICS 5 to<br>be added, a number of earlier<br>wave surveys to be added)|Ongoing since<br>1995, first<br>HEFPI data<br>from 1999|", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:007497:23:0:0", "start": 480, "end": 485, "surface": "HEFPI", "probe_tag": "confusion", "probe_score": 0.0715, "luna_label": 0}, {"key": "prwp:007497:23:0:2", "start": 1089, "end": 1099, "surface": "HEFPI data", "probe_tag": "confusion", "probe_score": 0.1277, "luna_label": 0}]}, {"key": "rafael-079", "text": "Looking at the pattern of commitments in the UR by the range of commitment and degree\n\n\nof development, a similar pattern develops to that observed in trade policy on goods: the degree of\n\n\nliberalization appears to increase with the level of income -- as shown in Table 14. Lower-income\n\n\ncountries appear to have committed to much less liberalization than higher-income ones. The\n\n\nsame conclusion can be drawn by looking at the commitments of the fifty developing countries\n\n\nwhose trade regimes were analyzed more systematically. Indeed, the pattern in Table 14 has a\n\n\nstriking parallel to the pattern in Table 3 above, with the lower the developing country income,\n\n\nthe lower the number of commitments and hence the highest the remaining protection. (See also\n\n\nBorchert, Goortiiz and Mattoo 2011, p.123.) The basic justification low-income countries make\n\n\nfor not liberalizing their service sector is the same infant industry argument used for so very long\n\n\nin the areas of merchandise trade. There are obvious dangers and limits to such a strategy as many\n\n\ndeveloping countries have realized in the areas of goods. These dangers have to be seriously\n\n\nevaluated by low-income developing countries which continue to protect their service sectors.\n\n\nOn the other hand the table shows that this relationship between the extent of liberalization\n\n\nand the level of development did not hold true for the financial services sector which includes\n\n\nbanking and insurance. In this case there is basically no pattern discernible, with most groups of\n\n\ncountries liberalizing about a quarter of the maximum possible -- if one weighs partial restrictions\n\n\nin each of the modes of supply. Again the LDCs made the fewest commitments. Only nine of the\n\n\ntwenty nine LDC made any commitments. But those which did, on average made greater\n\n\nliberalizing commitments than other developing countries.\n\n\nEarlier analysis (Mattoo 1998) suggests that in the area of commitments on commercial\n\n\npresence for financial services, Latin American countries tend to limit the number of suppliers,\n\n\nwhile Asian economies limit either solely the percentage of equity", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:005614:65:0:0", "start": 431, "end": 476, "surface": "commitments of the fifty developing countries", "probe_tag": "confusion", "probe_score": 0.8247, "luna_label": 0}]}, {"key": "rafael-080", "text": "\nduring this period drove the gains, and what elements of the approach were successful.\n\n\nFirst, we separate growth in the value of cereal production (teff, wheat, barley, maize and sorghum)\nfrom growth in the value of cash crop production (coffee, chat, nueg and sesame) and examine what\ntype of growth was most strongly associated with poverty reduction (Table 7, column 1). The regression\nresults show that gains were more strongly associated with growth in cereal, not cash crop, production.\n\n\nNext, we explore the determinants of growth in cereal production and examine the relationship\nbetween cereals growth and weather, prices and the use of improved inputs. Given these regressions do\nnot use poverty data, we are also able to include in the regression years in which poverty data are not\navailable. As such the panel is expanded to include all years from 1996 to 2011 (with the exception of\n2002 and 2003 for which there are no data). The regressions include only the main crop‐producing zones\nin the sample, and exclude Afar, Somali, Harari, Addis Ababa and Dire Dawa.\n\n\nDespite substantial increases in the use of fertilizer over this period, the regression estimates in columns\n2 and 3 indicate that on average increased fertilizer was not associated with growth. This is quite\nstriking, but consistent with other literature that shows that returns to use of fertilizer are highly\nweather dependent in Ethiopia (Christiaensen and Dercon 2010), and that the ratio of the price of\nfertilizer to cereals has been quite varied over time (Spielman et al 2007). Given the literature shows\n\n\n16", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:007323:17:1:0", "start": 702, "end": 714, "surface": "poverty data", "probe_tag": "confusion", "probe_score": 0.6968, "luna_label": 0}]}, {"key": "rafael-081", "text": "**<u>Variables</u>** **<u>Measures</u>** **<u>Sources</u>**\n**<u>Crisis measure:</u>**\nSignificant drop in stock market returns Basis for the crisis measure is the\npercentage change in US $ national,\ntotal return, stock market indices\nrelative to the previous month. A\nmonth is counted as a crisis month if\nthe total return drops at least 2\nstandard deviations below the sample\nmean. Subsequent months are also\ncounted as crisis months until returns\nmove back into the one standard\ndeviation band around the sample\n<u>mean.</u>\n\n\n\n2 sets of crisis measures: 1) IFC\ninvestable, total return indices,\n2) MSCI total return indices. For\nboth cases, we have to use local\nsources for Romania, Slovakia\nand Romania. To convert returns\nfrom these three national indices\ninto US dollars, we use end of\nperiod data on exchange rates\nfrom the IMF's International\nFinancial Statistics (IFS).\n\n\n\n**<u>Explanatory Variables:</u>**\nUncertainty measure Monthly data of the weighted\naverage of the standard deviations of\nthe current and following year\nforecast of GDP growth across\nsurvey respondents. In January, the\nstandard deviation of the current year\nforecast is given a weight of 11/12\nwhile the standard deviation of the\nfollowing year forecasts is given a\nweight of 1/12. In February, the\ncurrent year forecast receives a\nweight of 10/12 while the follwing\nyear forecast is weighted by 2/12.\nThis scheme continues until\nDecember where a weighting of 0/12\nis given for the current year forecast\nand 12/12 for the following year\nforecast.\n\nMean growth expectations Monthly data of the weighted\naverage of the mean forecast by\nsurvey respondents of the current and\nfollowing year GDP growth. The\nweighting scheme is the same as for\nthe standard deviation.", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:004188:31:0:1", "start": 838, "end": 872, "surface": "International\nFinancial Statistics", "probe_tag": "keep", "probe_score": 0.9486, "luna_label": 1}, {"key": "prwp:004188:31:0:3", "start": 937, "end": 949, "surface": "Monthly data", "probe_tag": "confusion", "probe_score": 0.4886, "luna_label": 0}]}, {"key": "rafael-082", "text": " coffee % total land size 0.58 0.58 0.00 1.00 0.00 0.79\nYield of coffee per hectare 3323.31 3273.56 49.75 0.83 100.09 0.72\nShare of red cherry sold to Fairtrade\nCoop (%) 94.47 94.55 -0.08 0.94 0.03 0.98\n\n**Panel B: Head of Household (N=1197)**\nGender (% Male) 0.89 0.87 0.02 0.24 0.01 0.53\nAge 50.22 49.65 0.57 0.53 0.41 0.65\nYears of schooling 3.92 3.82 0.11 0.63 0.09 0.73\nMarried 0.86 0.84 0.02 0.44 0.01 0.59\n\n**Panel C: Children aged 6-14 (N =1880)**\nGender (% Male) 0.01 0.69\nAge 0.14 0.25\nYears of schooling 0.05 0.65\nCurrently attending school -0.01 0.44\n\n**<u>Source:</u>** <u>Authors’ analysis based on an agricultural household survey conducted in 2015.</u>\n**Note:** Sample means computed from the first survey carried out in July 2015. P-values refer to the null\nhypothesis of equality of means between self-reported and proxy-reporting measures. Proxy respondents\ninclude head of households and spouses.", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000466:39:1:0", "start": 616, "end": 645, "surface": "agricultural household survey", "probe_tag": "confusion", "probe_score": 0.7684, "luna_label": 1}]}, {"key": "rafael-083", "text": "**9**\n\nthe conversion factor is determined **by** interacting supply and demand for foreign exchange.\n\nIt is shifts in these supply and demand schedules that give rise to price (i.e., exchange rate)\n\nchanges. Thus it should be recognized that the exchange rate is a price that balances the\n\ndesire to exchange one currency for another in order to effect trade in goods and services.\n\nThe introduction of restrictions into the price system prevents the price from fulfilling a\n\nstabilizing function of equalizing supply and demand, thus affecting relative prices, the\n\nprofitability of individual activities, and thus resulting in distortions in resource allocations.\n\n\nConversion Factor and Price Levels of Traded/NonTraded Goods\n\n**13.** The imposition of price measures (tariffs, specific taxes, import surcharges, advance\n\ndeposits on imports, export taxes and subsidies, and multiple exchange rates) and non-price\n\nmeasures (quotas, licencing **)** alter relative price levels between sectors, e.g., traded goods\n\nand nontraded goods. Here we are confronted with the issue of the distinction between\n\n\"traded goods (sector)\" and \"nontraded goods (sector)\" as well as between \"tradables\" and\n\n\"nontiadables.\" In very general terms, traded goods can be regarded as those that can be\n\nimported (and exported), while nontraded goods are those without close, foreign-produced\n\nsubstitutes and must therefore be supplied **by** domestic producers. More specifically, traded\n\ngoods can be regarded as \"goods that are sufficiently substitutable with goods produced\n\nabroad that there exists an international market; nontraded goods are those that are not\n\n**highly** substitutable with foreign-produced goods so that international trade in these goods\n\ndoes not exist\".' **A** very narrow dichotomy is that given in the foreign trade statistics in the\n\n\n**6**\nGeorge **", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000198:15:0:0", "start": 1816, "end": 1840, "surface": "foreign trade statistics", "probe_tag": "confusion", "probe_score": 0.8797, "luna_label": 1}]}, {"key": "rafael-084", "text": "Appendix Α: Information on data sources\n\n\nTable A1: List of candidate geospatial variables.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Variable|Source|Approximate<br>Resolution|Year|\n|---|---|---|---|\n|Population structure<br>|WorldPop (https://www.worldpop.org)<br>|100 m<br>|2018<br>|\n|Population density|WorldPop<br>|100 m<br>|2018<br>|\n|Temperature<br>|TerraClimate<br>(https://www.climatologylab.org/terraclimat<br>e.html)|4 km<br>|2018<br>|\n|Palmer Draught<br>Severity Index<br>(PSDI)<br>|TerraClimate|4 km<br>|2018<br>|\n|Distance to OSM<br>major roads<br>|WorldPop<br>|100 m<br>|2016<br>|\n|Radiance of night-<br>time lights|VIIRS<br>(https://eogdata.mines.edu/products/vnl/) <br>|500 m<br>|2018<br>|\n|Net primary<br>production|FAO Remote Sensing for Water<br>Productivity (WaPOR) 2.1<br>(https://data.apps.fao.org/wapor/?lang=en)<br>|240 m<br>|2018<br>|\n|Rainfall|Climate Hazards Group InfraRed<br>Precipitation with Station data (CHIRPS)<br>(https://www.chc.ucsb.edu/data/chirps) <br>|5", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001372:28:0:0", "start": 220, "end": 228, "surface": "WorldPop", "probe_tag": "confusion", "probe_score": 0.0615, "luna_label": 0}, {"key": "prwp:001372:28:0:2", "start": 350, "end": 362, "surface": "TerraClimate", "probe_tag": "drop", "probe_score": 0.0138, "luna_label": 0}, {"key": "prwp:001372:28:0:4", "start": 727, "end": 755, "surface": "FAO Remote Sensing for Water", "probe_tag": "confusion", "probe_score": 0.2623, "luna_label": 1}]}, {"key": "rafael-085", "text": " local content requirements. Last, the\ndevelopment of mechanisms that nudge countries toward greater climate policy alignment to reduce\nfrictions. Examples of such an alignment could be the harmonization of environmental standards and\nregulations as well as of compliance requirements or the adoption of a global carbon price that gradually\nincreases towards its social cost. Should the multilateral development of the framework prove\nchallenging, like-minded countries could start with a multi-party approach similar to the Multi-Party\nInterim Appeal Arbitration Arrangement (MPIA) developed when the WTO dispute settlement\nencountered difficulties.\n\n**Finally, international collaboration is necessary to tackle the challenges of emission intensity metrics**\n**and pricing, especially for goods with complex supply chains and cross-border issues** (WTO, OECD, IMF,\nUNCTAD, and World Bank Group WTO 2024). Efforts should streamline compliance through robust yet\nflexible Monitoring, Reporting, and Verification (MRV) systems and harmonized data for policy and\nstandards compliance. Multilateral solutions are needed to address varying regulatory and compliance\nrequirements, different carbon measurement methods, and non-recognition of domestic standards.\n\n\n**4.4** **Further research**\n\n**This paper has detailed various mitigation policies of the three largest global trading partners and the**\n**exposure of developing countries to their impacts. However, future work could expand this analysis in**\n**several dimensions** . First, the analysis focuses on each policy in isolation. This allows for granularity of the\nsectoral and country discussion. Future research could assess all these policies in a coherent framework\nthat takes into consideration interlinkages across sectors within countries and comparative advantages of\ncountries globally. Second, further research on the impact of various policies on SMEs and smallholder\n\n\n30 In the case of actionable subsidies under WTO rules that reduce foreign countries’ competitiveness, it will be\nimportant to agree on guardrails regarding the ways in which affected countries can respond. Such response\n(antidumping, countervailing duties,", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001477:29:1:0", "start": 1030, "end": 1045, "surface": "harmonized data", "probe_tag": "drop", "probe_score": 0.0402, "luna_label": 0}]}, {"key": "rafael-086", "text": "1-770.\n\nDechezleprêtre, Antoine, Matthieu Glachant, Ivan Hascic, Nick Johnstone, and Yann Ménière,\n\n(2011), “Invention and transfer of climate change mitigation technologies on a global\nscale: A study drawing on patent data,” Review of Environmental Economics and Policy,\n5(1), 109-130.\n\nDoranova, A., Costa, I., and G. Duysters (2009), “Knowledge base determinants of technology\n\nsourcing in the Clean Development Mechanism projects,” UNU-MERIT Working Paper\n#2009-015.\n\nDoukas, H., C. Karakosta, and J. Psarras (2009), “RES technology transfer within the new\n\nclimate regime: a ‘helicopter’ view under the CDM,” _Renewable and Sustainable Energy_\n_Reviews_, 13, 1138-1143.\n\nEnergy Information Administration (2010), _International Energy Outlook 2010_, Washington,\n\nDC: U.S. Department of Energy.\n\nEsty, Daniel C. (2001), “Bridging the trade-environment divide”, _Journal of Economic_\n\n_Perspectives_, 15(3):113-130.\n\nFisher-Vanden, K. and M.S. Ho (2010), “Technology, Development, and the Environment.”\n\n_Journal of Environmental Economics and Management,_ 59(1), 94-108.\n\nFisher-Vanden, K. and I. Sue Wing (2008), “Accounting for quality: Issues with modeling the\n\nimpact of R&D on economic growth and carbon emissions in developing countries”,\n_Energy Economic_ s, 2771-2784.\n\nGallagher, Kelly Sims. 2006. Limits to leapfrogging in energy technologies? Evidence from the\n\nChinese automobile industry. _Energy Policy._ 34:383-394.\n\n\n50", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:005403:51:1:0", "start": 212, "end": 223, "surface": "patent data", "probe_tag": "drop", "probe_score": 0.0229, "luna_label": 0}]}, {"key": "rafael-087", "text": "SRTM Digital Elevation Data Version 4; 90m <sup>2</sup> resolution (Jarvis et al., 2008)\n\n\nBuilt-up Land Cover\n\nGHSL: Global Human Settlement Layers, Built-Up Grid 1975-1990-2000-2015 (P2016);\n\n38m <sup>2</sup> resolution (Pesaresi et al., 2015)\n\n\nNormalized Difference Vegetation Index/NDVI\n\nLandsat 8 Collection 1 Tier 1 32-Day NDVI Composite; 30m <sup>2</sup> resolution\n\n\nNormalized Difference Water Index/NDWI (U.S. Geological Survey)\n\nLandsat 8 Collection 1 Tier 1 Annual NDWI Composite; 30m <sup>2</sup> resolution (U.S. Geolog\nical Survey)\n\n\nLand Use\n\nCopernicus Global Land Cover Layers: CGLS-LC100 Collection 3; 100m <sup>2</sup> resolution\n\n(Buchhorn et al., 2020)\n\n#### Appendix B Additional Tests\n\n\nThis section provides additional computations to investigate possible effects of data\n\ncollection and of choice of different spatial units.\n\n\nB.1 Heterogeneity in Employment Data Collection\n\n\nThe employment data used throughout this work is collected either via travel surveys\n\nor via population and firm censuses <sup>16</sup> . This introduces noise to the data both with respect\n\nto heterogeneity of polygon sizes within which the data was originally collected but also in\n\nterms of what specific employment is measured. Travel surveys overall typically include any\n\ntype of employment, whereas firm censuses are generally limited to collecting information\n\non formal employment and do not capture employment within the informal sector. As the\n\ninformal sector represents a substantial part of jobs within developing countries (Bryan et al.,\n\n2020), omitting this might introduce a noticeable measurement issue within the training of the\n\nalgorithms. In order to test this, we split the", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:002432:40:0:1", "start": 376, "end": 414, "surface": "Normalized Difference Water Index/NDWI", "probe_tag": "confusion", "probe_score": 0.6647, "luna_label": 1}, {"key": "prwp:002432:40:0:5", "start": 875, "end": 890, "surface": "Employment Data", "probe_tag": "drop", "probe_score": 0.0465, "luna_label": 1}]}, {"key": "rafael-088", "text": " as having gone ‘somewhat well’\n\n\n(26.4%) or ‘very well’ (71.7%).\n\n\nIn conjunction with the survey, we also collected data on the performance of public officials as assessed\n\n\nin their annual appraisal. Each year, public officials are evaluated by their direct manager on the tasks\n\n\nthat they were expected to contribute to. For example, tasks might include ‘Monitor and provide support\n\n\nto the [work] team preparing the budget’ and ‘Support the team to prepare soft and hard copy documents\n\n\nof the budget’. Managers evaluate the quality of contributions bureaucrats make to the tasks they were\n\n\ninvolved in and produce an overall ‘performance’ score. In addition to this performance-related score,\n\n\npublic officials are evaluated on their ‘attitude’ to work, which intends to measure their office behavior\n\n\nand alignment to the organization (Abagisa, 2014; Tereda, 2014). For the year 2016, we collected the\n\n\nperformance, attitude, and total scores (which are a weighted average of performance and attitude scores)\n\n\nfor each official from a subset of the organizations we visited for which they were available.\n\n\n9", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "sample:prwp:007560:10:1:0", "start": 118, "end": 161, "surface": "data on the performance of public officials", "probe_tag": "drop", "probe_score": 0.0194, "luna_label": 0}]}, {"key": "rafael-089", "text": " days 0.55 948 0.6 361 0.53 335 0.479 0.171\nEngaged in HH non-ag enterprise in last 7 days 0.09 948 0.1 361 0.13 335 0.100 0.563\nEngaged in any work in February 2020 0.82 948 0.84 361 0.86 335 0.088 0.344\n\n\nThe table presents means for treatment households (T) with a child in grade 4 or 8, control households (C) with a\nchild in grade 3, 5, 6, 7, or 9, and mixed households (M) with a child in both grade groups. Data are from the first time\na household is observed, typically in survey round 1 (May-early July) while schools were fully closed. Individual-level\ndata are for the survey respondent.\nColumns on the right present differences and means and p-values for tests of equality for control households compared to treatment and mixed households, separately. The joint F-stat for differences across control and treatment\nhouseholds is 1.12, with p-value 0.305. It is 4.37 (p<0.001) for differences across control and mixed households.\n\n- _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01\n\n\n26", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000092:27:2:0", "start": 546, "end": 567, "surface": "Individual-level\ndata", "probe_tag": "drop", "probe_score": 0.0322, "luna_label": 0}]}, {"key": "rafael-090", "text": "Table 6 shows the results from two estimations: one using annual data and the other using twoyear averages. <sup>7</sup> [^7: The simple two year average is performed for each of the variables; the first two-year average covers the period 1999-2000\nand the last two-year average the period 2007-08. CA is performed on these average values.] As noted earlier, the objective of using the two-year averages is to smooth out business\ncycle effects. Clearly, an even longer period would have been desirable from the perspective of purging\nbusiness cycle effects, but limitations in terms of the number of available data points prevented us from\ndoing so. The estimated coefficient in the multinomial regressions reflects the likelihood of being in one\ncluster relative to a reference cluster. As noted earlier, the reference group used is the lower growth\nand higher vulnerability (V) cluster, which is the worst of all possible outcomes.\n\n\n**Table 6. Multinomial Logit Estimations based on Growth and Vulnerability Clusters** 1/\n\n|Col1|Col2|Col3|VULNERABILITY|Col5|Col6|Col7|\n|---|---|---|---|---|---|---|\n||||**Annual Data**|**Annual Data**|**Two-Year Averages**|**Two-Year Averages**|\n||||**Lower**|**Higher**|**Lower**|**Higher**|\n|Trade openness; exports plus imports (% of GDP)<br>Export and import growth rates, difference in %<br>Financial openness; FX assets & liabilities (% of GDP)<br>FDI, net, % of GDP<br>Counter-cyclical fiscal policies 2/<br>Monetary tightening; Δ in velocity<br>Exchange rate flexibility<br>Capital controls<br>Initial level of income, share", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:004776:11:0:0", "start": 58, "end": 69, "surface": "annual data", "probe_tag": "confusion", "probe_score": 0.7644, "luna_label": 1}, {"key": "prwp:004776:11:0:2", "start": 1108, "end": 1119, "surface": "Annual Data", "probe_tag": "drop", "probe_score": 0.0349, "luna_label": 0}]}, {"key": "rafael-091", "text": "ita_ <sup>is a dichotomous variable of value 1 if we observe all sales, labor, materials and capital</sup>\n\nand zero otherwise. Symmetrically, in the case of sales, we have the following equation\n\n\nPr( _sib_  1| _Di_, _ICi_ )  ( 0 _b_   2 _b_  _Di_   3 _b_  _ICi_  _ib_ ),\n\n\nwhere in this case <sup>_s_</sup> _itb_ <sup>takes value 1 if we observe data for sales.</sup>\n\nTables 6.1 to 6.4 present the estimated results by applying a LPM to model the probability of\nhaving a missing value conditional on the investment climate faced by firms. Concretely, we propose\nfour models for each country. First we consider missing values in TFP conditioning in two different\nvectors of IC variables. The first specification includes the same set of IC variables as that included\nin equation (5); that is, the set of covariates statistically significant in the extended production\nfunction, before imputing missing values by the ICA method. The second specification chooses the\nset of significant correlates starting from the whole set of IC variables and applying a general-tospecific procedure of selection of variables. The case of sales is symmetrical in the sense that model\n\n[3] uses the same set of IC variables as in equation (5), while the specification shown in column [4]\nselects the set of variables as we did in the case of column [2].\n\n\n34", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:004536:35:1:0", "start": 360, "end": 374, "surface": "data for sales", "probe_tag": "drop", "probe_score": 0.0296, "luna_label": 0}]}, {"key": "rafael-092", "text": " (cap on reimbursement and/or services provided at the hospital level)<br>|<br>Global budget (cap on reimbursement and/or services provided at the hospital level)<br>|<br>Global budget (cap on reimbursement and/or services provided at the hospital level)<br>|\n|Uzbekistan||<br>Missing data|<br>Missing data|||||||||||||\n|Uzbekistan||||||||||||||||\n\n\n\nreports.", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:004181:48:5:0", "start": 277, "end": 289, "surface": "Missing data", "probe_tag": "drop", "probe_score": 0.0115, "luna_label": 0}]}, {"key": "rafael-093", "text": " OECD for more than 40 years as the OECD group.\n\n\nThe rest of countries are classified by region and income. We use the average of GDP per capita (World Bank\n\n\n2017e) over 1985–2014 to break the sample into income quintiles. Table B.1 shows the country list by region and\n\n\nincome quintile groups, indicating their inclusion in the samples by type of analysis (descriptive and statistical),\n\n\ndata source (PWT, GTAP, and WDI), and other characteristics (oil rent and population).\n\n\n1 Heavy dependence is defined as reliance on oil production for more than 32 percent of GDP on average during 2006-15, which is 90th –\n100th percentile among 98 countries with positive oil rents (World Bank 2017j); Angola (45%), Congo, Rep. (46%), Equatorial Guinea\n(42%), Gabon (32%), Iraq (52%), Kuwait (47%), Libya (54%), Oman (37%), Saudi Arabia (44%), and South Sudan (45%).\n\n\n12", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001996:13:1:1", "start": 411, "end": 415, "surface": "GTAP", "probe_tag": "confusion", "probe_score": 0.1171, "luna_label": 1}, {"key": "prwp:001996:13:1:2", "start": 421, "end": 424, "surface": "WDI", "probe_tag": "drop", "probe_score": 0.0201, "luna_label": 1}]}, {"key": "rafael-094", "text": " end of the\ntender. Less than 30 days later, three separate payments were made to different agents that\nwon the contracts, with total values of 11,377 BRL and 3,890 BRL. For the purchase of this\nrelatively simple good, a total of 235 days elapsed between the tender publication and the last\npayment.\n\nThe example above shows the potential of this newly constructed dataset in providing researchers, policy makers, and the civil society with a granular view of how local governments\nin Brazil acquire goods and services and execute their budgets. It also illustrates the potential\nfor similar datasets to be developed in other countries where scattered data might exist but\nrequire upfront investment to be collected, cleaned, and harmonized.\n\n##### **3 Validation**\n\n\nThe flagship dataset for information on municipal public finances in Brazil is the _Sistema de_\n_Informações Contábeis e Fiscais do Setor Público Brasileiro_ (SICONFI). <sup>19</sup> SICONFI contains selfreported information on municipalities’ revenues, expenditures, and balance sheets starting in\n1989, with details on amounts per category and budget execution phase (i.e. commitment, ver\n\n18We note that this connection between public procurement and budget execution is currently only possible\nfor the state of Parana (PR), which provides a table connecting tender IDs to commitment IDs. Approximately\n78% of the procurement IDs and 38% of total commitment IDs can be found within this correspondence table.\n19The dataset was formerly called _Finanças Brasileiras_ (Finbra).\n\n\n9", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000767:10:1:0", "start": 642, "end": 656, "surface": "scattered data", "probe_tag": "drop", "probe_score": 0.0369, "luna_label": 0}]}, {"key": "rafael-095", "text": "Estrin S, Hanousek J, Kočenda E, Svejnar, J., 2009. “The effects of privatization\n\n\nand ownership in transition economies.” _Journal of Economic Literature_,\n\n\n47(3), 699-728.\n\n\nEstrin, S. and V. Perotin, 1991. “Does ownership always matter?” _International_\n\n\n_Journal of Industrial Organization,_ 9(1), 55–73.\n\n\nGaricano, L. 2000. “Hierarchies and the Organization of Knowledge in Production”,\n\n\n_Journal of Political Economy_, 108(5), 874-904.\n\n\nGaricano, L. and E. Rossi-Hansberg. 2006. “Organization and inequality in a\n\n\nknowledge economy”. _Quarterly Journal of Economics_, 121, 1383-1435.\n\n\nGarmaise, M. and T. Moskowitz, 2004, “Confronting Information Asymmetries:\n\n\nEvidence from Real Estate Markets,” _Review of Financial Studies_, 16(4),\n\n\n1007-1040.\n\n\nGiroud, X. 2013, “Proximity and investment: evidence from plant-level data”.\n\n\n_Quarterly Journal of Economics_, 128(2), 861-915.\n\n\nGrinblatt, M. and M. Keloharju 2001. “How Distance, Language, and Culture\n\n\nInfluence Stockholdings and Trades,” _Journal of Finance_, 56, 1053-1073.\n\n\nLi, Lixing and Guangrong Ma 2015. “Government Size and Tax Evasion:\n\n\nEvidence from China.” _Pacific Economic Review_, 20(2), 346-364.\n\n\nLi, Yunsen and Cheryl Long 2013. “Historical Events and Regional Development:\n\n\nEvidence from China’s Third Front Construction.” Manuscript, Xiamen\n\n\nUniversity.\n\n\nMarkevich, A. and E. Zhuravskaya, 2011, “M", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:006378:27:0:0", "start": 823, "end": 839, "surface": "plant-level data", "probe_tag": "drop", "probe_score": 0.0291, "luna_label": 0}]}, {"key": "rafael-096", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nAn ageing population coupled with a growing\neconomy causes labour shortages. Despite\nfalling number of available workers, the Polish\neconomy is growing steadily, as it modernises\nand converges to the level of development of\nold EU Member States. Despite the Covid-19\npandemic, real GDP growth averaged 3.7%\nduring the last decade. As a result, due to\nsteady demand for labour since 2019, the\nharmonised unemployment rate in Poland\nhas not exceeded 4%, oscillating around\n3% in recent years. According to Eurostat\ndata, in September 2023, harmonized\nunemployment in Poland reached 2.8%, the\nsame level as Malta. In the entire EU, only the\nCzech Republic reached a lower level: 2.7%.\nVacancies stood at record heights before\n\n\n\nthe refugee inflow. Before the refugee\ninflux, the share of companies reporting\nvacancies in the quarterly NBP survey\nstood at 49% in Q4 2021 – the highest\nlevel on record. This helps to explain the\nrelative ease of the refugees' labour market\ninclusion in terms of employment rates.\nAfter refugee inflow, the share of firms\nreporting vacancies stopped growing and\ndeclined slightly to 45% in Q3 2023. This\npartly indicates the fact that many refugees\nentered the labour market, and partly a\ncyclical economic slowdown. Despite that,\nthe labour market remains relatively strong.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 9.** Change in the number of workers registered for social security with Ukrainian citizenship by NACE sector between end of Q3\n2023 and 2021\n\n\nThousands\n\n\n\n\n\nManufacturing\n\n\nAccommodation and food service activities\n\n\nWholesale and retail trade; repair of motor vehicles and motorcycles\n\n\nHuman heatlh and social work activities\n\n\nAdministrative and support service activities\n\n\nEducation\n\n\nInformation and communication\n\n\nProfessional, scientific and technical activities\n\n\nPublic administration and defense; compulsory social security\n\n\nConstruction\n\n\nFinancial and insurance activities\n\n\nAgriculture, forestry and fishing\n\n\nReal estate activities\n\n\nWater supply: sewerage, waste management and", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:9:0:0", "start": 580, "end": 593, "surface": "Eurostat\ndata", "probe_tag": "keep", "probe_score": 0.9993, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:9:0:1", "start": 899, "end": 919, "surface": "quarterly NBP survey", "probe_tag": "keep", "probe_score": 0.9884, "luna_label": 1}]}, {"key": "rafael-097", "text": " Poland of\n38%. Even before becoming refugees, they\nwould have required support to enter the\nworkforce and now, in the host country,\nthey would be likely to benefit from such\nhelp even more. Some of them may be\ndiscouraged by not having been able to find\na job, others may be marginally attached\nworkers who fell outside the labour\nforce, but have some desire and ability to\nreturn to work.\n\n\n\n**Chart 25. Ukrainian refugees’ employment rate in the 18-64 age group by previous**\n**status in Ukraine**\n\n\n91%\n\n\n\nHousehold\nresponsibilities\n\n\n\nOthers Studying Employed Self-employed\n\n\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey conducted in May and June 2024.\n\n\n\n29 A dummy variable that takes the value of 1 in case of none, beginner, or intermediate language knowledge, and 0 for other levels has been regressed against a\nnumber of explanatory variables.\n\n\n34 35", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "jad_paddy_docs:000001:17:3:0", "start": 624, "end": 641, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9715, "luna_label": 1}]}, {"key": "rafael-098", "text": " it is\nprovided, allows over a third of low-income <sup>10</sup> [^10: Those that are below the poverty line]\nhouseholds to de facto live above the poverty line.\nIn Slovakia this effect is the most dramatic –\nsubsidized housing allows an additional 46% of the\nrefugee population to escape poverty.\n\n\n**<u>DECREASE IN THE REFUGEE POVERTY RATE AS A RESULT</u>**\n**OF ACCOMMODATION RENT SUPPORT, %** <sup>**1,2,3**</sup> [^2: Results for the Czech Republic not individually presented due to\nsampling limitations] [^3: 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\nNot adoping\n\ncoping\nstrategies\n\n\n\nStress coping\n\nstrategies\n\n\n\nCrisis coping\n\nstrategies\n\n\n\nEmergency\n\ncoping\nstrategies\n\n\n\nWithout accommodation rent\nsupport\n\n\n\n1. The statistic on emergency strategies may have been affected by\nthe survey wording on illegal work\n\n\nThe Ukrainian refugee population also faces\nfinancial barriers to access critical services: one in\nten households have no health insurance and 22 %\nof those surveyed answered that they cannot afford\nfees at local health care clinics. Households that\ncontain a member with a disability are also more\nlikely to be below the poverty line. This group’s\npoverty rate stands at 59% versus 43% for\nhouseholds with no members with disabilities.\n\n\n**Support with accommodation expenses – an**\n**important vulnerability shield**\nOverall, almost half (48%) of refugee households in\nthe region report receiving accommodation or\nhousing assistance. Twenty percent are living in\n\n\n\n\n\n\n\n\n\n\n\n\n\nWith accommodation rent support\n\n\n\n\n\n\n\nBulgaria Hungary Poland Romania Slovakia Region\n\n\n1. Calculations for Moldova were not conducted, as this country is not\nincluded into the <u>[EU statistics on income and living conditions (SILC)](https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions)</u>\n<u>[survey,", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000000:4:1:0", "start": 1816, "end": 1861, "surface": "EU statistics on income and living conditions", "probe_tag": "keep", "probe_score": 0.9743, "luna_label": 1}]}, {"key": "rafael-099", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**<u>REGIONAL REFUGEE EMPLOYMENT RATE BY LEVEL OF</u>**\n\n**LOCAL LANGUAGE KNOWLEDGE (2024)**\n\n\nEmployment rate Share of the refugee population (rhs)\n\n\n\n80%\n\n\n60%\n\n\n40%\n\n\n20%\n\n\n0%\n\n\n\n<u>35%</u>\n\n\n\nDoes not\nunderstand\n\n\n\nBeginner Intermediate Advanced Fluent\n\n\n\nSource: Survey data\n\n\nThe 2024 survey introduced a new question on local language proficiency, reinforcing previous findings of a\nstrong correlation between language skills and employment. Respondents with at least an intermediate level of\nlocal language proficiency reported nearly twice the employment rate compared to those with no knowledge\n(9% of respondents). Even Ukrainians with only a basic understanding—limited to a few words or phrases (28%\nof the sample)—experienced a notable increase in employment compared to those with no local language\nskills <sup>16</sup> [^16: The data also demonstrates that the employment rate of refugees fluent in the local language is lower than for those with\nan intermediate or advanced knowledge. The reason for this is that the former group is heavily concentrated in lower age\nbrackets, with almost 30% being 15-19 years old] .\n\n\nFinally, unlike for the host population, refugee employment rates were found to be practically the same for all\neducation levels above technical or vocational <sup>17</sup>, implying that local employment markets may not be valuing\nadvanced degrees. Possible explanations include impediments to foreign qualifications recognition and other\nbarriers that are preventing placement into high-skilled jobs (such as language, a mismatch between\nqualifications and local demand, etc.).\n\n\n**Wages also improved but remain significantly below host levels**\n\n\nDespite a 28% increase in the regional weighted average wage of refugees in 2024, this figure still stands 30%\nlower than that of the local population. This means that, on average, Ukrainians earned roughly two-thirds of\nwhat their hosts did", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000010:11:0:0", "start": 376, "end": 387, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.999, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:11:0:1", "start": 394, "end": 405, "surface": "2024 survey", "probe_tag": "keep", "probe_score": 0.9953, "luna_label": 1}]}, {"key": "rafael-100", "text": " potential GDP should\nbe higher by around 0.9-1.35% due to\nrefugees contributions. <sup>44</sup> [^44: Note that long term refers to the time when the economy fully adjusts with no additional shocks. We do not model the current refugees from Ukraine\nchildren growing up and entering the labour market.]\n\n\nOur results are consistent with the\nprevious, similar studies. In estimating\nGDP impacts we take an approach that\nis most similar to the previous studies of\nthe pre-2022 Ukrainian migrants by NBP\neconomists (Gradzewicz, Jabłonowski,\nSasiela, and Żółkiewski, 2021; Strzelecki,\nGrowiec, and Wyszyński, 2022), but unlike\nthe previous Oxford Economics and ours\nimpact estimates of Ukrainian refugees\n(Urban, 2022; Deloitte, 2022) we do not\nallow for the possibility of a positive\nproductivity shock, because there is little\ndata to credibly estimate its size. Below,\nwe summarise impacts yielded by these\nstudies. As studies were done under\ndifferent assumptions on the number of\n\n\n\n\n\n\n\n\n\n41  Aggregate region in model consisting of Ukraine, Russia, Belarus, Moldova, Czechia, Slovakia, Hungary, Romania and Bulgaria.\n42  In other words it was assumed that money that would be spent e.g. through credit action for investments in Eastern Europe were spent for\nconsumption in Poland.\n\n\n34\n\n\n\n43  Data from forecast of Ministry of Finance from October 2023, <u>[Wytyczne dotyczące wskaźników makroekonomicznych - Ministerstwo Finansów - Portal](https://www.gov.pl/web/finanse/wytyczne-sytuacja-makroekonomiczna)</u>\n<u>[Gov.pl (www.gov.pl).](https://www.gov.pl/web/finanse/wytyczne-sytuacja-makroekonomiczna)</u>\n44  Note that long term refers to the time when the economy fully adjusts with no additional shocks", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:17:3:0", "start": 1295, "end": 1336, "surface": "Data from forecast of Ministry of Finance", "probe_tag": "keep", "probe_score": 0.973, "luna_label": 1}]}, {"key": "rafael-101", "text": " 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). Over the two-year period from\nQ2 2022 to Q2 2024, following the arrival\nof Ukrainian refugees, the occupational\ngroup distribution of Polish citizens shifted\n\n\n\n**Chart 30. Polish citizens occupational distribution change by salary bracket, Q2 2022 and Q2 2024**\nQ2 2022 distribution, Q2 2024 distribution, and percentage point changes between them\n\n\n-1.8 pp.\n\n\n\n\n\n\n\n\n\n<= 4,000 (4,000; 6,000] (6,000; 8,000] (8,000; 10,000] - 10,000\n\n\n\nQ2 2022 Q2 2024\n\n\n41\n\n\n\n30 Unfortunately, GUS data for these time periods and poviat level include the enterprise sector (firms that employ 10 or more persons) and public sector, which is\nmost of the labour market, but not the total economy.\n\n\n40\n\n\n\nSource: Deloitte own elaboration based on ZUS data and GUS data on salaries of 128 3-digit occupations in\nOctober 2022. Note that ZUS data is not comprehensive.", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "jad_paddy_docs:000001:20:4:0", "start": 956, "end": 964, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9232, "luna_label": 1}]}, {"key": "rafael-102", "text": ". <sup>11</sup> [^11: According to the social security data until 30th September 2023.]\nWhile public data does not distinguish\nbetween refugees entering these sectors\nand pre-2022 Ukrainian workers changing\njobs, it is largely consistent with the MSNA\nPoland 2023 survey, in which the most\nrefugees are employed in manufacturing\n(14%), accommodation and food service\n(12%), and trade and repair (6%).\n\n\nUkrainian refugee households in Poland\n\nto the MSNA Poland 2023 survey, 20% of\nUkrainian refugee households earn less\nthan 3 000 PLN, 41% earn between 3 000\nand 6 000 PLN, and 12% earn more than\n6 000 PLN, while 27% of respondents\npreferred not to answer. That said, the\nstandard of living of Ukrainian refugees\nmay be significantly lower than that of\nnative residents, even at similar incomes,\ndue to their lack of housing, which in\nPoland is usually occupant-owned.\n\n\n\n**Inflow of Ukrainian refugees into**\n**Poland**\nThe beginning of the full-scale war in\nUkraine in February 2022 resulted in large\nflows of refugees, reaching more than\n6 million globally. <sup>3</sup> [^3: As of December 2023, according to UNHCR, based on governmental sources <u>[Situation Ukraine Refugee Situation (unhcr.org)](https://data.unhcr.org/en/situations/ukraine)</u>] Much of this exodus\nhappened through the Polish border.\nAs of October 2023, almost 1 million\nUkrainian refugees were living in Poland <sup>4</sup> [^4: According to the active PESEL UKR database.]\n(Chapter 1). In the past decade, Poland\nexperienced large labour migration from\nUkraine. The number of workers with\nUkrainian citizenship that registered for\nsocial security (this data does not include\nthose working in the shadow economy\nor some minor cases that do not require\nregistration) grew from just 33 thousand interm", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:3:1:0", "start": 39, "end": 59, "surface": "social security data", "probe_tag": "keep", "probe_score": 0.9995, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:3:1:1", "start": 450, "end": 473, "surface": "MSNA Poland 2023 survey", "probe_tag": "keep", "probe_score": 0.9955, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:3:1:2", "start": 1432, "end": 1450, "surface": "PESEL UKR database", "probe_tag": "keep", "probe_score": 0.9866, "luna_label": 1}]}, {"key": "rafael-103", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nThe influx of refugees into Poland after the\nRussian invasion of Ukraine was large, with\nnearly 16 million border crossings <sup>18</sup> [^18: UNHCR data, <u>[https://data2.unhcr.org/en/situations/ukraine](https://data2.unhcr.org/en/situations/ukraine)</u>] from\nUkraine until the end of September 2023 and\ncumulatively 1.7 million PESEL registrations.\nIt must be noted that such a rapid population\nmovement of that scale was not seen in\nEurope since World War II. Not everyone\n\n\n\nstayed in Poland, however. A large number of\nthese refugees later returned to Ukraine or\nmoved to other European countries.\nBy October 2023, the remaining active PESEL\nUKR numbers stood at less than 1 million,\nwhile the border movement balance between\nPoland and Ukraine at 2.5 million.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 4.** Age and gender structure of refugees based with active PESEL numbers in\nOctober 2023\n\n\n|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|The s<br>imple<br>of for|udden<br>menta<br>eigner|drop in<br>tion of t<br>status a|registe<br>he 30-d<br>fter a r|red num<br>ay dead<br>egistere|bers is<br>line for<br>d depa|due to<br>revoca<br>rture fro|the<br>tion<br>m|Col18|\n|---|---|---|---|---|---|---|---|---|---|---|", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:7:0:0", "start": 220, "end": 230, "surface": "UNHCR data", "probe_tag": "keep", "probe_score": 0.9964, "luna_label": 1}]}, {"key": "rafael-104", "text": "Increased competition on the labour\nmarket could partially offset benefits\nfrom refugees. Although (due to a tight\nlabour market) increase in labour force\nis almost entirely absorbed into working\nforce, there should be slight increase\nin unemployment rate. Ultimately, it is\nestimated that it was higher by 0.14-0.25\npp. in 2022 and by 0.18-0.3 pp in 2023\nwhich corresponds to respectively 24-42\nthousand and 33-54 thousand additional\npeople being unemployed <sup>45</sup> [^45: Based on number of economically active people in III quarter of 2023 according to labour market survey.] . In the long\nrun, the unemployment rate should remain\nhigher by 0.15-0.3 pp. Because of that, it is\nestimated that growth of real wages was\nslower in 2022 and 2023. It is estimated\nthat due to the influx of refugees, real\nwages were lower in 2022 by 0.45-0.85%\nand in 2023 by 0.65-1.15%. Although in\neffect this is negative, it also means lower\ninflationary pressure from the labour\nmarket in the short term. Long-term real\nwages should be around 0.55-1.0% lower\nthan in a scenario without refugees.\nThat said, the actual labour market\neffect is likely to be null as evidenced by\neconometric studies (Gromadzki and\nLewandowski, 2023; Peri, 2014), which are\nelaborated on in Chapter 4. Gromadzki\nand Lewandowski (2023) in the early\nmonths of 2022 find no effect of Ukrainian\nrefugees on earnings, employment, and\nunemployment rate of natives and other\nimmigrants, except an actual slight positive\nimpact on the wages of native women.\n\n\nEven with the increase in unemployment\nand lower real wages, an increase in labour\nforce means a higher wage pool, which\nmeans higher tax income.\n\n\n\nMoreover, boosts in private consumption\nboth due to increase in population as\nwell as higher average spending rates\nmeans that refugees increased state\nincome from taxation on consumption.\nThese effects will be strengthened by\ninflux of capital from", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:18:0:0", "start": 561, "end": 581, "surface": "labour market survey", "probe_tag": "keep", "probe_score": 0.9911, "luna_label": 1}]}, {"key": "rafael-105", "text": ", and Zenou, 2010;\nÅslund and Rooth, 2007), and Switzerland\n(Müller, Pannatier, and Viarengo, 2022),\nas well as the previously described crosscountry study. This could in principle stem\nfrom scarring on an individual level from\nweak initial opportunities, or persistently\nweak local labour market combined with\nimperfect geographic mobility. The effects\nare hard to disentangle, while Åslund and\nRooth (2007) find some indication that both\nphenomena are at play, Godøy (2017) finds\nevidence only for persistently weak local\nlabour markets.\n\n\n##### Migrants and refugees dispersed away from ethnic enclaves experience weaker labour market inclusion outcomes, and vice versa.\n\nCo-nationals can provide important\ninformation to refugees about employment\nopportunities. In Germany, Battisti, Peri,\nand Romiti (2022) found that immigrants\ninitially located in places with more conationals, as well as well as refugees and\nrepatriated ethnic Germans dispersed to\nsuch places, are more likely to be employed\nin the first 3 years. It was found, however,\nthat these groups had lower probability of\ninvesting in human capital. In Swiss data,\nMartén, Hainmueller, and Hangartner\n(2019) found that refugees dispersed to\nlocations with more co-nationals are more\nlikely to find work, especially in the first 3\nyears. In Danish data, Damm (2014) found that\nhigher skill levels of non-Western immigrant\nmen in an area raises employment probability\nof refugee men, while higher employment\nrates of their co-national men raises their\nearnings. Also in Danish data, Damm (2009)\nfound that larger size of ethnic network in an\narea increases earnings of refugees.\nIn Swedish data, Edin, Fredriksson,\nand Aslund (2003) found that refugees\ndispersed to areas with more co-nationals\nexperience higher earnings.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\nKingdom,", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:12:2:0", "start": 1120, "end": 1130, "surface": "Swiss data", "probe_tag": "keep", "probe_score": 0.9927, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:12:2:1", "start": 1307, "end": 1318, "surface": "Danish data", "probe_tag": "keep", "probe_score": 0.9708, "luna_label": 1}]}, {"key": "rafael-106", "text": ">self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|advanced<br>fuent<br>self-employment<br>intermediate<br>Language|\n\n\n\nNote: Statistically significant results are given in green. Confidence bars reflect standard errors. Sample has been 833 individuals aged 18-64.\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 23. Average number of months since arrival of a Ukrainian refugee by Polish language fluency**\n\nN=702, age 18-64\n\n\n29\n\n\n\nAge Sector\n\n\nNote: Statistically significant results are given in green. n=681, age individuals aged 18-64.\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey.\n\n\n\nFluent\n\n\n\nAdvanced\n\n\n\nIntermediate\n\n\n\nNone\n\n\n\nBeginner\n\n\n\nSource: Deloitte own elaboration based on SEIS UNHCR survey.\n\n\n**Chart 24. What Ukrainian", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:16:2:0", "start": 952, "end": 969, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9952, "luna_label": 1}]}, {"key": "rafael-107", "text": " data. Based on the MultiSector Needs Assessment Poland 2023\nsurvey conducted in July-August 2023, we\ncalculate that 80% of the income of refugee\nhouseholds is derived from employment,\nwith an additional 5% coming from\nremittances and 2% from Ukrainian pension\nbenefits. In economic terms, refugees\nfrom Ukraine in Poland are not receivers\nof social services and charity, but primarily\nconsumers, employees, and entrepreneurs.\n\n\nIn Chapter 3, we estimate the economic\nimpact of refugees from Ukraine in Poland\nin general equilibrium Deloitte D.Climate\nmodel. This is the gold standard of\neconomic modelling on the macro-level,\nwhich comprehensively accounts for the\nsupply side of the economy including\nlabour supply and productivity, and the\ndemand side including consumption\nas well as taxation. Unfortunately, such\nmacroeconomic models cannot account for\nhow exactly refugees and other migrants\nenable native workers to specialise in\nbetter paid professions (occupational\nupgrading), or how firms allow for new\nskills by adapting different production\ntechnologies. As such, our estimates\nshould be treated as a conservative lower\nbound of the effect.\n\n\n\nFurthermore, in Chapter 4, we move\nbeyond theoretical modelling, to examine\nempirical studies on how immigrants\nand refugees impact not just output, but\nlabour productivity as well. This effect\ncomes not from traditional supply-demand\nanalysis, but from increased specialisation.\nImmigrants enable occupational upgrading\nof residents, supply new skills to firms,\nenter household works services that allow\nhighly productive native women to increase\nlabour supply, and exhibit high rates of\nentrepreneurship.", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:5:1:0", "start": 20, "end": 67, "surface": "MultiSector Needs Assessment Poland 2023\nsurvey", "probe_tag": "keep", "probe_score": 0.9999, "luna_label": 1}]}, {"key": "rafael-108", "text": ", to account\nfor spending of savings from Ukraine, data\nfrom the National Bank of Ukraine on\ncash withdrawals and retail transactions\nfrom Ukrainian bank cards in Poland was\nused <sup>55</sup> and then calibrated to data for\nprivate consumption in Poland. Additionally,\nthe same data was used to calibrate the\nnegative shock to investment in Eastern\nEurope.\n\n\n\nFor productivity two options were tested.\nFirstly, neutral impact: no shock to\nproductivity. Secondly, lower productivity of\nworkers from Ukraine negatively impacting\ntotal labour productivity. These shocks were\ncalibrated to match the difference in data\nbetween refugees income from labour in\nUNHCR survey and average wage in Poland\nfrom Statistics Poland weighted by refugees\nshare in total workforce.\n\n\nAs one period in the model is set to one\nyear and refugees started coming to Poland\nby the end of February 2022 it was assumed\nthat their impact on economy started being\nfelt starting from II quarter of the year.\nAs such shocks were set so that ¾ of shocks\nwere calibrated to data from 2022 and ¼ to\ndata for 2023 <sup>56</sup> .\n\n\n\n51  Computational General Equilibrium.\n52  Global Trade Analysis Project, <u>[GTAP Models: Current GTAP Model (purdue.edu).](https://www.gtap.agecon.purdue.edu/models/current.asp)</u>\n53  Region combined from: Russia, Belarus, Ukraine, Moldova, Czechia, Slovakia, Hungary, Romania, and Bulgaria.\n54  All shocks are percent deviations.\n55 <u>[Oversight of financial market infrastructures (bank.gov.ua)](https://bank.gov.ua/en/payments/oversite)</u>\n56  E.g. for total increase in number of workers of 350 thousand around 262.5 thou. was set to have happened in 2022 while rest in 2023.", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:21:1:0", "start": 51, "end": 89, "surface": "data\nfrom the National Bank of Ukraine", "probe_tag": "keep", "probe_score": 0.9987, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:21:1:1", "start": 216, "end": 254, "surface": "data for\nprivate consumption in Poland", "probe_tag": "keep", "probe_score": 0.9774, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:21:1:2", "start": 655, "end": 667, "surface": "UNHCR survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:21:1:3", "start": 1043, "end": 1057, "surface": "data from 2022", "probe_tag": "keep", "probe_score": 0.9704, "luna_label": 0}]}, {"key": "rafael-109", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n© UNHCR / Anna Liminowicz\n# **3.** Economic impact modelling\n\n\n\nSimple back-of-the-envelope calculations\ngive the intuition behind how additional\nworkers help to grow the economy.\nThe simplest estimate would be to assume\nthat an increase of employment by 1.4-2.2%\nwill grow the Gross Domestic Product by\nan equal percentage. In such a case, we\nwould need to assume that labour is the\nonly production factor, and thus all of GDP\ncan be equally divided between workers.\nHowever, this is not the case, as GDP is not\njust a function of labour, but also of capital\nthat workers have at their disposal – all the\nmachines, computer programs, offices, and\nthe like. As displacement is unexpected and\nrefugee status (both legal and intent to stay\nlong term) at first is uncertain, companies\ntake time to increase their stocks of capital\nto the new workers. A more elaborate\nestimate would account for the part of GDP\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\nthat is produced by labour alone. This can\nbe estimated by assuming that it is equal\nto the labour compensation share of GDP\n(GDP can be divided into compensation of\nlabour and capital), which in 2022 and 2023\nstood in Poland at 48% according to the\nEuropean Commission’s AMECO database.\nAccounting for that, gives a lower estimate\nof 0.7-1.0% GDP. Such calculations are\nvery abstract, and do not account for other\nphenomena developing simultaneously\nin the economy, like the various effects of\nthe war and energy shock. For this reason,\nwe turn next to formal general equilibrium\nmodelling, where equations of the model\nstate explicitly every assumption about\nthe workings of the economy and allow for\na credible estimation of counter-factual\nscenarios.\n\n\n\nRefugees’ impact on the economy manifests\nin a multi-layered fashion. An influx of\nrefugees means", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:15:0:0", "start": 1333, "end": 1347, "surface": "AMECO database", "probe_tag": "keep", "probe_score": 0.9688, "luna_label": 1}]}, {"key": "rafael-110", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\n**The only reliable timeseries of the**\n**number of Ukrainians in Poland over**\n**the past decade is social insurance**\n**data on insured Ukrainian nationals,**\n**though it accounts only for workers.**\nThe focus on workers rather than on\nthe entire group does not change much\nin the data from before February 2022,\nas the previous influx consisted mainly\nof Ukrainians seeking employment in\nPoland. However, the actual number\nof employed Ukrainian nationals must\nhave been higher. First, certain types\nof legal work often undertaken by\ntemporary employees do not require\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 4. Age and gender structure of Ukrainian refugees**\n\n\n\n**Most of the refugees from Ukraine**\n**currently living in Poland are women**\n**and children, though over half of**\n**the total population is of working**\n**age.** The best population data available\nis the regularly updated active PESEL\ndatabase. <sup>4</sup> [^4: Available in the repository maintained by the government <u>https://dane.gov.pl/pl/dataset/2715</u> as well as UNHCR data portal <u>https://app.powerbi.com/</u>\n<u>view?r=eyJrIjoiODhkOGZiMzctZTliMi00NzA5LTgyM2QtZGZhM2IwZjBiZDk2IiwidCI6ImU1YzM3OTgxLTY2NjQtNDEzNC04YTBjLTY1NDNkMmFmODBiZSIsImMiOjh9</u>] According to the registry, 61.5%\nof the registered are women and 38.5%\nare men. The database also includes the\nage of PESEL UKR holders, which indicates\nthat over half (57.4%), i.e. more than\n560 thousand people, are of working age\n(18-65). While the male and female shares\nof people below 18 years of age (20% and\n19%, respectively) and 66+ (1%", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:4:0:0", "start": 198, "end": 233, "surface": "data on insured Ukrainian nationals", "probe_tag": "keep", "probe_score": 0.9623, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:4:0:1", "start": 1020, "end": 1034, "surface": "PESEL\ndatabase", "probe_tag": "keep", "probe_score": 0.9643, "luna_label": 1}]}, {"key": "rafael-111", "text": " (2024) Hosts (2023)\n\n\n96%\n92%\n86%\n\n\n\nOverall\n\n\nMale\n\n\nFemale\n\n\nWith severe psychological\n\ndistress\n\n\nWith a disability\n\n\nSource: Survey data, SAG estimates\n\n\n\n64%\n\n\n67%\n\n\n63%\n\n\n57%\n\n\n\n72%\n\n\n\n\n\n49%\n\n\n\n\n\n\n\nTechnical or\n\nVocational\n\n\n\nBachelor's Master's Doctoral\n\n\n\n30%\n\n\n\nLower\nsecondary or\n\nbelow\n\n\n\nSource: Survey data, SAG estimates\n\n\n\n**11**", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000010:10:1:0", "start": 130, "end": 141, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9985, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:10:1:1", "start": 309, "end": 320, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9994, "luna_label": 1}]}, {"key": "rafael-112", "text": " those of their\nnative counterparts. Tani (2020) found\nthat in Australia, licensing raised hourly\n\n\n\nvalue to the economy. This estimate may\nunderestimate the potential benefits,\nas the boost in productivity would most\nlikely rise not just employee wages, but\nemployer profits as well. Conversely, the\noverall impact on wages might be lower, if\nincreased worker competition reduced the\naverage individual premium.\n\n\nwages and reduced over-education for\nmigrants working in licensed jobs, while\nproducing worse labour market outcomes\nfor those who did not gain licensure.\nAccording to Peterson et al. (2014), over the\n1973–2010 period, U.S. states with more\nstringent occupational licensing for migrant\nphysicians received fewer new migrant\nphysicians and struggled more with staffing\nshortages in healthcare. Aleksynska and\nTritah (2013) quoted data that migrants\nin France were denied legal access to\napproximately 30% of jobs in the country.\n\n\n\nUkrainian refugees Polish citizens\n\n\nSource: Deloitte own elaboration based on mid-2024 SEIS UNHCR survey\n(Ukrainian refugees’ educational attainment), 2023 Eurostat Labour Force\nSurvey Eurostat (Polish citizens educational attainment), and mid-2024 ZUS\nadministrative data (occupational groups).\n\n\n\nSource: Deloitte own elaboration based on mid-2024 SEIS UNHCR survey\n(Ukrainian refugees’ educational attainment and median net wages),\n2023 Eurostat Labour Force Survey Eurostat (Polish citizens educational\nattainment), and mid-2024 ZUS administrative data (occupational groups).\n\n\n\n**Widespread occupational licensing**\n**is a serious obstacle to an efficient**\n**use of Ukrainian refugees’ human**\n**capital.** Poland has the third highest\nnumber of regulated professions among\nthe 28 European Union member states,\naccording to European Commission’s\nRegulated Professions Database. This can\nbe a problem, as occupational licensing is\ncited in the literature among the reasons\nfor occupational downgrading of migrants.\nCassidy and Dacass (2021) found that in the\nUnited States, immigrants were significantly\nless likely to", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:14:1:0", "start": 1035, "end": 1052, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9974, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:14:1:1", "start": 1099, "end": 1141, "surface": "2023 Eurostat Labour Force\nSurvey Eurostat", "probe_tag": "keep", "probe_score": 0.999, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:14:1:2", "start": 1197, "end": 1220, "surface": "ZUS\nadministrative data", "probe_tag": "keep", "probe_score": 0.9499, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:14:1:3", "start": 1298, "end": 1315, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9983, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:14:1:4", "start": 1800, "end": 1830, "surface": "Regulated Professions Database", "probe_tag": "confusion", "probe_score": 0.8069, "luna_label": 1}]}, {"key": "rafael-113", "text": "%\n\n\n\n\n\n\n\n62%\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBulgaria Czechia Estonia Hungary Latvia Lithuania Moldova Poland Romania Slovakia Region\n\n\nNote: For comparability, employment rates for host countries have been recalculated assuming a similar gender distribution to that of refugees\n\n\nSource: ILO, survey data\n\n\n14. The employment rate is defined as the number of employed or self-employed individuals of working age (15-64) as a share of\nthe total number of people in this age group\n15. The labor force is defined as the number of people that are either employed or unemployed\n\n\n**10**", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "jad_paddy_docs:000010:9:1:0", "start": 278, "end": 294, "surface": "ILO, survey data", "probe_tag": "confusion", "probe_score": 0.869, "luna_label": 1}]}, {"key": "rafael-114", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n## Executive summary\n\n\n##### Progress in market integration\n\n**Ukrainian refugees have been**\n**increasingly successful in terms of**\n**labour market integration.** The large\ninflux of refugees since February 2022,\nhas further increased and changed the\ndemographics of the already significant\nUkrainian migrant population in Poland.\nRefugees from Ukraine are primarily\nwomen and children, with over 67% of\nfemale-headed households. Poland was\nquick to open its labour market to refugees\nfrom Ukraine, who – despite difficulties\n\n- surprisingly promptly began their\neconomic integration and soon supported\nthemselves primarily from employment.\nIn the past year, refugees’ employment\nrate grew from 61% to 69%, with the\nmedian net wage rising from PLN 3,100 to\nPLN 4,000 and narrowing the gap to the\nmedian net wage in the entire economy.\n**As Ukrainian refugees entered the**\n**labour market, the economy adapted,**\n**resulting in more specialization and**\n**higher productivity.** In a simplistic\nsupply-demand framework, the influx\nof Ukrainian refugees would have had\na negative impact on Polish workers\nemployment or caused a decline in real\nwages. However, this has not occurred.\nFirst, Polish citizens employment rates\nhave grown, and unemployment rates\nhave fallen. Second, poviats in which the\nemployment share of Ukrainian refugees\nhas grown by 1 pp. saw 0.5 pp. higher\nemployment rates among Polish citizens,\nand 0.3 pp. lower unemployment rates.\nThird, there is no evidence of lowered\nwages, in fact the limited available data\nindicates that a higher share of Ukrainian\nrefugees in a poviat may have caused local\nwages to rise. Such findings are in line with\nacademic literature, which documents\na positive impact of migrants on native\nworkers. With foreigners entering the\n\n\n04\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n*", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:2:0:0", "start": 1588, "end": 1610, "surface": "limited available data", "probe_tag": "confusion", "probe_score": 0.8446, "luna_label": 1}]}, {"key": "rafael-115", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n# Non-technical summary\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nSwift legal action facilitated labour market\nintegration of refugees. After the beginning\nof the full-scale war on 26th February\n2022, the European Union activated the\nTemporary Protection Directive on 4th\nMarch 2022, and the Polish parliament\npassed a special act to facilitate refugee\nintegration on 12th March 2022. Ukrainian\nrefugees in Poland were granted instant\naccess to the job market, health care, and\neducation. The authorities granted people\nescaping Ukraine legal residency for a\nperiod of eighteen months and enabled\nthem to access digital services and basic\nadministrative systems like PESEL.\nThus, the policies that have previously\nhampered job market integration of\nrefugees in other contexts, such as\ntemporary labour market bans (Fasani,\nFrattini, & Minale, 2021) and forced\ndispersals (Fasani, Frattini, & Minale, 2022),\nhave been avoided.\n\n\nThe high employment rate of refugees\nin Poland covers not only employees,\nbut also entrepreneurs. Five percent of\nUkrainian refugees registered for social\nsecurity have set up a business or work as\nfreelancers. Similar results can be gleaned\nfrom the Multi-Sector Needs Assessment\nPoland 2023 survey results, which show\nthat slightly more than 5% of respondent\nhouseholds receive income from selfemployment or similar activities.\n\n\n\nAll broad sectors of the economy saw an\nincrease in the number of workers with\nUkrainian citizenship and social insurance\nsince 2021, apart from storage and\ntransportation. The number increased\nthe most in manufacturing (almost by\n34 thousand), accommodation and food\n(more than 18 thousand), and wholesale\nand retail trade (more than 18 thousand). <sup>11</sup>\nWhile public data does not distinguish\nbetween refugees entering these sectors\nand pre-2022 Ukrainian workers changing\njobs, it is largely consistent with the MSNA\nPoland 2023 survey, in which the most\nrefugees are employed", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:3:0:0", "start": 1290, "end": 1338, "surface": "Multi-Sector Needs Assessment\nPoland 2023 survey", "probe_tag": "confusion", "probe_score": 0.886, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:3:0:1", "start": 1841, "end": 1852, "surface": "public data", "probe_tag": "keep", "probe_score": 0.9754, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:3:0:2", "start": 1994, "end": 2017, "surface": "MSNA\nPoland 2023 survey", "probe_tag": "confusion", "probe_score": 0.8983, "luna_label": 1}]}, {"key": "rafael-116", "text": " rate of native or other\nimmigrants, except an actual slight positive\nimpact on the wages of native women.\nEven in a conservative scenario that\nassumes negative effects of labour\nmarket competition in the form of higher\nunemployment and slower real wage\ngrowth, an increase in the labour force\ntranslates into larger personal incomes and\nhigher private consumption, which results\nin a larger tax revenue. These effects were\nstrengthened by an influx of capital from\nabroad.\n\n###### In total, the general government revenue increased by 0.8-1.1% in 2022 and 1.05-1.45% in 2023. In monetary terms, this amounts to 10.1-13.7 billion PLN in 2022 and 14.7-19.9 billion PLN in 2023.\n\n\n\nIf estimates quoted by a government offical\nof public expenses on refugees of around\n15 billion PLN in 2022 and 5 billion in 2023 <sup>13</sup> [^13: E.g. vice-president of Polish Development Found Bartosz Marczuk estimated it at around 16 billion PLN, but this estimation also included spending of\nNGOs which was combined with spendings of local governments <u>[Polska pomoc dla Ukrainy 2022 - ile kosztowała? - Infor.pl.](https://www.infor.pl/prawo/nowosci-prawne/5635962,Polska-pomoc-dla-Ukrainy-2022-ile-kosztowala.html)</u>]\nare accurate, we can conclude that they\nwere more than offset by the additional tax\nrevenue. In the long-term, refugees should\nincrease yearly government revenue by\naround 0.85-1.3%.\n\n\n**Unaccounted positive externalities**\nAll our theoretical economic modelling\nresults are conservative lower bound\nestimates, as econometric studies from\nother countries have found immigration\nto have additionally a positive impact\non labour productivity that cannot be\naccounted for using the available data.\nAccording to these econometric studies,\nimmigration can not only raise economic\noutput (i.e., more workers equal more\nproduction), but more importantly labour\nproductivity (i.e., more value added per\nworker)", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:4:2:0", "start": 1689, "end": 1703, "surface": "available data", "probe_tag": "confusion", "probe_score": 0.6607, "luna_label": 0}]}, {"key": "rafael-117", "text": " security administration, also known as Social Insurance Institution.\n\n\n44\n\n\n\nChart 1. Poland-Ukraine border movement balance and registered/active PESEL data\b 07\nChart 2. Ukrainians registered for social insurance\b 07\nChart 3. Number of Ukrainians registered in Poland for social insurance by sex\b 08\nChart 4. Age and gender structure of Ukrainian refugees\b 09\nChart 5. Ukrainian refugee households’ demographic composition\b 10\nChart 6. Local population shares of Ukrainian refugees\b 11\nChart 7. Income of Ukrainian refugee households by source\b 12\nChart 8. Ukrainian refugee household incomes from Poland and Ukraine in 2023 and 2024\b 12\nChart 9. Ukrainian refugee labour status 15\nChart 10. Ukrainian refugee median net wage 15\nChart 11. Main occupational groups of Ukrainian refugees, pre-war Ukrainians, other foreigners,\n\nand Polish citizens registered for social insurance, Q2 2022 and Q2 2024 (civilian, non-agricultural)\b 16\nChart 12. Ukrainian refugee wages relative to Polish citizens in the same employee-cells\b 17\nChart 13. Polish citizens and Ukrainian refugees’ employment rates by age group\b 18\nChart 14. Ukrainian refugee median net wage estimates in Q2 2024\b 19\nChart 15. Ukrainian refugees wages median net wage by age group\b 20\nChart 16. Median net wages of Ukrainian refugees median net wage by sector \b 21\nChart 17. Gross domestic product growth paths with and without Ukrainian refugees\b 23\nChart 18. Over-qualification rates by citizenship\b 27\nChart 19. Tertiary education and corresponding occupational groups shares\b 28\nChart 20. Ukrainian refugees median net wages by educational attainment\b 28\nChart 21. Share of regulated professions by citizenship and legal status, Q2 2024\b 29\nChart 22. Econometric model of determinants of Ukrainian refugees’ net wages\b 32\nChart 23. Average number of months since arrival of a Ukrainian refugee by Polish language fluency\b 33\nChart 24. What Ukrainian refugee groups have weakest Polish language fluency?\b 33\nChart 25. Ukrainian refugees’ employment rate in", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:22:1:0", "start": 148, "end": 158, "surface": "PESEL data", "probe_tag": "confusion", "probe_score": 0.6316, "luna_label": 0}]}, {"key": "rafael-118", "text": " net wage of\na Ukrainian refugee (estimated based on\nthe SEIS UNHCR survey in chapter 2) from\n80% to 98% of the median in the economy\nas a whole (or from 80% to 93% according\nto Ukrainian refugee’s median in the NBP’s\n2024 survey), almost closing the gap to the\neconomy as a whole in these terms. It is in\nfact higher than the PLN 500 median net\nwage premium of the pre-war Ukrainian\nmigrants over Ukrainian refugees in the\nNBP (2024) survey, even though 68% of the\nformer and only 28% of the latter said they\nhad a high level of fluency in Polish.\n\n\n31\n\n\n\nprofessions (physicians, dentists, nurses,\nand midwives) have opened to migrants\nand refugees to reduce shortages, and\nwhile the shares for all of them stand at\njust 0.9% compared to 1.9% for Polish\ncitizens, the gap is actually very narrow for\nphysicians and dentists, who constitute\n0.7% of Ukrainian refugees and 0.8% of\nPolish citizens. In legal professions (legal\ncounsels, barristers, notaries, and bailiffs),\nthe gap remains wide, although in addition\nto occupational licensing, the likely causes\nmay be the differences between the Polish\nand Ukrainian legal systems as well as\nnew entrants’ struggles with attracting\nclients due to advertising restrictions. In\neffect, just 0.02% of Ukrainian refugees\nwork in legal professions, compared to\n0.4% of Polish citizens. There are also\nhigh contrasts across the remaining\nregulated professions, which employ\n0.8% of Ukrainian refugees and 3.3% of\nPolish citizens. These are construction\nengineers (0.02% Ukrainian refugees),\npharmacists (0.01%), and psychologists\n(0.09%), compared to the following shares\nfor Polish citizens: 0.26%, 0.19%, and 0.18%,\nrespectively.\n\n\n\n**One of the", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:15:3:0", "start": 57, "end": 74, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9287, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:15:3:1", "start": 212, "end": 229, "surface": "NBP’s\n2024 survey", "probe_tag": "confusion", "probe_score": 0.533, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:15:3:2", "start": 424, "end": 441, "surface": "NBP (2024) survey", "probe_tag": "confusion", "probe_score": 0.0598, "luna_label": 1}]}, {"key": "rafael-119", "text": "## **HELPING HANDS** THE ROLE OF HOUSING SUPPORT AND EMPLOYMENT FACILITATION IN ECONOMIC VULNERABILITY OF REFUGEES FROM UKRAINE\n#### An inter-agency exploration of socio-economic data April 2024", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000000:11:0:0", "start": 164, "end": 183, "surface": "socio-economic data", "probe_tag": "confusion", "probe_score": 0.5559, "luna_label": 0}]}, {"key": "rafael-120", "text": " the Deloitte D.Climate model, economic\nimpact of Ukrainian refugees amounted\nto a higher real GDP by 1.5% in 2022, as\nthey initially entered the labour market.\nWith more refugees finding employment,\ntheir impact grew to 2.3% GDP in 2023,\nand further to 2.7% GDP in 2024. This\ncorresponds to GDP being higher by\nPLN 98.7 billion in 2024. In the long term, as\nthe refugees acquire more country-specific\nskills and firms invest to restore their\ncapital-to-labour ratio, the impact will grow\nto 3.2% GDP by 2030. Refugees contribute\nto the economy by increasing the labour\nsupply as both workers and entrepreneurs,\nand by boosting demand as consumers.\nThe increase in GDP is not directly\n\n\n\nproportional to the increase in population\nor employment. <sup>20</sup> [^20: As outlined in the Appendix on modelling strategy, the total number of refugees was set at 2.6% of the total population, while their share in total employment as\ngrowing from 1.5% in 2022 to 2.4% in 2024.] On the one hand,\nincrease in productivity further boosts\nthe economy, on the other net benefits\nare lowered both due to a decrease in\nthe capital-to-labour ratio, as well as an\nincrease in competition in the labour\nmarket. Moreover, the increase in demand\nin tight labour market conditions work in\nthe direction of higher inflation and lower\nprice competitiveness of Polish products\nwhich decrease its overall positive impact.\n\n\n**The results are in line with the**\n**optimistic scenario from the**\n**previous Deloitte (2024) report.** The\ncurrent report is different from the one\nfrom 2024 in that we account for the\npositive productivity shock reflected in\nthe labour market data, which further\n\n\n\n**The impact of Ukrainian refugees**\n**on the Polish economy is estimated**\n**with the Deloitte D.Climate general**\n**equilibrium model** <sup>", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:11:3:0", "start": 1635, "end": 1653, "surface": "labour market data", "probe_tag": "confusion", "probe_score": 0.0971, "luna_label": 1}]}, {"key": "rafael-121", "text": " shock\nto the total factor productivity growth. This\nyields GDP higher by 1.5-2.6% by 2030,\nsignificantly higher than our long-term\nestimate – the higher result is likely due to\nthe increase in TFP that we do not include\nin our modelling. Fourth, Monitor Deloitte\n(2022) provided an early wide estimate of\nthe economic impact of Ukrainian refugees.\nThis would translate into 0.9-2.4% higher\nGDP, also due to the possibility of a positive\nproductivity shock. We review the relevant\nliterature on the impacts of immigration on\nproductivity in Chapter 4.\n\n\n\nrefugees, we have adjusted all the results\nproportionally to a 1.4-2.2% employment\ngrowth. First, Gradzewicz, Jabłon\nowski, Sasiela, and Żółkiewski (2021)\nuse the NBP CGE model to estimate the\neconomic impact of pre-2022 Ukrainian\nmigrants over the 2015-2018 period,\ntreating their inflow as a positive unskilled\nlabour shock. If the Ukrainian refugee\ninflow had the same characteristics, it\nwould yield a 0.5-0.8% higher GDP – the\nlower bound of our estimate. Second,\nStrzelecki, Growiec, and Wyszyński (2022)\nperform a growth accounting exercise to\ngauge the economic impact of the pre-2022\nUkrainian migrants over the 2014-2018\nperiod, accounting for hours and worker\ncharacteristics to arrive at a productivityadjusted labour supply. If Ukrainian\nrefugees had the same characteristics, they\n\n\n© UNHCR / Anna Liminowicz\n\n\n\nAs such **4 scenarios** were calculated with options corresponding to different mixes\nof low and high estimation for employment and productivity while keeping stable\nimpact on consumption: high or low employment level, similar (baseline) or lower\n(conservative) productivity. In the conservative productivity scenario,\nwe assumed approximately 10% lower refugee productivity estimated based\non incomes reported in MSNA Poland 2023 and data from Statistics Poland.\n\n\n**Table 2", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "jad_paddy_docs:000007:17:1:0", "start": 1795, "end": 1811, "surface": "MSNA Poland 2023", "probe_tag": "keep", "probe_score": 0.9388, "luna_label": 1}, {"key": "jad_paddy_docs:000007:17:1:1", "start": 1816, "end": 1843, "surface": "data from Statistics Poland", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}]}, {"key": "rafael-122", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nImportantly, most of the income of\nUkrainian nationals living in Poland, both\nrefugees and pre-2022 migrants, comes\nfrom their work. Our calculations based\non the UNHCR MSNA Poland 2023 survey\nresults show that 80% of refugee income\ncomes from employment, with other\nsources on average playing a much lesser\nrole. The income brackets presented in the\nsurvey show that 20% of households earn\nless than 3000 PLN, 41% earn between\n3,000 and 6,000 PLN and 12% earn more\nthan 6,000 PLN, while 27% of respondents\npreferred not to answer. Meanwhile, in the\nNBP (2023) survey conducted in November\n2022 the net income of refugees oscillated\nbetween 2,000 and 3,000 PLN, while the\nnet income of pre-2022 migrants was\ncloser to between 3,000 and 4,000 PLN.\nIn the case of Ukrainians that were out\nof work, the monthly income was more\nvaried, especially because there were fewer\nprevious migrants in this situation.\nThe median income for non-employed prewar immigrants was around 2,500 PLN <sup>32</sup> .\nAmong refugees from Ukraine remaining\nout of work at the time of the survey, the\nmedian income equaled around 600 PLN.\n\n\nThe standard of living of refugees from\nUkraine may be significantly lower than\nnatives, even at similar incomes due to\ntheir lack of housing capital. 87% of the\npopulation in Poland resided in owneroccupied housing in 2021 and 2022,\naccording to Eurostat. As most refugees\nfrom Ukraine do not possess housing of\ntheir own in Poland, they need to rent in a\nrelatively tight market, especially when they\nreside in large metropolitan areas that offer\nthe most opportunities. Initially, in the first\nmonth after the outbreak of the full-scale\nwar in Ukraine, the number of renting\noffers in the OLX and Otodom portals\ndropped by approximately 60%, though it\nlater", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:14:0:0", "start": 239, "end": 268, "surface": "UNHCR MSNA Poland 2023 survey", "probe_tag": "keep", "probe_score": 0.9899, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:14:0:1", "start": 626, "end": 643, "surface": "NBP (2023) survey", "probe_tag": "confusion", "probe_score": 0.8845, "luna_label": 1}]}, {"key": "rafael-123", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n\n\nmeasles vaccination coverage for children\nstood at 83%, similar to 84% in 2023, falling\nshort of the 95% target.\n\n\n\n\n\n\n\nInformal support also played a vital role, with\n33% receiving help from family or friends and\n12% accessing spiritual support. Overall, 88%\nof those who received support reported\nimproved wellbeing, though there are notable\ndifferences depending on gender and age.\n\n\nThe recommendations drawn from this analysis\nfocus on addressing the health and mental health\nand psychosocial needs and barriers identified in\nthe SEIS, tailoring them to the specific data and\ncontext of each country. To enhance policy\ndevelopment, it will be crucial to improve monitoring\nof refugees’ health, including sexual and\nreproductive health and mental health, through\ninclusion of disaggregated refugee data into\nnational data systems. This will require effective\ncollaboration among health organizations, statistical\noffices, and partners. Addressing capacity issues in\nnational health systems, such as workforce\nshortages and long wait times, can be supported\nthrough telemedicine and temporarily integrating\nUkrainian healthcare workers. Refugees with\nchronic illnesses and disabilities require targeted\ninterventions to meet their health and MHPSS\nneeds, including through health financing\nmechanisms. Continued efforts are also required to\naddress persistent access barriers through contextspecific strategies, including providing refugees\nwith information on navigating health systems and\npreventive health services such as vaccination.\n\n\nExpanding community-based MHPSS services that\nintegrate formal services and promote the use of\ninformal supports will enhance service delivery.\nPublic awareness campaigns, tailored to both\nrefugees and host communities, should aim to\nreduce stigma and improve knowledge of available\nsupport. Gender-responsive and age-sensitive\napproaches are necessary, particularly for adult men\nand adolescent boys, to encourage help-seeking\nbehaviours and ensure that services are tailored to\nthe specific needs of children and adolescents.\nLastly, further research is required to gain a deeper\nunderstanding of unmet health needs—including\nSRH and MHPSS needs—and the barriers to access", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000004:3:0:0", "start": 608, "end": 612, "surface": "SEIS", "probe_tag": "confusion", "probe_score": 0.6005, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000004:3:0:1", "start": 853, "end": 879, "surface": "disaggregated refugee data", "probe_tag": "confusion", "probe_score": 0.4115, "luna_label": 0}]}, {"key": "rafael-124", "text": " for host country poverty](https://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions)</u>\nassessments\n\n3. Results for the Czech Republic not individually presented due to\nsampling limitations\n\n4. The host country poverty rate was based on <u>[OECD indicators for](https://stats.oecd.org/)</u>\n<u>[2021 that were indexed towards 2023 using consumer price index](https://stats.oecd.org/)</u>\n(CPI) data\n\n\nRefugee households also report having to engage\nin harmful coping strategies to meet basic needs.\nSome 8% and 15% had to resort to emergency\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n5 Belarus, Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, the Republic of Moldova, Poland, Romania, and Slovakia\n\n\n\n6 Defined as the total after-tax income of the household (including wages, transfers, social protection benefits, etc.) divided by the\nsquare root of the household size\n\n\n\n7 Based on <u>[OECD data from 2021, which was indexed by the CPI for 2022 and 2023 for each respective country](https://stats.oecd.org/)</u>\n\n\n\n**4**", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000000:3:2:0", "start": 287, "end": 302, "surface": "OECD indicators", "probe_tag": "keep", "probe_score": 0.9908, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000000:3:2:1", "start": 924, "end": 943, "surface": "OECD data from 2021", "probe_tag": "confusion", "probe_score": 0.8555, "luna_label": 1}]}, {"key": "rafael-125", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nTo account for refugees impact on\neconomy shocks for economy were\ncalibrated according to existing data.\nAs the refugees started coming to Poland\nby the end of February and in March\n2022 it was assumed that their impact\non the economy should be seen starting\nfrom the second quarter of 2022. As such\ntheir primary impact on yearly data was\ndivided as between 2022 and 2023 setting\n¾ of it in 2022 and ¼ in 2023. The total\nnumber of refugees was set according to\nthe newest data from the PESEL registry\n(Chart 3. in Chapter 1). Their employment\nwas calibrated to match data presented\nin Chapter 2. Changes in population and\n\n\n\nlabour supply in Poland were offset by\nequivalent changes for Eastern Europe\nregion <sup>41</sup> [^41: Aggregate region in model consisting of Ukraine, Russia, Belarus, Moldova, Czechia, Slovakia, Hungary, Romania and Bulgaria.] . It was also assumed that refugees\nshould have higher spending needs which\nmeans a lower saving rate than natives.\nMoreover, data shows that it is partially\nfinanced by savings they have in Ukrainian\nbanks which was calibrated as them having\nnegative saving rate while being offset\nby lowering investment levels in Eastern\nEurope <sup>42</sup> [^42: In other words it was assumed that money that would be spent e.g. through credit action for investments in Eastern Europe were spent for\nconsumption in Poland.] . Lastly, their productivity may\ndiffer from productivity of natives (Box 2)\nwhich also was taken into account.\nIn total there were three major sources\nof uncertainty: total level of employment,\nproductivity, and impact on consumption.\n\n\n\nwould increase GDP by 0.9-1.3% – the mid\nto higher bound of our estimate.\nThird, Urban (2022) uses an Oxford\nEconomics model to estimate the impact of\nUkrainian refugees on the potential GDP by\n2030", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:17:0:0", "start": 476, "end": 487, "surface": "yearly data", "probe_tag": "confusion", "probe_score": 0.8706, "luna_label": 0}, {"key": "sample:jad_paddy_docs:000007:17:0:1", "start": 639, "end": 653, "surface": "PESEL registry", "probe_tag": "keep", "probe_score": 0.9697, "luna_label": 1}]}, {"key": "rafael-126", "text": " working refugees there is a\nsignificant number of entrepreneurs.\nThe high employment rate of refugees in\nPoland covers not only employees, but\nalso the self-employed. The available data\nalso shows that refugees from Ukraine\nin Poland are likely to start their own\nbusinesses. ZUS (social security) statistics\non insured refugees indicate that around\n5% of them have set up a business or are\nfreelancers. Similar results can be gleaned\nfrom the MSNA Poland 2023 survey results,\nwhich show that slightly more than 5% of\nrespondent households receive income\nfrom self-employment or similar activities.\nThis percentage appears to be slightly\nhigher for men, at over 6%, than women.\n\n\n\n\n\n30  Eurostat data, <u>https://ec.europa.eu/eurostat/databrowser/view/migr_asytpsm/default/table?lang=en</u>\n\n\n\n26 27", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000007:13:1:0", "start": 277, "end": 329, "surface": "ZUS (social security) statistics\non insured refugees", "probe_tag": "confusion", "probe_score": 0.4385, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:13:1:1", "start": 445, "end": 476, "surface": "MSNA Poland 2023 survey results", "probe_tag": "confusion", "probe_score": 0.7324, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:13:1:2", "start": 688, "end": 701, "surface": "Eurostat data", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1}]}, {"key": "rafael-127", "text": " in the July-August 2023 MSNA\nsurvey to 76% in the May-June 2024 SEIS\nsurvey. <sup>7</sup> This is not surprising, as the\nsituation of Ukrainians in the Polish labour\nmarket has clearly improved. First,\nemployment rate of working-age refugees\n\n\n\nfrom Ukraine has increased from 61% to\n69%, while their unemployment rate has\nhalved from 15% to 8%. Second, median\nnet wage of Ukrainian refugees grew from\nPLN 3,100 to PLN 4,000, i.e. by 29%. While\nhalf of this wage growth came from the\noverall high earnings growth in the country\ndue to high inflation (gross wages in the\ngeneral economy grew by 15% <sup>8</sup> ), it was still\na major improvement. Gross wage gains\nappear lower, with ZUS social insurance\ncontributions data for the period from\n\n\n\nJune 30, 2023 and June 30, 2024 showing\nan increase of 18% (compared to 15% for\nPolish citizens). As of June 30, 2024, only\n44% of ZUS-insured Ukrainian refugees\nhad an employment contract (compared\nto 82% of Polish citizens). Many may opt for\ncivil law contracts and self-employment to\nlimit their social insurance contributions to\nthe level of minimum wage.\n\n\n\n7 Together with refugees working remotely in Ukraine, their income from work would add up to 80% in the SEIS survey.\n\n8 From Q2 2023 to Q2 2024, <u>https://stat.gov.pl/en/latest-statistical-news/communications-and-announcements/list-of-communiques-and-announcements/average-</u>\n<u>gross-wage-in-the-second-quarter-2024,281,43.html</u>\n\n\n14\n\n\n\n9 These employment rates are very close to the ones from the Polish central bank", "source": "jad_paddy_docs", "subset": "annotate_rafael", "spans": [{"key": "sample:jad_paddy_docs:000001:7:2:0", "start": 65, "end": 76, "surface": "SEIS\nsurvey", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:7:2:1", "start": 685, "end": 724, "surface": "ZUS social insurance\ncontributions data", "probe_tag": "confusion", "probe_score": 0.6881, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:7:2:2", "start": 1215, "end": 1226, "surface": "SEIS survey", "probe_tag": "confusion", "probe_score": 0.4802, "luna_label": 1}]}, {"key": "rafael-128", "text": "**The World Bank**\nSouth Sudan Enhancing Community Resilience and Local Governance Project (P169949)\n\n\n**I.** **STRATEGIC CONTEXT**\n\n\n**A. Country Context**\n\n\n1. **South Sudan was beset by decades of armed conflicts even prior to its independence in 2011,**\n**and these have only become increasingly complex in the years since.** Southern Sudan, as the region was\ncalled before independence, has been marred by conflict since 1955, just a year before Sudan attained its\nindependence from British colonial rule. The region experienced systematic marginalization and\nunderdevelopment under both British and Sudanese rule, inhibiting it from developing its physical and\nhuman capital. Consequently, at its independence in July 2011, South Sudan ranked almost at the bottom\nof the global development indicators with little infrastructure, basic services provided almost entirely\nthrough humanitarian aid, and an economy completely dependent on oil. Renewed civil conflict broke out\nin December 2013 and has only recently subsided with the formation of a new government in February\n2020, pursuant to the terms of the September 2018 Revitalized Peace Agreement. <mark>As a result of decades</mark>\n<mark>of violence, nearly 7.5 million people of the estimated 14 million total population rely on some type of</mark>\n<mark>humanitarian assistance or protection.</mark> <sup>1</sup> [^1: UNOCHA (United Nations Office for Coordination of Humanitarian Affairs). 2020. _Humanitarian Needs Overview 2020_, p. 3]\n\n\n2. **The country is highly vulnerable to climate change and natural disasters, and increased stress**\n**on natural resources is fueling local conflicts** . The Global Climate Risk Index ranked the country 125 out\nof 171 between 1998 and 2018. <sup>2</sup> [^2: Germanwatch. 2019. _Global Climate Risk Index 2020_, p. 42.] With a strong reliance on subsistence farming and", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000049:14:0:0", "start": 777, "end": 806, "surface": "global development indicators", "probe_tag": "keep", "probe_score": 0.9799, "luna_label": 1}, {"key": "sample:jdc_operational:000049:14:0:1", "start": 1663, "end": 1688, "surface": "Global Climate Risk Index", "probe_tag": "keep", "probe_score": 0.9976, "luna_label": 1}]}, {"key": "rafael-129", "text": "**The World Bank**\nUganda COVID-19 Education Response Project (P174033)\n\n\nalso increase with education level. For higher education the regional average is 21 percent, while the returns\nto primary and secondary education are 14.4 and 10.6 percent, respectively. As shown in the figure below,\nin Uganda, the average expected income also increases according to the highest education level attended.\n\n\n**Figure 1. Mean monthly wage, 14-64 years old, by highest education level attended, in UGX**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n_Source:_ UNHS 2016/17.\n_Notes:_ Monthly wage from main job, for those employed as paid employees, employers or self-employed.\n\n<mark>11.</mark> <mark>The direct project beneficiaries of the proposed project are 14 million</mark> <mark>students in preprimary, primary and</mark>\n<mark>lower-secondary students; and 520,000</mark> <mark>teachers and school administrators in Uganda. The main goal is to</mark>\n<mark>ensure learning continuity. The expected positive outcomes are therefore higher retention rates, as the</mark>\n<mark>pandemic might increase dropouts, affecting particularly harder children from poorer households and young</mark>\n<mark>girls.</mark>\n\n<mark>12.</mark> <mark>Costs are equivalent to the total cost of the project – which will disburse US$ 14.7M over a period of 18</mark>\n<mark>months – and additional costs due to the increased number of students enrolled in primary and lower</mark>\n<mark>secondary education as a result of the implementation of project’s activities. These additional students</mark>\n<mark>correspond to those who are currently enrolled in either primary or lower secondary education and would</mark>", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000034:46:0:0", "start": 517, "end": 521, "surface": "UNHS", "probe_tag": "keep", "probe_score": 0.9989, "luna_label": 1}]}, {"key": "rafael-130", "text": "**The World Bank**\nUganda Secondary Education Expansion Project (P166570)\n\n\n14. **On the national level, the main reasons for a girl dropping out of secondary school is pregnancy,**\n**marriage, and cost of schooling.** <sup>**18**</sup> [^18: Ibid, pp. 5.] Though the incidence of child marriage and early pregnancy have declined over\nthe years, Uganda’s levels of child marriage are above expectations given the level of income. The share of women\naged 18-22, who married before the age of 18, was 36.5 percent according to the latest Demographic and Health\nSurvey (2011). A more recent study supported by UNICEF <sup>19</sup> [^19: Amin et al., 2013.] indicated that child marriage in Uganda is even more\npervasive with 20 percent of girls in the country marring between the ages of 15 and 19 years old. One in seven\nwomen aged 18-22 have their first child before the age of 18. <sup>20</sup> [^20: Wodon, K. 2016, Uganda Note: Child Marriage and Education, p. 2.] The probability of completing secondary education\nfor a woman aged 25-34 who married after 18 is 12.9 points higher than for women who married earlier.\n\n15. **Additional evidence suggests that continued schooling delays marriage** when appropriate policies are\nimplemented by schools to prevent early marriage and pregnancy. These should include Violence Against Children\n(VAC) and Gender Based Violence (GBV) prevention activities and other measures ensuring a safe school\nenvironment that to ensure girls stay at school without the risk of falling victim to sexual violence. Additionally,\ndistance to lower secondary schools for young adolescents, especially girls from poor families, tends to raise\nopportunity costs and physical risks. Hence, increasing access to lower secondary schools, reducing cost of\neducation for poor households and providing incentives", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000018:17:0:0", "start": 536, "end": 565, "surface": "Demographic and Health\nSurvey", "probe_tag": "keep", "probe_score": 0.9207, "luna_label": 1}]}, {"key": "rafael-131", "text": "**The World Bank**\nUganda COVID-19 Education Response Project (P174033)\n\n\nFinancing Facility Advisory Services and Analytics is providing catalytic resources to provide technical assistance\nin three main areas linked to strengthening continuity of health services during the context of COVID-19.\n\n\n**B. Sectoral and Institutional Context**\n\n\n11. **In Uganda, there are over 15 million students enrolled in the education system, including higher**\n**education.** The majority of Ugandan students are enrolled in day schools while others are in boarding schools\nwhere many facilities are shared and students are constantly in close contact with each other, their teachers and\nother visitors on a daily basis, presenting an environment for easy transmission of the COVID-19. <mark>The education</mark>\n<mark>[system in Uganda has a structure of seven years of primary education](https://en.wikipedia.org/wiki/Primary_education)</mark> _<mark>,</mark>_ <mark>[six years of secondary education](https://en.wikipedia.org/wiki/Secondary_education)</mark> <mark>(divided</mark>\n<mark>into four years of lower secondary and two years of upper secondary school). Based on the data in 2017, the gross</mark>\n<mark>enrollment</mark> <sup>8</sup> [^8: EMIS data 2017, Ministry of Education and Sports] <mark>for preprimary was 14.4 percent, primary 115.7 percent, and secondary 28 percent, respectively.</mark>\n\n12. **All schools are currently closed as part of the Government’s COVID-19 response.** In response to the\ndanger posed by the pandemic to Ugandan students, the Government announced the closure of all schools from\nMarch 20, 2020 for a period of 30 days in a bid to", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000034:12:0:0", "start": 1166, "end": 1178, "surface": "data in 2017", "probe_tag": "keep", "probe_score": 0.9998, "luna_label": 0}, {"key": "sample:jdc_operational:000034:12:0:1", "start": 1239, "end": 1253, "surface": "EMIS data 2017", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1}]}, {"key": "rafael-132", "text": "**The World Bank**\nUganda Skills Development in Refugee and Host Communities (P176263)\n\n\nimprove quality of TVET, promote sustainable TVET financing, and ensure effectiveness of TVET management and organization. The\nBTVET Strategic Plan (2011) also provides a shift in the approach to employment-led skills development by providing labour marketrelevant skills and competencies. The core tenets are to increase internal efficiency and resource available to TVET, as well as to\nprovide more equitable access to skills development which is in line with this project’s PDO.\n14 See Table 16 (page 48) of the Education Response Plan for Refugees and Host Communities in Uganda (2018) presents data from\nRMIS and multiple sources in September 2017.\n15 Only 8 percent of refugees have received some form of formal training. See\nhttp://documents.worldbank.org/curated/en/571081569598919068/Informing-the-Refugee-Policy-Response-in-Uganda-Resultsfrom-the-Uganda-Refugee-and-Host-Communities-2018-Household-Survey\n16 In 2016, over 50% of GDP and 80% of the labor force can be attributed to the informal sector in Uganda (UBOS, 2014). While there\nare no official statistics on informal labor market in Uganda’s RHC, the UNHCR reports the economy in RHC as a predominantly informal\nsector.\n17 Violence against children in schools is widespread and has negative lasting impacts on physical and mental health, leading to risk of\nlow levels of educational attainment. A Raising Voices (2017) study identified that 93% of boys and 94% of girls aged 11-14 have\nexperienced physical violence from teachers in school. Sexual violence is likely to be underreported due to associated stigma.\n[18 For more details on Prospects, see the Vision Note (https://www.ilo", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000016:13:0:0", "start": 698, "end": 702, "surface": "RMIS", "probe_tag": "keep", "probe_score": 0.9689, "luna_label": 1}]}, {"key": "rafael-133", "text": " clear land and remove barriers,\nnegotiating with village chiefs to acquire plots of land for refugees, and increasing the number of water\npoints in the refugee camps.\n.\n\n\n10 According to the UNHCR SGBV Strategy for Chad 2012-2016. https://www.unhcr.org/protection/operations/56b1fd9f9/chadsgbv-strategy.html\n11 Percentage of women who have experienced physical and/or sexual violence from an intimate partner at some time in their\nlives. _Source:_ OECD, Gender, Institutions and Development Database 2015 (GID-DB) (accessed September 2017).\n\n\nPage 33 of 76", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000048:36:2:0", "start": 507, "end": 513, "surface": "GID-DB", "probe_tag": "keep", "probe_score": 0.9489, "luna_label": 1}]}, {"key": "rafael-134", "text": " in armed conflict may facilitate substantial population**\n**movement.** About 1.2 million people have returned from displacement within and outside of South Sudan since 2016 to\ndate. Among them, over 534,000 people (45 percent) returned since the signing of the revitalized peace agreement in\nSeptember 2018 until March 2019. <sup>13</sup> [^13: IOM DTM] This demonstrates that more people returned in a shorter period of time since the\nsigning of the agreement. According to both the UNHCR and IOM’s recent intention surveys, major pull factors for return\nare improved security, family reunification, access to basic services, and livelihood opportunities. For refugees, 30 percent\nof them in neighboring countries consider returning but majority are waiting to see how the situation unfolds. Slightly\nhigher numbers of IDPs have longer term return intentions to home areas. The prevailing security and basic living\nconditions are not yet conducive to more widespread return movements among both populations, however. In addition,\nthere have been new/secondary displacements triggered by intensifying inter-communal clashes in areas such as Unity,\nWarrap, Lakes, Western Bahr-el-Ghazal, Central Equatoria and Jonglei. <sup>14</sup> [^14: UNMISS (August 2019) ( _Mimeo_ )] Population movement thus remains volatile,\nwhere temporary visits by single family members to look after assets and property in home areas predominate. Should\nthe unity government be formed in November 2019 and security situation improve, however, the return of some of the\n4.2 million displaced (both refugees and IDPs) is likely to accelerate.\n\n9. **Unlike in other countries where significant numbers of returnees gravitate to major cities, in South Sudan the**\n**evidence suggests that IDPs and refugees are likely to return to their original villages or in their vicinity.** Existing data\nshows that a large majority (87 percent) of IDP and refugee returns are primarily to areas of habitual residence while\nrelocations to third areas is quite", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000014:4:1:0", "start": 347, "end": 354, "surface": "IOM DTM", "probe_tag": "keep", "probe_score": 0.9374, "luna_label": 1}, {"key": "sample:jdc_operational:000014:4:1:1", "start": 509, "end": 526, "surface": "intention surveys", "probe_tag": "confusion", "probe_score": 0.8434, "luna_label": 1}]}, {"key": "rafael-135", "text": "65. No Designated Account will be opened for this project. A Blanket Commitment will be\nset up for WFP and FAO for the full amount to be transferred to each UN agency as an Advance.\nThe Grant will finance 100 percent of eligible expenditures of the project, inclusive of taxes.\n\n\n66. On August 22, 2014, the Financial Management Operation Review Committee approved\nthe Request for Elimination of Audit Requirements for the proposed emergency project,\ncoordinated by the EAPSP, as part of project preparation. Alternative mechanisms (detailed in\nthe request) will be put in place to support the elimination of the Bank’s audit requirements,\nhowever. First, at least two field-based visits will be conducted during the first 12 months of the\nproject implementation period. The supervision intensity will be adjusted over time, taking into\naccount the project’s financial management performance and financial management risk level.\nSecond, the Government of Chad will have the entire responsibility to ensure during the project\nimplementation period that goods and services are delivered effectively to the beneficiaries.\nWhere deemed appropriate, however—for example, if the UN agencies’ systems or periodic\nreports have showed some weaknesses or deficiencies—the Bank team may request the\ngovernment to institute arrangements to physically inspect works, goods, and services delivered\nby WFP and FAO.\n\n\n**D.** **Procurement**\n\n67. **_Guidelines._** Procurement for the proposed project will be carried out in accordance with\nthe World Bank “Guidelines: Procurement of Goods, Works, and Non-Consulting Services under\nIBRD Loans and IDA Credits and Grants by World Bank Borrowers,” dated January, 2011, and\n“Guidelines: Selection and Employment of Consultants under IBRD Loans and IDA Credit and\nGrants by World Bank Borrowers,” dated January, 2011, and the provisions stipulated in the\nLegal Agreement. Contract awards will also be published in UNDB, in accordance with the\nBank’s Procurement Guidelines", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000007:27:0:0", "start": 1943, "end": 1947, "surface": "UNDB", "probe_tag": "confusion", "probe_score": 0.4866, "luna_label": 0}]}, {"key": "rafael-136", "text": "6.9|17,160.3|22.0|\n|Appraisal period<br>reduced to 3 years|4,296|19.9|2,781|28.6|(9,239)|−11.9|6,874|30.2|59,200|45.1|\n|Appraisal period<br>reduced to 5 years|13,780|34.3|7,990|45.8|2,454|9.0|19,152|47.2|136,276|60.9|\n\n\n\n_Source:_ Based on World Bank staff estimates.\n\n\n\nPage 81 of 94", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000049:86:1:0", "start": 240, "end": 266, "surface": "World Bank staff estimates", "probe_tag": "confusion", "probe_score": 0.2599, "luna_label": 1}]}, {"key": "rafael-137", "text": "22. **Moreover, investments in strengthening data systems (both technical and financial) need to**\n**continue to deepen the use of data for evidence‐based decision making in the sector and further**\n**improve resource allocation.** The MOE has successfully deployed an education management\ninformation system (EMIS), which is now hosting data on all schools and students in the system.\nAdditional investments in a geographical information system (GIS) are ongoing and will allow MOE to\nbetter plan for expansion of access across all regions in the country. Leveraging the data available\nthrough the EMIS for decision making in the sector is a key opportunity for the MOE which will require\nadditional technical assistance and capacity building to materialize. In addition, the collection,\nanalysis, and use of student learning data and disaggregated and gender‐sensitive data are essential\nfor monitoring, targeting pedagogical interventions, and improving teacher practices in the classroom.\n\n\n**C.** **Relationship to the Country Partnership Framework and Rationale for Use of**\n**Instrument**\n\n23. **_Relationship to the CPF._** The proposed operation is fully aligned with the Jordan Country\nPartnership Framework (CPF) discussed by the World Bank Group Board on July 14, 2016. The CPF\ncovers the period FY17–22 and highlights the economic, geopolitical, and social challenges that Jordan\nhas been facing, particularly with the Syrian refugee crisis. The CPF also acknowledges Jordan’s\ncommitment to reforms for a more sustainable growth path with stronger job creation, better service\ndelivery, a more conducive investment climate, and a larger involvement of citizens in the decision‐\nmaking process. In this respect, it is fully aligned with the government’s Vision Jordan 2025. The\nproposed operation directly supports the second pillar of the CPF, which aims to improve the quality\nand equity of service delivery, particularly its objective 2.2. “Improved delivery of education services.”\nThe proposed", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000041:14:0:0", "start": 810, "end": 831, "surface": "student learning data", "probe_tag": "confusion", "probe_score": 0.2375, "luna_label": 0}, {"key": "jdc_operational:000041:14:0:1", "start": 836, "end": 875, "surface": "disaggregated and gender‐sensitive data", "probe_tag": "confusion", "probe_score": 0.125, "luna_label": 0}]}, {"key": "rafael-138", "text": "**The World Bank**\nSouth Sudan Enhancing Community Resilience and Local Governance Project (P169949)\n\n\nbuilding power and voice through women’s identification of water and sanitation infrastructure needs.\nThis activity will be measured at the component level with the indicator: proportion of women in WASH\ninfrastructure/O&M groups. Progress on supporting the closing of the gender gaps through these efforts\nwill be measured through gender-disaggregated data in the M&E framework.\n\n\n81. **Citizen Engagement** . Citizen engagement is an essential element of the project, which is based on\na community-driven development approach, to improve community resilience and social cohesion. The\nproject mainstreams citizen engagement throughout the project cycle. Measures built in include:\nestablishment and strengthening of inclusive community institutions; a strong participatory planning\nprocess; inclusion of local communities in project implementation; community-based monitoring; training\non community-led O&M; a robust communication and outreach to communities; and a strong grievance\nredress mechanism to close the feedback loop. This will ensure community ownership of the project,\nadaptation of subprojects on local needs, efficient use of resources, and enhanced sustainability by\nincluding the communities in subproject O&M. Citizen engagement is also an essential tool for social risk\nmanagement, including a functioning grievance redress mechanism, meaningful consultations in line with\nESF/ESS10, prevention of elite capture, and inclusion of the most vulnerable. Implementing partners of\nthis project have considerable experience on citizen engagement in South Sudan, essential for effective\ncitizen engagement in the complex and diverse situations in different parts of the country.\n\n\n**B. Fiduciary**\n\n\n**(i) Financial Management**\n\n82. The overall fiduciary responsibility will be with UNOPS through the PMU with key staff responsible\nfor providing effective FM oversight. Since IOM will participate in the implementation of Components 1\nand 2 (and possibly some of Component 3 activities), IOM will establish similar FM arrangements. This will\nbe", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000049:43:0:0", "start": 435, "end": 460, "surface": "gender-disaggregated data", "probe_tag": "confusion", "probe_score": 0.0901, "luna_label": 0}]}, {"key": "rafael-139", "text": "**The World Bank**\nPromoting Financial Inclusion Policies and Regulations in Jordan ( P163719 )\n\n\nBank Universal Financial Access (UFA) data portal estimates that 0.8 million adults can be reached by the\ncountry opportunity of drafting and implementing the NFIS[1]. Affordable access to and use of financial\nservices helps young, women and small business owners (including those in the remote areas) generate\nincome, manage irregular cash flow, invest in opportunities, strengthen resilience to downturns, and work\ntheir way out of poverty.\n\n   - **The Project’s objective is aligned with the World Bank’s Twin Goals and global initiative to provide universal**\n\n**financial access around the world by 2020.** Financial inclusion - access and usage of quality financial services,\nincluding credit, savings, payments and insurance - is an enabler and a catalyst for achieving the Bank Group's\ngoals of ending extreme poverty by 2030 and boosting shared prosperity for the bottom 40 percent of the\npopulation in all developing countries.\n\n\n[1] http://ufa.worldbank.org/country-progress/jordan\n\n\n**C. Project Development Objective(s)**\nProposed Development Objective(s)\nThe project development objective is to support the implementation of the National Financial Inclusion Strategy (NFIS)\nin Jordan.\n\n\nKey Results\n\n   - Progress towards achieving the project’s objectives will be measured by a series of quantitative and qualitative\n\nindicators at the PDO level and at the intermediate level.\n\n   - The Financial Inclusion Monitoring and Evaluation (M&E) framework that will be developed under Component\n\n1, will include relevant NFIS indicators[1] and will track the implementation of NFIS and monitor progress and\nimpact. The framework will be segmented by intermediate outcomes, outcomes and national headline\nprogress. These indicators are meant to measure national progress, which has contributing factors beyond the\nscope of this project. Thus, this project will _only_ track national financial inclusion progress and will", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000013:5:0:0", "start": 98, "end": 147, "surface": "Bank Universal Financial Access (UFA) data portal", "probe_tag": "confusion", "probe_score": 0.2098, "luna_label": 1}]}, {"key": "rafael-140", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n\n\n\n\n\n\n\n\n\n\n|Table 6.2. Select Results of|the Survey Surveyed Provinces|Col3|Col4|Col5|\n|---|---|---|---|---|\n|<br> <br>|<br>**Surveyed Provinces**<br>|<br>**Surveyed Provinces**<br>|<br>**Surveyed Provinces**<br>|**Average**<br>|\n|<br> <br>|<br>**Guéra**<br>|<br>**Kanem**<br>|<br>**Logone**<br>**Occidental**<br>|<br>**Logone**<br>**Occidental**<br>|\n|Household size, number of people<br>|5.4<br>|4.6<br>|<br>6.8<br>|5.8<br>|\n|<br>Female-headed households, percentage<br>|<br>9.4<br>|<br>26.6<br>|<br>10.4<br>|<br>13.3<br>|\n|<br>Male-headed households, percentage<br>|<br>90.6<br>|<br>73.4<br>|<br>89.6<br>|<br>86.7<br>|\n|<br>Use of mobile money, percentage<br>|<br>12.0<br>|<br>11.0", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000051:90:0:0", "start": 19, "end": 54, "surface": "Chad Energy Access Scale Up Project", "probe_tag": "confusion", "probe_score": 0.8523, "luna_label": 0}, {"key": "sample:jdc_operational:000051:90:0:1", "start": 110, "end": 135, "surface": "Survey Surveyed Provinces", "probe_tag": "keep", "probe_score": 0.9685, "luna_label": 0}]}, {"key": "rafael-141", "text": ". <sup>15</sup> [^15: Informal refugees have not been profiled by the government.] Based on the lists of registered beneficiaries,\nvouchers will be delivered every month at designated distribution sites by NGO partners with the\nnecessary expertise, contracted by WFP. They will check the beneficiaries’ photo registration\ncards <sup>16</sup> [^16: Many refugees and returnees lose their national identification cards in their flight from CAR, so beneficiaries will\nreceive registration cards with their photos.] against the list of beneficiaries. To limit the possibility of unauthorized reproduction and\nredemption, each voucher has a specific security code and hologram.\n\n\n32. WFP will use its extensive experience in managing these processes to work with\ngovernment counterparts and establish agreements with a national bank, financial institutions,\nstorekeepers, and traders to implement the voucher/food delivery system. Under its current\nemergency operation, WFP assists about 75,000 displaced individuals, including the 31,200\nincluded under the proposed project. WFP intends to extend this operation through the end of\n2014, when a related WFP program (a Protracted Relief and Recovery Operation) will assume\nresponsibility for assisting these individuals. World Bank funding will represent about 42 percent\nof the operation’s needs for 12 months.\n\n\n33. **Component B: Agricultural Production and Livestock Stabilization (US$11 million**\n**IDA).** Component B aims to restore and maintain crop and livestock production capacity among\nthe affected populations in a context where pressure on the natural resource base is increasing\nrapidly. Under this component, the proposed project will finance the purchase and distribution of\n\n13 They may choose among 14 food items that have been selected based on local eating habits and healthy diets.\n14 When food prices rise during the lean season, the value of the vouchers declines, so people cannot obtain as much\nfood with the vouchers as before. Another drawback of using vouchers in the lean season is that they could trigger\nfood price inflation,", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000007:17:1:0", "start": 96, "end": 129, "surface": "lists of registered beneficiaries", "probe_tag": "confusion", "probe_score": 0.6806, "luna_label": 1}]}, {"key": "rafael-142", "text": "—Uganda National Household Survey\n2016–17.” World Bank, Washington, DC.\n2 Other contributing factors were the overall economic slowdown and widening in regional inequality observed since 2012/13. The\nonly region where poverty did not increase was the Northern region. Poverty declined in this region because of favorable weather\nconditions and increased transfers (remittances and social transfers).\n\n\nJun 15, 2021 Page 3 of 13", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000005:2:2:0", "start": 1, "end": 33, "surface": "Uganda National Household Survey", "probe_tag": "confusion", "probe_score": 0.8967, "luna_label": 1}]}, {"key": "rafael-143", "text": "<br>|\n|<br>N’Djamena<br>|<br>51.0<br>|<br>43.8<br>|<br>49.6|\n\n\n2. The gap in electricity access is also visible when looking at quintiles of wealth, although it only\nbecomes sizable for the highest quintile. From all male-headed households in the top quintile, 40.9\npercent have access to electricity while the same figure is 29.79 percent for female-headed households.\n\n\n\n\n\n\n|Location|Male (%)|Female (%)|All (%)|\n|---|---|---|---|\n|<br>Poorest|<br>0.01|<br>0.03|<br>0.01|\n\n\n\n43 The data on the distribution of household heads were updated by a survey on ability and willingness of households to pay for\nelectricity services, completed in 2021. Details are provided in annex 6.\n\n\nPage 71 of 87", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000051:76:2:0", "start": 484, "end": 527, "surface": "data on the distribution of household heads", "probe_tag": "confusion", "probe_score": 0.5761, "luna_label": 1}, {"key": "jdc_operational:000051:76:2:1", "start": 546, "end": 593, "surface": "survey on ability and willingness of households", "probe_tag": "keep", "probe_score": 0.9392, "luna_label": 1}]}, {"key": "rafael-144", "text": "**The World Bank**\nChad Energy Access Scale Up Project (P174495)\n\n\n**ANNEX 3: Gender Gap Analysis**\n\n\n\n**Rationale: Why a Focus on Gender in Energy Matters**\n\n1. Access to reliable household energy, clean and efficient cookstoves, or productive-use equipment\ncan reduce energy poverty and time spent on drudgery and give women and men additional incomeearning opportunities. In Chad, some key elements to consider include the following:\n\n\n\n(a) **Distribution of household heads.** About 22.1 percent of households in Chad are headed by\n\n\n\nfemales. The share is slightly higher in urban areas in general (23.6 percent); it is lower in\nN'Djamena (19.3 percent), <sup>43</sup> [^43: The data on the distribution of household heads were updated by a survey on ability and willingness of households to pay for\nelectricity services, completed in 2021. Details are provided in annex 6.] as detailed in Table 3.1. However, the project target with respect to\nelectrification of female-headed households is set at 15 percent according to more recent data\non the share of female-headed households in Chad that are provided in annex 6.\n\n\n\n\n\n|Location|Male (%)|Female (%)|\n|---|---|---|\n|<br>Nationwide<br>|<br>78.0<br>|<br>22.1<br>|\n|<br>Urban<br>|<br>76.5<br>|<br>23.6<br>|\n|<br>Rural<br>|<br>78.4<br>|<br>21.6<br>|\n|<br>N’Djamena<br>|<br>80.7<br>|<br>19.3<br>|\n\n\n(b)", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000051:76:0:0", "start": 1040, "end": 1093, "surface": "data\non the share of female-headed households in Chad", "probe_tag": "confusion", "probe_score": 0.841, "luna_label": 1}]}, {"key": "rafael-145", "text": "**The World Bank**\nSupport for Social Recovery Needs of Vulnerable Groups in Beirut (P176622)\n\n\n**3.** **Compounded by the global economic shock presented by COVID-19, disruptions in international food supply chains**\n**and trade networks exacerbate Lebanon’s food security vulnerabilities.** Lebanon's remittances dropped by 20%,\nfrom 3.9 billion U.S. dollars in the first half of 2019 to 3.1 billion dollars in the first half of 2020, according to Bank\nByblos' Lebanon This Week report released on Tuesday. <sup>6</sup> [^6: Bank Byblos (February 2020) Lebanon This Week ‘Lebanon’s expats’ remittances drop by 20% in H1 of 2020 in Xinhuanet.] Furthermore, the restrictions on movement to combat the\npandemic have hindered food-related logistic services, disrupting food supply chains and jeopardizing food security\nfor millions of people. The higher levels of export restrictions particularly leave food-importing countries vulnerable\nto commodity price fluctuations. The CPI witnessed an annual inflation of 133% between October 2019 and November\n2020ii, while Food Price Index (FPI) registered an inflation of 423% – representing an all-time high since CAS started\nprice monitoring on a monthly basis in 2007. <sup>7</sup> [^7: World Food Program (December 202) Lebanon, VAM Update of Food Price and Market Trends.] This is particularly relevant as Lebanon imports at least 80% of its food\nsupplies (ESCWA 2016). As a result of these crises, the real GDP growth of Lebanon contracted by 20.3% in 2020 and\na further contraction of about 9.3% is projected for 2021. These severe economic crises forced over 45% of the\nLebanese population below the poverty line.\n\n**4.** **The pandemic and ensuing lockdowns have affected the poor, refugees and other vulnerable populations**\n**disproportionately, on a global scale as well as on", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000054:3:0:1", "start": 1064, "end": 1080, "surface": "Food Price Index", "probe_tag": "confusion", "probe_score": 0.8807, "luna_label": 1}]}, {"key": "rafael-146", "text": " uniform data standards and\nclassification. Some of these key databases are; the maternal and neonatal registry, cancer registry,\ncommunicable disease surveillance registry, primary health care information system, and hospital\nutilization and billing system (visa billing system).\n\nThe aim of this component is to design a database that will allow for interoperability and serve as a\nmain repository of health sector data ready for analysis and dissemination. The database will not\nonly link the key MoPH databases, but will also pull relevant data from other sources such as CAS\nhousehold surveys (including Demographic and Health survey), NHA, and national hospital\nmorbidity and mortality reporting.\n\nThis component will work on the following activities:\n\n➢❨¢ Develop key performance indicators for assessing the performance of the health sector\nincluding disease burden, efficiency and equity of the health sector, PHC and hospital key\nperformance indicators, health economics indicators, as well as setting a plan to extract and monitor\nthe progress in the health and health-related Sustainable Development Goals indicators\n➢❨¢ Design an ICT system for ensuring interoperability of the existing databases including\nunification of patient identification numbers to facilitate statistical analyses for evidence based\ndecision making in the implementation of the national health sector plan\n\n\nPage 4 of 7", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000046:3:1:0", "start": 130, "end": 172, "surface": "communicable disease surveillance registry", "probe_tag": "confusion", "probe_score": 0.345, "luna_label": 0}, {"key": "jdc_operational:000046:3:1:1", "start": 576, "end": 597, "surface": "CAS\nhousehold surveys", "probe_tag": "keep", "probe_score": 0.9011, "luna_label": 0}, {"key": "jdc_operational:000046:3:1:2", "start": 609, "end": 638, "surface": "Demographic and Health survey", "probe_tag": "confusion", "probe_score": 0.4908, "luna_label": 1}, {"key": "jdc_operational:000046:3:1:3", "start": 641, "end": 644, "surface": "NHA", "probe_tag": "confusion", "probe_score": 0.2454, "luna_label": 0}, {"key": "jdc_operational:000046:3:1:4", "start": 650, "end": 701, "surface": "national hospital\nmorbidity and mortality reporting", "probe_tag": "confusion", "probe_score": 0.8719, "luna_label": 1}]}, {"key": "rafael-147", "text": "\nby facilitating citizens’ ability to monitor municipal investments and performance.\n\n  - _Local economic development:_ Based on the participatory needs identification and\n\nprioritization, UNDP will facilitate and support governorates and municipalities in\nanalyzing and assessing the local opportunities and challenges for economic growth and\njob creation. Such support will be designed around the results of a joint ILO-ACTED\nlabor needs assessment survey that UNDP is financing and an effort to bridge vocational\ntraining services with labor demands. It will also include market and value chain\nassessments, preparation/updating of participatory local economic development plans or\nother exploratory analysis related to livelihoods and income generating potential.\n\n20. Subcomponent 2A will finance the costs of Project related supervision, coordination and\nmonitoring, FM, procurement, safeguards management, audits of participating municipalities and\nother Project entities, etc. This subcomponent will also finance the preparation and dissemination\nof the Project OM, Project related information and communication activities, workshops,\ntrainings, and various studies and surveys related to monitoring and evaluation.\n\n21. Subcomponent 2B will seek to strengthen the institutional capacity and resilience of\nGovernment entities at the central, governorate and municipal level to better address the adverse\nimpacts of external shocks and stresses. While the municipal grants seek to alleviate some of the\n\n31", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000026:42:1:0", "start": 418, "end": 457, "surface": "ILO-ACTED\nlabor needs assessment survey", "probe_tag": "confusion", "probe_score": 0.4825, "luna_label": 0}]}, {"key": "rafael-148", "text": "**The World Bank**\nUganda COVID-19 Education Response Project (P174033)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Col1|of COVID-19 and mitigate<br>against negative impacts on<br>children’s wellbeing and<br>learning. This is intended to:<br>a) increase preventive<br>knowledge; b) promote<br>attitude change about for<br>example, a perceived risk of<br>infection and safe practices.<br>Design the messages in line<br>with existing SOPs, have it<br>verified and approved by<br>Communication & Advocacy<br>working group; and<br>dessiminate as: SMS, text in<br>national newspapers, short<br>commercial scripts on TVs<br>and radios.|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|IRI 2: Self-learning materials adapted to<br>large print and braille for students with<br>special needs (yes/no)|Defn: Self-learning materials <br>refer to any learning<br>resource that can be used<br>by a learner without the<br>physical presence of a<br>teacher.<br> <br>Braille is a symbolic code<br>used to write various<br>languages and are read by<br>touch rather than eyesight.<br>|Once a<br>year<br>|Delivery<br>records at<br>MoES<br>and SNED<br>|Review delivery records<br>at MoES (obtained from<br>DLGs)<br>|NCDC<br>|\n\n\n\nPage 32 of 43\n\n\nOfficial Use", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000034:36:0:0", "start": 1156, "end": 1160, "surface": "DLGs", "probe_tag": "confusion", "probe_score": 0.8546, "luna_label": 0}]}, {"key": "rafael-149", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\n\n\n\n\n\n\n\n|Col1|selected to receive SNSOP<br>who are refugees, defined<br>as forcibly displaced HHs<br>originating from a country<br>other than South Sudan and<br>registered as refugees in<br>South Sudan by the UNHCR.<br>A household will be<br>considered as a beneficiary<br>household if it is both<br>enrolled in the project and<br>have received a cash<br>transfer, at least for one<br>payment cycle.|measured at<br>least on a<br>quarterly<br>basis<br>including<br>through<br>missions and<br>ISRs|beneficiary<br>registration<br>and payment<br>data|beneficiary data during<br>targeting and<br>registration. The<br>payment service<br>provider will collect<br>payment data and<br>share with the<br>implementing partner.|Col6|\n|---|---|---|---|---|---|\n|Beneficiary households of social safety<br>net programs- Host Communities|The number of total<br>beneficiaries households<br>that are selected to receive<br>SNSOP who are from host<br>communities, defined as<br>local population groups<br>living in counties with a high<br>concentration of refugees. A<br>household will be<br>considered as a beneficiary<br>household if it is both<br>enrolled in the project and<br>have received a cash<br>transfer, at least for one<br>payment", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000057:56:0:0", "start": 639, "end": 655, "surface": "beneficiary data", "probe_tag": "confusion", "probe_score": 0.1255, "luna_label": 0}, {"key": "sample:jdc_operational:000057:56:0:1", "start": 748, "end": 760, "surface": "payment data", "probe_tag": "confusion", "probe_score": 0.0977, "luna_label": 0}]}, {"key": "rafael-150", "text": "**The World Bank**\nPromoting Financial Inclusion Policies and Regulations in Jordan ( P163719 )\n\n\nand evaluation studies.\n\n\nThis component will be conducted in close coordination with GIZ.\n\n\n**Component 2: Developing a country-specific program of peer-learning and knowledge sharing (US$400,000).**\n\n\n   - The project through partnership with AFI, AMF and WB will facilitate full engagement and peer learning among\n\nregulators and policymakers in Jordan by involving them in the broad array of services and offerings, with a\nspecial focus on the policy areas of priority that have been identified by the country based on the Financial\nInclusion Market Diagnostic results. Specifically, this component will finance the following sub-components:\n\n\n**Sub-component 2.1:** The Jordanian AFI membership during project duration will be funded under this\ncomponent and will engage Jordan in the following member services, which are designed to address the\nnational objectives of policymakers and regulators and will be modified to also address the national objectives\nof Jordan:\n\n\n   - Working Groups: These groups of 15-40 member institutions focus on specific policy areas, including: Digital\n\nFinancial Services; Consumer Protection and Market Conduct; Financial Inclusion Strategy; Financial Inclusion\nData; and SME Finance. The working groups are chaired by members and have led to important reforms in\nmany of the countries involved and facilitated the production of policy guidelines and tools that have practical\nrelevance.\n\n   - Participation in AFI's Global Policy Forum (GPF): The GPF is regarded as the leading global knowledge exchange\n\nevent on financial inclusion policymaking. Most sessions are hosted by members who share ‘on the ground’\nexperiences.\n\n   - Peer Learning Initiative with Standard Setting Bodies (SSBs): This will provide opportunities for Jordan to\n\nparticipate in shaping and influencing the global agenda through knowledge sharing with SSBs, which can have\na significant impact on how the global financial system helps or hinders financial inclusion.\n\n   - **Sub-component 2.2:** Organizing", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000013:7:0:0", "start": 625, "end": 670, "surface": "Financial\nInclusion Market Diagnostic results", "probe_tag": "confusion", "probe_score": 0.3955, "luna_label": 1}]}, {"key": "rafael-151", "text": "**The World Bank**\nGreater Beirut Public Transport Project (P160224)\n\n\nproject to be implemented by the CDR are (a) the lack of proper registry of asset lists; (b) delays in\nsubmission of timely audit reports; and (c) complex project where many other agencies are involved such\nas the RPTA and MPWT and that entails PPP and operators’ contributions. and the need to have strong\nFM staff within the PIU to be able to advise and support such activities and assume coordination among\nthe different stakeholders.\n\n\n6. Thus, to mitigate FM-related risks, (a) the CDR will ensure that the assets module of its accounting\nsoftware is well operationalized and is able to capture the work in progress and the assets acquired under\nthe project, (b) it will recruit an acceptable external auditor in the early stages of the project to enable\nconstant audit compliance, and (c) additional staff will be recruited as needed to ensure that FM\nimplementation is well supervised and followed up, in addition to the preparation of a Project\nImplementation Manual including an FM chapter that will detail the FM arrangements to be established\nfor carrying out the project FM implementation and defining the roles and responsibilities. The FM chapter\nwill include a detailed description of the process for expropriation and resettlement and the working\nrelationship with the RPTA, the PPP modalities, and the operators’ contributions mechanisms.\n\n\n7. **Staffing.** The existing CDR FO has adequate experience in managing World Bank-financed projects\nand will thus manage the project FM arrangements. This FO will be supervised by the Head of Funding\nDivision at the CDR and may be assisted by an additional financial staff as needed and as project activities\nbecome more complex.\n\n\n8. **Project accounting software.** The CDR has in place customized accounting software that has been\nused for the FM implementation of the World Bank-financed projects and can be used to record the\nproject’s accounting transactions and generate the", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000022:55:0:0", "start": 135, "end": 158, "surface": "registry of asset lists", "probe_tag": "confusion", "probe_score": 0.0575, "luna_label": 0}]}, {"key": "rafael-152", "text": "ary means for fostering improved teaching<br>practices. Successful completion of the training by the<br>awarding of a teacher certificate will serve as the verification<br>means. Data should be disaggregated by gender.|MOE Training and<br>certification records|Third Party|The verification agency will verify<br>training records for teachers'<br>training and will check teacher<br>certification records. The<br>certification mechanism will need<br>to be compliant with the<br>requirements agreed upon with the<br>WB as specified in the Program<br>Operations Manual.|\n\n\n38", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000041:45:3:0", "start": 239, "end": 260, "surface": "certification records", "probe_tag": "drop", "probe_score": 0.0044, "luna_label": 0}, {"key": "jdc_operational:000041:45:3:2", "start": 239, "end": 260, "surface": "certification records", "probe_tag": "drop", "probe_score": 0.0044, "luna_label": 0}, {"key": "jdc_operational:000041:45:3:1", "start": 312, "end": 328, "surface": "training records", "probe_tag": "drop", "probe_score": 0.0044, "luna_label": 0}]}, {"key": "rafael-153", "text": " under terms of reference satisfactory to the Bank. Apart from this, the\nPMU will be entrusted with compiling the annual financial statements and preparing any ad hoc financial\nreports to follow up on the Program financial activities, as necessary.\n\n\n**E.** **Capacity Building and Institutional Strengthening**\n\n\n46. **The World Bank and other development partners are undertaking substantial capacity**\n**building and institutional strengthening efforts in coordination with the PforR.** Some highlights of\nthis support include the following:\n\n\n(a) **Investment climate reform:** (i) Jordan Economic Legislation Reform (ASA financed by the\n\nMiddle East and North Africa Transition Fund), tackling the regulatory reform pillar; (ii)\nDoing Business Reform aiming at improving the business environment in areas measured by\nthe Doing Business report, including reforms on secured lending (collateral registry) that is\nkey to access to finance; and (iii) Inspections Reform (ongoing ASA), aiming at streamlining\nand simplifying the inspection regimes;\n\n\n(b) **Investment Promotion:** Jordan Competitiveness and Investment Promotion and Jordan\n\nInvestment Policy and Promotion projects (also financed by the Middle East and North Africa\nTransition Fund) are supporting the JIC—including relevant functions of the JIC: (i)\n\n\n14", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000045:22:1:0", "start": 826, "end": 847, "surface": "Doing Business report", "probe_tag": "drop", "probe_score": 0.0037, "luna_label": 1}]}, {"key": "rafael-154", "text": "---|---|---|\n|**1: Roads**<br>**Rehabilitation and**<br>**Maintenance**|Drainage structures and culverts; retaining<br>walls; routine maintenance.|184.6<br>|50<br>|0|\n|**2: Improving Road**<br>**Emergency**<br>**Response Capacity**|Increased MPWT’s capacity to deal with road<br>emergency works; purchase of necessary<br>equipment.|7.5<br>|7.5<br>|0|\n|**3.1: Strengthen**<br>**Road Asset**<br>**Management**|The creation of a road asset database for<br>Lebanon, and the revision of design and<br>maintenance standards to reflect changing<br>climate conditions.|2 <br>|2|0|\n|**3.2: Support the**<br>**planning and**<br>**implementation of**<br>**road safety**|n.a|2 <br>|0|0|\n|**3.3: Support**<br>**Planning and Design**<br>**Studies**|Finance necessary studies to produce<br>planning and design documents in the<br>transport sector, particularly public", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000008:34:1:0", "start": 426, "end": 445, "surface": "road asset database", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 0}]}, {"key": "rafael-155", "text": ">6 months<br>|MOPH<br>reports/ site<br>visit reports<br>|Administrative data<br>|PMU and MOPH<br>|\n|Number of communication initiatives<br>supported by the project to address<br>vaccine hesitancy <br>|<br>This indicator will track the <br>number of communication<br>initiatives that are either<br>conducted or/and<br>substantially supported by<br>MOPH for the public to<br>address the issue of<br>vaccine hesitancy. <br>|6 months<br> <br>|MOPH and<br>TPM reports<br> <br>|Administrative data<br> <br>|PMU/MOPH<br> <br>|\n|Percentage of vaccination sites visited by<br>the technical auditor in the last quarter|<br>This indicator will monitor<br>the percentage of project|3 months<br>|MOPH and<br>TPM reports|TPM reports<br>|PMU and TPM<br>|\n\n\nPage 46 of 54", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000000:50:1:0", "start": 57, "end": 76, "surface": "Administrative data", "probe_tag": "confusion", "probe_score": 0.7699, "luna_label": 0}, {"key": "sample:jdc_operational:000000:50:1:1", "start": 472, "end": 491, "surface": "Administrative data", "probe_tag": "drop", "probe_score": 0.0084, "luna_label": 0}]}, {"key": "rafael-156", "text": "):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|**Intermediate Results Area 1 (MDG Element):****_Improve urban service delivery through enhanced urban local development grant_ **|\n|**4.**Municipal** r**oads built<br>or rehabilitated with<br>related infrastructure<br>using urban LDG|√|3|Km<br>Targets|53.02|Measured<br>Annually|Measured<br>Annually|Measured<br>Annually|Measured<br>Annually|Measured<br>Annually|Annually|Municipal reports|Participating<", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000006:36:4:0", "start": 1379, "end": 1396, "surface": "Municipal reports", "probe_tag": "drop", "probe_score": 0.0017, "luna_label": 0}]}, {"key": "rafael-157", "text": " between construction firms it does not permit any\nforced partnerships. Therefore, the PPDA instruction that 30 percent of the Civil works should be outsourced\nto local staff and companies shall not apply. However, the contract will require that the Contractor only hires\nUgandans for non-skilled, and semi-skilled trades including operation of various equipment. UNRA has put in\nplace a robust contract management arrangement including strengthened its in house monitoring and\nsupervision capacity for the contract execution stage.\n\nFor high-value, high-risk, or complex contracts such as the road works, contract management plans will be\nprepared. To mitigate procurement capacity risks, there will be a need for staff capacity building and training,\n<u>continuous oversight, reviews and audits, and the use of real-time monitoring and tracking tools.</u>\n\n20. **Systematic Tracking of Exchanges in Procurement (STEP).** The project will use STEP, a planning and\ntracking system, which would provide data on procurement activities, establish benchmarks, monitor delays and\nmeasure procurement performance.\n\n21. **Use of National Procurement System.** National procurement procedures shall only apply if the\nrequirements as required by paragraph 5.3 of the Procurement Regulations <sup>64</sup> are met. In March 2017 (updated\n\n\n64 (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 Bank’s\nAnti-Corruption Guidelines, including without limitation the Bank’s right to sanction and the Bank’s inspection and audit rights; (d)\nProcurement Documents include provisions, as agreed with the Bank,", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000050:69:1:0", "start": 1002, "end": 1032, "surface": "data on procurement activities", "probe_tag": "drop", "probe_score": 0.0489, "luna_label": 0}]}, {"key": "rafael-158", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\n\n|Col1|project will undertake<br>proactive efforts to<br>communicate the service<br>standard to address and<br>respond to feedback that<br>will be received.|Col3|Col4|mechanism|Col6|\n|---|---|---|---|---|---|\n|Beneficiaries reporting satisfaction with<br>project activities<br>|Percentage of beneficiaries<br>in component 1 satisfied<br>with project application,<br>grant disbursement,<br>implementation, and<br>technical support.<br>Beneficiaries in component<br>2 reporting improved<br>community cohesion,<br>enhanced social inclusion,<br>and neighborhood<br>revitalization.<br>The findings of these<br>surveys will be published<br>and/or that the survey<br>findings will be used by the<br>implementing entity to<br>generate an action plan to<br>address the feedback<br>acquired through the<br>surveys.|At mid-<br>point of<br>project and<br>project<br>closure<br>|The scope of<br>the GRM will<br>include<br>complaints<br>and other<br>types of<br>feedback<br>such as<br>suggestions,<br>queries (e.g.<br>Quality of<br>Life<br>Survey) and<br>compliments<br>|A survey will be carried<br>out with direct<br>beneficiaries of the<br", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000012:46:0:0", "start": 712, "end": 719, "surface": "surveys", "probe_tag": "drop", "probe_score": 0.0087, "luna_label": 0}, {"key": "sample:jdc_operational:000012:46:0:1", "start": 903, "end": 910, "surface": "surveys", "probe_tag": "drop", "probe_score": 0.0022, "luna_label": 0}]}, {"key": "rafael-159", "text": ":<br>Fadi Yarak<br>  Title:<br>Director General|Contact:<br>Fadi Yarak<br>  Title:<br>Director General|Contact:<br>Fadi Yarak<br>  Title:<br>Director General|Contact:<br>Fadi Yarak<br>  Title:<br>Director General|Contact:<br>Fadi Yarak<br>  Title:<br>Director General|\n|Telephone No.:961-1-772-110<br>  Email:FYarak@MEHE.gov.lb|Telephone No.:961-1-772-110<br>  Email:FYarak@MEHE.gov.lb|Telephone No.:961-1-772-110<br>  Email:FYarak@MEHE.gov.lb|Telephone No.:961-1-772-110<br>  Email:FYarak@MEHE.gov.lb|Telephone No.:961-1-772-110<br>  Email:FYarak@MEHE.gov.lb|Telephone No.:961-1-772-110<br>  Email:FYarak@MEHE.gov.lb|Telephone No.:961-1-772-110<br>  Email:FYarak@MEHE.gov.lb|\n|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|**Project Financing Data(in USD Million)**|\n|[   ]", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000030:4:5:0", "start": 680, "end": 702, "surface": "Project Financing Data", "probe_tag": "drop", "probe_score": 0.0284, "luna_label": 0}]}, {"key": "rafael-160", "text": " the temporary rental of housing by displaced owners is projected\nat US$173.3 million for the same period. <sup>93</sup> [^93: RDNA]\n\n**7.** **Units and buildings remaining in need of repair are mainly those that are severely damaged, requiring**\n**more costly structural works, encompassing historic including heritage-grade buildings.** The wide national and\ninternational immediate response mostly focused on the less damaged units due to the technical complexity of\naddressing severely damaged units. As of 25 February 2021, 5,777 buildings remain with low or cosmetic damage\n(L1 damage level), 1,881 buildings with major but not structural damage (L2 level), and 1,093 with structural\ndamage (L3 level).\n\n**8.** **Most of the impacted residential buildings with cultural heritage value are still unattended and remain**\n**in precarious condition, requiring rehabilitation to allow habitability (see figure 3).** So far early recovery efforts\nhave focused on addressing affected residential buildings that suffered minor damage <sup>94</sup> [^94: Buildings with less than 10 percent of physical damage.] due to a lack of leadership\nfrom the authorities, as well as funds, capacity, and time required to assume the complex rehabilitation works of\nthe most severely damaged buildings during what was considered the “humanitarian phase”. According to the\nUN-Habitat damage inventory for all the buildings within the blast affected area of damaged buildings,\napproximately 25 buildings have not received any type of rehabilitation assistance yet, 20 have been propped but\nnot rehabilitated, 50 buildings are still under rehabilitation, and 80 have been completely rehabilitated. According\nto the DGA, 80 percent of the 640 heritage buildings which were damaged by the blast are of residential use.\nAmong these buildings, UNESCO interventions have focused on securing the most severely damaged buildings\nusing international and DGA standards <sup>95</sup>, with", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000012:57:1:0", "start": 1357, "end": 1384, "surface": "UN-Habitat damage inventory", "probe_tag": "drop", "probe_score": 0.0131, "luna_label": 1}]}, {"key": "rafael-161", "text": "**The World Bank**\nProductive Safety Net for Socioeconomic Opportunities Project (P177663)\n\n\nprotective support to HHs and investment in resilience building community assets will help sustain livelihoods, strengthen\nresilience, and prevent the most vulnerable from falling into destitution or being forcibly displaced. It will also directly\nsupport the Government’s Community Empowerment and Socioeconomic Development Strategy for Refugee Hosting\nAreas in South Sudan, with cash transfers promoting section 4.6 of the strategy on creation of livelihood and income\ngenerating opportunities given the lack of employment prospects in refugee-hosting environments.\n\n37. **In the absence of an enabling environment for widescale mobile payment systems, beneficiaries will receive**\n**physical cash at the time of payment, except for Juba where mobile money payment will be piloted.** A financial service\nprovider (i.e., paying agent), which will be competitively selected by the MAFS, will deliver cash to beneficiaries. The\nMAFS will provide the recipient list and amount of money to the financial service provider, and the list of beneficiaries\nwill be generated from the MIS. The MIS will capture beneficiaries' biometric data, which will be used to ensure that only\nthe eligible individuals will receive the cash transfer. The financial service provider pays beneficiaries verifying them\nbiometrically. In addition, implementing partners (i.e., UNOPS and NGOs contracted by MAFS to implement the project)\nand community leaders will be present and monitor the transfer process to ensure transparency and accountability.\nBased on the findings of a recently concluded analytical work, the project will pilot the use of mobile money payments\nin Juba. Mobile money payments would help strengthen transparency and safety and were assessed to be feasible in\nlarge urban center like Juba under the SSSNP. This pilot would inform potential future scale up of mobile money payments\nin urban areas.\n\n**Sub-component 1.1: Cash for Labor-Intensive Public Works and Complementary Social Measures** *", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000057:23:0:0", "start": 1169, "end": 1172, "surface": "MIS", "probe_tag": "confusion", "probe_score": 0.1458, "luna_label": 1}, {"key": "sample:jdc_operational:000057:23:0:1", "start": 1178, "end": 1181, "surface": "MIS", "probe_tag": "drop", "probe_score": 0.0462, "luna_label": 1}, {"key": "sample:jdc_operational:000057:23:0:2", "start": 1210, "end": 1224, "surface": "biometric data", "probe_tag": "confusion", "probe_score": 0.2475, "luna_label": 0}]}, {"key": "rafael-162", "text": " 335,000 hectares that require only modest\ninvestments in infrastructure to become productive. This resource, coupled with the country’s\nagro-ecological diversity, creates huge potential for crop diversification.\n\n\n16. The livestock subsector contributes significantly to the Chadian economy, although its\ncontribution to export revenues is suboptimal owing to the enormous uncontrolled migration of\nherds to markets in Nigeria each year. The dominant herding practices in Chad, affecting 80\npercent of livestock, are transhumance and extensive herding. Cattle herding is the primary\nagricultural activity in the Sahel and a secondary activity in the Sudanian zone, although the\nrelatively greater availability of food, agricultural by-products, and agro-industrial operations in\nthe Sudanian zone attract many herders from the north. In some instances, the herders’\ncoexistence with the agro-pastoralists poses land management problems and can be a source of\nconflict.\n\n\n17. Food security is volatile in Chad because agricultural production systems rely largely on\nrainfall rather than irrigation. Numbers of food-insecure individuals more than triple when\ndroughts deplete harvests and food prices soar. In southern Chad, where most of the refugees and\nreturnees are being settled, agriculture is dominated by small-scale farmers producing a\nmoderate, locally marketed surplus. Cross-border trade with Cameroon and CAR is also\nimportant. While local trade with CAR has been significantly curbed by the conflict, markets in\nthe areas where vouchers are being implemented continue to be well stocked and no unusual\nprice movements have been observed in the past few months since voucher transfers became\noperational. A January 2014 market assessment indicated that food production surpluses were\n\n\n4", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "sample:jdc_operational:000007:13:1:0", "start": 1719, "end": 1749, "surface": "January 2014 market assessment", "probe_tag": "drop", "probe_score": 0.0022, "luna_label": 1}]}, {"key": "rafael-163", "text": ">psycho-social support.|Defn: Emergency response<br>preparedness and psycho-<br>scial support refer to<br>strategic measures put in<br>place (by national COVID<br>taskforce) to manage the<br>spread and mitigate against<br>negative impacts of COVID-<br>19.<br> <br>Train or orient head<br>teachers of grant-supported<br>schools on the above<br>measures to help guide their<br>response at school level;|<br>once<br>|Training<br>reports<br>|Data collection through<br>monitoring of training<br>and review of<br>attendance registers<br>|MoES through<br>Directorate of Basic and<br>Secondary Education<br>|\n\n\nPage 34 of 43\n\n\nOfficial Use", "source": "jdc_operational", "subset": "annotate_rafael", "spans": [{"key": "jdc_operational:000034:38:1:0", "start": 508, "end": 528, "surface": "attendance registers", "probe_tag": "drop", "probe_score": 0.0147, "luna_label": 0}]}, {"key": "rafael-164", "text": "**The World Bank**\nUganda: Roads and Bridges in the Refugee Hosting Districts Project (P171339)\n\n\non the other sectors by enhancing connectivity of the host population and refugees to markets. The existing\nroad infrastructure in West Nile Sub-Region is of poor quality and not motorable especially during rains.\nAccess to health facilities and referral medical units is also encumbered by the dilapidated road network.\n\n\n11. **Uganda was ranked 127** <sup>**th**</sup> **out of 162 countries in the 2018 Gender Inequality Index** <sup>11</sup> . Prevalence rates of\ngender-based violence (GBV) in Uganda are high. According to the Uganda Demographic and Health Survey\n(UDHS) <sup>12</sup>, 56 percent of women have experienced spousal violence and 22 percent sexual violence. The\nfigures for Violence Against Children (VAC) are also high, with 59 percent of females and 68 percent of males\nreporting experiencing physical violence during childhood. <sup>13</sup> Adolescent girls in Uganda are more likely to\nbe poor, miss out on school and are at a greater risk of contracting HIV. <sup>14</sup> Of Ugandans ages 13-17 years,\none in four girls and one in ten boys reported sexual violence in 2015. <sup>15</sup> Nearly a quarter of teenage girls in\nUganda become pregnant. <sup>16</sup> The intersection with poverty and lack of access to education is the greatest\nrisk to violence against adolescent girls, particularly in rural areas. Refugee women/girls are at high risk of\nseveral forms of GBV including sexual exploitation and abuse (SEA), rape, forced and child marriage and\nintimate partner violence (IPV) <sup>17</sup>", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000146:14:0:0", "start": 499, "end": 527, "surface": "2018 Gender Inequality Index", "probe_tag": "keep", "probe_score": 0.9487, "luna_label": 1}, {"key": "refugee_pads:000146:14:0:1", "start": 631, "end": 667, "surface": "Uganda Demographic and Health Survey", "probe_tag": "keep", "probe_score": 0.9706, "luna_label": 1}]}, {"key": "rafael-165", "text": "s=NE)\n[2 World Bank Group. 2025. Niger. https://www.worldbank.org/en/country/niger/overview#3.](https://www.worldbank.org/en/country/niger/overview#3)\n3 Recent data from the World Food Program indicates that over 3.3 million individuals were classified as acutely food insecure during the 2023 season (June–August). An estimated 4.3 million people (2.4 million of whom are\nchildren) require humanitarian assistance. Additionally, Niger’s global acute malnutrition rate is estimated at 12.7 percent, and 42 percent of children under 5 years old are stunted. Severe recorded food crises in 1980, 1988,\n[1990, 1997, 2001, 2005, 2009, 2011–-.https://openknowledge.worldbank.org/handle/10986/37620).](https://openknowledge.worldbank.org/handle/10986/37620)\n[4 World Bank Group. 2021. Data. Poverty headcount ratio at US$2.15 a day (2017 PPP) (% of population) - Niger. https://data.worldbank.org/indicator/SI.POV.DDAY?locations=NE](https://data.worldbank.org/indicator/SI.POV.DDAY?locations=NE)\n5 In the transport sector women hold less than one percent of jobs. Although data on women in technical roles is unavailable, their share is likely lower due to inadequate skills and strong gender norms.\n6 When referring to host communities in this document, internally displaced persons are considered part of the host population unless noted otherwise.\n7 P. Thenkabail et al. 2016. Global Food Security Support Analysis Data (GFSAD) Crop Dominance 2010 Global 1 km", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000184:11:3:1", "start": 1374, "end": 1416, "surface": "Global Food Security Support Analysis Data", "probe_tag": "keep", "probe_score": 0.9406, "luna_label": 0}]}, {"key": "rafael-166", "text": "children, **l6** and child (under 5) mortality due to perinatal period conditions increased substantially in\n2002 to become the leading cause of death for children under 5 and the second leading cause of death\noverall). Per capita food consumption has declined by a quarter since 1998. The incidence o f Post\nTraumatic Stress Disorder (PTSD) has risen significantly and affects women and children in particular.\nNevertheless Palestine society remains fairly cohesive and families for the most part remain functional.\nThis situation together with the responses of the Palestine Authority i s described in more detail in _Twenty_\n_Seven Months, Intifada, Closures and Palestine Economic Crisis,_ May 2003, and ‘in _Palestine: The_\n_Current Socio-Economic Crisis and the Ministry of_ _Social Affairs,_ December, 2002, Report # 25262-02).\n\n\nThe coping capacity of poor households has been strained to the limit. Families have used a variety of\nstrategies to cope with a sharp decline in income.” Initially, many made up for lost income by drawing\non their savings (24 percent), assistance from family and friends (15 percent), cultivating land (10\npercent), or selling property (8 percent). B y November 2001, the number of families reporting income\nwas not enough for their needs had increased to 68 percent, and 17 percent were reducing their expenses,\n\n15 percent were relying on assistance from family and friends, and only 13 percent were able to draw\nfrom their savings. In terms of food, 82 percent of families rely on their own income for their food needs,\nwhile 11 percent rely on family and friends and 7 percent rely on relief assistance. Of those relying on\nrelief assistance for food, the majority were in Gaza. The surveys help illuminate the effectiveness of the\nrelief programs in reaching the poor. Households identified as Hardship Cases receive 56 percent of all\nfood assistance, and those below the poverty line receive another 26 percent", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000047:25:0:0", "start": 1725, "end": 1732, "surface": "surveys", "probe_tag": "keep", "probe_score": 0.9046, "luna_label": 1}]}, {"key": "rafael-167", "text": " from one stage to another. It is also driven in part by the curriculum which is geared to\npreparing students for the French baccalaureate examnination and may be contextually difficult for\nDjiboutians from less educated families.\n\n\n**3. Income and Gender Gaps in Enrollment Rates**\n\nEven though the main constraint at present appears to be school places, there is already evidence of\ngender and income gaps which cannot be explained by lack of school places alone. These are\nexpected to become more prominent over time as enrollment rates rise.\n\n\nAccording to the household expenditure survey data, in urban areas, the Net Enrollment Rate in\nPrimary Enrollment is 50% higher for the highest expenditure quintile compared to the lowest\nexpenditure quintile. The inequity is even worse in secondary education (lower secondary education\nis part of basic education but the survey data did not separate the two), where the NER of the highest\nquintile is 420% higher than the NER of the lowest quintile. The problem in urban areas is access demand exists among all groups but the rationing of sets ends up benefiting the better off who live in\nareas where schools have historically been located. Any further expansion of places will help the\npoorer segments of the population more particularly if care is taken to site the schools in areas where\nthe poor live.\n\n\nThere are also significant gender gaps and research indicates that educated mothers play a key role in\nthe country's overall development. There is a shortage of school places and any rationing works to\nthe detriment of girls enrollment. Parents are less willing for their girls to attend school because in\npar., they may view the curriculum as foreign. In addition, despite the fact the education is officially\nfree, poor families still have difficulty paying the cost of books and materials. They prefer to use\ntheir constrained resources for their boys who they feel have a better labor market potential. Finally,\nthe data from the Household Survey, showed that even if girls go to school, their parents pull them\nout at an", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000161:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1}, {"key": "refugee_pads:000161:38:1:2", "start": 1978, "end": 2008, "surface": "data from the Household Survey", "probe_tag": "keep", "probe_score": 0.9653, "luna_label": 1}]}, {"key": "rafael-168", "text": "**The World Bank**\nCosta Rica Results in Education (CORE) (P181174)\n\n\n**<u>Figure 1b: Technical education graduates by gender in strategic STEAM fields</u>**\n\n\n12. **By the end of 2022, Costa Rica was hosting 270,636 forcibly displaced and stateless people, and the uneven**\n**preparedness of these students strains the education system.** <sup>19</sup> [^19: UNHCR, Strategy 2024, Situation Analysis.] Even if the influx of migrants and refugees acts as a\npositive factor for some schools that would be facing closure due to decline of the native-born population, the education\nsystem faces stress because of the differential quality of preparedness and disruptions faced by immigrant students. In\nrecent decades, the country has seen the arrival of migrants and refugee applicants fleeing political and economic\nconditions in Colombia, Cuba, El Salvador, Nicaragua, and Venezuela. According to the United Nations High Commissioner\nfor Refugees and administrative records from the General Directorate of Migration and Foreigners, the number of asylum\nseekers in Costa Rica increased from 27,993 in 2018 to 273,066 by December 2023. While the country offers public\neducation to all children regardless of migratory status, more efforts are needed to ensure that enrollment requirements\ndo not exclude migrant children from the classroom, and that learning environments are inclusive. As of September 2023,\n64,455 students enrolled in the Costa Rican education system were foreigners (5.4 percent of the total student\npopulation). According to administrative registries, 38,870 migrant students (60.3 percent of the total migrant student\npopulation) are under irregular status or pending regularization. <sup>20</sup> [^20: Data from MEP’s Saber, as of September 26, 2023.] The issue of disabilities is also a priority item for\nGovernment policies on inclusion. MEP statistics indicate 19,643 students with disabilities, with alternative solutions\nproviding services", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000189:14:0:0", "start": 1543, "end": 1568, "surface": "administrative registries", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1}]}, {"key": "rafael-169", "text": "16\n\n\naddressed the Donors Roundtable and committed to increase Government resources to education to\nover 25% of the budget and noted that the government viewed education as the main source of future\ngrowth in Djibouti.\n\n\n**5. Value added of Bank support in this project**\n\nIDA has been supporting the national consensus building process through the National Education\nForum. The proposed project will help demonstrate that a consensus building approach that involves\n\nall elements of civil society is effective and produces results. In addition the use of an APL\ndemonstrates the long-term commitment by IDA to assist the Government in its strategic goal of\nreaching full enrollment in basic education. It is also hoped that the use of the IDA credit will further\ndecrease the construction unit cost (as IDA is supporting the use of local construction materials which\nshould be cheaper), help develop more cost-effective classroom designs, and provide the environment\nwith a more efficient procurement process.\n\n\n**E. SUMMARY PROJECT ANALYSIS** (Detailed assessments are in the project file, see Annex 8)\n\n\n**1. Economic (see Annex 4)**\n\n\nOther (specify) NPV=US$ million; ERR = ** % (see Annex 4)\n\n\n_** ERR = Over 11% based on system efficiency gains alone without allowing for development_\n_benefits, public goods nature of education and poverty reduction benefits._\n\n\nDjibouti's main resource base is its population and in order to achieve sustained development, the\ncountry needs to improve the quality of its human resource base. Quality starts with improved basic\neducation and school enrollments. In addition, the issue of equity arises. According to the household\nexpenditure survey data, in urban areas, the net enrollment rate (NER) at the primary level in the\nsurvey year (1996) was 50% greater for the highest expenditure quintile compared to the lowest\nexpenditure quintile. The inequity is even more pronounced in secondary education (lower secondary\neducation is part of basic education but the survey", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000038:19:0:0", "start": 1661, "end": 1694, "surface": "household\nexpenditure survey data", "probe_tag": "keep", "probe_score": 0.944, "luna_label": 1}]}, {"key": "rafael-170", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000086:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": 0.1197, "luna_label": 0}, {"key": "refugee_pads:000086:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "rafael-171", "text": "> <br>|<br>**Responsible: Implementing agencies**<br>**/Bank**|<br>**Responsible: Implementing agencies**<br>**/Bank**|<br>**Responsible: Implementing agencies**<br>**/Bank**|**Stage: Throughout**|**Due Date :**<br>**Ongoing**|**Status:**<br>**Ongoing**|\n|**Capacity**|**Capacity**|**Rating:**|**SUBSTANTIAL**|**SUBSTANTIAL**|**SUBSTANTIAL**|**SUBSTANTIAL**|**SUBSTANTIAL**|\n|**Description:**Lack of institutional capacity of the Project<br>Coordination Unit (PCU) located in DGE to carry out Project<br>management,<br>monitoring<br>and<br>evaluation,<br>including<br>procurement,<br>disbursement,<br>financial<br>management<br>and<br>safeguards could slow project implementation.<br> <br>This risk is compounded by extremely long national<br>**_procurement procedures_** which affect the speed of project<br>implementation. The delays result from the low threshold for<br>prior review and the centralization of notices and results<br>publication at DGMP. A survey conducted in 2010 showed that<br>the procurement process could take anywhere between nine to<br>fourteen months.|**Description:**Lack of institutional capacity of the Project<br>Co", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000136:69:2:0", "start": 958, "end": 982, "surface": "survey conducted in 2010", "probe_tag": "confusion", "probe_score": 0.656, "luna_label": 1}]}, {"key": "rafael-172", "text": " phase of the program that will look into extending the BRT lines to the southern\nand eastern suburbs of Beirut. This ambitious project will be the first mass transit and regular transport\nsystem in Lebanon in over 50 years, in a complex political-economy context in the country and the sector\n(alignment, informal operators, behavioral change, institutional). It is a vital project in tackling traffic\ncongestion, contributing to growth and connectivity between various Lebanese regions, and in providing\naffordable and reliable transport.\n\n\n14. **The proposed project will provide clean, affordable, and reliable transportation to middle- and**\n**low-income Lebanese and Syrians.** Currently, only the very poor Lebanese and Syrians use the existing\npublic transport system due to lack of alternatives. The planned system is designed to attract middle- and\nlow-income Lebanese and Syrians by substantially upgrading the quality of services to achieve modal shift\nwhile keeping prices affordable. Traffic surveys have shown that Syrians also rely significantly on public\ntransportation to meet their transportation needs and already represent between 30 percent and 40\npercent of users of the existing public transport system and are expected to significantly benefit from the\nnew improved services. The planned BRT project with its associated feeder network will cover about half\nof the country and will reach over 50 percent of all Lebanese and Syrians in Lebanon.\n\n\n15. **In addition, the project will create short-term jobs in the construction sector that has historically**\n**been a major employer for the low-skilled Lebanese and Syrians in Lebanon.** Before the Syria conflict,\nthe construction sector employed more than 100,000 workers (approximately 10 percent of the labor\nforce). The construction sector is also the second largest employer of Syrian refugees in Lebanon (24.1\npercent), after household work (26.5 percent). The employment of Syrians in the construction sector does\nnot displace the Lebanese labor force since over the past decade, the skilled labor force in construction\nhas been", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000074:16:1:0", "start": 998, "end": 1013, "surface": "Traffic surveys", "probe_tag": "confusion", "probe_score": 0.4189, "luna_label": 1}]}, {"key": "rafael-173", "text": "<br>supervisors. MOE will then evaluate teachers against this<br>framework to identify teacher professional development needs<br>and to determine teachers’ placement on the career path<br>(certification level). Data should be disaggregated by gender.|MOE Teacher<br>evaluation records|Third Party|The verification agency will verify<br>teacher evaluation records.|\n|**DLR#4.4.**Number of K‐12<br>teachers trained and certified <br>|In‐service training for grade 4 to 10 teachers to be delivered<br>with the aim of aligning teacher skills and competencies with<br>the new Teacher Standards. The training would also place<br>emphasis on the application of targeted instruction strategies<br>and the use of student assessments (formative and<br>summative). Instructional support materials, whether paper or<br>technology based, that are closely aligned with the curriculum<br>and that teachers will be trained on, will be introduced as<br>supplementary means for fostering improved teaching<br>practices. Successful completion of the training by the<br>awarding of a teacher certificate will serve ", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000127:45:2:0", "start": 266, "end": 284, "surface": "evaluation records", "probe_tag": "confusion", "probe_score": 0.5041, "luna_label": 0}, {"key": "refugee_pads:000127:45:2:1", "start": 336, "end": 362, "surface": "teacher evaluation records", "probe_tag": "confusion", "probe_score": 0.5041, "luna_label": 0}]}, {"key": "rafael-174", "text": " meet the high barriers to entry but are most likely to work\nin these areas are given opportunities by creating pathways and ladders to gain entry and broaden the set of\nrequirements of access by supporting programs that can validate acquired experience or through recognition of\nprior learning programs. While there are really no apparent systemic barriers within the training sector that\nprevents women from participating in technical education and vocational training programs, the number of\nwomen in non-traditional programs and courses continues to be small. The USAID’s Workforce Development\nprogram’s Gender Assessment reveals that a combination of prevalent social norms, parental influence, and poor\ncommunications, impacts decisions by women to participate in these programs. The assessment also highlights\nfinancial barriers for women to access to TVET (to cover the cost of courses including equipment as well as the\ncost of transportation), supply-side barriers (discrimination and harassment during trainings, few female trainers\nplaying as role models, lack of targeted trainings to female needs/interests due to their limited participation in\ndecision-making, etc.). These issues will need to be addressed to help improve system equity in terms of gender.\nNotwithstanding these concerns, it does need to be acknowledged that the official statistics suggest that nearly\n41 percent of students in the TVET sector are women, suggesting that the sheer lack of opportunities and access\nin other programs (such as, higher education) does result in a significant number of women accessing these\nprograms. Nevertheless, it is important and critical to support not only the expansion of opportunities for more\nwomen, but also to increase the range of opportunities thereby incentivizing women and young girls to access\ntraining in non-traditional programs.\n\n32. **The government is committed to ensuring access to quality education for all students considered as**\n**vulnerable, which include girls from host communities, refugees, and children with special needs.** Girls’\nenrollment is lower at all education levels and makes up 49 percent of preschool, 46 percent", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "sample:refugee_pads:000195:23:1:0", "start": 1345, "end": 1364, "surface": "official statistics", "probe_tag": "confusion", "probe_score": 0.1952, "luna_label": 1}]}, {"key": "rafael-175", "text": "**The World Bank**\nENHANCING CONNECTIVITY AND RESILIENCE IN THE FAR NORTH OF CAMEROON FOR INCLUSIVENESS\nPROJECT (P178207)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Col1|passability during the rainy<br>season.|Col3|Col4|passable road within<br>five kilometers of the<br>MDK road section|Col6|\n|---|---|---|---|---|---|\n|Number of refugees and host<br>communities population with access<br>to an all-weather passable road within<br>five kilometers of the MDK road<br>section.|<br>Number of refugees and<br>people in host communities<br>with access to an all-season<br>road. Climate change<br>impacts are expected to<br>affect road passability<br>during the rainy season.|Yearly<br>|Surveys<br>UNHCR data<br>|The methodology will<br>consist on using survey<br>to calculate the number<br>of refugees and host<br>communities<br>population<br>located within a 5km-<br>buffer zone of each<br>road section<br>rehabilitated and<br>maintained with<br>climate<br>resilience features.<br>|Project Implementation<br>Unit<br>|\n|Share of women with improved<br>access to an all-weather passable<br>road within five kilometers of the<br>MDK road section.|This sub-indicator measures<br>the percentage of women<br>with improved access to an<br>all-weather passable road<br>within five kilometers of the<br>MDK", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000003:62:0:0", "start": 664, "end": 674, "surface": "UNHCR data", "probe_tag": "confusion", "probe_score": 0.6174, "luna_label": 1}]}, {"key": "rafael-176", "text": " component, the project will reinforce the Government’s capacity by financing technical assistance and\nsocial protection system investments that will support achievement of the project objectives. Specifically, the component\nwill finance the scale-up of the national social registry of vulnerable households and strengthening of targeting\nprocedures, including the PMT methodology developed under the SSNP and community-based targeting. This will include\nmaking the necessary modifications to incorporate refugees in the social registry, given that they have become eligible\nfor several social programs in Djibouti. The program’s corresponding MIS will be reinforced for improved enrollment,\npayment, grievance and redress management, and monitoring as well as referral of potential beneficiaries to other\n\n\nPage 13 of 44", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000154:17:2:0", "start": 258, "end": 307, "surface": "national social registry of vulnerable households", "probe_tag": "confusion", "probe_score": 0.1555, "luna_label": 0}, {"key": "refugee_pads:000154:17:2:1", "start": 521, "end": 536, "surface": "social registry", "probe_tag": "confusion", "probe_score": 0.8106, "luna_label": 0}]}, {"key": "rafael-177", "text": "6\n\n\nThere is strong support in the Government for increasing resources for education, and the Government\nmade a commitment to increase education's share of budget from 16% in 2001-02 to 25% in 2009-10.\n\n\nOne of the reasons for choosing an APL with a ten-year perspective is that the education budget\nshortages will continue to be a constraint in the next few years. Over this period, Government\n\nexpenditures in non-priority areas will be brought under control and Government expenditures on\neducation can be expected to increase significantly. Despite the manageability in the long-run, the\nshort-run prospects on the budget are more challenging and donors will need to finance some recurrent\ncosts. The proposed APL will be implemented in three phases with distinct triggers (see Section B. 4).\nAs a result, a 10-year projection of enrollments and education costs has been developed (which is the\noverall framework for the APL), and a detailed five year plan and project proposals have been\nprepared (which is the framework for the first phase of the APL).\n\n\n3. Sector issues **to be addressed by the project and strategic choices**\n\nThe project will directly address all the issues below except for higher education.\n\n\n_Issues/Sector Problems_ _Government strategy and project proposal_\n\n**School Places**\n\nThe immediate problem in Djibouti City and The Government's strategy includes a combination\nsurrounding suburbs and other towns is the lack of of building more schools and continuing with the\nschool places due to the strong demand for schooling. double-shifting policy. The project will finance new\nclassrooms, sanitation services, and school furniture.\n\n**Equity, Gender, Disparities**\n\nChildren from poorer families, rural children, and The Government will construct schools in underespecially girls do not always attend school. The served areas, particularly in poorer parts of Djiboutirecent Household Expenditure Survey states that Ville where almost 70% of the population lives.\nmajor reasons for the", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000049:9:0:0", "start": 1906, "end": 1934, "surface": "Household Expenditure Survey", "probe_tag": "confusion", "probe_score": 0.8798, "luna_label": 1}]}, {"key": "rafael-178", "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_rafael", "spans": [{"key": "refugee_pads:000147:65:1:0", "start": 363, "end": 384, "surface": "photographic archives", "probe_tag": "confusion", "probe_score": 0.2148, "luna_label": 0}]}, {"key": "rafael-179", "text": "\nfor quarterly and bi-annual assessments moving to a bi-annual census of health facilities once monitoring capacity\nis established, anticipated in Year 2. Quarterly TPM visits will incorporate the following:\n(i) **Quarterly health facility functionality assessments.** At baseline and endline, the assessments will\nincorporate a sample of operational health facilities not supported by the project to generate\ncomprehensive information on health service delivery nationwide and the added value of the project.\nMeasures on disability access will be included in the assessments.\n(ii) Quarterly data quality verification to provide measures of partner data quality and reporting accuracy.\n(iii) Bi-annual health service quality assessment to capture the quality of key health services, focusing on health\nservice process and structural quality. On an annual basis the health service quality assessment will include\ndirect observation of health service process quality measures at hospitals and health centers.\n(iv) Bi-annual patient feedback using exit surveys.\n(v) Bi-annual visits to a sample of BHTs to measure service outputs and quality.\n**(b)** **Periodic TPM data collection:**\n\n(i) Biennial household coverage surveys as baseline/endline surveys in the project’s three-year timeframe. <sup>35</sup> [^35: Given the planned project length of three years, this is a baseline and an endline survey. Potential timeframe changes would include interim surveys, which are\nplanned to be light surveys focusing on key indicators.]\n(ii) Citizen engagement survey collected at the household level, with the coverage survey every other year.\n\n2. **Data Analysis and Visualization Platform.** The platform will emphasize development of an integrated,\ninstitutionalized, and sustainable system. The platform will include analysis of health service delivery in refugee and\nhost community areas. The platform will include the following:\n(a) Interactive data visualization platform presenting Results Framework and core indicators. The platform will use\n\ndata from DHIS2 and the TPM and will include BHI data. It will be updated at least on a", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000006:64:1:0", "start": 1187, "end": 1222, "surface": "Biennial household coverage surveys", "probe_tag": "confusion", "probe_score": 0.415, "luna_label": 0}, {"key": "refugee_pads:000006:64:1:1", "start": 1532, "end": 1557, "surface": "Citizen engagement survey", "probe_tag": "confusion", "probe_score": 0.5315, "luna_label": 0}, {"key": "refugee_pads:000006:64:1:2", "start": 2053, "end": 2058, "surface": "DHIS2", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}, {"key": "refugee_pads:000006:64:1:3", "start": 2088, "end": 2096, "surface": "BHI data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "rafael-180", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\n\n\n\n|Col1|financial support through<br>the project in the<br>reconstruction of their<br>residential unit from the<br>PoB explosion.<br>Rationale: 100 rental<br>contracts x 3.5 people/HH|Col3|and<br>Evaluation<br>Reports. Esti<br>mates by<br>Project<br>Management<br>Team.|disaggregating the<br>beneficiary data of the<br>progress reports|Team/UN-Habitat|\n|---|---|---|---|---|---|\n|Direct beneficiaries of cultural production<br>work|Number of people including<br>cultural practitioners,<br>individuals in cultural<br>entities and additional<br>workers involved in the<br>implementation of the<br>cultural productions.|Quarterly<br>|Progress,<br>Monitoring<br>and<br>Evaluation<br>Reports.<br>Third-Party<br>Monitoring<br>Agent<br>reports.<br>Estimates by<br>Project<br>Management<br>Team.<br>|Number of<br>beneficiaries will be<br>tracked in the progress<br>reports through<br>reporting updates by<br>each grant recipient of<br>the total number of<br>people participating on<br>each cultural<br>production work<br>|Project Management<br>Team/UN-Habitat<br>|\n|Of which, are female|Number of females<br>including cultural<br>", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "sample:refugee_pads:000034:40:0:0", "start": 400, "end": 416, "surface": "beneficiary data", "probe_tag": "confusion", "probe_score": 0.0908, "luna_label": 0}]}, {"key": "rafael-181", "text": "Annex 6\nPage 5 of 6\n\n\n\n**Table B: Thresholds for Procurement Methods and Prior Review 1**\n\n\n\n**Expenditure Category** **Contract Value** **Contracts Subject to**\n**Threshold** **Procurement** **Prior Review**\n(US$ thousands) Method (US$ millions)\n1. **Works** - US$50,000 NCB All ICB if any.\n< US$50,000 Simplified NCB NCB contracts above US$150,000\nFirst 5 contracts regardless of\nvalue; first 3 contracts for each\n\n\n\nyear starting January 1.\n**2. Goods** - US$100,000 ICB All ICB.\n< US$100,000 NCB NCB contracts above US$70,000\n< US$50,000 IS or NS where there are at First 5 contracts regardless of\n\n\n\nleast 3 capable national value; first 3 contracts for each\nsuppliers. year starting January 1.\n<US$10,000 DC\n**3. Services** - US$50,000 QCBS Contracts above US$200,000\n(Firms) would be advertised in the UNDB.\n\n              - US$25,000 Contracts for firms above\n(Indiv.) CQ US$50,000, and for individuals\nSS above US$25,000.\n< US$25,000 First 5 contracts regardless of\n=< US$15,000 value; first 3 contracts for each\nyear starting January 1.\nAll TORs\n_____________________________________________________________IAll Sole Source\nThresholds generally differ by country and project.", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000149:47:0:0", "start": 809, "end": 813, "surface": "UNDB", "probe_tag": "confusion", "probe_score": 0.223, "luna_label": 0}]}, {"key": "rafael-182", "text": "- World Bank diagnostics and international benchmarks also warrant the Program’s sectoral focus on education and\nhealth. According to the Global Digital Health Monitor 2023 <sup>13</sup> [^13: See State of Digital Health around the world today – Jordan profile.], the digital landscape in the health sector is\nconsidered more mature in Jordan compared to neighboring countries in the MENA region, especially in\ndeveloping digital services and applications (see the technical assessment for further detail). However, due to\nlimited governance and multiple services and systems designed and operated in silos, health information systems\nhave been fragmented with inconsistent data standards and quality. The education sector has also been advancing\non the digital front. Jordan was one of the first countries in the region to respond to the COVID-19 pandemic and\nschool closures by developing an online learning platform called Darsak covering the curriculum’s core subjects of\nArabic, English, math, and science for grades 1 through 12. In addition, a newly launched platform for teacher\ntraining offers courses on distance learning tools, blended learning, and educational technology. The MOE has\ndeveloped an integrated, comprehensive, flexible, and trusted educational management information system\n(EMIS) using OpenEMIS, which provides a system that is accessible countrywide, covering over 2 million students,\n7,300 schools, and 170,000 educational staff.\n\n  - World Bank diagnostics and international benchmarks also warrant the Program’s focus on transparency and\naccountability. Under international governance indicators, Jordan is on par with other upper middle-income\ncountries, but it lags on voice and accountability. Regarding transparency, although Jordan was the first country\nin the region to legislate access to information, it is poorly rated under the Right to Information Index and ranks\namong the 15 percent least well-performing countries. According to an assessment of the whole of government\ncitizen feedback platform, only 33 percent of citizens filing a grievance using the At Your Service online\ngovernment grievance redress platform surveyed in 2022 had received any response. Opinion surveys reflect a\npervasive perception", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000181:27:0:1", "start": 1870, "end": 1896, "surface": "Right to Information Index", "probe_tag": "confusion", "probe_score": 0.6406, "luna_label": 1}, {"key": "refugee_pads:000181:27:0:2", "start": 2204, "end": 2219, "surface": "Opinion surveys", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}]}, {"key": "rafael-183", "text": "required for local government involvement, and arrangements for maintenance, monitoring and\nevaluation (M&E). Social capital enhancing activities would be a mandatory part of all sub-projects, and\nwould be tailored to support activities chosen by the communities.\n\nSupport to Decentralized Government Structures. Most local administrations are beginning\nto operate again with a limited number of staff and other inputs. District and chiefdom authorities are\nvery weak, however, and lack the financial and human resources needed to address their concerns and\npnorities effectively. NGOs have demonstrated their ability to implement successful community-based\nsocial and economic projects and have played a key role in shelter reconstruction activities. With the\ngradual strengthening of local government capacity, partnerships between community groups and local\nauthorities are expected to increase. Upon completion of initial training, district and chiefdom authorities\nwould be required to demonstrate that they have used the training by showing that there have been some\nimprovements in their community. For instance, at the end of each training session, district authorities\nwould be required to develop a simple action plan that specifies some activities that NSAP or other\npartners could support. District and chiefdom authorities would also gain experience in implementing,\nsupporting or overseeing community development activities.\n\nHealth. The unfavorable health indicators in Sierra Leone can be attributed to several factors.\nHigh fertility, female genital mutilation and the presence of HIV/AIDS increase morbidity and mortality\nrisks for women and children. Many risk factors that have contributed to HIV/AIDS epidemics in other\nAfrican countries have long been present in Sierra Leone, and the protracted conflict has created the\nconditions for explosive growth in HIV/AIDS infection rates. The Centers for Disease Control carried\nout a survey in 2002 which found the HIV prevalence among adults (aged 1549) to be 6.1 %; in\nFreetown, 4% in rural areas and 4.9% nationwide. In response to the crisis, Government has developed\na multi-sector HIV/AIDS Program, which is being supported by various", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000050:10:0:0", "start": 1950, "end": 1964, "surface": "survey in 2002", "probe_tag": "confusion", "probe_score": 0.7887, "luna_label": 1}]}, {"key": "rafael-184", "text": "**The World Bank**\nTajikistan Water Supply and Sanitation Investment Project (P177325)\n\n\ndrinking water quality. The quality of drinking water sources may also be compromised by increased\nsediment and nutrient inputs due to extreme storm events. Sanitation-related public health risks tend to\nbe higher during/after the occurrence of extreme weather events such as floods and droughts, and the\nabsence of sustainable WASH solutions may further exacerbate the spread and transmission of\nwaterborne diseases. The impact of increasing temperatures on the incidence, transmission, season\nduration, and spread of diseases represents a major threat to rural communities, particularly those\naffected by heat waves. <sup>28</sup> Investments in piped water supply services to the rural areas, where wastewater\ncollection and treatment is almost nonexistent will result in an inevitable increase in volume of\nwastewater flows in the medium term, which if not considered and addressed duly may be associated\nwith the environmental and public health threats.\n\n\n16. The World Bank multi-sector rapid needs and impact assessment was conducted by the World\nBank in September 2021 to support the Government to prepare for potential influx of refugees under\ndifferent scenarios. Based on existing data and information, the impact assessment focused on Khatlon\nand GBAO provinces as locations for temporary refugee settlements. The assessment identified urgent\nand short- and medium-term needs to reduce the negative impacts of inflow on host communities and\nmaximize the positive benefits of refugee arrivals and settlement after registration. The assessment\nidentified provision of at least basic WSS services among priorities for the Khatlon and GBAO regions,\nparticularly along the border areas and high-density rural and peri-urban settlements characterized by\nlow coverage and poor quality of basic infrastructure and services (especially for children and women).\n\n\n**Sector Policy and Strategy**\n\n\n17. Recognizing the importance of water to its development agenda, Tajikistan has embarked on a\nprocess of water sector transformation in the last decade. Tajikistan’s goals and macro-strategies are laid", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "sample:refugee_pads:000162:16:0:0", "start": 1272, "end": 1301, "surface": "existing data and information", "probe_tag": "confusion", "probe_score": 0.8919, "luna_label": 1}]}, {"key": "rafael-185", "text": " of Expenditures (SOE) and the Special\nAccount (SA), would be audited quarterly internally and annually by an independent auditor, in\naccordance with internationally accepted standards. In addition, the auditor would carry out field spot\ncheck audits to ascertain compliance with contractual requirements. Compliance with conditional cash\ntransfers would be monitored by an independent external consultant (paragraph _C.3)._\n\n\n**4.** **Social**\n\nOpportunities, constraints, impacts, and risks arising. out of the socio-cultural and political context.\n\nThe impact of closure and incursions in the West Bank and Gaza has been extensively documented. On\nthe rise are poverty, unemployment, school drop out rates; on the decline or deteriorating are household\nincomes, living conditions, school attendance rates, at-large nutritional status and in particular of children\n0-5 years of age.13\n\nThe so called “newly poor”, those who are just above the poverty line before the outbreak of the second\nIntifada, in the third quarter of 2000, have been assisted by various interventions. To date, it has been\nmuch more difficult to find ways to assist the very poor, or first decile, whose coping mechanisms are\nessentially exhausted. The proposed project i s designed to support children (0-18 years old) in the first\ndecile. Although the SHC has targeted the first decile for more than 20 years, the program i s very small\ntoday in relation to need and requires new instruments to address the present situation and to shift\nMOSA’s overall strategy from one of coping to one of social springboard. Therefore, the project will\nsupport MOSA in reshaping its social assistance strategy and to improve effectiveness and efficiency of\n\n\n~\n\n**l3** PCBS quarterly household surveys, Palestinian Living Conditions quarterly surveys, IUCN, University of Geneva, Living\n\nStandards quarterly surveys, Nutrition Study, Johns Hopkins University, _2002,_ Nutrition Survey, PCBS and", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000047:20:1:0", "start": 1732, "end": 1764, "surface": "PCBS quarterly household surveys", "probe_tag": "confusion", "probe_score": 0.7827, "luna_label": 1}, {"key": "refugee_pads:000047:20:1:1", "start": 1766, "end": 1813, "surface": "Palestinian Living Conditions quarterly surveys", "probe_tag": "keep", "probe_score": 0.9613, "luna_label": 1}, {"key": "refugee_pads:000047:20:1:2", "start": 1843, "end": 1878, "surface": "Living\n\nStandards quarterly surveys", "probe_tag": "confusion", "probe_score": 0.7496, "luna_label": 1}]}, {"key": "rafael-186", "text": "Disbursement forecast\n\n\n\nContract number\n*Contract subject\nAwardee\n*Launching date\n-Expected delivery date\n-Non objection date\n*Expected date of final delivery\n*Bidder nationality\n*Contract allocation (general account, budget, loan\ncategory, geographic area)\n*List of contracts\nManagement of financial -Standard financial statements (balance sheet;\naccounts statement of sources and uses of funds/income\nstatement, ...)\n*LACI reports for the project duration\nFixed Assets management -Inventory of Fixed Assets (type, quantity, valuation,\ndate of service, etc.)\n\n\n\nSupplier\nAccounting category ; budgetary and accounting\nallocation of fixed assets\n\n\n\nLocation\nDepreciation\n-Disposal of Fixed assets\n\n\n\n**Module** Functions\nSorting parameters Project ID and currency used\n\n                  - Fiscal years\nCurrency\nDecentralized data entry locations\n\n\n\nChart of accounts, managerial reports, geographic\nareas of intervention, etc.\n\n\n\n\n                - Books of accounts\nDonors\n\n                 - Contracts\nCategories of disbursement\nUser Management Data storage ; restitution ; correction; cleaning; etc.\n\n                - Import/export of data to other Tempro modules\n\n\n\nIt is expected that the application would be modified to differentiate the operations from the\nprojects, as well as funding sources to allow for reporting in financial and accounting terms of the\nproject objectives and activities. The concept should allow for proper monitoring of the project\nduring the life of the credit, namely: (i) chart of accounts; (ii) by category, component, and subcomponent; (iii) by geography (type of establishment, site and district); (iv) by category of\nexpenses; and (v) in local and foreign currency. Reporting of multi-level data is planned, which\n**wiU** bring about a more dynamic approach to the management of the project, and which should", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000062:51:0:0", "start": 1720, "end": 1736, "surface": "multi-level data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "rafael-187", "text": " the government’s Vision Jordan 2025. The\nproposed operation directly supports the second pillar of the CPF, which aims to improve the quality\nand equity of service delivery, particularly its objective 2.2. “Improved delivery of education services.”\nThe proposed operation will seek to help Jordan improve the delivery of education services and\nstrengthen its resilience in response to the current Syrian refugee crisis.\n\n24. **_Rationale for the use of PforR_** **.** The PforR instrument will enhance the impact of the World\nBank’s financial and technical support and increase the overall results orientation of the government’s\nNational Human Resources Development Strategy (NHRDS). The decision to use this instrument was\nbased on the following considerations:\n\n- **Results orientation** : The GOJ’s program as depicted by the NESP provides a comprehensive set of\nactivities for making the education system more result‐oriented. The PforR will push forward the\nresults orientation of the sector plan by rewarding the achievement of results with\ndisbursements.\n\n\n- **Upfront momentum** : The PforR instrument will be used to generate momentum around key\nactivities that are potential bottlenecks in the system. Disbursement‐linked indicators (DLI) will\nbe a critical tool for shifting the policy dialogue toward results, especially in the initial years.\n\n\n- **Stakeholder harmonization** : The PforR will not only enhance the partnership between the\ngovernment and the World Bank by using the government’s own systems, but also the\nharmonization of donor interventions in the sector targeting a common results framework. The\nNESP lays out a comprehensive results framework that has been developed in coordination with\neducation partners.\n\n\n- **Institutionalization of measurement** : The use of the PforR instrument is an opportunity to\nleverage MOE’s investments in data systems, such as the OpenEMIS, and to strengthen", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000127:14:1:0", "start": 1896, "end": 1904, "surface": "OpenEMIS", "probe_tag": "confusion", "probe_score": 0.5297, "luna_label": 0}]}, {"key": "rafael-188", "text": " least once from project-supported<br>groups. The data is disaggregated by gender, youth (18-30 years) and refugee/host community status.|\n|Frequency|Quarterly|\n|Data source|Project MIS.|\n|Methodology for<br>Data Collection|Monitoring project implementation.|\n|Responsibility for<br>Data Collection|IA|\n|**New or improved jobs generated through the project (Number)**|**New or improved jobs generated through the project (Number)**|\n|Description|Quantitative indicator counting number of jobs created through all three main project components.|\n|Frequency|Quarterly|\n|Data source|Project MIS and Project Progress Reports.|\n|Methodology for<br>Data Collection|Monitoring project implementation.|\n|Responsibility for<br>Data Collection|IA|\n|**Project-supported groups trained on climate-resilient practices and technologies (Percentage)**|**Project-supported groups trained on climate-resilient practices and technologies (Percentage)**|\n|Description|Quantitative indicator counting percentage of project-supported groups under component 3 who receive<br>capacity-building support from the project on climate-resilient business planning, value chains, market<br>assessments, etc., and on climate-smart technologies, such as drought-resistant seeds, etc.|\n|Frequency|Quarterly|\n|Data source|Project MIS and Project Progress Reports.|\n|Methodology for<br>Data Collection|Monitoring project implementation|\n|Responsibility for<br>Data Collection|IA|\n|**Project-supported groups still operational one year after project support (Percentage)**|**Project-supported groups still operational one year after project support (Percentage)**|\n|Description", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000186:47:1:0", "start": 174, "end": 185, "surface": "Project MIS", "probe_tag": "drop", "probe_score": 0.0247, "luna_label": 0}, {"key": "refugee_pads:000186:47:1:1", "start": 580, "end": 620, "surface": "Project MIS and Project Progress Reports", "probe_tag": "drop", "probe_score": 0.0462, "luna_label": 0}]}, {"key": "rafael-189", "text": "**Annex 8: Procurement**\n\n**EGYPT: Early Childhood Education Enhancement Project (ECEEP)**\n\n\n**A. General**\n\n\n1. Procurement for the Bank-funded activities o f the project would be carried out in accordance\nwith the World Bank’s “Guidelines: Procurement Under IBRD Loans and IDA Credits” dated\nMay 2004; and “Guidelines: Selection and Employment o f Consultants by World Bank\nBorrowers” dated M a y 2004, and the provisions stipulated in the Legal Agreement. The general\ndescription o f various items under different expenditure categories are described below. For each\ncontract to be financed by the Loan, the different procurement methods or consultant selection\nmethods, the need for prequalification, estimated costs, prior review requirements, and time\nframe are agreed between the Borrower and the Bank project team in the Procurement Plan. The\nProcurement Plan will be updated at least annually or as required to reflect the actual project\nimplementation needs and improvements in institutional capacity.\n\n\n**Institutional Arrangements for Procurement**\n\n\n2. Procurement will be managed by the F S M O E designated procurement staff working closely\nwith the financial management officers, both at the central and the governorate level.\nProcurement financed by the loan would comprise civil works (US$16 m) which are essentially\nconstruction o f new KG classrooms and rehabilitated facilities donated by local communities for\nconversion into KGs; goods (US$2.35 m) which include equipment for new and rehabilitated\nKGs, and educational materials for KG instruction.\n\n\n3. The works contracts will be procured by GAEB. This will include construction and\nfurnishing o f the new KGs as well as rehabilitation o f community KGs. GAEB will also lead the\nidentification o f sites for construction o f KGs based on its extensive and well-tested GIS and\nschool mapping system. GAEB will work closely with the MOE/Finance Sector assigned\nProcurement Officers, at both the", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000040:60:0:0", "start": 1843, "end": 1872, "surface": "GIS and\nschool mapping system", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 1}]}, {"key": "rafael-190", "text": " / LHW and PMU,<br>DOH<br>|\n|Children under 1 year immunized with the<br>first dose of measles vaccination in target<br>districts|<br>Percentage of children<br>under 1 immunized at EPI<br>centres, PHC, and in the<br>community in target<br>districts.|Bi-Annually<br>|DHIS, EPI MIS<br>|Routine HMIS<br>|DHIS/EPI<br>|\n|Women receiving iron/folic acid<br>supplementation during pregnancy in<br>target districts|Percentage of all pregnant<br>women receiving iron/folic<br>acid supplementation at<br>PHC facilities, or in the<br>community in target<br>districts.|Bi-Annually<br>|DHIS, LHW<br>MIS<br>|Routine HMIS<br>|IMU, PMU, DOH<br>|\n|Health professionals (doctors, nurses,<br>non-medical staff) receiving refresher and<br>on-the-job training|Number provincial and<br>district staff trained<br>(Cumulative number).|Bi-Annually<br>|PMU<br>|PMU Records<br>|IMU, PMU, DOH<br>|\n\n\n\nPage 38 of 48", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000126:42:1:0", "start": 835, "end": 846, "surface": "PMU Records", "probe_tag": "drop", "probe_score": 0.047, "luna_label": 0}]}, {"key": "rafael-191", "text": "**The World Bank**\nBalochistan Human Capital Investment Project (P166308)\n\n\n(FMS), and an environmental and social safeguards specialist/officer. <sup>47</sup> [^47: During the early phase of implementation, the Governance and Policy Program (GPP) PMU will provide back‐up support.] The PMUs will be fully authorized\nto implement the planned activities approved by the Project Steering Committee (PSC).\n\n\n46. **A Project Coordination Committee (PCC) will be set up to coordinate project implementation**\n**and a PSC will be set up to provide strategic guidance and oversight.** The PCC, co‐chaired by Secretaries\nHealth and Secondary Education, will meet quarterly. The PSC, chaired by the Additional Chief Secretary,\nwill meet biannually (see figure 2).\n\n\n**Figure 2. Institutional and Implementation Arrangements**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n\n47. **Building on the results chain, the M&E framework identified indicators to track project**\n**implementation progress and impact.** The PDO‐level health indicators are taken from the RMNCHN\nindicators in the DHIS, while digitization and integration of various HMIS is an intermediate indicator. The\neducation indicators are taken from the EMIS. Where possible, relevant indicators will be disaggregated\nby gender. Discussions with the GoB and the UNHCR have confirmed, however, that beneficiary data by\nnationality will not be routinely collected or publicly released.\n\n\n48. **The project M&E will leverage and strengthen existing routine information systems, and finance**\n**the generation of user‐friendly evidence for efficient service delivery.** Routine surveys will be used to\ncollate data from target facilities, which will be triangulated through the existing management\ninformation system within the Health and Secondary Education Departments. The remote monitoring\nsystem within the SED uses technology‐based data management solutions with a dashboard to display\nthe broader analysis. The project will support the", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000085:25:0:0", "start": 1085, "end": 1102, "surface": "RMNCHN\nindicators", "probe_tag": "confusion", "probe_score": 0.7369, "luna_label": 1}, {"key": "refugee_pads:000085:25:0:1", "start": 1110, "end": 1114, "surface": "DHIS", "probe_tag": "confusion", "probe_score": 0.566, "luna_label": 1}, {"key": "refugee_pads:000085:25:0:2", "start": 1241, "end": 1245, "surface": "EMIS", "probe_tag": "confusion", "probe_score": 0.7093, "luna_label": 1}, {"key": "refugee_pads:000085:25:0:3", "start": 1385, "end": 1416, "surface": "beneficiary data by\nnationality", "probe_tag": "drop", "probe_score": 0.0448, "luna_label": 0}]}, {"key": "rafael-192", "text": " 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 and<br>implementing agency<br>will document payment<br>data<br>|Implementing Partner<br>|\n|Beneficiary households of social safety<br>net programs- Refugees|The number of total<br>beneficiaries HHs that are|This indicator<br>will be|SNSOP MIS<br>which hosts|The implementing<br>partner will collect|Implementing Partner<br>|\n\n\nPage 51 of 74", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000153:55:1:0", "start": 163, "end": 179, "surface": "beneficiary data", "probe_tag": "drop", "probe_score": 0.0106, "luna_label": 0}]}, {"key": "rafael-193", "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_rafael", "spans": [{"key": "refugee_pads:000062:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "drop", "probe_score": 0.0402, "luna_label": 0}]}, {"key": "rafael-194", "text": "br>beneficiaries affected by climate change to existing social<br>programs|**Sub-component 3.2:**<br>**Development of a**<br>**National**<br>**Social**<br>**Registry**<br>**(US$2**<br>**million)**<br> <br>Registration of beneficiaries<br>and<br>referral<br>to<br>social<br>programs<br>- <br>The Social Registry will collect socio-economic data from<br>beneficiaries that can help determine households’<br>vulnerability to climate change based on their own<br>indicators but also on the geographical location where<br>they live<br>- <br>The Social Registry will have the capacity to refer<br>beneficiaries affected by climate change to existing social<br>programs|**Sub-component 3.2:**<br>**Development of a**<br>**National**<br>**Social**<br>**Registry**<br>**(US$2**<br>**million)**<br> <br>Registration of beneficiaries<br>and<br>referral<br>to<br>social<br>programs<br>- <br>The Social Registry will collect socio-economic data from<br>beneficiaries that can help determine households’<br>vulnerability to climate change based on their own<br>indicators but also on the geographical location where<br>they live<br>- <br>The Social Registry will have the capacity to refer<br>beneficiaries affected by climate change to existing social<br", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000130:88:3:0", "start": 324, "end": 343, "surface": "socio-economic data", "probe_tag": "confusion", "probe_score": 0.0767, "luna_label": 0}, {"key": "refugee_pads:000130:88:3:1", "start": 324, "end": 343, "surface": "socio-economic data", "probe_tag": "drop", "probe_score": 0.0458, "luna_label": 0}]}, {"key": "rafael-195", "text": " and ranks according to the proxy-means testing (PMT) formula; (iv) maintaining the PMT formula;\n(v) analyzing national data and reporting findings to the Social Inter-Ministerial Committee (Social-IMC); (vi) monitoring the\n**NPTP** targeting; and (vii) auditing data processing. The central unit in **MOSA** (through its SDCs) is responsible for (i) comanaging the central **NPTP** database; (ii) receiving household applications; (iii) interfacing with applicants; (iv) entering data; **(v)**\nconducting household visits; (vi) checking for data errors; (vii) transmitting data to the central database of the **NPTP** **CMU;** and\n(viii) managing the public relations campaign.\n**3** Other options were considered at the time and are detailed in the EPP of the **ESPISP** II.\n\n\n**_52_**", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000109:51:2:0", "start": 111, "end": 124, "surface": "national data", "probe_tag": "drop", "probe_score": 0.0396, "luna_label": 1}]}, {"key": "rafael-196", "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_rafael", "spans": [{"key": "refugee_pads:000086:24:0:0", "start": 1670, "end": 1703, "surface": "data on social development issues", "probe_tag": "drop", "probe_score": 0.0226, "luna_label": 0}]}, {"key": "rafael-197", "text": "satisfied two or more rainy seasons; - Progress reports submitted\n2b.3 100% of houses benefit by implementing partners;\nmarginalized population                 - Beneficiary assessments\ngroups (female headed                 - NaCSA M&E data\nhouseholds, disabled and their\nfamilies); and\n2b.4 100% of beneficiaries\nwere selected by beneficiary\ncommunities.\n\n\n**3.** Proiect Management and\nInnovative Activities - NaCSA administrative data - Qualified implementing\n\n                                  - Capacity building event partners available to provide\n**3(a)** **Capacities of** assessments; capacity building and IEC\n**communities,** **chiefdomns,** **and** 3a.1 At least 5 successful - Participatory project activities at all levels;\n**district authorities to select,** capacity building events completion reviews; - A qualified full-time M&E\n**implement and maintain** carried out each year; - IDA supervision missions specialist is provided to\n**projects established** **and** NaCSA by another donor\n**strengthened** agency\n\n**3(b)** **Information, Education** 3b. 1 At least 40% of HHs are - Beneficiary assessments; - Non-NSAP activities\n**and Communication** aware of program; - NaCSA adrninistrative data; undertaken by NaCSA do not\n3b.2 At least 60%of chlefdom                  - IDA aide-memoires and detract from NaCSA ability to\nand district governments project status reports; and implement project.\naware of NSAP coverage,\ntargeting, methodology, and\nresults; and\n3b.3 At least 30% of general\npublic aware of NSAP                  - Public opinion survey\nprogram and results.\n\n\n**3(c)** **Performance** **of** 3c.1 M&E reports used for - NaCSA", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000096:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0}, {"key": "refugee_pads:000096:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": 0.1197, "luna_label": 1}, {"key": "refugee_pads:000096:31:0:2", "start": 1188, "end": 1214, "surface": "NaCSA adrninistrative data", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "rafael-198", "text": " <br>Technical<br>audit report<br> <br>Technical audit<br> <br>SEAS<br>|Community sub-projects functional one<br>year after completion<br>Percentage of infrastructure<br>assets rated as functional by<br>an independent technical<br>auditor one year after they<br>are completed (with<br>completion defined as<br>having passed the stage of<br>réception provisoire)<br>Once<br> <br>Technical<br>audit report<br> <br>Technical audit<br> <br>SEAS<br>|Community sub-projects functional one<br>year after completion<br>Percentage of infrastructure<br>assets rated as functional by<br>an independent technical<br>auditor one year after they<br>are completed (with<br>completion defined as<br>having passed the stage of<br>réception provisoire)<br>Once<br> <br>Technical<br>audit report<br> <br>Technical audit<br> <br>SEAS<br>|\n|Beneficiaries satisfied with the<br>community infrastructures financed by<br>the project<br>Percentage of direct<br>beneficiaries of community<br>infrastructures that are<br>globally satisfied with the<br>infrastructures<br>Once<br> <br>Survey<br> <br>Survey at end of project<br> <br>SEAS<br>|Beneficiaries satisfied with the<br>community infrastructures financed by<br>the project<br>Percentage of direct<br>beneficiaries of community<br>infrastructures that are<br>globally satisfied with the<br>", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000154:37:6:0", "start": 1072, "end": 1096, "surface": "Survey at end of project", "probe_tag": "drop", "probe_score": 0.0269, "luna_label": 0}]}, {"key": "rafael-199", "text": "|No|Key risks|Mitigation Actions|By Whom|By When|\n|---|---|---|---|---|\n|||<br>Organize a workshop to train procurement<br>staff in the DFM on World Bank<br>procurement procedures and work closely<br>with World Bank Procurement Specialist|<br>PIU/IDA|<br>At Project launch and<br>throughout project<br>life|\n|4|Lack of an adapted manual of<br>procedures with a section dedicated<br>to the procurement|Draft and submit to IDA a satisfactory<br>Project Implementation Manual (PIM)<br>with section on procurement detailing out<br>all applicable procedures, instructions and<br>guidance for handling procurement|MoD/PIU|Final version prior to<br>effectiveness|\n|<br>5|Lack of Procurement Plan|Preparation Procurement Plan for the first<br>18 months and agreed with the World<br>Bank<br>and<br>subsequently<br>updating<br>of<br>procurement plans in tandem with annual<br>work plan and budget and for agreed<br>Contracts. The critical approval times will<br>be reflected in the timelines of the<br>procurement plans.|MoD/ Others<br>ministries/DFM<br>|Throughout<br>project<br>life|\n|6|Poor record keeping system|Setting up adequate filling system for<br>project records to ensure easy retrieval of<br>information/data. Designate an officer to<br>be responsible for data management.|PIU|No<br>later<br>than<br>3 <", "source": "refugee_pads", "subset": "annotate_rafael", "spans": [{"key": "refugee_pads:000148:73:0:0", "start": 1148, "end": 1163, "surface": "project records", "probe_tag": "drop", "probe_score": 0.0329, "luna_label": 0}]}, {"key": "rafael-200", "text": " ensure the protection of stateless persons, the\nnaturalization of the Makonde community, and an agreement to naturalize qualifying members of the\nShona community. However, scarce socioeconomic information of stateless populations that is comparable to nationals prevents a deeper understanding of their living conditions, hence hindering efforts\nto design targeted policy aimed at solving statelessness.\n\n\n**The Shona SES provides comparable socioeconomic profiles for the Shona community and nation-**\n**als, while contributing toward informing a targeted response to address the socioeconomic impacts**\n**of the COVID-19 pandemic.** Together with the Department of Immigration Services (DIS) and the\nKenya National Bureau of Statistics (KNBS) of the GoK, UNHCR Kenya, with technical support from the\nWorld Bank, conducted a preregistration exercise and socioeconomic survey for the Shona commu\nnity. The Shona SES marks one of the first quantitative studies of a stateless population that is based\non a national socioeconomic assessment tool. The SES compares the living conditions of the Shona\ncommunity residing in Nairobi and urban Kiambu counties to the conditions of Kenyan nationals in\nsuch counties, as well as to the national urban average. <sup>3</sup> [^3: Results from the Shona survey are compared to those from the Kenya National Bureau of Statistics and its Kenya Integrated\nHousehold Budget Survey 2015/16.] This approach does not attempt to establish\na causal connection between their legal status and living conditions. It does, however, provide evidence that shows strong correlations between statelessness, access to rights, and key indicators of\nwell-being. In addition, the SES links its findings to the results of the first wave of the Kenya COVID-19\nRapid Response Phone Surveys (RRPS) designed to assess the socioeconomic impacts of the COVID19 pandemic on nationals, refugees, and stateless persons.\n\n\n1 Based on the information available from 76 countries.\n2 [https://www.unhcr.org/protection/statelessness/54621bf49/global-action-plan-end-statelessness-2014-2024.html](https", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "sample:reliefweb:001424:10:1:0", "start": 413, "end": 422, "surface": "Shona SES", "probe_tag": "keep", "probe_score": 0.96, "luna_label": 1}, {"key": "sample:reliefweb:001424:10:1:1", "start": 1761, "end": 1804, "surface": "Kenya COVID-19\nRapid Response Phone Surveys", "probe_tag": "keep", "probe_score": 0.9836, "luna_label": 1}]}, {"key": "rafael-201", "text": " The most notable national level analysis is the 2018 World Bank skills survey, but there are also other studies\nconducted at the regional level, including the recent Refugee Studies Centre reports on Dollo Ado and Addis Ababa,\nand a number of more specific assessments in Jigjiga. The World Bank skills survey provides a range of findings\nabout the level of education and skill of refugees, as well as their current level of economic engagement and poverty\nstatus in Ethiopia (see Table 1). The skills survey highlights both the significant variation in educational attainment\namong different refugee cohorts and the relatively small percentage of completion of secondary school education.\nThe educational challenges have been clearly recognised by the government to the extent that increasing educational\nattainment is a key commitment under the 2016 pledges and the draft NCRRS.\n\n\n28 UNHCR (2017a). Bringing the New York Declaration to Life Applying the Comprehensive Refugee Response Framework (CRRF).\n\n29 UNHCR (2005). Handbook for Self Reliance; see: https://www.unhcr.org/44bf7b012.pdf\n\n30 Available through the ReDSS website; see: https://www.dropbox.com/sh/8k983otoua3xucz/AADPmAkA-twjGyQDmzguMPMwa/Module%205%20Self%20reli<u>ance?dl=0&preview=Module+5+ODI+checklist.doc</u>\n\n31 Holzaepfel and Tadesse (2015). Evaluation: Evaluating the Effectiveness of Livelihoods Programs for Refugees in Ethiopia; Samuel Hall (2014). Living Out of\nCamp: Alternative to Camp-Based Assistance for Eritrean Refugees in Ethiopia; Samuel Hall (2018). Local Integration Focus: Refugees in Ethiopia. Gaps and\nOpportunities for Refugees Who Have Lived in Ethiopia for 20 Years or More.\n\n32 GoE (2019).\n\n33 Woldetsa", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001587:14:2:1", "start": 496, "end": 509, "surface": "skills survey", "probe_tag": "keep", "probe_score": 0.9486, "luna_label": 1}]}, {"key": "rafael-202", "text": "........................................................................................ 21\n\n\n5. CONCLUSION ...................................................................................................................... 23\n\n\n**Figures & Tables**\n\n\nFigure 1. Displacement as of 8 July 2015……………………………………………………………………………………………..5\n\n\nFigure 2. Displacement as of 30 September 2015………………………………………………………………………………..5\n\n\nFigure 3. Family tracing and reunification data, per quarter, 2015……………………………………………………….6\n\n\nFigure 4. Types of GBV reported in the South Sudan GBV IMS, per quarter, 2015…………………………………8\n\n\nFigure 5. Incidents of grave violations reported to", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "sample:reliefweb:000601:1:6:1", "start": 561, "end": 568, "surface": "GBV IMS", "probe_tag": "keep", "probe_score": 0.9576, "luna_label": 1}]}, {"key": "rafael-203", "text": "-19.pdf)</u>\nby the OECD in collaboration with UNHCR, and\nin which participation was on a voluntary basis. <sup>**23**</sup>\nFurther information on the survey, including the list of\nparticipants, is provided in the note on data sources\nand methodology at the end of this report.\n\n\n**The total volume of bilateral official**\n**assistance to refugee situations**\n**increased between 2016 and 2019**\n\n\nAccording to the 2020 OECD survey data, donors\ncontributed a cumulative total of USD 22.8 billion in\nbilateral Official Development Assistance (ODA) to\nrefugee situations in countries with lower incomes\nover 2018 and 2019. <sup>**24**</sup> This total is USD 24.2 billion\n\n\n\nwhen core contributions to refugee-mandated\nagencies (USD 1.44 billion), such as UNHCR and\nUNRWA, are included. The total amount of ODA\nincreased by 9 per cent (or 8 per cent when the core\ncontributions are included) from USD 10.9 billion\nin 2018 to USD 11.9 billion in 2019. <sup>**25**</sup> This growth in\nbilateral ODA to refugee situations (Figure 8) in host\ncountries with lower incomes continues the positive\ntrend observed in the previous survey conducted\nby the OECD in 2018. <sup>**26**</sup> Despite comparability\nlimitations between the two surveys (with several\nmethodological improvements made in the 2020\nsurvey), the data previously collected by the OECD\nrevealed an increase in bilateral ODA to refugee\nsituations of 23 per cent between 2015 and 2017. <sup>**27**</sup>\n\n\n**Figure 8:** Bilateral ODA to refugee situations, by\n\ntype of recipient, 2018 – 2019 (OECD Financing\n\nfor Refugee Situations Survey 2020, gross\ndisbursement, 2019 constant prices, US dollars)\n\n\n\n12", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000803:11:1:0", "start": 416, "end": 437, "surface": "2020 OECD survey data", "probe_tag": "keep", "probe_score": 0.9729, "luna_label": 1}, {"key": "reliefweb:000803:11:1:1", "start": 1307, "end": 1344, "surface": "data previously collected by the OECD", "probe_tag": "keep", "probe_score": 0.9157, "luna_label": 1}]}, {"key": "rafael-204", "text": "la grossesse et de l'accouchement sont la principale\ncause de décès chez les 15-19 ans. <sup>13</sup> Les\nadolescentes enceintes sont plus susceptibles que\nles adultes de recourir à l’avortement à risque ;\nenviron trois millions d'avortements à risque se\nproduisent chaque année chez les filles 15-19 ans. <sup>14</sup>\nLes avortements à risques chez les adolescentes et\nles jeunes de 15-24 ans représentent un peu moins\nde la moitié (40 pour cent) de tous les avortements\nà risque dans le monde. <sup>15</sup> Les effets néfastes de la\ngrossesse chez les adolescentes se répercutent sur\nleurs enfants. Les mortinatalités et décès néonatals\nsont 50 pour cent plus élevés chez les enfants nés\nde mères adolescentes que chez les enfants nés de\nfemmes âgées de 20-29 ans. <sup>16</sup> Les nouveau-nés de\nmères adolescentes sont plus susceptibles d'avoir\nun poids insuffisant à la naissance, avec le risque de\nconséquences à long terme. <sup>17</sup>\n\nUne étude réalisée en 2006 par Magadi et al.\ncomportant des données de l'enquête\ndémographique et de santé de 21 pays d'Afrique\nsubsaharienne a indiqué que les services de santé\nmaternelle ont été généralement sous-utilisés par\nles 15-19 ans par rapport aux femmes de 20", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000749:10:0:0", "start": 1022, "end": 1055, "surface": "enquête\ndémographique et de santé", "probe_tag": "keep", "probe_score": 0.9378, "luna_label": 1}]}, {"key": "rafael-205", "text": "\n7 Includes movement through official crossing points.\n8 [Movements recorded through border monitoring (UNHCR Afghanistan 2024 Border Monitoring Report).](https://data.unhcr.org/en/documents/details/114012)\n9 UNHCR Regional Protection Monitoring System (RPMS) dashboard indicates Türkiye as previous country of asylum for 27 per cent of Afghan nationals. Furthermore,\nRPMS indicates that 50 per cent of the Afghan nationals in Türkiye fled Afghanistan between one and five years ago, and 9 per cent fled Afghanistan more than five\nyears ago, before undertaking onward movements from Türkiye to the EU. Many Afghan nationals interviewed have lived for a long time in Türkiye and often in Iran\nbefore their onward movements due to, for example, administrative measures that force people to move from one province to another, loosing jobs and housing, and\nthe earthquakes.\n10 A significant pathway for refugees, asylum-seekers and migrants en route to Europe from the Middle East, Asia and Africa with Türkiye as the main transit country to\nreach Greece, Cyprus and Bulgaria.\n\n\n**REGIONAL BUREAU FOR ASIA AND THE PACIFIC (RBAP) & REGIONAL BUREAU FOR EUROPE (RBE) |** JULY 2025 3", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000518:2:2:1", "start": 104, "end": 151, "surface": "UNHCR Afghanistan 2024 Border Monitoring Report", "probe_tag": "keep", "probe_score": 0.9243, "luna_label": 1}]}, {"key": "rafael-206", "text": "**DEPARTMENT OF ARAUCA** | August 2024\n\n\n**Methodology**\n\n\nThe methodology of this Protection Analysis Update (PAU) has combined periodic monitoring by the Arauca Local\nCoordination Team, the Children’s Sub-Working Group and the GBV Sub-Working Group, as well as qualitative inputs from\nmeetings and consultations with local partners, key informants, and affected population. The analysis process has followed\nthe methodology of severity, estimations of Persons in Need (PIN), and Protection Analytical Framework (PAF).\n\n\n**Limitations**\n\n\nThis analysis has followed a logic of qualitative and quantitative analysis derived from official data for subsequent\ninterpretation by experts. On the other hand, to avoid potential risks that could be generated for the communities, the\nmeetings with them were limited.\n\n\nTherefore, the exercises for information gathering and analysis of the humanitarian situation focused on secondary data\nand interviews with local actors.\n\n\nFor more information, please contact: **Sebastián Díaz** <u>[diazj@unhcr.org](mailto:diazj@unhcr.org)</u> | **Gabriela Villota** <u>[gabriela.villota@drc.ngo](mailto:gabriela.villota@drc.ngo)</u>\n\n\nPage 10", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000935:9:0:0", "start": 629, "end": 642, "surface": "official data", "probe_tag": "confusion", "probe_score": 0.3055, "luna_label": 1}, {"key": "reliefweb:000935:9:0:1", "start": 918, "end": 932, "surface": "secondary data", "probe_tag": "confusion", "probe_score": 0.5749, "luna_label": 0}]}, {"key": "rafael-207", "text": ">|**Table 6. Annual asylum applications lodged in industrialized countries by origin, 2007**<br>Covering 29 major asylum countries which provided monthly data to UNHCR. Values between 1 and 4 have been replaced with an asterisk.<br>See Annex I for country codes used.<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>|**Table 6. Annual asylum applications lodged in industrialized countries by origin, 2007**<br>Covering 29 major asylum countries which provided monthly data to UNHCR. Values between 1 and 4 have been replaced with an asterisk.<br>See Annex I for country codes used.<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>|**Table 6. Annual asylum applications lodged in industrialized countries by origin, 2007**<br>Covering 29 major asylum countries which provided monthly data to UNHCR. Values between 1 and 4 have been replaced with an asterisk.<br>See Annex I for country codes used.<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>|**Table 6. Annual asylum applications lodged in industrialized countries by origin, 2007**<br>Covering 29 major asylum countries which provided monthly data to UNHCR. Values between 1 and 4 have been replaced with an asterisk.<br>See Annex I for country codes used.<br><br><", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001058:17:1:0", "start": 146, "end": 158, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.0935, "luna_label": 0}]}, {"key": "rafael-208", "text": "*35** All figures related to mid-2018.\n\n**36** Refugee figure for Iraqis was a Government estimate. UNHCR has registered\n15,700 Iraqis at the end of 2018. According to some reports many stateless\npersons were naturalized between 2011-2018, but no official figures are yet\nconfirmed.\n\n**37** Refugee figure for Syrians in Turkey was a Government estimate.\n\n**38** IDP figure in Ukraine includes 700,000 people who are in an IDP-like situation.\n\n**39** The statelessness figure refers to stateless persons with permanent residence\nreported by the Government of Uzbekistan in 2010. The figure has been adjusted\nto reflect that citizenship of Uzbekistan has been granted to 6,761 persons since\nDecember 2016.\n\n**40** A study is being pursued to provide a revised estimate of statelessness figure.\n\nSource: UNHCR/Governments.\n\n\n\nUNHCR > **GLOBAL TRENDS 2018** 69", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000649:67:6:0", "start": 47, "end": 72, "surface": "Refugee figure for Iraqis", "probe_tag": "confusion", "probe_score": 0.7506, "luna_label": 1}]}, {"key": "rafael-209", "text": "**A/AC.96/1178**\n\n\ncities in 2017. In April 2018, the OECD published research from 72 cities on local\napproaches to integration, accompanied by a checklist for cities and regions to use in\npromoting integration.\n\n\n56. Successful local integration programmes require efforts from all parties, including\nrefugees in their willingness to adapt, host communities in welcoming them and public\ninstitutions in meeting their needs. In some countries, significant additional support from\nthe international community, taking into account the needs of receiving communities, is\nessential.\n\n\n**D.** **Other pathways for admission**\n\n\n57. Other pathways for the admission of persons needing international protection can\nfacilitate access to protection and solutions, and alleviate pressure on host countries,\nparticularly in large-scale and protracted situations. Such pathways also create\nopportunities for refugees to learn new skills, acquire an education and reunite with family\nmembers in third countries.\n\n\n58. Although refugees sometimes find complementary pathways themselves, such\nprocesses may require the facilitation of administrative measures, complemented with\nprotection safeguards. To this end, UNHCR helped support the establishment and\nexpansion of complementary pathways, including in Argentina, Brazil, Chile, Colombia,\nFrance, Japan and Peru, along with other States in the MERCOSUR region. A new\npartnership was established with the United World Colleges to expand secondary education\nfor refugee students in third countries, and Talent Beyond Boundaries was commissioned to\ncreate a database of refugee talent in Jordan and Lebanon to facilitate labour mobility to\nthird countries. UNHCR and OECD initiated a mapping of non-humanitarian entry visas\nused by refugees in OECD countries to help develop guidance on complementary\npathways. UNHCR also supported the adoption of the African Union Protocol on Free\nMovement of Persons, Right of Residence and Right of Establishment, which will facilitate\naccess to other pathways for admission.\n\n\n59. Despite progress, refugees continue facing barriers and challenges in accessing\ncomplementary pathways, including being unable to obtain exit permits, entry visas and\ntravel documents. Other challenges include", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001472:15:0:0", "start": 1594, "end": 1620, "surface": "database of refugee talent", "probe_tag": "confusion", "probe_score": 0.1252, "luna_label": 0}, {"key": "reliefweb:001472:15:0:1", "start": 1720, "end": 1759, "surface": "mapping of non-humanitarian entry visas", "probe_tag": "confusion", "probe_score": 0.8482, "luna_label": 0}]}, {"key": "rafael-210", "text": "98<br>1,164<br>-<br>227<br>350<br>7,023<br>7,023<br>2,158<br>-<br>5<br>207<br>2,705<br>26,893<br>102<br>376<br>7,409<br>341<br>-<br>10,668<br>3,293<br>155<br>57,705<br>79<br>24<br>6,905<br>233<br>4,121<br>-<br>218<br>13,788<br>90<br>2,364<br>-<br>7<br>128<br>230<br>76,418<br>10<br>549<br>87<br>1,710<br>10,419|\n\n\n\n**62** UNHCR Global Trends 2015", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001367:60:5:0", "start": 322, "end": 346, "surface": "UNHCR Global Trends 2015", "probe_tag": "confusion", "probe_score": 0.1358, "luna_label": 1}]}, {"key": "rafael-211", "text": " government-controlled areas report slightly worse food security\nthan those in NSAG-controlled areas (68% report skipping a meal in government-controlled areas versus 64%\nin NSAG-controlled areas), possibly because the most\nvulnerable IDPs have fled NSAG areas. Female-headed\ndisplaced households face greater food-related challenges than male-headed households (77% versus 64%),\nbut there is no significant difference in food security\nbetween urban and rural IDPs. While there is need\nfor further analysis to determine the reasons for this\ndeterioration in food security, it is to be noted that last\nyear Afghanistan faced a nationwide drought, the worst\nin a lifetime affecting more than 3 million Afghans and\nresulting in massive displacement in several parts of the\ncountry, in particular the western region.\n\n\nwww.unhcr.org       9\n\n\n\nIDPs\n\n\n**2017 Survey**\n\n\n\n65%\n\n\n\nHost Community\n\n\n\n2017 Returnees 2016 Returnee\n\n\n\nIDPs\n\n\n\n55%\n\n\n\nHost Community\n\n\n\nYes", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000497:8:1:0", "start": 849, "end": 860, "surface": "2017 Survey", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 0}]}, {"key": "rafael-212", "text": "### **Introduction**\n\nThe recent Global Compact for Refugees has acknowledged that the increasing number of forcibly\ndisplaced persons and the difficulty to predict mass-movements of people have created an urgent\nneed for data-driven early warning systems that allow governments and humanitarian organisations\nto use their limited resources most efficiently [1, 2]. Efforts to install early warning systems have led to\nadvances in predicting and forecasting global migration flows; however, forced displacement remains\nthe most elusive and challenging migration form to predict [3, 2, 4].\n\n\nPeople base their decision to migrate on a complex set of different factors. However, in addition to the\ncomplexity of the individual decision-making process, predicting forced displacement flows is further\ncomplicated by the necessity to early detect or predict trigger events. Trigger events, which are often\nthe last link in a long chain of other events that tilt the individual’s decision towards flight, often happen\nrandomly and abruptly [5]. Both processes are difficult to model in and by themselves. However, the\nlack of timely and accurate data at the micro-, meso-, and macro-level further aggravates this problem.\nData needed to model these processes are often either unavailable (micro-level) or outdated (mesoand macro-level).\n\n\nFurthermore, whilst existing refugee flows between countries often are self-perpetuating and can be\npredicted based on historical data, refugee flows from new events pose a challenge because (i) the\nevent might no yet be known to the modeler; (ii) the effect of a new event on future refugee flows is\nunknown; (iii) no historical data from a recent event exist, and it is uncertain to which degree historical\ndata from other events are applicable to predict refugee flows from the new event. The prediction of\nforced displacement flows requires, therefore, first and foremost, a thorough understanding of the\nmechanisms of forced displacement. In particular:\n\n\n1. The factors that lead", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001630:5:0:0", "start": 1453, "end": 1468, "surface": "historical data", "probe_tag": "confusion", "probe_score": 0.8616, "luna_label": 1}, {"key": "reliefweb:001630:5:0:1", "start": 1453, "end": 1468, "surface": "historical data", "probe_tag": "confusion", "probe_score": 0.4185, "luna_label": 1}]}, {"key": "rafael-213", "text": "\n2. Для врегулювання конфлікту багато позитивних ініціатив були виконано донорами під керівництвом або за підтримки в тому числі компенсаційних фондів, що фінансувалися\nдонорськими організаціями. Таким чином, існує необхідність інформування донорів про їх роль в підтримці житлових програм в поєднанні з питаннями доступу до правосуддя на\nперіод відновлення.\n\n\n3. Географія важлива для забезпечення доступу до правосуддя. У ряді країн, які були найбільш успішними в управлінні, існувала добре розвинена система реєстрів нерухомості\n(кадастри) і шкала оцінки землі, які стали важливими чинниками як докази в ході роботи претензійних комісій. Для України потрібна підтримка в розробці системи кадастру країни,\nяка не була розвинена до кризи", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001579:0:2:0", "start": 511, "end": 531, "surface": "реєстрів нерухомості", "probe_tag": "confusion", "probe_score": 0.0923, "luna_label": 1}]}, {"key": "rafael-214", "text": "jados\n\n- abandonados por motivos de violencia, que busca\nestablecer un mecanismo de protección jurídica de\nbienes de las personas desplazadas por violencia\natendidas por la SEDH. La ruta inicia desde el momento\nde la solicitud de protección ante de la Dirección de\nProtección a Personas Desplazadas Internamente por\nla Violencia (DIPPDIV) de la SEDH, en donde se aplica\nuna ficha de identificación de bienes, posteriormente se\n\n\n\nrealiza un cruce de información con el Registro Unificado\nde Registros de la Propiedad del IP, se dictamina el caso\ny finalmente se procede a la inscripción del bien en\nun módulo que se creará en el sistema de información\ndel IP. En 2021, se continuará el proceso de revisión,\nsocialización y validación del instrumento, con el fin de\ndesarrollar ejercicios de pilotaje en 2022.\n\nLa Dirección de la Infancia, Niñez, Adolescencia y Familia\n(DINAF) como garante de derechos de la niñez y la\nadolescencia, con el apoyo del ACNUR y World Vision\nHonduras (WVH) han realizado diferentes actividades de\nfortalecimiento en el Programa de Antenas de Protección\nen las oficinas de Tegucigalpa, San Pedro Sula, Olancho\n\n\n**EL ARTE COMUNITARIO COMO ESTRATEGIA**\n**PARA FORTALECER LA PARTICIPACIÓN**\n**E INCLUSIÓN DE LAS COMUNIDADES EN**\n**RIESGO EN SAN PEDRO SULA**\n\nArte Comunitario es una estrategia de intervención\ncomunitaria liderada", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000936:39:2:0", "start": 629, "end": 658, "surface": "sistema de información\ndel IP", "probe_tag": "confusion", "probe_score": 0.6512, "luna_label": 0}]}, {"key": "rafael-215", "text": "in accordance with this MOU, UNHCR has deployed 18 support staff in 2014 to the Asylum Service\n(including the COI Department and Regional Asylum Offices), to assist with its quality assurance activities. It should also be noted that the Asylum Service regularly produces detailed, accurate and reliable\nstatistical data and analysis. In order to maintain the progress made to date, EU MS and institutions\nneed to continue support, including through UNHCR, to the Asylum Service.\n\n\nThe processing time for adjudicating applications for international protection has improved significantly. While there are still unprocessed cases pending appeal for more than seven years under the\n“old procedure” operated by the police, the average time in all RAOs from registration to issuance of\nfirst instance decision is 90 days, while the average time from the appeal to the issuance of an appeal\ndecision is 49 days. <sup>103</sup> [^103: These figures represent the average processing time, according to official data by the Asylum Service.] Processing times for applications lodged in administrative pre-removal detention take on average slightly more than 100 days for both first and second instance.\n\n\nImprovements in the quality of the decision-making process have also had an impact on protection\nrates. While under the old system operated by the police, protection rates ranged between 0.86 per\ncent and 2.05 per cent from 2005 to 2014, the new asylum procedure has a first instance recognition\nrate of 17.2 per cent for refugee status and a 7.6 per cent protection rate for subsidiary protection.\nThe average rejection rate is still higher than in a number of other EU MS, and stands at 75.2 per cent.\nIt should be noted, however, that the protection rate for Syrians is 99.5 per cent, Eritreans 79.7 per\ncent, Somalis 66 per cent, Afghans 61.9 per cent, and Ethiopians 61.4 per cent (all figures as of August\n2014).\n\n\nThe appeal stage comprises an administrative examination on issues of fact and", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "sample:reliefweb:000978:25:0:0", "start": 303, "end": 319, "surface": "statistical data", "probe_tag": "confusion", "probe_score": 0.5496, "luna_label": 0}, {"key": "sample:reliefweb:000978:25:0:1", "start": 994, "end": 1029, "surface": "official data by the Asylum Service", "probe_tag": "confusion", "probe_score": 0.8592, "luna_label": 1}]}, {"key": "rafael-216", "text": "Capítulo 9\n\n## **Limitaciones de datos**\n\n\n\nLa recolección, compilación, control de calidad y\ndifusión de estadísticas sobre personas refugiadas,\nsolicitantes de asilo, desplazadas internas (PDI) y\napátridas sigue siendo un reto. En este capítulo, se\nevalúan las principales limitaciones actuales en relación\ncon los datos y se describen varias medidas que el\nACNUR y sus socios están adoptando para subsanarlas.\n\n\n**Cobertura demográfica**\n\n\nEl desplazamiento forzado y la apatridia afectan a las\npersonas de forma diferente, según su edad, sexo,\ndiscapacidad y otras características de su diversidad.\nLa política del ACNUR basada en la edad, el género y la\ndiversidad (AGD) <sup>**139**</sup> busca garantizar que cada persona\n\n\n\nde la población de interés participe plenamente en las\ndecisiones que la afectan y pueda ejercer sus derechos\nen igualdad de condiciones que las demás.\n\n\nEl ACNUR recoge anualmente datos demográficos\ndesglosados por edad y sexo para sus estadísticas\noficiales. Los datos suelen proceder de los sistemas de\nregistro gestionados por el ACNUR o los Gobiernos de los\npaíses de acogida, o de estimaciones basadas en fuentes\nde datos alternativas, incluidos los datos de registro\nparcialmente disponibles o encuestas por muestreo.\n\n\nEl desglose por sexo está disponible para el 64%", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000160:65:0:1", "start": 1233, "end": 1255, "surface": "encuestas por muestreo", "probe_tag": "confusion", "probe_score": 0.5041, "luna_label": 0}]}, {"key": "rafael-217", "text": "**MYANMAR SITUATION**\n\n\n\n\n**Overview**\n\n\nMILLION\n### **2.4** **67%** **50%** **27%**\n\npersons of concern from Myanmar\n\n\nare stateless persons (Rohingya)\n\n\nare refugees or asylum-seekers\n\n\nare internally displaced persons\n\n\n### **30,700**\n\n\n\nfled to a neighbouring country in\n\n\n\n2021, following 1 February 2021\n\n\n\n26 Asia & the Pacific Regional Trends on Forced Displacement 2021", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000836:24:0:0", "start": 316, "end": 373, "surface": "Asia & the Pacific Regional Trends on Forced Displacement", "probe_tag": "confusion", "probe_score": 0.6936, "luna_label": 0}]}, {"key": "rafael-218", "text": "### ACRONYMS\n\n\n\n01\n\n\n\n**ACLED** Armed Conflict Data & Event\nData Project\n\n\n**AGD** Age, gender and diversity\n\n\n**BH** Boko Haram\n\n\n**DHS** Demographic Health Surveys\n\n\n**DRC** Democratic Republic of\nthe Congo\n\n\n**FTS** Financial Tracking System\n\n\n**GBV** Gender-based violence\n\n\n**GDFD** Gender Dimensions of\nForced Displacement\n\n\n**HRP** Humanitarian Response Plan\n\n\n**ICRC** International Committee of\nthe Red Cross\n\n\n**IDMC** Internal Displacement\nMonitoring Centre\n\n\n**IDP** Internally displaced person\n\n\n**IOM** International Organization\nfor Migration\n\n\n**IPV** Intimate partner violence\n\n\n**MENA** Middle East and North Africa\n\n\n**MPI** Multidimensional Poverty\nIndex\n\n\n\n**ODI** Overseas Development\nInstitute\n\n\n**SGBV** Sexual and gender-based\nviolence\n\n\n**UCDP** Uppsala Conflict Data\nProgram\n\n\n**UN** United Nations\n\n\n**UNFPA** United Nations Population\nFund\n\n\n**UNHCR** United Nations High\nCommissioner for Refugees\n\n\n**UNICEF** United Nations Children’s\nFund\n\n\n**UN OCHA** United Nations Office\n\nfor the Coordination of\nHumanitarian Affairs\n\n\n**USAID** United States Agency for\nInternational Development\n\n\n**VAW** Violence against women\n\n\n**WGSS** Women and girls’ safe\nspaces\n\n\n**WPS** Women, peace and security\n\n\n\nThe authors of this paper conducted their research under the Gender Dimensions of Forced\nDisplacement project. The project is co-led by Lucia Hanmer and Diana Arango under the guidance of\nHana Brixi, Global Director, Gender Unit, World Bank Group.\n\n\nThis work is part of the program ‘Building the Evidence on Protracted Forced Displacement: A MultiStakeholder Partnership’. The program is funded by UK aid from the United Kingdom’s Foreign", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001238:3:0:1", "start": 139, "end": 165, "surface": "Demographic Health Surveys", "probe_tag": "confusion", "probe_score": 0.2204, "luna_label": 0}, {"key": "reliefweb:001238:3:0:2", "start": 772, "end": 801, "surface": "Uppsala Conflict Data\nProgram", "probe_tag": "confusion", "probe_score": 0.0812, "luna_label": 0}]}, {"key": "rafael-219", "text": "### **ACHIEVEMENTS FROM THE FIELD:** **SUBNATIONAL CLUSTERS**\n\n\n\nthe subcluster led one meeting with all\n\nthe organizations working with the pro\nvision of assistance and information\n\nfor people on the move, to identify the\n\nmain protection risks and the possible\n\nresponse for each of them. The results\n\nwere shared with all the partners and\n\nenabled them to support their financial\n\npleas to donors.\n\n\nIn alliance with cluster partners and the\n\nGBV AoR, the subnational Cluster car\nried out a training on GBV prevention,\n\nsafe referrals, clinical management of\n\nsexual violence, the Child and Adoles\ncent Protection System in Venezuela,\n\nfirst-aid, and psychosocial support. In\n\naddition to the training, a joint response\n\nwas activated through additional ser\nvices related to sexual and reproductive\n\nhealth and GBV case management pro\nvided in the information points by some\n\npartners.\n\n\n\nAs part of the Mental Health and Psycho\nsocial Support (MHPSS) in Emergencies\n\nWorking Group, the subnational Clus\nter along with the health cluster, co-led\n\nmeetings with partners providing psy\nchosocial support services to identify the\n\ngaps and needs related to mental health\n\nin the States of Táchira and Mérida,\n\nupdate the mapping of MHPSS services,\n\nand prepare an action plan for the work\n\ngroup with roles and responsibilities for\n\nhumanitarian organizations.\n\n\n**Subnational** **Cluster** **Barinas.** In\n\nBarinas State, in coordination with sub\nnational Cluster members, assistance\n\nwas provided to returnees, and livelihood\n\nactivities were carried out to facilitate\n\ncommunity reintegration. Permanent\n\nlinks are maintained with the Administra\ntive Service for Identification, Migration,\n\nand Foreigners (SAIME by its Spanish\n\nacronym), the Department of Protection,\n\n\n\nthe Child Protection Council, and the pre\nfecture for the referral of cases involving\n\npeople on the move and returnees with\n\nspecific protection risks.\n\n\nStrategic campaigns were also con\nducted with", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001013:21:0:0", "start": 1221, "end": 1246, "surface": "mapping of MHPSS services", "probe_tag": "confusion", "probe_score": 0.3224, "luna_label": 0}]}, {"key": "rafael-220", "text": " countries studied in OECD\ncountries plus Brazil in the respective years.\n\n\nThere is further undercoverage with respect to data\non family permits: the data obtained for some host\ncountries exclude permits for family reunification\nwith a person under international protection (Japan,\nMexico) or under subsidiary international protection\n(Switzerland), whereas the data for Ireland exclude\nchildren below the age of 16 and the data for Canada\nexclude family members who reunified with persons\nissued with non-humanitarian permits.\n\n\n# 5 Analysis of admission trends\n\n\n\nOver the 2010–2019 period, a combined total of\nfour million new asylum applications were submitted\nin OECD countries and Brazil by nationals of\nAfghanistan, Eritrea, Iran, Iraq, Somalia, Syria and\nVenezuela. In the same period, close to 1.5 million\nfirst-time residence permits were granted by OECD\ncountries and Brazil to nationals of the seven\ncountries for family, work, or education-related\n\n\n\nreasons, including about 156,000 in 2019 alone.\nDuring the decade, about 2.2 million individuals\nof the same nationalities were recognized as\nrefugees or granted a subsidiary form of protection\nand 572,000 persons from the seven populations\nconcerned arrived in OECD countries and Brazil\nthrough resettlement programmes.\n\n\n\n<u>14</u>", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001237:12:1:1", "start": 425, "end": 440, "surface": "data for Canada", "probe_tag": "confusion", "probe_score": 0.3023, "luna_label": 1}]}, {"key": "rafael-221", "text": " Facebook products is that Facebook only provides very\nlimited information on their methods and definitions. It is, for example, unclear how Facebook defines\nits user’s ex-pat status [58, 6, 60].\n\n\nSince Zagheni et al. [58] introduced Facebook’s advertising platform as a new Big Data source, it\nhas become a popular alternative data source for migration and demographic researchers, as its\nfree API makes the data easily accessible [58, 59]. However, scraping the Facebook Advertising\nplatform might, under certain conditions, violate Facebook’s terms of services (Alex Pompe, verbatim). Zagheni et al. introduce data from Facebook’s Advertising platform to estimate migrant stocks.\nSpyratos et al. [60] use data on migration from Facebook’s advertising platform to map-out migration\nflows to 119 countries of residence for two time periods. The data allows them to detail migration\nflows by age, gender, and country of origin. They suggest a method to correct the bias in the Facebook data and find that the estimates positively correlate with official statistics on migration. They\nconclude that data from Facebook advertising, once corrected for biases, can be used as a cheap\nand globally available real-time supplement to official migration statistics and are accurate enough to\nbe used in trend-analyses and early-warning purposes. Palotti et al. [59] support this finding and develop a method to use Facebook’s advertising data for real-time monitoring of crises situations using\nVenezuelan migrants as a case study. They conclude that despite the biases in the Facebook data\nand the potential noise in the Facebook algorithm that determines a user’s country of origin, data from\nthe Facebook advertising platform correlate sufficiently with official statistics to be used for density\nmapping in crises.\n\n\n24 UNHCR", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001630:23:1:0", "start": 614, "end": 655, "surface": "data from Facebook’s Advertising platform", "probe_tag": "confusion", "probe_score": 0.8003, "luna_label": 1}, {"key": "reliefweb:001630:23:1:1", "start": 709, "end": 763, "surface": "data on migration from Facebook’s advertising platform", "probe_tag": "confusion", "probe_score": 0.8794, "luna_label": 1}, {"key": "reliefweb:001630:23:1:5", "start": 978, "end": 991, "surface": "Facebook data", "probe_tag": "confusion", "probe_score": 0.8975, "luna_label": 1}]}, {"key": "rafael-222", "text": "**BANGLADESH.** **_Amir Khan (65), works at_**\n**_sewing clothes, under the light provided_**\n**_by solar panels, at night, in Camp 1 East,_**\n**_Kutupalong camp, Cox’s Bazar,_**\n**_Bangladesh. An estimated 915,000_**\n**_Rohingya live in refugee camps in the_**\n**_area of Cox’s Bazar, Bangladesh._**\n**_More than half of them (nearly 55%)_**\n**_are children under the age of 18 years._**\n\n© UNHCR / VINCENT TREMEAU\n\n\nUNHCR > **MID-YEAR TRENDS REPORT 2020** 29", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000911:28:0:0", "start": 428, "end": 455, "surface": "MID-YEAR TRENDS REPORT 2020", "probe_tag": "confusion", "probe_score": 0.1293, "luna_label": 0}]}, {"key": "rafael-223", "text": " and in total children survivors\ncan be explained by the fact that most of those who seek help are\n\n\n6 \u0007Women and young persons with disabilities https://www.unfpa.org/featuredpublication/women-and-young-persons-disabilities\n\n\n7 \u0007Management Sciences for Health & UNFPA, We Decide Young Persons with\nDisabilities: Equal Rights and a Life Free of Violence (May 2016), https://www. msh.\norg/sites/msh.org/files/ we_decide_infographic.pdf\n\n\n\nsupported by child protection actors who are not part of the GBV\nIMS Task Force as per established standard operating procedures\n(SOPs) and referral pathways.\n\n\nGay and bisexual men face increased risks of sexual violence. In\nthis context, it is important to underline that the establishment\nor strengthening of services for male survivors should not affect\nservice provision for women and girls: funding for “Safe Spaces for\nWomen and Girls” (SSWG) should be maintained, while additional\nfunding should be sought for interventions for male survivors.\nNevertheless, there is a need to strengthen GBV prevention and\nresponse programming to outreach Lesbian and bisexual women\nwho may expose to further risks based on their sexual orientation.\n\n\nWomen and young persons with disabilities experience the same\nforms of GBV as individuals without disabilities <sup>6</sup> . In fact, studies\nshow that persons with disabilities are three times more likely\nto experience physical violence, sexual violence, and emotional\nviolence than persons without disabilities <sup>7</sup> .\n\n\nCollected data in 2020 indicates 28% increase in GBV incidents\nreported by survivors with disabilities compared to 2019. In line\nwith previous years trend more people with physical disability\nreported incidents compared to people with mental disability.\n\n\nThis comes as a result of continues capacity building efforts by\nthe Task Force members to other humanitarian aid workers and\ncommunity based organisations staff who work with persons with\n\n\n\n6", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001279:5:1:0", "start": 1513, "end": 1535, "surface": "Collected data in 2020", "probe_tag": "confusion", "probe_score": 0.5744, "luna_label": 1}]}, {"key": "rafael-224", "text": " credibility. When available, video material, lists of victims and supplementary\ninformation from protection cluster partners is incorporated.\n\nCIMP monitors civilian impact that occurs after an incident of armed violence have taken place, thus CIMP numbers on\n\ndisplacement, loss of livelihood and restriction of movements/obstruction to flight only covers households that have experienced a\ndirect impact from armed violence, e.g. a house destroyed or a vehicle hit. Therefore, CIMP data does not include full numbers of\npeople being displaced, loosing livelihood or experiencing restricted freedom of movement/obstruction to flight, where numbers are\nnaturally much higher than what is captured by CIMP.\n\nCivilian impact incidents recorded by CIMP are divided into direct and indirect impact, with associated direct and indirect protection\nimplications. Direct impact includes incidents in which individuals or households are directly affected by the incident, e.g. damage to\nhouses and farms, damage to markets and local businesses, impact on vehicles or as well as exposure to UXOs and armed conflict\ngenerating casualties. Indirect impact can broadly be defined as incidents of armed violence impacting on infrastructure and basic\nservices and in turn restricting access of civilians to various vital services, infrastructure and goods, e.g. healthcare, education, food\nand water and transport infrastructure. Due to the nature of the indirect impact, the number of households impacted is often much\nhigher than during direct impact.\n\nAs CIMP aims to collect and disseminate data on civilian impact that occurs as a result of armed conflict, some incidents are\nexcluded. This includes incidents related to crime, domestic violence and small arms fire incidents that occurs away from areas of\nactive conflict and have less than two casualties. Small arms fire incidents are always included when they occur in areas of active\nconflict.\n\n\n###### **3**", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "sample:reliefweb:000783:4:1:0", "start": 480, "end": 489, "surface": "CIMP data", "probe_tag": "confusion", "probe_score": 0.3748, "luna_label": 1}, {"key": "sample:reliefweb:000783:4:1:1", "start": 1581, "end": 1604, "surface": "data on civilian impact", "probe_tag": "drop", "probe_score": 0.0067, "luna_label": 0}]}, {"key": "rafael-225", "text": "EGYPT REFUGEE AND RESILIENCE RESPONSE PLAN (ERRRP)\nJanuary - December 2025 5\n\n### AT A GLANCE\n##### Country Planned Response January-December 2025\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**<mark>Population Planning Figures</mark>**\n\n\n**Country**\n\n\nSudanese Refugee Population\n\nSyrian Refugee Population\n\nOther Refugee Population\n\n\nHost Community\n\n\n\n**Refugees pre**\n**15 April 2023**\n\n\n60,779\n\n146,995\n\n85,846\n\n\n\n**Population as of**\n**end of Dec 2024**\n\n\n602,702\n\n147,797\n\n126,513\n\n\n\n**Planned**\n**Population as**\n**of end of 2025**\n\n\n1,142,331\n\n137,962\n\n149,035\n\n\n432,017\n\n\n\nTotal Projected Population in Need: **1,861,345**\n\n\n\n\n\n**14%**", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000675:4:0:0", "start": 176, "end": 203, "surface": "Population Planning Figures", "probe_tag": "drop", "probe_score": 0.0483, "luna_label": 0}]}, {"key": "rafael-226", "text": "BuildUp was also developed to counter hostile and toxic narratives against refugees online and promote digital](https://howtobuildup.org/)</u>\npeacebuilding and acceptance by host communities.\n\n\nImportantly, in relation to the COVID-19 pandemic, **89 per cent of 140 countries** reporting indicated that areas\ninhabited by refugees, internally displaced persons (IDPs) and other forcibly displaced and stateless persons were\nreached by information campaigns about COVID-19 pandemic risks.\n\n\n**~~Feedback and Response~~**\n\n\nIn 2021, important efforts were made to maintain or expand opportunities for feedback from the people that\nUNHCR worked with and for, though with higher reliance on remote methods and technology than in the past. New\ninitiatives were also initiated to address gaps in operations’ capacity to manage large unstructured quantities of\nfeedback data and investments were made in multiple communication channels to maintain proximity with people\nof concern to UNHCR. This included further development of contact centres’ referral and response mechanisms,\nincluding at the inter-agency level.\n\n\nAs a result, **65 per cent of reporting operations** have multi-channel feedback and response systems designed\nbased on consultations with communities.\n\n\n**~~Organizational Learning and Adaptation~~**\n\n\nThis is an area that requires further investment. Reporting on this aspect remained quite limited in 2021, highlighting\nthe need for strengthened knowledge-sharing to support learning and adaptation and the scale-up of innovative\napproaches. Interim findings from the ongoing longitudinal evaluation of the AGD Policy also suggested limited use\nof feedback from participatory exercises to inform adaptations of programme activities. <sup>4</sup> Moreover, siloed ways of\nworking often prevent a more systematic inclusion of feedback and learning.\n\n\n4 UNHCR, “Longitudinal evaluation of the implementation of UNHCR’s Age, Gender and Diversity Policy – Year 1 report” (2022). Available from <u>[www.un", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "sample:reliefweb:000524:7:1:0", "start": 855, "end": 868, "surface": "feedback data", "probe_tag": "drop", "probe_score": 0.0077, "luna_label": 0}]}, {"key": "rafael-227", "text": "**<u>2.3 Challenges and Limitations</u>**\n\n\n**The research instruments were translated**\n**from English to Bangla. The enumerators were**\n**unable to use Rohingya language to conduct**\n**research at field level. However, using Bangla**\n**questionnaires, the enumerators articulated**\n**the questions in the** **_Chittagonian_** **dialect,**\n**and took notes in Bangla. Some richness and**\n**nuances in data might have been lost due**\n**to applying different languages in the data**\n**collection process.**\n\n\n**Limited time was allocated for reviewing**\n**and adapting quantitative and qualitative**\n**tools from CARE’s gender analysis toolkit and**\n**customising training for this research. Limited**\n**comprehension of the tools could have led**\n**to misinterpretation of the survey questions**\n**by the enumerators as well as respondents.**\n**This could also have been due to the limited**\n**understanding of gender concepts.**\n\n\n**Qualitative data from the host community**\n**was very limited. More effort could also have**\n**been placed on identifying people living with**\n**disabilities in the host community, as well as**\n**from the camps.**\n\n\n\nWhile the experiences and views of people living with\ndisabilities from the host community were absent in the\ndata gathered, data collected from people living with\ndisabilities in the refugee communities gave limited\ninsight into their experiences. Out of four participants,\none pregnant woman was included as a person living\nwith a disability, indicating a limited understanding of the\ntopic among enumerators or little time to identify and\nrecruit people living with", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000732:28:0:0", "start": 934, "end": 950, "surface": "Qualitative data", "probe_tag": "drop", "probe_score": 0.0357, "luna_label": 1}, {"key": "reliefweb:000732:28:0:1", "start": 1276, "end": 1327, "surface": "data collected from people living with\ndisabilities", "probe_tag": "confusion", "probe_score": 0.2422, "luna_label": 1}]}, {"key": "rafael-228", "text": ">4,755<br>3,113<br>Before<br>2019<br>2019<br>2020<br>2021<br>2022<br>2023<br> <br>* Figures have been revised in June by removing individuals found in<br>VolRep and Data Transfer Request.<br> <br>*** The flow**s**<br>country of a<br>|926<br>3,276<br>1,777<br>2,051<br>4,755<br>3,113<br>Before<br>2019<br>2019<br>2020<br>2021<br>2022<br>2023<br> <br>* Figures have been revised in June by removing individuals found in<br>VolRep and Data Transfer Request.<br> <br>*** The flow**s**<br>country of a<br>|", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001463:3:2:0", "start": 154, "end": 186, "surface": "VolRep and Data Transfer Request", "probe_tag": "drop", "probe_score": 0.0163, "luna_label": 1}]}, {"key": "rafael-229", "text": " APC.\n## **CONCLUSION**\n\n\nThe APC Guidance Note facilitates the identification of key areas of work to redefine strategic priorities\nfor the APC. It provides highlights on some patterns of abuse and specific categories of population\nthat are exposed to threats. It also suggests key coordination and partnership areas for the APC to\nstrengthen.\n\n\nHowever, it also highlights the APC’s limited capacity to collect protection data and build up a more\nrobust and comprehensive protection analysis. The main challenges have been identified as limited\nhumanitarian access, sensitivity related to some protection thematic, a lack of information sharing\namong different agencies, limited capacity and knowledge to operationalize the protection risks\nequation, a lack of conflict sensitivity analysis and limited protection assessments. The available\nprotection analysis is usually limited to the overall chronic issues in the country, while conflict and\ndisplacement related risks and threats remain less explored and assessed.\n\n\nThose limitations can be overcome should the APC members prioritize the resources to fill those gaps\nand increase their commitment to the coordination system for the protection sector.\n\n\nA workshop with APC members will be organized in the course of August 2017 which will provide an\nopportunity to produce a joint protection analysis that would support a prioritization exercise.\n\n\nIntegrated protection response plans are also under development at regional level and will provide a\nregional perspective of the protection context and priorities.\n\n\nPage **10** of **12**", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000384:9:1:0", "start": 413, "end": 428, "surface": "protection data", "probe_tag": "drop", "probe_score": 0.047, "luna_label": 0}]}, {"key": "rafael-230", "text": "://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_eiord&lang=en)\n[have left the territory as a result of an order to leave (voluntary or forced) 2015-2017.](http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=migr_eirtn&lang=en)\n\n\n48 THE WAY FORWARD", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001185:47:2:0", "start": 52, "end": 62, "surface": "migr_eiord", "probe_tag": "confusion", "probe_score": 0.1202, "luna_label": 0}, {"key": "reliefweb:001185:47:2:1", "start": 220, "end": 230, "surface": "migr_eirtn", "probe_tag": "drop", "probe_score": 0.0384, "luna_label": 0}]}, {"key": "rafael-231", "text": "Refugee women founded the Heriyetu Foundation at Nakivale settlement in Uganda – a group that has launched a wine-making business, pharmacy and savings and\nloans programme. © UNHCR/Esther Ruth Mbabazi\n\n\n~~X~~ **~~Inclusive Programming~~**\n\n\n\n\n\n\n\n**3** Core Action 1 of the AGD Policy is applicable to UNHCR primary data collection activities. It applies only to operational data, defined as data and information that\npertains to a crisis/situation, the persons affected by the crisis/situation, and the response to the crisis/situation.\n\n\n14", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000271:12:0:0", "start": 362, "end": 378, "surface": "operational data", "probe_tag": "drop", "probe_score": 0.0281, "luna_label": 0}]}, {"key": "rafael-232", "text": "##### Appendix 1:\n## **Search terms**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|String Search #|Search terms|Date of the search|Database/source|Number of total results|Number of potentially relevant results (title screening)|Number of results of i fcially meeting inclusion criteria (abstract screening)|\n|---|---|---|---|---|---|---|\n|1|(“Myanmar” OR “Burma” OR “Rakhine” OR<br>“Bangladesh” OR “Malaysia” OR “India” OR<br>“Thailand” OR “Asia” OR Refugee* OR Asylum<br>Seeker* OR Displace* OR Rohingya*)**AND** <br>(“Humanitarian” OR “Emergency” OR “Disaster”<br>OR “Confict” OR “War” OR “Violence” OR<br>“Warfare” OR “Armed Confict” OR “Mass<br>Confict” OR Persecut* OR “Civil Confict” OR<br>“Genocide” OR “Mass Murder” OR “Human<br>Rights” OR “Ethnic Cleansing” OR “Mass<br>Violence”)|25/10/2017|CINAHL|3424|6|1|\n|1|(“Myanmar” OR “Burma” OR “Rakhine” OR<br>“Bangladesh” OR “Malaysia” OR “India” OR<br>“Thailand” OR “Asia” OR Refugee* OR Asylum<br>Seeker* OR Displace* OR Rohingya*)**AND** <br>(“Humanitarian” OR “Emergency” OR “Disaster”<br>OR “Confict” OR “War” OR “Violence", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001165:46:0:0", "start": 797, "end": 803, "surface": "CINAHL", "probe_tag": "drop", "probe_score": 0.0408, "luna_label": 1}]}, {"key": "rafael-233", "text": ">Men (51%)<br>Women (49%)|**Hosts**<br>(KIHBS<br>2015)<br>Men (52%)<br>Women (48%)|**Hosts**<br>(KIHBS<br>2015)<br>Men (52%)<br>Women (48%)|\n||Gender|Gender|Gender|Gender|Gender|Gender|Gender|\n||Age|Below 18:<br>71%<br>Above 64:<br>0.6%|Below 18:<br>61%<br>Above 64:<br>0.4%|Below 18: 60%<br>Above 64: 0.4%|Below 18:<br>45%<br>Above 64:<br>1.8%|Below 18: 32%<br>Above 64:<br>0.7%||\n||Dependency<br>ratio|1.9|1.2|1.4|0.6|0.4||\n||Women-<br>headed<br>households|66%|56%|47%|41%|32%|➢ Women and girls’ empowerment programmes in camps and urban<br>areas can help alleviate barriers to accessing socioeconomic<br>opportunities and build and maintain human capital.<br>➢ Financial inclusion programmes coupled with entrepreneurship skills,<br>business training and cash grants targeting women, especially those<br>with young dependents, can be a starting point to unlock refugee<br>women’s socioeconomic potential.|\n||Improved<br>housing|5%|3%|8%|82%|78%|➢ Scaling up permanent shelters in Kalobeyei with", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "sample:reliefweb:000574:5:1:0", "start": 40, "end": 45, "surface": "KIHBS", "probe_tag": "drop", "probe_score": 0.0011, "luna_label": 1}, {"key": "sample:reliefweb:000574:5:1:1", "start": 97, "end": 102, "surface": "KIHBS", "probe_tag": "drop", "probe_score": 0.0006, "luna_label": 1}]}, {"key": "rafael-234", "text": "|-|101,760|97,012|-|-|-|3,790|-|-|202,562|\n|Cayman Islands|36|-|36|13|-|-|-|-|-|52|101|\n|<br>Central African Rep.|7,175|-|7,175|311|46,523|669,906|90,672|-|-|-|814,587|\n|<br>Chad|442,672|-|442,672|3,759|308|170,278|-|-|122,359|-|739,376|\n|Chile|2,053|-|2,053|8,545|-|-|-|-|2,073|452,712|465,383|\n|China, Hong Kong SAR|130|-|130|-|-|-|-|-|-|-|130|\n\n\n72 UNHCR > **GLOBAL TRENDS 2019**", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:001657:71:5:0", "start": 362, "end": 380, "surface": "GLOBAL TRENDS 2019", "probe_tag": "drop", "probe_score": 0.023, "luna_label": 0}]}, {"key": "rafael-235", "text": "**Annex 1: Matrix for HIV/AIDS Interventions in Emergency Settings**\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Sectoral Response|Emergency preparedness|Minimum response (to be conducted<br>even in the midst of emergency)|Comprehensive response<br>(Stabilized phase)|\n|---|---|---|---|\n|1. Coordination|• Determine coordination structures<br>• Identify and list partners <br>• Establish network of resource<br>persons <br>• Raise funds <br>• Prepare contingency plans <br>• Include HIV/AIDS in humanitarian<br>action plans and train accordingly<br>relief workers|<br>1.1 Establish coordination mechanism <br> <br> <br> <br>|• Continue fundraising <br>• Strengthen networks <br>• Enhance information sharing <br>• Build human capacity <br>• Link emergency to development HIV action <br>• Work with authorities <br>• Assist government and non-state entities to promote and protect<br>human rights4 <br>|\n|2. Assessment and<br>monitoring <br>3. Protection|• Conduct capacity and situation<br>analysis <br>• Develop indicators and tools <br>• Involve local institutions and<br>beneficiaries <br>• Review existing protection laws<br>and policies <br>• Promote human rights and best<br>practices <br>• Ensure that humanitarian activitie s<br>minimize the risk of sexual violence,<br>and exploitation, and HIV-related<br>discrimination <br>• Train uniformed forces and<br>humanitarian workers on HIV/AIDS<br>and sexual violence|2.1 Assess baseline data <br> <br>2.2 Set up and manage a shared database <", "source": "reliefweb", "subset": "annotate_rafael", "spans": [{"key": "reliefweb:000612:12:0:0", "start": 1403, "end": 1416, "surface": "baseline data", "probe_tag": "drop", "probe_score": 0.0219, "luna_label": 0}]}, {"key": "rafael-236", "text": "_Figure 4 Ranking the criticality of links in Peru’s road network_\n\n\nFigure 5 maps the economic costs of disruption over all candidate critical links. The links with daily costs\nhigher than $2 million are shown in red and all are located on the Pan Americana Highway. This is not\nsurprising as this is the road with the highest daily traffic and it is located between the sea and the\nmountains; thus, it has little redundancy.\n\n\nNext, we explore the critical links most exposed to disruptions caused by natural disasters.\n\n\nFirst, we overlay the network with the flood maps described in section 2.2. The links with the highest cost\nand kilometers increase that are also exposed to floods are all on the Pan Americana highway (Figure 5).\nThe cluster Pan Americana – a set of critical links located on the Pan Americana highway – is exposed to\nriver and coastal floods, and if disrupted costs close to $3 million a day to its users – forcing them to drive\nan average of 300 additional kilometers. The area North of Arequipa presents the highest economic losses\nand very low redundancy, so we focus our analysis on this section (cluster Pan Americana in Figure 6).\n\n\n14", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:007005:15:0:0", "start": 563, "end": 573, "surface": "flood maps", "probe_tag": "confusion", "probe_score": 0.3683, "luna_label": 1}]}, {"key": "rafael-237", "text": "Systematically collecting and disseminating such data should follow best practices.\n\n\nBalancing reporting costs against obvious benefits, surveys could be updated to\n\n\nincorporate critical information missing in existing questionnaires. Closer collaboration\n\n\nbetween transport ministries or departments and statistical agencies would leverage\n\n\nexisting knowledge base, inform data needs and capabilities, and help avoid replication\n\n\nof efforts. As alluded to in Table 1, governmental agencies can increase the use and\n\n\nbenefit of their existing products by providing transparent procedures to facilitate access\n\n\nto micro-data and sharing aggregated statistics in their websites—and making these\n\n\navailable in English for global reach. Moreover, low-cost improvements to existing\n\n\nefforts could make them more informative for measuring overland transportation costs.\n\n\nFor example, to aide estimation from price gaps, data that is regularly collected for\n\n\ncompiling the consumer price index (CPI) could cover additional locations so as to\n\n\ngenerate sufficient geographic and spatial variation for this methodology to be more\n\n\nreliable. Another consideration described above is the comparability of products for\n\n\nwhich prices are collected. To the extent possible, targeting the same brands for any\n\n\ngiven product would be a good practice for not just precisely measuring transport\n\n\ncosts but also the CPI. Other potentially helpful data products are consistent and\n\n\nfrequent measures of road quality and traffic volumes. Once again, such data are\n\n\noften collected in order to monitor maintenance needs and congestion. There are well\n\nestablished methods to define and quantify road quality such as the International Road\n\n\nRoughness Index. Appropriately designing the spatial features of these collection efforts\n\n\nwould come at minimal additional cost but increase their usefulness for the purpose of\n\n\nmeasuring and lowering overland transportation costs.\n\n#### **References**\n\n\nAlder, S., Song, Z. and Zhu, Z. (2021). Unequal returns to China’s intercity road\n\n\nnetwork. Working Paper.\n\n\nAllen, T., and Arkolakis, C. (2014). Trade and the Topography of the Spatial Economy.\n\n\nThe Quarterly Journal of Economics, 129(3), 1085", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:001016:22:0:0", "start": 977, "end": 997, "surface": "consumer price index", "probe_tag": "confusion", "probe_score": 0.8056, "luna_label": 0}]}, {"key": "rafael-238", "text": "efficiency and equity. Likewise, they must guarantee the costs of the teaching, management, and\nadministrative personnel with their corresponding social benefits. Thus, an associated cost per\nstudent is assigned for each ETC and educational level, with differential costs between urban and\nrural areas.\n\nThe sources of information used come from: the initial distribution document of SGP-education\nresources in order to extract the allocation per student, ETC, educational level, and urban or rural\narea. The GEIH is used to identify students according to educational level (transition, primary,\nmiddle, and high school in official schools) and the information collected in the CEQ 2017 on the\nSole Regional Form (FUT, as per the acronym in Spanish) containing the municipalities'\nexpenditures on education.\n\nIn CEQ 2017, the distribution of resources executed in education was calculated between the SGP\nand other sources, within which the ETCs' own resources are included. This led to the calculation\nof a constant that is interpreted as the additional percentage to the SGP resources to finance\neducation in each ETC. Assuming that there are no significant changes in this percentage between\n2017 and 2021, this value is multiplied by the initial assigned value of the distribution document,\nupdated to 2021. In addition to this assumption, it is also assumed that each additional resource\nto the SGP is distributed proportionally to each student. The Table A20 presents the value\nassigned in the 2017 CEQ and current CEQ for 10 selected ETCs (out of 95 in total).\n\n**Table A20. Distribution of resources executed in education between SGP and other sources (in pesos)**\n\n<u><mark>2021 GEIH</mark></u>\n<u><mark>ETC</mark></u> <u><mark>Preschool</mark></u> <u><mark>Primary</mark></u> <u><mark>", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:000584:84:0:0", "start": 509, "end": 513, "surface": "GEIH", "probe_tag": "confusion", "probe_score": 0.6443, "luna_label": 1}]}, {"key": "rafael-239", "text": "24\n\n\nAlthough we cannot predict the future height of the Saigon River, this evidence\nsuggests that the height of the Saigon River may significantly surpass the 45 cm threshold\nto which the Baseline Strategy is robust. This further suggests that the Baseline Strategy\ndoes not meet its objectives in a wide enough range of plausible conditions, i.e. is not\nsufficiently robust. It strongly supports the city’s desire to seek additional flood risk\nmeasures, and supports Steering Center for Flood Control’s pursuit of an integrated flood\nrisk management strategy that augments the infrastructure described by the Baseline\nStrategy.\n\n\n**Figure 5.4. Range of future conditions in which the Baseline strategy meets**\n**decision makers’ objectives, defined as reducing risk for the poor and non-poor.**\n\n_Note: Vertical lines over rainfall intensity estimates show IPCC SREX mean and high projections_\n_for extreme precipitation events in Southeast Asia in 2045-2065. Horizontal lines over Saigon_\n_River levels show recent estimates of eustatic sea level rise and eustatic sea level rise with_\n_subsidence._\n\n###### **Scenarios Help Compare the Robustness of Alternative Strategies**\n\nThe analysis so far suggests that Ho Chi Minh City should consider augmenting the\nBaseline Strategy. This section examines the performance of the Baseline Strategy when\naugmented with a variety of adaptation and retreat options that Ho Chi Minh City might\npursue, in particular the nine alternative strategies described in Section 4. The analysis\naims to help decision makers ask two key questions: Which options or combination of", "source": "general_prwp", "subset": "annotate_rafael", "spans": [{"key": "prwp:005616:25:0:0", "start": 859, "end": 868, "surface": "IPCC SREX", "probe_tag": "confusion", "probe_score": 0.8141, "luna_label": 1}]}]