queue_annotate_paddy.json

[{"key": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-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_paddy", "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": "paddy-024", "text": "1.2 **Objectives of social audit**\n\n\nThe objective of this social audit is to validate the extent of the impact of the Ambowuha spring\ndevelopment at Hawassa water supply and sanitation project and Jimma water supply and sanitation\nproject on land and/or people (land acquisition, resettlement and livelihood restoration of the affected\npeople) and to confirm and corroborate whether farmers and other households have had land\nacquired, been resettled, and if so have been compensated for the loss of land and other assets as\ndetailed in the Environmental and Social Impact assessment of the two projects. Specifically the\naudit will establish:\n\n What compensations and livelihood restoration packages were provided to all affected\n\n\npersons including the farmers whose land has been acquired;\n\n\n What percentage of land was acquired from each of the farmers;\n\n\n Was there loss of any other assets such as trees, tukuls, water source, grazing land, etc.\n\n\n Were any of the farmers or other households, tenants or businesses affected and, if so what\n\n\nassets have been lost and have they received compensation for these assets?\n\n\n Whether farmers or other households were satisfied with the compensation packages offered\n\n\n(with particular focus on female headed households and other vulnerable groups);\n\n\n Whether farmers or other households were consulted in the process of determining the\n\n\ncompensations;\n\n\n Whether grievance mechanisms and procedures are put in place and affected persons are\n\n\nadequately aware of;\n\n\n Whether there have been adverse impacts on livelihood of farmers as a result of the land\n\n\nacquisition; and\n\n\n Whether there are potential adverse social impacts that could be caused by the project.\n\n\n1.3 **Expected output**\n\n\nA short report of key findings regarding the points outlined above, key lessons learned and\nrecommendations.\n\n\n1.4 **Methods of data collection**\n\n\nThe data for this report was collected both from secondary and primary sources. Secondary\ndocuments such as ESMF, RPF, ESIA", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:018910:5:0:0", "start": 2015, "end": 2019, "surface": "ESMF", "probe_tag": "keep", "probe_score": 0.9329, "luna_label": 1}, {"key": "fcv_pads_east_africa:018910:5:0:1", "start": 2021, "end": 2024, "surface": "RPF", "probe_tag": "keep", "probe_score": 0.9316, "luna_label": 1}]}, {"key": "paddy-025", "text": "14\n\n\n_Source: Crawford, S. CR2 Ltd._\n\nAt Regional Level, separate FGDs were held with the regional and woreda WaSH Teams. Within in\neach selected community/kebele separate FGDS were held with WaSHCO members, poor and\ndisadvantaged men and women, including people living with disabilities, from pastoralist\ncommunities and other under-served populations. All groups were disaggregated by gender and age.\nA total of 78 FGDs were conducted across the 3 regions involving 701 people: 353 women, 346 men,\nof which: 25 FGD, were held in SNNPR, (143 men and 97 women) 26 FGD in Somali (114 women and\n99 men) 27 FGD in Gambella (114 men and  142 women). A separate report contains all the regional\nreports and the breakdown of the sample size/focus group discussions.\n\n\n**_Regions and Woredas Consulted_**\nNWI (2011) and EDHS (2011) data was used to select Gambela, SSNPR, and Somali regions for the\nstakeholder consultations. This gave a purposive sample of regions with below average coverage of\nWaSH services and significant populations of under-served groups (pastoralists/agro-pastoralists and\nother minority groups; high percentage of households and individuals living below the poverty line as a proxy for vulnerability and disadvantage - and with locations where villagisation has taken\nplace). The 3 regions were considered a sufficiently robust sample; given the existing availability of\ndata  and the time constraints affecting the stakeholder consultations. In each Region, two woredas\nwere selected, both of which had been part of the WaSH I project in Somali and SNNPR and one of\nwhich was a WaSH 1 project area in Gabella). In all woredas selected, one woreda was showing\naccess levels above the national level and the other, with access levels below national levels. This", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:015974:13:0:0", "start": 798, "end": 801, "surface": "NWI", "probe_tag": "keep", "probe_score": 0.9137, "luna_label": 1}]}, {"key": "paddy-026", "text": " capita -O 2 -0 5 Lower-middle-income _group_\nESports of goods and servic\"s\n\n\n**STRUCTURE ofthe ECONOMY**\n\n**1979** **1989** **1998** **1999** **Growth rates of output and Investment ()**\n_{%I ol GOP)_\nAgriculture 3.4 _.._ **_2._**\nIndustry 21.0 O.Manufacturing 5.6 **_-2_** **_94_** _as_ _se_ _s_\nServices **75.6** 6\n\nPrivate consumption **-r.**\nGeneral government consumplion **G**\nImports of goods and services\n\n\n\n**1979-89** **1989-99** **1998** **1999**\n_(average annual orowth)_\nAgriculture\n\n\n\nIndustry\n\n\n\nManufacturing\nServices\n\n\n\nPrivate consumption\nGenerai government consumption\nGross domestic investment\nImports of goods and services\nGross national product 1 7 **1.4**\n\n\nNote. 1999 data are preliminary estimates.\nThis table was produced from the Development Economics central database.\n\nThe diamonds show four kev midicators in the country (in bold) compared with its income-group average. 11 data are missing, Ihe diamond will", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:019671:62:2:1", "start": 758, "end": 796, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.9824, "luna_label": 1}]}, {"key": "paddy-027", "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_paddy", "spans": [{"key": "fcv_pads_east_africa:019477:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1}, {"key": "fcv_pads_east_africa:019477:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1}]}, {"key": "paddy-028", "text": " from one stage to another. It is also driven in part by the curriculum which is geared to\npreparing students for the French baccalaureate examnination and may be contextually difficult for\nDjiboutians from less educated families.\n\n\n**3. Income and Gender Gaps in Enrollment Rates**\n\nEven though the main constraint at present appears to be school places, there is already evidence of\ngender and income gaps which cannot be explained by lack of school places alone. These are\nexpected to become more prominent over time as enrollment rates rise.\n\n\nAccording to the household expenditure survey data, in urban areas, the Net Enrollment Rate in\nPrimary Enrollment is 50% higher for the highest expenditure quintile compared to the lowest\nexpenditure quintile. The inequity is even worse in secondary education (lower secondary education\nis part of basic education but the survey data did not separate the two), where the NER of the highest\nquintile is 420% higher than the NER of the lowest quintile. The problem in urban areas is access demand exists among all groups but the rationing of sets ends up benefiting the better off who live in\nareas where schools have historically been located. Any further expansion of places will help the\npoorer segments of the population more particularly if care is taken to site the schools in areas where\nthe poor live.\n\n\nThere are also significant gender gaps and research indicates that educated mothers play a key role in\nthe country's overall development. There is a shortage of school places and any rationing works to\nthe detriment of girls enrollment. Parents are less willing for their girls to attend school because in\npar., they may view the curriculum as foreign. In addition, despite the fact the education is officially\nfree, poor families still have difficulty paying the cost of books and materials. They prefer to use\ntheir constrained resources for their boys who they feel have a better labor market potential. Finally,\nthe data from the Household Survey, showed that even if girls go to school, their parents pull them\nout at an", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:012502:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1}]}, {"key": "paddy-029", "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_paddy", "spans": [{"key": "fcv_pads_east_africa:010431:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1}]}, {"key": "paddy-030", "text": " the Registrar General in\nimplementation and coordination of all elements under this component to ensure that the silo approach to implementation is eliminated for better results.\nThe WBG technical team will support the PIU and URSB to re-focus implementation on the results framework and in particular, the outcomes. The\nmission team has also agreed to support the URSB in identifying quick wins that can be targeted for recognition in the 2017 Doing Business\nassessment. The team also noted that licensing reforms are largely focused on introduction of an ICT solution and very little has been done to ensure\nthat the administrative reforms are implemented in tandem with the ICT improvements for realization of the desired goal.\n_Component 3: Tourism Competitiveness Development_ has begun in a newly enhanced institutional environment including the strengthening of UTB.\nTORs for significant activities including the feasibility study and business plan for the Hotel and Tourism Training Institute have been prepared.\n_Component 4: Matching Grant Facility has progressed._ The PCU has completed the recruitment of MFG Unit staff and they have reported to work. They\ncomprise of: Fund Manager (1); Monitoring and Evaluation Officer (1); Business Advisors (4) – one for agriculture (coffee, edible oils, grains and pulse\nand horticulture; one for tourism; one for fisheries and One for ICT); and Accountant (1). Other staff to be shared with CEDP/PCU include:\nCommunication Officer and Environmental Officer. Additional 4 M&E regional staff will be hired on need-basis for monitoring of enterprise funded\nactivities. In the next 6 months, the studies to be undertaken include (i) Update the value chain analysis on the seven targeted subsectors, to be\ncompleted by end August, 2015. (ii) Undertake Baseline Data collection: which will be completed by end August, 2015 and following the availability of\nthe Baseline data, the Business Advisors will identify key performance indicators for their subsectors which will enable the MGF KPIs", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:012703:1:1:0", "start": 1800, "end": 1813, "surface": "Baseline Data", "probe_tag": "confusion", "probe_score": 0.1465, "luna_label": 0}, {"key": "fcv_pads_east_africa:012703:1:1:1", "start": 1908, "end": 1921, "surface": "Baseline data", "probe_tag": "confusion", "probe_score": 0.5417, "luna_label": 0}]}, {"key": "paddy-031", "text": "The World Bank\n\n\n\n\n\nReport No: ISR14738\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Number and percentage of health personnel<br>(health center to federal level) receiving<br>training on CBN|Col2|Number|Value|0.00|13000.00|12000.00|\n|---|---|---|---|---|---|---|\n|Number and percentage of  health personnel<br>(health center to federal level) receiving<br>training on CBN||Number|Date|29-Apr-2008|30-Apr-2014|31-May-2014|\n|Number and percentage of  health personnel<br>(health center to federal level) receiving<br>training on CBN||Number|Comments|Source: Routine reports,<br>FMOH|Source: Routine reports,<br>FMOH|Of a target of 1412 in 353<br>woredas. Source: Routine<br>reports, FMOH|\n|Percentage of CBN woredas providing monthly<br>nutrition data to federal level||Percentage|Value|0.00|80.00|50.00|\n|Percentage of CBN woredas providing monthly<br>nutrition data to federal level||Percentage|Date|29-Apr-2008|30-Apr-2014|31-May-2014|\n|Percentage of CBN woredas providing monthly<br>nutrition data to federal level||Percentage|Comments|FMOH Administration reports<br>(new indicator )|FMOH Administration reports|FMOH Administration reports|\n|Percentage of NNP operational research<br>studies completed and disseminated  (of a<br>target of 10)||Percentage|Value|0.00|100.00|80.00|\n|Percentage of N", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:018912:3:0:1", "start": 559, "end": 574, "surface": "Routine reports", "probe_tag": "confusion", "probe_score": 0.1247, "luna_label": 1}]}, {"key": "paddy-032", "text": " The sustainability of the sanitation measures will be carefully assessed from the point of view\nof good practices, cost effectiveness, affordability and the Djibouti water shortage environment.\n\n\n6. **Social**\n\n\n_6.1 Summarize key social issues relevant to the project objectives, and specify the project's social_\n_development outcomes._\n\n\nDjibouti is a small country and many key social issues were identified in the 1997 Poverty\n\nAssessment. The issues raised included the percentage of the population classified as poor in 1996\n(50-80% reaching the upper-bound when refugees, nomads and homeless are taken into account); large\nnumbers of refugees, nomads, and homeless populations; the majority of the poor live in urban areas\n(85%) even if the incidence of extreme poverty is overwhelmingly rural. Urban households can take\nadvantage of safety nets derived from the commodity market and services, and job opportunities are\nnot available in rural areas. The key problems faced by children include: (a) the high number of street\nchildren who have fled war ravaged Somalia and Ethiopia; (b) late entrance into school by poorer\nchildren (one out of four starts school at age 9 and leaves school at age 14); (c) health issues (diarrhea\n\nand malnutrition) are a leading cause of death for children under age 5. Other problems affecting the\nwhole population include respiratory infections on the increase (due to malnutrition); endemic health\nproblems (AIDS, tuberculosis, malaria, cholera); the widespread practice of Female Genital Mutilation\n(FGM); sanitation costs are high for poorer households not connected on the main water network as", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:017158:23:1:0", "start": 420, "end": 444, "surface": "1997 Poverty\n\nAssessment", "probe_tag": "confusion", "probe_score": 0.8166, "luna_label": 1}]}, {"key": "paddy-033", "text": "|Year|District<br>Garissa|District<br>Wajir|District<br>Mandera|District<br>Ijara|\n|---|---|---|---|---|\n|2001|151.41|140.34|137.00|142.35|\n|2002|153.54|137.44|129.95|142.81|\n|2003|141.55|136.39|134.79|144.66|\n|2004|152.69|141.22|148.11|151.46|\n|2005|144.19|162.78|163.38|168.08|\n|2006|156.66|178.92|181.69|102.76|\n\n\nAs illustrated, since project effectiveness in 2003, three of the four districts realized substantial\nincreases of between 10 to 35 percent in their examination results.\n\n**Outcome Indicator 2:** As a result of the provision of the instructional materials, every school\nhas put in place a school instructional materials management committee with signatory authority\nover local bank accounts and the expected outcome of the project would be an increase in the\nproportion of local school management committees and parents and children rating their school as\nsatisfactory or above for the learning process by June 2006.\n\n\n\n<u>Evidence to date: According to the</u> _“Final Report on Client Satisfaction Survey”_ 88.7% of pupils\nsaid they were either happy or very happy with the quality of teaching and learning in their\nschools; 82.4% of school management committee members reported that they were satisfied with\nthe quality of learning in the schools; and 66.7% of parents reported that they were satisfied with\nthe quality of teaching and learning. While no baseline data were available, interviews with all\nthree groups suggest that these percentages are higher than prior", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:014405:21:0:0", "start": 981, "end": 1023, "surface": "Final Report on Client Satisfaction Survey", "probe_tag": "confusion", "probe_score": 0.7341, "luna_label": 1}, {"key": "fcv_pads_east_africa:014405:21:0:1", "start": 1375, "end": 1388, "surface": "baseline data", "probe_tag": "confusion", "probe_score": 0.7259, "luna_label": 0}]}, {"key": "paddy-034", "text": "### **3. Context**\n\n#### **Legal and Policy Framework**\n\nGood governance is one of the key areas of focus for the third National Development Plan (NDP III).\nGood governance is key to accelerated development of national economic, political and social\nsectors. The NDP III aims to improve adherence to the rule of law over the next five years, pointing\nout that the weak adherence to the rule of law threatens governance. This was attributed to: i) weak\npolicy, legal and regulatory frameworks for effective governance; ii) low respect for and observance\nof human rights and fundamental freedoms, iii) limited access to and affordability of justice and (iv)\nlow recovery rate of public funds from individuals implicated in corruption. The NDP III aims to\nchange the Corruption Perception Index from a score of 26 to 35 out of 100.\n\n\nUganda has made strides towards putting in place relevant institutions, policies and frameworks\naimed at enhancing transparency and accountability in public procurement and contracts. However,\nGovernment of Uganda (GoU) public institutions are still faced with enforcement implementation\ngaps (Global Integrity, 2019).\n\n\nUganda’s legal framework towards ensuring transparency and accountability provides for proactive\nand reactive disclosure of information and citizens’ participation. There are a number of other anticorruption laws including: the Inspectorate of Government Act, 2002; the Leadership Code Act,\n2002; the Public Procurement and Disposal of Public Assets Act, 2003; the Public Finance and\nAccountability Act, 2003; the Budget Act, 2003; the Access to Information Act, 2005; the Local\nGovernments Amendment Act 2006; the Audit Act, 2008; the Anti-Corruption Act, 2009; the\nWhistle Blowers Act, 2010 and the Leadership Code Act, 2016, among others. These laws form a\nnational legal framework that is relevant for the fight against corruption, though enforcement is\ninadequate.\n\n\nIn 1988, the Inspectorate of Government was established in addition to the Directorate of Public\nProsecution, Criminal", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:007233:13:0:0", "start": 764, "end": 791, "surface": "Corruption Perception Index", "probe_tag": "confusion", "probe_score": 0.8601, "luna_label": 1}]}, {"key": "paddy-035", "text": "**KNBS**\nKenya National Bureau of Statistics **BUREAU**\nAnnual Reports <sup>and</sup> <sup>Financial</sup> <sup>Statements for the Year</sup> <sup>ended</sup> <sup>June</sup> <sup>**30,**</sup> <sup>2020</sup>\n\n\nTelephone Interviews (CATI) methodology. Two cycles of the survey were\n\ncarried out during the period.\n\n\n**COMESA** and PPRA funds market price surveys for comparative purposes.\n**COMESA** also funds the regional integration programme through The\nNational Treasury. The World Bank Consulting Services funded the\npreparation of County Gross Domestic Product **(CGDP).**\n\n\nThese amounts were received per Appendix II.\n\nStatistics Sweden meets the salary dues for **8** interns, attached within the\nBureau, for a period of one year. The expense amount is on a\nreimbursement basis.\n\n\nc. Revenue from other donors are funds from development partners and donors for\n\nspecific on-going projects. The projects have independent bank accounts. Their\nactual amounts have been adjusted **by** the unutilized grants as at the year end,\nper Note 21.\n\n\n**2019** / **2020** **2018/2019**\nKShs KShs\n**NIPFN** **52,266,925**\n\n**UNFPA** 4,320 4,260\n**UNICEF** **-** **MICS** 4,320 **8,940,966**\nTotal **52,275,565** 8,945,226\n\n\n**3.** Revenue from exchange transactions\na. These are made up of the", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:011741:57:0:1", "start": 539, "end": 568, "surface": "County Gross Domestic Product", "probe_tag": "confusion", "probe_score": 0.3973, "luna_label": 0}]}, {"key": "paddy-036", "text": "###### Resettlement Action Plan (RAP) For Dire Dam Rehabilitation Project\n\n**7.3 Method of Valuation**\n\nThere are three commonly known methods of valuing assets and properties, namely,\nincome based approach, replacement cost a market data or comparable sales\napproach. However, in this RAP, the methodology for valuing assets is referred at Full\nReplacement Cost. Full Replacement Cost is one method of valuation of property and\nthat determines the amount of replacement through compensation.\n\nThe concept of Full Replacement Cost is based on the premise that the costs of\nreplacing productive assets that would be expropriated for purpose of project activity.\nThe replacement cost approach involves; direct replacement of expropriated assets and\ncovers an amount that is sufficient for asset and property replacement.\n\n**7.4 Valuation for permanent loss of Farm Land / Crop Loss**\n\nThe project impact is perceived in terms of permanent land loss/crop loss will be\ncreated because of the need to expand the existing sanitary zone by limited size.\nAccordingly it is apparent some part of farm land that is owned by individuals will be\nexpropriated.\n\nAccording to the information obtained from PAPs and physical observation the farm\nland is used in most case to cultivate wheat and been. With this consideration the\nproductivity of the land is considered based on above mentioned dominantly produced\ncrop using data that is obtained from Woreda Agriculture office.\n\nHence, the land that will be expropriated from the farmers is initially valuated in\nconsidering the average crop yield obtained per hectare against the land size that would\nbe lost as the result of project activity.\n\n**7.5 Valuation for Grazing land**\n\nGrazing land in the area has a considerable economic benefit for the local communities\nin the aspect of raising livestock for different purpose including livestock products used\nfor domestic consumption and market consumption and livestock labour for different\npurpose.\n\nWith this consideration the benefit of grazing land for ones household is valuated\nagainst grass obtained from one", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:020132:48:0:0", "start": 1192, "end": 1196, "surface": "PAPs", "probe_tag": "confusion", "probe_score": 0.6107, "luna_label": 0}, {"key": "fcv_pads_east_africa:020132:48:0:1", "start": 1409, "end": 1461, "surface": "data that is obtained from Woreda Agriculture office", "probe_tag": "confusion", "probe_score": 0.87, "luna_label": 1}]}, {"key": "paddy-037", "text": "E data. The semi-annual reports were submitted regularly from 2018 to 2023 <sup>14</sup> [^14: In 2024, the PIU was finalizing the direct contracting with the same M&E consultant before project closure.] . This enabled\nthe PIU and the World Bank team to evaluate the progress toward achieving the PDO. This information was also\nconsistently reported on in Aide-memoires and Implementation Status and Results Reports (ISRs).\n\n\n**M&E Utilization**\n\n\n52. **The M&E data on PDO and intermediate outcomes were utilized to guide project management and decision-**\n**making.** As the Project progressed, restructurings were carried out to address issues identified through the M&E data.\nFor example, the second restructuring was prompted by insufficient progress towards the second part of the PDO. Two\nbus performance indicators, passenger trips carried by ABSE per day and in-service bus-km per bus per day were\nbenchmarked against bus services in other countries. These benchmarks inform policy dialogues on public transport\nreforms with AACATB. While the M&E framework effectively facilitated course corrections, there was an opportunity for\nfurther improvement. The World Bank could have supported AACATB and ABSE in designing and implementing a user\nsatisfaction survey for bus services. Such a survey would not only have provided valuable insights during the Project’s\nlifecycle but could also have been institutionalized as a regular practice post-project to assess service quality and guide\nfuture improvements.\n\n\n**Justification of Overall Rating of Quality of M&E**\n\n\n53. Based on the above discussions on the M&E design, implementation, and utilization, the overall rating of quality\nof M&E is **Substantial** .\n\n\nB. ENVIRONMENTAL, SOCIAL, AND FIDUCIARY COMPLIANCE\n\n\n54. **Environmental and Social (E&S) Safeguards.** The Project was classified as Environmental Assessment Category B,\nindicating substantial E&S risks. During the preparation phase, the Project", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:001340:23:1:0", "start": 0, "end": 6, "surface": "E data", "probe_tag": "confusion", "probe_score": 0.2555, "luna_label": 0}]}, {"key": "paddy-038", "text": "2. Genuine consultation must take place\n\n\nThe primary concern is to take seriously the rights and interests of the PAPs\nby listening to their voices. This can be done through formation of local\nlevel consultative forums. KPLC should formulate and coordinate the\nformation of such forums.\n\n3. Establishment of a pre-resettlement baseline data\n\n\nTo support the successful reestablishment of affected property, the\nfollowing activities will be undertaken prior to displacement.\n\n\n    - An inventory of landholdings and immovable/non-retrievable\nimprovements (buildings and structures) to determine fair and\nreasonable levels of compensation or mitigation.\n\n    - A census detailing household composition and demography, and\nother relevant socio-economic characteristics.\n\n\nThe asset inventories will be used to determine and negotiate\nentitlements, while the census information is required to monitor\nhomestead reestablishment. The information obtained from the inventories\nand census will be entered into a database to facilitate resettlement\nplanning, implementation and monitoring.\n\n4. Assistance in relocation must be made available\n\nKPLC should guarantee the provision of any necessary compensation for\npeople whose fields will be disturbed to make way for the transmission\nlines, or any other disturbances of productive land associated with the\nproject in proportion to their loss.\n\n5. A fair and equitable set of compensation options must be negotiated\n\nCompensation will be paid for structures, land and trees that are disturbed\naccording to set rates derived from market value comparables.\n\n6. Resettlement must take place as a development that ensures that PAPs\nbenefit\n\n\nWhere practical the employment and sub-contracting opportunities that\narise from the project will be made available to the affected population.", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:019339:12:0:0", "start": 662, "end": 715, "surface": "census detailing household composition and demography", "probe_tag": "confusion", "probe_score": 0.5222, "luna_label": 0}]}, {"key": "paddy-039", "text": "diploma and above) at Grade 1-8 was 64.7 per cent\nand 70.2 per cent in 2012-13 and 2013-14 respectively. In both the years, availability of qualified\nteachers remained significantly below the targets. However, disaggregated data shows that in 2013-14\n54.7 per cent of lower primary teachers were qualified compared to 91.7 per cent qualified upper\nprimary teachers. This means lack of qualified teachers is a problem only at Grade 1-4, and not at\nGrade 5-8. So, more attention is required for increasing qualified teachers at Grade 1-4.\n\n\n73. The PBS program has not considered learning achievement as a higher-level outcome although it\nis a proximate indicator to assess quality of education. Among others, quality of education depends to\na large extent on the availability of competent and motivated teachers. According to a small survey\nconducted in May 2014, most of the schoolteachers lack pedagogical skills and proficiency in\nteaching English <sup>34</sup> [^34: For details see Wendmsyamregne Mekasha Zike and Bereket Kelemu Ayele (2015): “State of Service\nDelivery in Ethiopian Primary Schools: Findings from the Ethiopian Education Service Delivery Indicator\nSurvey”, _Middle Eastern & African Journal of Educational Research_, No 16, 34-58.] . The same survey also reveals that the teacher absenteeism is not high (12 per\ncent), but proportion of teachers not taking scheduled classes is quite high (28 per cent). Thus, teacher\nmotivation is a challenge.\n\n\n74. **Health** : The _Penta-3 vaccine coverage_ increased from 84.9 per cent in 2011-12 to 87.6 per cent\nin 2012-13 and then to 91.1 per cent in 2013-14. Despite this increase, the vaccine coverage remained\nbelow the targets set for 2012-13 and 2013-14. The _ANC coverage for pregnant women_ declined\nmarginally from 2.86 million in 2012-13 to 2.52 million in 2013-14, but the coverage exceeded the\ntargets in both the years.\n\n\n75. The _population per HEW_ was below the norm of 2,500", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:010666:33:1:0", "start": 210, "end": 228, "surface": "disaggregated data", "probe_tag": "confusion", "probe_score": 0.7685, "luna_label": 1}, {"key": "fcv_pads_east_africa:010666:33:1:1", "start": 827, "end": 861, "surface": "small survey\nconducted in May 2014", "probe_tag": "keep", "probe_score": 0.9313, "luna_label": 1}]}, {"key": "paddy-040", "text": "**The World Bank** Implementation Status & Results Report\nPrimary Education Equity in Learning Program (P176867)\n\n\n\n\n\n\n|Completion<br>Measurement|Annual educational statistical booklet published for core education data, including refugee<br>children and learner with special needs.|\n|---|---|\n|Comments|This is an ongoing activity. The Ministry of Education is working with the Kenya National<br>Bureau of Statistics to update the annual statistical booklet.|\n\n\n\n\n\n\n\n\n|Action Description|Conduct policy dialogue for inclusion/mainstreaming of refugees in the NEMIS.|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|Source|DLI#|Responsibility|Timing|Timing Value|Status|\n|Technical|NA|MoE|Other|Continuous|In Progress|\n|Completion<br>Measurement|Data for refugee children included in the NEMIS|Data for refugee children included in the NEMIS|Data for refugee children included in the NEMIS|Data for refugee children included in the NEMIS|Data for refugee children included in the NEMIS|\n|Comments|The refugee registration module is in development, with ICT systems being enhanced for<br>universal access and equity in education.|The refugee registration module is in development, with ICT systems being enhanced for<br>universal access and equity in education.|The refugee registration module is in development, with ICT systems being enhanced for<br>universal access and equity in education.|The refugee registration module is in development, with ICT systems being enhanced for<br>universal access and equity in education.|The refugee registration module is in development, with ICT systems being enhanced for<br>universal access and equity in education.|\n\n\n\n\n\n\n\n\n|Action Description|Training", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:001761:2:0:1", "start": 744, "end": 791, "surface": "Data for refugee children included in the NEMIS", "probe_tag": "confusion", "probe_score": 0.3454, "luna_label": 0}]}, {"key": "paddy-041", "text": "_Environmental & Social Impact Assessment Project Report for the Construction of Kenol Hospital Road in_\n\n_<u>Murang’a County of the Nairobi Metropolitan Region</u>_\n\n\n**CHAPTER THREE: BASELINE INFORMATION OF THE STUDY AREA**\n\n\n**3.1** **Introduction**\n\n\nBaseline conditions cover all the biophysical and socio-economic conditions in the\nproject area. Gathering of baseline data is necessary to meet the following\nobjectives:\n\n`o` To _understand_ key biological, physical, ecological, social, cultural, economic,\n\nand political conditions in areas potentially affected by the proposed project;\n\n`o` To _understand_ the expectations and concerns of a range of stakeholders on the\n\nproposed development ;\n\n`o` To _inform_ the development of mitigation measures;\n\n`o` To _benchmark_ future socio-economic changes/ impacts and assess the\n\neffectiveness of mitigation measures.\n\n\n**3.2** **Project Location**\n\n\n**3.2** **Environmental Baseline Profile**\n\n\n**_3.2.1 Relief and physiography_**\n\n\nThe land of the project area has an altitude of about 1520 metres above sea level\nand rises to an altitude of 3,353m above sea level along the slopes of the Aberdares.\nThe highest areas to the West have deeply dissected topography and are well\n\n\n29", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "sample:fcv_pads_east_africa:013736:28:0:0", "start": 365, "end": 378, "surface": "baseline data", "probe_tag": "confusion", "probe_score": 0.0825, "luna_label": 0}]}, {"key": "paddy-042", "text": "*|**1.9%**|\n|**Borrower**||**411.2**|**   1,501.1**|**365%**|**78.7%**|\n|**FCDO (DfID)**|**Parallel**|**235.4**|**167.8**|**71%**|**8.8%**|\n|**UNICEF**|**Parallel**|**7.8**|**8.2**|**105%**|**0.4%**|\n|**Total**||**954.4**|**1,908.4**|**200%**|**100%**|\n\n\n_Source:_ SDSP and NDMA admin data, FCDO report, UNICEF report, PAD, Operations Portal\n\n\n_Note:_ Exchange rate of May 20, 2021 is applied to express the funding in US$ equivalent\n\n\n\nPage **57** of **69**", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:016570:60:1:1", "start": 291, "end": 302, "surface": "FCDO report", "probe_tag": "confusion", "probe_score": 0.1824, "luna_label": 1}, {"key": "fcv_pads_east_africa:016570:60:1:2", "start": 304, "end": 317, "surface": "UNICEF report", "probe_tag": "confusion", "probe_score": 0.5489, "luna_label": 1}]}, {"key": "paddy-043", "text": " **4,165,577.22** **0** delayed supporting\ndocuments\n10 POLARIS APPLIED SCIENCESdouet\nINC 1,806,546.24 14/04/2021 **0** 1,806,546.24 **0** delayed supporting\n11NCHLMEGROFHR\ndocuments\nSERVICE GROFHR\nSubRotlI05,1276 **5,736,960.00** **18/04/2018** **0** **5,736,960.00** **0** delayed supporting\n\ndocuments\nGrandTotal **105,466,766** **105,112,766.**\nGrad ota **15,46,66105,46,6**\n\n\n**ANNEX** **4 -** **SUMMARY** **OF FIXED ASSETS** **REGISTER**\n\nThe fixed asset summary is extracted from the asset registers maintained **by** the Project\n\n**26**", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:004952:30:2:0", "start": 491, "end": 506, "surface": "asset registers", "probe_tag": "confusion", "probe_score": 0.574, "luna_label": 0}]}, {"key": "paddy-044", "text": "sup>recently</sup> <sup>come</sup> <sup>under Government control.</sup>\n\n\n\n**2.** **Main sector** issues **and** <sup>**Government strategy:**</sup>\n\n\n\n_Sector Issues._\n_Poverty in Sierra Leone._ Sierra <sup>Leone has</sup> <sup>the lowest</sup> <sup>Human Development Index</sup> <sup>**in**</sup> <sup>the world</sup>\n\n\n\nand has a GNP per capita <sup>of only US$130</sup> <sup>compared</sup> <sup>to the average</sup> <sup>for Sub-Saharan</sup> <sup>Africa</sup> <sup>of $470.</sup>\n\n\n\nOver 82% of the population <sup>currently</sup> <sup>lives below</sup> <sup>the poverty line and life expectancy is only</sup> <sup>38 years.</sup>\n\n\n\nFertility, infant and child <sup>mortality are</sup> <sup>high</sup> <sup>and over</sup> <sup>a third</sup> <sup>of children and</sup> <sup>a fourth</sup> <sup>of adults</sup> <sup>are</sup>\n\n\n\nmalnourished. The pnmary <sup>school enrollment</s", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:014283:7:1:0", "start": 251, "end": 274, "surface": "Human Development Index", "probe_tag": "confusion", "probe_score": 0.8186, "luna_label": 1}]}, {"key": "paddy-045", "text": "**The World Bank**\nNational Youth Opportunities Towards Advancement Project (P179414)\n\n\n\n|Col1|beneficiaries who are living<br>with any kind of disability<br>that allows beneficiaries to<br>participate fully in the<br>program.|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|Youth beneficiaries in wage or self-<br>employment at least 6 months after<br>completing the package of project<br>interventions|This indicator measures<br>youth under components 1<br>and 2 who are employed<br>(wage or self-employment)<br>at least 6 months from date<br>of completion of package of<br>interventions|Annual<br>|Surveys,<br>Project MIS<br>data<br>|Tracer study survey on<br>youth employment<br>status and other<br>activities conducted at<br>least six months after a<br>cycle of training is<br>completed.<br> <br> <br>|MYAAS with survey<br>firm<br>|\n|Female beneficiaries in wage or self-<br>employment at least 6 months after<br>completing the package of project<br>interventions|This indicator measures<br>female beneficiaries under<br>components 1 and 2 who<br>are employed (wage or self-<br>employment) at least 6<br>months from date of<br>completion of package of<br>interventions|Annual<br>|Surveys<br>|Tracer Studies, Project<br>MIS data<br>|MYAAS with survey<br>", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "sample:fcv_pads_east_africa:002639:55:0:0", "start": 637, "end": 656, "surface": "Tracer study survey", "probe_tag": "keep", "probe_score": 0.948, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:002639:55:0:1", "start": 807, "end": 824, "surface": "MYAAS with survey", "probe_tag": "confusion", "probe_score": 0.1218, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:002639:55:0:2", "start": 1224, "end": 1232, "surface": "MIS data", "probe_tag": "keep", "probe_score": 0.9335, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:002639:55:0:3", "start": 1237, "end": 1254, "surface": "MYAAS with survey", "probe_tag": "confusion", "probe_score": 0.895, "luna_label": 0}]}, {"key": "paddy-046", "text": "## **2. Objective/Description of SEP**\n\nThe overall objective of this SEP is to define a program for stakeholder engagement, including public\ninformation disclosure and consultation throughout the entire project cycle. The SEP outlines the ways in\nwhich the project team will communicate with stakeholders and includes a mechanism by which people\ncan raise concerns, provide feedback, or make complaints about project activities or any activities related\nto the project.\n\n\nThis SEP has been updated to explicitly incorporate stakeholders introduced through the AF—namely\nrefugee learners, their families, refugee community leaders, and host communities—and to ensure their\nmeaningful, culturally appropriate, and accessible engagement throughout the project cycle\n\n## **3. Stakeholder identification and analysis**\n#### **3.1. Methodology**\n\nData to prepare this document are collected from five region and one city administration through\nstakeholders’ consultations, key informant interviews, and desk review. Field data from sample regions\nand a city administration were collected by MOE’s professionals. The sources of the field data were school\ncommunities, local communities, and selected regional bureaus and woreda offices. The experiences of\nthe stakeholders in environmental and social risks on previous WB financed projects which are similar to\nETOL project have been thoroughly explored. Stakeholders also forecasts the anticipated E&S risks of ETOL\nand suggest the mitigation strategies. The photos during the discussions and the attendance sheets are\nannexed to this plan.\n\n\nStakeholder engagement shall be informed by a set of principles defining core values underpinning\ninteractions with stakeholders. Common principles based on International Best Practice include the\nfollowing::\n\n- _Openness and life-cycle approach:_ Public consultations for the project(s) will be arranged during the\nwhole life cycle, carried out in an open manner, free of external manipulation, interference, coercion,\nor intimidation.\n\n- _Informed participation and feedback:_ Information will be provided to and widely distributed among all\nstakeholders in an appropriate format; opportunities are provided for", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:000921:6:0:1", "start": 1126, "end": 1136, "surface": "field data", "probe_tag": "confusion", "probe_score": 0.5395, "luna_label": 1}]}, {"key": "paddy-047", "text": "|IVA|\n|**Oversight, Participation and Accountability**|**Oversight, Participation and Accountability**|\n|**Participating counties with community-led project management committees established at ward level (Number)**|**Participating counties with community-led project management committees established at ward level (Number)**|\n|Description|The indicator measures the number of counties that have set up community-led project<br>management committees that are overseeing project identification, implementation,<br>and monitoring.|\n|Frequency|Annually|\n|Data source|IVA reports, county website|\n|Methodology for Data Collection|Firm will carry out the APA.|\n|Responsibility for Data Collection|NPCU|\n\n\n\nPage 39 of 57", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:005140:43:2:0", "start": 565, "end": 576, "surface": "IVA reports", "probe_tag": "confusion", "probe_score": 0.3085, "luna_label": 1}]}, {"key": "paddy-048", "text": " capita -O 2 -0 5 Lower-middle-income _group_\nESports of goods and servic\"s\n\n\n**STRUCTURE ofthe ECONOMY**\n\n**1979** **1989** **1998** **1999** **Growth rates of output and Investment ()**\n_{%I ol GOP)_\nAgriculture 3.4 _.._ **_2._**\nIndustry 21.0 O.Manufacturing 5.6 **_-2_** **_94_** _as_ _se_ _s_\nServices **75.6** 6\n\nPrivate consumption **-r.**\nGeneral government consumplion **G**\nImports of goods and services\n\n\n\n**1979-89** **1989-99** **1998** **1999**\n_(average annual orowth)_\nAgriculture\n\n\n\nIndustry\n\n\n\nManufacturing\nServices\n\n\n\nPrivate consumption\nGenerai government consumption\nGross domestic investment\nImports of goods and services\nGross national product 1 7 **1.4**\n\n\nNote. 1999 data are preliminary estimates.\nThis table was produced from the Development Economics central database.\n\nThe diamonds show four kev midicators in the country (in bold) compared with its income-group average. 11 data are missing, Ihe diamond will", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:014952:62:2:0", "start": 688, "end": 697, "surface": "1999 data", "probe_tag": "drop", "probe_score": 0.0227, "luna_label": 0}, {"key": "fcv_pads_east_africa:014952:62:2:1", "start": 758, "end": 796, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.9824, "luna_label": 1}]}, {"key": "paddy-049", "text": " ownership<br>❖ Project should be able to give back to the community in for of employment<br>and buying some materials from within the community.<br>❖ Need for timely payment of wages for workers to avoid stalling of the<br>construction works.<br>❖ Green environment and tree planting is key as passed by the district council.<br>❖ Routine meetings (preferably monthly) on the progress of the construction<br>to avoid having many grievances.<br>❖ Need to focus on prevention of teenage pregnancy.<br>❖ Need to consider constructing child friendly facilities for child mothers e. Play<br>areas with play facilities, sick bays and others.<br>❖ Government should be able to employ more staff upon completion.<br>❖ Water harvesting should be prioritized to minimize on hygiene problems at<br>the school.<br>❖ Need to focus on safeguarding the students by completing the fence.<br>❖ Need to avail the necessary documents like bill of quantity to the relevant<br>implementation stakeholders.<br>❖ The project is timely and relevant for the youth in the area.<br>❖ Final Construction designs to<br>be shared with stakeholders<br>❖ Code of conduct to be<br>reviewed and shared<br>❖ Accidents logs will be shared<br>❖ Engagement on protection of<br>learners on GBV, HIV, etc<br>❖ Collective monitoring of<br>progress for ownership based<br>on agreed schedules<br>❖ Engagement of communities|**Terego District: Omugo Technical Schools**|**Terego District: Omugo Technical Schools**|**Terego District: Omugo Technical Schools**|\n|❖ The district engineer must be involved to supervise works right from the<br>start.<br>❖ Accommodation for", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:002490:38:4:0", "start": 1174, "end": 1188, "surface": "Accidents logs", "probe_tag": "drop", "probe_score": 0.0221, "luna_label": 0}]}, {"key": "paddy-050", "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_paddy", "spans": [{"key": "fcv_pads_east_africa:005043:1:0:0", "start": 1250, "end": 1273, "surface": "Procurement Plan tables", "probe_tag": "drop", "probe_score": 0.0126, "luna_label": 0}]}, {"key": "paddy-051", "text": "**3.5 Overarching Themes, Other Outcomes and Impacts**\n**(a) Poverty Impacts, Gender Aspects, and Social Development**\n\n56. A gender focal point was established under the Research-Extension-Farmer Linkage Department\n(REFLD) to integrate gender issues into every aspect of the research endeavors i.e. research design;\nimplementation, and M&E processes. ARTP allocated support for recurrent budgets to facilitate research\nanalysis on gender issues. A major factor in the achievements of the unit in recent years has been the\nincrease in the budget allocated to the unit, which had previously operated with minimal financing. In the\npast year the unit held 10 training events on gender analysis for over 237 participants from EIAR\nmanagement, and federal and regional RCs. The unit also coordinated the development of case studies\nand baseline data collection through surveys at eleven RCs to identify gender disaggregated research\npriorities. A workshop presenting survey results was held and the findings are expected to be collected in\na proceedings and a summary report.\n\n57. The Tenth Supervision Mission in 2006 recommended the unit make an inventory of case studies\non how research outcomes have addressed issues faced by women. An initial assessment was undertaken\nand identified several research outputs that could be considered gender-responsive in increasing\nhousehold food security, reducing household drudgery and mitigating HIV/AIDs and poverty. No formal\ninventory was produced but the assessment provided an input into training activities and a more\nsubstantive report, including case studies, will instead be produced based on the results of the surveys.\nThe report is expected to be finalized in 2007. The unit also plans to continue to implement survey\nfindings in developing research proposals and undertake similar surveys at the regional level. While there\nhas been excellent progress in identifying gender issues and initiating a gender-focused research program,\nthere is a need to further institutionalize the collection of gender disaggregated data and the enhancement\nof gender focus of research operations.\n\n**(b)", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "sample:fcv_pads_east_africa:008304:28:0:0", "start": 865, "end": 886, "surface": "surveys at eleven RCs", "probe_tag": "confusion", "probe_score": 0.4354, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:008304:28:0:1", "start": 2045, "end": 2070, "surface": "gender disaggregated data", "probe_tag": "drop", "probe_score": 0.0175, "luna_label": 0}]}, {"key": "paddy-052", "text": "|\n|Business plan preparation and<br>design for restoration of Addis<br>Ababa museum|Actual|||||\n|||||||\n|Design for construction of two by-<br>pass roads in Lalibela core zone|Plan||12/12/2010|2/12/2011||\n|Design for construction of two by-<br>pass roads in Lalibela core zone|Actual|||||\n|||||||\n|Capacity  building gap assessment <br>and preparation of intervention<br>plan for MoCT, RTBs and local<br>tourism administration|<br>Plan||12/12/2010|2/12/2011||\n|Capacity  building gap assessment <br>and preparation of intervention<br>plan for MoCT, RTBs and local<br>tourism administration|<br>Actual|||||\n|||||||\n|Baseline survey for ESTDP<br>project|Plan<br>||1/27/2010|3/25/2011||\n|Baseline survey for ESTDP<br>project|~~Actual~~|||||\n|||||||\n|Design for construction of water<br>supply and sanitation schemes in<br>Lalibela.|Plan||12/11/2010|2/14/2011||\n|Design for construction of water<br>supply and sanitation schemes in<br>Lalibela.|Actual|||||\n|||||||\n|Assessment for improvement of<br>the completeness and reliability,<br>Design and deployment of tourism<br>information systems|Plan||6/20/2011|11/18/2011||\n|Assessment for improvement of<br>", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:010766:19:1:1", "start": 615, "end": 640, "surface": "Baseline survey for ESTDP", "probe_tag": "drop", "probe_score": 0.0495, "luna_label": 0}]}, {"key": "paddy-053", "text": "Resettlement Policy Framework MWUD Draft 7B\n\n\n### **Annex A: ULGDP ESMF and RPF Screening Form**\n\n<u>ULGDP investment project name:</u>\nLocation (include map/sketch): _(e.g. region, district, etc)_\nType of activity : _(e.g. new construction, rehabilitation, periodic_\n_maintenance)_\nEstimated Cost: (Birr)\nProposed Date of Works Commencement\nTechnical Drawing and Specifications (circle Yes No\n\n\n\nYes No\n\n\n\n\n\n(circle\n\n\n\n|Reviewed : an This report is to be kept short and concise. 1. Site Selection:|nswer):|\n|---|---|\n|<br> <br>**Physical data:**|_Yes/No answers and bullet lists preferred except_<br>_where descriptive detail is essential._|\n|Site area in ha||\n|Extension of or changes to existing alignment||\n|Any existing property to transfer to project||\n|Any plans for new construction<br>|<br>|\n\n\n**2.** **Impact identification and classification:**\n_When considering the location of a ULG investment project, rate the sensitivity of the_\n_proposed site in the following table according to the given criteria. Higher ratings do not_\n_necessarily mean that a site is unsuitable – it indicates a real risk of causing adverse impacts_\n_involving resettlement and compensation. The following table should be used as a reference_ .\n\n\n**Table 6** **Impact Identification and Classification**\n\n\n\n\n\n\n\n\n\n\n|Issues|Site Sensitivity|Col3|Col4|\n|---|---|---|---|\n|**Issues**|**Low**|**Medium**|**High**|\n|Involuntary<br>re", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "sample:fcv_pads_east_africa:018069:32:0:0", "start": 530, "end": 543, "surface": "Physical data", "probe_tag": "drop", "probe_score": 0.0117, "luna_label": 0}]}, {"key": "paddy-054", "text": "13\n\n\n**_Monitoring and Evaluation_**\n\n\nMonitoring will be done according to the development indicators given in the attachment to Annex 1.\nThe project will strengthen the capacity of CNOSEGE, and the Planning Unit of the Ministry so that\nmonitoring reports on the implementation of the reform can include key progress and impact\nindicators. Currently the Planning unit generates statistical data on all aspects of the education sector,\nhowever this can be further strengthened to monitor progress on key reform objectives such as access,\nequity and quality. In addition, during the donors round-table UNESCO offered support to develop an\nEducation Management Information System (EMIS). If this is not in place by the end of Phase I of the\nAPL, this would be a priority item for Phase II.\n\n\nEvaluation of the impact of the reforms will be done by CNOSEGE by recruiting experts in this field\n\nand an initial evaluation will be done at the end of Phase I. Particular areas of impact assessment will\nbe student performance and success in reaching out to disadvantaged groups. Normally, student\nperformance would be measured by overall test results but as the pool of students widens to include\nstudents from less advantaged socioeconomic groups, there will be a downward pressure on test\nscores. The Planning Unit of the Ministry will be strengthened to monitor progress in reaching out to\ndisadvantaged groups and test scores of students by socioeconomic background. Staff will carry out a\nrandom survey (5 to 10% sample) of students by socioeconomic background in 2001 to establish a\nbaseline. To keep the survey simple, the socioeconomic background questions will be limited to easily\nidentified categories such as day-laborers, civil servants, shopkeepers etc. The survey will be repeated\nin 2005 and 2110.\n\n\n**D.** PROJECT RATIONALE\n\n\n**1. Project alternatives considered and reasons for rejection**\n\nOriginally, the project was designed as a Sector Investment Loan, however, given the Government's\ncommitment to the education sector, and the", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "sample:fcv_pads_east_africa:012940:16:0:0", "start": 379, "end": 395, "surface": "statistical data", "probe_tag": "confusion", "probe_score": 0.582, "luna_label": 0}, {"key": "sample:fcv_pads_east_africa:012940:16:0:1", "start": 1487, "end": 1500, "surface": "random survey", "probe_tag": "drop", "probe_score": 0.012, "luna_label": 0}]}, {"key": "paddy-055", "text": "During my review, I observed some **inade** acies in the system; however, they are not senrous\n\n\n\nenough to withhold certification. hav detailed these inadequacies in the attachment, and\nincluded an action plan for remedy thsituation that was agreed upon with the Borrower.\n\n\n\nSigned by: _0 1_\nProcurement Specialist _\"_ i **°S** **TQI** 3 _1_ _,L_\nChristi fitAccraiT&~ Date\nProcureme taff, MNSt\n\n\n\n**Part III: Physical Monitorable Indicators and Overall Assessment**\n\nI have reviewed the project's system for monitoring physical implementation progress, including\nits monitorable indicators for major outputs. In my view, the system cannot provide the\nappropriate data on physical progress (PMR-Section 2) required by IDA.\n\nDuring my review, I observed some inadequacies in the system; however, they are not serious\nenough to withhold certification. I have detailed these inadequacies in the attachment, and\nincluded an action plan for remedying the situation that was agreed upon with the Borrower.\n\nSigned by: i A\n### Task Team Leader 1/ 4 -L- UJ 2, dO\n\nQaiser Khan, MNSHD Date\n\n\n**Part** IV: **Concurrence of** LOA for Eligibility of Project for PMR-Based Disbursements\n\n\nI have conducted a reasonable review of the process followed by the Task Team in assessing the\nproject, and I concur with its recommendation that this project is not eligible for PMR-Based\nDisbursements.\n\nSigned by: C____ __a _\nFMS-LOAADO Andrina Ambrose, LOAEL Date\n\n\nT.\n\n\nThu-Ha Nguyen, LOAEL Date", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:010697:55:0:0", "start": 665, "end": 690, "surface": "data on physical progress", "probe_tag": "drop", "probe_score": 0.0178, "luna_label": 0}]}, {"key": "paddy-056", "text": "*a**</sup> <sup>true</sup> <sup>and</sup> <sup>fair view of</sup>\n\nthe state of affairs of the Project for the financial <sup>year</sup> <sup>**eided**</sup> <sup>on June</sup> <sup>**30, 2025.**</sup>\nThis responsibility includes (i) Maintaining <sup>adequate</sup> <sup>financial</sup> <sup>management</sup> <sup>arrangements</sup> <sup>and</sup>\nensuring that these continue to **be** effective throughout the reporting period, (ii) Maintaining proper\naccounting records, which disclose with reasonable <sup>accuracy</sup> <sup>**at**</sup> <sup>any</sup> <sup>time the</sup> <sup>financial</sup> <sup>position of the</sup>\n\nproject, (iii) Designing, implementing and <sup>maintaining</sup> <sup>internal</sup> <sup>controls</sup> <sup>relevant</sup> <sup>to</sup> <sup>the</sup> <sup>preparation</sup>\nand fair presentation of the financial statement, and ensuring <sup>that</sup> <sup>they</sup> <sup>are</sup> <sup>free<", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:000432:20:1:0", "start": 455, "end": 473, "surface": "accounting records", "probe_tag": "drop", "probe_score": 0.0268, "luna_label": 0}]}, {"key": "paddy-057", "text": " National Accounts. Consumption data is used to derive potential VAT, and<br>Government revenue collection data is used to ascertain actual VAT collections. The difference is the total VAT Gap,<br>from which the policy gap will be subtracted to arrive at the VAT compliance gap.|\n|Responsibility for Data<br>Collection|PMCSO (MoR)|\n\n\n|Improved Public Financial Management Capabilities|Col2|\n|---|---|\n|**Public availability of annual recurrent and capital budget execution reports and contract award information (Text)**|**Public availability of annual recurrent and capital budget execution reports and contract award information (Text)**|\n|Description|Measures the extent to which budgetary, fiscal, and procurement information is publicly disclosed in a timely manner.|\n|Frequency|Annually|\n|Data source|Ministry of Finance Website and Federal Procurement and Property Administration Agency Website|\n|Methodology for Data<br>Collection|The Federal Government consistently makes available to the public: a) the annual recurrent and capital budget<br>execution reports, and b) contract awards information, through the MoF website and e-GP website respectively,<br>starting from EFY 2018 (FY2025/26). The quality and timeliness of budgetary and fiscal information will be assessed<br>using PEFA PI-5 (Budget Documentation) and PEFA PI-9 (Public Access to Fiscal Information). The quality of<br>procurement information will be assessed using the Open Contracting Data Standard (OCDS).|\n|Responsibility for Data<br>Collection|PMCSO (MoF)|\n\n\n\n\n\n|Beneficiary Citizen Feedback|Col2|\n|---|---|\n|**Increase in civil servant beneficiaries’ experience with the quality of HRM practices (Percentage) **|**", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "sample:fcv_pads_east_africa:004104:40:1:0", "start": 20, "end": 36, "surface": "Consumption data", "probe_tag": "drop", "probe_score": 0.0166, "luna_label": 1}, {"key": "sample:fcv_pads_east_africa:004104:40:1:1", "start": 77, "end": 111, "surface": "Government revenue collection data", "probe_tag": "confusion", "probe_score": 0.0742, "luna_label": 1}]}, {"key": "paddy-058", "text": " the EACC and other institutions)|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Responsibility**|**Recurrent**|**Frequency**|**Due Date**|**DLI#**|**Status**|\n|Client|Yes|SemiAnnual|||In Progress|\n|**Comments**||||||\n\n\n\n\n\n\n\n|Action Description|Implementing agencies to develop risk management registers. KDSP Secretariat to develop and periodically update<br>risk management registers|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Responsibility**|**Recurrent**|**Frequency**|**Due Date**|**DLI#**|**Status**|\n|Client|Yes|CONTINUOUS|||In Progress|\n|**Comments**||||||\n\n\n|Action Description|Sensitization and awareness campaigns on corruption reporting mechanisms|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Responsibility**|**Recurrent**|**Frequency**|**Due Date**|**DLI#**|**Status**|\n|client|Yes|CONTINUOUS|||In Progress|\n|**Comments**||||||\n\n\n6/27/2018 Page 5 of 13", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:013976:4:1:0", "start": 281, "end": 306, "surface": "risk management registers", "probe_tag": "drop", "probe_score": 0.0455, "luna_label": 0}]}, {"key": "paddy-059", "text": "2. Broader social capital building outcome: Capacity building interventions for implementing\npartners and local authorities would include conflict resolution training, and emphasize sub-project\ndesign and supervision processes which build social capital and mutual accountability between\ncommunities and the array of support organizations and institutions.\n\n\n3. Intra-community equity outcomes: To address intra-community equity issues in a more\nsystematic manner, NaCSA would revise its Operations Manual to enhance procedures for community\nneeds identification and sub-project selection. The enhanced guidelines would include, inter alia, a\nrequirement for implementing partners to fill out a Community Assessment Form. This would include\ninformation on community population (by gender and age group), basic infrastructure, main income\ngenerating activities, housing, organization, forms and levels of outside assistance, and particularly\nvulnerable groups.\n\n6.5 How will the project monitor performance in terms of social development outcomes?\n\nA Social Assessment is under preparation. It is being designed so as to ensure that the project\nresponds to social development concerns by identifying the opportunities, constraints and social risks\n\n\n\ninherent in the project and translating these ideas into practical design and implementation measures.\nThe World Bank's Social Capital Assessment Tool (SOCAT) and instruments for analyzing local\ninstitutions will be used in the Social Assessment. Subsequent iterative rounds of social analysis using\nsimple locally adapted variants of these tools would continue during the duration of the project to collect\nand analyze data on social development issues. These would include associational memberships and\ntrust, the process of creation and destruction of social capital and data on groups and networks,\n\n\n\nsubjective well-being, political engagement, sociability, community activities, cohesion and\ncommunication. The Assessment would thus provide baseline data on social issues, recommend and\n\n\n\napply social capital measurement tools, and provide a framework for continuing social analysis\nthroughout the project.\n\n\n\n7. Safeguard Policies:\n7.1 Are any of the following safeguard policies triggered by the prcect?\n\n     - '& **~** P19 ~ f _-", "source": "fcv_pads_east_africa", "subset": "annotate_paddy", "spans": [{"key": "fcv_pads_east_africa:017932:24:0:0", "start": 1670, "end": 1703, "surface": "data on social development issues", "probe_tag": "drop", "probe_score": 0.0226, "luna_label": 0}]}, {"key": "paddy-060", "text": "4.3 Impact of relaxing FAR regulations on estimated land value creation\nThere are Floor-Area Ratio (FAR) regulations in place in the Buenos Aires region, and in particular in the\ncity of Buenos Aires proper (CABA). We account for these in the model by adding a map that provides\ninformation about the maximum Floor Area Ratios – the ratio between the maximum amount of space\nbuilt for residential or other purposes and the land area the structure is built-on – in _CABA_ together with\nthe type of use allowed in each zone. We thus limit the maximum height of buildings within _CABA_ . We\nalso exclude zones that are uniquely destined to commercial or industrial uses from being urbanized for\nresidential purposes. To do so we intersected our grid with data from the 2011 Cόdigo de Planeamiento\nUrbano (CPU). <sup>15</sup> [^15: Accessible at: http://data.buenosaires.gob.ar/.] The CPU map provides for each zone the classification of buildings that can be built. From\nthis information we can retrieve the type of buildings (residential commercial etc.) and the maximum\nallowed height. For each grid cell we averaged the data to obtain the average building height for\nresidential purposes in _CABA_ (Figure 6). Beyond _CABA_ however we do not introduce maximum building\nheights. It should be noted that FARs only have a loose connection to the number of floors that can be\nbuilt on a given land plot so that equating both is a simplification that is valid for our modeling purposes\nonly. Indeed, a residential FAR of 1, for example, is the ratio between the amount of living space that can\nbe built and the area of a plot of land. This can correspond to one floor if the building occupies the total\nspace of the land plot, two floors if the building occupies only half the land plot area or even 4 floors if it\noccupies 25% of the land plot.\n\n\n_Figure 6: Maximum number of floors", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "sample:prwp:000444:22:0:0", "start": 766, "end": 800, "surface": "2011 Cόdigo de Planeamiento\nUrbano", "probe_tag": "keep", "probe_score": 0.9402, "luna_label": 1}, {"key": "sample:prwp:000444:22:0:1", "start": 881, "end": 888, "surface": "CPU map", "probe_tag": "keep", "probe_score": 0.9333, "luna_label": 1}]}, {"key": "paddy-061", "text": "frameworks) for evaluations related to local conflict building on a mixed qualitative-quantitative\n\n\napproach.\n\n\nThe PODES survey collects information on village/neighborhood institutions. We have\n\n\nconstructed variables which indicate the presence of religious groups, _adat_ (traditional\n\n\nlaw/customs) institutions, and the number of places for worship. We also used variables that\n\n\nindicate whether there is a BPD in the village and the gender of the village head. The BPD is a\n\n\nmandated democratically elected council in rural areas.\n\n\nReligious groups and _adat_ institutions are associated with higher levels of conflict. One\n\n\npossible explanation may be that their presence reflects differences in norms about conflict\n\n\nresolution. The qualitative work indicated that often varying expectations between different\n\n\ngroups in the community, or between the community and the state, about how tensions should be\n\n\nresolved resulted in conflict. The density of places of worship on the other hand is associated\n\n\nwith lower levels of conflict, especially in rural areas. This may support Varshneys’ hypothesis\n\n\nthat institutions that can resolve tensions that exist between their constituencies play an\n\n\nimportant role in preventing conflict from arising and escalating. The absence of a BPD in a\n\n\nrural village has no effect on conflict in the regression with provincial dummies, but does appear\n\n\nto be associated with lower levels of conflict in the regression that does not include these\n\n\ncontrols. The presence of a female village leader is associated with lower level of conflict in\n\n\nrural areas. This however, is still a rare phenomena: only 2 percent of the rural communities had\n\n\na female village leader.\n\n\n**_Hypothesis: Better public or communal provision of security is associated with lower_**\n\n\n**_conflict._** The PODES allows us to construct variables concerning the presence of formal and\n\n\ninformal security in a village, including whether the community has organized its own security\n\n\n29", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:002652:28:0:0", "start": 117, "end": 129, "surface": "PODES survey", "probe_tag": "keep", "probe_score": 0.9593, "luna_label": 1}]}, {"key": "paddy-062", "text": "used mobile money in the last three months. Although 80 percent of respondents\n\n\nreported saving some money, only 8 percent saved in a mobile money account.\n\n\nThe surveys included modules on remittances, asking if households had\n\n\nreceived money from somebody outside the household during the past six months.\n\n\nFor the households that reported receiving money, the surveys then asked for details\n\n\nof the transactions with the person they received money from most frequently. At\n\n\nbaseline, only 15 percent of respondents reported that they received money via any\n\n\nchannel with 7 percent receiving the transfer via mobile money. Almost all other\n\n\ntransfers were hand or bus delivered by somebody in the households or a friend.\n\n\nConditional on receiving money, the average transportation cost for picking up the\n\n\nmoney was 7,400 shillings. This amount corresponds to about 20 percent of\n\n\nmonthly household expenditures per capita (median of 32,859 shillings, mean of\n\n\n41,995 shillings).\n\n\nMost households in the sample engage in agriculture. At baseline, only\n\n\nabout 11 percent of the sample worked outside farming, with about 5 percent being\n\n\nself-employed.\n\n\n**4.2 Airtel Transactions Data**\n\n\nWe obtained monthly data from Airtel on mobile money transactions for June 2016\n\n\nto November 2017, including seven types of transactions: sending a peer-to-peer\n\n\n(P2P) transfer, receiving a P2P transfer, cash-in, cash-out, bill pay, airtime top-up\n\n\nand data top-up.\n\n\nTo analyze these data, we had to map them to our study clusters, which was\n\n\nnot straightforward. We have the phone number associated with each transaction,\n\n\nbut we do not have information on who owns the phone number or where the owner\n\n\nis located. The location can be approximated by the location of the cell phone tower\n\n\n11", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:002229:12:0:1", "start": 1216, "end": 1269, "surface": "monthly data from Airtel on mobile money transactions", "probe_tag": "keep", "probe_score": 0.9662, "luna_label": 1}]}, {"key": "paddy-063", "text": "Policy Research Working Paper 10762\n\n### **Abstract**\n\nThis study documents the impacts of climate change on\n\nfirm-level productivity by matching a globally comparable\nand standardized survey of nonagricultural firms covering\n154 countries with climate data. The findings show that\nthe overall effects of rising temperatures on productivity are negative but nonlinear and uneven across climate\nzones. Firms in hotter zones experience steeper losses with\nincreases in temperature. A 1 degree Celsius increase from\nthe typical wet-bulb temperature levels in the hottest climate zone (25.7 degrees Celsius and above) results in a\nproductivity decline of about 20.8 percent compared to\nfirms in the coldest climate zone. The effects vary not only\nbased on the temperature zones within which firms are\n\n\n\nlocated, but also on other factors such as firm size, industry\nclassification, income group, and region. Large firms, firms\nin manufacturing, and those in low-income countries and\nhotter climate zones tend to experience the biggest productivity losses. The uneven impacts, with firms in already\nhotter regions and low-income countries experiencing\nsteeper losses in productivity, suggest that climate change is\nreinforcing global income inequality. If the trends in global\nwarming are not reversed over the coming decades, there is\na heightened risk of widening inequality across countries.\nThe implications are especially dire for the poorest countries\n\nin the hottest regions.\n\n\n\nThis paper is a product of the World Bank Office of the Chief Economist, Africa Region and the International Monetary\n\nFund. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to\ndevelopment policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://\nwww.worldbank.org/prwp. The authors may be contacted at wkassa1@worldbank.org.\n\n\n_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development_\n_issues. An objective of the series is", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "sample:prwp:001529:1:0:0", "start": 185, "end": 216, "surface": "survey of nonagricultural firms", "probe_tag": "keep", "probe_score": 0.9933, "luna_label": 1}, {"key": "sample:prwp:001529:1:0:1", "start": 245, "end": 257, "surface": "climate data", "probe_tag": "keep", "probe_score": 0.9776, "luna_label": 1}]}, {"key": "paddy-064", "text": "A. Sanghi and D. Johnson\n\n\ndata from 130 countries and explicitly assesses the long-term behavior of income distributions,\ntracking demographic and educational changes over time.\n\n\n**Wealthier** **households** **in** **Sub-Saharan** **Africa** **benefit** **from** **demographic** **changes** **and** **wage**\n**changes across sectors** The wealthiest 40 percent of households will have higher per capita income growth from demographic changes, but the poorest 40 percent of households will see no\nbenefit. The poorest 20 percent of households will experience slower per capita income growth\nwhen demographic changes cause changes in relative wages across sectors. Upper middle income\nhouseholds —those between the 60th and 80th percentile of the income distribution —will gain\nthe most from the wage changes, earning the fastest per capita income growth <sup>8</sup>\n\n\n**Poorer** **households** **are** **hurt** **by** **changes** **in** **food** **to** **non-food** **relative** **prices,** **but** **gain** **overall**\n**from Chinese slowdown and rebalancing** Changes in food to non-food prices leave the poorest\n40 percent of households worse off: per capita income growth is 2.9 percent compared to the 3.07\npercent if China had continued to grow at seven percent. But thanks to greater Chinse consumption, the bottom 40 percent will increase their incomes; the number of people living in extreme\npoverty will fall by an additional 4.04 million people. The Chinese slowdown scenario increases\npoverty initially, but the rebalancing reduces poverty enough for an overall drop", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:006646:24:0:0", "start": 27, "end": 50, "surface": "data from 130 countries", "probe_tag": "keep", "probe_score": 0.9057, "luna_label": 1}]}, {"key": "paddy-065", "text": "Our perspective on EWAP dynamics in EMDEs is motivated by the literature on structural change.\nA key dynamic among EMDEs is the movement of economic activity out of agriculture and into\nnon-agriculture. Labor markets in these two sectors have very different operating features:\nagricultural labor markets are dominated by self-employment in low-density rural areas, whereas\nnon-agricultural labor markets are dominated by formal or informal employment in higher-density\nurban areas. Additionally, many policy and institutional differences are likely to have very\ndifferent effects on labor markets in these two sectors. For these reasons, the process of structural\nchange will plausibly give rise to dynamics in aggregate labor market outcomes, as activity\nswitches from largely self-employment in rural areas to formal or informal employment in urban\nareas. Long-run labor market outcomes will thus reflect the steady state outcomes in the nonagricultural sector.\nOur methodology for studying EWAP dynamics draws heavily on the growth literature that studies\nconvergence patterns in cross-country panel data sets. Specifically, following the literature on\nconditional convergence, we include country fixed effects in our convergence regressions to allow\nfor the possibility that each country is converging to its own steady-state level of EWAP. When\nimplementing this procedure, we also control for two time-varying driving forces: productivity\nand population.\nOur analysis of EWAP dynamics proceeds in two steps. The first step runs convergence\nregressions for EWAP. A by-product of this first step are country-specific steady-state values for\nthe EWAP. In the second step, we examine the correlation between these steady-state levels and\nvarious indicators.\nOur first step delivers three key results. First, while there are many countries with very similar\nsteady-state EWAP levels, there are many countries with steady-state EWAP levels that display\nlarge deviations from the mean. Second, higher population growth is associated with significantly\nlower EWAP. Third, higher productivity growth is also associated with", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001532:4:0:0", "start": 1084, "end": 1113, "surface": "cross-country panel data sets", "probe_tag": "keep", "probe_score": 0.9242, "luna_label": 0}]}, {"key": "paddy-066", "text": " and manufacturing sectors than in the services. Using data from the\ninternational input-output tables from TiVA, we further find that the negative association\nbetween trade and protectionism in government procurement concerns public and private\npurchases in qualitatively similar ways. This finding corroborates our initial hypothesis that\nprotectionism in government procurement can extend beyond the conduct of procurement\nauctions and affect the sourcing decisions of private firms as well (notably, the domestic ones\nwho are awarded procurement contracts).\n\nThe rest of the paper is organized as follows. Section II scopes out the existing literature on\ntrade policy and government procurement. Section III describes the construction of the\nindicators of protectionism based on national laws on government procurement, and the\ntrade data. We then present in Section IV the results from the cross-country regressions and\nfrom the estimations of the gravity model, both investigating the relationship between\nprotectionism in the law and trade openness. Section V concludes by summarizing the\nevidence and outlining limitations and policy implications.\n#### II. Literature review\n\n\nThis paper examines the importance of protectionist rules in government procurement across\ncountries and assesses their impact on cross-border flows, drawing on insights from different\n\n\n3", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "sample:prwp:001390:5:1:0", "start": 69, "end": 112, "surface": "international input-output tables from TiVA", "probe_tag": "confusion", "probe_score": 0.8319, "luna_label": 1}, {"key": "sample:prwp:001390:5:1:1", "start": 832, "end": 842, "surface": "trade data", "probe_tag": "confusion", "probe_score": 0.7863, "luna_label": 1}]}, {"key": "paddy-067", "text": " and better quantitative data is clearly a worthy aim in public\nadministration (and elsewhere). Many important gains in human welfare (e.g., recognizing and\nresponding to learning disabilities) can be directly attributed to interventions conceived and\nprioritized on the basis of the empirical documentation of the reality, scale, and consequences of\nthe underlying problem. The wonders of modern insurance are possible because actuaries can\nquantify all manner of risks over time, space and groups. What we will argue in the following\nsections, however, is that access to quantitative data alone is not a sufficient condition for\nachieving many of the objectives that are central to public administration and economic\ndevelopment.\n\nThis paper has five sections. Following this Introduction (Section I), we lay out in Section II the\nways in which the collection, curation, analysis, and interpretation of data is embedded in\ncontexts: no aspect takes place on a blank slate. On one hand, the institutional embeddedness of\nthe data collection and usage cycle – in rich and poor countries alike – leaves it susceptible to a\n\n\n[2 According to the Drucker Institute; see https://www.drucker.institute/thedx/measurement-myopia/](https://www.drucker.institute/thedx/measurement-myopia/) (accessed 6\nDecember 2021).\n[3 See https://www.theguardian.com/business/2008/feb/10/businesscomment1](https://www.theguardian.com/business/2008/feb/10/businesscomment1) (accessed 6 December 2021).\n4 See, for example, former USAID Administrator, Andrew Natsios (2011), citing Lord Wellington in 1812, on the\ninsidious manner in which measures of ‘accountability’ can compromise rather than enable central policy objectives\n(in Wellington’s case, winning a war). For his part, Stiglitz has argued that “", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000553:3:1:0", "start": 573, "end": 590, "surface": "quantitative data", "probe_tag": "confusion", "probe_score": 0.5288, "luna_label": 0}]}, {"key": "paddy-068", "text": "Figure 5: Distribution of R&D, Educational Attainment, and IPR Regime vs. Distance to\nTechnological Frontier\n\n\nSource: R&D series from UNESCO, OECD, Taiwan Statistical Yearbook and Lederman and Saenz (2005).\nData for education attainment comes from Barro and Lee (2001). Data for IPRs from Ginarte and Park\n(1997). The components are the coverage of patent laws across seven industries, membership in three key\ninternational patent agreements, provisions for loss of rights for loss of protection, three types of enforcement\nmechanisms, and the duration of patents relative to international standards. Distance to the frontier is GDP\nper capita relative to highest country value.\n\n\n32", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:005930:34:0:2", "start": 271, "end": 284, "surface": "Data for IPRs", "probe_tag": "confusion", "probe_score": 0.337, "luna_label": 1}]}, {"key": "paddy-069", "text": " not\nsystematic, we would risk bias in the analysis if we included only the available data.\n\n**We use data for the 2003 to 2017 period, from Eurostat, the Organisation for Economic**\n**Co-operation and Development (OECD), and** **_fDi Markets_** **(Financial Times).** Given our\nempirical goal, and considering data availability constraints, we have collected the largest possible\nset of data at the NUTS-3 level from three main sources to cover the longest possible time period,\nnamely from 2003 to 2017. On economic growth and industrial structure, we use Eurostat data from\nthe _Regio_ database on GDP, population, employment, land area, and sectoral Gross Value Added\n(GVA) – for agriculture, industry (mining; electricity; manufacturing), construction, market\nservices, <sup>13</sup> [^13: Wholesale and retail trade; transport; accommodation and food service activities; information and communication; financial and\ninsurance activities; real estate activities; professional, scientific and technical activities; administrative and support service activities.] and non-market services. <sup>14</sup> [^14: Public administration and defence; compulsory social security; education; human health and social work activities; arts,\nentertainment and recreation; other service activities; activities of household and extra-territorial organisations and bodies.] Missing values in the NUTS-3 level regional series have been\nfilled in by linearly interpolating NUTS-2 level data. On innovation, we use microdata on patents\nfiled under the Patent Co-operation Treaty (PCT) from the _REGPAT_ database provided by the OECD,\naggregated at the NUTS-3 level by priority year and inventor’s residence using the fractional count\ncriterion. On FDI, we use data on inward ‘greenfield’ FDI from the _fDi Markets_ database provided\nby the Financial Times. Of particular interest for our purposes, the _fDi Markets_ database collects\ninformation on individual investment projects in terms of year, destination region at the NUTS-3 (or\ncity) level", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000335:6:1:1", "start": 1459, "end": 1476, "surface": "NUTS-2 level data", "probe_tag": "confusion", "probe_score": 0.146, "luna_label": 1}, {"key": "prwp:000335:6:1:4", "start": 1786, "end": 1808, "surface": "_fDi Markets_ database", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}]}, {"key": "paddy-070", "text": "**References**\n\n\nDobbin International (2008). The Western Peninsula – Regional Development Strategy and\n\nInvestment Action Plan. Prepared for FIAS.\n\n\nEIU Country Profile (2008). Sierra Leone.\n\n\nFIAS (2006). Competitiveness and Corporate Social Responsibility in Sierra Leone. Industry\n\nSolutions for Tourism and Mining.\n\n\nFIAS (2007). Sierra Leone Economic Competitiveness and Corporate Social Responsibility:\n\nAction Steps for Sustainable Tourism Development in Sierra Leone 2007-2010.\n\n\nGovt. of Sierra Leone (1991). The Development of Tourism Act, 1990: Supplement to the Sierra\n\nleone Gazette vol. CXXII, No. 14, dated 28 <sup>th</sup> February 1991.\n\n\nGovt. of Sierra Leone (2008). Strategic Action Plan: Tourism Development in Sierra Leone.\n\n\nMiller, M. M. and L. J. Gibson (2005). ‘Cluster-based Development in the Tourism Industry:\n\nPutting Practice into Theory.’ In Applied Research in Economic Development. pp. 47-64.\n\n\nMINTeL (2006). The Gambia.\n\n\nPorter, M.; Ketels, C. and M. Delgado (2007). ‘The Microeconomic Foundations of Prosperity:\n\nFindings from the Business Competitiveness Index.’ In World Economic Forum (2007).\nThe Global Competitiveness Report 2007-08.\n\n\nThe Cluster Consortium (1999). South Africa tourism cluster study.\n\n<u>http://www.nedlac.org.za/find/index.htm</u>\n\n\nUNWTO Statistics and databases various years.\n\n\nWorld Bank (2006). Assessment of Socio-cultural and Economic Characteristics of Populations\n\nin Selected Project Areas and Potential for Establishing Alternative Livelihood Schemes.\n\n\nWorld Bank (2006). Sierra Leone: Adding Value through Trade for Poverty Reduction: A\n\nDiagnostic Trade Integration Study.\n\n\nWorld Bank (2007). Economic and Financial Feasibility Study on Ecotourism Potential in\n\nSelected Protected Areas.\n\n\nWorld Bank", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:004276:20:0:0", "start": 1297, "end": 1327, "surface": "UNWTO Statistics and databases", "probe_tag": "confusion", "probe_score": 0.2423, "luna_label": 0}]}, {"key": "paddy-071", "text": " but has little impact on the elasticity of average years of schooling.\nUnfortunately, measuring the impact of foreign capital goods on schooling by level of\neducation was hampered by implausibly high, albeit positive, parameter estimates for\nschooling. Tentatively, such an exercise reveals an altering of the coefficients for\nprimary education but has little impact on secondary and tertiary schooling. This\nclearly supports the earlier finding of the effect of trade openness on growth by level\nof education.\n\n\n**_Life Expectancy_**\n\n\nGiven the incomplete nature of education to proxy for human capital, a look at the\neffect of the health status yields important insights. Column 5 includes life\nexpectancy at birth into the regression. The schooling variable is removed due to\ncollinearity. The health variable is highly significant and has a very strong impact on\nlong-run growth. The estimate suggests that a 1 percent increase in life expectancy\nwould increase output by about 1.04 percent. Barro (2001) suggests that the variable\nhas such a strong impact on growth because it may proxy for features other than\nhealth, such as social capital, better work habits and a higher level of skill. The\nelasticities could be biased due to the reliance on interpolated data sources.\n\n\n-49", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:002824:53:1:0", "start": 1254, "end": 1279, "surface": "interpolated data sources", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}]}, {"key": "paddy-072", "text": " measured by labor productivity and three aspects of financial performance:\nprofitability, financial liquidity, and return on investments. We measure labor productivity as\noperating revenues per worker. Financial performance is assessed through financial liquidity using\n\n\n5 For instance, C is considered as a BOS through 3 layers of ownership in the case where the Ministry of Finance owns (by 10% or more) a company\nA, which owns company B (by 10% or more), which also owns company C.\n6 The WB BOS database does not provide information on golden shares or veto powers, so the level of participation and thresholds proposed are\nused as a proxy of control and influence of the state over the firms.\n7 The ORBIS dataset does not provide a variable to directly identify those with state participation Although a proxy variable in ORBIS denoted\nas the Global Ultimate Owner can serve to identify some companies with state shareholding, it provides an underestimation of the real footprint of\nthe state in the markets and suffer from omission errors that could misclassify SOEs as private firms (Dall'Olio, et al., 2022).\n8 Yet, there is also variation of coverage within these countries as documented by (Bajgar, Berlingieri, Calligaris, Criscuolo, & Timmis, 2020).\n\n\n6", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "sample:prwp:001299:7:2:0", "start": 493, "end": 508, "surface": "WB BOS database", "probe_tag": "confusion", "probe_score": 0.1389, "luna_label": 1}, {"key": "sample:prwp:001299:7:2:1", "start": 705, "end": 718, "surface": "ORBIS dataset", "probe_tag": "confusion", "probe_score": 0.769, "luna_label": 1}]}, {"key": "paddy-073", "text": " to below. Finally, it is notable that only once\na number of unknown mill characteristics are controlled for in the fixed effects\nformulation is the Fairtrade effect discernable (see footnote 50). This result is\nintuitive—there is a lot of mill-specific information that is simply unknown, including\nproportions of zoned coffee, exact destination markets, access to niche markets,\nproportion of unripe coffee, role of other NGOs and other interventions, etc. It is for\nthis reason that the fixed effects formulation is so very useful: Claims of the benefits\nof Fairtrade are notoriously difficult to attribute to Fairtrade in commodity markets\nwith many actors and many factors (Ronchi 2002b), many of which are unknown.\nControlling for them is the only way to isolate a Fairtrade effect.\n\n\n49 This result is robust across all version of (7) and (8) estimated using pooled OLS, between and fixed\neffect estimators (see Ronchi (2005) for a complete set of results).\n50 This result is only true only once mill-specific fixed effects are controlled for in the fixed effects\nformulation of (7). Pooled OLS and between estimators for the Fairtrade coefficient (see Ronchi (2005)\n) are not significant.\n51 It is important to recall that this fall in the mark-down measure is not due to the oft-superior Fairtrade\n\n                  -                   price (US $1.26/lb) as the data for p R in (p* - p R ) do not include the Fairtrade premium. Any effect\n\nmust be attributable to the other Fairtrade effects such as capacity building, export technical assistance,\norganizational support, liquidity, credit, etc.", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:003224:46:1:0", "start": 1373, "end": 1385, "surface": "data for p R", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}]}, {"key": "paddy-074", "text": "##### **Introduction**\n\nThe World Bank has declared that data are the next deprivation to end; they argue that the lack of data\n\n\ncauses many of the world’s poorest populations to be overlooked when resources are allocated to address\n\n\ntheir essential needs [1]. Data deprivation is a pressing challenge with as many as 74% of the global and 97%\n\n\nof the Sub-Saharan African population living in countries without adequate vital registration [2]; one-third of\n\n\ncountries lack any poverty statistics [1]; and only 17% of the estimated road traffic deaths are reported in\n\n\nofficial figures of low-income countries [3]. Without data to inform national and urban policies, the gap\n\n\nbetween low- and high-income countries will worsen [4]. However, while official statistics are poor, data in the\n\n\nhands of private providers are plentiful, populated by the rapid expansion of mobile phones and social media.\n\n\nGlobally, phone penetration reached 67% in 2019 [5], and social media penetration is almost 50% [6]. This\n\n\nprovides an opportunity for using crowdsourced data to study major urban and development policies [7–11].\n\n\nIn this project we test the hypothesis of whether privately maintained data can be transformed into a\n\n\nresource to better understand development challenges. Private data have been used to characterize\n\n\npopulations from determining poverty to understanding public emotions [12–17]. Here, we use private data\n\n\nto describe the urban environment that affects those populations, specifically analyzing events reported on\n\n\nsocial media that affect people’s safety such as road traffic crashes, crime or floods. We focus on road traffic\n\n\ncrashes (RTCs). Despite being the number one cause of death for children and young adults aged 5-29 years,\n\n\nthe lack of adequate data on RTCs is a recognized and unmet challenge <mark>[18]</mark> . The objective is to improve\n\n\nRTC data for urban planners so they can contribute to", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000063:3:0:2", "start": 1282, "end": 1294, "surface": "Private data", "probe_tag": "confusion", "probe_score": 0.5585, "luna_label": 1}]}, {"key": "paddy-075", "text": " non-consumption data are reliable. The consumption models use household\nand individual characteristics such as educational attainment and employment status of household members,\nhousehold composition and the like. This S2S approach is useful if price adjustments or poverty lines are\nnot comparable over time, but non-consumption data are comparable. Given some key assumptions are\nsatisfied, Christiansen et al. (2012) and Ahmed et al. (2013) corroborate the reliability of this approach\nusing data from various countries.\n\nNISR (2016) estimates a poverty trend between 2010/11 and 2013/14 using the S2S approach and compares\nit with the official poverty trend between 2010/11 and 2013/14 in NISR (2015). Following Tarozzi (2002),\nNISR (2016) estimates a logistic regression model to predict the probability of being poor from nonconsumption data using EICV4, predicts a poverty rate from a model using EICV4, and applies it to nonconsumption data in EICV3. Using this approach, they find that the poverty rate of EICV3 is 44.6 percent,\nwhich is almost the same as the official poverty estimate for EICV3, i.e., 44.9 percent. NISR (2016) also\nestimates the poverty rate of EICV4 using this approach (39.0 percent), which is almost identical to the\nofficial estimate of EICV4 (39.1 percent). The S2S analysis, therefore, also confirms that the official trend\nis reliable.\n\n\nIn Rwanda, the reduction in food budget shares over time also corroborates the poverty decline. Typically,\nincreases in income are strongly associated with a declining share of the budget spent on food (Engel’s\nlaw). We examine if this is true in the case of Rwanda. We estimate real food budget shares, expressed in\nJanuary 2014 prices, for 2010/11 and 2013/14 and find that the real food", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:007505:15:1:1", "start": 1, "end": 21, "surface": "non-consumption data", "probe_tag": "confusion", "probe_score": 0.0627, "luna_label": 1}, {"key": "prwp:007505:15:1:2", "start": 829, "end": 848, "surface": "nonconsumption data", "probe_tag": "confusion", "probe_score": 0.8965, "luna_label": 1}]}, {"key": "paddy-076", "text": " the measure of consumption based on the 2022 Demographic and WASH\nsurvey is still far from being an accurate welfare metric for poverty estimation in Syria, particularly for the\npurpose of assessing the evolution of poverty over time using pre-conflict estimates from the HIES as a\nbaseline.\n\n\nIn fact, the consumption module in each of the three rounds of HIES conducted prior to the conflict in\n2003, 2007 and 2009 was based on an extensive diary account of household expenditures covering more\nthan 500 items (goods and services). According to Beegle and others (2012), use of diary versus recall, and\ndifferences in the number of consumption items collected in surveys drastically affect poverty and\ninequality measures.\n\n3.2 Monetary poverty estimates based on the HNAP 2022 Demographic and WASH\nsurvey\n\n\nWhile differences in survey instruments between the HNAP 2022 and the HIES constrain the possibility of\nobtaining accurate pre-post conflict poverty comparisons, it is important to assess whether poverty\nestimates from the HNAP 2022 can be used to assess the profile of monetary poverty in Syria in 2022.\n\n\nThe welfare metric used for poverty analysis is constructed aggregating all available information on\nhousehold expenditure, with the sole exclusions of expenditures recorded under the “Other” categories\nfor both monthly and annual consumption items (see Table 4). Compared to a “standard” consumption\naggregate for welfare measurement, the consumption aggregate based on information collected in the\nHNAP 2022 has three major drawbacks:\n\n\n19 Engel’s law – the fact that the expenditure share dedicated to food consumption decreases as income rises – has been proved\nto be extraordinarily consistent across time and space. As such, the predictive character of this relationship has been widely used\nin poverty measurement, from the estimation of poverty lines to PPP assessment.", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001486:16:1:3", "start": 444, "end": 483, "surface": "diary account of household expenditures", "probe_tag": "confusion", "probe_score": 0.5736, "luna_label": 1}]}, {"key": "paddy-077", "text": " compute a weekly Merton’s distance-to\n\ndefault ( _ddi,j,t,w_ ). Then for each bank _i_ in year _t_, we run a time series regression of bank _i_ ’s\n\n\nweekly change in distance-to-default on country average weekly change in distance-to-default\n\n\nexcluding bank _i_ itself:\n\n\n(4)\n\n\nWe follow Morck, Yeung, and Yu (2000) and Karolyi, Lee, and Van Dijk (2011) and use\n\n\nthe logistic transformation of R-squared from the above regression, which is equal to _log(rsqi,j,t /_\n\n_(1-rsqi,j,t))_, to measure systemic risk posed by bank _i_ . R-squared is only computed for banks with\n\n\nat least twenty-six weeks of changes in weekly distance-to-default data in a year. In terms of\n\n\nmeasuring co-dependence, using R-squared has advantages over alternative measures as\n\n\n7", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:005156:8:1:0", "start": 616, "end": 647, "surface": "weekly distance-to-default data", "probe_tag": "confusion", "probe_score": 0.372, "luna_label": 1}]}, {"key": "paddy-078", "text": "; the effective weighted average is found to equal\n45 percent (data from DATAPREV, SUB, Plano Tabular da DIIE, and Ministerio da fazenda).\n\n\n<u>1</u>\n##### 10 The expression stated in dynamic terms: kGRt = (α( 1 + qt ) / [( 1 + nt )( 1 + gt )]α ) 1 −α .\n\n\n14", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:005733:15:3:0", "start": 73, "end": 81, "surface": "DATAPREV", "probe_tag": "keep", "probe_score": 0.9213, "luna_label": 1}, {"key": "prwp:005733:15:3:3", "start": 115, "end": 136, "surface": "Ministerio da fazenda", "probe_tag": "confusion", "probe_score": 0.7967, "luna_label": 1}]}, {"key": "paddy-079", "text": "changing each fuel’s share of total generation. Lastly, the pandemic may have disrupted the\n\nlogistics of resource extraction and fuel imports and exports, causing changes in prices large\n\nenough to stifle generation or encourage substitution from one type of generation to another.\n\nThis study investigates whether COVID-19 has affected total electricity generation, GHG\n\nemissions from the power sector, and electricity supply mix, particularly the penetration of solar\n\nand wind. Rather than focusing on a single system operator (Carvalho et al., 2021; Leach et al.,\n\n2020; Agdas and Barooah, 2020), this study uses daily electricity generation data from the United\n\nStates, Brazil, India, and the majority of the European countries from January 2018 to September\n\n2021. According to the latest data from the Emission Database for Global Atmospheric Research\n\n(EDGAR), these countries collectively account for 31% of global CO2 emissions in 2019.\n\nTo quantify the impact of the pandemic on electricity generation, we regress daily\n\ngeneration in each country on the stringency of the measures adopted to combat the pandemic,\n\ncontrolling for other factors thought to affect generation. We also regress the _share_ of a particular\n\ntype of electricity (e.g., coal-fired or hydro) out of total generation on the same determinants and\n\nmeasures of the pandemic. Identification comes from the stringency of lockdowns and other public\n\nhealth measures that vary across countries, depending on how early the pandemic hit them and\n\nhow proactive the governments were in implementing pandemic mitigation measures, as well as\n\nwithin a country over time.\n\nThe study finds that a 1-unit increase in the lockdown stringency measure is associated\n\nwith a 0.473 GWh decrease in the total daily generation. The lockdown stringency is measured\n\nusing the Oxford COVID-19 Government Response Tracker, which documents and compares\n\nworldwide government policy responses to the coronavirus using well-defined and consistent\n\ncriteria across countries and over time. On average, CO", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001035:5:0:0", "start": 619, "end": 652, "surface": "daily electricity generation data", "probe_tag": "confusion", "probe_score": 0.6962, "luna_label": 1}]}, {"key": "paddy-080", "text": "**Table 3. Business Cycle Synchronization – orthogonal to US business cycle**\n\n\n**2.1.2 Monthly Data: 1995 - 2003**\n\n\nThe business cycle is usually defined in the range of 6 to 32 quarters, as such, the low frequency\n\n\nof annual data might be insufficient to fully assess the degree of business cycle synchronization.\n\n\nIn this section we therefore complement our analysis in the previous section with an analysis of\n\n\nmonthly data, where output is proxied by seasonally adjusted monthly indices of industrial\n\n\nproduction and economic activity.\n\n\nWe use spectral analysis to estimate the correlation at different frequencies and use the average\n\n\ncoherence at business cycle frequency (6 to 32 quarters) of year-over-year changes in economic\n\n\nactivity as a summary measure of business cycle synchronization (Garnier, 2003). The\n\n\nadvantage of using cross-spectral densities over simple correlations in the analysis of business\n\n\ncycle synchronization is twofold. First, spectral analysis avoids possible business cycle\n\n\ndistortions due to filtering, it is well known that the cycles change with the de-trending method\n\n\n(Canova, 1998). Second, contemporaneous correlation is unable to take lagged co-movement\n\n\ninto account. As coherence measures the correlation between two series in the frequency domain\n\n\nand further provides information on the phase lead/lag it captures provides a richer analysis of\n\n\nthe business cycle dynamics. While the coherence measures to what extend two business cycles\n\n\nare dominated by the same frequency, the phase lag shows to what extend elements with the\n\n\nsame frequency lag each other. In sum, a high degree of business cycle synchronization implies\n\n\na high coherence and a low phase lag.\n\n\nTable 4 shows the average coherence at business cycle frequency between year-over-year growth\n\n\nrates of economic activity during 1995 and 2003. Encouragingly, the results broadly confirm the\n\n\nfindings of the previous section.\n\n\n7", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:002798:6:0:1", "start": 222, "end": 233, "surface": "annual data", "probe_tag": "confusion", "probe_score": 0.3662, "luna_label": 0}]}, {"key": "paddy-081", "text": "Portugal SIMPOC Labour Force Survey 2001\nCentral African Rep. MICS Multiple Indicator Cluster Survey 2000\nRwanda MICS Multiple Indicator Cluster Survey 2000\nSenegal MICS Multiple Indicator Cluster Survey 2000\nSierra Leone MICS Multiple Indicator Cluster Survey 2000\nSouth Africa SIMPOC Survey of Activities of young people 1999\nSudan MICS Multiple Indicator Cluster Survey 2000\nSwaziland MICS Multiple Indicator Cluster Surveys 2000\nTanzania SIMPOC Integrated Labour Force Survey 2001\nTogo MICS Multiple Indicator Cluster Survey 2000\nTrinidad and Tobago MICS Multiple Indicator Cluster Survey 2000\nTurkey SIMPOC Labor Force Survey 1999\n\nMultipurpose\n\nUganda Survey National Household Survey UNHS-2 2202-2003\n\nUzbekistan MICS Multiple Indicator Cluster Surveys 2000\n<u>Venezuela</u> <u>LSMS</u> <u>Encuesta de Hogares por Muestreo</u> <u>2003</u>\n\n\n18", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:003332:17:0:1", "start": 62, "end": 100, "surface": "MICS Multiple Indicator Cluster Survey", "probe_tag": "confusion", "probe_score": 0.8854, "luna_label": 1}, {"key": "prwp:003332:17:0:13", "start": 785, "end": 789, "surface": "LSMS", "probe_tag": "confusion", "probe_score": 0.5917, "luna_label": 1}]}, {"key": "paddy-082", "text": "**APPENDIX A: Data.**\n\n**A.1 Data Sources.**\n\nMost of the data utilized in this study come from a series of Encuesta de Ingresos y\n\n\nGastos de los Hogares (ENIGH), collected by the Instituto Nacional de Estadistica\n\n\nGeografica e Informatica (INEGI), and conducted in the third quarters of 1989, 1992,\n\n\n1994,1996,1998 and 2000. ENIGH is a rich households survey built for the purpose of\n\n\nmeasuring the consumption and earnings of Mexican households. Even if the size of the\n\n\nENIGH has been varying year to year, and its questionnaire updated from survey to\n\n\nsurvey, the conceptual framework remain the same. This ensures that ENIGH’s results\n\n\nare comparable across years. The survey is stratified according to urban and rural\n\n\nlocation. The sampling is done to assure that households are representative of geographic\n\n\nclusters with probability of being included proportional to cluster size. All the standard\n\n\nerrors in the results are corrected for survey design.\n\n\nThe income data and especially the consumption data are very disaggregated. The survey\n\n\nreports 43 income categories subdivided into monetary, non-monetary and financial\n\n\nincome. The consumption data consist of more than 600 different entries, about half of\n\n\nwhich are food items. Food and manufacturing products and services are finely\n\n\ndisaggregated.\n\n\nSince household size is not the same across income levels, and because the welfare\n\n\nmeasures are concerned with the well-being of individuals, all data were converted to a\n\n\nper capita basis. Since the measure of individual welfare still doesn’t have a firm\n\n\ntheoretical and empirical basis for the construction of equivalence scales, this paper\n\n\nadopts the standard practice of dividing household income and expenditure by its\n\n\nresidents, with children of age 14 or less counting as half of adults. The measure of total\n\n\nhousehold income is equal to the summation of financial, monetary and non-monetary\n\n\nincome. Non-monetary income includes payment in", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:002565:36:0:3", "start": 979, "end": 990, "surface": "income data", "probe_tag": "confusion", "probe_score": 0.7035, "luna_label": 1}]}, {"key": "paddy-083", "text": " with some specialized agencies that may be interested in conducting similar exercises. For\nexample, we have been collaborating with the International Telecommunications Union (ITU) to\nwiden the scope of the ITU annual telecommunications regulatory survey to incorporate relevant\ntrade policy questions. Such a collaborative approach would enable us to collect additional\ninformation of interest for this project, without unduly burdening the respondents of regulatory\nagencies in the economies.\n\nAlso, the World Bank and WTO Secretariat are engaged in the development of a new data set on\nservices PTAs that will, for the first time, cover in a comprehensive manner the most important\naspects of these agreements, from the design of the regulatory framework through to the specific\ncommitments on liberalization. We aim at quantifying commitments in the PTAs covered in the new\ndata set, which would allow us to compare the restrictiveness of services policies arising from\ndifferent PTAs, and to compare them with the restrictiveness of applied services policies, for the\neconomies covered by the World Bank -WTO Services Trade Policy Database.\n\nFinally, the World Bank, the WTO Secretariat and the OECD are continuing to collaborate on the\nproduction and dissemination of services trade policy information as a global public good. We are\ncommitted to ensuring the gradual convergence of the respective projects. The three agencies are\nalso working to increase the number of economies and sectors covered, and the inclusion of new\npolicy measures.\n\n\n28", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000062:29:1:0", "start": 208, "end": 255, "surface": "ITU annual telecommunications regulatory survey", "probe_tag": "confusion", "probe_score": 0.5672, "luna_label": 0}]}, {"key": "paddy-084", "text": "cases, including journey <br>planning, the mapping of <br>topography, as well as <br>demographic indicators.<br>|<br>Following data must be online to qualify for <br>assessment:<br>• <br>Markings of national traffic routes <br>• <br>Markings of relief/heights <br>• <br>Markings of water stretches <br>• <br>National borders Coordinates - <br>Note: To qualify, data must <br>contain geographic projections <br>that enable to interpret<br>coordinates<br>|\n|Administrative<br>Boundaries<br>|<br>Data on administrative units or areas <br>defined for the purpose of<br>administration by a (local) <br>government.The development of this <br>category draws on work ofFAO Global <br>Administrative Unit Layers <br>(GAUL)project, as well as theUNGIWG. <br>|Open data about <br>administrative zones has<br>many use cases: Who are the <br>candidates in my region? <br>Which government bodies <br>administer my region? How is <br>wealth distributed across <br>regions? The Index assesses <br>two administrative boundary <br>levels (e.g. federal states = <br>level 1, and municipalities = <br>level 2).<br>|Following data must be online to qualify for <br>assessment:<br>• <br>Boundary level 1 <br>• <br>Boundary level 2 (not required, if <br>country has only one level)<br>• <br>Coordinates of administrative <br>zone (latitude,", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001873:48:2:3", "start": 308, "end": 336, "surface": "National borders Coordinates", "probe_tag": "drop", "probe_score": 0.0213, "luna_label": 0}, {"key": "prwp:001873:48:2:4", "start": 493, "end": 530, "surface": "Data on administrative units or areas", "probe_tag": "confusion", "probe_score": 0.0991, "luna_label": 0}]}, {"key": "paddy-085", "text": "Jain, M., P. Mondal, G. L. Galford, G. Fiske, and R. S. DeFries (2017a), “An automated approach to map\n\nwinter cropped area of smallholder farms across large scales using MODIS imagery.” _Remote_ _Sensing_, 9,\n566, URL `https://doi.org/10.3390/rs9060566` .\n\n\nJain, M., P. Mondal, G. L. Galford, G. Fiske, and R. S. DeFries (2017b), “India annual winter cropped area,\n\n2001-2016.” URL `https://doi.org/10.7927/H47D2S3W` .\n\n\nMerfeld, Joshua D (2019), “Spatially heterogeneous effects of a public works program.” _Journal_ _of_ _Develop-_\n\n_ment_ _Economics_, 136, 151–167.\n\n\nModanesi, Sara, Christian Massari, Stefania Camici, Luca Brocca, and Giriraj Amarnath (2019), “Perfor\nmance of a drought standardized soil moisture index based on ESA CCI Soil Moisture product: validation\nin India using crop data.” In _Geophysical_ _Research_ _Abstracts_, volume 21.\n\n\nMukherjee, Abhijit, Dipankar Saha, Charles F Harvey, Richard G Taylor, Kazi Matin Ahmed, and Soumen\ndra N Bhanja (2015), “Groundwater systems of the Indian sub-continent.” _Journal_ _of_ _Hydrology:_ _Regional_\n_Studies_, 4, 1–14.\n\n\nMuralidharan, Karthik, Paul Niehaus, and Sandip Sukhtankar (2016), “Building state capacity: Evidence\n\nfrom biometric smartcards in India.” _American_ _Economic_", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001627:28:0:0", "start": 793, "end": 802, "surface": "crop data", "probe_tag": "drop", "probe_score": 0.0193, "luna_label": 0}]}, {"key": "paddy-086", "text": " Credit? Evidence from European Data.</mark> _<mark>Journal of</mark>_\n_<mark>Banking & Finance</mark>_ <mark>80 (C): 119-134.</mark>\n\n\n31", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000833:32:4:0", "start": 23, "end": 36, "surface": "European Data", "probe_tag": "drop", "probe_score": 0.0492, "luna_label": 0}]}, {"key": "paddy-087", "text": " to 9; higher scores indicate greater protection against<br>noncommunicable diseases. Data are for individuals ≥15 years of age from the 2021 Gallup World Poll for<br>2022<br>|World Gallup Poll<br>(GDQP) / Food systems<br>dashboard<br>|\n|**NCD-Risk** score among adults ≥ 15<br>|NCD-Risk scores are on a scale of 0 to 9; higher scores indicate greater risk for noncommunicable diseases.<br>Data are for individuals ≥15 years of age from the 2021 Gallup World Poll for 2022<br>|World Gallup Poll<br>(GDQP) / Food systems<br>dashboard<br>|\n|Prevalence of**overweight & obesity** <br>among women (20 - 49 years) %<br>|Percentage of women 20–49 years of age with a BMI greater than or equal to 25 kg/m2 in 2016<br>|UNICEF Global Database<br>(based on NCD-RisC)<br>|\n|Prevalence of**underweight** among<br>women (20 - 49 years) %<br>|Percentage of women 20–49 years of age with a BMI less than 18.5 kg/m2 in 2016<br>|UNICEF Global Database<br>(based on NCD-RisC)|\n|Adult**diabetes**prevalence<br>|Proportion of adults aged 18+ years with diabetes. Diabetes is defined as having a fasting glucose of 7.0<br>mmol/L or higher, being on medication for raised blood glucose, or having a past diagnosis of diabetes. Data<br>for 2014, except 1 country 2003, 1 country 2005, 2 countries 2010, 1 country 2012", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001312:33:1:3", "start": 747, "end": 755, "surface": "NCD-RisC", "probe_tag": "confusion", "probe_score": 0.0823, "luna_label": 1}, {"key": "prwp:001312:33:1:4", "start": 747, "end": 755, "surface": "NCD-RisC", "probe_tag": "drop", "probe_score": 0.0135, "luna_label": 1}]}, {"key": "paddy-088", "text": "*October 13,**<br>**2021 PPT**<br>**document**<br>**delivery starts**<br>**Around**<br>**2,351,351**<br>**registered in the**<br>**RUMV Census**<br>**Around 1,963,864**<br>**attended the**<br>**biometric**<br>**appointment**<br>**Around 1,492,295 PPT**<br>**documents were**<br>**delivered**|**Around 1,963,864**<br>**attended the**<br>**biometric**<br>**appointment**|**Around 1,492,295 PPT**<br>**documents were**<br>**delivered**||\n\n\n\nFigure A.2. ETPV Application Process\n\n\n45", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001137:46:4:0", "start": 131, "end": 142, "surface": "RUMV Census", "probe_tag": "drop", "probe_score": 0.0242, "luna_label": 1}]}, {"key": "paddy-089", "text": ", and policy makers often lead to\n\nambiguity. The definition of the agricultural holding is the primary unit of analysis in agricultural\n\nsurveys, whereas the household is the primary unit of analysis in household surveys. The Food and\n\nAgriculture Organization of the United Nations (FAO) (2015) defines the agricultural holding as\n\nan “economic unit of agricultural production under single management comprising all livestock\n\nkept and all land used wholly or partly for agricultural production purposes, without regard to title,\n\nlegal form or size...” Holdings can be divided into parcels, and the FAO notes that “a distinction\n\nshould be made between a parcel, a field and a plot”, where “a field is a piece of land in a parcel\n\nseparated from the rest of the parcel by easily recognizable demarcation lines such as paths,\n\ncadastral boundaries, fences, waterways or hedges. A field may consist of one or more plots, where\n\n\n28", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000049:29:1:0", "start": 124, "end": 145, "surface": "agricultural\n\nsurveys", "probe_tag": "drop", "probe_score": 0.0187, "luna_label": 0}]}, {"key": "paddy-090", "text": "**Table A2: UN classification of energy-intensive manufacturing sectors**\n\n\n\n\n\n\n\nSource: UNIDO (2010).\n\n\n**Table A3: Descriptive statistics WBES among sectors with at least one politically connected**\n**firm versus sectors with zero connected firms by types of connection**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Col1|Sectors<br>with PC All other<br>CEOs sectors|Sectors<br>with PC All other<br>owners sectors|Sectors<br>with any All other<br>PC firm sectors|\n|---|---|---|---|\n|||||\n|Share of firms acquired Land from Government<br>Share of firms in Industrial City<br>Share of firms with Bank Loan<br>Waiting days for construction permit<br>CoV (waiting days construction permit)|48%<br>37%<br>47%<br>36%<br>21%<br>17%<br>595<br>642<br>0.56<br>0.45|44%<br>33%<br>42%<br>33%<br>19%<br>17%<br>608<br>681<br>0.54<br>0.33|44%<br>30%<br>41%<br>34%<br>19%<br>13%<br>610<br>696<br>0.53<br>0.30|\n\n\nSource: WBES.\n\n\n38", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:006410:39:0:0", "start": 140, "end": 144, "surface": "WBES", "probe_tag": "drop", "probe_score": 0.0382, "luna_label": 0}, {"key": "prwp:006410:39:0:1", "start": 140, "end": 144, "surface": "WBES", "probe_tag": "confusion", "probe_score": 0.1601, "luna_label": 0}]}, {"key": "paddy-091", "text": ", _β_ <sup>ˆ</sup> 1, and _β_ <sup>ˆ</sup> 2 are estimated by ordinary least squares (OLS). <sup>7</sup> A consistent estimate of\n\n\nthe level of oil production needs to adjust for the fact that the expectation of the exponential of\n\n\na random variable is not equivalent to the exponential of its expectation.\n\n\n**A.4.1** **Consolidating oil production and field characteristics data**\n\n\nTo construct the dataset used to estimate equation 2, we combine data on field-level character\n\nistics (including oil output and GOR) with flare-level radiant heat measures. In this section, we\n\n\ndescribe the sources and preparation of calibration data.\n\n\n7The statistical software package used to that end is STATA version 14.\n\n\n38", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:007190:39:1:0", "start": 337, "end": 382, "surface": "oil production and field characteristics data", "probe_tag": "drop", "probe_score": 0.0443, "luna_label": 1}]}, {"key": "paddy-092", "text": " January 1, 2012.\n\n\nThe WTO commitments schedule became publicly available from the WTO\n\n\nafter the Russian Federation was formally invited to join the WTO on December 16,\n\n\n2011. The Russian Federation is scheduled to implement its commitments to the\n\n\nWTO in stages, beginning with the summer of 2012, with the longest transition period\n\n\neight years after accession, or 2020. We calculate the impact of the phased\n\n\nimplementation of the commitments, starting from the impact of the commitments in\n\n\n2012.\n\n\ncm3; and 870324 is for cylinder capacities exceeding 3 000 cm3. Within each six digit category, we\ntake a simple average of the lower and upper bounds as the expected value of the cylinder capacity, i.e.,\nfor 870321 we have 500 (0+1000)/2=500). Therefore, the following expected engine volumes apply:\n500 cm3 for 870321, 1250 cm3 for 870322, 2250 cm3 for 870323, and 3000 cm3 for 870324. Then we\nknow that all ten digit tariff lines with the same 6 digit code have this engine capacity and we can apply\nthe specific tariff to the numbers of cars in the import data based on the engine capacity that applies to\nthese ten digit tariff lines from the tariff schedule.\n\n[11 This document is available from the website of the Customs Union, http://www.tsouz.ru/.](http://www.tsouz.ru/)\n\n\n6", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "sample:prwp:005327:7:1:0", "start": 1064, "end": 1075, "surface": "import data", "probe_tag": "drop", "probe_score": 0.0119, "luna_label": 1}]}, {"key": "paddy-093", "text": "Ordered probit,<br>conditional fe logit and<br>re probit|Oredered Probit|Ordered logit|Ordered logit|Ordered probit|Ordered Logit|\n|**Data**|Ad-hoc questionnaire in<br>two villages in Israel|US-GSS 1989-1996|8 countries study 1972-<br>1994|GSOEP 1985-1998|BHPS 1991-2002<br>(Employed)|WVS ( 1980 – 1982,<br>1990 – 1991, and 1995<br>– 1997 waves)|US-GSS 1972-1997|EU Eurobarometer<br>1975-1992|RLMS 1994-2000|Latinobarometro 2004,<br>17 LAM countries|\n|**Study**|Morawetz et al., 1977|Hagerty 2000|Hagerty 2000|Schwarze and Harpfer,<br>2002|Clark, 2003|Helliwell, 2003|Alesina et al. 2004|Alesina et al. 2004|Senik, 2004|Graham and Felton,<br>2006|\n\n\n\n27", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:004758:29:3:4", "start": 191, "end": 197, "surface": "US-GSS", "probe_tag": "drop", "probe_score": 0.0247, "luna_label": 1}, {"key": "prwp:004758:29:3:5", "start": 363, "end": 379, "surface": "EU Eurobarometer", "probe_tag": "confusion", "probe_score": 0.2673, "luna_label": 1}]}, {"key": "paddy-094", "text": "composites combining processes, outputs and outcomes which make it hard to assess the effectiveness\nof government reforms. Fourth, many indicators which do not rely on expert judgement, instead track\nthe existence of specific regulatory management tools and thus not adequate for assessing how each\ntool is used in practice and how they interact with their contexts such as changing economic\ncircumstances.\n\n\nWhile such measurement has been useful, it clearly has limitations in terms of accuracy and validity,\ncalling for new metrics. The availability of large volumes of administrative data on laws and regulations\nopen up new avenues for better gauging the determinants, extent, and effects of volatile laws and\nregulations. Such legislative predictability indicators should further contribute to a more encompassing\nunderstanding of legislative quality.\n\n\n**3.1 Legislative and Regulatory Data**\n\nMore and more data has been made publicly available in recent years by political institutions through\ndedicated websites and open-data portals. Yet, the scope and quality of legislative data that are made\navailable still display a strong cross-country variation in terms of temporal scope and depth.\nNonetheless, most countries tend to disclose the title and text of legislation, its date of publication\nand the ID number of previous versions of legislation. The data collection work underpinning the\npresent analysis follows the data collection methods, database structure, and quality standards\nestablished by the research team (Fazekas et al, 2024). The wider data collection effort and process is\ndescribed in Appendix B. Here, we outline the specific data collection approach in Jordan.\n\n\nThe data collection process consists of four stages: source identification, source annotation, web\nscraping and parsing, as well as data evaluation. First, source identification was conducted in\nconsultation with Jordan’s Legislation and Opinion Bureau (LOB) which hosts the website and\nmanaged legislative data. Second, a detailed source annotation was created that precisely marks each\nvariable in the dataset (table 1) on the LOB website. Such annotations are highly technical, offering\nguidance for the subsequent programming work. Third", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001523:7:0:1", "start": 866, "end": 897, "surface": "Legislative and Regulatory Data", "probe_tag": "drop", "probe_score": 0.0268, "luna_label": 0}]}, {"key": "paddy-095", "text": "Policy Research Working Paper 8972\n\n### **Abstract**\n\nEnumeration areas are the operational geographic units\nfor the collection, dissemination, and analysis of census\ndata and are often used as a national sampling frame for\nvarious types of surveys. Traditionally, enumeration areas\nare created by manually digitizing small geographic units\non high-resolution satellite imagery or physically walking\nthe boundaries of units, both of which are highly time,\ncost, and labor intensive. In addition, creating enumeration areas requires considering the size of the population\nand area within each unit. This is an optimization problem that can best be solved by a computer. This paper, for\nthe first time, produces an automatic designation of predefined census enumeration areas based on high-resolution\ngridded population and settlement data sets and using publicly available natural and administrative boundaries. This\nautomated approach is compared with manually digitized\n\n\n\nenumeration areas that were created in urban areas in Mogadishu and Hargeisa for the United Nations Population\nEstimation Survey for Somalia in 2014. The automatically\ngenerated enumeration areas are consistent with standard\nenumeration areas, including having identifiable boundaries to field teams on the ground, and appropriate sizing and\npopulation for coverage by an enumerator. Furthermore,\nthe automated urban enumeration areas have no gaps. The\npaper extends this work to rural Somalia, for which no\nrecords exist of previous enumeration area demarcations.\nThis work shows the time, labor, and cost-saving value of\n\nautomated enumeration area delineation and points to\nthe potential for broadly available tools that are suitable\nfor low-income and data-poor settings but applicable to\npotentially wider contexts.\n\n\n\nThis paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to\n\nprovide open access to its research and make a contribution to development policy discussions around the world. Policy\nResearch Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at\nupape@worldbank.", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:000025:1:0:0", "start": 160, "end": 171, "surface": "census\ndata", "probe_tag": "drop", "probe_score": 0.0334, "luna_label": 0}]}, {"key": "paddy-096", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\nJust like last year, employment rates demonstrated disparity by age group, gender, education level, the\npresence of a disability, and being in need of MHPSS. Disability was found to be associated with the largest\ndrop in employment, with this factor halving the probability of working in 2024 when compared to the sample\noverall. Having MHPSS needs and being female were also associated with a lower employment likelihood,\nalthough to significantly smaller degrees.\n\n\nCompared to the host population, the refugee sample demonstrated higher employment rates at lower age\nbrackets (15-19 and 20-24), which, considering the findings on poverty, can be interpreted as a coping strategy\nassociated with low income. Higher age brackets (55-59 and 60-64) saw the highest gap in the employment\nrate compared to hosts, supporting the hypothesis that older age individuals have more difficulty integrating\ninto the local labor market.\n\n\n**<u>REGIONAL EMPLOYMENT RATE BY AGE AND POPULATION</u>**\n\n\nRefugees (2023) Refugees (2024) Hosts (2023)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n52%\n\n\n\n\n\n\n\n15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64\n\n\nSource: Survey data, ILO\n\n\n\n**<u>REGIONAL REFUGEE EMPLOYMENT RATE BY POPULATION</u>**\n\n**GROUP (2024)**\n\n\n\n**<u>EMPLOYMENT RATE BY EDUCATION LEVEL</u>**\n\n\nRefugees (2023) Refugees (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'", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000010:10:0:0", "start": 1238, "end": 1254, "surface": "Survey data, ILO", "probe_tag": "keep", "probe_score": 0.9987, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:10:0:1", "start": 1552, "end": 1565, "surface": "SAG estimates", "probe_tag": "keep", "probe_score": 0.9922, "luna_label": 1}]}, {"key": "paddy-097", "text": " not\nanticipate a significant negative impact\non wages due to increased competition.\nEnhancing language fluency is one way\nto address occupational downgrading. To\navoid double counting, we estimated that\naround PLN 1 billion has been already\nincluded in the previously estimated gains\nfrom reduced downgrading.\n\n\n\n**A further increase in labour**\n**participation of refugees would**\n**yield significant macroeconomic**\n**benefits.** Although the employment rate\nof Ukrainian refugees in Poland is already\nhigh when compared to other countries,\nthere is still room for improvement.\nIncreasing the employment of refugees by\n15 thousand people, which would close\nhalf of the gap between the employment\nrate of refugees and Poles, would yield at\nleast PLN 1 billion of value added in the\neconomy. This calculation is made under\nan assumption that those newly hired\nwould be paid minimum wage and should\nbe considered a lower-bound estimate, as\nit understates potential benefits. In part,\nthe higher productivity of workers might\nboost the profits for employers, further\nenhanced by increased investment.\n\n\n#### **4.4 Persons not previously in employment**\n\n\n\n**Employment rates among Ukrainian**\n**refugees are exceptionally high, with**\n**only a minority requiring targeted**\n**assistance to enter the labour market,**\n**in particular those who were not**\n**employed before their displacement.**\nIn the SEIS survey, employment rates\namong refugees aged 18-64 previously\nemployed or self-employed in Ukraine, are\n81% and 91% respectively. That is already\nvery high and any further increase would\nbe marginal. On the other hand, those who\nback in Ukraine managed the household\nhave an employment rate in 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", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:17:2:0", "start": 1400, "end": 1411, "surface": "SEIS survey", "probe_tag": "keep", "probe_score": 0.9295, "luna_label": 1}]}, {"key": "paddy-098", "text": "/data2.unhcr.org/en/situations/ukraine](https://data2.unhcr.org/en/situations/ukraine)</u>\n\n\n14\n\n\n\n**Source:** Deloitte own elaboration based on the results of the MSNA Poland 2023 survey\n\n\n\n15", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:7:3:0", "start": 164, "end": 187, "surface": "MSNA Poland 2023 survey", "probe_tag": "keep", "probe_score": 0.9996, "luna_label": 1}]}, {"key": "paddy-099", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nRefugees from Ukraine are not yet\nutilising their full potential in the Polish\nlabour market. In May 2022, 46% of\narriving refugees declared that they had\nno knowledge of the Polish language\n(NBP, 2023). Over time, this percentage\nimproved, with data from November 2022\nshowing a result of 21%, though this still\nrepresents a large group of people who\ndo not know the local language <sup>24</sup> [^24: Due to possible differences in methodologies data from this surveys should not be directly compared], placing\nthem at risk of limiting possible jobs to\nthose below their educational level. Nearly\n40% of Refugees from Ukraine insured\nat ZUS on 30th June 2023 in Poland were\nemployed in elementary occupations <sup>25</sup> [^25: Elementary occupations include: Cleaners and helpers; Agricultural, forestry and fishery labourers; Labourers in mining, construction, manufacturing\nand transport; Food preparation assistants; Street and related sales and services workers; Refuse workers and other elementary workers.],\n\n\n\nwhile for all employed persons in Q2 2023\nthis percentage was only 5%. Furthermore,\nthe Deloitte Ukraine Refugee Pulse report\nindicates that 50% of respondents point\nto language barriers as an obstacle to\naccessing services to meet basic needs\n(Deloitte, 2023). Meanwhile, in the MSNA\nPoland 2023 survey, when asked about\nencountered barriers for accessing the\nlabour market, 34% of respondents\npointed to lack of language knowledge.\n\n\nSmooth inclusion of refugees on labour\nmarket thus far was enabled by proper\npolicies. In response to the war escalation\n\n\n\nin Ukraine and the influx of refugees into\nthe EU, prompt actions were taken both at\nEU and national level.\n\n\nRefugees from Ukraine in the European\nUnion are covered by the Temporary\nProtection Directive TPD, which was\nactivated on 4th March 2022 (European\nCouncil, 2022). The regulation aims to\nsupport EU Member States’ asylum\nschemes and to ensure harmonised\nrights for the incoming", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:11:0:0", "start": 322, "end": 345, "surface": "data from November 2022", "probe_tag": "keep", "probe_score": 0.9875, "luna_label": 0}, {"key": "sample:jad_paddy_docs:000007:11:0:1", "start": 1185, "end": 1222, "surface": "Deloitte Ukraine Refugee Pulse report", "probe_tag": "keep", "probe_score": 0.9841, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:11:0:2", "start": 1377, "end": 1400, "surface": "MSNA\nPoland 2023 survey", "probe_tag": "keep", "probe_score": 0.9876, "luna_label": 1}]}, {"key": "paddy-100", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n**Republic of Moldova: the case for housing costs corrections in income-based poverty metrics**\n\n\nData from the Republic of Moldova highlights the limitations of poverty metrics that rely solely on disposable\nincome, as they fail to account for vulnerability related to asset ownership. Based on income alone, the 2024\npoverty rate suggests that Ukrainian refugees are able to attain a higher standard of living than their hosts, with\nonly 10% living in poverty compared to 32% of Moldovans. This apparent disparity is largely driven by the\nsizeable financial support that Ukrainians received from humanitarian organizations in 2024, which amounted\nto 44% of their total household income. While the above comparison could suggest that the provided aid is\nexcessive, such an interpretation overlooks an important factor – namely that only 5% of Moldovans incur rental\nexpenses compare to 41% of refugees. In fact, including utilities, housing costs were estimated to consume 46%\nof refugees’ disposable income (inclusive of humanitarian aid), whereas they account for just 14% for Moldovan\nhouseholds. In essence, these findings indicate that refugees are using the entirety of their aid to cover\naccommodation needs. Adjusting disposable income at the household level to reflect these additional housing\nexpenses raises the poverty rate for refugees to 43%, surpassing that of Moldovans.\n\n\n**Poverty is associated with tangibly inferior living conditions, worse healthcare coverage,**\n**more frequently children being out of school, and having to skip meals**\n\n\nRefugee households with members living in poverty were found to more often not report feeling safe when\nwalking alone in their neighborhood after dark (18% vs 10% for those not at risk), to significantly more frequently\nlive in collective housing (27% vs 8%), and to feel under pressure to leave their accommodation (29% vs 11%", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000010:6:0:0", "start": 206, "end": 239, "surface": "Data from the Republic of Moldova", "probe_tag": "keep", "probe_score": 0.943, "luna_label": 1}]}, {"key": "paddy-101", "text": "s/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 surveys of Ukrainian refugees, which showed 62% in July 2023 and 68% in July\n2024 (NBP, 2024). NBP (2024) age group was slightly different, describing adults as 18 years or older.\n\n10 See the note on median wage estimation method in the Online Technical Appendix.\n\n11 Since 2021, ZUS has been requesting information about the occupation of non-agricultural workers who first join the social insurance system (most farmers have a\nseparate social insurance system). Unfortunately, this data is not yet comprehensive, as on June 30, 2022, it included 4.8 million people, and June 30, 2024, 7.2 million\n\n- out of about 16 million socially insured workers. Nevertheless, it is still a very large sample and thus a useful proxy, especially in the case of Ukrainian refugees who\npresumably all should have their occupations listed as they did not arrive before 2022.\n\n\n15", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:7:3:0", "start": 141, "end": 190, "surface": "Polish central bank surveys of Ukrainian refugees", "probe_tag": "keep", "probe_score": 0.9856, "luna_label": 1}]}, {"key": "paddy-102", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\n© UNHCR / Anna Liminowicz\n\n\nDispersing refugees to weaker labour\nmarkets can also have negative social\noutcomes. Albarosa and Elsner (2022) report\nmore anti-immigrant incidents during the\nEuropean refugee crisis (2015/2016) in areas\nof Germany with high unemployment rates\nand shares of right-wing voters. Damm and\nDustmann (2014) show that immigrant men\ndispersed to areas of Denmark with more\nconvicted criminals, experience higher future\ncrime conviction probabilities, but not overall\nneighbourhood crime rate.\n\n\nThe estimated employment rate of refugees\nfrom Ukraine in Poland is above 60% and\nwas among the highest in the OECD <sup>27</sup> [^27: Deloitte elaboration based on the aggregation in OECD International Migration Outlook 2023] .\nAlthough there is uncertainty around\nemployment rate in different countries due\nto uncertainty regarding refugee numbers\n\nwhile MSNA Poland 2023 gives a slightly\nlower figure of 61%. In comparison, the\nemployment rate equalled 61% in the United\n\n\n\nBy refraining from any refugee dispersal\npolicies, Poland and other EU Member\nStates may have improved labour market\ninclusion. In many European countries\nrefugees are geographically dispersed after\narrival to spread the cost of hosting them,\nease the stress on the housing market\nand public services, and to avoid creating\nethnic enclaves. The effects, however, can\nbe detrimental to labour market inclusion\nand may be contrary to other aims of the\npolicy. Dispersal pushes at least some of\nthe refugees into regions with weak labour\nmarkets and few co-nationals already\nsettled, who otherwise could transmit\nimportant information about employment\nopportunities. Fasani, Frattini, and Minale\n(2022) conducted the first cross-country\nstudy of refugee dispersal policies.\nTheir LFS sample covers refugees who\nexperienced dispersal policies in Finland,\nGermany, Ireland, Netherlands, Norway,\nSweden, Switzerland and the UK (no data\nfor Denmark). They find that non-EU15\nrefugees aged 25-64 who arrived when a\ndispersal policy was in place experience\n4.5", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:12:0:0", "start": 778, "end": 819, "surface": "OECD International Migration Outlook 2023", "probe_tag": "keep", "probe_score": 0.9998, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:12:0:1", "start": 951, "end": 967, "surface": "MSNA Poland 2023", "probe_tag": "keep", "probe_score": 0.9984, "luna_label": 1}]}, {"key": "paddy-103", "text": "||Szczecin<br>||Gdańsk||||\n||Poznański|Bydgoszcz||~~Katowice~~||||\n|||||Łęczyń|ski||Lubiński|\n\n\n|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|\n|---|---|---|---|---|---|---|---|\n|||||||||\n|||||||||\n|||||||||\n|||||Warszawa||||\n|||||||||\n|||||||||\n|||Łodź|Wrocław|Kraków|||R² = 0,16|\n||Poz|nański|Szczecin|||||\n|||Gdańsk<br>~~Bydgoszcz~~||Katowice<br>Łęczy|ński||Lubiński|\n\n\n\n5000 6000 7000 8000 9000 10000 11000 12000\n\n\n**Average monthly gross earnings in enterprise sector in poviat of employment in 2022**\n\n\n\n\n\n8%\n\n\n7%\n\n\n6%\n\n\n5%\n\n\n4%\n\n\n3%\n\n\n2%\n\n\n1%\n\n\n0%\n\n4000\n\n\n10%\n\n\n9%\n\n\n8%\n\n\n7%\n\n\n6%\n\n\n5%\n\n\n4%\n\n\n3%\n\n\n2%\n\n\n1%\n\n\n0%\n\n4000\n\n\n\nUkrainian refugee educational attainment\nimplies from 18% (assuming refugees’\neducational attainment from November 2022\nsurvey) to 19% (July-August 2023) higher\n\nearnings than the general population. <sup>36</sup>\n\n\nPoviat-level geographical distribution of\nUkrainian refugees is more concentrated in\nhigh productivity agglomeration", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:16:1:0", "start": 726, "end": 746, "surface": "November 2022\nsurvey", "probe_tag": "keep", "probe_score": 0.967, "luna_label": 1}]}, {"key": "paddy-104", "text": "**Analysis of the impact of refugees from Ukraine on the economy of Poland**\n\n\n**Economists have advanced an array of factors that could be causing these macro-level effects. While macro-level**\n**regressions show a positive impact of immigration on productivity, they cannot show the exact channels through which**\n**these effects come about. It is thus uncertain which are dominant. Among them are:**\n\n\n# Conclusions\n\n\n\n**Analysis of the impact of refugees from Ukraine on the economy of Poland**\n\n\n\nDeloitte’s D.Climate general equilibrium\nmodel finds that refugees from Ukraine\ncontributed 0.7-1.1% to GDP cumulatively in\n2023. In the long-term this effect will grow\nto 0.9-1.35% as the economy fully adjusts.\nThe increases in government revenue from\ndirect and indirect taxes due to wages\nand private consumption of refugees from\nUkraine was likewise modelled.\nThese increases amounted to 0.8-1.0%\nhigher general government revenues in\n2022, 1.3-1.6% in 2023, and 0.95-1.13%\nin the long term. This implies that, while\nthere are no precise public data on the\ngovernment support to refugees from\nUkraine, while a government official quoted\ncost figures of 15-20 billion PLN in 2022\nand around 5 billion in 2023 <sup>50</sup> [^50: 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>] have been\nalready offset by refugees via taxes of\n12.3-15.2 billion PLN in 2022 and 18.2-22.5\nbillion PLN in 2023.\n\n\nAll the modelling results are conservative\nlower bound estimates, as econometric\nstudies from other", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:20:0:0", "start": 1044, "end": 1055, "surface": "public data", "probe_tag": "keep", "probe_score": 0.9561, "luna_label": 1}]}, {"key": "paddy-105", "text": "s tend to have higher rates of entrepreneurship**\n**than native workers.** There may be different reasons for this\nas perhaps immigrants have character traits that make them\nmore inclined towards entrepreneurial activity (unclear in the\ncase of refugees), or maybe it is a way to avoid occupational\ndowngrading as immigrants find it difficult to prove their\neducational and professional credentials from home countries.\nNotwithstanding the reason, such effects are observed across\nthe OECD countries (OECD, 2011). In a recent paper, Anelli,\nBasso, Ippedico, and Peri (2023) look at Italian data, finding that\none standard deviation increase in emigration rate generates\n4.8% decline of business formation in the municipality of origin.\nThe authors use existing migration networks to ensure causality\nof the results. Nevertheless, it is unclear whether immigrantcreated businesses are more successful from the native\nones, but one could speculate that a higher rate of trying new\nbusiness ideas spurs more successes.\n\n\n\nThe escalatation of war 2022 in Ukraine\ncaused a large inflow of refugees into\nPoland. While some refugees from Ukraine\nhave since returned to Ukraine or gone\nfurther to Germany or other countries,\nin October 2023 close to 957 thousand\nremained in Poland (identified by active\nPESEL UKR numbers). They consist\nprimarily of children and working aged\nwomen. Despite the forced nature of\ndisplacement, war trauma, and caregiving\nresponsibilities, refugees from Ukraine\nvery quickly entered the labour market\nas employees and entrepreneurs. The\nprecise number of refugees from Ukraine\nworking in Poland remains uncertain,\nwith our estimates ranging from 225 to\n350 thousand. The lower bound is the\nnumber of social security registrations and\nunderstates the actual figure, as some jobs\nmay not require social contributions or\nremain in the informal sector. The higher\nbound is the product of employment rates\nfrom surveys of refugees from Ukraine,\nand their working age population from\nthe active PESEL UKR database. By JulyAugust 2023 Ukrainian refugee households\nsupported themselves, with 80% of", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:20:3:0", "start": 582, "end": 594, "surface": "Italian data", "probe_tag": "keep", "probe_score": 0.9915, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:20:3:1", "start": 1296, "end": 1305, "surface": "PESEL UKR", "probe_tag": "keep", "probe_score": 0.9915, "luna_label": 1}]}, {"key": "paddy-106", "text": ",\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 most effective forms**\n**of support offered to refugees**\n**and other immigrants in terms of**\n**employment and earnings to be found**\n**in the literature is language training.**\nFoged et al. (2024) analysed labour market\noutcomes of language training, placement\nin strong labour markets, active labour\nmarket policies, cutting welfare benefits,\nand placement in co-ethnic networks that\nwere directed at refugees in Denmark.\nThanks to unusually detailed Danish data,\nthey could follow individual refugees who\narrived in Denmark between 1987 and\n2008, for at least 10 years, and in most\ncases for 15 years. They found intensive\nlanguage training introduced in 1999 to be\nthe most effective of all policies, accounting\nfor a 5-6 pp. increase in the probability of\nemployment and a USD 3,000 increase\nin annual earnings (2015 figures). While\nlocating refugees in strong labour markets\nalso had considerable positive effects,\nthe report found only some evidence that\nActive Labour Market Policies (ALMPs)\nfocused on matching refugees with deficit\noccupations improved their employment\nprospects and no evidence of positive\neffects of cutting benefits or placing\nrefugees in co-ethnic networks. Heller\nand Mumma (2023) exploited randomized\nenrolment lotteries for a publicly-funded", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:15:4:0", "start": 613, "end": 624, "surface": "Danish data", "probe_tag": "keep", "probe_score": 0.9788, "luna_label": 1}]}, {"key": "paddy-107", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n\n# Health analysis\n\n### **Health remains a** **priority need**\n\nAccess to health services has remained a priority\nneed and is ranked among the top three priorities\nby 33% of households, second only to employment\nand livelihoods support. The identification of health\nas a priority is consistent with 2023, where 34% of\nhouseholds ranked health among their top three\nneeds.\n\n\nVariations exist across countries, health care was\nthe top priority need for respondents in half the\ncountries (Bulgaria, Hungary, Moldova, Romania,\nand Latvia). In 2024, health care became a top\npriority for 49% of households in Romania, a shift\nfrom 38% in 2023, potentially indicating barriers and\nchanges in the level of access.\n\n\n\n(2023 N=9,466, 2024 N=7,140)\n\n- Not included in 2023 survey\n\n\n\n**<u>TOP 10 PRIORITY NEEDS (OUT OF THOSE WHO REPORTED)</u>**\n\n\n2023 2024\n\n\n\nEmployment,\n\nlivelihoods\n\n\nHealthcare\n\nservices\n\n\nAccommodation\n\n\nLanguage course\n\n\nEducation for\n\nchildren\n\n\nMedicines\n\n\nFood\n\n\nTrainings,\neducation adults\n\n\nLegal status*\n\n\nRegistration,\nlegal assistance\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n                                   - Not included in 2023 survey\n\nRegionally, women and men prioritized health care\nnearly equally with respectively 34% and 32%\nidentifying it as a priority need. For women, health was the second highest priority after employment\ncompared to men who prioritized it third after\nemployment and accommodation.\n\n\n\n**<u>TOP 10 PRIORITY NEEDS (OUT OF THOSE WHO REPORTED), BY COUNTRY</u>**\n\n\nTop 1 Top 2 Top 3 priority\n\n\n\n|Col1|Regional|Bulgaria|Czechia|Estonia|Hungary|Latvia|Lithuania|Moldova|Poland|Romania|Slovakia|\n|---|---|---|---|---|---|---|", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000004:9:0:0", "start": 829, "end": 840, "surface": "2023 survey", "probe_tag": "keep", "probe_score": 0.9978, "luna_label": 1}]}, {"key": "paddy-108", "text": "\nfell into the last two categories (GUS,\n2024). However, to become promoted\nto an appointed or chartered teacher, a\nforeigner must possess Polish, EU, Swiss,\nor other EEA citizenship (Babakova et al.,\n2024). In effect, only 0.4% of Ukrainian\nrefugees were teachers (ZUS-registered\nin mid-2024) compared to 2.2% of Polish\ncitizens. There were more teachers’\nassistants, who are not regulated, than\n\n\n\nactual teachers. Furthermore, the data\npresented in chapter 2 shows that the\nmedian net wages of Ukrainian refugees\nin the teaching profession are just\nPLN 3,500, constituting 66% of what the\nhost population earns, the lowest earnings\nof all sectors.\n\n\n**Occupational licensing rules regarding**\n**citizenship have been relaxed for**\n**medical professions in the wake of**\n**the COVID-19 pandemic in 2020 and in**\n**February 2022, but those were often**\n**makeshift arrangements.** According to\nthe Babakova et al. (2024) report, during\nthe COVID-19 pandemic, practicing medical\nprofessionals from Ukraine were allowed\n\n\n#### **4.3 Language wage premium**\n\n\n\nEnglish for Speakers of Other Languages\nprogramme in Massachusetts. The policy\ntargeted all immigrants, including those\nwho had lived in Massachusetts for\nseveral years, with data for 2008–2016 and\n4,700 individual lottery applicants. Authors\nfound a causal effect, with annual earnings\nincreasing by USD 2,400. These earnings\ngains generated additional tax revenue\nthat produced a 6% return for taxpayers.\nSchmid (2023) studied African refugees\nwho applied for asylum in Switzerland\nbetween 2008 and 2017. He exploited the\nrandom assignment of refugees to French,\nGerman, and Italian cantons, as well as\ntheir prior language knowledge (e.g. French\nspeakers placed in French or German\nspeaking cantons). The results showed that", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:15:1:0", "start": 434, "end": 461, "surface": "data\npresented in chapter 2", "probe_tag": "keep", "probe_score": 0.9968, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:15:1:1", "start": 1234, "end": 1252, "surface": "data for 2008–2016", "probe_tag": "keep", "probe_score": 0.9905, "luna_label": 0}]}, {"key": "paddy-109", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\n**Recently arrived Ukrainian refugees,**\n**those with below tertiary education**\n**and in older age group are most**\n**likely to communicate in Polish at**\n**intermediate and lower levels.** Based\non the SEIS UNHCR survey, a logistic\nregression has been performed to\nfind out which categories of Ukrainian\nrefugees may most require Polish language\nimprovement. <sup>29</sup> [^29: 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.] Results show that the\nodds of only zero to intermediate Polish\nknowledge decrease with every month\nsince arrival. Ukrainian refugees in the\n18 to 29 age group have the lowest odds\nof having an intermediate or lower level of\nPolish. It translates into a 38% probability,\neven lower than the 41% for refugees with\ntertiary education. The group with the\nhighest odds (70% probability) of zero to\n\n\n\nintermediate Polish are Ukrainian refugees\naged 50 to 64. The results by employment\nsectors are not statistically significant,\nother than for manufacturing. The results\nare intuitive, with the best language skills\namong refugees working in health and\neducation, and the lowest among those\nworking in construction, other services,\nand trade.\n\n\n**Addressing the gap in language**\n**fluency would yield significant**\n**macroeconomic benefits.** To illustrate\nthe macroeconomic impact of public\nintervention, we have assumed that half\nof the current language gap is addressed,\nso that the share of working refugees not\nspeaking Polish fluently falls from 82%\nto 41%. Assuming that the productivity\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n#### **4.5 Recommendations**\n\n##### Language courses\n\n\n**Ukrainian refugees in Poland earn higher wages when they", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:17:0:0", "start": 280, "end": 297, "surface": "SEIS UNHCR survey", "probe_tag": "keep", "probe_score": 0.9807, "luna_label": 1}]}, {"key": "paddy-110", "text": "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n||||||||||the te|rritory|of Polan|d.||||||\n|||||||||||||||||||\n|||||||||||||||||||\n|||||||||||||||||||\n|Lau|nch of ass|igning|PESEL n|umber|s to the|refugee|s|||||||||||\n|||||||||||||||||||\n\n\n\n40%\n\n\n30%\n\n\n20%\n\n\n10%\n\n\n0%\n\n\n\nmen\n\n\n\nwomen\n\n\n\n**Chart 3.** Poland-Ukraine border movement balance and registered/active PESEL data\n\n\n\n3000\n\n\n2500\n\n\n2000\n\n\n1500\n\n\n1000\n\n\n500\n\n\n0\n\n\n\n\n\n65+\n\n\n55-64\n\n\n45-54\n\n\n35-44\n\n\n25-34\n\n\n18-24\n\n\n<18\n\n\n25% 20% 15% 10% 5% 0% 5% 10% 15% 20% 25%\n\n\n**Source:** Deloitte own elaboration based on the PESEL database as of October 2023\n\n\n**Chart 5.** Composition of refugee households in Poland\n\n\n60%\n\n\n50%\n\n\n\n\n\nTotal entries-exits of the Polish-Ukrainian border Pesel data\n\n\n**Source:** Deloitte own elaboration based on Polish Border Guard Headquarter and PESEL data.\n\n\n\n\n\n\n\nMost of the refugees from Ukraine\ncurrently living in Poland are women and\nchildren, though over half of the total\npopulation is of working age.\nThe best population data available is the\nactive PESEL", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:7:1:0", "start": 371, "end": 381, "surface": "PESEL data", "probe_tag": "keep", "probe_score": 0.9994, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:7:1:1", "start": 578, "end": 592, "surface": "PESEL database", "probe_tag": "keep", "probe_score": 0.9995, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:7:1:2", "start": 739, "end": 749, "surface": "Pesel data", "probe_tag": "keep", "probe_score": 0.9773, "luna_label": 1}]}, {"key": "paddy-111", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\n\n**<u>ACCOMMODATION QUALITY BY POVERTY GROUP</u>**\n\n\nIncome above the poverty line Income below the poverty line\n\n\n\n**<u>LIVING CONDITIONS BY POVERTY GROUP</u>**\n\n\nIncome above the poverty line Income below the poverty line\n\n\n\nwalking in\nneighbourhood\n\n\n\nNot reporting\n\nfeeling safe\n\n\n\nUnable to\nstore or cook\n\nfood\n\n\n\nInsufficient\n\nprivacy\n\n\n\nLacking\nseparate\nshowers or\n\ntoilets\n\n\n\nleave\naccommodation\n\n\n\nFeeling under\n\n\n\npressure to\n\n\n\nLiving in\ncollective\n\nhousing\n\n\n\nafter dark\n\n\n\nSource: Survey data, SAG estimates\n\n\n**<u>POVERTY EFFECTS ON HEALTHCARE ACCESS</u>**\n\n\nIncome above the poverty line Income below the poverty line\n\n\n\nSource: Survey data, SAG estimates\n\n\n**<u>FOOD COPING STRATEGY OVER LAST 7 DAYS BY POVERTY</u>**\n**GROUP**\n\n\nIncome above the poverty line Income below the poverty line\n\n\n\n\n\n\n\nHad to reduce\n\nessential\n\nhealth\nexpenditures\nin last 30 days\n\n\n\nUnable to\nobtain needed\n\nhealthcare in\n\n\n\ndays\n\n\n\nCould not\nafford hospital\n\nor clinic fee in\n\nlast 30 days\n\n\n\nessential\n\n\n\nthe last 30\n\n\n\nHad to skip a\n\nmeal\n\n\n\nAdults had to\n\n\n\neat less to\nfeed small\n\n\n\nHad to borrow\n\n\n\nmoney for\n\n\n\n(including\n\n\n\nfood\n\n\n\ndrugs)\n\n\n\nchildren\n\n\n\nNote: Percentages of those that could not afford clinic fees are as\nshare of those not able to access healthcare in the last 30 days\n\n\nSource: Survey data, SAG estimates\n\n\n\nSource: Survey data, SAG estimates\n\n\n\n**Employment remains closely associated with significantly lower poverty rates, but size of**\n**employment income is key**\n\n\nJust like in the case of 2023 data, the current survey round suggests a strong link between employment and\npoverty. Whereas for individuals living in households with no one employed the poverty rate stands at a\nstaggering 62%, it drops to 10% for those with at least one person working. That", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000010:7:0:0", "start": 602, "end": 613, "surface": "Survey data", "probe_tag": "keep", "probe_score": 0.9939, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:7:0:1", "start": 1625, "end": 1634, "surface": "2023 data", "probe_tag": "keep", "probe_score": 0.9751, "luna_label": 0}]}, {"key": "paddy-112", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n## **2.** Ukrainian refugees in the Polish labour market\n\n##### In the past year, as far as labour market integration is concerned, refugees from Ukraine improved in terms of employment and wages, yet they continue to be disproportionately skewed towards elementary occupations. They are also the group to see the fastest improvements, with the gap towards Polish citizens visibly closing across the entire wage distribution (2.1). The current refugee employment rates are only slightly lower than those for Polish citizens, and median net wages are at about four-fifths of the economy as a whole, which may nevertheless be overly optimistic when compared to gross or average wages (2.2).\n\n#### **2.1 Improving economic situation**\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 9. Ukrainian refugee labour status** <sup>**9**</sup> [^9: These employment rates are very close to the ones from the Polish central bank surveys of Ukrainian refugees, which showed 62% in July 2023 and 68% in July] **Chart 10. Ukrainian refugee median net wage** <sup>**10**</sup> [^10: See the note on median wage estimation method in the Online Technical Appendix.]\n\nWorking age (women 15-59, men 15-64) PLN, 18-64 age group\n\n\n4,000\n\n\n69%\n\n\n\n**Ukrainian refugees are more likely**\n**to be employed in elementary**\n**occupations than pre-war Ukrainian**\n**migrants, non-Ukrainian foreigners,**\n**and Polish citizens, but they are**\n**also the group to have improved**\n**the most in the last two years.** The\ndata as of June 30, 2024 shows that\n38% of Ukrainian refugees worked in\nelementary occupations,", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:7:0:0", "start": 1010, "end": 1059, "surface": "Polish central bank surveys of Ukrainian refugees", "probe_tag": "keep", "probe_score": 0.9709, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:7:0:1", "start": 1602, "end": 1626, "surface": "data as of June 30, 2024", "probe_tag": "confusion", "probe_score": 0.7759, "luna_label": 0}]}, {"key": "paddy-113", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\nThe above results do not distinguish\nbetween Ukrainian refugees improving\nthe labour market outcomes in the\npoviats they arrived in and Ukrainian\nrefugees disproportionately moving to\nthe poviats with better performance,\nwhich continued to perform better in\nsubsequent quarters. This distinction is\nmostly academic, as causality is always\nuncertain in social sciences, as effects can\nrun both ways. However, instrumental\nvariable regressions have been performed\nto ascertain causal effects. This approach\nis widely used in studies focused on the\nimpact of migration on socio-economic\noutcomes. This econometric technique\nadditionally uses variables that correlate\nwell with Ukrainian refugees’ employment\nshares, but do not directly cause changes\nto the Polish citizens' employment rate\nor to the unemployment rate. Two\nvariables have been used: the first was\nthe share of Ukrainian children in Polish\nschools, and the second was the pre-war\ndistribution of Ukrainian citizens based\non notifications of entrusting work to\na foreigner. Unfortunately, panel fixed\neffects regressions with quarterly dummies\nshowed statistically insignificant results\nfor either instruments or both. In addition,\nregressions using only the instrumental\nvariable for the distribution of pre-war\nUkrainian citizens reveal only a modest\nlink to the employment share of Ukrainian\nrefugees.\n\n\nSimilarly to the above results, an early\nanalysis by Gromadzki and Lewandowski\n(2023) found no impact on the employment\nrate or unemployment rate. In a peerreviewed scientific article, they examined\nthe impact of Ukrainian refugees on the\nlabour market outcomes of Polish women\nfrom January to April 2022 (as most\nUkrainian refugees are female). They found\nthat the proportion of Ukrainian refugees\nin a given poviat had no statistically\nsignificant impact on the employment rate\nor unemployment rate of 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 poviat", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:20:0:0", "start": 1062, "end": 1109, "surface": "notifications of entrusting work to\na foreigner", "probe_tag": "confusion", "probe_score": 0.6331, "luna_label": 1}]}, {"key": "paddy-114", "text": " workers in the\neconomy, but also accounts for increased\nproductivity brought about by more\nspecialization. Considering all aspects, the\noverall impact of the refugees has been\npredominantly positive, and it moved the\nPolish economy to a higher growth path.\nRefugees contribute to the economy\nslightly more than their employment\nshare – they increase the labour supply\nas both workers and entrepreneurs and\nexpand demand as consumers. The rise\nin productivity due to more specialization\nacross the labour force further boosts\nthe economy. The impact of refugees\nis lowered by a temporary decrease in\nthe capital-to-labour ratio (companies\nneed time to invest in equipment and\nmachines to match the rise in the number\nof workers), as well as an increase in\ncompetition on the labour market.\n\n\n**The figures are higher than those in**\n**the previous Deloitte (2024) report,**\n**which – due to scant data available**\n**at the time – did not account for the**\n**positive impact on labour productivity.**\nIn the earlier report, the overall positive\nimpact of refugees was reduced based on\nconservative assumptions, in the absence\nof available/clear data on the increased\ncompetition in the labour market. The\n\n\n\nmodel notably considered lower wages\nand higher unemployment among native\nworkers. However, recent data indicates\nthat these concerns did not materialize.\nInstead, Polish workers have moved on to\nbetter paid occupations, and the economy\nhas benefited from a larger pool of talent,\nenabling deeper specialization and\nincreased productivity growth.\n\n##### Remaining challenges\n\n\n**Despite significant progress in**\n**integrating refugees into the labour**\n**market, several challenges persist.**\nRefugees are half as likely to have an\nemployment contract as Polish citizens\nand few of them achieve high incomes.\nAlthough refugees have been moving on\nto more desirable professions at a faster\nrate than other groups in the economy,\ntheir jobs continue to be disproportionately\nskewed towards elementary occupations.\nThese issues are most evident among\nthose with", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:2:3:0", "start": 1299, "end": 1310, "surface": "recent data", "probe_tag": "confusion", "probe_score": 0.4682, "luna_label": 1}]}, {"key": "paddy-115", "text": "HIGH EMPLOYMENT RATES, BUT LOW WAGES: A POVERTY ASSESSMENT OF UKRAINIAN REFUGEES IN NEIGHBORING COUNTRIES\n\n\nthe survey data side, only households that did not have any missing information on income (respondents were\nasked to provide both sources and amounts) were included in the calculation. For households claiming no or\nvery little income an imputation was made based on expenses and the duration of stay in the host country.\n\n\nIt is important to note that while the poverty definition adopted in this assessment allows for comparisons\nacross the countries covered (limitations apply to the Republic of Moldova), it may not be directly comparable\nto other studies, as approaches tend to vary quite significantly. The setup of the questionnaire, the sampling\nmethodology, the processing of income data (including any imputations), the approach to equivalizing income,\nand finally the location of the poverty line itself in the income range all have a substantial impact on poverty\nindicators.\n\n\nIn order to compute refugee wages, household net employment income was divided by the total number of\nworking hours reported by all employed members. This figure was then weighted by the total number of\nworking hours and averaged overall all households within a given country (while also respecting poststratification weights). The result was then converted to a monthly wage assuming employment at 40 hours per\nweek and 4.33 weeks in a month. For comparability with host population data, net wages were converted to\ntheir gross equivalent utilizing local tax regulations.\n\n# **Limitations**\n\nThe statistical significance of the SEIS results is limited by the non-probabilistic selection of respondents.\nMoreover, the use of convenience sampling likely led to a larger share of data being collected from more\nvulnerable households.\n\n\nThere was also a notably high non-response rate regarding questions related to income and expenditure,\nwhich likely resulted in non-response bias. The income module of the SEIS was also materially different from", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000010:14:0:0", "start": 112, "end": 123, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.7089, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:14:0:1", "start": 792, "end": 803, "surface": "income data", "probe_tag": "confusion", "probe_score": 0.6388, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000010:14:0:2", "start": 1464, "end": 1484, "surface": "host population data", "probe_tag": "keep", "probe_score": 0.9628, "luna_label": 1}]}, {"key": "paddy-116", "text": ". The complete questionnaires, along with the\nconsolidated anonymized dataset, are available in\nthe <u>[UNHCR Microdata Library.](https://microdata.unhcr.org/index.php/catalog/?page=1&from=2023&to=2024&region%5B%5D=3&ps=100)</u>\n\n\n# Limitations\n\nThis analysis has several limitations that should be\nconsidered when interpreting the findings. First, due\nto sampling constraints (lack of complete sampling\nframe) and the non-probabilistic selection of\nrespondents, the results may not fully represent the\nentire Ukrainian refugee population. Additionally,\nthe choice of sampling locations may have\nintroduced a bias toward more vulnerable segments\nof the population. Variations in sampling\napproaches and data collection periods across\ncountries can also affect comparability.\n\n\nThe findings on disability, chronic illness, and\nvaccination are based on self-reports and were not\nverified against medical records, which may impact\ntheir accuracy.\n\n\nA high non-response rate was observed for\nsensitive questions related to mental health,\npsychosocial well-being, protection, income and\nexpenditure which could affect the completeness of\nthe data. Additionally, the survey results for certain\nindicators, such as infant and young child feeding\nand SRH barriers, should be interpreted with\ncaution due to the small sample size or low\nresponse rates. As a result, some indicators could\nnot be further analyzed to assess how factors such\nas gender, age, disability, or place of residence\nimpact access to health and MHPSS services.\n\n\nIt is also important to note that there were slight\ndifferences in the questionnaire across countries\nand years, such as adjustments to answer options.\nTherefore, the regional trend analysis was limited to\nquestions that were consistently used across all\nparticipating countries and years to ensure\ncomparability. Furthermore, certain indicators were\nexcluded from the regional analysis due to\ninsufficient sample size or the unavailability of data\nacross all countries.\n\n\n\n7. <u>[", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000004:6:1:0", "start": 46, "end": 77, "surface": "consolidated anonymized dataset", "probe_tag": "confusion", "probe_score": 0.1552, "luna_label": 0}]}, {"key": "paddy-117", "text": " policies in Finland,\nGermany, Ireland, Netherlands, Norway,\nSweden, Switzerland and the UK (no data\nfor Denmark). They find that non-EU15\nrefugees aged 25-64 who arrived when a\ndispersal policy was in place experience\n4.5 percentage points lower employment\nrates than for those not exposed to such\na policy. This may understate the effect,\nas for refugees who arrived 10 or less\nyears before the survey, the effect is\n17.5 percentage points, while it becomes\nstatistically insignificant afterwards.\nFor the six countries for which data on\nresidence is available, non-dispersed\nrefugees show clearly stronger clustering in\neconomically stronger regions (measured\nby GDP per capita).\n\n\n24\n\n\n\n25\n\n\n\nEconomic research on dispersal policies\nand labour market inclusion focuses on\nthe strength of local labour markets and\nsize of local ethnic networks. While some\nscholars find only the strength of local\nlabour markets to be significant and not\nco-national networks (Foged, Hasager, and\nPeri, 2022), or inconsistent results for conational networks (Müller, Pannatier, and\nViarengo, 2022), others find effects only for\nco-national networks (Damm, 2014).\n\n\nMigrants dispersed to weaker labour\nmarkets experience poorer inclusion\noutcomes in subsequent years, and vice\nversa. These outcomes can pertain to\nemployment, earnings, or human capital\naccumulation, and have been shown in\nDenmark (Foged, Hasager, and Peri, 2022;\nAzlor, Damm, and Schiltz-Nielsen, 2020),\nGermany (Aksoy, Giray, Poutvaara, and\nSchikora, 2020), Norway (Godøy, 2017),\nSweden (Åslund, Östh, 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", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:12:1:0", "start": 532, "end": 549, "surface": "data on\nresidence", "probe_tag": "confusion", "probe_score": 0.3768, "luna_label": 1}]}, {"key": "paddy-118", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n# **1.** Inflow of refugees from Ukraine into Poland\n\n##### Even before the 2022 full-scale war in Ukraine the number of Ukrainians in Poland was substantial and growing, although their precise number is difficult to measure.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 1.** Ukrainian workers with social insurance.\n\n\nRate of change (right axis) Number (left axis)\n\n\n\n**Source:** Deloitte own elaboration based on ZUS data.\n\n\n\n1000\n\n\n750\n\n\n500\n\n\n250\n\n\n0\n\n\n\n\n\n\n\n120%\n\n\n100%\n\n\n80%\n\n\n60%\n\n\n40%\n\n\n20%\n\n\n0%\n\n\n-20%\n\n\n\n\n\n\n\n\n\nA steady inflow of migrants could be\nobserved in the last decade since 2014,\nwhen armed conflict erupted in Eastern\nUkraine. As the Ukrainian economy\nsuffered and its currency lost value, many\nUkrainians came to Poland looking for\nwork. Most of them came on a guest\nworker basis, enabled by a law from 2011,\nthat allowed Ukrainians and five other\nnations to work in Poland for 6 months\nduring a year without a work permit, based\non employers' declaration. This was a\ncircular migration, in which they returned to\nUkraine once the 6 month period expired.\nIn effect most stayed in Poland for less\nthan twelve months and therefore were\nnot included in the resident population or\nother national population estimates.\nA National Bank of Poland research paper\nestimates that between 2014 and 2018,\nbetween one and two million Ukrainian\nworkers arrived in the country (Strzelecki,\nGrowiec and Wyszyński, 2022).\nHard data on the number of Ukrainian\nworkers in Poland before 2022 are limited\n\n\n12\n\n\n\nand might understate their presence.\nThe only hard data on these flows is the\nnumber of Ukrainian workers with social\ninsurance, which nevertheless understates\nthe numbers, as certain kinds of legal\nwork often undertaken by temporary\nemployees did not require it, and some\nUkrainians worked in the shadow economy.\nThe", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:6:0:0", "start": 530, "end": 538, "surface": "ZUS data", "probe_tag": "keep", "probe_score": 0.9775, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:6:0:1", "start": 1671, "end": 1680, "surface": "hard data", "probe_tag": "confusion", "probe_score": 0.7393, "luna_label": 1}]}, {"key": "paddy-119", "text": "**\n**policymakers.** According to a previous\nresearch, usually about a third of migrants\nholding a university degree work in jobs\nrequiring only high-school diplomas, versus\n10% of natives (Tani, 2020). Migrants may\nlack language fluency or country-specific\nknowledge (e.g. of the law or business\ncontacts) and find it difficult to prove the\nprofessional experience or educational\ncredentials gained in their home country.\nThey also face regulatory barriers to\nentering some professions, e.g., in the\npublic sector or regulated specialist\noccupations. Ensuring that migrants`\nand refugees` skills are fully utilized to\nthe benefit of both the individuals and\nhost-country economies is a central policy\nchallenge for the countries facing largescale immigration.\n\n\n**In Poland, non-EU27 citizens are twice**\n**as likely to be over-qualified (for their**\n**job) as Polish citizens.** In its Labour\nForce Survey (LFS), Eurostat defines overqualification rate as the share of persons\nwith tertiary education (bachelor’s degree\nand higher) who are employed in the ISCO\n(International Standard Classification of\nOccupations) 4-9 occupational groups <sup>25</sup> [^25: This is the same classification as quoted from GUS and ZUS in the previous chapters. Codes 4-9 refer to clerical support workers; service and sales workers; skilled\nagricultural, forestry and fishery workers; craft and related trades workers; plant and machine operators and assemblers; elementary occupations.] .\nWhile the LFS is unlikely to cover all\nUkrainian refugees and migrants from\nother nationalities in Poland, there was a\nvisible increase in over-qualification rates\nfor non-EU27 citizens between 2022 and\n2023 that is not observed for Polish\ncitizens.\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 18. Over-qualification rates by citizenship**\n\n48%\n\n\nNon-EU27 Citizenship Polish Citizenship\n\n\n\nSource: Deloitte own elaboration based on Eurostat\n(Labour Force Survey) data.\n\n\n**Occupational downgrading is**\n**widespread among Ukrainian refugees**", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "jad_paddy_docs:000001:13:1:0", "start": 888, "end": 907, "surface": "Labour\nForce Survey", "probe_tag": "confusion", "probe_score": 0.8816, "luna_label": 1}]}, {"key": "paddy-120", "text": "NAVIGATING HEALTH AND WELL-BEING CHALLENGES FOR REFUGEES FROM UKRAINE\n\n# Recommendations\n\n\n\nHost countries and communities have made\ncommendable and continuous efforts to support\nrefugees in accessing essential health and MHPSS\nservices. While most respondents reported\nadequate access, barriers to services changed and\nincreased for some refugees. These changes do not\ntake the same form for all refugees, and\nintersectional factors related to gender, age,\ndisability, and chronic illness continue to impact the\nexperiences and needs of refugees from Ukraine in\nthis regard. The recommendations below highlight\nkey priorities and actions for governments and RRP\npartners to address these challenges and enhance\nrefugees’ access to care in host countries.\n\n\nThese actions aim to support governments and\nlocal communities in strengthening health and\nMHPSS systems in areas impacted by refugee\narrivals and advancing the integration of refugees\ninto national systems. Achieving these goals will\nrequire long-term planning and sustained funding\ncommitments.\n\n\n\n\n\n\n\n9. <u>[Integration of migrant and refugee data in health information systems in Europe: advancing evidence, policy and practice](https://doi.org/10.1016/j.lanepe.2023.100744)</u>\n\n\n**28**", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000004:27:0:0", "start": 1084, "end": 1108, "surface": "migrant and refugee data", "probe_tag": "confusion", "probe_score": 0.74, "luna_label": 0}]}, {"key": "paddy-121", "text": " 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 Chart 30).\n\n\n\nThird, cross-section regression analysis\nshows that poviats with a higher number of\nUkrainian refugees saw a greater increase\nin wages, which was caused by larger\nshare of refugees in local employment.\nDue to limitations in the wage dataset,\nour calculations incorporated yearly data.\nCross-section models were estimated using\na data sample for all 380 poviats in 2023.\nThe share of Ukrainian refugees among\nall employed, temporarily employed, and\nself-employed persons insured at ZUS in a\ngiven poviat was averaged across quarters\nto construct a yearly variable. The wage\nvariable is the GUS data series on gross\nmonthly wages and salaries <sup>30</sup> [^30: 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.], and taken\nas nominal change in 2023 from 2022.\nThe ordinary least squares cross-section\nmodel shows that in 2023, a 1 percentage\npoint increase in the employment share of\nUkrainian refugees was associated with an\nincrease in wages of PLN 66. Given that in a\nmean poviat gross wage growth amounted\nto PLN 757 and the gross wage in the\nprevious year was PLN 5,803, wages have\ngrown by 13.1% in nominal terms (including\ninflation), of which 0.7 percentage points\nwere associated with Ukrainian refugees\n(considering the fact that the employment\nshare of the Ukrainian refugees in a mean\npoviat was 0.61%).\n\n\nInstrumental variables regressions were\nperformed with the Two Stage Least\nSquares technique. In the first stage, it\nadjusts for the non-random distribution\nof Ukrainian refugees across poviats,\nand in the second stage, it calculates\nthe models. These results can be\ninterpreted causally. The", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:20:2:0", "start": 337, "end": 349, "surface": "wage dataset", "probe_tag": "keep", "probe_score": 0.9753, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:20:2:1", "start": 381, "end": 392, "surface": "yearly data", "probe_tag": "keep", "probe_score": 0.9971, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:20:2:2", "start": 698, "end": 713, "surface": "GUS data series", "probe_tag": "confusion", "probe_score": 0.8794, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:20:2:3", "start": 785, "end": 793, "surface": "GUS data", "probe_tag": "keep", "probe_score": 0.9712, "luna_label": 1}]}, {"key": "paddy-122", "text": "Analysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n**Chart 19. Tertiary education and corresponding**\n**occupational groups shares**\n\n\n\n**Chart 20. Ukrainian refugees median net wages by**\n**educational attainment**\n\n\n4,300 4,275\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\n\ncountry-specific knowledge and business\nnetworks) are more difficult to address\nthan others (such as language skills and\noccupational licensing). However, if the gap\nwas narrowed by half, the average wage of\nrefugees would increase by approximately\n10%. Assuming that the productivity\nincrease is equivalent to the wage increase,\nthis would result in PLN 3.5 billion of added\n\n\n\n**Addressing occupational downgrading**\n**could bring macroeconomic benefits.**\nTo demonstrate the potential impact,\na simulation was conducted in which\nthe underrepresentation of refugees in\nhigher-paying occupations was reduced by\nhalf. It was not assumed that there would\nbe no differences between refugees and\nPolish citizens, as some barriers (such as\n\n\n\n40%\n\n\nTertiary education share\n\n25-64 age group\nsurveys\n\n\n\nManagers, professionals,\nand technicians ZUS-insured\n\nemployment share\n\n\n\n\n#### **4.2 Access to regulated professions**\n\n\n\nnatives and this gap was largest for men,\nworkers in the highest education level, and\nnonnaturalized immigrants. This has an\nimportant impact on wages, because, as\nshowed in a seminal work by Kleiner and\nKrueger (2013) and confirmed in several\nanalyses, working in a regulated profession\ncomes with a significant wage premium.\nBrücker et al. (2021), who analysed\nGerman data, found that occupational\nrecognition led to full convergence of\nimmigrants’ earnings to 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", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:14:0:0", "start": 1104, "end": 1127, "surface": "25-64 age group\nsurveys", "probe_tag": "confusion", "probe_score": 0.5821, "luna_label": 0}, {"key": "sample:jad_paddy_docs:000001:14:0:1", "start": 1617, "end": 1628, "surface": "German data", "probe_tag": "keep", "probe_score": 0.9229, "luna_label": 1}]}, {"key": "paddy-123", "text": ", 2024 shows that\n38% of Ukrainian refugees worked in\nelementary occupations, much more than\npre-war Ukrainians (25%), non-Ukrainian\nforeigners (18%), and Polish citizens (10%).\n\n\n\n\n\n\n\n\n\n\n\nMSNA Jul-Aug 2023 SEIS May-Jun 2024\n\n\n\n\n\n\n\n\n\nMSNA Jul-Aug 2023 SEIS May-Jun 2024\n\n\n\nSource: Deloitte own elaboration based on MSNA and SEIS UNHCR surveys conducted in July-August 2023 and May-June 2024.\n\n\n\nWhile the share seems least favourable\namong Ukrainian refugees, their situation\nimproved the most in the two years since\nJune 30, 2022 (by 10 pp. compared with\n9 pp. for the pre-war Ukrainians, 2 pp. for\nnon-Ukrainian foreigners and 1 pp. for\nPolish citizens). On the other hand, in\nQ2 2024, managers and specialists, the\ntwo highest paid occupational groups,\ncomprised 8% of both Ukrainian refugees\nand pre-war Ukrainians, 22% of nonUkrainian foreigners (who include many\n\n\n\nIT specialists and executives), and 28% of\nPolish citizens. Their shares increased\nin the past two years by 3 pp. among\nUkrainian refugees and pre-war Ukrainians,\n2 pp. among other foreigners, and 1 pp.\namong Polish citizens – this data shows\nthat though Ukrainian refugees may be\nlargely employed in the less attractive\noccupational groups, they are also the\nfastest to progress toward more attractive\nprofessions. <sup>11</sup> [^11: Since 2021, ZUS has been requesting information about the occupation of non-agricultural workers who first join the social insurance system (most farmers have a\nseparate social insurance system). Unfortunately, this data is not yet comprehensive, as on June 30, 2022, it included 4.8 million people, and June 30, 2024, 7.2 million]\n\n\n\n**Ukrainian refugees in Poland have**\n**clearly improved their economic**\n**situation over the past year.** As\nindicated in chapter 1, the share of\nUkrainian refugee household incomes\nderived from work in Poland has increased\nfrom 74% in the July-August 2023 MSNA\nsurvey to 76% in the May-June 2024", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:7:1:0", "start": 324, "end": 342, "surface": "SEIS UNHCR surveys", "probe_tag": "keep", "probe_score": 0.9957, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:7:1:1", "start": 1902, "end": 1913, "surface": "MSNA\nsurvey", "probe_tag": "confusion", "probe_score": 0.645, "luna_label": 1}]}, {"key": "paddy-124", "text": " This is particularly\nevident among women, who make up the\nmajority of refugees. The employment\nrate for women aged 20-64 has grown\nconsistently from 68.2% in Q2 2021 to\n70.2% in Q2 2022, 71.7% in Q2 2023, and\n72.2% in Q2 2024 (see Chart 26). The\nunemployment rate for women aged\n20-64 has consistently fallen, from 3.5% in\nQ2 2021 to 3.0% in Q2 2022, 2.6% in both\nQ2 2023 and Q2 2024.\n\n\n\nSecond, panel regression analysis shows\nthat a larger influx of Ukrainian refugees\nat the poviat level was accompanied by\na larger increase in employment rates\nfor Polish citizens along with a larger\ndecrease in registered unemployment\nrates. The data sample for all regressions\nencompassed quarterly data for all\n380 poviats from Q1 2022 to Q2 2024.\nThe Ukrainian refugee share variable was\nconstructed as the share of Ukrainian\nrefugees among all employed, temporarily\nemployed, and self-employed persons\ninsured with ZUS in a given poviat at the\nend of the quarter. The employment rate\nof Polish citizens was constructed as the\nshare of Polish citizens who are employed,\n\n\n\nAnalysis of the impact of refugees from Ukraine on the economy of Poland\n\n\ntemporarily employed, and self-employed\nand who are insured with ZUS, divided by\nthe population from GUS in a given poviat\nin a given quarter (estimated based on halfyear population data). The unemployment\nrate is the registered unemployment\nrate series from GUS. Fixed effects panel\nregressions show that an increase in the\nemployment share of Ukrainian refugees\nby 1 percentage point correlates with an\nincrease in Polish citizens' employment\nrates by 0.5% and a decrease in the\nunemployment rate by 0.3%, in the\npreferred specification with dummy\nvariables for all quarters. All results are\nstatistically significant at a 0.01 level.\n\n\n\n**", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000001:19:1:0", "start": 680, "end": 714, "surface": "quarterly data for all\n380 poviats", "probe_tag": "confusion", "probe_score": 0.8312, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000001:19:1:1", "start": 1303, "end": 1327, "surface": "halfyear population data", "probe_tag": "confusion", "probe_score": 0.745, "luna_label": 1}]}, {"key": "paddy-125", "text": "|~~H*~~||~~IT~~|\n||||||\n\n\n\n2% 4% 6%\n\n**Female unemployment rate**\n\n\n\n34%\n\n\n\n\n\n\n\n\n\n\n\nThe high level of education of Ukrainians\ncoming to Poland helped them access the\nlabour market. The percentage of higher\neducation for refugees and pre-2022\nmigrants from Ukraine in NBP and UNHCR\nsurveys is significantly higher than for the\nPolish population and even higher than\nfor Ukraine, according to the Labour Force\nSurvey (LFS) in Poland, and its equivalent\nin Ukraine. According to NBP survey from\n2022 the percentage of refugees with\nhigher education was at 48%, while MSNA\n\n\n\n\n\n8% 10%\n\n\n\n\n\n\n\n\n\n\n\n**Source:** Deloitte own elaboration based on Eurostat, <u>[https://nbp.pl/wp-content/uploads/2023/04/Sytuacja-zyciowa-i-ekonomiczna-](https://nbp.pl/wp-content/uploads/2023/04/Sytuacja-zyciowa-i-ekonomiczna-migrantow-z-Ukrainy-w-Polsce_raport-z-badania-2022-r.pdf)</u>\n<u>[migrantow-z-Ukrainy-w-Polsce_raport-z-badania-2022-r.pdf, UNHCR survey conducted from 13.07.2023 to 21.08.2023, and State Statistics](https://nbp.pl/wp-content/uploads/2023/04/Sytuacja-zyciowa-i-ekonomiczna-migrantow-z-Ukrainy-w-Polsce_raport-z-badania-2022-r.pdf)</u>\nService of Ukraine (2021).", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:10:2:0", "start": 395, "end": 414, "surface": "Labour Force\nSurvey", "probe_tag": "confusion", "probe_score": 0.8876, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:10:2:1", "start": 476, "end": 486, "surface": "NBP survey", "probe_tag": "keep", "probe_score": 0.9825, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:10:2:2", "start": 924, "end": 936, "surface": "UNHCR survey", "probe_tag": "keep", "probe_score": 0.9833, "luna_label": 1}]}, {"key": "paddy-126", "text": " for 15-74 (broadest available) age group, and earnings by educational attainment are taken from the GUS (2022) “Structure of\nwages and salaries by occupations in October 2020”.\n37  Note that the numbers of Ukrainian refugees and all workers numbers by poviats are taken from ZUS Statistical Portal on 30th September\n2023 from the universe of workers registered for social security (this does not cover the informal sector and some jobs that do not require social\nsecurity). Ukrainian refugees are identified by PESEL UKR. Salaries and wages are taken from Statistics Poland BDL GUS database for 2022, but\ncover only the enterprise sector (firms with 10 or more employees).\n38  Note that there is no publicly available administrative data on the sectoral distribution of Ukrainian refugees. We proxied their sectors by\ntaking the difference in workers with Ukrainian citizenship registered for social security between H1 2023 and end of 2021 in A-Q 1-letter NACE\nsections. For the general population we used employment in the 15+ age group in Q2 2023 from the Eurostat Labour Force Survey. Earnings by\nNACE section have been taken from Statistics Poland Statistical Bulletin wages and salaries for the enterprise sector and public sector in Q2 2023.\n39  Note that Ukrainian refugees have been allocated to firm sizes based on the July-August 2023 UNHCR (2023) survey, while general workers\nfrom the GUS (2023) “Employment in the national economy in 2022”. Productivity of firms by size is based on gross value added per person\nemployed in industry, construction, and market services sectors (broadest available) in 2021 from Eurostat Structural Business Statistics.\n40  Note that data on occupations of Ukrainian refugees is for persons with PESEL UKR registered for social security on 30th September 2023. It\nis then compared to the general population by nine main occupational groups from GUS LFS in Q2 2023 and earnings from GUS (2023) “Structure", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000007:16:3:0", "start": 276, "end": 298, "surface": "ZUS Statistical Portal", "probe_tag": "confusion", "probe_score": 0.5255, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:16:3:1", "start": 557, "end": 591, "surface": "Statistics Poland BDL GUS database", "probe_tag": "keep", "probe_score": 0.9996, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:16:3:2", "start": 1060, "end": 1088, "surface": "Eurostat Labour Force Survey", "probe_tag": "keep", "probe_score": 0.9358, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:16:3:3", "start": 1136, "end": 1174, "surface": "Statistics Poland Statistical Bulletin", "probe_tag": "confusion", "probe_score": 0.8278, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:16:3:4", "start": 1347, "end": 1366, "surface": "UNHCR (2023) survey", "probe_tag": "drop", "probe_score": 0.0077, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:16:3:5", "start": 1625, "end": 1664, "surface": "Eurostat Structural Business Statistics", "probe_tag": "drop", "probe_score": 0.0009, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:16:3:6", "start": 1680, "end": 1721, "surface": "data on occupations of Ukrainian refugees", "probe_tag": "confusion", "probe_score": 0.388, "luna_label": 1}, {"key": "sample:jad_paddy_docs:000007:16:3:7", "start": 1891, "end": 1898, "surface": "GUS LFS", "probe_tag": "drop", "probe_score": 0.0019, "luna_label": 1}]}, {"key": "paddy-127", "text": " larger share of data being collected from more\nvulnerable households.\n\n\nThere was also a notably high non-response rate regarding questions related to income and expenditure,\nwhich likely resulted in non-response bias. The income module of the SEIS was also materially different from\nthe one employed by the EU SILC and the Republic of Moldova’s Household Budget Survey, which may limit\ncomparability of this data to that of host populations.\n\n\nIt is also important to highlight that there were slight differences in the questionnaire across countries. Not all\nquestions were consistently included in all country-level surveys, and some answer options were individually\nadjusted.\n\n\nLastly, the survey was conducted during the summer months, coinciding with both host country and Ukraine\nschool holidays. This period often sees many households temporarily visiting Ukraine, which impacted the\naccessibility of households and posed challenges in meeting targets, particularly in certain countries and\ngeographic locations.\n\n\n**15**", "source": "jad_paddy_docs", "subset": "annotate_paddy", "spans": [{"key": "sample:jad_paddy_docs:000010:14:1:0", "start": 347, "end": 370, "surface": "Household Budget Survey", "probe_tag": "drop", "probe_score": 0.0183, "luna_label": 1}]}, {"key": "paddy-128", "text": "**The World Bank**\nUganda Secondary Education Expansion Project (P166570)\n\n\nthat 40 percent of teachers in schools have been placed there based on factors other than the class time\nrequired by students. <sup>9</sup> [^9: UNESCO 2014, Teacher Issues in Uganda: A shared vision for an effective teachers’ policy.] Old curricula (replaced in 2020) used to further complicate teacher allocation\nacross schools by imposing too many subjects that required specialized teachers. This inefficiency was\nresolved by the new curricula.\n\n\n8. **In spite of increased access to schooling, the average level of education of the work force remains low**\n**and does not meet labor market requirements.** Uganda has been absorbing 600,000 new entrants to the labor\nmarket each year since 2014. In order to sustainably increase welfare, these entrants must find productive\nemployment. <sup>10</sup> [^10: Uganda job diagnostics/strategy, World Bank, 2018, draft.] Estimates from the Uganda National Household Survey (UNHS) (2016) show that only entrants\nwith post-secondary education can escape informal sector work. In order to increase the employability and\nproductivity of the expanding workforce, supply of quality education, especially for low-income, rural households\nand girls, is critical. According to the UNHS, only one in five people aged 15 and above completed full secondary\neducation. Thus, a large number of youth enter the job market without foundational skills of basic literacy and\nnumeracy, as well as generic skills essential for life and work.\n\n\n9. **Uganda is a pioneer in SSA in terms of setting the goal of achieving universal access to secondary**\n**education.** The secondary education sub-sector in Uganda is centrally managed and comprises six grades, Senior\n1 (S1) to Senior 6 (S6). S1-S4 is categorized as ordinary (‘O’) level, or lower secondary, while S5-S6 is Advanced\n(‘A’", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000018:14:0:0", "start": 964, "end": 996, "surface": "Uganda National Household Survey", "probe_tag": "keep", "probe_score": 0.9795, "luna_label": 1}, {"key": "sample:jdc_operational:000018:14:0:1", "start": 1296, "end": 1300, "surface": "UNHS", "probe_tag": "keep", "probe_score": 0.9429, "luna_label": 1}]}, {"key": "paddy-129", "text": "**The World Bank**\nSouth Sudan Emergency Food and Nutrition Security Project (P163559)\n\n\n16. **The agricultural potential is huge but largely unrealized.** Favorable soil, water, and climatic\nconditions render more than 70 percent of South Sudan’s total land area suitable for crop production and\n60 percent of the country’s population already lives in high to medium potential areas. However, less than\n4 percent of the total land area is currently cultivated, and at 0.8‐0.9 tons per ha (FAO/WFP, 2011),\naverage cereal yields are lower than those in Uganda (1.6 tons per ha), Kenya (2 tons per ha) and Ethiopia\n(3 tons per ha).\n\n\n17. **Years of conflict and displacement have destroyed household capacity as well as productive**\n**relationships, traditional markets, infrastructure, and social and economic institutions that support the**\n**sector.** As a result, most agriculture in the country is now subsistence and is characterized by limited use\nof productivity enhancing measures, limited acreage (0.32 ha per capita against a per capita land holding\nof 13 ha), high costs of production <sup>6</sup> [^6: High production costs are mainly due to high daily wage rates juxtaposed to high labor requirements for preparing (especially\nbush clearance) land for cultivation.], and low output per hectare. Most farmers produce less food than\nthey require for subsistence and nearly 75 percent of all farming households experience food shortages\n(hunger gap) for at least three months a year. National level data shows that despite some increase in\narea cropped under cereals, total production has been on the decline since 2005 and that production\ngenerally lags consumption by an average of 30 percent. IPC analyses show that populations in Phases 2\n(Stressed), 3 (Crisis) and 4 (Emergency) have been increasing steadily over time (Figure 3).", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000038:17:0:0", "start": 1493, "end": 1512, "surface": "National level data", "probe_tag": "keep", "probe_score": 0.9808, "luna_label": 1}]}, {"key": "paddy-130", "text": "/files/documents/UNACNorthern%20Uganda%20and%20West%20Nile%20Humanitarian%20and%20Development%20Report%20-January-February%202018.pdf)_\n_[%20Development%20Report%20-January-February%202018.pdf, p. 2.](http://ug.one.un.org/sites/default/files/documents/UNACNorthern%20Uganda%20and%20West%20Nile%20Humanitarian%20and%20Development%20Report%20-January-February%202018.pdf)_\n_19_ South Sudan Regional Refugee Response Plan, UNHCR, 2018\n20 Informing the Refugee Policy Response in Uganda: Results from the Uganda Refugee and Host Communities 2018 Household\nSurvey. Washington DC, World Bank, 2019.\n21 Uganda’s Education Response Plan for Refugees and Host Communities. Ministry of Education and Sports, 2018\n\n\nPage 10 of 80", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000050:14:3:0", "start": 501, "end": 558, "surface": "Uganda Refugee and Host Communities 2018 Household\nSurvey", "probe_tag": "keep", "probe_score": 0.913, "luna_label": 0}]}, {"key": "paddy-131", "text": "**The World Bank**\nUganda Electricity Access Scale-up Project (EASP) (P166685)\n\n\nproviding Tier 1 level access and above. The Uganda Solar Energy Association (USEA) reports over 210 solar\ncompanies operating in the Ugandan market as at end-December 2020, with most sales coming from a few\ninternational companies (for example, Fenix, M-Kopa, Solar Today, Village Power <sup>8</sup> [^8: USEA, Uganda Solar Market Report. Annual Sales and Impact Data. January-December 2019; GOGLA Global Off-Grid Solar\nMarket Reports. Sales and Impact Data for H1 2020.] ). These companies sold over\n482,100 off-grid solar products in 2019 and 128,242 off-grid solar products in the first half of 2019, positioning\nUganda as the third largest market in East Africa behind Kenya and Ethiopia.\n\n11. **About 58 percent of the population live without electricity.** The main access deficit is exhibited in rural\nareas, where 80 percent of the population resides and less than 40 percent has access to electricity. As reported\nin the Poverty Maps of Uganda Technical Report (World Bank, November 2019), the districts with the highest\naccess deficits below 10 percent, also correspond to the poorest ones, as visualized in the maps in figure 2. At the\nnational level, Uganda has one of the lowest electricity consumptions per capita in the world, estimated at an\naverage of 80 kWh per year in 2017, which is far below its peers (for example Kenya at 155 kWh per year, Ghana\nat 300 kWh per year). Such trends contribute to Uganda’s “adaptation deficit”, which limits the opportunity of\ncommunities to be resilient to external shocks, including those caused by disease or climate change.\n\n12. **The outcomes in clean cooking are even more dire with about 95 percent of Ugandans using solid**\n**biomass fuels for their meals** . The SDG7 tracking report <sup>9</sup> identified Uganda as one", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000040:6:0:0", "start": 474, "end": 516, "surface": "GOGLA Global Off-Grid Solar\nMarket Reports", "probe_tag": "keep", "probe_score": 0.9066, "luna_label": 1}, {"key": "jdc_operational:000040:6:0:1", "start": 1012, "end": 1034, "surface": "Poverty Maps of Uganda", "probe_tag": "keep", "probe_score": 0.9016, "luna_label": 1}]}, {"key": "paddy-132", "text": "**The World Bank**\nCHAD Improving Learning Outcomes Project (P175803)\n\n\n5. **Chad is highly vulnerable to shocks, whether climatic or security** including prolonged droughts in some areas and\nyearly floods in others, as well as wind erosion and desertification. These contribute to recurrent food crises and\npopulation displacements. Chad is also subject to repeated security threats, including Boko Haram and al Qaida in the\nIslamic Maghreb; and is classified as a Fragility, Conflict and Violence (FCV) country. As of June 30, 2021, there were\n508,307 refugees (mainly from the Sudan and the Central African Republic), as well as 401,511 internally displaced\npersons. <sup>3</sup> [^3: United Nations High Commissioner for Refugees (UNHCR), Operational Data Portal.] This places additional pressure on an education system which has already run out of steam.\n\n\nSectoral and Institutional Context\n\n\n6. **Pre-primary education is still nascent, a large share of primary education is managed by the community, and**\n**only few children can make it up to secondary education.** Pre-primary education is managed by the Ministry for Women\nand Early Childhood Protection (MFPPE) <sup>4</sup> [^4: Pre-primary education services are the responsibility of MFPPE. However, the Ministry of National Education and Civic Promotion (MENPC) is\nresponsible for pre-primary pedagogy.] . It lasts three years aimed at children aged 3-5 years, and the Gross Enrollment\nRate (GER) was 1.1 percent in 2019/20. Primary education is the first stage of basic education; by law, it is free and\ncompulsory. It consists of six years aimed at children aged 6-11 and is divided into three cycles of two years each:\npreparatory (CP), elementary (CE) and medium (CM). Successful completion of Grade Six, as determined by the primary\nschool, allows one to enter upon lower secondary education ( _enseignement", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000009:3:0:0", "start": 743, "end": 766, "surface": "Operational Data Portal", "probe_tag": "keep", "probe_score": 0.9372, "luna_label": 1}]}, {"key": "paddy-133", "text": "**The World Bank**\nChad Rural Mobility and Connectivity Project (P164747)\n\n\n**I.** **STRATEGIC CONTEXT**\n\n\n**A. Country Context**\n\n\n1. **Chad is a large landlocked country in central Africa covering an area of 1.3 million km² with**\n**approximately 14 million inhabitants.** The country is divided into multiple climatic zones: a desert zone\nin the north, an arid Sahelian belt in the center, and a more fertile Sudanese savanna zone in the south.\nWhile Arabic and French are the official languages, Chad is home to more than 200 distinct ethnic and\nlinguistic groups. A total of 78.2 percent of the population lives in rural areas, and 51.3 percent of the\npopulation is female, including 51 percent of youth under 15. <sup>1</sup> [^1: General Population and Housing Census (RGPH 2) of 2009.]\n\n\n2. **Since independence, Chad has been a fragile, violence- and conflict-affected country marked by**\n**chronic instability,** **political turmoil, security issues, armed conflict with neighboring countries, hunger,**\n**climate change, and local health crises.** These multiple shocks have slowed the development of the\ncountry, making it one of the poorest countries in the world. The 2018 United Nations Development\nProgramme (UNDP) Human Development Index ranks Chad 186 out of 189 countries. An oil producer since\n2003, Chad suffered a recession in 2014 when the sudden fall in the price of oil combined with the\nstructural weaknesses of the national economy pushed the country into two consecutive years of\neconomic contraction in 2016 and 2017 (−6.4 percent and −2.7 percent, respectively). Gross domestic\nproduct (GDP) per capita fell from US$969 in 2014 to US$843 in 2017.\n\n\n3. **Despite high levels of hunger, agriculture remains the main source", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000048:10:0:1", "start": 1231, "end": 1254, "surface": "Human Development Index", "probe_tag": "keep", "probe_score": 0.931, "luna_label": 1}]}, {"key": "paddy-134", "text": "(NFIS).** A Financial Inclusion Steering Committee (FISC) that includes all stakeholders in the area of financial\ninclusion was mandated by the Prime Minister to direct and approve high level policies supporting the NFIS.\nUnder the FISC, a Financial Inclusion Technical Committee (FITC) was established within CBJ and has drafted\nthe national governance structure and a roadmap for the NFIS process, which builds on thematic working\ngroups for microfinance, SME finance, digital payments, consumer protection, and financial education. On\nNovember 2016, CBJ published the NFIS vision document which identified five key pillars for NFIS, these\ninclude: (i) financial education among school students and various segments of society; (ii) consumer\nprotection; (iii) enhancing SMEs access to finance; (iv) access to microfinance services; and (v) digital payments.\nData collection, measurement, analysis and evidence based financial inclusion policies and targets are\nincorporated in the NFIS vision document as cross cutting pillars to make each of the above five pillars more\nrobust. Since then the thematic working groups are meeting regularly to feed in the NFIS developing process.\n\n   - **The CBJ mandated GIZ (with co-funding from the European Union) to conduct an in-depth market diagnostic**\n\n**study to provide a baseline for financial inclusion in preparation for the NFIS.** The diagnostic study of financial\ninclusion will build on full-fledged demand side and supply side studies. On the demand side, a nationally\nrepresentative, household-level survey of individuals is currently being conducted in order to analyze data\nacross key socio-economic (income levels), demographic (gender, age) and geographic segments. On the\nsupply side, relevant data relating to the financial infrastructure and providers is currently being compiled in\norder to map levels of financial access and usage as well as to analyze barriers across different product\ncategories for both households and businesses (i.e. micro", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000013:3:1:0", "start": 1512, "end": 1576, "surface": "nationally\nrepresentative, household-level survey of individuals", "probe_tag": "confusion", "probe_score": 0.6872, "luna_label": 0}, {"key": "jdc_operational:000013:3:1:1", "start": 1745, "end": 1813, "surface": "relevant data relating to the financial infrastructure and providers", "probe_tag": "confusion", "probe_score": 0.6825, "luna_label": 0}]}, {"key": "paddy-135", "text": ", resulting in substantial\nincreases in imported food prices. In January 2017, retail food prices in all monitored markets were much\nhigher than the same month in 2016. For example, the price of a kilo of sorghum had increased by about\n629 percent in Juba, and 86 percent in Bentiu compared to the same time last year. The price of maize\nincreased by 1,017 percent in Juba, while the price of beans, one of the major food imports, increased by\nmore than 150 percent compared to last year (despite the recent introduction of reduced customs duty\non imported food to relax the pressure on prices). On average, the prices of most food crops in major\nmarkets now hover around 345 percent to 1100 percent above their long‐term average (2007‐2015).\n\n\n7. **Overall, according to the National Bureau for Statistics, the Consumer Price Index increased by**\n**480 percent from February 2016 to February 2017 mainly on account of higher food prices** . The highest\nincreases are registered for bread and cereals, with prices increasing by more than 600 percent between\nDecember 2015 and December 2016 (see Figure 1). Such extreme increases in prices have had a serious\nadverse impact on the purchasing power and the food security of poor households, the majority of whom\ndepend on the market for their food supplies.\n\n\n1 These include: (i) the current drought in the Horn of Africa, which has reduced overall output in primary exporters Uganda and\nSudan and limited the food imports that South Sudan can viably access; (ii) high food prices mainly driven by depreciation of the\nlocal currency and a very high inflation rate; (iii) declines in crop production from already low levels due to insecurity and\ndisplacement of farmers; and (iv) destabilization of markets due to restrictions to movement in commodity‐supply corridors.\n\n\nPage 10 of 80", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000038:13:1:0", "start": 812, "end": 832, "surface": "Consumer Price Index", "probe_tag": "confusion", "probe_score": 0.8815, "luna_label": 1}]}, {"key": "paddy-136", "text": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\n26. **Uganda has remained committed to its refugee policies promoting integrated socio-economic opportunities for**\n**refugees and host communities despite the pressures of COVID-19.** This has seen refugee women productively join the\nworkforce and refugee households diversify incomes away from aid dependence. The project demonstrates substantial\npolicy content by enabling the implementation of policies allowing refugees to work in Uganda and by addressing the\npriorities identified in the government’s Jobs and Livelihoods Integrated Response Plan for Refugees and Host\nCommunities JLIRP (April 2021, and the Comprehensive Refugee Response Framework (CRRF) in Uganda. The project will\nimplement the gender sensitive JLIRP focusing on the two pillars: (a) enabling entrepreneurial led development and\nmarket growth systems; and (b) increasing access to market relevant skills training to enhance employability and job\ncreation. Under the first pillar strategic interventions include: increasing investment in micro and small enterprises and\nagricultural household enterprises; and strengthening market systems for enabling a business-friendly environment and\nbest practice. Under the second pillar strategic interventions include: increasing accesses and equity to technical and\nvocational training; and increasing job placement opportunities.\n\n27. **The World Bank, following consultation with the key actors such as United Nations High Commissioner for**\n**Refugees (UNHCR),** <sup>32</sup> [^32: Based on the Uganda Refugee Protection Assessment Update 3 –February 25, 2022.] **has determined that Uganda’s refugee protection framework remains adequate for accessing**\n**financing from the IDA19 WHR.** Uganda is recognized globally as having one of the refugee policies most aligned with\nthe Global Compact on Refugees. Not only is Uganda a state party to international or regional instruments protecting\nrefugees but also its laws, policies, and practices are largely consistent with international refugee law, guaranteeing nonrefoulement and adequate protection for refugees and asylum", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000025:18:0:0", "start": 1633, "end": 1678, "surface": "Uganda Refugee Protection Assessment Update 3", "probe_tag": "confusion", "probe_score": 0.8524, "luna_label": 1}]}, {"key": "paddy-137", "text": ", community teacher census<br>and<br>digital<br>personnel<br>dossier;<br>strengthening of salary payment<br>system|IT equipment and materials|\n\n\n\n27. The Project will also finance technical assistances capacity strengthening as well as critical equipment and\nmaterials to ensure that relevant MENPC departments are able to implement and monitor the project activities\neffectively, including the department in charge of education management and information system (EMIS) and the\nlearning assessment unit\n\n\n**Subcomponent 4.2: Project management, Monitoring and Evaluation**\n\n28. This subcomponent will finance monitoring, research and evaluation activities. All training activities will be\nmonitored during delivery, covering participation, logistics and quality. Monitoring of civil works and the installation of\ncovered spaces will include third-party site monitoring combined with community monitoring using technology (such as\ntablets and smartphones) to provide regular, real-time updates on progress. The same technology will be deployed to\nsupport the remote iterative beneficiary monitoring of other project activities, including school- and local-level activities\nto promote effective pedagogy, the use of covered spaces, remedial teaching of children returning to school, and the\navailability of textbooks and supplementary reading materials. The project will also finance research and capacity building\n(i) to assess system coverage and performance, including at least one national sample-based survey of learning outcomes;\n(ii) to better understand and address the scope and causes of children being out of school; (iii) to undertake a study on\nthe quality of Early Childhood Education (ECE) aiming at providing information for the revision of the preschool program\nand its alignment with the primary education curriculum, and for the establishment of minimum quality standards in this\nsub-sector ; and (iv) to determine the feasibility of integrating formal curricular standards pertaining to literacy and\nnumeracy into Quranic school education.\n\n\n29. **There will be five evaluations.** Effectiveness of the in-service training program will", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000009:23:1:0", "start": 2, "end": 26, "surface": "community teacher census", "probe_tag": "confusion", "probe_score": 0.0798, "luna_label": 0}, {"key": "jdc_operational:000009:23:1:1", "start": 1483, "end": 1532, "surface": "national sample-based survey of learning outcomes", "probe_tag": "confusion", "probe_score": 0.7406, "luna_label": 0}]}, {"key": "paddy-138", "text": "-grant agreements and agreed list of eligible\nexpenditures.\n\n22. **Training and Implementation Support:** The World Bank will provide focused training\nto participating municipalities on the World Bank FM and disbursement guidelines and\nprocedures, and will provide close support during the first year of Project implementation.\n\n_Disbursement Arrangements_\n\n23. The proceeds of the Grant will be disbursed in accordance with the World Bank's\ndisbursements guidelines as outlined in the Disbursement Letter and in accordance with the\nWorld Bank Disbursement Guidelines for Projects. Disbursements will be Report-based; the\ninitial disbursement for CVDB will be submitted to the Bank after Project effectiveness, based\non the forecast for two (2) quarters as provided in the quarterly IFRs. Thereafter, disbursements\nwill be made into the Designated Account (DA) based on quarterly IFRs which would provide\nactual expenditure for the preceding quarter (three months) and cash flow projections for the next\ntwo quarters (six months). All supporting documentation will be retained at CVDB. They will be\nkept in a manner readily accessible for review by Bank missions and internal and external\nauditors.\n\n24. The supporting documentation for reporting eligible expenditures paid from the DA will\nbe summary reports and records evidencing eligible expenditures for payments against contracts\nfor prior and post reviews. The supporting documentation for direct payment requests should be\nrecords evidencing eligible expenditures (i.e., copies of receipts, suppliers' invoices, etc.). CVDB\nwill prepare quarterly IFRs in form and content satisfactory to the Bank. The format and content\nof the IFR will be agreed upon between the Bank and CVDB. The contents of the IFR will\n\n\n39", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000026:50:1:0", "start": 773, "end": 787, "surface": "quarterly IFRs", "probe_tag": "confusion", "probe_score": 0.0737, "luna_label": 0}]}, {"key": "paddy-139", "text": "**The World Bank**\nSouth Sudan Emergency Food and Nutrition Security Project (P163559)\n\n\nwill develop detailed checklists to ensure consistent and compliant Project procurement.\n\n   - The PIU will also develop a contract management system to ensure that all contracts under the\nProject are effectively and efficiently managed. This will include the tracking of key contract\nmilestones and performance indicators as well as capturing all procurement and contract records.\n\n\n**Table A2.3. Procurement risk analysis and mitigation**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Risk Description|Description of Mitigation|Risk owner|\n|---|---|---|\n|**(i) Operational context:**  volatile political<br>situation<br>and<br>weak<br>macro‐economic<br>projection; current constraint in making<br>payments to another party outside of country<br>due to foreign exchange shortages.|Direct payments to suppliers at the request<br>of the Government.|Borrower|\n|**(ii) Fiduciary risk:**    corruption and bribery<br>concerns with regards to internal controls<br>within the Ministry and broader context of the<br>country;  South  Sudan  is  ranked  the  second<br>most corrupt country in the world on<br>transparency corruption perception index.|Project to be implemented with the support<br>of  UN  Agencies  as  the  main  suppliers  of<br>goods and services. In addition, direct<br>payments are proposed.|Borrower|\n|**(iii)Lack of complaints and resolution of**<br>**disputes system:**  the Government does not<br>have ", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000038:59:0:0", "start": 1158, "end": 1198, "surface": "transparency corruption perception index", "probe_tag": "confusion", "probe_score": 0.166, "luna_label": 1}]}, {"key": "paddy-140", "text": "**The World Bank**\nUganda: Investment for Industrial Transformation and Employment (P171607)\n\n\nGDP grew at 3.1 percent in FY20, less than half the 6.8 percent recorded in FY19, due to the effects of the COVID-19\ncrisis and is expected to grow at a similar level in FY21. Economic activity stalled during the latter part of the fiscal year\ndue to a domestic lockdown that lasted over four months, border closures for everything but essential cargo, and the\nspillover effects of disruption in global demand and supply chains due to the COVID-19 pandemic. This resulted in a sharp\ncontraction in public investment and deceleration in private consumption, which hit the industrial and certain service sectors\nparticularly hard. On a calendar year basis, real GDP growth is unlikely to exceed 1 percent during 2020, compared to 6.7\npercent in 2019, and, as a result, real per capita GDP growth is expected to contract by about 2.5 percent. Even if the GDP\ngrowth rebounds strongly by 2022, the level of per capita GDP is likely to remain well below its pre-COVID trajectory. <sup>1</sup> [^1: See Uganda Economic Update 16th Edition, September 2020.]\n\n\nSectoral and Institutional Context\n\n**COVID-19 is a significant threat to emerging economic transformation in Uganda and puts prospects of new jobs**\n**in danger.** Data from the June 2020 <sup>2</sup> [^2: Uganda Bureau of Statistics June 2020; conducted with the support of the World Bank.] Uganda Bureau of Statistics high frequency phone survey on the impact of the\nCOVID-19 pandemic, shows that the following sectors lost the highest number of workers: services 43 percent, commerce\n43 percent and transport 39 percent. It is expected that the hardest-hit firms will be exporters to international markets,\nmanufacturing companies and start-ups. The floriculture industry, for example, which employs over 10,000\npeople, is facing severe disruptions in its supply chains as air cargo companies", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000043:3:0:0", "start": 1313, "end": 1336, "surface": "Data from the June 2020", "probe_tag": "confusion", "probe_score": 0.5643, "luna_label": 0}, {"key": "jdc_operational:000043:3:0:1", "start": 1469, "end": 1496, "surface": "high frequency phone survey", "probe_tag": "confusion", "probe_score": 0.7856, "luna_label": 1}]}, {"key": "paddy-141", "text": "**Figure A3.1: Funds flow**\n\n\n\n\n\n15. Table A3.3 shows the categories of expenditure and percentages to be financed out of the\nGrant proceeds.\n\n\n**Table A3.3: Categories of expenditure and financing (in US$ and %) to be funded**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Category|Amount of the<br>financing<br>allocated<br>(US$ millions)|Percentage of<br>expenditures to be<br>financed<br>(inclusive of taxes)|\n|---|---|---|\n|(1) Goods, non-consulting services, consultants’<br>services, vouchers, food packages, and training (under<br>Component A of the project)|7.00|100%|\n|(2) Goods, works, non-consulting services, technology<br>packages, consultants’ services, and training (under<br>Component B of the project)|11.00|100%|\n|**Total amount**|18.00||\n\n\n**_Financial Reporting_**\n\n\n16. The government will prepare a consolidated report based on the IFRs submitted by WFP\nand FAO using the agreed reporting format and content on a quarterly basis. The consolidated\n\n\n37", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000007:46:0:0", "start": 827, "end": 831, "surface": "IFRs", "probe_tag": "confusion", "probe_score": 0.0971, "luna_label": 0}]}, {"key": "paddy-142", "text": " and host population—depends highly on local markets to buy food, voucher provision\nin the lean season has the potential to trigger food price inflation, which would reduce the\npurchasing power of all of these groups. For that reason, direct food transfers will be provided\nmonthly during the lean season (which lasts three to four months) at distribution sites. Food\nitems will include basic staples, oil, sugar, and salt. Every effort is made to ensure the safety of\nbeneficiaries, especially women recipients, at the distribution sites.\n\n\n12. The timing of the switch between food vouchers and direct transfers will be based on\nfood price monitoring data, which is collected weekly or biweekly in many local markets, as well\nas the local knowledge of WFP staff. For example, prices normally dip in August/September as\ntraders release their stocks to prepare for the coming harvest. Traders’ failure to release stocks at\nthat time indicates an anticipated poor harvest. The decision to procure food must be made at\nleast four to six months in advance of distribution to allow time for procurement, so decision\nmaking in this regard is an art supported by local knowledge and early trends in food prices and\nmarket behavior.\n\n13. In addition to the vouchers and direct food transfers, which target all household members,\nspecialized food assistance will be provided to children aged 6–23 months for four months to\nprevent seasonal spikes in malnutrition. This activity is planned for the initial 12 months of\ndisplacement only, as experience with camp-based populations in this area shows that over time\nnutritional status tends to stabilize at acceptable levels (defined in terms of the rate of Global\n\n\n26", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000007:35:1:0", "start": 631, "end": 657, "surface": "food price monitoring data", "probe_tag": "confusion", "probe_score": 0.4596, "luna_label": 1}]}, {"key": "paddy-143", "text": "Network coverage is another serious constraint to higher adoption of mobile broadband, with sharp**\n**regional disparities.** While more than 95 percent of the population is covered by mobile telephony networks (2G),\n\n\n23 World Bank Group. 2017. “Global Findex Database”, World Bank Group, 2017\nhttps://globalfindex.worldbank.org/sites/globalfindex/files/2018-04/2017%20Findex%20full%20report_0.pdf\n24 NITA-U (National Information Technology Authority of Uganda). 2018. National Information Technology Survey 2017/18 Report. NITA\nUganda, March 2018.\n25 NITA-U (National Information Technology Authority of Uganda). 2018. National Information Technology Survey 2017/18 Report. NITA\nUganda, March 2018.\n26 Economic Policy Research Centre. 2019. “Women’s Economic Empowerment in Uganda: Inequalities and Implications” Policy Brief No.\n110, Economic Policy Research Centre, November 2019. Kampala: Economic Policy Research Centre. Available at: https://eprcug.org/allpublications/614-women-s-economic-empowerment-in-uganda-inequalities-and-implications\n27 The State of ICT in Uganda. Research ICT Africa, 2019. “The State of ICT in Uganda.” https://researchictafrica.net/publication/the-stateof-ict-in-uganda\n28 ITU.\n29 The State of ICT in Uganda. Research ICT Africa, 2019. “The State of ICT in Uganda.” https://researchictafrica.net/publication/the-stateof-ict-in-uganda\n30 The State of ICT in Uganda. Research ICT Africa,2019. “The State of ICT in Uganda.” https://researchictafrica.net/publication/the", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000023:16:2:0", "start": 247, "end": 269, "surface": "Global Findex Database", "probe_tag": "confusion", "probe_score": 0.6843, "luna_label": 0}, {"key": "jdc_operational:000023:16:2:1", "start": 470, "end": 523, "surface": "National Information Technology Survey 2017/18 Report", "probe_tag": "confusion", "probe_score": 0.8559, "luna_label": 0}]}, {"key": "paddy-144", "text": " due to irregularities and lack of transparency in local tax administration, obstruction of\ninvestments by some unscrupulous politicians, and a general perception that LGs do not offer useful support,\nwithin their mandates, for local private sector development <sup>11</sup> . In addition, little support is provided to local\nfirms by municipal LGs, even though they have the mandate to provide support to micro-enterprises and\nother firms through the Commercial Office. There is also an absence of meaningful public private dialogue,\nparticularly in terms of consulting the private sector in the development of local development plans. The\nstudy made three main recommendations to LGs in Uganda: (i) to make infrastructure investments that are\nbetter prioritized according to local economic potentials – building on the major recent investments in roads\nand connectivity to transition to other strategic investments in tourism site development, market\n\n4 Arch Design Ltd, 2012 – Municipal Assets Inventory and Conditions Assessment Final Report\n5 From UGX 37 million in 1993/4 to UGX1.6 trillion in 2011/12.\n6 Local Government Finance Commission (2012) **.** Review of Local Government Financing: Financing Management and\nAccountability for Decentralized Service Delivery.\n7 FDA section 7.2\n8 DDEG has replaced the Local Development Grant (LDG) as part of the broader GoU IGFT reform\n9 For example, unit costs of paving 1 km of urban road ranges between US$800,000 to US$1 million and for a primary drainage\n(with box culverts at road crossings and armoflex linings) about US$500,000 to US$1.1 million per km.\n10 USAID (2015) Ugandan Decentralization Policy and Issues Arising in the Health and Education Sectors: A Political Economy\nStudy. October 2015.\n11 World Bank 2016. _Uganda - Repositioning local governments for economic growth_ . Washington, DC: World Bank.\n\n\n2", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000062:9:2:0", "start": 980, "end": 1032, "surface": "Municipal Assets Inventory and Conditions Assessment", "probe_tag": "confusion", "probe_score": 0.6535, "luna_label": 1}]}, {"key": "paddy-145", "text": "** **Cultural and Creative Industries (CCI) are key drivers of the creative economy and represent important sources**\n**of employment, economic growth, and innovation that contribute to city competitiveness and sustainability.** A\nrecent World Bank report points out that through their contribution to urban regeneration and sustainable urban\n\n\n6 Income headcount poverty rates based on upper poverty line of $14 income per day and lower poverty line of $8.5 per day. For the lower\npoverty line, the corresponding increase is from 8.2 percent to 23.2 percent, bringing the total number of poor Lebanese to 1.1 million for the\nlower poverty line and 2.7 million for the upper one. Source: Fakih, Ali, Makdissi, Paul, Marrouch, Walid, Tabri, Rami V., Yazbeck, Myra,\n“Confidence in Public Institutions and the Run up to the October 2019 Uprising in Lebanon,” Working Paper, 2020.\n7 Marot, “Jadaliyya - The End of Rent Control in Lebanon: Another Boost to the ‘Growth Machine?’”\n8 UNDP, “Leave No One Behind for an Inclusive and Just Recovery Process in Post-Blast Beirut”; UN-Habitat, “Lebanon Urban Profile,” 2011.\n9 Since its independence in 1942, the Lebanese State has rarely engaged in the production of public housing or introduced measures to protect\nor secure affordable housing for low-income groups such as property regularization and neighborhood upgrading\n10 InfoPro, “Business Opportunities in Lebanon – Year XI. Real Estate in Greater Beirut [Database],” 2014.\n\n\nPage 7 of 66", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000012:12:2:0", "start": 1423, "end": 1463, "surface": "Real Estate in Greater Beirut [Database]", "probe_tag": "confusion", "probe_score": 0.7441, "luna_label": 0}]}, {"key": "paddy-146", "text": ">CPF<br>|<br>Country Partnership Framework<br>|\n|<br>CPI<br>|<br>Corruption Perceptions Index<br>|\n|<br>DA<br>|<br>Designated Account<br>|\n|<br>DFIL<br>|<br>Disbursement and Financial Information Letter<br>|\n|<br>DHS<br>|<br>Demographic and Health Survey<br>|\n|<br>ECOSIT<br>|<br>Survey on Expenditure of Households and Informal Sector in Chad (_Enquête sur la_<br>_Consommation des Ménages et le Secteur Informel au Tchad)_ <br>|\n|ERR<br>|<br>Economic Rate of Return<br>|\n|<br>ESCP<br>|<br>Environmental and Social Commitment Plan<br>|\n|<br>ESF<br>|<br>Environmental and Social Framework<br>|\n|<br>ESIA<br>|<br>Environmental and Social Impact Assessment<br>|\n|<br>ESMF<br>|<br>Environmental and Social Management Framework<br>|\n|<br>ESS<br>|<br>Environmental and Social Standard<br>|\n|<br>FM<br>|<br>Financial Management<br>|\n|<br>FNPV<br>|<br>Financial Net Present Value<br>|\n|<br>GBV<br>|<br>Gender-Based Violence<br>|\n|<br>GDP<br>|<br>Gross Domestic Product<br>|\n|<br>GEMS<br>|<br>Geo-Enabling Initiative for Monitoring", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000051:2:1:0", "start": 225, "end": 254, "surface": "Demographic and Health Survey", "probe_tag": "confusion", "probe_score": 0.2894, "luna_label": 0}, {"key": "sample:jdc_operational:000051:2:1:1", "start": 280, "end": 343, "surface": "Survey on Expenditure of Households and Informal Sector in Chad", "probe_tag": "confusion", "probe_score": 0.3212, "luna_label": 0}]}, {"key": "paddy-147", "text": "CURRENCY EQUIVALENTS\n\n\n(Exchange Rate Effective: March 6, 2015)\n\n\nCurrency Unit = LBP Lebanese Pound\n\n1,503.51 Lebanese Pounds = US$1\n\nFISCAL YEAR\nJanuary 1                  - December 31\n\n\nABBREVIATIONS AND ACRONYMS\n\n\nCMU Country Management Unit (WB)\nCPS Country Partnership Strategy\nDfID Department for International Development (UK)\nDG Director General\nDOPS Pedagogical and Scholastic Guidance Office ( _Direction d’Orientation_\n_Pédagogique et Scolaire_\nECD Early Childhood Development\nECE Early Childhood Education\nECERS Early Childhood Environment Rating Scale\nECRD Educational Center for Research and Development\nECU Engineering Coordination Unit (MEHE)\nEDI Early Development Instrument\nEDP Education Development Project\nEDP II Second Education Development Project\nEESSP Emergency Education System Stabilization Project\nEMIS Education Management Information System\nESDS Educational Sector Development Secretariat\nESIA Economic and Social Impact Assessment\nESPISP II Second Emergency Social Protection Implementation Support Project\nETF European Training Foundation\nEU European Union\nGDP Gross Domestic Product\nGIS Geographic Information System\nGOL Government of Lebanon\nIBRD International Bank for Reconstruction and Development\nICB International Competitive Bidding\nIE Impact Evaluation\nKG Kindergarten\nLAES Lebanese Association for Educational Studies\nLAQA Lebanese Agency of Quality Assurance\nLSIN Lebanon School Improvement Network", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000030:1:0:0", "start": 1117, "end": 1150, "surface": "GIS Geographic Information System", "probe_tag": "confusion", "probe_score": 0.1428, "luna_label": 0}]}, {"key": "paddy-148", "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": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000022:16:1:0", "start": 998, "end": 1013, "surface": "Traffic surveys", "probe_tag": "confusion", "probe_score": 0.4189, "luna_label": 1}]}, {"key": "paddy-149", "text": "|Col1|Indicator 2.4: Teacher feedback on training and<br>certification system monitored, analyzed, and<br>included in the annual monitoring and progress<br>reports developed by ETC|Col3|No|Yes/No|No|Yes|Annually|MOE|Teacher surveys|\n|---|---|---|---|---|---|---|---|---|---|\n|Reformed<br>student<br>assessment and<br>certification<br>system<br>|Indicator 3.1: Grade 3 diagnostic test on early grade<br>reading and math implemented|7.2|No|Yes/No|No|Yes|Annually|MOE|Assessments records for a<br>sample of schools|\n|Reformed<br>student<br>assessment and<br>certification<br>system<br>|Indicator 3.2: Legal framework for the_Tawjihi_ exam<br>has been adopted so that its secondary graduation<br>and certification function is separated from its<br>function as a screening mechanism for university<br>entrance|7.4|No|Yes/No|No|Yes|Annually|MOE||\n|Reformed<br>student<br>assessment and<br>certification<br>system<br>|Indicator 3.3: Student and Teacher Feedback on first<br>phase_Tawjihi_ reform inform the", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000041:39:0:0", "start": 216, "end": 231, "surface": "Teacher surveys", "probe_tag": "confusion", "probe_score": 0.7636, "luna_label": 0}, {"key": "sample:jdc_operational:000041:39:0:1", "start": 465, "end": 484, "surface": "Assessments records", "probe_tag": "keep", "probe_score": 0.98, "luna_label": 0}]}, {"key": "paddy-150", "text": "</sup> 25.00 27.70 0.11 0.02 27.70 27.70\n\n\nMale Incomplete primary 8.00 5.60 ‐0.30 ‐0.06 3.93 2.60\n\n<sup>Incomplete lower secondary</sup> 22.00 23.00 0.05 0.01 21.33 20.00\n\n<sup>Incomplete upper secondary</sup> 34.00 32.40 ‐0.05 ‐0.01 30.73 29.40\n\nCompleted upper secondary but\nnot post‐secondary 12.00 13.50 0.13 0.03 0.01 18.50 0.02 22.50\n<u>Post‐secondary</u> <u>24.00</u> <u>25.50</u> <u>0.06</u> <u>0.01</u> <u>25.50</u> <u>25.50</u>\nSource: Income and Expenditure Survey, 2010, Department of Statistics and own calculations.\n\n\n59", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000041:66:1:0", "start": 447, "end": 476, "surface": "Income and Expenditure Survey", "probe_tag": "confusion", "probe_score": 0.8732, "luna_label": 1}]}, {"key": "paddy-151", "text": " Bousquet|Franck Bousquet|Franck Bousquet|Junaid Kamal Ahmad|Junaid Kamal Ahmad|Ferid Belhaj|Inger Andersen|\n||||||||\n|Recipient: Hashemite Kingdom of Jordan|Recipient: Hashemite Kingdom of Jordan|Recipient: Hashemite Kingdom of Jordan|Recipient: Hashemite Kingdom of Jordan|Recipient: Hashemite Kingdom of Jordan|Recipient: Hashemite Kingdom of Jordan|Recipient: Hashemite Kingdom of Jordan|\n|Responsible Agency: Ministry of Planning and International Cooperation|Responsible Agency: Ministry of Planning and International Cooperation|Responsible Agency: Ministry of Planning and International Cooperation|Responsible Agency: Ministry of Planning and International Cooperation|Responsible Agency: Ministry of Planning and International Cooperation|Responsible Agency: Ministry of Planning and International Cooperation|Responsible Agency: Ministry of Planning and International Cooperation|\n|Contact: H.E. Dr. Ibrahim Saif|Contact: H.E. Dr. Ibrahim Saif|Contact: H.E. Dr. Ibrahim Saif|Contact: H.E. Dr. Ibrahim Saif|Contact: H.E. Dr. Ibrahim Saif|Title: Minister of Planning and International<br>Cooperation|Title: Minister of Planning and International<br>Cooperation|\n|Telephone No. +962 6 4645437|Telephone No. +962 6 4645437|Telephone No. +962 6 4645437|Telephone No. +962 6 4645437|Telephone No. +962 6 4645437|Email:Ibrahim.Saif@mop.gov.jo|Email:Ibrahim.Saif@mop.gov.jo|\n||||||||\n|**Project Financing Data (in USD Million)**|", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000026:5:4:0", "start": 1390, "end": 1412, "surface": "Project Financing Data", "probe_tag": "confusion", "probe_score": 0.1033, "luna_label": 0}]}, {"key": "paddy-152", "text": " from hospitals, schools, and PHCs and\nauthorizing payments; (ix) managing the outreach campaign; (x) managing the ecard food voucher beneficiaries list, delivery of the e-cards to beneficiaries, and\nfollow up; and (xi) monitoring of the program (specifically inputs and outputs).\n\n     - The NPTP CMU in the PCM is responsible for the following: (i) managing the\n\ncentral database; (ii) validating data and cross-checking with national databases;\n(iii) processing household data, generating scores and ranks according to the\nproxy-means testing (PMT) formula, and providing the list of beneficiaries; (iv)\nmaintaining the PMT formula; (v) analyzing national data and reporting findings\nto the Social Inter-Ministerial Committee (Social-IMC); (vi) monitoring of\nprogram results including targeting performance; and (vii) auditing data\nprocessing.\n\n\n31", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000047:41:1:0", "start": 465, "end": 479, "surface": "household data", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 1}, {"key": "sample:jdc_operational:000047:41:1:1", "start": 650, "end": 663, "surface": "national data", "probe_tag": "drop", "probe_score": 0.0015, "luna_label": 0}]}, {"key": "paddy-153", "text": " project design are summarized below:\n\n\n     - _Strategic storage_ : Strategic reserves provide countries with critical lead time to secure alternative grain\nsupplies or supply routes during times of crisis. Reserves also offer psychological benefits that may prevent\nhoarding and pilferage. Moreover, historical data suggest a strong negative correlation between changes in\ngrain stocks and changes in grain prices. Not only could increasing strategic reserves reduce domestic price\nvolatility and the frequency of domestic price shocks, but it could also impact the global grain market and in\nturn mitigate international price risks. Importantly, the benefits of strategic reserves must be measured\nagainst the cost of maintaining them.\n\n\nPage 22 of 54", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000024:26:2:0", "start": 302, "end": 317, "surface": "historical data", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 1}]}, {"key": "paddy-154", "text": ">sanitation service delivery and support long-term<br>investments in infrastructure development, in RHDs<br>in the West Nile and Northern region.<br>Locations targeted for solar based water pumping<br>have already been identified in Arua, Yumbe, Moyo,<br>Adjumani, Lamwo, and Kiryandongo<br>|Provide digital enabling environment for<br>remove water monitoring and strengthen<br>efficiencies and effectiveness of water<br>management systems.<br> <br> <br> <br> <br>|\n|**Gender Based Violence**<br>**and Violence Against**<br>**Children Prevention and**<br>**Response Services in**<br>**Uganda’s Refugee-**<br>**Hosting Districts Report**<br> <br>_Status: Analysis_<br>_completed,_|Total<br>0.5<br> <br> <br>RSW/<br>WHR<br>N/A|To mitigate GBV and prevent violence against children<br>through engagement in productive activities in 4<br>RHDs.|Increased<br>access<br>to<br>more<br>affordable<br>connectivity will also increase likelihood of GB<br>online risks. Project will support the project<br>objective indirectly by including awareness<br>and mitigation measures in digital skills<br>training.<br> <br>Digital connectivity will strengthen case<br>management for GBV and violence against|\n\n\nPage 64 of 76", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000023:76:3:0", "start": 610, "end": 634, "surface": "Hosting Districts Report", "probe_tag": "drop", "probe_score": 0.0276, "luna_label": 0}]}, {"key": "paddy-155", "text": "11. The WFP has carried out lengthy assessments before deciding to switch from the paper\nfood voucher modality to the pre-paid electronic voucher. WFP finds that the electronic voucher\nis appropriate in Lebanon because of the favorable financial environment and high assessment\nratings in comparison with other MENA countries. The electronic system allows WFP to track\ntransactions, to gather data on the frequency the cards are used, and for which commodities.\n\n\n12. WFP has signed a partnership agreement in September 2013 with MasterCard, and the\nlocal bank, Banque Libano-Fran `ҫ` aise (BLF). BLF has been selected as the service provider to\ndevelop and implement the prepaid card system for registered Syrian refugees. The system will\nenable the beneficiaries to purchase food commodities at shops preselected by WFP. The\ncontracted shops opened accounts with the partner bank and installed Points of Sale (POS) in\ntheir businesses. WFP has contracted roughly 285 shops all over Lebanon. Each beneficiary\nhousehold registered with a unique UNHCR Case Number will receive a Card. The Card will be\nreloaded on a monthly basis, on the 5th of the month, to cover food needs. The monthly credit to\nbe received will be US$30 for each family member registered with UNHCR.\n\n\n13. The project will rely on WFP to provide technical assistance in implementing the prepaid\nE-card voucher system for the extremely poor Lebanese households, especially those affected by\nthe Syrian crisis. The NPTP will collaborate with WFP to provide all the necessary\ndocumentation to distribute the e-card food voucher benefit on a monthly basis.\n\n\n14. The NPTP will provide a file of eligible beneficiaries (Household Registry Statement)\nonce or twice a month to WFP based on the agreed on criteria between NPTP and WFP. The\nHousehold Registry statement includes the NPTP household registration number, the family size,\nthe date of birth of the family head, gender of the household head, and relevant distribution SDC.\nThe NPTP beneficiary file is imported into", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000047:38:0:0", "start": 1653, "end": 1683, "surface": "file of eligible beneficiaries", "probe_tag": "confusion", "probe_score": 0.696, "luna_label": 1}, {"key": "sample:jdc_operational:000047:38:0:1", "start": 1685, "end": 1713, "surface": "Household Registry Statement", "probe_tag": "confusion", "probe_score": 0.4386, "luna_label": 1}, {"key": "sample:jdc_operational:000047:38:0:2", "start": 1802, "end": 1830, "surface": "Household Registry statement", "probe_tag": "drop", "probe_score": 0.0097, "luna_label": 1}, {"key": "sample:jdc_operational:000047:38:0:3", "start": 2000, "end": 2021, "surface": "NPTP beneficiary file", "probe_tag": "confusion", "probe_score": 0.2304, "luna_label": 1}]}, {"key": "paddy-156", "text": "*Program Financing Data**|**Program Financing Data**|**Program Financing Data**|**Program Financing Data**|**Program Financing Data**|**Program Financing Data**|\n|[ X ]|Loan|[ ]|Grant|Grant|Grant|[x ]|[x ]|Other|Other|Other|Other|Other|Other|\n|[ X ]|Credit|||||||||||||\n|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|**For Loans/Credits/Others (US$, million):**|\n|", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000045:5:3:0", "start": 1, "end": 23, "surface": "Program Financing Data", "probe_tag": "drop", "probe_score": 0.0181, "luna_label": 0}]}, {"key": "paddy-157", "text": "**The World Bank**\nEmergency Food Security Project (P178936)\n\n\n|Indicator Name|PBC|Baseline|End Target|\n|---|---|---|---|\n|Monitoring tool for access to animal feed developed and<br>accessible to the public (Yes/No)||No|Yes|\n|External users of monitoring tool for access to bread satisfied<br>with information provided (Percentage)||0.00|90.00|\n|External users of monitoring tool for access to animal feed<br>satisfied with information provided (Percentage)<br>||0.00|90.00|\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Monitoring & Evaluation Plan: PDO Indicators|Col2|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|**Indicator Name **|**Definition/Description **|**Frequency **|**Datasource **|**Methodology for Data**<br>**Collection **|**Responsibility for Data**<br>**Collection **|\n|Cumulative amount of wheat procured<br>through the project|Cumulative amount of<br>wheat imports procured<br>with project financing since<br>the start of the project and<br>delivered to the port of<br>Aqaba|Monthly and<br>at the end of<br>the project<br>implementati<br>on period.<br>|MOITS<br>|Data collected regularly<br>and reported by the<br>MOITS<br>|Project Coordination<br>Team<br>|\n|Cumulative amount of barley procured", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000024:42:0:0", "start": 1054, "end": 1078, "surface": "Data collected regularly", "probe_tag": "drop", "probe_score": 0.0148, "luna_label": 0}]}, {"key": "paddy-158", "text": "**The World Bank**\nUganda: Investment for Industrial Transformation and Employment (P171607)\n\n\n**D. Project Description**\n\n**Component 1: Mitigating the impact of COVID-19.** The objective of this component is to ease liquidity constraints on\nMSMEs, including women led and refugee MSMEs. For the reasons discussed above, the component will seek to prioritize\nthe manufacturing and exporting sectors driving economic transformation, with the vision of connecting lower income\nregions, i.e. RHDs with more viable and sustainable markets. The component will operate three different windows targeting\ndifferent types of firms within the supply chain. All PFIs will be required to provide gender disaggregated data on their\nportfolios in order to address the lack of data on women-led firms as well as data on refugee or host community status to\nensure intersectional issues of exclusion are sufficiently addressed. _Window 1_ will support the extension of the loan period\nfor well performing firms by financing the cost of providing a grace period. _Window 2_ (supporting micro firms) will target\nmicro firms, including in RHDs, to enable them to restart or continue their operations as critical units in funding the link\nsay between producers with aggregators, processors, and distributors. _Window 3_ (receivables financing, including\ngovernment arrears) will provide finance to MSMEs based on security in the form of their receivables.\n\n**Component 2: Creating and Operating New Productive and Transformative Assets.** The component is focused on\nenabling new financing to restart and bolster economic growth. The component provides risk coverage for new lending to\nMSMEs, extends local currency liquidity on a long-term basis to larger investment projects, and de-risks or incentivizes\nprivate investment in RHDs through a competitive grant program. Component 2 seeks to mitigate the financial sector’s risk\naversion and thereby improve the availability of credit to MSMEs, and to provide longer", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000043:5:0:0", "start": 685, "end": 710, "surface": "gender disaggregated data", "probe_tag": "drop", "probe_score": 0.0329, "luna_label": 0}, {"key": "jdc_operational:000043:5:0:1", "start": 798, "end": 838, "surface": "data on refugee or host community status", "probe_tag": "drop", "probe_score": 0.0213, "luna_label": 0}]}, {"key": "paddy-159", "text": "include a section to report on the accountability of funds utilized and a section to access funds\nusing the report based method of disbursement. The reporting section of the IFRs should include\n(a) Statement of Sources and Uses of Funds; (b) Statement of Uses of Funds by Project\nActivity/Component; and (c) budgets compared to actual, and list of assets. The disbursement\nsection should include (a) Designated Account Activity Statement; (b) bank statements for DA\nand related bank reconciliation statements; (c) Summary Statement of DA expenditures for\ncontracts subject to prior review; and (d) Summary statement of DA expenditures not subject to\nprior review.\n\n\n25. **Designated Account:** CVDB will open a segregated DA at CBJ in US Dollars to cover\nthe Grant' shares of eligible expenditures. The Ceiling of the DA will be based on the forecast for\ntwo (2) quarters as provided in the quarterly IFR. CVDB will be responsible for submitting\nquarterly replenishing applications with appropriate supporting documentation.\n\n\n26. **Sub-Accounts:** Each participating municipality will be required to open a sub-account at\nCVDB for receiving the grant funds in local currency. The Grants will be provided annually to\nparticipating municipalities in two tranches: (i) the first tranche would be in the form of an\nadvance equal to 50 percent of the total annual allocated amount, and (ii) the second tranche\nwould be released only after submitting the progress reports and FM reports with bank\nreconciliations to CVDB for review and verification that funds were disbursed according to the\nsubsidiary agreements and positive list of expenditures.\n\n\n27. **E-Disbursement:** The World Bank has introduced e-disbursement for all projects in\nJordan. Under e-Disbursement, all transactions will be conducted and associated supporting\ndocuments and IFRs scanned and transmitted online through the World Bank’s Client", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000026:51:0:0", "start": 891, "end": 904, "surface": "quarterly IFR", "probe_tag": "drop", "probe_score": 0.0459, "luna_label": 0}]}, {"key": "paddy-160", "text": " emergency procedures while deferring exact projects/roads selection\nand associated safeguards to implementation;\n\n2. Agree on the first‐year road works program to speed up implementation;\n\n3. Include the purchase of necessary equipment which can be implemented quickly; and\n\n4. Introduce retroactive financing to support timely project implementation and initiate\nprocurement activities and required studies.\n\n  - **Deliver good quality infrastructure and asset management practices:**\n\n1. Prepare procurement strategy and packages to ensure a wider participation of local contractors\n(hence, broader benefits in different areas/communities) while maintaining well qualified\ncontractors to guarantee proper rehabilitation works in accordance with existing Lebanon’s high\nroad rehabilitation design standards;\n\n2. Introduce proper and objective road prioritization measures through the visual survey of the\nnetwork’s condition and safety, which will also later inform the creation of a new and integrated\nroad asset management system for Lebanon;\n\n3. Introduce road safety and climate resilient improvements to improve existing road design and\nconstruction standards and practices in Lebanon; and\n\n4. Introduce routine maintenance contracts as an important and efficient asset preservation\nmeasure (including possibly the piloting of performance‐based contracts).\n\n  - **Create significant number of short‐term jobs for Lebanese and Syrians:**\n\n      - Select road sections with required civil works such as drainage and slope stabilization structures\nto increase the labor content of contracts;\n\n\nPage 22 of 90", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000008:25:1:0", "start": 886, "end": 899, "surface": "visual survey", "probe_tag": "drop", "probe_score": 0.0002, "luna_label": 0}]}, {"key": "paddy-161", "text": "\n(b) Factories receive a draft of the full assessment report regarding compliance on all assessment\n\nquestions, including information on whether the issues subject to public reporting are in\nnoncompliance.\n\n\n(c) When the assessment report is finalized, the factory’s compliance with the 29 publicly\n\nreported issues is published online, on the Better Work Transparency Portal (for all factories\nthat have had at least two assessments).\n\n\n(d) In response, factories can upload documents and photos on the public reporting website\n\n(including information from assessment reports).\n\n\n(e) A factory’s compliance findings remain on the website until a new assessment report is\n\npublished, at which point the website is updated to reflect the factory’s most recent assessment\ndata.\n\n\n(f) Every time a new assessment is completed for a factory, new compliance data replaces old\ndata.\n\n\n(g) Compliance data on factories that had not yet had two assessments when public reporting was\n\nlaunched is published following a factory’s second assessment.\n\n\n27", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000045:35:1:0", "start": 759, "end": 774, "surface": "assessment\ndata", "probe_tag": "drop", "probe_score": 0.0491, "luna_label": 0}, {"key": "sample:jdc_operational:000045:35:1:1", "start": 842, "end": 857, "surface": "compliance data", "probe_tag": "confusion", "probe_score": 0.1116, "luna_label": 0}]}, {"key": "paddy-162", "text": " diligence for Financial Intermediaries.  The\nsystem can adequately provide, with reasonable assurance, accurate and timely information on the status of the project.\nThe financial management arrangements for the project have a **Substantial** residual risk rating. However, with planned\nimplementation support to the project as well as the implementation of agreed action plans, the financial management\narrangements will be strengthened to provide, with reasonable assurance, accurate and timely information on the status\nof the project resources required by IDA.\n\n**_Risk assessment and Mitigation_**\n\n13. The table below identifies the key risks that the project management may face in achieving these objectives and\nprovides a basis for determining how management should address these risks.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Risk Description|Risk Mitigating Measures Incorporated into Project Design|Risk rating|\n|---|---|---|\n|<br>**_Inherent risks_**<br>|<br> <br>|<br> <br>|\n|<br>**Country Level**- Recent fraud cases<br>in central government and the 2016<br>PEFA report show weaknesses in<br>government PFM systems. <br>|Weaknesses such as weak accounting capacity, budget credibility, payroll<br>rules and procurement compliance are being mitigated under a government<br>PFM reform program. New legislation is being crafted to freeze and<br>confiscate property acquired fraudulently. <br>|<br>S <br>|\n|<br>**Entity Level**-Line ministry could<br>delay in submitting relevant reports<br>due to weak capacity. <br>|<br>**MGLSD management will be enhanced by recruiting contracted personnel**<br>**to boost capacity. New measures to improve governance have been**<br>**", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "sample:jdc_operational:000025:61:1:0", "start": 1052, "end": 1063, "surface": "PEFA report", "probe_tag": "drop", "probe_score": 0.0006, "luna_label": 1}]}, {"key": "paddy-163", "text": "**The World Bank**\nStrengthening Lebanon’s Covid-19 Response (P178478)\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Col1|are refugees.|Col3|Col4|Col5|Col6|\n|---|---|---|---|---|---|\n|Percentage of feedback cases registered<br>in the project's grievance redress<br>mechanism (GRM) in the last quarter<br>addressed within a timeframe specified<br>by the project|<br>The project will maintain a<br>functioning grievance<br>redress mechanism (GRM).<br>Grievances will be tracked<br>and analyzed, and<br>feedback will be provided<br>to MOPH management for<br>corrective actions, as<br>needed. The project<br>operations manual will<br>include the specific process<br>to be followed. <br>|6 months<br> <br>|GRM reports<br>|Administrative data<br>|PMU/MOPH<br>|\n|Percentage of designated hospitals fully<br>equipped with commodities (e.g. PPE,<br>testing, infection control products and<br>supplies)|<br>This indicator measures the<br>proportion of designated<br>health facilities for COVID-<br>19 treatment that are fully<br>equipped with<br>commodities to support<br>COVID-19 response <br> <br>|<br>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", "source": "jdc_operational", "subset": "annotate_paddy", "spans": [{"key": "jdc_operational:000000:50:0:0", "start": 685, "end": 704, "surface": "Administrative data", "probe_tag": "drop", "probe_score": 0.0145, "luna_label": 0}]}, {"key": "paddy-164", "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_paddy", "spans": [{"key": "refugee_pads:000062:38:1:0", "start": 565, "end": 598, "surface": "household expenditure survey data", "probe_tag": "keep", "probe_score": 0.9572, "luna_label": 1}, {"key": "refugee_pads:000062:38:1:1", "start": 870, "end": 881, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9348, "luna_label": 1}]}, {"key": "paddy-165", "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": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000100:19:1:0", "start": 272, "end": 283, "surface": "survey data", "probe_tag": "keep", "probe_score": 0.9509, "luna_label": 1}]}, {"key": "paddy-166", "text": "imboneza_ if present (volunteer women in charge of addressing\ndomestic violence and children issues, with support from MDPHSAG), community health workers\nand the Red Cross, the community leader ( _bachingonazi_ ) or customary leader ( _Abagobo-_\n\n\n24 The poverty analysis on the household survey data (ECVMB 2014) for the poverty map will provide the PMT\ncoefficients and inform the design of a questionnaire to collect data on the variables associated with extreme poverty\nat the household-level.\n\n\n45", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000157:55:3:0", "start": 302, "end": 312, "surface": "ECVMB 2014", "probe_tag": "keep", "probe_score": 0.9459, "luna_label": 1}]}, {"key": "paddy-167", "text": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n\n19. The current economic analysis presents estimates of the efficiency gains in basic education to 2021, based on\nenrollment estimates employing UN population projections, average values from recent years for intake into\nGrade 1 of basic education (from the School Census) relative to population, and recent trends in promotion\nand retention in each grade of basic school. The analysis employs the same projections as the current\nEducation Sector Strategic Plan (ESSP) including for the GDP growth (IMF/World Bank), share of domestic\nresources spent on education (a 0.5 p.p. annual increase from 9.8 percent in 2017/18).\n\n\n20. _Gains from improved Internal Efficiency_ . The project objective is to sustain enrollment in public schools\nmeaning that the enrollment in target schools is to increase at the rate of the population growth – 2.5 percent\nper annum: from 5.40 million pupils to 5.54 million in 2021. The analysis is built on the assumption that survival\nand repetition rates will remain unchanged.\n\n21. We compare inefficient government spending under the expected scenario and a scenario, under which there\nis no increase in the student enrollment. The following formula is used to estimate inefficient spending:\n\n\n\n𝟖\n\n\n\n𝒙 <sup><u>𝒊</u></sup> ∗𝑮𝒙𝒊\n\n\n\n𝒙\n\n𝑰𝒏𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒕 𝑺𝒑𝒆𝒏𝒅𝒊𝒏𝒈 = ∑( <sup>𝑫𝒊</sup>\n\n\n\n𝑮𝒊\n\n\n\n𝒙 ) ∗𝑺 <sup>𝒙</sup>,\n\n\n\n𝒙𝒊\n\n\n\n𝒊=𝟏\n\n\n\nwhere 𝑫𝒊𝒙 is the dropout rates in grade 𝒊 in year 𝒙 in target schools; 𝑮𝒙𝒊 is the number of pupils enrolled in\n\ngrade 𝒊 in year 𝒙 ; 𝑺 <sup>𝒙</sup> is the projected government spending per pupil in year 𝒙 .\n\n22. According to the", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000024:43:0:0", "start": 224, "end": 249, "surface": "UN population projections", "probe_tag": "keep", "probe_score": 0.9507, "luna_label": 1}, {"key": "refugee_pads:000024:43:0:1", "start": 337, "end": 350, "surface": "School Census", "probe_tag": "keep", "probe_score": 0.9062, "luna_label": 1}]}, {"key": "paddy-168", "text": " of\nemployment in urban areas is in the informal sector <sup>2</sup>, characterized by low productivity and wages. In\naddition, congestion and lack of public transport options in many cities restricts the movement of goods and\npeople. The quality of housing remains inadequate for a large proportion of the urban population, with more\nthan 60 percent of the residents of urban areas living in slums. Finally, the delivery of social services of an\nadequate quality to a rapidly expanding urban population is also a source of concern <sup>3</sup> .\n\n4. **_Rapid Urbanization has resulted in a huge infrastructure backlog_** **.** For example, the backlog of\nbituminized roads in the 14 Municipalities targeted in the current phase of USMID was estimated at around\n\n\n1 Currently the Program targets 14 municipalities, namely: Arua, Gulu, Lira, Mbale, Soroti, Tororo, Jinja, Entebbe, Masaka, Mbarara,\nKabale, Fort Portal, Hoima, and Moroto.\n2 Uganda Urban Labor Force Survey 2009.\n3 World Bank. 2015. _The growth challenge: Can Ugandan cities get to work?_ . Washington, DC: World Bank Group.\n\n\n1", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "sample:refugee_pads:000014:8:2:0", "start": 939, "end": 970, "surface": "Uganda Urban Labor Force Survey", "probe_tag": "keep", "probe_score": 0.9985, "luna_label": 1}]}, {"key": "paddy-169", "text": "### 1.8 11\n\n\n\nCurrent account balance . **.2**\n\n\n\nFinancing items (net)\nChanges in net reserves .. .\n_Memo:_\nReserves including gold _(US$ millions)_\nConversion rate _(DEC, locaW/US$)_ 177.7 177.7 177.7\n\n\n**EXTERNAL DEBT and RESOURCE FLOWS**\n\n**1979** **1989** **1998** **1999**\n_(US$ millions)_ **Compositlon of total debt, 1998 (USS milIlona)**\nTotal debt outstanding and disbursed 26 **179** 288\nIBRD 0 0 0\nIDA 0 26 50 49 G 15 B\n\nTotal debt service 2 15 6\nIBRD 0 0 0 C: 9\nIDA 0 0 1 1\n\nComposition of net resource flows E: 119\nOfficial grants 5 32 48\nOfficial creditors -1 3 2\nPrivate creditors 0 -1 0\nForeign direct investment 0 0 6 D: **95**\nPortfolio equity 0 0 0\n\nWorld Bank program\n\nCommitments 0 9 3 A  - IBRD E - Bilateral\nDisbursements 0 2 2 1 B - IDA D - Other rrultilateral F **-** Private\nPrincipal repayments 0 0 0 0 C- IMF G - Short-term\nNet flows 0 2 2 1\nInterest payments 0 0 0 0\nNet transfers 0 2 1 1\n\n\nNote: This table was produced from the Development Economics central database. 9/13/00", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000097:63:1:0", "start": 960, "end": 998, "surface": "Development Economics central database", "probe_tag": "keep", "probe_score": 0.946, "luna_label": 1}]}, {"key": "paddy-170", "text": ". However, many\nissues remain with a large number of households connected to the water system, but not paying bills\nand those illegally connected (estimated between 30 to 50 percent). To compensate for cash shortfall,\nthe BWE also receives grants from the MoEW mainly earmarked for capital expenditures and for\nO&M service contracts.54A list of receivables from customers for previous years amounts to about\nUS$100 million in arrears. Some of these assets go back to 1992 and some of them may not be\ncollectible and need to be written off.\n\n166. **On the expense side**, the situation is more complex. As mentioned above, the records are based\non the cash accounting system, which does not show the entire picture of the BWE’s expenditures.\nHence, electricity consumption, part of the personnel payroll arrears, the social security debt as well as\ndepreciation allowances for capital assets are not fully reflected in the accounts. Operating expenses\nare relatively modest, represented mainly by personnel (38 percent); electricity expenses (40 percent);\nand O&M service contract (22 percent). Neither depreciation charge for fixed assets nor any provision\nfor doubtful accounts is set aside.\n\n\n167. **Cost recovery** . Efforts made during the last three years to increase revenues have made\npositive impact on the BWE’s finances. The BWE is now able to regularly pay its staff and part of its\nelectricity bills. Available data on revenues and expenses show that the BWE cost recovery for its\noperating expenses has increased from 31 percent in 2008 to nearly 50 percent in 2013; this is a\nnotable improvement in line with international standards.\n\n\n54Grants received from the MoEW ares LBP 13.7 billion in 2011 and 2012 (US$9.2 million); LBP 4.8 billion in 2013 (US$3.2 million);\nand LBP 12.8 billion in 2014 (US$8.5 million", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000036:51:1:0", "start": 337, "end": 371, "surface": "list of receivables from customers", "probe_tag": "keep", "probe_score": 0.9123, "luna_label": 0}, {"key": "refugee_pads:000036:51:1:1", "start": 1423, "end": 1452, "surface": "data on revenues and expenses", "probe_tag": "confusion", "probe_score": 0.8262, "luna_label": 1}]}, {"key": "paddy-171", "text": " Funds<br>(SLM) across the project<br>areas.|Bi-annually<br>|Project<br>Reports<br>|Project MIS<br>|NPCU<br>|\n|Share of households with Medium<br>Household Dietary Diversity Score in<br>refugee settlements|This will track the<br>percentage of households<br>with medium household<br>dietary diversity score in<br>refugee settlements|Biannually<br>|Project<br>Reports<br>|H/H survey-<br>beneficiaries<br>assessment<br>|NPCU<br>|\n|Percentage of which are women|This will track the<br>percentage of female<br>households with the<br>medium household diversity<br>score|<br>Biannually<br>|Project<br>Reports<br>|H/H surveys -<br>beneficiaries<br>assessment<br>|NPCU<br>|\n|Beneficiaries that feel project investments<br>reflected their needs|<br>This will measure the extent<br>to which decisions about the|<br>Biannually<br>|Project<br>Reports|H/H Surveys,<br>beneficiaries’|NPCU<br>|\n\n\n\nPage 54 of 81", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000001:59:1:1", "start": 606, "end": 617, "surface": "H/H surveys", "probe_tag": "confusion", "probe_score": 0.0836, "luna_label": 0}]}, {"key": "paddy-172", "text": "Annex 4\nPage 4 of 5\n\nextensive retraining of teachers and head-teachers. Temporary teachers will now be recruited\nearlier so that they can benefit from pedagogic training.\n\n\n**E. Justification of Public Finance to Support the Program**\n\nThere is a global consensus that universal access to basic education is the right of each child and this\nhas been widely endorsed most recently at the Education for All Conference in Dakar. The positive\nexternalities of basic education provide ample public finance justification for supporting universal\nbasic education with public funds. In addition, Djibouti's economic future depends heavily on the\nquality and productivity of its work-force as it has few other resources. Basic education is an\nessential pre-condition to improve workforce quality and productivity. Thus, public funding for it is\nalso justified on the grounds of improving Djibouti's development potential.\n\nPublic support for universal basic education in Djibouti can also be justified on the grounds of\nreducing income and gender inequities in enrollment which the Government proposes to address\nthrough a variety of ways discussed above. Random surveys of school children will be done over the\nten year period (including a base-line in 2001) to assess progress in reducing gender gaps and income\ngaps in enrollment.\n\n\n**F. Project Approach versus Budget Support Approach**\n\nThe project approach is considered more appropriate in Djibouti's case because given the urgent fiscal\nconstraints, budget support money may get diverted to finance immediate current needs and the\nschools may never get built. The supply of school places and the quality of schooling is easier to\naddress through a project approach.\n\n\n**G. Fiscal Impact of Program**\n\nAnnex 4, Table 1 illustrates the potential fiscal impact. This appears to be quite manageable. The\nrequired cost estimates include all levels of education, ministry overheads and potential grants to the\nprivate sector. The Government projects the most likely scenario for Djibouti's growth to be 2.4% per\nyear during the period 2000-2010 and expects the budget to grow slightly slower", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000170:40:0:0", "start": 1148, "end": 1181, "surface": "Random surveys of school children", "probe_tag": "confusion", "probe_score": 0.3714, "luna_label": 0}]}, {"key": "paddy-173", "text": "**The World Bank**\nDjibouti Health System Strengthening (P178033)\n\n\n\n\n\n|Inadequate inputs (esp. operating costs) for<br>health facilities|• PBF, DFF (at primary level in two regions)|\n|---|---|\n|_Inadequate data for real-time decision making_|• <br>DHIS2, CRVS, other surveys and data<br>initiatives|\n|_Limited institutional capacity_|• <br>Strengthening institutional capacity of<br>the MOH and other key entities to<br>enhance their stewardship|\n\n\n**C. Project Beneficiaries**\n\n\n53. The project will benefit the population of the whole country indirectly through health system\nstrengthening, service delivery optimization, improved health infrastructure, investments in community health\nplatforms, and SBCC. Direct beneficiaries of the project are women of childbearing age, adolescent and prepubescent girls, newborns and children under five years of age, both among nationals and refugees. Table 5\nsummarizes the estimated number of direct beneficiaries over the project’s implementation timeline. The\nnumbers in each column estimate the total number of individuals that will receive RMNCAH-N services that year.\nThe estimated number of registered refugees and asylum seekers receiving services could be on the conservative\nside, if this number increases due to a possible continuation of the conflict in neighboring Ethiopia despite the\ndeclared truce on March 24, 2022. These estimates do not take into account migrants and therefore\nunderestimate the total number of direct beneficiaries. The numbers of pre-pubescent and adolescent girls\nestimate the direct beneficiaries for counseling and prevention of FGM. The project will benefit health personnel,\nnotably doctors, nurses, nutritionists, and midwives, and CHWs from training on RMNCAH, nutrition, FGM, and\nmanagement of climate related health issues. The project will also benefit management at all levels of the health\nsector, including the MOH, regions, and", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000131:36:0:0", "start": 249, "end": 254, "surface": "DHIS2", "probe_tag": "confusion", "probe_score": 0.3011, "luna_label": 0}, {"key": "refugee_pads:000131:36:0:1", "start": 256, "end": 260, "surface": "CRVS", "probe_tag": "confusion", "probe_score": 0.5665, "luna_label": 0}]}, {"key": "paddy-174", "text": "**The World Bank**\nPublic Administration Modernization Project (P162904)\n\n\n**VII. RESULTS FRAMEWORK AND MONITORING**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Indicator Name|Core|Unit of<br>Measure|Baseline|End Target|Frequency|Data Source/Methodology|Responsibility for<br>Data Collection|\n|---|---|---|---|---|---|---|---|\n|**Name:**Percentage of<br>population with unique ID||Percentage|0.00|50.00|Quarterly<br>|ANSIE/PCU will create and<br>maintain a e-<br>ID registry which will<br>initially be populated via a<br>mass enrollment campaign<br>and by leveraging the work<br>and data gathered by the<br>national social security fund<br>and the social registry. The<br>percentage of population<br>with unique ID is obtain by<br>dividing total recipients of<br>e-ID to total population.<br>|ANSIE/PCU and the<br>General Directorate<br>of the Population at<br>the Ministry of<br>Interior.<br>|\n|||||||||\n\n\n\nPage 34 of 69", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000142:36:0:0", "start": 625, "end": 640, "surface": "social registry", "probe_tag": "confusion", "probe_score": 0.526, "luna_label": 1}]}, {"key": "paddy-175", "text": "_Sierra Leone_\n\n\nPRICES and GOVERNMENT FINANCE\n\n1981 1991 2000 2001 Inflation (%)\n_Domest)c_ _pHces_\n_(% change)_ c 1\nConsumer prices 16 7 102 7 -0.9 3 0 30 _< _\nImplicit GDP deflator 8 7 128 8 6 2 6 1 20\n\n_Govemment finance_ _10_\n_(% of GDP,_ _includes curent_ grants) 0\nCurrentrevenue .. 112 182 178 .10. 98 97 93 99 00 01\nCurrent budget balance .. -5 8 -4.5 -7 1 - GDP deflator _ CPI\nOverall surplus/deficit -10 4 -10.6 -12 3\n\n\nTRADE\n\n1981 1991 2000 2001 Export and Import levels (USS mnIll.)\n_(US$ millions)_\nTotal exports (fob) 147 176 75 78 400\nRutile . 72\nDiamonds (recorded) 32 10 21 300\nManufactures\nTotal imports (cHf) 317 158 161 303 20\nFood . 53 66 72 100\nFuel and energy 26 29 3d _ _8_\nCaptal goods .. 38 18 22  - __\n96 D6 97 9o 99 00 01\nExport price index _(1995=100)_ 90 86 87\nImport price Index (1995=100) 93 93 92 mExports ***Mrrports**\nTerms of trade (1995-100) 97 93 94\n\n\n\nBALANCE of PAYMENTS\n\n\n\n1981 1991 2000 2001 Curmnt account balance to GDP _(%)_\n_(US$_ _millions)_\nExports of goods and services 163 244 110 116 0\nImports of goods and services 349 226 212 252\nResource balance -186 18 -102 -137 .a*\n\n\n\nExports of goods and services 163 244 110 116 0\nImports of goods and services 349 226 212 252\nResource balance -186 18 -102 -137 .a*\n\nNet income -28 -60 -18 -20\nNet current", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000052:61:0:1", "start": 792, "end": 810, "surface": "Import price Index", "probe_tag": "confusion", "probe_score": 0.1084, "luna_label": 0}]}, {"key": "paddy-176", "text": "ficiaries 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>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>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>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", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000154:37:7:0", "start": 248, "end": 272, "surface": "Survey at end of project", "probe_tag": "confusion", "probe_score": 0.119, "luna_label": 0}, {"key": "refugee_pads:000154:37:7:1", "start": 248, "end": 272, "surface": "Survey at end of project", "probe_tag": "confusion", "probe_score": 0.1071, "luna_label": 0}]}, {"key": "paddy-177", "text": " in schools without any WASH facilities available to them, 0.3 million without\navailable water, and another 0.3 million without access to a sanitation facility. <sup>12</sup> [^12: Ministry of Education. 2020. _WASH in Schools Situation Analysis_ . Unpublished draft.] There are about 130,000 children living\nwith disabilities in formal public schools, requiring special consideration for access to WASH facilities. Poor access to\nWASH, and specifically to menstrual hygiene management (MHM) services, has been identified as one of the significant\nbarriers preventing girls from attending and completing school. <sup>13</sup> [^13: Alexander, Kelly T., et al. 2014. “Water, Sanitation and Hygiene Conditions in Kenyan Rural Schools: Are Schools Meeting the Needs of\nMenstruating Girls?” _Water_ 6 (5): 1453–1466. https://doi.org/10.3390/w6051453.] A sampling of healthcare facilities (HCFs) indicated a\n\n\n[6 United Nations High Commissioner for Refugees (UNHCR) (July 2023) https://www.unhcr.org/ke/wp-content/uploads/sites/2/2023/08/Kenya-](https://www.unhcr.org/ke/wp-content/uploads/sites/2/2023/08/Kenya-Statistics-Package-31-July-2023-DIMA.pdf)\n<u>[Statistics-Package-31-July-2023-DIMA.pdf.](https://www.unhcr.org/ke/wp-content/uploads/sites/2/2023/08/Kenya-Statistics-Package-31-July-2023-DIMA.pdf)</u>\n7 For a map, see https://data2.unhcr.org/en/country/ken.\n8 Socioeconomic Hubs for Integrated Refugee Inclusion in Kenya.\n9 UN University Institute for Water, Environment and Health (2022) Water Security in Africa: A Preliminary Assessment, Issue 13\nhttps://inweh.unu.edu/water-security-in-africa-a-preliminary-assessment/\n[10 Kenya Population and Housing Census", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000190:13:2:0", "start": 1635, "end": 1670, "surface": "Kenya Population and Housing Census", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}]}, {"key": "paddy-178", "text": "; sulfate (SO42); and total dissolved solids (TDS). The LRA then uses a simple Water Quality\nIndex (WQI), which presents the advantage of communicating water quality information in an\nunderstandable way for all stakeholders. The WQI summarizes a large amount of water quality data in\nscores reported as a total number between 1 and 100, with (a) 90–100 as excellent, (b) 75–90 as good,\n(c) 60–75 as fair, (d) 40–60 as marginal, and (e) 0–40 as poor. Under this component, the number of\nlocations will be increased to 20 and support will be mainly in the form of water quality measurement\nequipment, which could also expand the water quality indicator measured (such as BOD <sup>52</sup> ).\n\n\n116. **Improving water resources modeling** . Over time, the LRA will need to develop a systemwide modeling of the upper-catchment (hydrology, water quality, and so on), which will support\nmonitoring progress during the implementation of the MoE’s Business Plan for Combating Pollution\nin Qaraoun Lake. The LRA has already existing surface and groundwater flow models (such as HECRAS and Modflow) and these will be a good basis to expand to the wider upper catchment modeling.\nUnder this component, a water expert will be hired to support in assessing existing models, current,\nand projected water balance and integrate as possible current models to a wider upper catchment\nmodeling. The TOR of the water expert was discussed and is in advanced stage of preparation.\n\n\n117. **Raise awareness** . The LRA will continue to raise awareness about the need to clean up the\nLitani River by undertaking awareness and clean-up campaigns. It is important to involve all\nstakeholders in order to change the behavior of water users. The impact of awareness should be to the", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000036:39:1:0", "start": 262, "end": 280, "surface": "water quality data", "probe_tag": "confusion", "probe_score": 0.326, "luna_label": 1}]}, {"key": "paddy-179", "text": " GOY begins to deal with subsidy reforms. Potential\npartnerships with Civil Society Organizations (CSOs) to develop awareness tools and messages,\nencourage participation of women and gather community feedback will promote Fund transparency and\nincrease demand from beneficiaries for improved services. Ongoing communication to inform decision\nmakers, beneficiaries and other stakeholders concerning the SWF reforms and beneficiary recertification\nis essential.\n\n\n119. Further coordination and complementarity between social programs is possible through the use of\nthe SWF applicant database, to be further supported by ISP. The database will aim to be the most\ncomprehensive national database of poor and vulnerable individuals available in Yemen. Many countries\nuse such national databases to target and coordinate receipt of benefits across a range of social programs.\nFor example, Colombia’s national database in conjunction with a proxy means test is used to identify\nrecipients of the cash transfer program and for a national school feeding and day care program. Chile uses\nits database to target cash benefits for the main welfare program, cash for the poor elderly, for a school\nlunch program and for housing assistance. Palestine’s cash transfer database is used to cross-check with\nother state and donor-supported programs to ensure proper targeting and distribution of benefits. Yemen\nhas the potential to use the SWF database in a similar fashion, perhaps coordinating with SFD, PWP,\nschool and health interventions.\n\n\n120. Finally, considering that children are most at risk, and enrollment are low and dropout rates are\nhigh, over the medium run the SWF’s cash transfer program is a good candidate for the introduction of a\n**_conditional cash transfer_** program. Assistance can be tied to children’s schooling and\nhealthhmmunizations, as is now being implemented in a number of countries around the world including\nMexico, Pakistan, Turkey and Bangladesh.\n\n\n**28**", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000080:33:1:0", "start": 568, "end": 590, "surface": "SWF applicant database", "probe_tag": "keep", "probe_score": 0.922, "luna_label": 0}, {"key": "refugee_pads:000080:33:1:1", "start": 1240, "end": 1262, "surface": "cash transfer database", "probe_tag": "confusion", "probe_score": 0.8002, "luna_label": 1}, {"key": "refugee_pads:000080:33:1:2", "start": 1424, "end": 1436, "surface": "SWF database", "probe_tag": "keep", "probe_score": 0.9133, "luna_label": 0}]}, {"key": "paddy-180", "text": "**The World Bank**\nBeirut Housing Rehabilitation and Cultural and Creative Industries Recovery (P176577)\n\n\ncomprehensive framework is required to ensure that the various efforts on the ground are integrated and support the\nsame long-term goal of the city’s development. As such, rehabilitating the coastal (blast) zone for the urban population\nprovides a great opportunity to recover not only the physical condition of the infrastructure and residential assets,\nbut also to envision the regeneration of the waterfront neighborhoods as part of an integrated port-city development\napproach that considers social inclusion, sustainability, and local economic development. However, actions towards\nthis vision have not yet begun and the government does not have a clear ownership or capacity to kickstart this phase\nof the recovery.\n\n**11.** **The lack of coordinated policies and support systems for property owners and occupants has rendered housing**\n**conditions contingent on the owners’ ability or willingness to pay for necessary works.** <sup>**28**</sup> Low and middle-income\nhouseholds are under the greatest distress, and consequently in most need of financial and physical support for heavy\nrehabilitation and rental costs. Precarious tenure and the threat of evictions by property owners are of great concern\nin Beirut, particularly for the most vulnerable groups. A multisectoral needs assessment of two of the most blastaffected neighborhoods, Karantina and Mar Mikhael, highlights the precarious tenure conditions for many of those\nmost affected, with up to 57.2 percent of respondents without an official lease agreement and 3.2 percent of\nrespondents had lost these documents in the blast. <sup>29</sup> <sup>30</sup> Beyond the physical damage of the blast, housing speculation\nis threatening the preservation of the cultural and social identity of the central areas of Beirut.\n\n**12.** **The lack of effective coordination between entities in", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000034:15:0:0", "start": 1377, "end": 1407, "surface": "multisectoral needs assessment", "probe_tag": "confusion", "probe_score": 0.6943, "luna_label": 1}]}, {"key": "paddy-181", "text": " years, NCRRRINaCSA has been responsible for managing almost 60 percent of the\nresources allocated for social sector investments. This is expected to decrease as IDA and other\ndevelopment partners begin financing large education and health projects. Because of the limited\n\ncapacity of the line ministries, there is no evidence of crowding out investments that would otherwise\nhave been made by these ministries.\n\n\nNaCSA would continue to maximize the social rate of return of its portfolio through policies and\nprograms that ensure the poor are reached. Criteria applied to each sub-project would ensure that only\nthe most cost-effective investments are made and that NaCSA's administrative costs do not exceed 10\npercent of total program costs. NaCSA would use detailed technical criteria, standard designs and simple\neconomic criteria such as cost per beneficiary to establish sub-project economic viability. A list of unit\ncosts by sub-project type and region would be prepared and updated annually based on actual costs the\nprevious year.\n\n\n2. Financial (see Annex 4 and Annex 5):\nNPV=US$ million; FRR = % (see Annex 4)\n\n\n                               - 14", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000117:18:1:0", "start": 914, "end": 932, "surface": "list of unit\ncosts", "probe_tag": "confusion", "probe_score": 0.1106, "luna_label": 0}]}, {"key": "paddy-182", "text": ", and information about<br>the systems developed on its website, which is constantly being updated at https://id.gov.et/.<br>● The website contains frequently asked questions that address the risks related to some<br>misconceptions.<br>● NIDP organized a series of stakeholder consultations with civil society organizations, human and digital<br>rights groups, and legislative and executive wings of the Government among others during 2021–23.<br>The program will be able to further expand these activities once the project is operational, notably<br>further engaging with communities and local leaders.<br>● Based on the registrant’s request, all logs and audit trails of the user’s personal data and their<br>authentication history can be accessed and/or deleted. There are also facilities for the user to lock<br>their personal data so that it cannot be used for authentication.|\n\n\nPage 37 of 39", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "sample:refugee_pads:000005:47:2:0", "start": 684, "end": 697, "surface": "personal data", "probe_tag": "confusion", "probe_score": 0.3304, "luna_label": 0}, {"key": "sample:refugee_pads:000005:47:2:1", "start": 822, "end": 835, "surface": "personal data", "probe_tag": "confusion", "probe_score": 0.119, "luna_label": 0}]}, {"key": "paddy-183", "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": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "sample:refugee_pads:000147:19:1:0", "start": 272, "end": 283, "surface": "survey data", "probe_tag": "confusion", "probe_score": 0.8771, "luna_label": 1}]}, {"key": "paddy-184", "text": " functional CHUs was 7,780 and the total number of existing CHUs<br>(functional, non-functional, and semi-functional) was 10,382. <br>|\n|Frequency <br>|<br>Every six months <br>|\n|Data source <br>|Project report|\n|Methodology for Data<br>Collection <br>|Project monitoring|\n|Responsibility for Data<br>Collection <br>|MoH <br>|\n|**Percentage of quarterly priority HPTs orders placed within the required timeframe (Percentage)  **|**Percentage of quarterly priority HPTs orders placed within the required timeframe (Percentage)  **|\n|Description <br>|Percentage of quarterly priority HPTs orders placed by Counties within the required timeframe. The required timeframe<br>is by the 15th of the ordering month. <br>|\n|Frequency <br>|<br>Every six months <br>|\n|Data source <br>|Project report|\n|Methodology for Data<br>Collection <br>|Project monitoring|\n|Responsibility for Data<br>Collection <br>|KEMSA, MoH <br>|\n|**People who have received essential health, nutrition, and population (HNP) services (Number)CRI** <br> <br>|**People who have received essential health, nutrition, and population (HNP) services (Number)CRI** <br> <br>|\n|Description <br>|Total number of deliveries attended by skilled health personnel and total number of children immunized. <br>|\n|Frequency <br>|Every six months <br>|\n|Data source <br>|KHIS|\n|Methodology for Data<br>Collection|Routine HMIS", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000000:38:1:0", "start": 1321, "end": 1325, "surface": "KHIS", "probe_tag": "confusion", "probe_score": 0.4274, "luna_label": 1}]}, {"key": "paddy-185", "text": "\nrange of secondary and tertiary care facilities. However, continuity and sustainability of health\nservice delivery in Jordan is at risk of being severely compromised as a result of the upsurge in\ndemand for healthcare by the influx of the Syrian refugees.\n\n\n7. The healthcare needs of the Syrian refugees are different from those of the settled\npopulations because of the different demographic mix, living conditions and vulnerabilities,\nrequiring therefore a different mix of services and mode of health system response to respond to\ntheir needs. Such an upsurge both in demand and the mix of healthcare needs could easily\noverwhelm the existing facilities especially in Jordan where the absorptive capacity is relatively\nlow given its own population size relative to the size of the refugee population. According to\nUNHCR data, 78 percent of the Syrian refugees are vulnerable requiring additional assistance.\nThis include women (49 percent), children under the age of 12 (40 percent), and elderly (2.1\npercent). In addition, 23 percent of Syrian refugees have chronic diseases or serious medical\nconditions that require medical follow up. Comparative morbidity data show a different disease\nprofile with increased levels of morbidity for Syrian refugees than Jordanians which may affect\nthe disease burden in the future. According to Jordan’s national cancer statistics, Syrian refugees\npresenting with cancer at health facilities rose from 134 in 2011 to 169 in the first quarter of\n2013, representing 14 percent increase in Jordan’s total cancer disease burden. Similarly, MOH\nmorbidity data show a rise in selected communicable diseases. For example, TB case notification\nincreased from 5/100,000 among Jordanians in 2009 to 13/100,000 among Syrian refugees in\n2013. While no measles cases have been reported in Jordan since 2009, recent MOH data show\nthat 18 Jordanians and 23 Syrians have been diagnosed with the disease in 2013. Polio which had\nbeen eradicated since 1999 was also detected in two cases in 2013. With this higher demand for\n\n\n1414 World Health Organization Statistics, 2013\n\n\n34", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000064:33:1:2", "start": 1347, "end": 1373, "surface": "national cancer statistics", "probe_tag": "keep", "probe_score": 0.9173, "luna_label": 1}, {"key": "refugee_pads:000064:33:1:3", "start": 1579, "end": 1597, "surface": "MOH\nmorbidity data", "probe_tag": "confusion", "probe_score": 0.8645, "luna_label": 1}, {"key": "refugee_pads:000064:33:1:4", "start": 1845, "end": 1853, "surface": "MOH data", "probe_tag": "keep", "probe_score": 0.9389, "luna_label": 1}]}, {"key": "paddy-186", "text": " survey within\nthe duration of the ASPIRE MPA to seek feedback on benefits and services provided by the program. A\ncitizen engagement strategy will be developed to maintain continuous engagement and communication\nwith beneficiaries and citizens overall and contribute to building trust and a social contract. In addition,\nongoing citizens’ feedback will be considered when implementing the activities of the MPA phases, and\nPENRA will publish the results of the beneficiary surveys on its website, as a key results indicator for citizen\nengagement.\n\n\nPage 38 of 74", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000133:41:2:0", "start": 462, "end": 481, "surface": "beneficiary surveys", "probe_tag": "confusion", "probe_score": 0.5372, "luna_label": 0}]}, {"key": "paddy-187", "text": ", and geographical classifications. The only\nexpenditure of bodies controlled by the central government not reflected therein is that of the 10 public universities, whose\nexpenditures in 2020 were equivalent to approximately 5.4 percent of the total budget expenditures. In-year budget\nreports are published in the monthly General Government Finance Bulletin within four weeks of the end of the month.\nThey have become more informative as in-year internal reports show a breakdown by economic, administrative,\nprogrammatic, and functional classifications. However, they only cover payments and no commitments.\n\n\n**12.** **Annual financial statements cover revenues, expenditures, financial assets, liabilities, guarantees, long-term**\n**obligations, and cash balances comparable with the approved budget** . The Ministry of Finance is required by law to\n“submit to the Audit Bureau the final account of each fiscal year within not more than six months as of the date of year\nending.” The latest annual financial statements for the year ending December 31, 2022, were submitted to the Audit Bureau\nwithout delay. Consistent standards have been applied. The final accounts are published on the MOF’s website. The\ngovernment's consolidated financial statements are prepared according to International Public Sector Accounting\nStandards (IPSAS), and the Jordan Audit Bureau issues an audit opinion. There is a clear Audit Bureau opinion confirming\nthat cash based IPSAS were applied.\n\n\n**13. The MOF keeps a record of expenditure arrears reported by line ministries (including those ministries included in the**\n**Program), in conformity with the new Organic Budget law.** Despite GFMIS having a commitment control module, it is not\nutilized. The implementing agencies use Excel sheets to record commitments and follow up on payments. To enhance\ncommitment controls, an electronic system should be used by the implementing agencies to record and report on\ncommitments and be financially linked to the GFMIS. Until the latter is achieved, annual reports concerning commitments\nand arrears", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000181:74:1:0", "start": 1504, "end": 1533, "surface": "record of expenditure arrears", "probe_tag": "confusion", "probe_score": 0.3434, "luna_label": 0}]}, {"key": "paddy-188", "text": "**The World Bank**\nEducation Quality Improvement Project (P179363)\n\n\n\n\n\n\n\n\n\n\n\n\n\n|Components/<br>Subcomponents|Activity|Climate Mitigation<br>Measures (US$, millions)|Climate-Related Action|Anticipated<br>Mitigation|Anticipated Adaptation|\n|---|---|---|---|---|---|\n||planning||educational infrastructure, including data collection<br>related to safety, inclusiveness, sustainability such<br>as energy management systems, designing and<br>integrating sustainable O&M mechanisms for<br>energy efficiency and renewable energy, digital<br>provision, capacity building required for strategic<br>investment planning with durable and adequate<br>solutions in the face of the climate risks|reduce the<br>energy intensity<br>through<br>efficiency<br>measures and<br>renewable<br>energy options<br>based on the<br>data collection<br>on the energy<br>efficiency of<br>facilities|disasters will be a key<br>consideration in the<br>action plan, as well as<br>the potential for<br>educational institutions<br>to be designated disaster<br>shelters|\n\n\nPage 68 of 68", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "sample:refugee_pads:000185:77:0:0", "start": 315, "end": 330, "surface": "data collection", "probe_tag": "drop", "probe_score": 0.046, "luna_label": 0}, {"key": "sample:refugee_pads:000185:77:0:1", "start": 804, "end": 819, "surface": "data collection", "probe_tag": "drop", "probe_score": 0.0149, "luna_label": 0}]}, {"key": "paddy-189", "text": "**The World Bank**\nCameroon COVID-19 Preparedness and Response Project (P174108)\n\n\n90. **A grievance redress mechanism (GRM)** will be designed to allow affected stakeholders to raise\ngrievances and seek redress when they perceive that a negative impact has arisen from the project\ninterventions. The GRM will be designed in consultation with relevant government and non-government\nstakeholders and will build on the SEP. It will establish accessible processes to submit complaints as well as\nclear procedures from investigation to resolution and feedback. The GRM will include the provision for\nappeal if aggrieved parties are dissatisfied with the outcome. A communication campaign will be\nimplemented aiming at raising awareness and informing stakeholders on how to use the GRM and\nstipulating the investigation and resolution sequential process, timeline and procedures.\n\n91. **Large volumes of personal data, personally identifiable information and sensitive data are likely to**\n**be collected and used in connection with the management of the COVID-19 outbreak under circumstances**\n**where measures to ensure the legitimate, appropriate and proportionate use and processing of that data**\n**may not feature in national law or data governance regulations, or be routinely collected and managed**\n**in health information systems** . In order to guard against abuse of that data, the Project will incorporate\nbest international practices for dealing with such data in such circumstances. Such measures may include,\nby way of example, data minimization (collecting only data that is necessary for the purpose); data accuracy\n(correct or erase data that are not necessary or are inaccurate), use limitations (data are only used for\nlegitimate and related purposes), data retention (retain data only for as long as they are necessary),\ninforming data subjects of use and processing of data, and allowing data subjects the opportunity to correct\ninformation about them, etc. In practical terms, operations will ensure that these principles apply through\nassessments of existing", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000169:36:0:0", "start": 899, "end": 912, "surface": "personal data", "probe_tag": "drop", "probe_score": 0.0432, "luna_label": 0}]}, {"key": "paddy-190", "text": "**البنك الدولي**\n\n)P160224( بيروت الكُبرى مشروع النقل العام في منطقة\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n|زاجنإ|دوقعلا حنم|مييقتلا ةقيرط|رايتخلا قرط|ةسفانملا/ديروتلا جهن|ةباقر<br>فرصملا|ةردّقملا ةفلكلا<br>).أ.د نويلم(<br>رطخلا فينصتو|هناونعو هفيصوتو دقعلا ةئف|دقعلا عون|مقرلا<br>يفيرعتلا|\n|---|---|---|---|---|---|---|---|---|---|\n|30<br> <br> حزيران/يونيو2020<br>|1<br> حزيران/يونيو<br>2019<br>|الكلفة المقيمة الدنيا|التأهيل اللحق، طلب تسعيرة|وطني/مفتوح|لحقة|4.5<br> <br>متوسط|شبكة الباصات2<br> (التجهيزات وأشغال<br>)بسيطة|الشغال|9<br>|\n|31<br> <br> آذار/مارس2022<br>|<br>1<br> حزيران/يوني", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000065:78:0:0", "start": 546, "end": 559, "surface": "شبكة الباصات2", "probe_tag": "drop", "probe_score": 0.0242, "luna_label": 0}]}, {"key": "paddy-191", "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_paddy", "spans": [{"key": "refugee_pads:000165:31:0:0", "start": 226, "end": 240, "surface": "NaCSA M&E data", "probe_tag": "drop", "probe_score": 0.0279, "luna_label": 0}, {"key": "refugee_pads:000165:31:0:1", "start": 412, "end": 437, "surface": "NaCSA administrative data", "probe_tag": "confusion", "probe_score": 0.1197, "luna_label": 1}]}, {"key": "paddy-192", "text": "\ntargeted communities and assisted conmmunities; into national planning and\nbeneficiaries; and                  - Technical audits resource allocation\n\n                  - Proportion of sub-projects frameworks (such as the\noperative 24emonths after National Recovery Strategy,\ncorpletion. the PRSP, and the MTEF)\n\n\n**Output** **from** **each** **Output Indicators:** **Project** **reports:** **(from** **Outputs to Objective)**\n**Component:**\n**1.** Community-Driven\n**Program** (CDP)\nl(a) Rural social and Ia. 1 At least 1,000 - M&E data; - Targeting mnechanisms are\neconomic infrastructure and 'community based\" - NaCSA Progress reports efficient and implemented with\nservices are established, sub-projects implemented minimal political interference;\nupgraded and used. (breakdown by type and\nlocation).\n\n\nla.2 At least 90% of                  - Annual technical audit -Line agencies and/or other\n\n\n-25", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "sample:refugee_pads:000086:29:2:0", "start": 530, "end": 538, "surface": "M&E data", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 0}]}, {"key": "paddy-193", "text": "iii. **Data Validation and Verification:** Data validation and verification will be\ndone internally by MoPH. Internal verification will be aligned with the system\nused by the MoPH and will take place at two levels: (i) at the district level\n(Caza), the Caza Coordinator verifies a sample of 5 percent of all claims made\nthrough a combination of telephone and/or home visits (the sample is generated\nmonthly by the HIS and sent through the Coordinator tasked with this activity;\nand (ii) at the central level, a random sample of 2 percent of claims validated at\nthe district level is then re-validated by the central team at MoPH. Also at the\ncentral level, the MoPH will conduct trend and pattern analysis of the claims\nbeing processed to identify outlying data as an established anti-fraud tool.\n\n**b)** **Developing reporting mechanisms which will include:** (i) monthly reports from\n\nimplementing partners to PMU; (ii) quarterly reports from PMU to the Bank; and (iii) an\nAnnual Project Implementation Report, consolidating progress in the project\nimplementation by each of the institutions involved, based on administrative data, survey\ndata, beneficiary assessments and independent evaluations.\n\n\n_Evaluation_\n\n50. Evaluation will comprise mainly of an independent project evaluation, beneficiary\nassessment and evaluation by the Bank. The aim is to determine the relevance and fulfillment of\nobjectives, development effectiveness and sustainability. **_Independent Evaluation_** **:** The\nindependent project evaluation will be contracted by the MoPH, using the Word Bank\nprocurement processes and with Terms of Reference (TORs) acceptable to the Bank. The\npurpose of the independent evaluation will be to: (i) verify project’s progress towards\nachievement of the development objective; (ii) ensure the correct use of funds on a six monthly\nbasis; (iii) verify the services received by project beneficiaries through independently checking a\nsample", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000020:54:0:1", "start": 1134, "end": 1145, "surface": "survey\ndata", "probe_tag": "drop", "probe_score": 0.0418, "luna_label": 1}]}, {"key": "paddy-194", "text": " the rural labor\nmarket in pilot areas. Furthermore, seeking to promote the demand for employment through enhanced\ncontract farming has the potential to build this approach into the agricultural sector more broadly.\n\n\n82. Finally, the project aims to enhance the capacity of the ACC to ensure that the interventions and\nactivities supported by the project are scalable, thereby moving toward formalization of agriculture\nworkers more generally. These tools and practices (that is, registry of skilled workers and database of\nvacancies and training materials) are designed to be easily extended in other rural areas even beyond\nthose with presence of refugees.\n\n\n**IV.** **PROJECT APPRAISAL SUMMARY**\n\n\n**A. Technical and Economic Analysis**\n\n\n83. **The project design draws on international best practice by integrating supply- and demand-side**\n**interventions targeted to vulnerable populations in rural areas, both refugees and natives, in the**\n**agriculture sector in Turkey.** As explained in the section on lessons learned, integrated interventions that\ncombine supply- and demand-side approaches fare better in improving employability. In the current\ncontext, both demand and supply factors affect the employment capacity for vulnerable populations in\nthe rural areas in Turkey (Table 2). On the supply side, agricultural workers lack job-relevant skills including\nlanguage, technical, and behavioral. They also lack information about the possibility of and benefits from\nformal employment, particularly for the refugee population. Finally, workers can have trouble signaling\n\n\nPage 31 of 85", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "sample:refugee_pads:000114:34:1:0", "start": 481, "end": 508, "surface": "registry of skilled workers", "probe_tag": "drop", "probe_score": 0.0005, "luna_label": 0}, {"key": "sample:refugee_pads:000114:34:1:1", "start": 513, "end": 557, "surface": "database of\nvacancies and training materials", "probe_tag": "drop", "probe_score": 0.0036, "luna_label": 0}]}, {"key": "paddy-195", "text": "\ntargeted communities and assisted conmmunities; into national planning and\nbeneficiaries; and                  - Technical audits resource allocation\n\n                  - Proportion of sub-projects frameworks (such as the\noperative 24emonths after National Recovery Strategy,\ncorpletion. the PRSP, and the MTEF)\n\n\n**Output** **from** **each** **Output Indicators:** **Project** **reports:** **(from** **Outputs to Objective)**\n**Component:**\n**1.** Community-Driven\n**Program** (CDP)\nl(a) Rural social and Ia. 1 At least 1,000 - M&E data; - Targeting mnechanisms are\neconomic infrastructure and 'community based\" - NaCSA Progress reports efficient and implemented with\nservices are established, sub-projects implemented minimal political interference;\nupgraded and used. (breakdown by type and\nlocation).\n\n\nla.2 At least 90% of                  - Annual technical audit -Line agencies and/or other\n\n\n-25", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000104:29:2:0", "start": 530, "end": 538, "surface": "M&E data", "probe_tag": "drop", "probe_score": 0.023, "luna_label": 0}]}, {"key": "paddy-196", "text": " MINSANTE as well as the Director General\n(DG) ARMP, DG INS, and DG BUCREP. The Committee will meet at least every three months to monitor\nimplementation and address early any institutional bottlenecks.\n\n\n**B. Results Monitoring and Evaluation**\n\n\n50. **The project’s M&E framework relies on the ‘Guide On Operational-Level Public Financial**\n**Management Reform Indicator’.** The M&E framework will be a key instrument to monitor progress\ntoward achieving the PDOs and providing reports on performance including potential bottlenecks as\nthey arise. The M&E framework presented in section VII captures the high- and medium-level results\nthat are expected to be achieved. MINEPAT will be in charge of assessing and reviewing the projects’\nresult indicators data. A midterm review (2019) will be carried out to ensure the project is on track and\nwill recommend any needed actions or adaptations to address challenges and improve implementation\ntoward achievement of the PDO. During implementation, the team will revisit the currently agreed\nindicators to make adjustments as required in consultation with the Government and other partners.\n\n\n**C. Sustainability**\n\n\n51. **The sustainability of the project is underscored by the results-driven approach adopted** . The\nproject draws on diagnostics and recommendations for improving procurement and program budget\nprocesses. The introduction of public sector performance-based management and results and the\ndevelopment of robust systems and tools will provide a sound basis for ensuring Government\neffectiveness. The project’s design also includes activities shifting the incentives of key targeted civil\nservants in the Public Investment Management chain toward performance, using existing available\nbudget as much as possible (for example, per diem budgeted for the Procurement Committee). The\n\n\nPage 29 of 93", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "sample:refugee_pads:000044:32:1:0", "start": 738, "end": 760, "surface": "result indicators data", "probe_tag": "drop", "probe_score": 0.0241, "luna_label": 0}]}, {"key": "paddy-197", "text": " 4 or more ANC<br>visits. Denominator: Total number of expected live births during the reporting period within the host community of<br>Garissa and Turkana<br>|\n|Frequency <br>|Every six months <br>|\n|Data source <br>|KHIS|\n|Methodology for Data<br>Collection <br>|Routine HMIS data collection|\n|Responsibility for Data<br>Collection <br>|MoH <br>|\n|**Percentage of refugee pregnant women attending 4 or more ANC visits in Garissa and Turkana (Percentage)**<br>|**Percentage of refugee pregnant women attending 4 or more ANC visits in Garissa and Turkana (Percentage)**<br>|\n|Description<br>|Numerator: Number of refugee pregnant women attending 4 or more ANC visits.<br>Denominator: Total number of expected live births during the reporting period within the refugee community of Garissa<br>and Turkana<br>|\n|Frequency|Every six months <br>|\n|Data source<br>|UNHCR reports|\n|Methodology for Data<br>Collection<br>|Routine UNHCR data collection|\n|Responsibility for Data<br>Collection<br>|MoH <br>|\n|**Proportion of Children Under 5 with diarrhea treated with Zinc/ORS Co-Pack (Percentage) **|**Proportion of Children Under 5 with diarrhea treated with Zinc/ORS Co-Pack (Percentage) **|\n\n\nFeb 21, 2024 Page 31 of 43", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "sample:refugee_pads:000000:36:2:0", "start": 218, "end": 222, "surface": "KHIS", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 0}, {"key": "sample:refugee_pads:000000:36:2:1", "start": 273, "end": 293, "surface": "HMIS data collection", "probe_tag": "confusion", "probe_score": 0.2047, "luna_label": 0}, {"key": "sample:refugee_pads:000000:36:2:2", "start": 860, "end": 873, "surface": "UNHCR reports", "probe_tag": "confusion", "probe_score": 0.0992, "luna_label": 0}]}, {"key": "paddy-198", "text": " for the country and the climate, and how best to deal\nwith the operations and maintenance issues for school upkeep.\n\n\nThe list of sites for the Phase I schools follows. Note that the first year sites are fixed while there\nmay be some adjustments on the second year sites.", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "sample:refugee_pads:000149:32:2:0", "start": 123, "end": 160, "surface": "list of sites for the Phase I schools", "probe_tag": "drop", "probe_score": 0.0, "luna_label": 0}]}, {"key": "paddy-199", "text": "**The World Bank**\nAdvancing Sustainability in Performance, Infrastructure, and Reliability of the Energy Sector in the West Bank\nand Gaza (P170928)\n\n\ncapacity needs, the first phase will facilitate the creation of a community of practitioners (CoP) among the\nsector institutions (PENRA, PETL, DISCO, and PERC) to enable (a) alignment of PETL’s transmission master\nplan, DISCO’s distribution master plans, and demand projections from LGUs; (b) development and\nharmonization of the transmission code, distribution grid code including for utility-scale solar, and\ninterconnection standards for imports; (c) strategic prioritization of T&D investment needs for financing\nthrough tariffs, PA budget, and donor support; and (d) a cohesive approach to technical coordination with\nIEC. This community will create long-term capacity and coordination within the sector institutions that\nwill also inform future phases.\n\n\n30. **Service delivery, under Pillar 2, is the keystone of the electricity sector as it focuses on customer**\n**management and responsiveness.** Under ESPIP, DISCOs have started implementing a harmonized\nrevenue protection program (RPP) that targets the largest customers. ASPIRE Phase 1 will begin the\nprocess of enhancing metering, billing, and collection for a broader group of customers, which includes\nthe next tier of largest customers and potentially public sector consumers. This will involve installation of\nsmart meters. More importantly, Phase 1 will seek to identify citizen engagement, communication, and\nother approaches to improve the ability of DISCOs to install the meters, to ensure timely payments, to\nreduce nonpayment, and to improve monitoring of financial flows for each DISCO and the entire sector.\n\n\n31. **Private sector engagement, under Pillar 3, is a longer-term goal that requires longer-term**\n**planning, sustained effort, and innovative solutions.** Building on lessons learned from ESPIP and demand\nestimation", "source": "refugee_pads", "subset": "annotate_paddy", "spans": [{"key": "refugee_pads:000132:23:0:0", "start": 410, "end": 438, "surface": "demand projections from LGUs", "probe_tag": "drop", "probe_score": 0.0207, "luna_label": 1}]}, {"key": "paddy-200", "text": "\nState of Palestine\n\n\nEcuador\n\n\nBangladesh\n\n\nColombia\n\n\nUganda\n\n\nSyrian Arab Republic\n\n\nIraq\n\n\n\n\n\n3.0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n**The number of donors increased, but about two-**\n**thirds of ODA came from the five largest providers.**\n\n\nTo enhance burden- and responsibility-sharing, the\nGCR calls for broadening the support base, including\nthe number and types of donors. Compared to the\n2018-2019 data, where 36 donors reported financing\nfor refugee situations, 48 donors reported in 20202021. This included 32 DAC countries (USD 19.9\nbillion in bilateral ODA), 11 non-DAC countries (USD\n\n\n\n293 million in bilateral ODA), and five development\nbanks (USD 3 billion in multilateral ODA). Another\nUSD 3.2 billion was channelled as core contributions\nto 11 refugee-mandated United Nations agencies\nand four other multilateral entities, such as UNHCR,\nUNRWA, UNOCHA, Global Fund, i.a. (Figure 9).\nDespite the generosity of a larger number of financial\nproviders, the responsibility of supporting refugee\nsituations lay squarely with the five largest providers.\nThe United States of America (USD 6.6 billion),\n\n\nGLOBAL COMPACT ON REFUGEES INDICATOR REPORT 29", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "sample:reliefweb:000435:28:1:0", "start": 378, "end": 392, "surface": "2018-2019 data", "probe_tag": "keep", "probe_score": 0.9218, "luna_label": 0}, {"key": "sample:reliefweb:000435:28:1:1", "start": 1097, "end": 1140, "surface": "GLOBAL COMPACT ON REFUGEES INDICATOR REPORT", "probe_tag": "keep", "probe_score": 0.9859, "luna_label": 0}]}, {"key": "paddy-201", "text": "UNHCR IDP Data Report – May 2012\n\n\n**6.  PROTECTION CONCERNS REPORTED BY CONFLICT-INDUCED**\n**DISPLACED**\n\nThe key cause of displacement for conflict-induced IDPs has been the severity of their\nprotection concerns. These have included targeted threats, intimidation, and extortions, aerial\nattacks, forced recruitment, illegal taxation, night searches, and armed conflict among others.\nIDPs have also reported protection concerns during flight and in their places of displacement.\n\nMany IDPs continue to face threats to their physical security and well-being, including\nintimidation and persecution, which may lead to secondary displacement. In particular, male\nIDPs could face the additional risk of forced recruitment or being accused of association with\nparties to the conflict.\n\nThe protection risks facing women and children in displacement vary based on regional\ncharacteristics in each area in Afghanistan. However, the most relevant ones are, child\nrecruitment in armed force (Government and Anti-Government Elements), denial of access to\nbasic social services to single female headed households or unaccompanied women, risk of\nchild labor, economic constraints of a displaced family preventing the children from attending\nschool, threat of underage marriage/forced marriage to local power brokers. The groups\nespecially at risks among this population of women and children, are unaccompanied women,\nsingle female headed households and separated children.\n\nDue to the intensity of the conflict and often entrenched positions of the warring parties,\ndisplacement is becoming increasingly prolonged. Deterioration of the protection situation in\nplaces of displacement due to severe winters, absence of critical services in places of\ndisplacement, and the lack of livelihoods have rendered IDPs more vulnerable.\n\nThe existence of mines and other explosive remnants of war (ERWs) constitute a major threat\nto IDPs trying to settle in unfamiliar places. In areas of return, these similarly may restrict\naccess to shelter, water and land for grazing and cultivation, and it hampers the restoration of\nessential infrastructure. The", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "sample:reliefweb:000682:14:0:0", "start": 0, "end": 21, "surface": "UNHCR IDP Data Report", "probe_tag": "keep", "probe_score": 0.9809, "luna_label": 1}]}, {"key": "paddy-202", "text": " number of serious\nfactors, including: risks, threats and potential trauma faced by those who come forward; significant\ngaps in available services; restrictions on movement; shame, fear, stigma and discrimination; risks of\nretaliations and “honor killings”; impunity for perpetrators; and the existence of mandatory\nreporting of sexual violence by government health staff. Feedback from communities and service\nproviders indicate an increase of sexual violence within communities, directly related to the crisis\nand mass displacement. However, when reporting of GBV does happen, it is untimely, with only 9%\nof GBV cases reporting within three days of the incident. <sup>16</sup> [^16: GBVIMS, July 2015 – February 2016. This statistic represents all GBV cases, not just sexual violence.] Timely reporting is critical for addressing\nthe mental and physical health needs, including preventing long-lasting psychological effects of\ntrauma and providing lifesaving HIV post-exposure prophylaxis ad other immediate medical care.\n\nGaps in humanitarian aid have increased risks of violence and heightened the likelihood of women\nand girls being forced to engage in negative coping strategies (like survival sex or early and forced\nmarriage) to meet basic needs. Displaced women, especially widows and female-headed\nhouseholds, are particularly vulnerable to GBV. Safety audits in IDP and refugee sites identified\nsecurity risks for women and girls, including long distances to collect water, poor lighting at\nsanitation facilities, and overcrowded living. A recent study among IDPs living in critical shelters and\ncamps found that 64% of latrines surveyed were both unsegregated and without locks, representing\na significant risk of violence for women and girls. <sup>17</sup> [^17: GBV risks amongst IDPs Living in Critical Shelters and Camps, International Organization for Migration (IOM), September\n2015] Threats to their physical safety and security and\non-going harassment are part of the daily reality for many IDP women and girls.\n\n12 GBVIMS, July 2015 – February 2016. Data is only from reported cases and is in no way representative of", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000831:6:1:0", "start": 686, "end": 692, "surface": "GBVIMS", "probe_tag": "keep", "probe_score": 0.9643, "luna_label": 1}]}, {"key": "paddy-203", "text": "Venezolanos en Chile, Colombia, Ecuador y Perú\n\n- una oportunidad para el desarrollo\n\n\n**Figura 6.1 Población por categoría de ocupación**\n\n\n\n6 Resultados del mercado laboral para\nvenezolanos y locales\n\n\n35\n\n\n\n0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%\n\n\n**Chile**\n\n\nLocal\n\n\n63.9% 6.3% 29.7%\n\n\nMigrante\n\n\n88.6% 4.4% 7.1%\n\n\n**Colombia**\n\n\nLocal\n\n\n63.8% 11.3% 24.9%\n\n\nMigrante\n\n\n62.7% 12.5% 24.8%\n\n\n**Ecuador**\n\n\nLocal\n\n\n77.6% 6.2% 16.3%\n\n\nMigrante\n\n\n89.8% 4.5% 5.7%\n\n\n**Perú**\n\n\nLocal\n\n\n72.2% 6.7% 21.2%\n\n\nMigrante\n\n\n81.2% 6.3% 12.6%\n\n\nEmpleado Desempleado Inactivo\n\n\nFuentes: Elaboración propia a partir de las siguientes encuestas. Chile: Encuesta de Migración (Banco Mundial, SERMIG y Centro UC 2022) y Encuesta Nacional de\nEmpleo (INE 2022). Colombia: Pulso de la Migración (DANE 2022) y Gran Encuesta Integrada de Hogares (DANE 2021). Ecuador: Encuesta a Personas en Movilidad\nHumana y en Comunidades Receptoras en Ecuador (INEC 2019) y Encuestas Telefónicas de Alta Frecuencia ALC (Banco Mundial 2022). Perú: Encuesta dirigida a\nla población venezolana que reside en el país (INEI 2022) y Encuesta Nacional de Hogares (INEI", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "sample:reliefweb:001322:34:0:0", "start": 641, "end": 662, "surface": "Encuesta de Migración", "probe_tag": "keep", "probe_score": 0.9983, "luna_label": 1}, {"key": "sample:reliefweb:001322:34:0:1", "start": 706, "end": 733, "surface": "Encuesta Nacional de\nEmpleo", "probe_tag": "keep", "probe_score": 0.9948, "luna_label": 1}, {"key": "sample:reliefweb:001322:34:0:2", "start": 756, "end": 777, "surface": "Pulso de la Migración", "probe_tag": "keep", "probe_score": 0.9962, "luna_label": 1}, {"key": "sample:reliefweb:001322:34:0:3", "start": 792, "end": 826, "surface": "Gran Encuesta Integrada de Hogares", "probe_tag": "keep", "probe_score": 0.9968, "luna_label": 1}, {"key": "sample:reliefweb:001322:34:0:4", "start": 942, "end": 986, "surface": "Encuestas Telefónicas de Alta Frecuencia ALC", "probe_tag": "keep", "probe_score": 0.9998, "luna_label": 1}]}, {"key": "paddy-204", "text": "psychological or emotional abuse were the most commonly reported types of GBV incidents, accounting\nfor **36 percent** and **34 percent** of incidents, respectively.\n\nSexual abuse remains a risk with devastating consequences on women and girls. However, it remains\nunderreported because of the stigma associated with it. Rape and sexual assault constitute a 17 percent\nof all reported incidents according to GBVIMS report for Q2 2022.\n\nHome remains not a safe space for survivors of GBV. According to the GBVIMS report covering the\nsecond quarter of 2022, 27 percent of the GBV incidents reported took place in the perpetrators home,\nwhile 56 percent of incidents took place in the client’s home. These high percentages are linked to the\nhigh percentage of intimate partner violence incidents reported in the same quarter, with 57 percent of\nthe cases and marking a 2 percent increase compared to the first quarter of 2022.\n\n\n**_Intimate Partner Violence_**\n\n\nIntimate partner violence reporting continued to increase in 2022, with 2 percent increase compared to\nthe first quarter of 2022, accounting for **57 percent** in the second quarter of 2022, according to the\nGBVIMS. Data extracted from the GBVIMS indicate that both adults and children are subjected to intimate\npartner violence, with **7 percent** of female children reporting being subjected to intimate partner violence\nas well in the context of early marriage. According to KAFA internal reports <sup>5</sup> [^5: 2022. KAFA Internal reports on urgent cases of GBV in need for legal assistance. Accessed at: https://kafa.org.lb/en.] on urgent cases of women\nand girls’ reporting different incidents of gender-based violence in Bekaa, Beirut and Mount Lebanon and\nthe South, the majority of cases requesting legal assistance are cases of intimate partner violence. Women\nand girls are requesting legal protection, prosecution of the perpetrator/ intimate partner, safe shelter\nand childcare and the procedures weren’t put in action due to the", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001273:2:0:0", "start": 408, "end": 421, "surface": "GBVIMS report", "probe_tag": "keep", "probe_score": 0.9709, "luna_label": 1}]}, {"key": "paddy-205", "text": "a refugee haven” (Chatelard, 2010). This small state – one of the most water\nscarce in the world – has been home to the Palestinian refugees (and their descendants) who\nfled their homes in 1948 and 1967 as well as those who were expelled from Kuwait in 1991.\nEstimates vary greatly but it is believed that the Palestinians constitute at least 50% of the\npopulation. People fleeing violence in Iraq have come to Jordan at various junctures over the\npast three decades. And the recent Syrian crisis has brought refugees in numbers that exceed\neven the original influx of Palestinians in the mid-20 <sup>th</sup> Century. Thus any analysis of the\nJordanian Government’s response to the Iraqis who have entered the country since 2003 must\ntake into account the massive influx of refugees – both in volume and in relation to the size of\nthe citizenry. <sup>4</sup> [^4: According to the UNHCR 2012 Statistical Yearbook (the last available), in that year Jordan hosted the eighth\nlargest number of refugees in the world. When the number of refugees is considered in relation to the size of the\nhost country population Jordan came out number one. These figures do not reflect the continued influx of Syrian\n[and Iraqi refugees since December 2012. See http://www.unhcr.org/pages/4a02afce6.html](http://www.unhcr.org/pages/4a02afce6.html)] It is inconceivable that the leaders of European and other developed countries,\nparticularly at a time of rising popular opposition to immigration, would admit even a\nfraction of the number allowed to remain by the Jordanian authorities.\n\nWhilst acknowledging the hospitality of the authorities under very challenging conditions, it\nis also important to note that Jordan has received significant benefits through its role as a host\ncountry (Chatelard, 2010). It has been argued that the Iraqis have contributed significantly to\nthe growth of the economy. According to Human Rights Watch, in", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000556:20:1:0", "start": 882, "end": 913, "surface": "UNHCR 2012 Statistical Yearbook", "probe_tag": "keep", "probe_score": 0.9651, "luna_label": 1}]}, {"key": "paddy-206", "text": "\nparticuliers (y compris les personnes âgées, les handicapés, les minorités); droits humains; action contre les mines\nantipersonnel; droits à la terre, au logement et à la propriété; facilitation de solutions (PNUD); logistique; et gestion des\ninformations. Le Module global de la protection compte actuellement quatre AoR: protection de l’enfance ; lutte contre\nla violence sexuelle et sexiste; logement, foncier et propriété; et lutte antimines. Il existe implicitement un cinquième\nAoR, la « protection générale », catégorie fourre-tout qui inclut un éventail d’activités axées sur la protection qui ne\ndisposent plus de leur propre AoR telles que l’enregistrement, l’établissement du profil des populations affectées, la\ncapacité d’autoprotection de la communauté, l’assistance juridique, la prévention du retour forcé, des mesures de\nrenforcement de la confiance, etc.\n\n\n126 Après un examen très étendu et approfondi des données pour 2009-2010, qui comportaient des pratiques\nd’encodage incohérentes pour les données du HCR, nous avons procédé à trois corrections manuelles : les montants\ndu HCR pour la « situation en Iraq » en 2009 et 2010 ont été manuellement ajustés en fonction des montants effectifs\nalloués par le HCR à la protection des DI figurant dans ses Rapports globaux, et les montants alloués par le HCR en\n2010 à la protection pour les réfugiés d’Afrique", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001007:81:2:0", "start": 926, "end": 948, "surface": "données pour 2009-2010", "probe_tag": "confusion", "probe_score": 0.5206, "luna_label": 1}, {"key": "reliefweb:001007:81:2:1", "start": 1014, "end": 1028, "surface": "données du HCR", "probe_tag": "confusion", "probe_score": 0.248, "luna_label": 1}]}, {"key": "paddy-207", "text": " En Perú, no\nexiste correlación. El estado civil\nde los venezolanos en Perú está\ncorrelacionado con los salarios: los\nvenezolanos casados ganan en\npromedio un 2.5 por ciento más que\nlos solteros, con todos los demás\nfactores constantes. Si se tienen\nen cuenta otros factores (como la\neducación, la experiencia, el estado\ncivil y el tamaño del hogar), los salarios\nde las mujeres venezolanas son un\n28 por ciento inferiores a los de los\nhombres venezolanos en Colombia y\nun 27 por ciento inferiores en Perú.\n\n\n\n**Variable**\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFuentes: Elaboración propia a partir de las siguientes encuestas: Chile: Encuesta de Migración (Banco\nMundial, SERMIG y Centro UC 2022) y Encuesta Nacional de Empleo (INE 2022). Colombia: Pulso de la\nMigración (DANE 2022) y Gran Encuesta Integrada de Hogares (DANE 2021). Ecuador: Encuesta a Personas\nen Movilidad Humana y en Comunidades Receptoras en Ecuador (INEC 2019) y Encuestas Telefónicas de\nAlta Frecuencia ALC (Banco Mundial 2022). Perú: Encuesta dirigida a la población venezolana que reside\nen el país (INEI 2022) y Encuesta Nacional de Hogares (INEI 2021).\n\n\nNota: Errores estándar entre paréntesis. *** Estadísticamente significativo al nivel del 1 por ciento. **\nEstadísticamente significativo al nivel del 5 por ciento. * Estadísticamente significativo al nivel del 10 por\nciento.\n\n\n\nLa diferencia en las comunidades\nde a", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001322:41:4:1", "start": 676, "end": 703, "surface": "Encuesta Nacional de Empleo", "probe_tag": "confusion", "probe_score": 0.868, "luna_label": 1}]}, {"key": "paddy-208", "text": "Population Data Analysis – July 2023\n\n\nOverview\n\n\nAt the end of July 2023, Southern Africa hosts around **8.3 million people that UNHCR has the mandate**\n**and responsibility to protect and assist** . This includes 800,000 refugees, 201,000 asylum-seekers, 32,300\nothers of concern, <sup>1</sup> [^1: Others of concern refer to those who are linked to (but not classified as) refugees and asylum-seekers, and who need assistance by UNHCR.\nIn most cases in Southern Africa, they are family members, i.e., spouse or children, of refugees or asylum-seekers.] 6.8 million internally displaced persons (IDPs) induced by conflicts, as well as returned\nrefugees of around 600 and 482,100 returned IDPs. In addition, 1 million IDPs are induced by climate change\nand disaster. There are no siginificant changes in total numbers since June 2023. **The Democratic Republic**\n**of Congo (DRC) hosts 83 per cent of the population in the region** .\n\nRefugees, Asylum-Seekers and Others of Concern\n\n\nThere are **1 million refugees, asylum-seekers and others of concern** hosted in the region and 75 per cent\noriginate from countries outside Southern Africa region. <sup>2</sup> [^2: Southern Africa region refers to the 16 countries covered by the Regional Bureau for Southern Africa of UNHCR including Angola, Botswana,\nComoros, Congo, DRC, Eswatini, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Zambia and\nZimbabwe.] The top five countries of origin are: Central African\nRepublic (242,000), Rwanda (238,000), Democratic Republic of the Congo (217,600), Burundi (85,800) and\nSouth Sudan (57,000).\n\nDRC new arrivals\n\n\n\nIn preparation for the review of the refugee response plan\nfor the DRC situation the region’s", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001551:1:0:0", "start": 0, "end": 15, "surface": "Population Data", "probe_tag": "confusion", "probe_score": 0.5047, "luna_label": 0}]}, {"key": "paddy-209", "text": "UNHCR’s health information system tools continued\nto collect information on health and nutrition access,\nutilization, coverage and status disaggregated by\nage and sex. Primary-health-care information for\nrefugees living in camps/settlements was collected\nin **155 camps/settlements sites in 22 countries** .\n[Standardized Expanded Nutrition Surveys to assess](https://sens.unhcr.org/)\nmalnutrition levels were carried out in 93 sites across\n13 countries. Biometrics data are now also an integral\npart of UNHCR registration data and processed in **79**\n**operations** globally. <sup>31</sup> [^31: UNHCR, “Biometrics” (n.d.). Available from <u>[https://help.unhcr.org/jordan/wp-content/uploads/sites/46/2022/04/Biometrics-EN_Final_APRIL2022.pdf.](https://help.unhcr.org/jordan/wp-content/uploads/sites/46/2022/04/Biometrics-EN_Final_APRIL2022.pdf)</u>]\n\n\nAnother notable innovation in 2021 is the release of\na set of tools by the Division of International Protection\n(DIP), the Division of Resilience and Solution (DRS)\nand the Global Data Service (GDS) in August 2021 to\nsupport the systematic identification of persons with\ndisabilities at registration and in other data-collection\nefforts. <sup>32</sup> [^32: These included the revision of the Disability sub-categories under the Specific Needs entity in PRIMES, as well as the development of related guidance. For an\noverview of UNHCR’s work on disability inclusion in 2021, please refer to the relevant spotlight here.] More specifically, **UNHCR integrated the**\n**<u>[Washington Group questions on disability statistics](https://www.washingtongroup-disability.com/)</u>**\n**into its registration system across all refugee**\n**operations worldwide** .", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000524:20:0:0", "start": 309, "end": 348, "surface": "Standardized Expanded Nutrition Surveys", "probe_tag": "confusion", "probe_score": 0.8404, "luna_label": 0}, {"key": "reliefweb:000524:20:0:1", "start": 455, "end": 470, "surface": "Biometrics data", "probe_tag": "confusion", "probe_score": 0.4281, "luna_label": 0}]}, {"key": "paddy-210", "text": "6 \n \nSpecialized services: Among 325 respondents, needs \nfor specialized services were expressed according to \nthe accompanying chart, below. The need for \npsychosocial support was expressed by 55% of \nrespondents, compared to 20% two months earlier in \nthe rapid protection assessment.   \n6. Coping mechanisms \nAmong 395 respondents, the attempt to access aid \n(49%) was the most common coping mechanism in \nwhich respondents had noticed an increase in their \nneighborhoods. 38% reported an increase in staying \nhome, 36% an increase in begging, 28% in school drop-\nouts. 24% noted an increase in moving to other \nneighborhoods, 22% the restriction of movement of \nwomen and girls, 21% an increase in early marriage, \n4% an increase in visits to community or women’s \ncenters. 2% noted an increase in engagement in illegal \nactivities.  \n7. Gender-based violence \nGBV continues to pervade the lives of women and girls \nin East Aleppo city. Those findings, corroborated by \nfocus-group discussions conducted in 43 sub-districts \ncovered by the cross-border operation from the Turkey \nhub in July 2016, confirm that domestic violence, \nsexual violence and early marriage are ever-present \nrisks for women and girls. 21% respondents report an \nincrease in early marriage as a coping mechanism. 31% \nof respondents report domestic violence as concern, \n23% reporting that women are affected, 18% men, \n13% boys, and 11% girls. Sexual violence was reported \nby 7% of respondents: 5% reporting that boys are \naffected, 4% women, 3% girls, and 1% men. Request of \nsexual favours in exchange for aid was reported by 8%, \nwith men (4%) and boys (4%) most affected, followed \nby women (2%) and girls (2).  Sexual violence at \ndistribution points was reported by 5% of respondents, \nmostly as affecting women and girls (3% each). This \nillustrates the GBV risks", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001275:5:0:0", "start": 258, "end": 285, "surface": "rapid protection assessment", "probe_tag": "confusion", "probe_score": 0.2881, "luna_label": 1}]}, {"key": "paddy-211", "text": "**WHY INVEST NOW IN DATA AND INFORMATION MANAGMENT?** DATA TRANSFORMATION STRATEGY 2020-2025\n\n\n\nfinance in line with Grand Bargain commitments.\nExternal benchmarking shows that others have already\nmade investments in many of these areas.\n\n\nSixth, the UN Secretary General has embarked on an\nambitious set of reforms to align the UN development\nsystem with the 2030 Agenda. **In particular, the**\n**Common Country Analysis, the Cooperation**\n**Framework and the Joint Workplan will require**\n**stronger partnerships for data and information, joint**\n**needs assessments, and shared platforms for**\n**reporting.**\n\n\nSeventh, UNHCR recently issued the Internally\nDisplaced Population Policy and Guidance Package\nto strengthen UNHCR’s commitments to IDPs. The\npolicy outlines the data needs, systems and methods\nassociated with IDPs and describes how, in line with\nits role in managing evidence and information,\n**UNHCR aims to invest in data on internally and**\n**forcibly displaced populations.**\n\n\n\nEighth, the timeframe of this strategy encompasses the\nremaining years of the Global Action Plan to End\nStatelessness 2014−2024 (GAP). One of the key\nactions in the GAP is to **improve quantitative and**\n**qualitative data on stateless populations.** As\ngathering data continues to be a major challenge to\nresolving statelessness, stepping up efforts through\nthe analysis of civil registration data, population\ncensuses, targeted surveys and studies is key to\nreaching the GAP goal and will be pursued as part of\nthe strategy.\n\n\n\n_UNHCR staff member punches a card that records food distributions in South Sudan’s Gorom refugee camp. © UNHCR/Elizabeth Stuart_\n\n\nU N H C R / 1 3 S E P T E M B E R 2 0 1 9 9", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001112:8:0:0", "start": 1373, "end": 1396, "surface": "civil registration data", "probe_tag": "confusion", "probe_score": 0.1179, "luna_label": 1}]}, {"key": "paddy-212", "text": "1\nOVERVIEW\nThis report is the eleventh in the series of Protection \nTrends reports prepared by the South Sudan Protection \nCluster, with inputs from Child Protection, Sexual and \nGender Based Violence (SGBV), and Mine Action \nsub-clusters.1 This paper is a departure from previous \nTrends reports, instead providing an overview of the \nprotection trends for the entire year. The paper provides \nan overview of the protection situation followed by a \ndiscussion of trends based on data collected during \nreporting period for general protection trends, child \nprotection, GBV, and mine action. This includes an \noverview of the context, access to basic services, forced \ndisplacement and population movement patterns, \nfamily tracing and reunification, grave violations of child \nrights, sexual and gender based violence (SGBV), and \nexplosive hazards. \nThis report is not an exhaustive overview of the context \nin South Sudan in 2017, rather it highlights trends \nand observations of the serious protection trends \nimpacting the civilian population in South Sudan to \ninform the response of both humanitarian and political \nactors. For more detail on protection trends relating \nto specific periods, please refer to previous quarterly \ntrends reports. To contextualize the reporting period, \nthe paper uses data going back to 2013, to understand \nthe progressive impact of conflict and insecurity on \nprotection concerns. The information presented in \nthe report is based on a broad range of sources. The \noperational environment currently limits the availability \nof data on some key protection concerns. Furthermore, \nlack of humanitarian presence and reliance on remote \ndata collection for some locations present major \nchallenges in providing an accurate and comprehensive \ndepiction of the protection situation in some areas.\nCONTEXT OVERVIEW\nThe continued armed conflict in South Sudan in 2017 \nhas dire consequences for civilians resulting in civilian \ndeaths, separation, and conflict related sexual \nviolence. These issues are compounded by growing \nfood insecurity, limited basic services, widespread \nhealth issues, and exacerbated by heavy rains and \nflooding. There was little improvement in the overall \nprotection environment and civilians continued to \nflee from active conflict and reports of fighting across", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001475:1:0:0", "start": 480, "end": 519, "surface": "data collected during \nreporting period", "probe_tag": "confusion", "probe_score": 0.2496, "luna_label": 1}, {"key": "reliefweb:001475:1:0:1", "start": 1306, "end": 1329, "surface": "data going back to 2013", "probe_tag": "confusion", "probe_score": 0.2132, "luna_label": 1}]}, {"key": "paddy-213", "text": "## **I. INTRODUCTION ET CONTEXTE**\n\nFuyant les troubles sociopolitiques ayant prévalu dans leurs pays d’origine au milieu des\nannées 2000, des Tchadiens et Centrafricains se sont progressivement réfugiés au Cameroun\npour atteindre un effectif proche de 85 000 personnes en janvier 2012 <sup>1</sup> [^1: Données fournies par le HCR] . Ils sont disséminés\ndans 74 sites ou villages des régions de l’Adamaoua et de l’Est du Cameroun pour les\nCentrafricains, et installés au camp de Langui dans la région du Nord pour les Tchadiens. De\n2007 à 2010, ils ont bénéficié dans le cadre de l’opération d’urgence \"EMOP 107350\", d’une\nassistance alimentaire et nutritionnelle du PAM et d’autres formes d’assistance des agences\nsœurs du système des Nations-Unies (UNHCR, UNICEF et UNFPA), du Gouvernement\ncamerounais, des partenaires de mise en œuvre et des ONG (FICR, CRC, CRE, MSF, IMC, IRD,\nPU, Plan International) sous la coordination de l’UNHCR.\n\n\nLa dernière étude <sup>2</sup> [^2: Evaluation approfondie de la sécurité alimentaire et des conditions de vie des ménages des réfugiés centrafricains et de la population hôte des régions de l’Est et de\nl’Adamaoua (MINEPAT, PAM, HCR, FAO, FICR, MINADER, partenaires)] réalisée uniquement sur les réfugiés centrafricains en Juillet 2010 a établi\nune légère amélioration de leur situation alimentaire avec 9,2 % d’entre eux en insécurité\nalimentaire sévère et 22,8 % en insécurité alimentaire modérée.\n\n\nLes recommandations formulées à l’issue de cette étude et les", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000003:7:0:0", "start": 304, "end": 331, "surface": "Données fournies par le HCR", "probe_tag": "confusion", "probe_score": 0.4223, "luna_label": 1}]}, {"key": "paddy-214", "text": "\nde 18 años- que buscan la protección internacional\nen la región. La tendencia ha ido en aumento hasta\nllegar a finales de 2013 a más de 18,500 personas\nrefugiadas provenientes del TNAC. <sup>41</sup> Por otro lado, el\nnúmero de solicitudes de la condición de refugiado\nen todo el mundo pasó de 6,900 en 2009 a cerca de\n15,700 en 2013. <sup>42</sup> Llama la atención que actualmente,\ntres de cada cuatro solicitantes buscan protección\ninternacional en Estados Unidos <sup>43</sup>, aunque cada\nvez hay más aplicaciones en México, Belice, Costa\nRica, Nicaragua y Panamá, países vecinos de la\nregión del TNAC. <sup>44</sup> Al respecto, el informe Children\non the Run, antes mencionado, señala con base en\nestadísticas oficiales, que el número de solicitudes de\nnacionales del TNAC en estos 5 países ha registrado\nun incremento del 432%. <sup>45</sup>\n\nComo se puede observar (Gráfico 3), esta evolución\nse acentúa a partir de 2009-2010 <sup>46</sup> .\n\n\n\nEn México la CG COMAR, registra que el número\nde NNAS solicitantes de la condición de refugiado\nprocedentes de la región del TNAC ha ido en aumento\nen el periodo del 2008 al 2013 (Gráfico 4). Los datos\nseñalan que durante el mismo, de 191 solicitudes\npresentadas por NNAS, 150 pertenecen a nacionales", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001434:15:1:0", "start": 705, "end": 727, "surface": "estadísticas oficiales", "probe_tag": "confusion", "probe_score": 0.8068, "luna_label": 1}]}, {"key": "paddy-215", "text": "|Asylum Levels and Trends in Industrialized Countries, 2008|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|\n|---|---|---|---|---|---|---|---|---|---|\n|**Table 4. Origin of asylum applications lodged in the European Union (27), 2007 and 2008**<br>Covering 27 European Union countries which provided monthly data to UNHCR.<br>(2007 data for Italy is based on annual data; 2008 on monthly data).<br><br><br>|**Table 4. Origin of asylum applications lodged in the European Union (27), 2007 and 2008**<br>Covering 27 European Union countries which provided monthly data to UNHCR.<br>(2007 data for Italy is based on annual data; 2008 on monthly data).<br><br><br>|**Table 4. Origin of asylum applications lodged in the European Union (27), 2007 and 2008**<br>Covering 27 European Union countries which provided monthly data to UNHCR.<br>(2007 data for Italy is based on annual data; 2008 on monthly data).<br><br><br>|**Table 4. Origin of asylum applications lodged in the European Union (27), 2007 and 2008**<br>Covering 27 European Union countries which provided monthly data to UNHCR.<br>(2007 data for Italy is based on annual data; 2008 on monthly data).<br><br><br>|**Table 4. Origin of asylum applications lodged in the European Union (27), 2007 and 2008**<br", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000427:16:0:0", "start": 297, "end": 309, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.4543, "luna_label": 0}, {"key": "reliefweb:000427:16:0:4", "start": 297, "end": 309, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.2039, "luna_label": 0}, {"key": "reliefweb:000427:16:0:1", "start": 356, "end": 367, "surface": "annual data", "probe_tag": "confusion", "probe_score": 0.5535, "luna_label": 0}]}, {"key": "paddy-216", "text": "Au vu de ce tableau, nous constatons que la majorité des chef de ménages ont des cartes\nélecteurs / identité. Le 4,5% des ménages sans carte électeur constitue des ENA.\n\n\nData Center for IDP Population 39", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001384:38:0:0", "start": 171, "end": 201, "surface": "Data Center for IDP Population", "probe_tag": "confusion", "probe_score": 0.3016, "luna_label": 0}]}, {"key": "paddy-217", "text": " (%)<br>including<br>Origin<br>Q1<br>Q2<br>Q3<br>Q4<br>Total<br>Q2-Q1 Q3-Q2 Q4-Q3<br>Q1<br>Q2<br>Q3<br>Q4<br>Italy|**Table 10. Origin of asylum applicants in Europe by quarter, 2007**<br>Covering 37 European countries which provided monthly data to UNHCR (excluding Italy).<br>Total<br>2007<br>No. of applications (excluding Italy)<br>Change (%)<br>Share (%)<br>including<br>Origin<br>Q1<br>Q2<br>Q3<br>Q4<br>Total<br>Q2-Q1 Q3-Q2 Q4-Q3<br>Q1<br>Q2<br>Q3<br>Q4<br>Italy|**Table 10. Origin of asylum applicants in Europe by quarter, 2007**<br>Covering 37 European countries which provided monthly data to UNHCR (excluding Italy).<br>Total<br>2007<br>No. of applications (excluding Italy)<br>Change (%)<br>Share (%)<br>including<br>Origin<br>Q1<br>Q2<br>Q3<br>Q4<br>Total<br>Q2-Q1 Q3-Q2 Q4-Q3<br>Q1<br>Q2<br>Q3<br>Q4<br>Italy|**Table 10. Origin of asylum applicants in Europe by quarter, 2007**<", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000427:22:7:0", "start": 233, "end": 245, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.8566, "luna_label": 0}]}, {"key": "paddy-218", "text": " 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><br><br><br><br><br><br><br><br><br><br><br>", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001058:17:3:0", "start": 323, "end": 335, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.4452, "luna_label": 0}, {"key": "reliefweb:001058:17:3:1", "start": 323, "end": 335, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.5701, "luna_label": 0}, {"key": "reliefweb:001058:17:3:2", "start": 323, "end": 335, "surface": "monthly data", "probe_tag": "confusion", "probe_score": 0.5392, "luna_label": 0}]}, {"key": "paddy-219", "text": " of social pressures, fears associated with a lack of documentation, financial constraints to\nengaging in the legal system, and the fact that informal justice processes take less time to reach an\noutcome, even if the outcome is not in the favor of women and/ or the survivor’s needs. The same study\nreveals that LGBTIQ+ Syrian refugee women, despite of the significant GBV risks they are facing in the\ncommunity, confront significant barriers to accessing the formal justice system due to their gender\nidentity and/or sexual orientation, in addition to the fear of being criminilized because of their sexual\nand gender identity due to the existing laws and the Lebanese penal code.\n\n**Recommendations**\n\nThe Sexual- and Gender-Based Violence Task Force offers the following recommendations for\nsafeguarding at-risk populations and responding to survivors’ needs based on the data reported through\nthe GBVIMS during the year of 2021:\n\n\n   - Ensure proper implementation of the sexual harassment law through active coordination\nbetween GBV actors and legal actors throughout the case management process.\n\n   - Strengthen the dissemination of information related to the sexual harassment law and ensure\nawareness raising sessions in PSS activities.\n\n   - Strengthen and increase the gender-sensitive discussions and provide systematic and ongoing\ntraining for legal professionals and other justice actors on women’s rights, refugee rights, existing\nGBV laws, Lebanon’s international obligations under human rights frameworks, referral\nmechanisms, gendered experiences of justice, and the needs of survivors.\n\n\n5 Danish Refugee Council. Referrals Information Management System. “WOMEN’S BARRIERS TO ACCESS HUMANITARIAN SERVICES.”\n6 UN Women, _“Justice for me is living free and as a human being\"_ - An Analytical Study of Access to Justice for Sexual and Gender-Based\n[Violence case of Syrian Refugees in Lebanon. Accessed at: UN Women Lebanon and partners - SGBV A2J - 2022.pdf](https://arabstates.un", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000958:3:1:0", "start": 901, "end": 907, "surface": "GBVIMS", "probe_tag": "confusion", "probe_score": 0.6891, "luna_label": 1}]}, {"key": "paddy-220", "text": "## **Overall data trends**\n\n##### **1.5. Geographical spread of incidents**\n\nThe 844 civilian impact incidents were far from evenly distributed between the five target governorates. There was a significant\ndifference in the number of incidents between the areas, with Sa’ada seeing the majority of incidents (53%), more than double the\nsecond highest governorate, Al-Hudaydah (22%). The lowest number of incidents was recorded within the Sana’a hub, where Sana’a\nCapital, Sana’a governorate and Marib each saw about 8% of the total incidents.\n\nHowever, the number of casualties did not follow the same pattern, with Al-Hudaydah seeing 32% of casualties, despite only\nseeing 22% of incidents, and Sa’ada seeing 37% of casualties with 53% of incidents. Of interest, Sana’a capital saw 20% of\ncasualties despite only seeing 8% of incidents, illustrating that incidents of armed violence in the densely-populated city were\nmore likely to generate high casualty counts than in rural and less-populated areas.\n\nWithin each governorate a trend of incidents being concentrated in certain districts emerged. In Sa’ada, most incidents were\nrecorded in districts bordering Saudi Arabia, with 72% of incidents in the governorate occurring in border districts. These areas also\naccounted for the majority of shelling, small arms fire, and UXO incidents, in addition to airstrikes. Razih was particularly impacted,\nwith the district seeing 26% of all the incidents recorded in Sa’ada, more than double the second most impacted district, Monabbih.\n\nIn Al-Hudaydah, the impact was significantly higher in southern districts than the rest of the governorate, with 72% of incidents\noccurring in southern districts, where a military offensive was taking place. Hays (44 incidents), Al-Tuhayat (31), Al-Garrahi", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000253:13:0:0", "start": 85, "end": 110, "surface": "civilian impact incidents", "probe_tag": "confusion", "probe_score": 0.6132, "luna_label": 0}]}, {"key": "paddy-221", "text": ". IDPs induced\nby conflicts are reported in\nDRC, Congo and Mozambique,\nand IDPs induced by disaster\nare reported in DRC, Malawi,\nMozambique and Zimbabwe.\n\nSummary of Previous\nAnalyses\nThe monthly report has started\nsince March 2022. This part of\nthe report, last paragraph of this\npage, has been covering\ndifferent topics since April Table 1. Number of IDPs as of 31 May 2023\n2022, aiming to present different topics. In the last 12 reports, the topics have been various in specific\nprotection needs, durable solutions, education, mixed movements, statlessness and birth registration,\npopulation planning, data availability of demographics and assistance provided by UNHCR. Some topics\nhave been covered several times. Specific protection needs, for example, were presented three times, and\ndurable solutions, education and mixed movements were presented twice. They are the topics of available\ndata each month. Population planning and assistance numbers by UNHCR in the region were also analysed\nwith the annual data at the end and the start of the year.\n\nData Sources: proGres (PRIMES), government, OCHA and IOM DTM.\n\n\n1 Others of concern refer to those who are linked to (but not classified as) refugees and asylum-seekers, and who need assistance by UNHCR.\nIn most cases in Southern Africa, they are family members, i.e., spouse or children, of refugees or asylum-seekers.\n2 Southern Africa region refers to the 16 countries covered by the Regional Bureau for Southern Africa of UNHCR including Angola, Botswana,\nComoros, Congo, DRC, Eswatini, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Zambia and\nZimbabwe.\n\n\nUNHCR/May 2023", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001230:1:1:0", "start": 1006, "end": 1017, "surface": "annual data", "probe_tag": "confusion", "probe_score": 0.6117, "luna_label": 1}, {"key": "reliefweb:001230:1:1:1", "start": 1110, "end": 1117, "surface": "IOM DTM", "probe_tag": "confusion", "probe_score": 0.5344, "luna_label": 1}]}, {"key": "paddy-222", "text": "**INTRODUCTION**\n\nThe Whole of Syria (“WOS”) Shelter/NFI Humanitarian Needs Overview Analysis for 2017 provides detailed and extensive analysis based on data from the needs\nassessments that were conducted to inform the Humanitarian Needs Overview (HNO) in Syria for 2017. The data were combined from multiple assessments, with varied\ncoverage, methodologies and levels of analysis, which were conducted by a range of partners between April and August 2016. There were over 26,000 assessment\nentries in total, which were first aggregated to community level, and then to sub-district level. In total, 264 sub-districts were assessed for the NFI sector and 272 subdistricts for the Shelter sector.\n\n\nThis analysis is provided for members, agencies and implementing partners from the Shelter / NFI sector and for other sectors in order to support humanitarian planning,\nadvocacy, and fundraising. It may be freely shared and disseminated. Population figures included in this analysis of needs are derived from a combination of sector\nanalysis and OCHA population and IDP figures, and are for planning only. They do not represent a census of the Syrian population, and programming and implementation\nshould be supported by further detailed needs assessments in specific locations to determine accurate numbers of people in need, to specify target groups and to provide\ndemographic breakdowns.\n\n\n**Sources of information:**\n\n\n**Sector/Cluster-led assessments**\n\n\n  - Damascus Hub Needs Assessments: Structured Community Discussions and Expert Panel Discussions (119 Sub-Districts on NFIs and 118 Sub-Districts on Shelter)\n\n  - Amman Hub Shelter Needs Assessment: HH level assessment in coordination with WASH sector (17 SDs on Shelter)\n\n  - Turkey Hub Shelter Needs Assessment: Key Informant (2 SDs)\n\n  - Turkey Hub NFI Needs Assessment : Household level (2 SDs)\n\n  - Turkey Hub based individual operational partner assessments:\n\n1. Key Informants (17 SDs on NFIs and 7", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001274:1:0:0", "start": 153, "end": 184, "surface": "data from the needs\nassessments", "probe_tag": "confusion", "probe_score": 0.3808, "luna_label": 1}]}, {"key": "paddy-223", "text": "DTM Mali – Juillet 2021\n\n### **INTRODUCTION**\n\n\nDurant le premier semestre de l’année 2021, dans un contexte marqué par l’insécurité et la pandémie à\ncoronavirus, les groupes armés ont poursuivi leurs attaques contre les populations civiles dans le Nord et le\nCentre du pays. Le caractère violent des conflits a provoqué d’importants déplacements de populations tant\nà l’intérieur du territoire malien que vers les pays limitrophes.\n\n\nLa situation dans le Centre et le Nord du pays reste instable, et marquée par l’augmentation des attaques\ndirectes ou indirectes visant les forces armées nationales et internationales ainsi que la population civile. Ainsi,\nle caractère ponctuel de ces nouveaux déplacements exige une importante flexibilité des services disponibles\ndans les sites et autres zones d’accueil des déplacés.\n\n\nLe transfert du programme matrice de suivi des déplacements DTM (Displacement Tracking Matrix) au\nGouvernement par l’OIM, en novembre 2014 et la signature de deux accords de partenariat entre le\nGouvernement et l’UNHCR pour la gestion des sites PDIs et l’enregistrement des rapatriés, ont permis au\nGouvernement du Mali d’obtenir une meilleure compréhension des caractéristiques des populations\ndéplacées et retournées, ainsi que de mettre au jour leurs besoins et accès aux services de base. Ces\ninformations sont collectées puis disséminées auprès de la communauté humanitaire et des pouvoirs public", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000429:6:0:0", "start": 889, "end": 917, "surface": "Displacement Tracking Matrix", "probe_tag": "confusion", "probe_score": 0.6645, "luna_label": 1}]}, {"key": "paddy-224", "text": " be assured.\nSection II, clearly illustrates that there is insufficient information on IDP and HIV\nprogrammes to provide a clear picture of their needs and the gaps. A recent report\nin Northern Uganda reports that basic HIV services are lacking for IDPs. A\ncomprehensive HIV/AIDS needs assessment, combined with assessments from\nother sectors in a multi-sectoral fashion, is needed in all 8 IDP priority countries.\n\n**6.** **Prevention**\nThe same points as for recommendation 5.\n\n**7.** **Support, Care and Treatment**\nThe same points as for recommendation 5. As antiretroviral therapy (ART)\nbecomes available to IDP surrounding host communities, we must ensure that\nIDPs also have access.\n\n**8.** **Assessment, Surveillance, Monitoring and Evaluation**\nSections II and III clearly show a lack of data on HIV and IDP situations. As\nmentioned in recommendation 5, a comprehensive multi-sectoral assessment\nshould occur in all 8 IDP priority countries. Baseline data must be collected to\nallow for monitoring and evaluation of HIV interventions over time. In countries\nundertaking HIV sentinel surveillance or population-based HIV biological and/or\nbehavioural surveys, sample size should provide sufficient power to disaggregate\nbetween IDPs and non-displaced populations, as well as gender and age.\n\n\n8", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "sample:reliefweb:001616:7:1:0", "start": 951, "end": 964, "surface": "Baseline data", "probe_tag": "drop", "probe_score": 0.0037, "luna_label": 0}, {"key": "sample:reliefweb:001616:7:1:1", "start": 1108, "end": 1166, "surface": "population-based HIV biological and/or\nbehavioural surveys", "probe_tag": "confusion", "probe_score": 0.1936, "luna_label": 0}]}, {"key": "paddy-225", "text": "�„ Support the sector to interpret and analyse the differences for women, girls, boys and men and\nencourage this in order to shape the development of appropriate activities.\n\n\n�„ Support the inclusion of gender equality measures (ADAPT ACT C framework <sup>40</sup> ) in implementation\nactivities and monitoring and evaluation.\n\n\n�„ Contribute sector information and analysis to facilitate an overview of gender equality measures in\nhumanitarian action.\n\n\n�„ Promote inter-sector linkages for gender mainstreaming.\n\n\n**3. Capacity Development**\n\n\n�„ Identify the needs of colleagues for information and training in gender equality mainstreaming.\n\n\n�„ Support the Sector Coordinator to develop and deliver sector-appropriate gender in programming\nworkshops and training in the Gender Marker.\n\n\n�„ Encourage staff to raise gender equality issues in the sector.\n\n\n**4. Knowledge Management**\n\n\n�„ Participate in IATF’s Sector Gender Focal Point Network.\n\n\n�„ Share information and experiences with the Sector Gender Focal Point Network.\n\n\n40 **ADAPT ACT C** stands for: **A** nalyse gender differences; **D** esign services to meet needs of all; **P** articipate equally; **T** rain women and\nmen equally; **A** ddress GBV in sector programmes; **C** ollect, analyse and report sex- and age-disaggregated data; **T** arget actions\nbased on a gender analysis; **C** oordinate actions with all partners.\n\n\n64 **Gender Equality Promising Practices:** Syrian Refugees in the Middle East and North Africa (Geneva: UNHCR, December 2017)", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000401:65:0:0", "start": 1275, "end": 1306, "surface": "sex- and age-disaggregated data", "probe_tag": "drop", "probe_score": 0.0398, "luna_label": 0}]}, {"key": "paddy-226", "text": " Au moins 15 civils auraient été tués et une dizaine d’autres\nagriculteurs auraient été enlevés. Consécutivement à ces attaques, 331\nménages de 1,655 personnes en provenance des villages précités et le\nvillage Mamove se sont déplacés vers des familles d’accueil à la\ncommune rurale d’Oïcha.\n\n- En fin janvier, des éléments armés ont tué 11 civils et enlevé plusieurs\nautres lors de deux attaques contre les populations des localités de\nBayeti et de Kaza Roho (zone de santé d'Oicha), selon la société civile\nlocale. Plus de 2 000 personnes ont été contraintes de fuir vers des\nvillages voisins.\n\n\n2 <u>Rapports hebdomadaires du Monitoring de Protection_janvier 2024_INTERSOS et</u>\n<u>UNHCR ; les données statistiques n’ont pas été collectées pour janvier 2024.</u>\n\n\n\n\n- Du 5 au 8 janvier, 13 agriculteurs ont été tués dans leurs champs aux\nenvirons du village Molisho. Des éléments ADF étaient de passage dans\nla zone.\n\n- Le 23 janvier, dans le village Ngite-Mavivi (zone de santé d'Oicha), au\nmoins neuf personnes ont été tuées, cinq autres enlevées et 1 500\npersonnes ont été contraintes au déplacement vers Mbau et la ville de\nBeni.\n\n- Selon les acteurs de protection, au moins 57 civils ont été tués au cours\ndes attaques armées dans plusieurs villages du territoire de Beni.\n\n\n**<mark>LU", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "sample:reliefweb:001502:2:1:0", "start": 697, "end": 717, "surface": "données statistiques", "probe_tag": "drop", "probe_score": 0.0021, "luna_label": 0}]}, {"key": "paddy-227", "text": "Venezuelans in Chile, Colombia, Ecuador and\n\nPeru – A Development Opportunity\n\n\n8\n\n\n**CPP** Carnet de Permiso Temporal de Permanencia (Temporary Stay Permit)\n\n\n**DTM** Displacement Tracking Matrix\n\n\n**ENAHO** Encuesta Nacional de Hogares (National Household Survey on Living Conditions and Poverty)\n\n\n**ENPOVE** Encuesta Dirigida a la Población Venezolana que reside en el País (Survey Targeted at the Venezuelan Population Residing in Peru)\n\n\n**EPEC** Encuesta a Personas en Movilidad Humana y en Comunidades Receptoras en Ecuador (Human Mobility and Host Communities Survey)\n\n\n**EPTV** Estatuto Temporal de Protección para Migrantes Venezolanos (Temporary Protection Statute for Venezuelan Migrants)\n\n\n**FAO** Food and Agricultural Organization\n\n\n**GDP** Gross Domestic Product\n\n\n**GEIH** Gran Encuesta Integrada de Hogares (Great Integrated Household Survey)\n\n\n**HFPS** High-Frequency Phone Survey(s)\n\n\n**ILO** International Labor Organization\n\n\n**IMF** International Monetary Fund\n\n\n**INEI** Instituto Nacional de Estadística e Informática (National Institute of Statistics and Information)\n\n\n**IPE** Identificador Provisorio Escolar\n\n\n**IOM** International Organization for Migration\n\n\n**LAC** Latin American and the Caribbean\n\n\n**PEP** Permiso Especial de Permanencia (Special Permit of Permanence)\n\n\n**PPT** Permiso por Protección Temporal (Temporary Protection Status)\n\n\n**R4V** Interagency Coordination Platform for Refugees and Migrants from Venezuela\n\n\n**SERMIG** Servicio Nacional de Migraciones de Chile\n\n\n**UN** United Nations\n\n\n**UNDP** United Nations Development Programme\n\n\n*", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001190:7:0:3", "start": 533, "end": 575, "surface": "Human Mobility and Host Communities Survey", "probe_tag": "confusion", "probe_score": 0.309, "luna_label": 0}, {"key": "reliefweb:001190:7:0:6", "start": 873, "end": 900, "surface": "High-Frequency Phone Survey", "probe_tag": "drop", "probe_score": 0.0186, "luna_label": 0}]}, {"key": "paddy-228", "text": ",221,181|224,718|20,445,899|4,149,853|317,207|43,503,362|5,343,793|4,161,979|6,140,688|3,582,203|86,531,669|\n\n\n76 UNHCR > **GLOBAL TRENDS 2019**", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001657:75:8:0", "start": 124, "end": 142, "surface": "GLOBAL TRENDS 2019", "probe_tag": "drop", "probe_score": 0.0436, "luna_label": 0}]}, {"key": "paddy-229", "text": "S) developed\nguidance and tools to support field operations with\ndata collection, storing, analysis, and use. For example,\nto address gaps in population data disaggregation\nby age and sex, the GDS developed dedicated\ndemographic statistical models using multiple data\nsources to estimate sex- and age-disaggregated data.\nThis served to improve evidence-based advocacy at\nthe regional and global level.\n\n\n**Promoting AGD-inclusive Programming among**\n**Staff and Partners**\n\n\nSome operations reported that progress made in\nAGD data disaggregation was possible as a result\nof **technical support and trainings that had been**\n**offered to staff and partners on disaggregating data**,\nparticularly by disability, sexual orientation and gender\nidentity. Importantly, operations (for example, in **Algeria,**\n**Rwanda** and **Venezuela** ) encouraged, requested,\nor supported partners to apply the AGD approach,\nincluding in their data collection and analysis.\n\n\n**Generating and Collecting**\n**AGD-disaggregated Data**\n\n\n\nAt the global level, UNHCR’s GDS supported the\ndisaggregation and analysis of data through various\nresearch and products. For example, the GDS and\nthe Digital Engagement Section collaborated on\n**AGD-disaggregated qualitative and quantitative**\n**research into how persons of concern use digital**\n**services** and their expectations of UNHCR digital\nservices, particularly for the development of self-service\nsolutions. The AGD lens ensured usability and inclusion\nSenior Registration Assistant for UNHCR Jordan goes about remotely renewing for all persons of concern.\nrefugee asylum seeker certificates in Amman, Jordan. © UNHCR/Lilly Carlisle\n\n\n15", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000271:13:2:0", "start": 990, "end": 1012, "surface": "AGD-disaggregated Data", "probe_tag": "drop", "probe_score": 0.017, "luna_label": 0}]}, {"key": "paddy-230", "text": " can be used both to\nhelp those who need assistance the most and to hold\nrelevant parties accountable to their commitments.\n\nIn general, the quality of demographic data tends\nto be highest in countries where UNHCR has an operational role, undertakes registration and primary\ndata collection, and has been engaged for a year\nor more. For some populations – mostly in developing countries – UNHCR has additional demographic\nand socio-economic information, including date\nand place of birth, language, occupation, civil status, religion, and education level. In locations where\ngovernments are exclusively responsible for data\ncollection, comprehensive disaggregated data on\nrefugees, IDPs, and others of concern often are lacking or unavailable.\n\n\nUNHCR Global Trends 2015 **51**", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "sample:reliefweb:001367:49:1:0", "start": 152, "end": 168, "surface": "demographic data", "probe_tag": "drop", "probe_score": 0.0183, "luna_label": 1}, {"key": "sample:reliefweb:001367:49:1:1", "start": 650, "end": 668, "surface": "disaggregated data", "probe_tag": "drop", "probe_score": 0.0379, "luna_label": 0}]}, {"key": "paddy-231", "text": " Use Therapeutic Food<br>|\n|SAM<br>|Severe Acute Malnutrition<br>|\n|SC<br>|Stabilization Centre<br>|\n|SENS<br>|Standardised Expanded Nutrition Survey<br>|\n|SFP<br>|Supplementary Feeding Program<br>|\n|SMART<br>|Standardised Monitoring and Assessment of Relief and Transitions<br>|\n|TFP<br>|Therapeutic Feeding Program<br>|\n|TSFP<br>|Target Supplementary Feeding Program<br>|\n|UNHCR<br>|United Nations High Commissioner for Refugees<br>|\n|UNICEF<br>|United Nations Children's Fund<br>|\n|WFP<br>|World Food Program<br>|\n|WHO<br>|World Health Organization<br>|\n|WHZ|Weight-for-Length/Height Z-Score|", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001340:2:1:0", "start": 111, "end": 149, "surface": "Standardised Expanded Nutrition Survey", "probe_tag": "drop", "probe_score": 0.0089, "luna_label": 0}]}, {"key": "paddy-232", "text": "\na lack of understanding, expertise or medication for\nthe treatment of chronic illnesses.\n\n\n**Case study: West Darfur, Sudan**\n\nBy 2011, the Darfur emergency of 2003/4 had become\na protracted humanitarian crisis, with as many as\n2 million people becoming internally displaced –\nmany living in camps throughout Darfur. Of these, an\nestimated 8 per cent of the camp population were made\nup of older people.\n\nHelpAge had worked in West Darfur since 2004.\nIn 2005/6, it carried out a series of assessments and\nsurveys to consult older people about their\nvulnerabilities and health and nutrition needs. <sup>19</sup>\nResults showed that older people in Darfur were not\naccessing health services despite clinics being\navailable. This was for a number of complex reasons.\nMany older people were experiencing isolation and\nneglect, and were excluded from food aid and health\nprogrammes, while others with mobility concerns\nlacked transport. These factors left many older people\nreticent and unable to seek medical care.\n\nIn response to this gap in health services provision,\nHelpAge established a roster of community health\nworkers to visit housebound older people, providing\ncare and referral as required. They also introduced a\ndonkey cart ambulance to transport older people to\nclinics for emergency care. Another initiative involved\ndistributing supplementary food baskets to older people\nat risk of malnutrition, or who were caring for multiple\ndependents.", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "sample:reliefweb:001104:5:2:0", "start": 490, "end": 513, "surface": "assessments and\nsurveys", "probe_tag": "drop", "probe_score": 0.0141, "luna_label": 1}]}, {"key": "paddy-233", "text": "Venezolanos en Chile, Colombia, Ecuador y Perú\n\n      - una oportunidad para el desarrollo\n\n\n\n8\n\n\n**ACNUR** Alto Comisionado de las Naciones Unidas para los Refugiados\n\n\n**ALC** América Latina y el Caribe\n\n\n**CPP** Carnet de Permiso Temporal de Permanencia\n\n\n**DTM** Matriz de Seguimiento del Desplazamiento\n\n\n**ENAHO** Encuesta Nacional de Hogares\n\n\n**ENPOVE** Encuesta Dirigida a la Población Venezolana que reside en el País\n\n\n**EPEC** Encuesta a Personas en Movilidad Humana y en Comunidades Receptoras en Ecuador\n\n\n**EPTV** Estatuto Temporal de Protección para Venezolanos\n\n\n**FAO** Organización de las Naciones Unidas para la Alimentación y la Agricultura\n\n\n**FMI** Fondo Monetario Internacional\n\n\n**GEIH** Gran Encuesta Integrada de Hogares\n\n\n**HFPS** Encuestas Telefónicas de Alta Frecuencia\n\n\n**INEI** Instituto Nacional de Estadística e Informática\n\n\n**IPE** Identificador Provisorio Escolar\n\n\n**OIM** Organización Internacional para las Migraciones\n\n\n**OIT** Organización Internacional del Trabajo\n\n\n**ONU** Organización de Naciones Unidas\n\n\n**PNUD** Programa de las Naciones Unidas para el Desarrollo\n\n\n**PEP** Permiso Especial de Permanencia\n\n\n**PIB** Producto Interno Bruto\n\n\n**PPT** Permiso por Protección Temporal\n\n\n**R4V** Plataforma de Coordinación Interagencial para Refugiados y Migrantes de", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:001322:7:0:0", "start": 320, "end": 348, "surface": "Encuesta Nacional de Hogares", "probe_tag": "confusion", "probe_score": 0.1513, "luna_label": 0}, {"key": "reliefweb:001322:7:0:3", "start": 759, "end": 799, "surface": "Encuestas Telefónicas de Alta Frecuencia", "probe_tag": "drop", "probe_score": 0.0463, "luna_label": 0}]}, {"key": "paddy-234", "text": "|Table 2. Asylum applications submitted in selected countries in Eastern Europe, 2005-2009<br>All figures are based on annual data.|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Col16|Col17|Col18|Col19|Col20|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|Country<br>of asylum|2005|2006|2007|2008|2009|Total|Annual<br>change<br>'09-'08|Share|Share|Rank|Rank|Per 1,000 inhabitants|Per 1,000 inhabitants|Per 1,000 inhabitants|Per 1,000 inhabitants|Per 1 USD/GDP per capita|Per 1 USD/GDP per capita|Per 1 USD/GDP per capita|Per 1 USD/GDP per capita|\n|Country<br>of asylum|2005|2006|2007|2008|2009|Total|Annual<br>change<br>'09-'08|2009|'05-'09|2009|'05-'09|Total|Total|Rank|Rank|Total|Total|Rank|Rank|\n|Country<br>of asylum|2005|2006|2007|2008|2009|Total|Annual<br>change<br>'09-'08|2009|'05-'09|2009|'05-'09|2009|'", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000810:12:0:0", "start": 119, "end": 130, "surface": "annual data", "probe_tag": "drop", "probe_score": 0.0352, "luna_label": 0}]}, {"key": "paddy-235", "text": " IDPs/ refugees.\n\nRefugees and IDPs with disabilities also spoke about the need for better access to small business\ngrants and advice/ training on entrepreneurship.\n\n<u>Recommendations</u>\n\nRefugees and IDPs with disabilities identified access to information about job opportunities and\nadvice on business development as being particularly important, and that this information should\nbe available through various channels to improve accessibility.\n\nNumerous recommendations were also made regarding improving accessibility and inclusiveness\nof workplaces, both in regards to physical accessibility and attitudes. Refugees and IDPs\nrecommended awareness- raising of employers about the capacities and positive contributions of\npersons with disabilities.\nRefugees and IDPs also recommended for services to be available in community centers to support\nwith job searching, such as outreach by employment centers, advice on resume writing, provision\nof computers and business skills training.\n\n\n**3.3.** **ACCESS TO STATE ASSISTANCE**\n\n\n6 ‘The Washington Group’ was formed in 2001 with the authorization of the United Nations Statistical\nDivision to address the need for statistical and methodological work on an international level in order to\nfacilitate the comparison of data on disability cross- nationally. See\n<u>[http://www.cdc.gov/nchs/washington_group.htm](http://www.cdc.gov/nchs/washington_group.htm)</u>\n\n\nUnited Nations High Commissioner for Refugees (UNHCR) – www.unhcr.org\n\n3", "source": "reliefweb", "subset": "annotate_paddy", "spans": [{"key": "reliefweb:000520:2:1:0", "start": 1269, "end": 1287, "surface": "data on disability", "probe_tag": "drop", "probe_score": 0.0267, "luna_label": 0}]}, {"key": "paddy-236", "text": "#### **1 Introduction**\n\nTransport and communication costs fell precipitously during the last century, leading many\nobservers to posit that the world has ‘become flat’, that locational differences no longer matter.\nWith the help of cheap transport and communication, business can be done almost anywhere,\nor so the story goes, leaving policy makers with the impression that we have entered a ‘brave\nnew frictionless’ world. But, if this were true, the costs of trading and transporting goods\nshould no longer influence firms’ location choices and, thereby, the spatial structure of economic activity. Why is then the tendency for economic activity to cluster in space still strong?\nWhy do many industries nowadays still exhibit strong geographic patterns, including new\nentrants that should face little locational constraints?\n\nWe address this seeming contradiction head-on by identifying the causal effect of transport costs on the geographic concentration of industries. Using micro-level commodity flow\ndata and micro-geographic plant-level data, we construct industry-specific ad valorem trucking rates and continuous measures of geographic concentration. We find that, controlling for\ninternational trade exposure and input-output links, increasing trucking rates are significantly\nassociated with declining geographic concentration. The effect is large: had trucking rates not\nfallen between 1992 and 2008, the observed decline in geographic concentration of Canadian\nmanufacturing industries would have been about 20% greater. Our results hold up to a large\nvariety of robustness checks and to instrumental variables estimations that deal with potential\nendogeneity concerns of our key variables.\n\nAssessing empirically the impact of transport costs on the geographic concentration of industries is important for several reasons. First, it is fair to say that, despite their fundamental\ntheoretical role in spatial modeling, little is still known empirically on how transport costs\ndrive the geographic structure of industries. Second, among the forces that drive the clustering of industries, transport costs have been less studied, much less than the ‘Marshallian’\ndeterminants such as input-output", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:006871:3:0:1", "start": 1015, "end": 1048, "surface": "micro-geographic plant-level data", "probe_tag": "confusion", "probe_score": 0.7158, "luna_label": 1}]}, {"key": "paddy-237", "text": "Policy Research Working Paper 4924\n\n#### **Abstract**\n\n\nEmploying a view of culture as a communicative\nphenomenon involving discursive engagement, which is\ndeeply influenced by social and economic inequalities,\nthe authors argue that the struggle to break free of\npoverty is as much a cultural process as it is political\nand economic. In this paper, they analyze important\nexamples of discursive spaces—public meetings in Indian\nvillage democracies _(gram sabhas)_, where villagers make\nimportant decisions about budgetary allocations for\nvillage development and the selection of beneficiaries for\nanti-poverty programs. They examine 290 transcripts of\n_gram sabhas_ from South India to demonstrate how they\ncreate a culture of civic/political engagement among poor\npeople, and how definitions of poverty and beneficiary\n\n\nselection criteria are understood and interrogated within\nthem. Through this examination, they highlight the\nprocess by which _gram sabhas_ facilitate the acquisition\nof crucial cultural capabilities such as discursive skills\nand civic agency by poor and disadvantaged groups.\nThey illustrate how the poor and socially marginalized\ndeploy these discursive skills in a resource-scarce and\nsocially stratified environment in making material\nand non-material demands in their search for dignity.\nThe intersection of poverty, culture, and deliberative\ndemocracy is a topic of broad relevance because it sheds\nlight on cultural processes that can be influenced by\npublic action in a manner that helps improve the voice\nand agency of the poor.\n\n\n\nThis paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department\nto understand local government and citizen-based engagement in developing countries. Policy Research Working Papers\nare also posted on the Web at http://econ.worldbank.org. The author may be contacted at vrao@worldbank.org.\n\n\n_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development_\n_issues. An objective of the series is to get the findings out quickly, even", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:004119:1:0:0", "start": 634, "end": 666, "surface": "290 transcripts of\n_gram sabhas_", "probe_tag": "confusion", "probe_score": 0.1892, "luna_label": 1}]}, {"key": "paddy-238", "text": "ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE\n\n\nabout 39 percent. Looking ahead, the relative burden is expected to decrease from the 2009 level to about\n30 percent in 2010, as the projected nominal 2010 GDP is expected to continue rebounding. Even then,\nthe burden is comparatively very high.\n\n\nThus, Zimbabwe‘s ―ideal‖ investment scenario may lie out of reach for the time being. Therefore, two\nalternative scenarios were developed in this case (table 16).\n\n\n**Table 16. AICD annual spending needs estimates over a 10-year period**\n\n\n<u>Scenarios</u>\n\n\n\n<u>$ million per year</u>\n\n\n\n<u>Ideal</u> <u>Intermediate</u> <u>Minimalist</u>\n\n\n\n**<u>Total</u>** <u>2,009</u> <u>1,729</u> <u>1,219</u>\n\n\nPower 1,242 1,156 665\n\nWSS 427 342 313\n\nTransport 218 110 187\n\nICT 75 75 36\n\n<u>Irrigation</u> <u>47</u> <u>47</u> <u>18</u>\n\n\n**<u>New investment</u>**\n\n\nPower 577 491 0\n\nWSS 115 48 0\n\nTransport 31 16 0\n\nICT 39 39 0\n\n<u>Irrigation</u> <u>29</u> <u>29</u> <u>0</u>\n\n\n**<u>Rehabilitation</u>**\n\n\nPower 257 257 257\n\nWSS 171 171 171\n\nTransport 108 24 108\n\nICT 0 0 0\n\n<u>Irrigation</u> <u>17</u> <u>17</", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:004992:48:0:0", "start": 480, "end": 516, "surface": "AICD annual spending needs estimates", "probe_tag": "confusion", "probe_score": 0.6936, "luna_label": 0}]}, {"key": "paddy-239", "text": "##### A Appendix\n\nMalawi Longitudinal School Survey\n\n\nThe Malawi Longitudinal School Survey (MLSS) collects extensive data on school, classrooms,\nteachers, students, community members and parents. This Appendix provides summary details. For additional details, see Appendix B.\n\n\nA.0.1 Instruments\n\n\nThe survey contains the following instruments:\n\n\n1. Observation of school and classroom facilities\n\n\n2. Lesson observation\n\n\n3. Head Teacher interview, including details of teachers and committee members; information about their background and procedures; and school information from records\n\n\n4. Student interview\n\n\n5. Student learning assessment\n\n\n6. Teacher interview\n\n\n7. Teacher knowledge assessment\n\n\n8. Community interviews with members of the School Management Committee, ParentTeacher Association Executive Committee, and Mother Group\n\n\n9. Group Village Headman interview\n\n\nAll instruments were included in all rounds, except the Group Village Headman interview,\nwhich was not included in the 2016 phase of baseline. <sup>31</sup>\n\n\nThe MLSS instruments are based on similar tools used as part of the Service Delivery Indicators (SDI) survey implemented by the World Bank. The SDI instruments were adapted with\nadditional indicators which were appropriate for the Malawian context and/or specific to the\nMESIP program and related impact evaluations.\n\n\n31 A Group Village Headman is an intermediary-level official in Malawi’s Traditional Authority structure, broadly\nanalogous to a Village Chief\n\n\n38", "source": "general_prwp", "subset": "annotate_paddy", "spans": [{"key": "prwp:001315:40:0:0", "start": 18, "end": 51, "surface": "Malawi Longitudinal School Survey", "probe_tag": "confusion", "probe_score": 0.2047, "luna_label": 1}]}]